2018年 第28卷 第6期
2018, 28(6): 907-919.
doi: 10.1007/s11769-018-1002-2
摘要:
Vegetation is the main component of the terrestrial ecosystem and plays a key role in global climate change. Remotely sensed vegetation indices are widely used to detect vegetation trends at large scales. To understand the trends of vegetation cover, this research examined the spatial-temporal trends of global vegetation by employing the normalized difference vegetation index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) time series (1982-2015). Ten samples were selected to test the temporal trend of NDVI, and the results show that in arid and semi-arid regions, NDVI showed a deceasing trend, while it showed a growing trend in other regions. Mann-Kendal (MK) trend test results indicate that 83.37% of NDVI pixels exhibited positive trends and that only 16.63% showed negative trends (P < 0.05) during the period from 1982 to 2015. The increasing NDVI trends primarily occurred in tree-covered regions because of forest growth and re-growth and also because of vegetation succession after a forest disturbance. The increasing trend of the NDVI in cropland regions was primarily because of the increasing cropland area and the improvement in planting techniques. This research describes the spatial vegetation trends at a global scale over the past 30+ years, especially for different land cover types.
Vegetation is the main component of the terrestrial ecosystem and plays a key role in global climate change. Remotely sensed vegetation indices are widely used to detect vegetation trends at large scales. To understand the trends of vegetation cover, this research examined the spatial-temporal trends of global vegetation by employing the normalized difference vegetation index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) time series (1982-2015). Ten samples were selected to test the temporal trend of NDVI, and the results show that in arid and semi-arid regions, NDVI showed a deceasing trend, while it showed a growing trend in other regions. Mann-Kendal (MK) trend test results indicate that 83.37% of NDVI pixels exhibited positive trends and that only 16.63% showed negative trends (P < 0.05) during the period from 1982 to 2015. The increasing NDVI trends primarily occurred in tree-covered regions because of forest growth and re-growth and also because of vegetation succession after a forest disturbance. The increasing trend of the NDVI in cropland regions was primarily because of the increasing cropland area and the improvement in planting techniques. This research describes the spatial vegetation trends at a global scale over the past 30+ years, especially for different land cover types.
2018, 28(6): 920-934.
doi: 10.1007/s11769-018-0985-z
摘要:
Studies on long-term change of cropland is of great significance to the utilization of land resources and the implementation of scientific agricultural policies. The Korean Peninsula, adjacent to China, plays an important role in the international environment of Northeast Asia. The Korean Peninsula includes South Korea and North Korea-two countries that have a great difference in their institutions and economic developments. Therefore, we aim to quantify the spatiotemporal changes of croplands in these two countries using Landsat Thematic Imager (TM) and Operational Land Imager (OLI) imagery, and to compare the differences of cropland changes between the two countries. This paper take full advantage of ODM approach (object-oriented segmentation and decision-tree classification based on multi-season imageries) to obtain the distribution of croplands in 1990 and 2015. Results showed that the overall classification accuracy of cropland data is 91.10% in 1990 and 92.52% in 2015. The croplands were mainly distributed in areas with slopes that were less than 8° and with elevations that were less than 300 m in the Korean Peninsula. However, in other region (slope > 8° or elevation > 300 m), the area and proportion of North Korea's croplands were significantly higher than that of South Korea. Croplands significantly increased by 15.02% in North Korea from 1990 to 2015. In contrast, croplands in South Korea slightly decreased by 1.32%. During the 25 years, policy shift, economic development, population growth, and urban sprawl played primary roles for cropland changes. Additionally, the regional differences of cropland changes were mainly due to different agriculture policies implemented by different countries. The achievements of this study can provide scientific guidance for the protection and sustainability of land resources.
Studies on long-term change of cropland is of great significance to the utilization of land resources and the implementation of scientific agricultural policies. The Korean Peninsula, adjacent to China, plays an important role in the international environment of Northeast Asia. The Korean Peninsula includes South Korea and North Korea-two countries that have a great difference in their institutions and economic developments. Therefore, we aim to quantify the spatiotemporal changes of croplands in these two countries using Landsat Thematic Imager (TM) and Operational Land Imager (OLI) imagery, and to compare the differences of cropland changes between the two countries. This paper take full advantage of ODM approach (object-oriented segmentation and decision-tree classification based on multi-season imageries) to obtain the distribution of croplands in 1990 and 2015. Results showed that the overall classification accuracy of cropland data is 91.10% in 1990 and 92.52% in 2015. The croplands were mainly distributed in areas with slopes that were less than 8° and with elevations that were less than 300 m in the Korean Peninsula. However, in other region (slope > 8° or elevation > 300 m), the area and proportion of North Korea's croplands were significantly higher than that of South Korea. Croplands significantly increased by 15.02% in North Korea from 1990 to 2015. In contrast, croplands in South Korea slightly decreased by 1.32%. During the 25 years, policy shift, economic development, population growth, and urban sprawl played primary roles for cropland changes. Additionally, the regional differences of cropland changes were mainly due to different agriculture policies implemented by different countries. The achievements of this study can provide scientific guidance for the protection and sustainability of land resources.
