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Os processos relacionados a arenização inicialmente conceituados para a região sudoeste do Rio Grande do Sul, também ocorrem em outras regiões do Brasil. Estudos demonstram que o ponto convergente entre essas áreas é o tipo de solo, os Neossolos Quartzarênicos Órticos (RQo). Este artigo analisa as propriedades das séries temporais de NDVI e EVI da cobertura vegetal em áreas de solos RQo no Rio Grande do Sul, Goiás, Tocantins e Piauí, no período entre 2000 e 2015. Para tanto a metodologia proposta utiliza as seguintes propriedades: média, tendência, variabilidade interanual (IAV) e sazonalidade das séries temporais MOD13Q1 sensor MODIS, de NDVI e EVI; mapa de solos e índices pluviométricos do satélite Tropical Rainfall Measuring Mission (TRMM). Os resultados mostram que o comportamento dos valores das propriedades sugere que a cobertura vegetal em áreas com solos RQo em Gilbués/PI e Mateiros/TO se apresentam mais estáveis na série temporal, sem dinâmicas naturais ou antrópicas que ocasionem grandes mudanças no comportamento temporal da vegetação para serem expressas pelas propriedades tendência e IAV. Maçambará e Serranópolis com dinâmicas de arenização nas áreas de RQo mais intensas, apresentaram resultados mais expressivos. Serranópolis por abranger extensas áreas de pastagens e atividades agrícolas e ainda conservar uma boa parte de remanescentes de Cerrado se destacou com valores mais elevados em relação a tendência e sazonalidade. A variação da sazonalidade nas quatro áreas é consequência principalmente da dinâmica climática sazonal. As séries temporais de NDVI e EVI MODIS se mostraram eficientes em mostrar as variações temporais da biomassa e possibilitaram analisar satisfatoriamente a variação espaço-temporal do verdor da cobertura vegetal em áreas de solos RQo associadas a arenização. The sandization processes initially conceptualized for the southwestern region of Rio Grande do Sul, also occur in other regions of Brazil. Studies show that the converging point between these areas is the type of soil, the Neossolos Quartzarênicos Órticos (RQo). This article analyzes the properties of the NDVI and EVI time series of vegetation cover in areas of RQo soils in Rio Grande do Sul, Goiás, Tocantins and Piauí, in the period between 2000 and 2015. For this purpose, the proposed methodology uses the following properties: average, trend, interannual variability (IAV) and seasonality of the sensor MODIS, NDVI and EVI time series; soil map and rainfall index of the Tropical Rainfall Measuring Mission (TRMM) satellite. The results show that the behavior of the property values suggests that the vegetation cover in areas with RQo soils in Gilbués / PI and Mateiros / TO are more stable in the time series, without natural or anthropic dynamics that cause major changes in the vegetation’s temporal behavior to be expressed by the trend and IAV properties. Maçambará and Serranópolis with sanding dynamics in the most intense RQo areas, presented more expressive results. Serranópolis for covering extensive areas of pastures and agricultural activities and still conserving a good part of Cerrado remnants stood out with higher values in relation to the trend and seasonality. The variation in seasonality in the four areas is mainly due to seasonal climatic dynamics. The time series of NDVI and EVI MODIS proved to be efficient in showing the temporal variations of the biomass and made it possible to satisfactorily analyze the space-time variation of the greenness of the vegetation cover in areas of RQo soils associated with sanding.

[EN] The irrigation of green areas in cities should be managed appropriately to ensure its sustainability. In large cities, not all green areas might be monitored simultaneously, and the data acquisition time can skew the gathered value. Our purpose is to evaluate which parameter has a lower hourly variation. We included soil parameters (soil temperature and moisture) and plant parameters (canopy temperature and vegetation indexes). Data were gathered at 5 different hours in 11 different experimental plots with variable irrigation and with different grass composition. The results indicate that soil moisture and Normalized Difference Vegetation Index are the sole parameters not affected by the data acquisition time. For soil moisture, the maximum difference was in experimental plot 4, with values of 21% at 10:45 AM and 27% at 8:45 AM. On the other hand, canopy temperature is the most affected parameter with a mean variation of 15 degrees C in the morning. The maximum variation was in experimental plot 8 with a 19 degrees C at 8:45 AM and 39 degrees C at 12:45 PM. Data acquisition time affected the correlation between soil moisture and canopy temperature. We can affirm that data acquisition time has to be included as a variability source. Finally, our conclusion indicates that it is vital to consider data acquisition time to ensure water distribution for irrigation in cities., This research was funded by AREA VERDE-MG projects, by project PDR18-XEROCESPED funded under the PDR-CM 2014-2020 by the EU (European Agricultural Fund for Rural Development, EAFRD), Spanish Ministry of Agriculture, Fisheries and Food (MAPA) and Comunidad de Madrid regional government through IMIDRA, and by the Conselleria de Educacion, Cultura y Deporte through "Subvenciones para la contratacion de personal investigador en fase postdoctoral", grant number APOSTD/2019/04.

Submitted by Neusa Fagundes (neusa.fagundes@unioeste.br) on 2021-12-08T19:40:50Z No. of bitstreams: 2 Giuvane_Conti2021.pdf: 2544844 bytes, checksum: 90a782adec1b94bf10dd80becb985403 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Made available in DSpace on 2021-12-08T19:40:50Z (GMT). No. of bitstreams: 2 Giuvane_Conti2021.pdf: 2544844 bytes, checksum: 90a782adec1b94bf10dd80becb985403 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2021-02-19 Remote sensing has the ability to assist in the evolution of agricultural practices, providing periodic information about the state of a crop over a harvest, at different scales and for different segments. Applications in precision agriculture use remote sensing practices, such as vegetation indexes, from multispectral images, to measure physical and chemical parameters of plants, along their development cycle. Technological advances allowed the development of innovative services for the agricultural sector, based on the internet and hosted in the cloud. Therefore, the objective of this research was to develop a computational module that integrates and provides remote sensing data for the AgDataBox precision agriculture platform. The developed application allows the persistence of a new area (field), the search for raster images of orbital satellites, the selection vegetation indexes, as well as vectorizing and inserting images of interest in the AgDataBox platform. The proposed module was tested with data from the 2018/2019 corn crop (summer harvest), in a study area at Céu Azul, Paraná. Twelve vectors were generated from Sentinel-2 satellite images, using the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and enhanced vegetation index (EVI-2) of 11/12/2018, 12/16/2018, 15/01/2019 and 25/01/2019 and persisted in the AgDataBox platform. In addition, vectors were persisted with variables of productivity, altitude, sand, silt, clay, mechanical resistance to soil penetration at depths of 0-0.1, 0-0.2, 0.1-0.2, and 0.2-2.3 m. After autocorrelation analysis between the variables, with productivity as the target variable, EVI-2 and altitude were selected as the variables that showed the best cross autocorrelation with the target variable. Management zones (MZs) were delineated in the AgDataBox-Map module, using the fuzzy c-means method, for two, three, and four classes using three sets of input variables: (i) EVI-2_NM, (ii) Altitude, and (iii) EVI 2_NM + Altitude. After analyzing the results, it was concluded that the best design used the variable EVI-2 in the design of three classes of MZs. All designs for two classes showed statistical differences between their classes, with the best performance being obtained with the altitude variable. All designs with four classes were discarded, as there was no statistically significant difference between their classes. O sensoriamento remoto tem a capacidade de auxiliar na evolução das práticas agrícolas, fornecendo informações periódicas sobre o estado de uma cultura ao longo de uma safra, em diferentes escalas e para diferentes segmentos. Aplicações em agricultura de precisão utilizam práticas de sensoriamento remoto, como os índices de vegetação, derivados de imagens multiespectrais, para mensurar parâmetros físicos e químicos das plantas, no decorrer do seu ciclo de desenvolvimento. Os avanços tecnológicos oportunizaram o desenvolvimento de serviços inovadores para o setor agrícola, baseados na internet e hospedados em nuvem. Sendo assim, o objetivo dessa pesquisa foi desenvolver uma aplicação computacional que integre e forneça dados de sensoriamento remoto para a plataforma de agricultura de precisão AgDataBox. A aplicação desenvolvida permite o cadastro de uma nova área (talhão), buscar imagens raster de satélites orbitais, selecionar índices de vegetação, vetorizar e inserir as imagens de interesse na plataforma AgDataBox. A aplicação proposta foi testada com dados da safra de milho de 2018/201 (safra verão), em uma área de estudo no município de Céu Azul, Paraná. Foram gerados 12 vetores a partir de imagens do satélite Sentinel-2, utilizando o índice de vegetação por diferença normalizada (NDVI), índice de vegetação melhorado (EVI) e índice de vegetação melhorado 2 (EVI-2) dos dias 11/12/2018, 16/12/2018, 15/01/2019 e 25/01/2019 e inseridos na plataforma AgDataBox. Também foram inseridos vetores com variáveis de produtividade, altitude, areia, silte, argila, resistência mecânica a penetração do solo nas profundidades de 0-0,1 m, 0-0,2 m, 0,1-0,2 m e 0,2-0,3 m. Após a análise de autocorrelação entre as variáveis, tendo produtividade como variável alvo, foram selecionadas EVI2 e altitude como as variáveis que apresentaram melhor autocorrelação cruzada com a variável alvo. Foram delineadas zonas de manejo (ZMs) no módulo AgDataBox-Map, utilizando o método fuzzy c-means, para duas, três e quatro classes utilizando três conjuntos de variáveis de entrada: (i) EVI-2_NM, (ii) Altitude, e (iii) EVI 2_NM+Altitude. Após análise dos resultados, conclui-se que o melhor delineamento utilizou a variável EVI-2 no delineamento de três classes de ZMs. Todos os delineamentos para duas classes apresentaram diferença estatística entre suas classes, sendo o melhor desempenho obtido com a variável altitude. Todos os delineamentos com quatro classes foram descartados, pois não apresentaram diferença estatística significativa entre suas classes.

