The classification of Chinese traditional settlements(CTSs)is extremely important for their differentiated development and protection.The innovative double-branch classification model developed in this study comprehen...The classification of Chinese traditional settlements(CTSs)is extremely important for their differentiated development and protection.The innovative double-branch classification model developed in this study comprehensively utilized the features of remote sensing(RS)images and building facade pictures(BFPs).This approach was able to overcome the limitations of previous methods that used only building facade images to classify settlements.First,the features of the roofs and walls were extracted using a double-branch structure,which consisted of an RS image branch and BFP branch.Then,a feature fusion module was designed to fuse the features of the roofs and walls.The precision,recall,and F1-score of the proposed model were improved by more than 4%compared with the classification model using only RS images or BFPs.The same three indexes of the proposed model were improved by more than 2%compared with other deep learning models.The results demonstrated that the proposed model performed well in the classification of architectural styles in CTSs.展开更多
The productivity of vegetation is influenced by both climate change and human activities.Understanding the specific contributions of these influencing factors is crucial for ecological conservation and regional sustai...The productivity of vegetation is influenced by both climate change and human activities.Understanding the specific contributions of these influencing factors is crucial for ecological conservation and regional sustainability.This study utilized a combination of multi-source data to examine the spatiotemporal patterns of Net Primary Productivity(NPP)in the Yellow River Basin(YRB),China from 1982 to 2020.Additionally,a scenario-based approach was employed to compare Potential NPP(PNPP)with Actual NPP(ANPP)to determine the relative roles of climatic and human factors in NPP changes.The PNPP was estimated using the Lund-Potsdam-Jena General Ecosystem Simulator(LPJ-GUESS)model,while ANPP was evaluated by the Carnegie-Ames-Stanford Approach(CASA)model using different NDVI data sources.Both model simulations revealed that significant greening occurring in the YRB,with a gradual decrease observed from southeast to northwest.According to the LPJ_GUESS model simulations,areas experiencing an increasing trend in NPP accounted for 86.82% of the YRB.When using GIMMS and MODIS NDVI data with CASA model simulations,areas showing an increasing trend in NPP accounted for 71.42% and 97.02%,respectively.Furthermore,both climatic conditions and human factors had positive effects on vegetation restoration;approximated 41.15% of restored vegetation areas were influenced by both climate variation and human activities,while around 31.93% were solely affected by climate variation.However,it was found that human activities served as the principal driving force of vegetation degradation within the YRB,impacting 26.35% of degraded areas solely due to human activities.Therefore,effective management strategies encompassing both human activities and climate change adaptation are imperative for facilitating vegetation restoration within this region.These findings will valuable for enhancing our understanding in NPP changes and its underlying factors,thereby contributing to improved ecological management and the pursuit of regional carbon neutrality in China.展开更多
Understanding the spatial distribution of the crop yield gap(YG)is essential for improving crop yields.Recent studies have typically focused on the site scale,which may lead to considerable uncertainties when scaled t...Understanding the spatial distribution of the crop yield gap(YG)is essential for improving crop yields.Recent studies have typically focused on the site scale,which may lead to considerable uncertainties when scaled to the regional scale.To mitigate this issue,this study used a process-based and remote sensing driven crop yield model for winter wheat(PRYM-Wheat),which was derived from the boreal ecosystem productivity simulator(BEPS),to simulate the YG of winter wheat in the North China Plain from 2015 to 2019.Yield validation based on statistical yield data revealed good performance of the PRYM-Wheat Model in simulating winter wheat actual yield(Ya).The distribution of Ya across the North China Plain showed great heterogeneity,decreasing from southeast to northwest.The remote sensing-estimated results show that the average YG of the study area was 6400.6 kg ha^(–1).The YG of Jiangsu Province was the largest,at7307.4 kg ha^(–1),while the YG of Anhui Province was the smallest,at 5842.1 kg ha^(–1).An analysis of the responses of YG to environmental factors showed no obvious correlation between YG and precipitation,but there was a weak negative correlation between YG and accumulated temperature.In addition,the YG was positively correlated with elevation.In general,studying the specific features of the YG can provide directions for increasing crop yields in the future.展开更多
Random forest model is the mainstream research method used to accurately describe the distribution law and impact mechanism of regional population.We took Shijiazhuang as the research area,with comprehensive zoning ba...Random forest model is the mainstream research method used to accurately describe the distribution law and impact mechanism of regional population.We took Shijiazhuang as the research area,with comprehensive zoning based on endowments as the modeling unit,conducted stratified sampling on a hectare grid cell,and systematically carried out incremental selection experiments of population density impact factors,optimizing the population density random forest model throughout the process(zonal modeling,stratified sampling,factor selection,weighted output).The results are as follows:(1)Zonal modeling addresses the issue of confusion in population distribution laws caused by a single model.Sampling on a grid cell not only ensures the quality of training data by avoiding the modifiable areal unit problem(MAUP)but also attempts to mitigate the adverse effects of the ecological fallacy.Stratified sampling ensures the stability of population density label values(target variable)in the training sample.(2)Zonal selection experiments on population density impact factors help identify suitable combinations of factors,leading to a significant improvement in the goodness of fit(R^(2))of the zonal models.(3)Weighted combination output of the population density prediction dataset substantially enhances the model's robustness.(4)The population density dataset exhibits multi-scale superposition characteristics.On a large scale,the population density in plains is higher than that in mountainous areas,while on a small scale,urban areas have higher density compared to rural areas.The optimization scheme for the population density random forest model that we propose offers a unified technical framework for uncovering local population distribution law and the impact mechanisms.展开更多
