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A rule-based soil erosion model for a hilly catchment
Catena 37 Ž1999. 309–318 www.elsevier.comrlocatercatena
A rule-based soil erosion model for a hilly catchment J. Adinarayana a,) , K. Gopal Rao b, N. Rama Krishna a , P. Venkatachalam a , J.K. Suri a a
Centre of Studies in Resources Engineering, Indian Institute of Technology — Bombay, Powai, Mumbai 400076, India b Department of CiÕil Engineering, Indian Institute of Technology — Bombay, Powai, Mumbai 400076, India
Abstract Catchment resources, generated from multi-source data Žremotely-sensed, map- and groundbased systems., were input to a raster-based Geographical Information Systems ŽGIS. after geometrically co-registered to a standard grid Žpixel.. The GIS used was the PC-based, indigenously-developed package, called GRAM ŽGeo-Referenced Area Management.. A set of knowledge-based rules, for assessing the soil erosion of this heterogeneous hilly catchment, were formulated from the knowledge of the multi-disciplinary resource-experts and the knowledge of the local catchment resources, in addition to the field observations. This rule-based model, which is hopefully fast and cost-effective and without effect of the individual bias, helped in inferring the erosion intensity units that most likely to occur at any given pixel in the system. Finally, the catchment was grouped into four different erosion intensity units namely very severe, severe, moderate to severe and slight to moderate. The quantitative soil loss Žt hay1 yeary1 . ranges, estimated by USLE model by a spatial information analysis approach ŽGIS., were also computed: Ža. Very severe Ž) 50.: Žb. Severe Ž40–50.; Žc. Moderate to severe Ž20–40.; and Žd. Slight to moderate Ž- 20.. An integrated priority delineation survey, for taking up soilrwater conservation measures by Sediment Yield Index model, carried out in the catchment grouped the catchment into very high, high, medium and low priority classes. Apparently, the areal extent of high and moderate priority classes occupy about 80% of the catchment, which is closely following the areal extent of severe and moderate to severe erosion intensity unit categories by this rule-based model. The outcome of the results of this study will be immensely useful to monitor the dynamic changes in the catchment environment. The soil erosion information system thus generated, identifies the targeted problem areas for conservation planning and sustainable development of the catchment. Although it is an early airing of results obtained from this rule-based model, the introduction of multi-disciplinary expert’s informed opinion may provide an extension to the traditional methods
)
Corresponding author. Tel.: q91-22-5767689; Fax: q91-22-5783190; E-mail: [email protected]
0341-8162r99r$20.00 q 1999 Elsevier Science B.V. All rights reserved. PII: S 0 3 4 1 - 8 1 6 2 Ž 9 9 . 0 0 0 2 3 - 5
310
J. Adinarayana et al.r Catena 37 (1999) 309–318
of assessing the soil erosion statusrmagnitude which should enable andror evaluate different catchment management scenarios. q 1999 Elsevier Science B.V. All rights reserved. Keywords: Geographical Information Systems; Hilly catchment; Integrated resources unit; Multi-disciplinary knowledge-based rules; Rule-based approach; Soil erosion
1. Introduction Soil erosion, by water, is the resultant of interplay between catchment environmental factors such as soil, topography, drainage, rainfall and land use pattern, data upon which are available from different sourcesrsystems. Additional information through experience and knowledge of the multi-disciplinary experts also may be used for avoiding individual bias in assessing the soil erosion pattern of the area under interest. Hence, it is important to study the soil erosion by analysing these multi-source catchment resources with multi-disciplinary expertise in an integral manner. The problem is more serious with hilly heterogeneous catchments which are subject to heavy rainfall. The technology of Geographical Information Systems ŽGIS. provides a means of introducing information and knowledge from different sources in the decision-making process, and help in handling and manipulating the multi-source data. A catchment is the base unit for the study, being a complete hydrological and topographical unit. Many researchers have been reported in the literature on this catchment environment aspect using GIS ŽSpanner et al., 1983; De Roo et al., 1989—ANSWERS model; Bocco and Valenzuela, 1991; Young et al., 1994—AGNPS model.. Expert systems in the form of decision-rules have been used as a method of integrating multi-source data ŽMoore et al., 1983; Lee et al., 1987; Skidmore et al., 1991; Harris and Boardman, 1998.. The aim of this paper is to describe for natural resources scientists a classification method that interactively integrates conventional techniques, remote sensing, GIS and the interpreter’s expertise and knowledge. Particularly, our objective is to generate a GIS-assisted soil erosion map of a rain-infested heterogeneous hilly catchment in an integral manner avoiding any individual bias. 1.1. Background The approach was tested in a drainage basin of about 100 km2 in the difficult semi-arid, mountainous terrain of the Western Ghats Ž73845X –73855X E. 19816X –19823X N.. The catchment, which has a basaltic landscape, is located in the Western Plateau and Hill Agro-climatic Region of the Indian Peninsular. Sediments are deposited in the drainage ditches, and ultimately in Yedgaon reservoir, a terminus of drainage for the catchment. The siltation is largely due to poor soil conservation practices, scantily distributed vegetation cover, steep slopes and overall dry climate with an erratic monsoon rainfall, resulting in a high susceptibility to erosion. Cultivation Žcropping systems based on upland rice. is being introduced in the canyons extending into the steeper mountainous area with inadequate soil conservation practices. If erosion is not stopped, further increases in runoff, and sheet and gully erosion on
J. Adinarayana et al.r Catena 37 (1999) 309–318
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slopping ground will ultimately destroy the productive value of the land. Hence, a rule-based approach for assessing the soil erosion of this heterogeneous catchment is required for conservation planning.
