GIS and Remote Sensing Tools for Prevention of Agricultural Soil Degradation

Transcription

1 Angela Possinger NRS 509 Applications of GIS and Remote Sensing December 4, 2012 GIS and Remote Sensing Tools for Prevention of Agricultural Soil Degradation I. Overview In order to meet the food requirements of a growing and increasingly urbanized global population, the United Nations Food and Agriculture Organization (UN FAO) predicts the need for a 70% increase in food production worldwide, with a 100% increase in developing countries (UN FAO, 2009). As a result of increased food demand, more land area for production and higher yield per land area will be required, which may result in agricultural intensification (e.g. increased tillage and shift to annual vs. perennial cropping) and encroachment on marginal lands. These changes may ultimately result in accelerated soil degradation. In general, risks of soil degradation are greater in developing countries particularly those in arid climates due to both the relative magnitude of increased food demand, and current soil health status. In order to meet both food demands and maintain soil resources, the 2012 World Food Prize Winner, Daniel Hillel, calls for sustainable intensification increased production using innovative management technologies that also allows for selective use of non marginal lands (Fischer, 2012). The prediction of land suitability is a process that can be greatly enhanced using Geographic Information Systems (GIS) and remote sensing (RS) tools (Samrongpong et al., 2009). Soil degradation effects include accelerated erosion, nutrient depletion, salinization, and depletion of soil organic matter (SOM), among many other related effects (Diodato and Ceccarelli, 2004). In general, accelerated soil degradation is a result of combined physical, chemical, and biological parameters of soil, and depends on many different processes and conditions: among the most influential are terrain variables (e.g. slope, landscape position, aspect), soil textural class (percentage sand, silt, and clay), and SOM content. GIS and RS can be used to evaluate these variables in two primary ways: 1) estimation of numerical or categorical data using pre existing databases (e.g. Digital Elevation Models, DEM) and interpretation of aerial or satellite imagery; and 2) interpolation, integration, and analysis of collected data points for visual representation of the spatial distribution and relationships among variables. With the identification of high risk regions, soil degradation can be prevented more systematically, including support and encouragement of alternative agricultural techniques, as well as selection of high risk areas for land conservation programs (Samrongpong et al., 2009). A frequently employed model for prediction of soil degradation risk (particularly erosion) is the Universal Soil Loss Equation (USLE), or the Revised Universal Soil Loss Equation (RUSLE) (Lu et al., 2004; Lufafa et al., 2003). The USLE model is represented by the simplified equation: A = R x K x LS x CP, where A=estimated soil loss (t ha 1 year 1 ), R=rain erosivity, K=soil erodibility, LS=slope steepness and length, and CP=cover and management practices. Each of the USLE variables can be estimated in a number of ways; traditionally, erosion test plots were required for specific soil types, but GIS and RS tools can be used as a more efficient estimation technique that can be applied over large land areas. For instance, Zhang et al. (2012) estimated

2 the LS factor using a DEM dataset for a region of Beijing, China, as well as the estimation of terrain factors such as overall elevation, slope, aspect, and wetness/stream power indices. Additionally, Lu et al. (2004) estimated land use/land cover (LU/LC) in the Brazilian Amazonia region using LandSat 7 ETM+ imagery and a maximum likelihood classifier (MLC) method, corroborated by ground truthing at points for each LU/LC type (mature forest, successional forest, agroforestry, pasture, urban, and water). Numerous studies have emphasized the value of integrating the basic USLE model with GIS and RS tools, in order to strengthen the analysis process, include auxiliary variables, and increase the value of the model over heterogeneous landscapes of larger size (Zhang et al., 2012; Lu et al., 2004; Lufafa et al., 2003). In addition to USLE based modeling, a method increasing in popularity is the selection of essential indicators that effectively represent soil degradation risk. In general, indicator variables are useful due to experimentally and statistically confirmed correlation between the variable and predictable environmental results, allowing for simplified and cost effective analysis. A predominant indicator for soil degradation risk is SOM content (Zhang et al., 2012; Marchetti et al., 2012). SOM is a critical variable that affects many soil properties through complex physical, biological and chemical processes; in particular, SOM is correlated with physical factors such as aggregate stability and available water capacity, both important for soil degradation risk. Additionally, the increased rate of SOM cycling that offer occurs as a result of increased intensification (e.g. tillage) has implications for not only maintenance of soil health, but also for control and evaluation of greenhouse gas (e.g. CO 2 ) production from soil (Marchetti et al., 2012). Using SOM as an indicator may involve several difference GIS analysis strategies: among the most predominant are probabilistic interpolation (e.g. Kriging interpolation) of field point data, with inclusion of multiple auxiliary variables that enhance the strength of interpretations and inclusions. For instance, Zhang et al. (2012) evaluated SOM content at multiple georeferenced field sites, and incorporated terrain indices, components of the USLE, and additional categorical auxiliary variables (e.g. land use types, soil texture, and soil genetic types). Zhang et al. (2012) interpolated SOM point data using ordinary Kriging (OK), multiple linear stepwise regression (MLSR), and regression Kriging (RK) to achieve a continuous dataset, and statistically evaluated relationships among auxiliary variables to generate a final integrated index. Integrated indices, when supported by strong correlation among indicator variables, serve as simplified and effective tools for communication to the public, and consequently may be an improved means of informing targeted management and land conservation decisionmaking. Incorporation of socioeconomic factors in land suitability determination results in multilevel evidence and support for encouragement of conservation practices. Samrongpong et al. (2009) integrated soil physical factors, crop requirements, and socioeconomic data (e.g. crop prices, farmer income, etc.) in a custom GIS interface for a unified land suitability recommendation for Northern Thailand. In my opinion, it is integrated studies such as this that reflect the future applications for GIS in soil quality assessment, particularly when based on appropriate and relevant indicators (e.g. SOM content). The computing and analysis capabilities of GIS make possible the evaluation of many components, and when empirical evidence supports the selection of factors with the greatest potential influence, a streamlined and efficient analysis procedure can be completed. In my thesis research, I evaluate multiple