2018, 28(6): 935-945.
doi: 10.1007/s11769-018-1003-1
摘要:
Wetlands on the Qinghai-Tibetan Plateau (QTP) perform a dazzling array of vital ecological functions and are one of the most fragile ecosystems in the world. Timely and accurate information describing wetland resources and their changes over time is becoming more important in their protection and conservation. By using remote sensing data, this study intended to investigate spatial distribution and temporal variations of wetlands on the QTP at different watershed scales from 1970s to 2010s. Results show that wetlands on the QTP have undergone widespread degradation from 1970s to 2010s, with nearly 6.4% of their area being lost. Areas of freshwater marsh, salt marsh and wet meadow declined by 46.6%, 53.9% and 15.6%, respectively, while lake area increased by 14.6%. The most extensive losses of natural wetlands have occurred in endorheic basins, such as in the Kunlun-Altun-Qilian Drainage Basin and Qiangtang Basin, which shrank by 44.5% and 33.1%, respectively. A pronounced increase in temperature tends to facilitate the evaporation process and reduce water availability for wetlands. One-third of the wetlands on the QTP are under threat of being submerged due to lakes rising in recent years. More research is needed to gain insight into the interaction mechanisms behind observed variations and potential impacts from further warming in the future.
Wetlands on the Qinghai-Tibetan Plateau (QTP) perform a dazzling array of vital ecological functions and are one of the most fragile ecosystems in the world. Timely and accurate information describing wetland resources and their changes over time is becoming more important in their protection and conservation. By using remote sensing data, this study intended to investigate spatial distribution and temporal variations of wetlands on the QTP at different watershed scales from 1970s to 2010s. Results show that wetlands on the QTP have undergone widespread degradation from 1970s to 2010s, with nearly 6.4% of their area being lost. Areas of freshwater marsh, salt marsh and wet meadow declined by 46.6%, 53.9% and 15.6%, respectively, while lake area increased by 14.6%. The most extensive losses of natural wetlands have occurred in endorheic basins, such as in the Kunlun-Altun-Qilian Drainage Basin and Qiangtang Basin, which shrank by 44.5% and 33.1%, respectively. A pronounced increase in temperature tends to facilitate the evaporation process and reduce water availability for wetlands. One-third of the wetlands on the QTP are under threat of being submerged due to lakes rising in recent years. More research is needed to gain insight into the interaction mechanisms behind observed variations and potential impacts from further warming in the future.
2018, 28(6): 946-956.
doi: 10.1007/s11769-018-1004-0
摘要:
The dynamics of snow cover differs greatly from basin to basin in the Songhua River of Northeast China, which is attributable to the differences in the topographic shift as well as changes in the vegetation and climate since the hydrological year (HY) 2003. Daily and flexible multi-day combinations from the HY 2003 to 2014 were produced using Moderate Resolution Imaging Spectroradiometer (MODIS) from Terra and Aqua remote sensing satellites for the snow cover products in the three basins including the Nenjiang River Basin (NJ), Downstream Songhua River Basin (SD) and Upstream Songhua River Basin (SU). Snow cover duration (SCD) was derived from flexible multiday combination each year. The results showed that SCD was significantly associated with elevation, and higher SCD values were found out in the mountainous areas. Further, the average SCDs of NJ, SU and SD basins were 69.43, 98.14 and 88.84 d with an annual growth of 1.36, 2.04 and 2.71 d, respectively. Binary decision tree was used to analyze the nonlinear relationships between SCD and six impact factors, which were successfully applied to simulate the spatial distribution of depth and water equivalent of snow. The impact factors included three topographic factors (elevation, aspect and slope), two climatic factors (precipitation and air temperature) and one vegetation index (Normalized Difference Vegetation Index, NDVI). By treating yearly SCD values as dependent variables and six climatic factors as independent variables, six binary decision trees were built through the combination classification and regression tree (CART) with and without the consideration of climate effect. The results from the model show that elevation, precipitation and air temperature are the three most influential factors, among which air temperature is the most important and ranks first in two of the three studied basins. It is suggested that SCD in the mountainous areas might be more sensitive to climate warming, since precipitation and air temperature are the major factors controlling the persistence of snow cover in the mountainous areas.
