The accelerated erosion that occurs after wildfire in forest ecosystems is a major factor affecti... more The accelerated erosion that occurs after wildfire in forest ecosystems is a major factor affecting the sustainability of these environments. Soil erosion prediction models are crucial for estimating fire impacts and for planning post-fire soil stabilization measures. We evaluated the performance of the Revised Universal Soil Loss Equation (RUSLE) and the Pan-European Soil Erosion Risk Assessment (PESERA) models by comparing the soil losses predicted by the models and the soil losses measured in the first year after wildfires in NW Spain. Two alternative RUSLE approaches were also considered: i) the use of reduction coefficients for R and C factors and ii) the application of a soil erodibility reduction factor to take into account soil surface stone cover. The RUSLE model overestimated the observed erosion rates in all cases. The PESERA model performed reasonably well in predicting soil losses, but needs a better characterization of soil burn severity in the soil inputs (crusting and erodibility) because of the high sensitivity of the model to these parameters. In both cases, there is an urgent need for an appropriate assessment of the impact on soil burn severity in the ero-sional process. This seems critical if these models are to be used for post-fire soil stabilization activities planning.
Conventional soil mapping is costly and time consuming. Therefore, the development of quick, chea... more Conventional soil mapping is costly and time consuming. Therefore, the development of quick, cheap, but accurate methods is required. Several studies highlight the importance of developing regional soil spectral libraries for digital soil mapping, but few studies report on the use of these libraries to aid digital mapping of soil types. This study aims to produce a digital soil map using as training set Visible and Near Infra-Red (Vis–NIR) spectra from local soil samples, a regional spectral library and terrain attributes. The soils were sampled in 162 locations on a 270-ha farm in the municipality of Piracicaba, São Paulo, Brazil. Spectra from topsoil and subsoil were measured in laboratory (400–2500 nm) and arranged as multi-depth spectra. Information was summarized by principal component analysis. Regression tree models were calibrated to predict principal components (PC) scores based on terrain attributes. After calibration, the models were applied to the entire study site, resulting in PC score maps. Fuzzy c-means and PC maps were used to define the soil mapping units (MU). Based on fuzzy cen-troids, representative samples (RS) were defined to the MU. Munsell soil color and soil order were predicted from soil spectra and used to characterize the MU. The regression tree model had a good fit for PC1, with an r 2 of 0.92, and a satisfactory r 2 for PC3, PC4, and PC5, respectively 0.58, 0.66 and 0.53. The fuzzy clustering defined seven MU. The R 2 for Munsell color predictions were 0.94 (hue), 0.96 (value) and 0.73 (chroma). Soil order had good agreement in validation, with kappa coefficient of 0.41. The methodology indicates the potential of Vis–NIR spectra to improve soil mapping campaigns and consequently provides a product similar to a conventional soil map.
A comprehensive assessment of soil organic matter (SOM) dynamics in semi-arid agrosystems impleme... more A comprehensive assessment of soil organic matter (SOM) dynamics in semi-arid agrosystems implementing soil and water conservation (SWC) measures is still lacking despite their extent, ecological and economic significance. Therefore, we assessed the long-term impact of a commonly used SWC technique (Fanya-juu terracing) on SOM-related properties in South Eastern Kenya. A soil sampling campaign was conducted in a replicated stratified random manner on three land uses that had been continuously managed for over 30 years. Samples were analyzed for organic carbon and nitrogen contents, δ 13 C, δ 15 N, pH and texture. Compared to sites implementing conventional agriculture, the establishment of SWC structures in this erosion-prone landscape resulted in the recovery of SOM levels comparable to those observed in neighboring semi-natural ecosystems. Sites under conventional agriculture practices contained 20 Mg C ha −1 (0.85 m), while sites with SWC measures and those hosting semi-natural vegetation stored above a third more. There were significant differences in soil C/N ratios as well as in δ 13 C and δ 15 N values between SWC cultivation practices classified according to the presence or absence of trees. The presence of woody vegetation in sites with SWC structures had a strong impact on the spatial variability of SOM-related properties. There was also a significant negative relationship between δ 15 N values and C/N ratios across the different land uses. Our findings indicate the existence of contrasting SOM dynamics caused by vegetation-related effects, and provide suggestions for enhancing SOM storage in agricultural sites implementing SWC measures.
