939 resultados para Integration of GIS and remote sensing


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The main objective of this study is to reveal the housing patterns in Cairo as one of the most rapidly urbanizing city in the developing world. The study outlines the evolution of the housing problem and its influencing factors in Egypt generally and in Cairo specifically. The study takes into account the political transition from the national state economy to the open door policy, the neo-liberal period and finally to the housing situation after the January 2011 Revolution. The resulting housing patterns in Cairo Governorate were identified as (1) squatter settlements, (2) semi-informal settlements, (3) deteriorated inner pockets, and (4) formal settlements. rnThe study concluded that the housing patterns in Cairo are reflecting a multifaceted problem resulting in: (1) the imbalance between the high demand for affordable housing units for low-income families and the oversupply of upper-income housing, (2) the vast expansion of informal areas both on agricultural and desert lands, (3) the deterioration of the old parts of Cairo without upgrading or appropriate replacement of the housing structure, and (4) the high vacancy rate of newly constructed apartmentsrnThe evolution and development of the current housing problem were attributed to a number of factors. These factors are demographic factors represented in the rapid growth of the population associated with urbanization under the dictates of poverty, and the progressive increase of the prices of both buildable land and building materials. The study underlined that the current pattern of population density in Cairo Governorate is a direct result of the current housing problems. Around the depopulation core of the city, a ring of relatively stable areas in terms of population density has developed. Population densification, at the expense of the depopulation core, is characterizing the peripheries of the city. The population density in relation to the built-up area was examined using Landsat-7 ETM+ image (176/039). The image was acquired on 24 August 2006 and considered as an ideal source for land cover classification in Cairo since it is compatible with the population census 2006.rnConsidering that the socio-economic setting is a driving force of change of housing demand and that it is an outcome of the accumulated housing problems, the socio-economic deprivations of the inhabitants of Cairo Governorate are analyzed. Small administrative units in Cairo are categorized into four classes based on the Socio-Economic Opportunity Index (SEOI). This index is developed by using multiple domains focusing on the economic, educational and health situation of the residential population. The results show four levels of deprivation which are consistent with the existing housing patterns. Informal areas on state owned land are included in the first category, namely, the “severely deprived” level. Ex-formal areas or deteriorated inner pockets are characterized as “deprived” urban quarters. Semi-informal areas on agricultural land concentrate in the third category of “medium deprived” settlements. Formal or planned areas are included mostly in the fourth category of the “less deprived” parts of Cairo Governorate. rnFor a better understanding of the differences and similarities among the various housing patterns, four areas based on the smallest administrative units of shiakhat were selected for a detailed study. These areas are: (1) El-Ma’desa is representing a severely deprived squatter settlement, (2) Ain el-Sira is an example for an ex-formal deprived area, (3) El-Marg el-Qibliya was selected as a typical semi-informal and medium deprived settlement, and (4) El-Nozha is representing a formal and less deprived area.rnThe analysis at shiakhat level reveals how the socio-economic characteristics and the unregulated urban growth are greatly reflected in the morphological characteristics of the housing patterns in terms of street network and types of residential buildings as well as types of housing tenure. It is also reflected in the functional characteristics in terms of land use mix and its degree of compatibility. It is concluded that the provision and accessibility to public services represents a performance measure of the dysfunctional structure dominating squatter and semi-informal settlements on one hand and ample public services and accessibility in formal areas on the other hand.rn

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Winter maintenance, particularly snow removal and the stress of snow removal materials on public structures, is an enormous budgetary burden on municipalities and nongovernmental maintenance organizations in cold climates. Lately, geospatial technologies such as remote sensing, geographic information systems (GIS), and decision support tools are roviding a valuable tool for planning snow removal operations. A few researchers recently used geospatial technologies to develop winter maintenance tools. However, most of these winter maintenance tools, while having the potential to address some of these information needs, are not typically placed in the hands of planners and other interested stakeholders. Most tools are not constructed with a nontechnical user in mind and lack an easyto-use, easily understood interface. A major goal of this project was to implement a web-based Winter Maintenance Decision Support System (WMDSS) that enhances the capacity of stakeholders (city/county planners, resource managers, transportation personnel, citizens, and policy makers) to evaluate different procedures for managing snow removal assets optimally. This was accomplished by integrating geospatial analytical techniques (GIS and remote sensing), the existing snow removal asset management system, and webbased spatial decision support systems. The web-based system was implemented using the ESRI ArcIMS ActiveX Connector and related web technologies, such as Active Server Pages, JavaScript, HTML, and XML. The expert knowledge on snow removal procedures is gathered and integrated into the system in the form of encoded business rules using Visual Rule Studio. The system developed not only manages the resources but also provides expert advice to assist complex decision making, such as routing, optimal resource allocation, and monitoring live weather information. This system was developed in collaboration with Black Hawk County, IA, the city of Columbia, MO, and the Iowa Department of transportation. This product was also demonstrated for these agencies to improve the usability and applicability of the system.

