Paper Info
Title
A temporal-BRDF model-based approach to change detection.
Abstract
Remote Sensing provides the only practical means to monitor changes over large areas. This paper describes the development of a generic algorithm designed to map the temporal occurrence and spatial extent of areas exhibiting sudden change. The algorithm is demonstrated here applied to the problem of mapping fire affected areas. The research further develops the work of [1], which implemented a bi-directional reflectance (BRDF) model-based change detection algorithm to map the approximate day and location of burning, using daily 500m MODIS surface reflectance data. An original algorithm assumption is that the surface state remains static prior to the changes of interest. This is problematic in file presence of underlying change (for example, due to vegetation phenology) especially when there are missing and/or cloudy data. In an attempt to deal with this issue, an additional kernel has been added to the BRDF model in the form of a cubic function of time. In addition, a step function kernel has been introduced in order to more robustly detect step-like changes. These modifications and preliminary results over southern Africa using daily MODIS land surface reflectance data are presented.
Year
DOI
Venue
2004
10.1109/IGARSS.2004.1370772
IGARSS
Keywords
Field
DocType
fires,topography (Earth),vegetation mapping,BRDF model,MODIS land surface reflectance data,bidirectional reflectance model,burning location,change detection algorithm,cloudy data,fire affected areas,remote sensing,southern Africa,step function kernel,surface state,vegetation phenology
Kernel (linear algebra),Bidirectional reflectance distribution function,Vegetation,Change detection,Computer science,Remote sensing,Cubic function,Change detection algorithms,Genetic algorithm,Step function
Conference
Volume
ISSN
Citations 
3
2153-6996
2
PageRank 
References 
Authors
0.71
0
3
Name
Order
Citations
PageRank
Lisa Rebelo120.71
Philip Lewis232.16
David P. Roy330164.93