Tidewater glaciers form a significant drainage catchment of glacierised areas, directly transporting meltwater from the terrestrial to the marine environment. Surface melt of glaciers in the Arctic is increasing in response to warmer atmospheric temperatures, whilst tidewater glaciers are also exposed to warmer ocean temperatures, stimulating submarine melt. Increased freshwater discharge not only freshens fjord waters, but also plays a key role in glacimarine sedimentary processes, transporting sediment to glacial fjords. Despite this, the temporal evolution of meltwater production, storage and release from tidewater glacier systems at seasonal and interannual time scales is poorly understood. This leaves large uncertainties in the predictions for future sea level rise, ocean circulation and the impacts on the marine ecosystem. This study focuses on Kronebreen, a tidewater glacier which flows into the head of Kongsfjorden, north west Svalbard. Surface melt produces freshwater runoff, which is discharged from the grounding line as a buoyant, sediment laden plume, which spreads laterally across the surface water. This supraglacial melt is the dominant freshwater source, contributing an order of magnitude more freshwater to Kongsfjorden, than direct submarine melting of the ice face. Calibration of MODIS band 1 satellite imagery with in situ measurements of Total Suspended Solids and spectral reflectance, provides a method to quantify meltwater and sediment discharge. Plume extent has been determined for each cloud free day, from June to September, 2002 - 2013. Analysis of plume extent with atmospheric temperature and modeled surface runoff, gives a source to sea insight to meltwater production, storage and discharge. The extent of the plume changes in response to meltwater; larger plumes form when discharge increases. These results reveal that meltwater discharge into Kongsfjorden lags atmospheric temperature, the primary driver of meltwater production, by over a week during June and July. This is reduced to only 1 - 2 days in August and September, indicating a decline in meltwater storage as the ablation season progresses, and the development of more efficient glacial drainage. Sediment plumes respond to meltwater production, making them a valuable tool for quantifying meltwater discharge from a tidewater glacier. Insights to glacier hydrology can also be obtained when surface processes are also considered. This furthers the understanding of tidewater glacier hydrology, which is valuable for improving the accuracy of sea level rise predictions.

Magíster en Meteorología y Climatología / Ingeniero Civil Matemático / Se ha sugerido que el espesor óptico de aerosoles (AOD) derivado a partir de sensores a bordo de satélites puede ser un complemento a las mediciones superficiales de concentración de aerosoles en la capa límite. Se explora si esto es aplicable en el caso de Santiago de Chile, comparando el producto de AOD derivado de la señal satelital del instrumento MODerate resolution Imaging Spectroradiometer (MODIS) con diez años de mediciones in situ de concentraciones de material particulado parcialmente respirable (PM10) y totalmente respirable (PM2.5). Para ello, se desarrolla y aplica un modelo numérico simple de AOD en base a mediciones de concentración de material particulado en superficie, altura de capa límite y propiedades de aerosoles obtenidas de la literatura junto con la información disponible de estos parámetros para la ciudad de Santiago. El modelo captura la variabilidad estacional del AOD cuando es comparado con las observaciones obtenidas con un fotómetro solar de la red AErosol RObotic NETwork (AERONET) y también captura la variabilidad diurna en el caso de una campaña de un día. La variabilidad estacional opuesta entre la altura de la capa límite y la concentración de material particulado en superficie son las principales responsables de una estacionalidad débil del AOD simulado. Se observa una marcada estacionalidad del AOD satelital opuesta a la estacionalidad de la concentración de material particulado en superficie. Por otro lado y debido a la poca cantidad de mediciones simultáneas de AERONET y MODIS, se incluyen las simulaciones en la comparación de AOD. Se observa una considerable diferencia entre el comportamiento estacional del AOD simulado y el derivado de MODIS. En trabajos anteriores se sugiere que la presencia de nubes cirrus es la causa de la estacionalidad opuesta. En este estudio se deduce que aparentemente esta estacionalidad no se debe a la presencia de nubes cirrus. Con el uso de un modelo de reflectancia de superficie y sus parámetros derivados de MODIS, se propone que la diferencia entre la variación estacional del AOD de MODIS y aquella simulada se explica en gran parte por estimaciones inadecuadas de la reflectancia de la superficie y en menor grado por la selección inadecuada de propiedades ópticas y de proporción de fracción fina del aerosol en el algoritmo de MODIS. Se muestra que esta proposición se sustenta observacionalmente en el caso de Buenos Aires, donde se compara el AOD de MODIS con el observado por AERONET.

