An optimised land-use land-cover classification approach for general application in deserts and arid regions

Land-use/land-cover (LULC) is a critical driver of ecosystem dynamics globally. Arid regions are particularly vulnerable to global change factors, and comprehensive LULC assessments are crucial for evaluating the environmental stability of these areas. Despite considerable developments of remote sensing (RS) data/products and classification algorithms, the available multi-scale maps fail to represent the complex heterogeneity of LULC in these areas. The major limitation resides not on the improvements in data resolutions or the complexity of algorithmic decisions, but rather on the lack of approaches prioritizing detailed categorization of LULC in arid regions. Therefore, we propose an integrative multi-classification approach of LULC based on RS techniques and in-situ data for general application in arid regions, using the north-western Saudi Arabia as pilot area. Using in-situ data (N = 7523) and a Landsat-8 time-series, we applied a supervised classification of non-dynamic classes representing regional geodiversity, combined with a clustering analysis of dynamic harmonic regression model coefficients to categorize ecologically dynamic classes. The map was obtained with high accuracy for non-dynamic classes (Kappa≈0.84; overall accuracy≈0.87; overall producer's accuracy≈0.86; overall user's accuracy≈0.89) and dynamic classes (combined overall accuracy≈0.76). The final map presents a total of 15 classes, considerably improving the available categorical resolution for the study area. The approach is transferable to other arid regions, having the potential to integrate other finer-scale RS data and classification algorithms. We urge for increased efforts in data collection and the implementation of approaches considering the prominent diversity of LULC in arid regions. • We propose an approach of land-use/land-cover (LULC) classification in arid regions. • Multi-classification of LULC based on groups of non-dynamic and dynamic classes. • Final LULC map with high overall and class-wise accuracy measures. • The approach improved the LULC categorization in north-western arid Saudi Arabia. • Improved data collection efforts and target-based approaches needed in arid regions.