Fine scale spatial urban land cover factors associated with adult mosquito abundance and risk in Tucson, Arizona
|
|
- Norma Hines
- 5 years ago
- Views:
Transcription
1 Vol. 37, no. 2 Journal of Vector Ecology 407 Fine scale spatial urban land cover factors associated with adult mosquito abundance and risk in Tucson, Arizona Katheryn I. Landau 1 and Willem J.D. van Leeuwen 1,2 1 School of Geography and Development, The University of Arizona, Tucson, AZ 85721, U.S.A. klandau@ .arizona.edu 2 School of Natural Resources and the Environment, Office of Arid Land Studies, Arizona Remote Sensing Center, 1955 E. Sixth Street, The University of Arizona, Tucson, AZ 85721, U.S.A. Received 24 May 2012; Accepted 22 August 2012 ABSTRACT: It is currently unclear what role microhabitat land cover plays in determining the seasonal spatial distribution of Aedes aegypti and Culex quinquefasciatus, disease vectors of dengue and West Nile Virus, respectively, in Tucson, AZ. We compared mosquito abundance to sixteen land cover variables derived from 2010 NAIP multispectral data and 2008 LiDAR height data. Mosquitoes were trapped with traps from May to October of 2010 and Variables were extracted for five buffer zones (10-50 m radii at 10 m intervals) around trapping sites. Stepwise regression was performed to determine the best scale for observation and the influential land cover variables. The 30 m radius buffer was determined to be the best for observing the land cover-mosquito abundance relationship. Ae. aegypti presence was positively associated with structure and medium height trees and negatively associated with bare earth; Cx. quinquefasciatus presence was positively associated with pavement and medium height trees and negatively associated with shrubs. These findings emphasize vegetation, impervious surfaces, and soil influences on mosquito presence in an urban setting. Lastly, the land cover-mosquito abundance relationships were used to produce risk maps of seasonal presence that highlight high risk areas in Tucson, which may be useful for focusing mosquito control program actions. Journal of Vector Ecology 37 (2): Keyword Index: Aedes aegypti, Culex quinquefasciatus, land cover, mosquito abundance, seasonal presence, Arizona. INTRODUCTION Understanding the spatial dynamics of mosquito populations and how environmental factors affect mosquito abundance and distribution is central to preventing disease outbreaks by controlling mosquito populations. The mosquitoes Aedes aegypti and Culex quinquefasciatus, disease vectors for dengue fever and West Nile Virus (WNV), respectively, are commonly found throughout the Southwest. While neither species is native to the Southwest, both species have thrived in the human-modified desert environment. Because the mosquito s presence is highly dictated by the environment, understanding the spatial land cover-mosquito abundance relationship is essential for species distribution modeling and mapping the risk of seasonal Ae. aegypti and Cx. quinquefasciatus presence. Mosquito-borne diseases have had an increasing impact on human health in Arizona in the past decade, including active outbreaks of WNV and a rising potential for a dengue outbreak. The rapidly growing cities of AZ are providing an ever-expanding habitat for mosquitoes, increasing the potential human health impact the existence of these vectors present. In Arizona, climate factors have a known influence on the mosquito population; mosquito presence is highest during the summer months with the mosquito population increasing at the start of the summer monsoon season and decreasing as temperatures fall in the late summer months (Hoeck et al. 2003). Land cover factors have also been shown to play a controlling role in the distribution and abundance of Ae. aegypti (Focks et al. 1993) and Cx. quinquefasciatus (Reiter and LaPointe 2007). However, the role of the land cover in defining the spatial variability of mosquito presence in the urban desert environment is less clear. In response to potential outbreaks of vector-borne diseases, scientists and public health organizations have sought to use land cover-mosquito abundance relationships to develop place-based models to predict mosquito abundance and associated disease risk. A spatial epidemiology approach has produced many place-based models for malaria vectors, but fewer place-based models have been developed for other important disease vectors, such as dengue (Kolivras 2006, Fuller et al. 2010) and WNV (Liu et al. 2008, Ghosh 2011). Additionally, while general climate, land use, and mosquito abundance relationships are well documented for the Southwest (Hayden et al. 2010, Kolivras and Comrie 2004, Morin and Comrie 2010), no spatial models exist for dengue and WNV vectors that incorporate land cover variables or that highlight the risk of mosquito presence in Tucson, AZ. Mosquito-borne disease-vector-land cover relationships have shown that (1) land cover greatly influences mosquito presence and (2) disease-vector distribution is strongly governed by environmental factors that create suitable habitats (Kolivras 2006). However, a traditional ground-based approach to monitoring mosquitoes is labor intensive and cost prohibitive. To improve monitoring, a large scale approach to identify the areas at highest risk of mosquito presence would be the best method to allocate limited resources. Geospatial mapping using remote sensing and Geographic Information Systems (GIS) are highly useful tools for assessing the spatial epidemiology of vector-borne diseases as they
2 408 Journal of Vector Ecology December 2012 allow for accurate identification and mapping of the spatial distribution of high risk mosquito breeding habitat on a large scale. Until recently, technological limitations (i.e., low spatial resolution of satellite imagery, need for large storage capacity, etc.) limited epidemiological applications. Existing mosquito habitat and risk models often used imagery with too coarse a spatial resolution to adequately define relationships between microhabitat land cover and mosquito abundance in urban environments. Remote sensing technology advancements and increased access to high spatial resolution (~1m) multispectral aerial imagery data and three-dimensional (3- D) LiDAR (Light Detection and Ranging) data will allow for improved identification of the mosquito s habitat preferences as defined by land cover. Species distribution models (SDMs) provide the easiest way to incorporate remotely sensed data, such as land cover characteristics, into predictive models. SDM techniques can be applied to the modeling of many infectious diseases, beginning by predicting where the vector will be present. Modeling disease occurrence based on environmental conditions has been shown to be an effective way of developing a predictive spatial model (Kolivras 2006, Fuller et al. 2010). Many studies have used remotely-sensed data to identify mosquito habitat areas and to develop regressionbased predictive models. Developing such models would identify areas of high risk mosquito presence in Tucson, AZ. This study takes a spatial epidemiological approach to improve our understanding of mosquito habitat requirements and the spatial distribution of mosquito abundance. This study seeks to (1) use high spatial resolution image data to characterize urban mosquito microhabitat through land cover variables and (2) determine the best scale at which to monitor land cover variables to predict the risk of mosquito presence at the neighborhood level. Understanding the land covermosquito abundance relationships will assist in improving the identification of mosquito habitats in the urban Southwest and will allow for focusing of mosquito control actions. MATERIALS AND METHODS Study area The study area is located in the urban center of Tucson, AZ, covering approximately 76 km 2 between coordinates N N latitude and W W longitude (Figure 1). The study area was determined by the location of mosquito trapping sites. Located in the Sonoran Desert, Tucson experiences moderate winters (average January low temperature = 3.8 C), prolonged hot summers (average May-September high temperature = 35.7 C), and limited precipitation (average annual precipitation = mm) with high spatial variability in rainfall totals (Western Regional Climate Center Arizona climate summaries. Reno.) Some rainfall occurs during the winter, but the majority of the rainfall occurs during the summer monsoon season, from July through September was a dry hot year with above average temperatures and below average rainfall (National Weather Service Forecast Office climate report for Tucson. climate/monthly/2010.php.). The year 2011 had record low temperatures in January and February and record high temperatures in the summer, with one of the driest years on record up to June and one of the wettest years on record in the second half of the year (National Weather Service Forecast Office climate report for Tucson. gov/twc/climate/monthly/2011.php). The study area provides a representative snapshot of the urban desert environment with a high presence of impervious surfaces and native and introduced vegetation in natural and irrigated landscapes. The study area includes four land use categories: residential, managed