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We used the space-time K function and Kulldorff's scan statistic to analyze the spatial and spatial–temporal clustering of hemorrhagic disease (HD) in white-tailed deer in Alabama, Georgia, South Carolina, North Carolina, and Tennessee. The HD occurrence data were binary presence/absence data acquired annually on a county basis from 1980 to 2003. Space-time K function was employed to globally examine the existence of spatial–temporal clustering in the HD data. Three approaches of Kulldorff's scan statistic, i.e., spatial clustering analysis for the entire period, spatial–temporal clustering analysis, and spatial clustering analysis by individual years, were applied to detect potential HD clusters. Statistically significant spatial clusters and spatial–temporal clusters were detected in the five southeastern states during the 24-year study period. Some clusters were observed in multiple years. Clusters were most evident in west Alabama, south Alabama, central South Carolina, and along the border between South Carolina and North Carolina. The identification of HD clusters may provide a means to better understand the causal factors related to the HD outbreaks. Results also have potential application in improving or designing effective surveillance programs for this disease.

This study examines best image fusion approaches for generating pansharpened very high resolution (VHR) multispectral images to be utilized for monitoring coastal barrier island development. Selected fusion techniques assessed in this research come from the three categories of spectral substitution (e.g., Brovey transform and multiplicative merging), arithmetic merging (e.g., modified intensity-hue-saturation and principal component analysis), and spatial domain (e.g., high-pass filter, and subtractive resolution merge). The image fusion methods selected for this study were capable of producing pansharpened VHR images with more than three bands. Comparisons of fusion techniques were applied to images from three satellite sensors: United States commercial satellites IKONOS and QuickBird, and the Korean KOMPSAT II. Pansharpened VHR multispectral images were assessed by spectral and spatial quality measurements. Results satisfying both spectral and spatial quality revealed optimum pansharpened techniques nec...

This study geospatially analyzes beach availability for global recreational tourism management with focus on a case study of Jekyll Island off the coast of Georgia, USA. Aerial digital imagery in combination with Light Detection and Ranging (LiDAR) data and geographic information system (GIS) mapping and analysis are employed to delineate accurate shorelines with regard to accessible and available beach area. This analysis demonstrates geospatial techniques for the identification, delineation, qualification and geovisualization of dry beaches, a popular tourist destination worldwide that is subjected to diurnal flooding by tides. The accurate depiction of shorelines using remotely sensed differences in Mean Higher High Water (MHHW) and Mean Lower Low Water (MLLW) levels is required to identify dry beach areas available for coastal tourism and management. Such information allows tourists to choose beach areas suitable for recreational or ecological activities. Dry beach availability also assists coastal restoration managers to plan and implement beach conservation measures. Results predict shoreline changes and dry beach access while promoting minimal impacts by tourists on fragile coastal dune ecosystems.

In an innovative collaboration between government, university and private industry, researchers at the University of Georgia and Gainesville State College are collaborating with Photo Science, Inc. to acquire, process and quality control check lidar and or-thoimages of forest areas in the Southern Appalachian Mountains of the United States. Funded by the U.S. Geological Survey, this project meets the objectives of the ARRA initiative by creating jobs, preserving jobs and training students for high skill positions in geospatial technology. Leaf-off lidar data were acquired at 1-m resolution of the Tennessee portion of the Great Smoky Mountain National Park (GRSM) and adjacent Foothills Parkway. This 1400-sq. km. area is of high priority for national/global interests due to biodiversity, rare and endangered species and protection of some of the last remaining virgin forest in the U.S. High spatial resolution (30 cm) leaf-off 4-band multispectral orthoimages also were acquired for both the Chattahoochee National Forest in north Georgia and the entire GRSM. The data are intended to augment the National Elevation Dataset and orthoimage database of The National Map with information that can be used by many researchers in applications of LiDAR point clouds, high resolution DEMs and or-thoimage mosaics. Graduate and undergraduate students were involved at every stage of the workflow in order to provide then with high level technical educational and professional experience in preparation for entering the geospatial workforce. This paper will present geospatial workflow strategies, multi-team coordination, distance-learning training and industry-academia partnership.

Movement patterns of bearded capuchin monkeys (Cebus (Sapajus) libidinosus) in northeastern Brazil are likely impacted by environmental features such as elevation, vegetation density, or vegetation type. Habitat preferences of these monkeys provide insights regarding the impact of environmental features on species ecology and the degree to which they incorporate these features in movement decisions. In order to evaluate environmental features influencing movement patterns and predict areas suitable for movement, we employed a maximum entropy modelling approach, using observation points along capuchin monkey daily routes as species presence points. We combined these presence points with spatial data on important environmental features from remotely sensed data on land cover and topography. A spectral mixing analysis procedure was used to generate fraction images that represent green vegetation, shade and soil of the study area. A Landsat Thematic Mapper scene of the area of study was geometrically and atmospherically corrected and used as input in a Minimum Noise Fraction (MNF) procedure and a linear spectral unmixing approach was used to generate the fraction images. These fraction images and elevation were the environmental layer inputs for our logistic MaxEnt model of capuchin movement. Our models' predictive power (test AUC) was 0.775. Areas of high elevation (>450 m) showed low probabilities of presence, and percent green vegetation was the greatest overall contributor to model AUC. This work has implications for predicting daily movement patterns of capuchins in our field site, as suitability values from our model may relate to habitat preference and facility of movement.

Demarcation of upland and water/marsh boundary is a critical issue for management and protection of barrier island ecosystems. Most accurate, precise and confident approaches are required to resolve conflicts related to developable lands and conservation area. Barrier islands are especially prone to these conflicts because they are highly dynamic systems that are formed and reformed by rise and fall of tides and seal levels. In response, this research analyzes the demarcation of Jekyll Island State Park, Georgia, USA that is faced with management issues of balancing tourism and development with resource conservation. Questions addressed by this research include: (1) What are the standard sea levels used to demarcate barrier islands? (2) What are the best methods for most accurately estimating the total area of coastal barrier islands? (3) Do differences in tide elevations between back-barrier and ocean-front shorelines of barrier islands exist? Specifically, this research considers a number of environmental policies, regulations, and laws along with geospatial data such as aerial photography, Light Detection and Ranging (LiDAR) and GIS shapefiles. These data are used to define sea levels and outline of Jekyll Island State Park based on Mean High Water (MHW) levels with respect to 0.79-m, 0.85-m, 0.94-m, 1.00-m, and 1.49-m for the entire Jekyll Island State Park. The demarcations of different back-barrier marshland and ocean-front side of the island were also considered. Results indicate that the total area of Jekyll Island State Park may vary by as much as 3.31-km 2 or 7.2% depending on the jurisdictional sea level that is used to demarcate the island boundary.

