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Special Issue "Remote Sensing of Human-Environment Interactions along the Urban-Rural Gradient"

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

Deadline for manuscript submissions: closed (31 December 2016)

Special Issue Editors

Guest Editor
Prof. Yuhong He

Department of Geography, University of Toronto Mississauga, 3359 Mississauga Rd, Mississauga, ON L5L 1C6, Canada
Website | E-Mail
Phone: +1-905-569-4679
Interests: remote sensing; advanced spatial analysis; grassland health; wildlife habitat; climate change
Guest Editor
Prof. Qihao Weng

Center for Urban and Environmental Change, Department of Earth and Environmental Systems, Indiana State University, Terre Haute, IN 47809, USA
Website | E-Mail
Phone: +1-812-237-2255
Interests: remote sensing; GIS; land use and land cover change; urban environment and ecosystem

Special Issue Information

Dear Colleagues,

The presence and spread of human activities and impacts are major agents of most environmental problems along the urban–rural gradient. The human dimension of environmental change is ruled by a complex interaction of social, political, economic, and cultural factors that are coupled with growing globalization and global climate change. The consequences of human settlements and actions are altering the properties of natural landscapes, ultimately changing patterns of ecosystem processes and biodiversity. Thus, understanding of human–environment interactions is key to sound urban planning, management, mitigation, and conservation strategies. Increasingly, remote sensing data and technologies are being used to analyze, monitor, and model the impact of human settlements and activities on the environment and the impact of environmental changes on human society in the urban–rural space. Advances in the spatial, spectral, and temporal resolutions in remote sensors provide great opportunities to discern discrete patterns, disturbances, trends, and processes, from the macro- to micro-scale of systems of interest. This Special Issue calls for papers that present cutting-edge studies in human–environment interactions along the urban–rural gradient through remotely sensed data and techniques. The following list provides some examples of topics of interest:

  • Mapping of urbanized landscapes including but not limited to access to open green spaces, amount of impervious surfaces, and corridors such as road and rail networks, power lines, or irrigation canals.
  • Tracking urban growth and natural landscape degradation: area, speed, density, direction, and structure.
  • Mapping and analysis of land conversion impacts including rangeland alteration, agricultural intensification, wetland infringement, and deforestation.
  • Monitoring and analysis of land use impacts on biodiversity, endangered species, and invasive species.
  • Analyzing the impacts of urbanization on air and water quality, microclimate, habitat fragmentation, as well as potential environmental hazards such as floods, fires, earthquakes, tornadoes, hurricanes, landslides   and drought.

Prof. Yuhong He
Prof. Qihao Weng
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 papers will be 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 monthly 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 1600 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.


Published Papers (11 papers)