The dynamics of snow cover differs greatly from basin to basin in the Songhua River of Northeast China, which is attributable to the differences in the topographic shift as well as changes in the vegetation and climate since the hydrological year (HY) 2003. Daily and flexible multi-day combinations from the HY 2003 to 2014 were produced using Moderate Resolution Imaging Spectroradiometer (MODIS) from Terra and Aqua remote sensing satellites for the snow cover products in the three basins including the Nenjiang River Basin (NJ), Downstream Songhua River Basin (SD) and Upstream Songhua River Basin (SU). Snow cover duration (SCD) was derived from flexible multiday combination each year. The results showed that SCD was significantly associated with elevation, and higher SCD values were found out in the mountainous areas. Further, the average SCDs of NJ, SU and SD basins were 69.43, 98.14 and 88.84 d with an annual growth of 1.36, 2.04 and 2.71 d, respectively. Binary decision tree was used to analyze the nonlinear relationships between SCD and six impact factors, which were successfully applied to simulate the spatial distribution of depth and water equivalent of snow. The impact factors included three topographic factors (elevation, aspect and slope), two climatic factors (precipitation and air temperature) and one vegetation index (Normalized Difference Vegetation Index, NDVI). By treating yearly SCD values as dependent variables and six climatic factors as independent variables, six binary decision trees were built through the combination classification and regression tree (CART) with and without the consideration of climate effect. The results from the model show that elevation, precipitation and air temperature are the three most influential factors, among which air temperature is the most important and ranks first in two of the three studied basins. It is suggested that SCD in the mountainous areas might be more sensitive to climate warming, since precipitation and air temperature are the major factors controlling the persistence of snow cover in the mountainous areas.
2018, 28(6): 957-972.
doi: 10.1007/s11769-018-1005-z
摘要:
Remote sensing data have been widely applied to extract minerals in geologic exploration, however, in areas covered by vegetation, extracted mineral information has mostly been small targets bearing little information. In this paper, we present a new method for mineral extraction aimed at solving the difficulty of mineral identification in vegetation covered areas. The method selected six sets of spectral difference coupling between soil and plant (SVSCD). These sets have the same vegetation spectra reflectance and a maximum different reflectance of soil and mineral spectra from Hyperion image based on spectral reflectance characteristics of measured spectra. The central wavelengths of the six, selected band pairs were 2314 and 701 nm, 1699 and 721 nm, 1336 and 742 nm, 2203 and 681 nm, 2183 and 671 nm, and 2072 and 548 nm. Each data set's reflectance was used to calculate the difference value. After band difference calculation, vegetation information was suppressed and mineral abnormal information was enhanced compared to the scatter plot of original band. Six spectral difference couplings, after vegetation inhibition, were arranged in a new data set that requires two components that have the largest eigenvalue difference from principal component analysis (PCA). The spatial geometric structure features of PC1 and PC2 was used to identify altered minerals by spectral feature fitting (SFF). The collecting rocks from the 10 points that were selected in the concentration of mineral extraction were analyzed under a high-resolution microscope to identify metal minerals and nonmetallic minerals. Results indicated that the extracted minerals were well matched with the verified samples, especially with the sample 2, 4, 5 and 8. It demonstrated that the method can effectively detect altered minerals in vegetation covered area in Hyperion image.
Remote sensing data have been widely applied to extract minerals in geologic exploration, however, in areas covered by vegetation, extracted mineral information has mostly been small targets bearing little information. In this paper, we present a new method for mineral extraction aimed at solving the difficulty of mineral identification in vegetation covered areas. The method selected six sets of spectral difference coupling between soil and plant (SVSCD). These sets have the same vegetation spectra reflectance and a maximum different reflectance of soil and mineral spectra from Hyperion image based on spectral reflectance characteristics of measured spectra. The central wavelengths of the six, selected band pairs were 2314 and 701 nm, 1699 and 721 nm, 1336 and 742 nm, 2203 and 681 nm, 2183 and 671 nm, and 2072 and 548 nm. Each data set's reflectance was used to calculate the difference value. After band difference calculation, vegetation information was suppressed and mineral abnormal information was enhanced compared to the scatter plot of original band. Six spectral difference couplings, after vegetation inhibition, were arranged in a new data set that requires two components that have the largest eigenvalue difference from principal component analysis (PCA). The spatial geometric structure features of PC1 and PC2 was used to identify altered minerals by spectral feature fitting (SFF). The collecting rocks from the 10 points that were selected in the concentration of mineral extraction were analyzed under a high-resolution microscope to identify metal minerals and nonmetallic minerals. Results indicated that the extracted minerals were well matched with the verified samples, especially with the sample 2, 4, 5 and 8. It demonstrated that the method can effectively detect altered minerals in vegetation covered area in Hyperion image.