Mining throughout the Floridan aquifer system alters groundwater conditions. Determination of magnitude and extent of groundwater alterations in this regional karst aquifer system is difficult due to preferential flow paths, blasting, and widespread aquifer withdrawals and injections. Additionally, hydrologic models do not reveal subsurface impacts to the biotic environment, such as destruction of vegetative buffers, which are essential habitat for endangered and threatened species and other wildlife, and degradation from invasive species responding to hydroperiod disruptions. Our case study uses remote sensing and a geographic information system (GIS) to evaluate near-infrared (NIR) data for natural herbaceous depressional (wet prairie) wetlands in a central Florida phosphate mining area. These wetlands characteristically are dominated by graminoids, which have lower NIR reflectance and consequent digital numbers (DNs) in remotely sensed imagery than invasive species associated with hydroperiod alterations. Maximum aquifer withdrawals of 76,457 m 3 /d were permitted in November 1977 and remain active for study-area mines, which are surrounded primarily by un-irrigated pasture. Digital color infrared aerial imagery acquired in winter 2003–04 (1-m ground resolution) was used to extract NIR values within depressional wetlands. Digital historic (mid-1950s) U.S. Geological Survey topographic quadrangle maps (1:24,000 scale) were selected to delineate baseline wetland boundaries using ESRI ArcGIS 9.2. The study area included 567 of these wetlands, totaling 1,367 hectares. High mean NIR values (≥100 DN) consistent with hydroperiod-altered wetlands dominated by invasive plant species characterized 284 wetlands (50 and 60 percent total number and area, respectively), including some >5 km from study-area mines. Only 108 wetlands (20 and 10 percent total number and area, respectively) had low mean NIR values (≤80 DN) indicative of natural wet prairies without invasive species. Shallow ditches were not a statistically significant factor influencing NIR signatures in these wetlands. The spatial distribution of wetlands with high NIR DNs was inconsistent with conical groundwater drawdown predicted by groundwater models but suggests more linear fluid movement via subsurface preferential NW-SE flow paths.

This study used geographic object-based image analysis (GEOBIA) with very high spatial resolution (VHR) aerial imagery (0.3 m spatial resolution) to classify vegetation, channel and bare mud classes in a salt marsh. Three classification issues were investigated in the context of segmentation scale: (1) a comparison of single-and multi-scale GEOBIA using spectral bands, (2) the relative benefit of incorporating texture derived from the grey-level co-occurrence matrix (GLCM) in classifying the salt marsh features in single-and multi-scale GEOBIA and (3) the effect of quantization level of GLCM texture in the context of multi-scale GEOBIA. The single-scale GEOBIA experiments indicated that the optimal segmentation was both class and scale dependent. Therefore, the single-scale approach produced an only moderately accurate classification for all marsh classes. A multi-scale approach, however, facilitated the use of multiple scales that allowed the delineation of individual classes with increased between-class and reduced within-class spectral variation. With only spectral bands used, the multi-scale approach outperformed the single-scale GEOBIA with an overall accuracy of 82% vs. 76% (Kappa of 0.71 vs. 0.62). The study demonstrates the potential importance of ancillary data, GLCM texture, to compensate for limited between-class spectral discrimination. For example, gains in classification accuracies ranged from 3% to 12% when the GLCM mean texture was included in the multi-scale GEOBIA. The multi-scale classification overall accuracy varied with quantization level of the GLCM texture matrix. A quantization level of 2 reduced misclassifications of channel and bare mud and generated a statistically higher classification than higher quantization levels. Overall, the multi-scale GEOBIA produced the highest classification accuracy. The multi-scale GEOBIA is expected to be a useful methodology for creating a seamless spatial database of marsh landscape features to be used for further geographic information system (GIS) analyses.

We compared global-scale pansharpening of full- or large-scene QuickBird satellite imagery with local-scale pansharpening of a subset of these scenes by using modified intensity-hue-saturation (MIHS), principal component analysis (PCA), multiplicative (MP), and high pass filter ( HPF ) methods. The spectral properties of all pansharpened images were evaluated by using quantitative indices such as erreur relative globale adimensionnelle de synthese (ERGAS), correlation coefficient (CC), relative difference to mean (RDM), and relative difference to standard deviation (RDS). This study discovered that local-scale pansharpening was generally lower and higher than the global-scale approach in terms of ERGAs and CC, respectively. In particular, local-scale HPF produced pansharpening results very close to the original multispectral image with less than 0.18 percent and 0.07 percent of RDM and RDS, respectively. Local-scale fusion results with PCA and MP were similar to those of the global-scale approach. Local-scale pansharpening that uses very high spatial resolution imagery could lead to the rapid assessment of the magnitude and severity of humanitarian situations by producing a color image of high spatial resolution with reduced processing time.

Image fusion is an important component of digital image processing and quantitative image analysis. Image fusion is the technique of integrating and merging information from different remote sensors to achieve refined or improved data. A number of fusion algorithms have been developed in the past two decades, and most of these methods are efficient for applications especially for same-sensor and single-date images. However, colour distortion is a common problem for multi-sensor or multi-date image fusion. In this study, a new image fusion method of regression kriging is presented. Regression kriging takes consideration of correlation between response variable (i.e., the image to be fused) and predictor variables (i.e., the image with finer spatial resolutions), spatial autocorrelation among pixels in the predictor images, and the unbiased estimation with minimized variance. Regression kriging is applied to fuse multi-temporal (e.g., Ikonos, QuickBird, and OrbView-3) images. The significant properties of image fusion using regression kriging are spectral preservation and relatively simple procedures. The qualitative assessments indicate that there is no apparent colour distortion in the fused images that coincides with the quantitative checks, which show that the fused images are highly correlated with the initial data and the per-pixel differences are too small to be considered as significant errors. Besides a basic comparison of image fusion between a wavelet based approach and regression kriging, general comparisons with other published fusion algorithms indicate that regression kriging is comparable with other sophisticated techniques for multi-sensor and multi-date image fusion.

