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Remote Sensing of Urban Ecology

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 July 2017) | Viewed by 83284

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Prof. Dr. Conghe Song

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Guest Editor

Department of Geography, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-3220, USA
Interests: remote sensing of environment; land-cover/land-use change; ecosystem carbon and water exchange with atmosphere; human–environment interactions
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Junxiang Li

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Guest Editor

School of Design, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: urban ecology; landscape ecology; remote sensing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Weiqi Zhou

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Guest Editor

State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 10085, China
Interests: urban ecology; remote sensing; urban greenspace; spatial pattern; urban heat island; landscape ecology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As of 2007, there are more people living in urban areas than in the rural areas of the world. The global urban population is projected to reach 66 percent of the total population on the planet by 2050. As a result, cities around the world, particularly in developing countries, have expanded dramatically in recent decades, and will continue to expand given the expected growth of urban populations. Urbanization has profoundly changed the landscape within and around cities worldwide, profoundly influencing urban ecosystem structures and functions, across a wide range of scales, as well as the welfare of urban dwellers. While great benefits have been brought to society from the economic development, as a result of urbanization, it also caused serious negative ecological consequences, such as urban heat island, water and air pollutions (e.g. PM2.5 and NOx), habitat fragmentation and associated biodiversity loss and degradation, biological invasion, etc. Rapid urban growth has encroached on agricultural, natural and/or semi-natural lands, which intensify the conflicts between short-term human well-being and the long-term health of urban ecosystems. It is essential for sustainable urban ecosystem development and human wellbeing to monitor the spatiotemporal patterns of these changes and their consequences across a range of scales, and to identify how urbanization processes that drive these changes.

Remote sensing provides an efficient way to monitor the urban ecosystem in a real- or quasi-real-time manner. After more than five decades of development, there are unprecedented amounts of remote sensing data coming from sensors in optical, thermal infrared, and microwave spectra, as well as light detection and ranging (LiDAR) technology. Remotely sensed data from these sensors contain rich information on the structure and functions of urban ecosystems across a wide range of spatial and temporal resolutions. Urban landscapes are highly spatially heterogeneous and dynamically evolving with time. As a result, remote sensing techniques have become indispensable in monitoring environmental, ecological and socio-economic changes in urban and/or peri-urban areas. Information from remote sensing is needed, not only to those who conduct the traditional environmental and ecological research, but also to those who integrate ecological, environmental and socioeconomic factors in the study of urban ecology. We are requesting papers for a Special Issue of Remote Sensing on the remote sensing of urban ecology. Specific topics include, but are not limited to:

Use of multiple sensors through time to characterize key urban biophysical and socio-economic changes that may have important ecological consequences.

Use of remote sensing to understand the ecological consequences of urban expansion and within-city land use/cover change, such as loss of biodiversity, biological invasion, urban heat island effects, change in phenology and its implications, human health, and life quality.

Use of remote sensing to understand the ecological consequences of urban expansion, composition and configuration on the aquatic systems, such as water quantity and quality, and biogeochemical processes in urban streams.

Use of remote sensing to characterize the spatial/temporal patterns of air pollution and its relationship with urban structure, and to understand the ecological consequences of urban air quality on human and environmental health.

Authors are required to check and follow the specific Instructions to Authors at https://www.mdpi.com/journal/remotesensing/instructions.

Dr. Conghe Song
Dr. Junxiang Li
Dr. Weiqi Zhou
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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14 pages, 3264 KiB

Open AccessArticle

The Heterogeneity of Air Temperature in Urban Residential Neighborhoods and Its Relationship with the Surrounding Greenspace