Brazilian fruit culture has a great influence on the social and economic sector in the most diverse regions of the country, generating employment and income in the exercise of its activities. As it is an activity carried out most often in a manual and conventional manner, fruit culture has a great potential for technological growth, especially when adopting the concepts applied by precision agriculture on the crops of grains, fibers and energy, creating a new segment, Precision Fruit Farming. The present work aims to carry out a bibliographic review on the main trends that have emerged in the last five years on Precision Fruit growing, highlighting its future perspectives and the history of technological evolution. 83 articles were analyzed, classified in different perennial cultures and applications, such as machine learning, remote sensing, robotics, using UAV to obtain different vegetation indexes, among others. Index Terms: Machine Learning; Vegetation Indexes; Robotics; Tendências da agricultura de precisão em fruticultura no período de 2016 A 2020 Resumo - A fruticultura brasileira exerce grande influência sobre o setor social e econômico nas mais diversas regiões do País, gerando emprego e renda no exercício de suas atividades. Por se tratar de uma atividade realizada, na maioria das vezes, de forma manual e convencional, a fruticultura possui grande potencial de crescimento tecnológico, principalmente ao adotar os conceitos aplicados pela agricultura de precisão sobre as culturas de grãos, fibras e energia, criando um novo segmento, a Fruticultura de Precisão. O presente estudo objetivou realizar uma revisão bibliográfica sobre as principais tendências que surgiram nos últimos cinco anos sobre a Fruticultura de Precisão, destacando suas perspectivas futuras e o histórico de evolução tecnológica. Foram analisados 83 artigos, classificados em diferentes culturas perenes e aplicações, como machine learning (aprendizado de máquinas), sensoriamento remoto, robótica, utilização de VANT para obtenção de diferentes índices de vegetação, entre outras. Termos para indexação: Aprendizagem de Máquina; Índices de Vegetação; Robótica; Sensoriamento Remoto; VANT. Made available in DSpace on 2021-05-17T18:27:34Z (GMT). No. of bitstreams: 1 0100-2945-rbf-43-2-e-096.pdf: 1168727 bytes, checksum: 780ac6c71554fa71897d944981534b21 (MD5) Previous issue date: 2021

Timely diagnosis of sugar beet above-ground biomass (AGB) is critical for the prediction of yield and optimal precision crop management. This study established an optimal quantitative prediction model of AGB of sugar beet by using hyperspectral data. Three experiment campaigns in 2014, 2015 and 2018 were conducted to collect ground-based hyperspectral data at three different growth stages, across different sites, for different cultivars and nitrogen (N) application rates. A competitive adaptive reweighted sampling (CARS) algorithm was applied to select the most sensitive wavelengths to AGB. This was followed by developing a novel modified differential evolution grey wolf optimization algorithm (MDE−GWO) by introducing differential evolution algorithm (DE) and dynamic non-linear convergence factor to grey wolf optimization algorithm (GWO) to optimize the parameters c and γ of a support vector machine (SVM) model for the prediction of AGB. The prediction performance of SVM models under the three GWO, DE−GWO and MDE−GWO optimization methods for CARS selected wavelengths and whole spectral data was examined. Results showed that CARS resulted in a huge wavelength reduction of 97.4% for the rapid growth stage of leaf cluster, 97.2% for the sugar growth stage and 97.4% for the sugar accumulation stage. Models resulted after CARS wavelength selection were found to be more accurate than models developed using the entire spectral data. The best prediction accuracy was achieved after the MDE−GWO optimization of SVM model parameters for the prediction of AGB in sugar beet, independent of growing stage, years, sites and cultivars. The best coefficient of determination (R2), root mean square error (RMSE) and residual prediction deviation (RPD) ranged, respectively, from 0.74 to 0.80, 46.17 to 65.68 g/m2 and 1.42 to 1.97 for the rapid growth stage of leaf cluster, 0.78 to 0.80, 30.16 to 37.03 g/m2 and 1.69 to 2.03 for the sugar growth stage, and 0.69 to 0.74, 40.17 to 104.08 g/m2 and 1.61 to 1.95 for the sugar accumulation stage. It can be concluded that the methodology proposed can be implemented for the prediction of AGB of sugar beet using proximal hyperspectral sensors under a wide range of environmental conditions.

This study presents the use of a linear mixed model to accurately estimate the dynamics of canopy cover (CC) during an irrigation experiment on a tomato crop, performed in the Peruvian coastal desert. Initially, the normalized difference vegetation index (NDVI)-based CC was computed using multispectral sensors over consecutive weekly UAV flights, during the growing season of the crop. The drone high-resolution sequential imagery, combined with image segmentation on plant level, results in repeated measures growth curves of the coverage per plant. To analyze these repeated measures data per plant, cubic polynomials without intercept showed the best goodness-of-fit of the CC dynamics per plant. As a consequence, this polynomial was incorporated in a linear mixed model (LMM) as a random coefficient model to fit the plant-specific time evolution of CC as deviations from the mean time effects per irrigation treatment. The mixed model approach is capable to estimate the coverage curves per plant with high accuracy and a very limited number of model parameters. The results and statistical analysis demonstrate the potential benefits of the linear mixed model for incorporating plant-specific random components in addition to classical fixed effects models. Thus, the proposed linear mixed canopy model is a very efficient way to model plant-specific growth curves together with the treatment and design structure of the experiment.

Essa pesquisa objetivou ajustar modelos de regressão e classificação utilizando os índices de vegetação (IV) mais robustos derivados a partir de imagens digitais de Sentinel-2/MSI para a detecção de áreas atacadas por Sapajus nigritus. Os dados de campo foram obtidos em um povoamento de Pinus taeda L. localizado em Bocaina do Sul, estado de Santa Catarina. Foram alocadas 46 parcelas, nas quais os indivíduos arbóreos foram classificados de acordo com a intensidade do ano em: I: sem dano, II: moderado, III: severo e IV: morto. Foram utilizadas três imagens orbitais da constelação Sentinel-2/MSI em datas coincidentes ao pré ataque, ataque e pós ataque. Por meio de Análise de Componentes Principais, foram selecionados os índices de vegetação como variáveis regressoras para o desenvolvimento de modelos de regressão por Stepwise, Support Vector Machine (SVM) e Random Forest (RF) para a estimativa do percentual das classes de ataque por parcela. A seleção de melhor modelo foi baseada em estatísticas de ajuste de modelos de regressão. Os índices mais robustos foram o MCARI, NDI45 e NDVIRR, os quais estimaram para as classes um percentual de ataque de S. nigritus por parcela com R2 ajustado de 0,80, 0,46, 0,88 e 0,97; Syx de 13,49, 61,42, 15,11 e 8,69 (% 0,045 ha-1) e RMSE de 1,95, 4,49, 2,11 e 0,32 (% 0,045 ha1). Foi possível estimar/identificar áreas atacadas por esse primata no povoamento avaliado, destacando a modelagem Stepwise, seguida por SVM e RF. Entretanto, recomenda-se testar imagens digitais de alta/ultra alta resolução espacial para corroborar com tal metodologia. Made available in DSpace on 2021-03-26T18:34:04Z (GMT). No. of bitstreams: 1 Edilson-AgricPrec2021.pdf: 1320572 bytes, checksum: e3b686c13d0aea9fc18eb8587f083ab9 (MD5) Previous issue date: 2020

