A Novel Spatial Simulation Method for Mapping the Urban Forest Carbon Density in Southern China by the Google Earth Engine
Urban forest is an important component of terrestrial ecosystems and is highly related to global climate change. However, because of complex city landscapes, deriving the spatial distribution of urban forest carbon density and conducting accuracy assessments are difficult. This study proposes a nove...
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| Format: | Article |
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MDPI AG
2021-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/14/2792 |
| _version_ | 1797526108404449280 |
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| author | Fugen Jiang Chuanshi Chen Chengjie Li Mykola Kutia Hua Sun |
| author_facet | Fugen Jiang Chuanshi Chen Chengjie Li Mykola Kutia Hua Sun |
| author_sort | Fugen Jiang |
| collection | DOAJ |
| description | Urban forest is an important component of terrestrial ecosystems and is highly related to global climate change. However, because of complex city landscapes, deriving the spatial distribution of urban forest carbon density and conducting accuracy assessments are difficult. This study proposes a novel spatial simulation method, optimized geographically weighted logarithm regression (OGWLR), using Landsat 8 data acquired by the Google Earth Engine (GEE) and field survey data to map the forest carbon density of Shenzhen city in southern China. To verify the effectiveness of the novel method, multiple linear regression (MLR), k-nearest neighbors (kNN), random forest (RF) and geographically weighted regression (GWR) models were established for comparison. The results showed that OGWLR achieved the highest coefficient of determination (R<sup>2</sup> = 0.54) and the lowest root mean square error (RMSE = 13.28 Mg/ha) among all estimation models. In addition, OGWLR achieved a more consistent spatial distribution of carbon density with the actual situation. The carbon density of the forests in the study area was large in the central and western regions and coastal areas and small in the building and road areas. Therefore, this method can provide a new reference for urban forest carbon density estimation and mapping. |
| first_indexed | 2024-03-10T09:24:32Z |
| format | Article |
| id | doaj.art-3bbb16f22ac445f9bc800a5e67a13fb9 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T09:24:32Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-3bbb16f22ac445f9bc800a5e67a13fb92023-11-22T04:52:28ZengMDPI AGRemote Sensing2072-42922021-07-011314279210.3390/rs13142792A Novel Spatial Simulation Method for Mapping the Urban Forest Carbon Density in Southern China by the Google Earth EngineFugen Jiang0Chuanshi Chen1Chengjie Li2Mykola Kutia3Hua Sun4Research Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, ChinaResearch Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, ChinaForest Resources and Ecological Environment Monitoring Center of Guangxi Zhuang Autonomous Region, Nanning 530000, ChinaBangor College China, Bangor University, 498 Shaoshan Rd., Changsha 410004, ChinaResearch Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, ChinaUrban forest is an important component of terrestrial ecosystems and is highly related to global climate change. However, because of complex city landscapes, deriving the spatial distribution of urban forest carbon density and conducting accuracy assessments are difficult. This study proposes a novel spatial simulation method, optimized geographically weighted logarithm regression (OGWLR), using Landsat 8 data acquired by the Google Earth Engine (GEE) and field survey data to map the forest carbon density of Shenzhen city in southern China. To verify the effectiveness of the novel method, multiple linear regression (MLR), k-nearest neighbors (kNN), random forest (RF) and geographically weighted regression (GWR) models were established for comparison. The results showed that OGWLR achieved the highest coefficient of determination (R<sup>2</sup> = 0.54) and the lowest root mean square error (RMSE = 13.28 Mg/ha) among all estimation models. In addition, OGWLR achieved a more consistent spatial distribution of carbon density with the actual situation. The carbon density of the forests in the study area was large in the central and western regions and coastal areas and small in the building and road areas. Therefore, this method can provide a new reference for urban forest carbon density estimation and mapping.https://www.mdpi.com/2072-4292/13/14/2792forest carbon densityLandsat 8GEEgeographically weighted regression |
| spellingShingle | Fugen Jiang Chuanshi Chen Chengjie Li Mykola Kutia Hua Sun A Novel Spatial Simulation Method for Mapping the Urban Forest Carbon Density in Southern China by the Google Earth Engine Remote Sensing forest carbon density Landsat 8 GEE geographically weighted regression |
| title | A Novel Spatial Simulation Method for Mapping the Urban Forest Carbon Density in Southern China by the Google Earth Engine |
| title_full | A Novel Spatial Simulation Method for Mapping the Urban Forest Carbon Density in Southern China by the Google Earth Engine |
| title_fullStr | A Novel Spatial Simulation Method for Mapping the Urban Forest Carbon Density in Southern China by the Google Earth Engine |
| title_full_unstemmed | A Novel Spatial Simulation Method for Mapping the Urban Forest Carbon Density in Southern China by the Google Earth Engine |
| title_short | A Novel Spatial Simulation Method for Mapping the Urban Forest Carbon Density in Southern China by the Google Earth Engine |
| title_sort | novel spatial simulation method for mapping the urban forest carbon density in southern china by the google earth engine |
| topic | forest carbon density Landsat 8 GEE geographically weighted regression |
| url | https://www.mdpi.com/2072-4292/13/14/2792 |
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