Everglades National Park is a hydro-ecologically significant wetland experiencing salinity ingress over the years. This motivated our study to map water salinity using a spatially weighted optimization model (SWOM); and soil salinity using land cover classes and EC thresholds. SWOM was calibrated and validated at 3-km grids with actual salinity for 1998–2001, and yielded acceptable R2 (0.89-0.92) and RMSE (1.73-1.92 ppt). Afterwards, seasonal water salinity mapping for 1996–97, 2004–05, and 2016 was carried out. For soil salinity mapping, supervised land cover classification was firstly carried out for 1996, 2000, 2006, 2010 and 2015; with the first four providing average accuracies of 82%-94% against existing NLCD classifications. The land cover classes and EC thresholds helped mapping four soil salinity classes namely, the non saline (EC = 0~2 dS/m), low saline (EC = 2~4 dS/m), moderate saline (EC = 4~8 dS/m) and high saline (EC >8 dS/m) areas. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2017. / FAU Electronic Theses and Dissertations Collection

Thesis (MSc (Geography and Environmental Studies))--University of Stellenbosch, 2007. / Soil salinization is a world wide environmental problem affecting plant growth and agricultural yields. Remote sensing has been used as a tool to detect and/or manage soil salinity. Object-oriented image analysis is a relatively new image analysis technique which allows analysis at different hierarchical scales, the use of relationships between objects and contextual information in the classification process, and the ability to create a rule based classification procedure. The Lower Orange River in South Africa is a region of successful irrigation farming along the river floodplain but also with the potential risk of soil salinization. This research attempted to detect and map areas of potential high soil salinity using digital aerial photography and digital elevation models. Image orthorectification was conducted on the digital aerial photographs. The radiometric variances between photographs made radiometric calibration of the photographs necessary. Radiometric calibration on the photographs was conducted using Landsat 7 satellite images as radiometric correction values, and image segmentation as the correction units for the photographs. After radiometric calibration, object-oriented analysis could be conducted on one analysis region and the developed rule bases applied to the other regions without the need for adjusting parameters. A rule based hierarchical classification was developed to detect vegetation stress from the photographs as well as salinity potential terrain features from the digital elevation models. These rule bases were applied to all analysis blocks. The detected potential high salinity indicators were analyzed spatially with field collected soil data in order to assess the capability of the classifications to detect actual salinization, as well as to assess which indicators were the best indicators of salinity potential. Vegetation stress was not a good indicator of salinity as many other indicators could also cause vegetation stress. Terrain indicators such as depressions in the landscape at a micro scale were the best indicators of potential soil salinization.

