Land Cover In Urban Areas Research Articles

Use of evergreen and deciduous plants by nocturnal-roosting birds: A case study in Beijing

With continually increasing urbanization, the land cover in urban areas continues to change, resulting in the loss of biodiversity. Birds are highly sensitive to changes in habitat. Most forest birds perch on plants that provide increased safety to reduce the risk of predation, and small birds may also consider insulation when using roosting plants in winter because of cold weather. Landscaping plants thus shape the nocturnal roosting environment of urban birds, and proper planting is essential for the survival of birds at night. The use of roosting plants by urban birds should therefore be studied to provide a reference for landscaping. In the current study, we observed 1865 nocturnal roosting birds in Beijing from 2021 to 2022, with 23 species of birds from 12 families and 45 species of plants from 22 families recorded. Juniperus chinensis exhibited the highest bird rarity-weighted richness, followed by Fraxinus pennsylvanica, Phyllostachys propinqua, Pinus tabuliformis, and Ulmus pumila. The diameter at breast height, tree height, and crown width of plants used by birds was largest in summer and smallest in winter, and the perch height of birds was the highest in spring and summer and the lowest in winter. Birds used the highest proportion of deciduous plants in summer and the highest proportion of evergreen plants in winter. A significant seasonal difference in the use of evergreen and deciduous plants by small birds was noted, with a preference for deciduous plants in summer and evergreen plants in winter, while this preference was not found in large birds. These findings indicate that evergreen plants provide a vital nocturnal roosting environment for small birds in winter. To provide a better nocturnal roosting habitat for urban birds, we recommend paying attention to the combination of evergreen and deciduous plants when carrying out landscape construction.

Semantic Segmentation of Land Cover in Urban Areas by Fusing Multisource Satellite Image Time Series

Semantic Segmentation of Land Cover in Urban Areas by Fusing Multisource Satellite Image Time Series

Extraction of high-accuracy control points using ICESat-2 ATL03 in urban areas

• A method of extracting high-accuracy control points from ATL03 in urban areas is proposed. • Extract and classify building and ground photons in different urban areas. • The elevation and plane accuracy of control points are evaluated. • ICESat-2 ATL03 photons in urban areas can act as control points. • The land cover in urban areas has significant impact on the accuracy of control points. The ice, cloud, and land elevation satellite-2 (ICESat-2) can effectively measure global surface elevations and be used as a new data source for global elevation control. ICESat-2 has provided surface-specific data products on land/vegetation (ATL08), ice sheet (ATL06), inland water (ATL13), etc., but not on urban areas. The current research using ICESat-2 data to extract control points mostly based on products already provided, focusing on land, forest and other areas. Few studies have analyzed the feasibility of extracting control points in urban areas, as well as the position and elevation accuracy of control points. In this paper, we proposed a method for extracting high-accuracy control points from ATL03 in urban areas and evaluated the elevation and plane accuracy. First, building and ground photons were extracted and classified according to the photon shape features and neighborhood relationships, thereby addressing the insufficiency of ICESat-2 of not classifying photons in urban areas. Then, high-accuracy photons were selected from the ground photons as the laser control points considering the influence of land cover. Finally, the solid and undulating city outline was used for shape matching with DSM, thereby addressing the problem of plane accuracy evaluation in flat areas. We verified our method in two areas (Nanjing and Auckland) and showed that the method can adequately classify buildings and ground photons in different scenarios. The plane accuracy was better than 3.8 m, which lies well within ICESat-2′s mission requirement, with an elevation accuracy of better than 0.5 m, which meets the accuracy requirements of large-scale mapping, suggesting that photons in urban areas can also provide high-accuracy control data for stereo mapping.