2018, 28(6): 973-985.
doi: 10.1007/s11769-018-1006-y
摘要:
Understanding the effects of land cover changes on ecosystem carbon stocks is essential for ecosystem management and environmental protection, particularly in the transboundary region that has undergone marked changes. This study aimed to examine the impacts of land cover changes on ecosystem carbon stocks in the transboundary Tumen River Basin (TTRB). We extracted the spatial information from Landsat Thematic Imager (TM) and Operational Land Imager (OLI) images for the years 1990 and 2015 and obtained convincing estimates of terrestrial biomass and soil carbon stocks with the InVEST model. The results showed that forestland, cropland and built-up land increased by 57.5, 429.7 and 128.9 km2, respectively, while grassland, wetland and barren land declined by 24.9, 548.0 and 43.0 km2, respectively in the TTRB from 1990 to 2015. The total carbon stocks encompassing aboveground, belowground, soil and litter layer carbon storage pools have declined from 831.48 Tg C in 1990 to 831.42 Tg C in 2015 due to land cover changes. In detail, the carbon stocks decreased by 3.13 Tg C and 0.44 Tg C in Democratic People's Republic of Korea (North Korea) and Russia, respectively, while increased by 3.51 Tg C in China. Furthermore, economic development, and national policy accounted for most land cover changes in the TTRB. Our results imply that effective wetland and forestland protection policies among China, North Korea, and Russia are much needed for protecting the natural resources, promoting local ecosystem services and regional sustainable development in the transnational area.
Understanding the effects of land cover changes on ecosystem carbon stocks is essential for ecosystem management and environmental protection, particularly in the transboundary region that has undergone marked changes. This study aimed to examine the impacts of land cover changes on ecosystem carbon stocks in the transboundary Tumen River Basin (TTRB). We extracted the spatial information from Landsat Thematic Imager (TM) and Operational Land Imager (OLI) images for the years 1990 and 2015 and obtained convincing estimates of terrestrial biomass and soil carbon stocks with the InVEST model. The results showed that forestland, cropland and built-up land increased by 57.5, 429.7 and 128.9 km2, respectively, while grassland, wetland and barren land declined by 24.9, 548.0 and 43.0 km2, respectively in the TTRB from 1990 to 2015. The total carbon stocks encompassing aboveground, belowground, soil and litter layer carbon storage pools have declined from 831.48 Tg C in 1990 to 831.42 Tg C in 2015 due to land cover changes. In detail, the carbon stocks decreased by 3.13 Tg C and 0.44 Tg C in Democratic People's Republic of Korea (North Korea) and Russia, respectively, while increased by 3.51 Tg C in China. Furthermore, economic development, and national policy accounted for most land cover changes in the TTRB. Our results imply that effective wetland and forestland protection policies among China, North Korea, and Russia are much needed for protecting the natural resources, promoting local ecosystem services and regional sustainable development in the transnational area.
2018, 28(6): 986-997.
doi: 10.1007/s11769-018-1007-x
摘要:
Soil surface roughness, denoted by the root mean square height (RMSH), and soil moisture (SM) are critical factors that affect the accuracy of quantitative remote sensing research due to their combined influence on spectral reflectance (SR). In regards to this issue, three SM levels and four RMSH levels were artificially designed in this study; a total of 12 plots was used, each plot had a size of 3 m×3 m. Eight spectral observations were conducted from 14 to 30 October 2017 to investigate the correlation between RMSH, SM, and SR. On this basis, 6 commonly used bands of optical satellite sensors were selected in this study, which are red (675 nm), green (555 nm), blue (485 nm), near infrared (845 nm), shortwave infrared 1 (1600 nm), and shortwave infrared 2 (2200 nm). A negative correlation was found between SR and RMSH, and between SR and SM. The bands with higher coefficient of determination R2 values were selected for stepwise multiple nonlinear regression analysis. Four characterized bands (i.e., blue, green, near infrared, and shortwave infrared 2) were chosen as the independent variables to estimate SM with R2 and root mean square error (RMSE) values equal to 0.62 and 2.6%, respectively. Similarly, the four bands (green, red, near infrared, and shortwave infrared 1) were used to estimate RMSH with R2 and RMSE values equal to 0.48 and 0.69 cm, respectively. These results indicate that the method used is not only suitable for estimating SM but can also be extended to the prediction of RMSH. Finally, the evaluation approach presented in this paper highly restores the real situation of the natural farmland surface on the one hand, and obtains high precision values of SM and RMSH on the other. The method can be further applied to the prediction of farmland SM and RMSH based on satellite and unmanned aerial vehicle (UAV) optical imagery.
