Using remote sensing and geographical information systems to classify local landforms using a pattern recognition approach for improved soil mapping

Journal Title
Journal ISSN
Volume Title
Stellenbosch : Stellenbosch University
ENGLISH ABSTRACT: Presently, a major focus of digital soil mapping (DSM) in South Africa is unlocking the soil-landscape relationships of legacy soil data by disaggregating the only source of contiguous soil information for South Africa, the National Land Type Survey (LTS) (ARC, 2003). Each land type is best defined as a homogenous mapping unit with a unique combination of terrain type, soil pattern and macroclimate properties (Paterson et al., 2015). One of the prevailing reasons for the LTS longevity and continual temporal-interoperability is that terrain description is expressly related to a suite of catenary soil property descriptions (Milne, 1936). These terrain types are further divided into terrain morphological units (TMUs) representing a sequence of patterns based on a 5-unit landscape model of 1-crest, 2-scarp, 3-midslope, 4-footslope and 5-valley bottom. Importantly, dominant soil distribution patterns are defined by terrain units relying on an elementary terrain topo-sequence pattern approach, with much of the work done on modelling soil variation related to variation in terrain (van Zijl, 2019). Whilst the LTS remains a source of national interest, there is immense opportunity to build on the existing soil inventory data rather than only focus on “breaking it down” (disaggregation). However, what is needed is a standard operating procedure that not only leverages the ability of digital elevation models (DEM) to explicate soil-landscape associations beyond the limited 5-unit landscape model but allows better refinement of soil descriptions with landscape features. Only once the nuances of optimal DEM parametrisation under controlled conditions are fully understood can the complete scope of DSM and digital geomorphological mapping (DGM) applications be explored. This dissertation attempts to synthesise knowledge on theory, methods, and applications of using remote sensing (RS) and geographical information systems (GIS) to classify local landforms using a pattern recognition approach for improved soil mapping in the context of multiscale problems of digital terrain analysis in KwaZulu-Natal. The dissertation is divided into three parts. Part one (Chapter 2) represents the DEM pre- processing and generalisation method and establishes the protocols for soil-landscape covariate application derived from various sensor platforms and spatial scales. Part two (Chapter 3) introduces the concept of improved terrain unit mapping through the geomorphon approach and describes DEM optimisation for standardised geomorphon representation for uniformly describing soil-landscape properties for inputs to DSM applications. Finally, part three (Chapters 4 & 5) looks at applications of DEM sources and geomorphons first from a holistic landscape context by linking digital terrain and soil-landscape analysis to geodiversity. Finally, the benefit of improved RS and GIS combined with quantitative modelling approaches on improving natural resource predictions are explored by modelling soil-ecotope and soil type mapping units and proposing improvements to an existing DSS designed for KwaZulu-Natal Natal. Specifically, this research is organised into four (4) research chapters with an overview of each chapter’s contribution outlined hereafter. Chapter 2 accounts for the recognition and requirements of DEM generalisation from high to medium resolution RS platforms and the influence these pre-processing approaches have on the extraction of a wide range of terrain attributes. Digital elevation data are elemental in deriving primary topographic attributes that are input variables to various regional soil-landscape models. DEMs' utility to extract different topographic indices as primary inputs to DSM allows the generalised soil-formative relationship between topography and soil characteristics to be measured quantitatively. Traditional landscape-scale approaches to extracting and analysing soils remain subjective and an expensive last resort for large-scale regional soil distribution and variability prediction. Selecting the right DEMs is a critical step in the development of any soil-landscape model. Therefore, the ability to represent soil-landscape relationships rapidly and objectively between soil properties and landscape position using emerging technologies and elevation data in a digital environment and at varying scales is fundamental for using soil-landscape mapping as a regional planning tool. There is, however, still varied consensus on the effect of DEM source and resolution on the application of these topographic attributes to landscape and geomorphic characterisation within South Africa. However, Atkinson et al. (2017) have shown that topographic variable extraction is highly dependent on the DEM source and generalisation approach. However, while higher resolution DEMs may represent the “true” landscape surface more