Next Article in Journal
Above-Ground Biomass Retrieval over Tropical Forests: A Novel GNSS-R Approach with CyGNSS
Next Article in Special Issue
Remote Sensing Support for the Gain-Loss Approach for Greenhouse Gas Inventories
Previous Article in Journal
Deep Discriminative Representation Learning with Attention Map for Scene Classification
Previous Article in Special Issue
Predicting Forest Cover in Distinct Ecosystems: The Potential of Multi-Source Sentinel-1 and -2 Data Fusion
Open AccessArticle

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

1
Department of Forest resource & Environment management (Frem), Faculty of Agriculture and Forestry, Tay Nguyen University, Le Duan Str. 567, Buon Ma Thuot City 63000, Daklak Province, Vietnam
2
Department of Electronics and Nanoengineering, School of Electrical Engineering, Aalto University, P.O. Box 11000, 00076 Aalto, Finland
3
Department of Forest Sciences, University of Helsinki, Latokartanonkaari 7, P.O. Box 27 FI-00014 Helsinki, Finland
4
Raspberry Ridge Analytics, 15111 Elmcrest Avenue North, Hugo, MN 55038, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(9), 1367; https://doi.org/10.3390/rs12091367
Received: 23 March 2020 / Revised: 15 April 2020 / Accepted: 24 April 2020 / Published: 26 April 2020
(This article belongs to the Special Issue Advances in Remote Sensing for Global Forest Monitoring)
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 km2 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 km2 (1% of the study area) to 2200 km2 (34% of the study area) with greater uncertainties for smaller classes. View Full-Text
Keywords: classification; Sentinel 2; land use land cover; improved k-NN; logistic regression; random forest; support vector machine classification; Sentinel 2; land use land cover; improved k-NN; logistic regression; random forest; support vector machine
Show Figures

Graphical abstract

  • Externally hosted supplementary file 1
    Doi: Not published
    Description: Land Use/Land Cover Mapping Using Multitemporal Sentinel-2 Imagery and Four Classification Methods - A case study from Dak Nong, Vietnam
MDPI and ACS Style

Nguyen, H.T.T.; Doan, T.M.; Tomppo, E.; McRoberts, R.E. Land Use/Land Cover Mapping Using Multitemporal Sentinel-2 Imagery and Four Classification Methods—A Case Study from Dak Nong, Vietnam. Remote Sens. 2020, 12, 1367.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop