Transferability of multi- and hyperspectral optical biocrust indices

Rodríguez‐Caballero, Emilio; Escribano, Paula; Olehowski, Claas; Chamizo, Sonia; Hill, Joachim; Cantón, Yolanda; Weber, Bettina

doi:10.1016/j.isprsjprs.2017.02.007

Cited by 37 publications

(19 citation statements)

References 62 publications

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“…The spectral analysis of our experimental dataset corroborated that, according to several studies analyzing different biocrust communities around the world [2,[48][49][50][51][52], biocrusts modify soil surface reflectance with a main absorption detected in the reflectance red peak at about 680 nm, and along the red edge ( Figure 3) that corresponds to the well-known chlorophyll a spectral absorption [72][73][74][75]. However, contrary to previous studies [2,76], we found that the use of single band reflectance values at this wavelength were not able to accurately estimate Chla of biocrust communities, as shown by the lower coefficient of determination and higher RMSE values obtained compared to those obtained with the , CR, ND, and standard spectral indexes (Table 1).…”

Section: Discussionsupporting

confidence: 86%

“…Vastly different methodologies and indexes have been developed for these analyses using both multi-and hyper-spectral information [42][43][44][45][46][47]. Contrastingly, though there are numerous studies demonstrating that biocrusts have spectral traits related to the presence of photosynthetic pigments [2,[48][49][50][51][52], remote sensing applications for biocrust chlorophyll a quantification are mostly limited to a few local analyses using the normalized difference vegetation index (NDVI; e.g., Karnieli et al, 2001 [53]) or surface reflectance at 680 nm [2]. This occurs for two main reasons.…”

Section: Introductionmentioning

confidence: 99%

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Spectral Response Analysis: An Indirect and Non-Destructive Methodology for the Chlorophyll Quantification of Biocrusts

et al. 2019

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Chlorophyll a concentration (Chla) is a well-proven proxy of biocrust development, photosynthetic organisms' status, and recovery monitoring after environmental disturbances. However, laboratory methods for the analysis of chlorophyll require destructive sampling and are expensive and time consuming. Indirect estimation of chlorophyll a by means of soil surface reflectance analysis has been demonstrated to be an accurate, cheap, and quick alternative for chlorophyll retrieval information, especially in plants. However, its application to biocrusts has yet to be harnessed. In this study we evaluated the potential of soil surface reflectance measurements for non-destructive Chla quantification over a range of biocrust types and soils. Our results revealed that from the different spectral transformation methods and techniques, the first derivative of the reflectance and the continuum removal were the most accurate for Chla retrieval. Normalized difference values in the red-edge region and common broadband indexes (e.g., normalized difference vegetation index (NDVI)) were also sensitive to changes in Chla. However, such approaches should be carefully adapted to each specific biocrust type. On the other hand, the combination of spectral measurements with non-linear random forest (RF) models provided very good fits (R 2 > 0.94) with a mean root mean square error (RMSE) of about 6.5 µg/g soil, and alleviated the need for a specific calibration for each crust type, opening a wide range of opportunities to advance our knowledge of biocrust responses to ongoing global change and degradation processes from anthropogenic disturbance.

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Section: Discussionsupporting

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Spectral Response Analysis: An Indirect and Non-Destructive Methodology for the Chlorophyll Quantification of Biocrusts

et al. 2019

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“…Biocrust coverage within the Soebatsfontein and Knersvlakte region was mapped by means of a newly developed hyperspectral remote-sensing technique, which proved to be also transferable to other regions of the world (Weber et al 2008;Rodrίguez-Caballero et al 2017a, 2017b. This technique utilises the method of continuum removal (Clark and Roush 1984) to analyse the spectral traits of biocrusts allowing for discrimination between biocrusts and bare soil (Weber et al 2008;Rodrίguez-Caballero et al 2017a, 2017b. At Soebatsfontein, ~27% of a 2 837 ha site was classified as biocrusts, whereas in the Knersvlakte only ~16% of the area was classified as biocrusts, while ~42% of the area covering 2 351 ha was classified as quartz pebbles potentially hosting hypolithic shown as RGB composites from CASI 2 images with biocrust and quartz field classification.…”

Section: Biocrust Classification and Mappingmentioning

confidence: 99%

Biological soil crusts of the Succulent Karoo: a review

Weber

Tamm

Maier

et al. 2018

African Journal of Range & Forage Science

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Biological soil crusts (biocrusts) form a regular soil surface community covering about 12% (~18 × 10 6 km 2) of the global terrestrial surface area mainly in dryland regions all over the world (Rodrίguez-Caballero et al. 2018a). They are composed of varying proportions of photosynthesizing cyanobacteria, lichens and bryophytes, which grow together with heterotrophic fungi, bacteria and archaea as well as microfauna such as protozoa, nematodes and microarthropods (

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“…The BSCI was employed in the slope between the green and red band to extract BSCs dominated by lichen [11]. However, satisfactory results cannot be obtained when applying the CI and BSCI in regions covered by a mixture of photosynthetic and non-photosynthetic vegetation, bare sand, rocks, and BSCs [8,13] because it is difficult to extract the subtle spectral characteristics of BSCs [14]. In addition, there are no BSC indices for detecting moss-dominated BSCs.…”

Section: Introductionmentioning

confidence: 99%

A New Application of Random Forest Algorithm to Estimate Coverage of Moss-Dominated Biological Soil Crusts in Semi-Arid Mu Us Sandy Land, China

et al. 2019

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Biological soil crusts (BSCs) play an essential role in desert ecosystems. Knowledge of the distribution and disappearance of BSCs is vital for the management of ecosystems and for desertification researches. However, the major remote sensing approaches used to extract BSCs are multispectral indices, which lack accuracy, and hyperspectral indices, which have lower data availability and require a higher computational effort. This study employs random forest (RF) models to optimize the extraction of BSCs using band combinations similar to the two multispectral BSC indices (Crust Index-CI; Biological Soil Crust Index-BSCI), but covering all possible band combinations. Simulated multispectral datasets resampled from in-situ hyperspectral data were used to extract BSC information. Multispectral datasets (Landsat-8 and Sentinel-2 datasets) were then used to detect BSC coverage in Mu Us Sandy Land, located in northern China, where BSCs dominated by moss are widely distributed. The results show that (i) the spectral curves of moss-dominated BSCs are different from those of other typical land surfaces, (ii) the BSC coverage can be predicted using the simulated multispectral data (mean square error (MSE) < 0.01), (iii) Sentinel-2 satellite datasets with CI-based band combinations provided a reliable RF model for detecting moss-dominated BSCs (10-fold validation, R2 = 0.947; ground validation, R2 = 0.906). In conclusion, application of the RF algorithm to the Sentinel-2 dataset can precisely and effectively map BSCs dominated by moss. This new application can be used as a theoretical basis for detecting BSCs in other arid and semi-arid lands within desert ecosystems.

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Transferability of multi- and hyperspectral optical biocrust indices

Cited by 37 publications

References 62 publications

Spectral Response Analysis: An Indirect and Non-Destructive Methodology for the Chlorophyll Quantification of Biocrusts

Spectral Response Analysis: An Indirect and Non-Destructive Methodology for the Chlorophyll Quantification of Biocrusts

Biological soil crusts of the Succulent Karoo: a review

A New Application of Random Forest Algorithm to Estimate Coverage of Moss-Dominated Biological Soil Crusts in Semi-Arid Mu Us Sandy Land, China

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