Vegetation Types Variations to the South of Ngoring Lake from 2013 to 2020, Analyzed by Hyperspectral Imaging

Studying the variation in vegetation types within the source region of the Yellow River (SRYR) is of great significance for understanding the response of vegetation to climate change and human activities on the Qinghai-Tibet Plateau (QTP) permafrost. In order to understand the characteristics of the...

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Published in:Remote Sensing
Main Authors: Xiaole Liu, Guangjun Wang, Yu Shi, Sihai Liang, Jinzhang Jia
Format: Text
Language:English
Published: Multidisciplinary Digital Publishing Institute 2023
Subjects:
Online Access:https://doi.org/10.3390/rs15123174
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spelling ftmdpi:oai:mdpi.com:/2072-4292/15/12/3174/ 2023-08-20T04:09:15+02:00 Vegetation Types Variations to the South of Ngoring Lake from 2013 to 2020, Analyzed by Hyperspectral Imaging Xiaole Liu Guangjun Wang Yu Shi Sihai Liang Jinzhang Jia agris 2023-06-18 application/pdf https://doi.org/10.3390/rs15123174 EN eng Multidisciplinary Digital Publishing Institute Biogeosciences Remote Sensing https://dx.doi.org/10.3390/rs15123174 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 15; Issue 12; Pages: 3174 SRYR vegetation type variations hyperspectral remote sensing classification Thermopsis lanceolata Text 2023 ftmdpi https://doi.org/10.3390/rs15123174 2023-08-01T10:31:29Z Studying the variation in vegetation types within the source region of the Yellow River (SRYR) is of great significance for understanding the response of vegetation to climate change and human activities on the Qinghai-Tibet Plateau (QTP) permafrost. In order to understand the characteristics of the variation in vegetation associations in the SRYR under the influence of climate and human activities, two hyperspectral remote sensing images from HJ-1A in 2013 and OHS-3C in 2020 were used to extract the vegetation types located in the area south of Ngoring Lake, covering 437.11 km2 in Maduo County, from the perspective of vegetation associations. Here, the hybrid spectral CNN (HybridSN) model, which is dependent on both spatial and spectral information, was used for vegetation association classifications. On this basis, the variations in vegetation associations from 2013 to 2020 were studied using the transition matrix, and the variation in noxious weeds across different altitude and slope gradients was analyzed. As an example, Thermopsis lanceolata’s spatial distribution pattern and diffusion mechanism were analyzed. The results showed that (1) in addition to noxious weeds, herbage such as Poa poophagorum, Stipa purpurea, Kobresia humilis, and Carex moorcroftii increased, indicating that the overall ecological environment tended to improve, which may be attributed mainly to the development of a warm and humid climate. (2) Most of the noxious weeds were located at low altitudes with an area increase in the 4250–4400 m altitude range and a decrease in the 4400–4500 m altitude range. More attention should be given to the fact that the noxious weeds area increased from 2.88 km2 to 9.02 km2 between 2013 and 2020, which was much faster than that of herbage and may threaten local livestock development. (3) The Thermopsis lanceolate association characterized by an aggregated distribution tended to spread along roads, herdsmen sites, and degraded swamps, which were mainly affected by human activities and swamp degradation. Text permafrost MDPI Open Access Publishing Remote Sensing 15 12 3174
institution Open Polar
collection MDPI Open Access Publishing
op_collection_id ftmdpi
language English
topic SRYR
vegetation type variations
hyperspectral remote sensing
classification
Thermopsis lanceolata
spellingShingle SRYR
vegetation type variations
hyperspectral remote sensing
classification
Thermopsis lanceolata
Xiaole Liu
Guangjun Wang
Yu Shi
Sihai Liang
Jinzhang Jia
Vegetation Types Variations to the South of Ngoring Lake from 2013 to 2020, Analyzed by Hyperspectral Imaging
topic_facet SRYR
vegetation type variations
hyperspectral remote sensing
classification
Thermopsis lanceolata
description Studying the variation in vegetation types within the source region of the Yellow River (SRYR) is of great significance for understanding the response of vegetation to climate change and human activities on the Qinghai-Tibet Plateau (QTP) permafrost. In order to understand the characteristics of the variation in vegetation associations in the SRYR under the influence of climate and human activities, two hyperspectral remote sensing images from HJ-1A in 2013 and OHS-3C in 2020 were used to extract the vegetation types located in the area south of Ngoring Lake, covering 437.11 km2 in Maduo County, from the perspective of vegetation associations. Here, the hybrid spectral CNN (HybridSN) model, which is dependent on both spatial and spectral information, was used for vegetation association classifications. On this basis, the variations in vegetation associations from 2013 to 2020 were studied using the transition matrix, and the variation in noxious weeds across different altitude and slope gradients was analyzed. As an example, Thermopsis lanceolata’s spatial distribution pattern and diffusion mechanism were analyzed. The results showed that (1) in addition to noxious weeds, herbage such as Poa poophagorum, Stipa purpurea, Kobresia humilis, and Carex moorcroftii increased, indicating that the overall ecological environment tended to improve, which may be attributed mainly to the development of a warm and humid climate. (2) Most of the noxious weeds were located at low altitudes with an area increase in the 4250–4400 m altitude range and a decrease in the 4400–4500 m altitude range. More attention should be given to the fact that the noxious weeds area increased from 2.88 km2 to 9.02 km2 between 2013 and 2020, which was much faster than that of herbage and may threaten local livestock development. (3) The Thermopsis lanceolate association characterized by an aggregated distribution tended to spread along roads, herdsmen sites, and degraded swamps, which were mainly affected by human activities and swamp degradation.
format Text
author Xiaole Liu
Guangjun Wang
Yu Shi
Sihai Liang
Jinzhang Jia
author_facet Xiaole Liu
Guangjun Wang
Yu Shi
Sihai Liang
Jinzhang Jia
author_sort Xiaole Liu
title Vegetation Types Variations to the South of Ngoring Lake from 2013 to 2020, Analyzed by Hyperspectral Imaging
title_short Vegetation Types Variations to the South of Ngoring Lake from 2013 to 2020, Analyzed by Hyperspectral Imaging
title_full Vegetation Types Variations to the South of Ngoring Lake from 2013 to 2020, Analyzed by Hyperspectral Imaging
title_fullStr Vegetation Types Variations to the South of Ngoring Lake from 2013 to 2020, Analyzed by Hyperspectral Imaging
title_full_unstemmed Vegetation Types Variations to the South of Ngoring Lake from 2013 to 2020, Analyzed by Hyperspectral Imaging
title_sort vegetation types variations to the south of ngoring lake from 2013 to 2020, analyzed by hyperspectral imaging
publisher Multidisciplinary Digital Publishing Institute
publishDate 2023
url https://doi.org/10.3390/rs15123174
op_coverage agris
genre permafrost
genre_facet permafrost
op_source Remote Sensing; Volume 15; Issue 12; Pages: 3174
op_relation Biogeosciences Remote Sensing
https://dx.doi.org/10.3390/rs15123174
op_rights https://creativecommons.org/licenses/by/4.0/
op_doi https://doi.org/10.3390/rs15123174
container_title Remote Sensing
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