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BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-UVBQKM@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241002T094500
DTEND;TZID=Europe/Lisbon:20241002T100000
DESCRIPTION:Welcome plenary by Steffen Fritz - International Institute for
Applied Systems Analysis (IIASA)
DTSTAMP:20260613T183554Z
LOCATION:Theatre Hall (Conference Center Laxenburg)
SUMMARY:Welcome plenary -
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/UVBQKM/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-KD9R8T@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241002T100000
DTEND;TZID=Europe/Lisbon:20241002T103000
DESCRIPTION:Please add an abstract as soon as possible
DTSTAMP:20260613T183554Z
LOCATION:Theatre Hall (Conference Center Laxenburg)
SUMMARY:The Role of Earth Observation for the European Bauhaus Initiative"
- John Schellnhuber
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/KD9R8T/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-SUN8YE@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241002T103000
DTEND;TZID=Europe/Lisbon:20241002T104500
DESCRIPTION:The Open-Earth-Monitor Project - Coordinator of the Open-Earth-
Monitor project and director of the Opengeohub Foundation
DTSTAMP:20260613T183554Z
LOCATION:Theatre Hall (Conference Center Laxenburg)
SUMMARY:The Open-Earth-Monitor Project -
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/SUN8YE/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-ZTMYJL@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241002T113000
DTEND;TZID=Europe/Lisbon:20241002T120000
DESCRIPTION:Please provide an abstract as soon as possible
DTSTAMP:20260613T183554Z
LOCATION:Theatre Hall (Conference Center Laxenburg)
SUMMARY:10 years of Global Forest Watch – from data to impact - Elizabeth
Goldman
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/ZTMYJL/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-XRPRHR@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241002T120000
DTEND;TZID=Europe/Lisbon:20241002T122000
DESCRIPTION:Approximate five billion hectares (38%) of global land area is
used for agricultural system\, contributing significantly to the loss of b
iodiversity and having a substantial impact on water resources and greenho
use gas emissions of the World. Aiming to support multi-scale environmenta
l policies and decision making process\, several land monitoring systems /
products were launched in the last years\, including WorldCereal\, GLaNCE
\, Dynamic World\, UMD GLAD GLCLUC\, GLC_FCS30D and Global Pasture Watch.
Even though all these systems / products have different advantages\, limit
ation\, constraints and resolutions (thematic\, spatial and temporal)\, in
general they have a high potential to be combined to support different la
nd cover and land use applications at global\, national and local scale. H
ere we present a framework able integrate global monitoring systems / prod
ucts in an automated\, flexible and reproducible way\, taking advantages o
f new technologies as cloud-optimized formats and cloud services. We demon
strated it integrating different crop and pastures classes in seamless mon
itoring system for the tropics\, allowing the users to define their own ar
ea of interest\, harmonization rules and overlap criteria. The implementat
ion is publicly available in scikit-map (https://github.com/openlandmap/sc
ikit-map) and all input layers publicly accessible through SpatioTemporal
Asset Catalog (STAC).
DTSTAMP:20260613T183554Z
LOCATION:Theatre Hall (Conference Center Laxenburg)
SUMMARY:Monitoring livestock and agricultural systems: An ensemble approach
based on data harmonization - Leandro Leal Parente
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/XRPRHR/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-XQPMSH@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241002T120000
DTEND;TZID=Europe/Lisbon:20241002T122000
DESCRIPTION:Eddy covariance (EC) systems are commonly used to measure the n
et exchanges of energy\, water carbon dioxide (CO2) and other trace gasses
between the ecosystems and the atmosphere. Such measuring systems have be
en established in different ecosystems and climate regimes across the glob
e\, thereby providing invaluable ground information to understand ecosyste
m dynamics at global scale. Although the number of EC stations installed w
orldwide (e.g. FLUXNET sites) are constantly growing with time\, their spa
tial distribution is limited in comparison to the vast complexity of land
ecosystems. Furthermore\, EC towers track the exchange of energy and matte
r from an area (often referred to as a footprint) that spans some few hund
red meters around and upstream of the measurement site (the so-called fetc
h)\, and which can vary according to meteorological conditions. Remote sen
sing (RS) and in-situ flux datasets are commonly combined to upscale the e
xchanges of carbon and energy at a global scale (e.g. the FLUXCOM project)
\, as well as for calibration and validation activities. The challenge to
do this correctly lies in trying to link the footprints of the EC measurem
ents to those of the satellite measurements\, a task that is often disrega
rded or oversimplified. In this study we designed a methodological approac
h within the Open-Earth-Monitor (OEMC) project to estimate dynamically the
match (or mismatch) between some likely proxies of EC footprints (approxi
mated as circles with radius from 50 to 200 meters) and the footprints of
(coarse) spatial resolution RS time series. To quantify the degree of mism
atch we collect Sentinel-2 images at 10 meters resolution for several EC s
ites over Europe. Then\, we compute the kNDVI vegetation index for all the
sites masking clouds and cloud shadows. We also define proxies for differ
ent pixel sizes of satellite data ranging from 500 meters to 1500 meters r
adius around the tower. To compare the EC footprints with the Satellite pi
xel resolution we compute the Jensen-Shannon index that quantifies the amo
unt of information (in terms of kNDVI) shared between both scales at every
available time step. As a result\, we provide initial recommendations of
when in the year the sites are more suitable to be matched with satellite
data according to the surrounding phenology. We expect these will open the
possibility to correct biases in future upscaling fluxes exercises and re
mote sensing products calibration.
DTSTAMP:20260613T183554Z
LOCATION:Maria Theresia Seminar room (Conference Center Laxenburg)
SUMMARY:Quantifying the (mis)match between in-situ and satellite time serie
s: the case of eddy covariance flux observations - Daniel E. Pabon-Moreno
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/XQPMSH/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-ARXWB7@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241002T120000
DTEND;TZID=Europe/Lisbon:20241002T124500
DESCRIPTION:Daily gridded meteorological datasets are an important source o
f information for analysis of historical weather and many other research a
reas since they have no gaps in the spatio-temporal domain they cover. Mos
t of the daily gridded meteorological datasets represent reanalysis or est
imations from different remote sensing sensors or are generated by downsca
ling procedures. A daily gridded meteorological dataset for Europe at 1 km
spatial resolution\, named MeteoEurope1km\, is created\, covering the 196
1–2020 period and consists of five variables: maximum (TMAX)\, minimum (
TMIN)\, and mean (TMEAN) temperature\, sea-level pressure (SLP)\, and tota
l precipitation (PRCP). Spatio-temporal regression kriging\, an interpolat
ion method that combines multiple linear regression for trend modeling and
space-time kriging for the estimation of the residuals\, is used for inte
rpolation of daily temperature variables. Ordinary kriging is used for SLP
and PRCP\, except that for PRCP an additional step to predict PRCP occurr
ence is applied using Indicator kriging. Combination of GHCN-daily\, ECA&D
\, and SYNOP observations from OGIMET service is used as an observational
dataset\, with previous removal of duplicated stations and outliers. Geome
tric temperature trend\, digital elevation model and topographic wetness i
ndex are used as auxiliary variables for temperature datasets. Accuracy a
ssessment (leave-one-station-out cross-validation) shows high accuracy of
the fitted models. Coefficient of determination for all temperature parame
ters and SLP is greater than 96%\, while for PRCP is greater than 76 %. Ro
ot mean square error is 1.3°C\, 1.6°C\, 1.8°C\, 1.5 mbar\, and 2.5 mm f
or TMEAN\, TMAX\, TMIN\, SLP\, and PRCP\, respectively. MeteoEurope1km is
available as cloud optimized GeoTIFFs\, and are accessible through dailyme
teo.com portal\, ZENODO\, and R meteo package. Future work will be oriente
d towards increasing the spatial extent to other continents besides Europe
\, interpolation of other daily meteorological variables\, and improving m
odels performances by applying spatial machine learning methods\, such as
Random Forest Spatial Interpolation.
DTSTAMP:20260613T183554Z
LOCATION:Raiffa Room (IIASA)
SUMMARY:Workshop: MeteoEurope1km: a high-resolution daily gridded meteorolo
gical dataset for Europe for the 1961–2020 period - Aleksandar Sekulić
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/ARXWB7/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-WMWG8N@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241002T124000
DTEND;TZID=Europe/Lisbon:20241002T130000
DESCRIPTION:The WorldCereal project\, funded by the European Space Agency (
ESA)\, aims to provide a comprehensive understanding of global cropped are
as\, irrigation practices\, and the distribution of major commodity crops.
WorldCereal has developed a dynamic open-source system that generates a r
ange of products\, including temporary crop extent\, seasonal maize and ce
real maps\, seasonal irrigation maps\, seasonal active cropland maps\, and
confidence layers. These products are based on the analysis of Sentinel-1
and Sentinel-2 imagery at 10 m spatial resolution\, complemented by Lands
at 8 imagery and AgERA5 meteorological information\, and are updated at se
asonal intervals for each agricultural system. WorldCereal has demonstrate
d the feasibility of global crop mapping by producing the first global\, s
easonally updated crop and irrigation maps for the year 2021. WorldCereal
has also released a fully open\, harmonized database of in-situ reference
data related to land cover\, crop type\, and irrigation\, enabling a broad
community to access and contribute to this growing resource. WorldCereal
is now entering a new phase\, in which the system is being implemented as
a cloud-based processing service in the new Copernicus Data Space Ecosyste
m. The system will offer more flexibility and customization options to use
rs\, allowing them to generate tailored crop type products for their regio
ns of interest. Moreover\, the WorldCereal product suite will be extended
with eight new crops\, and the in-situ reference database will be updated
and expanded. WorldCereal will also conduct a series of regional use cases
and capacity building activities to demonstrate the system’s capabiliti
es and to boost user uptake by the broad agricultural monitoring community
. WorldCereal provides a vital tool for policymakers\, international organ
izations\, and researchers to better understand local to global cropping p
atterns and to inform decision-making related to food security and sustain
able agriculture.
DTSTAMP:20260613T183554Z
LOCATION:Maria Theresia Seminar room (Conference Center Laxenburg)
SUMMARY:WorldCereal: a dynamic open-source system for global-scale\, season
al\, and reproducible crop mapping - Kristof Van Tricht
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/WMWG8N/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-9VNA9W@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241002T124000
DTEND;TZID=Europe/Lisbon:20241002T130000
DESCRIPTION:Satellite radar remote sensing utilizes long-wavelength energy
that can penetrate clouds and is sensitive to changes in the physical stru
cture of vegetation. These characteristics\, in combination with the high
spatial and temporal detail of new and near-future radar satellites\, prov
ide major opportunities for monitoring forest disturbances and regrowth dy
namics.\n\nWe provide an overview of recent research activities on the use
of radar remote sensing to monitor forest dynamics and present key result
s achieved in the Open-Earth-Monitor project. These include forest disturb
ance monitoring\, monitoring of forest loss drivers and carbon\, and asses
sments of selective logging intensity. We will highlight how the near-futu
re availability of freely available multi-frequency radar data from Sentin
el-1 (C-band)\, NISAR (L-band)\, and BIOMASS (P-band) will improve our abi
lity to assess forest dynamics. We will also discuss our open-source initi
atives aimed at facilitating the adoption of radar data and change detecti
on approaches by both the scientific community and country stakeholders.
DTSTAMP:20260613T183554Z
LOCATION:Theatre Hall (Conference Center Laxenburg)
SUMMARY:Assessing forest disturbance dynamics and drivers using radar satel
lite data - Johannes Reiche
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/9VNA9W/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-WHDWVK@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241002T143000
DTEND;TZID=Europe/Lisbon:20241002T145000
DESCRIPTION:Air pollution\, particularly fine particulate matter (PM2.5)\,
ground-level ozone (O3)\, and nitrogen dioxide (NO2)\, poses a significant
global health risk\, contributing to early mortality. Measuring populatio
n exposure is crucial for understanding and mitigating these health impact
s. This paper leverages recent advancements in air pollution data to revie
w various global air pollution datasets based on a criteria set. The frame
work facilitates comparisons between various hybrid datasets (combining gr
ound-based and satellite measurements) and offers a methodology for constr
ucting air pollution exposure indicators for PM2.5\, O3\, and NO2. It uses
the Global Burden of Disease data to update the indicator set on the nati
onal and subnational levels across the 1990-2020 period. Results reveal th
at most OECD countries fall short of the World Health Organization's (WHO)
2021 air quality guidelines for PM2.5\, O3\, and NO2. Countries such as C
hile\, Korea\, Poland\, and Türkiye exhibit PM2.5 concentrations (populat
ion weighted) exceeding safe levels by a factor of four. Similarly\, sever
al OECD countries such as Korea\, Italy\, and Slovenia experienced severe
O3 exposure in 2020\, while non-OECD countries such as India displayed eve
n higher population weighted O3 concentrations\, exceeding safe levels by
more than double. A sensitivity analysis further indicates that despite si
milar trends observed across different air pollution datasets\, considerab
le differences are found between global datasets and national statistics.