by Yuguo Qian, Weiqi Zhou, Xiaofang Hu and Fan Fu

Remote Sens. 2018, 10(6), 965; https://doi.org/10.3390/rs10060965 - 16 Jun 2018

Cited by 20 | Viewed by 4436

Abstract

The thermal environment in residential areas is directly related to the living quality of residents. Therefore, it is important to understand thermal heterogeneity and ways to regulate temperature in residential neighborhoods. We investigated the spatial heterogeneity and temporal dynamics of air temperatures in 20 residential neighborhoods within the 5th ring road of Beijing, China. We further explored how the variations in air temperature were related to the patterns of the surrounding greenspace at different scales. We found that: (1) large air temperature differences existed among residential neighborhoods, with hourly maximum differences in air temperature reaching 5.30 °C on hot summer days; (2) not only the percentage but also the spatial configuration (e.g., edge density) of greenspace affected the local air temperature; and (3) the effects of spatial greenspace patterns on air temperature were scale dependent and varied by season. For example, increasing the proportion of greenspace in surrounding areas within a 100-m radius and increasing the edge density within radii from 500 to 1000 m could lower air temperatures in summer but not affect air temperatures in winter. In addition, decreasing the edge density of greenspaces within a 100-m radius of the surrounding areas would lead to an increase in air temperature in winter but not affect the air temperature in summer. These results extend our understanding of thermal environments and their relationships with greenspace patterns at the microscale (i.e., residential neighborhoods). They also provide useful information for urban planners to optimize greenspace patterns under better thermal conditions at the neighborhood scale. Full article

(This article belongs to the Special Issue Remote Sensing of Urban Ecology)

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3348 KiB

Open AccessArticle

Effects of Urban Expansion on Forest Loss and Fragmentation in Six Megaregions, China

by Weiqi Zhou, Sai Zhang, Wenjuan Yu, Jing Wang and Weimin Wang

Remote Sens. 2017, 9(10), 991; https://doi.org/10.3390/rs9100991 - 26 Sep 2017

Cited by 44 | Viewed by 6346

Abstract

Urban expansion has significant effects on forest loss and fragmentation. Previous studies mostly focused on how the amount of developed land affected forest loss and fragmentation, but neglected the impacts of its spatial pattern. This paper examines the effects of both the amount and spatial pattern of urban expansion on forest loss and fragmentation. We conducted a comparison study in the six largest urban megaregions in China—Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), Pearl River Delta (PRD), Wuhan (WH), Chengdu-Chongqing (CY), and Changsha-Zhuzhou-Xiangtan (CZT) urban megaregions. We first quantified both the magnitude and speed of urban expansion, and forest loss and fragmentation from 2000 to 2010. We then examined the relationships between urban expansion and forest loss and fragmentation by Pearson correlation and partial correlation analysis using the prefecture city as the analytical unit. We found: (1) urban expansion was a major driver of forest loss in the CZT, PRD, and CY megaregions, with 34.05%, 22.58%, and 19.65% of newly-developed land converted from forests. (2) Both the proportional cover of developed land and its spatial pattern (e.g., patch density) had significant impacts on forest fragmentation at the city level. (3) Proportional cover of developed land was the major factor for forest fragmentation at the city level for the PRD and YRD megaregions, but the impact of the spatial pattern of developed land was more important for the BTH and WH megaregions. Full article

(This article belongs to the Special Issue Remote Sensing of Urban Ecology)

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5076 KiB

Open AccessArticle

Sixty-Year Changes in Residential Landscapes in Beijing: A Perspective from Both the Horizontal (2D) and Vertical (3D) Dimensions

by Zhong Zheng, Weiqi Zhou, Jia Wang, Xiaofang Hu and Yuguo Qian

Remote Sens. 2017, 9(10), 992; https://doi.org/10.3390/rs9100992 - 25 Sep 2017

Cited by 37 | Viewed by 4581

Abstract

Landscape changes associated with urbanization can lead to many serious ecological and environmental problems. Quantifying the vertical structure of the urban landscape and its change is important to understand its social and ecological impacts, but previous studies mainly focus on urban horizontal expansion and its impacts on land cover/land use change. This papers focuses on the residential landscape to investigate how the vertical dimension of the urban landscape (i.e., building height) change through time, and how such change is related to changes in the horizontal dimension of the landscape, using Beijing, the capital of China, as a case study. We quantified the expansion of the residential neighborhoods from 1949 to 2009, and changes in vegetation coverage, building density, and building height within these neighborhoods, using 1 m spatial resolution imagery. One-way ANOVA and correlation analysis were used to examine the relationships of building height to vegetation coverage and building density. We found: (1) The residential areas expanded rapidly and were dominated by outward growth, with much less within-city infilling. The growth rate varied greatly through time, first increasing from 1949–2004 and then decreasing from 2005–2009. The expansion direction of newly built residential neighborhoods shifted from west to north in a clockwise direction. (2) The vertical structure of residential neighborhoods changed with time and varied in space, forming a “low-high” pattern from urban central areas to the urban edges within the 5th ring road of Beijing. (3) The residential neighborhoods built in different time periods had significant differences in vegetation coverage, building density, and building height. The residential neighborhoods built in more recent years tended to have taller buildings, lower building density and lower vegetation coverage. Full article

