Glacier recession is a globally occurring trend. Although a rich body of work has documented glacial response to climate warming, few studies have assessed vegetation cover change in recently deglaciated areas, especi...Glacier recession is a globally occurring trend. Although a rich body of work has documented glacial response to climate warming, few studies have assessed vegetation cover change in recently deglaciated areas, especially using geospatial technologies. Here, vegetation change at two glacier forefronts in Glacier National Park, Montana, U.S.A.was quantified through remote sensing analysis,fieldwork validation, and statistical modeling.Specifically, we assessed the spatial and temporal patterns of landcover change at the two glacier forefronts in Glacier National Park and determined the role of selected biophysical terrain factors(elevation, slope, aspect, solar radiation, flow accumulation, topographic wetness index, and surficial geology) on vegetation change(from nonvegetated to vegetated cover) at the deglaciated areas.Landsat imagery of the study locations in 1991, 2003,and 2015 were classified and validated using visual interpretation. Model results revealed geographic differences in biophysical correlates of vegetation change between the study areas, suggesting that terrain variation is a key factor affecting spatialtemporal patterns of vegetation change. At Jackson Glacier forefront, increases in vegetation over some portion or all of the study period were negatively associated with elevation, slope angle, and consolidated bedrock. At Grinnell Glacier forefront,increases in vegetation associated negatively with elevation and positively with solar radiation.Integrated geospatial and field approaches to the study of vegetation change in recently deglaciated terrain are recommended to understand and monitor processes and patterns of ongoing habitat change in rapidly changing mountain environments.展开更多
Rice(Oryza sativa L.) is the most important staple crop of China, and its production is related to both natural condition and human activities. It is fundamental to comprehensively assess the influence of terrain cond...Rice(Oryza sativa L.) is the most important staple crop of China, and its production is related to both natural condition and human activities. It is fundamental to comprehensively assess the influence of terrain conditions on rice production to ensure a steady increase in rice production. Although many studies have focused on the impact of one or several specific factors on crop production, few studies have investigated the direct influence of terrain conditions on rice production. Therefore, we selected Hunan Province, one of the major rice-producing areas in China, which exhibits complex terrain conditions, as our study area. Based on remote sensing data and statistical data, we applied spatial statistical analysis to explore the effects of terrain factors on rice production in terms of the following three aspects: the spatial patterns of paddy fields, the rice production process and the final yield. We found that 1) terrain has a significant impact on the spatial distribution of paddy fields at both the regional scale and the county scale; 2) terrain controls the distribution of temperature, sunlight and soil, and these three environmental factors consequently directly impact rice growth; 3) compared with the patterns of paddy fields and the rice production process, the influences of terrain factors on the rice yield are not as evident, with the exception of elevation; and 4) the spatial distribution of paddy fields mismatched that of production resources due to terrain factors. Our results strongly suggest that managers should scientifically guide farmers to choose suitable varieties and planting systems and allocate rice production resources in the northern plain regions to ensure food security.展开更多