Soil organic carbon(SOC)and total nitrogen(TN)play an important role in the global carbon and nitrogen cycles.Soil aggregates are critical reservoir of SOC and TN.Therefore,in areas with severe wind erosion,the change...Soil organic carbon(SOC)and total nitrogen(TN)play an important role in the global carbon and nitrogen cycles.Soil aggregates are critical reservoir of SOC and TN.Therefore,in areas with severe wind erosion,the changes in the accumulation of SOC,TN,clay,silt,and sand contents within different dry aggregate size fractions can offer crucial insights into soil conservation by the control of wind erosion.In this study,surface soil samples(0–5 cm depth)were collected from farmland and grassland in the Bashang region of northern China in 2020.The bulk soil and aggregate size fractions were used to determine the concentrations of SOC,TN,clay,silt,and sand.The results showed that:(1)farmland had lower SOC and higher TN than grassland;(2)SOC in the aggregates of farmland decreased with increasing aggregate size(P<0.010),while SOC in the aggregates of grassland increased with increasing aggregate size(P<0.010),and nonsignificant variation of TN and clay was observed among different aggregate sizes;(3)the mean of aggregate silt significantly decreased with increasing aggregate size and the mean of aggregate sand increased with increasing aggregate size(P<0.001);(4)no correlations between sand or silt of aggregate and TN or texture of bulk soil was found;and(5)SOC in bulk soil was correlated with those in different aggregate sizes,and was also affected by the texture of bulk soil(P<0.010).This study highlights the role of dry soil aggregate size in the redistribution of SOC,TN,clay,silt,and sand contents under different land uses,thereby facilitating the understanding of the process of wind erosion induced SOC,TN,and mineral dust emission.展开更多
The well-facilitated farmland projects(WFFPs)involve the typical sustainable intensification of farmland use and play a key role in raising food production in China.However,whether such WFFPs can enhance the nitrogen(...The well-facilitated farmland projects(WFFPs)involve the typical sustainable intensification of farmland use and play a key role in raising food production in China.However,whether such WFFPs can enhance the nitrogen(N)use efficiency and reduce environmental impacts is still unclear.Here,we examined the data from 502 valid questionnaires collected from WFFPs in the major grain-producing area,the Huang-Huai-Hai Region(HHHR)in China,with 429 samples for wheat,328 for maize,and 122 for rice.We identified gaps in N use efficiency(NUE)and N losses from the production of the three crops between the sampled WFFPs and counties based on the statistical data.The results showed that compared to the county-level(wheat,39.1%;maize,33.8%;rice,35.1%),the NUEs for wheat(55.2%),maize(52.1%),and rice(50.2%)in the WFFPs were significantly improved(P<0.05).In addition,the intensities of ammonia(NH3)volatilization(9.9-12.2 kg N ha–1),N leaching(6.5-16.9 kg N ha–1),and nitrous oxide(N2O)emissions(1.2-1.6 kg N ha–1)from crop production in the sampled WFFPs were significantly lower than the county averages(P<0.05).Simulations showed that if the N rates are reduced by 10.0,15.0,and 20.0%for the counties,the NUEs of wheat,maize,and rice in the HHHR will increase by 2.9-6.3,2.4-5.2,and 2.6-5.7%,respectively.If the N rate is reduced to the WFFP level in each county,the NUEs of the three crops will increase by 12.9-19.5%,and the N leaching,NH3,and N2O emissions will be reduced by 48.9-56.2,37.4-42.9,and 46.0-66.5%,respectively.Our findings highlight that efficient N management practices in sustainable intensive farmland have considerable potential for reducing environmental impacts.展开更多
The objective of this study is to quantitatively evaluate Tropical Rainfall Measuring Mission （TRMM） data with rain gauge data and further to use this TRMM data to drive a Dis- tributed Time-Variant Gain Model （DT...The objective of this study is to quantitatively evaluate Tropical Rainfall Measuring Mission （TRMM） data with rain gauge data and further to use this TRMM data to drive a Dis- tributed Time-Variant Gain Model （DTVGM） to perform hydrological simulations in the semi-humid Weihe River catchment in China. Before the simulations, a comparison with a 10-year （2001-2010） daily rain gauge data set reveals that, at daily time step, TRMM rainfall data are better at capturing rain occurrence and mean values than rainfall extremes. On a monthly time scale, good linear relationships between TRMM and rain gauge rainfall data are found, with determination coefficients R2 varying between 0.78 and 0.89 for the individual stations. Subsequent simulation results of seven years （2001-2007） of data on daily hydro- logical processes confirm that the DTVGM when calibrated by rain gauge data performs better than when calibrated by TRMM data, but the performance of the simulation driven by TRMM data is better than that driven by gauge data on a monthly time scale. The results thus suggest that TRMM rainfall data are more suitable for monthly streamfiow simulation in the study area, and that, when the effects of recalibration and the results for water balance components are also taken into account, the TRMM 3B42-V7 product has the potential to perform well in similar basins.展开更多
