Detailed Urban Land Use Research Articles

On the mitigation potential of higher urban albedo in a temperate oceanic metropolis

The urban overheating calls for the implementation of mitigation actions. The article aims at demonstrating the advantages and the risks associated with the increase of the city albedo in Melbourne (Australia), through high resolution numerical analyses based on a detailed urban land use. The Weather Research and Forecasting model was used to calculate the spatial and temporal local climate change for three increased albedo scenarios and three different urban densities. In the scenario with highest albedo, the ambient temperature decreases of about 1.6°C in the middle and high density zones and 0.9°C in the low density zones. The sea breeze extends the thermal benefit due to the albedo increase to non-urban areas, despite it is slightly weakened by the diminished land-sea temperature contrast. The pollutant levels are expected to increase, due to a combination of slower winds (drops up to 0.19 ms−1) and decreased planetary boundary layer heights (drops from 175 m up to 291 m). High and low density classes have the highest and lowest risk of stagnation, respectively. Due to the contrasting results in terms of thermal mitigation and pollution risks, the study calls for the identification of comprehensive metrics to asses optimal values of urban albedo.

From land cover to land use: Applying random forest classifier to Landsat imagery for urban land-use change mapping

The extensive record of Landsat imagery is commonly used to map urban land-cover and land-use change. Random forest (RF) classification was applied for mapping more detailed urban land-use and change categories than is typically attempted with Landsat data. Two dates of Landsat imagery (1990 and 2015) were utilized with surface reflectance, Vegetation-Impervious-Soil (V-I-S) fractions, grey-level cooccurrence matrix (GLCM) of V-I-S, and temporal variation of V-I-S inputs. GLCM V-I-S and temporal variation of Vegetation as input features of RF classifiers slightly improved accuracies of land use maps. A change map derived from an overlay analysis between the 2015 map and a Landsat-derived urban expansion map was more accurate than one from post-classification comparison of 1990 and 2015 maps. For the Taiwan study area, Transportation Corridor land use tended to lead conversion to Residential and Employment types in relatively undeveloped districts, and extensive urban land-use change occurred in peri-urban areas.

Mapping Essential Urban Land Use Categories in Beijing with a Fast Area of Interest (AOI)-Based Method

Urban land use mapping is critical to understanding human activities in space. The first national mapping result of essential urban land use categories of China (EULUC-China) was released in 2019. However, the overall accuracies in some of the plain cities such as Beijing, Chengdu, and Zhengzhou were lower than 50% because many parcel-based mapping units are large with mixed land uses. To address this shortcoming, we proposed an area of interest (AOI)-based mapping approach, choosing Beijing as our study area. The mapping process includes two major steps. First, grids with different sizes (i.e., 300 m, 200 m, and 100 m) were derived from original land parcels to obtain classification units with a suitable size. Then, features within these grids were extracted from Sentinel-2 spectral data, point of interest (POI), and Tencent Easygo crowdedness data. These features were classified using a random forest (RF) classifier with AOI data, resulting in a 10-category map of EULUC. Second, we superimposed the AOIs layer on classified units to do some rectification and offer more details at the building scale. The overall accuracy of the AOI layer reached 98%, and the overall accuracy of the mapping results reached 77%. This study provides a fast method for accurate geographic sample collection, which substantially reduces the amount of fieldwork for sample collection and improves the classification accuracy compared to previous EULUC mapping. The detailed urban land use map could offer more support for urban planning and environmental policymaking.

Detailed Mapping of Urban Land Use Based on Multi-Source Data: A Case Study of Lanzhou

Detailed urban land use information is the prerequisite and foundation for implementing urban land policies and urban land development, and is of great importance for solving urban problems, assisting scientific and rational urban planning. The existing results of urban land use mapping have shortcomings in terms of accuracy or recognition scale, and it is difficult to meet the needs of fine urban management and smart city construction. This study aims to explore approaches that mapping urban land use based on multi-source data, to meet the needs of obtaining detailed land use information and, taking Lanzhou as an example, based on the previous study, we proposed a process of urban land use classification based on multi-source data. A combination road network dataset of Gaode and OpenStreetMap (OSM) was synthetically applied to divide urban parcels, while multi-source features using Sentinel-2A images, Sentinel-1A polarization data, night light data, point of interest (POI) data and other data. Simultaneously, a set of comparative experiments were designed to evaluate the contribution and impact of different features. The results showed that: (1) the combination utilization of Gaode and OSM road network could improve the classification results effectively. Specifically, the overall accuracy and kappa coefficient are 83.75% and 0.77 separately for level I and the accuracy of each type reaches more than 70% for level II; (2) the synthetic application of multi-source features is conducive to the improvement of urban land use classification; (3) Internet data, such as point of interest (POI) information and multi-time population information, contribute the most to urban land use mapping. Compared with single-moment population information, the multi-time population distribution makes more contributions to urban land use. The framework developed herein and the results derived therefrom may assist other cities in the detailed mapping and refined management of urban land use.

Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018

Land use reflects human activities on land. Urban land use is the highest level human alteration on Earth, and it is rapidly changing due to population increase and urbanization. Urban areas have widespread effects on local hydrology, climate, biodiversity, and food production. However, maps, that contain knowledge on the distribution, pattern and composition of various land use types in urban areas, are limited to city level. The mapping standard on data sources, methods, land use classification schemes varies from city to city, due to differences in financial input and skills of mapping personnel. To address various national and global environmental challenges caused by urbanization, it is important to have urban land uses at the national and global scales that are derived from the same or consistent data sources with the same or compatible classification systems and mapping methods. This is because, only with urban land use maps produced with similar criteria, consistent environmental policies can be made, and action efforts can be compared and assessed for large scale environmental administration. However, despite of the fact that a number of urban-extent maps exist at global scales [3,4], more detailed urban land use maps do not exist at the same scale. Even at big country or regional levels such as for the United States, China and European Union, consistent land use mapping efforts are rare.

Detailed Urban Land Use Land Cover Classification at the Metropolitan Scale Using a Three-Layer Classification Scheme

Urban Land Use/Land Cover (LULC) information is essential for urban and environmental management. It is, however, very difficult to automatically extract detailed urban LULC information from remote sensing imagery, especially for a large urban area. Medium resolution imagery, such as Landsat Thematic Mapper (TM) data, cannot uncover detailed LULC information. Further, very high resolution (VHR) satellite imagery, such as IKONOS and QuickBird data, can only be applied to a small area, largely due to the data unavailability and high computation cost. As a result, little research has been conducted to extract detailed urban LULC information for a large urban area. This study, therefore, developed a three-layer classification scheme for deriving detailedurban LULC information by integrating newly launched Chinese GF-1 (medium resolution) and GF-2 (very high resolution) satellite imagery and synthetically incorporating geometry, texture, and spectral information through multi-resolution image segmentation and object-based image classification (OBIA). Homogeneous urban LULC types such as water bodies or large areas of vegetation could be derived from GF-1 imagery with 16 m and 8 m spatial resolutions, while heterogeneous urban LULC types such as industrial buildings, residential buildings, and roads could be extracted from GF-2 imagery with 3.2 m and 0.8 m spatial resolutions. The multi-resolution segmentation method and a random forest algorithm were employed to perform image segmentation and object-based image classification, respectively. An analysis of the results suggests an overall accuracy of 0.89 and 0.87 were achieved for the second and third level urban LULC classification maps, respectively. Therefore, the three-layer classification scheme has the potential to derive high accuracy urban LULC information through integrating medium and high-resolution remote sensing imagery.

Land use classification from social media data and satellite imagery

Detailed urban land use classification plays a highly important role in the development and management of cities and in the identification of human activities. The complexity of the urban system makes its functional zoning a challenge, which makes such maps underutilized. A detailed land use classification encompasses both the natural land features and the classification of structures closely related to human activities. The use of satellite imagery to classify land use can effectively benefit the recognition of natural objects, but its performance demands significant improvement in the recognition of social functions due to the lack of information regarding human activities. To identify such activities in an urban area, we added Point of Interests (POI) data. This dataset contains both geographical tags and attributes that describe human activities. However, it has an uneven spatial distribution, with gaps in coverage being readily apparent. This paper proposes a land use classification framework using satellite imagery and data from social media. The proposed method employs a kernel density estimation to handle the spatial unevenness of POI data. The solution of mixed programming of MPI and OpenMP was adopted to parallel the algorithm. The results are compared to data compiled manually by means of human interpretation. Considering the example of Wuhan city, results show that the overall accuracy of land use type classification is 86.2%, and the Kappa coefficient is 0.860. It is demonstrated that using both POI and satellite images, a detailed land use map can be created automatically with satisfactory robustness.

