Estimation of forest carbon stock using satellite imagery and NFI data - Comparing κnN algorithm and regression model

Jaehoon Jung, Honggyoo Sohn, Miseon Hong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

κ-Nearest Neighbor (κNN) algorithm and regression model have been widely used for a variety of forest parameter estimation and mapping application due to its intuitiveness and ease of use. The objective of this study is to comparing both algorithms for estimation of aboveground carbon stock in Danyang-Gun, South Korea. Field data from 5 th NFI and Landsat TM satellite image were used as dataset. Additionally, various ratio images, such as vegetation indices, topographic effect correction indices, and spectral angle indices, were generated and compared to the Landsat TM original bands. As a result, κNN algorithm and Landsat TM original bands were determined to be a suitable method and dataset for forest carbon stock estimation in Danyang-Gun, respectively.

Original languageEnglish
Title of host publication32nd Asian Conference on Remote Sensing 2011, ACRS 2011
Pages379-381
Number of pages3
Publication statusPublished - 2011 Dec 1
Event32nd Asian Conference on Remote Sensing 2011, ACRS 2011 - Tapei, Taiwan, Province of China
Duration: 2011 Oct 32011 Oct 7

Publication series

Name32nd Asian Conference on Remote Sensing 2011, ACRS 2011
Volume1

Other

Other32nd Asian Conference on Remote Sensing 2011, ACRS 2011
CountryTaiwan, Province of China
CityTapei
Period11/10/311/10/7

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All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

Cite this

Jung, J., Sohn, H., & Hong, M. (2011). Estimation of forest carbon stock using satellite imagery and NFI data - Comparing κnN algorithm and regression model. In 32nd Asian Conference on Remote Sensing 2011, ACRS 2011 (pp. 379-381). (32nd Asian Conference on Remote Sensing 2011, ACRS 2011; Vol. 1).