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The role of remote sensing data for monitoring different parameters in the study of ecosystems has been increasing. Particularly the development of different indices has played a great role to study the properties of vegetation and vegetation dynamics in large countries. In addition to this, satellite rainfall estimate data has been used to study the pattern of precipitation in areas where station rain-gauge data is not available. The Normalized Difference Vegetation Index (NDVI) and rainfall estimates data from the National Oceanic and Atmospheric Administration (NOAA) satellites were used to investigate the spatio-tempotal pattern of precipitation and the response of vegetation to precipitation in Ethiopia from 1996 to 2008. The patterns were studied in different land cover classes using data from the Global Land Cover Network (GLCN). The spatial patternof NDVI and precipitation showed that vegetation responded directly to precipitation. The seasonal patterns showed that there was between 0 to 3 months lag between precipitationand vegetation. However it was not possible to draw conclusion regarding the annual trendsof precipitation and NDVI because of the nature of the NDVI data, which was produced using the 10 day maximum composite values.

The Caspian Region includes the Caspian Sea and five littoral states: Azerbaijan, Iran, Turkmenistan, Kazakhstan and Russian. 40% of the Caspian coastal zone is arid, 69% of this territory undergone desertification according to international reports. Among the reasons are soil erosion caused by water, wind and irrigation, the salinization of soil, intense bioresources usage, and soil pollution due to oil extraction and production. Desertification is a serious problem, at global, national and local scales. It is important to know what should be sustained or developed in order to protect land from desertification. The generalization of data over desertification processes in Caspian countries, studying the dynamics of this process in space and time could help facilitate measures to counter regional desertification. To understand Caspian Region coastal desertification phenomenon, vegetation cover satellite images for the years 1982 – 2006 were investigated to give map vegetation changes over time. The Normalized Difference Vegetation Index (NDVI) data for this study was derived from the Global Inventory Modeling and Mapping Studies (GIMMS) dataset, with the spatial resolution of 8 km. A coastal strip 160 km from the coast, divided by countries, was investigated. Theanalyses were focused on extent and severity of vegetation cover degradation, and possible causes such as landscape, land use history and culture, climatic changes and policies. The aim was to address questions related to desertification phenomenon, by focusing on Caspian Region time-series of vegetation cover data and investigation patterns of desertification in the region. In this study evidence of land degradation in the Caspian Region countries was found to occur on local scales or sub-national scales rather than across the regional as a whole. Changes in vegetation cover revealed by AVHRR NDVI appeared to be reversible in character and were dependent on the climate conditions, and anthropogenic impact in approximately equal proportions.

Assessment of vegetation productivity on rangelands is needed to assist in timely decision making with regard to management of the livestock enterprise as well as to protect the natural resource. Characterization of the vegetation resource over large landscapes can be time consuming, expensive and almost impossible to do on a near real-time basis. The overarching goal of this study was to examine available technologies for implementing near real-time systems to monitor forage biomass available to livestock on a given landscape. The primary objectives were to examine the ability of the Climate Prediction Center Morphing Product (CMORPH) and Next Generation Weather Radar (NEXRAD) rainfall products to detect and estimate rainfall at semi-arid sites in West Texas, to verify the ability of a simulation model (PHYGROW) to predict herbaceous biomass at selected sites (patches) in a semi-arid landscape using NEXRAD rainfall, and to examine the feasibility of using cokriging for integrating simulation model output and satellite greenness imagery (NDVI) for producing landscape maps of forage biomass in Mongolia’s Gobi region. The comparison of the NEXRAD and CMORPH rainfall products to gage collected rainfall revealed that NEXRAD outperformed the CMORPH rainfall with lower estimation bias, lower variability, and higher estimation efficiency. When NEXRAD was used as a driving variable in PHYGROW simulations that were calibrated using gage measured rainfall, model performance for estimating forage biomass was generally poor when compared to biomass measurements at the sites. However, when model simulations were calibrated using NEXRAD rainfall, performance in estimating biomass was substantially better. A suggested reason for the improved performance was that calibration with NEXRAD adjusted the model for the general over or underestimation of rainfall by the NEXRAD product. In the Gobi region of Mongolia, the PHYGROW model performed well in predicting forage biomass except for overestimations in the Forest Steppe zone. Cross-validation revealed that cokriging of PHYGROW output with NDVI as a covariate performed well during the majority of the growing season. Cokriging of simulation model output and NDVI appears to hold promise for producing landscape maps of forage biomass as part of near real-time forage monitoring systems.