2. Data sources Keeping in view the integrated approach to be adopted, the following data were used: Remotely-sensed data: Landsat-TM and IRS-1A LISS II-digital ŽComputer Compatible Tapes-CCTs. and visual ŽFalse Colour Composites-FCC. products of monsoon Žkharif. and post-monsoon Žrabi. agriculture seasons. U Survey of India ŽSOI. topographical maps at 1:250 000 and 1:50 000 scales. U Available literature and maps on various themes. U Meteorological data Žrainfall, temperature, etc... U Ground survey information on soil and other terrain characteristics of the sample sites of the watershed. U
3. Database construction The GIS used was the PC-based, indigenously developed, user-friendly package called Geo-Referenced Area Management ŽGRAM.. This GIS package is a spin-off from the Indian Department of Science and Technology sponsored project ‘Natural Resources Data Management Systems’, which aimed to generate computer-compatible, spatially oriented databases of natural resources and socio-economic parameters to facilitate area-specific micro-level planning. The GRAM package operates on a small and relatively low-budget computer configuration, and can be used as a viable tool for natural resources management, rural and urban planning, image analysis of remotely sensed data, watershed management, impact assessment studies, etc. GRAM has been built with seven major modules: input module, attribute link, analysis module, terrain module, image processing module, statistics module and print module. The software is arranged in a hierarchical structure with sub-menus at different levels. The capability of the system is being tested for regional applications, and work is in progress to add on further analysis functions and to interface well-known mathematical models for resources applications ŽSengupta et al., 1996.. The data sources were integrated into the GIS in a grid cell format. GRAM allows rapid integration of data from image and map sources. Digitisation, georeferencing and rasterization were done in the input module. The output of this module was used as the base for the analysis module to integrate and process on logical combinations and knowledge-based rules. The rules were constructed from the knowledge of the local terrain parameters and field checks, in addition to the author’s expertise in various fields. Integration of these rules were carried out with graphic Žspatial. database Žloose-coupled way of integration. in the analysis module of GRAM. Using the query support, the spatial database was analysed and the final soil erosion map was produced.
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The erosion intensity map and the results were the output of the print module. The general procedure followed during the analysis is shown schematically in Fig. 1. 3.1. Coordinate system A defined cell Žpixel. was chosen, which is the limit of the resolution of the system. Each cell has an exact location in space, related to the grid orientation and cell size and a list of assigned attributes. The information database were geometrically corrected, using a SOI topographic map base and resampled to a 30 m resolution Žof Landsat-TM.. A grid size of 515 = 492 pixels covered the whole catchment. 3.2. Catchment resources Accurate information on the existing landuse pattern and its spatial distribution is a prerequisite for any catchment management programme. With the advent of remote sensing, it has been possible to prepare this dynamic resource map at various levels of confidence. However, the applications of multi-spectral classification techniques in extracting vegetation classes has been inconsistent because of similarities in spectral responses ŽBocco and Valenzuela, 1991; Adinarayana et al., 1994.. The problem is more serious with the digital techniques under heterogeneous hilly terrain. The other problem in the collection of landuse data is the presence of clouds and their shadows during the
Fig. 1. Generalised processing flow for soil erosion information system of a hilly catchment.
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monsoon period. In addition, it is also important to map the agricultural landuse pattern of both monsoon Žkharif. and post-monsoon Žrabi. seasons together in the rural catchments. Therefore, multi-seasonal remotely-sensed images of Landsat-TM digital Žkharif—when most of the crops are matured and harvested. and IRS-LISS II FCC Žrabi —cloud-free images showing vegetation in the upland areas. were used and classified using the maximum likelihood classifier and standard visual interpretation techniques, respectively. In addition, multi-thematic maps of soil, slope and drainage were also used to generate multi-disciplinary knowledge-based rules to overcome the above inherent problems in the hilly terrain. These maps were input and stored as separate layers in the GIS for integrated analysis. The link between above GIS data layers and knowledge of the multi-disciplinary group of Scientists and the field checks is provided by rules. Since each satellite classification on its own was insufficient to discriminate all the necessary classes, the rules were constructed by relating the GIS data layers and the aggregated improved agricultural landuse pattern of the catchment to overcome some inherent problems, viz. to discriminate the vegetation categories and to identify the information classes under the cloudrshadow regions in kharif season ŽAdinarayana and Rama Krishna, 1996.. Given that all the data sources were now in a common coordinate system, a number of logical combinations could be performed on a per pixel basis to consolidate the agricultural landuse pattern of this heterogeneous highland. To give a few examples: Ža. If Landsat-TM ŽKharif class. s Thick Forest and IRS LISS-II ŽRabi class. s Thick Forest; Then class s Thick Forest. Žb. If Kharif class s Sparse Forest and Rabi class s Open Žwithrwithout scrub. Area or Degraded Pasture; Then class s Open Žwithrwithout scrub. Area or Degraded Pasture, respectively. Žc. If Kharif s Sparse Forest and Rabi s Cropped; Then class s Cropped. Žd. If Kharif s Cloud Cover and Rabi s Thick Forest; Then class s Thick Forest. Že. If Kharifq Rabi s Vegetation, Drainage densitys Low, Soil s Typic Chromusterts and slope s- 58; Then class s Cropped. These informed-opinions, codified in the form of ‘if–then’ type of rules, were used to refine the classification accuracy. Initially, the correspondence between the LISS II-based visual and TM-based digital classification maps was at best 45%. After applying the rules on the classification output, the agreement between the maps increased to 85–100% ŽAdinarayana and Rama Krishna, 1996.. In the process, the visual and digital classification techniques interacted to improve the classification system. Finally, the GIS-assisted improved landuse map consists of five major categories, which were selected from the conservation planning point of view: agriculture, thick forest, sparse forest, degraded pastures and open Žwithrwithout scrub. lands. The introduction of multi-disciplinary expert rules provides an extension to the traditional methods of satellite-derived classification which should enable more relevant, and accurate, landusercover map to be prepared. A collective approach comprising satellite data ŽIRS. and topographic maps were used to delineate physiographic units. The soil associations of each physiographic unit were established by studying soil profiles in the landscape and by random checking outside the sample strip areas, and finally classifying the soils according to an estab-