This highlights the need to further examine the accuracy of the various da
ta sources and help guide policy analysis at the national and subnational
levels. Given the widespread failure to meet safe air quality standards\,
our findings emphasize the urgent need for global policy actions to reduce
population exposure to air pollution and safeguard public health.
DTSTAMP:20260613T183554Z
LOCATION:Maria Theresia Seminar room (Conference Center Laxenburg)
SUMMARY:Assessing population exposure to air pollution: A multi-pollutant i
ndicator framework for OECD countries and partners - Mikaël Maes\, Ivan H
aščič
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/WHDWVK/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-SYHL9S@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241002T143000
DTEND;TZID=Europe/Lisbon:20241002T145000
DESCRIPTION:There is currently a lack of high-resolution pan-European infor
mation on land use management\, especially in terms of how forests\, cropl
and and grassland are intensively and extensively managed. This is partly
due to the lack of ground-based information\, which is needed to downscale
these types of management practices (some of which are captured in differ
ent types of agricultural censuses and surveys and National Forest Invento
ries) as well as the inability of remote sensing to capture different kind
s of land use. This type of information is needed for economic land use mo
delling and for assessing policy impacts\, such as the latest reforms from
the Common Agricultural Policy (CAP) and other European Union (EU) Green
Deal targets. These types of analyses are undertaken using economic land u
se models such as GLOBIOM and CAPRI\, which is one of the main aims of the
Horizon Europe funded LAMASUS project (https://www.lamasus.eu/). \n \nOn
e of the main inputs to the development of a land use management map is Co
rine land cover\, which is a remotely sensed product developed by the Cope
rnicus Land Monitoring Service every six years. First\, we produced an ann
ual time series of Corine from 2000 to 2018 by using the high-resolution l
and cover times series produced by OpenGeoHub and the BFAST algorithm appl
ied to MODIS data to determine the year of change between the six-year pro
duction cycle of CORINE. Any remaining changes that were unaccounted for h
ad the year of change selected randomly. Transition rules were also applie
d to ensure that the land cover/land use transitions were reasonable. \n\n
Land use management classes for forest\, cropland\, grassland and urban ar
eas were then devised in collaboration with the modelers in the LAMASUS pr
oject as well as around 30 stakeholders who participated in the first LAMA
SUS stakeholder workshop. Using different input data sets from remote sens
ing\, in-situ data (from LUCAS)\, modelled data from CAPRI\, and statistic
al information from agricultural censuses\, surveys and other sources\, ru
les were developed to allocate the Corine land cover classes to more detai
led land use management classes. Here we will present the results of this
mapping along with a method for how the map has been fit to official area
statistics so that this information can be used by the economic land use m
odels in LAMASUS.
DTSTAMP:20260613T183554Z
LOCATION:Theatre Hall (Conference Center Laxenburg)
SUMMARY:Mapping land use management in Europe using remote sensing\, in sit
u data and statistical information - Linda See
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/SYHL9S/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-DNNSQM@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241002T145000
DTEND;TZID=Europe/Lisbon:20241002T153500
DESCRIPTION:This workshop equips participants with hands-on experience mana
ging their EO data on Zenodo using the zen Python library. Participants wi
ll learn to customize metadata and automate data management workflows usin
g zen scripts.
DTSTAMP:20260613T183554Z
LOCATION:Raiffa Room (IIASA)
SUMMARY:Workshop: Streamlining Earth Observation Data Sharing with the zen
Python Library - Deleted User
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/DNNSQM/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-GW8KJK@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241002T153000
DTEND;TZID=Europe/Lisbon:20241002T155000
DESCRIPTION:Improving the sustainability of the European livestock sector r
equires high resolution spatial data. Otherwise potential negative impacts
of livestock related to local ecosystem degradation\, as well as positive
ones such as preserving cultural landscapes through grazing cannot be ana
lysed. Data on livestock numbers usually used in scientific analyses are c
ollected and provided by the European statistical office\, but are provide
d on a rather coarse spatial resolution of statistical regions. In additio
n\, data on the actual use of grasslands\, whether grazed\, mown and the i
ntensity of their use is not collected systematically or not at all. We pr
ovide an approach for mapping grazing livestock (cattle\, small ruminants)
density and the use of grassland for Europe. We first collected livestock
numbers on a local level for all EU countries\, which we harmonized\, and
supplemented it with statistics on actual outdoor grazing of animals. We
then mapped areas that are grazed by combining EU-wide in-situ data on gra
zing with a set of socio-economic\, terrain\, soil and climate characteris
tics using machine learning. We then allocated grazing livestock on two di
fferent earth observation derived land use and land cover products: corine
land cover and the high resolution grassland layer. Our approach enables
identifying areas that are grazed\, and combined with livestock statistics
\, also how intensively these areas are used either for grazing or mowing.
Such information can support tracking the state of european grassland eco
systems\, landscape conservation\, as well as other environmental dimensio
ns related to the livestock sector\, such as nitrogen deposition\, with a
high spatial detail. Finally\, by using regularly updated systematically c
ollected data\, we can update the data in the future.
DTSTAMP:20260613T183554Z
LOCATION:Theatre Hall (Conference Center Laxenburg)
SUMMARY:High-resolution spatial information on livestock density and grassl
and management in Europe - Ziga Malek
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/GW8KJK/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-VHWEHP@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241002T153000
DTEND;TZID=Europe/Lisbon:20241002T155000
DESCRIPTION:Findable\, Accessible\, Interoperable\, and Reusable (FAIR) dat
a principles are composed of a set of guidelines focused on efficient disc
overy and data utilization\, which are crucial for sharing scientific data
effectively. Hence\, adapting to the FAIR principles benefits diverse env
ironmental applications and supports a diversity of policies. This study p
resents the findings of an extended user survey conducted within the Open
Earth Monitor Cyberinfrastructure (OEMC) project\, exploring user perspect
ives on FAIR environmental data. For this purpose\, an existing survey tar
geted at both users and producers of geospatial data was extended to enhan
ce the representability and have the widest feedback for understanding use
rs' and producers' needs\, expectations\, experiences\, and understanding
of FAIR principles. \nThe survey included three blocks. The first block ad
dressed the background and general information of the survey respondents.
The second block inquired about the characteristics of the geospatial data
that has been primarily used or produced. The third block investigated ho
w user and producer group participants are familiar with the FAIR principl
es and which of those seemed most relevant to them. In addition\, we foste
red a target-specific participant selection strategy to cover the main ins
titutions and relevant user groups. \nThe survey revealed a discrepancy in
the preferred observational scales between data producers and users. Whil
e producers primarily focus on generating data at global scales\, users fr
equently require data at local and regional levels. This finding underscor
es the need for improved communication and collaboration between data prov
iders and users to ensure data production aligns with user needs. Furtherm
ore\, the survey identified findability and openness as the top priorities
for FAIR environmental data\, alongside clear licensing\, comprehensive m
etadata availability\, and detailed documentation. \nThese findings emphas
ize the crucial role of robust data management practices and user-centric
approaches in promoting the effective utilization of environmental data. \
nFurther key findings from user responses will be presented\, highlighting
user perceptions of FAIRness in environmental data\, current gaps in FAIR
implementation\, and identified challenges. Based on these insights\, we
will discuss the implications of the survey results and propose recommenda
tions for advancing the FAIRness of environmental data in the future. \nTh
is research contributes to ongoing efforts within the OEMC project and bey
ond\, informing strategies for improving the discoverability\, usability\,
and overall value of environmental data for various stakeholders.
DTSTAMP:20260613T183554Z
LOCATION:Maria Theresia Seminar room (Conference Center Laxenburg)
SUMMARY:User and producer perspectives for FAIR environmental data - Katja
Berger
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/VHWEHP/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-XRCSRU@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241002T163000
DTEND;TZID=Europe/Lisbon:20241002T171500
DESCRIPTION:*nrt* is a Python package designed to streamline environmental
monitoring efforts by offering a unified Application Programming Interface
(API) for a diverse array of forest disturbance monitoring algorithms. Th
is unified API simplifies the process for users\, enabling easy comparison
and integration of different algorithms that are optimized for rapid comp
utation and scalable deployment.\n\nBeyond its core functionality\, the *n
rt* ecosystem encompasses additional tools that enhance its utility and ve
rsatility. These include **diagnostics**\, **time-series simulation**\, **
generation of reference data**\, and **computation of accuracy metrics**.
Collectively\, these features make *nrt* a valuable resource for environme
ntal monitoring and analysis\, catering to a wide range of research and op
erational needs.\n\nDuring the workshop\, participants will engage in hand
s-on demonstrations covering the various aspects of the *nrt* ecosystem. T
his practical experience aims to equip attendees with the knowledge and sk
ills necessary to effectively utilize this tool in their projects\, enhanc
ing their capability to leverage any of its components for their projects.
DTSTAMP:20260613T183554Z
LOCATION:Raiffa Room (IIASA)
SUMMARY:Workshop: The nrt Ecosystem: A Unified Approach to Forest Disturban
ce Monitoring - Kenji Ose
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/XRCSRU/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-QBNFXV@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241002T163000
DTEND;TZID=Europe/Lisbon:20241002T165000
DESCRIPTION:Earth observation data provides an invaluable resource to asses
s the state and condition of the environment. However\, many domain–spec
ific applications\, such as the mapping of species-specific habitats and v
egetation for conservation\, often require specific spatial and thematic r
esolutions\, rather than off–the–shelf products. And although remotely
sensed data is critical to assess actual coverage\, particularly for the
assessment of restoration opportunities\, knowledge on the potential distr
ibution of habitats and vegetation is usually required. Here I will provid
e an overview of ongoing efforts to estimate current and potential vegetat
ion types across global\, European and local extents. I will focus both on
approaches to integrate existing data sets for global and European habita
t estimates\, but also demonstrate the potential of earth observation data
and deep learning to identify vegetation types at high resolution. Finall
y\, I will highlight opportunities to bring the Earth observation and ecol
ogy community closer together particularly in the light of data gaps\, har
monization and standards.
DTSTAMP:20260613T183554Z
LOCATION:Theatre Hall (Conference Center Laxenburg)
SUMMARY:Actual and potential habitat and vegetation type mapping to support
conservation science - Martin Jung
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/QBNFXV/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-FJCFNF@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241002T163000
DTEND;TZID=Europe/Lisbon:20241002T171500
DESCRIPTION:Integrity of natural ecosystems is one of the main concerns of
current European and Global Green Policies\, e.g.\, the European Green Dea
l. Public administration managers need reliable and long-term information
for a better monitoring of the ecosystems and climate evolution and inform
decision makers. Data Spaces are intended to become the EC comprehensive
solution to integrate data from different sources with the aim to generate
and provide a more ready to use knowledge on climate change\, circular ec
onomy\, pollution\, biodiversity\, and deforestation. This workshop aims t
o discuss pros and cons of some technological solutions in terms of Data S
paces\, OGC standards\, semantic descriptions\, datacubes\, FAIR principle
s and sovereignty of data. It also intends to share lessons learned from m
ain EC projects dealing with the topic: AD4GD\, GREAT\, B-cubed\, Fairicub
e\, etc.\n\nRecording of the session: \nhttps://youtu.be/JH14NmIazpc?si=I8
jQUkY5uWhLfzE8
DTSTAMP:20260613T183554Z
LOCATION:Maria Theresia Seminar room (Conference Center Laxenburg)
SUMMARY:Workshop: Data Spaces: the EC solution for environmental\, biodiver
sity and climate challenges. Different approaches on multisource data\, se
mantics\, FAIRness and sovereignty - Joan Maso\, Ivette Serral
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/FJCFNF/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-Q9UQSZ@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241002T165000
DTEND;TZID=Europe/Lisbon:20241002T171000
DESCRIPTION:Biodiversity loss is a critical environmental concern\, with ha
bitat destruction and degradation identified as key drivers. Recent advanc
ements in computational methods and the ever-growing availability of Earth
Observation (EO) data enable detailed analyses of land cover changes at u
nprecedented spatial and temporal scales. This paper develops a set of ind
icators of land cover and land cover conversions to assess potential press
ures on terrestrial biodiversity and ecosystems. Key land cover conversion
s include deforestation/reforestation\, cropland expansion/contraction\, a
nd urban/infrastructure development. We leverage two high-resolution datas
ets (i.e. the Copernicus Climate Change Initiative Land Cover [CCI-LC] and
the Global Human Settlement Layer [GHSL] built-up area) to develop nation
al and subnational indicators for all countries globally\, spanning 2000-2
020 for CCI-LC and 1975-2030 for GHSL. The analysis reveals a continued de
cline in natural and semi-natural vegetation cover in many OECD countries
and partner countries since the 2000 baseline. For example\, Brazil experi
enced a substantial loss of tree cover (200\,000 km²) between 2000 and 20
20\, equivalent to an area exceeding Switzerland's landmass by a factor of
six. Meanwhile\, most OECD countries exhibited a net gain in tree cover d
uring the same period. Urban development is another key reason for the obs
erved decline in natural and semi-natural vegetated land where countries s
uch as China and India displayed a significantly higher increase in artifi
cial surfaces compared to OECD countries over the past two decades. Result
s currently only account for the ecosystem extent and do not account for t
he ecosystem condition. For instance\, some grassland land cover may have
been significantly modified by long-term grazing and is in fact intensivel
y managed grassland (wild prairies vs grassland pastures). Therefore\, the
se results should be considered alongside complementary data sources to pr
ovide a more comprehensive picture of biodiversity pressures and highlight
that current global land monitoring EO products do not adequately meet th
e needs of policy analysts who require data at the interface of land cover
and land use.
