Multi Source Remote Sensing Inversion of Biological Metabolism and Ecological Resilience Enhancement

Abstract

This paper delves into the spatiotemporal heterogeneity of thermal stress in biological metabolic processes and how this heterogeneity affects the resilience of ecosystems. This text used multiple remote sensing data sources, including Landsat, MODIS, and radar data, and integrated these data to construct an analysis framework that integrates spatiotemporal modeling and multi-objective optimization. In additions, This text employed a mixed effects model to reveal the spatial differentiation patterns of heat stress driving factors. Hence we innovatively proposed a theory that increasing vegetation coverage effectively reduced the intensity of thermal stress, and this process follows a nonlinear mechanism. Based on this theory, we designed differentiated improvement paths, such as optimizing the layout of green cold islands in urban areas, in order to achieve a 25% increase in ecological efficiency. In ecologically fragile areas, a collaborative strategy of vegetation restoration and artificial rainfall enhancement is implemented to enhance ecological resilience by 19%. Its root mean square error (RMSE) is 0.87, which is 33% higher than the accuracy of traditional models. Ultimately, we proposed a spatially explicit management plan, providing strong quantitative decision support for regional ecological security.