2017 Vol. 27, No. 4
Display Method:
2017, 27(4): 507-515.
doi: 10.1007/s11769-017-0884-8
Abstract:
Inspired by the importance of Redfield-type C:N:P ratios in global soils, we looked for analogous patterns in peatlands and aimed at deciphering the potential affecting factors. By analyzing a suite of peatlands soil data (n = 1031), mean soil organic carbon (SOC), total nitrogen (TN) and total phosphorous (TP) contents were 50.51%, 1.45% and 0.13%, respectively, while average C:N, C:P and N:P ratios were 26.72, 1186.00 and 46.58, respectively. C:N ratios showed smaller variations across different vegetation coverage and had less spatial heterogeneity than C:P and N:P ratios. No consistent C:N:P ratio, though with a general value of 1245:47:1, was found for entire peatland soils in China. The Northeast China, Tibet, Zoigê Plateau and parts of Xinjiang had high soil SOC, TN, TP, and C:P ratio. Qinghai, parts of the lower reaches of the Yangtze River, and the coast zones have low TP and N:P ratio. Significant differences for SOC, TN, TP, C:N, C:P and N:P ratios were observed across groups categorized by predominant vegetation. Moisture, temperature and precipitation all closely related to SOC, TN, TP and their pairwise ratios. The hydrothermal coefficient (RH), defined as annual average precipitation divided by temperature, positively and significantly related to C:N, C:P and N:P ratios, implying that ongoing climate change may prejudice peatlands as carbon sinks during the past 50 years in China.
Inspired by the importance of Redfield-type C:N:P ratios in global soils, we looked for analogous patterns in peatlands and aimed at deciphering the potential affecting factors. By analyzing a suite of peatlands soil data (n = 1031), mean soil organic carbon (SOC), total nitrogen (TN) and total phosphorous (TP) contents were 50.51%, 1.45% and 0.13%, respectively, while average C:N, C:P and N:P ratios were 26.72, 1186.00 and 46.58, respectively. C:N ratios showed smaller variations across different vegetation coverage and had less spatial heterogeneity than C:P and N:P ratios. No consistent C:N:P ratio, though with a general value of 1245:47:1, was found for entire peatland soils in China. The Northeast China, Tibet, Zoigê Plateau and parts of Xinjiang had high soil SOC, TN, TP, and C:P ratio. Qinghai, parts of the lower reaches of the Yangtze River, and the coast zones have low TP and N:P ratio. Significant differences for SOC, TN, TP, C:N, C:P and N:P ratios were observed across groups categorized by predominant vegetation. Moisture, temperature and precipitation all closely related to SOC, TN, TP and their pairwise ratios. The hydrothermal coefficient (RH), defined as annual average precipitation divided by temperature, positively and significantly related to C:N, C:P and N:P ratios, implying that ongoing climate change may prejudice peatlands as carbon sinks during the past 50 years in China.
2017, 27(4): 516-528.
doi: 10.1007/s11769-017-0869-7
Abstract:
The main aim of this paper was to calculate soil organic carbon stock (SOCS) with consideration of the pedogenetic horizons using expert knowledge and GIS-based methods in northeastern China. A novel prediction process was presented and was referred to as model-then-calculate with respect to the variable thicknesses of soil horizons (MCV). The model-then-calculate with fixed-thickness (MCF), soil profile statistics (SPS), pedological professional knowledge-based (PKB) and vegetation type-based (Veg) methods were carried out for comparison. With respect to the similar pedological information, nine common layers from topsoil to bedrock were grouped in the MCV. Validation results suggested that the MCV method generated better performance than the other methods considered. For the comparison of polygon based approaches, the Veg method generated better accuracy than both SPS and PKB, as limited soil data were incorporated. Additional prediction of the pedogenetic horizons within MCV benefitted the regional SOCS estimation and provided information for future soil classification and understanding of soil functions. The intermediate product, that is, horizon thickness maps were fluctuant enough and reflected many details in space. The linear mixed model indicated that mean annual air temperature (MAAT) was the most important predictor for the SOCS simulation. The minimal residual of the linear mixed models was achieved in the vegetation type-based model, whereas the maximal residual was fitted in the soil type-based model. About 95% of SOCS could be found in Argosols, Cambosols and Isohumosols. The largest SOCS was found in the croplands with vegetation of Triticum aestivum L., Sorghum bicolor (L.) Moench, Glycine max (L.) Merr., Zea mays L. and Setaria italica (L.) P. Beauv.