Improving Urban Population Distribution Models with Very-High Resolution Satellite Information

Built-up layers derived from medium resolution (MR) satellite information have proven their contribution to dasymetric mapping, but suffer from important limitations when working at the intra-urban level, mainly due to their difficulty in capturing the whole range of variation in terms of built-up densities. In this regard, very-high resolution (VHR) remote sensing is known for its ability to better capture small variations in built-up densities and to derive detailed urban land use, which plead in favor of its use when mapping urban populations. In this paper, we compare the added value of various combinations of VHR data sets, compared to a MR one. A top-down dasymetric mapping strategy is applied to reallocate population counts from administrative units into a regular 100 × 100 m grid, according to different weighting layers. These weighting layers are created from MR and/or VHR input data, using simple built-up proportion or reallocation “weights”, obtained from a set of multiple ancillary data used to train a Random Forest regression model. The results reveal that (1) a built-up mask derived from VHR can improve the accuracy of the reallocation by roughly 13%, compared to MR; (2) using VHR land-use information alone results in lower accuracy than using a MR built-up mask; and (3) there is a clear complementarity between VHR land cover and land use.

Land cover and land use change analysis using multi-spatial resolution data and object-based image analysis

Remote sensing data and techniques are reliable tools for monitoring and studying urban land cover and land use (LCLU) change. Fine spatial resolution (FRes) commercial satellite image in conjunction with geographic object-based image change analysis (GEOBICA) methods have been used to generate detailed and accurate urban LCLU maps. The integration of a backdating approach improves LCLU change classification results for the first date of a bi-temporal image sequences. Conversely, moderate spatial resolution satellite images such as those from Landsat sensors may not allow for detailed urban land use and land cover mapping. The objective of this study is to test a new bi-temporal change identification approach that integrates image classification of fine spatial resolution satellite imagery at time-2 and moderate spatial resolution satellite imagery (Landsat) at time-1, in a backdating and GEOBICA framework for mapping urban land use change. We compare the results from this approach to those of a GEOBICA approach based on fine spatial resolution imagery in both periods. The overall accuracy of the time-1 Landsat image classification is 0.82 and that of the fine spatial resolution image is 0.87. Moreover, the overall accuracy of the areal change data estimated from the pixel-wise spatial overlay of bi-temporal FRes LCLU maps is 0.80 while that from overlaying a time-2 FRes-generated map to that from a Landsat time-1 image is 0.81. The proposed method can be used in areas that lack FRes data due to limited coverage in the early 2000s.

Attribution of mortality to the urban heat island during heatwaves in the West Midlands, UK.

BackgroundThe Urban Heat Island (UHI) effect describes the phenomenon whereby cities are generally warmer than surrounding rural areas. Traditionally, temperature monitoring sites are placed outside of city centres, which means that point measurements do not always reflect the true air temperature of urban centres, and estimates of health impacts based on such data may under-estimate the impact of heat on public health. Climate change is likely to exacerbate heatwaves in future, but because climate projections do not usually include the UHI, health impacts may be further underestimated. These factors motivate a two-dimensional analysis of population weighted temperature across an urban area, for heat related health impact assessments, since populations are typically densest in urban centres, where ambient temperatures are highest and the UHI is most pronounced. We investigate the sensitivity of health impact estimates to the use of population weighting and the inclusion of urban temperatures in exposure data.MethodsWe quantify the attribution of the UHI to heat related mortality in the West Midlands during the heatwave of August 2003 by comparing health impacts based on two modelled temperature simulations. The first simulation is based on detailed urban land use information and captures the extent of the UHI, whereas in the second simulation, urban land surfaces have been replaced by rural types.Results and conclusionsThe results suggest that the UHI contributed around 50 % of the total heat-related mortality during the 2003 heatwave in the West Midlands. We also find that taking a geographical, rather than population-weighted, mean of temperature across the regions under-estimates the population exposure to temperatures by around 1 °C, roughly equivalent to a 20 % underestimation in mortality. We compare the mortality contribution of the UHI to impacts expected from a range of projected temperatures based on the UKCP09 Climate Projections. For a medium emissions scenario, a typical heatwave in 2080 could be responsible for an increase in mortality of around 3 times the rate in 2003 (278 vs. 90 deaths) when including changes in population, population weighting and the UHI effect in the West Midlands, and assuming no change in population adaptation to heat in future.Electronic supplementary materialThe online version of this article (doi:10.1186/s12940-016-0100-9) contains supplementary material, which is available to authorized users.

