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Integration of Ground Inventory Data with Landsat Imagery to Estimate Aboveground Biomass of Tropical Deciduous Forest in Bago Yoma, Myanmar

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Authors

카인죠윈

Advisor
Hyun Seok Kim
Major
농업생명과학대학 산림과학부
Issue Date
2015-08
Publisher
서울대학교 대학원
Keywords
aboveground biomass (AGB)Landsat 8 OLImultiple linear regression (MLR)principal component analysis (PCA)tropical deciduous forest
Description
학위논문 (석사)-- 서울대학교 대학원 : 산림과학부(산림환경학전공), 2015. 8. 김현석.
Abstract
Even with recently increased awareness of the environmental
conservation, the degradation of tropical forests are still becoming the major source for carbon emission to the atmosphere. The aboveground biomass (AGB) of these forests are, therefore, a vital role in global carbon sequestration. As the
initial step of the forest conservation in Myanmar, the aboveground biomass of South Zarmani Reserved Forest in Bago Yoma region were estimated using Landsat 8 OLI after the evaluation with 100 sample field inventory plots. Multiple linear regression (MLR) model of band values and their principal
component analysis (PCA) model were developed to estimate the AGB using the spectral reflectance from Landsat images and elevation as the input variables. TheMLR model had r2 = 0.43, RMSE = 60.2 tons/ha, relative RMSE
= 70.1%, Bias = -9.1 tons/ha, Bias (%) = -10.6%, and p < 0.0001, while the
PCA model showed r2 = 0.45, RMSE = 55.1 tons/ha, relative RMSE = 64.1%,
Bias = -8.3 tons/ha, Bias (%) = -9.7%, and p < 0.0001. The AGB maps of the
study area were generated based on both MLR and PCAmodels. The estimated
mean AGB values were 74.74±22.3 tons/ha and 73.04±17.6 tons/ha and the
total AGB of the study area are about 5.7 and 5.6 million tons from MLR and
PCA, respectively. In conclusion, we were able to generate solid regression
models from Landsat 8 OLI image after ground truth and two regression models
gave us very similar AGB estimation (less than 2%) of South Zarmani Reserved
Forest, Bago Yoma, Myanmar.
Language
English
URI
https://hdl.handle.net/10371/125692
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