A espacialização de atributos dos solos é necessária com vistas ao planejamento e monitoramento do solo. As imagens do satélite Landsat 5 Thematic Mapper (TM) são utilizadas em estudos relacionados aos recursos naturais por fornecerem informações da superfície das terras em áreas amplas e de difícil acesso. Nesse trabalho objetivou-se gerar uma imagem multitemporal de solo exposto através de imagens de satélite e, com ela, mapear atributos da superfície do solo. A área de estudo é a região de Piracicaba, SP, onde foram selecionadas treze imagens do Landsat TM. Amostras da camada mais superficial dos solos foram coletadas em 740 pontos, e nelas analisados vários atributos do solo. Por meio da reflectância espectral dos objetos das imagens de satélite foram obtidas informações de solo exposto e eliminados outros alvos. As imagens foram adquiridas em série histórica e sobrepostas, gerando uma composta final com solo exposto. Os atributos do solo que obtiveram boa correlação com as bandas dessa imagem foram quantificados por meio da técnica de regressão multivariada e espacializados. Mapas pré-existentes de geologia e pedologia auxiliaram no entendimento da variabilidade espacial da textura e cor dos solos na paisagem. A taxa de variação do solo exposto em uma imagem individual variou de 7 a 20 %, enquanto a unificada atingiu 53 % da área total. Valores de reflectância entre as bandas TM3 e TM4 contrapostos representando a linha do solo e curva espectral média de espectros de amostras de solos obtidas em laboratório apresentaram semelhança com as de satélite. Entre os atributos estudados, a argila obteve a melhor correlação com R2 de 0,75, erro baixo e RPD acima de 2. Outros atributos relacionados com a argila também obtiveram boa correlação, como matéria orgânica (MO) e capacidade de troca de cátions (CTC) com R2 de 0,4 e 0,34 respectivamente. / The knowledge of spatial distribution of soil attributes is necessary for soil planning and monitoring. Landsat 5 Thematic Mapper (TM) images are used in studies related to natural resources for providing the land surface information in large areas and in areas of difficult access. This work aimed to create a multi-temporal image of bare soil through satellite scenes and map soil attributes from the surface. The study area is located in Piracicaba region, SP, where thirteen Landsat TM scenes were selected. Samples of the soil superficial layer were collected at 740 points, and several soil properties were analyzed. Spectral reflectance of different objects from satellite images was obtained and only exposed soil information was selected. Images were acquired in historical series and overlapped, generating a final composed image with bare soil. Soil attributes that presented good correlation with the bands were quantified by multivariate regression and mapped. Pre-existing maps of geology and soil helped in understanding soil texture spatial variability and color in the landscape. The soil variation rate in an individual exposed image ranged from 7 to 20%, while the unified reached 53% of the total area. Obtained values of reflectance between TM3 and TM4 bands representing the soil line and average spectral curve of laboratory soil samples were similar to the satellite ones. Among the soil attributes studied, clay presented the best correlation with R2 value of 0.75, low error and RPD value above 2.0. Other attributes related to clay also presented good correlation, such as organic matter (OM) and cation exchange capacity (CEC) with R2 values of 0.4 and 0.34 respectively.

