Comparison of Pixel- and Object-Based Approaches in Phenology-Based Rubber Plantation Mapping in Fragmented Landscapes

doi:10.3390/rs10010044

	Comparison of Pixel- and Object-Based Approaches in Phenology-Based Rubber Plantation Mapping in Fragmented Landscapes
	Zhai, Deli 1,2; Dong, Jinwei 3,4,5; Cadisch, Georg 6; Wang, Mingcheng 1,2; Kou, Weili 7; Xu, Jianchu 1,2; Xiao, Xiangming 4,5,8; Abbas, Sawaid 9
刊名	REMOTE SENSING
	2018
卷号	10 期号:1 页码:20
关键词	rubber (Hevea brasiliensis) plantation phenology Xishuangbanna Landsat object-based approach pixel-based approach J0101
ISSN号	2072-4292
DOI	10.3390/rs10010044
通讯作者	Xu, Jianchu(j.c.xu@cgiar.org) ; Xiao, Xiangming(xiangming.xiao@ou.edu)
英文摘要	The increasing expansion of rubber plantations throughout East and Southeast Asia urgently requires improved methods for effective mapping and monitoring. The phenological information from rubber plantations was found effective in rubber mapping. Previous studies have mostly applied rule-pixel-based phenology approaches for rubber plantations mapping, which might result in broken patches in fragmented landscapes. This study introduces a new paradigm by combining phenology information with object-based classification to map fragmented patches of rubber plantations in Xishuangbanna. This research first delineated the time windows of the defoliation and foliation phases of rubber plantations by acquiring the temporal profile and phenological features of rubber plantations and natural forests through the Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data. To investigate the ability of finer resolution images at capturing the temporal profile or phenological information, 30 m resolution Landsat image data were used to capture the temporal profile, and a phenology algorithm to separate rubber plantations and natural forests was then defined. The derived phenology algorithm was used by both the object-based and pixel-based classification to investigate whether the object-based approach could improve the mapping accuracy. Whether adding the phenology information to the object-based classification could improve rubber plantation mapping accuracy in mountainous Xishuangbanna was also investigated. This resulted in three approaches: rule-pixel-based phenology, rule-object-based phenology, and nearest-neighbor-object-based phenology. The results showed that the rule-object-based phenology approaches (with overall accuracy 77.5% and Kappa Coefficients of 0.66) and nearest-neighbor-object-based phenology approach (91.0% and 0.86) achieved a higher accuracy than that of the rule-pixel-based phenology approach (72.7% and 0.59). The results proved that (1) object-based approaches could improve the accuracy of rubber plantation mapping compared to the pixel-based approach and (2) incorporating the phenological information from vegetation improved the overall accuracy of the thematic map.
资助项目	National Natural Science Foundation of China[41401647] ; National Natural Science Foundation of China[31300403] ; National Natural Science Foundation of China[31400493] ; National Natural Science Foundation of China[31760181] ; China Postdoctoral Science Foundation[2013M540722] ; Key Research Program of Frontier Sciences, the Chinese Academy of Sciences[QYZDB-SSW-DQC005] ; Key Research Program of Frontier Sciences, the Chinese Academy of Sciences[QYZDY-SSW-SMC014] ; NASA Land Use and Land Cover Change program[NNX14AD78G] ; Food Security Center (FSC) - German Academic Exchange Service (DAAD) ; Federal Ministry of Economic Cooperation and Development (BMZ) of Germany ; Federal Ministry for Economic Cooperation and Development, Germany[13.1432.7-001.00] ; Federal Ministry for Economic Cooperation and Development, Germany[01LL0919A] ; Kunming Institute of Botany
WOS关键词	MAINLAND SOUTHEAST-ASIA ; LAND-COVER CHANGE ; SOUTHWEST CHINA ; TIME-SERIES ; RAIN-FOREST ; TROPICAL CATCHMENT ; SW CHINA ; XISHUANGBANNA ; VEGETATION ; MODIS
WOS研究方向	Remote Sensing
语种	英语
出版者	MDPI AG
WOS记录号	WOS:000424092300043
资助机构	National Natural Science Foundation of China ; China Postdoctoral Science Foundation ; Key Research Program of Frontier Sciences, the Chinese Academy of Sciences ; NASA Land Use and Land Cover Change program ; Food Security Center (FSC) - German Academic Exchange Service (DAAD) ; Federal Ministry of Economic Cooperation and Development (BMZ) of Germany ; Federal Ministry for Economic Cooperation and Development, Germany ; Kunming Institute of Botany
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/56972]
专题	中国科学院地理科学与资源研究所
通讯作者	Xu, Jianchu; Xiao, Xiangming
作者单位	1.Chinese Acad Sci, Kunming Inst Bot, Key Lab Plant Divers & Biogeog East Asia KLPB, 132 Lanhei Rd, Kunming 650201, Yunnan, Peoples R China 2.World Agroforestry Ctr, East & Cent Asia Off, 132 Lanhei Rd, Kunming 650201, Yunnan, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, 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.Univ Hohenheim, Inst Agr Sci Trop 490G, Garbenstr 37, D-70599 Stuttgart, Germany 7.Southwest Forestry Univ, Sch Comp Sci & Informat, Kunming 650224, Yunnan, Peoples R China 8.Fudan Univ, Inst Biodivers Sci, Key Lab Biodivers Sci & Ecol Engn, Minist Educ, Shanghai 200433, Peoples R China 9.Hong Kong Polytech Univ, Dept Land Surveying & Geoinformat, Hong Kong, Hong Kong, Peoples R China
推荐引用方式 GB/T 7714	Zhai, Deli,Dong, Jinwei,Cadisch, Georg,et al. Comparison of Pixel- and Object-Based Approaches in Phenology-Based Rubber Plantation Mapping in Fragmented Landscapes[J]. REMOTE SENSING,2018,10(1):20.
APA	Zhai, Deli.,Dong, Jinwei.,Cadisch, Georg.,Wang, Mingcheng.,Kou, Weili.,...&Abbas, Sawaid.(2018).Comparison of Pixel- and Object-Based Approaches in Phenology-Based Rubber Plantation Mapping in Fragmented Landscapes.REMOTE SENSING,10(1),20.
MLA	Zhai, Deli,et al."Comparison of Pixel- and Object-Based Approaches in Phenology-Based Rubber Plantation Mapping in Fragmented Landscapes".REMOTE SENSING 10.1(2018):20.