High-precision remote sensing mapping of aeolian sand landforms based on deep learning algorithms
It is significant to adopt deep learning algorithms and higher-resolution remote sensing images in mapping large-scale and high-precision of aeolian landform. In this study, the western part of Horqin Sandy Land was taken as the study area. Based on the data collected from 2,786 verification points...
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| Format: | Article |
| Language: | English |
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De Gruyter
2022-03-01
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| Series: | Open Geosciences |
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| Online Access: | https://doi.org/10.1515/geo-2022-0351 |
| _version_ | 1811203729804754944 |
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| author | Du Huishi Wang Jingfa Han Cheng |
| author_facet | Du Huishi Wang Jingfa Han Cheng |
| author_sort | Du Huishi |
| collection | DOAJ |
| description | It is significant to adopt deep learning algorithms and higher-resolution remote sensing images in mapping large-scale and high-precision of aeolian landform. In this study, the western part of Horqin Sandy Land was taken as the study area. Based on the data collected from 2,786 verification points located in sandy land and remote sensing images of high-spectral and spatial resolution Sentinel-1, Sentinel-2, and GDEM (V3), this article made a research on data of large-scale and high-precision mapping classification of this area between 2015 and 2020 by using convolutional neural network deep learning algorithm. The results showed that the types of aeolian sandy landform in the west of Horqin Sandy Land mainly include longitudinal dune, flat sandy land, mild undulating sand land, nest-shaped land, parabolic dune, barchan dune, and dune chain, with an area of 1735.62, 51.32, 251.38, 902.07, 49.57, and 101.63 km2. Among them, longitudinal dune, barchan dune, and dune chain have the largest area, while parabolic dunes and flat sand land are smaller. Between 2015 and 2020, the area of aeolian landforms was reduced by 89.27 km2 and transformed into an oasis from a desert. This study adopted remote sensing data by high-resolution Sentinel and GDEM (V3) and convolutional neural network deep learning algorithm to map the aeolian landforms effectively. The precision of aeolian landform classification and Kappa coefficient in the western part of Horqin Sandy Land is as high as 95.51% and 0.8961. Combined with Sentinel-1, Sentinel-2, and GDEM (V3), the deep learning algorithm based on the convolution neural network can timely and effectively monitor the changes of sand dunes, which can be used for large-scale aeolian landforms. |
| first_indexed | 2024-04-12T02:59:55Z |
| format | Article |
| id | doaj.art-cf2329bae62a4b0bad8464d2eccc7426 |
| institution | Directory Open Access Journal |
| issn | 2391-5447 |
| language | English |
| last_indexed | 2024-04-12T02:59:55Z |
| publishDate | 2022-03-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Open Geosciences |
| spelling | doaj.art-cf2329bae62a4b0bad8464d2eccc74262022-12-22T03:50:41ZengDe GruyterOpen Geosciences2391-54472022-03-0114122423310.1515/geo-2022-0351High-precision remote sensing mapping of aeolian sand landforms based on deep learning algorithmsDu Huishi0Wang Jingfa1Han Cheng2Department of Geographic Information Science, College of Tourism and Geographic Science, Jilin Normal University, Siping Jilin 136000, ChinaDepartment of Geographic Information Science, College of Tourism and Geographic Science, Jilin Normal University, Siping Jilin 136000, ChinaDepartment of Geographic Information Science, College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, ChinaIt is significant to adopt deep learning algorithms and higher-resolution remote sensing images in mapping large-scale and high-precision of aeolian landform. In this study, the western part of Horqin Sandy Land was taken as the study area. Based on the data collected from 2,786 verification points located in sandy land and remote sensing images of high-spectral and spatial resolution Sentinel-1, Sentinel-2, and GDEM (V3), this article made a research on data of large-scale and high-precision mapping classification of this area between 2015 and 2020 by using convolutional neural network deep learning algorithm. The results showed that the types of aeolian sandy landform in the west of Horqin Sandy Land mainly include longitudinal dune, flat sandy land, mild undulating sand land, nest-shaped land, parabolic dune, barchan dune, and dune chain, with an area of 1735.62, 51.32, 251.38, 902.07, 49.57, and 101.63 km2. Among them, longitudinal dune, barchan dune, and dune chain have the largest area, while parabolic dunes and flat sand land are smaller. Between 2015 and 2020, the area of aeolian landforms was reduced by 89.27 km2 and transformed into an oasis from a desert. This study adopted remote sensing data by high-resolution Sentinel and GDEM (V3) and convolutional neural network deep learning algorithm to map the aeolian landforms effectively. The precision of aeolian landform classification and Kappa coefficient in the western part of Horqin Sandy Land is as high as 95.51% and 0.8961. Combined with Sentinel-1, Sentinel-2, and GDEM (V3), the deep learning algorithm based on the convolution neural network can timely and effectively monitor the changes of sand dunes, which can be used for large-scale aeolian landforms.https://doi.org/10.1515/geo-2022-0351deep learningaeolian landformremote sensing mappinghorqin sandy land |
| spellingShingle | Du Huishi Wang Jingfa Han Cheng High-precision remote sensing mapping of aeolian sand landforms based on deep learning algorithms Open Geosciences deep learning aeolian landform remote sensing mapping horqin sandy land |
| title | High-precision remote sensing mapping of aeolian sand landforms based on deep learning algorithms |
| title_full | High-precision remote sensing mapping of aeolian sand landforms based on deep learning algorithms |
| title_fullStr | High-precision remote sensing mapping of aeolian sand landforms based on deep learning algorithms |
| title_full_unstemmed | High-precision remote sensing mapping of aeolian sand landforms based on deep learning algorithms |
| title_short | High-precision remote sensing mapping of aeolian sand landforms based on deep learning algorithms |
| title_sort | high precision remote sensing mapping of aeolian sand landforms based on deep learning algorithms |
| topic | deep learning aeolian landform remote sensing mapping horqin sandy land |
| url | https://doi.org/10.1515/geo-2022-0351 |
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