Continuous change detection outperforms traditional post-classification change detection for long-term monitoring of wetlands

Abstract

Accurate long-term monitoring of wetlands using satellite archives is crucial for effective conservation. While new methods based on temporal profile classification have been useful for long-term monitoring of wetlands, their advantages over traditional classification methods have not yet been demonstrated. This study aimed to compare continuous change detection (using the continuous change detection and classification (CCDC) algorithm) to traditional post-classification change detection for monitoring wetland changes between 1984 and 2022 in a temperate coastal marsh (Marais Poitevin, France) from the Landsat archive. The reference dataset was collected mainly from field observations and used to train and test a random forest classifier. The accuracy of the resulting change map was then assessed for both methods using validation points collected via visual interpretation of historical aerial photographs and Landsat temporal profiles. The change map derived from CCDC had much higher unbiased overall accuracy (0.86 ± 0.02) than that derived from post-classification change detection (0.51 ± 0.03). In addition, wetland loss was much higher than wetland gain (18 % and 2 % of the area, respectively) and was due mainly to conversion of grassland to cropland and urbanization. The study demonstrated that, unlike traditional post-classification change detection, continuous change detection provides maps of wetland changes sufficiently accurate for operational use by managers. The study also confirmed the ongoing impact of agricultural intensification and artificialization on wetland degradation in Europe.

Keywords

• CCDC
• Time-series analysis
• Google Earth Engine
• Landsat
• Habitat monitoring