Les versants viticoles en Côte-d’Or (Bourgogne, France) présentent une forte diversité de sols résultant d’interactions entre des facteurs naturels et des facteurs anthropiques opérant à diverses échelles spatio-temporelles. Le sol représente un enjeu majeur en viticulture, car il détermine en partie la qualité de la production viticole. Or, il est soumis à d’importantes dégradations causées par l’érosion. Pour mieux gérer ce patrimoine sol et pérenniser la viticulture côte d’orienne, une meilleure connaissance de celui-ci est nécessaire. Ce travail a pour objectifs de caractériser les sols viticoles et d’identifier les facteurs qui contribuent à leur diversité par une approche interdisciplinaire croisant la géologie, la géomorphologie, la pédologie, l’histoire de l’occupation du sol et des pratiques. Les données ont été acquises à très haute résolution spatiale à partir de diverses méthodes d’imagerie i.e. télédétection par imagerie, géophysique de subsurface et modèle numérique de terrain. À l’échelle du versant, les cartes pédologiques produites à grande échelle permettent de discuter du modèle d’organisation des sols. À l’échelle de la parcelle, ces cartes mettent en évidence l’impact de l’homme sur la diversité des sols, par son rôle sur la structure du parcellaire et l’intensité de l’érosion notamment. La diversité des sols viticoles dépend de l’échelle spatiale à laquelle ils sont observés. À l’échelle du versant, les sols évoluent en fonction des variations du substrat géologique et de la topographie, suivant le modèle de topolithoséquence. À l’échelle de la parcelle, les variations du sol, telles que son épaisseur et son statut organique, peuvent être appréhendées, permettant d’en prédire le comportement agronomique. L’influence de l’homme se marque à la fois sur la structure du parcellaire et par les pratiques culturales anciennes et actuelles. Il participe ainsi à la construction des terroirs, à travers son action sur la diversité des sols. / The Burgundian vineyard hillslopes (Côte-d’Or, France) exhibit a high diversity of soils resulting from the combination of several natural and anthropogenic factors acting at various spatio-temporal scales. The soil types have major role in viticulture, since they partly determine wine-growing quality. However, soil undergoes important degradation caused by hydric erosion and vineyard management practices. To control this soil heritage for a sustained viticulture in Côte-d’Or, a better knowledge of soil is necessary. The objectives of this work is to characterise vineyard soils and to identify the factors governing their diversity using an interdisciplinary approach crossing geology, geomorphology, pedology and history of soil land use and vineyard management practices. Data have been acquired at a high spatial resolution from different imagery methods i.e. remote sensing, subsurface geophysics, and digital terrain model. At the hillslope scale, high resolution soil maps permit to predict soil agronomical comportment and discuss the spatial soil organisation of vineyard hillslope soils. At the plot scale, these maps highlight the human impact on soil diversity through its role on landscape structure and erosion intensity. Our work shows that the soil diversity of the vineyard hillslopes depends on the spatial scale used to analyse this diversity. At the hillslope scale, soil is gradually evolving along the slope, and is controlled by the geological substrate and topographical variations, and responds to topolithosequence model. At the plot scale, variations of soil thickness and organic status are taken into account and permit to predict soil agronomic behaviour. This soil diversity is partly related to human impact, due by both historical and present vineyard management practices. It appears that human activities have a past and present influence on the terroir construction in Côte-d’Or, through its action on soil diversity.

Conselho Nacional de Desenvolvimento Científico e Tecnológico / The possibility of mapping soil properties using soil spatial prediction functions (SSPFe) is a reality. But is it possible to SSPFe to estimate soil properties such as the particlesize distribution (psd) in a young, unstable and geologically complex geomorphologic surface? What would be considered a good performance in such situation and what alternatives do we have to improve it? With the present study I try to find answers to such questions. To do so I used a set of 339 soil samples from a small catchment of the hillslope areas of central Rio Grande do Sul. Multiple linear regression models were built using landsurface parameters (elevation, convergence index, stream power index). The SSPFe explained more than half of data variance. Such performance is similar to that of the conventional soil mapping approach. For some size-fractions the SSPFe performance can reach 70%. Largest uncertainties are observed in areas of larger geological heterogeneity. Therefore, significant improvements in the predictions can only be achieved if accurate geological data is made available. Meanwhile, SSPFe built on land-surface parameters are efficient in estimating the psd of the soils in regions of complex geology. However, there still are questions that I couldn t answer! Is soil mapping important to solve the main social and environmental issues of our time? What if our activities were subjected to a social control as in a direct democracy, would they be worthy of receiving any attention? / A possibilidade de mapear as propriedades dos solos através do uso de funções de predição espacial de solos (FPESe) é uma realidade. Mas seria possível construir FPESe para estimar propriedades como a distribuição do tamanho de partículas do solo (dtp) em um superfície geomorfológica jovem e instável, com elevada complexidade geológica e pedológica? O que seria considerado um bom desempenho nessas condições e que alternativas temos para melhorá-lo? Com esse trabalho tento encontrar respostas para essas questões. Para isso utilizei um conjunto de 339 amostras de solo de uma pequena bacia hidrográfica de encosta da região Central do RS. Modelos de regressão linear múltiplos foram construídos com atributos de terreno (elevação, índice de convergência, índice de potência de escoamento). As FPESe explicaram mais da metade da variância dos dados. Tal desempenho é semelhante àquele da abordagem tradicional de mapeamento de solos. Para algumas frações de tamanho o desempenho das FPESe pode chegar a 70%. As maiores incertezas ocorrem nas áreas de maior heterogeneidade geológica. Assim, melhorias significativas nas predições somente poderão ser alcançadas se dados geológicos acurados forem disponibilizados. Enquanto isso, FPESe construídas a partir de atributos de terreno são eficientes em estimar a dtp de solos de regiões com geologia complexa e elevada instabilidade. Mas restam dúvidas que não consegui resolver! O mapeamento de solos é importante para a resolução dos principais problemas sociais e ambientais do nosso tempo? E se nossas atividades estivessem submetidas ao controle da população como em uma democracia direta, seriam elas dignas de receber atenção?

