Submitted by Erika Demachki (erikademachki@gmail.com) on 2016-08-17T20:54:27Z No. of bitstreams: 2 Dissertação - Mayklyns Marcos de Almeida Linhares - 2016.pdf: 5918802 bytes, checksum: cc18441a8f9fd258681505cc840eb5cf (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2016-08-18T12:45:05Z (GMT) No. of bitstreams: 2 Dissertação - Mayklyns Marcos de Almeida Linhares - 2016.pdf: 5918802 bytes, checksum: cc18441a8f9fd258681505cc840eb5cf (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2016-08-18T12:45:05Z (GMT). No. of bitstreams: 2 Dissertação - Mayklyns Marcos de Almeida Linhares - 2016.pdf: 5918802 bytes, checksum: cc18441a8f9fd258681505cc840eb5cf (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2016-07-15 / This study aimed to evaluate the performance of an Unmanned Aerial Vehicle (UAV) used to analysis of cultivated pasture areas. This evaluation was based on the comparison between the products of Vegetation Index calculated by orthomosaics from photos acquired by a UAV and the Percentage of Green collected in the ground level. The UAV Vegetation Index was the Modified Photochemical Reflectance Index (MPRI). It was held also a comparison between the Vegetation Indexes obtained with the UAV and an orbital platform being these the MPRI and the Normalized Difference Vegetation Index (NDVI) respectively. The data were collected monthly between July and December 2014. For comparison between the Percentage of Green and the MPRI was used 25 samples distributed along of transect established in ground level into the pasture area. For comparison between MPRI and NDVI other 15 samples were distributed along of the pasture area. The Percentage of Green relied on vertical photos acquired in the ground level. The MPRI resulted from aerial surveys done by UAV’s model “TIRIBA” that used the Canon PowerShot S100 camera coupled. The orthomosaics from aerial photos acquired on aerial surveys were generated in the Agisoft PhotoScan to calculate the MPRI after. The NDVI was calculated from surface reflectance images acquired by Landsat-8 satellite. Has been observed that Percentage of Green and MPRI are correlated and the same to be observed for MPRI and NDVI. There is also relationship in behavior of these products over time so was possible to conclude that both products could provide the necessary information for the characterization of the development of pasture between July and December 2014. Both products – Percentage of Green, MPRI and NDVI – feature advantages and limitations: the Percentage of Green is most sensitive for the changes into the pasture because of their detail high level (spatial resolution) but doesn’t have the income that the MPRI/UAV has and not provide the analysis of the whole area being necessary use to sampling; the MPRI/UAV has a higher sensitivity to record of the behavior of pasture when compared to NDVI/Landsat-8 bus the gain in the area imaged by orbital platform should not be disregarded. The correlation between Percentage of Green and MPRI and between MPRI and NDVI also suggests that these products can be used in conjunction to perform the monitoring of pasture areas. / Esta pesquisa teve por objetivo avaliar o desempenho de um Veículo Aéreo Não Tripulado (VANT) quando destinado à análise de áreas com pastagem cultivada. Tal avaliação teve por base a comparação entre o produto de Índice de Vegetação, calculado por intermédio de ortomosaicos de fotografias adquiridas por um VANT, e o parâmetro de Percentual de Verde coletado em solo. O Índice de Vegetação calculado com os dados do VANT foi o Modified Photochemical Reflectance Index (MPRI). Foi realizada também uma comparação entre os Índices de Vegetação obtidos com o VANT e por plataforma orbital, sendo esses índices o MPRI e o Normalized Difference Vegetation Index (NDVI), respectivamente. A coleta de dados foi realizada mensalmente entre julho e dezembro de 2014. Para a comparação entre o Percentual de Verde e o MPRI foram utilizadas 25 amostras distribuídas ao longo de um transecto estabelecido em solo. Para a comparação entre o MPRI e o NDVI foram utilizadas outras 15 amostras, distribuídas ao longo da área da pastagem. O Percentual de Verde se baseou em fotografias verticais obtidas em nível de solo. Já o MPRI resultou de aerolevantamentos com um VANT modelo TIRIBA, embarcado com uma câmera Canon PowerShot S100. Os ortomosaicos que serviram de base para o cálculo do MPRI foram gerados no programa Agisoft PhotoScan. O NDVI foi calculado com base em imagens de reflectância da superfície adquiridas pelo satélite Landsat-8. Como resultados observou-se que há correlação entre o Percentual de Verde e o MPRI, bem como entre o MPRI e o NDVI. Há também relação no comportamento desses produtos ao longo do tempo, de forma que foi possível concluir que ambos poderiam subsidiar as informações necessárias para a caracterização do desenvolvimento da pastagem em questão entre julho e dezembro de 2014. Concluiu-se ainda que ambos os produtos apresentam vantagens e limitações: o Percentual de Verde, em virtude de seu detalhamento (resolução espacial), é o mais sensível as alterações na pastagem, mas não tem o rendimento que o MPRI/VANT agrega por viabilizar a análise da área no todo, o que dispensa amostragem; o MPRI/VANT tem maior sensibilidade se comparado ao NDVI/Landsat-8 no registro do comportamento da pastagem, mas o ganho em área imageada pelo satélite não deve ser desconsiderado. A correlação entre o Percentual de Verde e o MPRI e entre o MPRI e o NDVI sugere também que esses produtos possam ser utilizados em conjunto no monitoramento de áreas de pastagens.

