A new multiscale approach for monitoring vegetation using remote sensing-based indicators in laboratory, field, and landscape

Remote sensing is an important tool for studying patterns in surface processes on different spatiotemporal scales. However, differences in the spatiospectral and temporal resolution of remote sensing data as well as sensor-specific surveying characteristics very often hinder comparative analyses and effective up- and downscaling analyses. This paper presents a new methodical framework for combining hyperspectral remote sensing data on different spatial and temporal scales. We demonstrate the potential of using the “One Sensor at Different Scales” (OSADIS) approach for the laboratory (plot), field (local), and landscape (regional) scales. By implementing the OSADIS approach, we are able (1) to develop suitable stress-controlled vegetation indices for selected variables such as the Leaf Area Index (LAI), chlorophyll, photosynthesis, water content, nutrient content, etc. over a whole vegetation period. Focused laboratory monitoring can help to document additive and counteractive factors and processes of the vegetation and to correctly interpret their spectral response; (2) to transfer the models obtained to the landscape level; (3) to record imaging hyperspectral information on different spatial scales, achieving a true comparison of the structure and process results; (4) to minimize existing errors from geometrical, spectral, and temporal effects due to sensor- and time-specific differences; and (5) to carry out a realistic top- and downscaling by determining scale-dependent correction factors and transfer functions. The first results of OSADIS experiments are provided by controlled whole vegetation experiments on barley under water stress on the plot scale to model LAI using the vegetation indices Normalized Difference Vegetation Index (NDVI) and green NDVI (GNDVI). The regression model ascertained from imaging hyperspectral AISA-EAGLE/HAWK (DUAL) data was used to model LAI. This was done by using the vegetation index GNDVI with an R ² of 0.83, which was transferred to airborne hyperspectral data on the local and regional scales. For this purpose, hyperspectral imagery was collected at three altitudes over a land cover gradient of 25 km within a timeframe of a few minutes, yielding a spatial resolution from 1 to 3 m. For all recorded spatial scales, both the LAI and the NDVI were determined. The spatial properties of LAI and NDVI of all recorded hyperspectral images were compared using semivariance metrics derived from the variogram. The first results show spatial differences in the heterogeneity of LAI and NDVI from 1 to 3 m with the recorded hyperspectral data. That means that differently recorded data on different scales might not sufficiently maintain the spatial properties of high spatial resolution hyperspectral images.     

Monitoring and estimating scale-dependent hierarchical relationships between Sicyopterus japonicus density and stream habitat features in different seasons in northern Taiwan

Biological and physical processes operate collaboratively through spatial or temporal scales to form ecological patterns, which are considered as a key element for understanding the natural liens within an ecosystem. Given the importance of scaling in ecological inference, this study elucidates how physical and biological variables under or within scales interact with each other. Density of Sicyopterus japonicus and environmental variables are examined and quantified at 70 stream sections distributed among 14 reaches in the Datuan stream catchment of northern Taiwan during the fall and winter of 2007, as well as the spring and summer of 2008. Hierarchical linear regression analysis indicates that S. japonicus density and habitat features are related on two levels, i.e., sections within reaches and reaches within streams throughout the year. Moreover, parameter uncertainty is represented by the confidence interval, which is calculated by the variance-covariance matrix of hierarchical linear model (HLM) parameters. According to HLM results, environmental variables at the section level (water depth and current velocity) and the reach level (stream width, water temperature, stream slope, soil erosion index) influence S. japonicus density. Although S. japonicus density varies significantly among reaches and sections within reaches, cross-level interaction may not always influence the distribution, processes and activities of S. japonicus throughout the year. The HLMs of S. japonicus density associated with stream features describe thoroughly multiple processes and the activities of S. japonicus across scales and within scales during different seasons. The annual HLM results represent the overall biological and physical patterns of the Datuan stream annually, explaining why they do not reflect seasonal associations or explain S. japonicus-related activities and environmental features of the stream.     

