No Brasil, a área utilizada para pastagens é maior comparada com a área utilizada para as culturas agrícolas, com cerca de 158,6 milhões de hectares de pastagens. Sendo que as pastagens são extremamente importantes para a produção de carne bovina já que cerca do 95% é produzida para alimentar os rebanhos. O principal nutriente para a manutenção da produtividade em forrageiras é o nitrogênio (N), sendo um dos principais nutrientes que influencia diretamente as características morfofisiológicas, interferindo na produção e na qualidade da forragem. Nos últimos anos, o uso de técnicas de sensoriamento remoto tem se expandido nas áreas de ciências agrárias, mostrando-se uma ferramenta muito útil no monitoramento e gerenciamento da adubação nitrogenada nas culturas. Por tanto o objetivo do trabalho foi abordar o potencial de dados de sensoriamento remoto, mais especificamente para as forrageiras Panicum maximum cv. Mombaça e Urochloa brizantha cv. Marandú, obtidos por meio de sensor passivo e convertidos para diferentes índices de vegetação (IVs), na estimativa de parâmetros biofísicos, tais como: teor foliar de nitrogênio (TFN), produtividade, altura e índice de área foliar (IAF). Também foram simulados os dados para o satélite Sentinel-2 e testados, em áreas com plantio de Brachiaria brizantha cv. Piatã. Os IVs utilizados foram o NDVI (Índice de Vegetação por Diferença Normalizada), TBI (Three Band Index), DLH (Difference Line Height), NAOC (Normalized Area Over reflectance Curve) e TBDO (Three Band Dall ?Olmo). O experimento foi realizado na Escola Superior de Agricultura \"Luiz de Queiroz\" (ESALQ/USP), em Piracicaba, São Paulo. O delineamento experimental utilizado foi em blocos ao acaso, com quatro tratamentos e quatro repetições, sendo a ureia o fertilizante nitrogenado utilizado ao longo do experimento. As doses aplicadas para a cv. Mombaça foram três de 200, 400, 600 kg ha-1 e para a cv. Marandú foram aplicadas doses de 175, 350 e 525 kg ha-1, para ambas cultivares as parcelas testemunhas não receberam adubação nitrogenada (0 kg ha-1). Ao longo do ciclo da cultura, avaliou-se a sua altura, produtividade, IAF e o TFN e a sua resposta espectral de 400 até 920 nm. Os resultados demonstraram que as duas forrageiras foram responsivas à adubação nitrogenada, modificando a sua resposta espectral ao longo das aplicações, principalmente na região do visível (verde 550 nm) e do infravermelho próximo (a partir dos 700 nm). E também provaram que é possível predizer parâmetros biofísicos por meio de espectroscopia in situ e através do satélite Sentinel-2. / In Brazil, the area used for pasture is larger compared to the area used for agricultural crops, representing about 158,6 million hectares of pasture. Pastures are extremely important for beef production, as 95% is produced to feed the herds. The main nutrient for the maintenance of forage productivity is nitrogen (N), which influences directly the morphophysiological characteristics, interfering in the production and on the quality of the forage. In recent years, the use of remote sensing techniques has expanded in agricultural sciences, proving to be a very useful tool in the monitoring and management of nitrogen fertilization in crops. Therefore, the objective of this study was to address the potential of remote sensing data for the forages Panicum maximum cv. Mombasa and Urochloa brizantha cv. Marandú. Data were obtained by passive sensor and converted into different vegetation indices (IVs), in the estimation of biophysical parameters, such as: foliar nitrogen content (NTF), productivity, height and leaf area index (LAI). The data for the Sentinel-2 satellite were also simulated and tested in areas with Brachiaria brizantha cv. Piatã. The IVs used were NDVI (Normalized Difference Vegetation Index), TBI (Three Band Index), DLH (Difference Line Height), NAOC (Normalized Area Over Reflectance Curve) and TBDO (Three Band Dall\'Olmo). The experiment was carried out at the \"Luiz de Queiroz\" School of Agriculture (ESALQ/USP), in Piracicaba, São Paulo. The experimental design applied was a randomized block, with four treatments and four replicates, and with urea used throughout the experiment as a nitrogen fertilizer. The doses applied for cv. Mombaça were three of 200, 400 and 600 kg ha-1 and for cv. Marandú were applied 175, 350 and 525 kg ha-1 doses. For both cultivars the control plots did not receive nitrogen fertilization (0 kg ha-1). During the culture cycle, we evaluated its height, productivity, LAI and TFN, and its spectral response of 400 to 920 nm. The results showed that the two forages were responsive to nitrogen fertilization, modifying their spectral response along the applications, mainly in the region of visible (green 550 nm) and near infrared (from 700 nm). They also proved that it is possible to predict biophysical parameters using in situ spectroscopy and by using the Sentinel-2 satellite.

