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http://dx.doi.org/10.13087/kosert.2022.25.5.43

Normalized Difference Vegetation Index based on Landsat Images Variations between Artificial and Natural Restoration Areas after Forest Fire  

Noh, Jiseon (Forestland Information Center, Korea Forest Conservation Association)
Choi, Jaeyong (Department of Environment & Forest Resources, Chungnam National University)
Publication Information
Journal of the Korean Society of Environmental Restoration Technology / v.25, no.5, 2022 , pp. 43-57 More about this Journal
Abstract
This study aims to classify forest fire-affected areas, identify forest types by the intensity of forest fire damage using multi-time Landsat-satellite images before and after forest fires and to analyze the effects of artificial restoration sites and natural restoration sites. The difference in the values of the Normalized Burned Ratio(NBR) before and after forest fire damage not only maximized the identification of forest fire affected and unaffected areas, but also quantified the intensity of forest fire damage. The index was also used to confirm that the higher the intensity of forest fire damage in all forest fire-affected areas, the higher the proportion of coniferous forests, relatively. Monitoring was conducted after forest fires through Normalized Difference Vegetation Index(NDVI), an index suitable for the analysis of effects by restoration type and the NDVI values for artificial restoration sites were found to no longer be higher after recovering the average NDVI prior to the forest fire. On the other hand, the natural restoration site witnessed that the average NDVI value gradually became higher than before the forest fires. The study result confirms the natural resilience of forests and these results can serve as a basis for decision-making for future restoration plans for the forest fire affected areas. Further analysis with various conditions is required to improve accuracy and utilization for the policies, in particular, spatial analysis through forest maps as well as review through site checks before and immediately after forest fires. More precise analysis on the effects of restoration will be available based on a long term monitoring.
Keywords
Forest fire severity; Normalized burn ratio; Near-infrared; Short wave-infrared; Monitoring;
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Times Cited By KSCI : 5  (Citation Analysis)
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1 Choi SP and Park JS. 2004. Comparative Analysis between Normalized Burn Ration and Normalized Difference Vegetation Index in Forest Damage Area. Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 22(3): 261-268. (in Korean with English summary)
2 Doerr, S.H., Shakesby, R.A., Blake, W.H., Chafer, C.J., Humphreys, G.S. and Wallbrink, P.J. 2006. Effects of differing wildfire severities on soil wettability and implications for hydrological response. Journal of Hydrology 319: 298-311
3 Key, C.H. and N.C. Benson. 2002. Measuring and remote sensing of burn severity, Proc. of 2000 US Geological Survey(USGS) Wildland Fire Workshop, Los Alamos, NM, Oct. 31-Nov. 3, pp. 2-11.
4 Key, C.H. and N.C. Benson. 2006. Landscape assessment: sampling and analysis methods, Rocky Mountain Research Station General Technical Report RMRS-GTR-164-CD, USDA Forest Service, Ogden, UT, USA.
5 Korea Forest Service. 2021. Forest Fire Statistics. 74-128. (in Korean)
6 Lee SM and Jeong JC. 2019. Forest Fire Severity Classification Using Probability Density Function and KOMPSAT-3A. Korean Journal of Remote Sensing, 35(6-4): 1341-1350. (in Korean with English summary)   DOI
7 Lee SY.Won MS and Han SY. 2005. Developing of Forest Fire Occurrence Danger Index Using Fuel and Topographic Characteristics on the Condition of Ignition Point in Kore. Korea Institute of Fire Science & Engineering 19(4): 75-79. (in Korean with English summary)
8 Lee SY.Kang YS.An SH and Oh JS. 2002. Characteristic Analysis of Forest Fire Burned Area usin GIS. Journal of the Korean Association of Geographic Information Studies 5(1): 20-26. (in Korean with English summary)
9 Lim JH.Kim JH and Bae SW. 2012. Natural Regeneration Patten of Pine Seedlings on the Burned Forest Site in Gosung, Korea. Korea Journal of Agricultural and Forest Meteorology, 14(4): 222-228 (in Korean with English summary)   DOI
10 Brewer, C.K., Winne, J.C., Redmond, R.L., Opitz, D.W. and Mangrich, M.V. 2005. Classifying and mapping wildfire severity: A comparison of methods. Photogrammetric Engineering and Remote Sensing 71: 1311-1320.   DOI
11 Cocke, A.E., P.Z. Fule, and J.E. Crouse. 2005. Comparison of burn severity assessments using Differenced Normalized Burn Ratio and ground data, International Journal of Wildland Fire, 14(2): 189-198.   DOI