Maintaining forage availability is challenging for managers of grazing systems, especially in spatially heterogeneous swards. Remote sensing may help to overcome this problem. The objectives of this study were to (i) determine a method by which NDVI may be calibrated to estimate biomass, (ii) determine if NDVI can be used to assess spatial variability of yield in extensive grasslands, and (iii) to determine if NDVI can be used to evaluate grazing systems. We found that the calibration of NDVI values for the estimation of biomass was better correlated with the destructive harvesting procedure (R2 = 0.68) but far more laborious and time-consuming than estimation of biomass from the rising plate meter (R2 = 0.54). Semivariograms revealed that sampling at a 0.76 m distance provided information about the spatial variability structure of NDVI values from grazed swards. Frequency distributions of sward biomass derived from NDVI reflected foraging strategies of cattle. Negative skewness and high kurtosis of histograms indicated selective grazing, while positive skewness and low kurtosis indicated the opposite. Histograms also allowed for estimation of available forage within each field. We concluded that grassland biomass may be derived from high resolution NDVI and RPM data and used to evaluate condition of grassland landscapes and aid decision-making of managed grazing systems.

The normalized difference vegetative index (NDVI) has been correlated with physiological plant parameters and used to evaluate plant growth. There is little information about the use of this technique to detect soybean nutrient deficiencies. The objective of this work was to determine the ability of the NDVI sensor to detect P and K deficiencies, and grain yield reduction, in soybean. During 2010 and 2011, NDVI measurements were made on a soybean field trial site known to exhibit yield responses to both P and K nutrition. Four replicates of 8 levels each of P and K nutrition were evaluated. The NDVI measurements were made with an active proximal sensor held parallel to the soil surface every seven days after V2, and until R2. At each measurement a mean NDVI value was found for each plot. Phosphorus deficiency was detected with the first NDVI measurement. Potassium deficiency was first detected just after V4. Differences in NDVI values due to P or K nutrition increased with continued crop development. There were significant R1 leaf composition and grain yield responses to improved P or K nutrition. The active proximal sensor was able to detect soybean growth differences due to P or K deficiencies in soybean.

In the context of growing tourism and global warming, the fragile landscape of the Himalayas is under immense pressure because of rapid land cover changes in developing countries like Nepal. Remotely sensed data combined with ethnographic knowledge are useful tools for studying such changes. The quantitative change can be measured analyzing satellite images whereas local people’s perceptions provide supportive information. To measure such changes in Sagarmatha National Park of Nepal, Multispectral Scanner (MSS) and Thematic Mapper (TM) images since 1972 were used. Normalized Difference Vegetation Index (NDVI) was calculated for different elevation classes and land cover types. These measurements, along with land cover change (1992- 2006) analysis, shows a significant conversion of the areas covered by ice, shrub and grass to rock and soil. Factors including political conflict due to a Maoist rebellion group, inactive park management, increasing tourist demand, and consequent natural resources exploitation helped to explain the change in the forested areas. This is supported by the information from short, informal, semi-structured interviews with local people. However, the local people are unaware of global warming, which has caused the ice melting and glacial lake expansion. Although global causes are out of the immediate control of land managers, better management practices and managed tourism might help alleviate deteriorating Himalayan ecosystems.

