In cooperation with the USDA Forest Service Southern Research Station, an algorithm has been developed to replace the current aerial-photography-derived FIA Phase 1 estimates of forest/non-forest with a Landsat Thematic Mapper-based forest area estimation. Corrected area estimates were obtained using a new hybrid classifier called Iterative Guided Spectral Class Rejection (IGSCR) for portions of three physiographic regions of Virginia. Corrected area estimates were also derived using the Landsat Thematic Mapper-based Multi-Resolution Land Characteristic Interagency Consortium (MRLC) cover maps. Both satellite-based corrected area estimates were tested against the traditional photo-based estimates. Forest area estimates were not significantly different (at the 95% level) between the traditional FIA, IGSCR, and MRLC methods, although the precision of the satellite-based estimates was lower. The estimated percent forest area and the standard error (respectively) of the estimates for each region and method are as follows; Coastal Plain- Phase 1 66.06% and 1.08%, IGSCR 68.88% and 2.93%, MRLC 69.84% and 3.08%. Piedmont- Phase 1 63.87% and 1.91%, IGSCR 65.52% and 3.50%, MRLC 59.19% and 3.83%. Ridge and Valley- Phase 1 69.74% and 1.22%, IGSCR 70.02%, and 2.43%, MRLC 70.53% and 2.52%. Map accuracies were not significantly different (at the 95% level) between the IGSCR method and the MRLC method. Overall accuracies ranged from 80% to 89% using FIA definitions of forest and non-forest land use. Given standardization of the image rectification process and training data properties, the IGSCR methodology is objective and repeatable across users, regions, and time and outperforms the MRLC for FIA applications. / Master of Science

The increasing interest of modeling global carbon cycling during the past two decades has driven this research to map leaf area index (LAI) at multiple spatial resolutions by combining LAI field observations with various sensor images at local, regional, and global scale. This is due to its important role in process based models that are used to predict carbon sequestration of terrestrial ecosystems. Although a substantial research has been conducted, there are still many challenges in this area. One of the challenges is that various images with spatial resolutions varying from few meters to several hundred meters and even to 1 km have been used. However, a method that can be used to collect LAI field measurements and further conduct multiple spatial resolution mapping and accuracy assessment of LAI is not available. In this study, a pilot study in a complex landscape located in the Southern Illinois was carried out to map LAI by combining field observations and remotely sensed images. Multi-scale mapping and accuracy assessment of LAI using aerial photo, Landsat TM and MODIS images were explored by developing a multi-scale sampling design. The results showed that the sampling design could be used to collect LAI observations to create LAI products at various spatial resolutions and further conduct accuracy assessment. It was also found that the TM derived LAI maps at the original and aggregated spatial resolutions successfully characterized the heterogeneous landscape and captured the spatial variability of LAI and were more accurate than those from the aerial photo and MODIS. The aerial photo derived models led to not only over- and under-estimation, but also pixilated maps of LAI. The MODIS derived LAI maps had an acceptable accuracy at various spatial resolutions and are applicable to mapping LAI at regional and global scale. Thus, this study overcame some of the significant gaps in this field.

Change detection is currently a topic of great interest to theoretic geographic researchers. The necessity to map, monitor, and model land cover change is also important to a variety of applied fields as varied as urban planning and military intelligence. This research compares five algorithms to map urban land cover change in the greater Las Vegas, Nevada metropolitan area. Landsat Thematic Mapper imagery acquired on May 1990 and May 2000 was used as the primary data. The change detection methods yielded simple maps of change vs. no change. These algorithms included image differencing, image ratioing, image regression, vegetation index differencing, and principal components analysis. Each of these techniques accurately identified areas of land cover with moderate levels of accuracy and produced overall change detection accuracy values between 60% and 76% depending on the method. The highest accuracy was obtained by the image ratioing method using the red spectral band (76%). As expected, the determination of change detection thresholds for each technique was critical to the accuracy produced by the algorithm. Moreover, the type of statistic used in optimizing that threshold was also a significant impacting the final accuracy. The approach of using a set of ground points to calibrate the change detection threshold proved to have significant merit.

Carried out in collaboration with the Occoquan Water Monitoring Lab, this thesis presents the results of research that sought to ascertain the spatial distribution of chlorophyll-a concentrations in the Lake Manassas Reservoir using a combination of Landsat TM satellite imagery and ground based field measurements. Images acquired on May 14, 1998 and March 8, 2000 were analyzed with chlorophyll-a measurements taken on 13, 1998 and March 7, 2000. A ratio of Landsat TM band 3: Landsat Band 4 was used in a regression with data collected at eight water quality monitoring stations run by the Occoquan Watershed Monitoring Lab. Correlation coefficients of 0.76 for the 1998 data and 0.73 for the 2000 data were achieved. Cross validation statistical analysis was used to check the accuracy of the two models. The standard error and error of the estimate were reasonable for the models from both years. In each instance, the ground data was retrieved approximately 24 hours before the Landsat Image acquisition and was a potential source of error. Other sources of error were the small sample size of chlorophyll-a concentration measurements, and the uncertainty involved in the location of the water quality sampling stations. / Master of Science