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lished soil taxonomy ŽSoil Survey Staff, 1975.. In the end, a soil map at 1:50 000 scale was prepared by combining the soil information in each delineated physiographic unit. The contours and high points were digitised from 1:50 000 SOI topomaps Ž20 m interval. to generate the Digital Elevation Model ŽDEM. and the slope map with the help of the GRAM package. The catchment was divided into three major slope steepness classes for the evaluation of soil erosion and priority sub-watersheds delineation survey: Ž1. gently sloping lands Ž0–88.; Ž2. moderately sloping lands Ž8–188.; and Ž3. steeply sloping lands Ž) 188.. Steeper slopes promote concentrated run-off and show exposed rocks in some places. Moderate slopes support mostly degraded pastures or open scrub lands and have moderate to high run-off potential. Gently sloping lands covered with moderately deep cultivated black soils have low run-off potential. Once the contours are digitised, GRAM allows one to categorise any number of slope steepness classes as needed. An isohyetal map was compiled manually from 10 years of mean annual rainfall data from six meteorological stations available in and around the catchment area. The catchment was divided into two rainfall zones: a - 1500 mm low rainfall zone, and a G 1500 mm high rainfall zone. The drainage network was generated from SOI toposheet Žof 1975. and the high order river course systems were updated from Landsat-TM imagery Žof 1989.. The drainage density Žtotal length of streamsrunit area. was calculated for each sub-watershed and the catchment was divided into low, medium and high drainage density zones. The proximity analysis was done to establish the buffer zone along the streams to demarcate the zones where erosion was likely to be important. The catchment was divided into 33 sub-watersheds, which are smaller hydrologic units and constitute individual tributaries of the lowest order, or a group of such tributaries. The area of each sub-watershed was planometrically computed and ranged from 1.4 to 7.0 km2 , which is considered as a viable working size for conservation planning. All the previously described thematic maps were digitised Žexcept the TM-based digitally classified data which will be in the raster form., rasterized and brought to a common coordinate system, which thus acted as database for GIS analysis to generate the erosion map. 4. Database analysis One of the most important characteristics of GIS is its capability for data analysis and spatial modelling. GRAM includes conventional GIS data analysis and manipulation capabilities, such as overlaying, reclassification, etc. The system incorporates a set of decision-rules in which several knowledge-based rules are used to evaluate and input data at different levels of abstraction. 4.1. Generation of integrated resources units Given that all the data layers are now spatially part of a common coordinate system, a number of useful combinations can be performed. The first step in this process is to
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create a basic unit by overlay analysis, and create Integrated Resources Unit ŽIRU.. Each IRU comprises the catchment resource data on soil, slope, landuse, rainfall distribution and drainage buffers for one grid cell Ž30 m.. One IRU has been taken as the basic strategic unit for assessing the soil erosion. Since they exhibit strong uniformity, they can all be expected to respond similarly to given intensities of human use and management strategies. Each IRU descriptor was derived from catchment factors superimposed as layers in a GIS. Adjacent sets of IRU attributes were then separated. However, unlike photomorphic boundaries traced from aerial photographs or satellite images, an IRU in this system cannot be printed in map form, so each pixel was geocoded to a standard SOI topomap. In other words, the IRU can be ‘delineated’ in spatial domains using digital data and computer processing, since each pixel acts as an IRU. Taking all the catchment environmental factors and their attributes into consideration, 270 IRU combinations should occur in the catchment, whereas only 228 IRU combinations appeared in the catchment and are considered in our overlay analysis. 4.2. Knowledge-based rules Avoiding individual bias, in the form of giving equal weightings to each soil erosion parameter Žsoil, slope, drainage, rainfall and landuse., and in logical combinations, is the main basis for the rule-based approach in the study. The approach is more useful in complicated erosion-prone heterogeneous fragile ecosystems of hilly catchments which receives heavy rainfall. Hence, a top–down multi-disciplinary knowledge-based rules were formulated to map the soil erosion risk zones of this hilly catchment. Rules concerning environmental relationships cannot normally be expressed with absolute certainty Žtrue or false.. In other way, a rule lies in a continuum between true Žprobability of unit. and false Žprobability of zero., depending on how sure we are that the rule is true Žor false.. In this study, the link between the GIS database layers and the soil erosion intensity unit is provided by information-based rules. In the implementation of these rules, the multi-disciplinary expertise and knowledge of the local terrain parameters and field observations were taken into consideration, in addition to the authors expertise in soils, land-use planning, remote sensing and GIS. In the present study, the rules were expressed as the probability of an item of evidence occurring Žif - 58 slope; other drainage area; Typic Chromusterts; - 1500 mm rainfall with thick forest. given a particular hypothesis Žthen that pixel is slight to moderate erosion risk.. The rules are the most subjective aspect of the information-based system: they are heuristic, estimated from the informed opinion of the experts ŽSkidmore et al., 1991.. The well-developed and moderately deep Ž50–90 cm. black cultivated soils generally occupy the valleys and plains of the catchment. Undulating uplands and subdued hills have degraded pastures or open areas and partly cultivated lands with shallow soil depth Ž20–50 cm.. Undeveloped and very shallow Ž- 20 cm. soils cover the hilly regions with steep slopes and are mainly forest areas. The drainage density, in general, will be high, moderate and low in cases of forests, degraded pasturesropen Žwithrwithout scrub. areas and agriculture regions, respectively. These general natural conditions, in addition to the useful logical combinations of catchment environmental factors, were taken into
