Automated ridgeline recognition, using Kernel neighborhood pattern analysis
Subject Areas : Geospatial systems development
Kourosh Shirani
1
,
Sina Solhi
2
,
Fatemeh Nematolahi
3
1 - Assistant Professor, Soil Conservation and Watershed Management Research Department, Isfahan Agricultural and Natural Resources, Research and Education Center, AREEO, Isfahan, Iran
2 - Ph.D. Graduated of Geomorphology, Department of Physical Geography, Faculty of Geographical Sciences and Planning, University of Isfahan, Isfahan, Iran
3 - Postdoctoral Researcher, Department of Physical Geography, Faculty of Geographical Sciences and Planning, University of Isfahan, Isfahan, Iran
Keywords: Neighborhood pattern, Kernel, Automatic detection, Ridgeline,
Abstract :
Background and Objective Landform refers to any physical feature of the surface with a recognizable structure and shape. Landform elements and structural forms of the terrain surface could, directly and indirectly, drive many other environmental variables. Numerical representation of the surface and uneven pattern of the earth is a common topic in geographical, geomorphological, geological, and geophysical hazard mapping as well as sea-bed exploration. The combination of the earth and computer science with mathematics and geomorphometric engineering interacts with discrete and continuous landforms. Geomorphometry dates back to about 150 years ago and the work of Alexander von Humboldt and geomorphologists, and today with the revolution in computer science and especially digital computer models is developing rapidly. Detection and classification of landforms are of interest to GIS developers, geoscientists, and geomorphometry researchers. In this way, the desired work units are extracted with higher speed and accuracy and used in the form of vector and raster maps. Existing approaches are mainly based on height, terrain derivative, gradient, curvature, flow direction, slope position, morphometric indices, and the like. Also, less attention has been paid to the challenge of matching the diagnostic scale with the Landform scale, and most models have this shortcoming. On the other hand, less attention has been paid to the possibility of vectorization output results and also to the analysis of sensitivity and temporal response algorithms to machine processing. In this research, we attempted to recover and resolve the mentioned shortcoming and problems in the previous works. In this research, using basic algorithms of raster analysis and coding, new methods and algorithms for the automatic detection of landforms have been developed. Focal raster analysis is also emphasized and the moving window technique is used to implement the algorithms. Facing the scale challenge, sensitivity analysis, and the response algorithms to input changes as well as accuracy assessment are other aspects that have been addressed in this research.Materials and Methods In this study, the Digital Surface Model (DSM) published by the Japan Space Agency in May and October 2015 with a horizontal resolution of about 30 meters was used to work on the topography of the region. These data are obtained from ALOS satellite images. This database is based on DSM data (5m network version) 3D topography, one of the most accurate elevation data on a global scale. The digital elevation model was transformed into a matrix structure using a Python coding environment. Then, raster analysis was implemented using the moving window technique. The moving window algorithm was coded in a way that the dimensions of the moving window could be freely determined and changed. In proportion to the size of the moving window, some adaptive algorithms are implemented to automatically correct and organize the edge effect in proportion to the size of the moving window. In this study, automatic landform detection was performed using spatial analysis of kernel patterns in the raster grid of digital elevation models and the results were presented in the form of three algorithms applied in the detection of topographic peaks and ridges. These algorithms include Multilevel Mean Summit Recognition Algorithm (MLMSR), Complex Multilevel Summit Recognition Algorithm (CMLSR), and Single Point Summit Recognition (SPSR). Each of these three algorithms was first conceptually designed and then coded and executed using the Python programming language. In the next step, the sources of error and specific scenarios of the algorithms were examined. The sensitivity of each algorithm related to the dimensions of the moving window, the resolution, and the size of the raster file, was evaluated, and finally, the accuracy and validation of the three models, using reference layers that were manually prepared and plotted, were assessed. All the procedures were designed in a way that could easily be implemented in an official software and were completely compatible with the structure of machinery processing. Also, being automatic and working