Dynamic Responses of Vegetation in Arid Regions to Global Changes and Human Interference: Perspectives from Remote Sensing and GeoAI

Arid and semi-arid ecosystems, which span more than one-third of Earth’s terrestrial surface, are among the most climate-sensitive and ecologically fragile environments on the planet. Vegetation in these regions underpins ecosystem functions such as carbon sequestration, soil stabilization, and water regulation, yet it faces unprecedented challenges from both global change and human activities. Rising temperatures, altered precipitation patterns, and an increased frequency of droughts and extreme events are reshaping vegetation dynamics, while overgrazing, land-use conversion, and restoration interventions further alter ecological balance. Recent advances in remote sensing, unmanned aerial systems, and geospatial artificial intelligence (GeoAI) now allow researchers to capture fine-scale spatial and temporal variations in vegetation. Despite growing data availability, critical gaps remain in understanding long-term vegetation responses, the attribution of observed changes to particular climatic or anthropogenic drivers, and the translation of local observations into scalable models for regional and global monitoring.This Research Topic aims to advance mechanistic and predictive understanding of how vegetation in arid and semi-arid regions responds to ongoing global change and human disturbances. Specifically, it seeks to integrate diverse remote sensing datasets with emerging GeoAI and machine learning approaches to disentangle the complex interplay of natural and human-induced forces. The main objectives are to improve the accuracy and interpretability of vegetation monitoring, enhance causal attribution frameworks, and develop predictive modeling systems that inform restoration and sustainable land management practices. This Research Topic encourages the testing of new hypotheses on vegetation resilience, the identification of ecological thresholds, and the evaluation of adaptive management strategies in dryland ecosystems.This Research Topic will focus on arid and semi-arid ecosystems, emphasizing remote sensing and GeoAI applications across multiple spatial and temporal scales. It excludes purely descriptive analyses without methodological or ecological insights. To gather further insights into vegetation-climate-human interactions, we welcome articles addressing, but not limited to, the following themes: Long-term monitoring of vegetation dynamics using multi-source remote sensing data Attribution of vegetation changes to climate variability, extreme events, and human disturbances Applications of machine learning, deep learning, and GeoAI in vegetation mapping, forecasting, and resilience assessment Integration of remote sensing with field observations, ecological modeling, and socio-environmental data Scaling approaches to link local ecological processes with regional and global assessments Decision-support tools for ecosystem management and restoration under global change---------------------We welcome contributions from researchers across the field and are happy to support you as you prepare your submission. If you would like initial feedback on whether your planned manuscript aligns with the scope of this Research Topic, please reach out to us at plantscience@frontiersin.org before submitting. Early input can help ensure your work is a strong fit and save you time later in the process. To take part, click “Participate in this topic” at the top right of this page, and then confirm through the “Participate” link in the follow-up email. This lets us know to expect your contribution, keeps you informed of key deadlines, and gives you access to resources that guide you through submission. We look forward to your participation and to building a vibrant, cohesive Research Topic together.” Topic editors: Fanhao Meng, Yiqing Guo, Tim Van de Voorde, Min Luo, Tie Liu Source: https://www.frontiersin.org/research-topics/81471/dynamic-responses-of-vegetation-in-arid-regions-to-global-changes-and-human-interference-perspectives-from-remote-sensing-and-geoai