How ecosystems services drive urban growth: Integrating nature-based solutions

Addressing urban challenges with nature-based approaches can improve and protect ecosystem services. Yet, urban planning has not efficiently integrated such approaches to manage land use. This paper examines interactions between human and natural systems that result in ecosystem services and changes in land use and land cover in urban areas. It develops a social-ecological model for land use and land cover change, and for ecosystems services that integrates nature-based solutions in urban planning. The model treats spatial variations in ecosystems services as both drivers and consequences of human decision-making in choosing commercial and residential locations that drive land use and land cover change. We tested the social-ecological model in Stockholm County, Sweden, on a 30 × 30 m grid. Results show that accessibility in ecosystem services drives urban residential and commercial development, characterized by non-linearity. Areas around existing urban centers show high accessibility in ecosystem services and high development probabilities, whereas smaller population centers in large areas enjoy high accessibility to ecosystem services and low urban development probabilities. Model results suggest place-specific nature-based strategies for addressing the heterogeneous spatial relationships between ecosystem services and urban development.

Land cover analysis using object based image analysis based on Landsat 8 OLI images in the city of Jember

Jember area has a status as a PKW area (Regional Activity Center). This has resulted in diversifying activities of the population and increasing demand for land, so that land use change is inevitable. Real time land cover data updates in the City of Jember with Landsat 8 OLI imagery using OBIA can be used as an alternative to find out information on land cover classes. This study aims to determine the accuracy of land cover data extraction using OBIA. Using the OBIA method which involves the segmentation process with a multiresolution segmentation algorithm and a classification process with the nearest neighbor algorithm. The results showed the level of accuracy of land cover extraction using OBIA was 90%. These results indicate that the OBIA method for data extraction of land cover in urban areas has high accuracy.

Mapping Urban and Peri-Urban Land Cover in Zimbabwe: Challenges and Opportunities

Accurate and current land cover information is required to develop strategies for sustainable development and to improve the quality of life in urban areas. This study presents an approach that combines multi-seasonal Sentinel-1 (S1) and Sentinel-2 (S2) data, and a random forest (RF) classifier in order to map land cover in four major urban centers in Zimbabwe. The specific objective of this study was to assess the potential of multi-seasonal (rainy, post-rainy, and dry season) S1, rainy season S2, post-rainy season, dry season S2, multi-seasonal S2, and multi-seasonal composite S1 and S2 data for mapping land cover in urban areas. The study results show that the combination of multi-seasonal S1 and S2 data improve land cover mapping in urban and peri-urban areas relative to only multi-seasonal S1, mono-seasonal S2, and multi-seasonal S2 data. The overall accuracy scores for the multi-seasonal S1 and S2 land cover maps are above 85% for all urban centers. Our results indicate that rainy and post-rainy S2 spectral bands, as well as dry-season S1 VV and VH bands (ascending orbit) are the most important features for land cover mapping. In particular, S1 data proved useful in separating built-up areas from cropland, which is usually problematic when only optical imagery is used in the study area. While there are notable improvements in land cover mapping, some challenges related to the S1 data analysis still remain. Nonetheless, our land cover mapping approach shows a potential to map land cover in other urban areas in Zimbabwe or in Sub-Sahara Africa. This is important given the urgent need for reliable geospatial information, which is required to implement the United Nations Sustainable Development Goals (UN SDGs) and United Nations New Urban Agenda (NUA) programmes.

Extraction and Analysis of Finer Impervious Surface Classes in Urban Area

Impervious surfaces (IS), the most common land cover in urban areas, not only provide convenience to the city, but also exert significant negative environmental impacts, thereby affecting the ecological environment carrying capacity of urban agglomerations. Most of the current research considers IS as a single land-cover type, yet this does not fully reflect the complex physical characteristics of various IS types. Therefore, limited information for urban micro-ecology and urban fine management can be provided through one IS land-cover type. This study proposed a finer IS classification scheme and mapped the detailed IS fraction in Guangzhou City, China using Landsat imagery. The IS type was divided into seven finer classes, including blue steel, cement, asphalt, other impervious surface, and other metal, brick, and plastic. Classification results demonstrate that finer IS can be well extracted from the Landsat imagery as all root mean square errors (RMSE) are less than 15%. Specially, the accuracies of asphalt, plastic, and cement are better than other finer IS types with the RMSEs of 7.99%, 8.48%, and 9.92%, respectively. Quantitative analyses illustrate that asphalt, other impervious surface, and brick are the dominant IS types in the study area with the percentages of 9.68%, 6.27%, and 4.45%, respectively, and they are mainly located in Yuexiu, Liwan, Haizhu, and Panyu districts. These results are valuable for research into urban fine management and can support the detailed analysis of urban micro-ecology.

