Derivation of tasseled cap coefficients for RapidEye data

Schönert, M.; Weichelt, Horst; Zillmann, Erik; Jürgens, Carsten

doi:10.1117/12.2066842

Cited by 10 publications

(14 citation statements)

References 9 publications

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“…In addition, simple band transformations were performed, such as layer differencing, specified Tasseled Cap Transformation (sTCT) [13,37,38], Disturbance Index (DI) [16], Multivariate Alteration Detection (MAD) [39], and Spectral Angle Mapper (SAM) [40] (Figure 2).…”

Section: Input Variablesmentioning

confidence: 99%

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Windthrow Detection in European Forests with Very High-Resolution Optical Data

Einzmann

Immitzer

Böck

et al. 2017

Forests

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With climate change, extreme storms are expected to occur more frequently. These storms can cause severe forest damage, provoking direct and indirect economic losses for forestry. To minimize economic losses, the windthrow areas need to be detected fast to prevent subsequent biotic damage, for example, related to beetle infestations. Remote sensing is an efficient tool with high potential to cost-efficiently map large storm affected regions. Storm Niklas hit South Germany in March 2015 and caused widespread forest cover loss. We present a two-step change detection approach applying commercial very high-resolution optical Earth Observation data to spot forest damage. First, an object-based bi-temporal change analysis is carried out to identify windthrow areas larger than 0.5 ha. For this purpose, a supervised Random Forest classifier is used, including a semi-automatic feature selection procedure; for image segmentation, the large-scale mean shift algorithm was chosen. Input features include spectral characteristics, texture, vegetation indices, layer combinations and spectral transformations. A hybrid-change detection approach at pixel-level subsequently identifies small groups of fallen trees, combining the most important features of the previous processing step with Spectral Angle Mapper and Multivariate Alteration Detection. The methodology was evaluated on two test sites in Bavaria with RapidEye data at 5 m pixel resolution. The results regarding windthrow areas larger than 0.5 ha were validated with reference data from field visits and acquired through orthophoto interpretation. For the two test sites, the novel object-based change detection approach identified over 90% of the windthrow areas (≥0.5 ha). The red edge channel was the most important for windthrow identification. Accuracy levels of the change detection at tree level could not be calculated, as it was not possible to collect field data for single trees, nor was it possible to perform an orthophoto validation. Nevertheless, the plausibility and applicability of the pixel-based approach is demonstrated on a second test site.

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Section: Input Variablesmentioning

confidence: 99%

“…Since TCT is sensor specific [90] and RapidEye has no shortwave infrared band (SWIR) like Landsat, we applied specific transformations developed for RapidEye [13,38]. Specific TCC (sTCC) were calculated following both approaches.…”

Section: Appendix A2 Tasseled Cap Transformation and Disturbance Indexmentioning

confidence: 99%

Windthrow Detection in European Forests with Very High-Resolution Optical Data

Einzmann

Immitzer

Böck

et al. 2017

Forests

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“…The tasseled cap transformations and the total canopy cover are multivariate combinations of Landsat bands, and their coefficients are sensor specific. While tasseled cap transformations have been developed for other sensors (Zhang et al 2002;Schönert et al 2014), and so have canopy cover algorithms (Hansen et al 2002), here we used parameters that were Landsat-specific. For the set of ''universal'' indices we did not include the indices deemed Landsat specific, that is, it included all the indices in Table 1, except the tasseled cap transformations and the total canopy cover.…”

Section: Discussionmentioning

confidence: 99%

Testing remotely-sensed predictors of meso-carnivore habitat use in Mediterranean ecosystems

et al. 2016

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Context The legacy of human use of Mediterranean ecosystems results in spatial and temporal heterogeneity of resources for wildlife. Understanding wildlife use of these ecosystems may be improved by including information on ecosystem type, structure, and function extracted from remote sensing data. Objectives To assess whether we can improve our understanding of wildlife-habitat use by including information on ecosystem type, structure and function. Methods We tested whether remote sensing derived descriptors of ecosystem type, structure (tree cover and patch size) and function (productivity and stress) determine the habitat of stone martens (Martes foina), common genets (Genetta genetta), and European badgers (Meles meles) in southern Portugal. We linked radio-tracking data from five stone martens, five genets and eight badgers with aerial photography, and some spectra-selectivity to classify vegetation, its structure, productivity and drought stress. Results Statistically-derived generalized linear mixed regression models using combinations of remotely sensed descriptors of ecosystem type, structure and function, performed better than single ecosystem type descriptors. Conclusion Inclusion of information on ecosystem functioning in predictive models of habitat use is more informative than ecosystem type alone, suggesting functional relationships between wildlife and their habitat. However, inclusion of both ecosystem type and function maybe limited to finer spatial resolutions. Our results illustrate the untapped potential of remote sensing to provide detailed descriptors of habitat at adequate spatial scales, now that they are freely available and are systematically collected over space and time. This information adds useful insights on Electronic supplementary material The online version of this article

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“…For this purpose, we applied TCT to RapidEye satellite images to generate additional brightness, yellowness, and greenness bands, and we used them as supervised classification input data (Schönert et al, 2014). TCT is a representative spectral transformation technique that is used in the field of remote sensing; it was originally proposed by Kauth and Thomas (1976).…”

Section: ) Tct For Rapideyementioning

confidence: 99%

Land Cover Classification of RapidEye Satellite Images Using Tesseled Cap Transformation (TCT)

Moon¹,

Choi²,

Kim³

et al. 2017

Korean Journal of Remote Sensing

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:The RapidEye satellite sensor has various spectral wavelength bands, and it can capture large areas with high temporal resolution. Therefore, it affords advantages in generating various types of thematic maps, including land cover maps. In this study, we applied a supervised classification scheme to generate highresolution land cover maps using RapidEye images. To improve the classification accuracy, object-based classification was performed by adding brightness, yellowness, and greenness bands by Tasseled Cap Transformation (TCT) and Normalized Difference Water Index (NDWI) bands. It was experimentally confirmed that the classification results obtained by adding TCT and NDWI bands as input data showed high classification accuracy compared with the land cover map generated using the original RapidEye images.

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Derivation of tasseled cap coefficients for RapidEye data

Cited by 10 publications

References 9 publications

Windthrow Detection in European Forests with Very High-Resolution Optical Data

Windthrow Detection in European Forests with Very High-Resolution Optical Data

Testing remotely-sensed predictors of meso-carnivore habitat use in Mediterranean ecosystems

Land Cover Classification of RapidEye Satellite Images Using Tesseled Cap Transformation (TCT)

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