Radiometric correction effects in Landsat multi‐date/multi‐sensor change detection studies

Abstract

Radiometric corrections serve to remove the effects that alter the spectral characteristics of land features, except for actual changes in ground target, becoming mandatory in multi-sensor, multi-date studies. In this paper, we evaluate the effects of two types of radiometric correction methods (absolute and relative) for the determination of land cover changes, using Landsat TM and Landsat ETM + images. In addition, we present an improvement made to the relative correction method addressed. Absolute correction includes a crosscalibration between TM and ETM + images, and the application of an atmospheric correction protocol. Relative correction normalizes the images using pseudo-invariant features (PIFs) selected through band-to-band PCA analysis. We present a new algorithm for PIFs selection in order to improve normalization results. A post-correction evaluation index (Quadratic Difference Index (QD)), and post-classification and change detection results were used to evaluate the performance of the methods. Only the absolute correction method and the new relative correction method presented in this paper show good postcorrection and post-classification results (QD index < 0; overall accuracy .80%; kappa .0.65) for all the images used. Land cover change estimations based on uncorrected images present unrealistic change rates (two to three times those obtained with corrected images), which highlights the fact that radiometric corrections are necessary in multi-date multi-sensor land cover change analysis.

Land Use and Land Cover Changes and Their Effects on the Landscape of Abaya-Chamo Basin, Southern Ethiopia

“…However, their consistencies across the three accusation years were further assessed in this study. In addition, multi-temporal or multi-sensor images have scene-to-scene variability caused by the effects of solar zenith angles, earth-sun distance, atmospheric influence, and sensor differences [42,43]. Such data needs to be pre-processed in order to reduce scene-to-scene variability so that the data can be put on the same radiometric scale to reflect true changes on the ground [42,43].…”

“…In addition, multi-temporal or multi-sensor images have scene-to-scene variability caused by the effects of solar zenith angles, earth-sun distance, atmospheric influence, and sensor differences [42,43]. Such data needs to be pre-processed in order to reduce scene-to-scene variability so that the data can be put on the same radiometric scale to reflect true changes on the ground [42,43]. The absolute correction method was applied in all images using the image-based dark-object subtraction (DOS) model as described by Chander et al [42], Paolini et al [43], and Song et al [44].…”

Modeling the Relationship between the Gross Domestic Product and Built-Up Area Using Remote Sensing and GIS Data: A Case Study of Seven Major Cities in Canada

“…Then, atmospheric corrections were carried out on all of the multi-temporal Landsat images. The atmospheric correction model (ATCOR2) developed by Richter [19] was utilized to remove the effects that change the spectral characteristics of the land features [20]. To implement the ATCOR2 model, weather information (e.g., air temperature, visibility) were obtained from the Canadian national climate and weather data archive.…”

Implementación y evaluación del modelo Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS): estudio de caso en los Andes colombianos

“…Autores como Canty et al, 2004;Cheng et al, 2004;Lu et al, 2002;Paolini et al, 2006;Richter, 1998, tratan en sus artículos las dificultades en la implementación de modelos de corrección radiométrica y atmosférica en zonas de alta montaña para desarrollar estudios multitemporales. Sin embargo, en este estudio se evaluó la potencialidad de la implementación del modelo LEDAPS, como forma de corrección en reflectividad superficial en una zona de montaña dentro de los Andes colombianos, con cubierta de bosque tropical.…”