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Research

Open AccessArticle An Analysis of the Discrepancies between MODIS and INSAT-3D LSTs in High Temperatures
Remote Sens. 2017, 9(4), 347; doi:10.3390/rs9040347
Received: 17 December 2016 / Revised: 30 March 2017 / Accepted: 1 April 2017 / Published: 5 April 2017
Cited by 1 | PDF Full-text (6257 KB) | HTML Full-text | XML Full-text
Abstract
In many disciplines, knowledge on the accuracy of Land Surface Temperature (LST) as an input is of great importance. One of the most efficient methods in LST evaluation is cross validation. Well-documented and validated polar satellites with a high spatial resolution can be
[...] Read more.
In many disciplines, knowledge on the accuracy of Land Surface Temperature (LST) as an input is of great importance. One of the most efficient methods in LST evaluation is cross validation. Well-documented and validated polar satellites with a high spatial resolution can be used as references for validating geostationary LST products. This study attempted to investigate the discrepancies between a Moderate Resolution Imaging Spectro-radiometer (MODIS) and Indian National Satellite (INSAT-3D) LSTs for high temperatures, focusing on six deserts with sand dune land cover in the Middle East from 3 March 2015 to 24 August 2016. Firstly, the variability of LSTs in the deserts of the study area was analyzed by comparing the mean, Standard Deviation (STD), skewness, minimum, and maximum criteria for each observation time. The mean value of the LST observations indicated that the MYD-D observation times are closer to those of diurnal maximum and minimum LSTs. At all times, the LST observations exhibited a negative skewness and the STD indicated higher variability during times of MOD-D. The observed maximum LSTs from MODIS collection 6 showed higher values in comparison with the last versions of LSTs for hot spot regions around the world. After the temporal, spatial, and geometrical matching of LST products, the mean of the MODIS—INSAT LST differences was calculated for the study area. The results demonstrated that discrepancies increased with temperature up to +15.5 K. The slopes of the mean differences were relatively similar for all deserts except for An Nafud, suggesting an effect of View Zenith Angle (VZA). For modeling the discrepancies between two sensors in continuous space, the Diurnal Temperature Cycles (DTC) of both sensors were constructed and compared. The sample DTC models approved the results from discrete LST subtractions and proposed the uncertainties within MODIS DTCs. The authors proposed that the observed LST discrepancies in high temperatures could be the result of inherent differences in LST retrieval algorithms. Full article
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Open AccessArticle Urban Expansion and Its Impact on the Land Use Pattern in Xishuangbanna since the Reform and Opening up of China
Remote Sens. 2017, 9(2), 137; doi:10.3390/rs9020137
Received: 4 November 2016 / Revised: 17 January 2017 / Accepted: 25 January 2017 / Published: 7 February 2017
Cited by 3 | PDF Full-text (8363 KB) | HTML Full-text | XML Full-text
Abstract
Since the Chinese government carried out the reform and opening up policy, Xishuangbanna Dai Autonomous Prefecture has experienced rapid urbanization and dramatic land use change. This research aims at analyzing urban expansion in Xishuangbanna and its impact on the land use pattern using
[...] Read more.
Since the Chinese government carried out the reform and opening up policy, Xishuangbanna Dai Autonomous Prefecture has experienced rapid urbanization and dramatic land use change. This research aims at analyzing urban expansion in Xishuangbanna and its impact on the land use pattern using combined methods, including radar graph, the gradient-direction method and landscape metrics. Seven land use maps from 1976 to 2015 were generated and analyzed, respectively. The results showed that urban and rubber expanded rapidly, while forest decreased during the last 40 years. The city proper, the county town of Menghai and the county town of Mengla showed the most significant and fastest urban expansion rates. In response to rapid urban expansion, land use types outside urban areas changed dramatically. In Jinghong and Mengla, urban areas were usually surrounded by paddy, shrub, rubber and forest in 1976, while most areas were dominated by rubber by 2015. With the development of Xishuangbanna, landscape diversity increased along urban-rural gradients, but decreased in some key urban areas. Urban expansion slightly reduced the connectivity of forest and increased agglomeration of rubber at the same time. Based on the analyses above, we moved forward to discuss the consequences of urban expansion, rubber plantations and land fragmentation. Full article
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Open AccessArticle Monitoring Annual Urban Changes in a Rapidly Growing Portion of Northwest Arkansas with a 20-Year Landsat Record
Remote Sens. 2017, 9(1), 71; doi:10.3390/rs9010071
Received: 6 November 2016 / Revised: 31 December 2016 / Accepted: 9 January 2017 / Published: 13 January 2017
PDF Full-text (9422 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Northwest Arkansas has undergone a significant urban transformation in the past several decades and is considered to be one of the fastest growing regions in the United States. The urban area expansion and the associated demographic increases bring unprecedented pressure to the environment
