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...

Full description

Bibliographic Details
Main Authors: Dong-Dong Zhang, Lei Zhang
Format: Article
Language:English
Published: MDPI AG 2020-04-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/20/7/2091
id doaj-3ebe06a9cde64e8d9c5a11b74686662d
record_format Article
spelling doaj-3ebe06a9cde64e8d9c5a11b74686662d2020-11-25T03:10:55ZengMDPI AGSensors1424-82202020-04-01202091209110.3390/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
collection DOAJ
language English
format Article
sources DOAJ
author Dong-Dong Zhang
Lei Zhang
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
author_facet Dong-Dong Zhang
Lei Zhang
author_sort Dong-Dong Zhang
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_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_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_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
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2020-04-01
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.
topic land-use/cover change
Google Earth Engine
spatiotemporal analysis
driving mechanism
Nanjing
url https://www.mdpi.com/1424-8220/20/7/2091
work_keys_str_mv AT dongdongzhang landcoverchangeinthecentralregionoftheloweryangtzeriverbasedonlandsatimageryandthegoogleearthengineacasestudyinnanjingchina
AT leizhang landcoverchangeinthecentralregionoftheloweryangtzeriverbasedonlandsatimageryandthegoogleearthengineacasestudyinnanjingchina
_version_ 1724656408531566592