The main aim of this paper was to calculate soil organic carbon stock (SOCS) with consideration of the pedogenetic horizons using expert knowledge and GIS-based methods in northeastern China. A novel prediction process was presented and was referred to as model-then-calculate with respect to the variable thicknesses of soil horizons (MCV). The model-then-calculate with fixed-thickness (MCF), soil profile statistics (SPS), pedological professional knowledge-based (PKB) and vegetation type-based (Veg) methods were carried out for comparison. With respect to the similar pedological information, nine common layers from topsoil to bedrock were grouped in the MCV. Validation results suggested that the MCV method generated better performance than the other methods considered. For the comparison of polygon based approaches, the Veg method generated better accuracy than both SPS and PKB, as limited soil data were incorporated. Additional prediction of the pedogenetic horizons within MCV benefitted the regional SOCS estimation and provided information for future soil classification and understanding of soil functions. The intermediate product, that is, horizon thickness maps were fluctuant enough and reflected many details in space. The linear mixed model indicated that mean annual air temperature (MAAT) was the most important predictor for the SOCS simulation. The minimal residual of the linear mixed models was achieved in the vegetation type-based model, whereas the maximal residual was fitted in the soil type-based model. About 95% of SOCS could be found in Argosols, Cambosols and Isohumosols. The largest SOCS was found in the croplands with vegetation of Triticum aestivum L., Sorghum bicolor (L.) Moench, Glycine max (L.) Merr., Zea mays L. and Setaria italica (L.) P. Beauv.
2017, 27(4): 529-538.
doi: 10.1007/s11769-017-0885-7
Abstract:
Soil carbon to nitrogen (C/N) ratio is one of the most important variables reflecting soil quality and ecological function, and an indicator for assessing carbon and nitrogen nutrition balance of soils. Its variation reflects the carbon and nitrogen cycling of soils. In order to explore the spatial variability of soil C/N ratio and its controlling factors of the Ili River valley in Xinjiang Uygur Autonomous Region, Northwest China, the traditional statistical methods, including correlation analysis, geostatistic alanalys and multiple regression analysis were used. The statistical results showed that the soil C/N ratio varied from 7.00 to 23.11, with a mean value of 10.92, and the coefficient of variation was 31.3%. Correlation analysis showed that longitude, altitude, precipitation, soil water, organic carbon, and total nitrogen were positively correlated with the soil C/N ratio (P < 0.01), whereas negative correlations were found between the soil C/N ratio and latitude, temperature, soil bulk density and soil pH. Ordinary Cokriging interpolation showed that r and ME were 0.73 and 0.57, respectively, indicating that the prediction accuracy was high. The spatial autocorrelation of the soil C/N ratio was 6.4 km, and the nugget effect of the soil C/N ratio was 10% with a patchy distribution, in which the area with high value (12.00-20.41) accounted for 22.6% of the total area. Land uses changed the soil C/N ratio with the order of cultivated land > grass land > forest land > garden. Multiple regression analysis showed that geographical and climatic factors, and soil physical and chemical properties could independently explain 26.8%and 55.4% of the spatial features of soil C/N ratio, while human activities could independently explain 5.4% of the spatial features only. The spatial distribution of soil C/N ratio in the study has important reference value for managing soil carbon and nitrogen, and for improving ecological function to similar regions.
Soil carbon to nitrogen (C/N) ratio is one of the most important variables reflecting soil quality and ecological function, and an indicator for assessing carbon and nitrogen nutrition balance of soils. Its variation reflects the carbon and nitrogen cycling of soils. In order to explore the spatial variability of soil C/N ratio and its controlling factors of the Ili River valley in Xinjiang Uygur Autonomous Region, Northwest China, the traditional statistical methods, including correlation analysis, geostatistic alanalys and multiple regression analysis were used. The statistical results showed that the soil C/N ratio varied from 7.00 to 23.11, with a mean value of 10.92, and the coefficient of variation was 31.3%. Correlation analysis showed that longitude, altitude, precipitation, soil water, organic carbon, and total nitrogen were positively correlated with the soil C/N ratio (P < 0.01), whereas negative correlations were found between the soil C/N ratio and latitude, temperature, soil bulk density and soil pH. Ordinary Cokriging interpolation showed that r and ME were 0.73 and 0.57, respectively, indicating that the prediction accuracy was high. The spatial autocorrelation of the soil C/N ratio was 6.4 km, and the nugget effect of the soil C/N ratio was 10% with a patchy distribution, in which the area with high value (12.00-20.41) accounted for 22.6% of the total area. Land uses changed the soil C/N ratio with the order of cultivated land > grass land > forest land > garden. Multiple regression analysis showed that geographical and climatic factors, and soil physical and chemical properties could independently explain 26.8%and 55.4% of the spatial features of soil C/N ratio, while human activities could independently explain 5.4% of the spatial features only. The spatial distribution of soil C/N ratio in the study has important reference value for managing soil carbon and nitrogen, and for improving ecological function to similar regions.