Submitted by João Alberto Zago Bevenuto null (jbevenuto@yahoo.com.br) on 2018-03-22T21:46:10Z No. of bitstreams: 1 USO DE ÍNDICES DE REFLECTÂNCIA FOLIAR NO MONITORAMENTO DO PATOSSISTEMA Microcyclus ulei x SERINGUEIRA.pdf: 4476483 bytes, checksum: a17aef49bfab7709a3a8f6520055f96b (MD5) / Approved for entry into archive by Maria Lucia Martins Frederico null (mlucia@fca.unesp.br) on 2018-03-23T11:05:39Z (GMT) No. of bitstreams: 1 bevenuto_jaz_dr_botfca.pdf: 4389533 bytes, checksum: 8af5d4739088a6f44c131d62d3407908 (MD5) / Made available in DSpace on 2018-03-23T11:05:39Z (GMT). No. of bitstreams: 1 bevenuto_jaz_dr_botfca.pdf: 4389533 bytes, checksum: 8af5d4739088a6f44c131d62d3407908 (MD5) Previous issue date: 2017-12-15 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / O maior problema fitossanitário na heveicultura brasileira é a doença conhecida por mal das folhas, causada pelo fungo Microcyclus ulei, cujos sintomas ocorrem nas folhas jovens, causando intensas desfolhas, diminuindo a produção de látex e até mesmo a morte em plantas muito suscetíveis. A espécie Hevea brasiliensis possui um hábito deciduifólio completo que é um caráter importante, ou seja, o desfolhamento uniforme proporciona a recuperação da copa com folhas sadias e a redução do inóculo de patógenos que são descartados com as folhas velhas, onde as estruturas reprodutivas estão localizadas. Os objetivos deste trabalho foram: utilizar ferramentas de sensoriamento remoto para confrontar o efeito das variáveis climáticas em diferentes períodos do ano sobre interferência do mal das folhas em seis anos comparando o avanço da doença com os índices de vegetação, Índice de Vegetação com Diferença Normalizada (NDVI), Índice de Vegetação Ajustado para os Efeitos do Solo (SAVI) e Índice de Área Foliar (IAF), calculados para o período de cada ano estudado, analisar o efeito deciduifólio natural ou ocorrência do mal das folhas; e verificar a troca de folhas dos clones de seringueira em estudo comparando com a fenologia. A área utilizada para o estudo localiza-se no município de Registro, Estado de São Paulo, Brasil, na Fazenda Umuarama com plantio monoclonal de seringueira, foram avaliados seis clones diferentes: IAN 873, IAN 717; RRIM 600, Fx 3864, Fx 2261 e Fx 3844, por meio das imagens do satélite Landsat 5 utilizado os índices de vegetação NDVI, SAVI e IAF. Foram utilizados para análise estatística modelos lineares generalizados com a distribuição gama e função de ligação logarítmica tendo como fatores clones e índices de reflectância. Os modelos foram ajustados incluindo-se as covariáveis continuas mensurada no campo: folhas caídas totais e folhas caídas doentes Esses modelos foram comparados através do critério de informação de Akaike corrigido – AICC, para identificar o melhor modelo, verificada mediante desvios por graus de liberdade (scale deviance). Para comparações entre fatores foi utilizado foi o teste de Tukey–Kramer. Realizou a correlação de Spearman entre os índices de vegetação: NDVI, SAVI e IAF. Concluiu-se que as ferramentas de sensoriamento remoto são aplicáveis nos efeitos que as variações climáticas sobre a influência da doença mal das folhas. Os índices NDVI, SAVI e IAF foram significativos para a queda foliar da seringueira. Verificou também através dos índices a fenologia da seringueira nos períodos de troca de folhas: desfolha, reenfolha e densidade de copa. Ocorreu interação estatística significativa sobre o efeito dos clones, sua fenologia e índices de vegetação nos pixels da imagem. Obteve-se bom ajuste dos modelos dos índices com as imagens de satélites e as covariáveis dos dados de campo. A correlação de Spearman mostrou-se significativa entre os índices por valores dos pixels. Os índices são ferramentas de grande valia para estudos e análises sobre plantios florestais. / The biggest phytosanitary problem in Brazilian heveculture is the disease known as leaf blight caused by the fungus Microcyclus ulei. Whose symptoms occur in young leaves, causing severe defoliation, reducing the production of latex and even the death in susceptible plants. Rubber tree has a deciduous habit which is an important