Linking hydrological connectivity to gully erosion in savanna rangelands tributary to the Great Barrier Reef using structure‐from‐motion photogrammetry

Gully erosion is a major land management challenge globally and a particularly important issue in dry tropical savanna rangelands tributary to the Great Barrier Reef, Australia. This study investigated linkages between hillslope hydrological connectivity pathways and gully geomorphic change in the Burdekin River Basin. High‐resolution (0.1 m) topographic and land cover data derived from low‐cost aerial (via unmanned aircraft system) structure‐from‐motion with multiview stereo photogrammetry (SfM) were used to map fine‐scale connectivity patterns and quantify headcut retreat at the hillslope scale (~150,000 m2). Very high resolution (0.01 m) topographic models derived from ground‐based (via handheld digital camera) SfM were used to quantify the morphology and geomorphic change of several gully arms (300–700 m2) between 2016 and 2018. Median linear, areal, and volumetric headcut (n = 21) retreat rates were 0.2 m, 0.8 m2, and 0.3 m3 yr−1, respectively. At all study sites, the points where modelled hydrological flow lines intersected gullies corresponded to observed geomorphic change, enabling spatially explicit identification of gully extension pathways as a result of overland flow. Application of an index of connectivity demarcated parts of the hillslope most connected to the gully network. Bare areas, roads, and cattle trails were identified as important runoff source areas and hydrological conduits driving gully extension. Ground‐based SfM accurately reconstructed complex morphologic features including undercuts, overhangs, rills, and flutes, providing insights into within‐channel erosion processes. This study contributes to an improved understanding and modelling of hydrogeomorphic drivers of gully erosion in degraded savanna rangelands, ultimately benefiting gully management

Uncertainties in Digital Elevation Models: Evaluation and Effects on Landform and Soil Type Classification

Digital elevation models (DEMs) are a widely used source for the digital representation of the Earth's surface in a wide range of scientific, industrial and military applications. Since many processes on Earth are influenced by the shape of the relief, a variety of different applications rely on accurate information about the topography. For instance, DEMs are used for the prediction of geohazards, climate modelling, or planning-relevant issues, such as the identification of suitable locations for renewable energies. Nowadays, DEMs can be acquired with a high geometric resolution and over large areas using various remote sensing techniques, such as photogrammetry, RADAR, or laser scanning (LiDAR). However, they are subject to uncertainties and may contain erroneous representations of the terrain. The quality and accuracy of the topographic representation in the DEM is crucial, as the use of an inaccurate dataset can negatively affect further results, such as the underestimation of landslide hazards due to a too flat representation of relief in the elevation model. Therefore, it is important for users to gain more knowledge about the accuracy of a terrain model to better assess the negative consequences of DEM uncertainties on further analysis results of a certain research application. A proper assessment of whether the purchase or acquisition of a highly accurate DEM is necessary or the use of an already existing and freely available DEM is sufficient to achieve accurate results is of great qualitative and economic importance. In this context, the first part of this thesis focuses on extending knowledge about the behaviour and presence of uncertainties in DEMs concerning terrain and land cover. Thus, the first two studies of this dissertation provide a comprehensive vertical accuracy analysis of twelve DEMs acquired from space with spatial resolutions ranging from 5 m to 90 m. The accuracy of these DEMs was investigated in two different regions of the world that are substantially different in terms of relief and land cover. The first study was conducted in the hyperarid Chilean Atacama Desert in northern Chile, with very sparse land cover and high elevation differences. The second case study was conducted in a mid-latitude region, the Rur catchment in the western part of Germany. This area has a predominantly flat to hilly terrain with relatively diverse and dense vegetation and land cover. The DEMs in both studies were evaluated with particular attention to the influence of relief and land cover on vertical accuracy. The change of error due to changing slope and land cover was quantified to determine an average loss of accuracy as a function of slope for each DEM. Additionally, these values were used to derive relief-adjusted error values for different land cover classes. The second part of this dissertation addresses the consequences that different spatial resolutions and accuracies in DEMs have on specific applications. These implications were examined in two exemplary case studies. In a geomorphometric case study, several DEMs were used to classify landforms by different approaches. The results were subsequently compared and the accuracy of the classification results with different DEMs was analysed. The second case study is settled within the field of digital soil mapping. Various soil types were predicted with machine learning algorithms (random forest and artificial neural networks) using numerous relief parameters derived from DEMs of different spatial resolutions. Subsequently, the influence of high and low resolution DEMs with the respectively derived land surface parameters on the prediction results was evaluated. The results on the vertical accuracy show that uncertainties in DEMs can have diverse reasons. Besides the spatial resolution, the acquisition technique and the degree of improvements made to the dataset significantly impact the occurrence of errors in a DEM. Furthermore, the relief and physical objects on the surface play a major role for uncertainties in DEMs. Overall, the results in steeper areas show that the loss of vertical accuracy is two to three times higher for a 90 m DEM than for DEMs of higher spatial resolutions. While very high resolution DEMs of 12 m spatial resolution or higher only lose about 1 m accuracy per 10° increase in slope steepness, 30 m DEMs lose about 2 m on average, and 90 m DEMs lose more than 3 m up to 6 m accuracy. However, the results also show significant differences for DEMs of identical spatial resolution depending on relief and land cover. With regard to different land cover classes, it can be stated that mid-latitude forested and water areas cause uncertainties in DEMs of about 6 m on average. Other tested land cover classes produced minor errors of about 1 – 2 m on average. The results of the second part of this contribution prove that a careful selection of an appropriate DEM is more crucial for certain applications than for others. The choice of different DEMs greatly impacted the landform classification results. Results from medium resolution DEMs (30 m) achieved up to 30 % lower overall accuracies than results from high resolution DEMs with a spatial resolution of 5 m. In contrast to the landform classification results, the predicted soil types in the second case study showed only minor accuracy differences of less than 2 % between the usage of a spatial high resolution DEM (15 m) and a low resolution 90 m DEM. Finally, the results of these two case studies were compared and discussed with other results from the literature in other application areas. A summary and assessment of the current state of knowledge about the impact of a particular chosen terrain model on the results of different applications was made. In summary, the vertical accuracy measures obtained for each DEM are a first attempt to determine individual error values for each DEM that can be interpreted independently of relief and land cover and can be better applied to other regions. This may help users in the future to better estimate the accuracy of a tested DEM in a particular landscape. The consequences of elevation model selection on further results are highly dependent on the topic of the study and the study area's level of detail. The current state of knowledge on the impact of uncertainties in DEMs on various applications could be established. However, the results of this work can be seen as a first step and more work is needed in the future to extend the knowledge of the effects of DEM uncertainties on further topics that have not been investigated to date