2017, 27(4): 539-551.
doi: 10.1007/s11769-017-0886-6
Abstract:
Estuarial saline wetlands have been recognized as a vital role in CO2 cycling. However, insufficient attention has been paid to estimating CO2 fluxes from estuarial saline wetlands. In this study, the static chamber-gas chromatography (GC) method was used to quantify CO2 budget of an estuarial saline reed (Phragmites australis) wetland in Jiaozhou Bay in Qingdao City of Shandong Province, China during the reed growing season (May to October) in 2014. The CO2 budget study involved net ecosystem CO2 exchange (NEE), ecosystem respiration (Reco) and gross primary production (GPP). Temporal variation in CO2 budget and the impact of air/soil temperature, illumination intensity and aboveground biomass exerted on CO2 budget were analyzed. Results indicated that the wetland was acting as a net sink of 1129.16 g/m2during the entire growing season. Moreover, the values of Reco and GPP were 1744.89 g/m2 and 2874.05 g/m2, respectively; the ratio of Reco and GPP was 0.61. Diurnal and monthly patterns of CO2 budget varied significantly during the study period. Reco showed exponential relationships with air temperature and soil temperature at 5 cm, 10 cm, 20 cm depths, and soil temperature at 5 cm depth was the most crucial influence factor among them. Meanwhile, temperature sensitivity (Q10) of Reco was negatively correlated with soil temperature. Light and temperature exerted strong controls over NEE and GPP. Aboveground biomass over the whole growing season showed non-linear relationships with CO2 budget, while those during the early and peak growing season showed significant linear relationships with CO2 budget. This research provides valuable reference for CO2 exchange in estuarial saline wetland ecosystem.
Estuarial saline wetlands have been recognized as a vital role in CO2 cycling. However, insufficient attention has been paid to estimating CO2 fluxes from estuarial saline wetlands. In this study, the static chamber-gas chromatography (GC) method was used to quantify CO2 budget of an estuarial saline reed (Phragmites australis) wetland in Jiaozhou Bay in Qingdao City of Shandong Province, China during the reed growing season (May to October) in 2014. The CO2 budget study involved net ecosystem CO2 exchange (NEE), ecosystem respiration (Reco) and gross primary production (GPP). Temporal variation in CO2 budget and the impact of air/soil temperature, illumination intensity and aboveground biomass exerted on CO2 budget were analyzed. Results indicated that the wetland was acting as a net sink of 1129.16 g/m2during the entire growing season. Moreover, the values of Reco and GPP were 1744.89 g/m2 and 2874.05 g/m2, respectively; the ratio of Reco and GPP was 0.61. Diurnal and monthly patterns of CO2 budget varied significantly during the study period. Reco showed exponential relationships with air temperature and soil temperature at 5 cm, 10 cm, 20 cm depths, and soil temperature at 5 cm depth was the most crucial influence factor among them. Meanwhile, temperature sensitivity (Q10) of Reco was negatively correlated with soil temperature. Light and temperature exerted strong controls over NEE and GPP. Aboveground biomass over the whole growing season showed non-linear relationships with CO2 budget, while those during the early and peak growing season showed significant linear relationships with CO2 budget. This research provides valuable reference for CO2 exchange in estuarial saline wetland ecosystem.
2017, 27(4): 552-568.
doi: 10.1007/s11769-017-0887-5
Abstract:
Matching soil grid unit resolutions with polygon unit map scales is important to minimize the uncertainty of regional soil organic carbon (SOC) pool simulation due to their strong influences on the modeling. A series of soil grid units at varying cell sizes was derived from soil polygon units at six map scales, namely, 1:50 000 (C5), 1:200 000 (D2), 1:500 000 (P5), 1:1 000 000 (N1), 1:4 000 000 (N4) and 1:14 000 000 (N14), in the Taihu Region of China. Both soil unit formats were used for regional SOC pool simulation with a DeNitrification-DeComposition (DNDC) process-based model, which spans the time period from 1982 to 2000 at the six map scales. Four indices, namely, soil type number (STN), area (AREA), average SOC density (ASOCD) and total SOC stocks (SOCS) of surface paddy soils that were simulated by the DNDC, were distinguished from all these soil polygon and grid units. Subjecting to the four index values (IV) from the parent polygon units, the variations in an index value (VIV, %) from the grid units were used to assess its dataset accuracy and redundancy, which reflects the uncertainty in the simulation of SOC pools. Optimal soil grid unit resolutions were generated and suggested for the DNDC simulation of regional SOC pools, matching their respective soil polygon unit map scales. With these optimal raster resolutions, the soil grid units datasets can have the same accuracy as their parent polygon units datasets without any redundancy, when VIV < 1% was assumed to be a criterion for all four indices. A quadratic curve regression model, namely, y =-0.8×10-6x2 + 0.0228x + 0.0211 (R2 = 0.9994, P < 0.05), and a power function model = 10.394?0.2153 (R2 = 0.9759, P < 0.05) were revealed, which describe the relationship between the optimal soil grid unit resolution (y, km) and soil polygon unit map scale (1:10 000x), the ratio (?, %) of the optimal soil grid size to average polygon patch size (?, km2) and the ?, with the highest R2 among different mathematical regressions, respectively. This knowledge may facilitate the grid partitioning of regions during the investigation and simulation of SOC pool dynamics at a certain map scale, and be referenced to other landscape polygon patches' mesh partition.
