scholarly journals Analyses of Nocturnal Temperature Cooling-Rate Response to Historical Local-Scale Urban Land-Use/Land Cover Change

2011 ◽  
Vol 50 (9) ◽  
pp. 1872-1883 ◽  
Author(s):  
Winston T. L. Chow ◽  
Bohumil M. Svoma
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Cooling Rate ◽  
Rural Areas ◽  
Urban Areas ◽  
Local Scale ◽  
Urban Land Use ◽  
Cooling Rates ◽  
Near Surface ◽  
Over Time

AbstractUrbanization affects near-surface climates by increasing city temperatures relative to rural temperatures [i.e., the urban heat island (UHI) effect]. This effect is usually measured as the relative temperature difference between urban areas and a rural location. Use of this measure is potentially problematic, however, mainly because of unclear “rural” definitions across different cities. An alternative metric is proposed—surface temperature cooling/warming rates—that directly measures how variations in land-use and land cover (LULC) affect temperatures for a specific urban area. In this study, the impact of local-scale (<1 km2), historical LULC change was examined on near-surface nocturnal meteorological station temperatures sited within metropolitan Phoenix, Arizona, for 1) urban versus rural areas, 2) areas that underwent rural-to-urban transition over a 20-yr period, and 3) different seasons. Temperature data were analyzed during ideal synoptic conditions of clear and calm weather that do not inhibit surface cooling and that also qualified with respect to measured near-surface wind impacts. Results indicated that 1) urban areas generally observed lower cooling-rate magnitudes than did rural areas, 2) urbanization significantly reduced cooling rates over time, and 3) mean cooling-rate magnitudes were typically larger in summer than in winter. Significant variations in mean nocturnal urban wind speeds were also observed over time, suggesting a possible UHI-induced circulation system that may have influenced local-scale station cooling rates.

Spatiotemporal Pattern Analysis of Rapid Urban Expansion Using GIS and Remote Sensing

2010 ◽  
Vol 1 (2) ◽  
pp. 55-70 ◽  
Author(s):  
Hyun Joong Kim
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Urban Growth ◽  
Urban Areas ◽  
Temporal Dynamics ◽  
Urban Expansion ◽  
Local Scale ◽  
Spatiotemporal Pattern ◽  
Land Use Land Cover ◽  
Modeling Studies

Rapidly growing urban areas tend to reveal distinctive spatial and temporal variations of land use/land cover in a locally urbanized environment. In this article, the author analyzes urban growth phenomena at a local scale by employing Geographic Information Systems, remotely sensed image data from 1984, 1994, and 2004, and landscape shape index. Since spatial patterns of land use/land cover changes in small urban areas are not fully examined by the current GIS-based modeling studies or simulation applications, the major objective of this research is to identify and examine the spatial and temporal dynamics of land use changes of urban growth at a local scale. Analytical results demonstrate that sizes, locations, and shapes of new developments are spatio-temporally associated with their landscape variations and major transportation arteries. The key findings from this study contribute to GIS-based urban growth modeling studies and urban planning practices for local communities.

scholarly journals A 30-meter resolution national urban land-cover dataset of China, 2000–2015

2019 ◽  
Author(s):  
Wenhui Kuang ◽  
Shu Zhang ◽  
Xiaoyong Li ◽  
Dengsheng Lu
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Urban Areas ◽  
Vegetation Index ◽  
Urban Land ◽  
Google Earth ◽  
Urban Environments ◽  
Urban Land Use ◽  
Environmental Research ◽  
Urban Land Cover