El potencial del magnetismo en la clasificación de suelos: una revisión 365 Resumen Los óxidos de... more El potencial del magnetismo en la clasificación de suelos: una revisión 365 Resumen Los óxidos de hierro se encuentran en todos los suelos en cantidades diversas, son utilizados como indicadores de procesos pedogenéticos y reflejan los ambientes de formación del suelo. Por otro lado, estos componentes afectan las propiedades y funciones de los suelos, porque influyen en el color, la agregación, la capacidad de intercambio de cationes y la retención de fósforo en el suelo. Una característica de los óxidos de hierro es la formación de minerales ferrimagnéticos; debido a esto, y mediante la utilización de técnicas magnéticas se pueden identificar algunos procesos pedogenéticos. El objetivo de este trabajo es la descripción de las propiedadesmagnéticas de los suelos y sus aplicaciones potenciales, como parámetros proxy, en la clasificación de suelos. Se mencionan los portadores magnéticos, las propiedades magnéticas, las formas de análisis y sus aplicaciones. Las propiedades magnéticas de los suelos permiten identificar y cuantificar los minerales magnéticos y relacionarlos con los procesos pedogenéticos. Las mediciones magnéticas son fáciles de realizar y son de bajo costo, lo cual permite realizar el análisis de un gran número de muestras, con lo que es posible la elaboración de mapas de suelos, la identificación de procesos pedogenéticos y el mejoramiento de la clasificación de suelos. En países con alta diversidad edáfica como México, es posible la generación de nuevos tipos de aplicaciones de las propiedades magnéticas de los suelos.
An understanding of the nature of soils in natural and human influence ecosystems is essential if... more An understanding of the nature of soils in natural and human influence ecosystems is essential if progress is to be made in the determination and monitoring of soil quality. This paper briefly discuss the changing circumstances which have resulted in the increased awareness of the importance of soil as a key component of both natural and human influenced environmental systems. These changed circumstances and the recognition of the often crucial role that soils play within these systems has resulted in a demand for measures of soil quality, similar to those used in the characterisation of water and air. Whilst it is relatively straightforward to quantify the fitness to breathe of air, or the fitness to drink with respect to water, it is considerably more difficult to identify similar criteria to be applied to soil. In part this arises because of the wide range of uses to which soils are put and in part because of the complexity of the soil system and the possibility that changes in the soil may be slow and possibly only occur when some threshold is reached. The paper stresses that any index of soil quality must consider soil function, and these functions are varied and often complex. A soil which is considered to be of high quality for one function may not be so for other functions. As a consequence there are potentially many soil properties which might serve as indicators of soil quality, and research is required to identify the most suitable. It may also be questioned whether it is appropriate to pursue this notion of a limited number of soil quality indicators. If an indicator is chosen comparisons between sites and results from different investigators are not possible unless the methods of analysis are given. Standard methods of soil analysis must be developed. These standard methods are distinct from national standard values, or indicator values frequently set by national governments or regulatory agencies. The International Standardisation Organisation (ISO) has developed a programme for the development of standard methods of soil analysis across a range of possible properties including visible, biological, physical and chemical properties. If it is decided that soil quality indicators are required and progress is to be made on developing acceptable indicators of soil quality with agreed standard methods of determination there is need for co-operation amongst soil scientists and those involved in determining appropriate and sustainable use of the soil.
Precise soil quality assessment is critical for designing sustainable agriculture policies, resto... more Precise soil quality assessment is critical for designing sustainable agriculture policies, restoring degraded soils, carbon (C) modeling, and improving environmental quality. Although the consequences of soil quality reduction are generally recognized, the spatial extent of soil degradation is difficult to determine, because no universal equation or soil quality prediction model exists that fits all ecoregions. Furthermore, existing soil organic C (SOC) models generate estimates with uncertainties that may exceed 50%. Therefore it is possible that drastic changes in soil quality may be occurring in sites which are not identifiable on existing maps. Soil quality can either be directly inferred from SOC concentration, or through the assessment of the soil physical, chemical and biologic properties. Assessing the spatial distribution of SOC over large areas requires the calibration and development of models derived from laboratory or field based techniques. However, mapping SOC concentration in all soils is logistically challenging by using normal standard survey techniques. The availability of new generations of remotely sensed datasets and geographical information system (GIS) models (i.e. GEMS, RothC, and CENTURY) provides new opportunities for predicting soil properties and quality at different spatial scales. This article discusses the current approaches, identifies gaps and proposes improvements in techniques for measuring soil quality within agricultural fields.
