Is the change deforestation? Using time-series analysis of satellite data to disentangle deforestation from other forest degradation causes

Abstract

Protecting natural ecosystems requires monitoring approaches that work as early warning systems to avoid degradation and protect biodiversity. However, separating forest disturbance causes in change-detection pipelines is challenging due to the complex interplay of multiple drivers affecting vegetation. This study aims to detect deforestation in highly heterogeneous ecosystems. We used Landsat NDVI time-series data for testing three unsupervised change detection methods: 1) the non-parametric phenological anomaly detection (npphen), 2) the continuous change detection and classification (CCDC), and 3) the pruned exact linear time (PELT) algorithms. We used visual interpretation of Google Earth Pro high-resolution data (10 m) to depict deforestation, and natural-induced changes, like forest browning and fires, evaluating the performance of the unsupervised methods. Additionally, a Random Forest model trained with the outputs from detection algorithms together with elevation and radar vegetation index data were utilised to depict deforestation in a second step. While PELT slightly outperformed other methods for tracking general vegetation changes, with overall accuracies (OA) ranging from 0.78 to 0.99, depending on the vegetation type, it also showed the slowest deforestation tracking response. CCDC presented the fastest response and an OA between 0.78 and 0.95. Additionally, we observed a mean OA of only 0.47 when separating deforestation from other changes using only the unsupervised models. On the other hand, deforestation was accurately detected (OA = 0.93; kappa = 0.83) when using CCDC outputs within a secondary supervised classification, agreeing with selected citizen-based complaints from the Environmental Superintendence. The relatively fast response in deforestation tracking using CCDC makes it a viable alternative for near real-time monitoring. Commonly used unsupervised detection methods may be coupled with supervised techniques to depict vegetation change sources robustly. This application constitutes a step forward for managing and monitoring vegetation areas in highly complex and dynamic landscapes, like Mediterranean ecosystems.