Water retention is important in forest ecosystem services. The heterogeneity analysis of water-retention capacity and its influencing factors is of great significance for the construction of water-retention functional...Water retention is important in forest ecosystem services. The heterogeneity analysis of water-retention capacity and its influencing factors is of great significance for the construction of water-retention functional areas, restoration of vegetation, and the protection of forest ecosystems in the Beijing-Tianjin-Hebei region. A total of 1366 records concerning water-retention capacity in the canopy layer, litter layer, and soil layer of forest ecosystem in this region were obtained from 193 literature published from 1980 to 2017. The influencing factors of water-retention capacity in each layer were analyzed, and path analysis was used to investigate the contribution of the factors to the water-retention capacity of the three layers. The results showed that mixed forests had the highest water-retention capacity, followed by broad-leaved forests, coniferous forests, and shrub forests. In addition, no matter the forest type, the ranking of the water-retention capacity was soil layer, canopy layer, and litter layer from high to low. The main influencing factors of water-retention capacity in forest canopy were leaf area index and maximum daily precipitation(R2=0.49), and the influencing coefficients were 0.34 and 0.30, respectively. The main influencing factors of water-retention capacity in the litter layer were semi-decomposed litter(R2=0.51), and the influencing coefficient was 0.51. The main influencing factors of water-retention capacity in the soil layer were non-capillary porosity and soil depth(R2=0.61), the influencing coefficients were 0.60 and 0.38, respectively. This study verifies the simulation of the water balance model or inversion of remote sensing of the water-retention capacity at the site scale, and provides scientific basis for further study of the impact of global change on water retention.展开更多
Desertification caused by land degradation and overexploitation of natural resources is threatening large parts of eastern and southern Mediterranean. The actual state of desertification sensitivity in Lebanon was spa...Desertification caused by land degradation and overexploitation of natural resources is threatening large parts of eastern and southern Mediterranean. The actual state of desertification sensitivity in Lebanon was spatially assessed using site specific environmental bio-physical indicators, demographic pressure and socioeconomic conditions. Bio-physical assessment included the aridity index derived from integrated assessment of the historical data for 48 climatic stations spread throughout the country, the new detailed soil map at 1：50,000 scale, and the updated land cover/use map at 1：20,000 derived from IKONOS 2005. The methodology also included livelihood conditions and poverty at local administrative ＂Caza＂ level. Results showed the integrated impact of local climate, soil and vegetation quality and socioeconomic conditions on sensitivity to desertification. A total of 78% of the territories have low and very low climate quality index preconditioning the sensitivity to desertification. Fourteen Cazas out of 26 in total, representing more than 66% of the country, have low socioeconomic satisfaction index. Furthermore, negative trends are alleviated by good quality relict soils and vegetation cover. The actual extent of desertification covers 40.48% of the national territory, much of which occurs under semi-arid climate, moderate or low soil and vegetation quality and poor living conditions. The outcome of this research adjusted the previous coarse estimates of desertification prone areas at the national level. Results allow for realistic, policy oriented local assessment for responsive land use planning and proactive sustainable, national and local land management in the context of the national action plan to combat desertification.展开更多
The accurate simulation of regional-scale winter wheat yield is important for national food security and the balance of grain supply and demand in China.Presently,most remote sensing process models use the“biomass...The accurate simulation of regional-scale winter wheat yield is important for national food security and the balance of grain supply and demand in China.Presently,most remote sensing process models use the“biomass×harvest index(HI)”method to simulate regional-scale winter wheat yield.However,spatiotemporal differences in HI contribute to inaccuracies in yield simulation at the regional scale.Time-series dry matter partition coefficients(Fr)can dynamically reflect the dry matter partition of winter wheat.In this study,Fr equations were fitted for each organ of winter wheat using site-scale data.These equations were then coupled into a process-based and remote sensingdriven crop yield model for wheat(PRYM-Wheat)to improve the regional simulation of winter wheat yield over the North China Plain(NCP).The improved PRYM-Wheat model integrated with the fitted Fr equations(PRYM-Wheat-Fr)was validated using data obtained from provincial yearbooks.A 3-year(2000-2002)averaged validation showed that PRYM-Wheat-Fr had a higher coefficient of determination(R^(2)=0.55)and lower root mean square error(RMSE=0.94 t ha^(-1))than PRYM-Wheat with a stable HI(abbreviated as PRYM-Wheat-HI),which had R^(2) and RMSE values of 0.30 and 1.62 t ha^(-1),respectively.The PRYM-Wheat-Fr model also performed better than PRYM-Wheat-HI for simulating yield in verification years(2013-2015).In conclusion,the PRYM-Wheat-Fr model exhibited a better accuracy than the original PRYM-Wheat model,making it a useful tool for the simulation of regional winter wheat yield.展开更多
The Agulhas system is the strongest western boundary current system in the Southern Hemisphere and plays an important role in modulating the Indian-to-Atlantic Ocean water exchange by the Agulhas leakage.It is difficu...The Agulhas system is the strongest western boundary current system in the Southern Hemisphere and plays an important role in modulating the Indian-to-Atlantic Ocean water exchange by the Agulhas leakage.It is difficult to measure in situ transport of the Agulhas leakage as well as the Agulhas retroflection position due to their intermittent nature.In this study,an innovative kinematic algorithm was designed and applied to the gridded altimeter observational data,to ascertain the longitudinal position of Agulhas retroflection,the stability of Agulhas jet stream,as well as its strength.The results show that the east-west shift of retroflection is related neither to the strength of Agulhas current nor to its stability.Further analysis uncovers the connection between the westward extension of Agulhas jet stream and an anomalous cyclonic circulation at its northern side,which is likely attributed to the local wind stress curl anomaly.To confirm the effect of local wind forcing on the east-west shift of retroflection,numerical sensitivity experiments were conducted.The results show that the local wind stress can induce a similar longitudinal shift of the retroflection as altimetry observations.Further statistical and case study indicates that whether an Agulhas ring can continuously migrate westward to the Atlantic Ocean or re-merge into the main flow depends on the retroflection position.Therefore,the westward retroflection may contribute to a stronger Agulhas leakage than the eastward retroflection.展开更多
