The Tasseled Cap Transformation for RapidEye data and the estimation of vital and senescent crop parameters

Schönert, M.; Zillmann, Erik; Weichelt, Horst; Eitel, Jan U. H.; Magney, Troy S.; Lilienthal, Holger; Siegmann, Bastian; Jarmer, Thomas

doi:10.5194/isprsarchives-xl-7-w3-101-2015

Cited by 8 publications

(3 citation statements)

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“…Most studied land cover classes showed a peak in TCB at the beginning of summer, especially for the class "other". As the TCB captures overall brightness and variance in soil brightness (Schönert et al, 2015), this might be attributed to changes in soil conditions, such as moisture.…”

Section: Tasselled Cap Transformation Time-seriesmentioning

confidence: 99%

“…RapidEye Tasselled-Cap-transformed data has been successfully applied to map abandoned agricultural land (Löw, Fliemann, Abdullaev, Conrad, & Lamers, 2015), to estimate windthrow in forests (Einzmann et al, 2017) or for the prediction of biophysical crop parameters (Dahms et al, 2016;Schönert et al, 2015). As the correlation and data intensity is reduced by the TCT, its application can be an attractive approach for multi-temporal land cover mapping, which has not been extensively tested for RapidEye data, yet.…”

Section: Introductionmentioning

confidence: 99%

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Multi-temporal RapidEye Tasselled Cap data for land cover classification

Raab

Tonn

Meißner

et al. 2019

European Journal of Remote Sensing

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Land cover mapping can be seen as a key element to understand the spatial distribution of habitats and thus to sustainable management of natural resources. Multi-temporal remote sensing data are a valuable data source for land cover mapping. However, the increased amount of data requires effective machine learning algorithms and data compression approaches. In this study, the Random Forest and C 5.0 classification algorithms were applied to (1) a multi-temporal Tasselled-Cap-transformed, (2) top of atmosphere and (3) surface reflectance RapidEye time-series. The overall accuracies ranged from 91.44% to 91.80%, with only minor differences between algorithms and datasets. The McNemar test showed, however, significant differences between the Tasselled-Cap-transformed and untransformed mapping results in most cases. The temporal profiles for the Tasselled-Cap-transformed RapidEye data indicated a good separability between considered classes. The phenological profiles of vegetated surfaces followed a typical green-up curve for the Greenness Tasselled-Cap-index. A permutation-based variable importance measure indicated that late autumn should be considered as most important phenological phase contributing to the classification model performance. The results suggested that the RapidEye Tasselled Cap Transformation, which was designed for agricultural applications, can be an effective data compression tool, suitable to map heterogeneous landscapes with no measurable negative impact on classification accuracy.

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Section: Tasselled Cap Transformation Time-seriesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-temporal RapidEye Tasselled Cap data for land cover classification

Raab

Tonn

Meißner

et al. 2019

European Journal of Remote Sensing

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“…First, RapidEye images were selected due to the availability of the red edge band capable of detecting drastic reflectance changes in vegetation. 26,27 The open access, free of charge, and consistent availability of European Space Agency's high-resolution Sentinel-2 images favored their selection as the second set of imagery for the study. The subhumid wetland lies in a tropical region experiencing the presence of clouds exacerbated during the rainy season witnessed from March to May.…”

Section: Introductionmentioning

confidence: 99%

Assessing seasonal land cover dynamics in the tropical Kilombero floodplain of East Africa

Kirimi

Thiong’o

Gabiri

et al. 2018

J. Appl. Rem. Sens.

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, "Assessing seasonal land cover dynamics in the tropical Kilombero floodplain of East Africa," J. Appl. Remote Sens. 12(2), 026027 (2018), doi: 10.1117/1.JRS.12.026027. Abstract. Increasing demand for food in East Africa has created a shift to utilize wetlands for food production. Lack of spatial information hampers sustainable use of the Kilombero Valley floodplain. We take advantage of multispectral data from RapidEye, Landsat-8, and Sentinel-2 to derive high temporal resolution maps along three hydrological zones of the Kilombero Valley, assess seasonal land cover dynamics, and relate these dynamics to groundwater measurements. The depth of groundwater increases from June and declines from December, consistent with the end and the beginning of the rainy season, respectively. Bare land cover over the study area was 45% to 57% and increases to 62% to 69% as the season shifts from rainy to dry seasons while vegetation coverage, which was 34% to 47%, decreased to 25% to 27%. During the dry season, 68% to 81% of the total vegetation is within the riparian zone indicating the hydrological conditions favor plant growth. Vegetation growth in the fringe and middle zones mainly relies on precipitation whereas that in the riparian zone relies on saturation from the river. Our findings exemplify the relationship between seasonal land cover change and hydrological conditions and contribute to improved understanding of the spatial-temporal land cover dynamic in the Kilombero floodplain, required for planning sustained use of the wetland. © The Authors.Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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