How unique are spectral signatures?

Price, John C.

doi:10.1016/0034-4257(94)90013-2

Cited by 264 publications

(137 citation statements)

References 6 publications

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“…A unique biochemistry of species along with phenological and structural characteristics may enable a discrimination of vegetation, but is inevitable for a reliable mapping result. Problems may arise if different species feature a similar spectral response, which is a common phenomenon in remote sensing [4,5]. A unique biochemistry and spectral signal is considered neither in the biological definition of a species, nor in the ecological classification of plant assemblages [6].…”

Section: Introductionmentioning

confidence: 99%

Modeling Species Distribution Using Niche-Based Proxies Derived from Composite Bioclimatic Variables and MODIS NDVI

Feilhauer

Rocchini

2012

Remote Sensing

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Vegetation mapping based on niche theory has proven useful in understanding the rules governing species assembly at various spatial scales. Remote-sensing derived distribution maps depicting occurrences of target species are frequently based on biophysical and biochemical properties of species. However, environmental conditions, such as climatic variables, also affect spectral signals simultaneously. Further, climatic variables are the major drivers of species distribution at macroscales. Therefore, the objective of this study is to determine if species distribution can be modeled using an indirect link to climate and remote sensing data (MODIS NDVI time series). We used plant occurrence data in the US states of North Carolina and South Carolina and 19 climatic variables to generate floristic and climatic gradients using principal component analysis, then we further modeled the correlations between floristic gradients and NDVI using Partial Least Square regression. We found strong statistical relationship between species distribution and NDVI time series in a region where clear floristic and climatic gradients exist. If this precondition is given, the use of niche-based proxies may be suitable for predictive modeling of species distributions at regional scales. This indirect estimation of vegetation patterns may be a viable alternative to mapping approaches using biochemistry-driven spectral signature of species.

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Section: Introductionmentioning

confidence: 99%

Modeling Species Distribution Using Niche-Based Proxies Derived from Composite Bioclimatic Variables and MODIS NDVI

Feilhauer

Rocchini

2012

Remote Sensing

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“…Differences between the spectra were analyzed based on two indicators [65]: (I) the root mean square difference (D) between two spectra (Equation (5)); and (II) the difference angle (θ) between two spectra (Equation (6)), where θ normalizes for differences in offset.…”

Section: Difference Analysismentioning

confidence: 99%

“…Price [65] calculates Equation (6) with integrals instead of summations. We approximate integration using summation, which is a valid approach when using rectangular response functions.…”

Section: Difference Analysismentioning

confidence: 99%

Creating Multi-Temporal Composites of Airborne Imaging Spectroscopy Data in Support of Digital Soil Mapping

2016

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An increasing demand for full spatio-temporal coverage of soil information drives the growing use of soil spectroscopy. Soil spectroscopy application performed under laboratory conditions or in-field studies in semi-arid areas have shown promising results. However, when acquiring data in temperate zones, limitations by vegetation-free coverage, variation in soil moisture and management are driving coherent spatio-temporal data collection. This study explores the use of multi-temporal imaging spectroscopy data to increase the total mapping area of bare soils in a heterogeneous agricultural landscape. Spectrally and spatially high-resolution data from the Airborne Prism Experiment (APEX) were collected in September 2013, April 2014 and April 2015. Bare soils in all acquisitions were identified. To eliminate short-term differences in soil moisture and soil surface roughness, the empirical line method was used to calibrate the reflectance values of the singular images (2013 and 2015) towards the singular image with most bare soil pixels (2014). Difference indicators show that the calibration was successful (decrease in root mean square difference and angle difference, increase in R 2 and gain and offset close to one and zero). Finally, the multi-temporal composite image contained more than double the amount of bare soil pixels as compared to a singular acquisition. Summary statistics show that reflectance values of the multi-temporal composite approximate the single image data of 2014 (mean and standard deviation of 2014: 24.2 ± 8.9 vs. 24.0 ± 9.5 for the multi-temporal composite of 2013, 2014 and 2015). This indicates that global differences in soil moisture and land management have been corrected for. As a result, an improved spatial representation of soil parameters can be retrieved from the composite data. Spatial distribution of the correction factors and analysis of the spatial variability of all images, however, indicate that non-linear, short-term differences like variation in soil moisture and land management largely influence the result of the multi-temporal composite. Quantification and attribution of those factors will be required in the future to allow correcting for them.

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“…Furthermore, the assumption that individual plant species have unique spectral signatures may be wrong. Price [7] has argued that several species may actually have quantitatively similar spectra due to the spectral signature variation present within a species.…”

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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How unique are spectral signatures?

Cited by 264 publications

References 6 publications

Modeling Species Distribution Using Niche-Based Proxies Derived from Composite Bioclimatic Variables and MODIS NDVI

Modeling Species Distribution Using Niche-Based Proxies Derived from Composite Bioclimatic Variables and MODIS NDVI

Creating Multi-Temporal Composites of Airborne Imaging Spectroscopy Data in Support of Digital Soil Mapping

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

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