[...] Read more.
Northwest Arkansas has undergone a significant urban transformation in the past several decades and is considered to be one of the fastest growing regions in the United States. The urban area expansion and the associated demographic increases bring unprecedented pressure to the environment and natural resources. To better understand the consequences of urbanization, accurate and long-term depiction on urban dynamics is critical. Although urban mapping activities using remote sensing have been widely conducted, long-term urban growth mapping at an annual pace is rare and the low accuracy of change detection remains a challenge. In this study, a time series Landsat stack covering the period from 1995 to 2015 was employed to detect the urban dynamics in Northwest Arkansas via a two-stage classification approach. A set of spectral indices that have been proven to be useful in urban area extraction together with the original Landsat spectral bands were used in the maximum likelihood classifier and random forest classifier to distinguish urban from non-urban pixels for each year. A temporal trajectory polishing method, involving temporal filtering and heuristic reasoning, was then applied to the sequence of classified urban maps for further improvement. Based on a set of validation samples selected for five distinct years, the average overall accuracy of the final polished maps was 91%, which improved the preliminary classifications by over 10%. Moreover, results from this study also indicated that the temporal trajectory polishing method was most effective with initial low accuracy classifications. The resulting urban dynamic map is expected to provide unprecedented details about the area, spatial configuration, and growing trends of urban land-cover in Northwest Arkansas. Full article
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Open AccessArticle Estimation of Building Density with the Integrated Use of GF-1 PMS and Radarsat-2 Data
Remote Sens. 2016, 8(11), 969; doi:10.3390/rs8110969
Received: 25 July 2016 / Revised: 7 November 2016 / Accepted: 16 November 2016 / Published: 23 November 2016
Cited by 1 | PDF Full-text (10174 KB) | HTML Full-text | XML Full-text
Abstract
Building density, as a component of impervious surface fraction, is a significant indicator of population distribution as essentially all humans live and conduct activities in buildings. Because population spatialization usually occurs over large areas, large-scale building density estimation through a proper, time-efficient, and
[...] Read more.
Building density, as a component of impervious surface fraction, is a significant indicator of population distribution as essentially all humans live and conduct activities in buildings. Because population spatialization usually occurs over large areas, large-scale building density estimation through a proper, time-efficient, and relatively precise way is urgently required. Therefore, this study constructed a decision tree by the Classification and Regression Tree (CART) algorithm combining synthetic aperture radar (SAR) with optical images. The input features included four spectral bands (B14) of GF-1 PMS imagery; Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Ratio Built-up Index (RBI) derived from them; and backscatter intensity (BI) of Radarsat-2 SAR data. In addition, a new index called amended backscatter intensity (ABI), which takes the influence created by different spatial patterns into account, was introduced and calculated through fractal dimension and lacunarity. Result showed that before the integration use of multisource data, a model using B14, NDVI, NDWI, and RBI had the highest accuracy, with RMSE of 10.28 and R2 of 0.63 for Jizhou and RMSE of 20.34 and R2 of 0.36 for Beijing. In Comparison, the best model after combining two data sources (i.e., the model employing B14, NDVI, NDWI, RBI and ABI) reduced the RMSE to 8.93 and 16.21 raised the R2 to 0.80 and 0.64, respectively. The result indicated that the synergistic use of optical and SAR data has the potential to improve the building density estimation performance and the addition of ABI has a better capacity for improving the model than other input features. Full article
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Open AccessArticle Forms of Urban Expansion of Chinese Municipalities and Provincial Capitals, 1970s–2013
Remote Sens. 2016, 8(11), 930; doi:10.3390/rs8110930
Received: 31 August 2016 / Revised: 21 October 2016 / Accepted: 3 November 2016 / Published: 9 November 2016
Cited by 5 | PDF Full-text (12835 KB) | HTML Full-text | XML Full-text
Abstract
Urban expansion form is the most direct manifestation of urban expansion in space. Although it has been widely and vigorously studied, relatively little attention has been paid to reveal its spatiotemporal characteristics at the administrative level over a long timeframe. In this study,
[...] Read more.