The purpose of this study was to analyze the relationships between soil attributes and environmen... more The purpose of this study was to analyze the relationships between soil attributes and environmental covariates in a tropical hillslope environment on a regional scale to estimate spatial distribution of soil attributes and identify statistical and geostatistical techniques that could represent the variation of the soil attributes. The study was performed in Bom Jardim County, Brazil, and covered an area of 390 km 2 with a soil database of 208 sample points distributed in six depth layers (0.53 pts/km 2 ). The study used 18 environmental covariates derived from DEM and satellite imagery. The models evaluated were linear regression, regression trees and ordinary and regression kriging. An exploratory analysis showed that DEM, NDVI, MRVBF, MSP, b3/b2, b5/b7, SPI, SWI, SLOPE and ASPECT were correlated with soil properties. The models performance had a mean crossvalidation r 2 of 0.13. The best results were achieved with kriging models, with a crossvalidation r 2 of 0.19. A comparison between multiple linear regression and regression trees showed that the tree model yielded the best results. The sample density alone could not explain the results, but an interaction between DEM accuracy, sample density, covariates and geological conditions was suitable as an explanatory factor. Studies of tropical hillslope digital soil mapping on regional scales need to be more exhaustively focused to develop this research area.
Landsat thematic mapper data are used to estimate instantaneous regional-scale surface water and ... more Landsat thematic mapper data are used to estimate instantaneous regional-scale surface water and energy fluxes in a semi-arid Great Basin desert of the western United States. Results suggest that it is possible to scale from point measurements of environmental state variables to regional estimates of water and energy exchange. This research characterizes the unif3'ing thread in the classical climate-topography-soil-vegetation relation --the surface water and energy balance--through maps of the partitioning of energy throughout the landscape. The study was conducted in Goshute Valley of northeastern Nevada, which is characteristic of most faulted graben valleys of the Basin and Range Province of the western United States. The valley comprises a central playa and lake plain bordered by alluvial fans emanating from the surrounding mountains. The distribution of evapotranspiration (ET) is lowest in the middle reaches of the fans where the water table is deep and plants are small, resulting in low evaporation and transpiration. Highest ET occurs in the center of the valley, particularly in the playa, where limited to no vegetation occurs, but evaporation is relatively high because of a shallow water table and silty clay soil capable of large capillary movement. Intermediate values of ET are associated with large shrubs and is dominated by transpiration. quent weathering, mass movement and fluvial transport processes are superimposed on the mega-geomorphic processes to produce the current landscape. Geomorphologic evolution is closely coupled with the hydrologic cycle. Moisture and temperature are the two most important aspects of climate in controlling the physical, chemical, and biochemical processes that govern landscape development. Tempera-0169-555X/98/$19.00 ¢)
JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, a... more JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship.
Landsat TM images are used to define soil sealing in the Madrid Autonomous Region (Spain) between... more Landsat TM images are used to define soil sealing in the Madrid Autonomous Region (Spain) between 1989 and 2010. The comparison of images within this time frame obtains an estimated loss of 42,800 ha of agricultural land. Soil sealing mainly occurred in metropolitan areas as well as in the E, SW and NW of the capital. Landsat images overlaid on agrological classification maps and soil association maps show that the most significant losses correspond to agricultural classifications B and C and luvisols associated with cambisols, regosols, calcisols and fluvisols .
JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, a... more JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship.
Salinization and alkalinization are the most important land degradation processes in central Iran... more Salinization and alkalinization are the most important land degradation processes in central Iran. In this study we modelled the vertical and lateral variation of soil salinity (measured as electrical conductivity in saturation paste, ECe) using a combination of regression tree analysis and equal-area smoothing splines in a 72,000 ha area located in central Iran. Using the conditioned Latin hypercube sampling method, 173 soil profiles were sampled from the study area, and then analysed for ECe and other soil properties. Auxiliary data used in this study to represent predictive soil forming factors were terrain attributes (derived from a digital elevation model), Landsat 7 ETM + data, apparent electrical conductivity (ECa)-measured using an electromagnetic induction instrument (EMI), and a geomorphologic surfaces map. To derive the relationships between ECe (from soil surface to 1 m) and the auxiliary data, regression tree analysis was applied. In general, results showed that the ECa surfaces are the most powerful predictors for ECe at three depth intervals (i.e. 0-15, 15-30 and 30-60 cm). In the 60-100 cm depth interval, topographic wetness index was the most important parameter used in regression tree model. Validation of the predictive models at each depth interval resulted in R 2 values ranging from 78% (0-15 cm) to 11% (60-100 cm). Thus we can recommend similar applications of this technique could be used for mapping soil salinity in other parts in Iran.
