Gully erosion, land use and topographical thresholds during the last 60 years in a small rangeland catchment in SW Spain (Land Degradation & Development, 2009)
Gully erosion plays an important role in degradation processes of Mediterranean environments. In this paper aerial orthophotographs were used for (i) analysing the evolution of a valley bottom gully and its relation with land use and vegetation cover, (ii) exploring the role of land use and vegetation cover on the coefficients of the equation S = aA−b (where S is slope at the headcut and A is drainage area), which is based on the topographical threshold concept and is commonly used to predict gully initiation. The study was carried out in a small catchment (99·5 ha) located in the southwest of the Iberian Peninsula. Gullies and headcuts were mapped together with land use and vegetation cover using aerial photographs for the years 1945, 1956, 1989, 1998, 2002 and 2006, which had to be digitized and orthorectified in advance. The results showed an increase of the area affected by gullying from 695 m2 in 1945–1009 m2 in 2006, reaching a maximum of 1560 m2 in 1956. Gullying was closely related with land use, especially with the amount of cultivated areas within the catchment and also with grazing intensity. No clear relationship was found between the evolution of the gullied area and rainfall amounts. Finally, the values of the exponent b obtained for different headcuts and different dates (close to 0·4) were similar to those proposed by other authors for gully erosion caused by Hortonian overland flow in semiarid environments. Copyright © 2009 John Wiley & Sons, Ltd.

Modelling the occurrence of gullies in rangelands of southwest Spain (Earth Surface Processes and Landforms, 2009)
Gully erosion has been recognized as an important soil degradation process in rangelands of southwest Spain. In this paper, a relatively new data mining technique called Multivariate Adaptive Regression Splines (MARS) was applied to construct a model capable of predicting the location of gullies. A large database was used to support the model composed of a target variable (presence or absence of gullies) and 36 independent variables related to topography, lithology, soils, rainfall, land use and vegetation cover. The performance of the model was evaluated using the Receiver Operating Characteristic (ROC) curve for ﬁ ve external datasets. The model had high predictive power, with values for the area under the ROC curve of the external validation datasets varying from 0·75 to 0·98 (1·0 being perfect prediction). The most important variables explaining the spatial distribution of gullies were lithology and soil type. Finally the model was compiled and implemented into a geographical information system to obtain maps of susceptible areas for gully erosion. These maps show that approximately 7% of the study area presents favourable conditions for the development of gullies. The results demonstrate that MARS constitutes a valuable model in geomorphic research and could also be a useful tool for assessing the impacts of changing climate and land use on gully erosion.

Using and comparing two nonparametric methods (CART and MARS) to model the potential distribution of gullies - Gutiérrez, Álvaro Gómez; Schnabel, Susanne; Lavado Contador, J. Francisco (Ecological Modelling, 2009)
Gully erosion represents an important soil degradation process in rangelands. In order to take preventive or control measures and to reduce its environmental damages and economical costs it is useful to localize the points in the landscape where gullying takes place and to determine the importance of the different factors involved. The study is carried out in Extremadura, southwest Spain. The main objectives of this work are: (a) comparing two nonparametric schemes to model the potential distribution of gullies, (b) evaluating the importance of the different factors involved in gullying processes, (c) analyzing the role of prevalence in the success of the model and ﬁnally, (d) implementing and mapping the results with the help of a Geographical Information System (GIS). Two methods were used to model the response of a dependent variable (gullying) from a set of independent variables: Classiﬁcation And Regression Trees (CART) and Multivariate Adaptive Regression Splines (MARS). Three different datasets were used; the ﬁrst one for constructing the model (training dataset) and the others for validating the model (external datasets). These datasets are formed by a target variable (presence or absence of gullies) and a set of independent variables. The dependent variable was obtained by mapping the locations of gullies with the help of a GPS and high resolution aerial ortophotographs. A set of 32 independent variables reﬂecting topography, lithology, soil type, climate, land use and vegetation cover of each area were used. The performance of the models was evaluated using a non-dependent threshold method: the Receiver Operating Characteristic (ROC) curve. The results showed a better performance of MARS for predicting gullying with areas under the ROC curve of 0.98 and 0.97 for the validation datasets, while CART presented values of 0.96 and 0.66.
Keywords: CART; Gully erosion; MARS; Nonparametric modelling; Rangelands; ROC curve

Using and comparing two nonparametric methods (CART and MARS) to model the potential distribution of gullies (Ecological Modelling, 2009)
Gully erosion represents an important soil degradation process in rangelands. In order to take preventive or control measures and to reduce its environmental damages and economical costs it is useful to localize the points in the landscape where gullying takes place and to determine the importance of the different factors involved. The study is carried out in Extremadura, southwest Spain. The main objectives of this work are: (a) comparing two nonparametric schemes to model the potential distribution of gullies, (b) evaluating the importance of the different factors involved in gullying processes, (c) analyzing the role of prevalence in the success of the model and ﬁnally, (d) implementing and mapping the results with the help of a Geographical Information System (GIS). Two methods were used to model the response of a dependent variable (gullying) from a set of independent variables: Classiﬁcation And Regression Trees (CART) and Multivariate Adaptive Regression Splines (MARS). Three different datasets were used; the ﬁrst one for constructing the model (training dataset) and the others for validating the model (external datasets). These datasets are formed by a target variable (presence or absence of gullies) and a set of independent variables. The dependent variable was obtained by mapping the locations of gullies with the help of a GPS and high resolution aerial ortophotographs. A set of 32 independent variables reﬂecting topography, lithology, soil type, climate, land use and vegetation cover of each area were used. The performance of the models was evaluated using a non-dependent threshold method: the Receiver Operating Characteristic (ROC) curve. The results showed a better performance of MARS for predicting gullying with areas under the ROC curve of 0.98 and 0.97 for the validation datasets, while CART presented values of 0.96 and 0.66.