green spaces (golf courses and parks), mixed use, and washes. Adult mosquito collection Ae. aegypti and Cx. quinquefasciatus presence and abundance were measured at trapping sites throughout the study area. Adult mosquito trapping was carried out weekly from June to October (from now on referred to as the mosquito season) of 2010 and Trapping occurred at 49 sites during the 2010 mosquito season and continued at 30 of the sites during the 2011 mosquito season (Figure 1). Carbon dioxide (CO 2 )-baited suction traps were placed at trapping sites of various land use types (residential, mixed use, washes, and managed green spaces which includes parks and golf courses) (Table 1). Traps were retrieved approximately 14 h after placement. Trapping sites were documented with photographs, GPS points, and site descriptions. Mosquitoes were identified by species and sex. Analysis of mosquito trapping data was based on seasonal total counts of adult female Ae. aegypti and Cx. quinquefasciatus for predicting Table 1. Distribution of mosquito traps in the various land use classes and characterization of land use classes for the study area. The percentage of mosquito traps for the four land use types and the percentage of each land use type for the study area were calculated to determine how well represented each land use type was by the mosquito trap coverage. Land Use Type Number of Traps % of Traps % of Study Area Managed Green Spaces Mixed Use Residential Wash
3 Vol. 37, no. 2 Journal of Vector Ecology Figure 1. Study area and mosquito trap locations for 2010 and Near-infrared, red, and green false color multispectral NAIP (National Agricultural Imaging Program) image of the urban study area are a composite of a mosaic of the area in Tucson (AZ, USA) acquired June Figure 2. Land cover map based on classification and regression tree (CART) classification using band multispectral NAIP data, NDVI data, and 2008 LiDAR CHM data. 409
4 410 Journal of Vector Ecology December 2012 risk of seasonal mosquito presence. Land cover classification and quantification To quantify land cover around sites, a land cover map was created from National Imagery Agriculture Program (NAIP) multispectral (blue, green, red, and near-infrared) 1 m spatial resolution aerial imagery and LiDAR elevation data. The NAIP imagery was collected in June, 2010 by the United States Department of Agriculture. A Normalized Difference Vegetation Index (NDVI) was derived from the NAIP imagery. NDVI, derived from the red and near-infrared bands of the multispectral data, was used as a proxy for vegetation greenness (Tucker 1979). A canopy height model (CHM) was derived from February, 2008 LiDAR height and ground elevation data. The LiDAR data point sample density was one point/m or better with a horizontal accuracy of one m and a vertical accuracy of 37 cm (STGS 2009). The combination of LiDAR and multispectral image data has been shown to improve land cover classification, specifically identification of vegetation classes (Hartfield et al. 2011, Secord and Zakhor 2007). NAIP, NDVI, and CHM were used as inputs to Classification and Regression Tree (CART) classification to produce a land cover map (Rulequest Research 2008). Training points for the classification were selected through expert knowledge. The land cover map was comprised of eleven classes: bare earth, pavement, structure, pool, water, shadow, herbaceous (0 m), shrub ( m), and three tree height classes, low height tree ( m), medium height tree ( m), and high height tree (9.4+ m) (Figure 2). The water class is composed of all non-chlorinated water bodies such as ponds and lakes (natural or constructed), while the pool class refers to chlorinated water bodies. A distinction is made between chlorinated and non-chlorinated water bodies as chlorinated water bodies were determined to be unsuitable for larval development. Latitude and longitude coordinates of the trapping sites were recorded with a global positioning system for GIS processing using ArcGIS 10 (ESRI, Redlands CA, U.S.A). Vector layers were generated for trapping site locations and for presence and abundance of both species. To assess the micro-scale land cover factors, buffer zones were created around each trapping site. Five buffer zones were defined by radii (10-50 m at 10 m intervals). Sixteen land cover variables were extracted for each buffer zone to characterize the trapping sites. Extracted land cover variables included the percent coverage of the 11 land cover classes and three additional composite vegetation classes, and two additional landscape descriptors NDVI and distance to waterways. NDVI was used to characterize the sites separately from the land cover classification as it is calculated directly from the multispectral data and is without bias regarding land cover classes and plant physiology. Distance to waterways was calculated using Euclidean distance to the nearest water feature and was included in the site characterization as waterways may provide larval breeding habitat. Additional information of the land cover variables and extraction techniques is in Table 2. Land cover variable extraction was repeated for the five buffer zones to determine the best spatial scale at which to identify the significant land cover variables in the land cover-mosquito abundance relationship. Table 2. Land cover variables were extracted for the five buffer zones (10-50 m radii at 10 m intervals) around each trapping site. Land cover variables were used to characterize the mosquito habitat. Land cover was used as independent variables in the stepwise regression. Land cover Variable Description Composed of 11 classes: bare earth, pavement, structure, pool, water, shadow, and five vegetation classes herbaceous (0 m), shrub ( m), low height tree ( m), medium height tree ( m), and high height tree (9.4+ m). Percent coverage of each class was calculated All Vegetation Percent coverage of a composite of the five vegetation classes All Vegetation with height All Trees Distance to Water Average NDVI Percent coverage of a composite of the four vegetation classes defined by height (shrub, low tree, medium tree, high tree) Percent coverage of a composite of the three tree classes (low tree, medium tree, and high tree) Euclidean distance in meters from trapping site GPS point to nearest waterway (washes and man-made ponds). Pools were not included NDVI was used as proxy of vegetated cover. Average NDVI for buffer zone was extracted from NDVI raster
5 Vol. 37, no. 2 Journal of Vector Ecology 411 Land cover accuracy assessment A GPS receiver was used to select accuracy points throughout the entire study area. Accuracy points were not selected within the classification s training site pixels. At least 25 accuracy points were chosen for each of the 11 classes, except for the water class due to the lack of water bodies in the study area for both sampling and assessment. Points were selected in the middle of objects to avoid confusion that could be caused by mixed pixels possibly representing multiple classes. Forty-nine points were collected in proximity of mosquito trapping sites. The ERDAS Imagine 9.3 accuracy assessment tool was used to examine what the accuracy points actually represent. Assessment points were compared to the CART classification of those same points. Data from the accuracy assessment were used in an error matrix which provides producer s and user s accuracies along with kappa values for each of the 11 classes and overall accuracy and kappa values calculated for the entire classification product (Table 3) (Congalton 1991). Statistical analysis Relationships between seasonal Ae. aegypti and Cx. quinquefasciatus abundance and land cover variables at trapping sites (n=49 for both species) were determined using stepwise regression. The mosquito counts and land cover variables were log transformed and square-root transformed, respectively, to improve assumptions of normality and homogeneity of variance (Gomez and Gomez 1984). Stepwise regression was used to select the significant independent variables (P 0.15 thresholds for variable inclusion) with a manual backward elimination to remove noise variables. Manual backward elimination consisted of iteratively removing non-significant (P > 0.05) independent variables and observing the effect on the error rate of the model. Models for the five buffer zones were compared based on their R 2 values to determine the best scale for observing land cover-mosquito abundance relationships for both species. Prior to the stepwise regression, the multicollinearity of the explanatory variables was examined based on the variance inflation factor (VIF). We found high multicollienearity (VIF > 10) for several land cover variables, so multiple models were necessary to analyze them separately. JMP 9 (SAS Institute Inc.) was used for all statistical analysis. Species presence risk maps Based on the linear equation derived from the regression analysis, presence risk maps of seasonal Ae. aegypti and Cx. quinquefasciatus were produced using the raster calculator tool in ArcGIS 10. Binary raster layers were created for each land cover variable. The presence of each land cover variable within a 60 x 60 m moving window was calculated. Using the land cover