Технологии точного земледелия направлены на оптимизацию управления полями для повышения урожайности сельскохозяйственных культур, снижения воздействия на окружающую среду и потенциального усиления плодородности почв. Успех точного земледелия в значительной степени зависит от эффективного и точного метода определения свойств почвы. Целью данного исследования является оценка применения методов машинного обучения для определения уровня содержания фосфора в почве по космическим снимкам. Преимуществами данных дистанционного зондирования Земли (ДЗЗ) являются их актуальность, доступность, площадь покрытия. В данной статье, по проведенным ранее исследованиям для Северного Казахстана, была выявлена необходимость в проведении анализа содержания фосфора. Для достижения цели исследования был проведен отбор и анализ почвенных образцов по 1 га сетке, были выявлены основные вегетационные индексы важные для классификации, также проведено сравнение методов машинного обучения. Так в 2019 на площади 5236 га паровых полей агрохимический анализ был проведен по определению содержания фосфора. При моделировании методами машинного обучения на основе выявленных показателей классификация фосфора метод дерева решений показал 65,79% точности, метод опорных векторов – 68,5% и случайный лес – 71,8%. Входным параметрами являются рельеф, полученный по данным SRTM (Shuttle Radar Topography Mission), 9 спектральных каналов и 5 вегетационных индексов по снимку Sentinel 2. Также, были выявлены наиболее важные показатели для метода случайный лес с целью увеличения точности: рельеф, NDVI (Нормализованный вегетационный индекс), PSSRB (простой пигментный рацион для хлорофилла), NDRE (Нормализованный разностный красный краевой индекс), NDWI (Стандартизованный индекс различий увлажненности), каналы SWIR 2 (cредний коротковолновый инфракрасный) и синий. Таким образом, можно сделать вывод, что классификация содержания уровня фосфора в почве методами машинного обучения на основе данных ДЗЗ показывает относительно высокий результат и в дальнейшем разработанные модели могут быть усовершенствованы для получения более высокой точности., Precision farming technologies aim to optimize field management to increase crop yields, reduce environmental impacts, and potentially enhance soil fertility. The success of precision farming considerably depends on an efficient and accurate method for determining soil properties. The purpose of this study is to evaluate machine learning methods to determine the level of phosphorus in the soil from satellite images. The advantages of remote sensing data are their relevance, availability, and coverage area. In this article, based on the research conducted for Northern Kazakhstan earlier, the need for analysis of the phosphorus content was identified. To achieve the goal of the study, soil samples were selected and analyzed for 1 ha grid, the main vegetation indices important for classification were identified, and machine learning methods were compared. In 2019, an agrochemical analysis was carried out to determine the phosphorus content on an area of 5236 hectares of fallow fields. The modeling using machine learning methods based on the identified indicators of phosphorus classification showed accuracy 65.79% in the decision tree method, the support vector machine – 68.5%, and the random forest 71.8%. The input parameters are the digital elevation model (DEM) obtained from the SRTM (Shuttle Radar Topography Mission), 9 spectral bands, and 5 vegetation indices from the Sentinel 2 image. In addition, the feature important score for the random forest method was identified in order to increase accuracy: DEM, NDVI (Normalized vegetation index), PSSRB (Pigment Specific Simple Ratio for chlorophyll b), NDRE (Normalized difference red edge index), NDWI (Normalized Difference Water Index), SWIR 2 (short-wave infrared) and blue bands. Thus, we can conclude that the classification of the phosphorus content in the soil using machine learning methods based on remote sensing data shows a relatively high result and in the future, the developed models might be improved to obtain higher accuracy

Remote sensing technologies have been widely studied for the estimation of crop biometric and physiological parameters. The number of sensors and data acquisition methods have been increasing, and their evaluation is becoming a necessity. The aim of this study was to assess the performance of two remote sensing data for describing the variations of biometric and physiological parameters of durum wheat grown under different water regimes (rainfed, 50% and 100% of irrigation requirements). The experimentation was carried out in Policoro (Southern Italy) for two growing seasons. The Landsat 8 and Sentinel-2 images and radiometric ground-based data were acquired regularly during the growing season with plant biometric (leaf area index and dry aboveground biomass) and physiological (stomatal conductance, net assimilation, and transpiration rate) parameters. Water deficit index was closely related to plant water status and crop physiological parameters. The enhanced vegetation index showed slightly better performance than the normalized difference vegetation index when plotted against the leaf area index with R2 = 0.73. The overall results indicated that the ground-based vegetation indices were in good agreement with the satellite-based indices. The main constraint for effective application of satellite-based indices remains the presence of clouds during the acquisition time, which is particularly relevant for winter&ndash, spring crops. Therefore, the integration of remote sensing and field data might be needed to optimize plant response under specific growing conditions and to enhance agricultural production.

This study describes the results and conclusions obtained from vineyard zoning in Mendoza by using three vegetation indexes (NDVI, EVI and SAVI). Such indexes were calculated from spectral signals received by the Sentinel-2A satellite in the days close to the 2017 harvest. With this information, maps from the plot of land with the zoning given by each index were obtained. Based on the NDVI zoning, a stratified sampling was carried out. On each stratum, 14 plants were marked and the production variables total weight of grape per plant, number of grape bunchs per plant and the weight of 50 berries were measured. The results showed that NDVI and SAVI led to similar classifications in terms of vineyard zones (strata), while EVI captures high vigor levels with less sensitivity. There was a correspondence between the production variables and the strata of high, medium and low vigor. The three indexes clearly showed two different vineyard areas in terms of production. Consequently, these indexes may contribute to rationalize viticulture practices, adjusting the intensity of such practices to the characteristics and needs of each of these vineyard areas. El presente trabajo describe los resultados y conclusiones obtenidos en la zonificación de un viñedo de Mendoza, empleando tres índices de vegetación (NDVI, EVI y SAVI) calculados a partir de las señales espectrales captadas por Sentinel 2A, en los días próximos a la cosecha de 2017. A partir de esta información, se obtuvieron los mapas de la parcela con la zonificación dada por cada índice. En base a la zonificación dada por NDVI, se realizó un muestreo estratificado y en cada estrato se marcaron 14 plantas sobre las que se midieron las variables productivas peso total de uva, número de racimos por planta y peso de 50 bayas. Los resultados muestran que NDVI y SAVI conducen a clasificaciones similares en cuanto a zonas (estratos), mientras que sugieren que EVI capta con menos sensibilidad niveles de alto vigor. Hubo correspondencia entre los valores de las variables productivas con los estratos de alto, medio y bajo vigor. Los tres índices diferenciaron claramente dos zonas del viñedo en lo relativo a su producción, en consecuencia, los mismos podrían contribuir a racionalizar las prácticas de cultivo, adecuando su intensidad a las características y necesidades de cada una de estas zonas del viñedo.

