Submitted by Dominick Jesus (dominickdejesus@hotmail.com) on 2016-01-04T18:06:08Z No. of bitstreams: 2 Dissertação_Aline Pontes Lopes.pdf: 1051137 bytes, checksum: e3b333b260d053615da964dd23970780 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2016-01-04T18:06:08Z (GMT). No. of bitstreams: 2 Dissertação_Aline Pontes Lopes.pdf: 1051137 bytes, checksum: e3b333b260d053615da964dd23970780 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2015-03-18 / Conselho Nacional de Pesquisa e Desenvolvimento Científico e Tecnológico - CNPq / Fundação de Amparo à Pesquisa do Estado do Amazonas - FAPEAM / Financiadora de Estudos e Projetos - Finep / Leaf phenology may drive seasonal variation in canopy-scale photosynthetic capacity and carbon flux in the Central Amazon. Visual monitoring of leaf flush from the forest floor is time consuming and difficult against an overcast sky. Seasonal trends of vegetation indices from orbital sensors have been challenged due to artifacts from seasonal trends in solar illumination angle. Tower-mounted RGB cameras are an attractive alternative. The Green Chromatic Coordinate (GC = green brightness divided by summed brightness of the three RGB channels) has been employed for this purpose. However, subtle seasonal change of tropical forest GC at landscape-scale may be of smaller amplitude than artifacts related to color balance, crown shadow size, illumination angle, exposure speed and sensor drift. Furthermore, gradual change in GC is affected by both leaf age and leaf amount. Nonetheless, abrupt GC change of large amplitude occurs at the individual crown scale and can be reliably attributed to massive leaf flush or leaf loss events. We analyzed a full year of daily images from an RGB camera mounted 50 m above the forest canopy at the Amazon Tall Tower site (59.0ºW, 2.1ºS), on a well-drained plateau. We minimized artifacts by maintaining the sun behind the camera, accepting only images with diffuse illumination, by giving equal weight to each tree crown and by radiometric inter-calibration of all selected images against a standard. For each of 267 individual crown areas we then prepared a greenness timeline of daily GC values. Eighty-two percent of these upper canopy crowns showed a clear episode of rapid and massive leaf flush (smoothed GC slope >0.01/month, sustained for at least 0.5 month and peaking above 75th percentile of daily GC values) and 30% showed massive rapid leaf drop (smoothed GC slope <-0.01/month, sustained for at least 0.5 month, ending below 25th percentile of daily GC values). Flushing was strongly concentrated in the five driest months (50% of all crowns) compared to the five wettest months (10% of all crowns). This contradicts the recently proposed photo-period control over humid equatorial tree phenology. Trees with both abrupt leaf drop and abrupt leaf flush reached minimum greenness 26 +/- 16 days prior to maximum greenness, indicating that leaf drop was a brief preamble to dry season leaf flush. / A fenologia foliar tem sido apontada como vetor da sazonalidade da capacidade fotossintética do dossel florestal e do fluxo de carbono na Amazônia Central. O monitoramento visual a partir do chão é demorado e difícil, sobretudo, contra um céu nublado. Mudanças sazonais nos índices de vegetação obtidos por sensores orbitais têm sido questionados devido a tendências sazonais no ângulo de iluminação solar. Câmeras RGB montadas em torres são uma atrativa opção. A coordenada cromática verde (GC = brilho no canal verde dividido pela soma do brilho dos três canais RGB) tem sido empregada para este propósito. Entretanto, sutis mudanças sazonais no GC das florestas tropicais em escala de paisagem podem ter menor amplitude do que os artefatos relacionados com o balanço de cores, tamanho da sombra das copas, ângulo de iluminação, velocidade de exposição e deriva do sensor. Além disso, mudanças graduais no GC são afetadas tanto pela idade das folhas quanto pela quantidade de folhas. Mesmo assim, mudanças abruptas e de grande amplitude no GC ocorrem em escala de copas individuais e podem ser atribuídas com confiança a eventos de massiva emissão ou perda foliar. Nós analisamos um ano de imagens diárias obtidas por uma câmera RGB instalada a 50 metros acima do dossel florestal no sítio Observatório de Torre Alta da Amazônia (59.00ºW, 2.14ºS), em um platô bem drenado. Nós minimizamos os artefatos mantendo o sol atrás da câmera, aceitando apenas imagens de iluminação difusa, dando peso igual a cada uma das copas e procedendo a intercalibração radiométrica de todas as imagens selecionadas contra uma imagem padrão. Para cada uma das 267 áreas de copas individuais, nós então preparamos uma linha temporal do verdor com valores diários de GC. Oitenta e dois por cento destas copas do dossel superior mostrou um claro episódio de rápido e massivo flush foliar (inclinação suavizada do GC > 0,01/mês, sustentada por pelo menos 15 dias, culminando acima do 75º percentil dos valores diários do GC) e 30% mostrou uma perda rápida e massiva de folhas (inclinação suavizada do GC < -0,01/mês, sustentada por pelo menos 15 dias, atingindo valores abaixo do 25º percentil dos valores diários do GC). O flush foliar foi fortemente concentrado nos cinco meses mais secos (50% de todas as copas) comparado com os cinco meses mais chuvosos (10% de todas as copas). Isto contradiz o recentemente proposto controle por fotoperíodo na fenologia foliar das árvores na região equatorial úmida. Árvores com abrupta perda e emissão de folhas atingiram o seu verdor mínimo 26 +/- 16 dias antes do verdor máximo, indicando que a perda de folhas foi um rápido preâmbulo para o flush foliar na estação seca.

Digital image processing is one of the potential technologies used in precision agriculture to gather information, such as seed emergence, plant health, and phenology from the digital images. Despite its potential, the rate of adoption is slow due to limited accessibility, unsuitability to specific issues, unaffordability, and high technical knowledge requirement from the clientele. Therefore, the development of open source image processing applications that are task-specific, easy-to-use, requiring fewer inputs, and rich with features will be beneficial to the users/farmers for adoption. The Fiji software, an open source free image processing ImageJ platform, was used in this application development study. A collection of four different agricultural field applications were selected to address the existing issues and develop image processing tools by applying novel approaches and simple mathematical principles. First, an automated application, using a digital image and “pixel-march” method, performed multiple radial measurements of sunflower floral components. At least 32 measurements for ray florets and eight for the disc were required statistically for accurate dimensions. Second, the color calibration of digital images addressed the light intensity variations of images using standard calibration chart and derived color calibration matrix from selected color patches. Calibration using just three-color patches: red, green, and blue was sufficient to obtain images of uniform intensity. Third, plant stand count and their spatial distribution from UAS images were determined with an accuracy of ≈96 %, through pixel-profile identification method and plant cluster segmentation. Fourth, the soybean phenological stages from the PhenoCam time-lapse imagery were analyzed and they matched with the manual visual observation. The green leaf index produced the minimum variations from its smoothed curve. The time of image capture and PhenoCam distances had significant effects on the vegetation indices analyzed. A simplified approach using kymograph was developed, which was quick and efficient for phenological observations. Based on the study, these tools can be equally applied to other scenarios, or new user-coded, user-friendly, image processing tools can be developed to address specific requirements. In conclusion, these successful results demonstrated the suitability and possibility of task-specific, open source, digital image processing tools development for agricultural field applications. / United States. Agricultural Research Service / National Institute of Food and Agriculture (U.S.)