In this paper, introducing new remote sensing and geographic information technology to solve the problem of data collection and analysis, this makes the study of ecological risk assessment very quick and accurate. Tak...In this paper, introducing new remote sensing and geographic information technology to solve the problem of data collection and analysis, this makes the study of ecological risk assessment very quick and accurate. Taking the Shan Xin mining area of the tin mine in Lengshuijiang of Hunan Province as the research object, using the remote sensing image data of three periods in 2005, 2010 and 2015, the remote sensing image is classified carefully and the landscape classification map of the mining area is obtained. The ecological risk index is introduced and the ecological risk values are sampled and interpolated on the ArcGIS platform. The ecological risk spatial distribution map based on the landscape pattern index was obtained. The ecological risk was divided into 5 levels by using the Jenks natural classification method, and each ecological risk grade area was counted. The research results show that: from year 2005 to year 2010, landscape ecological risk trend of the mining area is growing up;the trend rising area of landscape ecological risk is mainly in the southwest and northeast of the Shan Xin mining field;the area of higher and high ecological risk is increasing year by year;and the trend of dispersed development in space is obvious;the development trend of ecological risk in the mining area is rapidly increasing;in 2010 - 2015, the higher and high ecological risk area decrease slightly with the increasing of area of grassland and residential low vulnerability of landscape types;the ecological risk area showed a slow decreasing trend. The research results provide an objective reference for decision making of ecological environment governance.展开更多
In view of the poor information integrity of the 3D model used to make the indoor road network and the lack of versatility of the constructed indoor road network, a method for building an indoor navigation network mod...In view of the poor information integrity of the 3D model used to make the indoor road network and the lack of versatility of the constructed indoor road network, a method for building an indoor navigation network model that can be seamlessly connected with outdoor paths is proposed in this paper. First, the IFC model is converted to the CityGML model using the BIM model as the indoor data source. Then, using GIS technology and limited Delaunay triangulation refinement algorithm, the necessary elements of indoor navigate on network model such as semantic information, geometric information and topological relationship contained in CityGML model are extracted. Finally, it is visualized and verified based on experimental model data. The results show that the indoor navigation network model constructed based on the CityGML model can accurately perform indoor navigation, make the constructed road network more general, and provide reference and technical support for the integrated construction of indoor and outdoor road network models.展开更多
Weighted total least squares(WTLS)have been regarded as the standard tool for the errors-in-variables(EIV)model in which all the elements in the observation vector and the coefficient matrix are contaminated with rand...Weighted total least squares(WTLS)have been regarded as the standard tool for the errors-in-variables(EIV)model in which all the elements in the observation vector and the coefficient matrix are contaminated with random errors.However,in many geodetic applications,some elements are error-free and some random observations appear repeatedly in different positions in the augmented coefficient matrix.It is called the linear structured EIV(LSEIV)model.Two kinds of methods are proposed for the LSEIV model from functional and stochastic modifications.On the one hand,the functional part of the LSEIV model is modified into the errors-in-observations(EIO)model.On the other hand,the stochastic model is modified by applying the Moore-Penrose inverse of the cofactor matrix.The algorithms are derived through the Lagrange multipliers method and linear approximation.The estimation principles and iterative formula of the parameters are proven to be consistent.The first-order approximate variance-covariance matrix(VCM)of the parameters is also derived.A numerical example is given to compare the performances of our proposed three algorithms with the STLS approach.Afterwards,the least squares(LS),total least squares(TLS)and linear structured weighted total least squares(LSWTLS)solutions are compared and the accuracy evaluation formula is proven to be feasible and effective.Finally,the LSWTLS is applied to the field of deformation analysis,which yields a better result than the traditional LS and TLS estimations.展开更多