Based on the estimating rule of the normal vector angles between two adjacent terrain units, we use the concept of terrain complexity factor to quantify the terrain complexity of DEM, and then the formula of terrain c...Based on the estimating rule of the normal vector angles between two adjacent terrain units, we use the concept of terrain complexity factor to quantify the terrain complexity of DEM, and then the formula of terrain complexity factor in Raster DEM and TIN DEM is deduced theoretically. In order to make clear how the terrain complexity factor ECF and the average elevation h affect the accuracy of DEM terrain representation RMSEEt, the formula of Gauss synthetical surface is applied to simulate several real terrain surfaces, each of which has different terrain complexity. Through the statistical analysis of linear regression in simula- tion data, the linear equation between accuracy of DEM terrain representation RMSEEt, terrain complexity factor ECF and the average elevation h is achieved. A new method is provided to estimate the accuracy of DEM terrain representation RMSEEt with a certain terrain complexity and it gives convincing theoretical evidence for DEM production and the corresponding error research in the future.展开更多
Topographic feature points and lines are the framework of topography,and their spatial distance relationship is an breakthrough in the study of topographical geometry,internal structure and development level.Proximity...Topographic feature points and lines are the framework of topography,and their spatial distance relationship is an breakthrough in the study of topographical geometry,internal structure and development level.Proximity distance(PD)is an indicator to describe the distance between the gully source point(GSP)and the watershed boundary.In the upstream catchment area,PDs can be expressed by the streamline proximity distance(SPD),as well as by the horizontal proximity distance(HPD)and the vertical proximity distance(VPD)in the horizontal and vertical dimensions,respectively.The series of indicators(e.g.,SPD,HPD and VPD)are important for quantifying the geomorphological development process of a loess basin because of the headward erosion of loess gullies.In this study,the digital elevation model data with 5 m resolution and a digital topographic analysis method are used for the statistical analyses of the SPD,VPD and HPD in 50 sample areas of 6 geomorphic types in the Loess Plateau of northern Shaanxi.The spatial characteristics and the influencing factors are also analysed.Results show that:1)Central tendencies for the HPDs and the VPDs for the whole study area and the six typical loess landforms are evident.2)Spatial patterns of the HPDs and the VPDs exhibit evident trends and zonal distributions over the whole study area.3)The HPDs have a strong positive correlation with gully density(GD)and hypsometric integral.The VPDs also correlates with GD to an extent.Vegetation cover,mean annual precipitation and loess thickness have stronger effects on the VPD than on the HPD.展开更多
In China’s Loess Plateau severe gully erosion(LPGE)region,the shoulder-line is the most intuitive and unique manifestation of the loess landform,which divides a landform into positive and negative terrains(PNTs).The ...In China’s Loess Plateau severe gully erosion(LPGE)region,the shoulder-line is the most intuitive and unique manifestation of the loess landform,which divides a landform into positive and negative terrains(PNTs).The spatial combination model of PNTs is of great significance for revealing the evolution of the loess landform.This study modeled and proposed the Surface Nibble Degree(SND),which is a new index that reflects the comparison of the areas of PNTs.Based on 5 m DEMs and matched high-resolution remote sensing images,the PNTs of 164 complete watersheds in the LPGE were extracted accurately,and the SND index was calculated.The spatial distribution trend of SND was discussed,and the relationship between SND and the factors that affect the evolution mechanism of regional landform was explored further.Results show that:(1)The SND can be calculated formally.It can quantify the development of the loess landform well.