Improving distributed runoff prediction in urbanized catchments with remote sensing based estimates of impervious surface cover

The amount and intensity of runoff on catchment scale are strongly determined by the presence of impervious land-cover types, which are the predominant cover types in urbanized areas. This paper examines the impact of different methods for estimating impervious surface cover on the prediction of peak discharges, as determined by a fully distributed rainfall-runoff model (WetSpa), for the upper part of the Woluwe River catchment in the southeastern part of Brussels. The study shows that detailed information on the spatial distribution of impervious surfaces, as obtained from remotely sensed data, produces substantially different estimates of peak discharges than traditional approaches based on expert judgment of average imperviousness for different types of urban land use. The study also demonstrates that sub-pixel estimation of imperviousness may be a useful alternative for more expensive high-resolution mapping for rainfall-runoff modelling at catchment scale

Large-Scale Urban Impervous Surfaces Estimation Through Incorporating Temporal and Spatial Information into Spectral Mixture Analysis

With rapid urbanization, impervious surfaces, a major component of urbanized areas, have increased concurrently. As a key indicator of environmental quality and urbanization intensity, an accurate estimation of impervious surfaces becomes essential. Numerous automated estimation approaches have been developed during the past decades. Among them, spectral mixture analysis (SMA) has been recognized as a powerful and widely employed technique. While SMA has proven valuable in impervious surface estimation, effects of temporal and spectral variability have not been successfully addressed. In particular, impervious surface estimation is likely to be sensitive to seasonal changes, majorly due to the shadowing effects of vegetation canopy in summer and the confusion between impervious surfaces and soil in winter. Moreover, endmember variability and multi-collinearity have adversely impacted the accurate estimation of impervious surface distribution with coarse resolution remote sensing imagery. Therefore, the main goal of this research is to incorporate temporal and spatial information, as well as geostatistical approaches, into SMA for improving large-scale urban impervious surface estimation. Specifically, three new approaches have been developed in this dissertation to improve the accuracy of large-scale impervious surface estimation. First, a phenology based temporal mixture analysis was developed to address seasonal sensitivity and spectral confusion issues with the multi-temporal MODIS NDVI data. Second, land use land cover information assisted temporal mixture analysis was proposed to handle the issue of endmember class variability through analyzing the spatial relationship between endmembers and surrounding environmental and socio-economic factors in support of the selection of an appropriate number and types of endmember classes. Third, a geostatistical temporal mixture analysis was developed to address endmember spectral variability by generating per-pixel spatial varied endmember spectra. Analysis results suggest that, first, with the proposed phenology based temporal mixture analysis, a significant phenophase differences between impervious surfaces and soil can be extracted and employed in unmxing analysis, which can facilitate their discrimination and successfully address the issue of seasonal sensitivity and spectral confusion. Second, with the analyzed spatial distribution relationship between endmembers and environmental and socio-economic factors, endmember classes can be identified with clear physical meanings throughout the whole study area, which can effectively improve the unmixing analysis results. Third, the use of the spatially varying per-pixel endmember generated from the geostatistical approach can effectively consider the endmember spectra spatial variability, overcome the endmember within-class variability issue, and improve the accuracy of impervious surface estimates. Major contributions of this research can be summarized as follows. First, instead of Landsat Thematic Mapper (TM) images, MODIS imageries with large geographic coverage and high temporal resolution have been successfully employed in this research, thus making timely and regional estimation of impervious surfaces possible. Second, this research proves that the incorporation of geographic knowledge (e.g. phonological knowledge, spatial interaction, and geostatistics) can effectively improve the spectral mixture analysis model, and therefore improve the estimation accuracy of urban impervious surfaces

Urban sprawl in the state of Missouri : current trends, driving forces, and predicted growth on Missouri's natural landscape

