Show simple item record

A Novel Spatial Simulation Method for Mapping the Urban Forest Carbon Density in Southern China by the Google Earth Engine

dc.contributor.authorFugen Jiang
dc.contributor.authorChuanshi Chen
dc.contributor.authorChengjie Li
dc.contributor.authorMykola Kutia
dc.contributor.authorHua Sun
dc.contributor.otherResearch Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China
dc.contributor.otherResearch Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China
dc.contributor.otherForest Resources and Ecological Environment Monitoring Center of Guangxi Zhuang Autonomous Region, Nanning 530000, China
dc.contributor.otherBangor College China, Bangor University, 498 Shaoshan Rd., Changsha 410004, China
dc.contributor.otherResearch Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China
dc.date.accessioned2025-10-09T04:53:47Z
dc.date.available2025-10-09T04:53:47Z
dc.date.issued01-07-2021
dc.identifier.urihttps://www.mdpi.com/2072-4292/13/14/2792
dc.identifier.urihttp://digilib.fisipol.ugm.ac.id/repo/handle/15717717/40829
dc.description.abstractUrban 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.
dc.language.isoEN
dc.publisherMDPI AG
dc.subject.lccScience
dc.titleA Novel Spatial Simulation Method for Mapping the Urban Forest Carbon Density in Southern China by the Google Earth Engine
dc.typeArticle
dc.description.keywordsforest carbon density
dc.description.keywordsLandsat 8
dc.description.keywordsGEE
dc.description.keywordsgeographically weighted regression
dc.description.doi10.3390/rs13142792
dc.title.journalRemote Sensing
dc.identifier.e-issn2072-4292
dc.identifier.oaioai:doaj.org/journal:3bbb16f22ac445f9bc800a5e67a13fb9
dc.journal.infoVolume 13, Issue 14


This item appears in the following Collection(s)

Show simple item record