Extracting land cover/use from remotely sensed imagery: Potentials for urban planning (original) (raw)
Related papers
2013
Remote sensing technology is useful for urban planning due to its capability in examining detailed spectral characteristic of urban land uses. This study attempts to review a relevant studied have been done in identified an appropriate spectral for urban land use using high resolution remote sensing images and GIS approach. The detailed spectral for urban land uses consist of residential, industrial and commercial in metropolitan and city center urban hierarchy will be discussed. The segmentation techniques through object oriented and the use of field measurement was highlighted, at once demonstrates the usability of such infrastructure to facilitate further progress of remote sensing and GIS application in urban planning in Malaysia. Finally, a discussion of the needs for further research is presented.
Remote Sensing in …, 2004
Very High Resolution (VHR) satellite images offer a great potential for the extraction of landuse and land-cover related information for urban areas. The available techniques are diverse and need to be further examined before operational use is possible. In this paper we applied two pixel-by-pixel classification techniques and the object-oriented image analysis approach (eCognition) for a land-cover classification of a Quickbird image of a study area in the northern part of the city of Ghent (Belgium). Only small differences in overall Kappa were noted between the best results of the pixel-based approach (neural network classification with Haralick texture measures) and the object-oriented classification (eCognition). A rule-based procedure using ancillary information on elevation derived from a digital surface model was applied on the pixel-based land-cover classification in order to obtain information on the spatial distribution of buildings and artificial surfaces.
Conclusion: Improve classification categories one of the image processing targets based on different kind of analyses to obtain the missing data or to dived the existing one for more class's levels. The first level of Residential urban fabric category obtained from Spot satellite images data sources as a homogeneous data (undivided data). Talking about residential density means the occupation of construction building areas of lands without volume, the neighbour categories such as Green, Street and industrial areas will affect on dividing the Residential density levels. We focused on the development of a methodology based on segmentation and buffer zone analysis for urban residential areas that may improve the urban investigation. We treat various fundamental steps based on: 1) Extract Residential Urban Fabric Classifications areas from the final classification image of. 2) Determine the homogeneity of residential pixels for special segmentation analysis for extraction of unlabelled homogeneous objects. 3) Understand the Buffer zone and segmentation Parameters for Residential Density. From last case areas, to determine the population density needs more information to improve such as adding the heights parameters using LiDAR information. We tried to observe the relations between land use areas to understand the compact construction areas. However, the residential density that shown focused on lands occupation without height information, the parameters and the results could have a percentage of error for example high construction density will not reflect high density of population or high volume construction density. Imagery was evaluated in three stages for its use, namely, urban change detection, urban structural classification and detail of imagery to allow for counts of buildings. Urban land use is a complex system that imposes a challenge for sciences and practice. Remote Sensing based land use modelling can provide quantified and visualized, special information on the future that is otherwise difficult to obtain, that it is hoped will draw public attention and increase environmental awareness. It is up to the elected officials, community leaders, local planners, landowners, developers, and conservationists to make wise decisions and take appropriate actions. However, we concentrate to illustrate more details of classification categories by detect the Residential urban areas that could gave more details of population behaviour 1. Abstract: Most major metropolitan areas face the growing problems of urban sprawl, loss of natural vegetation and open space. Almost everyone has seen these changes to their local environment but without a clear understanding of their impact. Remote sensing technology offers the potential for acquisition of detailed and accurate land-use information for management and planning of urban regions. 1 Esta ponencia se desarrolla como parte de las investigaciones realizadas el marco del Proyecto DESARROLLO DE UNA PLATAFORMA PARA EL MODELADO PROSPECTIVO DE LOS PROCESOS DE URBANIZACIÓN EN LAS ZONAS COSTERAS), financiado por el Ministerio de Ciencia e Innovación, MICINN, en la Convocatoria de ayudas de Proyectos de Investigación Fundamental no orientada, en el marco de algunos Programas Nacionales del Plan Nacional de I+D+i (2008-2011). Convocatoria 2009. (CSO2009-09057). 