Abstract. Accurate urban land-cover datasets are essential for mapping urban environments. However, a series of national urban land-cover data covering more than 15 years that characterizes urban environments is relatively rare. Here we propose a hierarchical principle on remotely sensed urban land-use/cover classification for mapping intra-urban structure/component dynamics. China's Land Use/cover Dataset (CLUD) is updated, delineating the imperviousness, green surface, waterbody and bare land conditions in cities. A new data subset called CLUD-Urban is created from 2000 to 2015 at five-year intervals with a medium spatial resolution (30 m). The first step is a prerequisite to extract the vector boundaries covered with urban areas from CLUD. A new method is then proposed using logistic regression between urban impervious surface area (ISA) and the annual maximum Normalized Difference Vegetation Index (NDVI) value retrieved from Landsat images based on a big-data platform with Google Earth Engine. National ISA and urban green space (UGS) fraction datasets for China are generated at 30-meter resolution with five-year intervals from 2000 to 2015. The overall classification accuracy of national urban areas is 92 %. The root mean square error values of ISA and UGS fractions are 0.10 and 0.14, respectively. The datasets indicate that the total urban area of China was 6.28 × 104 km2 in 2015, with average fractions of 70.70 % and 26.54 % for ISA and UGS, respectively. The ISA and UGS increased between 2000 and 2015 with unprecedented annual rates of 1,311.13 km2/yr and 405.30 km2/yr, respectively. CLUD-Urban can be used to enhance our understanding of urbanization impacts on ecological and regional climatic conditions and urban dwellers' environments. CLUD-Urban can be applied in future researches on urban environmental research and practices in the future. The datasets can be downloaded from https://doi.org/10.5281/zenodo.2644932.

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.

scholarly journals The value of OpenStreetMap Historical Contributions as a Source of Sampling Data for Multi‑temporal Land Use/Cover Maps

2019 ◽  
Vol 8 (3) ◽  
pp. 116 ◽  
Author(s):  
Cláudia M. Viana ◽  
Luis Encalada ◽  
Jorge Rocha
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Rural Areas ◽  
Urban Areas ◽  
Coverage Area ◽  
Training Samples ◽  
Spatial Coverage ◽  
Multi Temporal ◽  
One Year ◽  
Thematic Accuracy

OpenStreetMap (OSM) is a free, open-access Volunteered geographic information (VGI) platform that has been widely used over the last decade as a source for Land Use Land Cover (LULC) mapping and visualization. However, it is known that the spatial coverage and accuracy of OSM data are not evenly distributed across all regions, with urban areas being likelier to have promising contributions (in both quantity and quality) than rural areas. The present study used OSM data history to generate LULC datasets with one-year timeframes as a way to support regional and rural multi-temporal LULC mapping. We evaluated the degree to which the different OSM datasets agreed with two existing reference datasets (CORINE Land Cover and the official Portuguese Land Cover Map). We also evaluated whether our OSM dataset was of sufficiently high quality (in terms of both completeness accuracy and thematic accuracy) to be used as a sampling data source for multi-temporal LULC maps. In addition, we used the near boundary tag accuracy criterion to assesses the fitness of the OSM data for producing training samples, with promising results. For each annual dataset, the completeness ratio of the coverage area for the selected study area was low. Nevertheless, we found high thematic accuracy values (ranged from 77.3% to 91.9%). Additionally, the training samples thematic accuracy improved as they moved away from the features’ boundaries. Features with larger areas (> 10 ha), e.g., Agriculture and Forest, had a steadily positive correlation between training samples accuracy and distance to feature boundaries

Gradients of Atmospheric Temperature and Humidity Controlled by Local Urban Land-Use Intensity in Boston

2017 ◽  
Vol 56 (4) ◽  
pp. 817-831 ◽  
Author(s):  
J. A. Wang ◽  
L. R. Hutyra ◽  
D. Li ◽  
M. A. Friedl
Keyword(s):  
Land Use ◽  
Air Temperature ◽  
Rural Areas ◽  
Land Surface ◽  
Growing Season ◽  
Urban Land ◽  
Urban Land Use ◽  
Heat Island ◽  
Near Surface ◽  
Urban Rural