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consideration in formulating the knowledge-based rules to map the soil erosion risk zones of this heterogeneous hilly catchment. The knowledge-based rules for each IRU and their probability of erosion risk factor have been worked out. Two contrasting examples are: Ža. If 58 slope, other drainage area, Typic ChromustertsU , - 1500 mm rainfall with thick forest; then class s slight to moderate erosion risk. Žb. If ) 188 slope, 1–2 drainage orders, Typic UstorthentsUU , ) 1500 mm rainfall zone with open areas; then class s very severe erosion. ŽU . Piedmont black soils-deep, poorly drained, silty clay to clay, calcareous. ŽUU . Hilly greyish brown soils-shallow to very shallow, well drained, gravely sandy loam. Thus, these simple and useful logical combinations on a per pixel basis, enable mapping of the erosion intensity that is likely to occur at any given location, without any subjective bias. Finally, the soil erosion map was generated with four categories: slight to moderate, moderate to severe, severe and very severe soil erosion intensity units. An attempt has been made to use the multi-source data and GIS to estimate the soil loss by USLE ŽWeischmeir and Smith, 1978. for environmental conservation planning of this hilly catchment ŽAdinarayana and Gopal Rao, 1996.. The soil loss was computed for each pixel as per the attributes and their values, and summed up for each sub-watershed independently making use of the sub-watershed map. The soil loss yield of all the sub-watersheds were added to obtain the total sediment yield of the catchment. The rate of soil loss by USLE method of the catchment was 28.11 t hay1 yeary1 and closely matching with the field observed value of 24.67 t ha yeary1 . The range of soil loss values Žtons per hectare per year. for the erosion intensity categories was: Ž1. slight to moderate Ž- 20., Ž2. moderate to severe Ž20–30., severe Ž30–50., and very severe Ž) 50.. An integrated priority delineation survey, for taking up preferential soilrwater conservation measures, by Sediment Yield Index ŽSYI. model ŽAdinarayana et al., 1995., carried out in the catchment, grouped the catchment into very high, high, medium and low priority classes. Apparently, the areal extent of high and medium priority classes occupy about 80% of the catchment, which compares well with the occurrence of severe and moderate to severe erosion intensity unit categories by this rule-based model. 5. Conclusions The case study presented here suggests that the use of extensive multi-disciplinary knowledge-based rules helps in mapping the spatial pattern of magnituderseverity of soil erosion without suffering from individual bias by giving equal weightings to each catchment environmental factor. The soil erosion information system thus generated, identifies the targeted problem areas for catchment conservation planning. The methodology described will be more useful in hilly catchments with heterogeneous landscape features where the rainfall erosivity is a major environmental threat to the sustainable development of the area. The raster based GIS provides an easy method of integrating the catchment resource data into useful maps. Once in raster form, per pixel logical combinations are simple and could almost be handled in a standard database.
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The knowledge bases, with further research and testing on different and larger areas, generated for the purpose of soil erosion assessment could lead to form, in future, decision-support systems for conservation planning on catchment basis. Sophisticated methodologies, such as image processing and GIS, are useful tools for the assessment of soil erosion at regional scales. The model could be further improved by finer resolution terrain analysis, incorporating many more ecological variables and informed opinions from the experts and local people Žthe experiential knowledge-bases., and employing effective extrapolation of expert systemrneural network analysis. Eventually, with this improved approach, the local governmental bodies should be able to possess a highly versatile and responsive system that enables their resource managers to assess the breadth and depth of their needs in a geographically precise and consistent manner. The study also ensures the ability to monitor the dynamic changes of the catchment, particularly the landuse pattern by remote sensing data with periodic intervals, and to redefine the conservation planning strategies on the basis of detected change. This rule-based approach for hilly catchments with variable rainfall regimes and severe land degradation problems is likely to provide more sustainable and cost-effective conservation management strategies. The methodology attempted in the case study will be further tested in the other areas of hilly catchment ecosystem in the Western Ghat mountainous zone. Although it is an early airing of results obtained from the methodology described, the introduction of multi-disciplinary expert’s informed opinion may provide an extension to the traditional methods of soil erosion which should enable us to generate andror evaluate different conservation scenarios.
Acknowledgements This research project was financed by a research grant from the Department of Science and Technology, Government of India Žthrough SERC ProgrammeESr23r077r89.. The authors wish to thank CSRE, for providing facilities. Thanks are also due to our colleagues at CSRE, particularly Dr. G. Venkataraman ŽGeologist., for useful help, discussions and comments on this paper.
References Adinarayana, J., Gopal Rao, K., 1996. Prioritisation of watersheds based soil erosion studies–A remote sensing approach. Project report, Centre of Studies in Resources Engineering, IIT-Bombay. Adinarayana, J., Rama Krishna, N., 1996. Integration of multi-seasonal remotely-sensed images for improved landuse classification of a hilly watershed using Geographical Information Systems. International Journal of Remote Sensing 17 Ž9., 1679–1688. Adinarayana, J., Flach, J.D., Collins, W.G., 1994. Mapping land use patterns of a river catchment using Geographical Information Systems. Journal of Environmental Management 42 Ž1., 55–61. Adinarayana, J., Rama Krishna, N., Gopal Rao, K., 1995. An integrated approach for prioritization of watersheds. Journal of Environmental Management 44, 375–384.