Variación de la repelencia al agua de los suelos en función de la profundidad, del contenido de h... more Variación de la repelencia al agua de los suelos en función de la profundidad, del contenido de humedad, de la posición topográfica y de la cobertura vegetal, en la cuenca del río Los Gavilanes, Veracruz.
The accelerated erosion that occurs after wildfire in forest ecosystems is a major factor affecti... more The accelerated erosion that occurs after wildfire in forest ecosystems is a major factor affecting the sustainability of these environments. Soil erosion prediction models are crucial for estimating fire impacts and for planning post-fire soil stabilization measures. We evaluated the performance of the Revised Universal Soil Loss Equation (RUSLE) and the Pan-European Soil Erosion Risk Assessment (PESERA) models by comparing the soil losses predicted by the models and the soil losses measured in the first year after wildfires in NW Spain. Two alternative RUSLE approaches were also considered: i) the use of reduction coefficients for R and C factors and ii) the application of a soil erodibility reduction factor to take into account soil surface stone cover. The RUSLE model overestimated the observed erosion rates in all cases. The PESERA model performed reasonably well in predicting soil losses, but needs a better characterization of soil burn severity in the soil inputs (crusting and erodibility) because of the high sensitivity of the model to these parameters. In both cases, there is an urgent need for an appropriate assessment of the impact on soil burn severity in the ero-sional process. This seems critical if these models are to be used for post-fire soil stabilization activities planning.
Conventional soil mapping is costly and time consuming. Therefore, the development of quick, chea... more Conventional soil mapping is costly and time consuming. Therefore, the development of quick, cheap, but accurate methods is required. Several studies highlight the importance of developing regional soil spectral libraries for digital soil mapping, but few studies report on the use of these libraries to aid digital mapping of soil types. This study aims to produce a digital soil map using as training set Visible and Near Infra-Red (Vis–NIR) spectra from local soil samples, a regional spectral library and terrain attributes. The soils were sampled in 162 locations on a 270-ha farm in the municipality of Piracicaba, São Paulo, Brazil. Spectra from topsoil and subsoil were measured in laboratory (400–2500 nm) and arranged as multi-depth spectra. Information was summarized by principal component analysis. Regression tree models were calibrated to predict principal components (PC) scores based on terrain attributes. After calibration, the models were applied to the entire study site, resulting in PC score maps. Fuzzy c-means and PC maps were used to define the soil mapping units (MU). Based on fuzzy cen-troids, representative samples (RS) were defined to the MU. Munsell soil color and soil order were predicted from soil spectra and used to characterize the MU. The regression tree model had a good fit for PC1, with an r 2 of 0.92, and a satisfactory r 2 for PC3, PC4, and PC5, respectively 0.58, 0.66 and 0.53. The fuzzy clustering defined seven MU. The R 2 for Munsell color predictions were 0.94 (hue), 0.96 (value) and 0.73 (chroma). Soil order had good agreement in validation, with kappa coefficient of 0.41. The methodology indicates the potential of Vis–NIR spectra to improve soil mapping campaigns and consequently provides a product similar to a conventional soil map.
A comprehensive assessment of soil organic matter (SOM) dynamics in semi-arid agrosystems impleme... more A comprehensive assessment of soil organic matter (SOM) dynamics in semi-arid agrosystems implementing soil and water conservation (SWC) measures is still lacking despite their extent, ecological and economic significance. Therefore, we assessed the long-term impact of a commonly used SWC technique (Fanya-juu terracing) on SOM-related properties in South Eastern Kenya. A soil sampling campaign was conducted in a replicated stratified random manner on three land uses that had been continuously managed for over 30 years. Samples were analyzed for organic carbon and nitrogen contents, δ 13 C, δ 15 N, pH and texture. Compared to sites implementing conventional agriculture, the establishment of SWC structures in this erosion-prone landscape resulted in the recovery of SOM levels comparable to those observed in neighboring semi-natural ecosystems. Sites under conventional agriculture practices contained 20 Mg C ha −1 (0.85 m), while sites with SWC measures and those hosting semi-natural vegetation stored above a third more. There were significant differences in soil C/N ratios as well as in δ 13 C and δ 15 N values between SWC cultivation practices classified according to the presence or absence of trees. The presence of woody vegetation in sites with SWC structures had a strong impact on the spatial variability of SOM-related properties. There was also a significant negative relationship between δ 15 N values and C/N ratios across the different land uses. Our findings indicate the existence of contrasting SOM dynamics caused by vegetation-related effects, and provide suggestions for enhancing SOM storage in agricultural sites implementing SWC measures.
