War and Deforestation: Using Remote Sensing and Machine Learning to Identify the War-Induced Deforestation in Syria 2010–2019

Angham Daiyoub, Pere Gelabert, Sandra Saura‐Mas, Cristina Vega‐García

Land 2023 article cited by 49

Source Open-access PDF More in Climate & Environment

importance 5/10 solved Environmental and Biological Research in Conflict Zones environmental monitoring stage: research

Problem

Measuring and attributing forest cover loss in Syria during the 2011–2019 armed conflict.

Why it matters

Quantifying war-driven forest loss is needed for environmental accountability and post-war recovery planning.

What the AI did

Landsat and PALSAR imagery were processed with Tasseled Cap transformation and the LandTrendr algorithm to detect and simplify forest-change time series and identify change drivers.

Results

Forest cover decreased 19.3% between 2010 and 2019, predominantly in northwestern Syria.

Impact

Provides a quantified, remotely sensed estimate of conflict-induced forest loss in Syria.

Record

Venue	Land
Published	2023
Work type	article
Authors	Angham Daiyoub, Pere Gelabert, Sandra Saura‐Mas, Cristina Vega‐García
Citations	49 (OpenAlex, at import time)
References	74
Model or method	LandTrendr
Organization	not stated in source
Primary source	https://doi.org/10.3390/land12081509
Open access	gold
Keywords	deforestation, remote sensing, machine learning, Syria, Landsat, LandTrendr

Importance rating

5 / 10

real but narrower advance

Abstract quantifies 19.3% conflict-driven forest loss and mass displacement; supports environmental accountability and post-war recovery for one country, a real but regionally bounded advance.

Basis: stated. The source itself states the scale. Stated scale: 19.3% forest cover decrease 2010-2019; >5M refugees, >6M internally displaced. Method and limits.

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