DTSTAMP:20260613T183554Z
LOCATION:Theatre Hall (Conference Center Laxenburg)
SUMMARY:Land cover change and biodiversity pressures: A global analysis lev
eraging EO data - Mikaël Maes\, Ivan Haščič
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/Q9UQSZ/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-LYJ8FV@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241002T171000
DTEND;TZID=Europe/Lisbon:20241002T173000
DESCRIPTION:Accurate and timely flood risk assessment is paramount for effe
ctive disaster mitigation and preparedness. Traditional flood susceptibili
ty maps (FSMs) often fall short by providing static representations\, fail
ing to capture the dynamic nature of flood risk in a changing climate. Thi
s study presents a novel dynamic FSM framework that integrates high-resolu
tion climate data and temporal analysis to address these limitations. Deve
loped within the context of the Open-Earth-Monitor Cyberinfrastructure (OE
MC) project\, our methodology offers a significant advancement in flood ri
sk modeling.\nTo generate dynamic\, high-resolution (1 km) FSMs for the Me
diterranean region\, we utilized the Random Forest algorithm. These maps u
niquely adapt to varying seasonal conditions\, precipitation intensities\,
and post-drought scenarios. Our model's adaptability stems from its train
ing on a comprehensive dataset that combines flood and non-flood locations
from the Copernicus Emergency Management Service (EMS) and the Global Flo
od Database v1. Additionally\, we incorporated crucial factors influencing
flood events\, including elevation\, slope\, land cover\, drainage densit
y\, soil moisture\, and precipitation. Model evaluation employed cross-val
idation techniques utilizing both training and testing datasets. This comp
rehensive assessment confirmed the superior performance of the Random Fore
st model\, solidifying its effectiveness as a robust tool for flood suscep
tibility mapping.
DTSTAMP:20260613T183554Z
LOCATION:Theatre Hall (Conference Center Laxenburg)
SUMMARY:Dynamic Flood Susceptibility Assessment: Harnessing High-Resolution
Data for Effective Risk Reduction - Hamidreza Mosaffa
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/LYJ8FV/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-PTP77L@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241002T180000
DTEND;TZID=Europe/Lisbon:20241002T180500
DESCRIPTION:Tropical vegetation dynamics and ecosystem carbon (C) stocks ty
pically vary with local topography and forest disturbance history. Yet\, n
either remote sensing nor vegetation modeling captures the underlying mech
anistic processes determining ecosystem functioning and therefore the resu
lting estimates often do not match field observations of vegetation C stoc
ks\, especially so in hyperdiverse tropical forest ecosystems. This mismat
ch is further aggravated by the fact that multiple interacting factors\, s
uch as climatic drivers (i.e.\, temperature\, precipitation\, climate seas
onality)\, edaphic factors (i.e.\, soil fertility\,\ntopographic diversity
) and diversity-related parameters (i.e.\, species composition and associa
ted plant functional traits) in concert determine ecosystem functioning an
d therefore affect tropical forest C sink-strength. Here\, we propose a no
vel framework designed for integrating in-situ observations of local plant
species diversity with remotely sensed estimates of plant functional trai
ts\, with the goal to deduce parameters for a recently developed trait- an
d size-structured demographic vegetation model. Plant-FATE (Plant Function
al Acclimation and Trait Evolution) captures the acclimation of plastic tr
aits within individual plants in response to the local environment and sim
ulates shifts in species composition through demographic changes between c
oexisting species\, in association with differences in their life-history
strategies. Our framework allows to project the functional response of tro
pical forest ecosystems under present and future climate change scenarios
and thus should have crucial implications for assisted restoration and man
agement of tropical plant species threatened by extinction.
DTSTAMP:20260613T183554Z
LOCATION:Foyer
SUMMARY:Landscape-scale And Spatially Explicit Representation of tropical v
egetation dynamics and ecosystem carbon stocks (LASER) - Florian Hofhansl
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/PTP77L/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-ADD7VA@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241002T180500
DTEND;TZID=Europe/Lisbon:20241002T181000
DESCRIPTION:Among the many services in-situ datasets can provide to society
\, one of the more pressing interests currently active in the Earth Observ
ation (EO) sector is the integration of in-situ and satellite datasets. Th
e remote sensing community is actively using ICOS (Integrated Carbon Obser
vation System) outputs for calibration and validation activities of satell
ite products. However\, there are additional measurements currently exclud
ed from the ICOS portfolio that could be beneficial for calibration and va
lidation opportunities: for example\, fraction of absorbed photosynthetic
active radiation (fAPAR) and land surface temperature (LST) from thermal c
ameras. \nAn experimental setup was implemented on a subset of ICOS statio
ns for estimating leaf area index (LAI)\, strictly related to fAPAR\, from
above- and below-canopy measurements of photosynthetic active radiation (
PAR). The first longer-than-1-year datasets being available\, we present s
ome relevant preliminary results and the future direction of this activity
. \nNASA recently published some best practices on LST measurements for va
lidation of satellite products. At this scope\, a single thermal camera of
high accuracy is deployed on a network of measuring stations. We intend t
o check how this setup relates to different configurations\, such as diffe
rent camera models\, or the deployment of 3-4 lower-standard sensors looki
ng at different angles\, thus increasing the spatial resolution. \nAdditio
nal points under scrutiny are: what is the heterogeneity of these variable
s in the eddy covariance footprint\, and how can these measurements add va
lue to the net ecosystem exchange (NEE) and its derived products? And how
can the integration between satellite imagery and ground observations bene
fit from them?
DTSTAMP:20260613T183554Z
LOCATION:Foyer
SUMMARY:Exploring additional in-situ measurements for the integration of ed
dy covariance system observations with remote sensing time series - Simone
Sabbatini
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/ADD7VA/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-3MQPHQ@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241002T181000
DTEND;TZID=Europe/Lisbon:20241002T181500
DESCRIPTION:Air pollution is a health risk to millions of citizens in Europ
e. Critical concentrations of nitrogen-dioxide (NO2)\, ozone (O3)\, and pa
rticulate matter (PM10 and PM2.5) occur predominantly in densely populated
areas affected by high volumes of traffic or industry. Although several t
housand air quality stations scattered over Europe record hourly measureme
nts\, the EEA publishes continuous maps on an annual basis with considerab
le time lag. However\, there is a public benefit in accessing such maps mo
re timely.\nWith the OEMC Air Quality Monitor we design tools which stream
line the mapping workflow building on top of the EEA methodology. The proc
ess includes gathering and pre-processing data (both measurement and covar
iates) and making spatial predictions for the four mentioned air pollutant
s. We leverage public station measurements\, gridded climate and atmospher
ic transport model outputs\, and land cover and traffic information as wel
l as open source software. This combination facilitates a transparent way
to map air quality in Europe at one kilometer spatial resolution for daily
\, monthly\, and annual intervals.
DTSTAMP:20260613T183554Z
LOCATION:Foyer
SUMMARY:A European Air Quality Monitor - Johannes Heisig\, Brian Pondi
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/3MQPHQ/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-KDHV8A@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241002T181500
DTEND;TZID=Europe/Lisbon:20241002T182000
DESCRIPTION:The pervasive presence of microplastics in terrestrial ecosyste
ms has emerged as a pressing environmental concern. Recent studies have id
entified soil as a major sink for microplastics contamination\, potentiall
y surpassing oceanic levels by factors ranging from 4 to 23-fold. The smal
l size of microplastics and the complexity of soil matrices as sink substr
ates pose challenges for quantifying soil pollution. As a result\, current
analytical methods are limited in efficiency\, making large-scale environ
mental assessments unfeasible. The vertical incorporation of microplastics
into soil\, along with the challenges of recognizing microscopic objects
in satellite images\, restricts the practicality of using remote sensing f
or direct large-scale environmental assessments. Hence\, a more comprehens
ive approach is necessary to tackle these challenges. One potential soluti
on involves utilizing satellite imagery combined with a maximum entropy mo
del. By integrating locations where microplastic presence has been confir
med and extracted from soil samples\, the maximum entropy model can establ
ish a connection between satellite-derived environmental predictors and th
e presence of microplastics in soil. The aim of this research was to asses
s the practicality and viability of employing this approach in a real-worl
d setting.\n\nTo test our approach\, we designed a case study covering wid
er administrative area of the City of Osijek\, Croatia. For training data\
, we utilized 31 sampled locations where soil microplastics have been conf
irmed through previous research\, along with environmental variables prima
rily derived through signal enhancement of Sentinel-based imagery. After l
iterature review\, a preliminary list of 31 environmental predictor variab
les was generated\, covering various facets of microplastics input to the
soil and their dispersion in the environment. These were tested for varian
ce inflation factor (VIF) and spatial autocorrelation to identify statisti
cally significant variables for model calibration. To relate environmental
variables to microplastics presence\, we leveraged maximum entropy model.
The best-performing model underwent additional testing using various perm
utation tests to evaluate its robustness. We identified 4491 different set
s of three environmental variables eligible for further examination. We em
ployed each combination to train maximum entropy models using 5-fold cross
-validation to identify the most robust model. Additional testing included
jackknife cross-validation to identify and remove outlier samples.\n\nThe
best performing model\, with an AUC under the ROC of 0.863\, was the one
trained using combination of environmental predictors including land cover
(CLC+ Backbone raster product)\, soil moisture derived from Sentinel-1 im
agery\, and catchment areas determined through hydrological analysis of th
e digital elevation model. The output prediction map clearly delineates ar
eas that highly likely represent pollution hotspots. This research demonst
rates the feasibility of utilizing satellite imagery\, in conjunction with
topological analysis and maximum entropy models\, to conduct large-scale
environmental assessment and accurately pinpoint hotspots of soil micropla
stics contamination. This approach could significantly aid future stakeho
lders since the EU has taken proactive steps as of 2018 to tackle soil mic
roplastics pollution\, by implementing regulations\, action plans\, and in
itiatives to prevent plastic pellet loss. Furthermore\, the European Commi
ssion has incorporated impact assessments into its decision-making process
regarding microplastics. Advanced environmental monitoring techniques off
er potential in tracking progress and quantifying effectiveness of forthco
ming measures.
