Mapping aboveground forest biomass carbon stock by using satellite image and NFI data - A comparison between kNN and regression tree model

Hieu Cong Nguyen, Jaehoon Jung, Doyeon Kim, Joon Heo, Kyoungmin Kim

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

Abstract

To achieve quantitative information of aboveground forest biomass carbon stock by remote sensing approach, a number of methods have been conducted. This study is to examine application of regression tree (tree decision) and k-Nearest Neighbor (kNN) algorithm for forest carbon stock estimation of Chungnam province in South Korea. Dataset used for this research includes Landsat Thematic Mapper (TM) images and field data from 5th National Forest Inventory (NFI). As a result, total above forest carbon stock estimated by kNN (20,467,652.900 ton C) model is closer to the number given by Korean Institutes of Forest than regression tree's number (20,239,247.239 ton C), however RMSE from latter algorithm is less than the former, 19.168 ton C/ha and 20.063 ton C/ha, respectively.

Original languageEnglish
Title of host publication33rd Asian Conference on Remote Sensing 2012, ACRS 2012
Pages652-654
Number of pages3
Publication statusPublished - 2012 Dec 1
Event33rd Asian Conference on Remote Sensing 2012, ACRS 2012 - Pattaya, Thailand
Duration: 2012 Nov 262012 Nov 30

Publication series

Name33rd Asian Conference on Remote Sensing 2012, ACRS 2012
Volume1

Other

Other33rd Asian Conference on Remote Sensing 2012, ACRS 2012
CountryThailand
CityPattaya
Period12/11/2612/11/30

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All Science Journal Classification (ASJC) codes

  • Information Systems

Cite this

Nguyen, H. C., Jung, J., Kim, D., Heo, J., & Kim, K. (2012). Mapping aboveground forest biomass carbon stock by using satellite image and NFI data - A comparison between kNN and regression tree model. In 33rd Asian Conference on Remote Sensing 2012, ACRS 2012 (pp. 652-654). (33rd Asian Conference on Remote Sensing 2012, ACRS 2012; Vol. 1).