A Geographically-Based Land Use Suitability Assessment and Land Capability Classification

Transcription

1 A Geographically-Based Land Use Suitability Assessment and Land Capability Classification Item Type text; Proceedings Authors Cruz, Rex Victor O.; Ffolliott, Peter F. Publisher Arizona-Nevada Academy of Science Journal Hydrology and Water Resources in Arizona and the Southwest Rights Copyright, where appropriate, is held by the author. Download date 11/07/ :06:51 Link to Item

2 A GEOGRAPHICALLY -BASED LAND USE SUITABILITY ASSESSMENT AND LAND CAPABILITY CLASSIFICATION Rex Victor O. Cruz and Peter F. Ffolliott School of Renewable Natural Resources University of Arizona Tucson, Arizona Introduction Land capability classification generally refers to the description and classification of lands relative to their biophysical features and ability to sustain various kinds of uses. The USDA Soil Conservation Service land capability classification guide is perhaps the most popular system ever developed so far (Klingebiel and Montgomery, 1961; and Brakensiek, et al., 1979). It has been modified and used in other countries including Israel, the Philippines, and Zimbabwe (Hudson, 1981). In spite of its popularity, however, the USDA guide is based only on agronomic land uses and is qualitative in nature. Classification systems developed in recent years are more quantitative in nature as the understanding of the relationships among the different factors influencing soil productivity and stability increases. For example, Larson et al. (1988) and Warren et al. (1989) developed classification systems based on estimated measures of productivity, soil resistivity, and soil erosion, respectively. Land use suitability assessment is defined here as the measurement and rating of the impacts of a land use on the productivity and stability of an area. The impacts could be measured in terms of actual volume of production, decline in soil fertility, and the amount of soil erosion. It is the purpose of this paper to describe a methodology for land use suitability assessment and land capability classification based on estimated soil loss using the modified universal soil loss equation or MUSLE (Williams, 1975), with the aid of a geographic information system (GIS). The GIS Soil erosion is influenced by many factors which are geographic in nature, such as topography, soil properties, vegetation, and land use. As such, the use of a GIS would enhance the accuracy of soil erosion estimation. By dividing an area into smaller cells, the significance of geographic 51

3 characteristics which would otherwise be diminished as a result of parameter lumping when the area is treated as one who'e unit is preserved. Through the years, GIS has been defined in several ways (Burrough, 1986; Berry, 1986; Parker, 1988). It has been defined as a set of tools, a technology, and an automated spatial information system capable of processing spatial or geographically- referenced information. Most GIS are capable of data input, data storage and retrieval, data manipulation and analysis, and data reporting. Essentially, GIS can be applied to any activity where spatial considerations are important, and where large quantities of data need to be processed and reprocessed over a number of times. It has found many uses in natural resources management, economics and marketing, regional and urban planning, and engineering. Works by Berry and Sailor (1987), Gilliland and Potter(1987), Vasconcelos (1988), and Warren et al. (1989) are examples of the many uses of different kinds of GIS. In this study, Map Analysis Package (MAP) developed by Tomlin (1986) was used. Study Area The study area is located in the Ibulao Watershed, Philippines. It is a 65,000 -ha subwatershed of Magat River Basin which supports vast areas of agricultural lands. Existing land uses in Ibulao includes, forestry, grazing, and agriculture. The climate generally is humid, with an average annual rainfall of 2,200 mm. Soils are mostly clay loam, with topography ranging from flat to very steep and rugged terrain. Assessment Method There are four basic components to the methodology described in this paper, namely MUSLE, capability classification, land use suitability assessment, and GIS (Figure 1). Other components, RAINGEN, CREAMS, and IRSX are incidental to MUSLE for the estimation of the runoff factor. The study area generally was divided into 10 -ha cells. For the entire watershed, different data overlays, such as slope map, soils map, elevation map, and land use map, were prepared using MAP. From the data overlays, the features and parameters needed in the estimation of soil erosion, capability classification, and land use suitability 52

4 RAINGEN - organize and analyze rainfall data - simulate rainfall duration CREAMS - perform water budget analysis - compute for the soil moisture IRSX - simulate surface runoff volume and the peak runoff rate from each cell MUSLE - estimate the soil erosion from each cell (for each storm event, annual) CAPABILITY CLASSIFICATION - calculate the erosion index for each cell - classify cells into capability classes based on soil erosion index i SUITABILITY ASSESSMENT - evaluate the suitability of each landuse based on current legal land classification, LCCG of the Philippine Bureau of Soils, and soil erosion GIS Operations MAP - organize and create source data base maps - create derived maps (soils, slope, elevation, landuse maps) classify the Ibulao watershed into cells of homogenous soil, slope, and landuse features - estimate soil erosion from each cell - create output overlays (surface runoff and soil erosion maps) evaluate suitability of landuse for each cell based on present legal land classification, LCCG, and soil erosion create suitabililty maps based on present legal land classification, LCCG, and soil erosion - create erodibility map based on soil erosion index of each cell create capability map based on soil erodibility map IDRISI - refine and organize output overlays - print out copies of source maps, derived maps, and output maps Figure 1. Schematic Representation of the Generation and Flow of Information. 53