3 indicator variables for overall assessment of soil quality on a relatively small scale, and have selected soil quality variables for which experimental and agronomic support is relatively recent (e.g. active carbon (C) and potentially mineralizable nitrogen (N), indicators of biological integrity and nutrient supply). Consequently, integrated evaluation of soil degradation risk may be improved in the future as new indicators develop, some of which may be particularly applicable across diverse ecosystems. II. Literature Cited Diodato, N., and M. Ceccarelli Multivariate indicator Kriging approach using a GIS to classify land degradation for Mediterranean agricultural lands. Ecological Indicators 4: Fischer, M World food prize winner: Member Daniel Hillel awarded the Nobel Prize of agriculture. CSA News 57(11):4 8. Lu, D., G. Li, G.S. Valldares, and M. Batistella Mapping soil erosion risk in Rondônia, Brazilian Amazonia: Using RUSLE, remote sensing, and GIS. Land Degradation and Development 15: Lufafa, A., M. M. Tenywa, M. Isabirye, M.J.G Majaliwa, and P.L. Woomer Prediction of soil erosion in a Lake Victoria basin catchment using a GIS based Universal Soil Loss model. Agricultural Systems 76: Marchetti, A., C. Piccini, R. Francaviglia, and L. Mabit Spatial distribution of soil organic matter using geostatistics: a key indicator to assess soil degradation status in Central Italy. Pedosphere 22(2): Samranpong, C., B. Ekasingh, and M. Ekasingh Economic land evaluation for agricultural resource management in Northern Thailand. Environmental Modeling and Software 24: UN FAO High Level Expert Forum How to Feed the World in UN FAO Agricultural Development Economics Division, Economic and Social Development Department, Rome, Italy. Available from World_ii_2050.pdf. Zhang, S., Y. Huang, C. Shen, H. Ye, and Y. Du Spatial prediction of soil organic matter using terrain indices and categorical variables as auxiliary information. Geoderma :35 43.