Urban Air Pollution Patterns, Land Use, and Thermal Landscape: An Examination of the Linkage Using GIS

This article investigates the relationship of local air pollution pattern with urban land use and with urban thermal landscape using a GIS approach. Ambient air quality measurements for sulfur dioxide, nitrogen oxide, carbon monoxide, total suspended particles, and dust level were obtained for Guangzhou City in South China between 1981 and 2000. Landsat TM images and aerial photo derived maps were used to examine city's land use and land cover at different times and changes. Landsat thermal infrared data were employed to compute land surface temperatures and to assess urban thermal patterns. Relationships among the spatial patterns of air pollution, land use, and thermal landscape were sought through GIS and correlation analyses. Results show that the spatial patterns of air pollutants probed were positively correlated with urban built-up density, and with satellite derived land surface temperature values, particularly with measurements taken during the summer. It is suggested that further studies investigate the mechanisms of this linkage, and that remote sensing of air pollution delves into how the energy interacts with the atmosphere and the environment and how sensors see pollutants. Thermal infrared imagery could play a unique role in monitoring and modeling atmospheric pollution.     

Spatial scale-dependent policy planning for land management in southern Europe

This study outlines and original tool for rural policy planning in southern Europe. This new tool is a process-based, scale-dependent, rural policy-making approach, which is designed to address increasing land degradation problems in southern Europe. Seven important processes are identified (land abandonment, devegetation, intensification in agriculture, global climate change, accelerated soil erosion, increasing water demands, urbanisation) and plotted on a space-time diagram, which clearly shows the spatial and temporal scales for which these processes are significant for landscape change in southern Europe. Conclusions are derived concerning, in particular, sustainable (optimal) rural policy-making for southern Europe's problematic land management. An optimal spatial-temporal scale for land management in southern Europe may range spatially from the farm (0.5 km²) to sub-provincial level (450 km²) and temporally from 7 to 30 years. The study delineates methods and results derivable from such a new policy-planning approach and suggests the usefulness of combining this approach with ecological land classification at the landscape level.     

Spatial based compromise programming for multiple criteria decision making in land use planning

Today, competing land use is continuing to occur in many developed regions. In the Agricultural Development Zone of Western Sydney Region, which is characterised by complex landscape patterns, land use competition is widespread. From a land use planning perspective, identification of suitable locations for a given type of land use is necessary for decision makers to formulate land use alternatives in different locations, based on existing land potential and constraints. For such a region, use of a simple method that implements a categorical system and considers only inherent land characteristics in the analysis is often inadequate to arrive at an optimal spatial decision. The primary aim of this paper is to develop spatial modelling procedures for agricultural land suitability analysis using compromise programming (CoPr) and fuzzy set approach within a geographical information systems (GIS) environment. Five main sets of spatial data for use as decision criteria were developed by using fuzzy set methodology: a land suitability index (LSI) for maximising the land productivity objective; an erosion tolerance index (ETI) for minimising the erosion risk objective; a runoff curve number (CN) for maximising the water discharge regulation objective; an accessibility (RP) measure for maximising the land accessibility objective; and the proximity to water body (WP) for minimising the water pollution objective. An L _p -metric was used in the analysis utilising different strategies with representative indices ranging from a situation where full tradeoff among criteria occurs to a noncompensatory condition. Different weighting combinations were also applied, and decision analysis was carried out by using values ranging from 0 to 1.0, where 1.0 is considered as an ideal point. The CoPr model demonstrated in this paper yielded a promising result, as several different techniques of sensitivity analysis show reasonably good results. Likewise, an overlay of that result with the present land use/land cover indicates a good corresponding spatial matching between existing land use (orchard and cultivated land), and the cells (land parcels) classified as the best in CoPr. The results are amenable to various map display techniques, either using continuous values or by defining different cut off points in the data space within a raster GIS environment.     

