2019 Vol. 20, No. 3
Display Method:
2019, 20(3): 363-371.
doi: 10.1007/s11769-019-1038-y
Abstract:
Physical geography is the cornerstone of geography. In this article, the starting points of disciplines in physical geography in recent years in China are discussed. With the coupling of systems set as the research object, and sustainable development as the ultimate goal, the upgrade of physical geography can be deconstructed into three steps:deepen physical geography from the perspective of pattern-process coupling, improve the focus of physical geography from ecosystem processes to ecosystem services, and increase the understanding of the physical geography ‘pattern, process, service, sustainability’ research cascade. The incorporation of human activities into physical geographic processes is essential to conduct integrated analysis on physical and human factors at different scales. The development of ecosystem service models that couple supply-demand and sustainable development are of great importance to bridge the role of ecosystem services between the natural environment and human well-being. Moreover, human-land systems and sustainable development have become the core areas and frontiers of integrated physical geography and even geography in general. China faces the great strategic demand of constructing an ecological civilization in a new era, and the development of the disciplines of physical geography should give full access to the advantages of intersecting and comprehensive disciplines, focus on the human-land system patterns, processes, and services in key research areas, and provide disciplinary support for regional, national, and global sustainable development.
Physical geography is the cornerstone of geography. In this article, the starting points of disciplines in physical geography in recent years in China are discussed. With the coupling of systems set as the research object, and sustainable development as the ultimate goal, the upgrade of physical geography can be deconstructed into three steps:deepen physical geography from the perspective of pattern-process coupling, improve the focus of physical geography from ecosystem processes to ecosystem services, and increase the understanding of the physical geography ‘pattern, process, service, sustainability’ research cascade. The incorporation of human activities into physical geographic processes is essential to conduct integrated analysis on physical and human factors at different scales. The development of ecosystem service models that couple supply-demand and sustainable development are of great importance to bridge the role of ecosystem services between the natural environment and human well-being. Moreover, human-land systems and sustainable development have become the core areas and frontiers of integrated physical geography and even geography in general. China faces the great strategic demand of constructing an ecological civilization in a new era, and the development of the disciplines of physical geography should give full access to the advantages of intersecting and comprehensive disciplines, focus on the human-land system patterns, processes, and services in key research areas, and provide disciplinary support for regional, national, and global sustainable development.
2019, 20(3): 372-381.
doi: 10.1007/s11769-019-1039-x
Abstract:
The Yellow River Delta wetland is the youngest wetland ecosystem in China's warm temperate zone. To better understand how its landscape pattern has changed over time and the underlying factors responsible, this study analyzed the dynamic changes of wetlands using five Landsat series of images, namely MSS (Mulri Spectral Scanner), TM (Thematic Mapper), and OLI (Operational Land Imager) sensors in 1976, 1986, 1996, 2006, and 2016. Object-oriented classification and the combination of spatial and spectral features and both the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI), as well as brightness characteristic indices, were used to classify the images in eCognition software. Landscape pattern changes in the Yellow River Delta over the past 40 years were then delineated using transition matrix and landscape index methods. Results show that:1) from 1976 to 2016, the total area of wetlands in the study area decreased from 2594.76 to 2491.79 km2, while that of natural wetlands decreased by 954.03 km2 whereas human-made wetlands increased by 851.06 km2. 2) The transformation of natural wetlands was extensive:31.34% of those covered by Suaeda heteropteras were transformed into reservoirs and ponds, and 24.71% with Phragmites australis coverage were transformed into dry farmland. Some human-made wetlands were transformed into non-wetlands types:1.55% of reservoirs and ponds became construction land, and likewise 21.27% were transformed into dry farmland. 3) From 1976 to 2016, as the intensity of human activities increased, the number of landscape types in the study area continuously increased. Patches were scattered and more fragmented. The whole landscape became more complex. In short, over the past 40 years, the wetlands of the Yellow River Delta have been degraded, with the area of natural wetlands substantially reduced. Human activities were the dominant forces driving these changes in the Yellow River Delta.
The Yellow River Delta wetland is the youngest wetland ecosystem in China's warm temperate zone. To better understand how its landscape pattern has changed over time and the underlying factors responsible, this study analyzed the dynamic changes of wetlands using five Landsat series of images, namely MSS (Mulri Spectral Scanner), TM (Thematic Mapper), and OLI (Operational Land Imager) sensors in 1976, 1986, 1996, 2006, and 2016. Object-oriented classification and the combination of spatial and spectral features and both the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI), as well as brightness characteristic indices, were used to classify the images in eCognition software. Landscape pattern changes in the Yellow River Delta over the past 40 years were then delineated using transition matrix and landscape index methods. Results show that:1) from 1976 to 2016, the total area of wetlands in the study area decreased from 2594.76 to 2491.79 km2, while that of natural wetlands decreased by 954.03 km2 whereas human-made wetlands increased by 851.06 km2. 2) The transformation of natural wetlands was extensive:31.34% of those covered by Suaeda heteropteras were transformed into reservoirs and ponds, and 24.71% with Phragmites australis coverage were transformed into dry farmland. Some human-made wetlands were transformed into non-wetlands types:1.55% of reservoirs and ponds became construction land, and likewise 21.27% were transformed into dry farmland. 3) From 1976 to 2016, as the intensity of human activities increased, the number of landscape types in the study area continuously increased. Patches were scattered and more fragmented. The whole landscape became more complex. In short, over the past 40 years, the wetlands of the Yellow River Delta have been degraded, with the area of natural wetlands substantially reduced. Human activities were the dominant forces driving these changes in the Yellow River Delta.