Master of Science / Department of Horticulture and Natural Resources / Dale J. Bremer / Recent advances in small unmanned aerial systems (sUAS) may provide rapid and accurate methods for turf research and management. The study was to evaluate early drought detection ability of ultra-high resolution remote sensing with sUAS technology, and compare it with traditional techniques on fairway-height ‘Declaration’ creeping bentgrass (Agrostis stolonifera L.) treated from severe deficit to well-watered irrigation (15, 30, 50, 65, 80, and 100% evapotranspiration replacement). Airborne measurements with a modified digital camera mounted on a hexacopter included reflectance from broad bands (near infrared [NIR, 680-780 nm], and green and blue bands [overlapped, 400-580 nm]), from which eight vegetation indices (VIs) were derived for evaluation. Canopy temperature was measured only in the final year with a thermal infrared camera mounted on a drone. Traditional measurements were volumetric water content (VWC), visual quality (VQ), percentage green cover (PGC), and VIs from handheld devices. Declines in VWC in irrigation-deficit treatments were consistently detected by the NIR band and six VIs from sUAS, and NDVI and red band from a handheld device, before drought stress was evident in VQ. These bands and indices predicted drought stress at least one week before symptoms appeared in VQ. Canopy temperature predicted drought stress as early as the best VIs and NIR, 16 days before symptoms appeared in VQ in 2017. Only the NIR and GreenBlue VI [(green-blue)/(green+blue)] consistently predicted drought stress throughout three years. Results indicate using ultra-high resolution remote sensing with sUAS can detect drought stress before it is visible to the human eye and may prove viable for irrigation management on turfgrass.