layers and the land cover-mosquito abundance relationships, predictive SDMs were created to determine the risk of seasonal Ae. aegypti and Cx. quinquefasciatus presence. The presence risk maps highlight the spatial variability of seasonal mosquito abundance in Tucson. The presence risk maps of seasonal Ae. aegypti and Cx. quinquefasciatus presence were produced at a spatial resolution of 60 m. Residual plots (estimated mosquito counts were subtracted from the observed mosquito counts) and crossvalidation were used to assess the performance of the models. The residual plots provide information on whether the model s error is random and if the model can be improved by the inclusion of additional predictor variables. The models were evaluated by examining model residuals and the relationship between the modeled residual and observed data for both mosquito species. Cross-validation provides information on the generality of the risk maps and involved running ten iterations of the model that omitted a number of random trapping sites (5, 10, 15, 20, 25, 30 traps) and observing the effect on the average R 2 value for comparison purposes. RESULTS Vector abundance A total of 2,207 adult female Ae. aegypti and 3,944 adult female Cx. quinquefasciatus were collected from 49 trapping sites during the mosquito season. In both years, Cx. quinquefasciatus had an overall higher trapped presence than Ae. aegypti, however only a few sites were more productive in terms of the number of Cx. quinquefasciatus trapped. Individual trap totals varied considerably, even at trapping sites in close proximity (Figures 3a, 3b). Ae. aegypti and Cx. quinquefasciatus were both observed at 47 sites, and only one species was observed at two of the sites. However, at least one species was observed at each trapping site. The spatial distribution of mosquito abundance and trapping sites was not even, with a high percentage of mosquito trapping sites located in the residential neighborhoods near the center of the study area. Landscape scale To determine the best scale at which to define the landscape-abundance relationship, the stepwise regression models were compared based on the models R 2 values (Table 4). Land cover variables account for some of the variation in seasonal Ae. aegypti abundance for all buffer zone radii. Land cover accounts for some of the variation of seasonal Cx. quinquefasciatus abundance at the 30 m, 40 m, and 50 m radii buffer zones but does not account for any variation in seasonal Cx. quinquefasciatus abundance at the 10 m and 20 m radii buffer zones. The 30 m radius buffer zone had the highest R 2 values for both Ae. aegypti (0.66) and Cx. quinquefasciatus (0.21). Ae. aegypti abundance showed a stronger relationship (higher R 2 values) to land cover variables than Cx. quinquefasciatus abundance at all buffer sizes. The landscape scale results indicate that 60 m (30 m radius) is the best scale at which to observe the land cover-mosquito abundance relationship. The Ae. aegypti model s higher R 2 values indicate that land cover plays a more significant role in defining Ae. aegypti habitat and seasonal abundance than it does in defining Cx. quinquefasciatus habitat and abundance. Land cover-mosquito abundance relationships The results of the multicollinearity analysis did not
6 412 Journal of Vector Ecology December 2012 Figure 3. Distribution of mosquitoes. (a) Distribution of seasonal Ae. aegypti abundance per site; (b) distribution of seasonal Cx. quinquefasciatus abundance per site. The size of the circle represents the number of mosquitoes trapped at each site. High Ae. aegypti counts were observed in residential neighborhoods while high Cx. quinquefasciatus counts were observed in the parks, managed green areas around the university campus, and washes in the residential neighborhoods. identify a collinear relationship between the explanatory land cover variables. The results of the stepwise regressions (Table 4) reveal the relationship between land cover variables and log-transformed seasonal Ae. aegypti and Cx. quinquefasciatus counts at the five buffer sizes. The 30 m radius buffer models indicate that land cover variables explain 66% of the variation in seasonal Ae. aegypti abundance and 21% of the variation in seasonal Cx. quinquefasciatus abundance. The regression models indicate that at the best scale for observing the land cover-mosquito abundance relationship (30 m radius) three land cover variables define the habitat of both species. Structure and medium height tree presence have positive associations with Ae. aegypti abundance, while bare earth presence had negative associations with Ae. aegypti abundance. Pavement and medium height tree presence have positive associations with Cx. quinquefasciatus abundance, while shrub presence had negative associations with Cx. quinquefasciatus abundance. Predictive mosquito habitat maps Static maps were created using the land covermosquito abundance relationships for Ae. aegypti and Cx. quinquefasciatus to highlight the risk of seasonal mosquito presence in Tucson. The Ae. aegypti presence risk map (Figure 4 a) was created based on the significant predictive land cover variables percent structure, bare earth, and medium tree:
7 Vol. 37, no. 2 Journal of Vector Ecology 413 Table 3. Error matrix results for the land cover classification performed with band multispectral NAIP data, NDVI data, and 2008 LiDAR CHM data. Class Total User's Accuracy (%) Kappa 1 Structure Pavement Herbaceous Shrub Pool Water Bare Earth Shadow High Tree Low Tree Medium Tree Total Producer's Accuracy (%) Kappa Overall Accuracy 86.3% Kappa 0.85
8 414 Journal of Vector Ecology December 2012 Table 4. Overview of the significant land cover variables used to construct the mosquito abundance models for Ae. aegypti (Aedes) and Cx. quinquefasciatus (Culex) at the five buffer radii. Quantities in the spaces are the coefficients assigned to the variables that were significant (P<0.15). This table also shows the model goodness of fit represented by the R 2 value for each model. Land Cover Variables 10m 20m 30m 40m 50m Aedes Culex Aedes Culex Aedes Culex Aedes Culex Aedes Culex Structure Pavement Herbaceous Shrub Pool Water Bare Earth Shadow Low Tree Medium Tree High Tree All Tree All Vegetation with Height All Vegetation Water Dist (m) Buffer Avg NDVI R 2 Value Ae. aegypti = (Structure) 1.11(Bare Earth) (Medium Tree). While the Cx. quinquefasciatus presence risk map (Figure 4 b) was created using significant land cover variables pavement, shrub, and medium tree: Cx. quinquefasciatus = (Pavement) 5.65(Shrub) (Medium Tree). Because the 30 m radius (60 m diameter) buffer zone was identified as the best scale to observe the land covermosquito abundance relationship, the presence risk maps were produced at 60 m spatial resolution. The Ae. aegypti presence risk map shows that Ae. aegypti very high risk areas are isolated to small sections of residential neighborhoods. The majority of residential neighborhoods in the center and eastern portion of the study have moderate risk of seasonal Ae. aegypti presence, while the less developed portions of the study area, along the interstate and the southwestern section, shows very low risk of seasonal Ae. aegypti presence. In comparison to the relatively low risk of seasonal Ae. aegypti presence, the entire study area sees a significantly higher risk of seasonal Cx. quinquefasciatus presence. The Cx. quinquefasciatus presence risk map shows that Cx. quinquefasciatus very high risk areas are larger and less defined by specific land use designations (e.g., residential neighborhoods). The map indicates that a majority of the study area has at least a moderate risk of seasonal Cx. quinquefasciatus presence, including the less developed southwestern portion of the study area where very low Cx. quinquefasciatus presence would be expected. Accuracy assessment and model evaluation Accuracy assessment of the supervised classification of the land cover map found that the classification had an accuracy of 86.3% and a kappa value of 0.85 (Table 3). Performance of the land cover-mosquito abundance model was assessed examining the pattern of the residual plots (Figures 5a, 5b). Model accuracy was determined by the R 2 value of the residual plots. The residuals indicate that the Ae. aegypti model (residual plot R 2 =0.58) is performing better (smaller residuals) than the Cx. quinquefasciatus model (residual plot R 2 =0.84; larger residuals indicating an overestimation at lower range of the observed counts). The non-random pattern of the residual plots of both models indicates that the predictor variables are not capturing all of the explanatory information necessary for the most accurate prediction of seasonal mosquito abundance and that additional factors could play a significant role in determining seasonal Ae. aegypti and Cx. quinquefasciatus abundance.
9 Vol. 37, no. 2 Journal of Vector Ecology 415 Figure 4. Presence risk map of (a) seasonal Ae. aegypti and (b) seasonal Cx. quinquefasciatus presence. Presence risk maps have a spatial resolution of 60m. Risk of presence was determined from the predicted mosquito seasonal abundance. Five risk classes were then defined by equal intervals of the percent seasonal mosquito abundance.