Les prédateurs marins supérieurs, ici les cétacés et les oiseaux marins, doivent développer des stratégies optimales d’utilisation des ressources et des habitats. Notre objectif était d’explorer leurs habitats en fonction de leurs coûts de vie. Nous avons postulé que les prédateurs coûteux étaient contraints d’occuper les habitats de meilleure qualité alors que les prédateurs plus économes pouvaient occuper les habitats de qualité moindre. Nous nous sommes basés sur des guildes de cétacés et oiseaux définies selon leurs coûts de vie et les observations de survols aériens dans trois régions tropicales (l’Atlantique Ouest tropical, le Sud Ouest de l’Océan Indien et la Polynésie française). Nous avons construit des modèles additifs généralisés à partir de variables physiographiques (ex : profondeur), océanographiques (ex : activité tourbillonnaire) et biologiques (ex : chlorophylle et micronecton) pour décrire la qualité des habitats. Nous avons d’abord modélisé les habitats des cétacés et oiseaux à l’échelle régionale. Les cétacés coûteux occupaient les habitats de meilleure qualité alors que les cétacés plus économes occupaient aussi les habitats de qualité moindre. La distribution des oiseaux reflétait principalement celle des colonies et leur dépendance à la qualité de l’habitat semblait moins claire. Nous avons ensuite mis en évidence des propriétés génériques de distribution des cétacés et fourni des prédictions circumtropicales. Cette thèse a donné un nouvel aperçu des stratégies d’utilisation des habitats des prédateurs supérieurs à la lumière de leurs coûts de vie. Ces prédictions spatiales ont des implications majeures pour la gestion de ces espèces et de leurs écosystèmes. / Marine top predators, here cetaceans and seabirds, must develop optimal strategies of resource and habitat utilization. The main goal of this dissertation was to investigate cetacean and seabird strategies of habitat utilization in relation to their energetic costs of living. We hypothesized that predators with high costs of living should be constrained to high quality habitats, whereas less active predators could cope with habitats of lesser quality. We studied the habitats of cetacean and seabird guilds defined according to their likely costs of living. We relied on sightings collected from aerial surveys in three tropical regions (the western tropical Atlantic, the Southwest Indian Ocean and French Polynesia). We built generalized additive models based on a range of physiographic (e.g. depth), oceanographic (e.g. mesoscale activity) and biological variables (e.g. chlorophyll concentration and micronekton) to describe the quality of pelagic habitats. We first modeled cetacean and seabird habitats at the regional scale. Energetically costly cetaceans appeared to be constrained to the highest quality habitats, whereas less active cetaceans exploited habitats of lesser quality. Seabird distributions primarily reflected colony locations and their dependences on habitat quality were less clear. We then highlighted generic properties of cetacean distributions and provided predictions at the circumtropical scale. This dissertation gave new insights on top predator strategies of habitats utilization in light of their costs of living. These spatial predictions have significant implications for the management of these species and of their pelagic ecosystems.

The largest Florida manatee (Trichechus manatus latirostris) aggregation at a natural warm-water refuge occurs in Kings Bay, Crystal River, FL. Over the last 32 years, the U.S. Fish and Wildlife Service and the State of Florida have created a network of manatee protection areas within Kings Bay including a year-round refuge designation and seven Federal manatee sanctuaries during the winter manatee season (November 15 – March 31). Aerial survey data collected between 1983 and 2012 was used to examine the seasonal change in manatee distribution within Kings Bay in order to assess the effectiveness of current sanctuary sizes and locations. Regression analysis indicated a significant change in manatee abundance among the winter seasons (p < 0.05). The average winter manatee counts increased by 4.81 animals per year over the 30 year period. In contrast, no significant changes in average or peak manatee abundance was detected among the summer seasons (p = 0.71 and p = 0.45 respectively). The average manatee counts increased by only 0.109 animals per year over the summer periods. Spatially explicit models using Geographic Information System (GIS) analysis revealed a strong correlation between high manatee density and artesian springs during the winter seasons. Highest abundances were identified at three locations: King’s Spring, Three Sisters Springs, and Magnolia Springs. These three locations coincide well with pre-existing sanctuary designations, but additional coverage is needed to support the overflow of manatees outside of sanctuary boundaries. Manatees continued to use Kings Bay in the summer seasons but in lower numbers and densities. Because density patterns were not uniform across summer periods, a heavier reliance on boat speed regulation is recommended to provide adequate protection to the endangered Florida manatee. Within a habitat type, the Magnolia Springs, South Banana Island, and Three Sisters Springs sanctuaries exhibited a significant influence on manatee density, suggesting differences in quality among sanctuaries. Years coinciding with extreme cold weather events also had a significant influence on manatee density. Using GIS to investigate seasonal shifts in manatees can be very informative regarding many issues including habitat selection and may improve the design and management of protected areas.