Matching soil grid unit resolutions with polygon unit map scales is important to minimize the uncertainty of regional soil organic carbon (SOC) pool simulation due to their strong influences on the modeling. A series of soil grid units at varying cell sizes was derived from soil polygon units at six map scales, namely, 1:50 000 (C5), 1:200 000 (D2), 1:500 000 (P5), 1:1 000 000 (N1), 1:4 000 000 (N4) and 1:14 000 000 (N14), in the Taihu Region of China. Both soil unit formats were used for regional SOC pool simulation with a DeNitrification-DeComposition (DNDC) process-based model, which spans the time period from 1982 to 2000 at the six map scales. Four indices, namely, soil type number (STN), area (AREA), average SOC density (ASOCD) and total SOC stocks (SOCS) of surface paddy soils that were simulated by the DNDC, were distinguished from all these soil polygon and grid units. Subjecting to the four index values (IV) from the parent polygon units, the variations in an index value (VIV, %) from the grid units were used to assess its dataset accuracy and redundancy, which reflects the uncertainty in the simulation of SOC pools. Optimal soil grid unit resolutions were generated and suggested for the DNDC simulation of regional SOC pools, matching their respective soil polygon unit map scales. With these optimal raster resolutions, the soil grid units datasets can have the same accuracy as their parent polygon units datasets without any redundancy, when VIV < 1% was assumed to be a criterion for all four indices. A quadratic curve regression model, namely, y =-0.8×10-6x2 + 0.0228x + 0.0211 (R2 = 0.9994, P < 0.05), and a power function model = 10.394?0.2153 (R2 = 0.9759, P < 0.05) were revealed, which describe the relationship between the optimal soil grid unit resolution (y, km) and soil polygon unit map scale (1:10 000x), the ratio (?, %) of the optimal soil grid size to average polygon patch size (?, km2) and the ?, with the highest R2 among different mathematical regressions, respectively. This knowledge may facilitate the grid partitioning of regions during the investigation and simulation of SOC pool dynamics at a certain map scale, and be referenced to other landscape polygon patches' mesh partition.
2017, 27(4): 569-576.
doi: 10.1007/s11769-016-0829-7
Abstract:
All characteristics of vegetation, runoff and sediment from 1960 to 2010 in the Xiliu Gully Watershed, which is a representative watershed in wind-water erosion crisscross region in the upper reaches of the Yellow River of China, have been analyzed in this study. Based on the remote sensing image data, and used multi-spectral interpretation method, the characteristics of vegetation variation in the Xiliu Gully Watershed have been analyzed. And the rules of precipitation, runoff and sediment's changes have been illuminated by using mathematical statistics method. What's more, the influence mechanism of vegetation on runoff and sediment has been discussed by using the data obtained from artificial rainfall simulation test. The results showed that the main vegetation type was given priority to low coverage, and the area of the low vegetation coverage type was reducing year by year. On the country, the area of the high vegetation coverage type was gradually increasing. In a word, vegetation conditions had got better improved since 2000 when the watershed management project started. The average annual precipitation of the river basin also got slightly increase in 2000-2010. The average annual runoff reduced by 37.5%, and the average annual sediment reduced by 73.9% in the same period. The results of artificial rainfall simulation tests showed that the improvement of vegetation coverage could increase not only soil infiltration but also vegetation evapotranspiration, and then made the rainfall-induced runoff production decrease. Vegetation root system could increases the resistance ability of soil to erosion, and vegetation aboveground part could reduce raindrop kinetic energy and splash soil erosion. Therefore, with the increase of vegetation coverage, the rainfall-induced sediment could decrease.
All characteristics of vegetation, runoff and sediment from 1960 to 2010 in the Xiliu Gully Watershed, which is a representative watershed in wind-water erosion crisscross region in the upper reaches of the Yellow River of China, have been analyzed in this study. Based on the remote sensing image data, and used multi-spectral interpretation method, the characteristics of vegetation variation in the Xiliu Gully Watershed have been analyzed. And the rules of precipitation, runoff and sediment's changes have been illuminated by using mathematical statistics method. What's more, the influence mechanism of vegetation on runoff and sediment has been discussed by using the data obtained from artificial rainfall simulation test. The results showed that the main vegetation type was given priority to low coverage, and the area of the low vegetation coverage type was reducing year by year. On the country, the area of the high vegetation coverage type was gradually increasing. In a word, vegetation conditions had got better improved since 2000 when the watershed management project started. The average annual precipitation of the river basin also got slightly increase in 2000-2010. The average annual runoff reduced by 37.5%, and the average annual sediment reduced by 73.9% in the same period. The results of artificial rainfall simulation tests showed that the improvement of vegetation coverage could increase not only soil infiltration but also vegetation evapotranspiration, and then made the rainfall-induced runoff production decrease. Vegetation root system could increases the resistance ability of soil to erosion, and vegetation aboveground part could reduce raindrop kinetic energy and splash soil erosion. Therefore, with the increase of vegetation coverage, the rainfall-induced sediment could decrease.