Structural characteristics of post-wildfire and clearcut landscapes

A continuing discussion in the field of ecology and forest management concerns the implications of clearcutting as a functional replacement for wildfire in disturbance-driven ecosystems. At the landscape level, spatial pattern has been shown to influence many ecologically important processes. Satellite imagery allows the evaluation of structural patterns created by alternative forest management activities at broad scales. In Northwestern Ontario, both clearcutting and wildfire have occurred over large contiguous areas. Spatial characteristics including composition, patch size, patch shape, and interspersion were calculated from classified Landsat Thematic Mapper (TM) data at two thematic scales and used to compare post-wildfire and clearcut landscapes. Patches in the clearcut landscape were found to be larger in size, and had a more irregular shape than those in the wildfire landscape. Differences in landscape structure were much more pronounced at broad scales than at fine thematic scales.     

Land quality,sustainable development and environmental degradation in agricultural districts: A computational approach based on entropy indexes

Land Degradation (LD) in socio-environmental systems negatively impacts sustainable development paths. This study proposes a framework to LD evaluation based on indicators of diversification in the spatial distribution of sensitive land. We hypothesize that conditions for spatial heterogeneity in a composite index of land sensitivity are more frequently associated to areas prone to LD than spatial homogeneity. Spatial heterogeneity is supposed to be associated with degraded areas that act as hotspots for future degradation processes. A diachronic analysis (1960–2010) was performed at the Italian agricultural district scale to identify environmental factors associated with spatial heterogeneity in the degree of land sensitivity to degradation based on the Environmentally Sensitive Area Index (ESAI). In 1960, diversification in the level of land sensitivity measured using two common indexes of entropy (Shannon's diversity and Pielou's evenness) increased significantly with the ESAI, indicating a high level of land sensitivity to degradation. In 2010, surface area classified as “critical” to LD was the highest in districts with diversification in the spatial distribution of ESAI values, confirming the hypothesis formulated above. Entropy indexes, based on observed alignment with the concept of LD, constitute a valuable base to inform mitigation strategies against desertification.     

Land use/land cover change and their effects on landscape patterns in the Yanqi Basin, Xinjiang (China)

Human modification of land use and land cover change (LUCC) drives the change of landscape patterns and limits the availability of products and services for human and livestock. LUCC can undermine environmental health. Thus, this study aimed to develop an understanding of LUCC in the Yanqi Basin, Xinjiang, China, an arid area experiencing dramatic water and land resource use. A time series of satellite images (1964, 1973, 1989, 1999, and 2009) were used to calculate the index of landscape patterns to study the processes involved in changes to land uses and landscape patterns and the influence of this changes on landscape patterns. The results show that land uses in the Yanqi Basin have changed dramatically since 1964 with grassland being mainly converted to cropland. Landscape fragmentation and diversity have decreased in the study area, although landscape fragmentation increased from 1964 to 1999 and then decreased by 2009. The index of landscape diversity decreased from 1.64 in 1964 to 0.71 in 2009. The heterogeneity and complexity of the landscape increased during this period. In contrast, the index of dominance decreased from 0.85 in 1964 to 0.83 in 2009. Land use change drives landscape patterns of the development of the watershed toward diversity and a fragmented structure. Population growth, economic development, and industrial policies were the dominant driving forces behind LUCC in the Yanqi Basin. Sustainable use of land resources is a significant factor in maintaining economic development and environmental protection in this arid inland river basin.     