This study aims to investigate the effects of different mapping unit scales and study area scales on the uncertainty rules of landslide susceptibility prediction(LSP).To illustrate various study area scales,Ganzhou Ci...This study aims to investigate the effects of different mapping unit scales and study area scales on the uncertainty rules of landslide susceptibility prediction(LSP).To illustrate various study area scales,Ganzhou City in China,its eastern region(Ganzhou East),and Ruijin County in Ganzhou East were chosen.Different mapping unit scales are represented by grid units with spatial resolution of 30 and 60 m,as well as slope units that were extracted by multi-scale segmentation method.The 3855 landslide locations and 21 typical environmental factors in Ganzhou City are first determined to create spatial datasets with input-outputs.Then,landslide susceptibility maps(LSMs)of Ganzhou City,Ganzhou East and Ruijin County are pro-duced using a support vector machine(SVM)and random forest(RF),respectively.The LSMs of the above three regions are then extracted by mask from the LSM of Ganzhou City,along with the LSMs of Ruijin County from Ganzhou East.Additionally,LSMs of Ruijin at various mapping unit scales are generated in accordance.Accuracy and landslide suscepti-bility indexes(LSIs)distribution are used to express LSP uncertainties.The LSP uncertainties under grid units significantly decrease as study area scales decrease from Ganzhou City,Ganzhou East to Ruijin County,whereas those under slope units are less affected by study area scales.Of course,attentions should also be paid to the broader representativeness of large study areas.The LSP accuracy of slope units increases by about 6%–10%compared with those under grid units with 30 m and 60 m resolution in the same study area's scale.The significance of environmental factors exhibits an averaging trend as study area scale increases from small to large.The importance of environmental factors varies greatly with the 60 m grid unit,but it tends to be consistent to some extent in the 30 m grid unit and the slope unit.展开更多
MODerate resolution atmospheric TRANsmission(MODTRAN)is a commercial remote sensing(RS)software package that has been widely used to simulate radiative transfer of electromagnetic radiation through the Earth’s atmosp...MODerate resolution atmospheric TRANsmission(MODTRAN)is a commercial remote sensing(RS)software package that has been widely used to simulate radiative transfer of electromagnetic radiation through the Earth’s atmosphere and the radiation observed by a remote sensor.However,when very large RS datasets must be processed in simulation applications at a global scale,it is extremely time-consuming to operate MODTRAN on a modern workstation.Under this circumstance,the use of parallel cluster computing to speed up the process becomes vital to this time-consuming task.This paper presents PMODTRAN,an implementation of a parallel task-scheduling algorithm based on MODTRAN.PMODTRAN was able to reduce the processing time of the test cases used here from over 4.4 months on a workstation to less than a week on a local computer cluster.In addition,PMODTRAN can distribute tasks with different levels of granularity and has some extra features,such as dynamic load balancing and parameter checking.展开更多
Carbon Monoxide(CO)is an important urban pollutant with a relation to 5,transition economies based on emission intensities.In this study,Sentinel-MODerate resolution Imaging Spectroradiometer(MODIS),and Landsat-8 imag...Carbon Monoxide(CO)is an important urban pollutant with a relation to 5,transition economies based on emission intensities.In this study,Sentinel-MODerate resolution Imaging Spectroradiometer(MODIS),and Landsat-8 images were used to investigate the variations of CO and urban environmental indices and the correlations between them.From the assessed correlations for 932 Iranian cities,it occurred that the assessed indices were all correlated.The highest CO levels were 0.031 in the spring of 2019 and 2020,whereas in 2021 it was equal to 0.030 in both the spring and winter,respectively.In 2019 and 2020 the maximum values of the Enhanced Vegetation Index(EVI)in the spring were 0.181 and 0.183.Exceptionally high Absorbing Aerosol Index(AAI)values of–0.834 and–1.0,along with Urban Index(UI)of 0.102 and 0.092,were correlated with recorded spikes in CO level,despite that these seasons’EVI values were not so abnormal.It was forecasted that in 2030 rises in the CO level by 13.2%in the winter and by 17.5%in the fall are expected,with the simultaneous increase of AAI by 204.5%and 980.2%,and Aerosol Optical Depth(AOD)by 27%and 5%in the winter and spring,respectively.展开更多
基金The Science and Technology Project of Hebei Education Department,No.BJK2022031The Open Fund of Hebei Key Laboratory of Geological Resources and Environmental Monitoring and Protection,No.JCYKT202310。
文摘The classification of Chinese traditional settlements(CTSs)is extremely important for their differentiated development and protection.The innovative double-branch classification model developed in this study comprehensively utilized the features of remote sensing(RS)images and building facade pictures(BFPs).This approach was able to overcome the limitations of previous methods that used only building facade images to classify settlements.First,the features of the roofs and walls were extracted using a double-branch structure,which consisted of an RS image branch and BFP branch.Then,a feature fusion module was designed to fuse the features of the roofs and walls.The precision,recall,and F1-score of the proposed model were improved by more than 4%compared with the classification model using only RS images or BFPs.The same three indexes of the proposed model were improved by more than 2%compared with other deep learning models.The results demonstrated that the proposed model performed well in the classification of architectural styles in CTSs.
