Change assessment is a central and active area of inquiry in remote sensing. Broadly adopted probabilistic methods discriminate between change and no change based on image differencing, normalization and aggregation into a single band metric that is assumed to follow a Chi-square distribution. The adoption of the Chi-square distribution requires the application of band transformation to original data that is computer expensive and that operates under an untested assumption of multivariate distribution of pixel values in input bands. Despite the wide adoption of the Chisquare distribution, its appropriateness for discriminating between change and no change remains an open question. Here, we test the performance of the Chi-square distribution for change assessment compared to the use of the more-generic Gamma distribution. For this purpose, we implement an algorithm that iteratively removes observations labelled as change according to a pre-defined probabilistic distribution and a probability change threshold. We implement the algorithm in three study areas in tropical and subtropical regions representing contrasting ecological conditions and land cover types and changes. We also test whether input multispectral data meets the assumption of multivariate normality required for band transformation and for the use of the Chi-square distribution. We found that the Gamma distribution applied to untransformed data consistently performs more robustly to discriminate between change and no change compared to the application of band transformation and subsequent use of the Chi-square distribution. We also found that, in none of the evaluated cases, input multispectral data meet the assumption of multivariate normality required for band transformation. Our results suggest that assumptions about multivariate normality can affect the robustness of probabilistic change assessment in multispectral remote sensing. We encourage the remote sensing community to adopt the Gamma distribution applied to untransformed data as a probabilistic approach to differentiate between change and no change.
Spatial resources accessible for the derivation of biodiversity indicators of the class ecosystem structure are sparse and disparate, and their integration into computer algorithms for biodiversity monitoring remains problematic. We describe ecochange as an R‐package that integrates spatial analyses with a monitoring workflow for computing routines necessary for biodiversity monitoring. The ecochange comprises three modules for data integration, statistical analysis and graphics. The first module currently downloads and integrates diverse remote sensing products belonging to the essential biodiversity class of structure. The module for statistical analysis calculates RasterStack ecosystem‐change representations across areas of interest; this module also allows focusing on species habitats while deriving changes in a variety of indicators, including ecosystem areas, conditional entropy and fractal dimension indices. The graphics module produces level and bar plots that ease the development of indicator reports. Its functionality is described with an example workflow to calculate ecosystem‐class areas and conditional entropy across an area of interest contained in the package documentation. We conclude that ecochange features procedures necessary to derive ecosystem structure indicators integrating the retrieval of spatially explicit data with the use of workflows to calculate/visualize biodiversity indicators at the national/regional scales.
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