There is a need to understand the spatial distribution of soil taxonomic classes, the spatial distribution of potential biological soil crust, and soil properties related to wind erosion to address land use and management decisions in arid and semi-arid areas of the western USA. Digital soil mapping (DSM) can provide this information. Chapter 2 compared multiple DSM functions and environmental covariate sets at three geographically distinct semi-arid study areas to identify combinations that would best predict soil taxonomic classes. No single model or type of model was consistently the most accurate classifier for all three areas. The use of the “most important” variables consistently resulted in the highest model accuracies for all study areas. Overall classification accuracy was largely dependent upon the number of taxonomic classes and the distribution of pedons between taxonomic classes. Individual class accuracy was dependent upon the distribution of pedons in each class. Model accuracy could be increased by increasing the number of pedon observations or decreasing the number of taxonomic classes. Potential biological soil crust level of development (LOD) classes were predicted over a large area surrounding Canyonlands National Park in Chapter 3. The moderate LOD class was modeled with reasonable accuracy. The low and high LOD classes were modeled with poor accuracy. Prediction accuracy could likely be improved through the use of additional covariates. Spatial predictions of LOD classes may be useful for assessing the impact of past land uses on biological soil crusts. Threshold friction velocity (TFV) was measured and then correlated with other, easier-to-measure soil properties in Chapter 4. Only soils with alluvial surficial rocks or weak physical crusts reached TFV in undisturbed conditions. All soil surfaces reached TFV after disturbance. Soils with weak physical crusts produced the most sediment. Future work on wind erosion in the eastern Great Basin should focus on non-crusted/weakly crusted soils and soils formed in alluvium overlying lacustrine materials. Soils with other crust types are likely not susceptible to wind erosion. Threshold friction velocity in undisturbed soils with weak physical crusts and undisturbed soils with surficial rocks was predicted using a combination of penetrometer, rock cover, and silt measurements.

Airborne hyperspectral imagery has the potential to overcome the spectral and spatial resolution limitations of multispectral satellite imagery for monitoring salinity at both regional and farm scales. In particular, saline areas that have good cover of salt tolerant plants are difficult to map with multispectral satellite imagery. Hyperspectral imagery may provide a more reliable salinity mapping method because of its potential to discriminate halophytic plant cover from non - halophytes. HyMap and CASI airborne imagery ( at 3m ground resolution ) and Hyperion satellite imagery ( at 30 resolution ) were acquired over a 140 sq km dryland agricultural area in South Australia, which exhibits severe symptoms of salinity, including extensive patches of the perennial halophytic shrub samphire ( Halosarcia pergranulata ), sea barley grass ( Hordeum marinum ) and salt encrusted pans. The HyMap and Hyperion imagery were acquired in the dry season ( March and February respectively ) to maximise soil and perennial vegetation mapping. The optimum time of year to map sea barley grass, an annual species, was investigated through spectral discrimination analysis. Multiple reflectance spectra were collected of sea barley grass and other annual grasses with an ASD Fieldspec Pro spectrometer during the September spring flush and in November during late senescence. Comparing spectra of different species in November attempted to capture the spectral differences between the late senescing sea barley grass and other annual grasses. Broad NIR and SWIR regions were identified where sea barley grass differs significantly from other species in November during late senescence. The sea barley grass was therefore shown to have the potential to be discriminated and mapped with hyperspectral imagery at this time and as a result the CASI survey was commission for November. Other salinity symptoms were characterised by collecting single field and laboratory spectra for comparison to image derived spectra in order to provide certainty about the landscape components that were to be mapped. Endmembers spectra associated with saltpans and samphire patches were extracted from the imagery using automated endmember generation procedures or selected regions of interest and used in subsequent partial unmixing. Spectral subsets were evaluated for their ability to optimise salinity maps. The saltpan spectra contained absorption features consistent with montmorillonite and gypsum. A single gypsum endmember from one image strip successfully mapped saltpans across multiple images strips using the 1750 nm absorption feature as the input to matched filter unmixing. The individual spectra of green and red samphire are dominated by photosynthetic vegetation characteristics. The spectra of green samphire, often seen with red tips, exhibit peaks in both green and red wavebands whereas the red samphire spectra only contain a significant reflectance peak in the visible red wavelength region. For samphire, Mixture Tuned Matched Filtering using image spectra, containing all wavelength regions, from known samphire patches produced the most satisfactory mapping. Output salinity maps were validated at over 100 random sites. The HyMap salinity maps produced the most accurate results compared to CASI and Hyperion. HyMap successfully mapped highly saline areas with a good cover of samphire vegetation at Point Sturt without the use of multitemporal imagery or ancillary data such as topography or PIRSA soil attribute maps. CASI and Hyperion successfully mapped saltpan, however, their samphire maps showed a poor agreement with field data. These results suggest that perennial vegetation mapping requires all three visible, NIR and SWIR wavelength regions because the SWIR region contains important spectral properties related to halophytic adaptations. Furthermore, the unconvincing results of the CASI sea barley grass maps suggests that the optimal sensor for mapping both soil and vegetation salinity symptoms are airborne sensors with high spatial and spectral resolution, that incorporate the 450 to 1450 nm wavelength range, such as HyMap. This study has demonstrated that readily available software and image analysis techniques are capable of mapping indicators of varying levels of salinity. With the ability to map symptoms across multiple image strips, airborne hyperspectral imagery has the potential for mapping larger areas covering sizeable dryland agriculture catchments, closer in extent to single satellite images. This study has illustrated the advantage of the hyperspectral imagery over traditional soil mapping based on aerial photography interpretation such as the NLWRA Salinity 2000 and the PIRSA soil landscape unit maps. The HyMap salinity maps not only improved mapping of saline areas covered with samphire but also provided salinity maps that varied spatially within saline polygons. / Thesis (Ph.D.)--School of Earth and Environmental Sciences, 2006.