The objective of the thesis is the model-based analysis of spatial patterns of decomposition properties on the forested slopes of the montane level (ca. 1200-2200 m a.s.l.) in a study area in the Italian Alps (Val di Sole / Val di Rabbi, Autonomous Province of Trento). The analysis includes humus forms and enchytraeid assemblages as well as pH values, activities of extracellular enzymes and C/N ratios of the topsoil. The first aim is to develop, test and apply data-based techniques for spatial modelling of soil ecological parameters. This methodological approach is based on the concept of digital soil mapping. The second aim is to reveal the relationships between humus forms, soil organisms and soil microbiological parameters in the study area. The third aim is to analyze if the spatial patterns of indicators of decomposition differ between the landscape scale and the slope scale. At the landscape scale, sample data from six sites are used, covering three elevation levels at both north- and south-facing slopes. A knowledge-based approach that combines a decision tree analysis with the construction of fuzzy membership functions is introduced for spatial modelling. According to the sampling design, elevation and slope exposure are the explanatory variables. The investigations at the slope scale refer to one north-facing and one south-facing slope, with 30 sites occurring on each slope. These sites have been derived using conditioned Latin Hypercube Sampling, and thus reasonably represent the environmental conditions within the study area. Predictive maps have been produced in a purely data-based approach with random forests. At both scales, the models indicate a high variability of spatial decomposition patterns depending on the elevation and the slope exposure. In general, sites at high elevation on north-facing slopes almost exclusively exhibit the humus forms Moder and Mor. Sites on south-facing slopes and at low elevation exhibit also Mull and Amphimull. The predictions of those enchytraeid species characterized as Mull and Moder indicators match the occurrence of the corresponding humus forms well. Furthermore, referencing the mineral topsoil, the predictive models show increasing pH values, an increasing leucine-aminopeptidase activity, an increasing ratio alkaline/acid phosphomonoesterase activity and a decreasing C/N ratio from north-facing to south-facing slopes and from high to low elevation. The predicted spatial patterns of indicators of decomposition are basically similar at both scales. However, the patterns are predicted in more detail at the slope scale because of a larger data basis and a higher spatial precision of the environmental covariates. These factors enable the observation of additional correlations between the spatial patterns of indicators of decomposition and environmental influences, for example slope angle and curvature. Both the corresponding results and broad model evaluations have shown that the applied methods are generally suitable for modelling spatial patterns of indicators of decomposition in a heterogeneous high mountain environment. The overall results suggest that the humus form can be used as indicator of organic matter decomposition processes in the investigated high mountain area.

