Thesis (M.S.)--University of Montana, 2008. / Title from title screen. Description based on contents viewed May 7, 2009. Includes bibliographical references (p. 21-26).

Thesis (Ph.D.)--Boston University / Advances in remote sensing and associated capabilities are expected to proceed in a number of ways in the era of the Earth Observing System (EOS). More complex multitemporal, multi-source data sets will become available, requiring more sophisticated analysis methods. This research explores the applications of artificial neural networks in land-cover mapping, forward and inverse canopy modeling and change detection. For land-cover mapping a multi-layer feed-forward neural network produced 89% classification accuracy using a single band of multi-angle data from the Advanced Solidstate Array Spectroradiometer (ASAS). The principal results include the following: directional radiance measurements contain much useful information for discrimination among land-cover classes; the combination of multi-angle and multi-spectral data improves the overall classification accuracy compared with a single multi-angle band; and neural networks can successfully learn class discrimination from directional data or multi-domain data. Forward canopy modeling shows that a multi-layer feed-forward neural network is able to predict the bidirectional reflectance distribution function (BRDF) of different canopy sites with 90% accuracy. Analysis of the signal captured by the network indicates that the canopy structural parameters, and illumination and viewing geometry, are essential for predicting the BRDF of vegetated surfaces. The inverse neural network model shows that the R2 between the network-predicted canopy parameters and the actual canopy parameters is 0.85 for canopy density and 0.75 for both the crown shape and the height parameters. [TRUNCATED]

No description available.

Thesis (M.A.)--Boston University / The primary objective of this research is to test and validate a geometric-optical bidirectional reflectance canopy model developed by Li and Strahler, with respect to actual forest canopy reflectance measurments. This model treats forest canopies as scenes of discrete, three dimensional objects that are illuminated and viewed from different positions in the hemisphere. The shapes of the objects, their count densities and patterns of placement are the driving variables, and they condition the mixture of sunlit and shaded objects and background that is observed from a particular viewing direction, given a direction of illumination. This mixture, in turn, controls the brightness apparent to an observer or a radiometric instrument. The Advanced Solid-State Array Spectroradiometer (ASAS) is chosen to be the sensor having the ability of collecting measurements at various look angles and its imaged reflectance was used to validate the model. The modelled BRF's were compared to actual ASAS measured BRF's in sites with different canopy structures and densities. The comparision revealed execellent match between the modelled and measured reflectance, and great ability of the model in predicting the shape and magnitude of the BRDF, in almost all the sites investigated. It is concluded that the geometric optics approach provided a good way to model the bidirectional reflectance distribution function of natural vegetation canopies, that captures the most important features exhibited by bidirectional measurements of such canopies. Further modifications have been suggested that will improve the predicted BRF's, and yield better results. [TRUNCATED]

In the year 2000, croplands covered about 12% of the Earth’s ice-free land surface. Through cropland management, humankind momentarily appropriates about 25% of terrestrial ecosystem productivity. Not only are croplands a key element of human food supply, but also bear potential in increased carbon (C) uptake when best-practice land management approaches are adopted. A detailed assessment of the impact of land use on terrestrial ecosystems can be achieved by modelling, but the simulation of crop C cycling itself is a relatively new discipline. Observational data on crop net ecosystem exchange (NEE) are available only recently, and constitute an important tool for model development, diagnosis, and validation. Before crop functional types (CFT) had been introduced, however, large-scale biogeochemical models (BGCM) lacked crop-specific patterns of phenology, C allocation, and land management. As a consequence, the influence of cropland C cycling on biosphere-atmosphere C exchange seasonality and magnitude is currently poorly known. To date, no regional assessment of crop C cycling and yield formation exists that specifically accounts for spatially and temporally varying patterns of sowing dates within models. In this thesis, I present such an assessment for the first time. In the first step (chapter 2), I built a crop C mass balance model (SPAc) that models crop development and C allocation as a response to ambient meteorological conditions. I compared model outputs against C flux and stock observations of six different sites in Europe, and found a high degree of agreement between simulated and measured fluxes (R2 = 0.83). However, the model tended to overestimate leaf area index (LAI), and underestimate final yield. In a model comparison study (chapter 3), I found in cooperation with further researchers that SPAc best reproduces observed fluxes of C and water (owed to the model’s high temporal and process resolution), but is deficient due to a lack in simulating full crop rotations. I then conducted a detailed diagnosis of SPAc through the assimilation of C fluxes and biometry with the Ensemble Kalman Filter (EnKF, chapter 4), and identified potential model weaknesses in C allocation fractions and plant hydraulics. Further, an overestimation of plant respiration and seasonal leaf thickness variability were evident. Temporal parameter variability as a response to C flux data assimilation (DA) is indicative of ecosystem processes that are resolved in NEE data but are not captured by a model’s structure. Through DA, I gained important insights into model shortcomings in a quantitative way, and highlighted further needs for model improvement and future field studies. Finally, I developed a framework allowing for spatio-temporally resolved simulation of cropland C fluxes under observational constraints on land management and canopy greenness (chapter 5). MODIS (Moderate Resolution Imaging Spectroradiometer) data were assimilated both variationally (for sowing date estimation) and sequentially (for improved model state estimation, using the EnKF) into SPAc. In doing so, I was able to accurately quantify the multiannual (2000-2006) regional C flux and biometry seasonality of maize-soybean crop rotations surrounding the Bondville Ameriflux eddy covariance (EC) site, averaged over 104 pixel locations within the wider area. Results show that MODIS-derived sowing dates and the assimilation of LAI data allow for highly accurate simulations of growing season C cycling at locations for which groundtruth sowing dates are not available. Through quantification of the spatial variability in biometry, NEE, and net biome productivity (NBP), I found that regional patterns of land management are important drivers of agricultural C cycling and major sources of uncertainty if not appropriately accounted for. Observing C cycling at one single field with its individual sowing pattern is not sufficient to constrain large-scale agroecosystem behaviour. Here, I developed a framework that enables modellers to accurately simulate current (i.e. last 10 years) C cycling of major agricultural regions and their contribution to atmospheric CO2 variability. Follow-up studies can provide crucial insights into testing and validating large-scale applications of biogeochemical models.