AbstractCities are home to the majority of humanity. Therefore, understanding the mechanisms that control urban climates has substantial societal importance to a variety of sectors, including public health and energy management. In this study, data from an urban sensor network (25 stations) and moderate-resolution remote sensing were used to explore how spatial variation in near-surface air temperature Ta, vapor pressure deficit (VPD), and land surface temperature (LST) depend on local variations in urban land use, both diurnally and seasonally, in the Boston, Massachusetts, metropolitan area. Positive correlations were observed between the amount of local impervious surface area (ISA) and both Ta and VPD. Heat-island effects peaked during the growing-season nighttime, when mean Ta and VPD increased by up to 0.02°C and 0.008 kPa, respectively, per unit ISA. Air temperature and VPD were strongly coupled, but their relationship exhibited significant diurnal hysteresis during the growing season, with changes in VPD generally preceding changes in Ta. Over 79% of the urban–rural difference in VPD was explained by differences in near-surface atmospheric water content, which the authors attribute to reduced evapotranspiration from lower canopy cover in Boston’s urban core. Changes in daytime heat-island intensity were mediated by seasonal feedbacks between vegetation transpiration and VPD forcing. Differences between LST and Ta showed weaker coupling in highly urbanized areas than in rural areas, with summertime surface-urban-heat-island intensity (based on LST) being up to 14°C higher than corresponding urban–rural differences in Ta.

scholarly journals LAND USE CLASSIFICATION USING DEEP MULTITASK NETWORKS

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 17-21
Author(s):  
J. R. Bergado ◽  
C. Persello ◽  
A. Stein
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Urban Areas ◽  
Large Scale ◽  
Urban Land ◽  
Urban Land Use ◽  
The Other ◽  
Classification Task ◽  
Land Use Classification ◽  
Convolutional Network

Abstract. Updated information on urban land use allows city planners and decision makers to conduct large scale monitoring of urban areas for sustainable urban growth. Remote sensing data and classification methods offer an efficient and reliable way to update such land use maps. Features extracted from land cover maps are helpful on performing a land use classification task. Such prior information can be embedded in the design of a deep learning based land use classifier by applying a multitask learning setup—simultaneously solving a land use and a land cover classification task. In this study, we explore a fully convolutional multitask network to classify urban land use from very high resolution (VHR) imagery. We experimented with three different setups of the fully convolutional network and compared it against a baseline random forest classifier. The first setup is a standard network only predicting the land use class of each pixel in the image. The second setup is a multitask network that concatenates the land use and land cover class labels in the same output layer of the network while the other setup accept as an input the land cover predictions, predicted by a subpart of the network, concatenated to the original input image patches. The two deep multitask networks outperforms the other two classifiers by at least 30% in average F1-score.

scholarly journals DYNAMICS OF SPATIO-TEMPORAL URBAN EXPANSION IN SOUTH WEST DELHI REGION: A GEO-SPATIAL APPROACH FOR URBAN DISASTER MANAGEMENT

2019 ◽  
Vol XLII-3/W8 ◽  
pp. 373-379
Author(s):  
N. Sharma ◽  
A. Kaur ◽  
P. Bose
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Natural Resources ◽  
Land Cover Change ◽  
Rural Areas ◽  
Urban Areas ◽  
Urban Expansion ◽  
Urban Disaster ◽  
Spatio Temporal ◽  
The Impact

<p><strong>Abstract.</strong> Constantly increasing population and up-scaling economic growth has certainly contributed to fast-paced urban expansion, but simultaneously, as a result, has developed immense pressure on our natural resources. Among other unfavorable consequences, this has led to significant changes in the land use and land cover patterns in megacities all across the globe. As the impact of uncontrolled and unplanned development continues to alter life patterns, it has become imperative to study severe problems resulting from rapid development and leading to environmental pollution, disruptions in ecological structures, ever increasing pressure on natural resources and recurring urban disasters This paper presents an approach to address these challenges using geospatial data to study the land use and land cover change and the patterns and processes of urban growth. Spatio-temporal changes in land-use/land-cover were assessed over the years using multi-date high resolution satellite data. The land use classification was conducted using visual image interpretation technique wherein, study area was categorized into five different classes based on NRSC classification system namely agricultural, built-up, urban green (forest), and fallow land and water bodies. Post-classification change detection technique was used for the assessment of land-cover change and transition matrices of urban expansion were developed to quantify the changes. The results show that the city has been expanding majorly in its borders, where land masses have been converted from agriculture based rural areas to urban structures. An increase in the built-up category was observed with the transformation of agricultural and marginal land with an approximate change of 8.62% in the peri-urban areas. Urban areas are becoming more densely populated and open barren lands are converted into urban areas due to over population and migration from the rural areas of Delhi and thus increasing threat towards urban disaster. Conservation and sustainable management of various natural resources is recommended in order to minimize the impact of potential urban disasters.</p>

scholarly journals A New Approach to High-Resolution Urban Land Use Classification Using Open Access Software and True Color Satellite Images