Soil surface roughness, denoted by the root mean square height (RMSH), and soil moisture (SM) are critical factors that affect the accuracy of quantitative remote sensing research due to their combined influence on spectral reflectance (SR). In regards to this issue, three SM levels and four RMSH levels were artificially designed in this study; a total of 12 plots was used, each plot had a size of 3 m×3 m. Eight spectral observations were conducted from 14 to 30 October 2017 to investigate the correlation between RMSH, SM, and SR. On this basis, 6 commonly used bands of optical satellite sensors were selected in this study, which are red (675 nm), green (555 nm), blue (485 nm), near infrared (845 nm), shortwave infrared 1 (1600 nm), and shortwave infrared 2 (2200 nm). A negative correlation was found between SR and RMSH, and between SR and SM. The bands with higher coefficient of determination R2 values were selected for stepwise multiple nonlinear regression analysis. Four characterized bands (i.e., blue, green, near infrared, and shortwave infrared 2) were chosen as the independent variables to estimate SM with R2 and root mean square error (RMSE) values equal to 0.62 and 2.6%, respectively. Similarly, the four bands (green, red, near infrared, and shortwave infrared 1) were used to estimate RMSH with R2 and RMSE values equal to 0.48 and 0.69 cm, respectively. These results indicate that the method used is not only suitable for estimating SM but can also be extended to the prediction of RMSH. Finally, the evaluation approach presented in this paper highly restores the real situation of the natural farmland surface on the one hand, and obtains high precision values of SM and RMSH on the other. The method can be further applied to the prediction of farmland SM and RMSH based on satellite and unmanned aerial vehicle (UAV) optical imagery.
2018, 28(6): 998-1008.
doi: 10.1007/s11769-018-0996-9
摘要:
Global research progress on coastal flooding was studied using a bibliometric evaluation of publications listed in the Web of Science extended scientific citation index. There was substantial growth in coastal flooding research output, with increasing publications, a higher collaboration index, and more references during the 1995-2016 period. The USA has taken a dominant position in coastal flooding research, with the US Geological Survey leading the publications ranking. Research collaborations at institutional scales have become more important than those at global scales. International collaborative publications consistently drew more citations than those from a single country. Furthermore, coastal flooding research included combinations of multi-disciplinary categories, including ‘Geology’ and ‘Environmental Sciences & Ecology’. The most important coastal flooding research sites were wetlands and estuaries. While numerical modeling and 3S (Remote sensing, RS; Geography information systems, GIS; Global positioning systems, GPS) technology were the most commonly used methods for studying coastal flooding, Lidar gained in popularity. The vulnerability and adaptation of coastal environments, their resilience after flooding, and ecosystem services function showed increases in interest.
Global research progress on coastal flooding was studied using a bibliometric evaluation of publications listed in the Web of Science extended scientific citation index. There was substantial growth in coastal flooding research output, with increasing publications, a higher collaboration index, and more references during the 1995-2016 period. The USA has taken a dominant position in coastal flooding research, with the US Geological Survey leading the publications ranking. Research collaborations at institutional scales have become more important than those at global scales. International collaborative publications consistently drew more citations than those from a single country. Furthermore, coastal flooding research included combinations of multi-disciplinary categories, including ‘Geology’ and ‘Environmental Sciences & Ecology’. The most important coastal flooding research sites were wetlands and estuaries. While numerical modeling and 3S (Remote sensing, RS; Geography information systems, GIS; Global positioning systems, GPS) technology were the most commonly used methods for studying coastal flooding, Lidar gained in popularity. The vulnerability and adaptation of coastal environments, their resilience after flooding, and ecosystem services function showed increases in interest.