In Northeast China, permafrost advanced and retreated several times under the influences of fluctuating paleo-climates and paleo-environments since the Late Pleistocene. During the last 60 years, many new data were ob...In Northeast China, permafrost advanced and retreated several times under the influences of fluctuating paleo-climates and paleo-environments since the Late Pleistocene. During the last 60 years, many new data were obtained and studies were conducted on the evolution of permafrost in Northeast China, but so far no systematic summary and review have been made. Based on sedimentary sequences, remains of past permafrost, paleo-flora and-fauna records, and dating data, permafrost evolution since the Late Pleistocene has been analyzed and reconstructed in this paper. Paleo-temperatures reconstructed from the remains of past permafrost and those from paleo-flora and-fauna are compared, and thus the southern limit of permafrost(SLP) in each climate period is inferred by the relationship of the permafrost distribution and the mean annual air/ground temperatures(MAAT/MAGT). Thus, the evolutionary history of permafrost is here divided into five stages:(1) the Late Pleistocene(Last Glaciation, or LG)(65 to 10–8.5 ka), the Last Glaciation Maximum(LGM, 21–13 ka) in particular, the coldest period in the latest history with a cooling of about 6~10 ?C, characterized by extensive occurrences of glaciation, flourishing Mammathas-Coelodonta Faunal Complex(MCFC), widespread aeolian deposits, and significant sea level lowering, and permafrost greatly expanded southwards almost to the coastal plains(37?N–41?N);(2) the Holocene Megathermal Period(HMP, 8.5–7.0 to 4.0–3.0 ka), 3~5 ?C warmer than today, permafrost retreated to about 52°N;(3) the Late Holocene Cold Period(Neoglaciation)(4.0–3.0 to 1.0–0.5 ka), a cooling of 1~3 ?C, some earlier thawed permafrost was refrozen or attached, and the SLP invaded southwards to 46?N;(4) the Little Ice Age(LIA, 500 to 100–150 a), the latest cold period with significant permafrost expansion; and(5) climate warming since the last century, during which Northeast China has undergone extensive permafrost degradation. The frequent and substantial expansions and retreats of permafrost have greatly impacted cold-region environments in Northeast China. North of the SLP during the HMP, or in the present continuous permafrost zone, the existing permafrost was largely formed during the LG and was later overlapped by the permafrost formed in the Neoglaciation. To the south, it was formed in the Neoglaciation. However, many aspects of permafrost evolution still await further investigations, such as data integration, numerical reconstruction, and merging of Chinese permafrost history with those of bordering regions as well as collaboration with related disciplines. Of these, studieson the evolution and degradation of permafrost during the past 150 years and its hydrological, ecological, and environmental impacts should be prioritized.展开更多
Automatic and accurate classification is a fundamental problem to the analysis and modeling of LiDAR(Light Detection and Ranging)data.Recently,convolutional neural network(ConvNet or CNN)has achieved remarkable perfor...Automatic and accurate classification is a fundamental problem to the analysis and modeling of LiDAR(Light Detection and Ranging)data.Recently,convolutional neural network(ConvNet or CNN)has achieved remarkable performance in image recognition and computer vision.While significant efforts have also been made to develop various deep networks for satellite image scene classification,it still needs to further investigate suitable deep learning network frameworks for 3D dense mobile laser scanning(MLS)data.In this paper,we present a simple deep CNN for multiple object classification based on multi-scale context representation.For the pointwise classification,we first extracted the neighboring points within spatial context and transformed them into a three-channel image for each point.Then,the classification task can be treated as the image recognition using CNN.The proposed CNN architecture adopted common convolution,maximum pooling and rectified linear unit(ReLU)layers,which combined multiple deeper network layers.After being trained and tested on approximately seven million labeled MLS points,the deep CNN model can classify accurately into nine classes.Comparing with the widely used ResNet algorithm,this model performs better precision and recall rates,and less processing time,which indicated the significant potential of deep-learning-based methods in MLS data classification.展开更多
Aiming at the problem of lack of data model to analyze the level of transportation integration, the paper taking Changsha-Zhuzhou-Xiangtan City Group of China as the research object, based on the Gravity measurement m...Aiming at the problem of lack of data model to analyze the level of transportation integration, the paper taking Changsha-Zhuzhou-Xiangtan City Group of China as the research object, based on the Gravity measurement model, transportation comprehensive distance model, weighted road density model, analysis of Changsha-Zhuzhou-Xiangtan City Group accessibility and transportation integration level. A new method to measure the level of traffic integration is proposed and verified by the road network data and socio-economic data of Changsha-Zhuzhou-Xiangtan City Group. The results show that: Changsha-Zhuzhou-Xiangtan City Group traffic accessibility was “point to surface” shape distribution, taking the core region of Changsha as the optimal, Xiangtan, Zhuzhou, Changsha County next, in remote Yanling County, Chaling county has the lowest accessibility;the correlation between traffic network connection degree and economic connection degree reached 0.871, indicating that the transportation integration level of urban agglomerations has a high degree of fit with the level of economic integration. The research results on the one hand for the Chang-Zhuzhou-Xiangtan urban agglomeration traffic present situation to make an annotation;on the other hand, that provide a reference for further optimization of Changsha-Zhuzhou-Xiangtan urban agglomeration traffic planning.展开更多