Estima el espesor óptico de los aerosoles (⅄c =0,55 µm) a una resolución espacial de 1 km x 1 km, usando el modelo de transferencia radiativa Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART) (Ricchiazzi y Yang, 1998). Este modelo simula la transferencia radiativa en la atmósfera (scattering, absorción, emisión, etc), la interacción de la radiación tanto solar como terrestre con los componentes atmosféricos como el vapor de agua, dióxido de carbono, metano, etc. El modelo SBDART, genera un archivo de salida ASCII, que contiene la irradiancia en el tope de la atmosfera y en la superficie terrestre, obtenido mediante la aproximación de dos flujos y la aproximación de Eddington para resolver la ecuación de transferencia radiativa, considerando una atmosfera plano paralela. Para luego generar las ecuaciones definidas por el espesor óptico de los aerosoles (⅄c =0,55 µm) en función de la reflectancia en el tope de la atmosfera, mediante una regresión polinomial de grado 3, para la banda 1 (⅄c =0,66 µm) del sensor MODIS, con diferentes valores de espesor óptico de los aerosoles (⅄c =0,55 µm) y otras condiciones (geometría del Sol y del Sensor, un modelo de aerosoles y la reflectancia de la superficie (⅄c =0,66 µm). El área de estudio es el espacio aéreo de Perú, que se encuentra entre las latitudes 0002′00"S a 18021′03"S y las longitudes de 68039′00"O a 81019′35"O. Para el tratamiento de las imágenes MODIS Level 1B, se usa el lenguaje de programación IDL versión 7.8. Luego, usando los resultados del Modelo SBDART se calcula el promedio anual del espesor óptico de los aerosoles (⅄c =0,55 µm) a una resolución espacial de 1 km x 1 km en todo el área de estudio, para los años 2004 y 2005, con valores que oscilan entre 0,1 y 1,0, así como para la región amazónica tanto para el año 2004 y 2005, coincidiendo los valores máximos del espesor óptico de los aerosoles con el tiempo de inicio de los incendios forestales en Brasil. / Tesis

Student Number : 0216831W - MSc research report - School of Environmental Sciences - Faculty of Science / This report deals with the relationship between the NDVI obtained from the NOAA AVHRR sensor and that obtained from the MODIS sensor. The relationship is quantitatively assessed for distinct polygons over various land-cover types in the northeastern Kwa-Zulu Natal Province of South Africa. Spatial and temporal variations in the relationships are addressed and discussed with reference to spectral response, sunsensor- target geometries and atmospheric factors. Specifically, various methods are investigated to estimate a MODIS-equivalent NDVI from the AVHRR NDVI and in so doing create the potential to develop a self-consistent NDVI between the historically available AVHRR NDVI dataset and the relatively new MODIS NDVI dataset. NOAA-16 AVHRR NDVI data and AQUA MODIS NDVI data for the two-year period from January 2002 to December 2003 are used to develop the method. A linear relationship exists between the AVHRR and MODIS NDVI. However, spatial variations in the relationship in terms of land-cover and mean NDVI are pointed out. The potential of atmospheric corrections applied to AVHRR data through a radiative transfer atmospheric correction model to improve the relationship between the two NDVI datasets is also investigated. The importance of geo-location accuracy of the AVHRR NDVI dataset is assessed in the light of the accuracy obtainable with the proposed method to estimate a MODIS-equivalent NDVI from the AVHRR NDVI. A method to estimate the MODIS NDVI from the AVHRR NDVI that takes the mean AVHRR NDVI value into account, as opposed to a constant linear relationship over all the points, is proposed. Atmospheric correction is shown not to improve the accuracy of the method in a statistically significant way. The root-mean-square error of the proposed method is in the order of 0.05 NDVI units and varies between 0.5 and 2 standard deviations of the MODIS NDVI over an entire season.

