자연주의적 탐구조사(Naturalistic Inquiry)

Precision agriculture (PA) is the application of geospatial techniques and sensors (e.g., geographic information systems, remote sensing, GPS) to identify variations in the field and to deal with them using alternative strategies. In particular, high-resolution satellite imagery is now more commonly used to study these variations for crop and soil conditions. However, the availability and the often prohibitive costs of such imagery would suggest an alternative product for this particular application in PA. Specifically, images taken by low altitude remote sensing platforms, or small unmanned aerial systems (UAS), are shown to be a potential alternative given their low cost of operation in environmental monitoring, high spatial and temporal resolution, and their high flexibility in image acquisition programming. Not surprisingly, there have been several recent studies in the application of UAS imagery for PA. The results of these studies would indicate that, to provide a reliable end product to farmers, advances in platform design, production, standardization of image georeferencing and mosaicing, and information extraction workflow are required. Moreover, it is suggested that such endeavors should involve the farmer, particularly in the process of field design, image acquisition, image interpretation and analysis.

The application of small unmanned aerial systems for precision agriculture: a review

Classifying and valuing ecosystem services for urban planning

Aims Mapping vegetation through remotely sensed images involves various considerations, processes and techniques. Increasing availability of remotely sensed images due to the rapid advancement of remote sensing technology expands the horizon of our choices of imagery sources. Various sources of imagery are known for their differences in spectral, spatial, radioactive and temporal characteristics and thus are suitable for different purposes of vegetation mapping. Generally, it needs to develop a vegetation classification at first for classifying and mapping vegetation cover from remote sensed images either at a community level or species level. Then, correlations of the vegetation types (communities or species) within this classification system with discernible spectral characteristics of remote sensed imagery have to be identified. These spectral classes of the imagery are finally translated into the vegetation types in the image interpretation process, which is also called image processing. This paper presents an overview of how to use remote sensing imagery to classify and map vegetation cover. Methods Specifically, this paper focuses on the comparisons of popular remote sensing sensors, commonly adopted image processing methods and prevailing classification accuracy assessments. Important findings The basic concepts, available imagery sources and classification techniques of remote sensing imagery related to vegetation mapping were introduced, analyzed and compared. The advantages and limitations of using remote sensing imagery for vegetation cover mapping were provided to iterate the importance of thorough understanding of the related concepts and careful design of the technical procedures, which can be utilized to study vegetation cover from remote sensed images.

Remote sensing imagery in vegetation mapping: a review

*Ecologists require spatially explicit data to relate structure to function. To date, heavy reliance has been placed on obtaining such data from remote-sensing instruments mounted on spacecraft or manned aircraft, although the spatial and temporal resolutions of the data are often not suited to local-scale ecological investigations. Recent technological innovations have led to an upsurge in the availability of unmanned aerial vehicles (UAVs) – aircraft remotely operated from the ground – and there are now many lightweight UAVs on offer at reasonable costs. Flying low and slow, UAVs offer ecologists new opportunities for scale-appropriate measurements of ecological phenomena. Equipped with capable sensors, UAVs can deliver fine spatial resolution data at temporal resolutions defined by the end user. Recent innovations in UAV platform design have been accompanied by improvements in navigation and the miniaturization of measurement technologies, allowing the study of individual organisms and their spatiotemporal dynamics at close range.

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Lightweight unmanned aerial vehicles will revolutionize spatial ecology

The effect of urban leaf area on summertime urban surface kinetic temperatures: A Terre Haute case study

Although potential applications abound, small-scale unmanned aerial vehicles have not yet been widely used for environmental remote sensing. Several challenges remain to be overcome until widesprea...

Small-Scale Unmanned Aerial Vehicles in Environmental Remote Sensing: Challenges and Opportunities

This study examines urban quality of life by assessing the relationship between observed socioeconomic conditions and urban forest amenities in Terre Haute, Indiana, USA. Using remote-sensing methods and techniques, and ordinary least squares regression, the paper determines the relationship between urban leaf area and a population density parameter with median income and median housing value. Results demonstrate positive correlations between urban leaf area, population density, and their interaction with median income and median housing value. Furthermore, leaf area, density, and their interaction statistically account for observed variance in median income and median housing value, indicating that these variables may be used to study observed quality-of-life metrics. The methods used in this study may be useful to city managers, planners, and foresters who are concerned with urban quality-of-life issues, and who are interested in developing and implementing alternative policy assessment regimes.

Using Remote Sensing and Geographic Information Systems to Study Urban Quality of Life and Urban Forest Amenities

Mangrove wetlands are economically and ecologically important ecosystems and accurate assessment of these wetlands with remote sensing can assist in their management and conservation. This study was conducted to evaluate airborne AISA hyperspectral imagery and image transformation and classification techniques for mapping black mangrove populations on the south Texas Gulf coast. AISA hyperspectral imagery was acquired from two study sites and both minimum noise fraction (MNF) and inverse MNF transforms were performed. Four classification methods, including minimum distance, Mahalanobis distance, maximum likelihood, and spectral angle mapper (SAM), were applied to the noise-reduced hyperspectral imagery and to the band-reduced MNF imagery for distinguishing black mangrove from associated plant species and other cover types. Accuracy assessment showed that overall accuracy varied from 84 percent to 95 percent for site 1 and from 69 percent to 91 percent for site 2 among the eight classifications for each site. The MNF images provided similar or better classification results compared with the hyperspectral images among the four classifiers. Kappa analysis showed that there were no significant differences among the four classifiers with the MNF imagery, though maximum likelihood provided excellent overall and class accuracies for both sites. Producer’s and user’s accuracies for black mangrove were 91 percent and 94 percent, respectively, for site 1 and both 91 percent for site 2 based on maximum likelihood applied to the MNF imagery. These results indicate that airborne hyperspectral imagery combined with image transformation and classification techniques can be a useful tool for monitoring and mapping black mangrove distributions in coastal environments.

Evaluating AISA+ hyperspectral imagery for mapping black mangrove along the South Texas Gulf coast.

Hardin and Jensen (2011) presented six challenges to using small Unmanned Aerial Systems (sUAS) for environmental remote sensing: challenge of the hostile flying environment, challenge of power, ch...

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Ryan R. Jensen

Papers

The effect of urban leaf area on summertime urban surface kinetic temperatures: A Terre Haute case study

Small-Scale Unmanned Aerial Vehicles in Environmental Remote Sensing: Challenges and Opportunities

Using Remote Sensing and Geographic Information Systems to Study Urban Quality of Life and Urban Forest Amenities

Evaluating AISA+ hyperspectral imagery for mapping black mangrove along the South Texas Gulf coast.

Small Unmanned Aerial Systems (sUAS) for environmental remote sensing: challenges and opportunities revisited