Measuring, Modeling, and Evaluating the Spatial Properties of Northeast Oregon Forests Using Unmanned Aerial Systems

There is an ever expanding range of applications for the aerial images that unmanned aerial systems can uniquely provide. One such application is the use of high-resolution imagery for stand-level forest inventory. Inventory techniques utilizing unmanned aerial systems could be attractive where conditions demand high-resolution data, or where other aerial imagery sources are cost prohibitive. Here the effectiveness of unmanned aerial systems in this application was tested. Over the summer of 2015, a remote-controlled hexacopter equipped with a micro four thirds camera was flown over multiple 1600 meter-squared forested plots in Eastern Oregon. Additional ground-level validation measurements were collected including stem location, crown radius, and tree height. Agisoft Photoscan was used to construct 3-D point-clouds which then allowed the production of digital surface models of the stands. The first section of this project assesses the accuracy of stem locations derived from segmented imagery. The next section evaluates the accuracy of estimates for tree height, crown radius, and diameter at breast height. In the final section, various spatial metrics such as stand contagion and species mingling were compared with more commonly used metrics to see if significant correlations emerged. The utilized methods did not yield sufficiently accurate estimates for stem location or the various forest biometrics. Yet this work revealed stand density to be a significant influence on model accuracy. Finally, stand density and species diversity were found to be well correlated with the nearest neighbor and species mingling indexes, respectively, potentially supporting a complementary relationship indicating the clustering of various factors within the stand

Remote Sensing methods for power line corridor surveys

AbstractTo secure uninterrupted distribution of electricity, effective monitoring and maintenance of power lines are needed. This literature review article aims to give a wide overview of the possibilities provided by modern remote sensing sensors in power line corridor surveys and to discuss the potential and limitations of different approaches. Monitoring of both power line components and vegetation around them is included. Remotely sensed data sources discussed in the review include synthetic aperture radar (SAR) images, optical satellite and aerial images, thermal images, airborne laser scanner (ALS) data, land-based mobile mapping data, and unmanned aerial vehicle (UAV) data. The review shows that most previous studies have concentrated on the mapping and analysis of network components. In particular, automated extraction of power line conductors has achieved much attention, and promising results have been reported. For example, accuracy levels above 90% have been presented for the extraction of conductors from ALS data or aerial images. However, in many studies datasets have been small and numerical quality analyses have been omitted. Mapping of vegetation near power lines has been a less common research topic than mapping of the components, but several studies have also been carried out in this field, especially using optical aerial and satellite images. Based on the review we conclude that in future research more attention should be given to an integrated use of various data sources to benefit from the various techniques in an optimal way. Knowledge in related fields, such as vegetation monitoring from ALS, SAR and optical image data should be better exploited to develop useful monitoring approaches. Special attention should be given to rapidly developing remote sensing techniques such as UAVs and laser scanning from airborne and land-based platforms. To demonstrate and verify the capabilities of automated monitoring approaches, large tests in various environments and practical monitoring conditions are needed. These should include careful quality analyses and comparisons between different data sources, methods and individual algorithms

Fernando Jorge Pedro da Silva Pinto da Rocha e pelo Dr.