on different platforms where one of our priorities.Results and Discussion In the automatic detection of peaks and ridges using a digital terrain model, kernel spatial pattern analysis was used. In this regard, three proposed algorithms in this field were designed, coded, and executed. The output results of each of the algorithms were presented in the form of a raster and vector data model. Accuracy and sensitivity assessments were performed by considering changes in moving window size, resolution, and raster grid size (row x column) for each of the algorithms. The MLMSR algorithm tends to be in a more binary result in the lower dimensions of the moving window, while the CMLSR and SPSR algorithms do not. In all algorithms, increasing the size of the moving window causes a more generalization ratio. CMLSR and SPSR algorithms are more suitable for cartographic and visual purposes due to the higher degree of grading in the results. Regarding the temporal performance (Runtime) or sensitivity to input changes, the SPSR algorithm performs better. This is especially important when the input file size (number of rows and columns) is large. According to the results of validation and accuracy evaluation, MLMSR and SPSR had better performance than, the CMLSR algorithm. Python programming language has been widely used in the design and implementation of all algorithms, as well as in the field of sensitivity evaluation and validation. Totally more than 500 lines of codes were done for this purpose. All algorithms are automated and are able to execute and store results in raster and vector format using machine processing.Conclusion The results show that the MLMSR algorithm in smaller dimensions of the moving window is tending to more binary results, which is problematic in some graphical and cartographic applications, but the CMLSR and SPSR algorithms showed more gradual trends in their outputs and so, they performed better in this respect. Researchers who intend to study and develop in this field are advised to focus on adaptive algorithms and optimize the dimensions of the moving window in relation to the volume of input information and so, in this way, they increase the flexibility of algorithms in relation to input changes.
Adediran AO, Parcharidis I, Poscolieri M, Pavlopoulos K. 2004. Computer-assisted discrimination of morphological units on north-central Crete (Greece) by applying multivariate statistics to local relief gradients. Geomorphology, 58(1): 357-370. doi:https://doi.org/10.1016/j.geomorph.2003.07.024.
Ahnert F. 1996. The point of modelling geomorphological systems. Geomorphology Sans Frontières: 91-114.
Azanon J, Delgado J, Gómez A. 2004. Morphological terrain classification and analysis using geostatistical techniques. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 34(Part XXX).
Bates RL, Jackson JA. 1987. Glossary of geology, https://www.osti.gov/biblio/5128638.
Böhner J, Selige T. 2006. Spatial prediction of soil attributes using terrain analysis and climate regionalisation. In: SAGA-Analyses and modelling applications. Goltze, 45 p.
Brabyn L. 1997. Classification of macro landforms using GIS. ITC journal(1): 26-40.
Carrara A. 1983. Multivariate models for landslide hazard evaluation. Journal of the International Association for Mathematical Geology, 15(3): 403-426. doi:10.1007/BF01031290.
Chang K-T. 2008. Introduction to geographic information systems, vol 4, . McGraw-Hill Boston, 117-122.
Clayton K, Shamoon N. 1999. A new approach to the relief of Great Britain: III. Derivation of the contribution of neotectonic movements and exceptional regional denudation to the present relief. Geomorphology, 27(3): 173-189. doi:https://doi.org/10.1016/S0169-555X(98)00072-5.
Dikau R. 1990. Geomorphic landform modelling based on hierarchy theory. In: Proceedings of the 4th international symposium on spatial data handling. Department of Geography, University of Zürich Zürich, Switzerland, pp 230-239.
Dikau R. 2020. The application of a digital relief model to landform analysis in geomorphology. In: Three dimensional applications in geographical information systems. CRC Press, pp 51-77.
Dikau R, Brabb EE, Mark R, Pike R. 1995. Morphometric landform analysis of New Mexico. Zeitschrift für Geomorphologie Supplementband(101): 109-126.
Dikau R, Brabb EE, Mark RM. 1991. Landform classification of New Mexico by computer. US Dept. of the Interior, US Geological Survey, https://doi.org/10.3133/ofr91634.
Dobos E, Daroussin J, Montanarella L. 2010. A quantitative procedure for building physiographic units supporting a global SOTER database. Hungarian Geographical Bulletin, 59(2): 181-205.
Dymond J, Derose R, Harmsworth G. 1995. Automated mapping of land components from digital elevation data. Earth Surface Processes and Landforms, 20(2): 131-137. doi:https://doi.org/10.1002/esp.3290200204.
Dymond JR, Harmsworth GR. 1994. Towards automated land resource mapping using digital. ITC journal: 2, 129-138.