2 However, Satellite data is particularly useful for detecting major changes in urban land-use because of frequent coverage, low cost and the possibility of overlaying images from different dates exactly on top of each other. The determination of land-use data with high geometric and thematic accuracy is generally limited by the availability of adequate remote sensing data, in terms of special and temporal resolution and digital analysis image techniques. This study introduces a methodology using information on spatial images to describe urban land-use density and changes. The analysis is based on spatial analysis of land-cover structure mapped from digitally classified satellite images of the metropolitan region of Barcelona. The results show a useful separation and characterization of various types of land-uses of this area and several important structural land-cover features were identified for this study. The analysis shows the importance of the special measurements as second order image information that can contribute to more detailed mapping of urban areas and towards a more accurate characterization of spatial urban growth pattern. However, Improve classification categories one of the image processing targets based on different kind of analyses to obtain the missing data or to divide the existing one for more class's levels. The first level of Residential urban fabric category obtained from satellite images data sources as a homogeneous data (undivided data). When we are talking about residential density that's mean the occupation of construction building areas of lands because the volume is not exist in our case of study so the neighbour categories such as Green, Street and industrial areas will affect on dividing the Residential density levels. Our data source is formed by classified Spot 5 (year 2004) satellite image (False Colour image with 10m resolution) which cover the metropolitan area of Barcelona. This paper focused on the development of a methodology based on segmentation and buffer zone analysis for urban residential areas that may improve the urban investigation.
Remote Sensing Classification of Urban Area Using Multispectral Indices for Urban Planning
Remote Sensing: Special Issue "Remote Sensing-Based Urban Planning Indicators", 2020
An accelerating trend of global urbanization accompanying population growth makes frequently updated land use and land cover (LULC) maps critical. LULC maps have been widely created through the classification of remotely sensed imagery. Maps of urban areas have been both dichotomous (urban or non-urban) and entailing of discrete urban types. This study incorporated multispectral built-up indices, designed to enhance satellite imagery, for introducing new urban classification schemes. The indices examined are the new built-up index (NBI), the built-up area extraction index (BAEI), and the normalized difference concrete condition index (NDCCI). Landsat Level-2 data covering the city of Miami, FL, USA was leveraged with geographic data from the Florida Geospatial Data Library and Florida Department of Environmental Protection to develop and validate new methods of supervised and unsupervised classification of urban area. NBI was used to extract discrete urban features through object-oriented image analysis. BAEI was found to possess properties for visualizing and tracking urban development as a low-high gradient. NDCCI was composited with NBI and BAEI as the basis for a robust urban intensity classification scheme superior to that of the United States Geological Survey National Land Cover Database 2016. BAEI, implemented as a shadow index, was incorporated in a novel infill geosimulation of high-rise construction. The findings suggest that the proposed classification schemes are advantageous to the process of creating more detailed cartography in response to the increasing global demand.
Application of Remote sensing and GIS Techniques in Urban Planning, Development and Management
2019
Management and planning of urban space requires spatially accurate and timely information on land use and changing pattern. Evaluation provides the planners and decision-makers with required information about the current state of development and the nature of changes that have occurred. Remote sensing and Geographical Information system (GIS) provides vital tools which can be applied in the analysis at the district and as well, as the city level. This study evaluates the proposed future land use plan (2021) in context to the existing land use (2015) in Allahabad district. Land use /land cover map was prepared using supervised classification of landsat 8 (OLI) and LISS IV satellite data The results obtained from classified image were compared with the land use information obtained from National Remote Sensing Centre (NRSC, Govt. of India) for the Allahabad district. Though the land use information obtained from NRSC is for the period 2011-12, however in the absence of a more recent d...
Environment and Planning A, 2002
Over 70% of the population in developed countries lives in urbanized areas . Population growth, regional in-migration, and increasing ecological problems require advanced methods for city planners, economists, ecologists, and resource managers to support sustainable development in these rapidly changing regions. In order to make intelligent decisions, and to take timely and effective action, planners need extensive, comprehensive knowledge about the causes, chronology, and effects of these processes. Recent research has identified a number of different approaches for data acquisition and for land-use characterization and analysis which utilize remote sensing imagery as source data in the derivation of spatial data sets with high temporal and spatial resolution .