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Bocco, G., Valenzuela, C.R., 1991. Integration of GIS and remote sensing in land use and erosion studies. In: Valenzuela, C.R. ŽEd.., Introduction to GIS. ITC Publication No. LIS-22, Enschede, The Netherlands, pp. 192–205. De Roo, A.P.J., Hazelhoff, L., Burrough, P.A., 1989. Soil erosion modelling using ANSWERS and GIS. Earth Surface Processes and Landforms 14, 517–533. Harris, T.M., Boardman, J., 1998. Alternative approaches to soil erosion prediction and conservation using Expert Systems and Artificial Neural Networks. In: Boardman, J., Favis-Mortlock, D.T. ŽEds.., Modelling Soil Erosion by Water. Springer, NATO-ASI Series I-55, Berlin, pp. 461–478, in press. Lee, T., Richards, J.A., Swain, P.H., 1987. Probabilistic and evidential approaches for multisource data analysis. IEEE Transactions on Geoscience and Remote Sensing GE 25, 283–293. Moore, D.M., Lees, B.G., Davey, S.M., 1983. A new method for predicting vegetation distributions using decision tree analysis in a GIS. Journal of Environmental Management 15, 59–71. Sengupta, S., Patil, R.L., Venkatachalam, P., 1996. Assessment of population exposure and risk zones due to air pollution using geographical information systems. Computers, Environment and Urban Systems 20 Ž3., 191–199. Skidmore, A.K., Ryan, P.J., Dawes, W., Short, D., O’Loughlin, E., 1991. Use of expert system to map forest soils from a GIS. International Journal of Geographical Information Systems 5 Ž4., 431–445. Soil Survey Staff, 1975. Soil Taxonomy—A basic system of soil classification for making and interpreting soil survey. USDA Handbook No. 436, Washington DC. Spanner, M.A., Strahler, A.H., Estes, J.E., 1983. Soil loss prediction in a GIS format. Proceedings of 17th International Symposium on Remote Sensing of Environment. Ann Arbor, MI, 9–13 May 1983, pp. 89–100. Weischmeir, W.H., Smith, D.D., 1978. Predicting rainfall erosion losses—A guide to conservation planning. United States Department of Agriculture Handbook No. 537. Washington DC. Young, R.A., Onstad, C.A., Bosch, D.D., Anderson, W.P., 1994. Agricultural Non-Point Source Pollution Model, version 4.03. AGNPS User’s Guide Žftp: soils.mrsars.usda.gov..
A rule-based soil erosion model for a hilly catchment J. Adinarayana a,) , K. Gopal Rao b, N. Rama Krishna a , P. Venkatachalam a , J.K. Suri a a
Centre of Studies in Resources Engineering, Indian Institute of Technology — Bombay, Powai, Mumbai 400076, India b Department of CiÕil Engineering, Indian Institute of Technology — Bombay, Powai, Mumbai 400076, India
Abstract Catchment resources, generated from multi-source data Žremotely-sensed, map- and groundbased systems., were input to a raster-based Geographical Information Systems ŽGIS. after geometrically co-registered to a standard grid Žpixel.. The GIS used was the PC-based, indigenously-developed package, called GRAM ŽGeo-Referenced Area Management.. A set of knowledge-based rules, for assessing the soil erosion of this heterogeneous hilly catchment, were formulated from the knowledge of the multi-disciplinary resource-experts and the knowledge of the local catchment resources, in addition to the field observations. This rule-based model, which is hopefully fast and cost-effective and without effect of the individual bias, helped in inferring the erosion intensity units that most likely to occur at any given pixel in the system. Finally, the catchment was grouped into four different erosion intensity units namely very severe, severe, moderate to severe and slight to moderate. The quantitative soil loss Žt hay1 yeary1 . ranges, estimated by USLE model by a spatial information analysis approach ŽGIS., were also computed: Ža. Very severe Ž) 50.: Žb. Severe Ž40–50.; Žc. Moderate to severe Ž20–40.; and Žd. Slight to moderate Ž- 20.. An integrated priority delineation survey, for taking up soilrwater conservation measures by Sediment Yield Index model, carried out in the catchment grouped the catchment into very high, high, medium and low priority classes. Apparently, the areal extent of high and moderate priority classes occupy about 80% of the catchment, which is closely following the areal extent of severe and moderate to severe erosion intensity unit categories by this rule-based model. The outcome of the results of this study will be immensely useful to monitor the dynamic changes in the catchment environment. The soil erosion information system thus generated, identifies the targeted problem areas for conservation planning and sustainable development of the catchment. Although it is an early airing of results obtained from this rule-based model, the introduction of multi-disciplinary expert’s informed opinion may provide an extension to the traditional methods
)
Corresponding author. Tel.: q91-22-5767689; Fax: q91-22-5783190; E-mail: [email protected]
0341-8162r99r$20.00 q 1999 Elsevier Science B.V. All rights reserved. PII: S 0 3 4 1 - 8 1 6 2 Ž 9 9 . 0 0 0 2 3 - 5
310
J. Adinarayana et al.r Catena 37 (1999) 309–318
of assessing the soil erosion statusrmagnitude which should enable andror evaluate different catchment management scenarios. q 1999 Elsevier Science B.V. All rights reserved. Keywords: Geographical Information Systems; Hilly catchment; Integrated resources unit; Multi-disciplinary knowledge-based rules; Rule-based approach; Soil erosion
1. Introduction Soil erosion, by water, is the resultant of interplay between catchment environmental factors such as soil, topography, drainage, rainfall and land use pattern, data upon which are available from different sourcesrsystems. Additional information through experience and knowledge of the multi-disciplinary experts also may be used for avoiding individual bias in assessing the soil erosion pattern of the area under interest. Hence, it is important to study the soil erosion by analysing these multi-source catchment resources with multi-disciplinary expertise in an integral manner. The problem is more serious with hilly heterogeneous catchments which are subject to heavy rainfall. The technology of Geographical Information Systems ŽGIS. provides a means of introducing information and knowledge from different sources in the decision-making process, and help in handling and manipulating the multi-source data. A catchment is the base unit for the study, being a complete hydrological and topographical unit. Many researchers have been reported in the literature on this catchment environment aspect using GIS ŽSpanner et al., 1983; De Roo et al., 1989—ANSWERS model; Bocco and Valenzuela, 1991; Young et al., 1994—AGNPS model.. Expert systems in the form of decision-rules have been used as a method of integrating multi-source data ŽMoore et al., 1983; Lee et al., 1987; Skidmore et al., 1991; Harris and Boardman, 1998.. The aim of this paper is to describe for natural resources scientists a classification method that interactively integrates conventional techniques, remote sensing, GIS and the interpreter’s expertise and knowledge. Particularly, our objective is to generate a GIS-assisted soil erosion map of a rain-infested heterogeneous hilly catchment in an integral manner avoiding any individual bias. 1.1. Background The approach was tested in a drainage basin of about 100 km2 in the difficult semi-arid, mountainous terrain of the Western Ghats Ž73845X –73855X E. 19816X –19823X N.. The catchment, which has a basaltic landscape, is located in the Western Plateau and Hill Agro-climatic Region of the Indian Peninsular. Sediments are deposited in the drainage ditches, and ultimately in Yedgaon reservoir, a terminus of drainage for the catchment. The siltation is largely due to poor soil conservation practices, scantily distributed vegetation cover, steep slopes and overall dry climate with an erratic monsoon rainfall, resulting in a high susceptibility to erosion. Cultivation Žcropping systems based on upland rice. is being introduced in the canyons extending into the steeper mountainous area with inadequate soil conservation practices. If erosion is not stopped, further increases in runoff, and sheet and gully erosion on
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slopping ground will ultimately destroy the productive value of the land. Hence, a rule-based approach for assessing the soil erosion of this heterogeneous catchment is required for conservation planning.