Etzelmüller B, Sulebak JR. 2000. Developments in the use of digital elevation models in periglacial geomorphology and glaciology. Physische Geographie, 41: 35-58.
Evans IS. 1980. An integrated system of terrain analysis and slope mapping. Zeitschrift fur Geomorphologie, 36: 274-295.
Evans IS. 2019. General geomorphometry, derivatives of altitude, and descriptive statistics. In: Spatial analysis in geomorphology. Routledge, pp 17-90.
Felicísimo AM. 1994. Modelos digitales del terreno. Introducción y aplicaciones en las ciencias ambientales Oviedo: Pentalfa Ediciones, 122 p.
Fels JE, Matson KC. 1996. A cognitively-based approach for hydrogeomorphic land classification using digital terrain models. In: Proceedings of Third NCGIA International Conference/Workshop on Integrating GIS and Environmental Modeling. Santa Fe, New Mexico. Available: http://www. ncgia. ucsb. edu/conf/SANTA_FE_CD-ROM/sf_papers/fels_john/fels_and_matson. html.
Fenneman N, Johnson D. 1946. Physical division of the United States: US geological survey. Physiography Committee Special Map, scale, 1(7,000,000).
Florinsky IV. 1998. Combined analysis of digital terrain models and remotely sensed data in landscape investigations. Progress in physical geography, 22(1): 33-60.
Hammond EH. 1954. Small-scale continental landform maps. Annals of the Association of American Geographers, 44(1): 33-42.
Hammond EH. 1964. Analysis of properties in land form geography: an application to broad-scale land form mapping. Annals of the Association of American Geographers, 54(1): 11-19. doi:https://doi.org/10.1111/j.1467-8306.1964.tb00470.x.
Hengl T, Reuter HI. 2008. Geomorphometry: concepts, software, applications. Newnes, 796 p.
Hodgson ME. 1998. Comparison of angles from surface slope/aspect algorithms. Cartography and Geographic Information Systems, 25(3): 173-185. doi:https://doi.org/10.1559/152304098782383106.
Irvin BJ, Ventura SJ, Slater BK. 1997. Fuzzy and isodata classification of landform elements from digital terrain data in Pleasant Valley, Wisconsin. Geoderma, 77(2): 137-154. doi:https://doi.org/10.1016/S0016-7061(97)00019-0.
Iwahashi J, Pike RJ. 2007. Automated classifications of topography from DEMs by an unsupervised nested-means algorithm and a three-part geometric signature. Geomorphology, 86(3): 409-440. doi:https://doi.org/10.1016/j.geomorph.2006.09.012.
Lane SN, Richards KS, Chandler JH. 1998. Landform monitoring, modelling and analysis. John Wiley and Sons Ltd, 480 p.
MacMillan RA, Jones RK, McNabb DH. 2004. Defining a hierarchy of spatial entities for environmental analysis and modeling using digital elevation models (DEMs). Computers, Environment and Urban Systems, 28(3): 175-200. doi:https://doi.org/10.1016/S0198-9715(03)00019-X.
MacMillan RA, Pettapiece WW, Nolan SC, Goddard TW. 2000. A generic procedure for automatically segmenting landforms into landform elements using DEMs, heuristic rules and fuzzy logic. Fuzzy Sets and Systems, 113(1): 81-109. doi:https://doi.org/10.1016/S0165-0114(99)00014-7.
Moffat A, Catt J, Webster R, Brown E. 1986. A re‐examination of the evidence for a Plio‐Pleistocene marine transgression on the Chiltern Hills. I. Structures and surfaces. Earth Surface Processes and Landforms, 11(1): 95-106. doi:https://doi.org/10.1002/esp.3290110110.
Moore ID, Gessler PE, Nielsen G, Peterson G. 1993. Soil attribute prediction using terrain analysis. Soil Science Society of America Journal, 57(2): 443-452. doi:https://doi.org/10.2136/sssaj1993.03615995005700020026x.
Moore ID, Grayson R, Ladson A. 1991. Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrological processes, 5(1): 3-30. doi:https://doi.org/10.1002/hyp.3360050103.
Moore ID, Nieber JL. 1989. Landscape assessment of soil erosion and nonpoint source pollution. Journal of the Minnesota Academy of Science, 55(1): 18-25.
