Understanding drought related tree responses using deep learning approaches and satellite based proxy

Summary, in English

Recent droughts from 2017 to 2020 induced significant stress on woodland canopies across eastern Australia, resulting in widespread tree browning and mortality. However, the trajectory of post-drought recovery remains unclear, with uncertainty about whether canopy conditions are improving or continuing to decline. Identifying the key local environmental and climatic factors influencing drought-induced tree mortality and recovery is therefore critical for understanding these processes. In this study, we employed a data-driven deep learning framework that integrates CNNs and LSTM algorithms techniques that excel at capturing spatial dependencies and long-term temporal dynamics, respectively. We analyzed a seasonal time-series dataset spanning 2010–2022, which combined satellite-derived canopy stress anomalies (z-scores of the Normalized Burn Ratio, NBR) with environmental predictors including rainfall, temperature, and potential evapotranspiration (PET), soil texture (sand and clay fractions), vegetation type, and topographic variables (slope, aspect, and topographic wetness index, TWI). All predictors were at 30 m resolution to ensure spatial consistency. Among the compared models, the hybrid CNN-LSTM model performed the best, underscoring the superiority of hybrid architectures like that synergistically capture spatial patterns and temporal dependencies. Additionally, advanced sequential models, whether utilizing attention mechanisms, such as Selective Attention and TFT, or leveraging state-space formulations, such as TCN-Mamba and RWKV-TS, also outperformed traditional recurrent approaches. Beyond predictive performance, our aim was to interpret the ecological drivers of canopy stress and recovery by linking model sensitivities to physiological processes and landscape variability. Our phase-based analysis (wet, transition, drought, and post-drought conditions) revealed that in dry to mid-humid bioregions, canopy resilience during drought is shaped by the interplay of dynamic climatic stressors (e.g., evapotranspiration, rainfall variability) and static landscape features (soil texture, topography), highlighting how ecosystem vulnerability arises from synergistic abiotic thresholds under climatic extremes. Regions that followed a recovery pathway were primarily driven by rainfall and topographic variables, whereas non-recovery was largely associated with dominant climatic factors. In wet-humid bioregions, climatic variables were the primary triggers of drought impacts; recovery in these areas was influenced by an interaction between climatic and topographic variations, with topographic factors such as sand content and Topographic Wetness Index (TWI) playing a decisive role in non-recovered regions. Insights obtained through explainable algorithms can inform process-based models and facilitate a more robust mechanistic understanding of drought-impacted eucalypt woodlands. Ultimately, this remote sensing based approach holds promise for operational forest management and policy. By identifying key climatic drivers as early-warning indicators and mapping recovery versus non-recovery areas, our framework provides actionable information for prioritizing public awareness by targeting drought-prone habitats and supporting adaptive management strategies under climate change.

• Author: Rachid Oucheikh; Nuwanthi Arampola; Pengxiang Zhao; Ali Mansourian
• Department/s: Dept of Physical Geography and Ecosystem Science; eSSENCE: The e-Science Collaboration; Centre for Geographical Information Systems (GIS Centre); LU Profile Area: Nature-based future solutions; BECC: Biodiversity and Ecosystem services in a Changing Climate
• Publishing year: 2025-12
• Language: English
• Publication/Series: Science of Remote Sensing
• Volume: 12
• Links: http://dx.doi.org/10.1016/j.srs.2025.100317
• Document type: Journal article
• Publisher: Elsevier
• Topic: Earth and Related Environmental Sciences
• Keywords: Convolutional neural networks; Deep learning; Long short-term memory; Non-recovery; Recovery; Remote sensing; Topography variables
• Status: Published
• ISBN/ISSN/Other: ISSN: 2666-0172