Comparison of Lake Extraction and Classification Methods for the Tibetan Plateau Based on Topographic-Spectral Information

Wang, Xiaoliang; Zhou, Guangsheng; Lv, Xiaomin; Zhou, Li; Hu, Mingcheng; He, Xiaohui; Tian, Zengshan

doi:10.3390/rs15010267

Cited by 3 publications

References 31 publications

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Estimation of Wave Overtopping Over Breakwater Based on an Improved Stacking Model

Wang,

Wu,

Mao

et al. 2023

Preprint

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Estimation of Wave Overtopping Over Breakwater Based on an Improved Stacking Model

Wang,

Wu,

Mao

et al. 2023

Preprint

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Terrain Shadow Interference Reduction for Water Surface Extraction in the Hindu Kush Himalaya Using a Transformer-Based Network

Yan,

Song

2024

Remote Sensing

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Water is the basis for human survival and growth, and it holds great importance for ecological and environmental protection. The Hindu Kush Himalaya (HKH) is known as the “Water Tower of Asia”, where water influences changes in the global water cycle and ecosystem. It is thus very important to efficiently measure the status of water in this region and to monitor its changes; with the development of satellite-borne sensors, water surface extraction based on remote sensing images has become an important method through which to do so, and one of the most advanced and accurate methods for water surface extraction involves the use of deep learning networks. We designed a network based on the state-of-the-art Vision Transformer to automatically extract the water surface in the HKH region; however, in this region, terrain shadows are often misclassified as water surfaces during extraction due to their spectral similarity. Therefore, we adjusted the training dataset in different ways to improve the accuracy of water surface extraction and explored whether these methods help to reduce the interference of terrain shadows. Our experimental results show that, based on the designed network, adding terrain shadow samples can significantly enhance the accuracy of water surface extraction in high mountainous areas, such as the HKH region, while adding terrain data does not reduce the interference from terrain shadows. We obtained the water surface extraction results in the HKH region in 2021, with the network and training datasets containing both water surface and terrain shadows. By comparing these results with the data products of Global Surface Water, it was shown that our water surface extraction results are highly accurate and the extracted water surface boundaries are finer, which strongly confirmed the applicability and advantages of the proposed water surface extraction approach in a wide range of complex surface environments.

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A 10 m resolution land cover map of the Tibetan Plateau with detailed vegetation types

Huang,

Yin,

Feng

et al. 2024

Earth Syst. Sci. Data

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Abstract. The Tibetan Plateau (TP) hosts a variety of vegetation types, ranging from broadleaved and needle-leaved forests at the lower altitudes and in mesic areas to alpine grassland at the higher altitudes and in xeric areas. Accurate and detailed mapping of the vegetation distribution on the TP is essential for an improved understanding of climate change effects on terrestrial ecosystems. Yet, existing land cover datasets for the TP are either provided at a low spatial resolution or have insufficient vegetation types to characterize certain unique TP ecosystems, such as the alpine scree. Here, we produced a 10 m resolution TP land cover map with 12 vegetation classes and 3 non-vegetation classes for the year 2022 (referred to as TP_LC10-2022) by leveraging state-of-the-art remote-sensing approaches including Sentinel-1 and Sentinel-2 imagery, environmental and topographic datasets, and four machine learning models using the Google Earth Engine platform. Our TP_LC10-2022 dataset achieved an overall classification accuracy of 86.5 % with a kappa coefficient of 0.854. Upon comparing it with four existing global land cover products, TP_LC10-2022 showed significant improvements in terms of reflecting local-scale vertical variations in the southeast TP region. Moreover, we found that alpine scree, which is ignored in existing land cover datasets, occupied 13.99 % of the TP region, and shrublands, which are characterized by distinct forms (deciduous shrublands and evergreen shrublands) that are largely determined by the topography and are missed in existing land cover datasets, occupied 4.63 % of the TP region. Our dataset provides a solid foundation for further analyses which need accurate delineation of these unique vegetation types in the TP. TP_LC10-2022 and the sample dataset are freely available at https://doi.org/10.5281/zenodo.8214981 (Huang et al., 2023a) and https://doi.org/10.5281/zenodo.8227942 (Huang et al., 2023b), respectively. Additionally, the classification map can be viewed at https://cold-classifier.users.earthengine.app/view/tplc10-2022 (last access: 6 June 2024).

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Comparison of Lake Extraction and Classification Methods for the Tibetan Plateau Based on Topographic-Spectral Information

Cited by 3 publications

References 31 publications

Estimation of Wave Overtopping Over Breakwater Based on an Improved Stacking Model

Estimation of Wave Overtopping Over Breakwater Based on an Improved Stacking Model

Terrain Shadow Interference Reduction for Water Surface Extraction in the Hindu Kush Himalaya Using a Transformer-Based Network

A 10 m resolution land cover map of the Tibetan Plateau with detailed vegetation types

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