Urban expansion form is the most direct manifestation of urban expansion in space. Although it has been widely and vigorously studied, relatively little attention has been paid to reveal its spatiotemporal characteristics at the administrative level over a long timeframe. In this study, 31 Chinese municipalities and provincial capitals were selected as subjects to identify the urban expansion forms of provincial and higher level cities in China. First, urban expansion processes of these cities in the past four decades were reconstructed using remote sensing and geographical information system (GIS) technology. Then, the overall characteristics of urban expansion were presented to scientifically determine the urban expansion forms of the provincial and higher level cities in China. Afterwards, the annual expansion area per city (AEAC) index was employed to describe the urban expansion processes and determine the important time nodes of the 31 cities. Lastly, the urban expansion type (UET) index was adopted to analyze the spatiotemporal characteristics of urban expansion forms. Results indicate that (1) from the 1970s to 2013, urban lands in provincial and higher level cities in China expanded dramatically, with the central built-up area increasing by over 5 times, and urban expansion demonstrating an apparent spatial difference. The expansion rate of cities in East China was fastest with an AEAC of 13.78 km2, followed by that in Central China (AEAC = 9.67 km2). The urban expansion rate was slowest in West China (AEAC = 7.11 km2); (2) Affected by the national macro policies, urban expansion processes successively experienced four different stages: a slow expansion period (1970s–1987), an accelerating expansion period (1987–1995), a slowdown expansion period (1995–2000), and a high-speed fluctuating expansion period (after 2000); (3) The urban expansion forms of municipalities and provincial capitals were mainly edge-expansion supported by infilling expansion. The leapfrog form contributed minimally to urban expansion; (4) The edge-expansion form surged before 2010 and gradually slowed down after 2010. By contrast, infilling expansion kept increasing in the past four decades. Lastly, the rate of urban expansion via the leapfrog form fluctuated from the 1970s to 2013. Full article
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Open AccessArticle Monitoring Urban Dynamics in the Southeast U.S.A. Using Time-Series DMSP/OLS Nightlight Imagery
Remote Sens. 2016, 8(7), 578; doi:10.3390/rs8070578
Received: 28 April 2016 / Revised: 17 June 2016 / Accepted: 4 July 2016 / Published: 8 July 2016
Cited by 9 | PDF Full-text (6502 KB) | HTML Full-text | XML Full-text
Abstract
The Defense Meteorological Satellite Program (DMSP)’s Operational Line-scan System (OLS) stable nighttime light (NTL) imagery offers a good opportunity for characterizing the extent and dynamics of urban development at the global and regional scales. However, their ability to characterize intra-urban variation is limited
[...] Read more.
The Defense Meteorological Satellite Program (DMSP)’s Operational Line-scan System (OLS) stable nighttime light (NTL) imagery offers a good opportunity for characterizing the extent and dynamics of urban development at the global and regional scales. However, their ability to characterize intra-urban variation is limited due to saturation and blooming of the data values. In this study, we adopted the methods of Mann-Kendall and linear regression to analyze urban dynamics from time series Vegetation Adjusted NTL Urban Index (VANUI) data from 1992 to 2013 in the Southeast United States of America (U.S.A.), which is one of the fastest growing regions in the nation. The newly built urban areas were effectively detected based on the trend analysis. In addition, the VANUI-derived urban areas with an optimal threshold method were found highly consistent with the Landsat-derived National Land Cover Database. The total urbanized areas in large metropolitan areas in southeastern U.S.A. increased from 8524 km2 in 1992 to 14,684 km2 in 2010, accounting for 5% and 9% of the total area, respectively. The results further showed that urban expansion in the region cannot be purely explained by population growth. Our results suggested that the VANUI time series provided an effective method for characterizing the spatiotemporal dynamics of urban extent at the regional scale. Full article
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Open AccessArticle Estimation of Continuous Urban Sky View Factor from Landsat Data Using Shadow Detection
Remote Sens. 2016, 8(7), 568; doi:10.3390/rs8070568
Received: 29 April 2016 / Revised: 20 June 2016 / Accepted: 28 June 2016 / Published: 6 July 2016
Cited by 4 | PDF Full-text (3421 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Sky View Factor (SVF, a dimensionless value between 0 and 1 representing obstructed and unobstructed sky, respectively) has an important influence on urban energy balance, and is a key contributor to the Urban Heat Island (UHI) effect experienced by heavily built up regions.
[...] Read more.