2. Data sources Keeping in view the integrated approach to be adopted, the following data were used: Remotely-sensed data: Landsat-TM and IRS-1A LISS II-digital ŽComputer Compatible Tapes-CCTs. and visual ŽFalse Colour Composites-FCC. products of monsoon Žkharif. and post-monsoon Žrabi. agriculture seasons. U Survey of India ŽSOI. topographical maps at 1:250 000 and 1:50 000 scales. U Available literature and maps on various themes. U Meteorological data Žrainfall, temperature, etc... U Ground survey information on soil and other terrain characteristics of the sample sites of the watershed. U
3. Database construction The GIS used was the PC-based, indigenously developed, user-friendly package called Geo-Referenced Area Management ŽGRAM.. This GIS package is a spin-off from the Indian Department of Science and Technology sponsored project ‘Natural Resources Data Management Systems’, which aimed to generate computer-compatible, spatially oriented databases of natural resources and socio-economic parameters to facilitate area-specific micro-level planning. The GRAM package operates on a small and relatively low-budget computer configuration, and can be used as a viable tool for natural resources management, rural and urban planning, image analysis of remotely sensed data, watershed management, impact assessment studies, etc. GRAM has been built with seven major modules: input module, attribute link, analysis module, terrain module, image processing module, statistics module and print module. The software is arranged in a hierarchical structure with sub-menus at different levels. The capability of the system is being tested for regional applications, and work is in progress to add on further analysis functions and to interface well-known mathematical models for resources applications ŽSengupta et al., 1996.. The data sources were integrated into the GIS in a grid cell format. GRAM allows rapid integration of data from image and map sources. Digitisation, georeferencing and rasterization were done in the input module. The output of this module was used as the base for the analysis module to integrate and process on logical combinations and knowledge-based rules. The rules were constructed from the knowledge of the local terrain parameters and field checks, in addition to the author’s expertise in various fields. Integration of these rules were carried out with graphic Žspatial. database Žloose-coupled way of integration. in the analysis module of GRAM. Using the query support, the spatial database was analysed and the final soil erosion map was produced.
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The erosion intensity map and the results were the output of the print module. The general procedure followed during the analysis is shown schematically in Fig. 1. 3.1. Coordinate system A defined cell Žpixel. was chosen, which is the limit of the resolution of the system. Each cell has an exact location in space, related to the grid orientation and cell size and a list of assigned attributes. The information database were geometrically corrected, using a SOI topographic map base and resampled to a 30 m resolution Žof Landsat-TM.. A grid size of 515 = 492 pixels covered the whole catchment. 3.2. Catchment resources Accurate information on the existing landuse pattern and its spatial distribution is a prerequisite for any catchment management programme. With the advent of remote sensing, it has been possible to prepare this dynamic resource map at various levels of confidence. However, the applications of multi-spectral classification techniques in extracting vegetation classes has been inconsistent because of similarities in spectral responses ŽBocco and Valenzuela, 1991; Adinarayana et al., 1994.. The problem is more serious with the digital techniques under heterogeneous hilly terrain. The other problem in the collection of landuse data is the presence of clouds and their shadows during the
Fig. 1. Generalised processing flow for soil erosion information system of a hilly catchment.
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monsoon period. In addition, it is also important to map the agricultural landuse pattern of both monsoon Žkharif. and post-monsoon Žrabi. seasons together in the rural catchments. Therefore, multi-seasonal remotely-sensed images of Landsat-TM digital Žkharif—when most of the crops are matured and harvested. and IRS-LISS II FCC Žrabi —cloud-free images showing vegetation in the upland areas. were used and classified using the maximum likelihood classifier and standard visual interpretation techniques, respectively. In addition, multi-thematic maps of soil, slope and drainage were also used to generate multi-disciplinary knowledge-based rules to overcome the above inherent problems in the hilly terrain. These maps were input and stored as separate layers in the GIS for integrated analysis. The link between above GIS data layers and knowledge of the multi-disciplinary group of Scientists and the field checks is provided by rules. Since each satellite classification on its own was insufficient to discriminate all the necessary classes, the rules were constructed by relating the GIS data layers and the aggregated improved agricultural landuse pattern of the catchment to overcome some inherent problems, viz. to discriminate the vegetation categories and to identify the information classes under the cloudrshadow regions in kharif season ŽAdinarayana and Rama Krishna, 1996.. Given that all the data sources were now in a common coordinate system, a number of logical combinations could be performed on a per pixel basis to consolidate the agricultural landuse pattern of this heterogeneous highland. To give a few examples: Ža. If Landsat-TM ŽKharif class. s Thick Forest and IRS LISS-II ŽRabi class. s Thick Forest; Then class s Thick Forest. Žb. If Kharif class s Sparse Forest and Rabi class s Open Žwithrwithout scrub. Area or Degraded Pasture; Then class s Open Žwithrwithout scrub. Area or Degraded Pasture, respectively. Žc. If Kharif s Sparse Forest and Rabi s Cropped; Then class s Cropped. Žd. If Kharif s Cloud Cover and Rabi s Thick Forest; Then class s Thick Forest. Že. If Kharifq Rabi s Vegetation, Drainage densitys Low, Soil s Typic Chromusterts and slope s- 58; Then class s Cropped. These informed-opinions, codified in the form of ‘if–then’ type of rules, were used to refine the classification accuracy. Initially, the correspondence between the LISS II-based visual and TM-based digital classification maps was at best 45%. After applying the rules on the classification output, the agreement between the maps increased to 85–100% ŽAdinarayana and Rama Krishna, 1996.. In the process, the visual and digital classification techniques interacted to improve the classification system. Finally, the GIS-assisted improved landuse map consists of five major categories, which were selected from the conservation planning point of view: agriculture, thick forest, sparse forest, degraded pastures and open Žwithrwithout scrub. lands. The introduction of multi-disciplinary expert rules provides an extension to the traditional methods of satellite-derived classification which should enable more relevant, and accurate, landusercover map to be prepared. A collective approach comprising satellite data ŽIRS. and topographic maps were used to delineate physiographic units. The soil associations of each physiographic unit were established by studying soil profiles in the landscape and by random checking outside the sample strip areas, and finally classifying the soils according to an estab-
