Next Article in Journal
The Processing of Non-Timber Forest Products through Small and Medium Enterprises—A Review of Enabling and Constraining Factors
Next Article in Special Issue
An Automatic UAV Based Segmentation Approach for Pruning Biomass Estimation in Irregularly Spaced Chestnut Orchards
Previous Article in Journal
Temporal and Spatial Changes of Soil Organic Carbon Stocks in the Forest Area of Northeastern China
Previous Article in Special Issue
Characterizing Seedling Stands Using Leaf-Off and Leaf-On Photogrammetric Point Clouds and Hyperspectral Imagery Acquired from Unmanned Aerial Vehicle
Open AccessArticle

Using UAV Multispectral Images for Classification of Forest Burn Severity—A Case Study of the 2019 Gangneung Forest Fire

1
Geoinformatic Engineering Research Center, Inha University, Incheon 22212, Korea
2
Department of Geoinformatic Engineering, Inha University, Incheon 22212, Korea
3
School of Forest Science & Landscape Architecture, Kyungpook National University, Daegu 41566, Korea
4
Forest Disaster Management Division, National Institute of Forest Science, Seoul 02455, Korea
*
Authors to whom correspondence should be addressed.
Forests 2019, 10(11), 1025; https://doi.org/10.3390/f10111025
Received: 23 September 2019 / Revised: 5 November 2019 / Accepted: 11 November 2019 / Published: 14 November 2019
(This article belongs to the Special Issue Forestry Applications of Unmanned Aerial Vehicles (UAVs) 2019)
Unmanned aerial vehicle (UAV)-based remote sensing has limitations in acquiring images before a forest fire, although burn severity can be analyzed by comparing images before and after a fire. Determining the burned surface area is a challenging class in the analysis of burn area severity because it looks unburned in images from aircraft or satellites. This study analyzes the availability of multispectral UAV images that can be used to classify burn severity, including the burned surface class. RedEdge multispectral UAV image was acquired after a forest fire, which was then processed into a mosaic reflectance image. Hundreds of samples were collected for each burn severity class, and they were used as training and validation samples for classification. Maximum likelihood (MLH), spectral angle mapper (SAM), and thresholding of a normalized difference vegetation index (NDVI) were used as classifiers. In the results, all classifiers showed high overall accuracy. The classifiers also showed high accuracy for classification of the burned surface, even though there was some confusion among spectrally similar classes, unburned pine, and unburned deciduous. Therefore, multispectral UAV images can be used to analyze burn severity after a forest fire. Additionally, NDVI thresholding can also be an easy and accurate method, although thresholds should be generalized in the future. View Full-Text
Keywords: UAV; multispectral image; forest fire; burn severity; classification UAV; multispectral image; forest fire; burn severity; classification
Show Figures

Figure 1

MDPI and ACS Style

Shin, J.-I.; Seo, W.-W.; Kim, T.; Park, J.; Woo, C.-S. Using UAV Multispectral Images for Classification of Forest Burn Severity—A Case Study of the 2019 Gangneung Forest Fire. Forests 2019, 10, 1025.

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