In order to simplify the three-dimensional building group model, this paper proposes a clustering generalization method based on visual cognitive theory. The method uses road elements to roughly divide scenes, and the...In order to simplify the three-dimensional building group model, this paper proposes a clustering generalization method based on visual cognitive theory. The method uses road elements to roughly divide scenes, and then uses spatial cognitive elements such as direction, area, height and their topological constraints to classify them precisely, so as to make them conform to the urban morphological characteristics. Delaunay triangulation network and boundary tracking synthesis algorithm are used to merge and summarize the models, and the models are stored hierarchically. The proposed algorithm should be verified experimentally with a typical urban complex model. The experimental results show that the efficiency of the method used in this paper is at least 20% higher than that of previous one, and with the growth of test data, the higher efficiency is improved. The classification results conform to human cognitive habits, and the generalization levels of different models can be relatively unified by adaptive control of each threshold in the clustering generalization process.展开更多
Data scarcity is a major obstacle for high-resolution mapping of permafrost on the Tibetan Plateau(TP).This study produces a new permafrost stability distribution map for the 2010 s(2005–2015)derived from the predict...Data scarcity is a major obstacle for high-resolution mapping of permafrost on the Tibetan Plateau(TP).This study produces a new permafrost stability distribution map for the 2010 s(2005–2015)derived from the predicted mean annual ground temperature(MAGT)at a depth of zero annual amplitude(10–25 m)by integrating remotely sensed freezing degree-days and thawing degree-days,snow cover days,leaf area index,soil bulk density,high-accuracy soil moisture data,and in situ MAGT measurements from 237 boreholes on the TP by using an ensemble learning method that employs a support vector regression model based on distance-blocked resampled training data with 200 repetitions.Validation of the new permafrost map indicates that it is probably the most accurate of all currently available maps.This map shows that the total area of permafrost on the TP,excluding glaciers and lakes,is approximately 115.02(105.47–129.59)×10^4 km^2.The areas corresponding to the very stable,stable,semi-stable,transitional,and unstable types are 0.86×10^4,9.62×10^4,38.45×10^4,42.29×10^4,and 23.80×10^4 km^2,respectively.This new map is of fundamental importance for engineering planning and design,ecosystem management,and evaluation of the permafrost change in the future on the TP as a baseline.展开更多
The Nanwenghe Wetlands Reserve in the Yile'huli Mountains is a representative region of the Xing'an permafrost.The response of permafrost to climate change remains unclear due to limited field investigations.T...The Nanwenghe Wetlands Reserve in the Yile'huli Mountains is a representative region of the Xing'an permafrost.The response of permafrost to climate change remains unclear due to limited field investigations.Thus,longer-term responses of the ground thermal state to climate change since 2011 have been monitored at four sites with varied surface characteristics:Carex tato wetland(P1)and shrub-C.tato wetland(P2)with a multi-year average temperatures at the depth of zero annual amplitude(T_(ZAA))of−0.52 and−1.19℃,respectively;Betula platyphylla-Larix gmelinii(Rupr.)Kuzen mixed forest(P3)with T_(ZAA) of 0.17℃,and;the forest of L.gmelinii(Rupr.)Kuzen(P4)with T_(ZAA) of 1.65℃.Continuous observations demonstrate that the ecosystem-protected Xing'an permafrost experienced a cooling under a warming climate.The temperature at the top of permafrost(TTOP)rose(1.8℃ per decade)but the TZAA declined(−0.14℃ per decade),while the active layer thickness(ALT)thinned from 0.9 m in 2012 to 0.8 m in 2014 at P1.Both the TTOP and TZAA increased(0.89 and 0.06℃ per decade,respectively),but the ALT thinned from 1.4 m in 2012 to 0.7 m in 2016 at P2.Vertically detached permafrost at P3 disappeared in summer 2012,with warming rates of+0.42 and+0.17℃ per decade for TTOP and T_(ZAA),respectively.However,up to date,the ground thermal state has remained stable at P4.We conclude that the thermal offset is crucial for the preservation and persistence of the Xing'an permafrost at the southern fringe.展开更多