2017, 27(4): 577-588.
doi: 10.1007/s11769-017-0888-4
Abstract:
The lateral distributions of depth-averaged velocity in open compound channels with emerged and submerged vegetated floodplains were analyzed based on the analytical solution of the depth-integrated Reynolds-Averaged Navier-Stokes equation with a term to account for the effects of vegetation. The three cases considered for open channels were two-stage rectangular channel with emerged vegetated floodplain, rectangular channel with submerged vegetated corner, and two-stage rectangular channel with submerged vegetated floodplain, respectively. To predict the depth-averaged velocity with submerged vegetated floodplains, we proposed a new method based on a two-layer approach where flow above and through the vegetation layer was described separately. Moreover, further experiments in the two-stage rectangular channel with submerged vegetated floodplain were carried out to verify the results. The analytical solutions of the cases indicated that the corresponding analytical depth-averaged velocity distributions agree well with the simulated and experimental prediction. The analytical solutions of the cases with theoretical foundation and without programming calculation were reasonable and applicable, which were more convenient than numerical simulations. The analytical solutions provided a way for future researches to solve the problems of submerged vegetation and discontinuous phenomenon of depth-averaged velocity at the stage point for compound channels. Understanding the hydraulics of flow in compound channels with vegetated floodplains is very important for supporting the management of fluvial processes.
The lateral distributions of depth-averaged velocity in open compound channels with emerged and submerged vegetated floodplains were analyzed based on the analytical solution of the depth-integrated Reynolds-Averaged Navier-Stokes equation with a term to account for the effects of vegetation. The three cases considered for open channels were two-stage rectangular channel with emerged vegetated floodplain, rectangular channel with submerged vegetated corner, and two-stage rectangular channel with submerged vegetated floodplain, respectively. To predict the depth-averaged velocity with submerged vegetated floodplains, we proposed a new method based on a two-layer approach where flow above and through the vegetation layer was described separately. Moreover, further experiments in the two-stage rectangular channel with submerged vegetated floodplain were carried out to verify the results. The analytical solutions of the cases indicated that the corresponding analytical depth-averaged velocity distributions agree well with the simulated and experimental prediction. The analytical solutions of the cases with theoretical foundation and without programming calculation were reasonable and applicable, which were more convenient than numerical simulations. The analytical solutions provided a way for future researches to solve the problems of submerged vegetation and discontinuous phenomenon of depth-averaged velocity at the stage point for compound channels. Understanding the hydraulics of flow in compound channels with vegetated floodplains is very important for supporting the management of fluvial processes.
2017, 27(4): 589-599.
doi: 10.1007/s11769-017-0889-3
Abstract:
Improved understanding of the effect of shrub cover on soil erosion process will provide valuable information for soil and water conservation programs. Laboratory rainfall simulations were conducted to determine the effects of shrubs on runoff and soil erosion and to ascertain the relationship between the rate of soil loss and the runoff hydrodynamic characteristics. In these simulations a 20° slope was subjected to rainfall intensities of 45, 87, and 127 mm/h. The average runoff rates ranged from 0.51 to 1.26 mm/min for bare soil plots and 0.15 to 0.96 mm/min for shrub plots. Average soil loss rates varied from 44.19 to 114.61 g/(min·m2) for bare soil plots and from 5.61 to 84.58 g/(min·m2) for shrub plots. There was a positive correlation between runoff and soil loss for the bare soil plots, and soil loss increased with increased runoff for shrub plots only when rainfall intensity is 127 mm/h. Runoff and soil erosion processes were strongly influenced by soil surface conditions because of the formation of erosion pits and rills. The unit stream power was the optimal hydrodynamic parameter to characterize the soil erosion mechanisms. The soil loss rate increased linearly with the unit stream power on both shrub and bare soil plots. Critical unit stream power values were 0.004 m/s for bare soil plots and 0.017 m/s for shrub plots.
Improved understanding of the effect of shrub cover on soil erosion process will provide valuable information for soil and water conservation programs. Laboratory rainfall simulations were conducted to determine the effects of shrubs on runoff and soil erosion and to ascertain the relationship between the rate of soil loss and the runoff hydrodynamic characteristics. In these simulations a 20° slope was subjected to rainfall intensities of 45, 87, and 127 mm/h. The average runoff rates ranged from 0.51 to 1.26 mm/min for bare soil plots and 0.15 to 0.96 mm/min for shrub plots. Average soil loss rates varied from 44.19 to 114.61 g/(min·m2) for bare soil plots and from 5.61 to 84.58 g/(min·m2) for shrub plots. There was a positive correlation between runoff and soil loss for the bare soil plots, and soil loss increased with increased runoff for shrub plots only when rainfall intensity is 127 mm/h. Runoff and soil erosion processes were strongly influenced by soil surface conditions because of the formation of erosion pits and rills. The unit stream power was the optimal hydrodynamic parameter to characterize the soil erosion mechanisms. The soil loss rate increased linearly with the unit stream power on both shrub and bare soil plots. Critical unit stream power values were 0.004 m/s for bare soil plots and 0.017 m/s for shrub plots.
