Remote Sensing Method of Forest Logging in the Beijiang River Basin Based on Recent Intensive Satellite Data
Forests are important natural and strategic resources, and deforestation is a significant cause of soil erosion. Given the high uncertainty and limited temporal-spatial resolution of land features classified by remote sensing, especially the lack of regional studies on the dynamic distribution of fo...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Committee of Tropical Geography
2024-11-01
|
| Series: | Redai dili |
| Subjects: | |
| Online Access: | https://www.rddl.com.cn/CN/10.13284/j.cnki.rddl.20230862 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846157563031191552 |
|---|---|
| author | Zhao Zitong Chen Shuisen Yu Guorong Li Dan Jia Kai Zhao Chenyao Li Jian Qin Boxiong |
| author_facet | Zhao Zitong Chen Shuisen Yu Guorong Li Dan Jia Kai Zhao Chenyao Li Jian Qin Boxiong |
| author_sort | Zhao Zitong |
| collection | DOAJ |
| description | Forests are important natural and strategic resources, and deforestation is a significant cause of soil erosion. Given the high uncertainty and limited temporal-spatial resolution of land features classified by remote sensing, especially the lack of regional studies on the dynamic distribution of forest deforestation, it is urgent to extract the multi-temporal dynamic distribution of forest deforestation using remote sensing techniques. Based on the spectral features of ground objects before and after deforestation, 930 optical remote sensing images from Sentinel-2 in the Beijiang River Basin from 2017 to 2022 were selected as experimental data. The Google Earth Engine cloud platform was utilized for data collection and preprocessing to calculate the NDVI vegetation index from 2017 to 2022. Following the extraction of forest distribution using the threshold segmentation method, the dynamic change in deforestation distribution between 2017 and 2022 in the study area was analyzed. The results showed that: (1)229 sampling points were randomly selected in the deforestation area, and the accuracy of remote sensing mapping of deforestation in 2020-2021 was evaluated using historical high-resolution images, achieving a verification accuracy of 72.05%. (2)From 2017 to 2022, deforestation in the Beijiang River Basin exhibited an increasing trend year by year, except in 2020 and 2021, with an average annual increase of about 9%. In terms of distribution, the largest proportion of deforestation occurred in the Wujiang River Basin during 2017-2022, with an average annual deforestation rate of 3.27% of the total area of the basin. The lowest proportion of deforestation was observed in the Nanshui River Basin, with an average annual deforestation rate of 1.47% of the total area of that basin. (3)In the Beijiang River Basin, the distribution of deforestation across different slopes is more uniform. The deforestation area is primarily concentrated on slopes between 8° and 25°, which account for 53.3% of the total basin area and generate 48% to 57% of the deforestation area. Deforestation is more likely to occur on slopes below 15°, with a felling ratio of 3.76%, which is 1.14% higher than that of slopes above 15°. (4)The standardized NDVI average of 56 feature points decreased from 0.84 in 2017 to 0.43 in 2018 and then increased by an average of 0.08 per year thereafter. The NDVI characteristics of forest land generally recovered in the third year after deforestation. Using the Google Earth Engine cloud platform and the threshold segmentation method of NDVI, the dynamic characteristics of multi-temporal deforestation distribution were extracted. This approach addresses the limitations of remote sensing extraction and monitoring of the dynamic distribution of forest deforestation in the Beijiang River Basin, which is of great significance for the rational development and utilization of regional water resources and forest resource management. |
| format | Article |
| id | doaj-art-ee1b9e7a0d6b4b7fa184968884bce480 |
| institution | Kabale University |
| issn | 1001-5221 |
| language | zho |
| publishDate | 2024-11-01 |
| publisher | Editorial Committee of Tropical Geography |
| record_format | Article |
| series | Redai dili |
| spelling | doaj-art-ee1b9e7a0d6b4b7fa184968884bce4802024-11-25T09:05:43ZzhoEditorial Committee of Tropical GeographyRedai dili1001-52212024-11-0144112091210310.13284/j.cnki.rddl.202308621001-5221(2024)11-2091-13Remote Sensing Method of Forest Logging in the Beijiang River Basin Based on Recent Intensive Satellite DataZhao Zitong0Chen Shuisen1Yu Guorong2Li Dan3Jia Kai4Zhao Chenyao5Li Jian6Qin Boxiong7Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming650500, ChinaGuangzhou Institute of Geography, Guangdong Academy of Sciences, Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Research Center of Guangdong Province for Engineering Technology Application of Remote Sensing Big Data, Guangzhou510070, ChinaFaculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming650500, ChinaGuangzhou Institute of Geography, Guangdong Academy of Sciences, Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Research Center of Guangdong Province for Engineering Technology Application of Remote Sensing Big Data, Guangzhou510070, ChinaGuangzhou Institute of Geography, Guangdong Academy of Sciences, Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Research Center of Guangdong Province for Engineering Technology Application of Remote Sensing Big Data, Guangzhou510070, ChinaGuangzhou Institute of Geography, Guangdong Academy of Sciences, Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Research