Landscape fragmentation is generally viewed as an indicator of environmental stresses or risks,but the fragmentation intensity assessment also depends on the scale of data and the definition of spatial unit.This study...Landscape fragmentation is generally viewed as an indicator of environmental stresses or risks,but the fragmentation intensity assessment also depends on the scale of data and the definition of spatial unit.This study aimed to explore the scale-dependence of forest fragmentation intensity along a moisture gradient in Yinshan Mountain of North China,and to estimate environmental sensitivity of forest fragmentation in this semi-arid landscape.We developed an automatic classification algorithm using simple linear iterative clustering(SLIC)and Gaussian mixture model(GMM),and extracted tree canopy patches from Google Earth images(GEI),with an accuracy of 89.2%in the study area.Then we convert the tree canopy patches to forest category according to definition of forest that tree density greater than 10%,and compared it with forest categories from global land use datasets,FROM-GLC10 and GlobeLand30,with spatial resolutions of 10 m and 30 m,respectively.We found that the FROM-GLC10 and GlobeLand30 datasets underestimated the forest area in Yinshan Mountain by 16.88%and 21.06%,respectively;and the ratio of open forest(OF,10%<tree coverage<40%)to closed forest(CF,tree coverage>40%)areas in the underestimated part was 2:1.The underestimations concentrated in warmer and drier areas occupied mostly by large coverage of OFs with severely fragmented canopies.Fragmentation intensity of canopies positively correlated with spring temperature while negatively correlated with summer precipitation and terrain slope.When summer precipitation was less than 300 mm or spring temperature higher than 4℃,canopy fragmentation intensity rose drastically,while the forest area percentage kept stable.Our study suggested that the spatial configuration,e.g.,sparseness,is more sensitive to drought stress than area percentage.This highlights the importance of data resolution and proper fragmentation measurements for forest patterns and environmental interpretation,which is the base of reliable ecosystem predictions with regard to the future climate scenarios.展开更多
In response to the lack of objective evaluation criteria for interpreting the spatial scales of historical streets,as well as the problem of fragmented and complex textures,this research proposes an analysis method fo...In response to the lack of objective evaluation criteria for interpreting the spatial scales of historical streets,as well as the problem of fragmented and complex textures,this research proposes an analysis method for assessing the spatial scaling effects using the Ping Ge type map.Drawing on the Ping Ge cartographic methods from the Qing Dynasty(1644-1911),it connects ancient measurement systems with objective evaluation criteria based on object relations and utilizes surveying maps from the Republic of China(1912-1949)to clarify temporal attributes of texture.The study employs a typical case study to demonstrate the feasibility of using the Ping Ge type map in interpreting historical streets.By creating the Ping Ge type map of the East-West Street in Quanzhou Ancient City and utilizing form diagrams,it reveals patterns such as scale multiplications within respective plots,transformations in linear sequences of streets,and increases or decreases in plot series scales.The case analysis indicates that the Ping Ge type map effectively uncover the spatial scaling characteristics of historical street layouts in China and can transform them into design resources that preserve the inner order of cities,thereby promoting spatial scaling awareness in the planning and design of regional architectural clusters.展开更多
Amid the escalating frequency of climate extremes,it is crucial to determine their impact on agricultural water scarcity to preserve agricultural development.Current research does not often examine how different spati...Amid the escalating frequency of climate extremes,it is crucial to determine their impact on agricultural water scarcity to preserve agricultural development.Current research does not often examine how different spatial scales and compound climate extremes influence agricultural water scarcity.Using an agricultural water scarcity index(Awsl),this study examined the effects of precipitation and temperature extremes on AwSl across secondary and tertiary river basins in China from 1971 to 2010.The results indicated a marked increase in Awsl during dry years and elevated temperatures.The analysis underscores that precipitation had a greater impact on Awsl than temperature variation.In secondary basins,AwsI was about 26%higher than the