Thesis (Ph. D.)--Mississippi State University. Department of Entomology and Plant Pathology. / Title from title screen. Includes bibliographical references.

Esse trabalho utilizou sensores remotos, em escala local e regional, para caracterizar o padrão espectral da canola e propor metodologias de criação de máscaras de cultivo, através da classificação de imagens de satélite, e de geração de estimativas de variáveis biofísicas, a partir de índices de vegetação medidos ao longo do ciclo. As medições das variáveis biofísicas foram realizadas em parcelas experimentais, na Embrapa-Trigo no município de Coxilha, e em lavouras monitoradas nas mesorregiões Nordeste e Noroeste do Rio Grande do Sul, para as safras 2013 e 2014. As variáveis biofísicas medidas foram altura de plantas, matéria seca das folhas, da haste e das síliquas e, também, foi estimado o índice de área foliar. Os dados espectrais para as parcelas experimentais foram obtidos através de um espectrorradiômetro. Para as lavouras monitoradas, os dados espectrais foram obtidos dos produtos MCD43B4 e MOD09A1, medidos pelo sensor MODIS (satélites Terra/Aqua), e de imagens do sensor OLI (satélite Landsat 8). A partir destes foi realizada a caracterização espectral da canola ao longo do seu ciclo de desenvolvimento, gerando perfis completos e perfis das bandas espectrais. Os índices de vegetação foram utilizados para caracterizar o padrão espectral e para a criação de modelos de estimativas das variáveis biofísicas, os quais foram calculados usando as bandas espectrais simuladas. Os índices de vegetação foram utilizados para classificar as áreas cultivadas com canola para as mesorregiões Nordeste e Noroeste do Rio Grande do Sul e, posteriormente, aplicados os modelos de estimativas de variáveis biofísicas. A caracterização do padrão espectral da canola foi consistente entre os dois anos avaliados e para todos os sensores, com variação temporal semelhante a outras culturas agrícolas, exceto pela redução nos índices de vegetação durante a floração da cultura. Os modelos de estimativa das variáveis biofísicas, apresentaram coeficientes de determinação elevados, com exceção das variáveis matéria seca das folhas e índice de área foliar. A classificação da área cultivada com canola, utilizando os produtos MODIS, apresentou resultados coerentes com o esperado de acordo com dados de série histórica, apresentados pela CONAB. As estimativas de variáveis biofísicas mostraram coerência com os obtidos pelas medições nas lavouras monitoradas. Os resultados obtidos nesse estudo demonstram, portanto, o potencial da utilização de dados espectrais multisensor para o mapeamento de lavouras e realização de estimativas de variáveis biofísicas da cultura da canola. / This study used remote sensors, at local and regional levels, in order to characterize the spectral pattern of canola and propose methodologies to create crop masks, through satellite image classification, and generation of estimates of biophysical variables, from vegetation indices measured along the cycle. The measurements of biophysical variables were performed on experimental plots at Embrapa Trigo in Coxilha, and in crop sites monitored in the mesoregions Northeast and Northwest of Rio Grande do Sul, in 2013 and 2014. The biophysical variables measured were plant height, dry matter of the leaves, stem and pods and also, the leaf area index was estimated. The spectral data for the experimental plots were obtained using a spectroradiometer. For monitored crop fields, spectral data were obtained from the products MCD43B4 and MOD09A1, measured by MODIS (Terra / Aqua satellite) sensor, and images from the OLI sensor (Landsat 8). These data were used to perform the spectral characterization of canola along its development cycle, generating full spectral profiles and spectral bands profiles. The vegetation indices were used to characterize the spectral pattern and creating models to estimate the biophysical variables, which have been calculated using the simulated spectral bands. The vegetation indices were used to classify the areas planted with canola for the mesoregions Northeast and Northwest and then applied to the models for estimates of biophysical variables. The characterization of the canola's spectral pattern was consistent between the two years and for all sensors with temporal variation similar to other agricultural crops, except for the reduction in the vegetation indices during the flowering phase of culture. The biophysical variables estimation models showed high correlation coefficients, except for the variables dry matter of leaves and leaf area index. The canola classification using MODIS products, showed results consistent with the expected according to historical data series presented by CONAB. Estimates of biophysical variables were consistent with those obtained by measurements in the monitored fields. The results of this study show, therefore, the potential of using multi-sensor data for the spectral mapping of canola the estimation of biophysical variables.