The aim of this thesis was to examine environmental covariate-related issues, the resolution dependency, the contribution of vegetation covariates, and the use of LiDAR data, in the purposive sampling design for fuzzy logic-based digital soil mapping. In this design fuzzy c-means (FCM) clustering of environmental covariates was employed to determine proper sampling sites and assist soil survey and inference. Two subsets of the Laurel Creek Conservation area were examined for the purposes of exploring the resolution and vegetation issues, respectively. Both conventional and LiDAR-derived digital elevation models (DEMs) were used to derive terrain covariates, and a vegetation index calculated from remotely sensed data was employed as a vegetation covariate. A basic field survey was conducted in the study area. A validation experiment was performed in another area. The results show that the choices of optimal numbers of clusters shift with resolution aggregated, which leads to the variations in the optimal partition of environmental covariates space and the purposive sampling design. Combining vegetation covariates with terrain covariates produces different results from the use of only terrain covariates. The level of resolution dependency and the influence of adding vegetation covariates vary with DEM source. This study suggests that DEM resolution, vegetation, and DEM source bear significance to the purposive sampling design for fuzzy logic-based digital soil mapping. The interpretation of fuzzy membership values at sampled sites also indicates the associations between fuzzy clusters and soil series, which lends promise to the applicability of fuzzy logic-based digital soil mapping in areas where fieldwork and data are limited.

The aim of this thesis was to examine environmental covariate-related issues, the resolution dependency, the contribution of vegetation covariates, and the use of LiDAR data, in the purposive sampling design for fuzzy logic-based digital soil mapping. In this design fuzzy c-means (FCM) clustering of environmental covariates was employed to determine proper sampling sites and assist soil survey and inference. Two subsets of the Laurel Creek Conservation area were examined for the purposes of exploring the resolution and vegetation issues, respectively. Both conventional and LiDAR-derived digital elevation models (DEMs) were used to derive terrain covariates, and a vegetation index calculated from remotely sensed data was employed as a vegetation covariate. A basic field survey was conducted in the study area. A validation experiment was performed in another area. The results show that the choices of optimal numbers of clusters shift with resolution aggregated, which leads to the variations in the optimal partition of environmental covariates space and the purposive sampling design. Combining vegetation covariates with terrain covariates produces different results from the use of only terrain covariates. The level of resolution dependency and the influence of adding vegetation covariates vary with DEM source. This study suggests that DEM resolution, vegetation, and DEM source bear significance to the purposive sampling design for fuzzy logic-based digital soil mapping. The interpretation of fuzzy membership values at sampled sites also indicates the associations between fuzzy clusters and soil series, which lends promise to the applicability of fuzzy logic-based digital soil mapping in areas where fieldwork and data are limited.

Thesis (M.S.)--Northwest Missouri State University, 2006. / The full text of the thesis is included in the pdf file. Title from title screen of full text.pdf file (viewed on January 25, 2008) Includes bibliographical references.

Thesis (M.S.)--Oregon State University, 2009. / Printout. Includes bibliographical references (leaves 78-90). Also available on the World Wide Web.

Surface mining for coal in the Appalachian region destroys native forests and replaces them with reclaimed landscapes that are often revegetated as grasslands and are unacceptable for managed forest production without extensive remediation. Tree survival and growth are dependent on many reclaimed mine land properties. However, conventional mapping techniques using USDA soil series does not identify these critical soil property differences. This study was conducted to create a forest site quality classification system to be used to evaluate the potential productivity of specific tree species on mine soils. High soil bulk density is the most common limitation on mine soils and methods to efficiently measure this property were evaluated. No valid quantitative method of measuring mine soil bulk density was found due to the high rock fragment content in the soil profile, but a method for estimating relative soil density class was developed. Other soil chemical and physical properties were analyzed at abandoned mine sites in Virginia, West Virginia, and Ohio. Mine soil properties differed throughout the Appalachian region, with Ohio sites having finer textures and less rock fragments, West Virginia sites having coarser textures and a high quantity of dark-colored shale, and Virginia sites dominated by sandstone rock types. Selected field-measured soil and site properties were regressed with site index (SI) base age 50 at 52 sample locations in 10- to 18-year old white pine (Pinus strobus L.) stands on reclaimed mine lands. Sufficiency curves for nine soil and site properties were produced and a general productivity index (PI) calculated. Regression of the general PI and measured SI of white pine produced an R2 of 0.61. The general PI was simplified to four soil properties (soil density, rooting depth, texture, and pH) most significantly related to the SI of white pine, and the properties were weighted based on their importance to white pine growth on mine soils. The modified PI model produced an R2 of 0.69 for a linear relationship between PI and measured SI. The SI values were divided into five classes of equal interval and the corresponding PI values were used to define five forest site quality classes that could be identified by measuring and mapping differences in the PI on older mine soils. The model may be modified for determination of hardwood productivity after validation sites are located. Soil and site properties that are correlated with seedling survival appear different than those properties important for tree productivity. The forest site quality classification system proposed here proved practical for mapping a selected mine site, and the maps may be used as a validation test after future reforestation. / Master of Science