5 assessment for each cell were extracted. In turn, the results from these components were passed back to MAP for processing the various output overlays. MUSLE The general form of the MUSLE is shown as: E = 11.8 (Q *q)0.56 K L S C P where E is soil loss (Mg /ha) estimated as the product of runoff factor consisting of surface runoff depth (Q) and peak flow (q), soil erodibility factor (K), topography factor (LS), soil cover factor (C), and conservation practice factor (P). The factors KLSCP were estimated using the procedures described by Williams and Berndt (1972), Wischmeier and Smith (1978), Dissmeyer and Foster (1980), and David (1985). A detailed description of the parameter estimation is presented by Cruz (1990). The surface runoff and peak flow for each cell were simulated using IRSX, which is a modification of the infiltration- kinematic routing program (IRS9) developed by the USDA -ARS (Stone and Shirley, 1985). Most of the parameters used by IRSX, such as soil porosity, hydraulic conductivity, depth- discharge coefficient, and soil moisture, also are geographic in nature. The soil moisture for each cell was estimated by the hydrology component of CREAMS, a field -scale model for chemical, Runoff, and erosion from Agricultural Management aystems model (Knise1,1980). Likewise, most of the soil related parameters used in CREAMS simulation are geographic in nature. Land Capability Classification Land capability classification is based on soil erosion index (I). For each cell, the erosion index was estimated by: I = [11.8 (Q*g)056 KLS] / T where T is the soil loss tolerance which represents the amount of annual soil erosion that can be sustained by an area without jeopardizing its long term productivity. (The value of T usually ranges between 2.2 and 11.2 t /ha depending upon 54

6 the locally intrinsic rate of soil formation and soil depth; in this case, T was assumed to be 20 t /ha /year). The capability class of a cell was identified using the estimated average annual erosion index (Table 1 and Figure 2). The different land use recommendations were determined by solving for the maximum CP value (using the erosion index equation) for a given capability that would yield an annual soil erosion value not greater than the tolerance limit. Land Use Suitability Assessment Land use suitability in each cell was evaluated on the basis of the estimated annual soil erosion. A land use in a cell was rated suitable if the average annual soil erosion is less than or equal to the tolerance limit. Otherwise, land use was rated unsuitable (Table 2 and Figure 3). Applications The land use suitability assessment and land capability classification procedure described could be useful in identifying the land use most suitable to a given area. It also could provide a method of examining the status of existing land uses as far as impacts on soil productivity is concerned. It could be used to identify which existing land uses need to be changed or if they cannot be changed, the tool could help identify what kind of measures need to be taken to mitigate the adverse impacts of such land uses. Finally, the method would be instrumental in determining the different intensities of land uses that would best complement the capability of an area. Recommendations An erosion -based land capability classification and land use suitability assessment is only as useful as the soil erosion estimation component. Therefore, improvements in the existing tools for soil erosion estimation or development and use of methods applicable to the area of concern always should be considered. The accuracy of results obtained from the method described in this paper also is largely a function of the accuracy of the different input or source maps. In overlaying 55

7 Table 1. Erosion -Based Land Capability Classification of Ibulao Watershed. Capability class Erosion index Area (10ha) Maximum allowable CP Recommended landuse unrestricted grassland,rice terrace /paddy, agroforestry grassland,rice terrace /paddy, limited agroforestry rice terraces, limited agroforestry with conservation measures, forestry rice terrace, forestry limited rice terrace, forestry 7 > limited to forestry

8 ,,,,`.,.., class 1 INM LEGEND class 5 ;,.'';..,,:,. ii i: class 2 MIND class 6 class 3 class 7 - class 4 - Figure 2, Erosion -Based Land Capability Classification of Ibulao Watershed. 57

9 Table 2. Erosion Classes Coverage Under Different Land Uses in Ibulao Watershed. (The values represent the number of 10 -ha cells). Present Landuse Erosion Class TOTAL Forest mossy closed canopy open canopy Rice padd /terr Open grass * 112* 116* 103* 583* 1278 Diverse crop * 126* 260* 251* 707 Nonvegetated * 14* 2* 11* 95 Residential 8 1* 4* 26* 39 Mainstream TOTAL * Rated unsuitable based on the average annual soil erosion loss.

