Gis erosion risk assessment of the piracicaba river basin, southeastern brazil
TL;DR: In this paper, remote sensing data, a GIS, and the Universal Soil Loss Equation model (USLE) are used to develop maps of erosion risk in the Piracicaba River basin, southeastern Brazil.
Abstract: Remote sensing data, a GIS, and the Universal Soil Loss Equation model (USLE) are used to develop maps of erosion risk in the Piracicaba River basin, southeastern Brazil. The mapping program was designed to determine soil erosion losses under various land uses and the extent to which land use changes affected erosion risk during 1978–1993. To evaluate the latter, the USLE model was used to simulate erosion risk during January, the highest-precipitation month, in 1978 and 1993. This made it possible to identify the areas of highest erosion risk and to develop soil/water conservation countermeasures.
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TL;DR: In this paper, a review of water erosion assessment using satellite remote sensing is presented, which comprises the detection of erosion features and eroded areas, as well as the assessment of off-site impacts such as sediment deposition and water quality of inland lakes.
Abstract: Water erosion creates negative impacts on agricultural production, infrastructure, and water quality across the world. Regional-scale water erosion assessment is important, but limited by data availability and quality. Satellite remote sensing can contribute through providing spatial data to such assessments. During the past 30 years many studies have been published that did this to a greater or lesser extent. The objective of this paper is to review methodologies applied for water erosion assessment using satellite remote sensing. First, studies on erosion detection are treated. This comprises the detection of erosion features and eroded areas, as well as the assessment of off-site impacts such as sediment deposition and water quality of inland lakes. Second, the assessment of erosion controlling factors is evaluated. Four types of factors are discussed: topography, soil properties, vegetation cover, and management practices. Then, erosion mapping techniques are described that integrate products derived from satellite remote sensing with additional data sources. These techniques include erosion models and qualitative methods. Finally, validation methods used to assess the accuracy of maps produced with satellite data are discussed. It is concluded that a general lack of validation data is a main concern. Validation is of utmost importance to achieve regional operational monitoring systems, and close collaboration between the remote sensing community and field-based erosion scientists is therefore required.
387 citations
Cites background from "Gis erosion risk assessment of the ..."
..., 1997; Cihlar, 1987), large watersheds of more than 10,000 km (Cerri et al., 2001; Ma et al., 2003; Mati et al., 2000), the country scale for Morocco (Gay et al....
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...More common though are erosion surveys, in which rill dimensions are measured (Cerri et al., 2001; Mathieu et al., 1997), or which are limited to a visual field estimation of erosion risk based on observed features and erosion factors that depend on the study region (e....
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TL;DR: In this paper, the authors applied the Revised Universal Soil Loss Equation (RUSLE), remote sensing, and geographical information system (GIS) to the maping of soil erosion risk in Brazilian Amazonia.
Abstract: This article discusses research in which the authors applied the Revised Universal Soil Loss Equation (RUSLE), remote sensing, and geographical information system (GIS) to the maping of soil erosion risk in Brazilian Amazonia. Soil map and soil survey data were used to develop the soil erodibility factor (K), and a digital elevation model image was used to generate the topographic factor (LS). The cover-management factor (C) was developed based on vegetation, shade, and soil fraction images derived from spectral mixture analysis of a Landsat Enhanced Thematic Mapper Plus image. Assuming the same climatic conditions and no support practice in the study area, the rainfall–runoff erosivity (R) and the support practice (P) factors were not used. The majority of the study area has K values of less than 0·2, LS values of less than 2·5, and C values of less than 0·25. A soil erosion risk map with five classes (very low, low, medium, medium-high, and high) was produced based on the simplified RUSLE within the GIS environment, and was linked to land use and land cover (LULC) image to explore relationships between soil erosion risk and LULC distribution. The results indicate that most successional and mature forests are in very low and low erosion risk areas, while agroforestry and pasture are usually associated with medium to high risk areas. This research implies that remote sensing and GIS provide promising tools for evaluating and mapping soil erosion risk in Amazonia. Copyright © 2004 John Wiley & Sons, Ltd.
317 citations
Cites methods from "Gis erosion risk assessment of the ..."
...…(Millward and Mersey, 1999; Reusing et al., 2000; Ma et al., 2003), while GIS tools were used for derivation of the topographic factor from DEM data, data interpolation of sample plots, and calculation of soil erosion loss (Cerri et al., 2001; Bartsch et al., 2002; Wang et al., 2003)....
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...Different approaches have been used to assess the soil erosion risk, including empirical erosion models (Boggs et al., 2001; Cerri et al., 2001; Bartsch et al., 2002), a ranking method based on selected indicators such as percentage of bare ground, aggregate stability, organic carbon, percentage…...