[EN] A crop monitoring system was developed for monitor organic fertilization status of tomato plants at early stages. An automatic and nondestructive approach was used to analyze tomato plants with different levels of water-soluble organic fertilizer (3+5 NK) and vermicompost. The evaluation system was composed by a multispectral camera with five lenses: green (550nm), red (660nm), red edge (735nm), near infrared (790nm) + 16MP RGB and a computational image processing system. The water-soluble fertilizer was applied weekly in four different treatments: (T0: 0 ml, T1: 6.25 ml, T2: 12.5 ml and T3: 25 ml) and the vermicompost was added in the 1st (T0: 0ml; T1: 75 ml; T2:150ml; T3: 300 ml) and in the 5th week (T0: 0ml; T1: 237,5 ml; T2:475ml; T3: 950 ml). The trial was conducted in a greenhouse and 192 images were taken with each lens. An plant segmentation algorithm was developed and several vegetation indexes were calculated: Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Red-Edge Normalized Difference Vegetation Index (RENDVI), Nonlinear Vegetation Index (NLI), Optimized Soil Adjusted Vegetation Index (OSAVI), Green Ratio Vegetation Index (GRVI), Simple Ratio (SR), Modified Simple Ratio (MSR), Structure Intensive Pigment Index 2 (SPI2) and Leaf Chlorophyll Index (LCI). Multiple morphological features were obtained through image processing and the results were compared between treatments using Tukey¿s HSD test with 1% of probability. The morphological features such as, Area, Filled Area, Convex Area, Perimeter, Major Axis Length, Minor Axis Length and Equivalent Diameter revealed to be feasible to distinguish between the control and the organic fertilized plants despite the vegetation indexes. The system was developed in order to be assembled in a precision organic fertilization robotic platform., [ES] Se ha desarrollado un sistema de monitoreo de cultivos para supervisar el estado nutricional de las plantas de tomate en las primeras etapas de desarrollo. Se utilizó un enfoque automático y no destructivo para analizar las plantas de tomate con diferentes niveles de fertilizante orgánico soluble en agua (3 + 5 NK) y humus de lombriz. El sistema de evaluación estaba compuesto por una cámara multiespectral con cinco lentes: verde (550nm), rojo (660nm), borde rojo (735nm), infrarrojo cercano (790nm) + 16MP RGB y un sistema de procesamiento de imágenes computacional. El fertilizante soluble se aplicó semanalmente en cuatro tratamientos diferentes: (T0: 0 ml, T1: 6.25 ml, T2: 12.5 ml y T3: 25 ml) y el vermicompost se añadió en la primera (T0: 0 ml; ml, T2: 150 ml, T3: 300 ml) y en la quinta semana (T0: 0 ml, T1: 237.5 ml, T2: 475 ml, T3: 950 ml). El ensayo se realizó en invernadero y se tomaron 192 imágenes con cada lente. Se desarrolló un algoritmo de segmentación y múltiples índices de vegetación fueron calculados: Índice de Vegetación de Diferencia Normalizada (NDVI), Índice de Vegetación de Diferencia Normalizada Verde (GNDVI), Índice de Vegetación de Diferencia Normalizada de Borde Rojo (RENDVI), Índice de Vegetación No Lineal (NLI), Índice de Vegetación Ajustado y Optimizado para el Suelo (OSAVI), Índice de Vegetación de Proporción Verde (GRVI), Proporción Simple (SR), Proporción Simple Modificada (MSR), Índice de Pigmento Intensivo de Estructura 2 (SPI2) e Índice de Clorofila de la Hoja (LCI). Adicionalmente al cálculo de índices, se obtuvieron múltiples características morfológicas a través del procesamiento de imágenes y los resultados se compararon entre los tratamientos utilizando la prueba HSD de Tukey con un 1% de probabilidad. Las características morfológicas tales como: Área, Área rellena, Área convexa, Perímetro, Longitud del eje mayor, Longitud del eje menor y Diámetro Equivalente se revelaron más útiles para distinguir entre el control y las plantas fertilizadas orgánicamente que los índices de vegetación. El sistema fue desarrollado para ser ensamblado en una plataforma robótica de fertilización orgánica de precisión.

The objective of this work was to evaluate the replacement of conventional procedures of physical-chemical laboratory analysis of agronomic parameters in viticulture by remote sensing techniques through their correlation with biological variables and agronomic attributes of Vitis vinifera L. A sample grid with 74 points on 3 ha of vines was established. For reflectance readings, the FieldSpec Hand Held 2 portable spectrometer was used and 17 vegetation indices (VI) (SR, NDVI, mNDVI, SGR, RGR, NPCI, SRPI, NPQI, SIPI, PI1, PI2, PI3, EVI, PRI, PSRI, PWI e ARI) were calculated, in two field reading methods, by leaf clip (CP) and by proximal sensing (SP). The VI were related by Pearson's correlation coefficient, at 5% level, with the variables of apparent electrical conductivity (ECa), soil chemistry and physics, chemical analysis of plant tissue, oenological and phytotechnical parameters. Based on the obtained correlations it was possible to affirm that the use of IV instead of the physical / chemical analysis of agronomic parameters is viable, provided that they reach at least one correlation coefficient with moderate magnitude, where we found 36 moderate coefficients. The VI and the analyzed soil variables that presented the highest Pearson correlation coefficient and at least moderate magnitudes for the 0- 20cm depth were mNDVI-SP with Effective CTC (0.508), SR-SP with CTC pH 7,0 (0.534), RGR-SP with K (-0.447), NPQI-SP with Cu (-0.472), P1-SP with K (0.546), Pl2-SP with CTC pH 7.0 (-0.545), and PRI-CP which correlated with both CTC pH 7.0 (0.572) as well as K (-0.565). Already for the depth of 20-40 cm we obtained only the mNDVI-CP with the K (-0.534). For the relationship between IV and plant tissue variables the best results were ARI-CP with B (-0.402), Pl3-SP with K (-0.506) and ARI-SP with K (0.566). O objetivo deste trabalho foi avaliar a substituição de procedimentos convencionais de análise de laboratório físico-químico de parâmetros agronômicos na vitivinicultura por técnicas de sensoriamento remoto através da sua correlação com as variáveis biológicas e atributos agronômicos de solo em plantio de Vitis vinifera L. Foi estabelecida uma malha amostral com 74 pontos. Para as leituras de reflectâncias utilizou-se o espectrorradiometro portátil FieldSpec Hand Held 2 e foram calculados 17 índices de vegetação (IV) (SR, NDVI, mNDVI, SGR, RGR, NPCI, SRPI, NPQI, SIPI, PI1, PI2, PI3, EVI, PRI, PSRI, PWI e ARI), em dois métodos de leituras no campo, por clipagem na folha (leaf clip) (CP) e por sensoriamento próximal (SP). Os IV foram relacionados por meio do coeficiente de correlação de Pearson, ao nível de 5%, com as variáveis de condutividade elétrica aparente (CEa), química e física do solo, análise química do tecido vegetal, parâmetros enológicos e fitotécnicos. Com base nas correlações obtidas foi possível afirmar que a utilização de IV em substituição à análises físico/química de parâmetros agronômicos é viável, desde que atinjam, no mínimo, um coeficiente do correlação com magnitude moderada, onde encontramos 36 coeficientes moderadas. Os IV e as variáveis de solo analisadas que apresentaram os maiores coeficiente de correlação por Pearson e magnitudes pelo menos moderadas para a profundidade de 0-20cm foram mNDVISP com a CTC Efetiva (0,508), SR-SP com a CTC pH 7,0 (0,534), RGR-SP com o K (-0,447), NPQI-SP com Cu(-0,472), Pl1-SP com K(0,546), Pl2-SP com a CTC pH 7,0 (-0,545), e o PRI-CP, que apresentou correlação tanto com a CTC pH 7,0 (0,572), bem como com o K (-0,565). Já para a profundidade de 20-40 cm obtivemos apenas o mNDVI-CP com o K (-0,534). Para a relação entre IV e as variáveis do tecido vegetal os melhores resultados foram ARI-CP com B (-0,402), Pl3-SP com o K (- 0,506) e ARI-SP com o K (0,566).

RESUMEN La evaluación de las praderas destinadas a ganadería es esencial para la productividad de los animales. Los datos de sensores multiespectrales remotos aerotransportados (SM) permiten construir índices de vegetación (VI, por sus siglas en idioma inglés) y relacionarlos con características fisiológicas y biofísicas de las pasturas. El objetivo fue evaluar VI para la estimación de la cantidad y calidad de pasto kikuyo en sistemas lecheros, del norte de Antioquia, Colombia. Se calcularon 10 diferentes VI, con 168 muestras de pasto kikuyo. Las muestras fueron pesadas, para estimar la biomasa verde (BV) y analizadas por espectroscopia del infrarrojo cercano, para los contenidos de proteína bruta (PB), fibra en detergente neutro (FDN) y fibra en detergente ácido (FDA). Los datos, se analizaron usando componentes principales (CP) y modelos aditivos generalizados suavizados. Las variables que más contribuyeron a la formación de la primera componente principal (CP1) fueron el índice de vegetación de diferencia normalizada (NDVI), el índice de vegetación simple (RVI), el índice de vegetación de diferencia normalizada verde (GNDVI), el índice clorofílico verde (Clg) y la BV del pasto kikuyo. Para la segunda componente principal (CP2) fueron el índice de vegetación de diferencia normalizada borde del rojo (RNDVI), el índice borde del rojo de clorofila (Clrg) y PB, FDN y FDA del pasto kikuyo. La BV fue explicada por el NDVI y PB por el RNDVI. La estimación obtenida para FDN y FDA del pasto kikuyo no fueron precisas. ABSTRACT The evaluation of grazing lands is essential to improve livestock productivity. Data from multispectral airborne sensors allow calculating vegetation indexes (VI) and relating them to physiological and biophysical characteristics of the pastures. The objective of this study was to evaluate the usefulness of VI to estimate the quantity and quality of Kikuyu grass in dairy farms of northern Antioquia, Colombia. We calculated 10 different VI using 168 samples of Kikuyu grass. The samples were weighted to estimate green biomass (BV) and analyzed by near infrared spectroscopy for the contents of crude protein (PB), neutral detergent fiber (FDN) and acid detergent fiber (ADF). Data were analyzed using principal components (CP) and smoothed generalized additive models. The variables that contributed most to the formation of the first principal component (CP1) were the Normalized Difference Vegetation Index (NDVI), the Simple Vegetation Index (RVI), the Normalized Difference Vegetation Green Index (GNDVI), the Green Chlorophyll Index (Clg) and the BV of Kikuyu grass. The mayor contributors to the second principal component (CP2) were the Normalized Red-Edge Vegetation Index (RNDVI), the Red-Edge Chlorophyll Index (Clrg), and the PB, NDF and FDA of Kikuyu. The NDVI explained the BV, and the RNDVI explained the PB. The FDN and FDA estimations in Kikuyu were not precise.

