Abstract:Generating annual land cover maps in the tropics based on optical data is challenging because of the large amount of invalid observations resulting from the presence of clouds and haze or high moisture content in the atmosphere. This study proposes a strategy to build an annual time series from multi-year data to fill data gaps. The approach was tested using the Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index and spectral bands as input for land cover classification of Colombia. In a second step, selected ancillary variables, such as elevation, L-band Radar, and precipitation were added to improve overall accuracy. Decision-tree classification was used for assigning eleven land cover classes using the International Geosphere-Biosphere Programme (IGBP) legend. Maps were assessed by their spatial confidence derived from the decision tree approach and conventional accuracy measures using reference data and statistics based on the error matrix. The multi-year data integration approach drastically decreased the area covered by invalid pixels. Overall accuracy of land cover maps significantly increased from 58.36% using only optical time series of 2011 filtered for low quality observations, to 68.79% when using data for 2011¨2 years. Adding elevation to the feature set resulted in 70.50% accuracy.
This paper proposes a validation-comparison method for burned area (BA) products. The technique considers: (1) bootstrapping of scenes for validation-comparison and (2) permutation tests for validation. The research focuses on the tropical regions of Northern Hemisphere South America and Northern Hemisphere Africa and studies the accuracy of the BA products: MCD45, MCD64C5.1, MCD64C6, Fire CCI C4.1, and Fire CCI C5.0. The first and second parts consider methods based on random matrix theory for zone differentiation and multiple ancillary variables such as BA, the number of burned fragments, ecosystem type, land cover, and burned biomass. The first method studies the zone effect using bootstrapping of Riemannian, full Procrustes, and partial Procrustes distances. The second method explores the validation by using distance permutation tests under uncertainty. The results refer to Fire CCI 5.0 with the best BA description, followed by MCD64C6, MCD64C5.1, MCD45, and Fire CCI 4.1. It was also found that biomass, total BA, and the number of fragments affect the BA product accuracy.
Resumen: En este trabajo se hace un análisis de la reflectividad obtenida con una serie de imágenes Landsat procesadas con el modelo LEDAPS en una región de los Andes Colombianos. Fueron calibradas 38 imágenes de los sensores TM y ETM con el modelo LEDAPS con el fin de evaluar las diferencias de reflectividad entre las bandas de un mismo sensor, las diferencias entre sensores, y los patrones temporales. Se utilizaron pruebas estadísticas no paramétricas exactas que permitieron concluir: a) la reflectividad superficial entre bandas (1-5 y 7) es distinta y que esta diferencia se mantiene entre escenas de fechas distintas; b) al comparar las bandas de igual longitud de onda entre los sensores TM y ETM+ hay altas similitudes estadísticas entre las bandas; c) las variaciones temporales en reflectividad desde el año 1986 a 2013 con los sensores estudiados no son significativas. Estos resultados están sustentados con la implementación de modelación robusta con varios métodos resistentes a observaciones inusuales y otros problemas típicos de la modelación de mínimos cuadrados clásicos.Palabras clave: LEDAPS, Landsat, corrección atmosférica, prueba exacta de Wilcoxon-Mann-Whitney, modelo de Huber, regresión por mínimas desviaciones absolutas, mínimos cuadrados recortados, Bootstrap. Implementation and evaluation of the Landsat Ecosystem Disturbance Adaptive Processing Systems (LEDAPS) model: a case study in the Colombian AndesAbstract: This paper analyzes the reflectance obtained with a series of Landsat images processed with LEDAPS model in a region of the Colombian Andes. A total of 38 images of TM and ETM sensors were calibrated to surface reflectance using LEDAPS in order to determine difference among bands of the same sensor, difference between sensors and analyze temporal patterns. Exact nonparametric statistics allow to conclude that: a) surface reflectance for band 1-5 and 7 were significantly different and this difference remains among images of different dates; b) there are statistical similarities between the TM and ETM sensors bands; c) temporal variations on surface reflectance from the years 1986 to 2013 with the sensors studied are not statistically significant. These results are supported by the implementation of robust modeling with various methods resistant to unusual observations and other typical problems of the classical least squares modeling.
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