Thesis (Ph. D.)--Ohio State University, 2008. / Title from first page of PDF file. Includes bibliographical references (p. 117-122).

An airborne multispectral video system was used to collect soil spectral data over a four-square mile region in northeastern Arizona. Six multispectral video images were digitized. Using the red and blue bands of each image, an unsupervised classification was performed. Each was referenced to a digitized U.S. Soil Conservation Service map resulting in classification precisions ranging from 0-92.4 percent. Ground radiometric measurements were made to ascertain spectral separability of the soil samples. Soil color was determined to try to relate Munsell value to classification precision. Misclassification of soil map units was unrelated to soil brightness or areal extent of each soil. Rather, features such as slope, boundary complexity, and surface condition was responsible for misclassifications seen in this study. Best classification results occurred when soil mapping units were relatively homogeneous, possessed slight changes in slope, and had a regular surface with smooth and distinct boundaries.

Orientador: Teresa Cristina Tarlé Pissarra / Coorientador: Sérgio Campos / Banca: João Antônio Galbiatti / Banca: Maurício José Borges / Banca: Valdemir Antônio Rodrigues / Banca: Ellen Fittipaldi Brasilio Carrega / Resumo: Os estudos de caracterização, diagnóstico e prognóstico de bacias hidrográficas visando o manejo dos recursos naturais têm sido realizados para compreender e implantar práticas conservacionistas no sistema produtivo agrícola. A ocupação do meio é uma ação antrópica que ocorre ao longo dos anos, dado ao crescimento populacional e o conhecimento territorial é o primeiro passo para o planejamento ambiental. Este trabalho teve como objetivo principal caracterizar ambientalmente a bacia hidrográfica do Córrego Rico utilizando técnicas de geoprocessamento. Mapas da rede de drenagem, solos, uso e ocupação e declividade da área foram analisados utilizando técnicas estatísticas e sistema de informação geográfica. A divisão da bacia foi realizada em compartimentos hidrológicos e foram determinadas as principais características geomórficas. Nas áreas de preservação permanente foram identificadas as ocorrências de conflito de uso, tendo como referência a legislação ambiental. Os resultados analíticos obtidos refletiram características ambientais dos recursos hídricos, com sistema drenante de fluxo superficial na área para o Rio Mogi-Guaçú. No que refere a sua degradação ambiental, vem ocorrendo uma diminuição na cobertura vegetal original nos mananciais, causada pelo desmatamento da mata ciliar decorrente da expansão da área urbana, e um abandono destas áreas na área agrícola. Em algumas regiões ocorreram reflorestamentos, tanto nas nascentes como ao longo da rede de drenagem. A Bacia Hidrográfica do Córrego Rico apresenta uma área de 563,13 km2, e foi dividida em 25 compartimentos hidrológicos para o planejamento da implantação de práticas conservacionistas. As declividades predominantes foram de 3 a 8%, em 55,3% da área total, e o principal uso observado foi a cultura de... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: The characterization, diagnosis and prognosis studies on watersheds aimed to manage the natural resource have been performed to understand and implement conservation practices on agricultural production system. The occupation is a means of human action that occurs over the years, due to population growth, and the land use and land use change knowledge is the first step to environmental planning. This study aimed to characterize the environment of the Córrego Rico watershed using geographic information system techniques. Maps of the drainage network, soils, land use and land use change and slope were analyzed using statistical techniques and geographic information system. The division of the basin was performed in hydrological areas and the main physical characteristics were determined. In the permanent preservation areas were identified areas of land use conflict, with reference to Brazilian environmental legislation. Analytical results reflected environmental characteristics of water resources, with the drainage system in the area flowing to the Rio Mogi Guaçú. In terms of its environmental degradation, there has been a decrease in vegetation cover in the watershed caused by deforestation of riparian vegetation due to the expansion of the urban area, and an abandonment of these areas was observed. In some regions reforestation occurred in both the springs and along the drainage network. The watershed has an area of 563.13 km2, and was divided into 25 hydrological areas for planning the conservation practices. The predominant slopes were 3 - 8%, in 55,3% of the total area, and the main use was sugar cane. The soils that predominate at the downstream area was Oxisoil and Ultisols at upstream region. Of the total area, 6.12% are occupied with permanent preservation area totalizing... (Complete abstract click electronic access below) / Doutor