Time: Wednesday, 16/July/2025: 16:00 - 17:30 |
ID. 95531
ID. 95392
Oral
ID: 206 / S.3.5: 1
Dragon 6 Oral Presentation
ECOSYSTEMS: 95392 - Essential Grassland Degradation Variables Mapping Based on Multiple Remote Sensing Datasets
Essential Grassland Degradation Variables Mapping Based on Multiple Remote Sensing Datasets
1Aerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS), China, China, People's Republic of; 2University of Leeds, UK
Since the project launch last year, our project focuses on two critical challenges in current degradation monitoring, including large-scale quantitative assessment of shrub encroachment, and grassland degradation assessment with very high spatial resolution image. Through sensitivity analysis of optical and SAR features against shrub coverage, optimal monitoring periods and feature combinations were identified. By developing a feature fusion algorithm to amplify shrub signals, the team successfully constructed a shrub coverage index. The team conducted visual interpretation of Google Earth imagery in Hexigten Banner, Inner Mongolia. This enabled high-precision mapping of land cover and spatial patterns of vegetation/soil degradation, establishing a grassland degradation classification framework. The findings demonstrate that VHR image analysis coupled with cross-scale multidisciplinary frameworks serves as an effective tool for policy-making and land management decisions.
Oral
ID: 151 / S.3.5: 2
Dragon 6 Oral Presentation
ECOSYSTEMS: 95531 - Resilient Wetlands And Human-Water Relationship In Watersheds
Resilient Wetlands and Human-Water Relationship in Watersheds
1Key Laboratory of Wetland and Watershed Research, Ministry of Education/School of Geography and environment, Jiangxi Normal University; 2Centre for Research on Biodiversity and the Environment (CRBE), University of Toulouse; 3State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University; 4Southern marine science and engineering Guangdong laboratory (Guangzhou); 5School of Geography and Information Engineering, China University of Geosciences; 6School of Information and Software Engineering, East China Jiaotong University
Aiming at information extraction of wetland environment factors, a signal photon extraction method based on convolutional neural network and a hyperspectral vegetation feature band selection method based on quantum genetic spectral angle classification algorithm were proposed based on ICESat-2 and measured hyperspectral data respectively. At the basin scale, statistical analysis and model simulation method were used to analyze the characteristics and driving factors of water and sediment change, and separate the contribution of natural and human factors based on the long-term hydro-meteorological, natural and socio-economic factor and remote sensing data. The digital twin technology was used to construct more than 8 types of migratory bird models and 12 types of wetland vegetation models in Poyang Lake wetland, dynamically display the habitat activities of migratory birds and simulate the vegetation growth process synchronously, so as to realize the accurate restoration of the seasonal growth and decline of the virtual Poyang Lake water area, the inter-annual change of vegetation growth and the migratory activities of migratory birds. Above works are benefit for evaluation of wetlands resilience and reveal the human-water relationship in watersheds.