2018, 28(6): 1009-1026.
doi: 10.1007/s11769-018-0937-7
摘要:
Social psychology of people affected by hazards is different from normal psychology. For example, severe bank erosion in the lower reach of the Bhagirathi River in West Bengal has resulted in significant land loss (~60% of all households lost land over last 20 years) and affected the livelihoods of the people in the study villages along the river. Per capita income has almost halved from 1970-2012 due to land loss. This stark nature of land erosion and vulnerability of livelihood has had far-reaching repercussions on the fabric of society and the psychology of the people in this region. Results showed that erosion-affected villages have registered comparatively larger average family sizes (~4.1 as compared to ~3.9 in non-affected villages), lower literacy levels (< 50% compared to > 65% for the non-affected villages), and poor health. Reports of poor health as a result of land erosion include~60% of the respondents having reported physical ailments such as headache and abdominal discomfort, as well as 3%-5% reporting loss of emotional and psychological balance. Villages suffering from erosion showed higher positive loadings in average-coefficient of variation (CV) differential (25%-40%) depicting objectivity in their opinions for select variables of social processes. Principal component analysis (PCA) portrayed maximum eigenvalues in the first principal component for interpersonal processes (~98%) and a minimum for intergroup processes (~80%). Categorical principal component analysis (CATPCA) depicted a cluster between interpersonal and intergroup processes and another between intra-individual and group categories. The positive loadings in female-male differences in CV of perceptions portrayed relative consistency of males over the females concerning fear/phobia and physical stress while negative loadings exhibited higher consistency for females regarding psychological stress and shock. Lastly, the Tajfel matrix portrayed a distinction between hazard psychology characterized by maximum joint profit as found in Rukunpur, and normal psychology characterized by in-group favoritism as found in Matiari.
Social psychology of people affected by hazards is different from normal psychology. For example, severe bank erosion in the lower reach of the Bhagirathi River in West Bengal has resulted in significant land loss (~60% of all households lost land over last 20 years) and affected the livelihoods of the people in the study villages along the river. Per capita income has almost halved from 1970-2012 due to land loss. This stark nature of land erosion and vulnerability of livelihood has had far-reaching repercussions on the fabric of society and the psychology of the people in this region. Results showed that erosion-affected villages have registered comparatively larger average family sizes (~4.1 as compared to ~3.9 in non-affected villages), lower literacy levels (< 50% compared to > 65% for the non-affected villages), and poor health. Reports of poor health as a result of land erosion include~60% of the respondents having reported physical ailments such as headache and abdominal discomfort, as well as 3%-5% reporting loss of emotional and psychological balance. Villages suffering from erosion showed higher positive loadings in average-coefficient of variation (CV) differential (25%-40%) depicting objectivity in their opinions for select variables of social processes. Principal component analysis (PCA) portrayed maximum eigenvalues in the first principal component for interpersonal processes (~98%) and a minimum for intergroup processes (~80%). Categorical principal component analysis (CATPCA) depicted a cluster between interpersonal and intergroup processes and another between intra-individual and group categories. The positive loadings in female-male differences in CV of perceptions portrayed relative consistency of males over the females concerning fear/phobia and physical stress while negative loadings exhibited higher consistency for females regarding psychological stress and shock. Lastly, the Tajfel matrix portrayed a distinction between hazard psychology characterized by maximum joint profit as found in Rukunpur, and normal psychology characterized by in-group favoritism as found in Matiari.
2018, 28(6): 1027-1037.
doi: 10.1007/s11769-018-0999-6
摘要:
To investigate the spatio-temporal and compositional variation of selected water quality parameters and understand the purifying effects of wetland in Fujin National Wetland Park (FNWP), China, the trophic level index (TLI), paired samples t-test and correlation analysis were used for the statistical analysis of a set of 10 water quality parameters. The analyses were based on water samples collected from 22 stations in FNWP between 2014 and 2016. Results initially reveal that total nitrogen (TN) concentrations are above class V levels (2 mg/L), total phosphorus (TP) concentrations are below class Ⅲ levels (0.2 mg/L), and that all other parameters fall within standard ranges. Highest values for TN, pH, and Chlorophyll-a were recorded in 2016, while the levels of chemical oxygen demand (CODMn) and biochemical oxygen demand (BOD5) were lowest during this year. Similarly, TN values were highest between 2014 and 2016 while dissolved oxygen (DO) concentrations were lowest in the summer and TP concentrations were highest in the autumn. Significant variations were also found in Secchi depth (SD), TN, CODMn (P < 0.01), TP, and DO levels (P < 0.05) between the inlet and outlet of the park. High-to-low levels of TN, TP, and TDS were found in cattails, reeds, and open water (the opposite trend was seen in SD levels). Tested wetland water had a light eutrophication status in most cases and TN and TP removal rates were between 7.54%-84.36% and 37.50%-70.83%, respectively. Data also show no significant annual changes in water quality within this wetland, although obvious affects from surrounding agricultural drainage were nevertheless recorded. Results reveal a high major nutrient removal efficiency (N and P). The upper limits of these phenomena should be addressed in future research alongside a more efficient and scientific agricultural layout for the regions in and around the FNWP.
