Long Time Series Land Cover Classification in China from 1982 to 2015 Based on Bi-LSTM Deep Learning
Land cover classification data have a very important practical application value, and long time series land cover classification datasets are of great significance studying environmental changes, urban changes, land resource surveys, hydrology and ecology. At present, the starting point of continuou...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-07-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/11/14/1639 |
| _version_ | 1818959055361671168 |
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| author | Haoyu Wang Xiang Zhao Xin Zhang Donghai Wu Xiaozheng Du |
| author_facet | Haoyu Wang Xiang Zhao Xin Zhang Donghai Wu Xiaozheng Du |
| author_sort | Haoyu Wang |
| collection | DOAJ |
| description | Land cover classification data have a very important practical application value, and long time series land cover classification datasets are of great significance studying environmental changes, urban changes, land resource surveys, hydrology and ecology. At present, the starting point of continuous land cover classification products for many years is mostly after the year 2000, and there is a lack of long-term continuously annual land cover classification products before 2000. In this study, a long time series classification data extraction model is established using a bidirectional long-term and short-term memory network (Bi-LSTM). In the model, quantitative remote sensing products combined with DEM, nighttime lighting data, and latitude and longitude elevation data were used. We applied this model in China and obtained China’s 1982−2017 0.05° land cover classification product. The accuracy assessment results of the test data show that the overall accuracy is 84.2% and that the accuracies of wetland, water, glacier, tundra, city and bare soil reach 92.1%, 92.0%, 94.3%, 94.6% and 92.4%, respectively. For the first time, this study used a variety of long time series data, especially quantitative remote sensing products, for the classification of features. At the same time, it also acquired long time series land cover classification products, including those from the year 2000. This study provides new ideas for the establishment of higher-resolution long time series land cover classification products. |
| first_indexed | 2024-12-20T11:35:33Z |
| format | Article |
| id | doaj.art-2f980be7bb374beb9697001cadb7ffd4 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-20T11:35:33Z |
| publishDate | 2019-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-2f980be7bb374beb9697001cadb7ffd42022-12-21T19:42:07ZengMDPI AGRemote Sensing2072-42922019-07-011114163910.3390/rs11141639rs11141639Long Time Series Land Cover Classification in China from 1982 to 2015 Based on Bi-LSTM Deep LearningHaoyu Wang0Xiang Zhao1Xin Zhang2Donghai Wu3Xiaozheng Du4State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100875, ChinaState Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100875, ChinaState Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Science, Beijing 100101, ChinaCollege of Urban and Environmental Sciences, Peking University, Beijing 100871, ChinaState Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100875, ChinaLand cover classification data have a very important practical application value, and long time series land cover classification datasets are of great significance studying environmental changes, urban changes, land resource surveys, hydrology and ecology. At present, the starting point of continuous land cover classification products for many years is mostly after the year 2000, and there is a lack of long-term continuously annual land cover classification products before 2000. In this study, a long time series classification data extraction model is established using a bidirectional long-term and short-term memory network (Bi-LSTM). In the model, quantitative remote sensing products combined with DEM, nighttime lighting data, and latitude and longitude elevation data were used. We applied this model in China and obtained China’s 1982−2017 0.05° land cover classification product. The accuracy assessment results of the test data show that the overall accuracy is 84.2% and that the accuracies of wetland, water, glacier, tundra, city and bare soil reach 92.1%, 92.0%, 94.3%, 94.6% and 92.4%, respectively. For the first time, this study used a variety of long time series data, especially quantitative remote sensing products, for the classification of features. At the same time, it also acquired long time series land cover classification products, including those from the year 2000. This study provides new ideas for the establishment of higher-resolution long time series land cover classification products.https://www.mdpi.com/2072-4292/11/14/1639time seriesland cover classificationBi-LSTMquantitative remote sensing |
| spellingShingle | Haoyu Wang Xiang Zhao Xin Zhang Donghai Wu Xiaozheng Du Long Time Series Land Cover Classification in China from 1982 to 2015 Based on Bi-LSTM Deep Learning Remote Sensing time series land cover classification Bi-LSTM quantitative remote sensing |
| title | Long Time Series Land Cover Classification in China from 1982 to 2015 Based on Bi-LSTM Deep Learning |
| title_full | Long Time Series Land Cover Classification in China from 1982 to 2015 Based on Bi-LSTM Deep Learning |
| title_fullStr | Long Time Series Land Cover Classification in China from 1982 to 2015 Based on Bi-LSTM Deep Learning |
| title_full_unstemmed | Long Time Series Land Cover Classification in China from 1982 to 2015 Based on Bi-LSTM Deep Learning |
| title_short | Long Time Series Land Cover Classification in China from 1982 to 2015 Based on Bi-LSTM Deep Learning |
| title_sort | long time series land cover classification in china from 1982 to 2015 based on bi lstm deep learning |
| topic | time series land cover classification Bi-LSTM quantitative remote sensing |
| url | https://www.mdpi.com/2072-4292/11/14/1639 |
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