accurately, they do not necessarily offer the best results for all extracted terrain variables for modelling soil-landscape outputs. Given the convenience of a wide range of open-source elevation data for South Africa, there is a need to quantify the impact that DEM generalisation approaches have on simplifying detailed DEMs and compare the accuracy and reliability of results between high resolution and coarse resolution data on the extraction of localised topographic variables as a primer for soil-landscape or digital soil models. Chapter 3 explores the harmonisation of geomorphons derived from various RS platforms to define the landscape character in central KwaZulu-Natal. Robust DGM approaches that can simplify and translate the inclusion of “human knowledge” to automatic terrain classification across a broader spectrum of terrain morphological units and a range of DEM spatial scales offer great potential for improved topographic and landscape analysis and must have their utility investigated. Continual advances in quantitative modelling of surface processes, combined with new spatio-temporal and geo-computational algorithms, have revolutionised the auto-classification and mapping of landform components through the automated analysis of high-quality DEMs. Therefore, a thorough assessment of the effects that different pixel resolution (grain size) and DEM sources have on replicating observed geomorphic spatial patterns and representing selected terrain parameters using advanced automated geomorphometric mapping approaches is necessary. Specifically, it would be valuable to interrogate the self-adapting ability of these automated mapping approaches under regional conditions to quantitatively analyse how the choice of terrain model and scale influences the extraction, generalisation, and representation of digitally derived terrain attributes such as slope gradient, elevation and terrain unit feature extent. Equally important is understanding how the variation in resulting terrain unit representation is limited by spatial resolution discontinuities that ultimately influence the extraction and representation of elementary soil properties. Chapter 4 is a shift from the technical aspects of digital terrain preprocessing and modelling and instead attempts to explore the contribution of gridded soil-landscape products to the abiotic landscape development agenda. It would be worthwhile to contextualise and decode these technical aspects of terrain and soil analyses to a holistic landscape development agenda. It is argued that current global environmental problems and questions demand exploration into new scientific perspectives and improved related paradigms and methodologies. Geodiversity (abiotic complexity) has not received the same level of attention as biodiversity (biotic complexity) despite its intrinsic and indivisible linkages to ecosystem and landscape richness characterisation. The ability to better describe the substrate in which biological and human activities occur is of top standing and must have its potential explored. To date, only one landmark study has successfully investigated the influence of environmental factors on geodiversity mapping in South Africa (Kori et al., 2019). Using an array of multimodal environmental covariates, including hydrographic, lithostratigraphic, pedological, climatic, topographic, solar morphometric and geomorphic variables, I aim to provide further confirmation to regional and international geodiversity research agendas. Chapter 5 culminates in applying quantitative DSM methods, with improved terrain representation, to classify productive soil units (ecotopes) as a proposed methodology to improve the current Bioresource Report Writer (BRW) soil-landscape recommendations. In KwaZulu-Natal, it has been accepted that detailed natural resource information based on scientifically accurate and relevant criteria is required to develop spatial layers that planners, developers, local government, and other stakeholders can use to guide future development. At present, the KwaZulu-Natal Department of Agriculture and Rural Development (KZNDARD) can provide high-level crop production approximations for various crops based on BioResource Units (BRU). However, the BRW has not seen a significant revision for over two decades. Still, the natural resource information it contains provides land managers, policymakers and farmers with invaluable access to regional and farm level qualitative estimations of agricultural productivity. There is a need to preserve this information while simultaneously providing modern measures of land management recommendation at multiple scales to the end-user. Against this backdrop, access to readily interpretable soil and crop information is increasingly being prioritised by provincial planning commissions as critical inputs to DSS for sustainable land management within KwaZulu-Natal.