Na agricultura, técnicas de sensoriamento são um meio prático e barato de se obter informações sobre parâmetros de interesse agronômico, sendo os sensores ópticos uma alternativa para a avaliação da resposta de culturas agrícolas à sua adubação nitrogenada. Para a otimização da eficiência do uso de nitrogênio por culturas agrícolas, algumas estratégias de adubação se baseiam na coleta de dados espectrais em alta frequência no campo, utilizando-os para entender a variabilidade espacial do estado de nutrição da planta com este nutriente. Para a cana-de-açúcar, apesar da efetividade de sensores ópticos em idenificar alguns parâmetros desta cultura, ainda há a dificuldade de estabelecer relações com o seu Teor Foliar de Nitrogênio (TFN). Neste contexto, o presente trabalho acompanhou com sensor óptico hiperespectral (VisNIR) o desenvolvimento do dossel de cana-de-açúcar ao longo de seu ciclo, com o objetivo de avaliar temporalmente a relação entre a sua resposta espectral de reflectância e o seu TFN. Para tanto, foi avaliada uma área experimental com 28 parcelas de cana-deaçúcar, submetidas a tratamentos com diferentes doses de adubação nitrogenada. Ao longo do ciclo da cultura, avaliou-se a sua altura, o TFN e a sua resposta espectral de 400 a 900 nm; ao final do ciclo, foi estimada também a produtividade final de cada parcela. Para a avaliação do comportamento espectral da cultura em função da adubação nitrogenada e de seu desenvolvimento no campo, primeiramente, realizaramse análises de variância (ANOVA) para a altura, o TFN e as diferentes regiões espectrais e, em um segundo momento, análises descritivas e a análise de componentes principais foram conduzidas, ambas sobre os dados espectrais. Em seguida, foram aplicadas diferentes metodologias para a análise quantitativa dos espectros para a predição do TFN. Nessas análises quantitativas, buscou-se avaliar o período ideal do desenvolvimento da cana-de-açúcar para avaliações espectrais de seu TFN serem aplicadas, assim como comprimentos de onda e índices de vegetação (IVs) específicos com relações satisfatórias com o TFN. Os resultados obtidos pelo presente trabalho mostraram possível uma razoável predição do TFN da cana-de-açúcar através de espectroscopia in situ, contudo, esta avaliação só foi possível ao redor de 144 Dias Após o Corte (DAC), momento em que a cultura ainda apresentava resposta do TFN à adubação nitrogenada e no qual o dossel de plantas já estava desenvolvido o suficiente para interromper a influência do solo na leitura espectral. Os IVs avaliados que mais se destacaram para a predição do TFN utilizaram os comprimentos de onda de 490 nm da região do verde; 590 nm da região do laranja; 647 e 652 nm da região do vermelho; 730 nm da região do red-edge; 780 e 880 nm da região do infravermelho próximo. Por fim, o IV que mais se destacou foi o NDRE, índice já sugerido pela literatura com bons resultados para a determinação da biomassa da cana-de-açúcar. / In agriculture, sensing techniques are a practical and inexpensive way to obtain information on agronomic parameters. Optical sensors can be used as a tool to evaluate the response of agricultural crops to nitrogen (N) fertilization. In order to optimize the efficiency of N use in agricultural crops, some fertilization strategies are based on the collection and analysis of high frequency spectral data in the field to understand the spatial variability of N status of plants. Despite the effectiveness of optical sensors in identifying some agronomic parameters of the sugarcane, establishing relations between these data and the Leaf Nitrogen Content (TFN) of the sugarcane is still quite challenging. To address this issue, in this work the development of the sugarcane canopy was monitored during its cycle with a hyperspectral optical sensor (VisNIR), with the aim of evaluating the relations between its spectral reflectance response and its TFN in time. For this, an experimental area with 28 plots of sugarcane submitted to treatments with different doses of nitrogen fertilization was evaluated. Throughout the crop year were evaluated its height, TFN and spectral response from 400 to 900 nm; at the end of the cycle, the final yield of each plot was also evaluated. To begin with, the analysis of variance (ANOVA) for height, TFN and the different spectral regions was performed to assess the spectral behavior of the crop as a function of nitrogen fertilization and its development in the field. Furthermore, a descriptive analysis and analysis of principal components were conducted, both on spectral data. In addition to this, different methodologies were applied for the spectral quantitative analysis for the prediction of TFN. The aim of these quantitative analyses was to determine the ideal period of sugarcane development in order to apply spectral evaluations of its TFN and to find specific wavelengths and Vegetation Index (IVs) with satisfactory relations with the TFN. The results obtained by the present work showed a reasonable prediction of the sugarcane TFN by in situ spectroscopy. However, this evaluation was only possible around 144 Days After Harvest (DAC). During this period, the culture showed a response of the TFN to N fertilization and the canopy of plants was already developed enough to interrupt the influence of the soil in the spectral reading. The evaluated IVs that showed better results for the TFN prediction used the wavelengths 490 nm of the green region; 590 nm of the orange region; 647 and 652 nm of the red region; 730 nm of the red-edge region and; 780 and 880 nm of the near infrared region. The IV that showed the best result for the TFN prediciton was the NDRE, vegetation index, which was already suggested by the literature with good results for the determination the sugarcane biomass.

