Remote Sensing of Soils for Environmental Assessment and Management.

The next generation of imaging systems integrated with complex analytical methods will revolutionize the way we inventory and manage soil resources across a wide range of scientific disciplines and application domains. This special issue highlights those systems and methods for the direct benefit of environmental professionals and students who employ imaging and geospatial information for improved understanding, management, and monitoring of soil resources

Emergent Imaging and Geospatial Technologies for Soil Investigations

Soil survey investigations and inventories form the scientific basis for a wide spectrum of agronomic and environmental management programs. Soil data and information help formulate resource conservation policies of federal, state, and local governments that seek to sustain our agricultural production system while enhancing environmental quality on both public and private lands. The dual challenges of increasing agricultural production and ensuring environmental integrity require electronically available soil inventory data with both spatial and attribute quality. Meeting this societal need in part depends on development and evaluation of new methods for updating and maintaining soil inventories for sophisticated applications, and implementing an effective framework to conceptualize and communicate tacit knowledge from soil scientists to numerous stakeholders

A5. Identifying Susceptible Areas for Gully Erosion Using a Geospatial Analysis

Many studies have noted that gully erosion, the severe stage of soil erosion, has become one of the most challenging environmental problems restricting the long term productivity agriculture and water quality in developing countries. Even though several soil and water conservation practices have been implemented, the effects are far below expectations mainly due to lack of information to identify vulnerable areas for gully erosion. In this study, we specifically tested reliability of the topographic wetness index (TWI) to predict areas sensitive to gully erosion where saturation excess overland flow controls the erosion process. We used Debre Mewi watershed 30 km south of Lake Tana in the head waters of the Blue Nile where upland erosion takes place and gullies are actively forming in downhill locations. Wells were installed to measure groundwater table depths in the gully and in surrounding areas to assess the influence of subsurface flow on gully formation. Using geospatial analysis, TWI was correlated with ground water table depths during rainy months and can be used to estimate gully susceptibility in the studied region when data availability is limited

Spatial Prediction of Soil Organic Matter Content Using Cokriging with Remotely Sensed Data

Accurately measuring soil organic matter content (SOM) in paddy fields is important because SOM is one of the key soil properties controlling nutrient budgets in agricultural production systems. Estimation of this soil property at an acceptable level of accuracy is important; especially in the case when SOM exhibits strong spatial dependence and its measurement is a time- and labor-consuming procedure. This study was conducted to evaluate and compare spatial estimation by kriging and cokriging with remotely sensed data to predict SOM using limited available data for a 367-km(2) study area in Haining City, Zhejiang Province, China. Measured SOM ranged from 5.7 to 40.4 g kg(-1), with a mean of 19.5 g kg(-1). Correlation analysis between the SOM content of 131 soil samples and the corresponding digital number (DN) of six bands (Band 1-5 and Band 7) of Landsat Enhanced Thematic Mapper (ETM) imagery showed that correlation between SOM and DN of Band 1 was the highest (r= -0.587). We used the DN of Band I as auxiliary data for the SOM prediction, and used descriptive statistics and the kriging standard deviation (STD) to compare the reliabilities of the predictions. We also used cross-validation to validate the SOM prediction. Results indicate that cokriging with remotely sensed data was superior to kriging in the case of limited available data and the moderately strong linear relationship between remotely sensed data and SOM content. Remotely sensed data such as Landsat ETM imagery have the potential as useful auxiliary variables for improving the precision and reliability of SOM prediction

Symposium no. 38 Paper no. 608 Presentation: oral Watershed-scale terrain analysis for determining

As in other regions, individual states in the Northeast Region of the USA have developed indices to assess the risk of diffuse loss of phosphorus (P) and nitrogen (N) to surface waters at the field scale. Many of these indices have identified the soil and management conditions that are of state-specific importance for characterizing the potential for nutrient movement in landscapes. Some indices distinguish between the various modes of transport (erosion, runoff and leachate), assigning risk values for the various transport pathways. This study combines geographical information, nutrient indices and nutrient application data for identifying and ranking watersheds and larger geographical areas in the Northeast Region of the USA with respect to potential for nutrient export to streams. Concepts such as the delivery ratio (DR) developed by Draper et al. (1979) and the availability factor, an estimate of the proportion of nutrients that move into runoff from surface-applied manure (Heatwole and Shanholtz, 1991), are used in combination with soil drainage class and other soil parameters that influence risk of nutrient loss. This index approach to terrain analysis for assessing the environmental status of watersheds is a much simpler alternative to complex hydrologic modeling. Where water-monitoring data and modeling results are available, such as in the intensely studied Cannonsville Reservoir Basin, in Delaware County, NY, this index approach to terrain analysis may be calibrated and tested. Terrain analysis results show the potential for application of the index approach for selecting priority areas for implementing best management practices or enrollment in programs like the Conservation Reserve Program (Giasson et al., 2002). Comparisons across different physiographic re..

