Thesis (M.S.)--Indiana University, 2008. / Title from screen (viewed on June 3, 2009). Department of Earth Science, Indiana University-Purdue University Indianapolis (IUPUI). Advisor(s): Lin Li, Pierre Jacinthe, Gabriel M. Filippelli. Includes vita. Includes bibliographical references (leaves 78-81).

Thesis (M.S.)--Oregon State University, 2011. / Printout. Includes bibliographical references (leaves 44-50). Also available on the World Wide Web.

Digital soil mapping typically involves inputs of digital elevation models, remotely sensed imagery, and other spatially explicit digital data as environmental covariates to predict soil classes and attributes over a landscape using statistical models. Digital imagery from Landsat 5, a digital elevation model, and a digital geology map were used as environmental covariates in a 67,000-ha study area of the Great Basin west of Fillmore, UT. A “pre-map” was created for selecting sampling locations. Several indices were derived from the Landsat imagery, including a normalized difference vegetation index, normalized difference ratios from bands 5/2, bands 5/7, bands 4/7, and bands 5/4. Slope, topographic curvature, inverse wetness index, and area solar radiation were calculated from the digital elevation model. The greatest variation across the study area was found by calculating the Optimum Index Factor of covariates, choosing band 7, normalized difference ratio bands 5/2, normalized difference vegetation index, slope, profile curvature, and area solar radiation. A 20-class ISODATA unsupervised classification of these six data layers was reduced to 12. Comparing the 12-class map to a geologic map, 166 sites were chosen weighted by areal extent; 158 sites were visited. Twelve points were added using case-based reasoning to total 170 points for model training. A validation set of 50 sites was selected using conditioned Latin Hypercube Sampling. Density plots of sample sets compared to raw data produced comparable results. Geology was used to stratify the study area into areas above and below the Lake Bonneville highstand shoreline. Raster data were subset to these areas, and predictions were made on each area. Spatial modeling was performed with three different models: random forests, support vector machines, and bagged classification trees. A set of covariates selected by random forests variable importance and the set of Optimum Index Factor covariates were used in the models. The Optimum Index Factor covariates produced the best classification using random forests. Classification accuracy was 45.7%. The predictive rasters may not be useful for soil map unit delineation, but using a hybrid method to guide further sampling using the pre-map and standard sampling techniques can produce a reasonable soil map.

No description available.

Modeling is an important tool in watershed management. In much of the world, data needed for modeling, both for model inputs and for model evaluation, are very limited or non-existent. The overall objective of this research was to enable development of watershed management plans for agricultural dominated systems under situations where data are scarce. First, uncertainty of the SWAT model's outputs due to input parameters, specifically soils and high resolution digital elevation models, which are likely to be lacking in data-poor environments, was quantified using Monte Carlo simulation. Two sources of soil parameter values (SSURGO and STATSGO) were investigated, as well as three levels of DEM resolution (10, 30, and 90 m). Uncertainty increased as the input data became coarser for individual soil parameters. The combination of SSURGO and the 30 m DEM proved to adequately balance the level of uncertainty and the quality of input datasets. Second, methods were developed to generate appropriate soils information and DEM resolution for data-poor environments. The soils map was generated based on lithology and slope class, while the soil attributes were generated by linking surface soil texture to soils characterized in the SWAT soils database. A 30 m resolution DEM was generated by resampling a 90 m DEM, the resolution that is readily available around the world, by direct projection using a cubic convolution method. The effect of the generated DEM and soils data on model predictions was evaluated in a data-rich environment. When all soil parameters were varied at the same time, predictions based on the derived soil map were comparable to the predictions based on the SSURGO map. Finally, the methodology was tested in a data-poor watershed in Bolivia. The proposed methodologies for generating input data showed how available knowledge can be employed to generate data for modeling purposes and give the opportunity to incorporate uncertainty in the decision making process in data-poor environments. / Ph. D.

Wildland fires are increasing in both severity and intensity leading to severe and lasting biogeochemical effects on soil. The CZU lightning complex started on August 16th, 2020, and burned 86,509 acres causing severe social and ecological damage. To better understand the impact of fire on soil properties at the landscape scale, we created a digital soil mapping model with the inclusion of remotely sensed burn severity covariates. We combined a raster-stack of environmental covariates with rasters for fire severity and soil samples, to disentangle the relative contribution of fire to the spatial distribution of soil properties in the recently burned Little Creek watershed in Santa Cruz, Ca. Soils were sampled via a conditional Latin hypercube sampling design and analyzed for soil health and soil Fe/Al-oxide mineralogy. To ascertain the relative contribution of remotely sensed fire severity covariates and standard digital soil mapping covariates (e.g. SCORPAN factors) to explain the variance in post-fire soil properties, we deployed multi-linear regression and random forest modeling. We report that remotely sensed indicators of fire severity explained the variance of Ntotal, Caex, pH, oxalate extractable P, NO3-, and NH4+ in both the MLR and RF models at the watershed scale. The inclusion of rasters of fire effects improved the description of target soil property variance, in concert with more traditional raster-based proxies for the soil forming factors, indicating that fire helps explain the spatial variability of these soil properties in recently burned post-fire landscapes. Furthermore, we report that an increase in remotely sensed fire severity led to an increase in sorbed P (as measured via oxalate extractable P), suggesting a potentially unreported change to post-fire soil P dynamics. Results inform remotely sensed assessment of fire induced changes to soil properties at the landscape scale.