Sky View Factor (SVF, a dimensionless value between 0 and 1 representing obstructed and unobstructed sky, respectively) has an important influence on urban energy balance, and is a key contributor to the Urban Heat Island (UHI) effect experienced by heavily built up regions. Continuous urban SVF maps used in modeling the spatial distribution of UHI can be derived analytically using Lidar data; however, Lidar data are costly to obtain and often lack complete coverage of large cities or metropolitan areas. This study develops and validates a method for estimating continuous urban SVF from globally available Landsat TM data, based on the presence of shadows cast by SVF-reducing urban features. SVF and per-pixel shadow proportion (SP) were first calculated for synthetic grid cities to confirm a logarithmic relationship between the two properties; then Lidar data from four US cities were used to determine an empirical regression relating SP to SVF. Spectral Mixture Analysis was then used to estimate per-pixel SP in a Landsat 5 TM image covering the Greater Vancouver Area, Canada, and the empirical regression was used to calculate SVF from per-pixel SP. The accuracy of the resulting SVF map was validated using independent Lidar-derived SVF data (R2 = 0.78; RMSE = 0.056). Full article
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Open AccessArticle Evaluation of the Chinese Fine Spatial Resolution Hyperspectral Satellite TianGong-1 in Urban Land-Cover Classification
Remote Sens. 2016, 8(5), 438; doi:10.3390/rs8050438
Received: 24 March 2016 / Revised: 12 May 2016 / Accepted: 18 May 2016 / Published: 21 May 2016
Cited by 4 | PDF Full-text (2961 KB) | HTML Full-text | XML Full-text
Abstract
The successful launch of the Chinese high spatial resolution hyperspectral satellite TianGong-1 (TG-1) opens up new possibilities for applications of remotely-sensed satellite imagery. One of the main goals of the TG-1 mission is to provide observations of surface attributes at local and landscape
[...] Read more.
The successful launch of the Chinese high spatial resolution hyperspectral satellite TianGong-1 (TG-1) opens up new possibilities for applications of remotely-sensed satellite imagery. One of the main goals of the TG-1 mission is to provide observations of surface attributes at local and landscape spatial scales to map urban land cover accurately using the hyperspectral technique. This study attempted to evaluate the TG-1 datasets for urban feature analysis, using existing data over Beijing, China, by comparing the TG-1 (with a spatial resolution of 10 m) to EO-1 Hyperion (with a spatial resolution of 30 m). The spectral feature of TG-1 was first analyzed and, thus, finding out optimal hyperspectral wavebands useful for the discrimination of urban areas. Based on this, the pixel-based maximum likelihood classifier (PMLC), pixel-based support vector machine (PSVM), hybrid maximum likelihood classifier (HMLC), and hybrid support vector machine (HSVM) were implemented, as well as compared in the application of mapping urban land cover types. The hybrid classifier approach, which integrates the pixel-based classifier and the object-based segmentation approach, was demonstrated as an effective alternative to the conventional pixel-based classifiers for processing the satellite hyperspectral data, especially the fine spatial resolution data. For TG-1 imagery, the pixel-based urban classification was obtained with an average overall accuracy of 89.1%, whereas the hybrid urban classification was obtained with an average overall accuracy of 91.8%. For Hyperion imagery, the pixel-based urban classification was obtained with an average overall accuracy of 85.9%, whereas the hybrid urban classification was obtained with an average overall accuracy of 86.7%. Overall, it can be concluded that the fine spatial resolution satellite hyperspectral data TG-1 is promising in delineating complex urban scenes, especially when using an appropriate classifier, such as the hybrid classifier. Full article
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Open AccessArticle Estimating Tree Frontal Area in Urban Areas Using Terrestrial LiDAR Data
Remote Sens. 2016, 8(5), 401; doi:10.3390/rs8050401
Received: 9 March 2016 / Revised: 16 April 2016 / Accepted: 4 May 2016 / Published: 11 May 2016
Cited by 1 | PDF Full-text (5742 KB) | HTML Full-text | XML Full-text
Abstract
Surface roughness parameters, such as roughness length and displacement height, impact the estimation of surface moisture, and the frontal areas of buildings and trees are two components that contribute to surface roughness in urban areas. Research on tree frontal area has not been
[...] Read more.
Surface roughness parameters, such as roughness length and displacement height, impact the estimation of surface moisture, and the frontal areas of buildings and trees are two components that contribute to surface roughness in urban areas. Research on tree frontal area has not been conducted in urban areas before, and we hope to fill that gap in the literature with this study by using Terrestrial Light Detection and Ranging (LiDAR) data to estimate tree frontal areas in Warren Township, Indianapolis, IN, USA. We first estimated the frontal areas of individual trees based on their morphology, then calibrated a regression model to estimate the tree frontal area in 30 m pixels using parameters derived from LiDAR data and tree inventory data. The parameters included tree crown base area, height, width, conditions, defects, maintenances, genera, and land use. The validation shows that R2 yielded values ranging from 0.84 to 0.88, and RMSEs varied with tree category. The tree categories were identified based on the height and broadness of the canopy, which indicated the degree of resistance to air flow. This type of model can be used to empirically determine local roughness values at the tree-level for any city with a complete tree inventory. With the strong correlation between trees’ frontal area and crown base area, this model may also be used to determine local roughness value at 30 m resolution with NLCD (National Land Cover Database) tree canopy cover data as a component. A proper tree categorization according to the vertical air resistance, e.g., height and canopy density, was effective to reduce the RMSE in tree frontal area estimation. Geometric parameters, such as height, crown base height, and crown base area extracted from Airborne LiDAR, which demand less storage and computation capacity, may also be sufficient for tree frontal area estimation in the areas where Terrestrial LiDAR is not available. Full article