Submitted by Erika Demachki (erikademachki@gmail.com) on 2014-09-26T17:48:12Z No. of bitstreams: 2 Fernando_Moreira_Araujo_2010-dissertação.pdf: 9084097 bytes, checksum: 140c0836590b68685fd8324ba0d2a972 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Approved for entry into archive by Jaqueline Silva (jtas29@gmail.com) on 2014-09-26T18:12:38Z (GMT) No. of bitstreams: 2 Fernando_Moreira_Araujo_2010-dissertação.pdf: 9084097 bytes, checksum: 140c0836590b68685fd8324ba0d2a972 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Made available in DSpace on 2014-09-26T18:12:38Z (GMT). No. of bitstreams: 2 Fernando_Moreira_Araujo_2010-dissertação.pdf: 9084097 bytes, checksum: 140c0836590b68685fd8324ba0d2a972 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Previous issue date: 2010-12-16 / Conselho Nacional de Pesquisa e Desenvolvimento Científico e Tecnológico - CNPq / The Cerrado biome has many favorable characteristic, like soil, relief, and dense hydrography, for the development of mainly economic activity, as agriculture and pasture, this biome has suffered high human disturbance process. Along with human activities, the practice of burning was increased, particularly for the practice of grazing management, pest control, cleaning of areas for agricultural planting and others. The fires have several consequences for the biome, amongst them have increasing of temperature, decreasing rainfall, genetic impoverishment of natural species, increases the risk of respiratory diseases. In this research, for mapping burned areas in the Cerrado biome between 2002 to 2008 was used MCD45A1 MODIS product, which makes the mapping of burn scars globally scale. The result is that fires occur during the year in the Cerrado, reaching its peak between July to September, warm and dry period with the lowest relative humidity. The burned areas are located in larger amounts in the north-central part of the Cerrado, mainly in the expansion of agriculture in the states of Bahia, Piaui, Maranhao, Mato Grosso and Tocantins. However, the fires, based on mapping of the PROBIO Cerrado, occur in greater proportions in regions of natural vegetation cover (81.7%), and among vegetation types most affected are the wooded savanna (34.1%) and savanna parkland (29.7%). As for the river watershed, such as Amazon, San Francisco, Parnaíba, Paraná, and especially Tocantins-Araguaia (46.4% of fires between 2002 and 2008 were concentrated in the watershed) were the hardest hit by the effects of fire during the burning of biomass (combustible material). The fires hit large of Protected Areas (PAs) located in the biome, such as the sustainable use and integral protected areas, where the Environmental Protection Areas (APA) and National Parks (NP) were most affected. In relation to priority areas for conservation of biodiversity in the Cerrado biome the areas with extremely high priority received the greatest records of fires within its limits, as indigenous lands, protected areas, and was severely affected by fires in relation to others in the same period. As for the validation of the data MCD45A1 MODIS product, burned area from LANDSAT TM, satellite images, were quite satisfactory for the Cerrado, because of the area burned 11,126 polygons (MCD45A1) visually inspected, all were labeled as burnt / O bioma Cerrado por possuir características favoráveis, como solo, relevo e densa rede hidrográfica, para o desenvolvimento de importantes atividades econômicas, como a agricultura e pecuária, vêm sofrendo alto processo de antropização. Juntamente com as atividades antrópicas, a prática da queimada foi potencializada, principalmente para a prática do manejo de pasto, controle de pragas, limpeza de áreas para o plantio agrícola, etc. As queimadas trazem várias consequências para o bioma Cerrado, dentre elas temos o aumento da temperatura, diminuição das chuvas, empobrecimento genético das espécies naturais, aumenta os riscos de doenças respiratórias, etc. Nessa pesquisa, para o mapeamento de áreas queimadas no bioma Cerrado entre 2002 a 2008 foi utilizado o produto MODIS MCD45A1, o qual faz o mapeamento de cicatrizes de queimadas em escala global. Como resultado, temos que as queimadas ocorrem durante todo o ano no bioma Cerrado, atingindo seu ápice entre julho a setembro, período quente e seco com menor índice de umidade relativa do ar. As áreas queimadas localizam em maior proporção na região centro-norte do Cerrado, principalmente nas regiões de expansão da agricultura, nos estados da Bahia, Piauí, Maranhão, Mato Grosso e Tocantins. Contudo, as queimadas, com base no mapeamento do PROBIO Cerrado, ocorrem em maior proporção nas regiões de cobertura vegetal natural (81,7%), sendo que dentre as fitofisionomias mais afetadas são savana arborizada (34,1%) e savana parque (29,7%). Quanto às regiões hidrográficas, como Amazônica, São Francisco, Parnaíba, Paraná e, sobretudo, Tocantins-Araguaia (46,4% das queimadas entre 2002 e 2008 concentraram nessa bacia) foram as mais atingidas pelo efeito do fogo durante a queima de biomassa (material combustível). As queimadas atingiram extensas áreas das Unidades de Conservação (UCs) localizadas no bioma Cerrado, tais como as de uso sustentável e proteção integral, onde as Áreas de Proteção Ambiental (APA) e Parques Nacionais (PN) foram as mais afetadas. Em relação às áreas prioritárias para a conservação da biodiversidade do Cerrado, as áreas com prioridade extremamente alta obteve os maiores registros de queimadas em seus limites, já as Terras Indígenas, áreas protegidas, foi severamente atingidas pelas queimadas em relação às demais no mesmo período. Quanto à validação dos dados do produto MODIS MCD45A1 área queimada a partir de imagens do satélite LANDSAT TM se mostraram bastantes satisfatórios para o bioma Cerrado, pois, dos 11.126 polígonos de área queimada (MCD45A1) inspecionados visualmente, todos foram rotulados como queimada.

