Synoptic global remote sensing provides a multitude of land surface state variables. The continuous collection, for more than 30 years, of global observations has contributed to the creation of a unique and long term satellite imagery archive from different sensors. These records have become an invaluable source of data for many environmental and global change related studies. The problem, however, is that they are not readily available for use in research and application environment and require multiple preprocessing. Here, we looked at the daily global data records from the Advanced Very High Resolution Radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS), two of the most widely available and used datasets, with the objective of assessing their quality and suitability to support studies dealing with global trends and changes at the land surface. Findings show that clouds are the major data quality inhibitors, and that the MODIS cloud masking algorithm performs better than the AVHRR. Results show that areas of high ecological importance, like the Amazon, are most prone to lack of data due to cloud cover and aerosols leading to extended periods of time with no useful data, sometimes months. While the standard approach to these challenges has been compositing of daily images to generate a representative map over a preset time periods, our results indicate that preset compositing is not the optimal solution and a hybrid location dependent method that preserves the high frequency of these observations over the areas where clouds are not as prevalent works better. Using this data quality information the Vegetation Index and Phenology (VIP) Laboratory at The University of Arizona produced over 30 years of seamless sensor independent record of vegetation indices and land surface phenology metrics. These data records consist of 0.05-degree resolution global images for daily, 7-days, 15-days and monthly temporal frequency. These sort of remote sensing based products are normally made available through the internet by large data centers, like the Land Processes Distributed Active Archive Center (LP DAAC), however, in this project an online tool, the VIP Data Explorer, was developed to support the visualization, exploration, and distribution of these Earth Science Data Records (ESDRs) keeping it closer to the data generation center which provides a more active data support and distribution model. This web application has made it possible for users to explore and evaluate the products suite before download and use.

Land and vegetation changes are part of the continuous and dynamic cycle of earth system variation. This research examines vegetation changes in the 21-county eco-region along coastal Georgia. The Advanced Very High Resolution Radiometer (AVHRR) with Normalized Difference Vegetation Index (NDVI) data is used in tandem with a Principal Component Analysis (PCA) and climatic variables to determine where, and to what extent vegetation and land cover change is occurring. This research is designed around a 16 year time-series from 1990-2005. Findings were that mean NDVI values were either steady or slightly improved, and that PC1 (Healthiness) and PC2 (Time-Change) explained nearly 99 percent of the total mean variance. Healthiness declines are primarily the result of expanding urban districts and decreased soil moisture while increases are the results of restoration, and increased soil moisture. This research aims to use this analysis for the assessment of land changes as the conduit for future environmental research.

Cape Bounty, Melville Island is a partially vegetated High Arctic landscape with three main plant communities: polar semi-desert (47% of the study area), mesic tundra (31%) , and wet sedge meadows (7%). The objective of this research was to relate biophysical measurements of soil, vegetation, and CO2 exchange rates in each vegetation type to high resolution satellite data from IKONOS-2, extending plot level measurements to a landscape scale. Field data was collected through six weeks of the 2008 growing season. Two IKONOS images were acquired, one on July 4th and the other on August 2nd. Two products were generated from the satellite data: a land-cover classification and the Normalized Difference Vegetation Index (NDVI). The three vegetation types were found to have distinct soil and vegetation characteristics. Only the wet sedge meadows were a net sink for CO2; soil respiration tended to exceed photosynthesis in the sparsely vegetated mesic tundra and polar semi-desert. Scaling up the plot measurements by vegetation type area suggested that Cape Bounty was a small net carbon source (0.34 ± 0.47 g C m-2 day-1) in the summer of 2008. NDVI was strongly correlated with percent vegetation cover, vegetation volume, soil moisture, and moderately with soil nitrogen, biomass, and leaf area index (LAI). Photosynthesis and respiration of CO2 both positively correlated with NDVI, most strongly when averaged over the season. NDVI increased over time in every vegetation type, but this change was not reflected in any significant measured changes in vegetation or CO2 flux rates. A simple spatial model was developed to estimate Net Ecosystem Exchange (NEE) at every pixel on the satellite images based on NDVI, temperature and incoming solar radiation. It was found that the rate of photosynthesis per unit NDVI was higher early in the growing season. The model estimated a mean flux to the atmosphere of 0.21 g C m-2 day-1 at the time of image acquisition on July 4th, and -0.07 g C m-2 day-1 (a net C sink) on August 2nd. The greatest uncertainty in the relationship between NDVI and CO2 flux was associated with the polar semi-desert class. / Thesis (Master, Geography) -- Queen's University, 2011-12-28 23:27:34.824

The feasibility of using optical sensors and non-nitrogen limiting reference crops to determine post-emergent nitrogen fertilizer requirements of canola was evaluated. Normalized difference vegetation index was well suited for estimating yield potential and nitrogen status. Although sensor-based nitrogen management was generally agronomically feasible for canola, the economic benefits of doing so remain uncertain because of the added cost of applying post-emergent nitrogen.