DTSTAMP:20260613T183554Z
LOCATION:Foyer
SUMMARY:Satellite-based maximum entropy modelling for identifying potential
soil microplastics hotspots - Bruno Ćaleta
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/KDHV8A/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-TTZ89A@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241002T182000
DTEND;TZID=Europe/Lisbon:20241002T182500
DESCRIPTION:The collection of representative observational datasets in envi
ronmental sciences is crucial for advancing the understanding of the pheno
mena under consideration. The integration between in-situ datasets with re
mote sensing and machine learning techniques makes possible reliable predi
ctions and analyses with enhanced precision and resolution. The OEMC proje
ct aims at supporting informed decision making on environmental policies f
or the benefit of the whole society\, by combining in-situ measurements an
d remote sensing datasets. Here we investigate the impacts of some of the
OEMC in-situ datasets on society and policymakers: how are the in-situ da
tasets supporting the use-cases of the project? What is their combined pot
ential in terms of technological advancement and knowledge boost? The foll
owing categories of OEMC in-situ data\, their benefits\, and relation to s
ustainable development goals (SDGs) are scrutinised.\nGHG fluxes: GHG flux
es ground observations\, combined with satellite data\, can be proficientl
y used for calibration and validation of models\, with benefits in terms o
f better predictions\, development of early warning systems\, better under
standing of climate change impacts\, ecosystem services\, etc. Current and
potential stakeholders are the Intergovernmental Panel on Climate Change
(IPCC) and international projects such as the Global Carbon Project (GCP)
and FluxCom initiative. UNFCCC is also using GHG flux data. Related SDGs i
nclude 11\, 12\, 13 and 15.\nForest biomass: in-situ observations of fores
t biomass are fundamental in refining the assessment of global forest carb
on stocks and their change under natural and anthropogenic drivers. These
data serve the needs of a wide range of stakeholders\, from both the scien
tific and the policy making sectors\, interested in quantifying the actual
carbon sequestration capacity of forests and refining estimates of forest
inventories. Policies such as the European Forest strategy and monitoring
of SDG 15 will benefit from such datasets.\nMarine and terrestrial biodiv
ersity: these datasets support projects and activities of biodiversity con
servation\, a fundamental branch of Earth science and a crucial aspect for
the survival of humanity. Potential stakeholders include the European Env
ironmental Agency (EEA) and the Joint Research Centre of the European Comm
ission (JRC)\, and policies such as the European Biodiversity strategy and
SDGs 14 and 15.\nOcean and coastal datasets: the importance of ocean and
coastal organisms for the balance of the biosphere becomes more and more e
vident\, but scientific knowledge is still limited in comparison with the
terrestrial counterpart. Increasing the monitoring of these ecosystems is
crucial\, in particular for human communities living in coastal areas. EEA
and JRC are included in the stakeholders interested. Related SDG: 14\nLCL
U: in-situ land use and land cover information derived from processing lan
d surveys data and satellite imagery support land degradation alert system
s and EO mapping. Potentially supported SDGs are 11\, 12\, 13\, 14 and 15.
\nAutomated ground observations: automated measurements of biological proc
esses support the validation of EO products and provide input for ecologic
al modelling. Data consistency is enhanced by the availability of a contin
uous dataflow from field sites where sampling is logistically or financial
ly constrained. Possible applications include early warning systems in agr
icultural\, forestry\, and urban greening sectors\, improved agronomic and
silvicultural practices\, monitoring ecosystems productivity and biodiver
sity levels. Potential stakeholders are the EEA\, the JRC\, entities invol
ved in mandatory and voluntary carbon markets (UNFCCC\, UNDP\, private com
panies)\, national governments and local administrations. Related SDGs are
11\, 12\, 13 and 15.\nCitizen science: citizen science in-situ data for t
raining and validation of EO mapping models can play a fundamental part in
supporting environmental policies\, covering a wide range of topics\, fro
m deforestation to aboveground biomass assessment\, from crop type to land
use and land cover distributions. The European Green Deal is expected to
greatly benefit from this type of in-situ datasets\, and SDGs 13\, 14 and
15 will potentially be supported.\nIn-situ and gridded integration: althou
gh the combination of in-situ and gridded datasets is common\, their spati
al resolution often differs. A case study focusing on eddy covariance data
tries to shed light on the overlapping degree of ground and satellite foo
tprints\, with benefits for society in terms of technological advancements
and a deeper understanding of how ecosystems react to climate change\, wi
th potential benefits for SDGs 13 and 15.
DTSTAMP:20260613T183554Z
LOCATION:Foyer
SUMMARY:Ground measurements and in-situ observations from the OEMC project
for the support of environmental policies and the benefit of society - Sim
one Sabbatini
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/TTZ89A/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-UTXEKX@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241002T182500
DTEND;TZID=Europe/Lisbon:20241002T183000
DESCRIPTION:Assimilating Leaf Area Index and Soil Moisture from Optical and
SAR Data into the WOFOST Model to Improve Maize (Zea mays L.) Yield Estim
ation\n\nGebeyehu Abebe 1\,2\, Odunayo David Adeniyi1\,3\, Amazirh Abdelha
kim1\,4\, Zoltan Szantoi1\n1European Space Agency (ESA)/ESRIN\, Frascati R
M 00044\, Italy\n2Department of Natural Resources Management\, Debre Berha
n University\, Debre Berhan\, Ethiopia. \n3Department of Earth and Environ
mental sciences\, University of Pavia\, Italy\, Via Ferrata 1\, Pavia\, 27
100\, Italy.\n4Centre for Remote Sensing Applications (CRSA)\, Mohammed VI
Polytechnic University (UM6P)\, Hay My Rachid\, Ben Guerir 43150\, Morocc
o\nAbstract \nCrop Simulation Models (CSM) are commonly used to estimate c
rop yield at a local scale. Meanwhile\, Remote Sensing (RS) data provides
valuable information on crop parameters like soil moisture and leaf area i
ndex (LAI) across different spatial scales. Data Assimilation (DA) is a po
werful technique that combines CSM and RS data from satellite imagery to e
nhance simulated crop state variables and model outputs\, such as total bi
omass and yield. In this study\, we aimed to implement a joint assimilatio
n strategy for LAI and soil moisture data in the WOFOST model. The goal wa
s to simulate rainfed grain maize yield at the field scale and evaluate it
s performance at both the field and administrative zone levels. The Ensemb
le Kalman Filter (EnKF) algorithm was applied to achieve this integration.
The LAI and soil moisture data were sourced from Sentinel 3 and Soil Mois
ture Active Passive (SMAP) L3 Radiometer Global Daily 9 km Soil Moisture\,
respectively. The study tested various assimilation scenarios\, including
deterministic modeling\, independent assimilation of LAI from Sentinel 3\
, independent assimilation of soil moisture from SMAP\, and joint assimila
tion of both LAI and soil moisture data. Ongoing validation involves compa
ring the simulated grain maize yield with field observations and independe
nt grain maize statistics data in the major maize-growing administrative z
ones of western and southwestern Ethiopia. The expected outcomes include i
mproved accuracy in grain maize yield predictions at the field scale and e
nhanced crop monitoring and forecasting at local and national levels.\nKey
words: Data assimilation\; EnKF\; LAI\; soil moisture\; WOFOST\; grain mai
ze yield
DTSTAMP:20260613T183554Z
LOCATION:Foyer
SUMMARY:Assimilating Leaf Area Index and Soil Moisture from Optical and SAR
Data into the WOFOST Model to Improve Maize (Zea mays L.) Yield Estimatio
n - Gebeyehu A. Zeleke
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/UTXEKX/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-BSGLJR@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241002T183000
DTEND;TZID=Europe/Lisbon:20241002T183500
DESCRIPTION:The ‘Exploitation Platform’ concept derives from the need t
o access and process an ever-growing volume of data. Many web-based platfo
rms have emerged - offering access to a wealth of satellite Earth Observat
ion (EO) data. Increasingly\, these are collocated with cloud computing re
sources and applications for exploiting the data. Rather than downloading
the data\, the exploitation platform offers a cloud environment with acces
s to EO data and associated compute and tools that facilitate the analysis
and processing of large data volumes. The Exploitation Platform benefits
users\, data providers and infrastructure providers. Users benefit from th
e scalability & performance of the cloud infrastructure\, the added-value
services offered by the platform – and avoid the need to maintain their
own hardware. Data hosted in the cloud infrastructure reaches a wider audi
ence and Infrastructure Providers gain an increased cloud user base.\n\nUs
ers are beginning to appreciate the advantages of exploitation platforms.
However\, the market now offers a plethora of platforms with various added
value services and data access capabilities. This ever-increasing offer i
s rather intimidating and confusing for most users. In order to fully expl
oit the potential of these complementary platform resources we anticipate
the need to encourage interoperation amongst the platforms\, such that use
rs of one platform may consume the services of another directly platform-t
o-platform.\n\nEOEPCA (EO Exploitation Platform Common Architecture) is a
European Space Agency (ESA) funded project with the goal to define and agr
ee a re-usable exploitation platform architecture using standard interface
s to encourage interoperation and federation between operational exploitat
ion platforms - facilitating easier access and more efficient exploitation
of the rapidly growing body of EO and other data. Interoperability throug
h open standards is a key guiding force for the Common Architecture: platf
orm developers are more likely to invest their efforts in standard impleme
ntations that have wide usage\; off-the-shelf clients and software are mor
e likely to be found for standards-based solutions.\n\nThe EOEPCA system a
rchitecture is designed to meet a set of defined use cases for various lev
els of user\, from expert application developers to consumers. The archite
cture is defined as a set of Building Blocks (BBs)\, exposing well-defined
open-standard interfaces. These include Identity and Access Management\,
Resource Discovery\, Data Access\, Processing Workflows\, Data Cube Access
\, Machine Learning Operations\, and more. Each of these BBs are container
ized for Kubernetes deployment\, which provides an infrastructure-agnostic
deployment target.\n\nThe exploitation platform is conceived as a ‘virt
ual work environment’ where users can access data\, develop algorithms\,
conduct analysis and share their value-adding outcomes. The EOEPCA archit
ecture facilitates this through a Workspace BB that provides a user-centri
c platform experience in which the standard discovery\, visualisation and
access interfaces are re-used for user-owned resources maintained within t
he platform - including data\, applications\, added-value products (from p
rocessing)\, etc. This is supported by an Application Hub building-block t
hat provides interactive web-tooling for analysis\, algorithm development\
, data exploitation and provides a web dashboard capability through which
added-value outcomes can be showcased.\n\nOur presentation will highlight
the generalised architecture\, standards\, best practice and open source s
oftware components available.
DTSTAMP:20260613T183554Z
LOCATION:Foyer
SUMMARY:EO Exploitation Platform Common Architecture - Chandra Taposeea-Fis
her\, Garin Smith
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/BSGLJR/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-MSTF9Y@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241002T183500
DTEND;TZID=Europe/Lisbon:20241002T184000
DESCRIPTION:Air pollution has emerged as a critical global concern\, exerti
ng adverse impacts on natural ecosystems and exacerbating the pace of clim
ate change. Despite the existence of mitigation strategies\, the accurate
quantification of methane emissions remains a formidable challenge\, imped
ing progress towards meeting emission reduction targets set for 2030. This
study is dedicated to addressing the urgent global issue of air pollution
\, with a particular focus on methane emissions\, known for their signific
ant contribution to climate change and associated environmental and health
hazards. Conventional monitoring techniques have proven inadequate\, leav
ing millions of abandoned oil wells unchecked in their methane emissions\,
thus demanding a comprehensive solution. In response\, we present a nove
l technological advancement based on satellite data\, to facilitate the pr
ecise measurement\, detection\, and ongoing monitoring of methane leaks. B
y harnessing breakthroughs in deep tech disciplines such as Earth observat
ion integrated with machine learning\, astrophysical methodologies\, theor
etical chemistry\, and computational fluid dynamics\, this technology enab
les the identification of methane leaks across diverse geographical locati
ons worldwide.\nFurthermore\, this study underscores the critical importan
ce of fostering collaboration and information exchange among stakeholders
to optimize the effectiveness of emission reduction endeavors. Through its
innovative approach and interdisciplinary collaboration\, this work aspir
es to deliver a significant contribution towards mitigating climate change
impacts and safeguarding natural resources for the benefit of future gene
rations.
DTSTAMP:20260613T183554Z
LOCATION:Foyer
SUMMARY:Satellite-based methane discovering and monitoring: Revolutionizin
g air pollution control - Santiago Vargas\, Maria Fernanda González
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/MSTF9Y/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-TWTXRD@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241002T184000
DTEND;TZID=Europe/Lisbon:20241002T184500
DESCRIPTION:One of the major challenges in data manageme
nt is (and in the project OEMC) is demonstrating the correct implementatio
n of the FAIR
(Findable\, Accessible\, Interoperable and Reproducible) principles.
To make data accessible\, it is required that “data is retrievable by th
eir identifier using a standardised communications protocol that should be
open\, free\, and universally implementable”.
\nOEMC has produced a
list of datasets that are exposed to the public with and elegant Open-Earth-Monitor App. Our talk will f
ocus on demonstrating the interoperability of the taken approach\, showing
an alternative web map browser that gives access to the same OEMC dataset
s. This web map browser was deployed using the original MiraMon Map Browser technology wi
thout any customization and using only Open Geospatial Consortium (OGC) st
andards web services calls\, demonstrating the technical interoperability
of the OEMC services. The presented visualization portal goes beyond a simple visualization by combinin
g the OGC WMS standard with modern web browser capabilities. During the ta
lk\, we will demonstrate how to access OEMC datasets through MiraMon brows
er functionalities\, such as query by location\, multiple projections supp
ort\, reading storymaps\, and data multidimensional support among others.