Field data collection involves time and money consuming processes, additionally carrying possible measurement errors. With the technological advance in the last years, low cost remote sensing tools have emerged to facilitate procedures for in-field measurements, being one of the most known techniques the use of multispectral cameras coupled to RPA. These tools are complemented by the implementation of procedures in GIS and image-processing software, from which are developed methodologies leading to extract target values from a certain original set of data. In this work, multispectral images were used in two case studies: (1) for yield estimation in pea plots for breeding research, and (2) for plant counting in an apple nursery planted directly on the soil; both fields are located in Washington State, USA. In the first case, a reliable and replicable methodology for yield estimation was created as a high throughput phenotyping technique; while in the second case an algorithm capable of identifying the number of apple plants with more than 95% accuracy was developed. In both studies, remote sensing is used as an efficient and practical way to improve field operations under the specified conditions of each case. / A coleta de dados de campo envolve processos de grande consumo em tempo e dinheiro, ademais de levar o risco de possíveis erros de medição. Com o avanço tecnológico nos últimos anos, surgiram ferramentas de sensoriamento remoto de baixo custo para facilitar procedimentos de medição em campo, sendo uma das técnicas mais conhecidas o uso de câmeras multiespectrales acopladas a um ARP. Essas ferramentas são complementadas pela implementação de procedimentos em programas SIG e de processamento de imagens, a partir dos quais são desenvolvidas metodologias que visam extrair valores alvo desde um determinado conjunto original de dados. Neste trabalho, foram utilizadas imagens multiespectrais no desenvolvimento de dois estudos de caso: (1) para estimativa de produtividade em parcelas para pesquisa de ervilha, e (2) para contagem de plantas em um viveiro de maçã plantado diretamente no solo; ambos os campos localizados no estado de Washington, EUA. No primeiro caso, foi criada uma metodologia confiável e replicável para estimativa de produtividade como técnica de fenotipagem de alto rendimento; enquanto no segundo caso, foi desenvolvido um algoritmo capaz de identificar o número de plantas de maçã com mais de 95% de exatidão. Em ambos os estudos, o sensoriamento remoto é usado como uma ferramenta eficiente e prática na melhora de operações de campo.

Changes in the productivity of temperate forests have important implications for atmospheric carbon dioxide (CO2) concentrations, and many efforts have focused on methods to monitor both gross and net primary productivity in temperate forests. Remotely sensed vegetation indices provide spatially extensive measures of vegetation activity, and the Enhanced Vegetation Index (EVI) has been widely linked to photosynthetic activity of vegetation. Networks of tree ring width (TRW) chronologies provide ground-based estimates of annual net carbon (C) uptake in forests, and time series of EVI and TRW may capture common productivity signals. Robust correlations between mean TRW and EVI may enhance spatial extrapolations of TRW-based productivity estimates, ultimately improving understanding of spatio-temporal variability in forest productivity. The research presented in this thesis investigates potential empirical relationships between networks of TRW chronologies and time series of Landsat EVI and Landsat-based phenological metrics in the Northeast United States. We hypothesized that mean TRW is positively correlated with mean monthly EVI during the growing season, EVI integrated over the growing season, and growing season length. Results indicate that correlations between TRW and EVI are largely not significant in this region. The complex response of tree growth to a variety of limiting climatic factors in temperate forests may decouple measures of TRW growth and canopy reflectance. However, results also indicate that there may be important lag effects in which EVI affects mean TRW during the following year. These findings may improve understanding of links between C uptake and growth of tree stems over large spatial scales.

Hurricane Hugo struck the coast of South Carolina on September 21, 1989 as a category 4 hurricane on the Saffir-Simpson Scale. Landsat Thematic mapper was utilized to determine the extent of damage experienced at the Santee Experimental Forest (SEF) (a part of Francis Marion National Forest) in South Carolina. Normalized Difference Vegetation Index (NDVI) and the change detection techniques were used to determine initial forest damage and to monitor the recovery over a 22-year period following Hurricane Hugo. According to the results from the NDVI analysis the SEF made a full recovery after a 10-year period. The remote sensing techniques used were effective in identifying the damage as well as the recovery.

