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This review describes the National Agriculture Imagery Program (NAIP), explores strengths and weaknesses of the data, and summarizes how the data are used in land cover and feature extraction tasks in order to provide some recommendations for future research and best practices for working with NAIP data. NAIP orthoimagery is an often-overlooked source for remote sensing classification and feature extraction applications in the contiguous United States (CONUS). NAIP data are free, or nearly free; are in the public domain; are available for all of the CONUS; comprise a multitemporal data set that spans more than a decade; and are collected at a high spatial resolution with generally very low cloud coverage. However, there are challenges associated with the use of these data. The low spectral resolution limits spectral differentiation, while the high spatial resolution results in very large data sets for study areas even as small as a single county. Differences in acquisition dates and time of day result in varying illumination conditions and potentially even varying phenological state. As a consequence, image digital number (DN) values can vary between adjacent tiles, and shadow size and direction can be inconsistent between different tiles and different acquisitions. Therefore, taking full advantage of this valuable data source requires the analyst to be cognizant of such concerns and take measures to deal with such inconsistency and minimize its impact on the classification results; future research addressing these concerns would further enhance the value of NAIP data. [ABSTRACT FROM AUTHOR]

Ailanthus altissima is an invasive, dioecious deciduous tree common at the interface between urban and rural areas in the mid-Atlantic region, U.S.A. To examine spatial patterns of abundance and associations with land use type, we mapped all mature female trees in nine 89.5 ha plots (805.5 ha total area) across a typical urban-to-rural land use gradient using aerial images obtained via remote sensing supplemented by detailed ground referencing. Rural plots were dominated by forest and had the lowest density of mature females (0.007 females ha-1); urban and suburban plots did not differ significantly in mean density (0.37 females ha-1 vs. 0.34 females ha-1, respectively). Individuals in urban plots were more evenly distributed, but were not associated with a wider variety of land uses and were closer to roads or openings than those in suburban plots. Given less available habitat per unit area in urban than in suburban environments, these patterns suggest that Ailanthus fits the profile of an invasive species that may be proliferating outward from urban centers. With continued disturbances associated with development in the suburban areas, and timber harvesting in the rural areas, further spread of Ailanthus seems likely. [ABSTRACT FROM AUTHOR]

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 Maul, Hawai'i, we evaluated the accuracy of photointerpretation and automated censuses by imaging nine silversword census plots characterized by individuals of known size, life cycle status, and location. 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; however, the automated method often failed to segment and census tightly clustered plants. The photointerpreter commission error rate was lower than that of the automated method, and both methods tended to overestimate mean silversword size. These data outline the issues and challenges that will likely emerge as spatially explicit, high spatial resolution aerial censuses become more common. [ABSTRACT FROM AUTHOR]

Leaf-off individual trees in a deciduous forest in the eastern USA are detected and analysed in small footprint, high sampling density lidar data. The data were acquired February 1, 2001, using a SAAB TopEye laser profiling system, with a sampling density of approximately 12 returns per square meter. The sparse and complex configuration of the branches of the leaf-off forest provides sufficient returns to allow the detection of the trees as individual objects and to analyse their vertical structures. Initially, for the detection of the individual trees only, the lidar data are first inserted in a 2D digital image, with the height as the pixel value or brightness level. The empty pixels are interpolated, and height outliers are removed. Gaussian smoothing at different scales is performed to create a three-dimensional scale-space structure. Blob signatures based on second-order image derivatives are calculated, and then normalised so they can be compared at different scale-levels. The grey-level blobs with the strongest normalised signatures are selected within the scale-space structure. The support regions of the blobs are marked one-at-a-time in the segmentation result image with higher priority for stronger blobs. The segmentation results of six individual hectare plots are assessed by a computerised, objective method that makes use of a ground reference data set of the individual tree crowns. For analysis of individual trees, a subset of the original laser returns is selected within each tree crown region of the canopy reference map. Indices based on moments of the first four orders, maximum value and number of canopy and ground returns, are estimated. The indices are derived separately for height and laser reflectance of branches for the two echoes. Significant differences (p<0.05) are detected for numerous indices for three major native species groups: oaks (Quercus spp.), red maple (Acer rubrum) and yellow poplar (Liriodendron tuliperifera). Tree species classification results of different indices suggest a moderate to high degree of accuracy using single or multiple variables. Furthermore, the maximum tree height is compared to ground reference tree height for 48 sample trees and a 1.1-m standard error (R2=68% (adj.)) within the test-site is observed. [Copyright &y& Elsevier]

Despite the need for quality land cover information, large-area, high spatial resolution land cover mapping has proven to be a difficult task for a variety of reasons including large data volumes, complexity of developing training and validation datasets, data availability, and heterogeneity in data and landscape conditions. We investigate the use of geographic object-based image analysis (GEOBIA), random forest (RF) machine learning, and National Agriculture Imagery Program (NAIP) orthophotography for mapping general land cover across the entire state of West Virginia, USA, an area of roughly 62,000 km2. We obtained an overall accuracy of 96.7% and a Kappa statistic of 0.886 using a combination of NAIP orthophotography and ancillary data. Despite the high overall classification accuracy, some classes were difficult to differentiate, as highlight by the low user's and producer's accuracies for the barren, impervious, and mixed developed classes. In contrast, forest, low vegetation, and water were generally mapped with accuracy. The inclusion of ancillary data and first- and second-order textural measures generally improved classification accuracy whereas band indices and object geometric measures were less valuable. Including super-object attributes improved the classification slightly; however, this increased the computational time and complexity. From the findings of this research and previous studies, recommendations are provided for mapping large spatial extents. [ABSTRACT FROM AUTHOR]

Small format aerial photographs (SFAPJ can be acquired at a relatively low cost to obtain quantitative data for natural resource applications. A remote sensing strategy was developed for measuring logging road system length from SFAP. Aerial photography, digital image processing, and fieldwork procedures are described in sufficient detail fornatural resource managers to acquire their own SFAP with only basic skills and equipment. An alternative field-based strategy was developed and used to evaluate time, costs, and relative accuracy of the remote sensing strategy. The field-based strategy was assumed to consistently produce more accurate measurements of road system length than did the remote sensing strategy. The remote sensing strategy was less expensive than the field-based strategy because logging road system length can be measured in a shorter amount of time. Error for the remote sensing strategy has an average of 11 percent and can be expected to range from 8 to 14 percent. Accuracy is limited by use of uncontrolled mosaics, lens distortion, tilt displacement, topographic displacement, and scale variation. [ABSTRACT FROM AUTHOR]