2019, 20(3): 382-393.
doi: 10.1007/s11769-019-1036-0
Abstract:
Urban air pollution is a prominent problem related to the urban development in China, especially in the densely populated urban agglomerations. Therefore, scientific examination of regional variation of air quality and its dominant factors is of great importance to regional environmental management. In contrast to traditional air pollution researches which only concentrate on a single year or a single pollutant, this paper analyses spatiotemporal patterns and determinants of air quality in disparate regions based on the air quality index (AQI) of the Yangtze River Delta region (YRD) of China from 2014 to 2016. Results show that the annual average value of the AQI in the YRD region decreases from 2014 to 2016 and exhibit a basic characteristic of ‘higher in winter, lower in summer and slightly high in spring and autumn’. The attainment rate of the AQI shows an apparently spatial stratified heterogeneity, Hefei metropolitan area and Nanjing metropolitan area keeping the worst air quality. The frequency of air pollution occurring in large regions was gradually decreasing during the study period. Drawing from entropy method analysis, industrialization and urbanization represented by per capita GDP and total energy consumption were the most important factors. Furthermore, population agglomeration is a factor that cannot be ignored especially in some mega-cities. Limited to data collection, more research is needed to gain insight into the spatiotemporal pattern and influence mechanism in the future.
Urban air pollution is a prominent problem related to the urban development in China, especially in the densely populated urban agglomerations. Therefore, scientific examination of regional variation of air quality and its dominant factors is of great importance to regional environmental management. In contrast to traditional air pollution researches which only concentrate on a single year or a single pollutant, this paper analyses spatiotemporal patterns and determinants of air quality in disparate regions based on the air quality index (AQI) of the Yangtze River Delta region (YRD) of China from 2014 to 2016. Results show that the annual average value of the AQI in the YRD region decreases from 2014 to 2016 and exhibit a basic characteristic of ‘higher in winter, lower in summer and slightly high in spring and autumn’. The attainment rate of the AQI shows an apparently spatial stratified heterogeneity, Hefei metropolitan area and Nanjing metropolitan area keeping the worst air quality. The frequency of air pollution occurring in large regions was gradually decreasing during the study period. Drawing from entropy method analysis, industrialization and urbanization represented by per capita GDP and total energy consumption were the most important factors. Furthermore, population agglomeration is a factor that cannot be ignored especially in some mega-cities. Limited to data collection, more research is needed to gain insight into the spatiotemporal pattern and influence mechanism in the future.
2019, 20(3): 394-404.
doi: 10.1007/s11769-019-1040-4
Abstract:
Large amounts of data at various temporal and spatial scales require terabyte (TB) level storage and computation, both of which are not easy for researchers to access. Cloud data and computing services provide another solution to store, process, share and explore environmental data with low costs, stronger computation capacity and easy access. The purpose of this paper is to examine the benefits and challenges of using freely available satellite data products from Australian Geoscience DataCube and Google Earth Engine (GEE) online data with time series for integrative environmental analysis of the Macquarie-Castlereagh Basin in the last 15 years as a case study. Results revealed that the cloud platform simplifies the procedure of traditional catalog data processing and analysis. The integrated analysis based on the cloud computing and traditional methods represents a great potential as a low-cost, efficient and user-friendly method for global and regional environmental study. The user can save considerable time and cost on data integration. The research shows that there is an excellent promise in performing regional environmental analysis by using a cloud platform. The incoming challenge of the cloud platform is that not all kinds of data are available on the cloud platform. How data are integrated into a single platform while protecting or recognizing the data property, or how one portal can be used to explore data archived on different platforms represent considerable challenges.
Large amounts of data at various temporal and spatial scales require terabyte (TB) level storage and computation, both of which are not easy for researchers to access. Cloud data and computing services provide another solution to store, process, share and explore environmental data with low costs, stronger computation capacity and easy access. The purpose of this paper is to examine the benefits and challenges of using freely available satellite data products from Australian Geoscience DataCube and Google Earth Engine (GEE) online data with time series for integrative environmental analysis of the Macquarie-Castlereagh Basin in the last 15 years as a case study. Results revealed that the cloud platform simplifies the procedure of traditional catalog data processing and analysis. The integrated analysis based on the cloud computing and traditional methods represents a great potential as a low-cost, efficient and user-friendly method for global and regional environmental study. The user can save considerable time and cost on data integration. The research shows that there is an excellent promise in performing regional environmental analysis by using a cloud platform. The incoming challenge of the cloud platform is that not all kinds of data are available on the cloud platform. How data are integrated into a single platform while protecting or recognizing the data property, or how one portal can be used to explore data archived on different platforms represent considerable challenges.