The main objective of this study was to determine the spectral change technique best suited to detect complete forest harvests (clearcuts) in the Southern United States. In the pursuit of this objective eight existing change detection techniques were quantitatively evaluated and a hybrid method was also developed. Secondary objectives were to determine the impact of atmospheric corrections applied before the change detection, and the affect post-processing methods to eliminate small groups of misclassified pixels ("salt and pepper" effect) had on accuracy. Landsat TM imagery of Louisa County, Virginia was acquired on anniversary dates in both 1996 and 1998 (Path 16, Row 34), clipped to the study area boundary, and registered to one another. Previous to the change detection exercise, two levels of atmospheric corrections were applied to the imagery separately to produce three data sets. The three data sets were evaluated to determine what level of pre-processing is necessary for harvest change detection. In addition, eight change detection techniques were evaluated: 1) the 345 TM band differencing, 2) 35 TM band differencing, 3) NDVI differencing, 4) principal component 1 differencing, 5) selection of a change band in a multitemporal PCA, 6) tasseled cap brightness differencing, 7) tasseled cap greenness differencing, and 8) univariate differencing using TM band 7. A hybrid method that used the results from the eight previous techniques was developed. After performing the change detection, majority filters using window sizes of 3x3 pixels, 5x5 pixels, and 7x7 pixels were applied to the change maps to determine how eliminating small groups of misclassified pixels would affect accuracies. Accuracy assessments of the binary (harvested or not harvested) change maps were used to evaluate the accuracies of the various methods described using 256 validation points collected by the Virginia Department of Forestry. The atmospheric corrections did not seem to significantly benefit the change detection techniques, and in some cases actually degraded accuracies. Of the eight techniques applied to the original dataset, univariate differencing using TM band 7 performed the best with a 90.63% overall accuracy, while Tasseled Cap Greenness returned the worst result with an overall accuracy of 78.91%. Principal component 1 differencing and 35 differencing also performed well. The hybrid approach returned good results, but at its best returned an overall accuracy of 90.63%, matching the TM band 7 method. The majority filters using the 3x3 and 5x5 window sizes increased the accuracy in many cases, while the majority filter using the 7x7 window size degraded overall accuracy. / Master of Science

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.

The aim of this study was to detect surface temperature changes in the České Budějovice during last 30 years. The aim was also to asses the influence of vegetation on the surface temperature. The study is based on the thermal data acquired by the Landsat TM 4 and 5 and the study site was in the České Budějovice and surroundings.

In the event of a natural or anthropogenic disturbance, environmental resource managers require a reliable tool to quickly assess the spatial extent of potential damage to the seagrass resource. The temporal availability of the Landsat 5 Thematic Mapper (TM) imagery, 16-20 days, provides a suitable option to detect and assess damage to the seagrass resource. In this study, remote sensing Landsat 5 TM imagery is used to map the spatial extent of the seagrass resource. Various classification techniques are applied to delineate the seagrass beds in Clearwater Harbor and St. Joseph Sound, FL. This study aims to determine the most appropriate seagrass habitat mapping technique by evaluating the accuracy and validity of the resultant classification maps. Field survey data and high resolution aerial photography are available to use as ground truth information. Seagrass habitat in the study area consists of seagrass species and rhizophytic algae; thus, the species assemblage is categorized as submerged aquatic vegetation (SAV). Two supervised classification techniques, Maximum Likelihood and Mahalanobis Distance, are applied to extract the thematic features from the Landsat imagery. The Mahalanobis Distance classification (MDC) method achieves the highest overall accuracy (86%) and validation accuracy (68%) for the delineation of the presence/absence of SAV. The Maximum Likelihood classification (MLC) method achieves the highest overall accuracy (74%) and validation accuracy (70%) for the delineation of the estimated coverage of SAV for the classes of continuous and patchy seagrass habitat. The soft classification techniques, linear spectral unmixing (LSU) and artificial neural network (ANN), did not produce reasonable results for this particular study. The comparison of the MDC and MLC to the current Seagrass Aerial Photointerpretation (AP) project indicates that the classification of SAV from Landsat 5 TM imagery provides a map product with similar accuracy to the AP maps. These results support the application of remote sensing thematic feature extraction methods to analyze the spatial extent of the seagrass resource. While the remote sensing thematic feature extraction methods from Landsat 5 TM imagery are deemed adequate, the use of hyperspectral imagery and better spectral libraries may improve the identification and mapping accuracy of the seagrass resource.

Universiẗat, Diss., 2000--Freiburg (Breisgau).