AFRIKAANSE OPSOMMING: Tans ontsluit 'n groot fokus van digitale grond kartering (DSM) in Suid-Afrika die grond landskap verhoudings van nalatenskap grond data deur die enigste bron van aaneenlopende grond inligting vir Suid-Afrika, die Nasionale Grondtipe-opname (ARC, 2003) te distreun. Elke land tipe word die beste gedefinieer as 'n homogene karterings eenheid met 'n unieke kombinasie van terrein tipe, grondpatroon en makro klimaat eienskappe (Paterson et al. , 2015) . Een van die heersende redes vir die LTS-langlewendheid en voortdurende temporale interoperabiliteit is dat terrein beskrywing uitdruklik verband hou met 'n reeks katalise grondeiendom beskrywings (Milne, 1936). Hierdie terrein tipes word verder verdeel in terrein morfologiese eenhede (TMUs) wat 'n reeks patrone verteenwoordig wat gebaseer is op 'n 5-eenheid landskap model van 1- kuif, 2-serp, 3-midslope, 4-voet en 5-vallei bodem. Belangrik, dominante grond verspreidings patrone word gedefinieer deur terrein eenhede wat staatmaak op 'n elementêre terrein topo-volgorde patroon benadering, met baie van die werk gedoen op modellering grond variasie wat verband hou met variasie in terrein (van Zijl, 2019). Terwyl die LTS bly 'n bron van nasionale belang; daar is enorme geleentheid om voort te bou op die bestaande grond voorraad data eerder as om net te fokus op "afbreek" (disaggregasie). Wat egter nodig is, is 'n standaard bedryfsprosedure wat nie net die vermoë van digitale hoogte modelle(DEM) gebruik om grond landskap verenigings buite die beperkte 5-eenheid landskap model te vererger nie, maar beter verfyning van grond beskrywings met landskap kenmerke moontlik te maak. Slegs sodra die nuanses van optimale DEM parametrisasie onder beheerde toestande ten volle verstaan word, kan die volledige omvang van DSM- en digitale geomorfologiese kartering (DGM) aansoeke ondersoek word. Hierdie verhandeling poog om-kennis oor teorie, metodes en toepassings van ute sintetiseer om afstand waarneming (RS) en geografiese inligtingstelsels (GIS) tesing om plaaslike land vorms te klassifiseer deur 'n patroonherkenning benadering vir verbeterde grond kartering in die konteks van multiskaal probleme van digitale terrein analise te klassifiseer. In KwaZulu-Natal. Die verhandeling word in drie dele verdeel. Deel een (Hoofstuk 2) verteenwoordig die DEM-voor verwerker- en veralgemenings metode en vestig die protokolle vir grondlandskap-kovariaat toediening afgelei van verskeie sensor platforms en ruimtelike skale. Deel twee (Hoofstuk 3) stel die konsep van verbeterde terrein eenheid kartering deur die geomorfon benadering bekend en beskryf DEM-optimalisering vir gestandaardiseerde geomorfon verteenwoordiging om grond landskap eienskappe eenvormig te beskryf vir insette tot DSM-toepassings. Ten slotte, deel drie (Hoofstukke 4 & 5) kyk na toepassings van DEM bronne en geomorfon eerste vanuit 'n holistiese landskap konteks deur die koppeling van digitale terrein en grond landskap analise aan geodiversiteit. Ten slotte word die voordeel van verbeterde RS en GIS gekombineer met kwantitatiewe modellerings benaderings op die verbetering van natuurlike hulpbron voorspellings ondersoek deur grond-ekopeïen- en grondtipe karterings eenhede te modelleer en verbeterings voor te stel aan 'n bestaande DSS wat vir KwaZulu-Natal ontwerp is. Spesifiek, tsy navorsing is organiseer in vier (4) navorsing hoofstukke met 'n oorsig van elke hoofstuk se bydrae wat hierna uiteengesit word. Hoofstuk 2 is verantwoordelik vir die erkenning en vereistes van DEM veralgemening van hoë tot medium resolusie RS platforms en die invloed wat hierdie preprocessing benaderings het op die onttrekking van 'n wye verskeidenheid van terrein eienskappe. Digitale hoogte data is elementêr in die afleiding van primêre topografiese eienskappe wat inset veranderlikes aan verskeie plaaslike grond landskap modelle is. DEMs se nut om verskillende topografiese indekse as primêre insette tot DSM te onttrek, laat die algemene grond vormende verhouding tussen topografie en grondeienskappe kwantitatief gemeet word. Tradisionele landskap skaal benaderings tot die onttrekking en ontleding van grond bly subjektief en 'n duur laaste uitweg vir grootskaalse streeks grond verspreiding en veranderlikheid voorspelling. Die keuse van die regte DEMs is 'n kritieke stap in die ontwikkeling van enige grond landskap model. Daarom is die vermoë om grond landskap verhoudings vinnig en objektief tussen grondeienskappe en landskap posisie te verteenwoordig deur opkomende tegnologieë en hoogte data in 'n digitale omgewing te gebruik en op verskillende skale fundamenteel vir die gebruik van grond landskap kartering as 'n streeksbeplanning instrument. Daar is egter steeds uiteenlopende konsensus oor die uitwerking van DEM-bron en resolusie oor die toepassing van hierdie topografiese eienskappe aan landskap- en geomorfiese karakterisering binne Suid-Afrika. Atkinson et al. (2017) het egter getoon dat topografiese veranderlike onttrekking baie afhanklik is van die DEM-bron en veralgemenings benadering. Alhoewel hoër resolusie-DEMs die "ware" landskap oppervlak meer akkuraat kan verteenwoordig, bied hulle nie noodwendig die beste resultate vir alle onttrokke terrein veranderlikes vir die modellering van grond landskap-uitsette nie. Gegewe die gerief van 'n wye verskeidenheid oopbron-hoogte