A systematic review on remote sensing of dryland ecological integrity: Improvement in the spatiotemporal monitoring of vegetation is required

Background

Drylands cover more than 41% of Earth’s land surface and face high degradation risks due to water scarcity and low productivity. Remote sensing can be used for large-scale monitoring of dryland vegetation loss through satellites such as Landsat and Sentinel, yet common vegetation indices, like Normalized Difference Vegetation Index (NDVI), struggle to separate non-photosynthetic vegetation from bare soil. As an alternative, the authors present ecological integrity as a holistic framework that integrates ecosystem quality, diversity, management, and resilience beyond simple degraded or non-degraded classifications. This article presents ecological integrity as a framework linking monitoring with adaptive management decisions.

Goals and Methods

This article presents a systematic review of remote sensing approaches used to assess ecological integrity in drylands. The authors applied the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to analyze 137 studies retrieved from major scientific databases and expert-guided searches. Metadata and methodological data were extracted to examine geographic distribution, monitor ecosystem attributes, sensors, and spatial and temporal analytical approaches. This review categorizes indicators into ecosystem quality, diversity, management, and resilience, and evaluates how vegetation monitoring techniques contribute to broader ecosystem assessment. Special attention is given to emerging methods such as vegetation fractional cover analysis and time series monitoring.

Conclusions and Takeaways

This review concludes that vegetation remains the dominant proxy for ecological integrity monitoring, but current methods require improvement to better represent ecosystem condition and management outcomes. Time series analysis and fractional cover approaches show promise; yet benchmarking and field validation remain limited. This study highlights knowledge gaps linking remote sensing indicators with ecosystem quality and management effectiveness. For practitioners, this study’s findings emphasize the need to integrate remote sensing with ecological frameworks and adaptive management strategies to achieve more reliable dryland monitoring and restoration planning.