Share Email Print
cover

Proceedings Paper

Measuring grassland structure for recovery of grassland species at risk
Author(s): Xulin Guo; Wei Gao; John Wilmshurst
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

An action plan for recovering species at risk (SAR) depends on an understanding of the plant community distribution, vegetation structure, quality of the food source and the impact of environmental factors such as climate change at large scale and disturbance at small scale, as these are fundamental factors for SAR habitat. Therefore, it is essential to advance our knowledge of understanding the SAR habitat distribution, habitat quality and dynamics, as well as developing an effective tool for measuring and monitoring SAR habitat changes. Using the advantages of non-destructive, low cost, and high efficient land surface vegetation biophysical parameter characterization, remote sensing is a potential tool for helping SAR recovery action. The main objective of this paper is to assess the most suitable techniques for using hyperspectral remote sensing to quantify grassland biophysical characteristics. The challenge of applying remote sensing in semi-arid and arid regions exists simply due to the lower biomass vegetation and high soil exposure. In conservation grasslands, this problem is enhanced because of the presence of senescent vegetation. Results from this study demonstrated that hyperspectral remote sensing could be the solution for semi-arid grassland remote sensing applications. Narrow band raw data and derived spectral vegetation indices showed stronger relationships with biophysical variables compared to the simulated broad band vegetation indices.

Paper Details

Date Published: 1 September 2005
PDF: 12 pages
Proc. SPIE 5884, Remote Sensing and Modeling of Ecosystems for Sustainability II, 58840B (1 September 2005); doi: 10.1117/12.613198
Show Author Affiliations
Xulin Guo, Univ. of Saskatchewan (Canada)
Wei Gao, Colorado State Univ. (United States)
John Wilmshurst, Parks Canada (Canada)


Published in SPIE Proceedings Vol. 5884:
Remote Sensing and Modeling of Ecosystems for Sustainability II
Wei Gao; David R. Shaw, Editor(s)

© SPIE. Terms of Use
Back to Top