A5035292711- Land Use and Ecosystem Services
- Remote Sensing in Agriculture
- Remote Sensing and Land Use
- Oil Palm Production and Sustainability
- Remote Sensing and LiDAR Applications
- Conservation, Biodiversity, and Resource Management
- Remote-Sensing Image Classification
- Urban Heat Island Mitigation
- Species Distribution and Climate Change
- Environmental Changes in China
- Urban Green Space and Health
- Climate change impacts on agriculture
- Geochemistry and Geologic Mapping
- Rangeland Management and Livestock Ecology
- Hydrology and Watershed Management Studies
- Impact of Light on Environment and Health
- Agricultural and Environmental Management
- Climate variability and models
- Air Quality and Health Impacts
- Date Palm Research Studies
- Soil Geostatistics and Mapping
- Health disparities and outcomes
- Advanced Image Fusion Techniques
- Climate Change and Health Impacts
- IoT and Edge/Fog Computing
Tsinghua University
2016-2025
Chinese Academy of Surveying and Mapping
2023-2025
Yunnan University
2025
Harbin Medical University
2024
Second Affiliated Hospital of Harbin Medical University
2024
Renmin University of China
2021-2024
China Mobile (China)
2016-2024
Anhui Medical University
2023
University Town of Shenzhen
2023
First Affiliated Hospital of Anhui Medical University
2023
We have produced the first 30 m resolution global land-cover maps using Landsat Thematic Mapper (TM) and Enhanced Plus (ETM+) data. classified over 6600 scenes of TM data after 2006, 2300 ETM+ before all selected from green season. These images cover most world's land surface except Antarctica Greenland. Most these came United States Geological Survey in level L1T (orthorectified). Four classifiers that were freely available employed, including conventional maximum likelihood classifier...
Abstract Urban boundaries, an essential property of cities, are widely used in many urban studies. However, extracting boundaries from satellite images is still a great challenge, especially at global scale and fine resolution. In this study, we developed automatic delineation framework to generate multi-temporal dataset (GUB) using 30 m artificial impervious area (GAIA) data. First, delineated initial boundary by filling inner non-urban areas each city. A kernel density estimation approach...
Oil palm trees are important economic crops in Malaysia and other tropical areas. The number of oil a plantation area is information for predicting the yield oil, monitoring growing situation maximizing their productivity, etc. In this paper, we propose deep learning based framework tree detection counting using high-resolution remote sensing images Malaysia. Unlike previous studies, our study more crowded crowns often overlap. We use manually interpreted samples to train optimize...
Research on global environmental change requires new data processing and analysis tools that can integrate heterogeneous geospatial from real-time in situ measurement, remote sensing (RS) geographic information systems (GISs) at the scale. The rapid growth of virtual globes for management display holds promise to meet such a requirement. Virtual globes, Google Earth particular, enable scientists around world communicate their research findings an intuitive three-dimensional (3D) perspective....
Abstract The global urbanization rate is accelerating; however, data limitations have far prevented robust estimations of either urban expansion or its effects on terrestrial net primary productivity (NPP). Here, using a high resolution dataset land use/cover (GlobeLand30), we show that areas expanded by an average 5694 km 2 per year between 2000 and 2010. rapid in the past decade has turn reduced NPP, with loss 22.4 Tg Carbon (Tg C −1 ). Although small compared to total NPP fossil fuel...
Automatic extraction of building footprints from high-resolution satellite imagery has become an important and challenging research issue receiving greater attention. Many recent studies have explored different deep learning-based semantic segmentation methods for improving the accuracy extraction. Although they record substantial land cover use information (e.g., buildings, roads, water, etc.), public geographic system (GIS) map datasets rarely been utilized to improve results in existing...
Validating land-cover maps at the global scale is a significant challenge. We built validation data-set based on interpreting Landsat Thematic Mapper (TM) and Enhanced TM Plus (ETM+) images for total of 38,664 sample units pre-determined with an equal-area stratified sampling scheme. This was supplemented by MODIS enhanced vegetation index (EVI) time series data other high-resolution imagery Google Earth. Initially designed validating 30 m-resolution in Finer Resolution Observation...
For the two of most important agricultural commodities, soybean and corn, remote sensing plays a substantial role in delivering timely information on crop area for economic, environmental policy studies. Traditional long-term mapping corn is challenging as result high cost repeated training data collection, inconsistency image process interpretation, difficulty handling inter-annual variability weather progress. In this study, we developed an automated approach to map state Paraná, Brazil...
Earth system science has changed rapidly due to global environmental changes and the advent of observation technology. Therefore, new tools are required monitor, measure, analyze, evaluate, model data. Google (GE) was officially launched by in 2005 as a ”geobrowser”, Engine (GEE) released 2010 cloud computing platform with substantial computational capabilities. The use these two or platforms various applications, particularly used remote sensing community, developed rapidly. In this paper,...
As satellite observation technology develops and the number of Earth (EO) satellites increases, observations have become essential to developments in understanding its environment. However, current impacts remote sensing community different EO data possible future trends applications not been systematically examined. In this paper, we review use based on an analysis from 15 whose are widely used. Articles that reference missions included Web Science core collection for 2020 were analyzed...
Abstract Urban-living individuals are exposed to many environmental factors that may combine and interact influence mental health. While individual of an urban environment have been investigated in isolation, no attempt has made model how complex, real-life exposure living the city relates brain health, this is moderated by genetic factors. Using data 156,075 participants from UK Biobank, we carried out sparse canonical correlation analyses investigate relationships between environments...
Abstract. Accurate, detailed, and up-to-date information on cropland extent is crucial for provisioning food security environmental sustainability. However, because of the complexity agricultural landscapes lack sufficient training samples, it remains challenging to monitor dynamics at high spatial temporal resolutions across large geographical extents, especially regions where land use changing dramatically. Here we developed a cost-effective annual mapping framework that integrated...
Abstract A spatial multi-objective land use optimization model defined by the acronym 'NSGA-II-MOLU' or 'non-dominated sorting genetic algorithm-II for of use' is proposed searching optimal scenarios which embrace multiple objectives and constraints extracted from requirements users, as well providing support to planning process. In this application, we took MOLU was initially developed integrate coupled with a revised version algorithm NSGA-II based on specific crossover mutation operators....