2019, 20(3): 405-416.
doi: 10.1007/s11769-019-1041-3
Abstract:
The accurate assessment of forest damage is important basis for the forest post-disaster recovery process and ecosystem management. This study evaluates the spatial distribution of damaged forest and its damaged severity caused by ice-snow disaster that occurred in southern China during January 10 to February 2 in 2008. The moderate-resolution imaging spectroradiometer (MODIS) 13Q1 products are used, which include two vegetation indices data of NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index). Furtherly, after Quality Screening (QS) and Savizky-Golay (S-G) filtering of MODIS 13Q1 data, four evaluation indices are obtained, which are NDVI with QS (QSNDVI), EVI with QS (QSEVI), NDVI with S-G filtering (SGNDVI) and EVI with S-G filtering (SGEVI). The study provides a new way of firstly determining the threshold for each image pixel for damaged forest evaluation, by computing the pre-disaster reference value and change threshold with vegetation index from remote sensing data. Results show obvious improvement with the new way for forest damage evaluation, evaluation result of forest damage is much close to the field survey data with standard error of only 0.95 and 1/3 less than the result that evaluated from other threshold method. Comparatively, the QSNDVI shows better performance than other three indices on evaluating forest damages. The evaluated result with QSNDVI shows that the severe, moderate, mild damaged rates of Southern China forests are 47.33%, 34.15%, 18.52%, respectively. By analyzing the influence of topographic and meteorological factors on forest-vegetation damage, we found that the precipitation on freezing days has greater impact on forest-vegetation damage, which is regarded as the most important factor. This study could be a scientific and reliable reference for evaluating the forest damages from ice-snow frozen disasters.
The accurate assessment of forest damage is important basis for the forest post-disaster recovery process and ecosystem management. This study evaluates the spatial distribution of damaged forest and its damaged severity caused by ice-snow disaster that occurred in southern China during January 10 to February 2 in 2008. The moderate-resolution imaging spectroradiometer (MODIS) 13Q1 products are used, which include two vegetation indices data of NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index). Furtherly, after Quality Screening (QS) and Savizky-Golay (S-G) filtering of MODIS 13Q1 data, four evaluation indices are obtained, which are NDVI with QS (QSNDVI), EVI with QS (QSEVI), NDVI with S-G filtering (SGNDVI) and EVI with S-G filtering (SGEVI). The study provides a new way of firstly determining the threshold for each image pixel for damaged forest evaluation, by computing the pre-disaster reference value and change threshold with vegetation index from remote sensing data. Results show obvious improvement with the new way for forest damage evaluation, evaluation result of forest damage is much close to the field survey data with standard error of only 0.95 and 1/3 less than the result that evaluated from other threshold method. Comparatively, the QSNDVI shows better performance than other three indices on evaluating forest damages. The evaluated result with QSNDVI shows that the severe, moderate, mild damaged rates of Southern China forests are 47.33%, 34.15%, 18.52%, respectively. By analyzing the influence of topographic and meteorological factors on forest-vegetation damage, we found that the precipitation on freezing days has greater impact on forest-vegetation damage, which is regarded as the most important factor. This study could be a scientific and reliable reference for evaluating the forest damages from ice-snow frozen disasters.
2019, 20(3): 417-436.
doi: 10.1007/s11769-019-1042-2
Abstract:
This paper attempts to assess the vulnerability to climate change of human communities in selected mouzas of Sagar Island, South 24 Parganas District of India. A primary household survey has been conducted to collect data on socio-demographic profile, livelihood strategy, health, food, water, social network, natural disaster and climate variation indicators, were selected for Livelihood Vulnerability Index (LVI) and Livelihood Vulnerability Index-Intergovernmental Panel on Climate Change (LVI-IPCC) analyses to measure and compare the vulnerability of mouzas (administrative unit) currently suffering from frequent flooding, coastal erosion and embankment breaching on an annual basis. Secondary data collected from the Indian Meteorological Department, the Water Resources Information System of India and the Global Sea Level Observing System have been used to identify dynamics of climate change by employing statistical and Geographic Information System (GIS) techniques. A GPS survey has been conducted to identify locations of embankment breaching, and satellite images obtained from the National Aeronautics and Space Administration and U.S. Geological Survey (NASA USGS) Government website have been applied to shoreline and land use change detection, using a supervised maximum likelihood classification. The results indicate that the study area has experienced increasing temperature, changing precipitation patterns, rise in sea level, higher storm surges, shoreline change, constant land loss, embankment breaching and changing land use, which have had impact on vulnerability, particularly of poorer people. The LVI (0.48 to 0.68) and LVI-IPCC (0.04 to 0.14) scores suggest that the populations of Dhablat, Bankimnagar, Sumatinagar, Muri Ganga and Sibpur mouzas are highly vulnerable (LVI scores of 0.60 to 0.68 and LVI-IPCC scores of 0.11 to 0.14) to climate change both because the communities are more exposed to it, and because poor access to food, health facilities and water makes them extremely sensitive to it and lowers their adaptive capacity. The findings of this study could be crucial to framing further development and adaptation strategies relating to climate change, and to safeguarding the estuarine ecosystem and the vulnerable population.