10 416 Journal of Vector Ecology December 2012 Excluding climatic variables in the model accounts for some of models poor performance; both Ae. aegypti and Cx. quinquefasciatus have known population responses to climate (Hayden et al. 2010, Kolivras and Comrie 2004). Additionally, due the small spatial scale or area in which the mosquito exists, spatial resolution of the image data causes a limitation in pinpointing mosquito presence. The model uses high spatial resolution image data using a large scale approach to the region to identify areas at high and low risk for mosquito presence. However, the model cannot account for landscape variables that are too fine for the image data to pick up. Thus, the model s modest fit can be partially explained by the lack of ground based social factors such as artificial container coverage that is likely to be present in human modified environments. Cross-validation was performed to test the robustness of the land cover-mosquito abundance model by running ten iterations of the model that omitted a number of random trapping sites (5, 10, 15, 20, 25, 30 traps). The resulting R 2 values (Table 5) were compared and the results of all the trapping sites produced the most accurate model of Cx. quinquefasciatus abundance, while the Ae. aegypti model would be improved by using a sample of the trapping data. The absence of an observable drop in the Ae. aegypti R 2 values highlights the importance of selecting trapping site locations that are well representative of the potential Ae. aegypti habitat. DISCUSSION This study analyzed the spatial distribution of seasonal Ae. aegypti and Cx. quinquefasciatus abundance and their relationships with land cover in Tucson for the mosquito season. This study demonstrated that (1) urban micro-habitat land cover variables play an important role in the spatial distribution of mosquitoes and (2) the spatial scale of the analysis affects our ability to identify and quantify the land cover-mosquito abundance relationship. Furthermore, this study used the land cover-mosquito abundance relationships to produce presence risk maps of seasonal Ae. aegypti and Cx. quinquefasciatus that highlight the land cover influences on the spatial distribution of mosquitoes in Tucson. Because this study focuses on land cover influences, climatic factors such as temperature and precipitation were not included. However, it is possible that microclimate differences between trapping sites could be significant in explaining some of the observed variance in Ae. aegypti and Cx. quinquefasciatus presence and abundance. Previous studies have reported that mosquito presence is positively associated with higher vegetation coverage (Zhou et al. 2007, Vezzani et al. 2005). In Tucson, high mosquito activity has been recorded in residential neighborhoods (Fink 1998), areas typically characterized by higher vegetation coverage. By incorporating vegetation height data, this study was able to move beyond general vegetation presence and determine that both species have a distinct preference for the presence of medium height trees (3-9 m). The results of this study show that land cover partially determines Ae. aegypti and Cx. quinquefasciatus habitat suitability in urban environments. Presence of both Ae. aegypti and Cx. quinquefasciatus at 47 of the 49 sites indicate that the mosquitoes have common habitat requirements, with similar associations to human built environmental variables (structure and pavement) and vegetation cover (medium height trees and/or shrubs). While both species were found at a majority of the trapping sites, the differences in Ae. aegypti and Cx. quinquefasciatus seasonal abundance emphasizes the species different habitat requirements. The models show that Cx. quinquefasciatus has an overall higher predicted risk of presence than Ae. aegypti throughout the study area. This supports previous findings that while Cx. quinquefasciatus share habitat with Ae. aegypti, Cx. quinquefasciatus typically has a wider range of available habitats (Almiron and Brewer 1996, Pires and Gleiser 2007). Comparison of the models indicates that there is a significantly stronger relationship between land cover-ae. aegypti abundance than land cover-cx. quinquefasciatus abundance. The stronger performance of the Ae. aegypti model indicates that when not accounting for climatic influences, land cover may play a more significant role in defining Ae. aegypti seasonal abundance than land cover plays in defining it for Cx. quinquefasciatus. This suggests that Ae. aegypti potentially has more stringent habitat requirements that could explain why we have yet to see a dengue outbreak in the Tucson region. With less available potential habitat, Ae. aegypti has yet been unable to produce the large population for a continuous period of time, one environmental factor necessary for dengue to take hold. Similarly, Cx. quinquefasciatus less stringent habitat requirements, as Table 5. Cross-validation of the risk maps involved removing a number of randomly selected mosquito traps. Each iteration of the model was run 10 times and the average R 2 value was calculated for comparison. Additionally, the number of times the model was run where an R 2 value of 0.00 was calculated is recorded. # Traps Removed R 2 Value # With No Significant Variables Aedes Culex Aedes Culex
11 Vol. 37, no. 2 Journal of Vector Ecology 417 Figure 5. Model residual plots as a function of (a) Ae. aegypti seasonal abundance and model residuals (b) Cx. quinquefasciatus seasonal abundance and model residuals. Ae. aegypti residual plot has a R 2 value of 0.57; Cx. quinquefasciatus residual plot has a R 2 value of For visualization purposes, the Cx. quinquefasciatus plot x-axis ends at 300, there are points that are not displayed on the plot, however they are included in the calculation of the R 2 value. defined by land cover, means there is plenty of potential habitat and could explain why the mosquito and WNV have successfully established themselves in the Tucson region. The influence of scale can be seen in the buffer analysis. The best model for both species used the land cover variables from the 30 m radius buffer zone. This implies that if the scale of the analysis is too fine then the heterogeneous nature of the urban landscape will not be adequately defined and the significance of the land cover in determining the spatial variability of seasonal mosquito presence will be underrepresented. Aggregating data to 60 m spatial resolution produces presence risk maps at a far courser scale than the original input data. However, this aggregation does not mean that using lower spatial resolution original data would produce an equivalent presence risk map. Accurate modeling of the mosquito habitat still requires the initial use of high spatial resolution image data in order to produce an accurate land cover classification which can explain the finescale spatial relationship between land cover and mosquito abundance. Successful prediction of the spatial distribution of Ae. aegypti and Cx. quinquefasciatus seasonal abundance could allow vector control efforts to target areas at highest risk for mosquito presence, thereby focusing mosquito control actions to areas that would benefit the most from implementation of mosquito mitigation practices. Further research is needed to determine if the mechanisms behind the observed association between mosquitoes and land cover variables is limited to this particular region and climate. If land cover influences prove to be predictive at other scales and in other regions, this study s findings may be useful in guiding future disease-vector surveillance and mosquito control efforts. Additionally, because land cover does not fully explain the spatial distribution and risk of seasonal presence for either Ae. aegypti or Cx. quinquefasciatus, further research on the urban microclimate and land cover-microclimate interactions is needed to improve the predictive risk models. Acknowledgments Mosquito collection and identification was headed by Elizabeth Willott with assistance in weekly trapping provided by Cory Morin. We thank the Pima Association of Governments for sharing the LiDAR data and to the many residents who allowed mosquito trapping on their properties. NAIP data were provided by the USDA s Farm Service Agency (FSA) through the Aerial Photography Field Office. Special thanks to Kyle Hartfield for his invaluable assistance with this