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TL;DR: Wang et al. as mentioned in this paper applied the Revised Universal Soil Loss Equation (RUSLE), remote-sensing technique, and geographic information system (GIS) to map the soil erosion risk in Miyun Watershed, North China.
Abstract: This paper applied the Revised Universal Soil Loss Equation (RUSLE), remote-sensing technique, and geographic information system (GIS) to map the soil erosion risk in Miyun Watershed, North China. The soil erosion parameters were evaluated in different ways: the R factor map was developed from the rainfall data, the K factor map was obtained from the soil map, the C factor map was generated based on a back propagation (BP) neural network method of Landsat ETM+ data with a correlation coefficient (r) of 0.929 to the field collected data, and a digital elevation model (DEM) with a spatial resolution of 30 m was derived from topographical map at the scale of 1:50,000 to develop the LS factor map. P factor map was assumed as 1 for the watershed because only a very small area has conservation practices. By integrating the six factor maps in GIS through pixel-based computing, the spatial distribution of soil loss in the upper watershed of Miyun reservoir was obtained by the RUSLE model. The results showed that the annual average soil loss for the upper watershed of Miyun reservoir was 9.86 t ha−1 ya−1 in 2005, and the area of 47.5 km2 (0.3%) experiences extremely severe erosion risk, which needs suitable conservation measures to be adopted on a priority basis. The spatial distribution of erosion risk classes was 66.88% very low, 21.90% low, 6.19% moderate, 2.90% severe, and 1.84% very severe. Among all counties and cities in the study area, Huairou County is in the extremely severe level of soil erosion risk, about 39.6% of land suffer from soil erosion, while Guyuan County in the very low level of soil erosion risk suffered from 17.79% of soil erosion in 2005. Therefore, the areas which are in the extremely severe level of soil erosion risk need immediate attention from soil conservation point of view.
153 citations
Cites methods from "Gis erosion risk assessment of the ..."
...…classifications (Millward and Mersey 1999; Reusing et al. 2000; Ma et al. 2003), while GIS tools were used for derivation of the topographic factor from DEM data, data interpolation of sample plots, and calculation of soil erosion loss (Cerri et al. 2001; Bartsch et al. 2002; Wang et al. 2003)....
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...2003), while GIS tools were used for derivation of the topographic factor from DEM data, data interpolation of sample plots, and calculation of soil erosion loss (Cerri et al. 2001; Bartsch et al. 2002; Wang et al. 2003)....
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TL;DR: In this article, the influences of biomass burning emissions in the composition of aerosol have been studied during 1 year around the city of Piracicaba (Southeastern Brazil), in which inhalable particles, separated in PM2.5 and coarse particulate mode (CPM), with size in the range (2.4 to 2.5) were analyzed.
128 citations
TL;DR: The magnitude of the soil erosion was estimated in terms of the different soil units and land uses and the most erosion-prone areas where irreversible soil losses occurred were reasonably located in the Kazan watershed, which could be very useful for deciding restoration practices to control the soil degradation of the sites to be severely influenced.
Abstract: The Universal Soil Loss Equation (USLE) is an erosion model to estimate average soil loss that would generally result from splash, sheet, and rill erosion from agricultural plots. Recently, use of USLE has been extended as a useful tool predicting soil losses and planning control practices in agricultural watersheds by the effective integration of the GIS-based procedures to estimate the factor values in a grid cell basis. This study was performed in the Kazan Watershed located in the central Anatolia, Turkey, to predict soil erosion risk by the USLE/GIS methodology for planning conservation measures in the site. Rain erosivity (R), soil erodibility (K), and cover management factor (C) values of the model were calculated from erosivity map, soil map, and land use map of Turkey, respectively. R values were site-specifically corrected using DEM and climatic data. The topographical and hydrological effects on the soil loss were characterized by LS factor evaluated by the flow accumulation tool using DEM and watershed delineation techniques. From resulting soil loss map of the watershed, the magnitude of the soil erosion was estimated in terms of the different soil units and land uses and the most erosion-prone areas where irreversible soil losses occurred were reasonably located in the Kazan watershed. This could be very useful for deciding restoration practices to control the soil erosion of the sites to be severely influenced.
120 citations
References
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Book•
01 Jan 1978
TL;DR: The Universal Soil Loss Equation (USLE) as discussed by the authors is a model designed to predict the average rate of soil erosion for each feasible alternative combination of crop system and management practices in association with a specified soil type, rainfall pattern and topography.