Large amounts of carbon are stored in soils of the northern circumpolar permafrost region. High-resolution mapping of this soil organic carbon (SOC) is important to better understand and predict local to global scale carbon dynamics. In this thesis, studies from five different areas across the permafrost region indicate a pattern of generally higher SOC storage in Arctic tundra soils compared to forested sub-Arctic or Boreal taiga soils. However, much of the SOC stored in the top meter of tundra soils is permanently frozen, while the annually thawing active layer is deeper in taiga soils and more SOC may be available for turnover to ecosystem processes. The results show that significantly more carbon is stored in soils compared to vegetation, even in fully forested taiga ecosystems. This indicates that over longer timescales, the SOC potentially released from thawing permafrost cannot be offset by a greening of the Arctic. For all study areas, the SOC distribution is strongly influenced by the geomorphology, i.e. periglacial landforms and processes, at different spatial scales. These span from the cryoturbation of soil horizons, to the formation of palsas, peat plateaus and different generations of ice-wedges, to thermokarst creating kilometer scale macro environments. In study areas that have not been affected by Pleistocene glaciation, SOC distribution is highly influenced by the occurrence of ice-rich and relief-forming Yedoma deposits. This thesis investigates the use of thematic maps from highly resolved satellite imagery (&lt;6.5 m resolution). These maps reveal important information on the local distribution and variability of SOC, but their creation requires advanced classification methods including an object-based approach, modern classifiers and data-fusion. The results of statistical analyses show a clear link of land cover and geomorphology with SOC storage. Peat-formation and cryoturbation are identified as two major mechanisms to accumulate SOC. As an alternative to thematic maps, this thesis demonstrates the advantages of digital soil mapping of SOC in permafrost areas using machine-learning methods, such as support vector machines, artificial neural networks and random forests. Overall, high-resolution satellite imagery and robust spatial prediction methods allow detailed maps of SOC. This thesis significantly increases the amount of soil pedons available for the individual study areas. Yet, this information is still the limiting factor to better understand the SOC distribution in permafrost environments at local and circumpolar scale. Soil pedon information for SOC quantification should at least distinguish the surface organic layer, the mineral subsoil in the active layer compared to the permafrost and further into organic rich cryoturbated and buried soil horizons. / <p>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 3: Manuscript. Paper 4: Manuscript.</p>