2017, 27(4): 600-613.
doi: 10.1007/s11769-017-0880-z
Abstract:
Forest disturbance plays a vital role in modulating carbon storage, biodiversity and climate change. Yearly Landsat imagery from 1986 to 2015 of a typical plantation region in the northern Guangdong province of southern China was used as a case study. A Landsat time series stack (LTSS) was fed to the vegetation change tracker model (VCT) to map long-term changes in plantation forests' disturbance and recovery, followed by an intensive validation and a continuous 27-yr change analysis on disturbance locations, magnitudes and rates of plantations' disturbance and recovery. And the validation results of the disturbance year maps derived from five randomly identified sample plots with 25 km2 located at the four corners and the center of the scene showed the majority of the spatial agreement measures ranged from 60% to 83%. A confusion matrix summary of the accuracy measures for all four validation sites in Fogang County showed that the disturbance year maps had an overall accuracy estimate of 71.70%. Forest disturbance rates' change trend was characterized by a decline first, followed by an increase, then giving way to a decline again. An undulated and gentle decreasing trend of disturbance rates from the highest value of 3.95% to the lowest value of 0.76% occurred between 1988 and 2001, disturbance rate of 4.51% in 1994 was a notable anomaly, while after 2001 there was a sharp ascending change, forest disturbance rate spiked in 2007 (5.84%). After that, there was a significant decreasing trend up to the lowest value of 1.96% in 2011 and a slight ascending trend from 2011 to 2015 (2.59%). Two obvious spikes in post-disturbance recovery rates occurred in 1995 (0.26%) and 2008 (0.41%). Overall, forest recovery rates were lower than forest disturbance rates. Moreover, forest disturbance and recovery detection based on VCT and the Landsat-based detections of trends in disturbance and recovery (LandTrendr) algorithms in Fogang County have been conducted, with LandTrendr finding mostly much more disturbance than VCT. Overall, disturbances and recoveries in northern Guangdong were triggered mostly by timber needs, policies and decisions of the local governments. This study highlights that a better understanding about plantations' changes would provide a critical foundation for local forest management decisions in the southern China.
Forest disturbance plays a vital role in modulating carbon storage, biodiversity and climate change. Yearly Landsat imagery from 1986 to 2015 of a typical plantation region in the northern Guangdong province of southern China was used as a case study. A Landsat time series stack (LTSS) was fed to the vegetation change tracker model (VCT) to map long-term changes in plantation forests' disturbance and recovery, followed by an intensive validation and a continuous 27-yr change analysis on disturbance locations, magnitudes and rates of plantations' disturbance and recovery. And the validation results of the disturbance year maps derived from five randomly identified sample plots with 25 km2 located at the four corners and the center of the scene showed the majority of the spatial agreement measures ranged from 60% to 83%. A confusion matrix summary of the accuracy measures for all four validation sites in Fogang County showed that the disturbance year maps had an overall accuracy estimate of 71.70%. Forest disturbance rates' change trend was characterized by a decline first, followed by an increase, then giving way to a decline again. An undulated and gentle decreasing trend of disturbance rates from the highest value of 3.95% to the lowest value of 0.76% occurred between 1988 and 2001, disturbance rate of 4.51% in 1994 was a notable anomaly, while after 2001 there was a sharp ascending change, forest disturbance rate spiked in 2007 (5.84%). After that, there was a significant decreasing trend up to the lowest value of 1.96% in 2011 and a slight ascending trend from 2011 to 2015 (2.59%). Two obvious spikes in post-disturbance recovery rates occurred in 1995 (0.26%) and 2008 (0.41%). Overall, forest recovery rates were lower than forest disturbance rates. Moreover, forest disturbance and recovery detection based on VCT and the Landsat-based detections of trends in disturbance and recovery (LandTrendr) algorithms in Fogang County have been conducted, with LandTrendr finding mostly much more disturbance than VCT. Overall, disturbances and recoveries in northern Guangdong were triggered mostly by timber needs, policies and decisions of the local governments. This study highlights that a better understanding about plantations' changes would provide a critical foundation for local forest management decisions in the southern China.
2017, 27(4): 614-625.