Ecosystem impacts of disturbance in a dry tropical forest in southern India

Indian forests provide a multitude of services to vast populations. Common human activities including livestock grazing, fuelwood extraction and burning have the potential to impact forest ecosystem structure and function. The effects of these activities on vegetation, ecology and soil properties were investigated in Bandipur National Park (BNP) in southern India. Data were collected from 200 sites in four watersheds within the park. Sample sites spanned a degradation gradient measured by a field disturbance index (FDI). This paper focusses on the impacts on vegetation structure, diversity and composition, and integrates impacts on soil. Shrub and tree species were inventoried and evaluated in plots 10-m in diameter. The tree layer was dominated by Anogeissus–Emblica–Tectona species. The understory was dominated by invasives Chromolaena odorata and Lantana camara, and native Gymnosporia emarginata. Vegetation plot heights, canopy cover and tree diameters were negatively correlated with field disturbance resulting in stunted forest stature in degraded sites. Vegetation composition in degraded watersheds was dominated by small woody tree species and a greater diversity of shrub species. Ordination analysis was used to integrate soil data with vegetation and disturbance, revealing that deciduous forest in the park is degrading to scrub forest along with negative impacts on soil characteristics. Consequences of services currently enjoyed by local populations are discussed. Copyright © 2008 John Wiley & Sons, Ltd

B4. Improving Risk Estimates of Runoff Producing Areas: Formulating Variable Source Areas as a Bi-variate Process

Predicting runoff producing areas and their corresponding risks is important for developing watershed management strategies for protecting water quality from nonpoint source pollution. However, the currently proposed engineering methods to do this do not account for antecedent soil wetness status, which may substantially impact risk estimates, especially where variable source area (VSA) hydrology is a dominate storm runoff process. The objective of this study is to develop a simple approach to estimate spatially-distributed risks of runoff production. By considering the development of overland flow as a bivariate process, we incorporated both rainfall and antecedent soil moisture conditions into a method for predicting VSAs based on the Natural Resource Conservation Service-Curve Number equation. We used base flow immediately preceding storm events as an index of antecedent soil wetness status. Using the data from a study hillslope near Ithaca, NY, we demonstrated that our estimates agreed with independent field-observations. We further applied the proposed approach to the Upper Susquehanna River Basin and mapped predicted saturated areas in a Geographic Information System (GIS) using a Soil Topographic Index to demonstrate large-scale applicability and identify potential issues of the approach. The proposed methodology provides a new tool to watershed planners for quantifying runoff risks across watersheds, which can be used to target water quality protection strategies

Impacts of disturbance on soil properties in a dry tropical forest in Southern India

Grazing, fuelwood extraction and burning are common human activities in Indian forests. These activities can represent forest disturbances that drive the degradation of natural deciduous forest cover to scrub forest, with concurrent impacts on soils. The effects of human forest use on ecosystem functions were investigated in Bandipur National Park (BNP) in Peninsular India. This paper reports the impacts on surface soils. Soils were sampled from 200 locations covering four watersheds within the Park. These samples spanned a degradation gradient measured by a field disturbance index (FDI). Soil physical, nutrient and hydraulic properties were measured. Cation exchange capacity (CEC) and saturated hydraulic conductivity (Ks) were analyzed as key response variables describing nutrient availability and infiltration respectively. Effects of cattle and jeep trails on infiltration and bulk density were evaluated by sampling on-and-off trails. Trail density in research watersheds was estimated with satellite imagery. Soil nutrient availability is negatively impacted by disturbance, resulting from negative impacts on soil organic carbon (SOC) and clay content. Available water capacity (AWC) and saturated moisture content (SMC) were significantly higher in protected watersheds, attributed to reduced clay content in degraded watersheds. Off trails, high spatial variability in infiltration overwhelmed any meaningful trends with disturbance. However, infiltration was substantially reduced on trails as a result of significant increase in bulk density. The density of trails was considerably higher in degraded watersheds compared to protected watersheds. These results provide ground-based and remotely sensed evidence that forest disturbance within the Park has negative impacts on soil organic matter, nutrient availability and hydraulic characteristics. These have consequences for related ecological, nutrient cycling and hydrological processes, and the continuation of the services currently enjoyed by local human populations. Copyright 2008 John Wiley & Sons, Ltd

Estimating soil organic carbon stocks and spatial patterns with statistical and GIS-based methods.

Accurately quantifying soil organic carbon (SOC) is considered fundamental to studying soil quality, modeling the global carbon cycle, and assessing global climate change. This study evaluated the uncertainties caused by up-scaling of soil properties from the county scale to the provincial scale and from lower-level classification of Soil Species to Soil Group, using four methods: the mean, median, Soil Profile Statistics (SPS), and pedological professional knowledge based (PKB) methods. For the SPS method, SOC stock is calculated at the county scale by multiplying the mean SOC density value of each soil type in a county by its corresponding area. For the mean or median method, SOC density value of each soil type is calculated using provincial arithmetic mean or median. For the PKB method, SOC density value of each soil type is calculated at the county scale considering soil parent materials and spatial locations of all soil profiles. A newly constructed 1∶50,000 soil survey geographic database of Zhejiang Province, China, was used for evaluation. Results indicated that with soil classification levels up-scaling from Soil Species to Soil Group, the variation of estimated SOC stocks among different soil classification levels was obviously lower than that among different methods. The difference in the estimated SOC stocks among the four methods was lowest at the Soil Species level. The differences in SOC stocks among the mean, median, and PKB methods for different Soil Groups resulted from the differences in the procedure of aggregating soil profile properties to represent the attributes of one soil type. Compared with the other three estimation methods (i.e., the SPS, mean and median methods), the PKB method holds significant promise for characterizing spatial differences in SOC distribution because spatial locations of all soil profiles are considered during the aggregation procedure