Morgan JM, Lesh AM. 2005. Developing landform maps using ESRI’S Model-Builder. In: ESRI International User Conference.
Mulla DJ. 1988. Using geostatistics and spectral analysis to study spatial patterns in the topography of southeastern Washington State, USA. Earth Surface Processes and Landforms, 13(5): 389-405. doi:https://doi.org/10.1002/esp.3290130505.
Murphy RE. 1968. Annals map supplement number nine landforms of the world. Annals of the Association of American Geographers, 58(1): 198-200. doi:https://doi.org/10.1111/j.1467-8306.1968.tb01643.x.
Nogami M. 1995. Geomorphometric measures for digital elevation models. Z Geomorph, NF, Suppl, 101: 53-67.
Pike RJ. 1988. The geometric signature: Quantifying landslide-terrain types from digital elevation models. Mathematical Geology, 20(5): 491-511. doi:10.1007/BF00890333.
Pike RJ. 1995. Geomorphometry: progress, practice and prospect. Zeitschrift fur Geomorphologie NF SupplementBand, 101: 221-238.
Pike RJ. 2000. Geomorphometry-diversity in quantitative surface analysis. Progress in Physical Geography, 24(1): 1-20. doi:https://doi.org/10.1177/030913330002400101.
Prima ODA, Echigo A, Yokoyama R, Yoshida T. 2006. Supervised landform classification of Northeast Honshu from DEM-derived thematic maps. Geomorphology, 78(3): 373-386. doi:https://doi.org/10.1016/j.geomorph.2006.02.005.
Speight J. 2009. Landform. In ‘Australian soil and land survey field handbook’. CSIRO Publishing: Melbourne, 8-43 p.
Sulebak JR, Etzelmüller B, Sollid JL. 1997. Landscape regionalization by automatic classification of landform elements. Norsk Geografisk Tidsskrift-Norwegian Journal of Geography, 51(1): 35-45. doi:https://doi.org/10.1080/00291959708552362.
Tadono T, Ishida H, Oda F, Naito S, Minakawa K, Iwamoto H. 2014. Precise global DEM generation by ALOS PRISM. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2(4): 71. doi:https://doi.org/10.5194/isprsannals-II-4-71-2014.
Takaku J, Tadono T, Tsutsui K. 2014. Generation of High Resolution Global DSM from ALOS PRISM. ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, 2(4). doi:https://doi.org/10.5194/isprsarchives-XL-4-243-2014.
Wood J. 1996. The geomorphological characterisation of digital elevation models. University of Leicester (United Kingdom). Thesis submitted for the degree of Doctor of Philosophy at the University of Leicester, 450 p.
Zinck JA, Valenzuela CR. 1990. Soil geographic database: structure and application examples. ITC journal(3): 270-294.
_||_Adediran AO, Parcharidis I, Poscolieri M, Pavlopoulos K. 2004. Computer-assisted discrimination of morphological units on north-central Crete (Greece) by applying multivariate statistics to local relief gradients. Geomorphology, 58(1): 357-370. doi:https://doi.org/10.1016/j.geomorph.2003.07.024.
Ahnert F. 1996. The point of modelling geomorphological systems. Geomorphology Sans Frontières: 91-114.
Azanon J, Delgado J, Gómez A. 2004. Morphological terrain classification and analysis using geostatistical techniques. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 34(Part XXX).
Bates RL, Jackson JA. 1987. Glossary of geology, https://www.osti.gov/biblio/5128638.
Böhner J, Selige T. 2006. Spatial prediction of soil attributes using terrain analysis and climate regionalisation. In: SAGA-Analyses and modelling applications. Goltze, 45 p.
Brabyn L. 1997. Classification of macro landforms using GIS. ITC journal(1): 26-40.
Carrara A. 1983. Multivariate models for landslide hazard evaluation. Journal of the International Association for Mathematical Geology, 15(3): 403-426. doi:10.1007/BF01031290.
Chang K-T. 2008. Introduction to geographic information systems, vol 4, . McGraw-Hill Boston, 117-122.