(This article belongs to the Special Issue Remote Sensing of Urban Ecology)

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24659 KiB

Open AccessArticle

Impacts of Urbanization on Vegetation Phenology over the Past Three Decades in Shanghai, China

by Tong Qiu, Conghe Song and Junxiang Li

Remote Sens. 2017, 9(9), 970; https://doi.org/10.3390/rs9090970 - 20 Sep 2017

Cited by 40 | Viewed by 7732

Abstract

Vegetation phenology manifests the rhythm of annual plant life activities. It has been extensively studied in natural ecosystems. However, major knowledge gaps still exist in understanding the impacts of urbanization on vegetation phenology. This study addresses two questions to fill the knowledge gaps: (1) How does vegetation phenology vary spatially and temporally along a rural-to-urban transect in Shanghai, China, over the past three decades? (2) How do landscape composition and configuration affect those variations of vegetation phenology? To answer these questions, 30 m × 30 m mean vegetation phenology metrics, including the start of growing season (SOS), end of growing season (EOS), and length of growing season (LOS), were derived for urban vegetation using dense stacks of enhanced vegetation index (EVI) time series from images collected by Landsat 5–8 satellites from 1984 to 2015. Landscape pattern metrics were calculated using high spatial resolution aerial photos. We then used Pearson correlation analysis to quantify the associations between phenology patterns and landscape metrics. We found that vegetation in urban centers experienced advances of SOS for 5–10 days and delays of EOS for 5–11 days compared with those located in the surrounding rural areas. Additionally, we observed strong positive correlations between landscape composition (percentage of landscape area) of developed land and LOS of urban vegetation. We also found that the landscape configuration of local land cover types, especially patch density and edge density, was significantly correlated with the spatial patterns of vegetation phenology. These results demonstrate that vegetation phenology in the urban area is significantly different from its rural surroundings. These findings have implications for urban environmental management, ranging from biodiversity protection to public health risk reduction. Full article

(This article belongs to the Special Issue Remote Sensing of Urban Ecology)

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9763 KiB

Open AccessArticle

Diversification of Land Surface Temperature Change under Urban Landscape Renewal: A Case Study in the Main City of Shenzhen, China

by Yanxu Liu, Jian Peng and Yanglin Wang

Remote Sens. 2017, 9(9), 919; https://doi.org/10.3390/rs9090919 - 02 Sep 2017

Cited by 38 | Viewed by 6361

Abstract

Unprecedented rapid urbanization in China during the past several decades has been accompanied by extensive urban landscape renewal, which has increased the urban thermal environmental risk. However, landscape change is a sufficient but not necessary condition for land surface temperature (LST) variation. Many studies have merely highlighted the correlation between landscape pattern and LST, while neglecting to comprehensively present the spatiotemporal diversification of LST change under urban landscape renewal. Taking the main city of Shenzhen as a case study area, this study tracked the landscape renewal and LST variation for the period 1987–2015 using 49 Landsat images. A decision tree algorithm suitable for fast landscape type interpretation was developed to map the landscape renewal. Analytical tools that identified hot-cold spots, the gravity center, and transect of LST movement were adopted to identify LST changes. The results showed that the spatial variation of LST was not completely consistent with landscape change. The transformation from Green landscape to Grey landscape usually increased the LST within a median of 0.2 °C, while the reverse transformation did not obviously decrease the LST (the median was nearly 0 °C). The median of LST change from Blue landscape to Grey landscape was 1.0 °C, corresponding to 0.5 °C in the reverse transformation. The imbalance of LST change between the loss and gain of Green or Blue landscape indicates the importance of protecting natural space, where the benefits in terms of temperature mitigation cannot be completely substituted by reverse transformation. Full article