Esse trabalho utilizou sensores remotos, em escala local e regional, para caracterizar o padrão espectral da canola e propor metodologias de criação de máscaras de cultivo, através da classificação de imagens de satélite, e de geração de estimativas de variáveis biofísicas, a partir de índices de vegetação medidos ao longo do ciclo. As medições das variáveis biofísicas foram realizadas em parcelas experimentais, na Embrapa-Trigo no município de Coxilha, e em lavouras monitoradas nas mesorregiões Nordeste e Noroeste do Rio Grande do Sul, para as safras 2013 e 2014. As variáveis biofísicas medidas foram altura de plantas, matéria seca das folhas, da haste e das síliquas e, também, foi estimado o índice de área foliar. Os dados espectrais para as parcelas experimentais foram obtidos através de um espectrorradiômetro. Para as lavouras monitoradas, os dados espectrais foram obtidos dos produtos MCD43B4 e MOD09A1, medidos pelo sensor MODIS (satélites Terra/Aqua), e de imagens do sensor OLI (satélite Landsat 8). A partir destes foi realizada a caracterização espectral da canola ao longo do seu ciclo de desenvolvimento, gerando perfis completos e perfis das bandas espectrais. Os índices de vegetação foram utilizados para caracterizar o padrão espectral e para a criação de modelos de estimativas das variáveis biofísicas, os quais foram calculados usando as bandas espectrais simuladas. Os índices de vegetação foram utilizados para classificar as áreas cultivadas com canola para as mesorregiões Nordeste e Noroeste do Rio Grande do Sul e, posteriormente, aplicados os modelos de estimativas de variáveis biofísicas. A caracterização do padrão espectral da canola foi consistente entre os dois anos avaliados e para todos os sensores, com variação temporal semelhante a outras culturas agrícolas, exceto pela redução nos índices de vegetação durante a floração da cultura. Os modelos de estimativa das variáveis biofísicas, apresentaram coeficientes de determinação elevados, com exceção das variáveis matéria seca das folhas e índice de área foliar. A classificação da área cultivada com canola, utilizando os produtos MODIS, apresentou resultados coerentes com o esperado de acordo com dados de série histórica, apresentados pela CONAB. As estimativas de variáveis biofísicas mostraram coerência com os obtidos pelas medições nas lavouras monitoradas. Os resultados obtidos nesse estudo demonstram, portanto, o potencial da utilização de dados espectrais multisensor para o mapeamento de lavouras e realização de estimativas de variáveis biofísicas da cultura da canola. / This study used remote sensors, at local and regional levels, in order to characterize the spectral pattern of canola and propose methodologies to create crop masks, through satellite image classification, and generation of estimates of biophysical variables, from vegetation indices measured along the cycle. The measurements of biophysical variables were performed on experimental plots at Embrapa Trigo in Coxilha, and in crop sites monitored in the mesoregions Northeast and Northwest of Rio Grande do Sul, in 2013 and 2014. The biophysical variables measured were plant height, dry matter of the leaves, stem and pods and also, the leaf area index was estimated. The spectral data for the experimental plots were obtained using a spectroradiometer. For monitored crop fields, spectral data were obtained from the products MCD43B4 and MOD09A1, measured by MODIS (Terra / Aqua satellite) sensor, and images from the OLI sensor (Landsat 8). These data were used to perform the spectral characterization of canola along its development cycle, generating full spectral profiles and spectral bands profiles. The vegetation indices were used to characterize the spectral pattern and creating models to estimate the biophysical variables, which have been calculated using the simulated spectral bands. The vegetation indices were used to classify the areas planted with canola for the mesoregions Northeast and Northwest and then applied to the models for estimates of biophysical variables. The characterization of the canola's spectral pattern was consistent between the two years and for all sensors with temporal variation similar to other agricultural crops, except for the reduction in the vegetation indices during the flowering phase of culture. The biophysical variables estimation models showed high correlation coefficients, except for the variables dry matter of leaves and leaf area index. The canola classification using MODIS products, showed results consistent with the expected according to historical data series presented by CONAB. Estimates of biophysical variables were consistent with those obtained by measurements in the monitored fields. The results of this study show, therefore, the potential of using multi-sensor data for the spectral mapping of canola the estimation of biophysical variables.