The feasibility of using optical sensors and non-nitrogen limiting reference crops to determine post-emergent nitrogen fertilizer requirements of canola was evaluated. Normalized difference vegetation index was well suited for estimating yield potential and nitrogen status. Although sensor-based nitrogen management was generally agronomically feasible for canola, the economic benefits of doing so remain uncertain because of the added cost of applying post-emergent nitrogen.

Global and local climate patterns may affect barren-ground caribou (Rangifer tarandus groenlandicus) populations. I predicted global climate changes to be correlated with periods of population decline, and local changes to be more pronounced on the habitat of caribou with a declining population. In chapter 1, the Arctic Oscillation (AO), changes in normalized difference vegetation index and phenology were used as measures of global and local climate. In chapter 2 environmental variables and caribou presence points were used to build Maxent habitat models. There was no consistent correlation with the positive AO phase and periods of population decline, or phenology trends and the habitat of caribou with a declining population. Maxent models underestimated the amount of suitable habitat spatially and failed to model suitable habitat temporally. This thesis is the first to look at a range of density-independent variables over a long time period and model suitable habitat for multiple herds.

Este estudo analisa a variação dos valores do Índice de Vegetação por Diferença Normalizada (NDVI) na lagoa Itapeva-RS, por meio de imagens Landsat TM5 e ETM+7, compreendendo o período de 1985 a 2010. Para a análise do comportamento espaço-temporal foi utilizado classes de Índice de Vegetação por Diferença Normalizada (NDVI) e Análise por Séries Temporais (TSA) de imagens. Este estudo avalia o comportamento da lagoa com base na influência dos agentes hidrodinâmicos e sua relação com os padrões de distribuição espaço-temporal. Para validação dos padrões observados foram coletadas amostras de água em seis pontos amostrais na lagoa Itapeva para clorofila_a, Totais de Sólidos Suspensos e transparência do disco Secchi. Os resultados foram correlacionados com os valores de NDVI. Os mapas de classes de NDVI gerados após a análise da TSA evidenciaram padrões espaciais e sazonais, considerando a sua dinâmica associada as variáveis ambientais atuantes: os ventos, as chuvas, o período de floração do fitoplâncton, a concentração de clorofila_a, a profundidade da lagoa e a descarga de sedimentos pelos afluentes. Esses padrões espaço-temporais, permitiram estabelecer três grandes compartimentos para a lagoa Itapeva: norte, centro e sul. A série temporal de imagens mostrou que os valores de NDVI aumentam durante os periodos de floração do fitoplâncton no outono e primavera. As medidas in situ, mostraram baixas concentrações de clorofila_a e de Totais de Sólidos Suspensos na lagoa no dia da coleta das amostras. Os resultados gerados a partir da Análise de Séries Temporais de imagens Landsat foram satisfatórios no que se refere ao monitoramento da distribuição espaço-temporal da variação das classes de NDVI. / This study analyzes the variation of the Normalized Difference Vegetation Index (NDVI) in the Itapeva-RS Lake, using Landsat TM5 and ETM + 7 images, from 1985 through 2010. For the analysis of the space-temporal behavior was used Normalized Difference Vegetation Index (NDVI) classes and Temporal Series Analysis (TSA). This study also evaluates of the lake behavior based on the influence of hydrodynamic agents and the relationships on the spatial-temporal distribution pattern. To validate the observed patterns were collected water samples from six points in the Itapeva Lake for clorofila_a, Total suspended solids and Secchi disk transparency. The results were correlated with NDVI values. The NDVI classes maps that were generated from TSA, showed seasonal and spatial patterns considering the dynamics associated with environmental variables that were actuating: winds, rains, the period of phytoplankton bloom, the concentration of clorofila_a, depth of the coastal lake and the discharge of sediment by tributaries. These spatial and temporal patterns, allowed the establishment of three distinctive surfaces in the Itapeva Lake: north, center and south. The temporal series of NDVI values showed that the phytoplankton increases only during the flowering periods in autumn and spring. In situ measures, however, showed low concentrations of Chlorophyll_a and total suspended solids in the coastal lake on the day of sample collection. The results generated from the analysis of time series of Landsat images were satisfactory with regard to the monitoring of spatial and temporal distribution of the variation in NDVI classes.