12 418 Journal of Vector Ecology December 2012 work and Drs. Michael Crimmins and Stephen Yool for their comments on the manuscript. Support for this project was provided by the Arizona Remote Sensing Center and NSF grant DEB REFERENCES CITED Almirón, W.R. and M.E. Brewer Classification of immature stage habitats of Culicidae (Diptera) collected in Córdoba, Argentina. Mem. Em. I. Oswaldo Cruz 91: 1-9. Congalton, R A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens. Environ. 37: Fink, T., B. Hau, B. Baird, S. Palmer, S. Kaplan, F. Ramberg, D. Mead, and H. Hagedorn Aedes aegypti in Tucson, Arizona. Emerg. Infect. Dis. 4: Focks, D.A., D.G. Haile, E. Daniels, and G.A. Mount Dynamic life table model for Aedes aegypti (Diptera: Culicidae): Simulation results and validation. J. Med. Entomol. 30: Fuller, D.O., A. Troyo, O. Calderon-Arguedas, and J.C. Beier Dengue vector (Aedes aegypti) larval habitats in an urban environment of Costa Rica analysed with ASTER and QuickBird imagery. Int. J. Remote Sens. 31: Ghosh, D Geospatial analysis of West Nile Virus (WNV) incidences in a heterogeneous urban environment: A case study in the twin cities metropolitan area of Minnesota. In: J.A. Maantay and S. McLafferty (eds.) Geospatial Analysis of Environmental Health, pp Springer Netherlands, New York. Gomez, K.A. and A.A. Gomez Data that violate some assumptions of the analysis of variance. In: Statistical Procedures for Agricultural Research, 2nd ed. pp John Wiley & Sons, New York. Hartfield, K.A., K.I. Landau and W.J.D. van Leeuwen Fusion of high resolution aerial multispectral and LiDAR data: Land cover in the context of urban mosquito habitat. Remote Sens. 3: Hayden, M.H., C.K. Uejio, K. Walker, F. Ramberg, R. Moreno, C. Rosales, M. Gameros, L.O. Mearns, E. Zielinski- Gutierrez, and C.R. Janes Microclimate and human factors in the divergent ecology of Aedes aegypti along the Arizona, US/Sonora, MX Border. EcoHealth 7: Hoeck, P., F. Ramberg, S. Merrill, C. Moll, and H. Hagedorn Population and parity levels of Aedes aegypti collected in Tucson. J. Vector Ecol. 28: Kolivras, K.N. and A.C. Comrie Climate and infectious disease in the southwestern United States. Prog. Phys. Geog. 28: Kolivras, K.N Mosquito habitat and Dengue risk potential in Hawaii: A conceptual framework and GIS application. Prof. Geogr. 58: Liu, H., Q. Weng and D. Gaines Spatio-temporal analysis of the relationship between WNV dissemination and environmental variables in Indianapolis, USA. Int. J. Hlth. Geogr. 7: 66. Morin, C. and A.C. Comrie Modeled response of the West Nile virus vector Culex quinquefasciatus to changing climate using the dynamic mosquito simulation model. Int. J. Biometeorol. 54: Pires, D.A. and R.M. Gleiser Cuerpos de agua temporarios y permanentes como habitats larvales de mosquitos (Diptera: Culicidae) en la ciudad de Cordoba. Biol. Acuat. 23: 68. Reiter, M.E. and D.A. Lapointe Landscape factors influencing the spatial distribution and abundance of mosquito vector Culex quinquefasciatus (Diptera: Culicidae) in a mixed residential-agricultural community in Hawai i. J. Med. Entomol. 44: Secord, J. and A. Zakhor Tree detection in urban regions using aerial Lidar and image data. IEEE Geosci. Remote S. 4: STGS Pima Association of Governments 2008 Digital Orthophotography and LiDAR Report. Sanborn Total Geospatial Solutions. Tucker, C.J Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8: Vezzani, D., A. Rubio, S.M. Velazquez, N. Schweigmann, and T. Wiegand Detailed assessment of microhabitat suitability for Aedes aegypti (Diptera:Culicidae) in Buenos Aires, Argentina. Acta. Trop. 95: Zhou, G., S. Munga, N. Minakawa, A.K. Githeko and G. Yan Spatial relationship between adult malaria vector abundance and environmental factors in western Kenya highlands. Am. J. Trop. Med. Hyg. 77:
Evaluating Urban Vegetation Cover Using LiDAR and High Resolution Imagery
Evaluating Urban Vegetation Cover Using LiDAR and High Resolution Imagery Y.A. Ayad and D. C. Mendez Clarion University of Pennsylvania Abstract One of the key planning factors in urban and built up environments
More informationUrbanization, Land Cover, Weather, and Incidence Rates of Neuroinvasive West Nile Virus Infections In Illinois
Urbanization, Land Cover, Weather, and Incidence Rates of Neuroinvasive West Nile Virus Infections In Illinois JUNE 23, 2016 H ANNAH MATZ KE Background Uganda 1937 United States -1999 New York Quickly
More informationThe Road to Data in Baltimore
Creating a parcel level database from high resolution imagery By Austin Troy and Weiqi Zhou University of Vermont, Rubenstein School of Natural Resources State and local planning agencies are increasingly
More informationEPIDEMIOLOGY FOR URBAN MALARIA MAPPING
TELE-EPIDEMIOLOGY EPIDEMIOLOGY FOR URBAN MALARIA MAPPING @IRD/M Dukhan Vanessa Machault Observatoire Midi-Pyrénées, Laboratoire d Aérologie Pleiades days 17/01/2012 The concept of Tele-epidemiology The
More informationApplication of Remote Sensing and Global Positioning Technology for Survey and Monitoring of Plant Pests
Application of Remote Sensing and Global Positioning Technology for Survey and Monitoring of Plant Pests David Bartels, Ph.D. USDA APHIS PPQ CPHST Mission Texas Laboratory Spatial Technology and Plant
More informationRole of GIS in Tracking and Controlling Spread of Disease
Role of GIS in Tracking and Controlling Spread of Disease For Dr. Baqer Al-Ramadan By Syed Imran Quadri CRP 514: Introduction to GIS Introduction Problem Statement Objectives Methodology of Study Literature
More informationPlant Distribution in a Sonoran Desert City CAP LTER Data Explorations
in a Sonoran Desert City CAP LTER Data Explorations Author: Ecology Explorers Team, adapted from data analysis by J. Walker and the CAP LTER 200 point survey Time: 15-30 minutes Grade Level: 9-12 Background:
More informationUSING HYPERSPECTRAL IMAGERY
USING HYPERSPECTRAL IMAGERY AND LIDAR DATA TO DETECT PLANT INVASIONS 2016 ESRI CANADA SCHOLARSHIP APPLICATION CURTIS CHANCE M.SC. CANDIDATE FACULTY OF FORESTRY UNIVERSITY OF BRITISH COLUMBIA CURTIS.CHANCE@ALUMNI.UBC.CA
More informationSite-specific Prediction of Mosquito Abundance using Spatio-Temporal Geostatistics
Site-specific Prediction of Mosquito Abundance using Spatio-Temporal Geostatistics E.-H. Yoo 1, D. Chen 2 and C. Russell 3 1 Department of Geography, University at Buffalo, SUNY, Buffalo, NY, USA eunhye@buffalo.edu,
More informationTELE-EPIDEMIOLOGY URBAN MALARIA MAPPING
TELE-EPIDEMIOLOGY URBAN MALARIA MAPPING Ministère de la Défense Vanessa Machault Advances in Geospatial Technologies for Health 12-13/09/2011 Objective To develop a robust pre-operational methodology to
More informationWetland Mapping. Wetland Mapping in the United States. State Wetland Losses 53% in Lower US. Matthew J. Gray University of Tennessee
Wetland Mapping Caribbean Matthew J. Gray University of Tennessee Wetland Mapping in the United States Shaw and Fredine (1956) National Wetlands Inventory U.S. Fish and Wildlife Service is the principle
More information1. Introduction. Chaithanya, V.V. 1, Binoy, B.V. 2, Vinod, T.R. 2. Publication Date: 8 April DOI: https://doi.org/ /cloud.ijarsg.
Cloud Publications International Journal of Advanced Remote Sensing and GIS 2017, Volume 6, Issue 1, pp. 2088-2096 ISSN 2320 0243, Crossref: 10.23953/cloud.ijarsg.112 Research Article Open Access Estimation
More informationDROUGHT RISK EVALUATION USING REMOTE SENSING AND GIS : A CASE STUDY IN LOP BURI PROVINCE
DROUGHT RISK EVALUATION USING REMOTE SENSING AND GIS : A CASE STUDY IN LOP BURI PROVINCE K. Prathumchai, Kiyoshi Honda, Kaew Nualchawee Asian Centre for Research on Remote Sensing STAR Program, Asian Institute
More informationDROUGHT ASSESSMENT USING SATELLITE DERIVED METEOROLOGICAL PARAMETERS AND NDVI IN POTOHAR REGION
DROUGHT ASSESSMENT USING SATELLITE DERIVED METEOROLOGICAL PARAMETERS AND NDVI IN POTOHAR REGION Researcher: Saad-ul-Haque Supervisor: Dr. Badar Ghauri Department of RS & GISc Institute of Space Technology
More informationManitoba Weekly West Nile virus Surveillance Report
Manitoba Weekly West Nile virus Surveillance Report Week 26 (June 25 to July 1, 2017) Communicable Disease Control Active Living, Population and Public Health Branch Active Living, Indigenous Relations,
More informationDefining microclimates on Long Island using interannual surface temperature records from satellite imagery
Defining microclimates on Long Island using interannual surface temperature records from satellite imagery Deanne Rogers*, Katherine Schwarting, and Gilbert Hanson Dept. of Geosciences, Stony Brook University,
More informationPeninsular Florida p Modeled Water Table Depth Arboviral Epidemic Risk Assessment. Current Assessment: 06/08/2008 Week 23 Initial Wetting Phase
Peninsular Florida p Modeled Water Table Depth Arboviral Epidemic Risk Assessment Current Assessment: 06/08/2008 Week 23 Initial Wetting Phase Modeled Water Table Depth: MWTD has remained low across much
More informationDigital Change Detection Using Remotely Sensed Data for Monitoring Green Space Destruction in Tabriz
Int. J. Environ. Res. 1 (1): 35-41, Winter 2007 ISSN:1735-6865 Graduate Faculty of Environment University of Tehran Digital Change Detection Using Remotely Sensed Data for Monitoring Green Space Destruction
More informationGeo-statistical Dengue Risk Model Case Study of Lahore Dengue Outbreaks 2011
Geo-statistical Dengue Risk Model Case Study of Lahore Dengue Outbreaks 2011 BILAL TARIQ Department of Remote Sensing & Geo-information Science Institute of Space Technology (IST) Karachi Campus, Pakistan
More informationUrban Tree Canopy Assessment Purcellville, Virginia
GLOBAL ECOSYSTEM CENTER www.systemecology.org Urban Tree Canopy Assessment Purcellville, Virginia Table of Contents 1. Project Background 2. Project Goal 3. Assessment Procedure 4. Economic Benefits 5.
More informationProgress Report Year 2, NAG5-6003: The Dynamics of a Semi-Arid Region in Response to Climate and Water-Use Policy
Progress Report Year 2, NAG5-6003: The Dynamics of a Semi-Arid Region in Response to Climate and Water-Use Policy Principal Investigator: Dr. John F. Mustard Department of Geological Sciences Brown University
More informationAccuracy Assessment of Land Cover Classification in Jodhpur City Using Remote Sensing and GIS
Accuracy Assessment of Land Cover Classification in Jodhpur City Using Remote Sensing and GIS S.L. Borana 1, S.K.Yadav 1 Scientist, RSG, DL, Jodhpur, Rajasthan, India 1 Abstract: A This study examines
More informationGrant Opportunity Monitoring Bi-State Sage-grouse Populations in Nevada
Grant Opportunity Monitoring Bi-State Sage-grouse Populations in Nevada Proposals are due no later than November 13, 2015. Grant proposal and any questions should be directed to: Shawn Espinosa @ sepsinosa@ndow.org.
