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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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 |
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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 |
container_volume |
15 |
container_issue |
12 |
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3174 |
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