Monitoring Spatiotemporal Changes of Impervious Surfaces in Beijing City Using Random Forest Algorithm and Textural Features

As the capital city of China, Beijing has experienced unprecedented economic and population growth and dramatic impervious surface changes during the last few decades. An application of the classification method combining the spectral and textural features based on Random Forest was conducted to monitor the spatial and temporal changes of Beijing’s impervious surfaces. This classification strategy achieved excellent performance in the impervious surface extraction in complex urban areas, as the Kappa coefficient reached 0.850. Based on this strategy, the impervious surfaces inside Beijing’s sixth ring road in 1997, 2002, 2007, 2013, and 2017 were extracted. As the development of Beijing has a special regional feature, the changes of impervious surfaces within the sixth ring road were assessed. The findings are as follows: (1) the textural features can significantly improve the classification accuracy of land cover in urban areas, especially for the impervious surface with high albedo. (2) Impervious surfaces within the sixth ring road expanded dramatically from 1997 to 2017, had three expanding periods: 1997–2002, 2002–2007, and 2013–2017, and only shrank in 2007–2013. There are different possible major driving factors for each period. (3) The region between the fifth and sixth ring roads in Beijing underwent the most significant changes in the two decades. (4) The inner three regions are relatively highly urbanized areas compared to the outer two regions. Urbanization processes in the interior regions tend to be completed compared to the exterior regions.

GEOSPATIAL TECHNIQUES APPROACH FOR LANDUSE AND LANDCOVER CHANGE ANALYSIS IN BALTANA OF ZIRAKPUR, PUNJAB AND PART OF SECTOR-19, PANCHKULA CITY, HARYANA

In urban area information on landuse and land cover is important for monitoring, planning and management land resources. Remote sensing satellite data and Geographical Information System (GIS) are vital tools in mapping of land use and land cover in urban areas because of availability of historical high resolution satellite data. In the present study landuse and landcover change analysis have been done in Baltana area of Zirakpur of Punjab and part of Sector-19, Panchkula city,Haryana. The study area is located between the latitudes 30 40'50.62”N to 30 40'6.91”N and longitudeso76 49'18.18”E to 76.50'5.61”E. The study area covers an area of 96.49 Hect. Google earth satellite data for the years 2002 and 2018 have been used in the study for change analysis in land use and land cover. The study shows that during 2002 to 2018 built-up area increased 29.57 Hect., agriculture land increased 2.92 Hect., agriculture plantation area increased 1.48 Hect., park area increased 0.54 Hect, road area increased 7.53 Hect,, vacant land area decreased 41.30 Hect and drainage area decreased 0.74 Hect. This study is highly useful for planning and management of land use and land cover in the study area.

Spending time in the garden is positively associated with health and wellbeing: Results from a national survey in England

Research has found that natural environments within urban areas are associated with benefits for human health and wellbeing. However, most studies have primarily focused on publically accessible green space. Less is known about domestic gardens, which in the UK comprise a high proportion of land cover in urban areas and could form a resource for health promotion. This study analysed secondary data from the Monitor of Engagement with the Natural Environment (MENE) survey, a representative survey of the English population (n = 7,814). We investigated the relationships between garden access and use and: general health; evaluative and eudaimonic wellbeing; meeting physical activity guidelines; and visiting nature in the last week. Statistical models included a range of individual and area-level socio-demographic variables. Compared to no garden access, access to a private garden was associated with better evaluative wellbeing, and people with access to a private space such as a balcony, yard or patio were more likely to meet physical activity guidelines. Respondents who reported both gardening and using a garden to relax also reported better health and wellbeing, more physical activity, and more nature visits than those who did not. These findings indicate that domestic gardens are a potential health resource and are not necessarily substituted for by other natural environments, highlighting the importance of their provision alongside green space in urban policy and planning.