More informationThis is trial version
Journal of Rangeland Science, 2012, Vol. 2, No. 2 J. Barkhordari and T. Vardanian/ 459 Contents available at ISC and SID Journal homepage: www.rangeland.ir Full Paper Article: Using Post-Classification
More informationSpatio-temporal dynamics of the urban fringe landscapes
Spatio-temporal dynamics of the urban fringe landscapes Yulia Grinblat 1, 2 1 The Porter School of Environmental Studies, Tel Aviv University 2 Department of Geography and Human Environment, Tel Aviv University
More informationA diffusion model to predict spatial and temporal population dynamics of Rift valley fever vectors in Northern Senegal
A diffusion model to predict spatial and temporal population dynamics of Rift valley fever vectors in Northern Senegal Soti V., Tran A., Fontenille D, Lancelot R, Chevalier V., Thiongane Y., Degenne P.,
More informationMAPPING LAND USE/ LAND COVER OF WEST GODAVARI DISTRICT USING NDVI TECHNIQUES AND GIS Anusha. B 1, Sridhar. P 2
MAPPING LAND USE/ LAND COVER OF WEST GODAVARI DISTRICT USING NDVI TECHNIQUES AND GIS Anusha. B 1, Sridhar. P 2 1 M. Tech. Student, Department of Geoinformatics, SVECW, Bhimavaram, A.P, India 2 Assistant
More informationLand cover/land use mapping and cha Mongolian plateau using remote sens. Title. Author(s) Bagan, Hasi; Yamagata, Yoshiki. Citation Japan.
Title Land cover/land use mapping and cha Mongolian plateau using remote sens Author(s) Bagan, Hasi; Yamagata, Yoshiki International Symposium on "The Imp Citation Region Specific Systems". 6 Nove Japan.
More informationMODELLING AND UNDERSTANDING MULTI-TEMPORAL LAND USE CHANGES
MODELLING AND UNDERSTANDING MULTI-TEMPORAL LAND USE CHANGES Jianquan Cheng Department of Environmental & Geographical Sciences, Manchester Metropolitan University, John Dalton Building, Chester Street,
More informationProgram Update. Lisle Township August 2018 Status Report SEASON PERSPECTIVE
Lisle Township August 2018 Status Report SEASON PERSPECTIVE Introduction. Weather conditions critically affect the seasonal mosquito population. Excessive rainfall periods trigger hatches of floodwater
More informationUsing GIS: to analyze vector borne disease. David Attaway Esri
Using GIS: to analyze vector borne disease David Attaway Esri Why do I care? Example: YELP Most of us have used it Most of us let it help guide our decisions Most see it helpful But. How does this relate
More informationCITY OF FORT COLLINS AUGUST 2016 MONTHLY REPORT
CITY OF FORT COLLINS AUGUST 2016 MONTHLY REPORT MONTHLY REPORT: SEPTEMBER 6, 2016 COLORADO MOSQUITO CONTROL West Nile Virus Risk Contact CMC: City of Fort Collins Mosquito Control Program Broox Boze, Operations
More informationMODELING LIGHTNING AS AN IGNITION SOURCE OF RANGELAND WILDFIRE IN SOUTHEASTERN IDAHO
MODELING LIGHTNING AS AN IGNITION SOURCE OF RANGELAND WILDFIRE IN SOUTHEASTERN IDAHO Keith T. Weber, Ben McMahan, Paul Johnson, and Glenn Russell GIS Training and Research Center Idaho State University
More informationTemporal and Spatial Autocorrelation Statistics of Dengue Fever
Temporal and Spatial Autocorrelation Statistics of Dengue Fever Kanchana Nakhapakorn a and Supet Jirakajohnkool b a Faculty of Environment and Resource Studies, Mahidol University, Salaya, Nakhonpathom
More informationFMEL Arboviral Epidemic Risk Assessment: Second Update for 2012 Week 23 (June 12, 2012)
FMEL Arboviral Epidemic Risk Assessment: Second Update for 2012 Week 23 (June 12, 2012) Current Assesment of SLW\WN Epidemic Risk Figure 1. A map of peninsular Florida indicating areas currently at Medium
More informationApplications of GIS and Remote Sensing for Analysis of Urban Heat Island
Chuanxin Zhu Professor Peter V. August Professor Yeqiao Wang NRS 509 December 15, 2016 Applications of GIS and Remote Sensing for Analysis of Urban Heat Island Since the last century, the global mean surface
More informationThe Wide Dynamic Range Vegetation Index and its Potential Utility for Gap Analysis
Summary StatMod provides an easy-to-use and inexpensive tool for spatially applying the classification rules generated from the CT algorithm in S-PLUS. While the focus of this article was to use StatMod
More informationUrban Growth Analysis: Calculating Metrics to Quantify Urban Sprawl
Urban Growth Analysis: Calculating Metrics to Quantify Urban Sprawl Jason Parent jason.parent@uconn.edu Academic Assistant GIS Analyst Daniel Civco Professor of Geomatics Center for Land Use Education
More informationLesson 4b Remote Sensing and geospatial analysis to integrate observations over larger scales
Lesson 4b Remote Sensing and geospatial analysis to integrate observations over larger scales We have discussed static sensors, human-based (participatory) sensing, and mobile sensing Remote sensing: Satellite
More informationThe Current SLE/WN Epidemic Assesment
FMEL Arboviral Epidemic Risk Assessment: First Update for 2014 Week 18 (May 1, 2014) The Current SLE/WN Epidemic Assesment Funding for the Florida Medical Entomology Laboratory Epidemic Risk Model ended
More informationKNOWLEDGE-BASED CLASSIFICATION OF LAND COVER FOR THE QUALITY ASSESSEMENT OF GIS DATABASE. Israel -
KNOWLEDGE-BASED CLASSIFICATION OF LAND COVER FOR THE QUALITY ASSESSEMENT OF GIS DATABASE Ammatzia Peled a,*, Michael Gilichinsky b a University of Haifa, Department of Geography and Environmental Studies,
More informationUSE OF RADIOMETRICS IN SOIL SURVEY
USE OF RADIOMETRICS IN SOIL SURVEY Brian Tunstall 2003 Abstract The objectives and requirements with soil mapping are summarised. The capacities for different methods to address these objectives and requirements
More informationGIS APPLICATIONS IN SOIL SURVEY UPDATES
GIS APPLICATIONS IN SOIL SURVEY UPDATES ABSTRACT Recent computer hardware and GIS software developments provide new methods that can be used to update existing digital soil surveys. Multi-perspective visualization
More informationMonitoring Vegetation Growth of Spectrally Landsat Satellite Imagery ETM+ 7 & TM 5 for Western Region of Iraq by Using Remote Sensing Techniques.