Title from PDF of title page (University of Missouri--Columbia, viewed on March 5, 2013).The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.Dissertation advisor: Dr. Hong S. HeIncludes bibliographical references.Vita.Ph.D. University of Missouri--Columbia 2012."December 2012"Missouri reflects a full range of sprawl characteristics that include large metropolitan centers, which led growth in 1980s, and smaller metropolitan and rural areas, which led growth in 1990s. In order to study the historical patterns of sprawl, there is a need to quantitatively and geographically depict the extent and density of impervious surface for three time periods of 1980, 1990, and 2000 for the entire state of Missouri. Mapped impervious surface is the best candidate of ancillary data for dasymetric mapping of population in several comparison studies. The current research examines the performances of dasymetric mapping of population with imperviousness as ancillary data and regression analysis of population using imperviousness as a predictor Results from this work can be aggregated to any geographical unit (hydrologic boundaries, administrative boundaries, etc.). A pilot future urban growth study for the two decades of 1980s and 1990s was done in Missouri. The historical urban growth of the two decades were analyzed then coupled with various predictor variables to investigate the influence of each predictor variables towards the process of urban growth. The knowledge learned from the process is then used to build an urban growth simulation model that is GIS-based with open framework for ease of management and improvement. Pixel level urban growth was simulated for year 2010, 2020 and 2030. This model framework is developed with the ultimate goal of simulating urban growth for the entire state of Missouri.Includes bibliographical reference

Small-Area Population Estimation: an Integration of Demographic and Geographic Techniques

Knowledge of detailed and accurate population information is essential to analyze and address a wide variety of socio-economic, political, and environmental issues and to support necessary planning practices for both public agencies and the private sector. However, such important data are generally only available once every decade through the National Census. Moreover, populations in some rapidly-developing areas may increase quickly, such that this ten-year frequency does not meet the needs of these areas. Therefore, a cost-effective method for population estimation is necessary. To address this issue, this research integrated geographic, sociological, and demographic theories and exploited remotely sensed imagery and geographic information system (GIS) datasets to derive better population estimates at the census block level, the finest level of the national census. Specifically, three new approaches have been proposed in this dissertation to assist in the improvement of small-area population estimation accuracy. First, existing remotely sensed and GIS data have been adopted to estimate two major components of a demographic framework, including the redistribution of newly built dwelling units from the aggregated geographic level to the census block level and the estimation of persons per household (PPH) at such a fine scale. Second, in addition to the use of existing data, new urban environmental indicators were also extracted and employed to improve population estimation. In particular, to implement the automatic enumeration for individual housing units, a new spectral index, biophysical composition index (BCI), has been proposed to derive impervious surface information, a desirable urban environmental parameter. Third, using the extracted high-resolution urban environmental information and GIS data, a new bottom-up method was developed for small-area population estimation at the census block level by incorporating these high-resolution data into the demographic framework. Analyses of the results suggest three major conclusions. First, existing GIS spatial factors, together with demographic information, can assist in improving the accuracy of small-area population estimation. Second, the BCI has a closer relationship with impervious surface area than do other popular indices. Moreover, it was shown to be the most effective index of the four evaluated for separating impervious surfaces and bare soil, which consequently might assist in more accurately deriving fractional land cover values. Third, the use of the new environmental indicators extracted from remote sensing imagery and GIS data and the integration of demographic and geographic approaches has significantly improved the estimation accuracy of housing unit (HU) numbers, PPH, and population counts at the census block level. Therefore, this research contributes to both the remote sensing and applied demography fields. The contribution to the remote sensing field lies in the development of a novel spectral index to characterize urban land for monitoring and analyzing urban environments. This index provided more significant separability between impervious surfaces and bare soil than did other existing indices. Moreover, three major contributions have been made in the field of applied demography: 1) the generation of accurate HU estimates using high-resolution remote sensing and GIS datasets, 2) the development of a model to derive an accurate PPH estimate, and 3) the improvement of small-area population estimation accuracy through the integration of geographic and demographic approaches

A Comparison of Different Machine Learning Algorithms in the Classification of Impervious Surfaces: Case Study of the Housing Estate Fort Bema in Warsaw (Poland)