A Comparison of Two Morphological Techniques in the Classification of Urban Land Cover

Understanding the often-heterogeneous land cover in urban areas is critical for, among other things, environmental monitoring, spatial planning, and enforcement. Recently, several earth observation satellites were developed with an enhanced spatial resolution that provides for precise and detailed representations of image objects. Morphological image analysis techniques provide useful tools for extracting spatial features from high-resolution, remotely sensed images. This study investigated the efficacy of mathematical morphological (MM) techniques in the land cover classification of a heterogeneous urban landscape using very high-resolution pan-sharpened Pleiades imagery. Specifically, the study evaluated two morphological profiles (MP) techniques (i.e., concatenation of morphological profiles (CMPs) and multi-morphological profiles (MMPs)) in the classification of a heterogeneous urban land cover. The overall accuracies for CMP were 83.14% and 83.19% over the two study areas. Similarly, the MMP overall accuracies were 84.42% and 84.08% for the two study sites. The study concluded that CMP and MMP can greatly improve the classification of heterogeneous landscapes that typify urban areas by effectively representing the structural landscape information necessary for discriminating related land cover classes. In general, similar and visually acceptable results were produced for land cover classification using either CMP or MMP image analysis techniques

Методика классификации городской территории по данным многозональных изображений Sentinel 2 и Landsat 8 (на примере города Тхань Хоа, Вьетнам)

In recent years, land cover changes very quickly in urban areas due to the impact of population growth and socio-economic development. The authors present the method of land cover/land use classification based on the combination of Sentinel 2 and Landsat 8 multi-resolution satellite images. A middle infrared band (band 11), a near infrared (band 8) of Sentinel 2 image and a thermal infrared one (band 10) of Landsat 8 image were used to calculate EBBI (Enhanced Built-up and Barreness Index). The EBBI index and Sentinel 2 spectral bands with spatial resolution 10 m (band 2, 3, 4, 8) were used to classify the land cover. The obtained results showed that, the method of land cover classification based on combination of Sentinel 2 and Landsat 8 satellite images improves the overall accuracy by about 5 % compared with the one using only Sentinel 2 data. The results obtained at the study can be used for the management, assessment and monitoring the status and dynamics of land cover in urban areas.

GEOSPATIAL TECHNIQUES APPROACH FOR LANDUSE AND LANDCOVER CHANGE ANALYSIS IN BALTANA OF ZIRAKPUR, PUNJAB AND PART OF SECTOR-19, PANCHKULA CITY, HARYANA

In urban area information on landuse and land cover is important for monitoring, planning and management land resources. Remote sensing satellite data and Geographical Information System (GIS) are vital tools in mapping of land use and land cover in urban areas because of availability of historical high resolution satellite data. In the present study land use and land cover change analysis have been done in Baltana area of Zirakpur of Punjab and part of Sector-19, Panchkula city, Haryana. The study area is located between the latitudes 30 40'50.62”N to 30 40'6.91”N and longitudes 76 49'18.18”E to 76.50'5.61”E. The study area covers an area of 96.49 Hect. Google earth satellite data for the years 2002and 2018 have been used in the study for change analysis in land use and land cover. The study shows that during 2002 to2018 built-up area increased 29.57 Hect., agriculture land increased 2.92 Hect., agriculture plantation area increased1.48 Hect., park area increased 0.54 Hect, road area increased 7.53 Hect,, vacant land area decreased 41.30 Hect anddrainage area decreased 0.74 Hect. This study is highly useful for planning and management of land use and land cover in the study area

Linear Spectral Mixture Analysis Land Cover for Assessment Level Subpixel: A Case Study of Tasikmalaya City Area Based on Landsat Imagery

