Application of remote sensing technology in South Dakota to assess wildlife habitat change, describe meandering lakes, improve agricultural censusing, map Aspen, and quantify cell selection criteria for spatial data

There are no author-identified significant results in this report

Remote sensing applications to resource problems in South Dakota

The author has identified the following significant results. Change in the vegetative structure was taking place in the Black Hills. Temporal analysis of the areal extent of open meadows was accomplished using black and white and color infrared aerial photography. A reduction of nearly 1100 hectares of open meadows was determined using photointerpretation. Techniques were developed for the management of meandering lakes, including use of LANDSAT imagery for continuous monitoring, classification of hydrophytes on low altitude CIR imagery, and planning and evaluation of improvements and multiple uses on aerial photography and photo mosaics. LANDSAT data were analyzed statistically from small and entire study scene areas to determine the effect of soils stratifications of corn signatures. Band 5 early season and band 7 later season recorded the strongest evidence of the influence of soils on corn signatures. Significant strata were determined by a multiple range test

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

Cornell University remote sensing program

Aircraft and satellite remote sensing technology were applied in the following areas: (1) evaluation of proposed fly ash disposal sites; (2) development of priorities for drainage improvements; (3) state park analysis for rehabilitation and development; (4) watershed study for water quality planning; and (5) assistance project-landfill site selection. Results are briefly summarized. Other projects conducted include: (1) assessment of vineyard-related problems; (2) LANDSAT analysis for pheasant range management; (3) photo-historic evaluation of Revolutionary War sites; and (4) thermal analysis of building insulation. The objectives, expected benefits and actions, and status of these projects are described

Remote sensing in Michigan for land resource management

The application of NASA earth resource survey technology to resource management and environmental protection in Michigan was investigated. Remote sensing techniques to aid Michigan government agencies were applied in the following activities: (1) land use inventory and management, (2) great lakes shorelands protection and management, (3) wetlands protection and management, and (4) soil survey. In addition, information was disseminated on remote sensing technology, and advice and assistance was provided to a number of users

Vegetation Detection and Classification for Power Line Monitoring

Electrical network maintenance inspections must be regularly executed, to provide a continuous distribution of electricity. In forested countries, the electrical network is mostly located within the forest. For this reason, during these inspections, it is also necessary to assure that vegetation growing close to the power line does not potentially endanger it, provoking forest fires or power outages. Several remote sensing techniques have been studied in the last years to replace the labor-intensive and costly traditional approaches, be it field based or airborne surveillance. Besides the previously mentioned disadvantages, these approaches are also prone to error, since they are dependent of a human operator’s interpretation. In recent years, Unmanned Aerial Vehicle (UAV) platform applicability for this purpose has been under debate, due to its flexibility and potential for customisation, as well as the fact it can fly close to the power lines. The present study proposes a vegetation management and power line monitoring method, using a UAV platform. This method starts with the collection of point cloud data in a forest environment composed of power line structures and vegetation growing close to it. Following this process, multiple steps are taken, including: detection of objects in the working environment; classification of said objects into their respective class labels using a feature-based classifier, either vegetation or power line structures; optimisation of the classification results using point cloud filtering or segmentation algorithms. The method is tested using both synthetic and real data of forested areas containing power line structures. The Overall Accuracy of the classification process is about 87% and 97-99% for synthetic and real data, respectively. After the optimisation process, these values were refined to 92% for synthetic data and nearly 100% for real data. A detailed comparison and discussion of results is presented, providing the most important evaluation metrics and a visual representations of the attained results.Manutenções regulares da rede elétrica devem ser realizadas de forma a assegurar uma distribuição contínua de eletricidade. Em países com elevada densidade florestal, a rede elétrica encontra-se localizada maioritariamente no interior das florestas. Por isso, durante estas inspeções, é necessário assegurar também que a vegetação próxima da rede elétrica não a coloca em risco, provocando incêndios ou falhas elétricas. Diversas técnicas de deteção remota foram estudadas nos últimos anos para substituir as tradicionais abordagens dispendiosas com mão-de-obra intensiva, sejam elas através de vigilância terrestre ou aérea. Além das desvantagens mencionadas anteriormente, estas abordagens estão também sujeitas a erros, pois estão dependentes da interpretação de um operador humano. Recentemente, a aplicabilidade de plataformas com Unmanned Aerial Vehicles (UAV) tem sido debatida, devido à sua flexibilidade e potencial personalização, assim como o facto de conseguirem voar mais próximas das linhas elétricas. O presente estudo propõe um método para a gestão da vegetação e monitorização da rede elétrica, utilizando uma plataforma UAV. Este método começa pela recolha de dados point cloud num ambiente florestal composto por estruturas da rede elétrica e vegetação em crescimento próximo da mesma. Em seguida,múltiplos passos são seguidos, incluindo: deteção de objetos no ambiente; classificação destes objetos com as respetivas etiquetas de classe através de um classificador baseado em features, vegetação ou estruturas da rede elétrica; otimização dos resultados da classificação utilizando algoritmos de filtragem ou segmentação de point cloud. Este método é testado usando dados sintéticos e reais de áreas florestais com estruturas elétricas. A exatidão do processo de classificação é cerca de 87% e 97-99% para os dados sintéticos e reais, respetivamente. Após o processo de otimização, estes valores aumentam para 92% para os dados sintéticos e cerca de 100% para os dados reais. Uma comparação e discussão de resultados é apresentada, fornecendo as métricas de avaliação mais importantes e uma representação visual dos resultados obtidos

Automatic detection of powerlines in UAV remote sensed images

Powerline detection is one of the important applications of Uninhabited Aerial Vehicle (UAV ) based remote sensing. In this paper, powerlines are detected from UAV remote sensed images. The images are acquired from a Quad rotor UAV fitted with a GoPro® camera. In the proposed method pixel intensity-based clustering is performed followed by morphological operations. K-means clustering is applied for clustering. The number of clusters to be used in k-means clustering is automatically generated using Davies-Bouldin (DB) index. Further, the clustered data is processed to improvise the extraction using mathematical morphological operations. Performance of powerline extraction is analysed using confusion matrix method. In the observed results of powerline extraction using DB index, evaluation features derived from confusion matrix is close to one, indicating good classification

Evaluation of Skylab (EREP) data for forest and rangeland surveys

The author has identified the following significant results. Four widely separated sites (near Augusta, Georgia; Lead, South Dakota; Manitou, Colorado; and Redding, California) were selected as typical sites for forest inventory, forest stress, rangeland inventory, and atmospheric and solar measurements, respectively. Results indicated that Skylab S190B color photography is good for classification of Level 1 forest and nonforest land (90 to 95 percent correct) and could be used as a data base for sampling by small and medium scale photography using regression techniques. The accuracy of Level 2 forest and nonforest classes, however, varied from fair to poor. Results of plant community classification tests indicate that both visual and microdensitometric techniques can separate deciduous, conifirous, and grassland classes to the region level in the Ecoclass hierarchical classification system. There was no consistency in classifying tree categories at the series level by visual photointerpretation. The relationship between ground measurements and large scale photo measurements of foliar cover had a correlation coefficient of greater than 0.75. Some of the relationships, however, were site dependent

Third ERTS Symposium: Abstracts

Abstracts are provided for the 112 papers presented at the Earth Resources Program Symposium held at Washington, D.C., 10-14 December, 1973