Remote sensing estimation of forest ecosystem service values

A precise estimation of the value of forest ecosystem services (FESVs) has become essential due to the growing significance of the influence of global climate change on them. The primary objective of this study was to enhance the precision of forest service value monitoring and evaluation, thereby providing a scientific foundation for the promotion of sustainable forest resource management and ecological civilization construction. The study presented a remote sensing estimation (RSE) technique that combined time series characterization (TSC) and double-logistic regression (DLR) in order to achieve this. Through the integration of TSC analysis and a substantial amount of remote sensing data, the RSE approach increased the accuracy of monitoring changes in the service values of forest ecosystems (F-Ecos) in various dimensions. The data were obtained from the field investigation and data reference of the forest ecological Demonstration Zone of Longgang District, Shenzhen, China, from January 2017 to January 2023. Based on the collected data, the FESV function evaluation and analysis experiment were carried out. The experiments were conducted in local forest resources laboratories to calculate and update estimates of the value of FESVs on a regular basis using the above information. The outcomes of the study indicated that the method achieved 69.26%, 66.59%, 63.77%, and 72.86% identification stability for impacts of the atmospheric carbon dioxide concentration and biodiversity index, vegetation cover, soil erosion rate, and hydrological regulation services, respectively. Moreover, the recognition stability of the forest carbon storage and sequestration capacity, degradation and recovery rates, and productivity indicators were 64.89%, 69.89%, 76.82%, and 63.78%, respectively. In the context of verifying the accuracy of feature extraction efficiency and consistency, the results of the DLR method yielded a 96.95% accuracy rate and a 98.04% consistency rate, respectively. The results obtained in the absence of DLR were 97.33% and 96.92%, respectively. The efficacy and reliability of this method in evaluating and forecasting the value of FESVs were empirically demonstrated. Hence, this study provides an efficient tool for policymakers and environmental managers to monitor and assess changes in service values of F-Ecos under the impact of global climate change.