Recent advances in satellite sensor technology and micrometeorological instrumentation for water flux measurement, coupled with the expansion of automatic weather station networks that provide routine measurements of near-surface climate variables, present new opportunities for combining satellite and ground-based instrumentation to obtain distributed estimates of vegetation water use over wide areas in South Africa. In this study, a novel approach is tested, which uses satellite leaf area index (LAI) data retrieved by the Moderate Resolution Imaging Spectroradiometer (MODIS) to inform the FAO-56 Penman-Monteith equation for calculating reference evaporation (ET₀) of vegetation phenological activity. The model (ETMODIS) was validated at four sites in three different ecosystems across the country, including semi-arid savanna near Skukuza, mixed community grassland at Bellevue, near Pietermaritzburg, and Groenkop, a mixed evergreen indigenous forest near George, to determine potential for application over wider areas of the South African land surface towards meeting water resource management objectives. At Skukuza, evaluated against 170 days of flux data measured at a permanent eddy covariance (EC) flux tower in 2007, the model (ETMODIS) predicted 194.8 mm evapotranspiration relative to 148.9 mm measured fluxes, an overestimate of 31.7 %, (r² = 0.67). At an adjacent site, evaluated against flux data measured on two discrete periods of seven and eight days in February and May of 2005 using a large aperture scintillometer (SLS), ETMODIS predicted 27.4 mm and 6.7 mm evapotranspiration respectively, relative to measured fluxes of 32.5 and 8.2 mm, underestimates of 15.7 % and 18.3 % in each case (r² = 0.67 and 0.34, respectively). At Bellevue, evaluated against 235 days of evapotranspiration data measured using a surface layer scintillometer (SLS) in 2003, ETMODIS predicted 266.9 mm evapotranspiration relative to 460.2 mm measured fluxes, an underestimate of 42 % (r² = 0.67). At Groenkop, evaluated against data measured using a SLS over three discrete periods of four, seven and seven days in February, June and September/October respectively, ETMODIS predicted 9.7 mm, 10.3 mm and 17.0 mm evapotranspiration, relative to measured fluxes of 10.9 mm, 14.6 mm and 23. 9 mm, underestimates of 22.4 %, 11.2 % and 24.1 % in each case (r² = 0.98, 0.43 and 0.80, respectively). Total measured evapotranspiration exceeded total modelled evapotranspiration in all cases, with the exception of the flux tower site at Skukuza, where evapotranspiration was overestimated by ETMODIS by 31.7 % relative to measured (EC) values for the 170 days in 2007 where corresponding modelled and measured data were available. The most significant differences in measured versus predicted data were recorded at the Skukuza flux tower site in 2007 (31.7 % overestimate), and the Bellevue SLS flux site in 2003 (42 % underestimate); coefficients of determination, a measure of the extent to which modelled data are able to explain observed data at validation periods, with just two exceptions, were within a range of 0.67 – 0.98. Several sources of error and uncertainty were identified, relating predominantly to uncertainties in measured flux data used to evaluate ETMODIS, uncertainties in MODIS LAI submitted to ETMODIS, and uncertainties in ETMODIS itself, including model assumptions, and specific uncertainties relating to various inputs; further application of the model is required to test these uncertainties however, and establish confidence limits in performance. Nevertheless, the results of this study suggest that the technique is generally able to produce estimates of vegetation water use to within reasonably close approximations of measurements acquired using micrometeorological instruments, with r² values within the range of other peer-reviewed satellite remote sensing-based approaches.

