Remote Sensing–Based Assessment of Forest Carbon Sequestration and Land Use Change Dynamics under Climate Variability

Abstract

Forest ecosystems are among the most significant terrestrial carbon reservoirs and play a crucial role in regulating the global carbon cycle through carbon sequestration. However, rapid land use and land cover change (LULC), combined with increasing climate variability, has significantly altered the stability and efficiency of these ecosystems. This review paper examines the application of remote sensing technologies in assessing forest carbon sequestration and monitoring land use dynamics under changing climatic conditions. The study highlights how urbanization, agricultural expansion, deforestation, and wildfire activities contribute to carbon emissions and ecosystem degradation across tropical, coastal, semi-arid, and urban forest landscapes. Special emphasis is placed on the integration of optical, LiDAR, and Synthetic Aperture Radar (SAR) systems for estimating above-ground biomass (AGB), soil organic carbon (SOC), and vegetation structural characteristics. The review further explores the role of vegetation indices, multi-sensor fusion, and machine learning algorithms such as Random Forest (RF), Support Vector Machines (SVM), XGBoost, and Convolutional Neural Networks (CNNs) in improving biomass estimation accuracy and carbon accounting. Additionally, the paper discusses the influence of climatic variables, including temperature rise, altered precipitation regimes, droughts, and wildfires, on forest carbon sequestration potential and ecosystem resilience. Emerging technologies such as ESA Biomass, NISAR missions, and Digital Twin Earth systems are also evaluated for their transformative potential in global forest monitoring and carbon management. The findings suggest that integrating advanced remote sensing with artificial intelligence and climate modeling provides an effective framework for sustainable forest management, climate mitigation strategies, and accurate carbon monitoring at regional and global scales.