IGSNRR OpenIR
Integrated Analyses of PALSAR and Landsat Imagery Reveal More Agroforests in a Typical Agricultural Production Region, North China Plain
Yang, Zhiqi1,2,3; Dong, Jinwei1; Qin, Yuanwei4,5; Ni, Wenjian3; Zhao, Guosong1; Chen, Wei3; Chen, Bangqian6,7; Kou, Weili8; Wang, Jie4,5; Xiao, Xiangming4,5,9
2018-09-01
Source PublicationREMOTE SENSING
ISSN2072-4292
Volume10Issue:9Pages:18
Corresponding AuthorDong, Jinwei(dongjw@igsnrr.ac.cn) ; Xiao, Xiangming(xiangming.xiao@ou.edu)
AbstractAs the largest among terrestrial ecosystems, forests are vital to maintaining ecosystem services and regulating regional climate. The area and spatial distribution of trees in densely forested areas have been focused on in the past few decades, while sparse forests in agricultural zones, so-called agroforests or trees outside forests (TOF), have usually been ignored or missed in existing forest mapping efforts, despite their important role in regulating agricultural ecosystems. We combined Landsat and PALSAR data to map forests in a typical agricultural zone in the North China Plain. The resultant map, based on PALSAR and Landsat (PL) data, was also compared with five existing medium resolution (30-100 m) forest maps from PALSAR (JAXA forest map) and Landsat: NLCD-China, GlobeLand30, ChinaCover, and FROM-GLC. The results show that the PL-based forest map has the highest accuracy (overall accuracy of 95 +/- 1% with a 95% confidence interval, and Kappa coefficient of 0.86) compared to those forest maps based on single Landsat or PALSAR data in the North China Plain (overall accuracy ranging from 85 +/- 2% to 92 +/- 1%). All forest maps revealed higher accuracy in densely forested mountainous areas, while the PL-based and JAXA forest maps showed higher accuracy in the plain, as the higher omission errors existed in only the Landsat-based forest maps. Moreover, we found that the PL-based forest map can capture more patched forest information in low forest density areas. This means that the radar data have advantages in capturing forests in the typical agricultural zones, which tend to be missing in published Landsat-based only forest maps. Given the significance of agroforests in regulating ecosystem services of the agricultural ecosystem and improving carbon stock estimation, this study implies that the integration of PALSAR and Landsat data can provide promising agroforest estimates in future forest inventory efforts, targeting a comprehensive understanding of ecosystem services of agroforests and a more accurate carbon budget inventory.
Keywordforest mapping agroforests Landsat PALSAR North China Plain
DOI10.3390/rs10091323
WOS KeywordALOS PALSAR ; CARBON SEQUESTRATION ; SNOW DETECTION ; CLOUD SHADOW ; FOREST ; RESOLUTION ; ACCURACY ; CLIMATE ; CONSERVATION ; BIODIVERSITY
Indexed BySCI
Language英语
Funding ProjectStrategic Priority Research Program of the Chinese Academy of Sciences (CAS)[XDA19040301] ; Key Research Program of Frontier Sciences of the Chinese Academy of Sciences (CAS)[QYZDB-SSW-DQC005] ; Open Fund of State Key Laboratory of Remote Sensing Science[OFSLRSS201606] ; National Natural Science Foundation of China[31760181] ; National Natural Science Foundation of China[31400493] ; International Fellowship Initiative, Institute of Geographic Sciences and Natural Resources Research, CAS[2017VP02]
Funding OrganizationStrategic Priority Research Program of the Chinese Academy of Sciences (CAS) ; Key Research Program of Frontier Sciences of the Chinese Academy of Sciences (CAS) ; Open Fund of State Key Laboratory of Remote Sensing Science ; National Natural Science Foundation of China ; International Fellowship Initiative, Institute of Geographic Sciences and Natural Resources Research, CAS
WOS Research AreaRemote Sensing
WOS SubjectRemote Sensing
WOS IDWOS:000449993800003
PublisherMDPI
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/52459
Collection中国科学院地理科学与资源研究所
Corresponding AuthorDong, Jinwei; Xiao, Xiangming
Affiliation1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China
4.Univ Oklahoma, Dept Microbiol & Plant Biol, Norman, OK 73019 USA
5.Univ Oklahoma, Ctr Spatial Anal, Norman, OK 73019 USA
6.Chinese Acad Trop Agr Sci, Rubber Res Inst, Danzhou City 571737, Peoples R China
7.Minist Agr, Danzhou Invest & Expt Stn Trop Crops, Danzhou 571737, Peoples R China
8.Southwest Forestry Univ, Coll Big Data & Intelligence Engn, Kunming 650224, Yunnan, Peoples R China
9.Fudan Univ, Inst Biodivers Sci, Minist Educ, Key Lab Biodivers Sci & Ecol Engn, Shanghai 200438, Peoples R China
Recommended Citation
GB/T 7714
Yang, Zhiqi,Dong, Jinwei,Qin, Yuanwei,et al. Integrated Analyses of PALSAR and Landsat Imagery Reveal More Agroforests in a Typical Agricultural Production Region, North China Plain[J]. REMOTE SENSING,2018,10(9):18.
APA Yang, Zhiqi.,Dong, Jinwei.,Qin, Yuanwei.,Ni, Wenjian.,Zhao, Guosong.,...&Xiao, Xiangming.(2018).Integrated Analyses of PALSAR and Landsat Imagery Reveal More Agroforests in a Typical Agricultural Production Region, North China Plain.REMOTE SENSING,10(9),18.
MLA Yang, Zhiqi,et al."Integrated Analyses of PALSAR and Landsat Imagery Reveal More Agroforests in a Typical Agricultural Production Region, North China Plain".REMOTE SENSING 10.9(2018):18.
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