Digital soil mapping, downscaling and updating conventional soil maps using GIS, RS, statistics and auxiliary data

Spatial distribution of soil types and soil properties in the landscape are important in many environmental researches. Conventional soil surveys are not designed to provide the high-resolution soil information required in environmental modelling and site-specific farm management. The objectives of this study were to investigate the relationship between soil development, soil evolution in the landscape, updating legacy soil maps and pedodiversity in an arid and semi-arid region. The application of Digital Soil Mapping (DSM) techniques was investigated with a particular focus to predict soil taxonomic classes and spatial distribution of soil types by soil observations and covariate sets representative of s,c,o,r,p,a,n factors. In the first study, focus is on establishing relationships between pedodiversity and landform evolution in a 86,000 ha region in Borujen, Chaharmahal-Va-Bakhtiari Province, Central Iran. From an overview study, we could conclude that landform evolution was mainly affected by topography and its components. A second study compares various DSM-methods and a conventional soil mapping approach for soil class maps in terms of accuracy, information value and cost in central Iran. Also, the effects of different sample sizes were investigated. Our results demonstrated that in most predicted maps, in DSM approaches, the best results were obtained using the combination of terrain attributes and the geomorphology map. Furthermore, results showed that the conventional soil mapping approach was not as effective as DSM approach. In the third study, different models of the DSM approach were compared to predict the spatial distribution of some important soil properties such as clay content, soil organic carbon and calcium carbonate content. Among all studied models, the terrain attribute “elevation” is the most important variable to predict soil properties. Random forest had promising performance to predict soil organic carbon. But results revealed that all models could not predict the spatial distributions of clay content properly. The minimum area of land that can be legibly delineated in a traditional (printed) map is highly dependent upon mapping scale. For example, this area at a mapping scale of 1:24,000 is about 2.3 ha but at a mapping scale of 1:1,000,000 it is about 1000 ha. A mapping scale of 1:1,000,000 is just too coarse to show a fine-scale pattern or soil type with any degree of legibility, but finer-scale soil maps are more expensive and time-consuming to produce. Thus, spatial variation is often unavoidably obscured. The fourth study of this dissertation focuses on downscaling and updating soil map methods. Thus, the objectives were to apply supervised and unsupervised disaggregation approaches to disaggregate soil polygons of conventional soil map at a scale of 1: 1,000,000 in the selected area. Therefore, soil subgroups and great groups were selected because it is a basic taxonomic level in regional and national soil maps in Iran. In general, we conclude that DSM approach and also disaggregation approach are capable to predict soil types and properties, produce and update legacy soil maps. However, still a number of challenges need to be evaluated e.g. influence of expert knowledge on CSM approach, resolution of ancillary data, georeferenced legacy soil samples data to validate disaggregated soil maps

Remote sensing and GIS in support of sustainable agricultural development

Over the coming decades it is expected that the vast amounts of area currently in agricultural production will face growing pressure to intensify as world populations continue to grow, and the demand for a more Western-based diet increases. Coupled with the potential consequences of climate change, and the increasing costs involved with current energy-intensive agricultural production methods, meeting goals of environmental and socioeconomic sustainability will become ever more challenging. At a minimum, meeting such goals will require a greater understanding of rates of change, both over time and space, to properly assess how present demand may affect the needs of future generations. As agriculture represents a fundamental component of modern society, and the most ubiquitous form of human induced landscape change on the planet, it follows that mapping and tracking changes in such environments represents a crucial first step towards meeting the goal of sustainability. In anticipation of the mounting need for consistent and timely information related to agricultural development, this thesis proposes several advances in the field of geomatics, with specific contributions in the areas of remote sensing and spatial analysis: First, the relative strengths of several supervised machine learning algorithms used to classify remotely sensed imagery were assessed using two image analysis approaches: pixel-based and object-based. Second, a feature selection process, based on a Random Forest classifier, was applied to a large data set to reduce the overall number of object-based predictor variables used by a classification model without sacrificing overall classification accuracy. Third, a hybrid object-based change detection method was introduced with the ability to handle disparate image sources, generate per-class change thresholds, and minimize map updating errors. Fourth, a spatial disaggregation procedure was performed on coarse scale agricultural census data to render an indicator of agricultural development in a spatially explicit manner across a 9,000 km2 watershed in southwest Saskatchewan for three time periods spanning several decades. The combination of methodologies introduced represents an overall analytical framework suitable for supporting the sustainable development of agricultural environments