An important feature of the visualization portal is that it allows the fin
al users to provide common feedback about the data (such as star rating an
d comments) that are shared with other users as well as to produce and sha
re their own storymaps and this way share the knowledge gained by analysin
g the data.
DTSTAMP:20260613T183554Z
LOCATION:Foyer
SUMMARY:The interoperable alternative map browser for the datasets produced
in OEMC - Joan Maso\, Imma Serra
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/TWTXRD/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-GBXGKF@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241002T184500
DTEND;TZID=Europe/Lisbon:20241002T185000
DESCRIPTION:Hydrological planners need accurate and up-to-date information
on snow dynamics. The OEMC project aims to improve the measurement of the
Snow Water Equivalent as an estimation of the available water stored in sn
ow covered areas in the Alps to support planning activities such as hydrop
ower\, agriculture and drinking water. To reach this objective high spatia
l and temporal information is required. Several remote sensing sensors exi
st with different spatial and temporal resolutions and hence different pot
entiality. To produce optimal results\, an integration of different data s
ources is necessary. This requires large computational resources as well a
s huge data amounts. In this context\, standardized cloud processing APIs
such as OpenEO serve as powerful processing tools that can promote opennes
s and reproducibility. In this talk we will present how we exploited cloud
native EO to improve the development of snow products\, such as snow cove
r fraction and snow water equivalent maps.
DTSTAMP:20260613T183554Z
LOCATION:Foyer
SUMMARY:Integrating different remote sensing products to produce high spati
al and temporal snow estimates in the cloud - Valentina Premier
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/GBXGKF/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-PYZRQT@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241002T185000
DTEND;TZID=Europe/Lisbon:20241002T185500
DESCRIPTION:At the OEMC Global Workshop in 2023\, we presented a community
led initiative part of the wider Open Innovation framework at European Spa
ce Agency that worked to implement an open\, interactive\, user intuitive
platform for a constantly updated\, comprehensive and detailed overview of
the dynamic environment of the open source digital infrastructure for geo
spatial data storage\, processing and visualisation systems. Today\, we h
ave over 450 documented geospatial FOSS projects\, interconnected into the
FOSS4G ecosystem. \nAt the OEMC Global Workshop of 2024\, the team presen
ts the work done within the next steps\, identifying quality metrics for o
pen source software and assess the connection with the health of the assoc
iated project and thus paving the way to understand the benefits as well a
s the pitfalls of certification in geospatial open source software. \nThe
work is supported by ESA\, under the Permanently Open Call for Proposals f
or Future EO-1: EO Science for Society.
DTSTAMP:20260613T183554Z
LOCATION:Foyer
SUMMARY:The evolution of the OSS4gEO\, a FOSS4G resources platform initiati
ve - Codrina Maria Ilie
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/PYZRQT/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-TJJTXG@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241002T185500
DTEND;TZID=Europe/Lisbon:20241002T185800
DESCRIPTION:Over the last decade\, UAV systems have enabled high-resolution
data collection for various applications at relatively low\n\ncosts and w
ith great flexibility in acquisition time and parameters [5]. This data ca
n serve as a valuable reference for large-\nscale space-borne applications
. However\, the flexibility in image acquisition presents challenges relat
ed to varying data types\n\nand quality\, which are affected by environmen
tal conditions\, sensor specifications\, and radiometric calibration. Capt
uring\ncomparable reflectance values with UAV systems is particularly chal
lenging\, and many early studies relied on minimal\npreprocessing or raw d
igital number (DN) values [4]. Given that some datasets' spectral informat
ion (reflectance/DN) may\nnot be directly comparable\, a classifier that e
mphasizes generalized texture information is needed rather than relying so
lely\non spectral data. Among common machine learning (ML) techniques\, th
e convolutional neural network (CNN) of deep\nlearning (DL) has proven to
be a successful tool in classifying images of land use from remote sensing
data [1\, 2]. CNN\nallows high-order representation based on generalized
texture information already used in crop classification [7\, 3\, 6].\nOur
research explores the potential of using low-to-moderate-quality UAV data
for agricultural pattern classification\,\nfocusing on how color-balancing
techniques can enhance data consistency when images are captured under va
riable lighting\n\nconditions. We evaluated the performance of CNNs in cla
ssifying agricultural patterns using moderate- to low-quality\, high-\nres
olution (0.07-meter) optical multispectral data collected from three agric
ultural test sites in Germany between 2019 and\n\n2021. We used models tra
ined exclusively on samples converted to reflectance values and applied th
em to images impacted\nby different sunlight conditions\, including digita
l number (DN) and reflectance data. The models were trained to classify\ns
mall-scale agricultural patterns\, such as damaged and undamaged canopy\,
weed-infested and bare soil areas\, across four\ncrop types: winter wheat\
, rapeseed\, corn\, and spring barley.\nThis study\, funded by the German
Federal Ministry for Economic Affairs and Energy (FKZ: 50EE1901)\, is carr
ied out in\ncollaboration with CLAAS E-Systems GmbH to develop an applicat
ion for crop monitoring based on Sentinel-1 data.
DTSTAMP:20260613T183554Z
LOCATION:Foyer
SUMMARY:OPTIMIZING UAV DATA PROCESSING FOR PATTERN CLASSIFICATION WITH CNN
ON LOW TO MODERATE-QUALITY IMAGERY - Linara Arslanova
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/TJJTXG/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-MKMTKA@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241003T090000
DTEND;TZID=Europe/Lisbon:20241003T093000
DESCRIPTION:In the face of the escalating global climate crisis we are faci
ng\, the integration of open data\, open science\, and open platforms has
emerged as a transformative approach in Earth Observation (EO) and its app
lications. This abstract explores the pivotal role of these interconnected
principles in addressing these climate challenges in the European Space A
gency (ESA).\nOpen data initiatives have democratized access to valuable E
O datasets\, fostering collaboration and innovation across a wide range of
stakeholders from policy makers\, policy owners to scientists\, and end u
sers as farmers. By facilitating transparency and accessibility\, these in
itiatives enable a deeper understanding of Earth's systems\, crucial for i
nformed decision-making amidst climate uncertainty.\nCoupled with open dat
a\, open science practices advocate for transparency\, reproducibility\, a
nd the sharing of methodologies\, results\, and findings. This collaborati
ve view not only accelerates scientific discovery but also cultivates a cu
lture of accountability essential in confronting the multifaceted complexi
ties of climate change and the climate finance behind it.\nFurthermore\, t
he integration of open platforms also in ESA provides a dynamic infrastruc
ture for EO research and application development. These platforms not only
streamline data management and analysis but also empower communities to c
o-create solutions tailored to their unique challenges\, fostering resilie
nce in the face of environmental threats\, which ESA is supporting through
many of its projects and programmes.\nAs the climate crisis intensifies\,
the synergy between open data\, open science\, and open platforms offers
a promising pathway forward in EO endeavours. By fostering inclusivity\, i
nnovation\, and collective action\, this integrated approach holds the pot
ential to catalyse transformative change\, safeguarding our planet for fut
ure generations making good use of all ESA’s EO missions and options.
DTSTAMP:20260613T183554Z
LOCATION:Theatre Hall (Conference Center Laxenburg)
SUMMARY:Open data\, open science and open platforms: way forward with Earth
Observation in the actual climate crisis - Inge Jonckheere
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/MKMTKA/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-8WPVGA@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241003T093000
DTEND;TZID=Europe/Lisbon:20241003T100000
DESCRIPTION:Due to their ability to observe the land surface irrespective o
f weather and lightning conditions\, radar satellite constellations are in
dispensable for monitoring of highly dynamic land surface processes. While
in the past only scatterometer missions allowed consistent monitoring at
global scale\, albeit at very coarse spatial scales\, this has changed fun
damentally with the Copernicus Sentinel-1 mission that stands out as one o
f the most successful Synthetic Aperture Radar (SAR) missions. With its no
vel combination of high spatial and temporal resolution\, long-term missio
n planning\, and open data policy it has served as a role model for the co
nceptualization of future radar missions. With the upcoming launches of th
e Japanese Advanced Land Observing Satellite-4 (ALOS-4) satellite\, the NA
SA-ISRO SAR Mission (NISAR)\, ESA’s Biomass mission\, and the Copernicus
Radar Observing System for Europe in L-band (ROSE-L) satellites\, there i
s now the opportunity to monitor dynamic processes at high spatial resolut
ion (10-20m) with short revisit times (1-3 days) at multiple frequencies (
C-\, L-\, and P-band). In this presentation I will discuss a collaborative
effort of the Vienna University of Technology (TU Wien) and the EODC Eart
h Observation Data Centre to build a global multi-frequency SAR datacube s
uited for applying hybrid algorithms combining physical models and machine
learning. Furthermore\, I will show examples of how we use this tailored
datacube for the monitoring of soil moisture\, floods\, vegetation\, and s
oil structural characteristics.
DTSTAMP:20260613T183554Z
LOCATION:Theatre Hall (Conference Center Laxenburg)
SUMMARY:Towards a multi-frequency SAR datacube for global monitoring of dyn
amic land surface processes - Wolfgang Wagner
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/8WPVGA/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-QV3EBZ@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241003T100000
DTEND;TZID=Europe/Lisbon:20241003T103000
DESCRIPTION:Please provide an abstract and exact title as soon as possible
DTSTAMP:20260613T183554Z
LOCATION:Theatre Hall (Conference Center Laxenburg)
SUMMARY:Brazilian use case of economic land-use modelling to impact policy
- Gilberto Camara
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/QV3EBZ/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-W3JBXW@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241003T113000
DTEND;TZID=Europe/Lisbon:20241003T121500
DESCRIPTION:This hands-on workshop will present the use of big data analyti
cs to work with data available at the Open GEO Hub cloud service
DTSTAMP:20260613T183554Z
LOCATION:Maria Theresia Seminar room (Conference Center Laxenburg)
SUMMARY:Workshop: Big Data Analytics in Open Geo Hub Cloud using SITS - Gil
berto Camara\, Deleted User
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/W3JBXW/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-DSGFLG@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241003T113000
DTEND;TZID=Europe/Lisbon:20241003T115000
DESCRIPTION:The primary objective of this study was to develop and evaluate
different remote sensing techniques for mapping Other Wooded Lands (OWL)\
, while also assessing the accuracy and uncertainties associated with clas
sifying OWL class compared to forest and grasslands. Additionally\, we aim
ed to design a scalable process for large-scale OWL mapping. As defined by
the Food and Agriculture Organization (FAO)\, OWLs are areas with 5-10% t
ree canopy cover for trees reaching a height of 5 meters at maturity\, or
with a combined cover of shrubs\, bushes\, and trees above 10 percent. Als
o\, OWLs must span a minimum land area of 0.5 hectares and exclude predomi
nantly agricultural or urban land uses. Three diverse landscapes were chos
en based on expert input\, encompassing natural regions globally and repre
senting the three main land cover classes of interest: forest\, OWL\, and
grassland. The selected areas were (1) Cheringoma\, Sofala\, Mozambique\;
(2) Cerrado biome\, Goiás\, Brazil\; and (3) Albacete and Jaén\, Spain.
For each Area of Interest (AOI)\, we selected a Sentinel-2 MGRS tile that
entirely covered the area. A stratified random sampling approach ensured r
obust sample collection across all land cover classes within each scene\,
resulting in over 1.7 million samples per scene. High-resolution imagery f
rom Google Earth/Bing was utilized for visual interpretation. The mapping
utilized data from 2022\, encompassing a six-month window before and after
the year of interest (totaling two years). A total of 174 metrics were ca
lculated on data from various sources to characterize land cover for OWL m
odeling. Data processing was conducted using Google Earth Engine (GEE)\, a
nd a Random Forest algorithm was employed for OWL land cover modeling. The
resulting maps exhibited a global accuracy of 74.5% (Mozambique) and 76.5
% (Brazil)\, Spain is currently under analysis. In Mozambique\, the produc
er accuracy for OWL was 42.4%\, with omissions associated with grasslands
and forests at 34.5% and 21.5%\, respectively. For the Cerrado region\, bo
th user and producer accuracies were notably higher\, at 71.6% and 74.7% r
espectively. Mapping results were combined with ICESat-2 satellite lidar\,
where available\, to investigate the vegetation height and structure of l
and cover classes. Top of canopy heights\, median heights\, and percent fo
rest cover decreased between forest\, OWL\, and grassland classes. This me
thodology offers a scalable approach for mapping OWLs\, contributing to im
proved deforestation monitoring and environmental protection efforts.