This paper attempts to assess the vulnerability to climate change of human communities in selected mouzas of Sagar Island, South 24 Parganas District of India. A primary household survey has been conducted to collect data on socio-demographic profile, livelihood strategy, health, food, water, social network, natural disaster and climate variation indicators, were selected for Livelihood Vulnerability Index (LVI) and Livelihood Vulnerability Index-Intergovernmental Panel on Climate Change (LVI-IPCC) analyses to measure and compare the vulnerability of mouzas (administrative unit) currently suffering from frequent flooding, coastal erosion and embankment breaching on an annual basis. Secondary data collected from the Indian Meteorological Department, the Water Resources Information System of India and the Global Sea Level Observing System have been used to identify dynamics of climate change by employing statistical and Geographic Information System (GIS) techniques. A GPS survey has been conducted to identify locations of embankment breaching, and satellite images obtained from the National Aeronautics and Space Administration and U.S. Geological Survey (NASA USGS) Government website have been applied to shoreline and land use change detection, using a supervised maximum likelihood classification. The results indicate that the study area has experienced increasing temperature, changing precipitation patterns, rise in sea level, higher storm surges, shoreline change, constant land loss, embankment breaching and changing land use, which have had impact on vulnerability, particularly of poorer people. The LVI (0.48 to 0.68) and LVI-IPCC (0.04 to 0.14) scores suggest that the populations of Dhablat, Bankimnagar, Sumatinagar, Muri Ganga and Sibpur mouzas are highly vulnerable (LVI scores of 0.60 to 0.68 and LVI-IPCC scores of 0.11 to 0.14) to climate change both because the communities are more exposed to it, and because poor access to food, health facilities and water makes them extremely sensitive to it and lowers their adaptive capacity. The findings of this study could be crucial to framing further development and adaptation strategies relating to climate change, and to safeguarding the estuarine ecosystem and the vulnerable population.
2019, 20(3): 437-445.
doi: 10.1007/s11769-019-1043-1
Abstract:
Deyeuxia angustifolia wetlands were widely distributed in the Sanjiang Plain in Northeast China. Due to strong demand for food production, large-area wetlands were reclaimed to farmlands, which threatened regional ecological security greatly. Since the 21th century, returning farmlands to wetlands was widely adopted for natural restoration in the Sangjiang Plain. As the first reflection of wetland restoration, vegetation succession of restored D. angustifolia wetlands should be fully assessed. In this study, vegetation investigation was carried out in three restored D. angustifolia wetlands with 5, 8 and 12 yr restoration, respectively. Meanwhile, a natural D. angustifolia wetland was selected as reference wetland. Results showed that community composition changed greatly and there was visible community succession. Community dominant species changed from composite to gramineae as restoration time increasing. At first, weeds community appeared in the restored wetlands, especially the xerophytes developed to the pioneer species rapidly. And then, mesophytes and wetland species became the dominant species in the restored wetlands. Finally, wetland species, especially D. angustifolia, occupied the dominant position of restored community. Shannon-wiener index (H) and Simpson index (D) both decreased to close to natural D. angustifolia wetlands. Compared with natural D. angustifolia wetland, species composition and diversity in restored wetlands were more complex and higher. As restoration time increasing, there were not significant differences between community characteristics of restored wetlands and natural wetland. All these suggested that vegetation in reclaimed D. angustifolia wetland could be restored naturally, but its restored period is 10 yr at least. From another angle, it is important to protect current natural wetlands.
Deyeuxia angustifolia wetlands were widely distributed in the Sanjiang Plain in Northeast China. Due to strong demand for food production, large-area wetlands were reclaimed to farmlands, which threatened regional ecological security greatly. Since the 21th century, returning farmlands to wetlands was widely adopted for natural restoration in the Sangjiang Plain. As the first reflection of wetland restoration, vegetation succession of restored D. angustifolia wetlands should be fully assessed. In this study, vegetation investigation was carried out in three restored D. angustifolia wetlands with 5, 8 and 12 yr restoration, respectively. Meanwhile, a natural D. angustifolia wetland was selected as reference wetland. Results showed that community composition changed greatly and there was visible community succession. Community dominant species changed from composite to gramineae as restoration time increasing. At first, weeds community appeared in the restored wetlands, especially the xerophytes developed to the pioneer species rapidly. And then, mesophytes and wetland species became the dominant species in the restored wetlands. Finally, wetland species, especially D. angustifolia, occupied the dominant position of restored community. Shannon-wiener index (H) and Simpson index (D) both decreased to close to natural D. angustifolia wetlands. Compared with natural D. angustifolia wetland, species composition and diversity in restored wetlands were more complex and higher. As restoration time increasing, there were not significant differences between community characteristics of restored wetlands and natural wetland. All these suggested that vegetation in reclaimed D. angustifolia wetland could be restored naturally, but its restored period is 10 yr at least. From another angle, it is important to protect current natural wetlands.
