Estimation of urban vegetation abundance by spectral mixture analysis

Publication Type  Journal Article
Year of Publication  2001
Authors  Small, C.
Journal Title  International Journal of Remote Sensing
Volume  22
Issue  7
Pages  1305-1334
Journal Date  May 10
ISBN Number  0143-1161
Accession Number  ISI:000168699900008
Key Words  aviris data; multispectral images; grapevine mountains; green vegetation; heat-island; reflectance; index; evapotranspiration; nevada; scale

The spatio-temporal distribution of vegetation is a fundamental component of the urban environment that can be quantified using multispectral imagery. However, spectral heterogeneity at scales comparable to sensor resolution limits the utility of conventional hard classification methods with multispectral reflectance data in urban areas. Spectral mixture models may provide a physically based solution to the problem of spectral heterogeneity. The objective of this study is to examine the applicability of linear spectral mixture models to the estimation of urban vegetation abundance using Landsat Thematic Mapper (TM) data. The inherent dimensionality of TM imagery of the New York City area suggests that urban reflectance measurements may be described by linear mixing between high albedo, low albedo and vegetative endmembers. A three-component linear mixing model provides stable, consistent estimates of vegetation fraction for both constrained and unconstrained inversions of three different endmember ensembles. Quantitative validation using vegetation abundance measurements derived from high-resolution (2m) aerial photography shows agreement to within fractional abundances of 0.1 for vegetation fractions greater than 0.2. In contrast to the Normalised Difference Vegetation Index (NDVI), vegetation fraction estimates provide a physically based measure of areal vegetation abundance that may be more easily translated to constraints on physical quantities such as vegetative biomass and evapotranspiration.


432PATimes Cited:57Cited References Count:60

URL  <Go to ISI>://000168699900008