Share Email Print
cover

Proceedings Paper

Quantitative retrieving forest ecological parameters based on remote sensing in Liping County of China
Author(s): Qingjiu Tian; Jing M. Chen; Guang Zheng; Xueqi Xia; Junying Chen
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

Forest ecosystem is an important component of terrestrial ecosystem and plays an important role in global changes. Aboveground biomass (AGB) of forest ecosystem is an important factor in global carbon cycle studies. The purpose of this study was to retrieve the yearly Net Primary Productivity (NPP) of forest from the 8-days-interval MODIS-LAI images of a year and produce a yearly NPP distribution map. The LAI, DBH (diameter at breast height), tree height, and tree age field were measured in different 80 plots for Chinese fir, Masson pine, bamboo, broadleaf, mix forest in Liping County. Based on the DEM image and Landsat TM images acquired on May 14th, 2000, the geometric correction and terrain correction were taken. In addition, the "6S"model was used to gain the surface reflectance image. Then the correlation between Leaf Area Index (LAI) and Reduced Simple Ratio (RSR) was built. Combined with the Landcover map, forest stand map, the LAI, aboveground biomass, tree age map were produced respectively. After that, the 8-days- interval LAI images of a year, meteorology data, soil data, forest stand image and Landcover image were inputted into the BEPS model to get the NPP spatial distribution. At last, the yearly NPP spatial distribution map with 30m spatial resolution was produced. The values in those forest ecological parameters distribution maps were quite consistent with those of field measurements. So it's possible, feasible and time-saving to estimate forest ecological parameters at a large scale by using remote sensing.

Paper Details

Date Published: 17 October 2006
PDF: 10 pages
Proc. SPIE 6359, Remote Sensing for Agriculture, Ecosystems, and Hydrology VIII, 63591I (17 October 2006); doi: 10.1117/12.689408
Show Author Affiliations
Qingjiu Tian, Nanjing Univ. (China)
Key Lab. of China Remote Sensing Satellite Ground Station (China)
Jing M. Chen, Univ. of Toronto (Canada)
Guang Zheng, Nanjing Univ. (China)
Xueqi Xia, Nanjing Univ. (China)
Junying Chen, Nanjing Univ. (China)


Published in SPIE Proceedings Vol. 6359:
Remote Sensing for Agriculture, Ecosystems, and Hydrology VIII
Manfred Owe; Guido D'Urso; Christopher M. U. Neale; Ben T. Gouweleeuw, Editor(s)

© SPIE. Terms of Use
Back to Top