long-term average during dry years,increasing to nearly 49%in exceptionally dry conditions.By comparison,in tertiary basins,the increases were 28%and 55%,respectively.In hot years,AwSl rose by about 6.8%(7.3%for tertiary basins)above the average,surging to about 19.1%(15.5%for tertiary basins)during extremely hot periods.These results show that AwSl assessment at the tertiary basin level better captured the influence of climate extremes on Awsl than assessments at the secondary basin level,which highlights the critical importance of a finer spatial scale for a more precise assessment and forecast of water scarcity within basin scales.Also,this study has highlighted the paramount urgency of implementing strategies to tackle water scarcity issues under compound extreme dry and hot conditions.Overall,this study offers an in-depth evaluation of the influence of both precipitation and temperature variation,and research scale on water scarcity,which will help formulate better water resource management strategies.展开更多
The leaf area index(LAI) is a critical biophysical variable that describes canopy geometric structures and growth conditions.It is also an important input parameter for climate,energy and carbon cycle models.The scali...The leaf area index(LAI) is a critical biophysical variable that describes canopy geometric structures and growth conditions.It is also an important input parameter for climate,energy and carbon cycle models.The scaling effect of the LAI has always been of concern.Considering the effects of the clumping indices on the BRDF models of discrete canopies,an effective LAI is defined.The effective LAI has the same function of describing the leaf density as does the traditional LAI.Therefore,our study was based on the effective LAI.The spatial scaling effect of discrete canopies significantly differed from that of continuous canopies.Based on the directional second-derivative method of effective LAI retrieval,the mechanism responsible for the spatial scaling effect of the discrete-canopy LAI is discussed and a scaling transformation formula for the effective LAI is suggested in this paper.Theoretical analysis shows that the mean values of effective LAIs retrieved from high-resolution pixels were always equal to or larger than the effective LAIs retrieved from corresponding coarse-resolution pixels.Both the conclusions and the scaling transformation formula were validated with airborne hyperspectral remote sensing imagery obtained in Huailai County,Zhangjiakou,Hebei Province,China.The scaling transformation formula agreed well with the effective LAI retrieved from hyperspectral remote sensing imagery.展开更多
基金the Natural Science Foundation of China(Grant No.41790425).
文摘Landscape fragmentation is generally viewed as an indicator of environmental stresses or risks,but the fragmentation intensity assessment also depends on the scale of data and the definition of spatial unit.This study aimed to explore the scale-dependence of forest fragmentation intensity along a moisture gradient in Yinshan Mountain of North China,and to estimate environmental sensitivity of forest fragmentation in this semi-arid landscape.We developed an automatic classification algorithm using simple linear iterative clustering(SLIC)and Gaussian mixture model(GMM),and extracted tree canopy patches from Google Earth images(GEI),with an accuracy of 89.2%in the study area.Then we convert the tree canopy patches to forest category according to definition of forest that tree density greater than 10%,and compared it with forest categories from global land use datasets,FROM-GLC10 and GlobeLand30,with spatial resolutions of 10 m and 30 m,respectively.We found that the FROM-GLC10 and GlobeLand30 datasets underestimated the forest area in Yinshan Mountain by 16.88%and 21.06%,respectively;and the ratio of open forest(OF,10%<tree coverage<40%)to closed forest(CF,tree coverage>40%)areas in the underestimated part was 2:1.The underestimations concentrated in warmer and drier areas occupied mostly by large coverage of OFs with severely fragmented canopies.Fragmentation intensity of canopies positively correlated with spring temperature while negatively correlated with summer precipitation and terrain slope.When summer precipitation was less than 300 mm or spring temperature higher than 4℃,canopy fragmentation intensity rose drastically,while the forest area percentage kept stable.Our study suggested that the spatial configuration,e.g.,sparseness,is more sensitive to drought stress than area percentage.This highlights the importance of data resolution and proper fragmentation measurements for forest patterns and environmental interpretation,which is the base of reliable ecosystem predictions with regard to the future climate scenarios.