Land cover in urban areas can be detected through surveys or using high-resolution imagery with a better accuracy. Especially if the need related to land cover information regionally in the period of the nineties that require the availability of data and unavailability of high resolution images. Therefore, images with intermediate spatial resolution are still required. However, the use of medium-resolution images such as Landsat is constrained by the presence of mixed pixels that cause land cover in urban areas to vary. The mixed pixel will be the source of error in the multispectral classification process, so it takes analysis up to the subpixel level. The need for information up to the subpixel level for ground cover detection can be obtained through the Linear Spectral Mixture Analysis method, where one pixel in the Landsat image in this study will be separated into four endmember, ie vegetation, impervious surface, bare soil, and water. These four endmembers are assumed to represent linear combinations of land coverings contained in urban areas in the form of proportions in each pixel. The results show that the endmember can be well separated, whereas the RMS error of 1994th is 0.013 with an accuracy of 94.44%.

Automatic Classification of Major Urban Land Covers Based on Novel Spectral Indices

Urban land cover classification and mapping is an important and ongoing research field in monitoring and managing urban sprawl and terrestrial ecosystems. The changes in land cover largely affect the terrestrial ecosystem, thus information on land cover is important for understanding the ecological environment. Quantification of land cover in urban areas is challenging due to their diversified activities and large spatial and temporal variations. To improve urban land cover classification and mapping, this study presents three new spectral indices and an automated approach to classifying four major urban land types: impervious, bare land, vegetation, and water. A modified normalized difference bare-land index (MNDBI) is proposed to enhance the separation of impervious and bare land. A tasseled cap water and vegetation index (TCWVI) is proposed to enhance the detection of vegetation and water areas. A shadow index (ShDI) is proposed to further improve water detection by separating water from shadows. An approach for optimizing the thresholds of the new indices is also developed. Finally, the optimized thresholds are used to classify land covers using a decision tree algorithm. Using Landsat-8 Operational Land Imager (OLI) data from two study sites (Hong Kong and Dhaka City, Bangladesh) with different urban characteristics, the proposed approach is systematically evaluated. Spectral separability analysis of the new indices is performed and compared with other common indices. The urban land cover classifications achieved by the proposed approach are compared with those of the classic support vector machine (SVM) algorithm. The proposed approach achieves an overall classification accuracy of 94–96%, which is superior to the accuracy of the SVM algorithm.

Assessment of urban growth in Guangzhou using multi-temporal, multi-sensor Landsat data to quantify and map impervious surfaces

ABSTRACTOver the past few decades, one of the most important global environmental issues has been the increasing transformation of natural landscape cover to impervious surface (IS) as a result of urbanization. A wide range of urban ecology fields, such as urban hydrology, urban climatology, urban planning, and urban ecosystem monitoring, demand accurate and timely information on land use and land cover in urban areas. As part of one of the fastest growing regions in China, Guangzhou is experiencing rapid urban expansion. We developed a combined method that integrates spectrum normalization, normalized spectral mixture analysis (NSMA), threshold segmentation, and a spatiotemporal modification approach to map the Guangzhou urban extent, through quantification of IS as a continuous variable using Landsat series images. We also developed a method to automatically extract water from Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+)/Operational Land Imager (OLI) imagery. The results indicated that the integrated approach is accurate, powerful, and effective. The Guangzhou assessment showed that from 1973 to 2013 the amount of urban area with sub-pixel IS larger than or equal to 55% increased from 0.27% to 30.58%, an increase in urban extent of more than 100-fold over 40 years.

Embedded Zero Tree Wavelet based Artificial Neural Network Image Classification Algorithm - A Study

In this work, an urban area land cover is proposed to classify the large resolution image. It aims to extract the features like texture, shape, size and spectral information in the feature extraction process. Embedded Zero tree Wavelet transform is a lossy image compression algorithm. Most of the coefficients at low bit rates bent through a sub band transform will be zero, or very close to zero. These features data are used for the classification process. Here, we used various classification algorithms namely, Radial Basis Function, SMO, Multilayer Perceptron and Random Forest are implemented. The classification accuracy constantly depends on the efficiency of the extracted features and classification algorithms. The result of the proposed classification algorithms are merged with EZW. Experimental results illustrate that the better accuracy performance is obtained by the Multilayer Perceptron algorithm than other classification algorithms.