Diploma thesis was focused on data yield map processing, their interpretation and analysis of relations with another spatial data. Aims of the work were identification error values, statistical process yield data and with help statistical methods evaluate correlation between yield map and another spatial data. Methods were based on gathering yield data with combine harvester during two years in corn and malting barley in conditions of farm. With using GIS and predefined tolls were removed error values from yield data and created yield maps. Subsequently, yield maps were analyzed with vegetation index NDVI, digital elevation model (DEM), sensor measurements of soil electrical conductivity and historical yield maps. In results was achieved removing from 11,3 to 13,8 % yield points in average, increasing of average yield from 3,6 to 4,7 % and decreasing of variation coefficient between 7,0 and 8,6 % with comparing original yield data and depending on planting crops during data yield processing. In case of evaluation of relations between yield map and NDVI obtained from satellite remote sensing was achieved moderately correlation by corn (r = 0,44) and malting barley (r = 0,65) in average depending on times of imaging and vegetative phase of the crop. In the evaluation relations between yield maps and DEM and slope was achieved weak and very weak direct and indirect correlation. Likewise, by soil electrical conductivity and yield was achieved very weak to negligible correlation. Conversely, the highest correlation was achieved between yield map and historical yield map (r = 0,63) in average. Obtained information serve as starting point for making agronomy decisions in conditions of particular farm in case of replacement of creating yield maps by prediction of yield based on another spatial data.

The present thesis deals with evaluation of crop water stress by means of remote sensing. The first part of the research is focused on the assessment of water stress of sunflowers in a laboratory environment using spectral analysis of the reflected radiation and thermography. Leaf of suflowers temperature were evaluated and using them, the index of water stress CWSI were calculated. Reflection of leaf was evaluated in the wavelengths 350 -2500 nm. From these wavelengths spectral indexes were calculated - NDVI (Normalized difference vegetation index) which detect the presence of living green vegetation, WI (Water Index) which serve to calculate the approximate amount of water in the vegetation and RVI (simple ratio vegetation index) which determine the content of biomass or photosynthetic capacity. After that, they were compared with CWSI. Finally CWSI was compared with the spectral data, and from the resulting correlations they were apparent wavelengths, which correlated with given index more or less. The second part is focused on the assessment of water stress in terms of 4th. level catchment, specifically on catchment "Dehtáře" near Pelhřimov. There were primarily evaluated three biggest areas - fields of winter wheat, field corn and TTP.

Este estudo analisa a variação dos valores do Índice de Vegetação por Diferença Normalizada (NDVI) na lagoa Itapeva-RS, por meio de imagens Landsat TM5 e ETM+7, compreendendo o período de 1985 a 2010. Para a análise do comportamento espaço-temporal foi utilizado classes de Índice de Vegetação por Diferença Normalizada (NDVI) e Análise por Séries Temporais (TSA) de imagens. Este estudo avalia o comportamento da lagoa com base na influência dos agentes hidrodinâmicos e sua relação com os padrões de distribuição espaço-temporal. Para validação dos padrões observados foram coletadas amostras de água em seis pontos amostrais na lagoa Itapeva para clorofila_a, Totais de Sólidos Suspensos e transparência do disco Secchi. Os resultados foram correlacionados com os valores de NDVI. Os mapas de classes de NDVI gerados após a análise da TSA evidenciaram padrões espaciais e sazonais, considerando a sua dinâmica associada as variáveis ambientais atuantes: os ventos, as chuvas, o período de floração do fitoplâncton, a concentração de clorofila_a, a profundidade da lagoa e a descarga de sedimentos pelos afluentes. Esses padrões espaço-temporais, permitiram estabelecer três grandes compartimentos para a lagoa Itapeva: norte, centro e sul. A série temporal de imagens mostrou que os valores de NDVI aumentam durante os periodos de floração do fitoplâncton no outono e primavera. As medidas in situ, mostraram baixas concentrações de clorofila_a e de Totais de Sólidos Suspensos na lagoa no dia da coleta das amostras. Os resultados gerados a partir da Análise de Séries Temporais de imagens Landsat foram satisfatórios no que se refere ao monitoramento da distribuição espaço-temporal da variação das classes de NDVI. / This study analyzes the variation of the Normalized Difference Vegetation Index (NDVI) in the Itapeva-RS Lake, using Landsat TM5 and ETM + 7 images, from 1985 through 2010. For the analysis of the space-temporal behavior was used Normalized Difference Vegetation Index (NDVI) classes and Temporal Series Analysis (TSA). This study also evaluates of the lake behavior based on the influence of hydrodynamic agents and the relationships on the spatial-temporal distribution pattern. To validate the observed patterns were collected water samples from six points in the Itapeva Lake for clorofila_a, Total suspended solids and Secchi disk transparency. The results were correlated with NDVI values. The NDVI classes maps that were generated from TSA, showed seasonal and spatial patterns considering the dynamics associated with environmental variables that were actuating: winds, rains, the period of phytoplankton bloom, the concentration of clorofila_a, depth of the coastal lake and the discharge of sediment by tributaries. These spatial and temporal patterns, allowed the establishment of three distinctive surfaces in the Itapeva Lake: north, center and south. The temporal series of NDVI values showed that the phytoplankton increases only during the flowering periods in autumn and spring. In situ measures, however, showed low concentrations of Chlorophyll_a and total suspended solids in the coastal lake on the day of sample collection. The results generated from the analysis of time series of Landsat images were satisfactory with regard to the monitoring of spatial and temporal distribution of the variation in NDVI classes.