The opening of large archives of satellite data such as LANDSAT, MODIS and the SENTINELs has given researchers unprecedented access to data, allowing them to better quantify and understand local and global land change. The need to analyze such large data sets has led to the development of automated and semi-automated methods for satellite image time series analysis. However, few of the proposed methods for remote sensing time series analysis are available as open source software. In this paper we present the R package dtwSat. This package provides an implementation of the time-weighted dynamic time warping method for land cover mapping using sequence of multi-band satellite images. Methods based on dynamic time warping are flexible to handle irregular sampling and out-of-phase time series, and they have achieved significant results in time series analysis. Package dtwSat is available from the Comprehensive R Archive Network (CRAN) and contributes to making methods for satellite time series analysis available to a larger audience. The package supports the full cycle of land cover classification using image time series, ranging from selecting temporal patterns to visualizing and assessing the results.

Abstract After deregulation of the agricultural markets in South Africa in 1997, the estimated maize crop could no longer be verified against the actual crop, due to the lack of control data from the Maize Control Board. This drove the need to explore remotely sensed data as a supplement to the current crop estimation methodology to improve crop estimations. Input data for the development of a Geographic Information System (GIS)-based model consisted of objective yield point data, Moderate Resolution Imaging Spectroradiometer (MODIS) Normalised Difference Vegetation Index (NDVI) images and rainfall grids. Rainfall grids were interpolated from weather station data. NDVI values were obtained from the MODIS sensor aboard the Terra platform. Objective yield point field survey data for the 2001/2002 growing season were utilised since dry-land or irrigated conditions were recorded for that season. MODIS NDVI values corresponded well with the growing stages and age of the maize plants after being adjusted to reflect the crop’s age rather than the Julian date. Rainfall values were extracted from rainfall grids and also aligned with the age of the maize plants. This is a suggested alternative to the traditional method of using the mean NDVI for several districts in a region over a Julian growing period of 11 months according to Julian dates. South African maize production areas extend over seven (7) provinces with eight (8) different temperature and rainfall zones (du Plessis, 2004). Planting-date zones based on the uniform age of the maize plants were developed from objective yield Global Positioning System (GPS) points for the 2001/2002 growing season and compared with the 2004/2005 growing season (Frost and Kneen, 2006). Planting dates were interpolated from these planting zones for objective yield GPS points which were missing planting dates in the survey database. MODIS imagery is affordable (free) and four (4) images cover the whole of South Africa daily, while one (1) image covers the study area daily. Several recommendations, such as establishing yield equations for a normal, dry, and wet season were made. It is also suggested that dry-land and irrigated areas continue to be evaluated separately in future.

Extensive research on soot in snow and snow grain size has been carried out in the Polar Regions. However, North American prairie snowpacks lack observations of soot in snow on snow albedo which adds uncertainty to the overall global effect that black carbon on snow has on climate. Measurements in freshly fallen prairie snowpacks in Northwestern Iowa and Central Texas were collected from February 25 to March 3, 2007 and April 6, 2007, respectively. Multi-day monitoring locations and a frozen lake were study sites at which snow samples were collected to measure soot in snow concentrations. Ancillary measurements were collected at a subset of the sample sites that included: temperature, density, depth, and grain size. At some locations snow reflectance and snow radiance was collected with an Analytical Spectral Device visible/near infra-red spectroradiometer (350 ? 1500 nm). Snow impurity, consisting of light-absorbing particulate matter, was measured by filtering meltwater through a nucleopore 0.4 micrometer filter. Filters were examined using a photometer to measure mass impurity concentration. Soot observations indicate prairie snowpack concentrations ranging from 1 ng C gm^-1 to 115 ng C gm^-1 with an average of 34.9 ng C gm^-1. These measurements are within range of previously published values and can lower snow albedo. As expected, spectral albedo was found to decrease with increasing impurities. Additionally, as grain size increased impurity concentration increased. Differences in soot concentration were observed between the two Iowa snowfall events. The Texas event had higher soot concentrations than both Iowa snowfalls. Validation of an ADEOS-II snow product algorithm that compares simulated radiances to measured sensor radiances for retrieval of snow grain size and mass fraction of soot in snow was attempted using satellite images acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS). The algorithm was unable to uniquely identify a particular snow grain size and soot concentration that would lead to a converging radiance solution in the two spectral bands measured and compared by the algorithm. The in situ data at the validation site fell within published ranges for freshly fallen snow for both snow grain size and soot concentration; however; the closest algorithm retrievals were considerably higher than in situ measurements for both grain size and impurity concentrations.