DTSTAMP:20260613T183554Z
LOCATION:Theatre Hall (Conference Center Laxenburg)
SUMMARY:A Multi-Source Remote Sensing Approach for Large-Scale Mapping of O
ther Wooded Lands - Nathália Teles
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/DSGFLG/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-BXHX3T@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241003T113000
DTEND;TZID=Europe/Lisbon:20241003T121500
DESCRIPTION:In this workshop\, the participants will apply an automated mac
hine learning framework suitable for EO data.
DTSTAMP:20260613T183554Z
LOCATION:Raiffa Room (IIASA)
SUMMARY:Workshop: Geo-AutoML with Scikit-map - Leandro Leal Parente
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/BXHX3T/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-8NF7GF@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241003T113000
DTEND;TZID=Europe/Lisbon:20241003T121500
DESCRIPTION:The workshop aims to present new data sets related to tree mana
gement available on the GFW website and tools for collecting feedback on t
hese data sets and tools to collect training and validation data.\n The li
st of new data sets includes the new version of Spatial Database of Plante
d Trees\, the\nNatural Lands Map\, and a new version of the Forest managem
ent layer for the year 2020. Discussion will focus on current challenges d
ata producers face such as dataset definitions\, data gaps\, and quality a
ssurance of the presented datasets.
DTSTAMP:20260613T183554Z
LOCATION:Wodak Room (IIASA)
SUMMARY:Workshop: Global Forest Watch: the latest data and tools to better
protect forests - Myroslava Lesiv\, Elise Mazur\, Liz Goldman
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/8NF7GF/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-FN3KHE@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241003T115000
DTEND;TZID=Europe/Lisbon:20241003T121000
DESCRIPTION:Climate change poses a significant threat to the distribution a
nd composition of forest tree species worldwide. European forest tree spec
ies’ range is expected to shift to cope with the increasing frequency an
d intensity of extreme weather events\, pests and diseases caused by clima
te change. Despite numerous regional studies\, a continental scale assessm
ent of current changes in species distributions in Europe is missing due t
o the difficult task of modeling a species realized distribution and to qu
antify the influence of forest disturbances on each species. In this study
we conducted a trend analysis on the realized distribution of 6 main Euro
pean forest tree species (Abies alba Mill.\, Fagus sylvatica
L.\, Picea abies L. H. Karst.\, Pinus nigra J. F. Arnold\,
Pinus sylvestris L. and Quercus robur L.) to capture and map
the prevalent trends in probability of occurrence for the period 2000–2
020. We also analyzed the impact of forest disturbances on each species’
range and identified the dominant disturbance drivers. Our results reveal
ed an overall trend of stability in species’ distributions (85% of the p
ixels are considered stable by 2020 for all species) but we also identifie
d some hot spots characterized by negative trends in probability of occurr
ence\, mostly at the edges of each species’ latitudinal range. Additiona
lly\, we identified a steady increase in disturbance events in each specie
s’ range by disturbance (affected range doubled by 2020\, from 3.5% to 7
% on average) and highlighted species-specific responses to forest disturb
ance drivers such as wind and fire. Overall\, our study provides insights
into distribution trends and disturbance patterns for the main European fo
rest tree species. The identification of range shifts and the intensifying
impacts of disturbances call for proactive conservation efforts and long-
term planning to ensure the resilience and sustainability of European fore
sts.
DTSTAMP:20260613T183554Z
LOCATION:Theatre Hall (Conference Center Laxenburg)
SUMMARY:Multi-decadal trend analysis and forest disturbance assessment of E
uropean tree species: concerning signs of a subtle shift - Carmelo Bonanne
lla
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/FN3KHE/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-LGDAKZ@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241003T121000
DTEND;TZID=Europe/Lisbon:20241003T123000
DESCRIPTION:Earth Observation (EO) biomass and carbon datasets are increasi
ng and their potential as inputs to the environmental-economic accounting
framework based on SEEA was assessed in this study toward accounting for a
ll carbon pools: above-ground\, below-ground\, deadwood\, litter and soil
carbon. This demonstration allowed the compilation of carbon accounts in f
our accounting periods 2010-2017\, 2017-2018\, 2018-2019 and 2019-2020 for
six case countries namely Brazil\, Mozambique\, the Netherlands\, the Phi
lippines\, Sweden and USA\, and later on compared with the accounts from a
counterpart carbon accounting framework based on UNFCCC. The compiled car
bon accounts revealed the above-ground component being the dominant carbon
pool in Brazil and the Philippines\, while soil organic carbon outweighs
other carbon pools in the Netherlands\, Sweden and surprisingly Mozambique
. We found decreasing carbon stocks especially for Brazil even in shorter
accounting periods i.e.\, 2018-2019 captured by the EO dataset. This is in
contrast to what has been reported by countries to UNFCCC mostly reportin
g stability in the carbon flows over the years. Part of the discrepancy is
the country definitions of managed forests which can be inconsistent with
forest management datasets from EO (this study). Another reason is the de
pendency of countries on national forest inventories which are rarely upda
ted on an annual basis. Moreover\, our compiled accounts showed minimal ca
rbon emissions from forest degradation mainly driven by the choice of ecos
ystem extent input\, and lower soil carbon emissions than UNFCCC reports\,
potentially underestimating peatland emissions. The findings and outputs
from this demonstration echo the potential of EO datasets for carbon accou
nting especially with the advent of time series biomass data\, higher spat
ial resolution of ecosystem extent maps 5-10 m and online ecosystem accoun
ting tools for efficient use cases.
DTSTAMP:20260613T183554Z
LOCATION:Theatre Hall (Conference Center Laxenburg)
SUMMARY:SEEA carbon accounting using Earth Observation datasets and its com
parison with carbon accounts following the UNFCCC framework - Arnan Araza
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/LGDAKZ/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-CW3LLQ@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241003T133000
DTEND;TZID=Europe/Lisbon:20241003T135000
DESCRIPTION:Here we propose a planetary health diagnostic framework\, which
aims to track\, understand\, and characterize the Earth system during the
onset and progression of both chronic change (such as climate change) and
abrupt disruptions (stemming from climate extremes and socio-economic sho
cks). However\, monitoring a single component of the Earth system to guide
policy\, but ignoring other essential components\, could lead to misleadi
ng diagnostics and maladaptation of global sustainability. To gain insight
s into the integration of climate\, biosphere\, and society\, we apply an
interactive dimensionality reduction to the annual variability of multi-st
ream global data from 2003-2022\, including data representing the biospher
e and climate combined with national socio-economic indicators.\n \nWe fin
d that the interactions between biosphere\, atmosphere and socio-economy c
an be captured by three principal axes\, which cumulatively explain 17.3%\
, 22.8% and 24.5% of the variability condensed by non-interactive dimensio
nality reduction in each individual domain\, respectively. The 1st and 2rd
pairs of Biosphere-atmosphere-socioeconomic interactive axes describe ter
restrial vegetation and land surface water syndromes. The first axes posit
ively correlate to terrestrial vegetation productivity\, air temperature\,
and technology and public health. The second axes negatively correlate to
soil moisture\, potential evaporation\, and reflect several combined soci
oeconomic aspects such as land use and inequality. We find distinct trajec
tories across countries with high-income countries more resistant COVID-19
-induced economic shock. High and low income groups show contrasting traje
ctories that are related to poverty reduction and methane emission in the
low-income country group. This study advocates for a data-driven paradigm
to jointly monitor the recent trajectories of the biosphere\, atmosphere\,
and society that could provide a better understanding and early warning o
f the state of the Earth system for human well-being.
DTSTAMP:20260613T183554Z
LOCATION:Maria Theresia Seminar room (Conference Center Laxenburg)
SUMMARY:Systemic human-biosphere-atmosphere monitoring and diagnostics - Gr
egory Duveiller
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/CW3LLQ/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-BMUDD8@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241003T133000
DTEND;TZID=Europe/Lisbon:20241003T135000
DESCRIPTION:xcube is a mature and capable Python software package and frame
work for EO data ingestion\, processing\, analysis\, visualization\, and d
issemination. In the scope of the Open Earth Monitor project\, xcube is be
ing updated and expanded to support new data sources\, improve on-demand c
luster processing capabilities\, and run seamlessly on the new Copernicus
Data Space Ecosystem. Recent work also focuses on providing a maximally pr
econfigured turnkey distribution of xcube\, increasing its suitability as
a drop-in compute engine for cloud infrastructures such as CDSE. xcube’s
features are complemented by the new zappend tool\, which provides robust
creation and updating of large\, slice-structured Zarr datasets.\n\nThis
talk will describe and demonstrate a typical large-scale processing workfl
ow using the xcube framework in the CDSE ecosystem – running the gamut t
hrough data ingestion from multiple sources through the xcube data store s
ubsystem\, data cube construction and normalization\, data synthesis and p
rocessing to export\, dissemination\, and seamless visualization via the s
erver and viewer components. Scalability and big data capability is accoun
ted for throughout through approaches such as object storage\, paralleliza
tion\, on-demand cluster processing\, dataset pyramidization\, and lazy co
mputation. The newly implemented components and improved integration make
xcube an ideal tool for the realization of typical Open Earth Monitor work
flows.
DTSTAMP:20260613T183554Z
LOCATION:Theatre Hall (Conference Center Laxenburg)
SUMMARY:Large-scale EO processing with xcube on CDSE - Pontus Lurcock
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/BMUDD8/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-AMCPTF@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241003T133000
DTEND;TZID=Europe/Lisbon:20241003T141500
DESCRIPTION:Climate change is profoundly affecting the global water cycle\,
increasing the likelihood and severity of extreme water-related events. D
roughts are becoming more frequent and intense. Extreme precipitation even
ts are more localised and of unprecedented magnitude\, causing widespread
flooding and severe impacts on our lives and assets.\nAccurately predictin
g and monitoring water-related environmental disasters\, as well as optima
l water resource management\, require better decision support systems. The
se systems should integrate remote sensing\, in-situ and citizen observati
ons with high-resolution Earth system modelling\, artificial intelligence\
, information and communication technologies\, and high-performance comput
ing.\nWithin the Digital Twin Earth for Hydrology and the Open Earth Monit
or Cyberinfrastructure projects\, we have developed advanced interactive t
ools for building what-if scenarios for flood risk assessment\, drought mo
nitoring and water resources management. The workshop will describe the de
veloped tools (current version here: https://explorer.dte-hydro.adamplatfo
rm.eu/) and the recent advances developed within the Open Earth Monitor Cy
berinfrastructure and related projects. An interactive session will be hel
d to demonstrate the potential and limitations of the developed what-if sc
enarios.
DTSTAMP:20260613T183554Z
LOCATION:Raiffa Room (IIASA)
SUMMARY:Workshop: Playing the water cycle game: data from space for flood r
isk mitigation and better managing water resources - Luca Brocca
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/AMCPTF/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-7H8WET@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241003T133000
DTEND;TZID=Europe/Lisbon:20241003T141500
DESCRIPTION:The workshop aims to exchange on recent policy requirements\, p
rogress in providing EO-based data and products and equip participants wit
h better knowledge and skills to analyze the drivers of deforestation and
associated carbon emissions using remote sensing and Machine learning. The
workshop aligns with recent European Union(EU) regulations to curb the EU
market’s impact on global deforestation and provides valuable informati
on for monitoring land use following deforestation\, crucial for environme
ntal initiatives and carbon neutrality goals.
DTSTAMP:20260613T183554Z
LOCATION:Wodak Room (IIASA)
SUMMARY:Workshop: Monitoring Deforestation-related land use change and Carb
on Emissions for EUDR and climate policies - Robert Masolele
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/7H8WET/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-LT8X3B@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241003T143000
DTEND;TZID=Europe/Lisbon:20241003T145000
DESCRIPTION:While various imputation methods are available to reconstruct g
appy time series of images\, most of them are inadequate for large dataset
s like the full Landsat archive.\nTo address this need\, this work propose
s a new methodology called seasonally weighted average generalization (SWA
G). SWAG works solely on the time dimension\, reconstructing images by emp
loying a weighted average of available samples in the original time series
. It prioritizes images collected at integer multiples of a year to enforc
e annual seasonality and gives higher weights to more recent images to avo
id propagating land cover changes. The method is implemented as part of th
e open source Python package scikit-map and optimized for computational ef
ficiency.
