Multiscale Assessment of Binary and Continuous Landcover Variables for MODIS Validation, Mapping, and Modeling Applications

Abstract Validation, mapping, and modeling efforts require accurate methods to transform process rates and ecosystem attributes estimated from small field plots to the 250–1000-m-wide cells used by a new generation of land cover mapping sensors. We provide alternative scale transformations, each with attendant assumptions and limitations. The choice of method depends on spatial characteristics of the land cover variables in question and consequently may vary between biomes or with the intended application. We extend the fractal similarity dimension renormalization method, previously developed for binary maps, to continuous variables. The method can 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 and out of register with each other, thereby opening another dimension upon which to detect the scales at which various land use or terrain processes operate. Alternatively, landscapes can be selectively rescaled 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 Resolution Imaging Spectrometer (MODIS) products be based on continuous variables to reduce errors that originate from uncertainties in binary maps.