4 III. Annotated Bibliography Diodato, N., and M. Ceccarelli Multivariate indicator Kriging approach using a GIS to classify land degradation for Mediterranean agricultural lands. Ecological Indicators 4: The susceptibility of soil to degradation is can be predicted by the integration of many physical, chemical, and biological variables, though the complex interactions among soil properties can limit the development of effective analysis methods. Geographic Information Systems (GIS) and geostatistics can be employed to improve the ease of spatially evaluating multiple variables. Diodato et al. (2004) aimed to integrate several measurements (e.g. susceptibility to water erosion, aridity, and soil depth) into a single indicator of soil degradation risk for Mediterranean agricultural lands, followed by interpolation using a probabilistic approach (e.g. Kriging interpolation). Variables were subdivided into geomorphologic, bioclimatic, and pedologic categories, including analysis of specific parameters such as subsoil exposure, degree of aridity, and topsoil depth, respectively. Due to the lack of normally distributed data as a result of spatial heterogeneity, multivariate indicator Kriging (MVIK), a non parametric interpolation method, was used. The relative influence of variables shifted as a result of land area size for evaluation; for instance, at short range, depth of topsoil was a critical influence, and at longerrange, climate characteristics more strongly influenced soil degradation risk. The authors constructed maps highlighting regions where soil can withstand no further degradation (i.e. high susceptibility to degradation) and regions where adverse consequences are less likely to occur (i.e. sustainable soil). Overall, this paper offers useful insight into a process that could be adapted based on regional characteristics to develop an integrated indicator for soil degradation. Additionally, results can be easily communicated via susceptibility maps to stakeholders and land managers. Lu, D., G. Li, G.S. Valldares, and M. Batistella Mapping soil erosion risk in Rondônia, Brazilian Amazonia: Using RUSLE, remote sensing, and GIS. Land Degradation and Development 15: The Revised Universal Soil Loss Equation (RUSLE), which builds on the original USLE, is a frequently used model for predicting soil erosion risk, but is often limited in geographic scope due to its reliance on collection of erosion test plot data. Lu et al. (2004) assert that GIS and RS analytical techniques complement the RUSLE model. In this study, the authors evaluated erosion risk in the Brazilian Amazonia, a region in which soil degradation by deforestation is increasing. The authors employed a simplified RUSLE model, wherein Soil Erosion Risk (A) = K x LS x C, and K=soil erodibility, LS=slope steepness and length, and C=cover and management practices. GIS and RS procedures were used to determine estimate each component, as well as determine the spatial distribution of erosion risk. For instance, land use/land cover (LU/LC) in the region (factor C) was determined from the LandSat 7 ETM+ image using the maximum

5 likelihood classifier (MLC) method and ground truthed at points for each LU/LC type (mature forest, successional forest, agroforestry, pasture, urban, and water). The authors conclude that the combined approach used in this study is a useful procedure for large areas with little existing soil erosion risk data. However, rapid changes in land cover, particularly in the Brazilian Amazonia, may necessitate the use of multitemporal satellite imagery. Additionally, the quality and format of data from multiple sources may require careful evaluation and correction for accurate modeling. Lufafa, A., M. M. Tenywa, M. Isabirye, M.J.G Majaliwa, and P.L. Woomer Prediction of soil erosion in a Lake Victoria basin catchment using a GIS based Universal Soil Loss model. Agricultural Systems 76: Lufafa et al. (2003) discuss design and implementation of an analytical procedure for assessment of soil erosion risk in the Lake Victoria basin (Uganda) using the widespread Universal Soil Loss Equation (USLE) model, supported by GIS analytical technologies. The USLE model is represented by the simplified equation: A = R x K x LS x CP, where A=estimated soil loss (t ha 1 year 1 ), R=rain erosivity, K=soil erodibility, LS=slope steepness and length, and CP=cover and management practices. For each of these parameters, various spatial analyses were employed. Values for R, K, LS, and CP were estimated and geographically referenced using: 1) contour interpolation of annual precipitation in combination with Erosivity Index data, 2) soil order and generic soil property mapping (1:10,000 scale), 3) point interpolation of equal square gradient values to construct a Digital Elevation Model (DEM), and 4) interpretation of aerial photo and Landsat TM imagery, respectively. In particular, land cover and management practices were assessed using a Normalized Difference Vegetation Index. In the Lake Victoria basin, the authors determined highest erosion risk for annual croplands, and no erosion risk for forest or papyrus marsh. Backslope landscape positions were also associated with increased erosion risk. Overall, the authors conclude that the USLE model is improved by GIS data manipulation, input, and display techniques. With emphasis on estimating soil erosion risk over large areas with little existing soil property data, this paper has value for developing an appreciation for the application of simplified models (e.g. USLE), supported by GIS analyses. However, many of the procedures employed in this study may have more sophisticated modern counterparts (e.g. manual equal grid estimation of slope and elevation vs. NIMA Digital Terrain Elevation Data for Uganda), so may be better used as a historical and contextual reference than as a template for current erosion risk assessment analyses. Marchetti, A., C. Piccini, R. Francaviglia, and L. Mabit Spatial distribution of soil organic matter using geostatistics: a key indicator to assess soil degradation status in Central Italy. Pedosphere 22(2): Marchetti et al. (2012) discuss the application of soil organic matter (SOM) point data as an indicator for soil degradation risk. In contrast to soil degradation risk models (e.g. the RUSLE) that primarily incorporate soil physical factors, the authors emphasize the collection and interpolation of point data for a small number of specific properties with particularly important implications for soil health and susceptibility to degradation. SOM is associated with improved