Vegetation mapping was performed using geographic object-based image analysis (GEOS1A) and very high spatial resolution (VHR) imagery for two study areas in Great Smoky Mountains National Park. This study investigated how accurately GEOB1A with ancillary data emulates manual interpretation in rugged mountain areas for multi-level vegetation classes of the National Vegetation Classification System (NVCS). It was discovered that the incorporation of texture and topographic variables with spectral data from scanned color infrared aerial photographs increased the overall accuracy of GEOBIA vegetation classification by 2.8 percent and 5.0 percent Kappa. In a separate study using multispectral Ikonos imagery, the use of elevation, aspect, slope and proximity to streams produced NVCS macro-group vegetation segmentations that resembled manual interpretation and significantly improved the overall accuracy to 76.6 percent, Kappa 0.57. Ancillary information may thus aid in GEOBIA vegetation mapping for updating vegetation inventories in rugged mountain areas.

Site selection analysis was carried out to find the best suitable lands for development activities in an example of promising coastal plains, southwestern Sinai, Egypt. Two GIS models were developed to represent two scenarios of land use suitability in the study area using GIS Multi Criteria Analysis Modeling. The factors contributed in the analysis are the Topography, Land cover, Existing Land use, Flash flood index, Drainage lines and Water points. The first scenario was to classify the area according to various gradual ranges of suitability. According to this scenario, the area is classified into five classes of suitability. The percentage of suitability values are 51.16, 6.13, 22.32, 18.49 and 1.89% for unsuitable, least suitable, low suitable, suitable and high suitable, respectively. The second scenario is developed for a particular kind of land use planning; tourism and recreation projects. The suitability map of this scenario was classified into five values. Unsuitable areas represent 51.18% of the study area, least suitable 16.67%, low suitable 22.85%, suitable 8.61%, and high suitable 0.68%. The best area for locating development projects is the area surrounding El-Tor City and close to the coast. This area could be an urban extension of El-Tor City with more economical and environmental management.

This project combines qualitative data of personal narratives with geographic information science (GIScience) technologies to explore the potential for critical cartography in the study of mass atrocity. The case study used is northern Uganda, where millions have been affected by physical violence and hardship, displacement, and fear. Web-based virtual globes as a ready source of imagery for remote areas and derived spatial data imported to geographic information systems (GIS) provide quantified data that complement testimonials and other qualitative data from the field. Cartographic functions, geovisualization, and spatial analyses available in GIS are used to extract information from high-resolution remote sensing images documenting internally displaced persons (IDP) camps and quantifying evidence of crimes against humanity. These techniques explore spatial relationships and communicate results on the extent and impact of the atrocities in northern Uganda.

This study investigated the use of a geographic object-based image analysis (GEOBIA) approach with the incorporation of object-specific grey-level co-occurrence matrix (GLCM) texture measures from a multispectral Ikonos image for delineation of deciduous, evergreen, and mixed forest types in Guilford Courthouse National Military Park, North Carolina. A series of automated segmentations was produced at a range of scales, each resulting in an associated range of number and size of objects (or segments). Prior to classification, the spatial autocorrelation of each segmentation was evaluated by calculating Moran’s I using the average image digital numbers (DNs) per segment. An initial assumption was made that the optimal segmentation scales would have the lowest spatial autocorrelation, and conversely, that over- and under-segmentation would result in higher autocorrelation between segments. At these optimal segmentation scales, the automated segmentation was found to yield information comparable to manually interpreted stand-level forest maps in terms of the size and number of segments. A series of object-based classifications was carried out on the image at the entire range of segmentation scales. The results demonstrated that the scale of segmentation directly influenced the object-based forest type classification results. The accuracies were higher for classification of images identified from a spatial autocorrelation analysis to have an optimal segmentation, compared to those determined to have over- and under-segmentation. An overall accuracy of 79 percent with a Kappa of 0.65 was obtained at the optimal segmentation scale of 19. The addition of object-specific GLCM multiple texture analysis improved classification accuracies up to a value of 83 percent overall accuracy and a Kappa of 0.71 by reducing the confusion between evergreen and mixed forest types. Although some misclassification still remained because of local segmentation quality, a visual assessment of the texture-enhanced GEOBIA classification generally agreeable with manually interpreted forest types.

Completely cloud-free remotely sensed images are preferred, but they are not always available. Although the average cloud coverage for the entire planet is about 40 percent, the removal of clouds and cloud shadows is rarely studied. To address this problem, a closest spectral fit method is developed to replace cloud and cloud-shadow pixels with their most similar nonclouded pixel values. The objective of this paper is to illustrate the methodology of the closest spectral fit and test its performance for removing clouds and cloud shadows in images. The closest spectral fit procedures are summarized into six steps, in which two main conceptions, location-based one-to-one correspondence and spectral-based closest fit, are defined. The location-based one-to-one correspondence is applied to identify pixels with the same locations in both base image and auxiliary images. The spectral-based closest fit is applied to determine the most similar pixels in an image. Finally, this closest spectral fit approach is applied to remove cloud and cloud-shadow pixels and diagnostically checked using Landsat TM images. Additional examples using QuickBird and MODIS images also indicate the efficiency of the closest spectral fit for removing cloud pixels.

summary At the Little River Watershed (LRW) heterogeneous landscape near Tifton Georgia US an in situ network of stations operated by the US Department of Agriculture–Agriculture Research Service-Southeast Watershed Research Lab (USDA–ARS-SEWRL) was established in 2003 for the long term study of climatic and soil biophysical processes. To develop an accurate interpolation of the in situ readings that can be used to produce distributed representations of soil moisture (SM) and energy balances at the landscape scale for remote sensing studies, we studied (1) the temporal and spatial variations of ground temperature (GT) and infra red temperature (IRT) within 30 by 30 m plots around selected network stations; (2) the relationship between the readings from the eight 30 by 30 m plots and the point reading of the network stations for the variables SM, GT and IRT; and (3) the spatial and temporal variation of GT and IRT within agriculture landuses: grass, orchard, peanuts, cotton and bare soil in the surrounding landscape. The results showed high correlations between the station readings and the adjacent 30 by 30 m plot average value for SM; high seasonal independent variation in the GT and IRT behavior among the eight 30 by 30 m plots; and site specific, in-field homogeneity in each 30 by 30 m plot. We found statistical differences in the GT and IRT between the different landuses as well as high correlations between GT and IRT regardless of the landuse. Greater standard deviations for IRT than for GT (in the range of 2–4) were found within the 30 by 30 m, suggesting that when a single point reading for this variable is selected for the validation of either remote sensing data or water-energy models, errors may occur. The results confirmed that in this landscape homogeneous 30 by 30 m plots can be used as landscape spatial units for soil moisture and ground temperature studies. Under this landscape conditions small plots can account for local expressions of environmental processes, decreasing the errors and uncertainties in remote sensing estimates caused by landscape heterogeneity. Published by Elsevier B.V.