Delineating the cropping area of cocoa agroforests is a major challenge in quantifying the contribution of land use expansion to tropical deforestation. Discriminating cocoa agroforests from tropical transition forests using multispectral optical images is difficult due to the similarity of the spectral characteristics of their canopies. Moreover, the frequent cloud cover in the tropics greatly impedes optical sensors. This study evaluated the potential of multiseason Sentinel-1 C-band synthetic aperture radar (SAR) imagery to discriminate cocoa agroforests from transition forests in a heterogeneous landscape in central Cameroon. We used an ensemble classifier, Random Forest (RF), to average the SAR image texture features of a grey level co-occurrence matrix (GLCM) across seasons. We then compared the classification performance with results from RapidEye optical data. Moreover, we assessed the performance of GLCM texture feature extraction at four different grey levels of quantization: 32 bits, 8 bits, 6 bits, and 4 bits. The classification&rsquo, s overall accuracy (OA) from texture-based maps outperformed that from an optical image. The highest OA (88.8%) was recorded at the 6 bits grey level. This quantization level, in comparison to the initial 32 bits in the SAR images, reduced the class prediction error by 2.9%. The texture-based classification achieved an acceptable accuracy and revealed that cocoa agroforests have considerably fragmented the remnant transition forest patches. The Shannon entropy (H) or uncertainty provided a reliable validation of the class predictions and enabled inferences about discriminating inherently heterogeneous vegetation categories.

Submitted by Edineia Teixeira (edineia.teixeira@unioeste.br) on 2019-05-17T14:25:37Z No. of bitstreams: 2 DIANDRA_GANASCINI_2019.pdf: 1668212 bytes, checksum: a6268c93137904e11cdcc0cd75a03296 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Made available in DSpace on 2019-05-17T14:25:37Z (GMT). No. of bitstreams: 2 DIANDRA_GANASCINI_2019.pdf: 1668212 bytes, checksum: a6268c93137904e11cdcc0cd75a03296 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2019-01-24 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES An alternative to the bean harvest at the appropriate time (physiological maturation) is the use of desiccants; however, depending on the way it is applied, type, time and mode of action, it can compromise the quality of the product, rendering it not usable. The storage of grains tends to cause the loss of quality and can lead to increasing of the hardness of the grains, cooking time, change of taste and color of the integument, and such loss can be augmented by the usage of disiccants. Therefore, the objective of this work was to evaluate the influence of the application of two different desiccant herbicides (diquat and ammonium glufosinate) on the bean crop by means of vegetation indexes, spectral curves and their derivatives after application, and through quality evaluation of grains submitted to different storage times. The experiment was carried out in two farms, in the municipality of Santa Tereza do Oeste, state of Paraná. The experimental design was completely randomized in subdivided plots, the plots were divided into five storage times (0, 2, 4, 6, and 8 months) and redivided into two different desiccants (diquat and ammonium glyphosinate) and control with five repetitions. In Article 1, the spectral characterization of the plots in the different treatments was performed, then the vegetation indices (IVs) and those derived from the spectral curve were calculated. The vegetation indices (IV), the IVP and IVM length ranges of the treatments with desiccant application presented ≤0.05 from the second day after application (DAD), being possible to identify visually in the wavelengths (705, 750, 860 and 1240 nm) in the spectral curves. The NDWI and mNDI IVs presented p≤0.05 in the first DAD. The use of desiccant causes degradation of chlorophyll, which can be observed in the first DAD in the region of the wavelengths (650 and 800 nm). By means of the red edge parameter and the first derivative it is possible to observe the stress generated in the plant, being more expressive with the application of diquat, since the magnitude of the peaks with the application of diquat becomes smaller with the passing of days. It has been observed that the bean is a human consumed legume, the application of desiccant interfered negatively in the quality of the grains, with increase in its cooking time (TC) and reduction of Hue (coloring angle). In addition, it was observed that the Hue presented a negative correlation (-0.52) with the water content of the plant and (-0.59) with TC. It is concluded that it is possible to differentiate the effects caused by the desiccants, showing that the ammonium-glufosinate presents less damage to the quality, presenting loss of water similar to the control. In the Article 2 the grain characterization was carried out, evaluating the following grain variables: productivity, water content, grain size, color parameters, cooking time, texture, and electrical conductivity. The results for productivity and water content did not present statistical differences, and the control obtained higher productivity. It was observed that the prolonged storage time leads to a decrease in grain size. As for color, it was observed that storage causes browning of the grains and with the application of desiccants this effect is more pronounced. The cooking time and texture of the dried grains is higher than the control and the diquat presented the greatest time and resistance, and it is possible to observe that these values increase progressively along with storage time. It is concluded that the application of pre-harvest desiccants influences the technological quality of the grains, being this effect more accentuated with the grain storage Uma alternativa para a realização da colheita de feijão fora do tempo aproriado de maturação fisiológica é a utilização de dessecantes; porém, dependendo da maneira como é aplicado, época e modo de ação, pode comprometer a qualidade do produto, inviabilizando a utilização. O armazenamento de grãos tende a ocasionar a perda de qualidade, podendo levar ao aumento da dureza dos grãos e do tempo de cocção, mudando sabor e cor do tegumento, e essa perda pode ser acentuada com o uso de dessecantes. Portanto, o objetivo do trabalho foi avaliar a influência da aplicação de dois diferentes herbicidas dessecantes (diquat e amônio glufosinato) na cultura do feijão por meio de índices de vegetação, curvas espectrais e suas derivadas após aplicação, e por meio da avaliação da qualidade de grãos submetidos a diferentes tempos de armazenamento. O experimento foi realizado em duas propriedades agrícolas, no município de Santa Tereza do Oeste, PR. O delineamento experimental utilizado foi inteiramente casualizado em parcelas subdivididas, divididas em cinco tempos de armazenamento (0, 2, 4, 6 e 8 meses) e subdivididas nos dois diferentes dessecantes (diquat, e glifosinato de amônio) e testemunha, com cinco repetições. No Artigo 1 foi realizada a caracterização espectral das parcelas nos diferentes tratamentos; em seguida, foram calculados os índices de vegetação (IVs) e as derivadas da curva espectral. Os índices de vegetação (IV), as faixas de comprimento do IVP e IVM dos tratamentos com aplicação de dessecantes apresentaram p≤0,05 a partir do segundo dia após aplicação (DAD), sendo possível identificar visualmente nos comprimentos de onda (705, 750,860 e 1240 nm) nas curvas espectrais. Os IVs NDWI e mNVDI apresentaram p≤ 0,05 no primeiro DAD. O uso de dessecante acarreta na degradação da clorofila, observado no primeiro DAD na região dos comprimentos de onda (650 e 800 nm). Por meio da red edge e da primeira derivada é possível observar o estresse gerado na planta, sendo mais expressivo com a aplicação do diquat, pois a magnitude dos picos com a aplicação do diquat se torna menor com o passar dos dias. O feijão é uma leguminosa de consumo humano e a aplicação de dessecante interferiu negativamente na qualidade dos grãos, com aumento do tempo de cozimento (TC) e redução do Hue (ângulo de coloração). Além disso, observou-se que o Hue apresentou correlação negativa (-0,52) com a diferença de teor de água da planta e (-0,59) com TC. Foi possível diferenciar os efeitos causados pelos dessecantes, mostrando que o amônio-glufosinato apresenta menor dano à qualidade, apresentando perda de água semelhante à testemunha. No Artigo 2 foi realizada a caracterização dos grãos, sendo avaliadas as seguintes variáveis dos grãos: produtividade, teor de água, tamanho do grão, parâmetros de cor, tempo de cozimento, textura e condutividade elétrica. Os resultados de produtividade e teor de água não apresentaram diferenças estatísticas, sendo que a testemunha obteve maior produtividade. Foi possível observar que o tempo prolongado de armazenamento acarreta na diminuição do tamanho dos grãos. Quanto à cor, observou-se que o armazenamento causa o escurecimento dos grãos, e com a aplicação de dessecantes este efeito é mais acentuado. O tempo de cozimento e textura dos grãos dessecados é maior que a testemunha e o diquat apresentou o maior tempo e resistência, sendo possível observar que com o armazenamento esses valores aumentam progressivamente. Conclui-se que a aplicação de dessecantes em pré-colheita influencia na qualidade tecnológica dos grãos, sendo este efeito mais acentuado com o armazenamento dos grãos.