10 LEGEND suitable - unsuitable Figure 3. Landuse Suitability Map of Ibulao Watershed Based on Soil Erosion. 59

11 several maps, format considerations, such as map resolution and projection, should be appropriately defined to keep the joint probability of coincidence the same at all locations. The success of using geographically -based tools for land use suitability assessment and land capability classification also would depend upon the adequacy of definition and quantification of the different spatial relationships involved. For example, the relationship between neighboring cells as far as surface runoff generated from each cell should be clearly defined and represented to obtain satisfactory prediction or simulation results. References Cited Berry, J.K Learning computer assisted map analysis. J of Forestry 84(10): Berry, J.K., and J.K. Sailor Use of geographic information system for storm runoff prediction from small urban watersheds. Environ. Management 11(1): Brakensiek, D.L.; H.B. Osborn; and W.J. Rawls Field manual for research in agricultural hydrology. USDA -SEA, Washington D.C. Burrough, P.A Principles of geographical systems for land resources assessment. Clarendon Press, Oxford. Cruz, R.V.O Landuse suitability assessment and land capability classification in Ibulao watershed, Philippines. Ph D Dissertation, University of Arizona, Tucson, Arizona. David, W.P Erosion and sediment transport. Upland Resource Policy Program. Technical Report Series No Philippine Institute for Development Studies. Quezon City, Philippines. Dangermond, J., and C. Killpack Mapping for the future. Landscape Architecture. July /August: Dissmeyer, G.E., and G.R. Foster A guide for predicting sheet and rill erosion on forest land. Technical Publication R8 -TP 6. USDA -FS Southern Region. Atlanta, GA. 60

12 Foster, G.R., L.J. Lane, J.D. Nowlin, J.M. Laflen, and R.A. Young A model to estimate sediment yield from field -sized areas: Development of model. In CREAMS: A field scale model for chemicals, runoff, and erosion from agricultural management systems. Vol 1. Model Documentation, Ch 3. USDA Conser. Res. Report No.26. Gilliland, M.W. and W. Potter A geographic information system to predict non -point source pollution potential. Water Resources Bulletin 23(2): Hudson, N Soil conservation. Batsford Academic and Education, Ltd., London. Klingebiel, A.A., and P.H. Montgomery Land capability classification. USDA Agricultural Handbook 210. Knisel, W.G., Ed CREAMS: a field -scale model for chemicals, runoff, and erosion from agricultural management systems. USDA Conservation Research Report No. 26. Larson, G.A., G. Roloff, and W.E. Larson A new approach to marginal agricultural land classification. J of Soil and Water Conservation. 43: Marble, D.F., and S.E. Amundson Microcomputer -based geographic information systems and their role in urban and regional planning. Environmental and Planning B: Planning and Design. 15: Mein, R.G., and C.L. Larson Modeling infiltration during a steady rain. Water Resources Res. 9: Parker, D.H The unique qualities of geographic information system: A commentary. Photogrammetric Eng. and Remote Sensing. 54: Smith, D.D., and W.H. Wischmeier Factors affecting sheet and rill erosion. Trans. Amer. Geophys. Union. 38: Stone, J.J.; L.J. Lane; and E.D. Shirley Program IRS, model documentation. Personal communication. Stone, J.J., and E.D. Shirley Unpublished model documentation of IRS9. USDA -ARS. Tucson, AZ. 61

13 Tomlin, D.C The IBM personal computer version of the Map Analysis Package. Laboratory for Computer Graphics and Spatial Analysis. Harvard Graduate School of Design, Harvard University. Van Roessel, J.W Guidelines for forestry information processing with particular reference to developing countries. Food and Agricultural Organization- United Nations. Rome. Vasconcelos, M Simulation of fire behavior with a geographic information system. Masters Thesis. SRNR, University of Arizona, Tucson, AZ. Warren, S.D., V.E. Diersing, P.J. Thompson, and W.D. Goran An erosion -based land classification system for military installations. Environ. Management. 13: Welch, R., and M.M. Remillard Remote sensing and geographic information system techniques for aquatic resource evaluation. Photogrammetric Eng. and Remote Sensing. 54: Williams, J.R Sediment yield predicted with universal equation using runoff energy factor. In: Present and Prospective Technology for Predicting Sediment and Resources. USDA- ARS -S- 40: Williams, J.R. and H.D. Berndt Sediment yield computed with universal equation. Proc. of the Amer. Soc. of Civil Eng. Journal of the Hydraulics Division. HY 12. Wischmeier, W.H Use and misuse of the universal soil loss equation. Jo. of Soil and Water Conservation. 31:5-9. Wischmeier, W.H.; and D.D. Smith Predicting rainfall erosion losses. Agric. Handbook 537. USDA - SEA -ARS. 62