In order to successfully support current and future US military operations in coastal zones, geospatial information must be rapidly integrated and analyzed to meet ongoing force structure evolution and new mission directives. Coastal zones in a military-operational environment are complex regions that include sea, land and air features that demand high-volume databases of extreme detail within relatively narrow geographic corridors. Static products in the form of analog maps at varying scales traditionally have been used by military commanders and their operational planners. The rapidly changing battlefield of 21st Century warfare, however, demands dynamic mapping solutions. Commercial geographic information system (GIS) software for military-specific applications is now being developed and employed with digital databases to provide customized digital maps of variable scale, content and symbolization tailored to unique demands of military units. Research conducted by the Center for Remote Sensing and Mapping Science at the University of Georgia demonstrated the utility of GIS-based analysis and digital map creation when developing large-scale (1:10,000) products from littoral warfare databases. The methodology employed–selection of data sources (including high resolution commercial images and Lidar), establishment of analysis/modeling parameters, conduct of vehicle mobility analysis, development of models and generation of products (such as a continuous sea–land DEM and geo-visualization of changing shorelines with tidal levels)–is discussed. Based on observations and identified needs from the National Geospatial-Intelligence Agency, formerly the National Imagery and Mapping Agency, and the Department of Defense, prototype GIS models for military operations in sea, land and air environments were created from multiple data sets of a study area at US Marine Corps Base Camp Lejeune, North Carolina. Results of these models, along with methodologies for developing large-scale littoral warfare databases, aid the National Geospatial-Intelligence Agency in meeting littoral warfare analysis, modeling and map generation requirements for US military organizations.

This research investigates the spatial covariation of soil and land use/land cover (LULC) at the Little River watershed to assess landscape heterogeneity and the spatial extent to which point data ...

Large area forest inventory is important for understanding and managing forest resources and ecosystems. Remote sensing, the Global Positioning System (GPS), and geographic information systems (GIS) provide new opportunities for forest inventory. This paper develops a new systematic geostatistical approach for predicting forest parameters, using integrated Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images, GPS, and GIS. Forest parameters, such as basal area, height, health conditions, biomass, or carbon, can be incorporated as a response variable, and the geostatistical approach can be used to predict parameter values for uninventoried points. Using basal area as the response and Landsat ETM+ images of pine stands in Georgia as auxiliary data, this approach includes univariate kriging (ordinary kriging and universal kriging) and multivariable kriging (co-kriging and regression kriging). The combination of bands 4, 3, and 2, as well as the combination of bands 5, 4, and 3, normalized difference vegetation index (NDVI), and principal components (PCs) were used in this study with co-kriging and regression kriging. Validation based on 200 randomly sampling points withheld field inventory was computed to evaluate the kriging performance and demonstrated that band combination 543 performed better than band combination 432, NDVI, and PCs. Regression kriging resulted in the smallest errors and the highest R-squared indicating the best geostatistical method for spatial predictions of pine basal area.

Summary This research addressed the temporal and spatial variation of soil moisture (SM) in a heterogeneous landscape. The research objective was to investigate soil moisture variation in eight homogeneous 30 by 30 m plots, similar to the pixel size of a Landsat Thematic Mapper (TM) or Enhanced Thematic Mapper plus (ETM+) image. The plots were adjacent to eight stations of an in situ soil moisture network operated by the United States Department of Agriculture-Agriculture Research Service USDA-ARS in Tifton, GA. We also studied five adjacent agricultural fields to examine the effect of different landuses/land covers (LULC) (grass, orchard, peanuts, cotton and bare soil) on the temporal and spatial variation of soil moisture. Soil moisture field data were collected on eight occasions throughout 2005 and January 2006 to establish comparisons within and among eight homogeneous plots. Consistently throughout time, analysis of variance (ANOVA) showed high variation in the soil moisture behavior among the plots and high homogeneity in the soil moisture behavior within them. A precipitation analysis for the eight sampling dates throughout the year 2005 showed similar rainfall conditions for the eight study plots. Therefore, soil moisture variation among locations was explained by in situ local conditions. Temporal stability geostatistical analysis showed that soil moisture has high temporal stability within the small plots and that a single point reading can be used to monitor soil moisture status for the plot within a maximum 3% volume/volume (v/v) soil moisture variation. Similarly, t -statistic analysis showed that soil moisture status in the upper soil layer changes within 24 h. We found statistical differences in the soil moisture between the different LULC in the agricultural fields as well as statistical differences between these fields and the adjacent 30 by 30 m plots. From this analysis, it was demonstrated that spatial proximity is not enough to produce similar soil moisture, since t -test’s among adjacent plots with different LULCs showed significant differences. These results confirm that a remote sensing approach that considers homogeneous LULC landscape fragments can be used to identify landscape units of similar soil moisture behavior under heterogeneous landscapes. In addition, the in situ USDA-ARS network will serve better in remote sensing studies in which sensors with fine spatial resolution are evaluated. This study is a first step towards identifying landscape units that can be monitored using the single point reading of the USDA-ARS stations network.

In image fusion, the spatial resolution ratio can be defined as the ratio between the spatial resolution of the high-resolution panchromatic image and that of the low-resolution multispectral image. This paper attempts to assess the effects of the spatial resolution ratio of the input images on the quality of the fused image. Experimental results indicate that a spatial resolution ratio of 1 : 10 or higher is desired for optimal multisensor image fusion provided the input panchromatic image is not downsampled to a coarser resolution. Due to the synthetic pixels generated from resampling, the quality of the fused image decreases as the spatial resolution ratio decreases (e.g. from 1 : 10 to 1 : 30). However, even with a spatial resolution ratio as small as 1 : 30, the quality of the fused image is still better than the original multispectral image alone for feature interpretation. In cases where the spatial resolution ratio is too small (e.g. 1 : 30), to obtain better spectral integrity of the fused image, one may downsample the input high-resolution panchromatic image to a slightly lower resolution before fusing it with the multispectral image.