El objetivo de este trabajo es analizar la disponibilidad, accesibilidad y calidad de los Espacios Verdes Urbanos (EVU) de carácter público de la ciudad de General José de San Martín, ciudad que se localiza en el noreste de la Provincia del Chaco. Este asentamiento, al igual que otras ciudades intermedias de la provincia, desde las últimas dos décadas del siglo pasado, ha visto modificada su configuración territorial producto del crecimiento de la población y de la expansión urbana. Es en este contexto en el que se hace necesario identificar las diferenciaciones intraurbanas que marcan desigualdades espaciales con el propósito de orientar las acciones que sean más beneficiosas para mitigar las asimetrías territoriales. The objective of this work is to analyze the availability, accessibility and quality of the Urban Green Spaces (UGS) of a public nature in the city of General José de San Martín, city located in the northeast of Chaco Province. These settlements, like other intermediate cities in the province, has been around since the last two decades of the last century, has seen its territorial configuration modified as a result of population growth and urban expansion. It is in this context that it is necessary to identify intra-urban differentiations that mark spatial inequalities with the purpose to guide actions that are most beneficial to mitigate territorial asymmetries. Fil: Meza, Julio César. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Investigación para el Desarrollo Territorial y del Hábitat Humano. Universidad Nacional del Nordeste. Facultad de Arquitectura y Urbanismo. Instituto de Investigación para el Desarrollo Territorial y del Hábitat Humano.; Argentina Fil: Ramirez, Mirta Liliana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Investigación para el Desarrollo Territorial y del Hábitat Humano. Universidad Nacional del Nordeste. Facultad de Arquitectura y Urbanismo. Instituto de Investigación para el Desarrollo Territorial y del Hábitat Humano.; Argentina

In recent decades, remote sensing has increasingly been used to estimate the spatio-temporal evolution of crop biophysical parameters such as the above-ground biomass (AGB). On a local scale, the advent of unmanned aerial vehicles (UAVs) seems to be a promising trade-off between satellite/airborne and terrestrial remote sensing. This study aims to evaluate the potential of a low-cost UAV RGB solution to predict the final AGB of Zea mays. Besides evaluating the interest of 3D data and multitemporality, our study aims to answer operational questions such as when one should plan a combination of two UAV flights for AGB modeling. In this case, study, final AGB prediction model performance reached 0.55 (R-square) using only UAV information and 0.8 (R-square) when combining UAV information from a single flight with a single-field AGB measurement. The adding of UAV height information to the model improves the quality of the AGB prediction. Performing two flights provides almost systematically an improvement in AGB prediction ability in comparison to most single flights. Our study provides clear insight about how we can counter the low spectral resolution of consumer-grade RGB cameras using height information and multitemporality. Our results highlight the importance of the height information which can be derived from UAV data on one hand, and on the other hand, the lower relative importance of RGB spectral information.

In the present work, the role played by vegetation parameters, necessary to the hydrological distributed modeling, is investigated focusing on the correct use of remote sensing products for the evaluation of hydrological losses in the soil water balance. The research was carried out over a medium-sized river basin in Southern Italy, where the vegetation status is characterised through a data-set of multi-temporal NDVI images. The model adopted uses one layer of vegetation whose status is defined by the Leaf Area Index (LAI), which is often obtained from NDVI images. The inherent problem is that the vegetation heterogeneity – including soil disturbances – has a large influence on the spectral bands and so the relation between LAI and NDVI is not unambiguous. We present a rationale for the basin scale calibration of a non-linear NDVI-LAI regression, based on the comparison between NDVI values and literature LAI estimations of the vegetation cover in recognized landscape elements of the study catchment. Adopting a process-based model (DREAM) with a distributed parameterisation, the influence of different NDVI-LAI regression models on main features of water balance predictions is investigated. The results show a significant sensitivity of the hydrological losses and soil water regime to the alternative LAI estimations. These crucially affects the model performances especially in low-flows simulation and in the identification of the intermittent regime.

Perennial ryegrass (Lolium perenne) is a perennial crop used in temperate regions as forage. In L. perenne breeding programs, persistency is an important trait. Poor persistency results in sward degradation and associated yield and nutritive value losses. Breeders assess persistency of accessions using visual scoring in field plots during the 2nd or 3rd growing season. This evaluation system is easy and cheap but is not free from human bias. In this study, the correlation between the scoring done by different breeders was only 0.243. As an alternative we have developed a methodology to assess persistency of L. perenne breeding materials based on vegetation indices (VIs) derived from Unmanned Aerial Vehicle (UAV) imagery. The VIs Excess green (ExG2), Green Leaf Index and Normalized green intensity (GCC) were found to provide consistent results for flights carried out under different light conditions and were validated by ground reference information. The correlation between the VIs and the percentage of ground cover extracted from on-ground imagery was 0.885. To test the implementation of the method we compared the ExG2 value based approach to selection with a visual score based selection methodology as applied by two breeders. The breeding decisions of Breeder A agreed well with decisions based on ExG2 values (74.6%), but those of Breeder B displayed a lower agreement (54.0%). In contrast, agreement between decisions based on different flights was very high (91.6%). The methodology was validated for general applicability. In summary, the results demonstrate that basing persistency selection in L. perenne breeding programs on ExG2 values from UAV imagery is likely to be more objective in comparison to the currently-used visual scoring method. ispartof: EUPHYTICA vol:214 issue:8 status: published

The photochemical reflectance index (PRI) is calculated from vegetation narrowband reflectance in two bands in the visible part of the spectrum. Variations in PRI are associated with changes in the xanthophyll cycle pigments which regulate the light use efficiency of vegetation. Correlations have been found between remotely-sensed PRI and various photosynthetic productivity parameters at the scales from leaves to landscapes. Environmental satellites can provide only an instantaneous value of this index at the time of overpass. The diurnal course of needle (leaf) PRI needs to be known in order to link the instantaneous values robustly with photosynthetic parameters at time scales exceeding one day. This information is not currently available in the scientific literature. Here we present the daily cycle of Scots pine needle and canopy PRI in a southern boreal forest zone in the presence of direct solar radiation during the peak growing season of two consecutive years. We found the PRI of the needles which are exposed to direct radiation to have a distinct diurnal cycle with constant or slightly increasing values before noon and a daily minimum in the afternoon. The cycle in needle PRI was not correlated with that in the incident photosynthetic photon flux density (PPFD). However, when PPFD was above 1000 &mu, mol m&minus, 2 s&minus, 1, approximately between 8 a.m. and 5 p.m., needle PRI was correlated with the light use efficiency (LUE), measured with shoot chambers. The timing of the minimum needle PRI coincided with the minimum canopy value, as measured by an independent sensor above the canopy, but the correlation between the two variables was not significant. Our field results corroborate the applicability of needle PRI in monitoring the daily variation in LUE. However, to apply this to remote sensing of seasonal photosynthetic productivity, the daily cycle of leaf PRI needs to be known for the specific vegetation type.

12 páginas : figuras. En gran parte del territorio colombiano, se está presentando un cambio paisajístico pasando de ser principalmente rurales a suburbanos o urbanos, y dicho cambio del paisaje puede afectar la biota presente. En el caso partículas de los cerros orientales de Bogotá han aumentado el cambio del uso del suelo en área urbanas con fines residenciales, educativos e infraestructura vial por los procesos de migración poblacional pasando de territorios rurales a urbanos. De acuerdo con lo anterior, este estudio tiene como hipótesis que el desarrollo urbanístico afecta la cobertura vegetal de los cerros orientales de Bogotá de 1997 a 2018. In much of the Colombian territory, a landscape change is being presented, changing from being mainly rural to suburban or urban, and this change in the landscape can affect the present biota. In the case of particles from the eastern hills of Bogotá, the change in land use in urban areas for residential, educational and road infrastructure has increased due to population migration processes from rural to urban areas. In agreement with the previous thing, this study has like hypothesis that the urban development affects the vegetal cover of the oriental hills of Bogota from 1997 to 2018.

