Distribution and Structure Analysis of Mountain Permafrost Landscape in Orulgan Ridge (Northeast Siberia) Using Google Earth Engine
An analysis of the landscape spatial structure and diversity in the mountain ranges of Northeast Siberia is essential to assess how tundra and boreal landscapes may respond to climate change and anthropogenic impacts in the vast mountainous permafrost of the Arctic regions. In addition, a precise la...
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ftmdpi:oai:mdpi.com:/2073-445X/11/8/1187/ 2023-08-20T04:04:58+02:00 Distribution and Structure Analysis of Mountain Permafrost Landscape in Orulgan Ridge (Northeast Siberia) Using Google Earth Engine Moisei Zakharov Sébastien Gadal Jūratė Kamičaitytė Mikhail Cherosov Elena Troeva agris 2022-07-29 application/pdf https://doi.org/10.3390/land11081187 EN eng Multidisciplinary Digital Publishing Institute Land Systems and Global Change https://dx.doi.org/10.3390/land11081187 https://creativecommons.org/licenses/by/4.0/ Land; Volume 11; Issue 8; Pages: 1187 permafrost landscape Google Earth Engine Support Vector Machine time-series image classification terrain analysis landscape structure landscape mapping Northeast Siberia Text 2022 ftmdpi https://doi.org/10.3390/land11081187 2023-08-01T05:53:22Z An analysis of the landscape spatial structure and diversity in the mountain ranges of Northeast Siberia is essential to assess how tundra and boreal landscapes may respond to climate change and anthropogenic impacts in the vast mountainous permafrost of the Arctic regions. In addition, a precise landscape map is required for knowledge-based territorial planning and management. In this article, we aimed to explore and enhanced methods to analyse and map the permafrost landscape in Orulgan Ridge. The Google Earth Engine cloud platform was used to generate vegetation cover maps based on multi-fusion classification of Sentinel 2 MSI and Landsat 8 OLI time series data. Phenological features based on the monthly median values of time series Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), and Normalized Difference Moisture Index (NDMI) were used to recognize geobotanical units according to the hierarchical concept of permafrost landscapes by the Support Vector Machine (SVM) classifier. In addition, geomorphological variables of megarelief (mountains and river valleys) were identified using the GIS-based terrain analysis and landform classification of the ASTER GDEM scenes mosaic. The resulting environmental variables made it possible to categorize nine classes of mountain permafrost landscapes. The result obtained was compared with previous permafrost landscape maps, which revealed a significant difference in distribution and spatial structure of intrazonal valleys and mountain tundra landscapes. Analysis of the landscape structure revealed a significant distribution of classes of mountain Larix-sparse forests and tundra. Landscape diversity was described by six longitudinal and latitudinal landscape hypsometric profiles. River valleys allow boreal–taiga landscapes to move up to high-mountainous regions. The features of the landscape structure and diversity of the ridge are noted, which, along with the specific spatial organization of vegetation and relief, can be ... Text Arctic Climate change permafrost taiga Tundra Siberia MDPI Open Access Publishing Arctic Land 11 8 1187 |
institution |
Open Polar |
collection |
MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
permafrost landscape Google Earth Engine Support Vector Machine time-series image classification terrain analysis landscape structure landscape mapping Northeast Siberia |
spellingShingle |
permafrost landscape Google Earth Engine Support Vector Machine time-series image classification terrain analysis landscape structure landscape mapping Northeast Siberia Moisei Zakharov Sébastien Gadal Jūratė Kamičaitytė Mikhail Cherosov Elena Troeva Distribution and Structure Analysis of Mountain Permafrost Landscape in Orulgan Ridge (Northeast Siberia) Using Google Earth Engine |
topic_facet |
permafrost landscape Google Earth Engine Support Vector Machine time-series image classification terrain analysis landscape structure landscape mapping Northeast Siberia |
description |
An analysis of the landscape spatial structure and diversity in the mountain ranges of Northeast Siberia is essential to assess how tundra and boreal landscapes may respond to climate change and anthropogenic impacts in the vast mountainous permafrost of the Arctic regions. In addition, a precise landscape map is required for knowledge-based territorial planning and management. In this article, we aimed to explore and enhanced methods to analyse and map the permafrost landscape in Orulgan Ridge. The Google Earth Engine cloud platform was used to generate vegetation cover maps based on multi-fusion classification of Sentinel 2 MSI and Landsat 8 OLI time series data. Phenological features based on the monthly median values of time series Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), and Normalized Difference Moisture Index (NDMI) were used to recognize geobotanical units according to the hierarchical concept of permafrost landscapes by the Support Vector Machine (SVM) classifier. In addition, geomorphological variables of megarelief (mountains and river valleys) were identified using the GIS-based terrain analysis and landform classification of the ASTER GDEM scenes mosaic. The resulting environmental variables made it possible to categorize nine classes of mountain permafrost landscapes. The result obtained was compared with previous permafrost landscape maps, which revealed a significant difference in distribution and spatial structure of intrazonal valleys and mountain tundra landscapes. Analysis of the landscape structure revealed a significant distribution of classes of mountain Larix-sparse forests and tundra. Landscape diversity was described by six longitudinal and latitudinal landscape hypsometric profiles. River valleys allow boreal–taiga landscapes to move up to high-mountainous regions. The features of the landscape structure and diversity of the ridge are noted, which, along with the specific spatial organization of vegetation and relief, can be ... |
format |
Text |
author |
Moisei Zakharov Sébastien Gadal Jūratė Kamičaitytė Mikhail Cherosov Elena Troeva |
author_facet |
Moisei Zakharov Sébastien Gadal Jūratė Kamičaitytė Mikhail Cherosov Elena Troeva |
author_sort |
Moisei Zakharov |
title |
Distribution and Structure Analysis of Mountain Permafrost Landscape in Orulgan Ridge (Northeast Siberia) Using Google Earth Engine |
title_short |
Distribution and Structure Analysis of Mountain Permafrost Landscape in Orulgan Ridge (Northeast Siberia) Using Google Earth Engine |
title_full |
Distribution and Structure Analysis of Mountain Permafrost Landscape in Orulgan Ridge (Northeast Siberia) Using Google Earth Engine |
title_fullStr |
Distribution and Structure Analysis of Mountain Permafrost Landscape in Orulgan Ridge (Northeast Siberia) Using Google Earth Engine |
title_full_unstemmed |
Distribution and Structure Analysis of Mountain Permafrost Landscape in Orulgan Ridge (Northeast Siberia) Using Google Earth Engine |
title_sort |
distribution and structure analysis of mountain permafrost landscape in orulgan ridge (northeast siberia) using google earth engine |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2022 |
url |
https://doi.org/10.3390/land11081187 |
op_coverage |
agris |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Climate change permafrost taiga Tundra Siberia |
genre_facet |
Arctic Climate change permafrost taiga Tundra Siberia |
op_source |
Land; Volume 11; Issue 8; Pages: 1187 |
op_relation |
Land Systems and Global Change https://dx.doi.org/10.3390/land11081187 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/land11081187 |
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Land |
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11 |
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8 |
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1187 |
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