DTSTAMP:20260613T183554Z
LOCATION:Theatre Hall (Conference Center Laxenburg)
SUMMARY:Time-series reconstruction of global scale historical Earth observa
tion data by seasonally weighted average - Davide Consoli
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/LT8X3B/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-LAQNPD@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241003T143000
DTEND;TZID=Europe/Lisbon:20241003T145000
DESCRIPTION:Spatiotemporal prediction of SOCD for Europe (2000–2022) in 3
D+T
DTSTAMP:20260613T183554Z
LOCATION:Maria Theresia Seminar room (Conference Center Laxenburg)
SUMMARY:Spatiotemporal prediction of SOCD for Europe (2000–2022) in 3D+T
- Xuemeng Tian
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/LAQNPD/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-YMSRCM@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241003T153000
DTEND;TZID=Europe/Lisbon:20241003T161500
DESCRIPTION:Access methods and processing pipeline of Landsat bi-monthly\,
complete and cloud optimized collection
DTSTAMP:20260613T183554Z
LOCATION:Raiffa Room (IIASA)
SUMMARY:Workshop: Accessing global scale\, historical and complete Landsat
data - Davide Consoli
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/YMSRCM/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-LGF33B@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241003T153000
DTEND;TZID=Europe/Lisbon:20241003T161500
DESCRIPTION:Machine Learning is commonly used to map environmental variable
s in 2D\, but what about generating predictions of dynamic variables such
as above ground biomass\, forest species\, soil carbon and similar? The di
fference between spatiotemporal vs purely 2D / 3D mapping is in the three
main aspects: (1) points and covariate layers are matched in spacetime (us
ually month-year period or at least year)\, (2) covariate layers are based
on time-series data and include also accumulative indices (e.g. cumulativ
e rainfall\, cumulative snow cover\, cumulative cropping fraction and simi
lar) and derivatives\, (3) during model training and validation\, points a
re subset in both spacetime to avoid overfitting and bias in predictions.
The rationale for using spatiotemporal machine learning is fitness of data
for reliable time-series analysis: the predictions for anywhere in the sp
acetime cube need to be unbiased\, with objectively quantified prediction
errors (uncertainty)\, so that hence changes can be derived without a risk
for serious over-/under-estimation. We have tested this framework on loca
l and regional data sets (e.g. LUCAS soil samples covering 2009\, 2012\, 2
015\, 2018 for Europe) and can be now potentially applied using global com
pilations of soil points (https://opengeohub.github.io/SoilSamples/). Spat
iotemporal machine learning could also potentially be used for predicting
future states of soil\, e.g. by extrapolating models to future climate sce
narios and future land use systems (Bonannella et al.\, 2023).
DTSTAMP:20260613T183554Z
LOCATION:Wodak Room (IIASA)
SUMMARY:Workshop: Spatiotemporal Machine Learning: fitting models and gener
ating predictions using time-series data - Tom Hengl (OpenGeoHub)
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/LGF33B/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-CCLAG9@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241003T153000
DTEND;TZID=Europe/Lisbon:20241003T155000
DESCRIPTION:The UN has declared this to be the Decade of Ecosystem Restorat
ion\, which should foster the development of restoration projects in many
parts of the world suffering from land degradation. In parallel\, there is
growing demand for deforestation-free and sustainably produced products\,
as reflected partly by the establishment of the new EU Regulation on Defo
restation-free products. The combination of these trends will likely lead
to local land use changes resulting in increases in landscape heterogeneit
y. Here we place an interest in the effects that such changes have on biop
hysical variables that directly impact the Earth system and the local clim
ate\, such as short-wave radiation\, land surface temperature and evapotra
nspiration\, as estimated diurnally from geostationary satellite observati
ons. In this study\, we explore how the tree density and tree spatial arra
ngement in different ecosystems of the African continent have an impact on
the energetic budget at local and regional scales. We perform a space for
time analysis where local changes on vegetation are used to disentangle t
he effect of land cover transitions on biophysical variables. We expect th
e results of the study to provide insights into where increasing landscape
complexity may provide additional benefits in terms of ecosystem services
and thereby contribute towards guidelines in sustainable land planning.
DTSTAMP:20260613T183554Z
LOCATION:Theatre Hall (Conference Center Laxenburg)
SUMMARY:Exploring the biophysical impacts of potential changes in tree cove
r in Africa - Gregory Duveiller
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/CCLAG9/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-PLVP8N@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241003T153000
DTEND;TZID=Europe/Lisbon:20241003T155000
DESCRIPTION:Please provide an abstract of your talk as soon as possible
DTSTAMP:20260613T183554Z
LOCATION:Maria Theresia Seminar room (Conference Center Laxenburg)
SUMMARY:The GEO-trees project - Dmitry Shchepashchenko
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/PLVP8N/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-MWTFSU@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241003T163000
DTEND;TZID=Europe/Lisbon:20241003T165000
DESCRIPTION:The achievement of ambitious LULUCF mitigation targets for 2030
and the EU 2050 climate\nneutrality goals strongly rely on forests. Curre
ntly\, there is a large discrepancy in data for monitoring\nthe status of
EU forests with large differences across sources of information. In partic
ular\, remote\nsensing data and national statistics are not sufficiently d
etailed and consistently integrated to allow\nfor comprehensive monitoring
of forest status and consistently modelling biomass and carbon over\ntime
\, by showing a latency in capturing changes in forest cover and forest bi
omass.\nForestNavigator aims at modelling a series of forest sector policy
pathways aligned to EU climate\nneutrality goals. These pathways rely on
integrating various data sources\, including high resolution\nremote sensi
ng derived datasets (forest area\, disturbances)\, ground data sources (NF
I structural\ndata) and national statistics (forest harvest and products).
In ForestNavigator\, we consistently\ncombine these sources allowing for
for a consistent representation of forests and forest sector\nstatus featu
red in forest biophysical and socioeconomic models. Additionally\, ForestN
avigator\ndevelops workflows that enable to timely update mitigation pathw
ays according to near-real time\ndetection of changes in forests and in th
e forest bioeconomy. This near-real time update of policy\npathways\, acco
rding to the continuously changing conditions\, enables to timely correct
efforts for\nachieving policy mitigation targets. We present recent develo
pments ongoing in ForestNavigator\nproject for a model-data fusion towards
the assessment of EU consistent forest policy pathways.
DTSTAMP:20260613T183554Z
LOCATION:Theatre Hall (Conference Center Laxenburg)
SUMMARY:ForestNavigator: combining forest monitoring and modelling for asse
ssing policy pathways towards EU climate neutrality - Fulvio Di Fulvio\, A
ndrey Lessa Derci Augustynczik\, Petr Havlik
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/MWTFSU/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-S8FTSE@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241003T163000
DTEND;TZID=Europe/Lisbon:20241003T165000
DESCRIPTION:Drought monitoring across scales is increasingly feasible with
the use of open data. Multiple missions dedicated to monitor specific vari
ables as indicators of the status of the earth system contribute to the gr
owing availability of earth observation datasets. Soil moisture is one of
these key indicators to monitor the status of drought. \n\nHowever\, droug
ht\, as a process dependent on multiple conditions from the atmospheric sc
ale to the local land surface scale\, expresses itself as a pattern of pat
terns. This nested nature consisting of vast anomalies conditioned in frag
ments\, frequently complicates the characterization of drought from only o
ne type of observations (e.g. ground data or only certain scale of remote
sensing observations). Therefore\, soil moisture data at multiple spatial
scales are needed. \n\nCurrently\, soil moisture datasets cover a reasonab
ly wide range of scales to enable the monitoring of drought from continent
al to local scale. Multiple products exist to cover the monitoring of soil
moisture anomalies with resolutions in the order of tens of kilometres ei
ther from active and passive radiometric technologies like ASCAT (Advanced
SCATterometer) and the European Space Agency - Climate Change Initiative
(ESA-CCI) products. Similarly\, the pursuit of high-resolution observation
s is already evidencing the advantage of high-resolution data such as that
of Sentinel-1 mission for dealing with the small-scale heterogeneity. Eva
luation of these two scales of available data over Europe and Italy serve
as examples of their suitability for multiple drought applications\, also
in an operational context \n\nFor this study we benefit from the Open Eart
h Monitoring Cyberinfrastructure project aiming to democratize the use of
earth observations open the path to generalize the integration of open dat
asets across scales. Overall\, our goal is to support this initiative and
improve the comparison and combination of open data sources. This is cruc
ial for addressing the multi-scale challenges of earth system sciences.
DTSTAMP:20260613T183554Z
LOCATION:Maria Theresia Seminar room (Conference Center Laxenburg)
SUMMARY:Drought monitoring across scales with open soil moisture remote sen
sing data - Jaime Gaona
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/S8FTSE/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-QNCDG9@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241004T090000
DTEND;TZID=Europe/Lisbon:20241004T093000
DESCRIPTION:GEO now since 2 decades has been working\, to advocate Earth O
bservations Open data and Open knowledge\, it is urgent to make sure that
users are able to discover\, access and re-use the available open applicat
ions\, enhance knowledge sharing and solve most urgent countries socio env
ironmental issues. The GEO Knowledge Hub is a promising tool to enhance kn
owledge sharing among the scientific community and accelerate the impact t
hat EO Data and EO Knowledge can have.
DTSTAMP:20260613T183554Z
LOCATION:Theatre Hall (Conference Center Laxenburg)
SUMMARY:Implementing Open EO Knowledge and the journey towards users engage
ment - Paola De Salvo
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/QNCDG9/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-HJNYMX@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241004T093000
DTEND;TZID=Europe/Lisbon:20241004T100000
DESCRIPTION:Satellite imagery is traditionally stored and processed on rect
angular grids. However\, the widespread usage of such grids has normalized
their inherent distortions\, particularly near the poles. Previous attemp
ts to address this issue\, such as employing multiple local projections li
ke the UTM-based Sentinel 2 L1C grid\, have led to inefficiencies\, includ
ing a significant increase in data volume (~30%) due to overlaps that need
to be stored\, downloaded\, and processed. Additionally\, there is a lack
of a unified global indexing system and the choice of pixel cell shape\,
which further complicate the analysis.\n\nIn this keynote talk\, we advoca
te for a paradigm shift towards Discrete Global Grid Systems (DGGS) to mit
igate these challenges. DGGS tessellate the Earth's surface with hierarchi
cal cells of equal area\, minimizing distortion and reducing loading time
of large geospatial datasets. This approach would greatly improve spatial
statistics and convolutional Machine Learning models\, where accurate repr
esentation of global phenomena is paramount at a global scale.
DTSTAMP:20260613T183554Z
LOCATION:Theatre Hall (Conference Center Laxenburg)
SUMMARY:Rethinking the grid: Towards less distorted imagery and AI - Daniel
Loos
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/HJNYMX/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-BMCQN9@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241004T100000
DTEND;TZID=Europe/Lisbon:20241004T103000
DESCRIPTION:An abstract needs to be submitted as soon as possible
DTSTAMP:20260613T183554Z
LOCATION:Theatre Hall (Conference Center Laxenburg)
SUMMARY:EO for Policy making in the EU - Mark Dowell
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/BMCQN9/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-DMBRC7@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241004T110000
DTEND;TZID=Europe/Lisbon:20241004T114500
DESCRIPTION:Policies on opening satellite image archives have shifted earth
observation to the big data era. However\, due to the associated data-hun
gry analytics\, such as deep learning\, satellite observations have to be
combined (trained and then validated) with a large amount of in-situ data
to get meaningful results. Yet\, the collection of in-situ data is often l
aborious\, and the resulting observations are rarely open for others to us
e. To bridge this in-situ data gap\, this workshop will analyze the suitab
ility of a citizen science mobile app for measuring biomass and tree speci
es of individual trees and forest plots\, i.e.\, the TreeQuest and ForestQ
uest modules\, respectively. The app has been developed by the Internation
al Institute for Applied Systems Analysis (IIASA) and will be freely avail
able for Android and iOS phones by the workshop. We will first present the
app and then initiate a citizen science campaign motivating the conferenc
e participants to take part by testing the app and surveying selected tree
s around the conference center. Members from TU Wien will measure and mode
l selected trees using a terrestrial laser scanner. The resulting 3D point
cloud will allow the extraction of detailed information on vegetation str
ucture\, which will be used for comparison with the mobile app and forest
inventory measurements acquired with traditional forest measurement tools
(e.g. caliper\, vertex). Finally\, we will present the results and discuss
the performance and potential further development of the app with worksho
p participants. \n\nThe workshop will also discuss the relevance of collec
ted data and the approach for the two ongoing initiatives such as (a) the
Citizens for Copernicus project that is funded by the Austrian Research Pr
omotion Agency\, application No. 47907528\, and (b) the Open Earth Monitor
Cyberinfrastructure project funded from the European Union's Horizon Euro
pe research and innovation programme under grant agreement No. 101059548.