基金Under the auspices of National Natural Science Foundation of China(No.41991231,U21A2011)。
文摘The productivity of vegetation is influenced by both climate change and human activities.Understanding the specific contributions of these influencing factors is crucial for ecological conservation and regional sustainability.This study utilized a combination of multi-source data to examine the spatiotemporal patterns of Net Primary Productivity(NPP)in the Yellow River Basin(YRB),China from 1982 to 2020.Additionally,a scenario-based approach was employed to compare Potential NPP(PNPP)with Actual NPP(ANPP)to determine the relative roles of climatic and human factors in NPP changes.The PNPP was estimated using the Lund-Potsdam-Jena General Ecosystem Simulator(LPJ-GUESS)model,while ANPP was evaluated by the Carnegie-Ames-Stanford Approach(CASA)model using different NDVI data sources.Both model simulations revealed that significant greening occurring in the YRB,with a gradual decrease observed from southeast to northwest.According to the LPJ_GUESS model simulations,areas experiencing an increasing trend in NPP accounted for 86.82% of the YRB.When using GIMMS and MODIS NDVI data with CASA model simulations,areas showing an increasing trend in NPP accounted for 71.42% and 97.02%,respectively.Furthermore,both climatic conditions and human factors had positive effects on vegetation restoration;approximated 41.15% of restored vegetation areas were influenced by both climate variation and human activities,while around 31.93% were solely affected by climate variation.However,it was found that human activities served as the principal driving force of vegetation degradation within the YRB,impacting 26.35% of degraded areas solely due to human activities.Therefore,effective management strategies encompassing both human activities and climate change adaptation are imperative for facilitating vegetation restoration within this region.These findings will valuable for enhancing our understanding in NPP changes and its underlying factors,thereby contributing to improved ecological management and the pursuit of regional carbon neutrality in China.
基金the Shandong Key Research and Development Project,China(2018GNC110025)the National Natural Science Foundation of China(41871253)+2 种基金the Central Guiding Local Science and Technology Development Fund of Shandong—Yellow River Basin Collaborative Science and Technology Innovation Special Project,China(YDZX2023019)the Natural Science Foundation of Shandong Province,China(ZR2020QD016)the“Taishan Scholar”Project of Shandong Province,China(TSXZ201712)。
文摘Understanding the spatial distribution of the crop yield gap(YG)is essential for improving crop yields.Recent studies have typically focused on the site scale,which may lead to considerable uncertainties when scaled to the regional scale.To mitigate this issue,this study used a process-based and remote sensing driven crop yield model for winter wheat(PRYM-Wheat),which was derived from the boreal ecosystem productivity simulator(BEPS),to simulate the YG of winter wheat in the North China Plain from 2015 to 2019.Yield validation based on statistical yield data revealed good performance of the PRYM-Wheat Model in simulating winter wheat actual yield(Ya).The distribution of Ya across the North China Plain showed great heterogeneity,decreasing from southeast to northwest.The remote sensing-estimated results show that the average YG of the study area was 6400.6 kg ha^(–1).The YG of Jiangsu Province was the largest,at7307.4 kg ha^(–1),while the YG of Anhui Province was the smallest,at 5842.1 kg ha^(–1).An analysis of the responses of YG to environmental factors showed no obvious correlation between YG and precipitation,but there was a weak negative correlation between YG and accumulated temperature.In addition,the YG was positively correlated with elevation.In general,studying the specific features of the YG can provide directions for increasing crop yields in the future.
基金National Natural Science Foundation of China,No.42071167,No.42201197,No.40871073The Second Tibetan Plateau Scientific Expedition and Research Program,No.2019QZKK0406Natural Science Foundation of Hebei Province,No.D2007000272。
文摘Random forest model is the mainstream research method used to accurately describe the distribution law and impact mechanism of regional population.We took Shijiazhuang as the research area,with comprehensive zoning based on endowments as the modeling unit,conducted stratified sampling on a hectare grid cell,and systematically carried out incremental selection experiments of population density impact factors,optimizing the population density random forest model throughout the process(zonal modeling,stratified sampling,factor selection,weighted output).The results are as follows:(1)Zonal modeling addresses the issue of confusion in population distribution laws caused by a single model.Sampling on a grid cell not only ensures the quality of training data by avoiding the modifiable areal unit problem(MAUP)but also attempts to mitigate the adverse effects of the ecological fallacy.Stratified sampling ensures the stability of population density label values(target variable)in the training sample.(2)Zonal selection experiments on population density impact factors help identify suitable combinations of factors,leading to a significant improvement in the goodness of fit(R^(2))of the zonal models.(3)Weighted combination output of the population density prediction dataset substantially enhances the model's robustness.(4)The population density dataset exhibits multi-scale superposition characteristics.On a large scale,the population density in plains is higher than that in mountainous areas,while on a small scale,urban areas have higher density compared to rural areas.The optimization scheme for the population density random forest model that we propose offers a unified technical framework for uncovering local population distribution law and the impact mechanisms.