Clayton K, Shamoon N. 1999. A new approach to the relief of Great Britain: III. Derivation of the contribution of neotectonic movements and exceptional regional denudation to the present relief. Geomorphology, 27(3): 173-189. doi:https://doi.org/10.1016/S0169-555X(98)00072-5.
Dikau R. 1990. Geomorphic landform modelling based on hierarchy theory. In: Proceedings of the 4th international symposium on spatial data handling. Department of Geography, University of Zürich Zürich, Switzerland, pp 230-239.
Dikau R. 2020. The application of a digital relief model to landform analysis in geomorphology. In: Three dimensional applications in geographical information systems. CRC Press, pp 51-77.
Dikau R, Brabb EE, Mark R, Pike R. 1995. Morphometric landform analysis of New Mexico. Zeitschrift für Geomorphologie Supplementband(101): 109-126.
Dikau R, Brabb EE, Mark RM. 1991. Landform classification of New Mexico by computer. US Dept. of the Interior, US Geological Survey, https://doi.org/10.3133/ofr91634.
Dobos E, Daroussin J, Montanarella L. 2010. A quantitative procedure for building physiographic units supporting a global SOTER database. Hungarian Geographical Bulletin, 59(2): 181-205.
Dymond J, Derose R, Harmsworth G. 1995. Automated mapping of land components from digital elevation data. Earth Surface Processes and Landforms, 20(2): 131-137. doi:https://doi.org/10.1002/esp.3290200204.
Dymond JR, Harmsworth GR. 1994. Towards automated land resource mapping using digital. ITC journal: 2, 129-138.
Etzelmüller B, Sulebak JR. 2000. Developments in the use of digital elevation models in periglacial geomorphology and glaciology. Physische Geographie, 41: 35-58.
Evans IS. 1980. An integrated system of terrain analysis and slope mapping. Zeitschrift fur Geomorphologie, 36: 274-295.
Evans IS. 2019. General geomorphometry, derivatives of altitude, and descriptive statistics. In: Spatial analysis in geomorphology. Routledge, pp 17-90.
Felicísimo AM. 1994. Modelos digitales del terreno. Introducción y aplicaciones en las ciencias ambientales Oviedo: Pentalfa Ediciones, 122 p.
Fels JE, Matson KC. 1996. A cognitively-based approach for hydrogeomorphic land classification using digital terrain models. In: Proceedings of Third NCGIA International Conference/Workshop on Integrating GIS and Environmental Modeling. Santa Fe, New Mexico. Available: http://www. ncgia. ucsb. edu/conf/SANTA_FE_CD-ROM/sf_papers/fels_john/fels_and_matson. html.
Fenneman N, Johnson D. 1946. Physical division of the United States: US geological survey. Physiography Committee Special Map, scale, 1(7,000,000).
Florinsky IV. 1998. Combined analysis of digital terrain models and remotely sensed data in landscape investigations. Progress in physical geography, 22(1): 33-60.
Hammond EH. 1954. Small-scale continental landform maps. Annals of the Association of American Geographers, 44(1): 33-42.
Hammond EH. 1964. Analysis of properties in land form geography: an application to broad-scale land form mapping. Annals of the Association of American Geographers, 54(1): 11-19. doi:https://doi.org/10.1111/j.1467-8306.1964.tb00470.x.
Hengl T, Reuter HI. 2008. Geomorphometry: concepts, software, applications. Newnes, 796 p.
Hodgson ME. 1998. Comparison of angles from surface slope/aspect algorithms. Cartography and Geographic Information Systems, 25(3): 173-185. doi:https://doi.org/10.1559/152304098782383106.
Irvin BJ, Ventura SJ, Slater BK. 1997. Fuzzy and isodata classification of landform elements from digital terrain data in Pleasant Valley, Wisconsin. Geoderma, 77(2): 137-154. doi:https://doi.org/10.1016/S0016-7061(97)00019-0.
Iwahashi J, Pike RJ. 2007. Automated classifications of topography from DEMs by an unsupervised nested-means algorithm and a three-part geometric signature. Geomorphology, 86(3): 409-440. doi:https://doi.org/10.1016/j.geomorph.2006.09.012.
Lane SN, Richards KS, Chandler JH. 1998. Landform monitoring, modelling and analysis. John Wiley and Sons Ltd, 480 p.