Comparison and Validation of Satellite-Derived Digital Surface/Elevation Models over India

© 2020, Indian Society of Remote Sensing. India presents among the world’s most topographically complex geomorphologies, with land elevations ranging from –2 m to + 8586 m and terrain gradients sometimes exceeding 45°. Here, we present an evaluation of four freely available digital surface models (DSMs) on a model-to-model basis, as well as a validation using independent ground-truth data from levelled benchmarks in India. The DSMs tested comprise SRTM1″, SRTM3″, ASTER1″ and Cartodem1″ [an India-only model]. Along with these four DSMs, the MERIT3″ digital elevation model (DEM) is also tested with the ground-truth data. Our results for India indicate some mismatch of these DEMs/DSMs from their claimed accuracies/precisions. All DSMs/DEMs (except for ASTER) have > 90% of pixels satisfying ± 16 m at the one-sigma level, but only in the low-lying (< 500 m) parts of India, i.e. the Gangetic plains and the Thar desert

Limitations posed by free DEMs in watershed studies: The case of river Tanaro in Italy

Topography is a critical element in the hydrological response of a drainage basin and its availability in the form of digital elevation models (DEMs) has advanced the modeling of hydrological and hydraulic processes. However, progress experienced in these fields may stall, as intrinsic characteristics of free DEMs may limit new findings, while at the same time new releases of free, high-accuracy, global digital terrain models are still uncertain. In this paper, the limiting nature of free DEMs is dissected in the context of hydrogeomorphology. Ten sets of terrain data are analyzed: the SRTM GL1 and GL3, HydroSHEDS, TINITALY, ASTER GDEM, EU DEM, VFP, ALOS AW3D30, MERIT and the TDX. In specific, the influence of three parameters are investigated, i.e., spatial resolution, hydrological reconditioning and vertical accuracy, on four relevant geomorphic terrain descriptors, namely the upslope contributing area, the local slope, the elevation difference and the flow path distance to the nearest stream, H and D, respectively. The Tanaro river basin in Italy is chosen as the study region and the newly released LiDAR for the Italian territory is used as benchmark to reassess vertical accuracies. In addition, the EU-Hydro photo-interpreted river network is used to compare DEM-based river networks. Most DEMs approximate well the frequency curve of elevations of the LiDAR, but this is not necessarily reflected in the representation of geomorphic features. For example, DEMs with finer spatial resolution present larger contributing areas; differences in the slope can reach 10%; between 5 m and 12 m H, none of the considered DEMs can faithfully represent the LiDAR; D presents significant variability between DEMs; and river network extraction can be problematic in flatter terrain. It is also found that the lowest mean absolute error (MAE) is given by the MERIT, 2.85 m, while the lowest root mean square error (RMSE) is given by the SRTM GL3, 4.83 m. Practical implications of choosing a DEM over another may be expected, as the limitations of any particular DEM in faithfully reproducing critical geomorphic terrain features may hinder our ability to find satisfactory answers to some pressing problems