基金supported by the National Natural Science Foundation of China(4227100242201002)+2 种基金the Foundation of Central Guidance for Local Scientific and Technological Development(246Z3705G)the Water Conservancy Science and Technology Plan Project of Hebei Province(2023-64)the Hebei Natural Science Foundation(D2021205013).
文摘Soil organic carbon(SOC)and total nitrogen(TN)play an important role in the global carbon and nitrogen cycles.Soil aggregates are critical reservoir of SOC and TN.Therefore,in areas with severe wind erosion,the changes in the accumulation of SOC,TN,clay,silt,and sand contents within different dry aggregate size fractions can offer crucial insights into soil conservation by the control of wind erosion.In this study,surface soil samples(0–5 cm depth)were collected from farmland and grassland in the Bashang region of northern China in 2020.The bulk soil and aggregate size fractions were used to determine the concentrations of SOC,TN,clay,silt,and sand.The results showed that:(1)farmland had lower SOC and higher TN than grassland;(2)SOC in the aggregates of farmland decreased with increasing aggregate size(P<0.010),while SOC in the aggregates of grassland increased with increasing aggregate size(P<0.010),and nonsignificant variation of TN and clay was observed among different aggregate sizes;(3)the mean of aggregate silt significantly decreased with increasing aggregate size and the mean of aggregate sand increased with increasing aggregate size(P<0.001);(4)no correlations between sand or silt of aggregate and TN or texture of bulk soil was found;and(5)SOC in bulk soil was correlated with those in different aggregate sizes,and was also affected by the texture of bulk soil(P<0.010).This study highlights the role of dry soil aggregate size in the redistribution of SOC,TN,clay,silt,and sand contents under different land uses,thereby facilitating the understanding of the process of wind erosion induced SOC,TN,and mineral dust emission.
基金supported by the National Key Research and Development Program of China(2022YFB3903505)the National Natural Science Foundation of China(72221002)。
文摘The well-facilitated farmland projects(WFFPs)involve the typical sustainable intensification of farmland use and play a key role in raising food production in China.However,whether such WFFPs can enhance the nitrogen(N)use efficiency and reduce environmental impacts is still unclear.Here,we examined the data from 502 valid questionnaires collected from WFFPs in the major grain-producing area,the Huang-Huai-Hai Region(HHHR)in China,with 429 samples for wheat,328 for maize,and 122 for rice.We identified gaps in N use efficiency(NUE)and N losses from the production of the three crops between the sampled WFFPs and counties based on the statistical data.The results showed that compared to the county-level(wheat,39.1%;maize,33.8%;rice,35.1%),the NUEs for wheat(55.2%),maize(52.1%),and rice(50.2%)in the WFFPs were significantly improved(P<0.05).In addition,the intensities of ammonia(NH3)volatilization(9.9-12.2 kg N ha–1),N leaching(6.5-16.9 kg N ha–1),and nitrous oxide(N2O)emissions(1.2-1.6 kg N ha–1)from crop production in the sampled WFFPs were significantly lower than the county averages(P<0.05).Simulations showed that if the N rates are reduced by 10.0,15.0,and 20.0%for the counties,the NUEs of wheat,maize,and rice in the HHHR will increase by 2.9-6.3,2.4-5.2,and 2.6-5.7%,respectively.If the N rate is reduced to the WFFP level in each county,the NUEs of the three crops will increase by 12.9-19.5%,and the N leaching,NH3,and N2O emissions will be reduced by 48.9-56.2,37.4-42.9,and 46.0-66.5%,respectively.Our findings highlight that efficient N management practices in sustainable intensive farmland have considerable potential for reducing environmental impacts.
基金National Key Technology P&D Program,No.2012BAB02B00The Fundamental Research Funds for the Central Universities
文摘The objective of this study is to quantitatively evaluate Tropical Rainfall Measuring Mission （TRMM） data with rain gauge data and further to use this TRMM data to drive a Dis- tributed Time-Variant Gain Model （DTVGM） to perform hydrological simulations in the semi-humid Weihe River catchment in China. Before the simulations, a comparison with a 10-year （2001-2010） daily rain gauge data set reveals that, at daily time step, TRMM rainfall data are better at capturing rain occurrence and mean values than rainfall extremes. On a monthly time scale, good linear relationships between TRMM and rain gauge rainfall data are found, with determination coefficients R2 varying between 0.78 and 0.89 for the individual stations. Subsequent simulation results of seven years （2001-2007） of data on daily hydro- logical processes confirm that the DTVGM when calibrated by rain gauge data performs better than when calibrated by TRMM data, but the performance of the simulation driven by TRMM data is better than that driven by gauge data on a monthly time scale. The results thus suggest that TRMM rainfall data are more suitable for monthly streamfiow simulation in the study area, and that, when the effects of recalibration and the results for water balance components are also taken into account, the TRMM 3B42-V7 product has the potential to perform well in similar basins.