Monitoring Vegetation Growth of Spectrally Landsat Satellite Imagery ETM+ 7 & TM 5 for Western Region of Iraq by Using Remote Sensing Techniques. Fouad K. Mashee, Ahmed A. Zaeen & Gheidaa S. Hadi Remote
More informationDescribing Greater sage-grouse (Centrocercus urophasianus) Nesting Habitat at Multiple Spatial Scales in Southeastern Oregon
Describing Greater sage-grouse (Centrocercus urophasianus) Nesting Habitat at Multiple Spatial Scales in Southeastern Oregon Steven Petersen, Richard Miller, Andrew Yost, and Michael Gregg SUMMARY Plant
More informationSensitivity Analysis of WRF Forecasts in Arizona During the Monsoon Season Case Study: August 2, 2005 to August 3, 2005
Sensitivity Analysis of WRF Forecasts in Arizona During the Monsoon Season Case Study: August 2, 2005 to August 3, 2005 Christopher L. Castro and Stephen Bieda III University of Arizona Susanne Grossman-Clarke
More informationIntroduction to Geographic Information Systems (GIS): Environmental Science Focus
Introduction to Geographic Information Systems (GIS): Environmental Science Focus September 9, 2013 We will begin at 9:10 AM. Login info: Username:!cnrguest Password: gocal_bears Instructor: Domain: CAMPUS
More informationTemporal and spatial mapping of hand, foot and mouth disease in Sarawak, Malaysia
Geospatial Health 8(2), 2014, pp. 503-507 Temporal and spatial mapping of hand, foot and mouth disease in Sarawak, Malaysia Noraishah M. Sham 1, Isthrinayagy Krishnarajah 1,2, Noor Akma Ibrahim 1,2, Munn-Sann
More informationSpatial Process VS. Non-spatial Process. Landscape Process
Spatial Process VS. Non-spatial Process A process is non-spatial if it is NOT a function of spatial pattern = A process is spatial if it is a function of spatial pattern Landscape Process If there is no
More informationPreparation of LULC map from GE images for GIS based Urban Hydrological Modeling
International Conference on Modeling Tools for Sustainable Water Resources Management Department of Civil Engineering, Indian Institute of Technology Hyderabad: 28-29 December 2014 Abstract Preparation
More informationA comparison of pixel and object-based land cover classification: a case study of the Asmara region, Eritrea
Geo-Environment and Landscape Evolution III 233 A comparison of pixel and object-based land cover classification: a case study of the Asmara region, Eritrea Y. H. Araya 1 & C. Hergarten 2 1 Student of
More informationNew Land Cover & Land Use Data for the Chesapeake Bay Watershed
New Land Cover & Land Use Data for the Chesapeake Bay Watershed Why? The Chesapeake Bay Program (CBP) partnership is in the process of improving and refining the Phase 6 suite of models used to inform
More informationAN INVESTIGATION OF AUTOMATIC CHANGE DETECTION FOR TOPOGRAPHIC MAP UPDATING
AN INVESTIGATION OF AUTOMATIC CHANGE DETECTION FOR TOPOGRAPHIC MAP UPDATING Patricia Duncan 1 & Julian Smit 2 1 The Chief Directorate: National Geospatial Information, Department of Rural Development and
More informationSpatio-temporal modeling of weekly malaria incidence in children under 5 for early epidemic detection in Mozambique
Spatio-temporal modeling of weekly malaria incidence in children under 5 for early epidemic detection in Mozambique Katie Colborn, PhD Department of Biostatistics and Informatics University of Colorado
More informationANALYSIS AND VALIDATION OF A METHODOLOGY TO EVALUATE LAND COVER CHANGE IN THE MEDITERRANEAN BASIN USING MULTITEMPORAL MODIS DATA
PRESENT ENVIRONMENT AND SUSTAINABLE DEVELOPMENT, NR. 4, 2010 ANALYSIS AND VALIDATION OF A METHODOLOGY TO EVALUATE LAND COVER CHANGE IN THE MEDITERRANEAN BASIN USING MULTITEMPORAL MODIS DATA Mara Pilloni
More informationQuantifying Weather and Climate Impacts on Health in Developing Countries (QWeCI)
Quantifying Weather and Climate Impacts on Health in Developing Countries (QWeCI) Science Talk QWeCIis funded by the European Commission s Seventh Framework Research Programme under the grant agreement
More informationUrban remote sensing: from local to global and back
Urban remote sensing: from local to global and back Paolo Gamba University of Pavia, Italy A few words about Pavia Historical University (1361) in a nice town slide 3 Geoscience and Remote Sensing Society
More informationMapping Coastal Change Using LiDAR and Multispectral Imagery
Mapping Coastal Change Using LiDAR and Multispectral Imagery Contributor: Patrick Collins, Technical Solutions Engineer Presented by TABLE OF CONTENTS Introduction... 1 Coastal Change... 1 Mapping Coastal
More informationUsing Geomatics in Urban Forestry
Using Geomatics in Urban Forestry By Kieran Hunt PAUL COWIE AND ASSOCIATES c o n s u l t i n g a r b o r i s t s / u r b a n f o r e s t e r s All maps in this presentation were created by Kieran Hunt
More informationAnalysis of Relative Humidity in Iraq for the Period
International Journal of Scientific and Research Publications, Volume 5, Issue 5, May 2015 1 Analysis of Relative Humidity in Iraq for the Period 1951-2010 Abdulwahab H. Alobaidi Department of Electronics,
More informationSUPPORTING INFORMATION. Ecological restoration and its effects on the
SUPPORTING INFORMATION Ecological restoration and its effects on the regional climate: the case in the source region of the Yellow River, China Zhouyuan Li, Xuehua Liu,* Tianlin Niu, De Kejia, Qingping
More informationGIS and Remote Sensing
Spring School Land use and the vulnerability of socio-ecosystems to climate change: remote sensing and modelling techniques GIS and Remote Sensing Katerina Tzavella Project Researcher PhD candidate Technology
More informationCORRELATION BETWEEN URBAN HEAT ISLAND EFFECT AND THE THERMAL INERTIA USING ASTER DATA IN BEIJING, CHINA
CORRELATION BETWEEN URBAN HEAT ISLAND EFFECT AND THE THERMAL INERTIA USING ASTER DATA IN BEIJING, CHINA Yurong CHEN a, *, Mingyi DU a, Rentao DONG b a School of Geomatics and Urban Information, Beijing
More informationApplications of GIS in Health Research. West Nile virus
Applications of GIS in Health Research West Nile virus Outline Part 1. Applications of GIS in Health research or spatial epidemiology Disease Mapping Cluster Detection Spatial Exposure Assessment Assessment
More informationRating of soil heterogeneity using by satellite images
Rating of soil heterogeneity using by satellite images JAROSLAV NOVAK, VOJTECH LUKAS, JAN KREN Department of Agrosystems and Bioclimatology Mendel University in Brno Zemedelska 1, 613 00 Brno CZECH REPUBLIC
More informationArizona Climate Summary February 2012
Arizona Climate Summary February 2012 Summary of conditions for January 2012 January 2012 Temperature and Precipitation Summary January 1 st 20 th : The New Year has started on a very dry note. The La
More informationVISUALIZATION URBAN SPATIAL GROWTH OF DESERT CITIES FROM SATELLITE IMAGERY: A PRELIMINARY STUDY
CO-439 VISUALIZATION URBAN SPATIAL GROWTH OF DESERT CITIES FROM SATELLITE IMAGERY: A PRELIMINARY STUDY YANG X. Florida State University, TALLAHASSEE, FLORIDA, UNITED STATES ABSTRACT Desert cities, particularly
More informationAssessing the benefit of green infrastructure/wsud on urban microclimate
Supporting the strategic planning of City of Unley (SA) towards a water sensitive city by quantifying the urban microclimate benefits using the Water Sensitive Cities Modelling Toolkit A Collaboration
More informationMcHenry County Property Search Sources of Information
Disclaimer: The information in this system may contain inaccuracies or typographical errors. The information in this system is a digital representation of information derived from original documents; as
More informationKimberly J. Mueller Risk Management Solutions, Newark, CA. Dr. Auguste Boissonade Risk Management Solutions, Newark, CA
1.3 The Utility of Surface Roughness Datasets in the Modeling of United States Hurricane Property Losses Kimberly J. Mueller Risk Management Solutions, Newark, CA Dr. Auguste Boissonade Risk Management
More informationCentral Ohio Air Quality End of Season Report. 111 Liberty Street, Suite 100 Columbus, OH Mid-Ohio Regional Planning Commission
217 218 Central Ohio Air Quality End of Season Report 111 Liberty Street, Suite 1 9189-2834 1 Highest AQI Days 122 Nov. 217 Oct. 218 July 13 Columbus- Maple Canyon Dr. 11 July 14 London 11 May 25 New Albany
More informationDAMAGE DETECTION OF THE 2008 SICHUAN, CHINA EARTHQUAKE FROM ALOS OPTICAL IMAGES
DAMAGE DETECTION OF THE 2008 SICHUAN, CHINA EARTHQUAKE FROM ALOS OPTICAL IMAGES Wen Liu, Fumio Yamazaki Department of Urban Environment Systems, Graduate School of Engineering, Chiba University, 1-33,
More informationLanduse and Landcover change analysis in Selaiyur village, Tambaram taluk, Chennai
Landuse and Landcover change analysis in Selaiyur village, Tambaram taluk, Chennai K. Ilayaraja Department of Civil Engineering BIST, Bharath University Selaiyur, Chennai 73 ABSTRACT The synoptic picture
More informationGeographical Information System (GIS)-based maps for monitoring of entomological risk factors affecting transmission of chikungunya in Sri Lanka
Geographical Information System (GIS)-based maps for monitoring of entomological risk factors affecting transmission of chikungunya in Sri Lanka M.D. Hapugoda 1, N.K. Gunewardena 1, P.H.D. Kusumawathie
More informationClassification of High Spatial Resolution Remote Sensing Images Based on Decision Fusion
Journal of Advances in Information Technology Vol. 8, No. 1, February 2017 Classification of High Spatial Resolution Remote Sensing Images Based on Decision Fusion Guizhou Wang Institute of Remote Sensing
More informationLand Use MTRI Documenting Land Use and Land Cover Conditions Synthesis Report
Colin Brooks, Rick Powell, Laura Bourgeau-Chavez, and Dr. Robert Shuchman Michigan Tech Research Institute (MTRI) Project Introduction Transportation projects require detailed environmental information
More informationSpatial Effects on Current and Future Climate of Ipomopsis aggregata Populations in Colorado Patterns of Precipitation and Maximum Temperature
A. Kenney GIS Project Spring 2010 Amanda Kenney GEO 386 Spring 2010 Spatial Effects on Current and Future Climate of Ipomopsis aggregata Populations in Colorado Patterns of Precipitation and Maximum Temperature
More informationAerial Photography and Imagery Resources Guide
Aerial Photography and Imagery Resources Guide Cheyenne and Laramie County Cooperative GIS Created and Maintained by the GIS Coordinator for the Cooperative GIS Program May 2016 CHEYENNE / LARAMIE COUNTY
More informationTechnical Drafting, Geographic Information Systems and Computer- Based Cartography
Technical Drafting, Geographic Information Systems and Computer- Based Cartography Project-Specific and Regional Resource Mapping Services Geographic Information Systems - Spatial Analysis Terrestrial
More informationNatural and Human Influences on Flood Zones in Wake County. Georgia Ditmore
Natural and Human Influences on Flood Zones in Wake County Georgia Ditmore Prepared for GEOG 591 December 5, 2014 2 Table of Contents Introduction.3 Objectives...5 Methods...6 Conclusion.11 References
More informationWisconsin River Floodplain Project: Overview and Plot Metadata
Wisconsin River Floodplain Project: Overview and Plot Metadata CLASS I. DATA SET DESCRIPTORS Data set identity: Plot-level variable information for Wisconsin River Floodplain Project. Relevant for following
More informationidentify tile lines. The imagery used in tile lines identification should be processed in digital format.