Spatio-temporal analyses of cropland degradation in the irrigated lowlands of Uzbekistan using remote-sensing and logistic regression modeling

Advancing land degradation in the irrigated areas of Central Asia hinders sustainable development of this predominantly agricultural region. To support decisions on mitigating cropland degradation, this study combines linear trend analysis and spatial logistic regression modeling to expose a land degradation trend in the Khorezm region, Uzbekistan, and to analyze the causes. Time series of the 250-m MODIS NDVI, summed over the growing seasons of 2000–2010, were used to derive areas with an apparent negative vegetation trend; this was interpreted as an indicator of land degradation. About one third (161,000 ha) of the region’s area experienced negative trends of different magnitude. The vegetation decline was particularly evident on the low-fertility lands bordering on the natural sandy desert, suggesting that these areas should be prioritized in mitigation planning. The results of logistic modeling indicate that the spatial pattern of the observed trend is mainly associated with the level of the groundwater table (odds?=?330 %), land-use intensity (odds?=?103 %), low soil quality (odds?=?49 %), slope (odds?=?29 %), and salinity of the groundwater (odds?=?26 %). Areas, threatened by land degradation, were mapped by fitting the estimated model parameters to available data. The elaborated approach, combining remote-sensing and GIS, can form the basis for developing a common tool for monitoring land degradation trends in irrigated croplands of Central Asia.     

Assessment of land cover changes & water quality changes in the Zayandehroud River Basin between 1997–2008

Water quality of rivers is strongly influenced by landscape characteristics of their watershed, including land use /cover types, and their spatial configuration. This research evaluates the effects of land cover changes on the water quality of the Zayandehroud River, which is the most important river in the center of Iran. The main goal of this study was to quantify the change in rangelands, forests, and bare lands in the Zayandehroud river basin, which suffered intense human interference, in a period of 11 years (1997–2008), and to evaluate how landscape patterns (including the number of patches, edge density, percentage of rangelands, forests, and bare lands) influence on the 14 water quality indices (including BOD5, EC, NO3, P, and TDS) measured in 10 stations along the river. Results showed that from 1997 to 2008, bare lands increased from 5.8 to 20 %, while rangelands decreased from 70 to 55 % in the whole basin. The results indicated that water quality was significantly correlated with both the proportions and configuration of rangeland and bare land areas. The total edge (TE) of rangeland area had positive effects on water quality, especially on BOD5 and EC. Percentage of landscape (PLAND) and largest patch index (LPI) metrics of rangeland had positive effect on decreasing nutrient (NO₃, PO₄). The results showed that water quality was more likely degraded when there was high edge density (ED) of bare lands. Results of this study also revealed that degradation of rangeland lead to the degradation of water quality. Finding of this study highlights the importance of rangeland conservation in water quality management at landscape scale.     

Seasonal variations in the relationship between landscape pattern and land surface temperature in Indianapolis, USA

This paper intended to examine the seasonal variations in the relationship between landscape pattern and land surface temperature based on a case study of Indianapolis, United States. The integration of remote sensing, GIS, and landscape ecology methods was used in this study. Four Terra's ASTER images were used to derive the landscape patterns and land surface temperatures (LST) in four seasons in the study area. The spatial and ecological characteristics of landscape patterns and LSTs were examined by the use of landscape metrics. The impact of each land use and land cover type on LST was analyzed based on the measurements of landscape metrics. The results show that the landscape and LST patterns in the winter were unique. The rest of three seasons apparently had more agreeable landscape and LST patterns. The spatial configuration of each LST zone conformed to that of each land use and land cover type with more than 50% of overlap in area for all seasons. This paper may provide useful information for urban planers and environmental managers for assessing and monitoring urban thermal environments as result of urbanization.     

Modeling Desertification Change in Minqin County,China

Monitoring environmental processes is becoming increasingly important wherever there is increasing population and economic development pressure placed on fragile environments. Remote sensing, digital image processing, and spatial analysis have proven to be useful technologies in both assessing and monitoring environmental change. In this study, they were used to assess desertification processes and change in Minqin County, China from 1988 to 1997. The results suggest that wind erosion was the dominant cause of desertification in more than half of the study area. Coupled with this were increases in salinization processes, affecting 33.62% of the land area in 1997. Overall, moderate desertification was found to be the dominant desertification grade (43.64% of total area), followed by extreme/severe desertification (26.15% of total area) in 1997. In addition, examination of landscape pattern changes indicated that desertification processes at the landscape level were becoming evident at increasing levels of fragmentation, complexity in shape, and isolation of patches. Major fluctuations in desertification type and grade were found at the fringes of oases, where an ongoing shift was taking place between cultivation, abandonment, and reclamation.     

