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大面积遥感植被成图方法的述评(PDF)

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

Issue:
2005年05期
Page:
1-7
Column:
研究论文
publishdate:
2005-05-20

Article Info:/Info

Title:
Large Vegetation Mapping: A Methodology Review
Article ID:
1000-2006(2005)05-0001-07
Author(s):
ZHU Zhi-liang1 Peng Shi-kui2
1. U.S. Geological Survey, EROS Data Center Sioux Falls, SD 57198, USA; 2. College of Forest Resources and Environment Nan}ing Forestry University, Nanjing 210037, China
Keywords:
Natural vegetation Mapping Remote sensing methodology
Classification number :
S757
DOI:
10.3969/j.jssn.1000-2006.2005.05.001
Document Code:
A
Abstract:
Geospatial distribution of natural vegetation is among some of very important environmental parameters required for applications ranging from global climate change to monitoring of natural hazards, monitoring of ecosystem vitality, and fire management practices. Increasingly sophisticated applications require vegetation datasets to cover large areas at a suitable scale and provide sufficiently detailed information. In this paper, we describe a research effort to develop a remote sensing methodology capable of producing 30 m resolution, wallto-wall coverage of natural vegetation types and structure variables in support of a multi-agency fire fuels and fire risks assessment project. Success of this remote sensing research effort is dependent on improved sensor and data qualities, a thorough understanding of regional and local vegetation ecology, successful integration of remote sensing with large amount of field plot data, and flexible mapping algorithms. Preliminary results produced in Wasatch Range and Uinta Mountains of central Utah include 28 vegetation types with an overall accuraccy of 60% (average by life forms), percent canopy density (sub-pixel density) of forest, sbrub, and herbaceous cover, and average top canopy height of forest, shrub, and herbaceous cover. Techniques to improve the first-round results are discussed, including refinements of mapping models and use of relevant environmental gradients and potential vegetation classification associated with actual vegetation types.

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Last Update: 2013-05-20