基金National Key R&D Program of China,No.2017YFA0604703National Natural Science Foundation of China,No.41771111+4 种基金Hebei Natural Science Foundation,No.D2019205123Youth Innovation Promotion Association,No.2018071Research Fund Project of Hebei Normal University,No.L052018Z09Key Subject of Physical Geography of Hebei ProvinceInvestigation and Monitoring Project of Ministry of Natural Resources,No.JCQQ191504-06。
文摘Water retention is important in forest ecosystem services. The heterogeneity analysis of water-retention capacity and its influencing factors is of great significance for the construction of water-retention functional areas, restoration of vegetation, and the protection of forest ecosystems in the Beijing-Tianjin-Hebei region. A total of 1366 records concerning water-retention capacity in the canopy layer, litter layer, and soil layer of forest ecosystem in this region were obtained from 193 literature published from 1980 to 2017. The influencing factors of water-retention capacity in each layer were analyzed, and path analysis was used to investigate the contribution of the factors to the water-retention capacity of the three layers. The results showed that mixed forests had the highest water-retention capacity, followed by broad-leaved forests, coniferous forests, and shrub forests. In addition, no matter the forest type, the ranking of the water-retention capacity was soil layer, canopy layer, and litter layer from high to low. The main influencing factors of water-retention capacity in forest canopy were leaf area index and maximum daily precipitation(R2=0.49), and the influencing coefficients were 0.34 and 0.30, respectively. The main influencing factors of water-retention capacity in the litter layer were semi-decomposed litter(R2=0.51), and the influencing coefficient was 0.51. The main influencing factors of water-retention capacity in the soil layer were non-capillary porosity and soil depth(R2=0.61), the influencing coefficients were 0.60 and 0.38, respectively. This study verifies the simulation of the water balance model or inversion of remote sensing of the water-retention capacity at the site scale, and provides scientific basis for further study of the impact of global change on water retention.
文摘Desertification caused by land degradation and overexploitation of natural resources is threatening large parts of eastern and southern Mediterranean. The actual state of desertification sensitivity in Lebanon was spatially assessed using site specific environmental bio-physical indicators, demographic pressure and socioeconomic conditions. Bio-physical assessment included the aridity index derived from integrated assessment of the historical data for 48 climatic stations spread throughout the country, the new detailed soil map at 1：50,000 scale, and the updated land cover/use map at 1：20,000 derived from IKONOS 2005. The methodology also included livelihood conditions and poverty at local administrative ＂Caza＂ level. Results showed the integrated impact of local climate, soil and vegetation quality and socioeconomic conditions on sensitivity to desertification. A total of 78% of the territories have low and very low climate quality index preconditioning the sensitivity to desertification. Fourteen Cazas out of 26 in total, representing more than 66% of the country, have low socioeconomic satisfaction index. Furthermore, negative trends are alleviated by good quality relict soils and vegetation cover. The actual extent of desertification covers 40.48% of the national territory, much of which occurs under semi-arid climate, moderate or low soil and vegetation quality and poor living conditions. The outcome of this research adjusted the previous coarse estimates of desertification prone areas at the national level. Results allow for realistic, policy oriented local assessment for responsive land use planning and proactive sustainable, national and local land management in the context of the national action plan to combat desertification.
基金supported by the National Natural Science Foundation of China(42101382 and 42201407)the Shandong Provincial Natural Science Foundation China(ZR2020QD016 and ZR2022QD120)。
文摘The accurate simulation of regional-scale winter wheat yield is important for national food security and the balance of grain supply and demand in China.Presently,most remote sensing process models use the“biomass×harvest index(HI)”method to simulate regional-scale winter wheat yield.However,spatiotemporal differences in HI contribute to inaccuracies in yield simulation at the regional scale.Time-series dry matter partition coefficients(Fr)can dynamically reflect the dry matter partition of winter wheat.In this study,Fr equations were fitted for each organ of winter wheat using site-scale data.These equations were then coupled into a process-based and remote sensingdriven crop yield model for wheat(PRYM-Wheat)to improve the regional simulation of winter wheat yield over the North China Plain(NCP).The improved PRYM-Wheat model integrated with the fitted Fr equations(PRYM-Wheat-Fr)was validated using data obtained from provincial yearbooks.A 3-year(2000-2002)averaged validation showed that PRYM-Wheat-Fr had a higher coefficient of determination(R^(2)=0.55)and lower root mean square error(RMSE=0.94 t ha^(-1))than PRYM-Wheat with a stable HI(abbreviated as PRYM-Wheat-HI),which had R^(2) and RMSE values of 0.30 and 1.62 t ha^(-1),respectively.The PRYM-Wheat-Fr model also performed better than PRYM-Wheat-HI for simulating yield in verification years(2013-2015).In conclusion,the PRYM-Wheat-Fr model exhibited a better accuracy than the original PRYM-Wheat model,making it a useful tool for the simulation of regional winter wheat yield.
基金The National Key R&D Program of China under contract No.2019YFA0606702the National Natural Science Foundation of China under contract Nos 42176222,91858202,41630963,and 41776003+1 种基金the National Science Foundation under contract No.NSF-IIS-2123264the fund suported by the National Aeronautics and Space Administration under contract No.NASA-80NSSC20M0220.
