Volume XLII-3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 751-756, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-751-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 751-756, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-751-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Apr 2018

30 Apr 2018

DEVELOPMENT OF SPATIAL SCALING TECHNIQUE OF FOREST HEALTH SAMPLE POINT INFORMATION

J. H. Lee1, J. E. Ryu1, H. I. Chung1, Y. Y. Choi1, S. W. Jeon1, and S. H. Kim2 J. H. Lee et al.
  • 1Department of Environmental Science and Ecological Engineering, Graduate School, Korea University 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Korea
  • 2National Institute of Forest Science, Korea

Keywords: Forest, Forest management, Forest Health, Species diversity, Shannon’s index, Spatial interpolation, Kriging, IDW

Abstract. Forests provide many goods, Ecosystem services, and resources to humans such as recreation air purification and water protection functions. In rececnt years, there has been an increase in the factors that threaten the health of forests such as global warming due to climate change, environmental pollution, and the increase in interest in forests, and efforts are being made in various countries for forest management. Thus, existing forest ecosystem survey method is a monitoring method of sampling points, and it is difficult to utilize forests for forest management because Korea is surveying only a small part of the forest area occupying 63.7 % of the country (Ministry of Land Infrastructure and Transport Korea, 2016). Therefore, in order to manage large forests, a method of interpolating and spatializing data is needed. In this study, The 1st Korea Forest Health Management biodiversity Shannon;s index data (National Institute of Forests Science, 2015) were used for spatial interpolation. Two widely used methods of interpolation, Kriging method and IDW(Inverse Distance Weighted) method were used to interpolate the biodiversity index. Vegetation indices SAVI, NDVI, LAI and SR were used. As a result, Kriging method was the most accurate method.