Knowledge-Guided Machine Learning for Spatiotemporal Environmental Data Analysis

With the emergence of ubiquitous environmental monitoring systems in the past few decades, we are gaining unprecedented ability to collect vast amounts of spatiotemporal environmental data. However, it still remains a challenge to mine information from the spatiotemporal data for many environmental sustainability tasks. This dissertation focuses on utilizing machine learning to analyze spatiotemporal environmental data with the guidance of domain knowledge and specifically proposes several novel algorithms for the soil moisture gap-filling task and crop yield prediction task. First, we study the problem of soil moisture gap-filling. Large spatiotemporal gaps can be incurred for daily soil moisture product that adopts the radar-radiometer fusion approach. This is normally due to the relatively low revisit schedule and the associated poor spatiotemporal coverage of radar observations. Gap-fill high resolution soil moisture in regional scale for remote sensing soil moisture product is however a great challenge. It requires models learned at neighboring regions to produce predictions at a new region with reasonable accuracy. To address this issue, we propose a novel two-layer machine learning-based algorithm that is motivated by the soil moisture radar-radiometer fusion retrieval algorithm. It predicts the brightness temperature and subsequently the soil moisture at gap areas. Compared with the traditional one-layer machine learning approach, this two-layer approach shows superior performance in extensive experiments at four study areas with distinct climate regimes. We then focus on information mining from the multi-channel geo-spatiotemporal data. Existing approaches adopt various dimensionality reduction techniques without fully taking advantage of the data. In addition, the lack of labeled training data raises another challenge for modeling such data. We propose a novel semi-supervised self-attentive model that learns global spatiotemporal representations for prediction tasks. Spatial and temporal variations in the geo-spatiotemporal data are extracted to produce accurate predictions. To overcome the data scarcity issue, we introduce sampled spatial and temporal context that naturally reside in the largely-available unlabeled geo-spatiotemporal data. The proposed algorithm is validated specifically on a large-scale real-world crop yield prediction task. The results show that our semi-supervised self-attentive model outperforms existing state-of-the-art yield prediction methods and its counterpart, the supervised-only self-attentive model, especially under the stress of training data scarcity

Artificial intelligence for decision making in energy demand-side response

This thesis examines the role and application of data-driven Artificial Intelligence (AI) approaches for the energy demand-side response (DR). It follows the point of view of a service provider company/aggregator looking to support its decision-making and operation. Overall, the study identifies data-driven AI methods as an essential tool and a key enabler for DR. The thesis is organised into two parts. It first provides an overview of AI methods utilised for DR applications based on a systematic review of over 160 papers, 40 commercial initiatives, and 21 large-scale projects. The reviewed work is categorised based on the type of AI algorithm(s) employed and the DR application area of the AI methods. The end of the first part of the thesis discusses the advantages and potential limitations of the reviewed AI techniques for different DR tasks and how they compare to traditional approaches. The second part of the thesis centres around designing machine learning algorithms for DR. The undertaken empirical work highlights the importance of data quality for providing fair, robust, and safe AI systems in DR — a high-stakes domain. It furthers the state of the art by providing a structured approach for data preparation and data augmentation in DR to minimise propagating effects in the modelling process. The empirical findings on residential response behaviour show better response behaviour in households with internet access, air-conditioning systems, power-intensive appliances, and lower gas usage. However, some insights raise questions about whether the reported levels of consumers’ engagement in DR schemes translate to actual curtailment behaviour and the individual rationale of customer response to DR signals. The presented approach also proposes a reinforcement learning framework for the decision problem of an aggregator selecting a set of consumers for DR events. This approach can support an aggregator in leveraging small-scale flexibility resources by providing an automated end-to-end framework to select the set of consumers for demand curtailment during Demand-Side Response (DR) signals in a dynamic environment while considering a long-term view of their selection process

Machine learning to generate soil information

This thesis is concerned with the novel use of machine learning (ML) methods in soil science research. ML adoption in soil science has increased considerably, especially in pedometrics (the use of quantitative methods to study the variation of soils). In parallel, the size of the soil datasets has also increased thanks to projects of global impact that aim to rescue legacy data or new large extent surveys to collect new information. While we have big datasets and global projects, currently, modelling is mostly based on "traditional" ML approaches which do not take full advantage of these large data compilations. This compilation of these global datasets is severely limited by privacy concerns and, currently, no solution has been implemented to facilitate the process. If we consider the performance differences derived from the generality of global models versus the specificity of local models, there is still a debate on which approach is better. Either in global or local DSM, most applications are static. Even with the large soil datasets available to date, there is not enough soil data to perform a fully-empirical, space-time modelling. Considering these knowledge gaps, this thesis aims to introduce advanced ML algorithms and training techniques, specifically deep neural networks, for modelling large datasets at a global scale and provide new soil information. The research presented here has been successful at applying the latest advances in ML to improve upon some of the current approaches for soil modelling with large datasets. It has also created opportunities to utilise information, such as descriptive data, that has been generally disregarded. ML methods have been embraced by the soil community and their adoption is increasing. In the particular case of neural networks, their flexibility in terms of structure and training makes them a good candidate to improve on current soil modelling approaches