The aim of this study is to extract impervious surfaces and show their spatial distribution, using different machine learning algorithms. For this purpose, geoprocessing and remote sensing techniques were used and three classification methods for digital images were compared, namely Support Vector Machines (SVM), Maximum Likelihood (ML) and Random Trees (RT) classifiers. The study area is one of the most prestigious and the largest housing estates in Warsaw (Poland), the Fort Bema housing complex, which is also an exemplary model for hydrological solutions. The study was prepared on the Geographic Information System platform (GIS) using aerial optical images, orthorectified and thus provided with a suitable coordinate system. The use of these data is therefore supported by the accuracy of the resulting infrared channel product with a pixel size of 0.25 m, making the results much more accurate compared to satellite imagery. The results of the SVM, ML and RT classifiers were compared using the confusion matrix, accuracy (Root Mean Square Error /RMSE/) and kappa index. This showed that the three algorithms were able to successfully discriminate between targets. Overall, the three classifiers had errors, but specifically for impervious surfaces, the highest accuracy was achieved with the SVM classifier (the highest percentage of overall accuracy), followed by ML and RT with 91.51%, 91.35% and 84.52% of the results, respectively. A comparison of the visual results and the confusion matrix shows that although visually the RT method appears to be the most detailed classification into pervious and impervious surfaces, the results were not always correct, e.g., water/shadow was detected as an impervious surface. The NDVI index was also mapped for the same spatial study area and its application in the evaluation of pervious surfaces was explained. The results obtained with the GIS platform, presented in this paper, provide a better understanding of how these advanced classifiers work, which in turn can provide insightful guidance for their selection and combination in real-world applications. The paper also provides an overview of the main works/studies dealing with impervious surface mapping, with different methods for their assessment (including the use of conventional remote sensing, NDVI, multisensory and cross-source data, ‘social sensing’ and classification methods such as SVM, ML and RT), as well as an overview of the research results

Landscape and Impervious Surface Mapping in the Twin Cities Metropolitan Area using Feature Recognition and Decision Tree techniques

Land Use and Land Cover (LULC) and Impervious Surface Area (ISA) are important parameters for many environmental studies, and serve as an essential tool for decision makers and stakeholders in Urban & Regional planning. Newly available high spatial resolution aerial ortho-imagery and LiDAR data, in combination with specialized, object-oriented and decision-tree classification techniques, allow for accurate mapping of these features. In this study, a method was developed to first classify LULC using an object-based classifier, and then use the resulting map as input for a decision-tree model to classify ISA in the Twin Cities Metropolitan Area in Minnesota. It was found that vegetation cover classes were the most prevalent in the study area, making up over half of the land area. Water was the smallest class, followed by urban land cover, which made up 11%. Impervious surface was determined to make up 14% of the TCMA area.Overall classification accuracy for LULC cover was estimated to be 74%, and 95% for the ISA classification

Mapping impervious surfaces with the integrated use of Landsat Thematic Mapper and radar data: a case study in an urban-rural landscape in the Brazilian Amazon.

This research explored the integrated use of Landsat Thematic Mapper (TM) and radar (i.e., ALOS PALSAR L-band and RADARSAT-2 C-band) data for mapping impervious surface distribution to examine the roles of radar data with different spatial resolutions and wavelengths. The wavelet-merging technique was used to merge TM and radar data to generate a new dataset. A constrained least-squares solution was used to unmix TM multispectral data and multisensor fusion images to four fraction images (high-albedo, low-albedo, vegetation, and soil). The impervious surface image was then extracted from the high-albedo and low-albedo fraction images. QuickBird imagery was used to develop an impervious surface image for use as reference data to evaluate the results from TM and fusion images. This research indicated that increasing spatial resolution by multisensor fusion improved spatial patterns of impervious surface distribution, but cannot significantly improve the statistical area accuracy. This research also indicated that the fusion image with 10-m spatial resolution was suitable for mapping impervious surface spatial distribution, but TM multispectral image with 30 m was too coarse in a complex urban?rural landscape. On the other hand, this research showed that no significant difference in improving impervious surface mapping performance by using either PALSAR L-band or RADARSAT C-band data with the same spatial resolution when they were used for multi-sensor fusion with the wavelet-based method