Accurate areal measurements of snow-cover extent are important for hydrological and climate modeling. The traditional method of mapping snow cover is binary where a pixel is approximated to either snow-covered or snow-free. Fractional snow cover (FSC) mapping achieves a more precise estimate of areal snow-cover extent by determining the fraction of a pixel that is snow-covered. The two most common FSC methods using Moderate Resolution Imaging Spectroradiometer (MODIS) images are linear spectral unmixing and the empirical Normalized Difference Snow Index (NDSI) method. Machine learning is an alternative to these approaches for estimating FSC, as Artificial Neural Networks (ANNs) have been used for estimating the subpixel abundances of other surfaces. The advantages of ANNs over the other approaches are that they can easily incorporate auxiliary information such as land-cover type and are capable of learning nonlinear relationships between surface reflectance and snow fraction. ANNs are especially applicable to mapping snow-cover extent in forested areas where spatial mixing of surface components is nonlinear. This study developed an ANN approach to snow-fraction mapping. A feed-forward ANN was trained with backpropagation to estimate FSC from MODIS surface reflectance, NDSI, Normalized Difference Vegetation Index (NDVI) and land cover as inputs. The ANN was trained and validated with high spatial-resolution FSC derived from Landsat Enhanced Thematic Mapper Plus (ETM+) binary snow-cover maps. ANN achieved best result in terms of extent of snow-covered area over evergreen forests, where the extent of snow cover was slightly overestimated. Scatter plot graphs of the ANN and reference FSC showed that the neural network tended to underestimate snow fraction in high FSC and overestimate it in low FSC. The developed ANN compared favorably to the standard MODIS FSC product with the two methods estimating the same amount of total snow-covered area in the test scenes.

Whitings on both the Great Bahama Bank (GBB) and Little Bahama Bank (LBB) were evaluated using data collected from 2000-2010 by the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments onboard the Terra and Aqua satellites. A semi-objective method was developed to classify whiting patches from other look-alike features using the recently developed Floating Algae Index (FAI) algorithm, an empirical cloud masking algorithm, and a gradient analysis from the 250-m resolution MODIS data. A total of 1,500 images with minimal cloud cover was used to calculate long-term and seasonal trends as well as an average daily coverage for both banks. Annual and monthly frequency of occurrences for whitings at every location was also calculated. Based on the results, the distribution of whitings over the GBB was restricted between 25–30'N and 23–45'N and occurred most frequently on the edge of the bank. Whitings were observed throughout the LBB and at much higher frequencies than in the GBB, especially on the east side from November to February. Results from daily whiting coverage indicate whitings cover nearly twice as much area over the LBB compared to the GBB. Whitings show a clear seasonal variation with respect to coverage on both banks. Whiting coverage over the LBB has a clear seasonal variation with peak coverage in spring (April) and fall (November) and minimum coverage during summer. Whiting coverage over the GBB peaks in spring (April), but no second peak or seasonal minimum was observed. Sea surface temperature (SST), photosynthetically available radiation (PAR) and wind were compared to the observed long-term and seasonal trends of whiting coverage. Using multi-variable analyses, the influence of SST and PAR on monthly whiting coverage over the GBB from 2000-2010 was found to be statistically significant, though the correlation between the three values was low. The results indicate that these parameters may not directly influence whiting origin and coverage but rather have an effect through influence mechanism, for example through phytoplankton blooms. It is hypothesized that whitings are directly influenced by cyanobacterial phytoplankton, which are dependent on SST and PAR. Long-term trends in whiting coverage differ between the two banks. In general, whiting coverage appeared to be decreasing from 2000-2010 over the LBB, while the opposite trend was observed over the GBB during the same time period. It is currently unclear what led to these opposite trends due to lack of long-term, in-situ measurements of the water environments in the two banks. However, this is the first study that documents the long-term trends for both banks, from which one may infer that the processes affecting whiting occurrence in the two banks vary greatly and future research is needed to understand the driving forces of whitings in order to improve the current understanding of their contributions in the global carbon cycle.