Functions and services provided by soils play an important role for numerous sustainable development goals involving mainly food supply and environmental health. In many regions of the Earth, water erosion is a major threat to soil functions and is mostly related to land-use change or poor agricultural management. Selecting proper soil management practices requires site-specific indicators such as water erosion, which follow a spatio-temporal variation. The aim of this study was to develop monthly soil erosion risk maps for the data-scarce catchment of Ruiru drinking water reservoir located in Kenya. Therefore, the Revised Universal Soil Loss Equation complemented with the cubist–kriging interpolation method was applied. The erodibility map created with digital soil mapping methods (R2 = 0.63) revealed that 46% of the soils in the catchment have medium to high erodibility. The monthly erosion rates showed two distinct potential peaks of soil loss over the course of the year, which are consistent with the bimodal rainy season experienced in central Kenya. A higher soil loss of 2.24 t/ha was estimated for long rains (March–May) as compared to 1.68 t/ha for short rains (October–December). Bare land and cropland are the major contributors to soil loss. Furthermore, spatial maps reveal that areas around the indigenous forest on the western and southern parts of the catchment have the highest erosion risk. These detected erosion risks give the potential to develop efficient and timely soil management strategies, thus allowing continued multi-functional use of land within the soil–food–water nexus.

Soil carbon is the largest sink and source of the global carbon cycle and is disturbed by several natural, anthropogenic, and environmental factors. The global increase of atmospheric CO2 affects soil carbon cycling through varied biogeochemical processes. The first chapter is a compilation of current information on potential factors triggering soil acidification and weathering mechanisms under elevated CO2 and their consequences on soil inorganic carbon (SIC) pool and quality. Soil water content and precipitation were critical factors influencing elevated CO2 effects on the SIC pool. The second chapter examines a detailed column experiment in which six soils from the state of Mississippi, USA, representing acidic, neutral, and alkaline pH, were exposed to different CO2 enrichments (100%, 10%, and 1%) for 30 days. The leachates’ pH tended to attain an equilibrium state (neutral) with time under CO2 saturation. SIC increased under CO2 saturation, whereas cation exchange capacity (CEC) showed a decreasing pattern in all soils. In the third chapter, an eXplainable artificial intelligence (XAI) was performed to visualize the different forms of soil carbon variability across the Mississippi River Basin area. This model explains key insights and local discrepancies, suggesting a solution to the “Black-Box” issue. The best performing model, stack ensemble, showed improved RMSE (3 to 8%) and spatial variability for soil carbons than other ML models, especially after adding the residuals from regression analyses. Land cover type > soil pH > total nitrogen, > NDVI were identified as the top four crucial factors for predicting SOC when bulk density > precipitation, soil pH > mean annual temperature described SIC. The proposed automatic machine learning (AutoML) model with model agnostic interpolations might be a hallmark to mitigate the C loss under adverse climate change conditions and allow diverse knowledge groups to adopt a new interpretable ML algorithm more confidently. Findings from this study help predict the impact of elevated atmospheric CO2 on soil pH, acidification, and nutrient availability and develop strategies for sustainable land management practices under a changing climate.

The objective of this thesis was to identify the mechanisms associated with the recurrence of rock-slope instability along the R518 and R523 roads in Limpopo. Advanced geotechnical methods and ASTER imagery were used for the purpose while a predictive rockfall hazard rating matrix chart and rock slope stability charts for unsaturated sensitive clay soil and rock slopes were to be developed. The influence of extreme rainfall on the slope stability of the sensitive clay soil was also evaluated. To achieve the above, field observations, geological mapping, kinematic analysis, and limit equilibrium were performed. The latter involved toppling, transitional and rotational analyses. Numerical simulation was finally resorted to. The following software packages were employed: SWEDGE, SLIDE, RocData, RocFall, DIPS, RocPlane, and Phase 2. The simulation outputs were analyzed in conjunction with ASTER images. The advanced remote sensing data paved the way for landslide susceptibility analysis. From all the above, rockfall hazard prediction charts and slope stability prediction charts were developed. Several factors were also shown by numerical simulation to influence slope instability in the area of study, i.e. sites along the R518 and R523 roads in the Thulamela Municipality. The most important factors are extreme rainfall, steep slopes, geological features and water streams in the region, and improper road construction. Owing to the complexity of the failure mechanisms in the study area, it was concluded that both slope stability prediction charts and rock hazard matrix charts are very useful. They indeed enable one to characterize slope instability in sensitive clay soils as well as rockfall hazards in the study area. It is however recommended that future work is undertaken to explore the use of sophisticated and scientific methods. This is instrumental in the development of predictive tools for rock deformation and displacement in landslide events. / Electrical and Mining Engineering / D. Phil. (Mining Engineering)