A espacialização de atributos dos solos é necessária com vistas ao planejamento e monitoramento do solo. As imagens do satélite Landsat 5 Thematic Mapper (TM) são utilizadas em estudos relacionados aos recursos naturais por fornecerem informações da superfície das terras em áreas amplas e de difícil acesso. Nesse trabalho objetivou-se gerar uma imagem multitemporal de solo exposto através de imagens de satélite e, com ela, mapear atributos da superfície do solo. A área de estudo é a região de Piracicaba, SP, onde foram selecionadas treze imagens do Landsat TM. Amostras da camada mais superficial dos solos foram coletadas em 740 pontos, e nelas analisados vários atributos do solo. Por meio da reflectância espectral dos objetos das imagens de satélite foram obtidas informações de solo exposto e eliminados outros alvos. As imagens foram adquiridas em série histórica e sobrepostas, gerando uma composta final com solo exposto. Os atributos do solo que obtiveram boa correlação com as bandas dessa imagem foram quantificados por meio da técnica de regressão multivariada e espacializados. Mapas pré-existentes de geologia e pedologia auxiliaram no entendimento da variabilidade espacial da textura e cor dos solos na paisagem. A taxa de variação do solo exposto em uma imagem individual variou de 7 a 20 %, enquanto a unificada atingiu 53 % da área total. Valores de reflectância entre as bandas TM3 e TM4 contrapostos representando a linha do solo e curva espectral média de espectros de amostras de solos obtidas em laboratório apresentaram semelhança com as de satélite. Entre os atributos estudados, a argila obteve a melhor correlação com R2 de 0,75, erro baixo e RPD acima de 2. Outros atributos relacionados com a argila também obtiveram boa correlação, como matéria orgânica (MO) e capacidade de troca de cátions (CTC) com R2 de 0,4 e 0,34 respectivamente. / The knowledge of spatial distribution of soil attributes is necessary for soil planning and monitoring. Landsat 5 Thematic Mapper (TM) images are used in studies related to natural resources for providing the land surface information in large areas and in areas of difficult access. This work aimed to create a multi-temporal image of bare soil through satellite scenes and map soil attributes from the surface. The study area is located in Piracicaba region, SP, where thirteen Landsat TM scenes were selected. Samples of the soil superficial layer were collected at 740 points, and several soil properties were analyzed. Spectral reflectance of different objects from satellite images was obtained and only exposed soil information was selected. Images were acquired in historical series and overlapped, generating a final composed image with bare soil. Soil attributes that presented good correlation with the bands were quantified by multivariate regression and mapped. Pre-existing maps of geology and soil helped in understanding soil texture spatial variability and color in the landscape. The soil variation rate in an individual exposed image ranged from 7 to 20%, while the unified reached 53% of the total area. Obtained values of reflectance between TM3 and TM4 bands representing the soil line and average spectral curve of laboratory soil samples were similar to the satellite ones. Among the soil attributes studied, clay presented the best correlation with R2 value of 0.75, low error and RPD value above 2.0. Other attributes related to clay also presented good correlation, such as organic matter (OM) and cation exchange capacity (CEC) with R2 values of 0.4 and 0.34 respectively.

Master of Arts / Department of Geography / Kevin P. Price / Due primarily to changes in land management practices, eastern redcedar (Juniperus virginiana L.), a native Kansas conifer, is rapidly invading onto valuable rangelands. The suppression of fire and increase of intensive grazing, combined with the rapid growth rate, high reproductive output, and dispersal ability of the species have allowed it to dramatically expand beyond its original range. There is a growing interest in harvesting this species for use as a biofuel. For economic planning purposes, density and biomass quantities for the trees are needed. Three methods are explored for mapping eastern redcedar and quantifying its biomass in Riley County, Kansas. First, a land cover classification of redcedar cover is performed using a method that utilizes a support vector machine classifier applied to a multi-temporal stack of Landsat TM satellite images. Second, a Small Unmanned Aircraft System (sUAS) is used to measure individual redcedar trees in an area where they are encroaching into a pasture. Finally, a hybrid approach is used to estimate redcedar biomass using high resolution multispectral and light detection and ranging (LiDAR) imagery. These methods showed promise in the forestry, range management, and bioenergy industries for better understanding of an invasive species that shows great potential for use as a biofuel resource.

Agriculture has an important role in Turkey / hence automated approaches are crucial to maintain sustainability of agricultural activities. The objective of this research is to classify eight crop types cultivated in Karacabey Plain located in the north-west of Turkey using multi-temporal Kompsat-2 and Envisat ASAR satellite data. To fulfill this objective, first, the fused Kompsat-2 images were segmented separately to define homogenous agricultural patches. The segmentation results were evaluated using multiple goodness measures to find the optimum segments. Next, multispectral single-date Kompsat-2 images with the Envisat ASAR data were classified by MLC and SVMs algorithms. To combine the thematic information of the multi-temporal data set, probability maps were generated for each classification result and the accuracies of the thematic maps were then evaluated using segment-based manner. The results indicated that the segment-based approach based on the SVMs method using the multispectral Kompsat-2 and Envisat ASAR data provided the best classification accuracies. The combined thematic maps of June-August and June-July-August provided the highest overall accuracy and kappa value around 92% and 0.90, respectively, which was 4% better than the highest result computed with the MLC method. The produced thematic maps were also evaluated based on field-based manner and the analysis revealed that the classification performances are directly proportional to the size of the agricultural fields.