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

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Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Systematic Review

Keywords: Arid Regions, Vegetation Dynamics, Global Change, Human Disturbances, Remote Sensing, GeoAI, Machine Learning, Dryland Ecosystems

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.