El potencial del magnetismo en la clasificación de suelos: una revisión 365 Resumen Los óxidos de... more El potencial del magnetismo en la clasificación de suelos: una revisión 365 Resumen Los óxidos de hierro se encuentran en todos los suelos en cantidades diversas, son utilizados como indicadores de procesos pedogenéticos y reflejan los ambientes de formación del suelo. Por otro lado, estos componentes afectan las propiedades y funciones de los suelos, porque influyen en el color, la agregación, la capacidad de intercambio de cationes y la retención de fósforo en el suelo. Una característica de los óxidos de hierro es la formación de minerales ferrimagnéticos; debido a esto, y mediante la utilización de técnicas magnéticas se pueden identificar algunos procesos pedogenéticos. El objetivo de este trabajo es la descripción de las propiedadesmagnéticas de los suelos y sus aplicaciones potenciales, como parámetros proxy, en la clasificación de suelos. Se mencionan los portadores magnéticos, las propiedades magnéticas, las formas de análisis y sus aplicaciones. Las propiedades magnéticas de los suelos permiten identificar y cuantificar los minerales magnéticos y relacionarlos con los procesos pedogenéticos. Las mediciones magnéticas son fáciles de realizar y son de bajo costo, lo cual permite realizar el análisis de un gran número de muestras, con lo que es posible la elaboración de mapas de suelos, la identificación de procesos pedogenéticos y el mejoramiento de la clasificación de suelos. En países con alta diversidad edáfica como México, es posible la generación de nuevos tipos de aplicaciones de las propiedades magnéticas de los suelos.
An understanding of the nature of soils in natural and human influence ecosystems is essential if... more An understanding of the nature of soils in natural and human influence ecosystems is essential if progress is to be made in the determination and monitoring of soil quality. This paper briefly discuss the changing circumstances which have resulted in the increased awareness of the importance of soil as a key component of both natural and human influenced environmental systems. These changed circumstances and the recognition of the often crucial role that soils play within these systems has resulted in a demand for measures of soil quality, similar to those used in the characterisation of water and air. Whilst it is relatively straightforward to quantify the fitness to breathe of air, or the fitness to drink with respect to water, it is considerably more difficult to identify similar criteria to be applied to soil. In part this arises because of the wide range of uses to which soils are put and in part because of the complexity of the soil system and the possibility that changes in the soil may be slow and possibly only occur when some threshold is reached. The paper stresses that any index of soil quality must consider soil function, and these functions are varied and often complex. A soil which is considered to be of high quality for one function may not be so for other functions. As a consequence there are potentially many soil properties which might serve as indicators of soil quality, and research is required to identify the most suitable. It may also be questioned whether it is appropriate to pursue this notion of a limited number of soil quality indicators. If an indicator is chosen comparisons between sites and results from different investigators are not possible unless the methods of analysis are given. Standard methods of soil analysis must be developed. These standard methods are distinct from national standard values, or indicator values frequently set by national governments or regulatory agencies. The International Standardisation Organisation (ISO) has developed a programme for the development of standard methods of soil analysis across a range of possible properties including visible, biological, physical and chemical properties. If it is decided that soil quality indicators are required and progress is to be made on developing acceptable indicators of soil quality with agreed standard methods of determination there is need for co-operation amongst soil scientists and those involved in determining appropriate and sustainable use of the soil.