(This article belongs to the Special Issue Remote Sensing of Urban Ecology)

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4593 KiB

Open AccessFeature PaperArticle

“Kill Two Birds with One Stone”: Urban Tree Species Classification Using Bi-Temporal Pléiades Images to Study Nesting Preferences of an Invasive Bird

by Marine Le Louarn, Philippe Clergeau, Elodie Briche and Magali Deschamps-Cottin

Remote Sens. 2017, 9(9), 916; https://doi.org/10.3390/rs9090916 - 01 Sep 2017

Cited by 27 | Viewed by 7279

Abstract

This study presents the results of object-based classifications assessing the potential of bi-temporal Pléiades images for mapping broadleaf and coniferous tree species potentially used by the ring-necked parakeet Psittacula krameri for nesting in the urban area of Marseille, France. The first classification was performed based solely on a summer Pléiades image (acquired on 28 July 2015) and the second classification based on bi-temporal Pléiades images (a spring image acquired on 24 March 2016 and the summer image). An ensemble of spectral and textural features was extracted from both images and two machine-learning classifiers were used, Random Forest (RF) and Support Vector Machine (SVM). Regardless of the classifiers, model results suggest that classification based on bi-temporal Pléiades images produces more satisfying results, with an overall accuracy 11.5–13.9% higher than classification using the single-date image. Textural and spectral features extracted from the blue and the NIR bands were consistently ranked among the most important features. Regardless of the classification scheme, RF slightly outperforms SVM. RF classification using bi-temporal Pléiades images allows identifying 98.5% of the tree species used by the ring-necked parakeet for nesting, highlighting the promising value of remote sensing techniques to assess the ecological requirements of fauna in urban areas. Full article

(This article belongs to the Special Issue Remote Sensing of Urban Ecology)

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5974 KiB

Open AccessArticle

Urban Land-Cover Dynamics in Arid China Based on High-Resolution Urban Land Mapping Products

by Tao Pan, Dengsheng Lu, Chi Zhang, Xi Chen, Hua Shao, Wenhui Kuang, Wenfeng Chi, Zhengjia Liu, Guoming Du and Liangzhong Cao

Remote Sens. 2017, 9(7), 730; https://doi.org/10.3390/rs9070730 - 14 Jul 2017

Cited by 18 | Viewed by 5647

Abstract

Rapid urbanization has occurred in northwestern China, threatening the sustainability of its fragile dryland ecosystems. A lack of precise urban land-cover information has limited our understanding on the urbanization in the dryland. Here, we examined urban land-cover changes from 2000 to 2014 in 21 major cities that comprise over 50% of the developed land in arid China, using Landsat Enhanced Thematic Mapper Plus and Operational Land Imager data, and a hybrid classification method. The 15-m resolution urban land-cover products (including impervious surfaces, vegetation, bare soil, and water bodies) had an overall accuracy of 90.37%. Based on these new land use products, we found the urbanization in arid China was characterized by the dramatic expansion of impervious surface (+13.23%) and reduction of bare soil (−13.41%), while the proportions of vegetation (+0.27%) and water (−0.10%) remained stable. The observed dynamic equilibrium of vegetated ratio implies an increasing harmonization of urbanization and greening, which was particularly important for the sustainability of fragile urban ecosystems in arid regions. From an economic perspective, gross domestic product and population were significantly correlated with impervious surfaces, and oasis cities displayed a stronger ability to attract new residents than desert cities. Full article

(This article belongs to the Special Issue Remote Sensing of Urban Ecology)

