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Remote Sensing of Impervious Surfaces in the Urban Areas: Requirements,
- Methods, and Trends.” Remote Sensing of Environment
, 2012
"... Urban mapping requirements Pixel-based algorithms Sub-pixel based algorithms Object-oriented method Artificial neural networks The knowledge of impervious surfaces, especially the magnitude, location, geometry, spatial pattern of impervious surfaces and the perviousness-imperviousness ratio, is sig ..."
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Cited by 37 (1 self)
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Urban mapping requirements Pixel-based algorithms Sub-pixel based algorithms Object-oriented method Artificial neural networks The knowledge of impervious surfaces, especially the magnitude, location, geometry, spatial pattern of impervious surfaces and the perviousness-imperviousness ratio, is significant to a range of issues and themes in environmental science central to global environmental change and human-environment interactions. Impervious surface data is important for urban planning and environmental and resources management. Therefore, remote sensing of impervious surfaces in the urban areas has recently attracted unprecedented attention. In this paper, various digital remote sensing approaches to extract and estimate impervious surfaces will be examined. Discussions will focus on the mapping requirements of urban impervious surfaces. In particular, the impacts of spatial, geometric, spectral, and temporal resolutions on the estimation and mapping will be addressed, so will be the selection of an appropriate estimation method based on remotely sensed data characteristics. This literature review suggests that major approaches over the past decade include pixel-based (image classification, regression, etc.), sub-pixel based (linear spectral unmixing, imperviousness as the complement of vegetation fraction etc.), object-oriented algorithms, and artificial neural networks. Techniques, such as data/image fusion, expert systems, and contextual classification methods, have also been explored. The majority of research efforts have been made for mapping urban landscapes at various scales and on the spatial resolution requirements of such mapping. In contrast, there is less interest in spectral and geometric properties of impervious surfaces. More researches are also needed to better understand temporal resolution, change and evolution of impervious surfaces over time, and temporal requirements for urban mapping. It is suggested that the models, methods, and image analysis algorithms in urban remote sensing have been largely developed for the imagery of medium resolution (10-100 m). The advent of high spatial resolution satellite images, spaceborne hyperspectral images, and LiDAR data is stimulating new research idea, and is driving the future research trends with new models and algorithms.
Synergistic Use of Lidar and Color Aerial Photography for Mapping Urban Parcel Imperviousness
"... The imperviousness of land parcels was mapped and evaluated using high spatial resolution digitized color orthophotography and surface-cover height extracted from multiple-return lidar data. Maximum-likelihood classification, spectral clustering, and expert system approaches were used to extract the ..."
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Cited by 27 (0 self)
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The imperviousness of land parcels was mapped and evaluated using high spatial resolution digitized color orthophotography and surface-cover height extracted from multiple-return lidar data. Maximum-likelihood classification, spectral clustering, and expert system approaches were used to extract the impervious information from the datasets. Classified pixels (or segments) were aggregated to parcels. The classification model based on the use of both the orthophotography and lidar-derived surface-cover height yielded impervious surface results for all parcels that were within 15 percent of reference data. The standard error for the rule-based per-pixel model was 7.15 percent with a maximum observed error of 18.94 percent. The maximum-likelihood per-pixel classification yielded a lower standard error of 6.62 percent with a maximum of 14.16 percent. The regression slope (i.e., 0.955) for the maximum-likelihood per-pixel model indicated a near perfect relationship between observed and predicted imperviousness. The additional effort of using a per-segment approach with a rule-based classification resulted in slightly better standard error (5.85 percent) and a near-perfect regression slope (1.016).
Cartography and Geographic Information Science How users interact with a 3D geo-browser under time pressure
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Comparing rapid scene categorization of aerial and terrestrial views: A new perspective on scene gist
"... Scene gist, a viewer's holistic representation of a scene from a single eye fixation, has been extensively studied for terrestrial views, but not for aerial views. We compared rapid scene categorization of both views in three experiments to determine the degree to which diagnostic information ..."
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Scene gist, a viewer's holistic representation of a scene from a single eye fixation, has been extensively studied for terrestrial views, but not for aerial views. We compared rapid scene categorization of both views in three experiments to determine the degree to which diagnostic information is view dependent versus view independent. We found large differences in observers' ability to rapidly categorize aerial and terrestrial scene views, consistent with the idea that scene gist recognition is viewpoint dependent. In addition, computational modeling showed that training models on one view (aerial or terrestrial) led to poor performance on the other view, thereby providing further evidence of viewpoint dependence as a function of available information. Importantly, we found that rapid categorization of terrestrial views (but not aerial views) was strongly interfered with by image rotation, further suggesting that terrestrial-view scene gist recognition is viewpoint dependent, with aerial-view scene recognition being viewpoint independent. Furthermore, rotationinvariant texture images synthesized from aerial views of scenes were twice as recognizable as those synthesized from terrestrial views of scenes (which were at chance), providing further evidence that diagnostic information for rapid scene categorization of aerial views is viewpoint invariant. We discuss the results within a perceptual-expertise framework that distinguishes between configural and featural processing, where terrestrial views are more effectively processed due to their predictable view-dependent configurations whereas aerial views are processed less effectively due to reliance on view-independent features.
Potential of Digital Color Imagery for Censusing Haleakala
"... Spatially explicit, high spatial resolution remotely sensed imagery offers a largely untapped potential for censusing and monitoring rare plant populations that exist in remote, exposed environments. Using digital color imagery acquired over the Haleakala Crater on Maui, Hawai’i, we evaluated the ac ..."
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Spatially explicit, high spatial resolution remotely sensed imagery offers a largely untapped potential for censusing and monitoring rare plant populations that exist in remote, exposed environments. Using digital color imagery acquired over the Haleakala Crater on Maui, Hawai’i, we evaluated the accuracy of photointerpretation and automated cen-suses by imaging nine silversword census plots character-ized by individuals of known size, life cycle status, and lo-cation. Due to spatial resolution limitations, both methods tended to omit small individuals, but omissions varied by size class and type of omission. Omission rates were low for demographically important medium and large plants; how-ever, the automated method often failed to segment and census tightly clustered plants. The photointerpreter com-mission error rate was lower than that of the automated method, and both methods tended to overestimate mean sil-versword size. These data outline the issues and challenges that will likely emerge as spatially explicit, high spatial res-olution aerial censuses become more common.