Spatiotemporal Characteristics of Rainstorm-Induced Hazards Modified by Urbanization in Beijing

AbstractThe rainfall data of 20 rain gauge stations are used for analysis on the spatiotemporal characteristics of rainstorm-induced hazards in Beijing. A local model used to calculate rainstorm hazards index values (RHIVs) has been developed to reflect the degree of rainstorm-induced hazards. The Mann–Kendall test on the RHIVs series recognizes 1984 as the changepoint in the series, which is shortly after the beginning of a rapid urban expansion period in 1981. The RHIV trend analysis reveals that the trends indices of all stations are negative before 1984 but mostly positive after 1984. Although the climate in north China shows drought conditions, and the annual rainfalls have decreased in recent years, no relationship is implied to a reduction in rainstorm-induced hazards. By using the lognormal distribution model, the probability analysis on the RHIVs of 20 stations indicates that very extreme precipitation occurred in increasing frequency after 1984. Moreover, the estimated spatial distributions of 100-, 150-, and 200-mm daily rainfall exceedance probabilities (EPs) indicate that these EPs have increased mainly in the urban areas and northward, which are the downwind of the summer monsoon, whereas the EPs to the south of the urban areas have decreased since 1984. Such spatiotemporal characteristics of the RHIVs can be attributed to modification of precipitation by the changed land use and land cover in urban areas. Moreover, the urban-induced rainfall downwind of the urban areas enhanced the rain intensity and rain rate, which led to an increase in RHIVs and contributed to the frequent occurrences of flash floods in Beijing metropolis.

English

Abstract Urban land cover is dominated by impervious surface that degrades both terrestrial and aquatic ecosystems relative to predevelopment conditions. There are significant opportunities for designers of urban landscapes to use alternative land covers that have multiple functions, benefiting both human and nonhuman components of the urban ecosystem. Vegetated (green) roofs are one form of alternative land cover that has shown the potential to provide a variety of ecological benefits in urban areas. We evaluated how stormwater retention, building energy and temperature, and rooftop habitat are influenced by the use of green roofs using test plots in Georgia and Massachusetts. Green roofs were shown to recreate part of the predevelopment hydrology through increasing interception, stormwater storage, evaporation, and transpiration on the rooftop and worked extremely well for small storm events. Temperature reductions were found on the green rooftop as compared to an asphalt surface, although other roof technologies that minimize temperatures, such as lighter colored membranes, provide similar benefits. Novel habitat was created on the rooftop, although the extent of this habitat was limited in part by plant survivability and the need for additional water inputs for diverse plant communities to survive. Despite the challenges, the green roof benefits reported here suggest that green roofs can be used effectively as a multifunctional land cover in urban areas. Keywords Green roof; urbanization; stormwater; energy; habitat.

Fine Spatial Resolution Simulated Satellite Sensor Imagery for Land Cover Mapping in the United Kingdom

This article presents a set of techniques developed to classify land cover on a per-parcel (herein termed per-field) basis by integrating fine spatial resolution simulated satellite sensor imagery with digital vector data. Classification, based on the spectral and spatial properties of the imagery, was carried out on a per-pixel basis. The resulting classified images were then integrated with vector data to classify on a per-field basis. Four tools were adopted or developed to increase the accuracy and utility of the per-field classification and a fifth was proposed. The spectral variability within agricultural fields resulted in misclassification within the per-pixel classification, and this was overcome using a per-field classification. Mixed land cover in urban areas also resulted in misclassification. A low pass smoothing filter and a “texture” filter applied to the per-pixel classified image increased the classification accuracy of this land cover prior to per-field classification. The flexibility of the integration process enabled the exploitation of spectral and spatial variation between pixels within individual parcels to produce new classes during per-field classification and to identify fields with a high likelihood of misclassification.