doi: 10.1007/s11769-017-0882-x
Abstract:
The sub-pixel impervious surface percentage (SPIS) is the fraction of impervious surface area in one pixel, and it is an important indicator of urbanization. Using remote sensing data, the spatial distribution of SPIS values over large areas can be extracted, and these data are significant for studies of urban climate, environment and hydrology. To develop a stabilized, multi-temporal SPIS estimation method suitable for typical temperate semi-arid climate zones with distinct seasons, an optimal model for estimating SPIS values within Beijing Municipality was built that is based on the classification and regression tree (CART) algorithm. First, models with different input variables for SPIS estimation were built by integrating multi-source remote sensing data with other auxiliary data. The optimal model was selected through the analysis and comparison of the assessed accuracy of these models. Subsequently, multi-temporal SPIS mapping was carried out based on the optimal model. The results are as follows: 1) multi-seasonal images and nighttime light (NTL) data are the optimal input variables for SPIS estimation within Beijing Municipality, where the intra-annual variability in vegetation is distinct. The different spectral characteristics in the cultivated land caused by the different farming characteristics and vegetation phenology can be detected by the multi-seasonal images effectively. NLT data can effectively reduce the misestimation caused by the spectral similarity between bare land and impervious surfaces. After testing, the SPIS modeling correlation coefficient (r) is approximately 0.86, the average error (AE) is approximately 12.8%, and the relative error (RE) is approximately 0.39. 2) The SPIS results have been divided into areas with high-density impervious cover (70%-100%), medium-density impervious cover (40%-70%), low-density impervious cover (10%-40%) and natural cover (0%-10%). The SPIS model performed better in estimating values for high-density urban areas than other categories. 3) Multi-temporal SPIS mapping (1991-2016) was conducted based on the optimized SPIS results for 2005. After testing, AE ranges from 12.7% to 15.2%, RE ranges from 0.39 to 0.46, and r ranges from 0.81 to 0.86. It is demonstrated that the proposed approach for estimating sub-pixel level impervious surface by integrating the CART algorithm and multi-source remote sensing data is feasible and suitable for multi-temporal SPIS mapping of areas with distinct intra-annual variability in vegetation.
The sub-pixel impervious surface percentage (SPIS) is the fraction of impervious surface area in one pixel, and it is an important indicator of urbanization. Using remote sensing data, the spatial distribution of SPIS values over large areas can be extracted, and these data are significant for studies of urban climate, environment and hydrology. To develop a stabilized, multi-temporal SPIS estimation method suitable for typical temperate semi-arid climate zones with distinct seasons, an optimal model for estimating SPIS values within Beijing Municipality was built that is based on the classification and regression tree (CART) algorithm. First, models with different input variables for SPIS estimation were built by integrating multi-source remote sensing data with other auxiliary data. The optimal model was selected through the analysis and comparison of the assessed accuracy of these models. Subsequently, multi-temporal SPIS mapping was carried out based on the optimal model. The results are as follows: 1) multi-seasonal images and nighttime light (NTL) data are the optimal input variables for SPIS estimation within Beijing Municipality, where the intra-annual variability in vegetation is distinct. The different spectral characteristics in the cultivated land caused by the different farming characteristics and vegetation phenology can be detected by the multi-seasonal images effectively. NLT data can effectively reduce the misestimation caused by the spectral similarity between bare land and impervious surfaces. After testing, the SPIS modeling correlation coefficient (r) is approximately 0.86, the average error (AE) is approximately 12.8%, and the relative error (RE) is approximately 0.39. 2) The SPIS results have been divided into areas with high-density impervious cover (70%-100%), medium-density impervious cover (40%-70%), low-density impervious cover (10%-40%) and natural cover (0%-10%). The SPIS model performed better in estimating values for high-density urban areas than other categories. 3) Multi-temporal SPIS mapping (1991-2016) was conducted based on the optimized SPIS results for 2005. After testing, AE ranges from 12.7% to 15.2%, RE ranges from 0.39 to 0.46, and r ranges from 0.81 to 0.86. It is demonstrated that the proposed approach for estimating sub-pixel level impervious surface by integrating the CART algorithm and multi-source remote sensing data is feasible and suitable for multi-temporal SPIS mapping of areas with distinct intra-annual variability in vegetation.
2017, 27(4): 626-637.
doi: 10.1007/s11769-017-0890-x
Abstract:
The world is marching into a new development period when the digital technology, physical technology, and biological technology have achieved an unprecedented development respectively in their own fields, and at the same time their applications are converging greatly. These are the three major technological drivers for the Fourth Industrial Revolution. This paper discusses the specific technology niches of each kind technological driver behind the Fourth Industrial Revolution, and then evaluates impacts of the Fourth Industrial Revolution on global industrial, economic, and social development. At last this paper proposes possible measures and policies for both firms and governments to cope with the changes brought by the Fourth Industrial Revolution.
The world is marching into a new development period when the digital technology, physical technology, and biological technology have achieved an unprecedented development respectively in their own fields, and at the same time their applications are converging greatly. These are the three major technological drivers for the Fourth Industrial Revolution. This paper discusses the specific technology niches of each kind technological driver behind the Fourth Industrial Revolution, and then evaluates impacts of the Fourth Industrial Revolution on global industrial, economic, and social development. At last this paper proposes possible measures and policies for both firms and governments to cope with the changes brought by the Fourth Industrial Revolution.