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. / February 2008

Alluvial groundwater systems are an important source of water for communities and biodiverse riparian corridors throughout the arid and semi-arid Basin and Range Geological Province of western North America. These aquifers and their attendant desert streams have been depleted to support a growing population, while projected climate change could lead to more extreme episodes of drought and precipitation in the future. The only source of replenishment to these aquifers is recharge. This dissertation builds upon previous work to characterize and quantify recharge in arid and semi-arid basins by characterizing the intra-annual seasonality of recharge across the Basin and Range Province, and considering how climate change might impact recharge seasonality and volume, as well as fragile riparian corridors that depend on these hydrologic processes. First, the seasonality of recharge in a basin in the sparsely-studied southern extent of the Basin and Range Province is determined using stable water isotopes of seasonal precipitation and groundwater, and geochemical signatures of groundwater and surface water. In northwestern Mexico in the southern reaches of the Basin and Range, recharge is dominated by winter precipitation (69% ± 42%) and occurs primarily in the uplands. Second, isotopically-based estimates of seasonal recharge fractions in basins across the region are compared to identify patterns in recharge seasonality, and used to evaluate a simple water budget-based model for estimating recharge seasonality, the normalized seasonal wetness index (NSWI). Winter precipitation makes up the majority of annual recharge throughout the region, and North American Monsoon (NAM) precipitation has a disproportionately weak impact on recharge. The NSWI does well in estimating recharge seasonality for basins in the northern Basin and Range, but less so in basins that experience NAM precipitation. Third, the seasonal variation in riparian and non-riparian vegetation greenness, represented by the normalized difference vegetation index (NDVI), is characterized in several of the study basins and climatic and hydrologic controls are identified. Temperature was the most significant driver of vegetation greenness, but precipitation and recharge seasonality played a significant role in some basins at some elevations. Major contributions of this work include a better understanding of recharge in a monsoon-dominated basin, the characterization of recharge seasonality at a regional scale, evaluation of an estimation method for recharge seasonality, and an interpretation of the interaction of seasonal hydrologic processes, vegetation dynamics, and climate change.

Droughts are a recurrent part of our climate, and are still considered to be one of the most complex and least understood of all natural hazards in terms of their impact on the environment. In recent years drought has become more common and more severe across the world. For more than a decade, the US southwest has faced extensive and persistent drought conditions that have impacted vegetation communities and local water resources. The focus of this work is achieving a better understanding of the impact of drought on the lands of the Hopi Tribe and Navajo Nation, situated in the Northeastern corner of Arizona. This research explores the application of remote sensing data and geospatial tools in two studies to monitor drought impacts on vegetation productivity. In both studies we used land surface phenometrics as the data tool. In a third related study, I have compared satellite-derived land surface phenology (LSP) to field observations of crop stages at the Maricopa Agricultural Center to achieve a better understanding of the temporal sensitivity of satellite derived phenology of vegetation and understand their accuracy as a tool for monitoring change. The first study explores long-term vegetation productivity responses to drought. The paper develops a framework for drought monitoring and assessment by integrating land cover, climate, and topographical data with LSP. The objective of the framework is to detect long-term vegetation changes and trends in the Normalized Difference Vegetation Index (NDVI) related productivity. The second study examines the major driving forces of vegetation dynamics in order to provide valuable spatial information related to inter-annual variability in vegetation productivity for mitigating drought impacts. The third study tests the accuracy of remote sensing-derived LSP by comparing them to the actual seasonal phases of crop growth. This provides a way to compare and validate the various LSP algorithms, and more crucially, helps to characterize the remote sensing-based metrics that contrast with the actual biological phenophases of the crops. These studies demonstrate how remote sensing data and simple statistical tools can be used to assess drought effects on vegetation productivity and to inform about land conditions, as well as to better understand the accuracy of satellite derived LSP.

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