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Multi-Temporal Sentinel-1 and Sentinel-2 Data for Orchards Discrimination in Khairpur District, Pakistan Using Spectral Separability Analysis and Machine Learning Classification

文献类型：期刊论文

作者Rehman, Arif Ur1,2; Zhang, Lifu1,2; Sajjad, Meer Muhammad2,3; Raziq, Abdur4
刊名REMOTE SENSING
出版日期2024-02-01
卷号16期号:4页码:21
关键词multi-temporal spectral separability orchard classification random forest support vector machine
DOI10.3390/rs16040686
通讯作者Zhang, Lifu(zhanglf@aircas.ac.cn)
英文摘要Generating orchards spatial distribution maps within a heterogeneous landscape is challenging and requires fine spatial and temporal resolution images. This study examines the effectiveness of Sentinel-1 (S1) and Sentinel-2 (S2) satellite data of relatively high spatial and temporal resolutions for discriminating major orchards in the Khairpur district of the Sindh province, Pakistan using machine learning methods such as random forest (RF) and a support vector machine. A Multicollinearity test (MCT) was performed among the multi-temporal S1 and S2 variables to remove those with high correlations. Six different feature combination schemes were tested, with the fusion of multi-temporal S1 and S2 (scheme-6) outperforming all other combination schemes. The spectral separability between orchards pairs was assessed using Jeffries-Matusita (JM) distance, revealing that orchard pairs were completely separable in the multi-temporal fusion of both sensors, especially the indistinguishable pair of dates-mango. The performance difference between RF and SVM was not significant, SVM showed a slightly higher accuracy, except for scheme-4 where RF performed better. This study concludes that multi-temporal fusion of S1 and S2 data, coupled with robust ML methods, offers a reliable approach for orchard classification. Prospectively, these findings will be helpful for orchard monitoring, improvement of yield estimation and precision based agricultural practices.
WOS关键词TREE SPECIES CLASSIFICATION ; LAND-COVER CLASSIFICATION ; REMOTE-SENSING DATA ; TIME-SERIES ; RANDOM FOREST ; IMAGE CLASSIFICATION ; CROP CLASSIFICATION ; VEGETATION ; LANDSAT-8 ; SAR
资助项目National Natural Science Foundation of China ; Chinese Academy of Sciences ; Alliance of International Science Organizations (ANSO)
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:001172605200001
出版者MDPI
资助机构National Natural Science Foundation of China ; Chinese Academy of Sciences ; Alliance of International Science Organizations (ANSO)
源URL[http://ir.igsnrr.ac.cn/handle/311030/203629]  
专题中国科学院地理科学与资源研究所
通讯作者Zhang, Lifu
作者单位1.Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
4.Islamia Coll Univ, Dept Geog, Peshawar 25120, Pakistan
推荐引用方式
GB/T 7714		 Rehman, Arif Ur,Zhang, Lifu,Sajjad, Meer Muhammad,et al. Multi-Temporal Sentinel-1 and Sentinel-2 Data for Orchards Discrimination in Khairpur District, Pakistan Using Spectral Separability Analysis and Machine Learning Classification[J]. REMOTE SENSING,2024,16(4):21.
APA Rehman, Arif Ur,Zhang, Lifu,Sajjad, Meer Muhammad,&Raziq, Abdur.(2024).Multi-Temporal Sentinel-1 and Sentinel-2 Data for Orchards Discrimination in Khairpur District, Pakistan Using Spectral Separability Analysis and Machine Learning Classification.REMOTE SENSING,16(4),21.
MLA Rehman, Arif Ur,et al."Multi-Temporal Sentinel-1 and Sentinel-2 Data for Orchards Discrimination in Khairpur District, Pakistan Using Spectral Separability Analysis and Machine Learning Classification".REMOTE SENSING 16.4(2024):21.

入库方式： OAI收割

来源：地理科学与资源研究所

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