Comparison of methods for land-use classification incorporating remote sensing and GIS inputs
Applied Geography, 2011
Over the last few decades, dramatic land-use changes have occurred throughout Israel. Previously-grazed areas have been afforested, converted to irrigated or rain-fed agriculture, turned into natural reserves, often used as large military training sites, converted to rural and urban settlements, or left unused. Landuse maps provided by the Israeli governmental are more detailed for agricultural and urban land-use classes than for others. While rangelands still account for a substantial part of the northern Negev, their extent today is not well defined. In light of continuous land-use changes and lack of regard to rangelands in existing land-use maps, there is a need for creating a current land-use information database, to be utilized by planners, scientists, and decision makers. Remote-sensing (RS) data are a viable source of data from which land-use maps could be created and updated efficiently. The purpose of this work is to explore low-cost techniques for combining current satellite RS data together with data from the Israeli Geographic Information System (GIS) in order to create a relatively accurate and current land-use map for the northern Negev. Several established methods for land-use classification from RS data were compared. In addition, ancillary land-use data were used to update and improve the RS classification accuracy within a GIS framework. It was found that using a combination of supervised and unsupervised training classes produces a more accurate product than when using either of them separately. It was also found that updating this product using ancillary data and GIS techniques can improve the product accuracy by up to 10%. The final product's overall accuracy was 81%. It is suggested that applying the presented technique for more RS images taken at different times can facilitate the creation of a database for land-use changes.
2013
New aerial cameras and new advanced geoprocessing tools improve the generation of urban land cover maps. Elevations can be derived from stereo pairs with high density, positional accuracy, and efficiency. The combination of multispectral high-resolution imagery and high-density elevations enable a unique method for the automatic generation of urban land cover maps. In the present paper, imagery of a new medium-format aerial camera and advanced geoprocessing software are applied to derive normalized digital surface models and vegetation maps. These two intermediate products then become input to a tree structured classifier, which automatically derives land cover maps in 2D or 3D. We investigate the thematic accuracy of the produced land cover map by a class-wise stratified design and provide a method for deriving necessary sample sizes. Corresponding survey adjusted accuracy measures and their associated confidence intervals are used to adequately reflect uncertainty in the assessment based on the chosen sample size. Proof of concept for the method is given for an urban area in Switzerland. Here, the produced land cover map with six classes (building, wall and carport, road and parking lot, hedge and bush, grass) has an overall accuracy of 86% (95% confidence interval: 83-88%) and a kappa coefficient of 0.82 (95% confidence interval: 0.78-0.85). The classification of buildings is correct with 99% and of road and parking lot with 95%. To possibly improve the classification further, classification tree learning based on recursive partitioning is investigated. We conclude that the open source software "R" provides all the tools needed for performing statistical prudent classification and accuracy evaluations of urban land cover maps.
International Journal of Remote Sensing, 2012
Detailed, up-to-date information on intra-urban land cover is important for urban planning and management. Differentiation between permeable and impermeable land, for instance, provides data for surface run-off estimates and flood prevention, whereas identification of vegetated areas enables studies of urban micro-climates. In place of maps, high-resolution images, such as those from the satellites IKONOS II, Quickbird, Orbview and WorldView II, can be used after processing. Object-based image analysis (OBIA) is a well-established method for classifying high-resolution images of urban areas. Despite the large number of previous studies of OBIA in the context of intra-urban analysis, there are many issues in this area that are still open to discussion and resolution. Intra-urban analysis using OBIA can be lengthy and complex because of the processing difficulties related to image segmentation, the large number of object attributes to be resolved and the many different methods needed to classify various image objects. To overcome these issues, we performed an experiment consisting of land-cover mapping based on an OBIA approach using an IKONOS II image of a southern sector of São José dos Campos city (covering an area of 12 km2 with 50 neighbourhoods), which is located in São Paulo State in south-eastern Brazil. This area contains various occupation and land-use patterns, and it therefore contains a wide range of intra-urban targets. To generate the land-cover map, we proposed an OBIA-based processing framework that combines multi-resolution segmentation, data mining and hierarchical network techniques. The intra-urban land-cover map was then evaluated through an object-based error matrix, and classification accuracy indices were obtained. The final classification, with 11 classes, achieved a global accuracy of 71.91%.