Application of remote sensing in ecosystem health assessment in times of global change

Abstract

Climate change, Land Use/Land Cover Changes (LULCCs), and biological invasions are transforming ecosystems globally, posing significant challenges to human well-being. Understanding and monitoring ecosystem health—a multifaceted concept reflecting an ecosystem’s structure, function, resilience, and recovery capacity—is essential for sustainable development. Combining ground-based methods with advanced remote sensing technologies makes it possible to assess and monitor ecosystem health over extensive spatial scales, providing critical insights for Sustainable Development Goals (SDGs). This research focused on evaluating ecosystem health in two contrasting regions: the semi-arid landscapes of central Iran and the temperate suboceanic forests of central Germany. The study pursued three key objectives: (1) comparing ground-based and remote sensing methods for ecosystem health assessment, (2) employing the Dynamic Habitat Index (DHI) to monitor ecosystem dynamics over time, and (3) analyzing the sensitivity of DHIs to environmental changes across diverse LULC types. In Iran, ground-based assessments provided factors such as biotic integrity, site stability, and hydrological functions to classify ecosystem health as healthy, at risk, or unhealthy. Complementing this, Landsat imagery and machine learning techniques produced detailed ecosystem health maps, closely aligned with field-based findings. Historical health maps derived from Landsat and evaluated using aerial orthophotos historical changes in ecosystem health. However, the specific climatic and ecological context of the region limited the generalizability of these methods. In Germany, the study shifted to exploring the potential of DHIs—derived from Normalized Difference Vegetation Index (NDVI) data—to evaluate the health of coniferous forests under extreme drought conditions. We also evaluated the sensitivity of the DHI to changing environmental conditions across various Land Use/Land Cover (LULC) types. The analysis highlighted the effectiveness of DHIs in capturing the impacts of drought on Central European coniferous forest ecosystems. DHIs successfully distinguished between damaged and nondamaged forest areas, showing promise as an early warning system for ecosystem degradation and functional changes. Integrating DHIs with meteorological and ancillary geodata enhanced their interpretive power, highlighting the dynamic interplay of pedo-climatic factors in shaping ecosystem health. The findings illustrate the strengths and limitations of different approaches, emphasizing the importance of indicator selection related to regional contexts, historical background, and environmental conditions. The integrated methodologies developed in this research offer valuable tools for land managers and decision-makers, contributing to sustainable land use strategies and advancing SDG indicators related to land degradation.