An integrated framework for exploring finite mixture heterogeneity in travel demand and behavior

In recent years we have faced a plethora of social trends and new technologies such as shared mobility, micro-mobility, and information and communication technologies, and we will be facing many more in the future (e.g. self-driving cars, disruptive events). In this context, the perennial mission of transportation behavior analysts and modelers - to model behavior/demand so as to understand behavior, help craft responsive policies, and accurately forecast future demand - has become far more challenging. Specifically, behavioral realism and predictive ability are two key goals of modeling (travel) behavior/demand, and a key strategy for achieving those goals has been to introduce some type of heterogeneity in modeling. Thus, this thesis aims to improve our behavioral modeling by accounting for heterogeneity, with clues from the ideas of data/market segmentation, finite mixture, and mixture modeling. The objectives of the thesis are: (1) to build a framework for modeling finite mixture heterogeneity that connects seemingly less related models and various methodological ideas across domains, (2) to tackle various heterogeneity-related research questions in travel behavior and thus show the empirical usefulness of the models under the framework; and (3) to examine the potential, challenges, and implications of the framework with conceptual considerations and practical applications. Five inter-related studies in this thesis illuminate some part(s) of the framework and delineate how key concepts in the framework are connected to each other. (a) The thesis overviews the topics of heterogeneity and mixture modeling in transportation and provides the landscape and details of how we have used mixture modeling. (b) Extending the idea of a finite segmentation approach, the thesis connects and compares three models for treating finite-valued parameter heterogeneity: deterministic segmentation, endogenous switching, and latent class models. The study discusses their similarities and differences from conceptual and empirical standpoints. (c) The thesis explains the confirmatory latent class approach and its potential usefulness, as opposed to the conventional exploratory approach. Adopting this perspective, the study embraces zero-inflated models under the confirmatory latent class approach and demonstrates their empirical value. (d) The thesis introduces the idea of combining latent class and endogenous switching models. Conceptual and empirical differences between the standard latent class model and the proposed approach are discussed. (e) The dissertation illuminates the linkage between finite mixture modeling (specifically in “indirect application”) and the mixture of experts (MoE) architecture, introduced in machine learning. The study proposes to use MoE as a data-driven exploratory tool to capture nonlinear/interaction effects (which are types of parameter heterogeneity), and exhibits its ability using synthetic and empirical data. The thesis concludes with discussions about challenges, potential technical advances, and outlook for the framework. The dissertation is expected to give conceptual/methodological insights on the framework for modeling finite mixture heterogeneity and how various methodologies are connected under the framework. As well, the studies provide rich discussions about study-specific empirical findings and their implications. Thus, the dissertation can help improve our behavior/demand models by serving as a navigational compass for analysts.Ph.D

Urban expansion and loss of prime agricultural land in Sub-Saharan Africa: a challenge to soil conservation and food security

Urbanisation often involves the conversion of various land use and land cover (LULC) classes including agricultural land to urban uses and leads to loss of soil diversity. Unfortunately, there is limited literature on soils lost in Sub-Saharan African (SSA) cities due to lack of detailed soil information yet this region is the fastest growing globally. This study attempts to bridge this gap by using remote sensing data and digital soil mapping (DSM) techniques to assess the rate of agricultural land conversion, determine the appropriate pre-urban soil prediction model and estimate the extent of loss of soils diversity and urban and peri-urban agriculture (UPA) using urban centres in Uganda as case studies. Multi-temporal LULC classification of Landsat ETM+ and TM images showed that built-up area in Kampala expanded 8 times between 1989 and 2015 and 5 times in Mbarara between 2002 and 2016 as a result of the conversion of savannah, wetlands and systematic targeting of agricultural land. DSM techniques involving legacy soil data and soil observations were used to predict the pre-urban soil patterns by modelling the relationships between observed soil classes and environmental covariates using random forests (RF), Multinomial Logistic Regression (MLR) and Boosted Regression Trees (BT) algorithms. The overall prediction accuracy was over 70% producing soil maps at 30m resolution. The soil diversity loss was determined by overlaying RF soil map with the multi-temporal LULC maps. The results show that 74% of the soils lost were in high and medium quality class for agricultural production. Moreover, the average size of farms under UPA decreased from about 1.9 acres in 2002 to about 0.5 acres in 2015. This study has revealed that DSM techniques and remote sensing can be useful in quantifying the loss of soil diversity to urbanisation and provides quantitative evidence that rapid urbanisation could lead to loss of good soils and increase food insecurity in SSA cities

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

Thesis (PhDAgric)--Stellenbosch University, 2022.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.Doctora