2019, 20(3): 446-462.
doi: 10.1007/s11769-019-1033-3
Abstract:
Satellite-based precipitation products have been widely used to estimate precipitation, especially over regions with sparse rain gauge networks. However, the low spatial resolution of these products has limited their application in localized regions and watersheds. This study investigated a spatial downscaling approach, Geographically Weighted Regression Kriging (GWRK), to downscale the Tropical Rainfall Measuring Mission (TRMM) 3B43 Version 7 over the Lancang River Basin (LRB) for 2001-2015. Downscaling was performed based on the relationships between the TRMM precipitation and the Normalized Difference Vegetation Index (NDVI), the Land Surface Temperature (LST), and the Digital Elevation Model (DEM). Geographical ratio analysis (GRA) was used to calibrate the annual downscaled precipitation data, and the monthly fractions derived from the original TRMM data were used to disaggregate annual downscaled and calibrated precipitation to monthly precipitation at 1 km resolution. The final downscaled precipitation datasets were validated against station-based observed precipitation in 2001-2015. Results showed that:1) The TRMM 3B43 precipitation was highly accurate with slight overestimation at the basin scale (i.e., CC (correlation coefficient)=0.91, Bias=13.3%). Spatially, the accuracies of the upstream and downstream regions were higher than that of the midstream region. 2) The annual downscaled TRMM precipitation data at 1 km spatial resolution obtained by GWRK effectively captured the high spatial variability of precipitation over the LRB. 3) The annual downscaled TRMM precipitation with GRA calibration gave better accuracy compared with the original TRMM dataset. 4) The final downscaled and calibrated precipitation had significantly improved spatial resolution, and agreed well with data from the validated rain gauge stations, i.e., CC=0.75, RMSE (root mean square error)=182 mm, MAE (mean absolute error)=142 mm, and Bias=0.78% for annual precipitation and CC=0.95, RMSE=25 mm, MAE=16 mm, and Bias=0.67% for monthly precipitation.
Satellite-based precipitation products have been widely used to estimate precipitation, especially over regions with sparse rain gauge networks. However, the low spatial resolution of these products has limited their application in localized regions and watersheds. This study investigated a spatial downscaling approach, Geographically Weighted Regression Kriging (GWRK), to downscale the Tropical Rainfall Measuring Mission (TRMM) 3B43 Version 7 over the Lancang River Basin (LRB) for 2001-2015. Downscaling was performed based on the relationships between the TRMM precipitation and the Normalized Difference Vegetation Index (NDVI), the Land Surface Temperature (LST), and the Digital Elevation Model (DEM). Geographical ratio analysis (GRA) was used to calibrate the annual downscaled precipitation data, and the monthly fractions derived from the original TRMM data were used to disaggregate annual downscaled and calibrated precipitation to monthly precipitation at 1 km resolution. The final downscaled precipitation datasets were validated against station-based observed precipitation in 2001-2015. Results showed that:1) The TRMM 3B43 precipitation was highly accurate with slight overestimation at the basin scale (i.e., CC (correlation coefficient)=0.91, Bias=13.3%). Spatially, the accuracies of the upstream and downstream regions were higher than that of the midstream region. 2) The annual downscaled TRMM precipitation data at 1 km spatial resolution obtained by GWRK effectively captured the high spatial variability of precipitation over the LRB. 3) The annual downscaled TRMM precipitation with GRA calibration gave better accuracy compared with the original TRMM dataset. 4) The final downscaled and calibrated precipitation had significantly improved spatial resolution, and agreed well with data from the validated rain gauge stations, i.e., CC=0.75, RMSE (root mean square error)=182 mm, MAE (mean absolute error)=142 mm, and Bias=0.78% for annual precipitation and CC=0.95, RMSE=25 mm, MAE=16 mm, and Bias=0.67% for monthly precipitation.
2019, 20(3): 463-473.
doi: 10.1007/s11769-019-1044-0
Abstract:
In China, promoting the development of coastal areas has been included in a series of national strategic development plans. At the same time, many marine environmental problems have been associated with the rapid development of coastal sea use. In order to quantify the impact of human activities on the coast, the characteristics of coastlines and near-shore sea use of Liaodong Bay, Northeast China, were first classified using multi-source, remotely sensed imagery using automatic or semi-automatic extraction methods for five periods between 1993 and 2015. Sea use dynamics and coastline dynamics resulting from human activates were analyzed. Results showed a significant trend of continuous growth in sea use and a progressive increase in the total length of artificial coastline, but a noticeable loss of natural coastline during the five periods. Reclaimed land and enclosed areas were the main types of sea use. Most coastal human activities were distributed in the northern part of the bay. In recent years, rapid industrialization and urbanization in China's coastal areas have promoted large-scale land reclamation. Accordingly, the observed coastline changes during each period had a close relationship with coastal development and sea area utilization. Based on marine functional zoning (MFZ), the sea use carrying capacity was evaluated by means of indexes to describe human exploitation of the marine and coastal environments in the bay. This showed that the intensity of coastal utilization in Liaodong Bay has increased year-on-year. Sea use carrying capacity reached a ‘critically loaded’ state by 2008 and was ‘overloaded’ by 2015.
