Analysis of impacts of urban land use and land cover on air quality in the Las Vegas region using remote sensing information and ground observations

2007 ◽  
Vol 28 (24) ◽  
pp. 5427-5445 ◽  
Author(s):  
G. Xian
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Air Quality ◽  
Land Cover ◽  
Las Vegas ◽  
Urban Land ◽  
Urban Land Use

scholarly journals An Ensemble Learning Approach for Urban Land Use Mapping Based on Remote Sensing Imagery and Social Sensing Data

2020 ◽  
Vol 12 (19) ◽  
pp. 3254
Author(s):  
Zhou Huang ◽  
Houji Qi ◽  
Chaogui Kang ◽  
Yuelong Su ◽  
Yu Liu
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Ensemble Learning ◽  
Urban Land ◽  
Urban Land Use ◽  
Learning Approach ◽  
Land Use Classification ◽  
Remote Sensing Imagery ◽  
Sensing Data

Urban land use mapping is crucial for effective urban management and planning due to the rapid change of urban processes. State-of-the-art approaches rely heavily on the socioeconomic, topographical, infrastructural and land cover information of urban environments via feeding them into ad hoc classifiers for land use classification. Yet, the major challenge lies in the lack of a universal and reliable approach for the extraction and combination of physical and socioeconomic features derived from remote sensing imagery and social sensing data. This article proposes an ensemble-learning-approach-based solution of integrating a rich body of features derived from high resolution satellite images, street-view images, building footprints, points-of-interest (POIs) and social media check-ins for the urban land use mapping task. The proposed approach can statistically differentiate the importance of input feature variables and provides a good explanation for the relationships between land cover, socioeconomic activities and land use categories. We apply the proposed method to infer the land use distribution in fine-grained spatial granularity within the Fifth Ring Road of Beijing and achieve an average classification accuracy of 74.2% over nine typical land use types. The results also indicate that our model outperforms several alternative models that have been widely utilized as baselines for land use classification.

Reviewing the Pertinence of Sentinel-1 SAR for Urban Land Use Land Cover Classification

2020 ◽  
Vol 11 (5) ◽  
pp. 529-535
Author(s):  
Dan Abudu ◽  
Nigar Sultana Parvin ◽  
Geoffrey Andogah
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Urban Areas ◽  
Land Use Changes ◽  
Land Cover Classification ◽  
Urban Land ◽  
Urban Land Use ◽  
Land Use Land Cover ◽  
Sensing Technology

Conventional approaches for urban land use land cover classification and quantification of land use changes have often relied on the ground surveys and urban censuses of urban surface properties. Advent of Remote Sensing technology supporting metric to centimetric spatial resolutions with simultaneous wide coverage, significantly reduced huge operational costs previously encountered using ground surveys. Weather, sensor’s spatial resolution and the complex compositions of urban areas comprising concrete, metallic, water, bare- and vegetation-covers, limits Remote Sensing ability to accurately discriminate urban features. The launch of Sentinel-1 Synthetic Aperture Radar, which operates at metric resolution and microwave frequencies evades the weather limitations and has been reported to accurately quantify urban compositions. This paper assessed the feasibility of Sentinel-1 SAR data for urban land use land cover classification by reviewing research papers that utilised these data. The review found that since 2014, 11 studies have specifically utilised the datasets.

The Use of Remote Sensing and Landscape Metrics to Describe Structures and Changes in Urban Land Uses

10.1068/a3496 ◽  
2002 ◽  
Vol 34 (8) ◽  
pp. 1443-1458 ◽  
Author(s):  
Martin Herold ◽  
Joseph Scepan ◽  
Keith C Clarke
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Urban Growth ◽  
Landscape Metrics ◽  
Urban Land ◽  
Urban Land Use ◽  
Urban Region ◽  
Santa Barbara

Remote sensing technology has great potential for acquisition of detailed and accurate land-use information for management and planning of urban regions. However, 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 spatial and temporal resolution, and digital image analysis techniques. This study introduces a methodology using information on image spatial form—landscape metrics—to describe urban land-use structures and land-cover changes that result from urban growth. The analysis is based on spatial analysis of land-cover structures mapped from digitally classified aerial photographs of the urban region Santa Barbara, CA. Landscape metrics were calculated for segmented areas of homogeneous urban land use to allow a further characterization of the land use of these areas. The results show a useful separation and characterization of three urban land-use types: commercial development, high-density residential, and low-density residential. Several important structural land-cover features were identified for this study. These were: the dominant general land cover (built up or vegetation), the housing density, the mean structure and plot size, and the spatial aggregation of built-up areas. For two test areas in the Santa Barbara region, changes (urban growth) in the urban spatial land-use structure can be described and quantified with landscape metrics. In order to discriminate more accurately between the three land-cover types of interest, the landscape metrics were further refined into what are termed ‘landscape metric signatures’ for the land-use categories. The analysis shows the importance of the spatial 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.

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