The automatic classification of power lines from airborne light detection and ranging(LiDAR)data is a crucial task for power supply management.The methods for power line classification can be either supervised or unsu...The automatic classification of power lines from airborne light detection and ranging(LiDAR)data is a crucial task for power supply management.The methods for power line classification can be either supervised or unsupervised.Supervised methods might achieve high accuracy for small areas,but it is time consuming to collect training data over areas of different conditions and complexity.Therefore,unsupervised methods that can automatically work over different areas without sophisticated parameter tuning are in great demand.In this paper,we presented a hierarchical unsupervised LiDAR-based power line classification method that first screened the power line candidate points(including the power line corridor direction detection based on a layered Hough transform,connectivity analysis,and Douglas–Peucker simplification algorithm),followed by the extraction of contextual linear and angular features for each candidate laser points,and finally by setting the feature threshold values to identify the power line points.We tested the method over both forest and urban areas and found that the precision,recall and quality rates were up to 96.7%,88.8%and 78.3%,respectively,for the test datasets and were higher than the ones from a previously developed supervised classification method.Overall,our approach has the advantages of achieving relatively high accuracy and being relatively fast.展开更多
Land surface albedo is a critical variable in determining surface energy balance,and regulating climate and ecosystem processes through feedback mechanisms.Therefore,climatic modelers and radiative monitoring require ...Land surface albedo is a critical variable in determining surface energy balance,and regulating climate and ecosystem processes through feedback mechanisms.Therefore,climatic modelers and radiative monitoring require accurate estimates of land surface albedo.With the instrument development,algorithm upgrade,spectral-band-adjustment in wavelength center or band width,and the increasing distinct requirement from diversified communities,various albedo terms have been generated in related satellite-based products.The lack of understanding on the divergence of these terminologies can introduce potential considerable errors in the subsequent applications,or an elevated probability to invert the deduced conclusion.We surveyed the basic concepts of reflectance quantities,retrieval strategies,and models developed since the 1970s,and discuss both strength and opportunity for improvements on land surface albedo extraction,and product generation.In addition,we exemplified the difference of albedo terms using the daily MODIS product(MCD43A)to emphasize the potential risk of the ambiguous usage,over typical IGBP land covers in Northern Kazakhstan.Our investigation shows that relative differences among various albedo terms can reach up to 181%and 50%,while 0.266 and 0.118 of absolute variance respectively in the narrow and broad-band surface albedo,which illuminated cautions against the ambiguous understanding of albedo terminologies or erroneous usage of albedo products.展开更多
文摘In this paper, introducing new remote sensing and geographic information technology to solve the problem of data collection and analysis, this makes the study of ecological risk assessment very quick and accurate. Taking the Shan Xin mining area of the tin mine in Lengshuijiang of Hunan Province as the research object, using the remote sensing image data of three periods in 2005, 2010 and 2015, the remote sensing image is classified carefully and the landscape classification map of the mining area is obtained. The ecological risk index is introduced and the ecological risk values are sampled and interpolated on the ArcGIS platform. The ecological risk spatial distribution map based on the landscape pattern index was obtained. The ecological risk was divided into 5 levels by using the Jenks natural classification method, and each ecological risk grade area was counted. The research results show that: from year 2005 to year 2010, landscape ecological risk trend of the mining area is growing up;the trend rising area of landscape ecological risk is mainly in the southwest and northeast of the Shan Xin mining field;the area of higher and high ecological risk is increasing year by year;and the trend of dispersed development in space is obvious;the development trend of ecological risk in the mining area is rapidly increasing;in 2010 - 2015, the higher and high ecological risk area decrease slightly with the increasing of area of grassland and residential low vulnerability of landscape types;the ecological risk area showed a slow decreasing trend. The research results provide an objective reference for decision making of ecological environment governance.
文摘In view of the poor information integrity of the 3D model used to make the indoor road network and the lack of versatility of the constructed indoor road network, a method for building an indoor navigation network model that can be seamlessly connected with outdoor paths is proposed in this paper. First, the IFC model is converted to the CityGML model using the BIM model as the indoor data source. Then, using GIS technology and limited Delaunay triangulation refinement algorithm, the necessary elements of indoor navigate on network model such as semantic information, geometric information and topological relationship contained in CityGML model are extracted. Finally, it is visualized and verified based on experimental model data. The results show that the indoor navigation network model constructed based on the CityGML model can accurately perform indoor navigation, make the constructed road network more general, and provide reference and technical support for the integrated construction of indoor and outdoor road network models.