2019 ◽  
Vol 11 (19) ◽  
pp. 5266 ◽  
Author(s):  
Fernando Chapa ◽  
Srividya Hariharan ◽  
Jochen Hack
Keyword(s):  
Land Use ◽  
High Resolution ◽  
Land Cover ◽  
Urban Areas ◽  
Google Earth ◽  
Urban Land Use ◽  
Land Use Land Cover ◽  
Wide Range ◽  
True Color ◽  
Land Cover Maps

Urbanization nowadays results in the most dynamic and drastic changes in land use/land cover, with a significant impact on the environment. A detailed analysis and assessment of this process is necessary to take informed actions to reduce its impact on the environment and human well-being. In most parts of the world, detailed information on the composition, structure, extent, and temporal changes of urban areas is lacking. The purpose of this study is to present a methodology to produce high-resolution land use/land cover maps by the use of free software and satellite imagery. These maps can help to understand dynamic urbanizations processes to plan, design, and coordinate sustainable urban development plans, especially in areas with limited resources and advancing environmental degradation. A series of high-resolution true color images provided by Google Earth Pro were used to do initial classifications with the Semi-Automatic Classification Plug-in in QGIS. Afterwards, a new methodology to improve the classification by the elimination of shadows and clouds, and a reduction of misclassifications through superimposition was applied. The classification was carried out for three urban areas in León, Nicaragua, with different degrees of urbanization for the years 2009, 2015, and 2018. Finally, the accuracy of the classification was analyzed using randomly defined validation polygons. The results are three sets of high-resolution land use/land cover maps of the initial and the improved classification, showing the detailed structures and temporal dynamics of urbanization. The average accuracy of classification reaches 74%, but up to 85% for the best classification. The results clearly identify advancing urbanization, the loss of vegetation and riparian zones, and threats to urban ecosystems. In general, the level of detail and simplicity of our methodology is a valuable tool to support sustainable urban management, although its application is not limited to these areas and can also be employed to track changes over time, providing therefore, relevant information to a wide range of decision-makers.

scholarly journals Quantification of Land Cover Changes in Sub-urban Areas of Pekanbaru City

2021 ◽  
Vol 887 (1) ◽  
pp. 012020
Author(s):  
F. Firmansyah ◽  
A. B. Raharja
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Rural Areas ◽  
Economic Activity ◽  
Urban Areas ◽  
The Road ◽  
On The Road ◽  
Urban And Rural Areas ◽  
High Level ◽  
Two Stages

Abstract Morphologically, land cover, urban and rural areas have different characteristics. It is the same as Pekanbaru City area that has unique characteristics including its surrounding regencies. However, the high level of land demand caused by increasing economic activity, high natural and non-natural population growth, makes the morphology of land cover in urban and rural areas unclear. Empirically this beginning to be considered common in urban areas that have a role as a strategic point or center of economic activity, but one of the concerns is the development of unplanned and dominating areas in a space that later create a more fragile environmental conditions in suburban areas. This study aimed to identify changes in land cover and assess the level of conformity of land use in the suburbs of Pekanbaru City. This study used a description method with two stages, (1). Identifying land cover using temporal images, (2). Analyze the level of conformity of land use. The results showed that there are four patterns of land cover change in the suburbs of Pekanbaru City, especially on the road axis connecting the surrounding area. These developments indicate nonconformity of land use which has an impact on the loss of protected land and productive plantation land in the suburbs of Pekanbaru City.

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