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UID:pretalx-global-workshop-2026-DJBM9P@pretalx.earthmonitor.org
DTSTART;TZID=Europe/Amsterdam:20261007T181500
DTEND;TZID=Europe/Amsterdam:20261007T182000
DESCRIPTION:The coastal border of Semarang and Demak in Central Java\, Indo
 nesia\, faces unprecedented mangrove deforestation driven by rapid land su
 bsidence\, sea-level rise\, aquaculture expansion\, and industrialization.
  Traditional optical remote sensing approaches are severely constrained by
  persistent cloud cover in this tropical environment\, resulting in detect
 ion lags of weeks to months that preclude timely intervention. This study 
 presents an iterative Bayesian updating framework for near-real-time mangr
 ove deforestation monitoring through multi-sensor fusion of Sentinel-1 Syn
 thetic Aperture Radar (SAR) and optical imagery from Landsat-8/9 and Senti
 nel-2. We formulate a probabilistic change detection model where posterior
  deforestation probabilities are sequentially updated with each new satell
 ite observation\, incorporating VH-polarized backscatter from Sentinel-1 a
 longside three complementary optical indices: Normalized Difference Vegeta
 tion Index (NDVI)\, Mangrove Vegetation Index (MVI)\, and Enhanced Mangrov
 e Index (EMI). Four experimental scenarios were evaluated across the 2018-
 2025 period: (1) SAR-Optical Baseline (VH + NDVI)\, (2) Structure-Focused 
 (VH + MVI)\, (3) Moisture/Soil-Focused (VH + EMI)\, and (4) Full Integrate
 d Suite (VH + EMI + NDVI + MVI). Validation through field surveys\, high-r
 esolution imagery\, and comparison with existing deforestation maps demons
 trated that Scenario 4 achieved the highest F1-score (0.89) and lowest det
 ection lag (8.3 days median)\, reducing false positives from tidal floodin
 g by 67% compared to single-sensor approaches. The integration of structur
 al information from SAR and MVI with spectral-moisture signals from EMI an
 d NDVI enabled robust discrimination between genuine deforestation events 
 and natural tidal dynamics. Mathematical formulations for prior specificat
 ion\, likelihood functions\, and posterior updating are presented in detai
 l\, alongside practical implementation considerations for tropical coastal
  environments. These findings provide actionable guidance for local coasta
 l management agencies in Semarang-Demak to implement operational near-real
 -time monitoring systems that can trigger rapid response to illegal loggin
 g and land conversion.
DTSTAMP:20260624T084636Z
LOCATION:Aula Magna
SUMMARY:Iterative Bayesian Updating for Near Real-Time Mangrove Deforestati
 on Monitoring: A Multi-Sensor Fusion Approach in Semarang-Demak\, Indonesi
 a - Munawaroh Munawaroh
URL:https://pretalx.earthmonitor.org/global-workshop-2026/talk/DJBM9P/
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