(2)The SND of the LPGE has evident spatial differentiation that increases from southwest to northeast.High values appear in Shenmu of Shaanxi,Shilou of Shanxi,and northern Yanhe River,whereas the low values are mainly distributed in the southern loess tableland and the inclined elongated ridge area of Pingliang in Gansu and Guyuan in Ningxia.(3)In the Wuding River and Yanhe River,the SND decreases with the increase in flow length(FL).In the North-Luohe River and Jinghe River,the SND increases with FL.(4)SND is significantly correlated with gully density and sediment modulus and moderately correlated with hypsometric integral.As for the mechanism factors analysis,the relationship between loess thickness and SND is not obvious,but SND increased first and then decreased with the increase of precipitation and vegetation in each geographical division,and we found that the land use type of low coverage grassland has greater erosion potential.展开更多
[目的]分析雅砻河流域生态系统服务价值的变化,为流域生态文明建设提供科学依据。[方法]基于高精度土地利用数据,运用地形梯度分级、标准差椭圆和地理探测器,探究了近20年流域生态系统服务价值(Ecosystem service value,ESV)的时空演变...[目的]分析雅砻河流域生态系统服务价值的变化,为流域生态文明建设提供科学依据。[方法]基于高精度土地利用数据,运用地形梯度分级、标准差椭圆和地理探测器,探究了近20年流域生态系统服务价值(Ecosystem service value,ESV)的时空演变和地形梯度分异特征。[结果](1)草地是流域主要的土地利用类型,占比达92.45%。研究期内林地、灌木和草地面积有所减少,其他类型有所增加;(2)流域ESV在研究期间增长了0.13%(0.16亿元),ESV在空间上呈现出东南部高西北部低的特征。随着海拔、坡度和地形起伏度的抬升,ESV呈先增后降的分布规律,坡向梯度呈阴坡高于阳坡的特征。(3)流域ESV的标准差椭圆和重心在研究期内逐渐向西南方向移动,并趋于聚集。ESV的空间分异主要受自然和经济因子的影响,其中年均地温是主导因子(q=0.24),任意因子交互均增强了其分异性。[结论]依据雅砻河流域ESV的时空和地形分布特征,采取因地制宜的生态保护措施,促进流域生态环境的可持续发展。展开更多
探究安宁河流域生态系统服务价值(ecosystem service value,ESV)的地形梯度分布规律及其空间分异的驱动因子,对流域实施因地制宜的国土空间规划和生态环境保护策略具有积极意义。利用2000—2020年高精度土地利用数据,通过CA-Markov模型...探究安宁河流域生态系统服务价值(ecosystem service value,ESV)的地形梯度分布规律及其空间分异的驱动因子,对流域实施因地制宜的国土空间规划和生态环境保护策略具有积极意义。利用2000—2020年高精度土地利用数据,通过CA-Markov模型预测流域2030年土地利用结构,分析2000—2030年安宁河流域ESV变化,并借助地形因子和地理探测器分析ESV的地形梯度分布特征和空间分异驱动因素。结果表明:(1)2000—2020年安宁河流域ESV减少0.51%,2020—2030年预计受林地锐减影响,ESV将继续减少;(2)林地贡献了安宁河流域74%以上的ESV;气候调节和水文调节是ESV贡献率突出的生态系统服务功能,合计达47.79%以上;(3)总ESV和各项生态系统服务功能的ESV随海拔、坡度和地形起伏度增大呈先增后减的分布特征,坡向上分布较均匀;其中2300~2579 m海拔、30.92°~35.73°坡度、507~602 m地形起伏度和西坡分布最多;(4)流域ESV空间分异受自然和经济因子的共同作用影响,其中地形起伏度为主导因子。展开更多
基金Financial support was provided by the Virginia Tech, Department of Geography, Sidman P. Poole Endowment
文摘Glacier recession is a globally occurring trend. Although a rich body of work has documented glacial response to climate warming, few studies have assessed vegetation cover change in recently deglaciated areas, especially using geospatial technologies. Here, vegetation change at two glacier forefronts in Glacier National Park, Montana, U.S.A.was quantified through remote sensing analysis,fieldwork validation, and statistical modeling.Specifically, we assessed the spatial and temporal patterns of landcover change at the two glacier forefronts in Glacier National Park and determined the role of selected biophysical terrain factors(elevation, slope, aspect, solar radiation, flow accumulation, topographic wetness index, and surficial geology) on vegetation change(from nonvegetated to vegetated cover) at the deglaciated areas.Landsat imagery of the study locations in 1991, 2003,and 2015 were classified and validated using visual interpretation. Model results revealed geographic differences in biophysical correlates of vegetation change between the study areas, suggesting that terrain variation is a key factor affecting spatialtemporal patterns of vegetation change. At Jackson Glacier forefront, increases in vegetation over some portion or all of the study period were negatively associated with elevation, slope angle, and consolidated bedrock. At Grinnell Glacier forefront,increases in vegetation associated negatively with elevation and positively with solar radiation.Integrated geospatial and field approaches to the study of vegetation change in recently deglaciated terrain are recommended to understand and monitor processes and patterns of ongoing habitat change in rapidly changing mountain environments.