Precise soil quality assessment is critical for designing sustainable agriculture policies, resto... more Precise soil quality assessment is critical for designing sustainable agriculture policies, restoring degraded soils, carbon (C) modeling, and improving environmental quality. Although the consequences of soil quality reduction are generally recognized, the spatial extent of soil degradation is difficult to determine, because no universal equation or soil quality prediction model exists that fits all ecoregions. Furthermore, existing soil organic C (SOC) models generate estimates with uncertainties that may exceed 50%. Therefore it is possible that drastic changes in soil quality may be occurring in sites which are not identifiable on existing maps. Soil quality can either be directly inferred from SOC concentration, or through the assessment of the soil physical, chemical and biologic properties. Assessing the spatial distribution of SOC over large areas requires the calibration and development of models derived from laboratory or field based techniques. However, mapping SOC concentration in all soils is logistically challenging by using normal standard survey techniques. The availability of new generations of remotely sensed datasets and geographical information system (GIS) models (i.e. GEMS, RothC, and CENTURY) provides new opportunities for predicting soil properties and quality at different spatial scales. This article discusses the current approaches, identifies gaps and proposes improvements in techniques for measuring soil quality within agricultural fields.
The purpose of this study was to analyze the relationships between soil attributes and environmen... more The purpose of this study was to analyze the relationships between soil attributes and environmental covariates in a tropical hillslope environment on a regional scale to estimate spatial distribution of soil attributes and identify statistical and geostatistical techniques that could represent the variation of the soil attributes. The study was performed in Bom Jardim County, Brazil, and covered an area of 390 km 2 with a soil database of 208 sample points distributed in six depth layers (0.53 pts/km 2 ). The study used 18 environmental covariates derived from DEM and satellite imagery. The models evaluated were linear regression, regression trees and ordinary and regression kriging. An exploratory analysis showed that DEM, NDVI, MRVBF, MSP, b3/b2, b5/b7, SPI, SWI, SLOPE and ASPECT were correlated with soil properties. The models performance had a mean crossvalidation r 2 of 0.13. The best results were achieved with kriging models, with a crossvalidation r 2 of 0.19. A comparison between multiple linear regression and regression trees showed that the tree model yielded the best results. The sample density alone could not explain the results, but an interaction between DEM accuracy, sample density, covariates and geological conditions was suitable as an explanatory factor. Studies of tropical hillslope digital soil mapping on regional scales need to be more exhaustively focused to develop this research area.
Landsat thematic mapper data are used to estimate instantaneous regional-scale surface water and ... more Landsat thematic mapper data are used to estimate instantaneous regional-scale surface water and energy fluxes in a semi-arid Great Basin desert of the western United States. Results suggest that it is possible to scale from point measurements of environmental state variables to regional estimates of water and energy exchange. This research characterizes the unif3'ing thread in the classical climate-topography-soil-vegetation relation --the surface water and energy balance--through maps of the partitioning of energy throughout the landscape. The study was conducted in Goshute Valley of northeastern Nevada, which is characteristic of most faulted graben valleys of the Basin and Range Province of the western United States. The valley comprises a central playa and lake plain bordered by alluvial fans emanating from the surrounding mountains. The distribution of evapotranspiration (ET) is lowest in the middle reaches of the fans where the water table is deep and plants are small, resulting in low evaporation and transpiration. Highest ET occurs in the center of the valley, particularly in the playa, where limited to no vegetation occurs, but evaporation is relatively high because of a shallow water table and silty clay soil capable of large capillary movement. Intermediate values of ET are associated with large shrubs and is dominated by transpiration. quent weathering, mass movement and fluvial transport processes are superimposed on the mega-geomorphic processes to produce the current landscape. Geomorphologic evolution is closely coupled with the hydrologic cycle. Moisture and temperature are the two most important aspects of climate in controlling the physical, chemical, and biochemical processes that govern landscape development. Tempera-0169-555X/98/$19.00 ¢)
JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, a... more JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship.
Landsat TM images are used to define soil sealing in the Madrid Autonomous Region (Spain) between... more Landsat TM images are used to define soil sealing in the Madrid Autonomous Region (Spain) between 1989 and 2010. The comparison of images within this time frame obtains an estimated loss of 42,800 ha of agricultural land. Soil sealing mainly occurred in metropolitan areas as well as in the E, SW and NW of the capital. Landsat images overlaid on agrological classification maps and soil association maps show that the most significant losses correspond to agricultural classifications B and C and luvisols associated with cambisols, regosols, calcisols and fluvisols .
JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, a... more JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship.