character. Uniform defoliation of clones provides a reduction in the interior of pathogens, whose locations to reproduction occur in the old leaves. The objectives of this work were: Using remote sensing tools to compare the effect of climatic variables in different periods of the year on leaf mischief interference in six years comparing the disease progression with Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI) and Leaf Area Index (LAI) vegetation indices, calculated for the period of each year studied, verifying the natural deciduous effect or occurrence of leaf blight; Estimate an exchange of leaves of the rubber tree clones under study, comparing them with phenology. The area used for the study is located in the municipality of Registro, São Paulo State, Brazil, at Umuarama Farm with monoclonal rubber plantation, IAM 873, IAN 717; RRIM 600, Fx 3864, Fx 2261 and Fx 3844, using Landsat 5 satellite images used in NDVI, SAVI and IAF vegetation indices. They were used for statistical analysis generalized linear model with logarithmic range distribution and binding function having as clones reflectance factors and indices. Set the same models including whether the continuous covariates measured in the field: total fallen leaves sick and fallen leaves. Compared these models through the Akaike information criterion corrected - AICC to identify the best model, verified through the deviations by degrees of freedom (scale deviance). Was compared between factors was used was the Tukey-Kramer test. Spearman correlation was performed between the vegetation indexes: NDVI, SAVI and LAI. It was concluded that remote sensing tools are applicable in the effects that climatic variations on the influence of evil leaf disease. The NDVI, SAVI and IAF indexes showed the leaf fall effect of the rubber tree. Also verified through the indices the phenology of the rubber tree in the periods of leaf change: defoliation, re-foliage and cup density. There was a statistically significant interaction on the effect of clones’ phenology and vegetation index in the pixels of the image. It obtained good adjustment of the models of the contents with the satellite images and field data covariates. The Spearman correlation coefficient was significant between the indexes through the values of the pixels. The indices are valuable tools for studies and analyzes on forest plantations.

Submitted by NATÁLIA SOARES DA SILVA null (nataliasoasilva@hotmail.com) on 2017-01-25T15:52:11Z No. of bitstreams: 1 Natalia Tese com ficha.pdf: 3382712 bytes, checksum: bec77c34de08a6140f88f32e659fd0e8 (MD5) / Approved for entry into archive by LUIZA DE MENEZES ROMANETTO (luizamenezes@reitoria.unesp.br) on 2017-01-27T12:21:22Z (GMT) No. of bitstreams: 1 silva_ns_dr_bot.pdf: 3382712 bytes, checksum: bec77c34de08a6140f88f32e659fd0e8 (MD5) / Made available in DSpace on 2017-01-27T12:21:22Z (GMT). No. of bitstreams: 1 silva_ns_dr_bot.pdf: 3382712 bytes, checksum: bec77c34de08a6140f88f32e659fd0e8 (MD5) Previous issue date: 2016-11-25 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / O sensoriamento remoto tem sido uma ferramenta bastante utilizada em diferentes campos das ciências e não seria diferente na agricultura sendo um dos principais motivos de sua utilização a facilidade para a obtenção de dados dos sensores, já que muitos são disponibilizados gratuitamente em plataformas na internet. É indiscutível que a agricultura é um dos maiores consumidores dos recursos hídricos e que seu uso quando de forma adequada pode gerir excelentes resultados na produção dos cultivos agrícolas. A hipótese do estudo é que técnicas de sensoriamento remoto, aplicadas na área de interesse, podem se transformar em ferramenta para a gestão dos recursos hídricos dedicados à agricultura irrigada com cobertura de palha no solo. Nesse contexto o objetivo principal da pesquisa foi monitorar através do sensoriamento remoto o desenvolvimento do feijoeiro conduzido em sistema de Pivô Central cultivado com cobertura de palha no solo, na região de Paranapanema-SP, determinando quais parâmetros poderão ser utilizados para a gestão da irrigação. O estudo foi desenvolvido através da