The K nearest neighbor (KNN) method of image analysis is practical, relatively easy to implement, and is becoming one of the most popular methods for conducting forest inventory using remote sensing data. The KNN is often named K nearest neighbor classifier when it is used for classifying categorical variables, while KNN is called K nearest neighbor regression when it is applied for predicting noncategorical variables. As an instance-based estimation method, KNN has two problems: the selection of K values and computation cost. We address the problems of K selection by applying a new approach, which is the combination of the Kolmogorov-Smirnov (KS) test and cumulative distribution function (CDF) to determine the optimal K. Our research indicates that the KS tests and CDF are much more efficient for selecting K than cross-validation and bootstrapping, which are commonly used today. We use remote sensing data reduction techniques—such as principal components analysis, layer combination, and computation of a ...

Using forest inventory data and Landsat ETM+ data, linear fixed-effects models and linear mixed-effects models are developed based on the allometric growth model. The surface area of the normalized difference vegetation index (NDVIsa) is developed from the triangulated irregular network (TIN) with the aid of image-processing and the three-dimensional analysis extensions of Environmental Systems Research Institute's ArcView GIS software. The NDVIsa is used as the predictor, and it implies the area of trees for both site and area. Linear fixed-effects and linear mixed-effects models based on the allometric growth model are developed to fit the relationships between either biomass or volume and NDVIsa. Linear mixed-effects model with both intercept and slope having random-effects best fits the data, and the relatively high R 2 (about 0.57) was achieved. This linear mixed-effects model is significantly different from the linear fixed-effects model and the linear mixed-effects model with only intercept having random-effects. The best fitted linear mixed-effects model discovers different spatial characteristics of biomass and volume of trees across the whole state of Georgia. The Piedmont ecoregion has positive allometric characteristics, the Upper Coastal Plain has negative allometry, whereas the other three ecoregions have isometric characteristics. At last, the linear mixed-effects models were compared with the extreme situation, fitting linear fixed-effects models within each region. Model diagnostics indicated that the linear mixed-effects models were the best modeling approach.

Existing image fusion techniques such as the intensity–hue–saturation (IHS) transform and principal components analysis (PCA) methods may not be optimal for fusing the new generation commercial high-resolution satellite images such as Ikonos and QuickBird. One problem is color distortion in the fused image, which causes visual changes as well as spectral differences between the original and fused images. In this paper, a fast Fourier transform (FFT)-enhanced IHS method is developed for fusing new generation high-resolution satellite images. This method combines a standard IHS transform with FFT filtering of both the panchromatic image and the intensity component of the original multispectral image. Ikonos and QuickBird data are used to assess the FFT-enhanced IHS transform method. Experimental results indicate that the FFT-enhanced IHS transform method may improve upon the standard IHS transform and the PCA methods in preserving spectral and spatial information.

In theory, “aquifer storage and recovery” (ASR) is a form of artificial aquifer recharge consisting of threecomponents: (1) aquifer injections of fluids; (2) withdrawals of the injected fluids; and (3) a period of timebetween the injections and withdrawals that is considered to be aquifer “storage” of the injected fluids. Theinjected fluids may be: (1) treated sewage effluent (also known as reclaimed, reuse, or bright water); (2)stormwater runoff pumped out of canals, mine pits or other areas; (3) surface waters from natural lakes andstreams; or (4) ground water from different aquifer zones. An evaluation of existing data from more than 80injection/withdrawal cycle tests at 18 ASR sites in 9 counties throughout southern Florida, in the regional karstFloridan aquifer system of the United States (US) revealed that less than 25% actual “recovery” was achievedfrom ASR wells where water from various sources was injected into the regional karst aquifer system.Determination of actual “recovery” was based on the reported chloride content of injected and recovered waterand was more suggestive of fluid disposal than aquifer “recharge.” Actual “recovery” for those ASR tests,adjusted to the chloride concentrations of injected fluids, ranged from 0-17% for “storage” periods that rangedfrom 0-181 days. Although results of actual “recovery” provide little support for the concept of “stored” water, inreality those results also over-estimate the volume of injected water that is stored because it assumes that waterrecovered at the same chloride concentration is the same water that was injected. There is no evidence in theASR data to support that assumption. The low actual “recovery” rates occurred despite the fact that 28 of thecycle tests had a “storage” period < 1 day and the longest “storage” period tested did not exceed 181 days. Thosebrief “storage” periods also were insufficient to meet the stated agency objectives of retrieving the injected fluidsduring the dry season, more than six months after injection of what is termed “excess water” during the rainyseason. Despite those results, the agency’s Final Technical Data Report (TDR) and groundwater model releasedin 2014 concluded that: (1) “recovery” from those wells would range from 70-100% and (2) 232 ASR wells (94in the upper Floridan aquifer, 37 in the Avon Park Permeable Zone of the middle Florida aquifer and 101 in theBoulder Zone) could be completed in Florida’s Greater Everglades Basin (basin) as restoration. That Final TDRdid not consider differences in chloride content between water that was injected into and withdrawn from theASR cycle tests and that groundwater model for the basin did not include the anisotropy option or preferentialflow through karst conduits such as fractures. Preferential flow of water injected and withdrawn could result inboth low ASR “recovery” rates and environmental harm, such as submarine groundwater discharge (SGD)contaminated with pollutants, including nutrients that result in harmful algal blooms (HABs). That TDR andgroundwater model for southern Florida also did not evaluate the direct, indirect or cumulative adverse impactsof preferential flow from ASR injections and withdrawals (e.g., through fractures) on federally threatened andendangered species, environmentally sensitive areas or on sinkhole formation. Our evaluation of the basinvicinity also included analyses of fracture frequency, length and proximity to ASR wells, other injection andwithdrawal wells and modern sinkholes, based on georeferenced and transformed lineaments from threeindependent sources: the US Army Corps of Engineers (ACOE, 2004c), Florida Department of Transportation(FDOT, 1973), and Vernon (1951). Based on the three lineament data sets included in our study, includingwww.ccsenet.org/jgg Journal of Geography and Geology Vol. 7, No. 2; 2015118fractures that extend for considerable distances through environmentally sensitive areas in the basin such as theBig Cypress National Preserve, Biscayne National Park, Estero Bay, Everglades National Park, Florida KeysNational Marine Sanctuary, Indian River Lagoon and Marquesas Keys Wildlife Management Area. Some ofthese fractures also extend beyond the boundaries of the basin.