Submitted by Suzana Maria Inácio De Carvalho (suzana-carvalho@live.com) on 2018-07-28T15:12:36Z No. of bitstreams: 1 Dissertacao_SMIC_2307_novo_repositorio.pdf: 7752307 bytes, checksum: ff8be58adddeedea30e008b00bd7e4b1 (MD5) Approved for entry into archive by Lucimara Kurokawa Shinoda null (lucimaraks@sorocaba.unesp.br) on 2018-07-30T18:15:59Z (GMT) No. of bitstreams: 1 carvalho_smi_me_soro.pdf: 7752307 bytes, checksum: ff8be58adddeedea30e008b00bd7e4b1 (MD5) Made available in DSpace on 2018-07-30T18:15:59Z (GMT). No. of bitstreams: 1 carvalho_smi_me_soro.pdf: 7752307 bytes, checksum: ff8be58adddeedea30e008b00bd7e4b1 (MD5) Previous issue date: 2018-06-04 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Alterações no clima causam impactos significativos sobre a estrutura e distribuição dos ecossistemas afetando a biodiversidade terrestre. O objetivo da pesquisa foi descrever os padrões da dinâmica da vegetação natural da Mata Atlântica Paulista na região da bacia hidrográfica do rio Paraíba do Sul (UGRHI 2) e identificar a relação que se estabelece entre a variabilidade climática e a vegetação. Dados de imagens de séries temporais de índices de vegetação como o EVI (Índice de Vegetação Melhorado), LSWI (Índice de umidade da vegetação) e LAI (Índice de Área Foliar) derivados de produtos de Sensoriamento Remoto (SR), foram adquiridos do sensor MODIS, pois são poderosas ferramentas indicadoras do desenvolvimento da vegetação. Dados de precipitação pluvial adquiridas por estações pluviométricas disponibilizadas pela ANA foram aplicados para cálculo dos índices climáticos como o SPI (Índice de Precipitação Padronizado) e RAI (Índice de Anomalia de chuva). Os índices conseguiram detectar a variação entre anos extremamente úmidos e anos extremamente secos. Destacaram-se os anos 2009 e 2014, sendo que o ano de 2009 representou um ano de anomalia positiva, com precipitação acima da média climatológica. Já em 2014, foi possível detectar uma condição de anomalia negativa, com condições mais severas com precipitação pluvial abaixo da média climatológica. Com a finalidade de avaliar como essa dinâmica climática influencia na dinâmica da vegetação, os índices EVI, LSWI e LAI foram analisados e mostraram que o comportamento da dinâmica da vegetação natural das regiões da Mata Atlântica Paulista acompanha a variabilidade do clima, já que foram observados padrões de maior produtividade no período da estação chuvosa, época em que a condição hídrica é mais intensa e favorece o verdejamento das folhas, o oposto foi observado quando há o déficit hídrico, que ocorre principalmente na estação seca onde nota-se o declínio dos valores dos índices, ou seja, indicando a queda foliar. Os índices capturaram o padrão esperado, vale destacar que o índice LSWI apresentou maior sensibilidade à condição hídrica, o que era esperado devido às bandas que compõe este índice, sendo possível verificar o potencial das imagens do sensor MODIS em capturar a resposta da floresta e consequentemente contribuir de forma significativa para estudos de mudanças climáticas. Climate change causes significant impacts on the structure and distribution of ecosystems affecting terrestrial biodiversity. The objective of the research was to describe the patterns of the dynamics of the natural vegetation of the Atlantic forest in the region of the watershed of Rio Paraíba do Sul (UGRHI 2) and to identify the relationship that is established between climatic variability and vegetation. Image data from temporal series of vegetation indices as the improved Vegetation Index (EVI), Vegetation Humidity Index (LSWI) and foliar area index (LAI) derived from remote sensing products (SR), were acquired from the MODIS sensor, as they are Powerful tools indicating the development of vegetation. Rainfall precipitation data acquired by precipitation stations provided by the National Water Agency (ANA) were applied for calculating climatic indexes such as the Standardized Precipitation Index (SPI) and Rain Anomaly Index (RAI). The indexes were able to detect the variation between extremely humid years and extremely dry years. The years 2009 and 2014 were highlighted, and the year 2009 represented a year of positive anomaly, with precipitation above the average climatological. In 2014, it was possible to detect a negative anomaly condition, with harsher conditions with rainfall precipitation below the climatological average. For the purpose of evaluating how this climate dynamics influences the dynamics of vegetation, the indices EVI, LSWI and LAI were analyzed and showed that the behavior of the dynamics of the natural vegetation of the Regions of Atlantic Forest Paulista accompanies the Climate variability, since there were observed higher productivity patterns during the rainy season, when the water condition is more intense and favors the greenishing of the leaves, the opposite was observed when there is the water deficit, which occurs Mainly in the dry season where it is noted the decline of the values of the indices, that is, indicating the foliage fall. The indexes captured the expected pattern, it is worth noting that the LSWI index presented greater sensitivity to the water condition, which was expected due to the bands that make up this index, it is possible to verify the potential of the images of the MODIS sensor in capturing the Forest response and consequently contribute significantly to climate change studies.

Degradação de pastagem é um processo segundo qual ocorre perda da produtividade, do valor nutricional e da capacidade de recuperação natural. As técnicas de geoprocessamento permitem identificar diferentes níveis de degradação por meio de dados consistentes e medições repetidas. Objetivou-se com a presente pesquisa utilizar Índices de Vegetação (NDVI) para detecção de processos de degradação de pastagens cultivadas no município de Colorado do Oeste – RO. Para o estudo, utilizaram-se imagens do satélite OLI/Landsat 8 cenas 230/69 e 229/69 para o ano de 2015, com posterior verificação a campo das condições das pastagens. Os índices indicaram que 66,69% do município apresentam pastagens com algum grau de degradação. Desse percentual, 35,19% classificada com moderadamente (Pastagem 3), 24,88% fortemente (Pastagem 2) e 6,62%, extremamente degradadas (Pastagem 1), e apenas 33,30% das pastagens da área de estudo estão em condições adequadas de produção. A metodologia utilizada mostrou-se satisfatória para diferenciação de níveis de degradação das pastagens. La dégradation des pâturages est un processus qui cause la perte de productivité, de la valeur nutritive et de la capacité de récupération naturelle. Les techniques de géotraitement permettent d'identifier différents niveaux de dégradation grâce à des données cohérentes et à des mesures répétées. L'objectif de cette recherche était d'utiliser les indices de végétation (NDVI) pour détecter les processus de dégradation des pâturages cultivés dans la commune de Colorado do Oeste - RO. Pour l'étude, des images satellites OLI / Landsat 8 scènes 230/69 et 229/69 ont été utilisées pour l'année 2015, avec une vérification sur le terrain des conditions de pâturage. Les indices indiquaient que 66,69% de la commune présentait des pâturages avec un certain degré de dégradation. De ce pourcentage, 35,19% classé comme modérément (Grassland 3), fortement 24,88% (Grassland 2) et 6,62%, extrêmement dégradé (Grassland 1), et seulement 33,30% de la zone d'étude ont des pâturages dans des conditions de production appropriées. La méthodologie utilisée était satisfaisante pour différencier les niveaux de dégradation des pâturages. Grassland degradation is a process according to which occurs loss of productivity, nutritional value and natural recovery capacity. The geoprocessing techniques to identify different levels of degradation through consistent data and repeated measurements. The objective of this research was to use Vegetation Indexes (NDVI) to detect degradation processes of pastures grown in the municipality of Colorado do Oeste – RO. For the study, were used OLI / Landsat satellite images 8 scenes 230/69 and 229/69 for the year 2015, with subsequent verification of a field of pasture conditions. The indexes indicated that 66.69% of the municipality present pastures with some degree of degradation. Of this percentage, 35.19% were classified with moderately (Grassland 3), 24.88% strongly (Grassland 2) and 6.62%, extremely degraded (Grassland 1), and only 33.30% of the pastures in the study area are in adequate production conditions. The methodology used was satisfactory for differentiation of pasture degradation levels.

In the Mediterranean, durum wheat is one of the major crops, but a high variability of grain yield and protein concentration (GPC) prevents an adequate agronomic planning at the farm or consortium level. Although there are many studies on monitoring of crop production and early prediction of yields, little has been done at the local scale. The aim of this study was to assess simplified integration algorithms (SIAs) for integrating remote sensing information with a crop model, to forecast the GPC and grain yield at the field scale. To this end, the CERES-Wheat model was run to simulate the seasonal average of grain yield (AVE) and GPC in Val d’ Orcia (Tuscany Region, Italy) during the 2009–2010 and 2010–2011 growing seasons. The performances of different vegetation indices from MODIS imagery in harvest forecasting were assessed and compared. The SIA formulation was based on the simulated AVE and GPC, and on their spatialization in relation to the intraannual variability between the fields described by vegetation indices. The simulated AVE traced the observed trend. The fraction of absorbed photosynthetically active radiation (fPAR) was the best index in describing grain yield, and the related SIA showed at validation good performance at the field scale (r² = 0.74). Conversely, the SIA was unable to predict GPC due to the low performance of CERES-Wheat in capturing the interannual variability and to the failure of the fPAR in describing the GPC interfields variability at intermediate canopy reflectance values.