análise de imagens Landsat e Terra para obtenção do índice de vegetação por diferença normalizada (NDVI) por sensoriamento remoto e suas relações com outras variáveis (umidade do solo, índice de área foliar e evapotranspiração) a fim de parametrizar o desenvolvimento do feijoeiro, além da aplicação do modelo de índice de umidade do solo (IUS). Observou-se uma similaridade no comportamento do NDVI tanto nas imagens obtidas pelo satélite Landsat quanto Terra, onde no início do cultivo o NDVI é baixo devido à baixa porcentagem de cobertura verde e à medida que a cultura se desenvolve esses valores aumentam com o acréscimo da cobertura vegetal onde o ponto máximo é verificado na fase de enchimento do grão e decréscimo na maturação. Com a determinação de um índice (IUS) por sensoriamento remoto infere-se a umidade do solo e é possível monitorar as condições do feijoeiro durante o período de cultivo. / Remote sensing has been a tool widely used in different fields of science and it would not be different for agriculture. It is ease to obtaining data from sensors, since many are available on platforms on the internet. There is no doubt that agriculture is one of the largest consumers of water resources, and when properly manage, excellent results are obtained from agricultural crops production. The main objective of the study was to monitor through remote sensing the development of bean conducted under Central Pivot irrigation, cultivated with no-till and direct seedling, in the region of Paranapanema-SP. Additionally determining which parameters may be used for the irrigation management. The study hypothesis was that remote sensing techniques, applied in the area of interest, can become a tool for the management of water resources devoted to irrigated agriculture with no-till and direct seedling. The study was developed through the analysis of Landsat and Terra images, obtaining the normalized difference vegetation index (NDVI) by remote sensing and its relations with other variables (soil moisture, leaf area index and evapotranspiration) in order to the parametrization of the development of common bean, as well as the application of the model index of soil moisture (IUS). There were similarities in the behavior of NDVI for images from Landsat satellite and Terra. At the beginning of bean development NDVI was low due to the low percentage of cover; as the crop develops these values increased with the development of vegetation cover. The maximum value of NDVI was obtained during the filling phase of the grain in the pods and a decrease in maturation. With the determination of the IUS by remote sensing it can be infers soil water content.

The phenology of plants in northern ecosystems is currently changing. Roots have a key role in these ecosystems, though the phenology of roots is still poorly understood. The aim of this report was to investigate if above- and belowground phenology of the circumpolar sedge Eriophorum vaginatum was synchronized in a subarctic peatland underlain by permafrost, and to investigate which abiotic factors are limiting root growth. Additionally, the length of the belowground growing season was examined. The study was performed with a non-destructive in situ method (minirhizotrons and NDVI measurements) in the northernmost part of Sweden. Both above- and belowground phenology was measured biweekly during the whole growing season in 2016. The depth of the active layer, air temperature, soil temperature and soil moisture were measured to investigate the determinants of root growth. Root growth and aboveground activity was asynchronous, as peak in root growth occurred on average 21 days before maximum NDVI was reached. Soil temperature and thaw depth seem to be important factors regulating root growth in this peatland. The result highlight that solely studying the aboveground parts of plants can give a misleading interpretation about the phenology of the entire plant and thus during which time periods important ecosystem processes take place. Hence, to more accurate forecast ecosystem responses to global warming, both aboveground and belowground phenology should be considered.