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7569 KiB

Open AccessArticle

Evaluating Urban Land Carrying Capacity Based on the Ecological Sensitivity Analysis: A Case Study in Hangzhou, China

by Jinyeu Tsou, Yanfei Gao, Yuanzhi Zhang, Sun Genyun, Jinchang Ren and Yu Li

Remote Sens. 2017, 9(6), 529; https://doi.org/10.3390/rs9060529 - 25 May 2017

Cited by 57 | Viewed by 8312

Abstract

Abstract: In this study, we present the evaluation of urban land carrying capacity (ULCC) based on an ecological sensitivity analysis. Remote sensing data and geographic information system (GIS) technology are employed to analyze topographic conditions, land-use types, the intensity of urban development, and ecological environmental sensitivity to create reasonable evaluation indicators to analyze urban land carrying capacity based on ecological sensitivity in the rapidly developing megacity of Hangzhou, China. In the study, ecological sensitivity is grouped into four levels: non-sensitive, lightly sensitive, moderately sensitive, and highly sensitive. The results show that the ecological sensitivity increases progressively from the center to the periphery. The results also show that ULCC is determined by ecologically sensitive levels and that the ULCC is categorized into four levels. Even though it is limited by the four levels, the ULCC still has a large margin if compared with the current population numbers. The study suggests that the urban ecological environment will continue to sustain the current population size in the short-term future. However, it is necessary to focus on the protection of distinctive natural landscapes so that decision makers can adjust measures for ecological conditions to carry out the sustainable development of populations, natural resources, and the environment in megacities like Hangzhou, China. Full article

(This article belongs to the Special Issue Remote Sensing of Urban Ecology)

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4335 KiB

Open AccessArticle

Quantifying Changes of Villages in the Urbanizing Beijing Metropolitan Region: Integrating Remote Sensing and GIS Analysis

by Kun Wang, Weiqi Zhou, Kaipeng Xu, Hanmei Liang, Wenjuan Yu and Weifeng Li

Remote Sens. 2017, 9(5), 448; https://doi.org/10.3390/rs9050448 - 06 May 2017

Cited by 10 | Viewed by 5527

Abstract

Rapid urbanization has resulted in great changes in rural landscapes globally. Using remote sensing data to quantify the distribution of rural settlements and their changes has received increasing attention in the past three decades, but remains a challenge. Previous studies mostly focused on the residential changes within a grid or administrative boundary, but not at the individual village level. This paper presents a new change detection approach for rural residential settlements, which can identify different types of rural settlement changes at the individual village level by integrating remote sensing and Geographic Information System (GIS) analyses. Using multi-temporal Landsat TM image data, this approach classifies villages into five types: “no change”, “totally lost”, “shrinking”, “expanding”, and “merged”, in contrast to the commonly used “increase” and “decrease”. This approach was tested in the Beijing metropolitan area from 1984 to 2010. Additionally, the drivers of such changes were investigated using multinomial logistic regression models. The results revealed that: (1) 36% of the villages were lost, but the total area of developed lands in existing villages increased by 34%; (2) Changes were dominated by the type of ‘expansion’ in 1984–1990 (accounted for 43.42%) and 1990–2000 (56.21%). However, from 2000 to 2010, 49.73% of the villages remained unchanged; (3) Both topographical factors and distance factors had significant effects on whether the villages changed or not, but their impacts changed through time. The topographical driving factors showed decreasing effects on the loss of rural settlements, while distance factors had increasing impacts on settlement expansion and merging. This approach provides a useful tool for better understanding the changes in rural residential settlements and their associations with urbanization. Full article

(This article belongs to the Special Issue Remote Sensing of Urban Ecology)

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5701 KiB

Open AccessArticle

Examining Spatial Distribution and Dynamic Change of Urban Land Covers in the Brazilian Amazon Using Multitemporal Multisensor High Spatial Resolution Satellite Imagery

by Yunyun Feng, Dengsheng Lu, Emilio F. Moran, Luciano Vieira Dutra, Miquéias Freitas Calvi and Maria Antonia Falcão De Oliveira