In China, promoting the development of coastal areas has been included in a series of national strategic development plans. At the same time, many marine environmental problems have been associated with the rapid development of coastal sea use. In order to quantify the impact of human activities on the coast, the characteristics of coastlines and near-shore sea use of Liaodong Bay, Northeast China, were first classified using multi-source, remotely sensed imagery using automatic or semi-automatic extraction methods for five periods between 1993 and 2015. Sea use dynamics and coastline dynamics resulting from human activates were analyzed. Results showed a significant trend of continuous growth in sea use and a progressive increase in the total length of artificial coastline, but a noticeable loss of natural coastline during the five periods. Reclaimed land and enclosed areas were the main types of sea use. Most coastal human activities were distributed in the northern part of the bay. In recent years, rapid industrialization and urbanization in China's coastal areas have promoted large-scale land reclamation. Accordingly, the observed coastline changes during each period had a close relationship with coastal development and sea area utilization. Based on marine functional zoning (MFZ), the sea use carrying capacity was evaluated by means of indexes to describe human exploitation of the marine and coastal environments in the bay. This showed that the intensity of coastal utilization in Liaodong Bay has increased year-on-year. Sea use carrying capacity reached a ‘critically loaded’ state by 2008 and was ‘overloaded’ by 2015.
2019, 20(3): 474-487.
doi: 10.1007/s11769-019-1035-1
Abstract:
Using the ‘theoretical hypothesis-empirical study-case verification’ method, this paper studies the spatial distribution and differentiation of port & shipping service enterprises (PSSE), as well as the variation process and underlying mechanism in the Yangtze River Delta (YRD). First, through inductive and deductive reasoning, we propose the following hypothesis:the regional distribution of different types of PSSE would show different spatial agglomeration-decentralization tendency; and there would be distinct regional differentiation in the industrial structure of the enterprises. Second, based on data obtained from enterprises, empirical research is conducted using Gini coefficient and spatial interpolation simulation methods. Results show that:1) The overall enterprise distribution is decentralized within a city. 2) Different types of enterprises show different spatial agglomeration-decentralization tendencies. At 3000 m×3000 m grid scale, there is an agglomeration tendency along seas and rivers in the spatial distribution of enterprises. Shanghai has been identified consistently as a hot spot. 3) There is significant regional differentiation in 12 port cities with respect to the industrial structures of enterprises. Finally, the transportization and the increase of shipping service demand, the globalization and the expansion of multinational corporate activities, the hierarchization and the cooperation among port cities as well as the decentralization and the behavioral difference between the central and local states can be seen as main driving mechanism of the spatial phenomenon.
Using the ‘theoretical hypothesis-empirical study-case verification’ method, this paper studies the spatial distribution and differentiation of port & shipping service enterprises (PSSE), as well as the variation process and underlying mechanism in the Yangtze River Delta (YRD). First, through inductive and deductive reasoning, we propose the following hypothesis:the regional distribution of different types of PSSE would show different spatial agglomeration-decentralization tendency; and there would be distinct regional differentiation in the industrial structure of the enterprises. Second, based on data obtained from enterprises, empirical research is conducted using Gini coefficient and spatial interpolation simulation methods. Results show that:1) The overall enterprise distribution is decentralized within a city. 2) Different types of enterprises show different spatial agglomeration-decentralization tendencies. At 3000 m×3000 m grid scale, there is an agglomeration tendency along seas and rivers in the spatial distribution of enterprises. Shanghai has been identified consistently as a hot spot. 3) There is significant regional differentiation in 12 port cities with respect to the industrial structures of enterprises. Finally, the transportization and the increase of shipping service demand, the globalization and the expansion of multinational corporate activities, the hierarchization and the cooperation among port cities as well as the decentralization and the behavioral difference between the central and local states can be seen as main driving mechanism of the spatial phenomenon.