DTSTAMP:20260613T183554Z
LOCATION:Maria Theresia Seminar room (Conference Center Laxenburg)
SUMMARY:Workshop: Citizen Science Mobile App and Data in Support of Forest
Mapping: Laxenburg Park Campaign - Milutin Milenković
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/DMBRC7/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-9ELMGE@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241004T110000
DTEND;TZID=Europe/Lisbon:20241004T112000
DESCRIPTION:Evidence-based policy is gaining importance\, also in the envir
onmental policy domain in Flanders\, Belgium. However\, the most prevalent
source of policy-relevant information still remains ground sampling\, wit
h limited spatial and temporal detail and coverage. The ease of access to
freely available (Sentinel) satellite imagery from the Copernicus program
through the new OpenEO API provides a golden opportunity for filling this
information gap. During the GEO.INFORMED project\, remote sensing and deep
learning researchers engaged in a co-creation trajectory with regional en
vironmental policy makers to develop machine learning workflows for transf
orming Copernicus satellite data into policy-relevant geodata. The main ch
allenges encountered in the project where associated with ensuring mutual
understanding between scientists and policy-makers\; and with the technica
l implications of non-standard model inputs and limited reference data ava
ilability. Within the project\, a range of strategies for overcoming these
challenges were tested\, and the lessons learned will be the main focus o
f this talk.
DTSTAMP:20260613T183554Z
LOCATION:Theatre Hall (Conference Center Laxenburg)
SUMMARY:Deriving policy-relevant geodata from satellite images: lessons lea
rned in the GEO.INFORMED project - Stien Heremans
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/9ELMGE/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-CNSFUF@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241004T114000
DTEND;TZID=Europe/Lisbon:20241004T120000
DESCRIPTION:Global Trait-Based Vegetation Monitoring: Leveraging Multispect
ral Imagery for Restoration Project Assessment\n\nRestoration projects are
crucial for ecosystem recovery and biodiversity conservation\, but their
large-scale monitoring poses significant challenges. Conventional approach
es often rely on intensive manual work\, incur high costs and need help wi
th standardisation\, making monitoring on a global scale impossible. Publi
c satellite missions such as Sentinel-2 have great potential to transform
ecosystem monitoring due to their high spatial and temporal resolution whe
n linked directly to ecosystem characteristics. Here\, we present several
global\, high-resolution (20m) maps of vegetation traits derived from Sent
inel-2 multispectral imagery\, reflecting the mean trait value during the
vegetation period at annual intervals from 2019 onwards. Using a hybrid in
version approach of the physically-based radiative transfer model PROSAIL\
, we estimate leaf functional traits (e.g. chlorophyll content\, equivalen
t water thickness\, or leaf mass per area) and canopy structural traits (e
.g. leaf area index). Validation using in-situ data suggests that the trai
t maps can effectively track local temporal changes. Further\, we show how
the generated trait maps can map functional trait diversity at a coarser
resolution. Altogether\, these products provide deeper insights into ecosy
stem health\, biodiversity status and restoration efforts.
DTSTAMP:20260613T183554Z
LOCATION:Theatre Hall (Conference Center Laxenburg)
SUMMARY:Global Trait-Based Vegetation Monitoring - Felix Specker
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/CNSFUF/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-SMBD8J@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241004T114500
DTEND;TZID=Europe/Lisbon:20241004T123000
DESCRIPTION:Various modelling techniques are available to understand the te
mporal and spatial variations of the phenology of species. Scientists ofte
n rely on correlative models\, which establish a statistical relationship
between a response variable (such as species abundance or presence-absence
) and a set of predominantly abiotic covariates. The choice of the modelli
ng approach\, i.e.\, the algorithm\, is a crucial factor in addressing the
multiple sources of variability that can lead to disparate outcomes when
different models are applied to the same dataset. This inter-model variabi
lity has led to the adoption of ensemble modelling techniques\, among whic
h stacked generalisation\, which has recently demonstrated its capacity to
produce robust results. Stacked ensemble modelling incorporates predictio
ns from multiple base learners or models as inputs for a meta-learner. The
meta-learner\, in turn\, assimilates these predictions and generates a fi
nal prediction by combining the information from all the base learners. Ou
r study utilized a recently published dataset documenting egg abundance ob
servations of Aedes albopictus collected using ovitraps. This dataset span
s various locations in southern Europe\, covering four countries -Albania\
, France\, Italy\, and Switzerland- and encompasses multiple seasons from
2010 to 2022. Utilising these ovitrap observations and a set of environmen
tal predictors\, we employed a stacked machine learning model to forecast
the weekly average number of mosquito eggs. This approach enabled us to i)
unearth the seasonal dynamics of Ae. albopictus for 12 years\; ii) genera
te spatio-temporal explicit forecasts of mosquito egg abundance in regions
not covered by conventional monitoring initiatives. Beyond its immediate
application for public health management\, our work presents a versatile m
odelling framework adaptable to infer the spatio-temporal abundance of var
ious species\, extending its relevance beyond the specific case of Ae. alb
opictus.
DTSTAMP:20260613T183554Z
LOCATION:Maria Theresia Seminar room (Conference Center Laxenburg)
SUMMARY:Workshop: Inferring spatiotemporal dynamics of mosquitoes in Italy
using machine learning - Carmelo Bonannella\, Daniele Da Re
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/SMBD8J/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-3BJ8YK@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241004T140000
DTEND;TZID=Europe/Lisbon:20241004T142000
DESCRIPTION:Cocoa cultivation serves as a crucial source of income for coun
tless farmers across pantropical regions. However\, this agricultural prac
tice often leads to deforestation in tropical forests. While previous stud
ies have highlighted the expansion of cocoa farms\, particularly in select
African countries\, there remains a significant gap in comprehensive data
regarding the location of cocoa farms on a pantropical scale. To address
this challenge\, our study employs deep learning models trained on Sentine
l-1 and Sentinel-2 satellite imagery\, coupled with annotated reference da
tasets\, to map cocoa farms across pantropical regions.\nOur findings prov
ide valuable insights for governments\, cocoa companies\, consumers\, NGOs
\, and international organizations striving to mitigate the challenges ass
ociated with escalating deforestation linked to cocoa production. Of parti
cular significance is the utility of this dataset in addressing the recent
European Union Regulation mandating companies to refrain from importing c
ommodity crops associated with deforestation. By providing a comprehensive
understanding of cocoa farm distribution across pantropical regions\, our
research contributes to informed decision-making and sustainable practice
s in cocoa production and trade.
DTSTAMP:20260613T183554Z
LOCATION:Theatre Hall (Conference Center Laxenburg)
SUMMARY:Mapping Cocoa Farms Across Pantropical Regions Using High-Resolutio
n Satellite Imagery and Deep Learning - Robert Masolele
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/3BJ8YK/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-WGKG8X@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241004T140000
DTEND;TZID=Europe/Lisbon:20241004T143000
DESCRIPTION:This workshop will explore the high-resolution mapping of gross
primary productivity (GPP) using light-use efficiency models. During the
workshop\, we will cover how to access the bi-monthly GPP maps and assess
the accuracy of the maps via eddy covariance flux measurements. Participan
ts will gain insight on how to exploit high-resolution GPP maps across div
erse ecosystems.
DTSTAMP:20260613T183554Z
LOCATION:Maria Theresia Seminar room (Conference Center Laxenburg)
SUMMARY:Workshop: High-Resolution Gross Primary Productivity: Modeling and
Mapping Dynamics - Mustafa Serkan Isik
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/WGKG8X/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-LEYNUZ@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241004T142000
DTEND;TZID=Europe/Lisbon:20241004T144000
DESCRIPTION:The EarthCODE (Earth Science Collaborative Open Development Env
ironment) vision provides an integrated\, cloud-based\, user-centric devel
opment environment which can be used to support the European Space Agency
’s (ESA) science activities and projects. Building on activities that de
veloped the European EO open-source ecosystem and the Open Earth System Sc
ience community (e.g. EOEPCA - Exploitation Platform Common Architecture\,
DeepESDL - Deep Earth System Data Lab\, openEO Platform\, ESA Euro Data C
ube\, etc.)\, ESA is implementing EarthCODE as a collaborative platform fo
r conducting Earth System Science sustainably and adhering to FAIR and Ope
n Science Principles. EarthCODE will enable the long-term persistence of r
esearch outputs from science activities. \n\nEarthCODE looks to maximise r
eproducibility\, reuse\, and consumption of research outputs by the wider
community\, promoting a flexible and scalable architecture developed with
interoperable open-source blocks\, with a long-term vision evolving by inc
rementally integrating industrially provided services from a portfolio of
the Network of Resources. EarthCODE platform collaborators will participat
e in creating integrated architecture\, with interoperable solutions and f
ederated capabilities.\n \nEarthCODE will use EOEPCA Open Standards to hel
p support Open Science\, and help drive these standards. Open science prin
ciples are increasingly being embraced in the field of Earth Sciences\, pr
omoting transparency\, collaboration\, and accessibility of research. This
is being done by promoting open access publications\, preprints and open
review processes\, sharing data/methodologies for verification\, reproduci
bility and reuse. In software development\, these principles allow inspect
ion\, modification\, and code contribution\, encouraging collaboration amo
ng researchers through various platforms (i.e. GitHub\, GitLab\, etc.). Sh
aring of educational resources openly allow for global audience\, and invo
lvement of the public through citizen science for scientific research.\n \
nEarthCODE will provide an Integrated Development Platform\, giving develo
pers the tools needed to develop high quality workflows that allow experim
ents to be executed in the cloud and the reproduced by other scientists\,
following Open Science principles. Our solution is built around existing o
pen-source solutions and building blocks\, primarily the Open Science Cata
logue\, EOxHub and EOEPCA. With it’s adopted federated approach\, EarthC
ODE will have the capability to facilitate processing on other platforms\,
i.e. DeepESDL\, ESA EURO Data Cube\, Open EO Cloud/Open EO Platform and A
IOPEN/AI4DTE. \n \nCollaboration and Federation are at the heart of Earth
CODE. As EarthCODE evolves we expect providing solutions allowing allow fe
deration of data and processing. EarthCODE has ambition to deliver a model
for a Collaborative Open Development Environment for Earth system science
\, where researchers can leverage the power of the wide range of EO platfo
rm services available to conduct their science\, while also making use of
FAIR Open Science tools to manage data\, code and documentation\, create e
nd-to-end reproducible workflows on platforms\, and have the opportunity t
o discover\, use\, reuse\, modify and build upon the research of others in
a fair and safe way. EarthCODE thus aims to make possible the eight enabl
ing elements of the EO Open Science and Innovation vision: open data\, ope
n-source code\, linked data & code\, open access documentation\, end-to-en
d workflows reproducible on platforms\, open science resources\, open scie
nce tools\, and a healthy community applying all the elements in their pra
ctice.
DTSTAMP:20260613T183554Z
LOCATION:Theatre Hall (Conference Center Laxenburg)
SUMMARY:EarthCODE – A FAIR Open Science environment for the Earth science
s - Garin Smith
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/LEYNUZ/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-open-earth-monitor-global-workshop-2024-PHH7DJ@pretalx.earthmon
itor.org
DTSTART;TZID=Europe/Lisbon:20241004T143000
DTEND;TZID=Europe/Lisbon:20241004T150000
DESCRIPTION:The workshop starts with accessing a global ensemble digital te
rrain model\, cropping to tile and reprojecting to Equi7 projection system
. Secondly\, the attendants will set up a docker containing GRASS\, SAGA a
nd Whtieboxtool with R/Python that enables to script land relief parameter
ization process. Lastly\, attendant will follow a workflow that produces d
ifferent land relief parameters by tiles and mosaics with consideration of
boundary effect\, in order to achieve high-resolution global scale terrai
n parameter mapping.
DTSTAMP:20260613T183554Z
LOCATION:Maria Theresia Seminar room (Conference Center Laxenburg)
SUMMARY:Workshop: Using GRASS\, SAGA and Whiteboxtool to map global high-re
solution land relief parameterization adopting Equi7 projection system - Y
u-Feng Ho
URL:https://pretalx.earthmonitor.org/open-earth-monitor-global-workshop-202
4/talk/PHH7DJ/
END:VEVENT
END:VCALENDAR