1Department of Agricultural Science and Technology, School of Agriculture and Environmental Studies, Kenyatta University, Nairobi, Kenya
2Central Highlands Eco-Region Research Programme, Kenya Forestry Research Institute, Nairobi, Kenya
3Kenya Sugar Research and Training Institute, Miwani Road, Kisumu, Kenya
4Department of Agronomy, Dr. Rajendra Prasad Central Agricultural University, Pusa, Samastipur, Bihar, India
5Department of Agronomy and Agroforestry, Centurion University of Technology and Management, Paralakhemundi, Odisha, India
*Corresponding author: hgitari@gmail.com
Forests serve as critical regulators of the global climate through carbon sequestration, biodiversity conservation, and ecosystem services. Nonetheless, climate change is fundamentally disrupting these functions by driving shifts in species distributions, reducing biomass productivity, altering carbon storage capacity, and intensifying disturbance regimes such as wildfires, pest outbreaks, droughts, and storms. Such transformations threaten forest resilience and challenge sustainable management, necessitating advanced monitoring tools capable of capturing complex, multi-scale ecological responses. Remote sensing technologies, including satellite-based optical sensors (Landsat, Sentinel, MODIS), radar systems, LiDAR, hyperspectral imaging, and unmanned aerial vehicles (UAVs), provide indispensable insights into forest canopy structure, phenological cycles, biomass dynamics, and physiological stress indicators like chlorophyll fluorescence and water deficits. Integration of these technologies with machine learning (ML), artificial intelligence (AI), and ecological models enables unprecedented detection of subtle climate-driven impacts and predictive capacity for ecosystem shifts. Key advances include Sentinel-1/2 fusion for cloud-free deforestation monitoring, GEDI-Landsat integration, enhancing biomass estimation accuracy by 30% and convolutional neural networks (CNNs) achieving >90% accuracy in early pest outbreak detection. Despite persistent challenges such as tropical cloud cover, scaling mismatches, computational demands, and ground-validation gaps, remote sensing has become fundamental for informing climate adaptation strategies, conservation planning, and policy frameworks like REDD+. This chapter synthesizes technological innovations, regional case studies, and emerging opportunities to advance predictive monitoring and strengthen forest resilience under accelerating climate change, bridging critical gaps between scientific research and actionable management solutions.
⓿ Climate change has intensified calamities such as wildfires, pest outbreaks, droughts, and storms in forest ecosystems, thus threatening forest resilience and challenging sustainable management.
⓿ Remote sensing technologies provide indispensable insights into forest canopy structure, phenological cycles, biomass dynamics, and physiological stress indicators.
⓿ Such technologies can be integrated into machine learning (ML), artificial intelligence (AI), and ecological models for the detection of subtle climate-driven impacts.
Forest dynamics, Remote sensing, Hyperspectral imaging, Carbon storage, Forest productivity