To investigate the spatio-temporal and compositional variation of selected water quality parameters and understand the purifying effects of wetland in Fujin National Wetland Park (FNWP), China, the trophic level index (TLI), paired samples t-test and correlation analysis were used for the statistical analysis of a set of 10 water quality parameters. The analyses were based on water samples collected from 22 stations in FNWP between 2014 and 2016. Results initially reveal that total nitrogen (TN) concentrations are above class V levels (2 mg/L), total phosphorus (TP) concentrations are below class Ⅲ levels (0.2 mg/L), and that all other parameters fall within standard ranges. Highest values for TN, pH, and Chlorophyll-a were recorded in 2016, while the levels of chemical oxygen demand (CODMn) and biochemical oxygen demand (BOD5) were lowest during this year. Similarly, TN values were highest between 2014 and 2016 while dissolved oxygen (DO) concentrations were lowest in the summer and TP concentrations were highest in the autumn. Significant variations were also found in Secchi depth (SD), TN, CODMn (P < 0.01), TP, and DO levels (P < 0.05) between the inlet and outlet of the park. High-to-low levels of TN, TP, and TDS were found in cattails, reeds, and open water (the opposite trend was seen in SD levels). Tested wetland water had a light eutrophication status in most cases and TN and TP removal rates were between 7.54%-84.36% and 37.50%-70.83%, respectively. Data also show no significant annual changes in water quality within this wetland, although obvious affects from surrounding agricultural drainage were nevertheless recorded. Results reveal a high major nutrient removal efficiency (N and P). The upper limits of these phenomena should be addressed in future research alongside a more efficient and scientific agricultural layout for the regions in and around the FNWP.
2018, 28(6): 1038-1047.
doi: 10.1007/s11769-018-1000-4
摘要:
To examine the effects of microtopography on the stoichiometry of carbon (C), nitrogen (N) and phosphorus (P) in mosses along the hummock-hollow gradient in boreal peatlands, we investigated species-level C:N, C:P and N:P ratios of five mosses (Sphagnum magellanicum, S. perichaetiale, S. palustre, S. girgensohnii and Aulacomnium palustre) in the hummocks, hollows and their intermediate zones, and then assessed community-level spatial patterns in a boreal ombrotrophic peatland of north of the Great Xing'an Mountain, Northeast China. The results show that at the species level, C:N, C:P and N:P ratios of the selected Sphagnum mosses remained stable in the hummock-hollow complexes due to unchanged C, N and P concentrations, whereas the non-Sphagnum moss (A. palustre) in the hummocks and intermediate zones had lower P concentrations and thus greater C:P ratios than that in the hollows. At the community level, moss N concentration and C:N ratio remained constant along the hummock-hollow gradient, whereas hummocks and intermediate zones had higher community-level moss C:P and N:P ratios than hollows because of greater C and lower P concentrations. These findings imply that the effects of microtopography on moss C:N:P stoichiometry are scale-dependent and reveal spatial heterogeneity in C and nutrient dynamics. These results provide a more comprehensive understanding of biogeochemical cycles in boreal peatlands.
To examine the effects of microtopography on the stoichiometry of carbon (C), nitrogen (N) and phosphorus (P) in mosses along the hummock-hollow gradient in boreal peatlands, we investigated species-level C:N, C:P and N:P ratios of five mosses (Sphagnum magellanicum, S. perichaetiale, S. palustre, S. girgensohnii and Aulacomnium palustre) in the hummocks, hollows and their intermediate zones, and then assessed community-level spatial patterns in a boreal ombrotrophic peatland of north of the Great Xing'an Mountain, Northeast China. The results show that at the species level, C:N, C:P and N:P ratios of the selected Sphagnum mosses remained stable in the hummock-hollow complexes due to unchanged C, N and P concentrations, whereas the non-Sphagnum moss (A. palustre) in the hummocks and intermediate zones had lower P concentrations and thus greater C:P ratios than that in the hollows. At the community level, moss N concentration and C:N ratio remained constant along the hummock-hollow gradient, whereas hummocks and intermediate zones had higher community-level moss C:P and N:P ratios than hollows because of greater C and lower P concentrations. These findings imply that the effects of microtopography on moss C:N:P stoichiometry are scale-dependent and reveal spatial heterogeneity in C and nutrient dynamics. These results provide a more comprehensive understanding of biogeochemical cycles in boreal peatlands.