Urban land use mapping using high resolution SAR data based on density analysis and contextual information

This paper presents a procedure for urban land use interpretation from a single high-resolution synthetic aperture radar (SAR) image. The approach involves two semi-automatic steps: urban extent delineation and urban land use mapping. In the first step, two general classes (urban and nonurban) are mapped using an existing method that involves analysis of speckle characteristics and intensity information. In the second step, more detailed urban land use classification is undertaken based on analysis of regional radar backscatter patterns in terms of density of dark linear features, density of bright features, and urban contextual information. Density analysis was conducted at three levels: individual building–road, urban block, and suburban commercial–industrial. Contextual information, including density, building size, and distance between buildings and parking places, was used to quantify urban morphological patterns. Tests were conducted for mapping Ottawa, Canada, using five Radarsat-2 images of different incidence angles and three TerraSAR-X images of the same incidence angles but different dates. The results show that the proposed method could be used to map five urban land uses including low-density residential, commercial–industrial, high-density urban, open land, and nonurban with accuracies in the range from 74% to 82%.

Assessing urbanisation effects on rainfall-runoff using a remote sensing supported modelling strategy

This paper aims at developing a methodology for assessing urban dynamics in urban catchments and the related impact on hydrology. Using a multi-temporal remote sensing supported hydrological modelling approach an improved simulation of runoff for urban areas is targeted. A time-series of five medium resolution urban masks and corresponding sub-pixel sealed surface proportions maps was generated from Landsat and SPOT imagery. The consistency of the urban mask and sealed surface proportion time-series was imposed through an urban change trajectory analysis. The physically based rainfall-runoff model WetSpa was successfully adapted for integration of remote sensing derived information of detailed urban land use and sealed surface characteristics.A first scenario compares the original land-use class based approach for hydrological parameterisation with a remote sensing sub-pixel based approach. A second scenario assesses the impact of urban growth on hydrology. Study area is the Tolka River basin in Dublin, Ireland.The grid-based approach of WetSpa enables an optimal use of the spatially distributed properties of remote sensing derived input.Though change trajectory analysis remains little used in urban studies it is shown to be of utmost importance in case of time series analysis. The analysis enabled to assign a rational trajectory to 99% of all pixels. The study showed that consistent remote sensing derived land-use maps are preferred over alternative sources (such as CORINE) to avoid over-estimation errors, interpretation inconsistencies and assure enough spatial detail for urban studies. Scenario 1 reveals that both the class and remote sensing sub-pixel based approaches are able to simulate discharges at the catchment outlet in an equally satisfactory way, but the sub-pixel approach yields considerably higher peak discharges. The result confirms the importance of detailed information on the sealed surface proportion for hydrological simulations in urbanised catchments. In addition a major advantage with respect to hydrological parameterisation using remote sensing is the fact that it is site- and period-specific. Regarding the assessment of the impact of urbanisation (scenario 2) the hydrological simulations revealed that the steady urban growth in the Tolka basin between 1988 and 2006 had a considerable impact on peak discharges. Additionally, the hydrological response is quicker as a result of urbanisation. Spatially distributed surface runoff maps identify the zones with high runoff production.It is evident that this type of information is important for urban water management and decision makers. The results of the remote sensing supported modelling approach do not only indicate increased volumes due to urbanisation, but also identifies the locations where the most relevant impacts took place.

Using Geometrical, Textural, and Contextual Information of Land Parcels for Classification of Detailed Urban Land Use

