Improving Discrimination of Savanna Tree Species Through a Multiple-Endmember Spectral Angle Mapper Approach: Canopy-Level Analysis

Cho, Moses Azong; Debba, Pravesh; Mathieu, Renaud; Naidoo, Laven; Aardt, Jan van; Asner, Gregory P.

doi:10.1109/tgrs.2010.2058579

Cited by 88 publications

(81 citation statements)

References 40 publications

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“…SAM is applied both in hyperspectral remote sensing [92,93] as well as in multispectral CD [68,94]. Any change over time leads to higher angles, since the pixel vectors vary more compared to unchanged pixels.…”

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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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: 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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“…This method computes the extent of similarity among the analyzed spectral vector data, test vectors and reference spectral vectors, based on the obtained angle between these vectors, where the vectors are considered to have a dimension equal to the number of bands (nb) [53]. This method has been applied for several purposes, such as mineral prospect mapping [62], land cover mapping [63,64], vegetation mapping [65] and water body HCRF spectrum analysis [58,66] including water macrophyte mapping [15].…”

Section: Spectral Angle Mapper (Sam)mentioning

confidence: 99%

Signal Classification of Submerged Aquatic Vegetation Based on the Hemispherical–Conical Reflectance Factor Spectrum Shape in the Yellow and Red Regions

et al. 2013

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Abstract:The water column overlying the submerged aquatic vegetation (SAV) canopy presents difficulties when using remote sensing images for mapping such vegetation. Inherent and apparent water optical properties and its optically active components, which are commonly present in natural waters, in addition to the water column height over the canopy, and plant characteristics are some of the factors that affect the signal from SAV mainly due to its strong energy absorption in the near-infrared. By considering these interferences, a hypothesis was developed that the vegetation signal is better conserved and less absorbed by the water column in certain intervals of the visible region of the spectrum; as a consequence, it is possible to distinguish the SAV signal. To distinguish the signal from SAV, two types of classification approaches were selected. Both of these methods consider the hemispherical-conical reflectance factor (HCRF) spectrum shape, although one type was supervised and the other one was not. The first method adopts cluster analysis and uses the parameters of the band (absorption, asymmetry, height and width) obtained by continuum removal as the input of the classification. The spectral angle mapper (SAM) was adopted as the supervised classification approach. Both approaches tested different wavelength intervals in the visible and near-infrared spectra. It was demonstrated that the Remote Sens. 2013, 5 1857 585 to 685-nm interval, corresponding to the green, yellow and red wavelength bands, offered the best results in both classification approaches. However, SAM classification showed better results relative to cluster analysis and correctly separated all spectral curves with or without SAV. Based on this research, it can be concluded that it is possible to discriminate areas with and without SAV using remote sensing.

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“…High spectral and spatial resolution imagery provides researchers the potential to map vegetation at species' level, provided the plant species under study are spectrally distinct [5]. Species discrimination using remote sensing is based on the assumption that each species is characterized by a set of unique biophysical features and biochemical composition that control the variability in its spectral signature [6].…”

Section: Introductionmentioning

confidence: 99%

Spectral Characteristics of Common Reed Beds: Studies on Spatial and Temporal Variability

Tuominen

Lipping

2016

Remote Sensing

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Reed beds are the second largest producer of biomass in Olkiluoto Island. Quantitative information on the extent and amount of reed stands is an integral part of the biosphere assessment related to long-term safety analysis of nuclear fuel repository site currently under construction. The major challenge in reed bed mapping is discrimination between reed and other green vegetation. Spectral field measurements were used to study the temporal and spatial variability of spectral characteristics of reed beds. Feasibility of discriminating reed beds from other vegetation based on hyperspectral measurements was studied as well. Results indicate that there is large temporal variation of reed bed spectra and the optimal time for data acquisition differs for old and new reed bed types. Comparing spectral characteristics of the reed bed and meadow classes in a local neighborhood indicated that the classes have high within-class spectral variability and similar mean spectra, however, 10 out of 11 targets had lower angle to the mean spectrum of the corresponding class than that of the other class when Spectral Angle Mapper (SAM) was used. Comparing the spectral characteristics of reed beds at four test sites within the Olkiluoto Island indicated that while some of the sites had similar spectra, the difference between others was remarkable. This is partly explained by different density and height of dead and live reed stems at the four sites.

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Improving Discrimination of Savanna Tree Species Through a Multiple-Endmember Spectral Angle Mapper Approach: Canopy-Level Analysis

Cited by 88 publications

References 40 publications

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

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

Signal Classification of Submerged Aquatic Vegetation Based on the Hemispherical–Conical Reflectance Factor Spectrum Shape in the Yellow and Red Regions

Spectral Characteristics of Common Reed Beds: Studies on Spatial and Temporal Variability

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