基金Creative Research Groups of National Natural Science Foundation of China,No.41621061National Natural Science Foundation of China,No.41571493,No.31561143003
文摘Rice(Oryza sativa L.) is the most important staple crop of China, and its production is related to both natural condition and human activities. It is fundamental to comprehensively assess the influence of terrain conditions on rice production to ensure a steady increase in rice production. Although many studies have focused on the impact of one or several specific factors on crop production, few studies have investigated the direct influence of terrain conditions on rice production. Therefore, we selected Hunan Province, one of the major rice-producing areas in China, which exhibits complex terrain conditions, as our study area. Based on remote sensing data and statistical data, we applied spatial statistical analysis to explore the effects of terrain factors on rice production in terms of the following three aspects: the spatial patterns of paddy fields, the rice production process and the final yield. We found that 1) terrain has a significant impact on the spatial distribution of paddy fields at both the regional scale and the county scale; 2) terrain controls the distribution of temperature, sunlight and soil, and these three environmental factors consequently directly impact rice growth; 3) compared with the patterns of paddy fields and the rice production process, the influences of terrain factors on the rice yield are not as evident, with the exception of elevation; and 4) the spatial distribution of paddy fields mismatched that of production resources due to terrain factors. Our results strongly suggest that managers should scientifically guide farmers to choose suitable varieties and planting systems and allocate rice production resources in the northern plain regions to ensure food security.
基金Supported by Innovation Program of Shanghai Municipal Education Commission (No.10ZZ25)the Key Laboratory of Geo-informatics of State Bureau of Surveying and Mapping (No.200914)
文摘Based on the estimating rule of the normal vector angles between two adjacent terrain units, we use the concept of terrain complexity factor to quantify the terrain complexity of DEM, and then the formula of terrain complexity factor in Raster DEM and TIN DEM is deduced theoretically. In order to make clear how the terrain complexity factor ECF and the average elevation h affect the accuracy of DEM terrain representation RMSEEt, the formula of Gauss synthetical surface is applied to simulate several real terrain surfaces, each of which has different terrain complexity. Through the statistical analysis of linear regression in simula- tion data, the linear equation between accuracy of DEM terrain representation RMSEEt, terrain complexity factor ECF and the average elevation h is achieved. A new method is provided to estimate the accuracy of DEM terrain representation RMSEEt with a certain terrain complexity and it gives convincing theoretical evidence for DEM production and the corresponding error research in the future.
基金supported by the National Natural Science Foundation of China (Grant Nos. 41871288, 41930102 and 41602182)the Fundamental Research Funds for the Central Universities (Grant No. 2018CSLZ002)
文摘Topographic feature points and lines are the framework of topography,and their spatial distance relationship is an breakthrough in the study of topographical geometry,internal structure and development level.Proximity distance(PD)is an indicator to describe the distance between the gully source point(GSP)and the watershed boundary.In the upstream catchment area,PDs can be expressed by the streamline proximity distance(SPD),as well as by the horizontal proximity distance(HPD)and the vertical proximity distance(VPD)in the horizontal and vertical dimensions,respectively.The series of indicators(e.g.,SPD,HPD and VPD)are important for quantifying the geomorphological development process of a loess basin because of the headward erosion of loess gullies.In this study,the digital elevation model data with 5 m resolution and a digital topographic analysis method are used for the statistical analyses of the SPD,VPD and HPD in 50 sample areas of 6 geomorphic types in the Loess Plateau of northern Shaanxi.The spatial characteristics and the influencing factors are also analysed.Results show that:1)Central tendencies for the HPDs and the VPDs for the whole study area and the six typical loess landforms are evident.2)Spatial patterns of the HPDs and the VPDs exhibit evident trends and zonal distributions over the whole study area.3)The HPDs have a strong positive correlation with gully density(GD)and hypsometric integral.The VPDs also correlates with GD to an extent.Vegetation cover,mean annual precipitation and loess thickness have stronger effects on the VPD than on the HPD.