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lished soil taxonomy ŽSoil Survey Staff, 1975.. In the end, a soil map at 1:50 000 scale was prepared by combining the soil information in each delineated physiographic unit. The contours and high points were digitised from 1:50 000 SOI topomaps Ž20 m interval. to generate the Digital Elevation Model ŽDEM. and the slope map with the help of the GRAM package. The catchment was divided into three major slope steepness classes for the evaluation of soil erosion and priority sub-watersheds delineation survey: Ž1. gently sloping lands Ž0–88.; Ž2. moderately sloping lands Ž8–188.; and Ž3. steeply sloping lands Ž) 188.. Steeper slopes promote concentrated run-off and show exposed rocks in some places. Moderate slopes support mostly degraded pastures or open scrub lands and have moderate to high run-off potential. Gently sloping lands covered with moderately deep cultivated black soils have low run-off potential. Once the contours are digitised, GRAM allows one to categorise any number of slope steepness classes as needed. An isohyetal map was compiled manually from 10 years of mean annual rainfall data from six meteorological stations available in and around the catchment area. The catchment was divided into two rainfall zones: a - 1500 mm low rainfall zone, and a G 1500 mm high rainfall zone. The drainage network was generated from SOI toposheet Žof 1975. and the high order river course systems were updated from Landsat-TM imagery Žof 1989.. The drainage density Žtotal length of streamsrunit area. was calculated for each sub-watershed and the catchment was divided into low, medium and high drainage density zones. The proximity analysis was done to establish the buffer zone along the streams to demarcate the zones where erosion was likely to be important. The catchment was divided into 33 sub-watersheds, which are smaller hydrologic units and constitute individual tributaries of the lowest order, or a group of such tributaries. The area of each sub-watershed was planometrically computed and ranged from 1.4 to 7.0 km2 , which is considered as a viable working size for conservation planning. All the previously described thematic maps were digitised Žexcept the TM-based digitally classified data which will be in the raster form., rasterized and brought to a common coordinate system, which thus acted as database for GIS analysis to generate the erosion map. 4. Database analysis One of the most important characteristics of GIS is its capability for data analysis and spatial modelling. GRAM includes conventional GIS data analysis and manipulation capabilities, such as overlaying, reclassification, etc. The system incorporates a set of decision-rules in which several knowledge-based rules are used to evaluate and input data at different levels of abstraction. 4.1. Generation of integrated resources units Given that all the data layers are now spatially part of a common coordinate system, a number of useful combinations can be performed. The first step in this process is to
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create a basic unit by overlay analysis, and create Integrated Resources Unit ŽIRU.. Each IRU comprises the catchment resource data on soil, slope, landuse, rainfall distribution and drainage buffers for one grid cell Ž30 m.. One IRU has been taken as the basic strategic unit for assessing the soil erosion. Since they exhibit strong uniformity, they can all be expected to respond similarly to given intensities of human use and management strategies. Each IRU descriptor was derived from catchment factors superimposed as layers in a GIS. Adjacent sets of IRU attributes were then separated. However, unlike photomorphic boundaries traced from aerial photographs or satellite images, an IRU in this system cannot be printed in map form, so each pixel was geocoded to a standard SOI topomap. In other words, the IRU can be ‘delineated’ in spatial domains using digital data and computer processing, since each pixel acts as an IRU. Taking all the catchment environmental factors and their attributes into consideration, 270 IRU combinations should occur in the catchment, whereas only 228 IRU combinations appeared in the catchment and are considered in our overlay analysis. 4.2. Knowledge-based rules Avoiding individual bias, in the form of giving equal weightings to each soil erosion parameter Žsoil, slope, drainage, rainfall and landuse., and in logical combinations, is the main basis for the rule-based approach in the study. The approach is more useful in complicated erosion-prone heterogeneous fragile ecosystems of hilly catchments which receives heavy rainfall. Hence, a top–down multi-disciplinary knowledge-based rules were formulated to map the soil erosion risk zones of this hilly catchment. Rules concerning environmental relationships cannot normally be expressed with absolute certainty Žtrue or false.. In other way, a rule lies in a continuum between true Žprobability of unit. and false Žprobability of zero., depending on how sure we are that the rule is true Žor false.. In this study, the link between the GIS database layers and the soil erosion intensity unit is provided by information-based rules. In the implementation of these rules, the multi-disciplinary expertise and knowledge of the local terrain parameters and field observations were taken into consideration, in addition to the authors expertise in soils, land-use planning, remote sensing and GIS. In the present study, the rules were expressed as the probability of an item of evidence occurring Žif - 58 slope; other drainage area; Typic Chromusterts; - 1500 mm rainfall with thick forest. given a particular hypothesis Žthen that pixel is slight to moderate erosion risk.. The rules are the most subjective aspect of the information-based system: they are heuristic, estimated from the informed opinion of the experts ŽSkidmore et al., 1991.. The well-developed and moderately deep Ž50–90 cm. black cultivated soils generally occupy the valleys and plains of the catchment. Undulating uplands and subdued hills have degraded pastures or open areas and partly cultivated lands with shallow soil depth Ž20–50 cm.. Undeveloped and very shallow Ž- 20 cm. soils cover the hilly regions with steep slopes and are mainly forest areas. The drainage density, in general, will be high, moderate and low in cases of forests, degraded pasturesropen Žwithrwithout scrub. areas and agriculture regions, respectively. These general natural conditions, in addition to the useful logical combinations of catchment environmental factors, were taken into