文摘The Agulhas system is the strongest western boundary current system in the Southern Hemisphere and plays an important role in modulating the Indian-to-Atlantic Ocean water exchange by the Agulhas leakage.It is difficult to measure in situ transport of the Agulhas leakage as well as the Agulhas retroflection position due to their intermittent nature.In this study,an innovative kinematic algorithm was designed and applied to the gridded altimeter observational data,to ascertain the longitudinal position of Agulhas retroflection,the stability of Agulhas jet stream,as well as its strength.The results show that the east-west shift of retroflection is related neither to the strength of Agulhas current nor to its stability.Further analysis uncovers the connection between the westward extension of Agulhas jet stream and an anomalous cyclonic circulation at its northern side,which is likely attributed to the local wind stress curl anomaly.To confirm the effect of local wind forcing on the east-west shift of retroflection,numerical sensitivity experiments were conducted.The results show that the local wind stress can induce a similar longitudinal shift of the retroflection as altimetry observations.Further statistical and case study indicates that whether an Agulhas ring can continuously migrate westward to the Atlantic Ocean or re-merge into the main flow depends on the retroflection position.Therefore,the westward retroflection may contribute to a stronger Agulhas leakage than the eastward retroflection.
基金the Natural Science Foundation of China(41807285)Interdisciplinary Innovation Fund of Natural Science,NanChang University(9167-28220007-YB2107).
文摘This study aims to investigate the effects of different mapping unit scales and study area scales on the uncertainty rules of landslide susceptibility prediction(LSP).To illustrate various study area scales,Ganzhou City in China,its eastern region(Ganzhou East),and Ruijin County in Ganzhou East were chosen.Different mapping unit scales are represented by grid units with spatial resolution of 30 and 60 m,as well as slope units that were extracted by multi-scale segmentation method.The 3855 landslide locations and 21 typical environmental factors in Ganzhou City are first determined to create spatial datasets with input-outputs.Then,landslide susceptibility maps(LSMs)of Ganzhou City,Ganzhou East and Ruijin County are pro-duced using a support vector machine(SVM)and random forest(RF),respectively.The LSMs of the above three regions are then extracted by mask from the LSM of Ganzhou City,along with the LSMs of Ruijin County from Ganzhou East.Additionally,LSMs of Ruijin at various mapping unit scales are generated in accordance.Accuracy and landslide suscepti-bility indexes(LSIs)distribution are used to express LSP uncertainties.The LSP uncertainties under grid units significantly decrease as study area scales decrease from Ganzhou City,Ganzhou East to Ruijin County,whereas those under slope units are less affected by study area scales.Of course,attentions should also be paid to the broader representativeness of large study areas.The LSP accuracy of slope units increases by about 6%–10%compared with those under grid units with 30 m and 60 m resolution in the same study area's scale.The significance of environmental factors exhibits an averaging trend as study area scale increases from small to large.The importance of environmental factors varies greatly with the 60 m grid unit,but it tends to be consistent to some extent in the 30 m grid unit and the slope unit.
基金This work was mainly supported by the National High-Technology Research and Development Program(863)[grant number 2013AA122801]the National Science Foundation of the United States[Award No.1251095]+3 种基金Also it was partially supported by the Fundamental Research Funds for the Central Universities[grant number ZYGX2015J111]the project entitled‘Design and development of the parallelism for typical remote sensing image algorithm based on heterogeneous computing’from the Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciencesthe project entitled‘CAST Innovation Fund:the Study of Agent and Cloud Based Spatial Big Data Service Chain’also the National Natural Science Foundation of China[grant number 51277167].
文摘MODerate resolution atmospheric TRANsmission(MODTRAN)is a commercial remote sensing(RS)software package that has been widely used to simulate radiative transfer of electromagnetic radiation through the Earth’s atmosphere and the radiation observed by a remote sensor.However,when very large RS datasets must be processed in simulation applications at a global scale,it is extremely time-consuming to operate MODTRAN on a modern workstation.Under this circumstance,the use of parallel cluster computing to speed up the process becomes vital to this time-consuming task.This paper presents PMODTRAN,an implementation of a parallel task-scheduling algorithm based on MODTRAN.PMODTRAN was able to reduce the processing time of the test cases used here from over 4.4 months on a workstation to less than a week on a local computer cluster.In addition,PMODTRAN can distribute tasks with different levels of granularity and has some extra features,such as dynamic load balancing and parameter checking.
文摘Carbon Monoxide(CO)is an important urban pollutant with a relation to 5,transition economies based on emission intensities.In this study,Sentinel-MODerate resolution Imaging Spectroradiometer(MODIS),and Landsat-8 images were used to investigate the variations of CO and urban environmental indices and the correlations between them.From the assessed correlations for 932 Iranian cities,it occurred that the assessed indices were all correlated.The highest CO levels were 0.031 in the spring of 2019 and 2020,whereas in 2021 it was equal to 0.030 in both the spring and winter,respectively.In 2019 and 2020 the maximum values of the Enhanced Vegetation Index(EVI)in the spring were 0.181 and 0.183.Exceptionally high Absorbing Aerosol Index(AAI)values of–0.834 and–1.0,along with Urban Index(UI)of 0.102 and 0.092,were correlated with recorded spikes in CO level,despite that these seasons’EVI values were not so abnormal.It was forecasted that in 2030 rises in the CO level by 13.2%in the winter and by 17.5%in the fall are expected,with the simultaneous increase of AAI by 204.5%and 980.2%,and Aerosol Optical Depth(AOD)by 27%and 5%in the winter and spring,respectively.