Question and Answers: Automated identification of tile drainage from remotely sensed data Bibi Naz, Srinivasulu Ale, Laura Bowling and Chris Johannsen Introduction: Subsurface drainage (popularly known
More informationNR402 GIS Applications in Natural Resources. Lesson 9: Scale and Accuracy
NR402 GIS Applications in Natural Resources Lesson 9: Scale and Accuracy 1 Map scale Map scale specifies the amount of reduction between the real world and the map The map scale specifies how much the
More informationThe AIR Bushfire Model for Australia
The AIR Bushfire Model for Australia In February 2009, amid tripledigit temperatures and drought conditions, fires broke out just north of Melbourne, Australia. Propelled by high winds, as many as 400
More informationGIS in Weather and Society
GIS in Weather and Society Olga Wilhelmi Institute for the Study of Society and Environment National Center for Atmospheric Research WAS*IS November 8, 2005 Boulder, Colorado Presentation Outline GIS basic
More informationAFTERMATTHew, Came Irma!
AFTERMATTHew, Came Irma! MOSQUITOES & STORMS LAURA PEATY CHATHAM COUNTY MOSQUITO CONTROL SAVANNAH GA Major factors contributing to high mosquito numbers in our area include: Rain Tides Dredging operations
More informationThe Climate of Texas County
The Climate of Texas County Texas County is part of the Western High Plains in the north and west and the Southwestern Tablelands in the east. The Western High Plains are characterized by abundant cropland
More informationGeospatial Technologies
An Overview of Prepared by: John McGee Jennifer McKee With support from: NSF DUE-0903270 in partnership with: Geospatial Technician Education Through Virginia s Community Colleges (GTEVCC) What is Geospatial?
More informationSEASONAL CLIMATE OUTLOOK VALID FOR JULY-AUGUST- SEPTEMBER 2013 IN WEST AFRICA, CHAD AND CAMEROON
SEASONAL CLIMATE OUTLOOK VALID FOR JULY-AUGUST- SEPTEMBER 2013 IN WEST AFRICA, CHAD AND CAMEROON May 29, 2013 ABUJA-Federal Republic of Nigeria 1 EXECUTIVE SUMMARY Given the current Sea Surface and sub-surface
More informationQuick Response Report #126 Hurricane Floyd Flood Mapping Integrating Landsat 7 TM Satellite Imagery and DEM Data
Quick Response Report #126 Hurricane Floyd Flood Mapping Integrating Landsat 7 TM Satellite Imagery and DEM Data Jeffrey D. Colby Yong Wang Karen Mulcahy Department of Geography East Carolina University
More informationIntegrating GIS into West Nile Virus Planning and Surveillance
Integrating GIS into West Nile Virus Planning and Surveillance Fairfax County Health Department Adrian Joye, Environmental Health Specialist Agenda Background/Benefits Routes/Trap Locations Dead Bird Complaint
More informationAn Analysis of Urban Cooling Island (UCI) Effects by Water Spaces Applying UCI Indices
An Analysis of Urban Cooling Island (UCI) Effects by Water Spaces Applying UCI Indices D. Lee, K. Oh, and J. Seo Abstract An urban cooling island (UCI) involves an area that has a lower temperature compared
More informationResearch Article A Quantitative Assessment of Surface Urban Heat Islands Using Satellite Multitemporal Data over Abeokuta, Nigeria
International Atmospheric Sciences Volume 2016, Article ID 3170789, 6 pages http://dx.doi.org/10.1155/2016/3170789 Research Article A Quantitative Assessment of Surface Urban Heat Islands Using Satellite
More informationCUYAHOGA COUNTY URBAN TREE CANOPY & LAND COVER MAPPING
CUYAHOGA COUNTY URBAN TREE CANOPY & LAND COVER MAPPING FINAL REPORT M IKE GALVIN S AVATREE D IRECTOR, CONSULTING GROUP P HONE: 914 403 8959 E MAIL: MGALVIN@SAVATREE. COM J ARLATH O NEIL DUNNE U NIVERSITY
More informationAbstract. TECHNOFAME- A Journal of Multidisciplinary Advance Research. Vol.2 No. 2, (2013) Received: Feb.2013; Accepted Oct.
Vol.2 No. 2, 83-87 (2013) Received: Feb.2013; Accepted Oct. 2013 Landuse Pattern Analysis Using Remote Sensing: A Case Study of Morar Block, of Gwalior District, M.P. Subhash Thakur 1 Akhilesh Singh 2
More informationSTUDY OF NORMALIZED DIFFERENCE BUILT-UP (NDBI) INDEX IN AUTOMATICALLY MAPPING URBAN AREAS FROM LANDSAT TM IMAGERY
STUDY OF NORMALIZED DIFFERENCE BUILT-UP (NDBI) INDEX IN AUTOMATICALLY MAPPING URBAN AREAS FROM LANDSAT TM IMAGERY Dr. Hari Krishna Karanam Professor, Civil Engineering, Dadi Institute of Engineering &
More informationIllinois Drought Update, December 1, 2005 DROUGHT RESPONSE TASK FORCE Illinois State Water Survey, Department of Natural Resources
Illinois Drought Update, December 1, 2005 DROUGHT RESPONSE TASK FORCE Illinois State Water Survey, Department of Natural Resources For more drought information please go to http://www.sws.uiuc.edu/. SUMMARY.
More informationLAND USE AND LAND COVER ANALYSIS USING 8- BAND DATA: A CASE STUDY OF BELGAUM CITY AND ITS SURROUNDING.
LAND USE AND LAND COVER ANALYSIS USING 8- BAND DATA: A CASE STUDY OF BELGAUM CITY AND ITS SURROUNDING. Mrs. Rita Basanna *, Dr. A.K. Wodeyar ** ABSTRACT Monitoring land-use change has become an important
More informationGeospatial Technologies for the Agricultural Sciences
Geospatial Technologies for the Agricultural Sciences Maggi Kelly Assoc. Cooperative Extension Specialist Department of Environmental Science, Policy & Management Director, GIIF UC Berkeley Karin Tuxen
More informationThe Climate of Payne County
The Climate of Payne County Payne County is part of the Central Great Plains in the west, encompassing some of the best agricultural land in Oklahoma. Payne County is also part of the Crosstimbers in the
More information