Detailed urban land use data are important to government officials, researchers, and businesspeople for a variety of purposes. This article presents an approach to classifying detailed urban land use based on geometrical, textural, and contextual information of land parcels. An area of 6 by 14 km in Austin, Texas, with land parcel boundaries delineated by the Travis Central Appraisal District of Travis County, Texas, is tested for the approach. We derive fifty parcel attributes from relevant geographic information system (GIS) and remote sensing data and use them to discriminate among nine urban land uses: single family, multifamily, commercial, office, industrial, civic, open space, transportation, and undeveloped. Half of the 33,025 parcels in the study area are used as training data for land use classification and the other half are used as testing data for accuracy assessment. The best result with a decision tree classification algorithm has an overall accuracy of 96 percent and a kappa coefficient of 0.78, and two naive, baseline models based on the majority rule and the spatial autocorrelation rule have overall accuracy of 89 percent and 79 percent, respectively. The algorithm is relatively good at classifying single-family, multifamily, commercial, open space, and undeveloped land uses and relatively poor at classifying office, industrial, civic, and transportation land uses. The most important attributes for land use classification are the geometrical attributes, particularly those related to building areas. Next are the contextual attributes, particularly those relevant to the spatial relationship between buildings, then the textural attributes, particularly the semivariance texture statistic from 0.61-m resolution images.

Per‐field urban land use classification based on tax parcel boundaries

The objective of this study is to test a per‐field approach for classifying detailed urban land use, such as single‐family, multi‐family, industrial and commercial. Tax parcel boundaries are used as the field boundaries for classification. Twelve attributes of parcels, such as parcel sizes, parcel shape, building counts and building heights, are used as the discriminant factors between different land use types. For our study area that consists of 33 025 parcels, we first derived parcel attributes from geographic information system (GIS) and remote sensing data. We then converted the parcel vector data to an image of 12 bands with pixel values from parcel attributes. After that, we performed a standard supervised classification to classify the image into nine land use types. The best classification result with a decision tree classifier had an overall accuracy of 93.53% and a Kappa Coefficient of 0.7023. This study shows the feasibility of applying a per‐field approach based on tax parcel boundaries to classify detailed urban land use.

Population Distribution and Spatial Structure in Transitional Chinese Cities: A Study of Nanjing

Two geographers use population surface model techniques to analyze the distribution of population in Chinese cities. Focusing on Nanjing, they utilize detailed urban land use and building distribution data to develop a productive alternative method to reveal spatial variations in the distribution of inhabitants. The findings indicate that despite suburbanization, Nanjing remains a compact city with a population density that declines rapidly as distance from its central business district increases. Also, suburbanization has been limited to an inner area where population is densely distributed while commercial and office development is less prominent. Lastly, commercial activities influence the distribution of urban inhabitants and suburbanization more significantly than industrial development in suburbia. Journal of Economic Literature, Classification Numbers: O15, O18, R12, R14. 9 figures, 3 tables, 37 references.

A rule-based urban land use inferring method for fine-resolution multispectral imagery

Detailed urban land use mapping requires high-resolution remotely sensed data. The pan-sharpened multispectral IKONOS imagery of 1 m pixel resolution is experimented with for urban land use classification. With the increase of spatial resolution, between-class spectral confusion and within-class spectral variation increase. Spectral-based traditional image classification methods cannot be directly applied to the IKONOS data for urban land use mapping. In this study, a rule-based urban land use inferring method is proposed and tested on 36 samples of typical land use classes and an IKONOS subscene of various classes in London, Ontario, Canada. The proposed method includes two general steps. First, the conventional multispectral classification method is applied to produce a preliminary land cover map. Second, urban land use information is inferred from the combination of several land cover classes existing in a neighbourhood by a rule-based modelling process. The inferring rules involve the percent composition ranges of compatible land cover categories for a certain land use class, the interrelationship of the compatible land covers, and exclusion of incompatible land covers. The results show that the proposed method has successfully identified level II and level III land use classes using the U.S. Geological Survey land use classification system. The proposed method has successfully identified the land use classes in the sample image with over 90% accuracy. For the subscene, the proposed method has produced a land use map with 88.5% overall accuracy.

Land use mapping of Hong Kong from Landsat images An evaluation

Abstract The use of Landsat 70 mm film images for the production of land use maps of Hong Kong, a small territory of 1061 km2 in area, was evaluated according to their planimetric accuracy and the accuracy of land use interpretation. Land use maps were produced at a scale around 1:250 000 with the aid of a Fairey additive viewer and at a scale of 1:88 500 through manual interpretation. Finally, an urban land use map at a scale of 1:25 000 was generated with a computer-assisted method of analysis of the Landsat data. It was concluded that these land use maps were all of an acceptable planimetric (±400m) and semantic accuracy (89 per cent), especially if a high level of generalization was intended. The computer-assisted method of analysis permits more detailed urban land use information to be extracted, but with an accuracy of only 69 per cent.