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Open AccessArticle Mapping Urban Land Use by Using Landsat Images and Open Social Data
Remote Sens. 2016, 8(2), 151; doi:10.3390/rs8020151
Received: 23 October 2015 / Revised: 31 January 2016 / Accepted: 4 February 2016 / Published: 17 February 2016
Cited by 21 | PDF Full-text (3683 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
High-resolution urban land use maps have important applications in urban planning and management, but the availability of these maps is low in countries such as China. To address this issue, we have developed a protocol to identify urban land use functions over large
[...] Read more.
High-resolution urban land use maps have important applications in urban planning and management, but the availability of these maps is low in countries such as China. To address this issue, we have developed a protocol to identify urban land use functions over large areas using satellite images and open social data. We first derived parcels from road networks contained in Open Street Map (OSM) and used the parcels as the basic mapping unit. We then used 10 features derived from Points of Interest (POI) data and two indices obtained from Landsat 8 Operational Land Imager (OLI) images to classify parcels into eight Level I classes and sixteen Level II classes of land use. Similarity measures and threshold methods were used to identify land use types in the classification process. This protocol was tested in Beijing, China. The results showed that the generated land use map had an overall accuracy of 81.04% and 69.89% for Level I and Level II classes, respectively. The map revealed significantly more details of the spatial pattern of land uses in Beijing than the land use map released by the government. Full article
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Open AccessArticle Quantitative Estimation of the Velocity of Urbanization in China Using Nighttime Luminosity Data
Remote Sens. 2016, 8(2), 94; doi:10.3390/rs8020094
Received: 9 November 2015 / Revised: 7 January 2016 / Accepted: 18 January 2016 / Published: 26 January 2016
Cited by 4 | PDF Full-text (4788 KB) | HTML Full-text | XML Full-text
Abstract
Rapid urbanization with sizeable enhancements of urban population and built-up land in China creates challenging planning and management issues due to the complexity of both the urban development and the socioeconomic drivers of environmental change. Improved understanding of spatio-temporal characteristics of urbanization processes
[...] Read more.
Rapid urbanization with sizeable enhancements of urban population and built-up land in China creates challenging planning and management issues due to the complexity of both the urban development and the socioeconomic drivers of environmental change. Improved understanding of spatio-temporal characteristics of urbanization processes are increasingly important for investigating urban expansion and environmental responses to corresponding socioeconomic and landscape dynamics. In this study, we present an artificial luminosity-derived index of the velocity of urbanization, defined as the ratio of temporal trend and spatial gradient of mean annual stable nighttime brightness, to estimate the pace of urbanization and consequent changes in land cover in China for the period of 2000–2010. Using the Defense Meteorological Satellite Program–derived time series of nighttime light data and corresponding satellite-based land cover maps, our results show that the geometric mean velocity of urban dispersal at the country level was 0.21 km·yr−1 across 88.58 × 103 km2 urbanizing areas, in which ~23% of areas originally made of natural and cultivated lands were converted to artificial surfaces between 2000 and 2010. The speed of urbanization varies among urban agglomerations and cities with different development stages and urban forms. Particularly, the Yangtze River Delta conurbation shows the fastest (0.39 km·yr−1) and most extensive (16.12 × 103 km2) urban growth in China over the 10-year period. Moreover, if the current velocity holds, our estimates suggest that an additional 13.29 × 103 km2 in land area will be converted to human-built features while high density socioeconomic activities across the current urbanizing regions and urbanized areas will greatly increase from 52.44 × 103 km2 in 2010 to 62.73 × 103 km2 in China’s mainland during the next several decades. Our findings may provide potential insights into the pace of urbanization in China, its impacts on land changes, and accompanying alterations in environment and ecosystems in a spatially and temporally explicit manner. Full article
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