Salmonellosis cases in the in the United States show distinct geographical trends, with the southeast reporting among the highest rates of illness. In the state of Georgia, USA, non-outbreak associated salmonellosis is especially high in the southern low-lying coastal plain. Here we examined the distribution of Salmonella enterica in environmental waters and associated wildlife in two distinct watersheds, one in the Atlantic Coastal Plain (a high case rate rural area) physiographic province and one in the Piedmont (a lower case rate rural area). Salmonella were isolated from the two regions and compared for serovar and strain diversity, as well as distribution, between the two study areas, using both a retrospective and prospective design. Thirty-seven unique serovars and 204 unique strain types were identified by pulsed-field gel electrophoresis (PFGE). Salmonella serovars Braenderup, Give, Hartford, and Muenchen were dominant in both watersheds. Two serovars, specifically S. Muenchen and S. Rubislaw, were consistently isolated from both systems, including water and small mammals. Conversely, 24 serovars tended to be site-specific (64.8%, n = 37). Compared to the other Salmonella serovars isolated from these sites, S. Muenchen and S. Rubislaw exhibited significant genetic diversity. Among a subset of PFGE patterns, approximately half of the environmental strain types matched entries in the USA PulseNet database of human cases. Ninety percent of S. Muenchen strains from the Little River basin (the high case rate area) matched PFGE entries in PulseNet compared to 33.33% of S. Muenchen strains from the North Oconee River region (the lower case rate area). Underlying the diversity and turnover of Salmonella strains observed for these two watersheds is the persistence of specific Salmonella serovars and strain types that may be adapted to these watersheds and landscapes.

The distribution of West Nile virus (WNV) is dependent on the occurrence of both susceptible avian reservoir hosts and competent mosquito vectors. Both factors can be influenced by geographic variables such as land use/landcover, elevation, human population density, physiographic region, and temperature. The current study uses geographic information systems (GIS) and logistic regression analyses to model the distribution of WNV in the state of Georgia based on a wild bird indicator system, and to identify human and environmental predictor variables that are important in the determination of WNV distribution. A database for Georgia was constructed that included (1) location points of all the avian samples tested for WNV, (2) local land use classifications, including temperature, physiographic divisions, land use/landcover, and elevation, (3) human demographic data from the U.S. Census, and (4) statistics summarizing land cover, elevation, and climate within a 1-km-radius landscape around each sample point. Logistic regression analysis was carried out using the serostatus of avian collection sites as the dependent variable. Temperature, housing density, urban/suburban land use, and mountain physiographic region were important variables in predicting the distribution of WNV in the state of Georgia. While weak, the positive correlation between WNV-antibody positive sites and the urban/suburban environment was consistent throughout the study period. The risks associated with WNV endemicity appear to be increased in urban/ suburban areas and decreased in the mountainous region of the state. This information may be used in addressing regional public health needs and mosquito control programs.

Resource managers realize the potential of remote sensing and geographic information system (GIS) operations for detecting, monitoring, mapping, and modeling distributions of invasive plants. However, to make wise decisions, imagery must be evaluated in terms of geometric fidelity, spatial resolution, spectral resolution, area coverage, and cost. These data characteristics are discussed relative to mapping vegetation distributions and detecting exotic plants. Examples are drawn from projects conducted during the past 20 yr by the Department of Geography's Center for Remote Sensing and Mapping Science at the University of Georgia using remotely sensed data, GIS, and Global Positioning System techniques to produce detailed vegetation maps and databases for study areas in the southeastern United States. Many of these projects include a component of invasive plant detection and distribution mapping for monitoring spread and effectiveness of control. Additional index words: Hyperspectral image data, national parks, natural resource management, photo interpretation, satellite imagery. Abbreviations: ASTER, Advanced Spaceborne Thermal Emission and Relection Radiometer; AVHRR, Advanced Very High Resolution Radiometer; AVIRIS, Airborne Visible/Infrared Imaging Spectrometer; CIR, color infrared; DMC, Digital Modular Camera; GIS, geographic information system; GPS, Global Positioning System; GR, ground resolution; LIDAR, light detection and ranging; lprs/mm, line pairs per millimeter; MODIS, Moderate Resolution Imaging Spectroradiometer; PAN, panchromatic; SPOT, System Probatoire d'Observation de la Terre; USGS, United States Geological Survey; XS, multispectral.

Data acquired by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) with 224 bands, each with 0.01-μm spectral resolution and 20-meter spatial resolution, were used to produce a vegetation map for a portion of Everglades National Park, Florida, USA. The vegetation map was tested for classification accuracy with a pre-existing detailed GIS wetland vegetation database compiled by manual interpretation of 1∶40,000-scale color infrared (CIR) aerial photographs. Although the accuracy varied greatly for different classes, ranging from 40 percent for scrub red mangroves (Rhizophora mangle) to 100 percent for spike rush (Eleocharis cellulosa) prairies, the Everglades communities generally were successfully identified, averaging 66 percent correct for all classes. In addition, the hyperspectral image data proved suitable for detecting the invasive exotic species lather leaf (Colubrina asiatica) that is sometimes difficult to differentiate on aerial photographs. The findings from this study have implications for operational uses of spaceborne hyperspectral image data that are now becoming available. Practical limitations of using such image data for wetland vegetation mapping include inadequate spatial resolution, complexity of image processing procedures, and lack of stereo viewing.