6 aggregate stability and available water capacity, important factors for erosion prevention. The rate of SOM cycling with associated reversal of C sequestration and increased CO 2 production is often increased with higher levels of disturbance, and in terms of agronomic practices, with increased intensity of cultivation. In this study, the authors generated a georeferenced database of SOM, soil textural class and C:N ratio point data for soil systems (recurring groups of soils in the same landscape position) in Central Italy. Point data were interpolated using ordinary Kriging (OK), quantifying both the spatial data variability and the correlation among analyzed parameters. Overall, the parameters displayed spatial structure, and the authors determined that combined SOM, textural class, and C:N data provides more meaningful information for prediction of degradation risk than SOM data alone. While a relatively straightforward approach to land evaluation, the rationale supporting and details of each analysis step are thoroughly described in this paper (e.g. the mathematics behind the OK analysis). For users with limited exposure to the methods, this explanation may be a valuable resource; in contrast, the ease of reading and interpreting this paper could be limited by the extent of detail presented. Samranpong, C., B. Ekasingh, and M. Ekasingh Economic land evaluation for agricultural resource management in Northern Thailand. Environmental Modeling and Software 24: Prediction of land suitability for crop production has long incorporated calculation of estimated crop yields, usually specific to a certain crop type. Samranpong et. al (2009) explore methods of incorporating multiple factors in the land suitability determination process, including socioeconomic and soil degradation parameters, for an improved system of land assessment in Northern Thailand. The authors use a custom GIS interface based on Boolean logic fuzzy set methods, characterized by a membership function, wherein an object s degree of membership (in this case, similarity to land suitability index data points) ranges from 1 (full membership) to 0 (least membership). In order to construct a continuous spatial dataset, land mapping units (LMU) were created by overlaying basic land evaluation parameters (e.g. soil types, rainfall, temperature). LMU were integrated with land use requirements (LRU) for certain crops (e.g. irrigation and pesticide requirements) to generate a final physical suitability index based on overall land quality. Socioeconomic data were collected through farmer surveys (e.g. materials, yields, costs, crop prices, etc.) and spatially interpolated into economic input zones using Voroni polygons and analyzed using EconSuit software. Ultimately, the assessment method was determined to be useful for predicting suitability on a crop by crop basis, and also reflected changes in suitability over time (e.g. soybean suitability shift from inundated to dry season conditions). Additionally, the economic evaluation component, resulting in a visual representation of spatial analysis, may be an effective tool for communication of land planning information to farmers and decision makers. The benefit of the author s land suitability analysis is a strengthened argument for avoidance of marginal lands susceptible to soil degradation, due to combined economic and physical factors. In this paper, the authors emphasize the details of the socioeconomic analyses employed, with less emphasis on details of the land suitability interpretations; as such, the paper may apply more strongly as a supplement to integrated GISbased agroecological and socioeconomic analyses.

7 Zhang, S., Y. Huang, C. Shen, H. Ye, and Y. Du Spatial prediction of soil organic matter using terrain indices and categorical variables as auxiliary information. Geoderma : Zhang et al. (2012) investigated methods of improving soil degradation risk projection using soil organic matter (SOM) as a primary indicator. In particular, the authors focused on the selection of auxiliary variables that, in combination, provide optimal integrated information. Auxiliary variables evaluated in this study include terrain indices and categorical variables (e.g. land use type, textural class, and soil genetic type). Land evaluation was conducted in a Miyun County, Beijing, China. For SOM analysis, bulk samples were collected at multiple locations across land use and soil types, and were processed (sieved and ground) for extraction using potassium dichromate wet combustion. Terrain variables were obtained from a 25 m spatial resolution DEM of the study area, and included elevation, slope, aspect, wetness and stream power indices, and the length slope factor (LS) of the RUSLE (see Lu et al above). Categorical variables evaluated include land use types, soil texture, and soil genetic types. For SOM interpolation, ordinary Kriging (OK), multiple linear stepwise regression (MLSR), and regression Kriging (RK) were applied using GIS software. Relationships among SOM, terrain indices, and categorical variables were analyzed using the Least Significant Difference (LSD) and Pearson correlation analysis. Overall, the authors determined that the spatial distribution of SOM was jointly affected by the parameters evaluated. For instance, SOM content was predictably low for sand soil textures, and high for loam. However, the strength of correlation with SOM varied for individual parameters, so the authors emphasize the need for exploratory analyses before selection of optimal auxiliary variables. This study succinctly and clearly summarizes the methods employed, and is a reflection of current methods in the use of spatial analysis for soil degradation risk.