2017, 27(4): 638-647.
doi: 10.1007/s11769-017-0891-9
Abstract:
In this paper, we study the interactive relationship between the agglomeration of urban elements and the evolution of eco-environmental pressure. We build an index system for evaluating the agglomeration of urban elements and eco-environmental pressure. Using the entropy method and response intensity model, we analyze how urban elements agglomeration influenced eco-environmental pressure in Changchun from 1990 to 2012, eliciting the changing features and influential factors. Ultimately, we conclude there is a significant interactive relationship between the agglomeration of urban elements and the evolution of eco-environmental pressure in Changchun. This is inferred from the degree of this agglomeration in Changchun having increased since 1990, with the degree of eco-environmental pressure first decreasing and then increasing. Alongside this, the impact of urban elements agglomeration on eco-environmental pressure has changed from negative to positive. The main reasons behind this shift are arguably the rapid growth of urban investment and ongoing urbanization.
In this paper, we study the interactive relationship between the agglomeration of urban elements and the evolution of eco-environmental pressure. We build an index system for evaluating the agglomeration of urban elements and eco-environmental pressure. Using the entropy method and response intensity model, we analyze how urban elements agglomeration influenced eco-environmental pressure in Changchun from 1990 to 2012, eliciting the changing features and influential factors. Ultimately, we conclude there is a significant interactive relationship between the agglomeration of urban elements and the evolution of eco-environmental pressure in Changchun. This is inferred from the degree of this agglomeration in Changchun having increased since 1990, with the degree of eco-environmental pressure first decreasing and then increasing. Alongside this, the impact of urban elements agglomeration on eco-environmental pressure has changed from negative to positive. The main reasons behind this shift are arguably the rapid growth of urban investment and ongoing urbanization.
2017, 27(4): 648-659.
doi: 10.1007/s11769-017-0892-8
Abstract:
While climate is an important factor attracting tourists to certain destinations, it can also motivate people residing in a country with a harsh climate to move to another location. By applying X-12 decompositions and a panel data regression analysis, this study analyzes the pull and push effects of climatic seasonal factors between destination (Hainan Island, China) and source countries (Russia and South Korea). The findings show that climatic seasonal factors have significant pulling and pushing effects on seasonal patterns of tourism demand, with temperature being the main factor. Furthermore, the number of paid vacation days in the source country affects that country's sensitivity to climatic seasonal factors; countries with a higher numbers of paid vacation days are more sensitive to climatic conditions. Lastly, future global warming may causes the aforementioned pull and push effects to abate, which will have an unavoidable influence on tourism industries.
While climate is an important factor attracting tourists to certain destinations, it can also motivate people residing in a country with a harsh climate to move to another location. By applying X-12 decompositions and a panel data regression analysis, this study analyzes the pull and push effects of climatic seasonal factors between destination (Hainan Island, China) and source countries (Russia and South Korea). The findings show that climatic seasonal factors have significant pulling and pushing effects on seasonal patterns of tourism demand, with temperature being the main factor. Furthermore, the number of paid vacation days in the source country affects that country's sensitivity to climatic seasonal factors; countries with a higher numbers of paid vacation days are more sensitive to climatic conditions. Lastly, future global warming may causes the aforementioned pull and push effects to abate, which will have an unavoidable influence on tourism industries.
2017, 27(4): 660-672.
doi: 10.1007/s11769-017-0893-7
Abstract:
We use the directional slacks-based measure of efficiency and inverse distance weighting method to analyze the spatial pattern evolution of the industrial green total factor productivity of 108 cities in the Yangtze River Economic Belt in 2003-2013. Results show that both the subprime mortgage crisis and ‘the new normal’ had significant negative effects on productivity growth, leading to the different spatial patterns between 2003-2008 and 2009-2013. Before 2008, green poles had gathered around some capital cities and formed a tripartite pattern, which was a typical core-periphery pattern. Due to a combination of the polarization and the diffusion effects, capital cities became the growth poles and ‘core’ regions, while surrounding areas became the ‘periphery’. This was mainly caused by the innate advantage of capital cities and ‘the rise of central China’ strategy. After 2008, the tripartite pattern changed to a multi-poles pattern where green poles continuously and densely spread in the midstream and downstream areas. This is due to the regional difference in the leading effect of green poles. The leading effect of green poles in midstream and downstream areas has changed from polarization to diffusion, while the polarization effect still leads in the upstream area.
We use the directional slacks-based measure of efficiency and inverse distance weighting method to analyze the spatial pattern evolution of the industrial green total factor productivity of 108 cities in the Yangtze River Economic Belt in 2003-2013. Results show that both the subprime mortgage crisis and ‘the new normal’ had significant negative effects on productivity growth, leading to the different spatial patterns between 2003-2008 and 2009-2013. Before 2008, green poles had gathered around some capital cities and formed a tripartite pattern, which was a typical core-periphery pattern. Due to a combination of the polarization and the diffusion effects, capital cities became the growth poles and ‘core’ regions, while surrounding areas became the ‘periphery’. This was mainly caused by the innate advantage of capital cities and ‘the rise of central China’ strategy. After 2008, the tripartite pattern changed to a multi-poles pattern where green poles continuously and densely spread in the midstream and downstream areas. This is due to the regional difference in the leading effect of green poles. The leading effect of green poles in midstream and downstream areas has changed from polarization to diffusion, while the polarization effect still leads in the upstream area.
Email
RSS