Veredas are wetlands of relevant ecological and social value that may be closely related to the maintenance of the water regime of the springs. Remotely Piloted Aircraft Systems (RPAS) have proved to be great allies in the space-time monitoring of wetlands. This study evaluates the effectiveness of multispectral sensors attached to an RPAS to discriminate habitats from paths through the Object-Based Image Analysis (OBIA) approach. Multispectral camera overflights were performed on September 25, 2020 (dry) and January 28, 2021 (wet). Radiometrically corrected orthomosaics were generated with five spectral bands. Multiscale segmentations were applied, and later the classification by the OBIA approach through the classifier of the nearest neighbor, the results were post-processed by applying the algorithm of a class assignment. The classification separated the objects into 14 and 12 classes with an overall accuracy of 92.21% and 88.01% (kappa 0.92 and 0.87), for September and January, respectively. Among these, are the phytophysiognomies of Cerrado stricto sensu (surrounding) and Gallery forest (centralized), in addition to eight classes of habitats in the vereda. The multispectral sensor was sensitive to differentiate these habitats in the vereda and the occurrence of areas covered by the pteridophyte Dicranopteris flexuosa, its distribution, and physiological stages. The classification of two seasonal seasons made it possible to characterize the behavior of habitats according to water availability. The multispectral sensor on board the RPAS is a powerful tool to determine the diagnosis and management of wetlands, contributing to the establishment of public policies for the conservation of vereda environments.
RESUMOO traçado urbano do centro histórico de Cáceres é caracterizado por ruas e calçadas estreitas, onde o ordenamento espacial era focado nas construções geralmente sem recuo frontal ou lateral, típico das edificações coloniais. No entanto, devido às mudanças ocorridas na dinâmica das cidades é importante ressaltarmos a preocupação com a arborização, principalmente, na região dos centros históricos, onde inicialmente a estrutura verde não era uma prioridade nas tomadas de decisão. O objetivo do seguinte trabalho foi fazer um levantamento da situação da arborização e sua relação com o desenho urbano no centro histórico de Cáceres-MT. Para a realização do trabalho, primeiramente foi feito um inventário arbóreo, em seguida verificada a situação fitossanitária e a relação das árvores com o desenho urbano. A área inventariada no centro da cidade possui 127 indivíduos, dos quais 56,79% são nativas e 43,21% exóticas. As condições fitossanitárias dos indivíduos apresentaram 61,42% em condições sadias e 38,58%, com injúrias mecânicas, pragas ou doenças. Em relação ao desenho urbano, apenas 15 árvores, 11,6%, conflitam com a fiação elétrica. As ruas possuíam dois tipos diferentes de pavimentação em boas condições, as calçadas sofreram danos causados pelas raízes das árvores, devido à falta de manutenção em geral. O centro histórico da cidade de Cáceres tem grande potencial para implementar um plano de arborização urbana, as mais importantes adequações devem ser a padronização dos tipos de pavimentos, largura das calçadas e plantio de espécies adequadas.
In our planet there are thousands of plant species, being important to catalog these to help in the biodiversity preservation. However, identifying various plant species is not an easy task, even for specialists. Methods of computer vision for identifying plant species are interesting solutions for these difficulties. This work aims to analyze the efficiency of texture feature extraction methods applied in the identification of plant species by means of images of its leaves. For this, different texture descriptors were applied in three different databases. The obtained results indicate that local phase quantization (LPQ)-based methods achieve great efficiency and robustness. Additionally, the combination of LPQ-based methods with a segmentation based fractal texture analysis (SFTA) has increased the correct classification rate in all databases.
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