12 DeBano. L.F., Neary, D.G. and Ffolliott, P.F. 1998. Fire's effects on ecosystems, John Wiley and Sons:New York, NY.
13 Jeon SW and Park JH. 1997. Uses of Remote Sensing Techniques in Managing Ecosystem. Korea Environment Institute: 1-2. (in Korean)
14 Korea Forest Research Institute. 1996. Report on the research the ecological environment at Forest fire area in Gosung. Korea Forest Service. 169. (in Korean)
15 Lee SY.Jun KW.Lee MW and Chun KW. 2008. Mortality in Pine Stand and Vegetation Recovery after Forest Fire. Korean Society of Hazard Mitigation 8(1): 71-79. (in Korean with English summary)
16 Lee JM.Won MS.Lim JH and Lee SW. 2012. Effects of Edge Area and Burn Severity on Early Vegetation Regeneration in Damaged Area. Journal of Korean Fores Society, 101(1): 121-129 (in Korean with English summary)
17 Morgan, P. and Neuenschwander, L.F. 1988. Shrub response to high and low severity burns. Western Journal of Applied Forestry 3(1): 5-9   DOI
18 Roy, D. P., L. Boschetti, and S. N. Trigg. 2006. Remote sensing of fire severity: assessing the performance of the normalized burn ratio, IEEE Geoscience and Remote Sensing Letters, 3(1): 112-116.   DOI
19 Won MS.Koo KS and Lee MB. 2007. An Quantitative Analysis of Severity Classification and Burn Severity for the Large Forest Fire Areas using Normalized Burn Ratio of Landsat Imagery. Journal of the Korean Association of Geographic Information Studies 10(3): 80-97 (in Korean with English summary)
20 Won MS.Jang KC.Yoon SH and Lee HT. 2019. Change Detection of Damaged Area and Burn Severity due to Heat Damage from Gangwon Large Fire Area in 2019. Korea Journal of Remote Sensing, 35(6-2): 1083-1093 (in Korean with English summary)
21 Wang, G.G. 2002. Fire severity in relation to canopy com-position within burned boreal mixewood stands. Forest Ecology and Management 163: 85-92   DOI
22 White, J.D., Ryan, K.C., Key, C.C. and Running, S.W. 1996. Remote sensing of forest fire severity and vegeta-tion recovery. International Journal of Wildland Fire 6: 125-136   DOI
23 Youn HJ and Jeong JC. 2019. Detection of Forest Fire and NBR Mis-classified Pixel Using Multi-temporal Sentinel-2A Images. Korean Journal of Remote Sensing 35(6-2): 1107-1115. (in Korean with English summary)   DOI
24 Ministry of Environment. 2002. Studies on the Ecosystem Restoration and the Polices in the East Coast Fire Regions. 158-172. (in Korean)
25 National Geographic Information Institute. 2021. National Land Satellite Center Research Report. 124-125. (in Korean)
26 National Institute of Forest Science. 2006. Post-Fire Restoration - To Establish a Healthy and Sustainable Forest Ecosystem. Korea Forest Service. 7-60.
27 Sohn HJ.Kim DH.Kim NY.Hong JP and Song YK. 2019. Evaluation indicators for the restoration of degraded urban ecosystems and the analysis of restoration performance. Journal of Korean Society of Environmental Restoration Technology, 22(6): 97-114 (in Korean with English summary)
28 Van Wagtendonk, J.W., R.R. Root, and C.H. Key. 2004. Comparison of AVIRIS and Landsat ETM+ detection capabilities for burn severity, Remote Sensing of Environment, 92(3): 397-408   DOI