Quantifying the physical composition of urban morphology throughout Wales based on the time series (1989-2011) analysis of Landsat TM/ETM+ images and supporting GIS data

Knowledge of impervious surface areas (ISA) and on their changes in magnitude, location, geometry and morphology over time is significant for a range of practical applications and research alike from local to global scales. Despite this, use of Earth Observation (EO) technology in mapping ISAs within some European Union (EU) countries, such as the United Kingdom (UK), is to some extent scarce. In the present study, a combination of methods is proposed for mapping ISA based on freely distributed EO imagery from Landsat TM/ETM+ sensors. The proposed technique combines a traditional classifier and a linear spectral mixture analysis (LSMA) with a series of Landsat TM/ETM+ images to extract ISA. Selected sites located in Wales, UK, are used for demonstrating the capability of the proposed method. The Welsh study areas provided a unique setting in detecting largely dispersed urban morphology within an urban-rural frontier context. In addition, an innovative method for detecting clouds and cloud shadow layers for the full area estimation of ISA is also presented herein. The removal and replacement of clouds and cloud shadows, with underlying materials is further explained. Aerial photography with a spatial resolution of 0.4 m, acquired over the summer period in 2005 was used for validation purposes. Validation of the derived products indicated an overall ISA detection accuracy in the order of ~97%. The latter was considered as very satisfactory and at least comparative, if not somehow better, to existing ISA products provided on a national level. The hybrid method for ISA extraction proposed here is important on a local scale in terms of moving forward into a biennial program for the Welsh Government. It offers a much less subjectively static and more objectively dynamic estimation of ISA cover in comparison to existing operational products already available, improving the current estimations of international urbanization and soil sealing. Findings of our study provide important assistance towards the development of relevant EO-based products not only inaugurate to Wales alone, but potentially allowing a cost-effective and consistent long term monitoring of ISA at different scales based on EO technology

GIS-based urban land use characterization and population modeling with subpixel information measured from remote sensing data

This dissertation provides deeper understanding on the application of Vegetation-Impervious Surface-Soil (V-I-S) model in the urban land use characterization and population modeling, focusing on New Orleans area. Previous research on the V-I-S model used in urban land use classification emphasized on the accuracy improvement while ignoring the discussion of the stability of classifiers. I developed an evaluation framework by using randomization techniques and decision tree method to assess and compare the performance of classifiers and input features. The proposed evaluation framework is applied to demonstrate the superiority of V-I-S fractions and LST for urban land use classification. It could also be applied to the assessment of input features and classifiers for other remote sensing image classification context. An innovative urban land use classification based on the V-I-S model is implemented and tested in this dissertation. Due to the shape of the V-I-S bivariate histogram that resembles topological surfaces, a pattern that honors the Lu-Weng’s urban model, the V-I-S feature space is rasterized into grey-scale image and subsequently partitioned by marker-controlled watershed segmentation, leading to an urban land use classification. This new approach is proven to be insensitive to the selection of initial markers as long as they are positioned around the underlying watershed centers. This dissertation links the population distribution of New Orleans with its physiogeographic conditions indicated by the V-I-S sub-pixel composition and the land use information. It shows that the V-I-S fractions cannot be directly used to model the population distribution. Both the OLS and GWR models produced poor model fit. In contrast, the land use information extracted from the V-I-S information and LST significantly improved regression models. A three-class land use model is fitted adequately. The GWR model reveals the spatial nonstationarity as the relationship between the population distribution and the land use is relatively poor in the city center and becomes stronger towards the city fringe, depicting a classic urban concentric pattern. It highlighted that New Orleans is a complex metropolitan area, and its population distribution cannot be fully modeled with the physiogeographic measurements