Monitoring the dynamic changes in vegetation cover and driving factors from 2000 to 2020 in the Maoershan Forest Farm region, China, using satellite remote sensing data
Aim of study: Natural climate change is a central driver of global ecosystem and forest change. Climate change and topographical factors have had the greatest impact on different types of forests around the world. We used remote sensing technology to detect and analyze the temporal and spatial chan...
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| Format: | Article |
| Language: | English |
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Consejo Superior de Investigaciones Científicas (CSIC)
2023-07-01
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| Series: | Forest Systems |
| Subjects: | |
| Online Access: | https://revistas.inia.es/index.php/fs/article/view/20348 |
| _version_ | 1827162696194195456 |
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| author | Teng LI Yuanke GAO |
| author_facet | Teng LI Yuanke GAO |
| author_sort | Teng LI |
| collection | DOAJ |
| description |
Aim of study: Natural climate change is a central driver of global ecosystem and forest change. Climate change and topographical factors have had the greatest impact on different types of forests around the world. We used remote sensing technology to detect and analyze the temporal and spatial changes of forest vegetation to provide reference for regional management.
Area of study: Maoershan Forest Farm, China.
Material and methods: The Landsat images were preprocessed using ArcGIS and ENVI software. The normalized difference vegetation index (NDVI) was calculated to identify vegetation changes from 2000 to 2020. In addition, the vegetation fraction cover (VFC) was calculated using the pixel binary model. The driving factors and their influences on vegetation changes in this region were determined using the random forest algorithm and Pearson correlation analysis method.
Main results: From 2000 to 2020, the NDVI showed an overall increasing trend. The results indicated that compared with the climatic factors, topographic factors were more important to vegetation growth in the study area. Among the topographic factors, elevation was the most important factor affecting vegetation growth and both showed a negative correlation. Among the climatic factors, relative humidity was the primary driving factor affecting vegetation growth and both showed a positive correlation.
Research highlights: Accurate and timely assessment of vegetation change and its relationship to climate and topographical changes can provide very useful information for policy makers, governments and planners in formulating management policies.
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| first_indexed | 2024-03-12T21:34:53Z |
| format | Article |
| id | doaj.art-cbf9bc3fe2134e95b11bf76eac72d128 |
| institution | Directory Open Access Journal |
| issn | 2171-9845 |
| language | English |
| last_indexed | 2025-03-21T00:48:55Z |
| publishDate | 2023-07-01 |
| publisher | Consejo Superior de Investigaciones Científicas (CSIC) |
| record_format | Article |
| series | Forest Systems |
| spelling | doaj.art-cbf9bc3fe2134e95b11bf76eac72d1282024-08-03T05:46:26ZengConsejo Superior de Investigaciones Científicas (CSIC)Forest Systems2171-98452023-07-0132210.5424/fs/2023322-20348Monitoring the dynamic changes in vegetation cover and driving factors from 2000 to 2020 in the Maoershan Forest Farm region, China, using satellite remote sensing dataTeng LI0Yuanke GAO1Liaoning Vocational College of Ecological Engineering, Shenyang 110101, ChinaDepartment of Teaching and Research Management, Maoershan Teaching Area, Northeast Forestry University, Shangzhi 150611, China Aim of study: Natural climate change is a central driver of global ecosystem and forest change. Climate change and topographical factors have had the greatest impact on different types of forests around the world. We used remote sensing technology to detect and analyze the temporal and spatial changes of forest vegetation to provide reference for regional management. Area of study: Maoershan Forest Farm, China. Material and methods: The Landsat images were preprocessed using ArcGIS and ENVI software. The normalized difference vegetation index (NDVI) was calculated to identify vegetation changes from 2000 to 2020. In addition, the vegetation fraction cover (VFC) was calculated using the pixel binary model. The driving factors and their influences on vegetation changes in this region were determined using the random forest algorithm and Pearson correlation analysis method. Main results: From 2000 to 2020, the NDVI showed an overall increasing trend. The results indicated that compared with the climatic factors, topographic factors were more important to vegetation growth in the study area. Among the topographic factors, elevation was the most important factor affecting vegetation growth and both showed a negative correlation. Among the climatic factors, relative humidity was the primary driving factor affecting vegetation growth and both showed a positive correlation. Research highlights: Accurate and timely assessment of vegetation change and its relationship to climate and topographical changes can provide very useful information for policy makers, governments and planners in formulating management policies. https://revistas.inia.es/index.php/fs/article/view/20348forest managementclimate changerandom forestNDVI |
| spellingShingle | Teng LI Yuanke GAO Monitoring the dynamic changes in vegetation cover and driving factors from 2000 to 2020 in the Maoershan Forest Farm region, China, using satellite remote sensing data Forest Systems forest management climate change random forest NDVI |
| title | Monitoring the dynamic changes in vegetation cover and driving factors from 2000 to 2020 in the Maoershan Forest Farm region, China, using satellite remote sensing data |
| title_full | Monitoring the dynamic changes in vegetation cover and driving factors from 2000 to 2020 in the Maoershan Forest Farm region, China, using satellite remote sensing data |
| title_fullStr | Monitoring the dynamic changes in vegetation cover and driving factors from 2000 to 2020 in the Maoershan Forest Farm region, China, using satellite remote sensing data |
| title_full_unstemmed | Monitoring the dynamic changes in vegetation cover and driving factors from 2000 to 2020 in the Maoershan Forest Farm region, China, using satellite remote sensing data |
| title_short | Monitoring the dynamic changes in vegetation cover and driving factors from 2000 to 2020 in the Maoershan Forest Farm region, China, using satellite remote sensing data |
| title_sort | monitoring the dynamic changes in vegetation cover and driving factors from 2000 to 2020 in the maoershan forest farm region china using satellite remote sensing data |
| topic | forest management climate change random forest NDVI |
| url | https://revistas.inia.es/index.php/fs/article/view/20348 |
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