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This study used Landsat 8 satellite imagery to identify environmental variables of households with malaria vector breeding sites in a malaria endemic rural district in Western Kenya. Understanding the influence of environmental variables on the distribution of malaria has been critical in the strengthening of malaria control programs. Using remote sensing and GIS technologies, this study performed a land classification, NDVI, Tasseled Cap Wetness Index, and derived land surface temperature values of the study area and examined the significance of each variable in predicting the probability of a household with a mosquito breeding site with and without larvae. The findings of this study revealed that households with any potential breeding sites were characterized by higher moisture, higher vegetation density (NDVI) and in urban areas or roads. The results of this study also confirmed that land surface temperature was significant in explaining the presence of active mosquito breeding sites (P< 0.000). The present study showed that freely available Landsat 8 imagery has limited use in deriving environmental characteristics of malaria vector habitats at the scale of the Bungoma East District in Western Kenya.

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Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies

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An efficient and reliable automated model that can map physical Soil and Water Conservation (SWC) structures on cultivated land was developed using very high spatial resolution imagery obtained from Google Earth and ArcGIS, ERDAS IMAGINE, and SDC Morphology Toolbox for MATLAB and statistical techniques. The model was developed using the following procedures: (1) a high-pass spatial filter algorithm was applied to detect linear features, (2) morphological processing was used to remove unwanted linear features, (3) the raster format was vectorized, (4) the vectorized linear features were split per hectare (ha) and each line was then classified according to its compass direction, and (5) the sum of all vector lengths per class of direction per ha was calculated. Finally, the direction class with the greatest length was selected from each ha to predict the physical SWC structures. The model was calibrated and validated on the Ethiopian Highlands. The model correctly mapped 80% of the existing structures. The developed model was then tested at different sites with different topography. The results show that the developed model is feasible for automated mapping of physical SWC structures. Therefore, the model is useful for predicting and mapping physical SWC structures areas across diverse areas.

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Kirjallisuusarvostelu

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In this study, an attempt has been made to find the textural, geochemical, sedimentological characteristics of sediments and water phases of the kayamkulam estuary located in the Southwest coast of Kerala, besides the impact of gas based thermal power plant located at the northern part of the estuary. Estuaries are an important stage in the transport of the solid weathering product of the earth’s crust. These weathered products or sediments are complex mixtures of a number of solid phases that may include clays, silica, organic matter, metal oxides, carbonates, sulfides and a number of minerals. Studies on the aquatic systems revealed the fact that it posses severe ecological impairments due to heavy discharge of sediments from 44 rivers, the continued disposal of pollutants rich materials from industries, sewage channels, agricultural areas and retting yards

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mbikulam Tiger Reserve of Western Ghats using Geospatial technology. The major objectives of the study are Land use land cover mapping (LULC) and Phytodiversity analysis. Satellite data was used to map the land use / land cover using supervised classification techniques in Erdas imagine. The change for a period of 32 years was assessed using the multi-temporal satellite datasets from Landsat MSS (1973), Landsat TM (1990), and IRS P6 LISS III (2005). A geospatial approach was used for the land cover analysis. Digital elevation models, Satellite imageries and SOI topo sheets were the data sets used in the analysis. Vegetation sampling plots distributed over the different forest types were enumerated and studied for Phytodiversity analysis.

