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Publication Title: Multiscale assessment of binary and continuous landcover variables for MODIS validation, mapping, and modeling applications
Year: 1999 Publication Type: Journal Article
H. J. Andrews Publication Number: 2609
Citation: Milne, Bruce T.; Cohen, Warren B. 1999. Multiscale assessment of binary and continuous landcover variables for MODIS validation, mapping, and modeling applications. Remote Sensing of Environment. 70(1): 82-98.
Online PDF: http://andrewsforest.oregonstate.edu/pubs/pdf/pub2609.pdf
Abstract: Validation, mapping, and modeling efforts require accurate methods to transform process rates and ecosystemattributes estimated from small field plots to the 250—1000-m-wide cells used by a new generation of landcover mapping sensors. We provide alternative scaletransformations, each with attendant assumptions andlimitations. The choice of method depends on spatialcharacteristics of the land cover variables in question andconsequently may vary between biomes or with the intended application. We extend the fractal similarity dimension renormalization method, previously developedfor binary maps, to continuous variables. The methodcan preserve both the mean and the multifractal properties of the image, thereby satisfying a major goal, namely,to provide accurate areal estimates without sacrificing information about within-site variation. The scale transformation enables the multifractal scaling exponents of landscapes or individual spectral bands to be brought in andout of register with each other, thereby opening anotherdimension upon which to detect the scales at which various land use or terrain processes operate. Alternatively,landscapes can be selectively resealed to highlight patterns due to particular processes. We recommend geostatistical procedures with which to assess spatial characteristics both within a site and within individual image cells. We recommend that aggregation of fine-grain measurements during validation of the Moderate ResolutionImaging Spectrometer (MODIS) products be based oncontinuous variables to reduce errors that originate fromuncertainties in binary maps. ©Elsevier Science Inc.,1999
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