Assessing the impact of multiple drivers of land sensitivity to desertification in a Mediterranean country

Experiencing climate changes and increased human pressure, Mediterranean regions are considered representative hotspots of desertification. However, relatively few studies have been devoted to quantify the individual impact of different factors shaping land sensitivity to desertification in these contexts. Our study contributes to this deserving (positive and normative) issue with a diachronic analysis of the impact of multiple drivers of desertification risk on six indicators of land sensitivity based on the Environmentally Sensitive Area (ESA) approach. Indicators (average and maximum ESA score, coefficient of variation and normalized range in the ESA scores, share of ‘fragile’ and ‘critical’ areas in total landscape) were calculated in 777 rural districts of Italy at three time points (early-1960s, early-1990s, and early-2010s). Multivariate models were used to determine the impact of 12 predictors (climate, soil, vegetation, and land management quality) on each indicator of land sensitivity. Results of the analysis identified two non-redundant dimensions respectively associated with the average level of land sensitivity and its intrinsic variability across space. Impacts of climate and vegetation qualities on the level of land sensitivity were high, decreasing over time, and more intense respectively in Northern and Southern Italy. Impacts of soil and land management qualities were moderate, increasing over time, and involving almost all the country's area. Our study emphasizes the role of context-based measures promoting sustainable land management. The ‘local’ dimension proved to be crucial in any strategy of risk mitigation undertaken at disaggregated spatial scales.     

Assessing the impact of alternative land-use zoning policies on future ecosystem services

Land use conversions rank among the most significant drivers of change in ecosystem services worldwide, affecting human wellbeing and threatening the survival of other species. Hence, predicting the effects of land use decisions on ecosystem services has emerged as a crucial need in spatial planning, and in the associated Strategic Environmental Assessment (SEA) practice. The paper presents a case-study research aimed at empirically exploring how the implementation of different land-use zoning policies affect the future provision of a set of ecosystem services (water purification, soil conservation, habitat for species, carbon sequestration and timber production). The study area is located in The Araucanía, one of Chile's Administrative Regions. The first part of the methods consisted in the construction of land-use scenarios associated to different policies. Subsequently, the effects of the land-use scenarios on the provision of the selected ecosystem services were assessed in a spatially explicit way, by using modeling tools. Finally, a set of metrics was developed to compare scenarios, and trade-offs in the provision of different ecosystem services were made explicit through trade-off curves. The results indicate that, for this case study, spatial configuration of land uses is as an important factor as their size. This suggests that the analysis of land-use patterns deserves attention, and that this information should be included in scenario exercises aimed to support spatial planning. The paper concludes by discussing the potential contribution of the approach to support SEA of spatial plans.     

Monitoring the urban green spaces and landscape fragmentation using remote sensing: a case study in Osmaniye,Turkey

Urban green spaces play a significant role in management of physical activity, psychological well-being, and public health of urban residents. With the expansion of urban areas in Turkey during the past decades, urban green spaces have been fragmented and dispersed causing impairment and environmental degradation. The purpose of this study is to model urban green space distribution by focusing on the landscape fragmentation in city of Osmaniye using remote sensing and geographic information system technology. Normalized difference vegetation index (NDVI) and urban landscape ratio (ULR) were used to assess the proximity and spatial arrangement of urban green spaces within the neighbor landscapes to quantify the urban land use effect. The geospatial analysis results showed that increase in total built-up area and population has significantly decreased the urban green space cover because of high levels of landscape fragmentation in urban city center. Also, due to high levels of landscape fragmentation, approximately 45% of the Osmaniye city is estimated to become urbanized by 2030. This study demonstrated the benefits of directional vegetation index application with geospatial analyses in characterizing the environmental quality for planning and management of urban green spaces. This approach could be used for determining the future urban land development scenarios correlating with regional planning procedures.     

