Land Use/Land Cover Mapping Using Multitemporal Sentinel-2 Imagery and Four Classification Methods-A Case Study from Dak Nong, Vietnam

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dc.contributor.author Huong Thi Thanh Nguyen
dc.contributor.author Trung Minh Doan
dc.contributor.author Tomppo, Erkki
dc.contributor.author McRoberts, Ronald E.
dc.date.accessioned 2020-07-20T12:16:01Z
dc.date.available 2020-07-20T12:16:01Z
dc.date.issued 2020-05
dc.identifier.citation Huong Thi Thanh Nguyen , Trung Minh Doan , , Tomppo , E & McRoberts , R E 2020 , ' Land Use/Land Cover Mapping Using Multitemporal Sentinel-2 Imagery and Four Classification Methods-A Case Study from Dak Nong, Vietnam ' , Remote Sensing , vol. 12 , no. 9 , 1367 . https://doi.org/10.3390/rs12091367
dc.identifier.other PURE: 140921161
dc.identifier.other PURE UUID: c6690c17-936a-453a-bc03-4240711a0f7e
dc.identifier.other WOS: 000543394000014
dc.identifier.uri http://hdl.handle.net/10138/317823
dc.description.abstract Information on land use and land cover (LULC) including forest cover is important for the development of strategies for land planning and management. Satellite remotely sensed data of varying resolutions have been an unmatched source of such information that can be used to produce estimates with a greater degree of confidence than traditional inventory estimates. However, use of these data has always been a challenge in tropical regions owing to the complexity of the biophysical environment, clouds, and haze, and atmospheric moisture content, all of which impede accurate LULC classification. We tested a parametric classifier (logistic regression) and three non-parametric machine learning classifiers (improved k-nearest neighbors, random forests, and support vector machine) for classification of multi-temporal Sentinel 2 satellite imagery into LULC categories in Dak Nong province, Vietnam. A total of 446 images, 235 from the year 2017 and 211 from the year 2018, were pre-processed to gain high quality images for mapping LULC in the 6516 km(2) study area. The Sentinel 2 images were tested and classified separately for four temporal periods: (i) dry season, (ii) rainy season, (iii) the entirety of the year 2017, and (iv) the combination of dry and rainy seasons. Eleven different LULC classes were discriminated of which five were forest classes. For each combination of temporal image set and classifier, a confusion matrix was constructed using independent reference data and pixel classifications, and the area on the ground of each class was estimated. For overall temporal periods and classifiers, overall accuracy ranged from 63.9% to 80.3%, and the Kappa coefficient ranged from 0.611 to 0.813. Area estimates for individual classes ranged from 70 km(2) (1% of the study area) to 2200 km(2) (34% of the study area) with greater uncertainties for smaller classes. en
dc.format.extent 27
dc.language.iso eng
dc.relation.ispartof Remote Sensing
dc.rights cc_by
dc.rights.uri info:eu-repo/semantics/openAccess
dc.subject classification
dc.subject Sentinel 2
dc.subject land use land cover
dc.subject improved k-NN
dc.subject logistic regression
dc.subject random forest
dc.subject support vector machine
dc.subject TIME-SERIES
dc.subject FOREST VARIABLES
dc.subject ACCURACY
dc.subject ALGORITHMS
dc.subject SELECTION
dc.subject SUPPORT
dc.subject REGION
dc.subject METAANALYSIS
dc.subject CLASSIFIERS
dc.subject MAPS
dc.subject 1171 Geosciences
dc.subject 119 Other natural sciences
dc.title Land Use/Land Cover Mapping Using Multitemporal Sentinel-2 Imagery and Four Classification Methods-A Case Study from Dak Nong, Vietnam en
dc.type Article
dc.contributor.organization Department of Forest Sciences
dc.description.reviewstatus Peer reviewed
dc.relation.doi https://doi.org/10.3390/rs12091367
dc.relation.issn 2072-4292
dc.rights.accesslevel openAccess
dc.type.version publishedVersion

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