Land Cover Change in the Central Region of the Lower Yangtze River Based on Landsat Imagery and the Google Earth Engine: A Case Study in Nanjing, China
Urbanization in China is progressing rapidly and continuously, especially in the newly developed metropolitan areas. The Google Earth Engine (GEE) is a powerful tool that can be used to efficiently investigate these changes using a large repository of available optical imagery. This work examined la...
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MDPI AG
2020-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/7/2091 |
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| author | Dong-Dong Zhang Lei Zhang |
| author_facet | Dong-Dong Zhang Lei Zhang |
| author_sort | Dong-Dong Zhang |
| collection | DOAJ |
| description | Urbanization in China is progressing rapidly and continuously, especially in the newly developed metropolitan areas. The Google Earth Engine (GEE) is a powerful tool that can be used to efficiently investigate these changes using a large repository of available optical imagery. This work examined land-cover changes in the central region of the lower Yangtze River and exemplifies the application of GEE using the random forest classification algorithm on Landsat dense stacks spanning the 30 years from 1987 to 2017. Based on the obtained time-series land-cover classification results, the spatiotemporal land-use/cover changes were analyzed, as well as the main factors driving the changes in different land-cover categories. The results show that: (1) The obtained land datasets were reliable and highly accurate, with an overall accuracy ranging from 88% to 92%. (2) Over the past 30 years, built-up areas have continued to expand, increasing from 537.9 km<sup>2</sup> to 1500.5 km<sup>2</sup>, and the total area occupied by built-up regions has expanded by 178.9% to occupy an additional 962.7 km<sup>2</sup>. The surface water area first decreased, then increased, and generally showed an increasing trend, expanding by 17.9%, with an area increase of approximately 131 km<sup>2</sup>. Barren areas accounted for 6.6% of the total area in the period 2015–2017, which was an increase of 94.8% relative to the period 1987–1989. The expansion of the built-up area was accompanied by an overall 25.6% (1305.7 km<sup>2</sup>) reduction in vegetation. (3) The complexity of the key factors driving the changes in the regional surface water extent was made apparent, mainly including the changes in runoff of the Yangtze River and the construction of various water conservancy projects. The effects of increasing the urban population and expanding industrial development were the main factors driving the expansion of urban built-up areas and the significant reduction in vegetation. The advantages and limitations arising from land-cover mapping by using the Google Earth Engine are also discussed. |
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| language | English |
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| publishDate | 2020-04-01 |
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| series | Sensors |
| spelling | doaj.art-3ebe06a9cde64e8d9c5a11b74686662d2023-11-19T20:59:55ZengMDPI AGSensors1424-82202020-04-01207209110.3390/s20072091Land Cover Change in the Central Region of the Lower Yangtze River Based on Landsat Imagery and the Google Earth Engine: A Case Study in Nanjing, ChinaDong-Dong Zhang0Lei Zhang1Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai 201100, ChinaMOE International Joint Lab of Trustworthy Software, East China Normal University, Shanghai 200062, ChinaUrbanization in China is progressing rapidly and continuously, especially in the newly developed metropolitan areas. The Google Earth Engine (GEE) is a powerful tool that can be used to efficiently investigate these changes using a large repository of available optical imagery. This work examined land-cover changes in the central region of the lower Yangtze River and exemplifies the application of GEE using the random forest classification algorithm on Landsat dense stacks spanning the 30 years from 1987 to 2017. Based on the obtained time-series land-cover classification results, the spatiotemporal land-use/cover changes were analyzed, as well as the main factors driving the changes in different land-cover categories. The results show that: (1) The obtained land datasets were reliable and highly accurate, with an overall accuracy ranging from 88% to 92%. (2) Over the past 30 years, built-up areas have continued to expand, increasing from 537.9 km<sup>2</sup> to 1500.5 km<sup>2</sup>, and the total area occupied by built-up regions has expanded by 178.9% to occupy an additional 962.7 km<sup>2</sup>. The surface water area first decreased, then increased, and generally showed an increasing trend, expanding by 17.9%, with an area increase of approximately 131 km<sup>2</sup>. Barren areas accounted for 6.6% of the total area in the period 2015–2017, which was an increase of 94.8% relative to the period 1987–1989. The expansion of the built-up area was accompanied by an overall 25.6% (1305.7 km<sup>2</sup>) reduction in vegetation. (3) The complexity of the key factors driving the changes in the regional surface water extent was made apparent, mainly including the changes in runoff of the Yangtze River and the construction of various water conservancy projects. The effects of increasing the urban population and expanding industrial development were the main factors driving the expansion of urban built-up areas and the significant reduction in vegetation. The advantages and limitations arising from land-cover mapping by using the Google Earth Engine are also discussed.https://www.mdpi.com/1424-8220/20/7/2091land-use/cover changeGoogle Earth Enginespatiotemporal analysisdriving mechanismNanjing |
| spellingShingle | Dong-Dong Zhang Lei Zhang Land Cover Change in the Central Region of the Lower Yangtze River Based on Landsat Imagery and the Google Earth Engine: A Case Study in Nanjing, China Sensors land-use/cover change Google Earth Engine spatiotemporal analysis driving mechanism Nanjing |
| title | Land Cover Change in the Central Region of the Lower Yangtze River Based on Landsat Imagery and the Google Earth Engine: A Case Study in Nanjing, China |
| title_full | Land Cover Change in the Central Region of the Lower Yangtze River Based on Landsat Imagery and the Google Earth Engine: A Case Study in Nanjing, China |
| title_fullStr | Land Cover Change in the Central Region of the Lower Yangtze River Based on Landsat Imagery and the Google Earth Engine: A Case Study in Nanjing, China |
| title_full_unstemmed | Land Cover Change in the Central Region of the Lower Yangtze River Based on Landsat Imagery and the Google Earth Engine: A Case Study in Nanjing, China |
| title_short | Land Cover Change in the Central Region of the Lower Yangtze River Based on Landsat Imagery and the Google Earth Engine: A Case Study in Nanjing, China |
| title_sort | land cover change in the central region of the lower yangtze river based on landsat imagery and the google earth engine a case study in nanjing china |
| topic | land-use/cover change Google Earth Engine spatiotemporal analysis driving mechanism Nanjing |
| url | https://www.mdpi.com/1424-8220/20/7/2091 |
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