A susceptibility model for post wildfire soil erosion in a temperate oceanic mountain area of Spain

Fernández, Susana; Marquínez, J.; Duarte, Rosana Menéndez

doi:10.1016/j.catena.2005.03.006

Cited by 20 publications

(23 citation statements)

References 25 publications

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“…The results of the chemical analysis of the 39 soil samples showed that the study area was heterogeneous, with large variations in both TOC and OC, which is in good agreement with the results obtained in previous studies in this region [54,78]. TOC and OC maximum values were relatively high (greater than 35%) and related to the high amount of organic matter accumulated in the existing paleo surfaces.…”

Section: Descriptive Statistics Of Socsupporting

confidence: 89%

“…The soils are classified as Lithosols, Histosols, and Regosols, according to the World Reference Base [52], and are sandy, shallow, and stony. We chose this region because it has been affected by frequent wildfires [53], and soil in this area contains high amounts of organic matter [54], which makes it well-suited for the study. As a result of the degradation caused by the repeated burning of this region, the landscape is dominated by extensive areas of heath formations, and deciduous forests, to a lesser extent [55].…”

Section: Study Areamentioning

confidence: 99%

“…Total organic C (TOC) was analyzed by dry combustion at 950 • C using a Vario C-N Analyzer (Elementar Analysensysteme GmbH, Langenselbold, Germany). In our study area, TOC is equivalent to total C, as the soil parent material is non-calcareous [54]. Oxidizable C (OC) was determined using the Walkley-Black method [10].…”

Section: Field Datamentioning

confidence: 99%

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Prediction of Topsoil Organic Carbon Using Airborne and Satellite Hyperspectral Imagery

et al. 2017

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Abstract:The Airborne Hyperspectral Scanner (AHS) and the Hyperion satellite hyperspectral sensors were evaluated for their ability to predict topsoil organic carbon (C) in burned mountain areas of northwestern Spain slightly covered by heather vegetation. Predictive models that estimated total organic C (TOC) and oxidizable organic C (OC) content were calibrated using two datasets: a ground observation dataset with 39 topsoil samples collected in the field (for models built using AHS data), and a dataset with 200 TOC/OC observations predicted by AHS (for models built using Hyperion data). For both datasets, the prediction was performed by stepwise multiple linear regression (SMLR) using reflectances and spectral indices (SI) obtained from the images, and by the widely-used partial least squares regression (PLSR) method. SMLR provided a performance comparable to or even better than PLSR, while using a lower number of channels. SMLR models for the AHS were based on a maximum of eight indices, and showed a coefficient of determination in the leave-one-out cross-validation R 2 = 0.60-0.62, while models for the Hyperion sensor showed R 2 = 0.49-0.61, using a maximum of 20 indices. Although slightly worse models were obtained for the Hyperion sensor, which was attributed to its lower signal-to-noise ratio (SNR), the prediction of TOC/OC was consistent across both sensors. The relevant wavelengths for TOC/OC predictions were the red region of the spectrum (600-700 nm), and the short wave infrared region betweeñ 2000-2250 nm. The use of SMLR and spectral indices based on reference channels at~1000 nm was suitable to quantify topsoil C, and provided an alternative to the more complex PLSR method.

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Section: Descriptive Statistics Of Socsupporting

confidence: 89%

Section: Study Areamentioning

confidence: 99%

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Prediction of Topsoil Organic Carbon Using Airborne and Satellite Hyperspectral Imagery

et al. 2017

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“…These factors include slope, altitude (Odeh et al, 1994), rainfall, and historic and current land management practices, e.g. the existence or absence of vegetation cover, soil preparation or fertilisation particularly for fastgrowing species plantations, or the use of fire as a management tool (Fernández et al, 2005).…”

Section: Usefulness For Land Use Planningmentioning

confidence: 99%

Potential productivity of forested areas based on a biophysical model. A case study of a mountainous region in northern Spain

Benavides

Roig²,

Osoro

2009

Ann. For. Sci.