MacMillan RA, Jones RK, McNabb DH. 2004. Defining a hierarchy of spatial entities for environmental analysis and modeling using digital elevation models (DEMs). Computers, Environment and Urban Systems, 28(3): 175-200. doi:https://doi.org/10.1016/S0198-9715(03)00019-X.
MacMillan RA, Pettapiece WW, Nolan SC, Goddard TW. 2000. A generic procedure for automatically segmenting landforms into landform elements using DEMs, heuristic rules and fuzzy logic. Fuzzy Sets and Systems, 113(1): 81-109. doi:https://doi.org/10.1016/S0165-0114(99)00014-7.
Moffat A, Catt J, Webster R, Brown E. 1986. A re‐examination of the evidence for a Plio‐Pleistocene marine transgression on the Chiltern Hills. I. Structures and surfaces. Earth Surface Processes and Landforms, 11(1): 95-106. doi:https://doi.org/10.1002/esp.3290110110.
Moore ID, Gessler PE, Nielsen G, Peterson G. 1993. Soil attribute prediction using terrain analysis. Soil Science Society of America Journal, 57(2): 443-452. doi:https://doi.org/10.2136/sssaj1993.03615995005700020026x.
Moore ID, Grayson R, Ladson A. 1991. Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrological processes, 5(1): 3-30. doi:https://doi.org/10.1002/hyp.3360050103.
Moore ID, Nieber JL. 1989. Landscape assessment of soil erosion and nonpoint source pollution. Journal of the Minnesota Academy of Science, 55(1): 18-25.
Morgan JM, Lesh AM. 2005. Developing landform maps using ESRI’S Model-Builder. In: ESRI International User Conference.
Mulla DJ. 1988. Using geostatistics and spectral analysis to study spatial patterns in the topography of southeastern Washington State, USA. Earth Surface Processes and Landforms, 13(5): 389-405. doi:https://doi.org/10.1002/esp.3290130505.
Murphy RE. 1968. Annals map supplement number nine landforms of the world. Annals of the Association of American Geographers, 58(1): 198-200. doi:https://doi.org/10.1111/j.1467-8306.1968.tb01643.x.
Nogami M. 1995. Geomorphometric measures for digital elevation models. Z Geomorph, NF, Suppl, 101: 53-67.
Pike RJ. 1988. The geometric signature: Quantifying landslide-terrain types from digital elevation models. Mathematical Geology, 20(5): 491-511. doi:10.1007/BF00890333.
Pike RJ. 1995. Geomorphometry: progress, practice and prospect. Zeitschrift fur Geomorphologie NF SupplementBand, 101: 221-238.
Pike RJ. 2000. Geomorphometry-diversity in quantitative surface analysis. Progress in Physical Geography, 24(1): 1-20. doi:https://doi.org/10.1177/030913330002400101.
Prima ODA, Echigo A, Yokoyama R, Yoshida T. 2006. Supervised landform classification of Northeast Honshu from DEM-derived thematic maps. Geomorphology, 78(3): 373-386. doi:https://doi.org/10.1016/j.geomorph.2006.02.005.
Speight J. 2009. Landform. In ‘Australian soil and land survey field handbook’. CSIRO Publishing: Melbourne, 8-43 p.
Sulebak JR, Etzelmüller B, Sollid JL. 1997. Landscape regionalization by automatic classification of landform elements. Norsk Geografisk Tidsskrift-Norwegian Journal of Geography, 51(1): 35-45. doi:https://doi.org/10.1080/00291959708552362.
Tadono T, Ishida H, Oda F, Naito S, Minakawa K, Iwamoto H. 2014. Precise global DEM generation by ALOS PRISM. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2(4): 71. doi:https://doi.org/10.5194/isprsannals-II-4-71-2014.
Takaku J, Tadono T, Tsutsui K. 2014. Generation of High Resolution Global DSM from ALOS PRISM. ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, 2(4). doi:https://doi.org/10.5194/isprsarchives-XL-4-243-2014.
Wood J. 1996. The geomorphological characterisation of digital elevation models. University of Leicester (United Kingdom). Thesis submitted for the degree of Doctor of Philosophy at the University of Leicester, 450 p.
Zinck JA, Valenzuela CR. 1990. Soil geographic database: structure and application examples. ITC journal(3): 270-294.