基金National Natural Science Foundation of China,No.41871288,No.41930102The Fundamental Research Funds for the Central Universities,No.GK202003064。
文摘In China’s Loess Plateau severe gully erosion(LPGE)region,the shoulder-line is the most intuitive and unique manifestation of the loess landform,which divides a landform into positive and negative terrains(PNTs).The spatial combination model of PNTs is of great significance for revealing the evolution of the loess landform.This study modeled and proposed the Surface Nibble Degree(SND),which is a new index that reflects the comparison of the areas of PNTs.Based on 5 m DEMs and matched high-resolution remote sensing images,the PNTs of 164 complete watersheds in the LPGE were extracted accurately,and the SND index was calculated.The spatial distribution trend of SND was discussed,and the relationship between SND and the factors that affect the evolution mechanism of regional landform was explored further.Results show that:(1)The SND can be calculated formally.It can quantify the development of the loess landform well.(2)The SND of the LPGE has evident spatial differentiation that increases from southwest to northeast.High values appear in Shenmu of Shaanxi,Shilou of Shanxi,and northern Yanhe River,whereas the low values are mainly distributed in the southern loess tableland and the inclined elongated ridge area of Pingliang in Gansu and Guyuan in Ningxia.(3)In the Wuding River and Yanhe River,the SND decreases with the increase in flow length(FL).In the North-Luohe River and Jinghe River,the SND increases with FL.(4)SND is significantly correlated with gully density and sediment modulus and moderately correlated with hypsometric integral.As for the mechanism factors analysis,the relationship between loess thickness and SND is not obvious,but SND increased first and then decreased with the increase of precipitation and vegetation in each geographical division,and we found that the land use type of low coverage grassland has greater erosion potential.
文摘[目的]分析雅砻河流域生态系统服务价值的变化,为流域生态文明建设提供科学依据。[方法]基于高精度土地利用数据,运用地形梯度分级、标准差椭圆和地理探测器,探究了近20年流域生态系统服务价值(Ecosystem service value,ESV)的时空演变和地形梯度分异特征。[结果](1)草地是流域主要的土地利用类型,占比达92.45%。研究期内林地、灌木和草地面积有所减少,其他类型有所增加;(2)流域ESV在研究期间增长了0.13%(0.16亿元),ESV在空间上呈现出东南部高西北部低的特征。随着海拔、坡度和地形起伏度的抬升,ESV呈先增后降的分布规律,坡向梯度呈阴坡高于阳坡的特征。(3)流域ESV的标准差椭圆和重心在研究期内逐渐向西南方向移动,并趋于聚集。ESV的空间分异主要受自然和经济因子的影响,其中年均地温是主导因子(q=0.24),任意因子交互均增强了其分异性。[结论]依据雅砻河流域ESV的时空和地形分布特征,采取因地制宜的生态保护措施,促进流域生态环境的可持续发展。
文摘探究安宁河流域生态系统服务价值(ecosystem service value,ESV)的地形梯度分布规律及其空间分异的驱动因子,对流域实施因地制宜的国土空间规划和生态环境保护策略具有积极意义。利用2000—2020年高精度土地利用数据,通过CA-Markov模型预测流域2030年土地利用结构,分析2000—2030年安宁河流域ESV变化,并借助地形因子和地理探测器分析ESV的地形梯度分布特征和空间分异驱动因素。结果表明:(1)2000—2020年安宁河流域ESV减少0.51%,2020—2030年预计受林地锐减影响,ESV将继续减少;(2)林地贡献了安宁河流域74%以上的ESV;气候调节和水文调节是ESV贡献率突出的生态系统服务功能,合计达47.79%以上;(3)总ESV和各项生态系统服务功能的ESV随海拔、坡度和地形起伏度增大呈先增后减的分布特征,坡向上分布较均匀;其中2300~2579 m海拔、30.92°~35.73°坡度、507~602 m地形起伏度和西坡分布最多;(4)流域ESV空间分异受自然和经济因子的共同作用影响,其中地形起伏度为主导因子。