Submitted by Jorge Silva (jorgelmsilva@ufrrj.br) on 2016-10-21T17:28:48Z No. of bitstreams: 1 2016 - Alessandro Samuel-Rosa.pdf: 15092171 bytes, checksum: bbe06c922805d4196e0a50c4f2aee7a5 (MD5) / Made available in DSpace on 2016-10-21T17:28:48Z (GMT). No. of bitstreams: 1 2016 - Alessandro Samuel-Rosa.pdf: 15092171 bytes, checksum: bbe06c922805d4196e0a50c4f2aee7a5 (MD5) Previous issue date: 2016-02-24 / CNPq / Modern soil spatial modelling is based on statistical models to explore the empirical relation-ship among environmental conditions and soil properties. These models are a simplification of reality, and their outcome (soil map) will always be in error. What a soil map conveys is what we expect the soil to be, acknowledging that we are uncertain about it. The objective of this thesis is to evaluate important sources of uncertainty in spatial soil modelling, with emphasis on soil and covariate data. Case studies were developed using data from a catchment located in Southern Brazil. The soil spatial distribution in the study area is highly variable, being deter-mined by the geology and geomorphology (coarse spatial scales), and by agricultural practices (fine spatial scales). Four topsoil properties were explored: clay content, organic carbon con-tent, effective cation exchange capacity and bulk density. Five covariates, each with two levels of spatial detail, were used: area-class soil maps, digital elevation models, geologic maps, land use maps, and satellite images. These soil and covariate data constitute the Santa Maria dataset. Two packages for R were created in support to the case studies, the first (pedometrics) con-taining various functions for spatial exploratory data analysis and model calibration, the second (spsann) designed for the optimization of spatial samples using simulated annealing. The case studies illustrated that existing covariates are suitable for calibrating soil spatial models, and that using more detailed covariates results in only a modest increase in the prediction ac-curacy that may not outweigh the extra costs. More efficient means of increasing prediction accuracy should be explored, such as obtaining more soil observations. For this end, one should use objective means for selecting observation locations to minimize the effects of psycholog-ical responses of soil modellers to conceptual and operational factors on the sampling design. This because conceptual and operational difficulties encountered in the field determine how the motivation of soil modellers shifts between learning/verifying soil-landscape relationships and maximizing the number of observations and geographic coverage. For the sole purpose of spa-tial trend estimation, it should suffice to optimize spatial samples aiming only at reproducing the marginal distribution of the covariates. For the joint purpose of optimizing sample configu-rations for spatial trend and variogram estimation, and spatial interpolation, one can formulate a sound multi-objective optimization problem using robust versions of existing sampling algo-rithms. Overall, we have learned that a single, universal recipe for reducing our uncertainty in soil spatial modelling cannot be formulated. Deciding upon efficient ways of reducing our uncertainty requires, first, that we explore the full potential of existing soil and covariate data using sound spatial modelling techniques. / A modelagem espacial do solo moderna usa modelos estat?sticos para explorar a rela??o em-p?rica entre as condi??es ambientais e as propriedades do solo. Esses modelos s?o uma sim-plifica??o da realidade, e seu resultado (mapa do solo) estar? sempre errado. O que um mapa do solo transmite ? o que esperamos que o solo seja, reconhecendo que somos incertos sobre ele. O objetivo dessa tese ? avaliar importantes fontes de incerteza na modelagem espacial do solo, com ?nfase nos dados do solo e covari?veis. Estudos de caso foram desenvolvidos usando dados de uma bacia hidrogr?fica do sul do Brasil. A distribui??o espacial do solo na ?rea de estudo ? vari?vel, sendo determinada pela geologia e geomorfologia (escalas espaciais maiores) e pr?ticas agr?colas (escalas espaciais menores). Quatro propriedades do solo foram explora-das: teor de argila, teor de carbono org?nico, capacidade de troca cati?nica efetiva e densidade. Cinco covari?veis, cada um com dois n?veis de detalhe espacial, foram utilizadas: mapas areais de classes de solo, modelos digitais de eleva??o, mapas geol?gicos, mapas de uso da terra, e imagens de sat?lite. Esses dados constituem o conjunto de dados de Santa Maria. Dois paco-tes para R foram criados, o primeiro (pedometrics) contendo v?rias fun??es para a an?lise explorat?ria espacial de dados e calibra??o de modelos, o segundo (spann) projetado para a optimiza??o de amostras espaciais usando recozimento simulado. Os estudos de caso ilustraram que as covari?veis existentes s?o apropriadas para calibrar modelos espaciais do solo, e que o uso de covari?veis mais detalhadas resulta em modesto aumento na acur?cia de predi??o que pode n?o compensar os custos adicionais. Meios mais eficientes de aumentar a acur?cia de pre-di??o devem ser explorados, como obter mais observa??es do solo. Para esse fim, deve-se usar meios objetivos para a sele??o dos locais de observa??o a fim de minimizar os efeitos das res-postas psicol?gicas dos modeladores do solo a fatores conceituais e operacionais sobre o plano de amostragem. Isso porque as dificuldades conceituais e operacionais encontradas no campo determinam mudan?as na motiva??o dos modeladores do solo entre aprendizagem/verifica??o das rela??es solo-paisagem e maximiza??o do n?mero de observa??es e cobertura geogr?fica. Para estimar a tend?ncia espacial, deve ser suficiente otimizar as amostras espaciais visando so-mente reproduzir a distribui??o marginal das covari?veis. Para otimizar configura??es amostrais para estimar a tend?ncia espacial e o variograma, e interpola??o espacial, pode-se formular um problema de otimiza??o multi-objetivo s?lido usando vers?es robustas de algoritmos de amos-tragem existentes. No geral, aprendemos que uma receita ?nica, universal para a redu??o da incerteza na modelagem espacial do solo n?o pode ser formulada. Decidir sobre formas efi-cazes de redu??o da incerteza requer, em primeiro lugar, que exploremos todo o potencial dos dados existentes usando t?cnicas de modelagem espacial s?lidas.