基金the financial support of the National Natural Science Foundation of China(Grant No.42074016,42104025,42274057and 41704007)Hunan Provincial Natural Science Foundation of China(Grant No.2021JJ30244)Scientific Research Fund of Hunan Provincial Education Department(Grant No.22B0496)。
文摘Weighted total least squares(WTLS)have been regarded as the standard tool for the errors-in-variables(EIV)model in which all the elements in the observation vector and the coefficient matrix are contaminated with random errors.However,in many geodetic applications,some elements are error-free and some random observations appear repeatedly in different positions in the augmented coefficient matrix.It is called the linear structured EIV(LSEIV)model.Two kinds of methods are proposed for the LSEIV model from functional and stochastic modifications.On the one hand,the functional part of the LSEIV model is modified into the errors-in-observations(EIO)model.On the other hand,the stochastic model is modified by applying the Moore-Penrose inverse of the cofactor matrix.The algorithms are derived through the Lagrange multipliers method and linear approximation.The estimation principles and iterative formula of the parameters are proven to be consistent.The first-order approximate variance-covariance matrix(VCM)of the parameters is also derived.A numerical example is given to compare the performances of our proposed three algorithms with the STLS approach.Afterwards,the least squares(LS),total least squares(TLS)and linear structured weighted total least squares(LSWTLS)solutions are compared and the accuracy evaluation formula is proven to be feasible and effective.Finally,the LSWTLS is applied to the field of deformation analysis,which yields a better result than the traditional LS and TLS estimations.
基金supported by the Subproject No. XDA05120302 (Permafrost Extent in China during the Last Glaciation Maximum and Megathermal)Strategic Pilot Science and Technology Program of the Chinese Academy of Sciences (Identification of Carbon Budgets for Adaptation to Changing Climate and the Associated Issues) (Grant No. XDA05000000)the auspices of the International Permafrost Association (IPA) Action Group on "Last Permafrost Maximum and Minimum (LPMM) on the Eurasian Continent"
文摘In Northeast China, permafrost advanced and retreated several times under the influences of fluctuating paleo-climates and paleo-environments since the Late Pleistocene. During the last 60 years, many new data were obtained and studies were conducted on the evolution of permafrost in Northeast China, but so far no systematic summary and review have been made. Based on sedimentary sequences, remains of past permafrost, paleo-flora and-fauna records, and dating data, permafrost evolution since the Late Pleistocene has been analyzed and reconstructed in this paper. Paleo-temperatures reconstructed from the remains of past permafrost and those from paleo-flora and-fauna are compared, and thus the southern limit of permafrost(SLP) in each climate period is inferred by the relationship of the permafrost distribution and the mean annual air/ground temperatures(MAAT/MAGT). Thus, the evolutionary history of permafrost is here divided into five stages:(1) the Late Pleistocene(Last Glaciation, or LG)(65 to 10–8.5 ka), the Last Glaciation Maximum(LGM, 21–13 ka) in particular, the coldest period in the latest history with a cooling of about 6~10 ?C, characterized by extensive occurrences of glaciation, flourishing Mammathas-Coelodonta Faunal Complex(MCFC), widespread aeolian deposits, and significant sea level lowering, and permafrost greatly expanded southwards almost to the coastal plains(37?N–41?N);(2) the Holocene Megathermal Period(HMP, 8.5–7.0 to 4.0–3.0 ka), 3~5 ?C warmer than today, permafrost retreated to about 52°N;(3) the Late Holocene Cold Period(Neoglaciation)(4.0–3.0 to 1.0–0.5 ka), a cooling of 1~3 ?C, some earlier thawed permafrost was refrozen or attached, and the SLP invaded southwards to 46?N;(4) the Little Ice Age(LIA, 500 to 100–150 a), the latest cold period with significant permafrost expansion; and(5) climate warming since the last century, during which Northeast China has undergone extensive permafrost degradation. The frequent and substantial expansions and retreats of permafrost have greatly impacted cold-region environments in Northeast China. North of the SLP during the HMP, or in the present continuous permafrost zone, the existing permafrost was largely formed during the LG and was later overlapped by the permafrost formed in the Neoglaciation. To the south, it was formed in the Neoglaciation. However, many aspects of permafrost evolution still await further investigations, such as data integration, numerical reconstruction, and merging of Chinese permafrost history with those of bordering regions as well as collaboration with related disciplines. Of these, studieson the evolution and degradation of permafrost during the past 150 years and its hydrological, ecological, and environmental impacts should be prioritized.