基金funded by the National Natural Science Foundation of China(NSFC)Key Program(Grant No.52038007)。
文摘In response to the lack of objective evaluation criteria for interpreting the spatial scales of historical streets,as well as the problem of fragmented and complex textures,this research proposes an analysis method for assessing the spatial scaling effects using the Ping Ge type map.Drawing on the Ping Ge cartographic methods from the Qing Dynasty(1644-1911),it connects ancient measurement systems with objective evaluation criteria based on object relations and utilizes surveying maps from the Republic of China(1912-1949)to clarify temporal attributes of texture.The study employs a typical case study to demonstrate the feasibility of using the Ping Ge type map in interpreting historical streets.By creating the Ping Ge type map of the East-West Street in Quanzhou Ancient City and utilizing form diagrams,it reveals patterns such as scale multiplications within respective plots,transformations in linear sequences of streets,and increases or decreases in plot series scales.The case analysis indicates that the Ping Ge type map effectively uncover the spatial scaling characteristics of historical street layouts in China and can transform them into design resources that preserve the inner order of cities,thereby promoting spatial scaling awareness in the planning and design of regional architectural clusters.
基金supported by the National Natural Science Foundation of China(32361143871,52109071,and 52239002)the Chinese Universities Scientific Fund(2024RC033 and 2023RC026)the Pinduoduo-China Agricultural University Research Fund(Grant No.PC2023A02002).
文摘Amid the escalating frequency of climate extremes,it is crucial to determine their impact on agricultural water scarcity to preserve agricultural development.Current research does not often examine how different spatial scales and compound climate extremes influence agricultural water scarcity.Using an agricultural water scarcity index(Awsl),this study examined the effects of precipitation and temperature extremes on AwSl across secondary and tertiary river basins in China from 1971 to 2010.The results indicated a marked increase in Awsl during dry years and elevated temperatures.The analysis underscores that precipitation had a greater impact on Awsl than temperature variation.In secondary basins,AwsI was about 26%higher than the long-term average during dry years,increasing to nearly 49%in exceptionally dry conditions.By comparison,in tertiary basins,the increases were 28%and 55%,respectively.In hot years,AwSl rose by about 6.8%(7.3%for tertiary basins)above the average,surging to about 19.1%(15.5%for tertiary basins)during extremely hot periods.These results show that AwSl assessment at the tertiary basin level better captured the influence of climate extremes on Awsl than assessments at the secondary basin level,which highlights the critical importance of a finer spatial scale for a more precise assessment and forecast of water scarcity within basin scales.Also,this study has highlighted the paramount urgency of implementing strategies to tackle water scarcity issues under compound extreme dry and hot conditions.Overall,this study offers an in-depth evaluation of the influence of both precipitation and temperature variation,and research scale on water scarcity,which will help formulate better water resource management strategies.
基金supported by the National Natural Science Foundation of China(Grant Nos.91025006,40871186,40730525)National Basic Research Program of China(Grant No.2007CB714402)National High Technology Research and Development Program of China(Grant Nos.2009AA12Z143,2009AA122103)
文摘The leaf area index(LAI) is a critical biophysical variable that describes canopy geometric structures and growth conditions.It is also an important input parameter for climate,energy and carbon cycle models.The scaling effect of the LAI has always been of concern.Considering the effects of the clumping indices on the BRDF models of discrete canopies,an effective LAI is defined.The effective LAI has the same function of describing the leaf density as does the traditional LAI.Therefore,our study was based on the effective LAI.The spatial scaling effect of discrete canopies significantly differed from that of continuous canopies.Based on the directional second-derivative method of effective LAI retrieval,the mechanism responsible for the spatial scaling effect of the discrete-canopy LAI is discussed and a scaling transformation formula for the effective LAI is suggested in this paper.Theoretical analysis shows that the mean values of effective LAIs retrieved from high-resolution pixels were always equal to or larger than the effective LAIs retrieved from corresponding coarse-resolution pixels.Both the conclusions and the scaling transformation formula were validated with airborne hyperspectral remote sensing imagery obtained in Huailai County,Zhangjiakou,Hebei Province,China.The scaling transformation formula agreed well with the effective LAI retrieved from hyperspectral remote sensing imagery.