Determining the size of a complete disturbance landscape: multi-scale,continental analysis of forest change

The scale of investigation for disturbance-influenced processes plays a critical role in theoretical assumptions about stability, variance, and equilibrium, as well as conservation reserve and long-term monitoring program design. Critical consideration of scale is required for robust planning designs, especially when anticipating future disturbances whose exact locations are unknown. This research quantified disturbance proportion and pattern (as contagion) at multiple scales across North America. This pattern of scale-associated variability can guide selection of study and management extents, for example, to minimize variance (measured as standard deviation) between any landscapes within an ecoregion. We identified the proportion and pattern of forest disturbance (30 m grain size) across multiple landscape extents up to 180 km². We explored the variance in proportion of disturbed area and the pattern of that disturbance between landscapes (within an ecoregion) as a function of the landscape extent. In many ecoregions, variance between landscapes within an ecoregion was minimal at broad landscape extents (low standard deviation). Gap-dominated regions showed the least variance, while fire-dominated showed the largest. Intensively managed ecoregions displayed unique patterns. A majority of the ecoregions showed low variance between landscapes at some scale, indicating an appropriate extent for incorporating natural regimes and unknown future disturbances was identified. The quantification of the scales of disturbance at the ecoregion level provides guidance for individuals interested in anticipating future disturbances which will occur in unknown spatial locations. Information on the extents required to incorporate disturbance patterns into planning is crucial for that process.     

Analysis of the changes of vegetation coverage of western Beijing mountainous areas using remote sensing and GIS

Mentougou District acts as a crucial component in the ecological buffer in western Beijing mountainous areas, Beijing, China. Using two Landsat MSS/TM images acquired on July 14, 1979 and July 23, 2005, the vegetation coverage of Mentougou District was calculated based on normalized difference vegetation index and spectral mixture analysis (NDVI-SMA) model. Its temporal and spatial changes were analyzed according to digital elevation model (DEM) image, social and economic data. The results showed that the vegetation coverage decreased from 76.4% in 1979 to 72.7% in 2005. Vegetation degradation was probably the result of human disturbance, such as outspreading of resident areas, and coal and stone mining activities, while vegetation restoration might be contributed by the combined effects of both natural processes and ecological construction effort. Vegetation changes were closely related to topographical characteristics. Plants at high altitude were more stable and less degraded than the plants at low altitude, while the plants on steep slope or northwest aspect were more vulnerable to degradation. During the period of 26 years, landscape appeared to become more fragmental, and ecological quality of the land seemed deteriorated sharply in that highly-covered vegetation area has been decreased by 24%.     

Tailoring a non-path-dependent model for environmental risk management and polycentric urban land-use planning

This study attempts to develop a non-path-dependent model for environmental risk management and polycentric urban land-use planning in Gorgan Township area, Iran. Applying three suitability layers of environmental risk (soil erosion, flood risk, fire risk, and land susceptibility), urbanization potential, and integrated surface (environmental risk plus urbanization potential layers), a non-path-dependent Cellular Automata-Markov Chain (CA-MC) model was configured to execute three scenarios of polycentric urban growth allocation. Specifically, the modeling approach improved the traditional functionality of the CA-MC model from a prediction algorithm into an innovative land allocation tool. Besides, due to its flexibility, the non-path-dependent model was able to explicitly include different characteristics of the landscape structure ranging from physical land attributes to landscape functions and processes (natural hazards). Accordingly, three polycentric urban growth allocation efforts were undertaken and compared in terms of connectivity and compactness of the resultant patterns and consumption of other land resources. Based on results, the polycentric allocation procedure based on integrated suitability layer produced a more manageable pattern of urban landscape, while the growth option based on environmental risk layer was more successful for protecting farmlands against excessive urbanization. This study suggests that polycentric urban land-use planning under the strategy of rural land development programs is an available option for designing an urban landscape with lower exposure to natural hazards and more economic benefits to rural residents. Finally, the non-path-dependent modeling is a recommended approach, when highly flexible and interactive decision-support systems as well as trend-breaking scenarios are desired.     

