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基于ASTER遥感数据的杨树林分因子建模及制图研究(PDF)

《南京林业大学学报(自然科学版)》[ISSN:1000-2006/CN:32-1161/S]

Issue:
2006年05期
Page:
123-126
Column:
研究论文
publishdate:
2006-05-20

Article Info:/Info

Title:
A Study on Modeling & Mapping for Poplar Stands’ Parameters Based on ASTER Remote Sensed Datasets
Article ID:
1000-2006(2006)05-0123-04
Author(s):
LI Ming shi TAN Ying PENG Shi-kui
College of Forest Resources and Environment Nanjing Forestry University, Nanjing 210037, China
Keywords:
ASTER Poplar Regression tree Modeling Mapping
Classification number :
S757
DOI:
10.3969/j.jssn.1000-2006.2006.05.030
Document Code:
A
Abstract:
The methods of image fusion and transform, including PCA, wavelet based fusion, MNF and RBV transform etc. were executed to generate 37 feature bands on the basis of the ASTER original 9 bands. Coupling with observations of 48 poplar sample plots, traditional univariate regression models and regression tree models for average height, age and stem volume of poplar were cstablished respectively. After comparing the performance of models fit ring and grouud-truthing, it was found that regression tree models were superior to traditional univariate models in mapping spatial distribution of poplar stands’ parameters. Consequently, taking regression tree models to retrieve and map biophysical variables at a regional scale based on remote sensed data was more viable and reliable.

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Last Update: 2013-05-20