Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
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Basal Area Mapping using Remote Sensing and Ecological Data

원격 탐사 자료와 현장 조사 자료를 이용한 기저면적 예측 지도 제작

  • Lee, Jung-Bin (School of Civil & Environmental Engineering, College of Engineering) ;
  • Jayakumar, S. (School of Civil & Environmental Engineering, College of Engineering) ;
  • Heo, Joon (School of Civil & Environmental Engineering, College of Engineering)
  • 이정빈 (연세대학교 공과대학 사회환경시스템 공학부) ;
  • ;
  • 허준 (연세대학교 공과대학 사회환경시스템 공학부)
  • Published : 2008.12.30
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Abstract

This study was carried out in part of Tamil Nadu, India. Also, Landsat ETM+ image and field sampling data were acquired. The field data were basal area, number of trees and number of species. Using the data set, this study performed a three steps processing, (1) Image classification (2) extracting the vegetation indices(NDVI, Tasseled cap brightness, greenness and wetness) (3) mapping the prediction of biodiversity distribution using basal area and NDVI image value. Basal area was significantly correlated with NDVI. The result of classification showed 69% overall accuracy.

인도의 Tamil Nadu 지역을 대상지역으로 선택하여 Landsat ETM+ 영상과 현장 조사 자료(기저면적, 개체 수, 종의 수)를 취득하였다. 취득된 자료를 통하여 (1) 영상의 분류, (2) 식생지수 영상의 추출(NDVI, Tasseled Cap 토양명도, 녹색식생, 토양습도), (3) 가장 상관관계가 높은 결과를 보인 NDVI와 기저면적(Basal area)을 이용한 식생다양성 분포 예측 지도 제작이 이루어 졌다. 기저면적과 NDVI가 가장 높은 상관관계를 가지며 대상지역 영상분류 결과 69%정도의 정확도를 보였다.

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References

  1. Baatz, M., Benz, U., Dehghani, S., Heynen, M., Holtje, A., Hofmann, P., Lingenfelder, I., Mimler, M., Sohlbach, M., Weber, M., Willhauck, G., 2004. eCognition Professional: User guide 4., Munich, Definiens-Imaging
  2. Barrett, James W., 1977. A Field Guide for Stand Basal Area, Average Diameter and Tree Spacing Relationships, Research Note, PNW-298, USDA Forest Service
  3. Erten, E., V. Kurgun, and N. Musaoglu, 2004. Forest Fire Risk Zone Mapping From Satellite Imagery and GIS a Case Study. XXth ISPRS Congress, Istanbul, Turkey
  4. Jayakumar, S., A. Ramachandran, J. B. Lee, and J. Heo, 2007. Object-oriented Classification and Quickbird Multi-spectral Imagery in Forest Density Mapping, Korean Journal of Remote Sensing, 23(3): 153-160 https://doi.org/10.7780/kjrs.2007.23.3.153
  5. Jensen, J. R., 2004. Introductory Digital Image Processing: A Remote Sensing Perspective. 3rd ed, Upper Saddle River, New Jersey, Prentice Hall
  6. Minami, M., 2000. Using ArcMap: GIS by ESRI, Environmental System Research Institute, Redlands
  7. Nagendra, H., 2001. Using Remote Sensing to Assess Biodiversity, International Journal of Remote Sensing, 22(12): 2377-2400 https://doi.org/10.1080/01431160117096
  8. Padalia, H., N. Chauhan, M. C. Porwal, and P. S. Roy, 2004. Phytosociological Observations on Tree Species Diversity of Andaman Islands, India, Current Science, 87: 799-806
  9. Saura, S., 2002. Effect of Minimum Mapping Unit on Land Cover Data Spatial Configuration and Composition, International Journal of Remote Sensing, 23(22): 4853-4880 https://doi.org/10.1080/01431160110114493