Data-driven identification of high-nature value grasslands using Harmonized Landsat Sentinel-2 time series data

Abstract

Europe’s high-nature value (HNV) grasslands have significantly declined in recent decades. European conservation strategies are mainly confined to protected areas, and many national initiatives aiming for comprehensive coverage suffer from long and irregular monitoring intervals. Addressing this, we propose a data-driven approach to derive information about the location, extent and HNV status of grasslands to improve the efficiency of large-scale field mappings. Serving as a representative example of European and national grassland monitoring, we utilize the regional habitat map of Schleswig-Holstein, Germany, in conjunction with Harmonized Landsat Sentinel-2 time series data to train XGBoost models for the period of 2017-2022. Our models achieved high classification performance, distinguishing eight grassland classes with average F1-scores of 0.89 before and 0.86 after feature selection. We examined model decision-making patterns using an adapted version of SHapley Additive exPlanation values, finding that start-of-season, end-of-season, Red-Edge, and spectral change features significantly impacted predictions. We produced annual HNV grassland maps and, by aggregating yearly results, derived a robust estimate of the HNV status in our study area. Applying our HNV estimate to an independent dataset comprising 2363 km${}^2$ of grassland plots with unknown HNV status, we identified 84 km${}^2$ as HNV, highlighting the significance of our result. Overall, our study demonstrates how integrating remote sensing data enhances the efficiency and comprehensiveness of large-scale mapping initiatives.