Remote Sens. 2017, 9(4), 381; https://doi.org/10.3390/rs9040381 - 19 Apr 2017

Cited by 39 | Viewed by 5840

Abstract

The construction of the Belo Monte hydroelectric dam began in 2011, resulting in rapidly increased population from less than 80,000 persons before 2010 to more than 150,000 persons in 2012 in Altamira, Pará State, Brazil. This rapid urbanization has produced many problems in urban planning and management, as well as challenging environmental conditions, requiring monitoring of urban land-cover change at high temporal and spatial resolutions. However, the frequent cloud cover in the moist tropical region is a big problem, impeding the acquisition of cloud-free optical sensor data. Thanks to the availability of different kinds of high spatial resolution satellite images in recent decades, RapidEye imagery in 2011 and 2012, Pleiades imagery in 2013 and 2014, SPOT 6 imagery in 2015, and CBERS imagery in 2016 with spatial resolutions from 0.5 m to 10 m were collected for this research. Because of the difference in spectral and spatial resolutions among these satellite images, directly conducting urban land-cover change using conventional change detection techniques, such as image differencing and principal component analysis, was not feasible. Therefore, a hybrid approach was proposed based on integration of spectral and spatial features to classify the high spatial resolution satellite images into six land-cover classes: impervious surface area (ISA), bare soil, building demolition, water, pasture, and forest/plantation. A post-classification comparison approach was then used to detect urban land-cover change annually for the periods between 2011 and 2016. The focus was on the analysis of ISA expansion, the dynamic change between pasture and bare soil, and the changes in forest/plantation. This study indicates that the hybrid approach can effectively extract six land-cover types with overall accuracy of over 90%. ISA increased continuously through conversion from pasture and bare soil. The Belo Monte dam construction resulted in building demolition in 2015 in low-lying areas along the rivers and an increase in water bodies in 2016. Because of the dam construction, forest/plantation and pasture decreased much faster, while ISA and water increased much faster in 2011–2016 than they had between 1991 and 2011. About 50% of the increased annual deforestation area can be attributed to the dam construction between 2011 and 2016. The spatial patterns of annual urban land-cover distribution and rates of dynamic change provided important data sources for making better decisions for urban management and planning in this city and others experiencing such explosive demographic change. Full article

(This article belongs to the Special Issue Remote Sensing of Urban Ecology)

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5005 KiB

Open AccessArticle

Human Activity Influences on Vegetation Cover Changes in Beijing, China, from 2000 to 2015

by Meichen Jiang, Shufang Tian, Zhaoju Zheng, Qian Zhan and Yuexin He

Remote Sens. 2017, 9(3), 271; https://doi.org/10.3390/rs9030271 - 15 Mar 2017

Cited by 66 | Viewed by 6919

Abstract

For centuries, the rapid development of human society has already made human activity the dominant factor in the terrestrial ecosystem. As the city of greatest importance in China, the capital Beijing has experienced eco-environmental changes with unprecedented economic and population growth during the past few decades. To better understand the ecological transition and its correlations in Beijing, Landsat Thematic Mapper (TM) and Operational Land Imager (OLI) images were used to investigate vegetation coverage changes using a dimidiate pixel model. Piecewise linear regression, bivariate-partial correlation analysis, and factor analysis were applied to the probing of the relationship between vegetation coverage changes and climatic/human-induced factors. The results showed that from 2000 to 2005, 2005 to 2010, and 2010 to 2015, Beijing experienced both restoration (6.33%, 10.08%, and 12.81%, respectively) and degradation (13.62%, 9.35%, and 9.49%, respectively). The correlation analysis results between climate and vegetation changes demonstrated that from 2000 to 2015, both the multi-year annual mean temperature (r = −0.819, p < 0.01) and the multi-year annual mean precipitation (r = 0.653, p < 0.05) had a significantly correlated relationship with vegetation change. The Beijing-Tianjin Sandstorm Source Control Project (BTSSCP) has shown beneficial spatial effects on vegetation restoration; the total effectiveness in conservation areas (84.94 in 2000–2010) was much better than non-BTSSCP areas (34.34 in 2000–2010). The most contributory socioeconomic factors were the population (contribution = 54.356%) and gross domestic product (GDP) (contribution = 30.677%). The population showed a significantly negative correlation with the overall vegetation coverage (r = −0.684, p < 0.05). The GDP was significantly negatively correlated with vegetation in Tongzhou, Daxing, Central city, Fangshan, Shunyi, and Changping (r = −0.601, p < 0.01), while positively related in Huairou, Miyun, Pinggu, Mentougou and Yanqing (r = 0.614, p < 0.01). These findings confirm that human activity is a very significant factor in impacting and explaining vegetation changes, and that some socioeconomic influences on vegetation coverage are highly spatially heterogeneous, based on the context of different areas. Full article

