We investigated how weather conditions and environmental factors affect the spatiotemporal variability in Culex pipiens population using the data collected from a surveillance program in Ontario, Canada, from 2005 to 2008. This study assessed the relative influences of temperature and precipitation on the temporal patterns of mosquito abundance using harmonic analysis and examined the associations with major landscape predictors, including land-use type, population density, and elevation, on the spatial patterns of mosquito abundance. The intensity of trapping efforts on the mosquito abundance at each trap site was examined by comparing the spatial distribution of mosquito abundance in relation to the spatial intensity of trapping efforts. The authors used a mixed effects modeling approach to account for potential dependent structure in mosquito surveillance data due to repeated observations at single trap sites and/or similar mosquito abundance at nearby trap sites each week. The model fit was improved by taking into account the nested structure of mosquito surveillance data and incorporating the temporal correlation in random effects.

Total suspended matter is an important water quality parameter, and plays a key role in water quality evaluation, especially of inland waters. Many different methods have been developed to estimate TSM from remote sensing data, in which empirical methods and model-based methods are two types of commonly used methods. Compared with empirical methods, model-based methods have the advantages of definite physical meanings, high robustness and retrieval accuracy. In model-based methods, matrix inversion method is commonly used in monitoring water qualities of inland waters. However, matrix inversion method has to predetermine some optical parameters by empirical values or simplified optical model, which may introduce some errors in retrieved water quality parameters. In order to overcome the shortcomings of matrix inversion method and increase the estimating accuracy of total suspended mater, in this paper, a bio-optical model based method is developed, which estimates total suspended mater by using remote sensing reflectance of two near-infrared bands. This method is validated by in-situ experiment data measured in Lake Taihu, a big turbid lake in eastern China. The results show that this method has better performance than matrix inversion method. The average relative error of the estimated total suspended matter by this method is only 13.0%, which is much smaller than the errors by matrix inversion method (32.7%). This method has the advantages of definite physical meaning, easiness to carry out, and high estimating accuracy. However, the applicable scope of this method has limitations: it can only be applied to optically deep waters with high concentrations of total suspended matter.

ABSTRACT. This paper presents a series of experiments on classification of remotely sensed images, to investigate the effectiveness of various combinations of different types of feature sets, including spectral features, variance features and wavelet-based features. All the experiments use the identical study area, training data, reference data, testing data, and classification algorithm while varying the feature sets. The classification accuracy from different feature sets is evaluated using the traditional accuracy assessment from reference data. The experimental results show that the spectral-based feature set has the basic discrimination power to distinguish classes with middle and high homogeneity value. However, it has little success in correctly classifying classes with low homogeneity value, such as the residential class. Compared with spectral features, the multi-scale wavelet-based feature set can improve the discrimination power for classes with both low and high homogeneity value. The variance-based feature set alone has little discrimination power, no matter what homogeneity level the class has. However, adding the variance features into the spectral feature or wavelet-based feature set can dramatically increase the classification accuracy for classes with low homogeneity value.

Abstract. The urban heat island (UHI) is a common effect caused by urbanization and has been studied to evaluate the thermal condition in cities worldwide. However, most previous UHI analyses are performed in major metropolitan cities. This study conducts a spatiotemporal analysis of UHI in a rapidly expanding low-density suburban centre and determines how bio-productive land covers react and the extent of the disturbance to each land cover based on time series land surface temperatures extracted from Landsat 7 ETM+ images. Two methods applied and compared are the single exponential decay method, which measures UHI footprint (UHIFP) on vegetation phenology, and the two dimensional Gaussian surface, which quantifies the influence based on distance from the local urban perimeter. Three spectral indices (Normalized Difference Vegetation Index (NDVI), Moisture Index (NDMI), and the Enhanced Vegetation Index (EVI)) were extracted and the residuals from the Gaussian model were compared based on these indices in order to better understand the thermal variations of each land cover within a UHI. The results show that the UHIFP of the studied low-density suburban centre is 1.4 times larger than the size of the urban centre, marginally smaller than previous analyses performed within high-density metropolises. All vegetated land covers experienced their maximum cooling effects before reaching the UHIFP perimeter while urban surfaces begin to diverge from the Gaussian model outside of the UHIFP. The residuals of sparse vegetation maintained strong correlations with each index throughout the growing season while NDMI retained the strongest relationships with every land cover. This study has helped us better understand the UHI effects of small communities with varied vegetation phonology based on the distribution of built-up pervious and impervious surfaces within the neighbourhood structure. The similar results from both methods indicate a strong urban cover influence overpowering the dominant distribution of agricultural surfaces throughout the growing season.

Abstract. The socioeconomic data, such as household income, is an important indicator of people’s well-being. However, due to the limited resource in many developing countries such as Thailand, the data obtained from household income surveys are often incomplete. As a result, the annual household survey usually contains a gap at the municipality household level. In this study, we aim to quantify the household income with K-NN imputation models at the sub-district level using satellite imageries and geospatial data as proxies to socioeconomic indicators. We examined the role of satellite and geospatial data in household income estimation, applied the K-NN imputation methods to estimate the missing income data by using various geographical and statistical variables, and quantified how these data improved the accuracy of sub-district household income estimation. Our results illustrated a significant correlation between sub-district household income and geographical data extracted from day-night satellite data, such as night light intensity (r = 0.53), urban density (r = 0.44), residential area (r = 0.68), urban area (r = 0.64), and statistical data as well as household expenditure (r = 0.97). These can be used to improve the socioeconomic indicators’ estimation as well as household income in sub-district level. The income imputation from geographical data perform better result than purely statistical variables. Especially, the night light intensity can infer the wealth of people living in large scale areas, while day-time satellite images can be interpreted for land use and land cover also implying socioeconomic status. Such socioeconomic proxy from space provides spatially explicit information in further study.