DOI: 10.1016/j.jag.2016.07.014
Scopus记录号: 2-s2.0-84997706106
论文题名: Enhancing the performance of regional land cover mapping
作者: Wu W ; , Zucca C ; , Karam F ; , Liu G
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2016
卷: 52 起始页码: 422
结束页码: 432
语种: 英语
英文关键词: Accuracy
; Multisource data integration
; Phenological contrast
; Separability
; Topographic features
Scopus关键词: accuracy assessment
; artificial neural network
; GIS
; land cover
; land use change
; machine learning
; mapping
; phenology
英文摘要: Different pixel-based, object-based and subpixel-based methods such as time-series analysis, decision-tree, and different supervised approaches have been proposed to conduct land use/cover classification. However, despite their proven advantages in small dataset tests, their performance is variable and less satisfactory while dealing with large datasets, particularly, for regional-scale mapping with high resolution data due to the complexity and diversity in landscapes and land cover patterns, and the unacceptably long processing time. The objective of this paper is to demonstrate the comparatively highest performance of an operational approach based on integration of multisource information ensuring high mapping accuracy in large areas with acceptable processing time. The information used includes phenologically contrasted multiseasonal and multispectral bands, vegetation index, land surface temperature, and topographic features. The performance of different conventional and machine learning classifiers namely Malahanobis Distance (MD), Maximum Likelihood (ML), Artificial Neural Networks (ANNs), Support Vector Machines (SVMs) and Random Forests (RFs) was compared using the same datasets in the same IDL (Interactive Data Language) environment. An Eastern Mediterranean area with complex landscape and steep climate gradients was selected to test and develop the operational approach. The results showed that SVMs and RFs classifiers produced most accurate mapping at local-scale (up to 96.85% in Overall Accuracy), but were very time-consuming in whole-scene classification (more than five days per scene) whereas ML fulfilled the task rapidly (about 10 min per scene) with satisfying accuracy (94.2–96.4%). Thus, the approach composed of integration of seasonally contrasted multisource data and sampling at subclass level followed by a ML classification is a suitable candidate to become an operational and effective regional land cover mapping method. © 2016 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/80056
Appears in Collections: 气候变化事实与影响
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作者单位: State-Key Lab of Nuclear Resources and Environment, East China Institute of Technology (ECIT), Nanchang, Jiangxi, China; ICARDA (International Center for Agricultural Research Center in the Dry Areas), Amman, Jordan; Litani River AuthorityBeirut, Lebanon; Faculty of Sciences, East China Institute of Technology (ECIT), Nanchang, Jiangxi, China
Recommended Citation:
Wu W,, Zucca C,, Karam F,et al. Enhancing the performance of regional land cover mapping[J]. International Journal of Applied Earth Observation and Geoinformation,2016-01-01,52