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UID:pretalx-soil-health-now-2025-CJYR3A@pretalx.earthmonitor.org
DTSTART:20250408T182500Z
DTEND:20250408T182900Z
DESCRIPTION:Peatlands are unique ecosystems with high biodiversity and envi
 ronmental services such as water filtration and retention as well as carbo
 n storage. Interestingly\, however\, in contrast to other soils and ecosys
 tems\, little is known about the extent and health of European peatlands (
 Andersen et al.\, 2017). With increasing human-induced drainage\, degradat
 ion and restoration\, there is an even greater need to monitor the extent 
 and health of European peatlands (Andersen et al.\, 2017). We developed a 
 conceptual framework to (1) distinguish between (unforested) peatlands and
  surrounding areas (forest and grassland)\, and (2) separate drained/degra
 ded from natural/rewetted peatlands. Our study includes 11 European peatla
 nds across three Köppen-Geiger climate classes (Kottek et al.\, 2006). We
  use remote sensing data because they provide objective\, spatially explic
 it and temporally extensive data (Chasmer et al.\, 2020). We use  Sentinel
  2 and Planet Scope optical bands with high spatial and temporal resolutio
 n\, focusing on red\, red edge\, near infrared (NIR)\, and shortwave infra
 red (SWIR) band reflectances to discriminate between peatland vegetation a
 nd surrounding areas (Burdun et al.\, 2023). Normalized Difference Vegetat
 ion Index (NDVI)\, Enhanced Vegetation Index Red (EVI). Green Normalized V
 egetation Index (gNDVI)\, and Greenness Index (GI) were used as indicators
  of vegetation composition and health (Burdrun et al.\, 2023\;  Räsänen 
 et al\, 2022)\, while Normalized Moisture Index (NDMI) was used as measure
  for vegetation water stress (Räsänen et al.\, 2022). Ground truthing of
  our data was performed with biogeochemical analyses\, including pyrolysis
  gas chromatography with integrated mass spectroscopy (PYGCMS) to study th
 e molecular composition of surface soils. In particular\, we investigated 
 molecules specific to different vegetation classes and their transformatio
 n products. To verify our results\, we also used established biogeochemica
 l parameters such as C:N ratio and oxidation state (Cox)\, which are indic
 ators of the degree of microbial transformation and decomposition processe
 s in soils (Leifeld et al.\, 2020). As a first step\, we separated peatlan
 ds from surrounding forest using existing European Forest layers. We furth
 er distinguished grasslands from peatlands using red edge and NIR reflecta
 nce data\, which were significantly higher for grasslands than for peatlan
 ds (p<0.001). To distinguish between natural/rewetted and degraded/drained
  sites\, we will correlate the specific reflectance with the molecular and
  biochemical data to establish a framework for an inventory of peatland si
 tes and their health on a regional scale using a machine learning approach
 .
DTSTAMP:20260807T115651Z
LOCATION:W - Invite
SUMMARY:Remote Sensing-Based Framework for Differentiating between Natural 
 and Drained Peatlands in Europe - Miriam Gross-Schmoelders
URL:https://pretalx.earthmonitor.org/soil-health-now-2025/talk/CJYR3A/
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