Pond-cypress (Taxodium ascendens Brong.) is a dominant canopy species in depressional wetlands of the south-eastern Coastal Plain. Unsustainable withdrawals from the karst Floridan aquifer system have caused premature decline and death of pond-cypress trees, presumably owing to altered hydroperiods (which alter the flow of water and nutrients in trees). There has been no scientifically based means to determine sustainable yield from this regional aquifer system or to detect early stages of physical/ecological damage associated with groundwater mining and aquifer storage and recovery (ASR, which also can alter natural hydroperiods). In this study, the relationship between visual symptoms (indicators) of stress or premature decline, and spectral reflectance was evaluated using dried, milled branch tips collected from natural stands of mature pond-cypress. Depressional systems evaluated represented four of the six aquifer system subregions where subsurface perturbations from groundwater mining: (i) were presumed not to be occurring (reference wetlands); (ii) may be occurring but are not documented; and (iii) have been confirmed. Sampled trees were assigned to one of three stress classes (1, no/minimal; 2, moderate; 3, severe) based on the visual indicators. Partial least squares–linear discriminant analysis of second derivative spectral transformations in the visible/shortwave near-infrared (NIR) region (400–1100 nm) and the NIR region (1100–2500 nm) was used to evaluate the samples in assigned classes. Class 1 samples were discriminated from combined class 2 and 3 samples in the NIR region with 100% and 97% accuracy for consecutive winter sample periods (before bud-break). The percentage of correctly classified samples in this spectral region was lower (85%) for summer samples (full leaf-out). Second-derivative models for the NIR region developed from the winter data sets predicted assigned classes for alternate winter’s samples with an accuracy of 97% and 100%. High correlation between spectral reflectance of dried, milled branch tips collected from mature pond-cypress in winter and visual indicators of premature decline suggests in situ pond-cypress are hydroecological indicators of anthropogenic subsurface hydroperiod perturbations. This approach provides objective means for early detection of unsustainable aquifer yield and adverse impacts from ASR activities in the south-eastern Coastal Plain. Used in conjunction with hydrological monitoring and modelling, the hydroecological indicators should provide the means with which sustainable yield in the south-eastern Coastal Plain can be achieved and maintained. Copyright  2003 John Wiley & Sons, Ltd.

Detailed vegetation databases and associated maps of the Great Smoky Mountains National Park, a rugged, forested area of more than 2000 km 2 , were constructed to support resource management activities of the U.S. National Park Service (NPS). These detailed vegetation databases and associated maps have a terrain relief exceeding 1700 m and a continuous forest cover over 95% of the Park. The requirement to use 1:12,000- and 1:40,000-scale color infrared aerial photographs as the primary data source for mapping overstory and understory vegetation, respectively, necessitated the integration of analog photointerpretation with both digital softcopy photogrammetry and geographic information system (GIS) procedures to overcome problems associated with excessive terrain relief and a lack of ground control. Applications of the vegetation database and associated large-scale maps include assessments of vegetation patterns related to management activities and quantification of forest fire fuels.

The regional Floridan aquifer system (FAS) extends from the submerged carbonate platform of the Atlantic Ocean, Gulf of Mexico, and Straits of Florida in the southeastern United States (US), throughout Florida and the coastal plain of Alabama, Georgia, and South Carolina. This carbonate aquifer system is characterized by bedding planes, fractures, dissolution cavities, and other karst features that result in preferential flow of ground water, particularly in response to anthropogenic perturbations such as groundwater withdrawals and aquifer injections. The FAS was divided into six sub-regions for groundwater-modeling purposes in 1989, with results concluding that breaches of those groundwater divides had occurred and those breaches were attributed to large withdrawals of ground water in the US southeastern coastal plain. Those results suggest the model did not elucidate preferential flow conditions through fractures and other karst conduits. We hypothesized that incorporating fractures and sinkholes into groundwater models could improve results and predict adverse impacts to environmentally sensitive areas. We analyzed extensive fracture networks and sinkholes previously mapped throughout Florida and in Dougherty County, Georgia. Some of those fractures extend from one sub-region into an adjacent sub-region of the FAS and may be facilitating the breaching of groundwater divides described in the 1989 groundwater model for this regional aquifer system. The greater total fractures and fracture density in Dougherty County (1,225 and 141.3/100 km2, respectively) compared to 21 north-Florida counties (10-91fractures per county and 0.6-3.8/100 km2, respectively) presumably is due to the scale of fracture mapping and shorter mean lengths of mapped fractures in Dougherty County (1.2 km), compared to north Florida counties (26-118 km), rather than to orders of magnitude increases in fracture densities in that part of the FAS. The number of sinkholes identified in Dougherty County in a recent, unrelated project using 2011 Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images, was approximately an order of magnitude greater than the number of sinkholes mapped in analog form in that county and published in 1986. Extension of the dense network of those fractures that occurred within the boundaries of a Priority Amphibian and Reptile Conservation Area (PARCA) that encompassed Dougherty County covered the Elmodel Wildlife Management Area (WMA) and ASR demonstration well in Baker County, Georgia. Those extensions also passed through numerous agricultural areas with center-pivot irrigation wells in southwest Georgia; intersected other Georgia PARCAs near the Florida-Georgia state line; and clumped in two areas of dense sinkhole clusters in northwest Florida. No determination has been made regarding the contributions of pirated water from the Apalachicola-Chattahoochee-Flint (ACF) River Basins and Wakulla Springshed from the magnitude and extent of agricultural, municipal, and industrial groundwater withdrawals in Georgia’s coastal plain, that exceed groundwater withdrawals in Florida for that area of the FAS, to the increase in sinkholes in Dougherty County and the dense clusters of sinkholes in northwest Florida, via preferential flow through fractures. Similarly, the survival and recovery of at least 24 animal species in Georgia that are either federally listed or high-priority state species may be jeopardized by adverse direct, indirect, and cumulative impacts from preferential flow through fractures, sinkholes, and other karst conduits in response to aquifer injections and withdrawals that have not been evaluated. Currently no regional groundwater model has been constructed to evaluate such preferential groundwater flow in the FAS. A model incorporating preferential flow via mapped fractures and sinkholes is essential to determine the magnitude and extent of environmental impacts from ASR wells and other supply and disposal wells in this regional aquifer system, such as pirated water from the ACF and other river basins, alterations in submarine groundwater discharge to Apalachicola Bay and other coastal areas, saltwater intrusion, upconing of saline ground water and resulting impacts to federally endangered and threatened species and high-priority state species.