2018, 28(6): 1048-1060.
doi: 10.1007/s11769-018-0982-2
摘要:
With the increasing number of vehicles in large-and medium-sized cities challenges in urban traffic management, control, and road planning are being faced. Taxi GPS trajectory data is a novel data source that can be used to study the potential dynamic traffic characteristics of urban roads, and thus identify locations that show a notable lack of road planning. Considering that road traffic characteristics on their own are insufficient for a comprehensive understanding of urban traffic, we develop a road traffic characteristic time series clustering model to analyze the relationship between urban road traffic characteristics and road grade based on existing taxi trajectory data. We select the main urban area of Nanjing as our study area and use the taxi trajectory data of a single month for evaluating our method. The experiments show that the clustering model exhibit good performance and can be successfully used for road traffic characteristic classification. Moreover, we analyze the correlation between traffic characteristics and road grade to identify road segments with planning designs that do not match the actual traffic demands.
With the increasing number of vehicles in large-and medium-sized cities challenges in urban traffic management, control, and road planning are being faced. Taxi GPS trajectory data is a novel data source that can be used to study the potential dynamic traffic characteristics of urban roads, and thus identify locations that show a notable lack of road planning. Considering that road traffic characteristics on their own are insufficient for a comprehensive understanding of urban traffic, we develop a road traffic characteristic time series clustering model to analyze the relationship between urban road traffic characteristics and road grade based on existing taxi trajectory data. We select the main urban area of Nanjing as our study area and use the taxi trajectory data of a single month for evaluating our method. The experiments show that the clustering model exhibit good performance and can be successfully used for road traffic characteristic classification. Moreover, we analyze the correlation between traffic characteristics and road grade to identify road segments with planning designs that do not match the actual traffic demands.
2018, 28(6): 1061-1076.
doi: 10.1007/s11769-018-1008-9
摘要:
Rural poverty and poverty reduction are not only the focal issues that have attracted worldwide attention, but also the vital issues on people's livelihood that has attached great importance and aimed to be solved by the central and local governments of China. Based on the survey data of 354 farming households, this paper, taking the national poverty county of Lingao County, Hainan Province for an example, examined the characteristics of rural poverty of the county. Moreover, this paper established the spatial lag model (SLM) from five dimensions, namely, status of the household head, household structure, health status, income composition and traffic accessibility, to analyze the main influencing factors of rural poverty according to the values of Moran's I and the diagnosis of spatial dependence of the OLS model. It is found that the poor farming households gathered mainly in five towns in the north and southwest of the county, and the rural poverty have the characteristics of low educational level of the heads, more minor children, high population of farming peasants, high incidence of disease and low proportion of household wage-equivalent income. The results also showed that the variables such as the number of minor children, the number of migrant worker, the number of farming peasants and the proportion of wage-equivalent income have significant effectiveness on rural poverty, while the status of the household head, health status and traffic accessibility have little influence. It is an important way to realize the goal of poverty alleviation by controlling the number of farmers' fertility, strengthening the vocational skills training of farmers, vigorously developing specialization and large-scale agriculture and increasing the employment opportunities of farmers.
Rural poverty and poverty reduction are not only the focal issues that have attracted worldwide attention, but also the vital issues on people's livelihood that has attached great importance and aimed to be solved by the central and local governments of China. Based on the survey data of 354 farming households, this paper, taking the national poverty county of Lingao County, Hainan Province for an example, examined the characteristics of rural poverty of the county. Moreover, this paper established the spatial lag model (SLM) from five dimensions, namely, status of the household head, household structure, health status, income composition and traffic accessibility, to analyze the main influencing factors of rural poverty according to the values of Moran's I and the diagnosis of spatial dependence of the OLS model. It is found that the poor farming households gathered mainly in five towns in the north and southwest of the county, and the rural poverty have the characteristics of low educational level of the heads, more minor children, high population of farming peasants, high incidence of disease and low proportion of household wage-equivalent income. The results also showed that the variables such as the number of minor children, the number of migrant worker, the number of farming peasants and the proportion of wage-equivalent income have significant effectiveness on rural poverty, while the status of the household head, health status and traffic accessibility have little influence. It is an important way to realize the goal of poverty alleviation by controlling the number of farmers' fertility, strengthening the vocational skills training of farmers, vigorously developing specialization and large-scale agriculture and increasing the employment opportunities of farmers.
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