Mapping Temperate Grassland Dynamics in China Inner Mongolia (1980s-2010s) Using Multi-Source Data and Deep Neural Network

As a vital part of the Eurasian temperate grassland, the Chinese temperate grassland is primarily distributed in the Inner Mongolia Plateau. This paper focuses on mapping temperate grassland dynamics from the 1980s to the 2010s in Inner Mongolia, which was divided into temperate meadow steppe (TMS), temperate typical steppe (TTS), temperate desert steppe (TDS), temperate steppe desert (TSD) and temperate desert (TD). Multi-source features, including multispectral reflectance, vegetation growth, topography, water bodies, meteorological data, and soil characteristics, were selected based on their distinct physical properties and remote sensing variations. Then, we applied deep neural network (DNN) models to classify them, achieving an accuracy of 79.4% in the 1980s and 81.1% in the 2000s. Additionally, validation in the 2010s through field reconnaissance demonstrated an accuracy of 72.7%, which was acceptable, confirming that DNN is an effective method for classifying temperate grasslands. The results revealed that TTS had the highest proportion in the study area (39%), while TMS and TSD had the lowest (8.2% and 8.1%, respectively). Grassland types have the distribution law of aggregation; according to statistics, 61.1% of the grassland area remained unchanged, and the transition zone between adjacent grassland classes was highly easy to change. The area variation mainly came from TTS, TDS, and TSD, but not TD. The mutual transformation of different grassland types occurred mainly in adjacent areas between them. This study demonstrates the potential of DNN for long-term grassland mapping and provides the most comprehensive classification maps of Inner Mongolia grasslands to date, which are invaluable for grassland research and conservation efforts in the area.