Salinization and alkalinization are the most important land degradation processes in central Iran... more Salinization and alkalinization are the most important land degradation processes in central Iran. In this study we modelled the vertical and lateral variation of soil salinity (measured as electrical conductivity in saturation paste, ECe) using a combination of regression tree analysis and equal-area smoothing splines in a 72,000 ha area located in central Iran. Using the conditioned Latin hypercube sampling method, 173 soil profiles were sampled from the study area, and then analysed for ECe and other soil properties. Auxiliary data used in this study to represent predictive soil forming factors were terrain attributes (derived from a digital elevation model), Landsat 7 ETM + data, apparent electrical conductivity (ECa)-measured using an electromagnetic induction instrument (EMI), and a geomorphologic surfaces map. To derive the relationships between ECe (from soil surface to 1 m) and the auxiliary data, regression tree analysis was applied. In general, results showed that the ECa surfaces are the most powerful predictors for ECe at three depth intervals (i.e. 0-15, 15-30 and 30-60 cm). In the 60-100 cm depth interval, topographic wetness index was the most important parameter used in regression tree model. Validation of the predictive models at each depth interval resulted in R 2 values ranging from 78% (0-15 cm) to 11% (60-100 cm). Thus we can recommend similar applications of this technique could be used for mapping soil salinity in other parts in Iran.
Variación de la repelencia al agua de los suelos en función de la profundidad, del contenido de h... more Variación de la repelencia al agua de los suelos en función de la profundidad, del contenido de humedad, de la posición topográfica y de la cobertura vegetal, en la cuenca del río Los Gavilanes, Veracruz.
Soil salinity is a global threat to agricultural production and necessitates the monitoring there... more Soil salinity is a global threat to agricultural production and necessitates the monitoring thereof. Saline conditions in South African irrigation schemes generally occur in small patches (some only a few metres in diameter) and this study, which forms part of a Water Research Commission (WRC) project, evaluates the use of very high resolution (VHR) satellite imagery, in particular those produced by the WorldView 2 (WV2) sensor, for the detection of salt accumulation in irrigated areas. A range of features derived from the WV2 image were evaluated, namely 8 WV2 bands, 10 vegetation indices (VIs), 25 texture measures and 2 principle component analysis (PCA) components. These features were generated at six spatial resolutions (0.5 m, 2 m, 6 m, 10 m, 15 m and 20 m) to investigate the value of high spatial resolution for detecting affected areas. The relationships between the image features and electric conductivity measurements of 30 soil samples were studied by means of regression analysis and classification and regression tree (CART) modelling. The regression analysis results showed that a spatial resolution of 6 m or higher is ideal when VIs are used as input. When texture measures are used, higher spatial resolution (0.5 m) produced better models. The regression results also showed that the relatively high spectral resolution of the WV2 sensor (compared to other VHR sensors) did not provide a significant improvement in accuracy. The CART results provided a categorical quantification of salt accumulation and showed that VIs generated at 0.5 m resolution were the best features to use. Because the use of WV2 images is not financially viable for operational use in very large irrigation schemes, the study concludes with some guidelines on the spectral and spatial requirements of images when monitoring salt accumulation in irrigation schemes with similar conditions.
This paper highlights the impacts of soil sealing on agricultural soils in Nile Delta using remot... more This paper highlights the impacts of soil sealing on agricultural soils in Nile Delta using remote sensing and GIS. The current work focuses on two aims. The first aim is to evaluate soil productivity lost to urban sprawl, which is a significant cause of soil sealing in Nile Delta. The second aim is to evaluate the Land Use and Land Cover Changes (LU LC) from 2001 to 2014 in El Gharbia governorate as a case study. Three temporal data sets of images from two different sensors: Landsat 7 Enhanced Thematic Mapper (ETM+) with 30 m resolution acquired in 2001 and Landsat 8 acquired in 2013 with 30 m resolution, and Egypt sat acquired in 2010 with 7.8 m resolution, consequently were used. Four different supervised classification tech niques (Maximum Likelihood (ML), Minimum Distance, Neural Networks (NN); and Support Vector Machine (SVM) were applied to monitor the changes of LULC in the investigated area. The results showed that the agricultural soils of the investigated area are characterized by the moderate to high productivity depending on the soil chemical and physical properties. In 2001-2010, 3992 ha of highly productive soils were lost from agriculture because of soil sealing. In 2010-2013, soil sealing took place on 1397 ha. It is expected that the area under constructions will increase by 12.4% by 2020, which means that additional 3400 ha of productive soils will be lost from agriculture.
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Papers by Geovanni A.O