Three vegetation indexes derived from remote sensing (NDVI, SAVI and EVI) were used to assess, in a citrus orchard, the performance of three irrigation treatments: control (full irrigation), regulated deficit irrigation and partial root-zone drying. In addition, midday steam water potential and canopy temperature were measured for comparison with the vegetation indexes values. No statistically significant differences were found among treatment in the evaluated parameters, indicating the irrigation treatments did not induce a severe soil water deficit affecting tree water status. The good agreement found between the ground measurements and the indexes derived from remote sensing highlights the potentialities of this technology for assessing the performance of different irrigation methods.

Submitted by JOSÉ CARLOS DE SOUZA null (jcsouza1974@gmail.com) on 2017-09-02T20:13:33Z No. of bitstreams: 1 Tese_final_PDF.pdf: 5571055 bytes, checksum: 5bf13179b0dee373575a49ac318f9a43 (MD5) Approved for entry into archive by Luiz Galeffi (luizgaleffi@gmail.com) on 2017-09-06T14:02:59Z (GMT) No. of bitstreams: 1 souza_jc_dr_soro.pdf: 5571055 bytes, checksum: 5bf13179b0dee373575a49ac318f9a43 (MD5) Made available in DSpace on 2017-09-06T14:02:59Z (GMT). No. of bitstreams: 1 souza_jc_dr_soro.pdf: 5571055 bytes, checksum: 5bf13179b0dee373575a49ac318f9a43 (MD5) Previous issue date: 2017-08-18 A espacialização de dados ambientais através de técnicas de geoprocessamento e o monitoramento do comportamento espectral da vegetação por meio de imagens orbitais são instrumentos importantes nos estudos ambientais de análise da paisagem, em especial os aplicados a bacias hidrográficas. O objetivo da presente pesquisa foi desenvolver uma metodologia de avaliação ambiental aplicada à bacia hidrográfica, através da estimativa de parâmetros biofísicos, gerados por imagens Landsat 8 e determinação de parâmetros físico-químicos dos solos. O estudo foi aplicado na Bacia Hidrográfica do Rio Una, localizada em Ibiúna, no Estado de São Paulo, Brasil. Foram estimados o índice de vegetação por diferença normalizada – NDVI; o índice de vegetação ajustado do solo – SAVI; o índice de água por diferença normalizada – NDWI e o índice de ressecamento da vegetação por temperatura – TVDI. O estudo foi desenvolvido utilizando imagens do satélite Landsat 8 datadas de janeiro (período úmido) e agosto (período seco) do ano de 2015. O processamento digital das imagens foi realizado no software ArcGis 10.3. Foram geradas matrizes de correlação com base na combinação pixel a pixel para os índices NDVI, SAVI e NDWI utilizando o software Matlab para a análise espacial e temporal da cobertura vegetal. Além disso, foram coletados amostras de solo para determinação dos teores de matéria orgânica e carbono orgânico total – COT e levantamento in situ, dos teores de umidade e pH da área de estudo. Os resultados levantados foram interpolados pelo método do inverso do quadrado da distância - IQD. Os índices estimados apresentaram padrão espacial com forte relação com a sazonalidade climática, com ênfase ao regime de chuvas. Os resultados obtidos mostraram uma redução dos índices NDVI e SAVI, em 87,5% e 92% respectivamente, do período úmido para o seco. No NDWI foi identificado perda em conteúdo de água na vegetação em 73,03% dos pixels. As reduções nos valores dos pixels, nos três índices analisados, evidenciam a influência direta das condições climáticas no vigor e no teor de umidade da vegetação. O TVDI também apresentou influencia da sazonalidade climática. No mês de janeiro predominaram valores entre 0.2 e 0.4, indicando umidade normal, e em agosto predominaram valores entre 0.4 e 0.8, indicando condições ligeiramente seca ou estresse hídrico moderado. O TVDI se mostrou adequado para avaliar o déficit hídrico em diferentes coberturas do solo, com potencial para monitorar as perdas de umidade do solo e da vegetação. A umidade do solo, com medição in loco, registrou valores maiores nos meses com maior total de chuva, em todas as amostras, com exceção de algumas áreas agrícolas que ocorriam irrigação por aspersão, no período seco. Os solos apresentaram fraca acidez em todas as amostras, registrando pequena variação do pH entre os períodos, indicando condições de menor acidez no período de estiagem. Os teores de COT e matéria orgânica dos solos, indicaram as áreas de pastagens com teores mais elevados e as culturas temporárias com teores mais reduzidos, em relação aos demais tipos de cobertura do solo da bacia. A prática de cultivo e o processo de intemperismo explicam a redução nos teores de TOC e MO nos solos de culturas temporárias. Já as pastagens com suas estruturas radiculares, explicam a potencialização no armazenamento do carbono. As imagens Landsat 8 se mostraram eficientes na estimativa de parâmetros biofísicos da vegetação e as técnicas de geoprocessamento contribuíram substancialmente nas análises espaciais, se apresentando como ferramentas indispensáveis em estudos ambientais aplicados a bacias hidrográficas. The spatialization of environmental data through geoprocessing techniques and the monitoring of vegetation spectral behavior through orbital images are important tools in environmental studies of landscape analysis, in special those applied to watershad. The objective of the present research was to develop an environmental assessment methodology applied to the watershad, through the estimation of biophysical parameters, generated by Landsat 8 images and determination of chemical-physical parameters of the soils. The study was applied in the Una Watershad, located in Ibiúna, in the State of São Paulo, Brazil. Were estimated the Normalized Difference Vegetation Index - NDVI; The Soil Adjusted Vegetation Index - SAVI; Normalized Difference Water Index - NDWI and the Temperature Vegetation Dryness Index - TVDI. The study was developed using Landsat 8 satellite images dated January (wet period) and August (dry period) of the year 2015. The digital process of images was performed in ArcGis 10.3 software. Correlation matrices were generated based on the pixel by pixel combination for the NDVI, SAVI and NDWI indexes using the Matlab software for the spatial and temporal analysis of the vegetation cover. In addition, soil samples were collected to determine the organic matter and total organic carbon (TOC) and survey in loco of soil moisture content and pH of the study area. The results obtained were interpolated by the Inverse Distance Weighting - IDW. The estimated indices presented a spatial pattern with strong relation with climatic seasonality, with emphasis on the rainfall regime. The results showed a reduction of the NDVI and SAVI indices, in 87.5% and 92%, respectively, from the wet to the dry period. In the NDWI, loss of water content in the vegetation was detected in 73.03% of the pixels. The reductions in the values of the pixels, in the three indices analyzed, show the direct influence of the climatic conditions on the vigor and the moisture content of the vegetation. TVDI also had an influence of climatic seasonality. In January values between 0.2 and 0.4 predominated, indicating normal humidity, and in August, values between 0.4 and 0.8 were predominant, indicating conditions slightly dry or moderate water stress. The TVDI proved to be suitable to evaluate the water deficit in different soil coverages, with potential to monitor soil and vegetation moisture losses. Soil moisture, with in loco measurement, recorded higher values in the months with the highest total rainfall, in all samples, except for some agricultural areas that were irrigated by sprinkling in the dry period. Soils presented low acidity in all samples, registering a small pH variation between the periods, indicating conditions of lower acidity in the dry season. The TOC and organic matter contents of the soils indicated the pasture areas with higher contents and the temporary crops with lower levels, in relation to the other types of soil cover in the watershed. The cultivation practice and the weathering process explain the reduction in TOC and OM contents in soils of temporary crops. On the other hand, pastures with their root structures explain the potential of carbon storage. Landsat 8 images were efficient in estimating biophysical parameters of the vegetation and geoprocessing techniques contributed substantially to spatial analysis, presenting themselves as indispensable tools in environmental studies applied to watersheds.

Quantifying the accuracy of remote sensing products is a timely endeavor given the rapid increase in Earth observation missions. A validation site for Sentinel-2 products was hence established in central Germany. Automatic multispectral and hyperspectral sensor systems were installed in parallel with an existing eddy covariance flux tower, providing spectral information of the vegetation present at high temporal resolution. Normalized Difference Vegetation Index (NDVI) values from ground-based hyperspectral and multispectral sensors were compared with NDVI products derived from Sentinel-2A and Moderate-resolution Imaging Spectroradiometer (MODIS). The influence of different spatial and temporal resolutions was assessed. High correlations and similar phenological patterns between in situ and satellite-based NDVI time series demonstrated the reliability of satellite-based phenological metrics. Sentinel-2-derived metrics showed better agreement with in situ measurements than MODIS-derived metrics. Dynamic filtering with the best index slope extraction algorithm was nevertheless beneficial for Sentinel-2 NDVI time series despite the availability of quality information from the atmospheric correction procedure.