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

Geovisualization

Geovisualization involves the depiction of spatial data in an attempt to facilitate the interpretation of observational and simulated datasets through which Earth's surface and solid Earth processes may be understood. Numerous techniques can be applied to imagery, digital elevation models, and other geographic information system data layers to explore for patterns and depict landscape characteristics. Given the rapid proliferation of remotely sensed data and high-resolution digital elevation models, the focus is on the visualization of satellite imagery and terrain morphology, where manual human interpretation plays a fundamental role in the study of geomorphic processes and the mapping of landforms. A treatment of some techniques is provided that can be used to enhance satellite imagery and the visualization of the topography to improve landform identification as part of geomorphological mapping. Visual interaction with spatial data is an important part of exploring and understanding geomorphological datasets, and a variety of methods exist ranging across simple overlay, panning and zooming, 2.5D, 3D, and temporal analyses. Specific visualization outputs are also covered that focus on static and interactive methods of dissemination. Geomorphological mapping legends and the cartographic principles for map design are discussed, followed by details of dynamic web-based mapping systems that allow for greater immersive use by end users and the effective dissemination of data

Semi-automated geomorphological mapping applied to landslide hazard analysis

Computer-assisted three-dimensional (3D) mapping using stereo and multi-image (“softcopy”) photogrammetry is shown to enhance the visual interpretation of geomorphology in steep terrain with the direct benefit of greater locational accuracy than traditional manual mapping. This would benefit multi-parameter correlations between terrain attributes and landslide distribution in both direct and indirect forms of landslide hazard assessment. Case studies involve synthetic models of a landslide, and field studies of a rock slope and steep undeveloped hillsides with both recently formed and partly degraded, old landslide scars. Diagnostic 3D morphology was generated semi-automatically both using a terrain-following cursor under stereo-viewing and from high resolution digital elevation models created using area-based image correlation, further processed with curvature algorithms. Laboratory-based studies quantify limitations of area-based image correlation for measurement of 3D points on planar surfaces with varying camera orientations. The accuracy of point measurement is shown to be non-linear with limiting conditions created by both narrow and wide camera angles and moderate obliquity of the target plane. Analysis of the results with the planar surface highlighted problems with the controlling parameters of the area-based image correlation process when used for generating DEMs from images obtained with a low-cost digital camera. Although the specific cause of the phase-wrapped image artefacts identified was not found, the procedure would form a suitable method for testing image correlation software, as these artefacts may not be obvious in DEMs of non-planar surfaces.Modelling of synthetic landslides shows that Fast Fourier Transforms are an efficient method for removing noise, as produced by errors in measurement of individual DEM points, enabling diagnostic morphological terrain elements to be extracted. Component landforms within landslides are complex entities and conversion of the automatically-defined morphology into geomorphology was only achieved with manual interpretation; however, this interpretation was facilitated by softcopy-driven stereo viewing of the morphological entities across the hillsides.In the final case study of a large landslide within a man-made slope, landslide displacements were measured using a photogrammetric model consisting of 79 images captured with a helicopter-borne, hand-held, small format digital camera. Displacement vectors and a thematic geomorphological map were superimposed over an animated, 3D photo-textured model to aid non-stereo visualisation and communication of results

Limitations Posed by Free DEMs in Watershed Studies: The Case of River Tanaro in Italy

Topography is a critical element in the hydrological response of a drainage basin and its availability in the form of Digital Elevation Models (DEM) has advanced the modelling of hydrological and hydraulic processes. However, progress experienced in these fields may stall, as intrinsic characteristics of free DEMs may limit new findings, while at the same time new releases of free, high-accuracy, global digital terrain models are still uncertain. In this paper, the limiting nature of free DEMs is dissected in the context of hydrogeomorphology. Nine sets of terrain data are analysed: the SRTM GL1 and GL3, HydroSHEDS, TINITALY, ASTER GDEM, EU DEM, VFP, ALOS AW3D30, MERIT and the TDX. In specific, the influence of three parameters are investigated, i.e., spatial resolution, hydrological reconditioning and vertical accuracy, on four relevant geomorphic terrain descriptors, namely the upslope contributing area, the local slope, the elevation difference and the flow path distance to the nearest stream, H and D, respectively. The Tanaro river basin in Italy is chosen as the study region and the newly released LiDAR for the Italian territory is used as benchmark to reassess vertical accuracies. In addition, the EU-Hydro photo-interpreted river network is used to compare DEM-based river networks. Most DEMs approximate well the frequency curve of elevations of the LiDAR, but this is not necessarily reflected in the representation of geomorphic features. For example, DEMs with finer spatial resolution present larger contributing areas; differences in the slope can reach 10%; between 5 m and 12 m H, none of the considered DEMs can faithfully represent the LiDAR; D presents significant variability between DEMs; and river network extraction can be problematic in flatter terrain. It is also found that the lowest mean absolute error (MAE) is given by the MERIT, 2.85 m, while the lowest root mean square error (RMSE) is given by the SRTM GL3, 4.83 m. Practical implications of choosing a DEM over another may be expected, as the limitations of any particular DEM in faithfully reproducing critical geomorphic terrain features may hinder our ability to find satisfactory answers to some pressing problems