Exploring the Relationship Between Hydrologic Parameters and Nutrient Loads Using Digital Elevation Model and GIS – A Case Study from Sugarcreek Headwaters, Ohio, U.S.A.

Ohio is typical among the Midwestern and Eastern United States with high levels of water pollutants, the main sources being from agriculture. In this study, we used a digital elevation model in conjunction with hydrological indices to determine the role of landscape complexity affecting the spatial and temporal variation in pollutant levels, in one of the most impaired headwater streams in Ohio. More than eighty five percent of the study area is dominated by agriculture. Spatial distribution of slope (S), altitude and wetness index along with other watershed parameters such as flow direction, flow accumulation, stream networks, flow stream orders and erosion index were used within a Geographic Information Systems framework to quantify variation in nitrate and phosphate loads to headwater streams. Stream monitoring data for nutrient loads were used to correlate the observed spatial and temporal patterns with hydrological parameters using multiple linear regressions. Results from the wetness index calculated from a digital elevation model suggested a range of 0.10–16.39, with more than 35% having values less than 4.0. A Revised Universal Soil Loss Equation (RUSLE) predicted soil loss in the range of 0.01–4.0 t/ha/yr. Nitrate nitrogen levels in the study area paralleled precipitation patterns over time, with higher nitrate levels corresponding to high precipitation. Atmospheric deposition through precipitation could explain approximately 35% of total nitrate levels observed in streams. Among the different topographic variables and hydrological indices, results from the step-wise multiple regression suggested the following best predictors, (1) elevation range and upstream flow length for nitrate, (2) flow direction and upstream flow length for ammonia-nitrogen and slope, and (3) elevation range for phosphate levels. Differences in the landscape models observed for nitrate, phosphate and ammonia-nitrogen in the surface waters were attributed partly to differences in the chemical activity and source strengths of the different forms of these nutrients through agricultural management practices. The results identify geomorphologic and landscape characteristics that influence pollutant levels in the study area.     

An examination of the effect of landscape pattern, land surface temperature, and socioeconomic conditions on WNV dissemination in Chicago

This paper developed an approach by the synthesis of remote sensing, landscape metrics, and statistical methods to examine the effects of landscape pattern, land surface temperature, and socioeconomic conditions on the spread of West Nile virus (WNV) caused by mosquitoes and animal hosts in Chicago, USA. Land use/land cover and land surface temperature images were derived from Terra’s Advanced Spaceborne Thermal Emission and Reflection Radiometer imagery. An analytical procedure using landscape metrics was developed, applying configuration analysis of landscape patterns in the study area. The positive reports of mosquitoes and animal hosts for WNV in fall, 2001–2006, were collected from the Cook County Public Health Department. Forty-nine municipalities were found to have WNV-positive records in mosquitoes and animal hosts in fall 2004. Socioeconomic data were obtained from the 2000 US Census. Statistical analysis was applied to WNV data in fall 2004 to identify the relationship between potential predictors and WNV spread. As a result, landscape factors, such as landscape aggregation index and the urban areas and areas of grass and water, showed strong correlations with the WNV-positive records. Socioeconomic conditions, such as the population over 65 years old, also showed a strong correlation with WNV-positive records. Thermal conditions of water showed a less but still considerable correlation to WNV-positive records. This research offers an opportunity to explore the effects of landscape pattern, land surface temperature, and socioeconomic conditions on the spread of WNV caused by mosquitoes and animal hosts. Results can contribute to public health and environmental management in the study area.