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The evaluation of geospatial data quality and trustworthiness presents a major challenge to geospatial data users when making a dataset selection decision. The research presented here therefore focused on defining and developing a GEO label – a decision support mechanism to assist data users in efficient and effective geospatial dataset selection on the basis of quality, trustworthiness and fitness for use. This thesis thus presents six phases of research and development conducted to: (a) identify the informational aspects upon which users rely when assessing geospatial dataset quality and trustworthiness; (2) elicit initial user views on the GEO label role in supporting dataset comparison and selection; (3) evaluate prototype label visualisations; (4) develop a Web service to support GEO label generation; (5) develop a prototype GEO label-based dataset discovery and intercomparison decision support tool; and (6) evaluate the prototype tool in a controlled human-subject study. The results of the studies revealed, and subsequently confirmed, eight geospatial data informational aspects that were considered important by users when evaluating geospatial dataset quality and trustworthiness, namely: producer information, producer comments, lineage information, compliance with standards, quantitative quality information, user feedback, expert reviews, and citations information. Following an iterative user-centred design (UCD) approach, it was established that the GEO label should visually summarise availability and allow interrogation of these key informational aspects. A Web service was developed to support generation of dynamic GEO label representations and integrated into a number of real-world GIS applications. The service was also utilised in the development of the GEO LINC tool – a GEO label-based dataset discovery and intercomparison decision support tool. The results of the final evaluation study indicated that (a) the GEO label effectively communicates the availability of dataset quality and trustworthiness information and (b) GEO LINC successfully facilitates ‘at a glance’ dataset intercomparison and fitness for purpose-based dataset selection.

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This study compares the precision of three image classification methods, two of remote sensing and one of geostatistics applied to areas cultivated with citrus. The 5,296.52ha area of study is located in the city of Araraquara - central region of the state of São Paulo (SP), Brazil. The multispectral image from the CCD/CBERS-2B satellite was acquired in 2009 and processed through the Geographic Information System (GIS) SPRING. Three classification methods were used, one unsupervised (Cluster), and two supervised (Indicator Kriging/IK and Maximum Likelihood/Maxver), in addition to the screen classification taken as field checking.. Reliability of classifications was evaluated by Kappa index. In accordance with the Kappa index, the Indicator kriging method obtained the highest degree of reliability for bands 2 and 4. Moreover the Cluster method applied to band 2 (green) was the best quality classification between all the methods. Indicator Kriging was the classifier that presented the citrus total area closest to the field check estimated by -3.01%, whereas Maxver overestimated the total citrus area by 42.94%.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Although Recovery is often defined as the less studied and documented phase of the Emergency Management Cycle, a wide literature is available for describing characteristics and sub-phases of this process. Previous works do not allow to gain an overall perspective because of a lack of systematic consistent monitoring of recovery utilizing advanced technologies such as remote sensing and GIS technologies. Taking into consideration the key role of Remote Sensing in Response and Damage Assessment, this thesis is aimed to verify the appropriateness of such advanced monitoring techniques to detect recovery advancements over time, with close attention to the main characteristics of the study event: Hurricane Katrina storm surge. Based on multi-source, multi-sensor and multi-temporal data, the post-Katrina recovery was analysed using both a qualitative and a quantitative approach. The first phase was dedicated to the investigation of the relation between urban types, damage and recovery state, referring to geographical and technological parameters. Damage and recovery scales were proposed to review critical observations on remarkable surge- induced effects on various typologies of structures, analyzed at a per-building level. This wide-ranging investigation allowed a new understanding of the distinctive features of the recovery process. A quantitative analysis was employed to develop methodological procedures suited to recognize and monitor distribution, timing and characteristics of recovery activities in the study area. Promising results, gained by applying supervised classification algorithms to detect localization and distribution of blue tarp, have proved that this methodology may help the analyst in the detection and monitoring of recovery activities in areas that have been affected by medium damage. The study found that Mahalanobis Distance was the classifier which provided the most accurate results, in localising blue roofs with 93.7% of blue roof classified correctly and a producer accuracy of 70%. It was seen to be the classifier least sensitive to spectral signature alteration. The application of the dissimilarity textural classification to satellite imagery has demonstrated the suitability of this technique for the detection of debris distribution and for the monitoring of demolition and reconstruction activities in the study area. Linking these geographically extensive techniques with expert per-building interpretation of advanced-technology ground surveys provides a multi-faceted view of the physical recovery process. Remote sensing and GIS technologies combined to advanced ground survey approach provides extremely valuable capability in Recovery activities monitoring and may constitute a technical basis to lead aid organization and local government in the Recovery management.

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Department of Atmospheric Sciences, Cochin University of Science and Technology