What question did this study set out to answer?

To evaluate the drivers of woody plant encroachment and its ecological impacts using remote sensing methods.

January 26, 2026Open Access

Remote Sensing of Woody Plant Encroachment: A Global Systematic Review of Drivers, Ecological Impacts, Methods, and Emerging Innovations

Key Points

To evaluate the drivers of woody plant encroachment and its ecological impacts using remote sensing methods.
Conducted a systematic literature review of 159 peer-reviewed studies.
Analyzed remote sensing approaches including machine learning and classification techniques.
Synthesized findings to identify knowledge gaps and future directions for monitoring.
Drivers of woody plant encroachment include climate variability and land use transitions.
Ecological impacts affect biodiversity, ecosystem functioning, and services.
Emerging technologies enable long-term monitoring but face challenges in early-stage identification.

Abstract

Globally, grasslands, savannas, and wetlands are degrading rapidly and increasingly being replaced by woody vegetation. Woody Plant Encroachment (WPE) disrupts natural landscapes and has significant consequences for biodiversity, ecosystem functioning, and key ecosystem services. This review synthesizes findings from 159 peer-reviewed studies identified through a PRISMA-guided systematic literature review to evaluate the drivers of WPE, its ecological impacts, and the remote sensing (RS) approaches used to monitor it. The drivers of WPE are multifaceted, involving interactions among climate variability, topographic and edaphic conditions, hydrological change, land use transitions, and altered fire and grazing regimes, while its impacts are similarly diverse, influencing land cover structure, water and nutrient cycles, carbon and nitrogen dynamics, and broader implications for ecosystem resilience. Over the past two decades, RS has become central to WPE monitoring, with studies employing classification techniques, spectral mixture analysis, object-based image analysis, change detection, thresholding, landscape pattern and fragmentation metrics, and increasingly, machine learning and deep learning methods. Looking forward, emerging advances such as multi-sensor fusion (optical– synthetic aperture radar (SAR), Light Detection and Ranging (LiDAR)–hyperspectral), cloud-based platforms including Google Earth Engine, Microsoft Planetary Computer, and Digital Earth, and geospatial foundation models offer new opportunities for scalable, automated, and long-term monitoring. Despite these innovations, challenges remain in detecting early-stage encroachment, subcanopy woody growth, and species-specific patterns across heterogeneous landscapes. Key knowledge gaps highlighted in this review include the need for long-term monitoring frameworks, improved socio-ecological integration, species- and ecosystem-specific RS approaches, better utilization of SAR, and broader adoption of analysis-ready data and open-source platforms. Addressing these gaps will enable more effective, context-specific strategies to monitor, manage, and mitigate WPE in rapidly changing environments.

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