As florestas desempenham um papel de extrema importância na sociedade e, consequentemente, o conhecimento sobre ela é essencial para a sua gestão e usufruto sustentável. A recolha de informação florestal é um processo complexo, tradicionalmente alcançado através de inventários florestais. A Deteção Remota tem sofrido um desenvolvimento crescente que tem vindo a possibilitar soluções inovadoras e eficazes em diversos campos do conhecimento, nomeadamente no que diz respeito à gestão florestal. A Individualização de Copas de Árvores (ICA) a partir de fotografia aérea é uma operação que poderá facilitar a recolha de informação florestal, permitindo a obtenção indireta de parâmetros físicos das árvores, medidas estas de extrema importância para a gestão florestal. Assim, o presente relatório de estágio tem como principal objetivo explorar diversos algoritmos que permitem a ICA de forma mais automatizada, num sistema florestal característico de Portugal: o Montado. Trata-se de um sistema florestal único no contexto nacional, no qual a monotorização e correta gestão florestal pode ser melhorada e desenvolvida a partir de novas metodologias. Neste seguimento foi utilizada como base de trabalho uma fotografia aérea de alta resolução correspondente a uma zona de montado e uma metodologia de trabalho dividida em três fases distintas: a construção de objetos representados na imagem, uma classificação por limares para seleção dos objetos correspondentes a copas de árvores e a validação da cartografia de Copas de Árvores obtida. Foram aplicados cinco algoritmos que permitiram a construção dos objetos representados na imagem, assentes em bases técnicas distintas. Desde os mais usualmente utilizados nesta tarefa, baseados na segmentação de imagens - Watershed, Crescimento de Regiões e Multiresolução – até a técnicas mais avançadas, como os algoritmos de aprendizagem automática (machine learning) - Random Forests e Máxima Entropia. Na fase seguinte foram selecionados os objetos que correspondiam a copas de árvores através de um processo de classificação por limiares, fazendo uso de um conjunto de variáveis espectrais e morfológicas de interesse.Foram produzidos mapas de Copas de Árvores para cada algoritmo, verificando-se para os algoritmos de segmentação uma exatidão global entre 0,67 - 0,73 e coeficiente de concordância Kappa abaixo de 0,46 e para os algoritmos de machine learning uma exatidão global entre 0,79 - 0,81 e coeficiente de concordância Kappa acima de 0,55. Estes últimos demonstraram-se assim promissores na tarefa de ICA, em detrimento dos já convencionalmente aplicados.Forests play an important role on world’s society and, as a result, the knowledge about them is essential for their sustainable management and use. Forest data collection is a complex task, traditionally achieved by forest inventory. Remote Sensing has undergone a growing development, which has enabled new and effective solutions in several fields of knowledge, especially regarding to forest management. Tree Crown Individualization (TCI) from aerial photography can make forest data collection an easier task, allowing indirect data collection of tree measures, which are extremely important for forest management. Therefore, the main purpose of this work is to explore several algorithms that allow TCI from remote sensing data, in a more automated way, in a characteristic Portuguese forest: the cork oak forest. It is a national unique forest, in which monitoring and correct forest management can be of improved and developed through new methodologies. In order to achieve the presented purpose, was developed a methodology divided into three phases, to apply to a high-resolution aerial photography: object construction phase, threshold classification phase for selection of tree crown objects and the Tree Crown Cartography validation phase. For the first phase, five algorithms were applied to build image objects. From the most commonly used, based on image segmentation – Watershed, Region Growing and Multiresolution – to more advanced techniques, such as machine learning algorithms – Random Forests and Maximum Entropy. Later, objects corresponding to tree crowns were selected by a threshold classification, making use of a variable set of interest. Tree Crown maps were produced for each algorithm used, with a global accuracy between 0,67-0,73 and a Kappa coefficient of agreement below 0,46 for image segmentation algorithms, and a global accuracy between 0,79-0,81 and a Kappa coefficient of agreement above 0,55 for machine learning algorithms. Thus, the later ones have shown promising results in the TCI task, in opposition to those conventionally applied

Deep Learning Methods for Remote Sensing

Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing

Towards automatic tree crown detection and delineation in spectral feature space using PCNN and morphological reconstruction

The application of object-based approaches to the problem of extracting vegetation information from images requires accurate delineation of individual tree crowns. This paper presents an automated method for individual tree crown detection and delineation by applying a simplified PCNN model in spectral feature space followed by post-processing using morphological reconstruction. The algorithm was tested on high resolution multi-spectral aerial images and the results are compared with two existing image segmentation algorithms. The results demonstrate that our algorithm outperforms the other two solutions with the average accuracy of 81.8%