As fotografias aéreas, bem como as técnicas de estereoscopia, foram amplamente utilizadas para estudos ambientais e da paisagem. Com o avanço do mapeamento digital de solos os parâmetros da rede de drenagem foram sendo substituídos por parâmetros derivados do relevo, nas metodologias de predição de atributos do solo. No entanto, a literatura é ampla na atribuição da rede de drenagem como um fator determinante no mapeamento de solos, havendo a necessidade de desenvolver técnicas para inserir as características dos canais nos diferentes métodos de mapeamento do solo. Dessa forma, objetiva-se desenvolver um mapa de compartimentação da paisagem, através da rede de drenagem e um modelo digital de elevação (MDE), ambos com alta resolução espacial, visando avaliar as suas correlações com os atributos do solo (teor de argila nas profundidades 0-20 e 80-100 cm, gradiente textural, Ferro total (Fe2O3) e a cor do solo) e classes pedológicas. Tais procedimentos poderão auxiliar na produção de métodos base para relacionar a paisagem com a pedologia e o mapeamento. A área de estudo está localizada no município de Rio das Pedras, no estado de São Paulo, Brasil, com 538 km². O levantamento da rede de drenagem foi realizado a partir de fotografias aéreas com a visualização em 3D por estereoscopia digital. O MDE foi criado a partir de curvas altimétricas com equidistância vertical de 5 m. A partir das características da rede de drenagem e do relevo foram calculados os parâmetros morfométricos que representassem os dois elementos ao longo de toda a área de estudo. Com o processamento dos parâmetros foi utilizada a técnica fuzzy k-médias para fazer uma compartimentação da paisagem não supervisionada. Os resultados mostraram que a densidade de drenagem (DD) possui uma correlação negativa com os teores de argila (r = - 0.63), enquanto a correlação com o gradiente textural foi positiva (r = 0.42). O ferro total (Fe2O3) apresentou baixa variabilidade na área e não resultou em correlações significativas. A maior correlação foi com o matiz (r = 0.67), determinando solos mais amarelos nos locais de maior DD. A compartimentação da paisagem separou bem as posições do relevo em relação a DD. Cada compartimento se apresentou como uma unidade de mapeamento, havendo relação direta com classes pedológicas. / Aerial photographs, as well as stereoscopy were widely used for environmental and landscape studies. As digital soil mapping techniques have been developed, drainage network was replaced for relief parameters. However, literature studies have shown vast attribuition between drainage network and soil mapping, bringing the need to develop ways to insert the drainage parameters on different soil mapping strategies. Therefore, this study aims to create a landscape compartment map through drainage network and a digital elevation model (DEM), both with high spatial resolution, in order to evaluate its correlation with five soil attributes (clay content at 0-20 and 80-100 cm, textural gradient, total Iron (Fe2O3) and color) and soil classes. Wih these procedures it will be possible to create base methods to associate landscape with pedology and mapping. The study area has 538 km² and is located at Rio das Pedras municipality in the state of São Paulo, Brazil. The drainage network was created using aerial photographs with digital stereoscopy in 3D and the DEM with altimetric curves. These two geographic basis were used to calculate morphometric parameters that represents the patterns along the study area. The parameters were processed with fuzzy k-means technique to create a non-supervised landscape compartments map. Results showed that drainage density (DD) had a negative correlation with clay content (r = - 0.63), while textural gradient was positive (r = 0.42). The (Fe2O3) had low spatial variability, resulting in non-significant results. The greatest correlation was achieved with soil color (r = 0.67), indicating yellow soils at high DD landscapes. The landscape compartment was able to distinguish the relief positions related to DD. Each compartment was assumed as a soil map unit, presenting straight connections with soil classes.

Digital soil mapping (DSM) relies on quantitative relationships between easily measured environmental covariates and field and laboratory data. We applied innovative sampling and inference techniques to predict the distribution of soil attributes, taxonomic classes, and dominant vegetation across a 30,000-ha complex Great Basin landscape in southwestern Utah. This arid rangeland was characterized by rugged topography, diverse vegetation, and intricate geology. Environmental covariates calculated from digital elevation models (DEM) and spectral satellite data were used to represent factors controlling soil development and distribution. We investigated optimal sample size and sampled the environmental covariates using conditioned Latin Hypercube Sampling (cLHS). We demonstrated that cLHS, a type of stratified random sampling, closely approximated the full range of variability of environmental covariates in feature and geographic space with small sample sizes. Site and soil data were collected at 300 locations identified by cLHS. Random forests was used to generate spatial predictions and associated probabilities of site and soil characteristics. Balanced random forests and balanced and weighted random forests were investigated for their use in producing an overall soil map. Overall and class errors (referred to as out-of-bag [OOB] error) were within acceptable levels. Quantitative covariate importance was useful in determining what factors were important for soil distribution. Random forest spatial predictions were evaluated based on the conceptual framework developed during field sampling.