Abstract: The Universal Soil Loss Equation (USLE) enables planners to predict the average rate of soil erosion for each feasible alternative combination of crop system and management practices in association with a specified soil type, rainfall pattern, and topography. When these predicted losses are compared with given soil loss tolerances, they provide specific guidelines for effecting erosion control within specified limits. The equation groups the numerous interrelated physical and management parameters that influence erosion rate under six major factors whose site-specific values can be expressed numerically. A half century of erosion research in many states has supplied information from which at least approximate values of the USLE factors can be obtained for specified farm fields or other small erosion prone areas throughout the United States. Tables and charts presented in this handbook make this information available for field use. Significant limitations in the data are identified. The USLE is an erosion model designed to compute longtime average soil losses from sheet and rill erosion under specified conditions. It is also useful for construction sites and other non-agricultural conditons, but it does not predict deposition and does not compute sediment yields from gully, streambank, and streambed erosion
6,947 citations
"Gis erosion risk assessment of the ..." refers background or methods in this paper
...13 m long) and on a 9% slope, in continuous bare fallow, tilled up and down slope (Wischmeier and Smith, 1978)....
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...The C factor represents the ratio of soil loss from land cropped under specific conditions to the corresponding loss from continuous fallow conditions (Wischmeier and Smith, 1978)....
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...To map areas of soil erosion risk, we adopted the Universal Soil Loss Equation (USLE) (see Equation 1) (Wischmeier and Smith, 1978)....
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Book•
01 Dec 1995
TL;DR: Introductory Digital Image Processing: A Remote Sensing Perspective focuses on digital image processing of aircraft- and satellite-derived, remotely sensed data for Earth resource management applications.
Abstract: For junior/graduate-level courses in Remote Sensing in Geography, Geology, Forestry, and Biology. Introductory Digital Image Processing: A Remote Sensing Perspective focuses on digital image processing of aircraft- and satellite-derived, remotely sensed data for Earth resource management applications. Extensively illustrated, it explains how to extract biophysical information from remote sensor data for almost all multidisciplinary land-based environmental projects. Part of the Pearson Series Geographic Information Science. Now in full color, the Fourth Edition provides up-to-date information on analytical methods used to analyze digital remote sensing data. Each chapter contains a substantive reference list that can be used by students and scientists as a starting place for their digital image processing project or research. A new appendix provides sources of imagery and other geospatial information.
5,478 citations
"Gis erosion risk assessment of the ..." refers methods in this paper
..., 1991) Landsat 5 Thematic Mapper images, using the maximum likelihood method (Jensen, 1996)....
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TL;DR: In this article, the authors describe elevation data sources, digital elevation model structures, and the analysis of digital elevation data for hydrological, geomorphological, and biological applications.
Abstract: The topography of a catchment has a major impact on the hydrological, geomorphological. and biological processes active in the landscape. The spatial distribution of topographic attributes can often be used as an indirect measure of the spatial variability of these processes and allows them to be mapped using relatively simple techniques. Many geographic information systems are being developed that store topographic information as the primary data for analysing water resource and biological problems. Furthermore, topography can be used to develop more physically realistic structures for hydrologic and water quality models that directly account for the impact of topography on the hydrology. Digital elevation models are the primary data used in the analysis of catchment topography. We describe elevation data sources, digital elevation model structures, and the analysis of digital elevation data for hydrological, geomorphological, and biological applications. Some hydrologic models that make use of digital representations of topography are also considered.
2,855 citations
TL;DR: Although this rate of deforestation is lower than previous estimates, the effect on biological diversity is greater and tropical forest habitat, severely affected with respect to biological diversity, increased.
Abstract: Landsat satellite imagery covering the entire forested portion of the Brazilian Amazon Basin was used to measure, for 1978 and 1988, deforestation, fragmented forest, defined as areas less than 100 square kilometers surrounded by deforestation, and edge effects of 1 kilometer into forest from adjacent areas of deforestation. Tropical deforestation increased from 78,000 square kilometers in 1978 to 230,000 square kilometers in 1988 while tropical forest habitat, severely affected with respect to biological diversity, increased from 208,000 to 588,000 square kilometers. Although this rate of deforestation is lower than previous estimates, the effect on biological diversity is greater.
1,574 citations
Journal Article•
TL;DR: In this paper, a new soil particle-size PARAMETER was found and used to devise a convenient ERODIBILITY EQUATION that is suitable for exposed subsoil as well as FARMLAND.
Abstract: A NEW SOIL PARTICLE-SIZE PARAMETER WAS FOUND AND USED TO DERIVE A CONVENIENT ERODIBILITY EQUATION THAT IS VALID FOR EXPOSED SUBSOILS AS WELL AS FARMLAND. A SIMPLE NOMOGRAPH PROVIDES QUICK SOLUTIONS TO THE EQUATION. ONLY FIVE SOIL PARAMETERS NEED TO BE KNOWN: PERCENT SILT, PERCENT SAND, ORGANIC MATTER CONTENT, STRUCTURE, AND PERMEABILITY. THE NEW WORKING TOOL OPENS THE DOOR TO SEVERAL NEW CONSIDERATIONS IN SEDIMENT- CONTROL PLANNING. /AUTHOR/
872 citations