(This article belongs to the Special Issue Remote Sensing of Urban Ecology)

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9630 KiB

Open AccessArticle

The Spatiotemporal Pattern of Urban Expansion in China: A Comparison Study of Three Urban Megaregions

by Wenjuan Yu and Weiqi Zhou

Remote Sens. 2017, 9(1), 45; https://doi.org/10.3390/rs9010045 - 06 Jan 2017

Cited by 70 | Viewed by 7192

Abstract

Urban megaregions have emerged as a new urbanized form. However, previous studies mostly focused on urban expansion at the city scale, particularly for large cities. Understanding urban expansion at the regional scale including cities having different sizes is important for extending current knowledge of urban growth and its environmental and ecological impacts. Here, we addressed two questions: (1) How do the extent, rate, and morphological model of urban expansion vary at both the regional and city scales? (2) How do factors, such as city size and expansion rate, influence urban expansion models? We focused on the three largest urban megaregions in China, Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD) and Pearl River Delta (PRD). We quantified and compared the spatiotemporal pattern of urban expansion during 2000–2010 at both the regional and city scales based on remote sensing data. We used correlation analysis and linear regressions to address our research questions. We found that (1) the three urban megaregions experienced rapid and massive urban growth, but the spatiotemporal pattern varied greatly. Urban expansion was dominated by edge-expansion in the BTH, edge-expansion and infilling in the YRD, and infilling in the PRD. Cities in the same megaregion tended to have similar expansion morphology; (2) geographical location influenced the model of urban expansion the most, followed by city size and by its expansion rate. Small-sized cities were more likely to develop in a leapfrogging model, while cities with relatively rapid expansion tended to grow in an edge-expansion model. Full article

(This article belongs to the Special Issue Remote Sensing of Urban Ecology)

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2643 KiB

Open AccessArticle

Deriving and Evaluating City-Wide Vegetation Heights from a TanDEM-X DEM

by Johannes Schreyer and Tobia Lakes

Remote Sens. 2016, 8(11), 940; https://doi.org/10.3390/rs8110940 - 11 Nov 2016

Cited by 8 | Viewed by 4955

Abstract

Vegetation provides important functions and services in urban areas, and vegetation heights divided into vertical and horizontal units can be used as indicators for its assessment. Conversely, detailed area-wide and updated height information is frequently missing for most urban areas. This study sought to assess three vegetation height classes from a globally available TanDEM-X digital elevation model (DEM, 12 × 12 m spatial resolution) for Berlin, Germany. Subsequently, height distribution and its accuracy across biotope classes were derived. For this, a TanDEM-X intermediate DEM, a LiDAR DTM, an UltraCamX vegetation layer, and a biotope map were included. The applied framework comprised techniques of data integration and raster algebra for: Deriving a height model for all of Berlin, masking non-vegetated areas, classifying two canopy height models (CHMs) for bushes/shrubs and trees, deriving vegetation heights for 12 biotope classes and assessing accuracies using validation CHMs. The findings highlighted the possibility of assessing vegetation heights for total vegetation, trees and bushes/shrubs with low and consistent offsets of mean heights (total CHM: −1.56 m; CHM for trees: −2.23 m; CHM bushes/shrubs: 0.60 m). Negative offsets are likely caused by X-band canopy penetrations. Between the biotope classes, large variations of height and area were identified (vegetation height/biotope and area/biotope: ~3.50–~16.00 m; 4.44%–96.53%). The framework and results offer a great asset for citywide and spatially explicit assessment of vegetation heights as an input for urban ecology studies, such as investigating habitat diversity based on the vegetation’s heterogeneity. Full article

(This article belongs to the Special Issue Remote Sensing of Urban Ecology)

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