2019, 20(3): 488-502.
doi: 10.1007/s11769-019-1021-7
Abstract:
In order to make assessment on urbanization coordination, we developed a comprehensive model by integrating entropy weight method (EWM), coupling degree model (CDM), coupling coordination degree model (CCDM), multi-index grading method (MIGM) and Remote Sensing & Geographic Information System (RS & GIS) technology. Then we applied this integrated model to a case study in Jiangxi Province, China. Our study finds that:1) EWM, CDM and CCDM can evaluate the temporal dynamic of urbanization. Urbanization process of Jiangxi Province can be divided into three periods, the stable development period (1990-2001), the accelerated development period (2002-2009) and the rapid development period (2010-2015). Coordinated development of urbanization in Jiangxi Province can be divided into two phases, an increasingly coordinated phase (1990-2003) and an increasingly incongruous phase (2003-2015). The state transition was due to low development rate of population urbanization. 2) RS & GIS technology is an effective tool for detecting urban growth. Urban construction land area of Jiangxi Province increased from 615.8 km2 in 1990 to 2896.8 km2 in 2015, and the per capita urban construction land area (PCUCLA) reached 122.9 m2, with the maximum value of 343 m2 in Gongqingcheng City. 3) MIGM and RS & GIS technology can analyze spatial difference of urbanization. There is a significant spatial difference in socioeconomic development at county scale, with the maximum value six times the minimum value for both PCUCLA and per capita GDP in 2015. Population urbanization lag and excessive land use are the main reasons for uncoordinated urbanization. There were 15 counties with a lag in demographic urbanization and 33 counties where PCUCLA exceeded the national standard in 2015, among which 20 exceeded the national standard of PCUCLA by 50% (≥ 165 m2). Since there are significant spatio-temporal differences in urbanization, it is necessary to carry out a comprehensive assessment to facilitate differential urbanization strategy making.
In order to make assessment on urbanization coordination, we developed a comprehensive model by integrating entropy weight method (EWM), coupling degree model (CDM), coupling coordination degree model (CCDM), multi-index grading method (MIGM) and Remote Sensing & Geographic Information System (RS & GIS) technology. Then we applied this integrated model to a case study in Jiangxi Province, China. Our study finds that:1) EWM, CDM and CCDM can evaluate the temporal dynamic of urbanization. Urbanization process of Jiangxi Province can be divided into three periods, the stable development period (1990-2001), the accelerated development period (2002-2009) and the rapid development period (2010-2015). Coordinated development of urbanization in Jiangxi Province can be divided into two phases, an increasingly coordinated phase (1990-2003) and an increasingly incongruous phase (2003-2015). The state transition was due to low development rate of population urbanization. 2) RS & GIS technology is an effective tool for detecting urban growth. Urban construction land area of Jiangxi Province increased from 615.8 km2 in 1990 to 2896.8 km2 in 2015, and the per capita urban construction land area (PCUCLA) reached 122.9 m2, with the maximum value of 343 m2 in Gongqingcheng City. 3) MIGM and RS & GIS technology can analyze spatial difference of urbanization. There is a significant spatial difference in socioeconomic development at county scale, with the maximum value six times the minimum value for both PCUCLA and per capita GDP in 2015. Population urbanization lag and excessive land use are the main reasons for uncoordinated urbanization. There were 15 counties with a lag in demographic urbanization and 33 counties where PCUCLA exceeded the national standard in 2015, among which 20 exceeded the national standard of PCUCLA by 50% (≥ 165 m2). Since there are significant spatio-temporal differences in urbanization, it is necessary to carry out a comprehensive assessment to facilitate differential urbanization strategy making.
2019, 20(3): 503-516.
doi: 10.1007/s11769-019-1045-z
Abstract:
The location pattern of different commercial stores in Shichahai, a historic conservation area in Beijing, was investigated from a street centrality perspective. Many previous studies have investigated the relationship between street centrality and land use patterns or commercial activities at interurban or intraurban scales. We considered Shichahai in this study to determine if street centrality applied at the street scale and if the street network was the only factor influencing the selection of store location. First, the nearest neighbor index, nearest neighbor hierarchical spatial cluster (NNHSC), and kernel density estimation (KDE) methods were used to provide baseline spatial distributions of commercial stores. Second, urban network analysis (UNA) tools were used to measure the street centrality indices under two conditions, with and without the weighting of cultural relics calculated by a principal component analysis (PCA). Finally, both store locations and centrality values at nodes were transformed to one unit (raster pixel) for a correlation analysis. The results showed that three of the four store types were clustered and had their own hotspots that were mostly located in the eastern and central parts of city blocks. The most momentous findings were determined from the street centrality indices. Among the three store types with correlation coefficients above 0.5, all centrality indices with landmark weighting, except straightness, had higher correlations, with closeness with landmark weighting having the highest correlation, followed by betweenness with landmark weighting. Therefore, we statistically concluded that street centrality could apply at the street scale and that the street network was not the only factor that influenced store location pattern, with landmarks also playing a significant role. The results provide guidance in determining the selection strategy for stores in a historic conservation area.