Center of Guangdong Province for Engineering Technology Application of Remote Sensing Big Data, Guangzhou510070, ChinaFaculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming650500, ChinaGuangzhou Institute of Geography, Guangdong Academy of Sciences, Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Research Center of Guangdong Province for Engineering Technology Application of Remote Sensing Big Data, Guangzhou510070, ChinaForests are important natural and strategic resources, and deforestation is a significant cause of soil erosion. Given the high uncertainty and limited temporal-spatial resolution of land features classified by remote sensing, especially the lack of regional studies on the dynamic distribution of forest deforestation, it is urgent to extract the multi-temporal dynamic distribution of forest deforestation using remote sensing techniques. Based on the spectral features of ground objects before and after deforestation, 930 optical remote sensing images from Sentinel-2 in the Beijiang River Basin from 2017 to 2022 were selected as experimental data. The Google Earth Engine cloud platform was utilized for data collection and preprocessing to calculate the NDVI vegetation index from 2017 to 2022. Following the extraction of forest distribution using the threshold segmentation method, the dynamic change in deforestation distribution between 2017 and 2022 in the study area was analyzed. The results showed that: (1)229 sampling points were randomly selected in the deforestation area, and the accuracy of remote sensing mapping of deforestation in 2020-2021 was evaluated using historical high-resolution images, achieving a verification accuracy of 72.05%. (2)From 2017 to 2022, deforestation in the Beijiang River Basin exhibited an increasing trend year by year, except in 2020 and 2021, with an average annual increase of about 9%. In terms of distribution, the largest proportion of deforestation occurred in the Wujiang River Basin during 2017-2022, with an average annual deforestation rate of 3.27% of the total area of the basin. The lowest proportion of deforestation was observed in the Nanshui River Basin, with an average annual deforestation rate of 1.47% of the total area of that basin. (3)In the Beijiang River Basin, the distribution of deforestation across different slopes is more uniform. The deforestation area is primarily concentrated on slopes between 8° and 25°, which account for 53.3% of the total basin area and generate 48% to 57% of the deforestation area. Deforestation is more likely to occur on slopes below 15°, with a felling ratio of 3.76%, which is 1.14% higher than that of slopes above 15°. (4)The standardized NDVI average of 56 feature points decreased from 0.84 in 2017 to 0.43 in 2018 and then increased by an average of 0.08 per year thereafter. The NDVI characteristics of forest land generally recovered in the third year after deforestation. Using the Google Earth Engine cloud platform and the threshold segmentation method of NDVI, the dynamic characteristics of multi-temporal deforestation distribution were extracted. This approach addresses the limitations of remote sensing extraction and monitoring of the dynamic distribution of forest deforestation in the Beijiang River Basin, which is of great significance for the rational development and utilization of regional water resources and forest resource management.https://www.rddl.com.cn/CN/10.13284/j.cnki.rddl.20230862sentinel-2deforestationdynamic monitoringgoogle earth enginebeijiang river basin |
| spellingShingle | Zhao Zitong Chen Shuisen Yu Guorong Li Dan Jia Kai Zhao Chenyao Li Jian Qin Boxiong Remote Sensing Method of Forest Logging in the Beijiang River Basin Based on Recent Intensive Satellite Data Redai dili sentinel-2 deforestation dynamic monitoring google earth engine beijiang river basin |
| title | Remote Sensing Method of Forest Logging in the Beijiang River Basin Based on Recent Intensive Satellite Data |
| title_full | Remote Sensing Method of Forest Logging in the Beijiang River Basin Based on Recent Intensive Satellite Data |
| title_fullStr | Remote Sensing Method of Forest Logging in the Beijiang River Basin Based on Recent Intensive Satellite Data |
| title_full_unstemmed | Remote Sensing Method of Forest Logging in the Beijiang River Basin Based on Recent Intensive Satellite Data |
| title_short | Remote Sensing Method of Forest Logging in the Beijiang River Basin Based on Recent Intensive Satellite Data |
| title_sort | remote sensing method of forest logging in the beijiang river basin based on recent intensive satellite data |
| topic | sentinel-2 deforestation dynamic monitoring google earth engine beijiang river basin |
| url | https://www.rddl.com.cn/CN/10.13284/j.cnki.rddl.20230862 |
| work_keys_str_mv | AT zhaozitong remotesensingmethodofforestlogginginthebeijiangriverbasinbasedonrecentintensivesatellitedata AT chenshuisen remotesensingmethodofforestlogginginthebeijiangriverbasinbasedonrecentintensivesatellitedata AT yuguorong remotesensingmethodofforestlogginginthebeijiangriverbasinbasedonrecentintensivesatellitedata AT lidan remotesensingmethodofforestlogginginthebeijiangriverbasinbasedonrecentintensivesatellitedata AT jiakai remotesensingmethodofforestlogginginthebeijiangriverbasinbasedonrecentintensivesatellitedata AT zhaochenyao remotesensingmethodofforestlogginginthebeijiangriverbasinbasedonrecentintensivesatellitedata AT lijian remotesensingmethodofforestlogginginthebeijiangriverbasinbasedonrecentintensivesatellitedata AT qinboxiong remotesensingmethodofforestlogginginthebeijiangriverbasinbasedonrecentintensivesatellitedata |