Sustainable development of any type that utilizes water or involves excavations in karst aquifer systems, such as the regional Floridan aquifer system (FAS), requires knowledge of preferential groundwater flow pathways that can extend adverse impacts beyond the development site and alter natural hydroperiods. Such pathways include fractures and other types of karst conduits that are associated with modern and relict sinkholes. Developments, including power plants and mines, that have not accounted for these features have caused induced recharge, altered hydroperiods and saltwater intrusion in the FAS, resulting in destruction of wetlands and adverse impacts to other surface waters, wildlife habitat and threatened and endangered species. This study analyzed indicators of preferential groundwater flow by considering surface expressions of underlying geological conditions (lineaments and modern sinkholes) in the FAS, which coincide with the United States southeastern coastal plain. Lineament mapping by Vernon (1951) and the Florida Department of Transportation (FDOT, 1973), incorporating analog mapping techniques and hardcopy prints of satellite imagery, preceded extensive urbanization, groundwater extractions, and mining in the region. All of these alterations limit the ability to identify fractures using lineaments by reducing groundwater discharges and vegetation indicative of those discharges. In this study, established methods for georectification, including control-point identification and spatial matching of scanned maps and remotely sensed images, were applied to these previously mapped lineaments. These results were applied to the environmentally sensitive karst study area of Citrus and Levy Counties, Florida in the southern extent of the FAS. Geospatial analyses of lineament distribution and modern sinkhole locations from the state database showed a dense network of lineaments with associated sinkholes throughout the study area and seven surrounding counties, including the proposed sites for a nuclear power plant and two mines in Levy County. Proposed excavations and water use for construction and operation of the power plant and mines would result in irreversible adverse environmental impacts on extensive depressional wetlands beyond the surface-footprint impact of these developments via these preferential flow pathways that were not evaluated during the review process.

Since the Ramsar Convention on Wetlands in 1971, wetland conservation (maintenance and sustainable use) and restoration (recovery of degraded natural wetlands) have been high priorities for many countries. China has the world’s fourth largest wetland area, which exceeds the whole territory of Great Britain. While the Chinese government has increasingly recognized the importance of wetland protection, particularly after joining the Ramsar Convention in 1992, natural wetlands in China have suffered great loss and degradation. To address this problem, China has implemented the National Wetland Conservation Program (NWCP)—one of the largest of its kind in the world—with ambitious goals, massive investments, and potentially enormous impacts. Furthermore, NWCP has global implications because it aims to rehabilitate habitats for water birds of international importance, enhance carbon sequestration, conserve soil and water, and protect important headwaters of international rivers and lakes.

The present study constructed a spatial-temporal statistical model to identify the risk and protective factors for haemorrhagic disease (HD) in white-tailed deer in the five states of Alabama, Georgia, South Carolina, North Carolina and Tennessee. The response variable was binary, indicating the presence or absence of HD in an individual county, measured annually from 1983 to 2000. Predictor variables included climatic factors of temperature, rainfall, wind speed and dew point, remotely sensed data of normalised difference vegetation index (NDVI) and land surface temperature derived from archived remotely sensed advanced very-high-resolution radiometer (AVHRR) satellite data, elevation, a spatial autocorrelation (SA) term and a temporal autocorrelation term. This study first applied principal component factor analysis to reduce the volume of climatic data and remotely sensed data. Then, a generalised linear mixed model framework (GLMM) was used to develop a spatial-temporal statistical model. The results showed that the area under receiver operating characteristic curve (ROC) was 0.728, indicating a good overall fit of the model. The total prediction accuracy over the 18 year period with optimal cut-off probability was 67 per cent. The prediction accuracy for individual years ranged from 48 to 75 per cent.

The determination of segments that represents an optimal image object size is very challenging in object-based image analysis (OBIA). This research employs local variance and spatial autocorrelation to estimate the optimal size of image objects for segmenting forest stands. Segmented images are visually compared to a manually interpreted forest stand database to examine the quality of forest stand segmentation in terms of the average size and number of image objects. Average local variances are then graphed against segmentation scale in an attempt to determine the appropriate scale for optimally derived segments. In addition, an analysis of spatial autocorrelation is performed to investigate how between-object correlation changes with segmentation scale in terms of over-, optimal, and under-segmentation.

The Florida Everglades are subtropical marshlands located in the southern portion of the United States state of Florida. Over the past few decades, municipal encroachment has impacted the ecosystem through numerous recreational activities. One particular activity involves ecotourism and the use of airboats to transport customers to unique locations. This study considered the impact of airboat use created by such transportation activities. Remote Sensing, Geographic Information Systems and Global Positioning Systems where used to analyze the impact of airboat use. The total length of airboat trails and total length based on width where compared with total area of vegetation. Comparisons among percent area of vegetation and the average total length of airboat trails by region, along with the difference between the percent vegetation near and far where all analyzed. A significant positive correlation was found between airboat trails ges 10 meters (m) wide and total area of cattail and between airboat trails ges 10m wide and airboat trails ges3m wide.

Over the past ten years, the Center for Remote Sensing and Mapping Science (CRMS) at The University of Georgia has worked cooperatively with the National Park Service to create digital vegetation databases for 21 National Parks, Preserves, Home Sites and Battlefields in the southeastern United States. These databases were created from manually interpreted large-scale color infrared aerial photographs using a combination of Global Positioning System (GPS) surveys, softcopy photogrammetry and GIS modeling procedures. Throughout the process, geovisualization techniques have been used to aid in the extraction and assessment of vegetation patterns, quality control evaluation and communication of information to managers and users of park resources. In one example, three-dimensional (3D) drapes of orthorectified images and vegetation maps were used to aid in the interpretation process by providing the interpreter with multiple 3D perspective views and information on elevation range, slope and aspect. Upon completion of the databases, geovisualization techniques also were used to qualitatively identify and assess areas prone to errors in the geometric orthorectification. In this way, geovisualization contributed to improvements in both the thematic and geometric accuracy of the National Park Service vegetation data sets. In another example, animations of spatio-temporal data sets and 3D drapes were developed to assess the impacts of surrounding development and land use changes on resources within park boundaries. Geovisualizations provided to the National Park Service are being used to prioritize the acquisition of additional lands to help preserve water quality and habitats of ecological importance.

A landscape is defined as an area having a common geomorphology, climate, and disturbance regime encompassing all types, frequencies, and intensities of disturbance through time (Mooney and Godron 1983; Forman and Godron 1986; and Risser, Chapter 1). Disturbances are discrete events in time which disrupt an ecosystem, community, or population structure and change resources, substrate availability, or the physical environment (Pickett and White 1985). The relationship between disturbance and heterogeneity in a landscape is complex and depends on the scale of the disturbance and important underlying environmental gradients. Disturbances may either increase or decrease heterogeneity (Denslow 1985), whereas landscape heterogeneity may enhance or inhibit the spread of disturbance (Risser et al. 1984).