The location pattern of different commercial stores in Shichahai, a historic conservation area in Beijing, was investigated from a street centrality perspective. Many previous studies have investigated the relationship between street centrality and land use patterns or commercial activities at interurban or intraurban scales. We considered Shichahai in this study to determine if street centrality applied at the street scale and if the street network was the only factor influencing the selection of store location. First, the nearest neighbor index, nearest neighbor hierarchical spatial cluster (NNHSC), and kernel density estimation (KDE) methods were used to provide baseline spatial distributions of commercial stores. Second, urban network analysis (UNA) tools were used to measure the street centrality indices under two conditions, with and without the weighting of cultural relics calculated by a principal component analysis (PCA). Finally, both store locations and centrality values at nodes were transformed to one unit (raster pixel) for a correlation analysis. The results showed that three of the four store types were clustered and had their own hotspots that were mostly located in the eastern and central parts of city blocks. The most momentous findings were determined from the street centrality indices. Among the three store types with correlation coefficients above 0.5, all centrality indices with landmark weighting, except straightness, had higher correlations, with closeness with landmark weighting having the highest correlation, followed by betweenness with landmark weighting. Therefore, we statistically concluded that street centrality could apply at the street scale and that the street network was not the only factor that influenced store location pattern, with landmarks also playing a significant role. The results provide guidance in determining the selection strategy for stores in a historic conservation area.
2019, 20(3): 517-527.
doi: 10.1007/s11769-019-1046-y
Abstract:
There are two opposing viewpoints on which kind of network configuration provides a more competitive advantage, namely, network closure or structural holes, with the latter occupying the dominant position in the literature. Using social network analysis and negative binomial regression methods, we graph the co-patent network of Dongying's petroleum equipment industry in China and explore its impact on enterprise innovation. The analysis is based on 17 face-to-face interviews, 31 enterprise questionnaires, and 354 co-patent records from the China State Intellectual Property Office identifying cooperative innovation for the years 1988-2013. We find that this network is closed, controlled by state-owned enterprises, and its closure has positive effects on enterprise innovation performance. This may be related to China's unique industrial development history, state system and policies, regional culture and circumstances, and enterprise characteristics. Therefore, for some industries in specific regions, the advantages usually attributed to structural holes and open innovation may not necessarily apply.
There are two opposing viewpoints on which kind of network configuration provides a more competitive advantage, namely, network closure or structural holes, with the latter occupying the dominant position in the literature. Using social network analysis and negative binomial regression methods, we graph the co-patent network of Dongying's petroleum equipment industry in China and explore its impact on enterprise innovation. The analysis is based on 17 face-to-face interviews, 31 enterprise questionnaires, and 354 co-patent records from the China State Intellectual Property Office identifying cooperative innovation for the years 1988-2013. We find that this network is closed, controlled by state-owned enterprises, and its closure has positive effects on enterprise innovation performance. This may be related to China's unique industrial development history, state system and policies, regional culture and circumstances, and enterprise characteristics. Therefore, for some industries in specific regions, the advantages usually attributed to structural holes and open innovation may not necessarily apply.
2019, 20(3): 528-540.
doi: 10.1007/s11769-019-1047-x
Abstract:
It is important to optimize the cooperative innovation network for the improvement of economic competence and innovative power. Based on the patent information services platform, we obtain invention patent data for the electronic information industry in Liaoning from 1985 to 2015. This paper analyzes the cooperative innovation network structure, its spatiotemporal evolution and the triple helix relationship of government-industry-university (GIU) by using the social network analysis method and the triple helix theory as well as UCINet, ArcGIS and NetDraw. The empirical results show that:1) the number of the subjects of the electronic information industry GIU cooperative innovation network in Liaoning demonstrates a gradual increase from 1985 to 2015, with the same trend in concentration. In terms of its subject and its centrality, the universities have a higher position, and the industries have a lower position, while the status of the government is still unclear. 2) The cooperative innovation network presents a core-periphery structure, and the polarization effect of innovation subjects tends to be obvious. There is certain distance-decay regularity in the cooperative innovation network, and a strong geographical proximity to cooperative innovation. 3) The compactness shows a downward trend as a whole. In terms of the extent of participation, the industries are better than the government but worse than the universities. This means that the cooperative innovation network of GIU in the electronic information industry in Liaoning is in the initial stage of formation.
It is important to optimize the cooperative innovation network for the improvement of economic competence and innovative power. Based on the patent information services platform, we obtain invention patent data for the electronic information industry in Liaoning from 1985 to 2015. This paper analyzes the cooperative innovation network structure, its spatiotemporal evolution and the triple helix relationship of government-industry-university (GIU) by using the social network analysis method and the triple helix theory as well as UCINet, ArcGIS and NetDraw. The empirical results show that:1) the number of the subjects of the electronic information industry GIU cooperative innovation network in Liaoning demonstrates a gradual increase from 1985 to 2015, with the same trend in concentration. In terms of its subject and its centrality, the universities have a higher position, and the industries have a lower position, while the status of the government is still unclear. 2) The cooperative innovation network presents a core-periphery structure, and the polarization effect of innovation subjects tends to be obvious. There is certain distance-decay regularity in the cooperative innovation network, and a strong geographical proximity to cooperative innovation. 3) The compactness shows a downward trend as a whole. In terms of the extent of participation, the industries are better than the government but worse than the universities. This means that the cooperative innovation network of GIU in the electronic information industry in Liaoning is in the initial stage of formation.
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