Evaluación de los diferentes índices para cartografiar biocostras a partir de información espectral

Abstract

Resumen: Las biocostras o costras biológicas del suelo (CBS) son comunidades formadas por la asociación de partí-culas de suelo con microorganismos como cianobacterias, algas, hongos, líquenes, hepáticas o briófitos, que viven en la superficie del suelo. Estas comunidades bióticas cubren las áreas desprovistas de vegetación en los ecosistemas áridos y semiáridos, modificando las propiedades del suelo e influyendo en numerosos procesos clave para el funcionamiento de los ecosistemas. Aunque representan una porción insignificante del perfil del suelo, las CBS regulan el intercambio de gases, agua y nutrientes desde y hacia el suelo, por lo que desempeñan un papel fundamental en los ecosistemas en los que aparecen. Debido al importante papel que ejercen las CBS en los ecosistemas donde aparecen y a su fragilidad frente a las acciones antrópicas, es necesario conocer de forma precisa su distribución espacial. Para ello, recientemente, se han desarrollado varios índices que permiten cartografiarlas a partir de imágenes obtenidas de sensores remotos. Este trabajo tiene por objetivo evaluar los diferentes índices desarrollados para cartografiar CBS, aplicándolos a una zona distinta para las que han sido desarrolladas. Los diferentes índices se evaluarán a dos escalas espaciales diferentes, parcela y ecosistema. A ambas escalas se analizará la fiabilidad de cada uno de los índices para la cartografía de CBS y su capacidad de diferenciar entre diferentes tipos, así como las principales cubiertas presentes en los sistemas áridos y semiáridos. La gran heterogeneidad espacial de la zona y la similitud espectral entre las CBS y la vegetación seca dificultan la correcta cartografía de ecosistemas en los que las CBS son una componente importante utilizando información multiespectral. Sin embargo, los índices basados en información hiperespectral y alta resolución espacial constituyen una herramienta sólida para cartografiar CBS proporcionando una fiabilidad de 71%.Palabras clave: costras biológicas del suelo, hiperespectral, semiárido, teledetección, radiometría, índices espectrales. evaluation of the different spectral indices to map biocrust using spectral informationAbstract: Biological soil crusts (BSC) are complex communities formed by a close association of soil particles and cyanobacteria, algae, microfungi, lichens or bryophytes that live within or immediately on top of the uppermost millimeters of the soil surface. These communities cover non vegetated areas in most of the arid and semiarid ecosystems, and modify numerous soil surface properties and ecosystem processes. Given the importance of BSC in ecosystem functioning, accurate and spatially explicit information on the distribution of BSC is mandatory. With this objective, considerable effort has been devoted to identify and map BSC using remote sensing data, and some spectral indices have been developed. These indexes use the spectral differences among BSC and other surface components like vegetation or bare soil to identify the areas dominated by BSC. Our main o...

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

“…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).…”

“…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.…”

Imaging Spectroscopy for Soil Mapping and Monitoring

“…With this, the subtle spectral differences between sparse vegetation, bare soil and biocrust could be identified. Nevertheless, important classification errors were observed in heterogeneous areas where each pixel is covered by a mixture of semiarid vegetation, bare soil and biocrusts (Alonso et al, 2014). Similarly, Hill et al (1999) and Rodríguez-Caballero et al (2014) used spectral unmixing methods to successfully quantify the amount of biocrust coverage within a pixel based on hyperspectral imagery in the Nitzana region (Israel), and in El Cautivo area (Spain).…”

Transferability of multi- and hyperspectral optical biocrust indices

“…Published studies have described two main absorption features of biocrusts at 516 and 679 nm, related to the presence of carotenoids and chlorophyll a, respectively (Weber et al, 2008;Chamizo et al, 2012b), a decrease in reflectivity (Zaady et al, 2007;Zhang et al, 2014;Rodriguez-Caballero et al, 2015), and an increase in emissivity (Rozenstein and Karnieli, 2015) as crust cover and developmental stage increases. Spectral differences betweenbiocrusts, vegetation and bare soil have been used to map areas dominated by biocrusts (Karnieli, 1997;Cheng et al, 2005;Weber et al, 2008;Moghtaderi et al, 2011;Chamizo et al, 2012b;Alonso et al, 2014;Rozenstein and Karnieli, 2015) and to quantify their relative cover (Rodriguez-Caballero et al, 2014a). These studies have resulted in the development of several biocrust mapping indices using optical reflectivity (Karnieli, 1997;Cheng et al, 2005;Weber et al, 2008;Chamizo et al, 2012b).The Crust Index (CI; Karnieli, 1997) and the Biological Soil Crust Index (BSCI; Chen et al, 2005) use multispectral optical information from LANDSAT ETM+ images to identify areas dominated by biocrusts in desert ecosystems.Whereas CI was developed for the detection of cyanobacteriadominated areas, based on the assumption that phycobilines of cyanobacteria cause increased reflectance values in the blue region, BSCI used the slope between the green and red part of the spectrum to discriminate lichen-dominated biocrustsagainst vegetation and bare sand.…”

“…These studies have resulted in the development of several biocrust mapping indices using optical reflectivity (Karnieli, 1997;Cheng et al, 2005;Weber et al, 2008;Chamizo et al, 2012b).The Crust Index (CI; Karnieli, 1997) and the Biological Soil Crust Index (BSCI; Chen et al, 2005) use multispectral optical information from LANDSAT ETM+ images to identify areas dominated by biocrusts in desert ecosystems.Whereas CI was developed for the detection of cyanobacteriadominated areas, based on the assumption that phycobilines of cyanobacteria cause increased reflectance values in the blue region, BSCI used the slope between the green and red part of the spectrum to discriminate lichen-dominated biocrustsagainst vegetation and bare sand. Application of these indices in complex and heterogeneous areas, like most drylands, where the surface is covered by a mixture of green and dry vegetation, bare soil, rocks and physical and biological soil crusts, does not provide satisfactory results (Weber et al, 2008;Alonso et al, 2014). This is mainly caused by the subtle spectral differences between the areas covered by sparse vegetation and biocrusts (Escribano et al, 2010).…”