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consideration in formulating the knowledge-based rules to map the soil erosion risk zones of this heterogeneous hilly catchment. The knowledge-based rules for each IRU and their probability of erosion risk factor have been worked out. Two contrasting examples are: Ža. If 58 slope, other drainage area, Typic ChromustertsU , - 1500 mm rainfall with thick forest; then class s slight to moderate erosion risk. Žb. If ) 188 slope, 1–2 drainage orders, Typic UstorthentsUU , ) 1500 mm rainfall zone with open areas; then class s very severe erosion. ŽU . Piedmont black soils-deep, poorly drained, silty clay to clay, calcareous. ŽUU . Hilly greyish brown soils-shallow to very shallow, well drained, gravely sandy loam. Thus, these simple and useful logical combinations on a per pixel basis, enable mapping of the erosion intensity that is likely to occur at any given location, without any subjective bias. Finally, the soil erosion map was generated with four categories: slight to moderate, moderate to severe, severe and very severe soil erosion intensity units. An attempt has been made to use the multi-source data and GIS to estimate the soil loss by USLE ŽWeischmeir and Smith, 1978. for environmental conservation planning of this hilly catchment ŽAdinarayana and Gopal Rao, 1996.. The soil loss was computed for each pixel as per the attributes and their values, and summed up for each sub-watershed independently making use of the sub-watershed map. The soil loss yield of all the sub-watersheds were added to obtain the total sediment yield of the catchment. The rate of soil loss by USLE method of the catchment was 28.11 t hay1 yeary1 and closely matching with the field observed value of 24.67 t ha yeary1 . The range of soil loss values Žtons per hectare per year. for the erosion intensity categories was: Ž1. slight to moderate Ž- 20., Ž2. moderate to severe Ž20–30., severe Ž30–50., and very severe Ž) 50.. An integrated priority delineation survey, for taking up preferential soilrwater conservation measures, by Sediment Yield Index ŽSYI. model ŽAdinarayana et al., 1995., carried out in the catchment, grouped the catchment into very high, high, medium and low priority classes. Apparently, the areal extent of high and medium priority classes occupy about 80% of the catchment, which compares well with the occurrence of severe and moderate to severe erosion intensity unit categories by this rule-based model. 5. Conclusions The case study presented here suggests that the use of extensive multi-disciplinary knowledge-based rules helps in mapping the spatial pattern of magnituderseverity of soil erosion without suffering from individual bias by giving equal weightings to each catchment environmental factor. The soil erosion information system thus generated, identifies the targeted problem areas for catchment conservation planning. The methodology described will be more useful in hilly catchments with heterogeneous landscape features where the rainfall erosivity is a major environmental threat to the sustainable development of the area. The raster based GIS provides an easy method of integrating the catchment resource data into useful maps. Once in raster form, per pixel logical combinations are simple and could almost be handled in a standard database.
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The knowledge bases, with further research and testing on different and larger areas, generated for the purpose of soil erosion assessment could lead to form, in future, decision-support systems for conservation planning on catchment basis. Sophisticated methodologies, such as image processing and GIS, are useful tools for the assessment of soil erosion at regional scales. The model could be further improved by finer resolution terrain analysis, incorporating many more ecological variables and informed opinions from the experts and local people Žthe experiential knowledge-bases., and employing effective extrapolation of expert systemrneural network analysis. Eventually, with this improved approach, the local governmental bodies should be able to possess a highly versatile and responsive system that enables their resource managers to assess the breadth and depth of their needs in a geographically precise and consistent manner. The study also ensures the ability to monitor the dynamic changes of the catchment, particularly the landuse pattern by remote sensing data with periodic intervals, and to redefine the conservation planning strategies on the basis of detected change. This rule-based approach for hilly catchments with variable rainfall regimes and severe land degradation problems is likely to provide more sustainable and cost-effective conservation management strategies. The methodology attempted in the case study will be further tested in the other areas of hilly catchment ecosystem in the Western Ghat mountainous zone. Although it is an early airing of results obtained from the methodology described, the introduction of multi-disciplinary expert’s informed opinion may provide an extension to the traditional methods of soil erosion which should enable us to generate andror evaluate different conservation scenarios.
Acknowledgements This research project was financed by a research grant from the Department of Science and Technology, Government of India Žthrough SERC ProgrammeESr23r077r89.. The authors wish to thank CSRE, for providing facilities. Thanks are also due to our colleagues at CSRE, particularly Dr. G. Venkataraman ŽGeologist., for useful help, discussions and comments on this paper.
References Adinarayana, J., Gopal Rao, K., 1996. Prioritisation of watersheds based soil erosion studies–A remote sensing approach. Project report, Centre of Studies in Resources Engineering, IIT-Bombay. Adinarayana, J., Rama Krishna, N., 1996. Integration of multi-seasonal remotely-sensed images for improved landuse classification of a hilly watershed using Geographical Information Systems. International Journal of Remote Sensing 17 Ž9., 1679–1688. Adinarayana, J., Flach, J.D., Collins, W.G., 1994. Mapping land use patterns of a river catchment using Geographical Information Systems. Journal of Environmental Management 42 Ž1., 55–61. Adinarayana, J., Rama Krishna, N., Gopal Rao, K., 1995. An integrated approach for prioritization of watersheds. Journal of Environmental Management 44, 375–384.
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