Esse trabalho utilizou sensores remotos, em escala local e regional, para caracterizar o padrão espectral da canola e propor metodologias de criação de máscaras de cultivo, através da classificação de imagens de satélite, e de geração de estimativas de variáveis biofísicas, a partir de índices de vegetação medidos ao longo do ciclo. As medições das variáveis biofísicas foram realizadas em parcelas experimentais, na Embrapa-Trigo no município de Coxilha, e em lavouras monitoradas nas mesorregiões Nordeste e Noroeste do Rio Grande do Sul, para as safras 2013 e 2014. As variáveis biofísicas medidas foram altura de plantas, matéria seca das folhas, da haste e das síliquas e, também, foi estimado o índice de área foliar. Os dados espectrais para as parcelas experimentais foram obtidos através de um espectrorradiômetro. Para as lavouras monitoradas, os dados espectrais foram obtidos dos produtos MCD43B4 e MOD09A1, medidos pelo sensor MODIS (satélites Terra/Aqua), e de imagens do sensor OLI (satélite Landsat 8). A partir destes foi realizada a caracterização espectral da canola ao longo do seu ciclo de desenvolvimento, gerando perfis completos e perfis das bandas espectrais. Os índices de vegetação foram utilizados para caracterizar o padrão espectral e para a criação de modelos de estimativas das variáveis biofísicas, os quais foram calculados usando as bandas espectrais simuladas. Os índices de vegetação foram utilizados para classificar as áreas cultivadas com canola para as mesorregiões Nordeste e Noroeste do Rio Grande do Sul e, posteriormente, aplicados os modelos de estimativas de variáveis biofísicas. A caracterização do padrão espectral da canola foi consistente entre os dois anos avaliados e para todos os sensores, com variação temporal semelhante a outras culturas agrícolas, exceto pela redução nos índices de vegetação durante a floração da cultura. Os modelos de estimativa das variáveis biofísicas, apresentaram coeficientes de determinação elevados, com exceção das variáveis matéria seca das folhas e índice de área foliar. A classificação da área cultivada com canola, utilizando os produtos MODIS, apresentou resultados coerentes com o esperado de acordo com dados de série histórica, apresentados pela CONAB. As estimativas de variáveis biofísicas mostraram coerência com os obtidos pelas medições nas lavouras monitoradas. Os resultados obtidos nesse estudo demonstram, portanto, o potencial da utilização de dados espectrais multisensor para o mapeamento de lavouras e realização de estimativas de variáveis biofísicas da cultura da canola. / This study used remote sensors, at local and regional levels, in order to characterize the spectral pattern of canola and propose methodologies to create crop masks, through satellite image classification, and generation of estimates of biophysical variables, from vegetation indices measured along the cycle. The measurements of biophysical variables were performed on experimental plots at Embrapa Trigo in Coxilha, and in crop sites monitored in the mesoregions Northeast and Northwest of Rio Grande do Sul, in 2013 and 2014. The biophysical variables measured were plant height, dry matter of the leaves, stem and pods and also, the leaf area index was estimated. The spectral data for the experimental plots were obtained using a spectroradiometer. For monitored crop fields, spectral data were obtained from the products MCD43B4 and MOD09A1, measured by MODIS (Terra / Aqua satellite) sensor, and images from the OLI sensor (Landsat 8). These data were used to perform the spectral characterization of canola along its development cycle, generating full spectral profiles and spectral bands profiles. The vegetation indices were used to characterize the spectral pattern and creating models to estimate the biophysical variables, which have been calculated using the simulated spectral bands. The vegetation indices were used to classify the areas planted with canola for the mesoregions Northeast and Northwest and then applied to the models for estimates of biophysical variables. The characterization of the canola's spectral pattern was consistent between the two years and for all sensors with temporal variation similar to other agricultural crops, except for the reduction in the vegetation indices during the flowering phase of culture. The biophysical variables estimation models showed high correlation coefficients, except for the variables dry matter of leaves and leaf area index. The canola classification using MODIS products, showed results consistent with the expected according to historical data series presented by CONAB. Estimates of biophysical variables were consistent with those obtained by measurements in the monitored fields. The results of this study show, therefore, the potential of using multi-sensor data for the spectral mapping of canola the estimation of biophysical variables.