Spectral properties and sources of variability of ecosystem components in a Mediterranean semiarid environment

Escribano, Paula; Palacios-Orueta, Alicia; Oyonarte, Cecilio; Chabrillat, Sabine

doi:10.1016/j.jaridenv.2010.02.001

Cited by 30 publications

(33 citation statements)

References 41 publications

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“…2a). The spectral differences in the 760-2500 nm region were much more obvious than those in the 350-760 nm range, a result also noted in other studies (Morgan et al, 2009;Escribano et al, 2010;Nocita et al, 2011). The noticeable differences in the near and shortwave infrared regions are likely the effect of other factors, such as soil moisture (Morgan et al, 2009), clay (Summers et al, 2011), and SOM contents (Uno et al, 2005).…”

Section: Soil Spectrasupporting

confidence: 84%

“…Cross-validation is a way to predict the fit of a model to a hypothetical validation set when an explicit validation set is not available. Due to the low number of soil samples, all samples were used to develop the calibration model in the study, and the crossvalidation was used to verify the models (Bartholomeus et al, 2008;Melendez-Pastror et al, 2008;Escribano et al, 2010).…”

Section: Evaluation Of Som Prediction Modelmentioning

confidence: 99%

“…In recent years, a comprehensive literature review found that partial least squares regression (PLSR) is the most common VNIR calibration method (Dunn et al, 2002;Morgan et al, 2009;Escribano et al, 2010;Fontan et al, 2010). Although PLSR has many advantages, such as its simplicity, robustness, predictability, precision, and clearly quantitative explanations, the principal disadvantage is that PLSR does not provide a quantitative explanation for the relationship between predictor variables and response variables, and it does not support re-use of model algorithms between variable instrumentations.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Prediction of soil organic matter content in a litchi orchard of South China using spectral indices

Chen

Peng

et al. 2012

Soil and Tillage Research

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Section: Soil Spectrasupporting

confidence: 84%

Section: Evaluation Of Som Prediction Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of soil organic matter content in a litchi orchard of South China using spectral indices

Chen

Peng

et al. 2012

Soil and Tillage Research

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“…Acacia longifolia, too, has a high growth rate and extended growth period [42,55], efficient nutrient acquisition [41] and high transpiration rates [45] while having a different leaf type (large phyllodes) compared to native species in this dune ecosystem [23,111]. This was reflected in the slightly, but significantly lower CAI value for A. longifolia which can be related to less dry, non-photosynthetically active plant material in the canopy [83,112]. In contrast to directly selecting one specific biochemical parameter, e.g., leaf tannin content [51], our data mining approach identified suitable vegetation indices from a large set of easily produced predictors.…”

Section: Interpretation Of the Relevant Vegetation Indices For Identimentioning

confidence: 99%

Evaluation of Continuous VNIR-SWIR Spectra versus Narrowband Hyperspectral Indices to Discriminate the Invasive Acacia longifolia within a Mediterranean Dune Ecosystem

et al. 2016

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Hyperspectral remote sensing is an effective tool to discriminate plant species, providing vast potential to trace plant invasions for ecological assessments. However, necessary baseline information for the use of remote sensing data is missing for many high-impact invaders. Furthermore, the identification of the suitable classification algorithms and spectral regions for successfully classifying species remains an open field of research. Here, we tested the separability of the invasive tree Acacia longifolia from adjacent exotic and native vegetation in a Natura 2000 protected Mediterranean dune ecosystem. We used continuous visible, near-infrared and short wave infrared (VNIR-SWIR) data as well as vegetation indices at the leaf and canopy level for classification, comparing five different classification algorithms. We were able to successfully distinguish A. longifolia from surrounding vegetation based on vegetation indices. At the leaf level, radial-basis function kernel Support Vector Machine (SVM) and Random Forest (RF) achieved both a high Sensitivity (SVM: 0.83, RF: 0.78) and a high Positive Predicted Value (PPV) (0.86, 0.83). At the canopy level, RF was the classifier with an optimal balance of Sensitivity (0.75) and PPV (0.75). The most relevant vegetation indices were linked to the biochemical parameters chlorophyll, water, nitrogen, and cellulose as well as vegetation cover, which is in line with biochemical and ecophysiological properties reported for A. longifolia. Our results highlight the potential to use remote sensing as a tool for an early detection of A. longifolia in Mediterranean coastal ecosystems.

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“…Modern remote sensing has become a novel tool, not only for detecting and quantifying geological materials [1], but also for monitoring dynamic processes and induced changes in their physical/chemical properties [2][3][4][5][6]. Multispectral and superspectral imagery have been effectively used for mapping geology/minerals [7][8][9][10][11] as well as for monitoring mining impacts [12][13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Integration of Absorption Feature Information from Visible to Longwave Infrared Spectral Ranges for Mineral Mapping

Kopačková

Koucká

2017

Remote Sensing

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Merging hyperspectral data from optical and thermal ranges allows a wider variety of minerals to be mapped and thus allows lithology to be mapped in a more complex way. In contrast, in most of the studies that have taken advantage of the data from the visible (VIS), near-infrared (NIR), shortwave infrared (SWIR) and longwave infrared (LWIR) spectral ranges, these different spectral ranges were analysed and interpreted separately. This limits the complexity of the final interpretation. In this study a presentation is made of how multiple absorption features, which are directly linked to the mineral composition and are present throughout the VIS, NIR, SWIR and LWIR ranges, can be automatically derived and, moreover, how these new datasets can be successfully used for mineral/lithology mapping. The biggest advantage of this approach is that it overcomes the issue of prior definition of endmembers, which is a requested routine employed in all widely used spectral mapping techniques. In this study, two different airborne image datasets were analysed, HyMap (VIS/NIR/SWIR image data) and Airborne Hyperspectral Scanner (AHS, LWIR image data). Both datasets were acquired over the Sokolov lignite open-cast mines in the Czech Republic. It is further demonstrated that even in this case, when the absorption feature information derived from multispectral LWIR data is integrated with the absorption feature information derived from hyperspectral VIS/NIR/SWIR data, an important improvement in terms of more complex mineral mapping is achieved.

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Spectral properties and sources of variability of ecosystem components in a Mediterranean semiarid environment

Cited by 30 publications

References 41 publications

Prediction of soil organic matter content in a litchi orchard of South China using spectral indices

Prediction of soil organic matter content in a litchi orchard of South China using spectral indices

Evaluation of Continuous VNIR-SWIR Spectra versus Narrowband Hyperspectral Indices to Discriminate the Invasive Acacia longifolia within a Mediterranean Dune Ecosystem

Integration of Absorption Feature Information from Visible to Longwave Infrared Spectral Ranges for Mineral Mapping

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