“3s” Technologies and Application for Dynamic Monitoring Soil and Water Loss in the Yangtze River Basin, China

Li, C.; Yao, Jing; Li, R.; Zhu, Yonghua; Yao, He; Zhang, P.; D, Wei; Zhao, Shengchuan; Li, Y.; Wu, Ying

doi:10.5194/isprs-archives-xliii-b3-2020-1563-2020

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…To address this problem, field surveys are carried out to correct the manual labeling errors and machine errors generated by DL networks. in regions with terrain or potential hazards, we employ unmanned aerial vehicles (UAVs) to facilitate the manual validation [88].…”

Section: Misclassified Results Correctionmentioning

confidence: 99%

National-Standards- and Deep-Learning-Oriented Raster and Vector Benchmark Dataset (RVBD) for Land-Use/Land-Cover Mapping in the Yangtze River Basin

Zhang

et al. 2023

Remote Sensing

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A high-quality remote sensing interpretation dataset has become crucial for driving an intelligent model, i.e., deep learning (DL), to produce land-use/land-cover (LULC) products. The existing remote sensing datasets face the following issues: the current studies (1) lack object-oriented fine-grained information; (2) they cannot meet national standards; (3) they lack field surveys for labeling samples; and (4) they cannot serve for geographic engineering application directly. To address these gaps, the national-standards- and DL-oriented raster and vector benchmark dataset (RVBD) is the first to be established to map LULC for conducting soil water erosion assessment (SWEA). RVBD has the following significant innovation and contributions: (1) it is the first second-level object- and DL-oriented dataset with raster and vector data for LULC mapping; (2) its classification system conforms to the national industry standards of the Ministry of Water Resources of the People’s Republic of China; (3) it has high-quality LULC interpretation accuracy assisted by field surveys rather than indoor visual interpretation; and (4) it could be applied to serve for SWEA. Our dataset is constructed as follows: (1) spatio-temporal-spectrum information is utilized to perform automatic vectorization and label LULC attributes conforming to the national standards; and (2) several remarkable DL networks (DenseNet161, HorNet, EfficientNetB7, Vision Transformer, and Swin Transformer) are chosen as the baselines to train our dataset, and five evaluation metrics are chosen to perform quantitative evaluation. Experimental results verify the reliability and effectiveness of RVBD. Each chosen network achieves a minimum overall accuracy of 0.81 and a minimum Kappa of 0.80, and Vision Transformer achieves the best classification performance with overall accuracy of 0.87 and Kappa of 0.86. It indicates that RVBD is a significant benchmark, which could lay a foundation for intelligent interpretation of relevant geographic research about SWEA in the Yangtze River Basin and promote artificial intelligence technology to enrich geographical theories and methods.

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Section: Misclassified Results Correctionmentioning

confidence: 99%

National-Standards- and Deep-Learning-Oriented Raster and Vector Benchmark Dataset (RVBD) for Land-Use/Land-Cover Mapping in the Yangtze River Basin

Zhang

et al. 2023

Remote Sensing

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“…As such, accurate data on abandonment and recultivation patterns are crucial to conduct this research. Currently, the remote sensing based studies for monitoring of cropland in the Yangtze River basin are diverse and cover various aspects of land use and environmental monitoring, including urbanization [59], water and soil monitoring [60], and crop patterns [61]. However, studies focusing on the spatiotemporal dynamics of the interaction between cropland abandonment and recultivation in this region are lacking.…”

Section: Introductionmentioning

confidence: 99%

Mapping the Spatiotemporal Dynamics of Cropland Abandonment and Recultivation across the Yangtze River Basin

Long,

Sun,

Wellens

et al. 2024

Remote Sensing

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Whether China can achieve the United Nations’ Sustainable Development Goals (SDGs) largely depends on the ability of main food-producing areas to cope with multiple land use change challenges. Despite the fact that the Yangtze River basin is one of the key regions for China’s food security, the spatiotemporal dynamics of cropland abandonment and recultivation remain largely unexplored in this region. The present study assesses the evolution of the agricultural system within the Yangtze River basin between 2000 and 2020 by mapping cropland abandonment and recultivation using MODIS time series and multiple land cover products. The results highlight a widespread cropland abandonment process (i.e., 10.5% of the total study area between 2000 and 2020), predominantly in Western Sichuan, Eastern Yunnan, and Central Jiangxi. Although 70% of abandoned cropland is situated in areas with slopes less than 5°, the highest rates of abandonment are in mountainous regions. However, by 2020, 74% of this abandoned cropland had been recultivated at least once, whereas half of the abandoned croplands got recultivated within three years of their initial abandonment. Hence, as this is one of the first studies that unravels the complex interaction between cropland abandonment and recultivation in a spatiotemporal explicit context, it offers (i) scientists a novel methodological framework to assess agricultural land use issues across large geographical entities, and (ii) policy-makers new insights to support the sustainable transition of the agricultural sector.

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Object-Oriented and Deep-Learning-Based High-Resolution Mapping from Large Remote Sensing Imagery

Zhang

et al. 2021

Canadian Journal of Remote Sensing

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“3s” Technologies and Application for Dynamic Monitoring Soil and Water Loss in the Yangtze River Basin, China

Cited by 3 publications

References 6 publications

National-Standards- and Deep-Learning-Oriented Raster and Vector Benchmark Dataset (RVBD) for Land-Use/Land-Cover Mapping in the Yangtze River Basin

National-Standards- and Deep-Learning-Oriented Raster and Vector Benchmark Dataset (RVBD) for Land-Use/Land-Cover Mapping in the Yangtze River Basin

Mapping the Spatiotemporal Dynamics of Cropland Abandonment and Recultivation across the Yangtze River Basin

Object-Oriented and Deep-Learning-Based High-Resolution Mapping from Large Remote Sensing Imagery

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