Developing an early warning land degradation indicator based on geostatistical analysis of Ecosystem Functional Types dynamics

Identifying and quantifying ecosystem degradation and recovery is of critical importance for ecosystem health, biodiversity, food security and the livelihoods of local communities. Remote sensing datasets and techniques, particularly land cover maps, provide crucial data for capturing spatial and temporal changes, supporting informed decision-making and targeted interventions. However, these maps often emphasize structural rather than functional attributes and, also due to their thematic and temporal resolution, may not detect early degradation signs. This study evaluates the effectiveness of Ecosystem Functional Attributes (EFAs) and Ecosystem Functional Types (EFTs) as early warning indicators of ecosystem degradation. Using Sentinel-2 derived Normalized Difference Vegetation Index (NDVI) data from 2016 to 2022, we conducted qualitative and quantitative EFA/EFT analysis on two fragile protected areas, Moyowosi Game Reserve (Kigoma region, Tanzania) and the Sheikh Jamal Inani National Park (Cox’s Bazar district, Bangladesh). Firstly, EFA analysis characterized vegetation productivity and seasonality, revealing temporal trends and spatial patterns of change. Secondly, EFTs served as indicators of change levels. Our findings showed significant insights into productivity shifts due to human activities and climate anomalies, identifying specific temporal events and turning points. Variogram-based geostatistical analysis highlighted changes in vegetation diversity’s spatial distribution. Integrating EFA/EFT analysis with geostatistical methods proved effective for early detection of land degradation, surpassing traditional land cover change analysis. Hence, the presented approach forms a robust framework for an early warning system, aimed at monitoring and evaluating environmentally fragile areas and aiding decision-makers in mitigating environmental degradation and promoting sustainable land management.