data vir Suid-Afrika, is dit 'n behoefte om die impak wat DEM-veralgemenings benaderings het op die vereenvoudiging van gedetailleerde DEMs te kwantifiseer en die akkuraatheid en betroubaarheid van resultate tussen hoë resolusie en growwe resolusie data te vergelyk oor die onttrekking van gelokaliseerde topografiese veranderlikes as 'n primer vir grond landskap of digitale grond modelle. Hoofstuk 3 ondersoek die harmonisering van geomorfon wat van verskeie RS-platforms afkomstig is om die landskap karakter in Sentraal-KwaZulu-Natal te definieer. Robuuste DGM benaderings wat die insluiting van "menslike kennis" kan vereenvoudig en vertaal na outomatiese terrein klassifikasie oor 'n breër spektrum van terrein morfologiese eenhede en 'n verskeidenheid DEM ruimtelike skale bied groot potensiaal vir verbeterde topografiese en landskap analise en moet hul nut ondersoek. Voortdurende vooruitgang in kwantitatiewe modellering van oppervlak prosesse, gekombineer met nuwe spatio-temporale en geo-berekenings algoritmes, het die ou toklassifikasie en kartering van land vorm komponente omwentel deur die outomatiese analise van hoë gehalte DEMs. Daarom is 'n deeglike assessering van die effekte wat verskillende pixel resolusie (graan grootte) en DEM-bronne het op die replisering van waargenome geomorfiese ruimtelike patrone en verteenwoordig geselekteerde terrein parameters met behulp van gevorderde outomatiese geomorfon metriese karterings benaderings nodig. Spesifiek, dit sal waardevol wees om die self-aanpassing vermoë van hierdie outomatiese kartering benaderings onder streeks toestande te ondervra om kwantitatief te analiseer hoe die keuse van terrein model en skaal die onttrekking, veralgemening en voorstelling van digitaal afgeleide terrein kenmerke soos hellings gradiënt, hoogte- en terrein eenheid-funksie omvang beïnvloed. Ewe belangrik is om te verstaan hoe die variasie in gevolglike terrein eenheid verteenwoordiging beperk word deur ruimtelike resolusie-stakings wat uiteindelik die onttrekking en voorstelling van elementêre grondeienskappe beïnvloed Hoofstuk 4 is 'n verskuiwing van die tegniese aspekte van digitale terrein voor verwerking en modellering en poog eerder om die bydrae van geroosterde grond landskap produkte na die abiotiese landskap ontwikkelings agenda te verken. Ek sou die moeite werd wees om hierdie tegniese aspekte van terrein- en grond ontledings na 'n holistiese landskap ontwikkelings agenda te kontekstualiseer en te dekodeer. Daar word aangevoer dat huidige globale omgewingsprobleme en vrae eksplorasie in nuwe wetenskaplike perspektiewe en verbeterde verwante paradigmas en metodologieë vereis. Geodiversiteit (abiotiese kompleksiteit) het nie dieselfde vlak van aandag as biodiversiteit (biotiese kompleksiteit) ontvang nie, ten spyte van sy intrinsieke en ondeelbare verbande met ekosisteem- en landskap ryke karakterisering. Die vermoë om die substraat waarin biologiese en menslike aktiwiteite voorkom, beter te beskryf, is van bostaande en moet sy potensiaal ondersoek. Tot op hede het slegs een ander landmerk studie die invloed van omgewingsfaktore op geodiversiteits kartering in Suid-Afrika (Kori et al. , 2019). Met behulp van 'n verskeidenheid multimodale omgewings kovariaat, insluitend hidrografiese, lithostratigraphic, pedologiese, klimaat-, topografiese, son morfometriese en geomorfiese veranderlikes, beoog ek om verdere bevestiging te gee aan streeks- en internasionale geodiversiteits navorsing agendas. Hoofstuk 5 kulmineer in die toepassing van kwantitatiewe DSM-metodes, met verbeterde terrein verteenwoordiging, om produktiewe grondeenhede (ekotipes) te klassifiseer as 'n voorgestelde metodologie om die huidige BRW-grondlandskap aanbevelings te verbeter. In KwaZulu-Natal is aanvaar dat gedetailleerde natuurlike hulpbron inligting gebaseer op wetenskaplik akkurate en relevante kriteria nodig is om ruimtelike lae te ontwikkel wat beplanners, ontwikkelaars, plaaslike regering en ander belanghebbendes kan gebruik om toekomstige ontwikkeling te lei. Tans kan die KwaZulu-Natal Departement van Landbou en Landelike Ontwikkeling (KZNDARD) hoëvlak-gewasproduksie-benaderings vir verskeie gewasse op grond van BRUs verskaf. Die BRW het egter vir meer as twee dekades nie 'n beduidende hersiening gesien nie. Tog bied die natuurlike hulpbron inligting wat dit bevat, grond bestuurders, beleidmakers en boere van onskatbare waarde toegang tot streeks- en plaasvlak kwalitatiewe beramings van landbou produktiwiteit. Daar is 'n behoefte om hierdie inligting te bewaar, terwyl dit terselfdertyd moderne maatreëls van grondbestuur aanbeveling op verskeie skale aan die eindgebruiker verskaf. Teen hierdie agtergrond word toegang tot geredelik interpreteerbare grond- en gewas inligting toenemend deur provinsiale beplanningskommissie geprioritiseer as kritiese insette tot DSS vir volhoubare grondbestuur binne KwaZulu-Natal.
Thesis (PhDAgric)--Stellenbosch University, 2022.
Digital elevation models -- South Africa -- KwaZulu- Natal, Geographic information systems -- South Africa, Geodiversity, Digital soil mapping, Remote sensing -- South Africa, UCTD