<p dir="ltr">Digital soil mapping (DSM) combines field and laboratory data with environmental factors to predict soil properties. The accuracy of these predictions depends on factors such as model selection, data quality and quantity, and landscape characteristics. In our study, we investigated the impact of sample density and the use of various environmental covariates (ECs) including slope, topographic position index, topographic wetness index, multiresolution valley bottom flatness, and multiresolution ridge top flatness, as well as the spatial resolution of these ECs on the predictive accuracy of four predictive models; Cubist (CB), Random Forest (RF), Regression Kriging (RK), and Ordinary Kriging (OK). Our analysis was conducted at three sites in Indiana: the Purdue Agronomy Center for Research and Education (ACRE), Davis Purdue Agriculture Center (DPAC), and Southeast Purdue Agricultural Center (SEPAC). Each site had its unique soil data sampling designs, management practices, and topographic conditions. The primary focus of this study was to predict the spatial distribution of soil properties, including soil organic matter (SOM), cation exchange capacity (CEC), and clay content, at different depths (0-10cm, 0-15cm, and 10-30cm) by utilizing five environmental covariates and four spatial resolutions for the ECs (1-1.5 m, 5 m, 10 m, and 30 m).</p><p dir="ltr">Various evaluation metrics, including R², root mean square error (RMSE), mean square error (MSE), concordance coefficient (pc), and bias, were used to assess prediction accuracy. Notably, the accuracy of predictions was found to be significantly influenced by the site, sample density, model type, soil property, and their interactions. Sites exhibited the largest source of variation, followed by sampling density and model type for predicted SOM, CEC, and clay spatial distribution across the landscape.</p><p dir="ltr">The study revealed that the RF model consistently outperformed other models, while OK performed poorly across all sites and properties as it only relies on interpolating between the points without incorporating the landscape characteristics (ECs) in the algorithm. Increasing sample density improved predictions up to a certain threshold (e.g., 66 samples at ACRE for both SOM and CEC; 58 samples for SOM and 68 samples for CEC at SEPAC), beyond which the improvements were marginal. Additionally, the study highlighted the importance of spatial resolution, with finer resolutions resulting in better prediction accuracy, especially for SOM and clay content. Overall, comparing data from the two depths (0-10cm vs 10-30cm) for soil properties predications, deeper soil layer data (10-30cm) provided more accurate predictions for SOM and clay while shallower depth data (0-10cm) provided more accurate predictions for CEC. Finally, higher spatial resolution of ECs such as 1-1.5 m and 5 m contributed to more accurate soil properties predictions compared to the coarser data of 10 m and 30 m resolutions.</p><p dir="ltr">In summary, this research underscores the significance of informed decisions regarding sample density, model selection, and spatial resolution in digital soil mapping. It emphasizes that the choice of predictive model is critical, with RF consistently delivering superior performance. These findings have important implications for land management and sustainable land use practices, particularly in heterogeneous landscapes and areas with varying management intensities.</p>

This research work concentrate on developing digital soil maps to support field based plant phenotyping research. We have developed soil organic matter content (OM), cation exchange capacity (CEC), natural soil drainage class, and tile drainage line maps using topographic indices and aerial imagery. Various prediction models (universal kriging, cubist, random forest, C5.0, artificial neural network, and multinomial logistic regression) were used to estimate the soil properties of interest.