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Ann. For. Sci. 66 (2009) • In a first approach, the climatic productivity is assessed by modelling the required parameters using different geostatistical techniques and software supported by GIS. A second approach includes the correction of the former productivity classes considering the different lithological facies. The potential forest productivity model involves the integration of both models.• Finally, data from the National Forest Inventory (NFI) are used to compare the real and potential yield data within different regions of the studied area.• The results of these analyses demonstrate the usefulness of the model, particularly in mountainous regions, where no significant differences are found between the data from the NFI and the model, but they also show the discrepancies between the estimates and real data when the latter are considered for different tree species, diameter classes or management. Mots-clés :techniques géostatistiques / SIG / gestion de l'usage des sols / modélisation / rendements Résumé -Productivité potentielle des forêts à partir d'un modèle biophysique. Étude du cas d'une région montagneuse dans le nord de l'Espagne.• Les gestionnaires forestiers doivent actuellement faire face à de nombreux défis qui impliquent un besoin croissant d'estimateurs précis de la structure et de la biomasse, de la productivité potentielle et de la croissance des forêts.• L'objectif de ce travail est la modélisation de la productivité potentielle dans une région montagneuse de l'Espagne. Le modèle combine des données climatiques, topographiques et lithologiques et se base sur une variante d'un modèle biophysique classique : l'indice de Paterson.• Dans une première approche, la productivité climatique est estimée en modélisant les paramètres requis grâce à différentes techniques géostatistiques et de logiciels relevant des systèmes d'information géographique (SIG). Une deuxième approche consiste corriger les anciennes classes de productivité en prenant en compte les facies lithologiques. Le modèle de productivité forestière potentielle a été obtenu en combinant ces deux modèles. Finalement, les données de l'Inventaire Forestier National (IFN) sont utilisées pour comparer les rendements réels et potentiels dans les différentes régions de la zone étudiée.• Les résultats de ces analyses ont montré l'utilité du modèle, en particulier dans les régions montagneuses, où aucune différence significative n'a été décelée entre les données IFN et le modèle. Ces résultats ont cependant mis aussi en évidence des divergences entre la productivité potentielle et données réelles lorsque l'on compare différentes espèces, classes de diamètre ou modes de gestion.

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“…Las consecuencias medioambientales de los fuegos se dejan notar no sólo en la vegetación, sino en los suelos afectados por el incendio (Shakesby y Doerr, 2006), modificando su estabilidad estructural (Molina et al, 1994, Martínez Fernández y Díaz Pereira, 1994, Fernández et al, 2005 y, por lo tanto, su susceptibilidad a la erosión (De Bano et al, 1979, Díaz-Fierros et al, 1987, Díaz-Fierros y Rubio, 1990, Coelho et al, 1990, Soler y Sala, 1992, Sala y Rubio,1994, Soler et al, 1994, Kutiel et al, 1995, Úbeda y Sala, 1998 y su comportamiento hidrológico, al reducir tanto la cantidad de materia orgánica (Giovannini y Lucchesi, 1983, Sánchez et al, 1994, como la tasa de infiltración (DeBano et al, 1970, DeBano, 1971, Díaz-Fierros et al, 1987, Marqués y Mora, 1992, Calvo-Cases y Cerdà, 1994, Soto et al, 1994, Cerdà et al, 1995, Bissonais, 1996, Cerdà et al, 1998, Pérez-Cabello et al, 2003a y b, Strunk, 2005.…”

Section: Introductionunclassified

Efectos del fuego en el comportamiento hidrológico de dos cuencas en el Prepirineo aragonés (España)

Arnedo

Benlloch

López

et al. 2013

CIG

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A susceptibility model for post wildfire soil erosion in a temperate oceanic mountain area of Spain

Cited by 20 publications

References 25 publications

Prediction of Topsoil Organic Carbon Using Airborne and Satellite Hyperspectral Imagery

Prediction of Topsoil Organic Carbon Using Airborne and Satellite Hyperspectral Imagery

Potential productivity of forested areas based on a biophysical model. A case study of a mountainous region in northern Spain

Efectos del fuego en el comportamiento hidrológico de dos cuencas en el Prepirineo aragonés (España)

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