基金funded by the Chinese National Fund Projects (Nos. 41401028, 41201066)by the State Key Laboratory of Frozen Soils Engineering (Project No. SKLFSE201201)
文摘daily air temperature;;gap filling;;Kriging spatial interpolation;;northeast
基金National Natural Science Foundation of China(Nos.41971423,31972951,41771462)Hunan Provincial Natural Science Foundation of China(No.2020JJ3020)+1 种基金Science and Technology Planning Project of Hunan Province(No.2019RS2043,2019GK2132)Outstanding Youth Project of Education Department of Hunan Province(No.18B224)。
文摘Automatic and accurate classification is a fundamental problem to the analysis and modeling of LiDAR(Light Detection and Ranging)data.Recently,convolutional neural network(ConvNet or CNN)has achieved remarkable performance in image recognition and computer vision.While significant efforts have also been made to develop various deep networks for satellite image scene classification,it still needs to further investigate suitable deep learning network frameworks for 3D dense mobile laser scanning(MLS)data.In this paper,we present a simple deep CNN for multiple object classification based on multi-scale context representation.For the pointwise classification,we first extracted the neighboring points within spatial context and transformed them into a three-channel image for each point.Then,the classification task can be treated as the image recognition using CNN.The proposed CNN architecture adopted common convolution,maximum pooling and rectified linear unit(ReLU)layers,which combined multiple deeper network layers.After being trained and tested on approximately seven million labeled MLS points,the deep CNN model can classify accurately into nine classes.Comparing with the widely used ResNet algorithm,this model performs better precision and recall rates,and less processing time,which indicated the significant potential of deep-learning-based methods in MLS data classification.
文摘Aiming at the problem of lack of data model to analyze the level of transportation integration, the paper taking Changsha-Zhuzhou-Xiangtan City Group of China as the research object, based on the Gravity measurement model, transportation comprehensive distance model, weighted road density model, analysis of Changsha-Zhuzhou-Xiangtan City Group accessibility and transportation integration level. A new method to measure the level of traffic integration is proposed and verified by the road network data and socio-economic data of Changsha-Zhuzhou-Xiangtan City Group. The results show that: Changsha-Zhuzhou-Xiangtan City Group traffic accessibility was “point to surface” shape distribution, taking the core region of Changsha as the optimal, Xiangtan, Zhuzhou, Changsha County next, in remote Yanling County, Chaling county has the lowest accessibility;the correlation between traffic network connection degree and economic connection degree reached 0.871, indicating that the transportation integration level of urban agglomerations has a high degree of fit with the level of economic integration. The research results on the one hand for the Chang-Zhuzhou-Xiangtan urban agglomeration traffic present situation to make an annotation;on the other hand, that provide a reference for further optimization of Changsha-Zhuzhou-Xiangtan urban agglomeration traffic planning.
文摘In order to simplify the three-dimensional building group model, this paper proposes a clustering generalization method based on visual cognitive theory. The method uses road elements to roughly divide scenes, and then uses spatial cognitive elements such as direction, area, height and their topological constraints to classify them precisely, so as to make them conform to the urban morphological characteristics. Delaunay triangulation network and boundary tracking synthesis algorithm are used to merge and summarize the models, and the models are stored hierarchically. The proposed algorithm should be verified experimentally with a typical urban complex model. The experimental results show that the efficiency of the method used in this paper is at least 20% higher than that of previous one, and with the growth of test data, the higher efficiency is improved. The classification results conform to human cognitive habits, and the generalization levels of different models can be relatively unified by adaptive control of each threshold in the clustering generalization process.
基金This paper is funded jointly by projects of the National Natural Science Foundation of China （41571374）, the key research project of Hunan Education Ministry （No.16A070）, Nature Science Joint Funding of Hunan province and Xiangtan Local （No.2017JJ4037）.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDA19070204)the National Natural Science Foundation of China(Grant Nos.42071421,41630856)。
文摘Data scarcity is a major obstacle for high-resolution mapping of permafrost on the Tibetan Plateau(TP).This study produces a new permafrost stability distribution map for the 2010 s(2005–2015)derived from the predicted mean annual ground temperature(MAGT)at a depth of zero annual amplitude(10–25 m)by integrating remotely sensed freezing degree-days and thawing degree-days,snow cover days,leaf area index,soil bulk density,high-accuracy soil moisture data,and in situ MAGT measurements from 237 boreholes on the TP by using an ensemble learning method that employs a support vector regression model based on distance-blocked resampled training data with 200 repetitions.Validation of the new permafrost map indicates that it is probably the most accurate of all currently available maps.This map shows that the total area of permafrost on the TP,excluding glaciers and lakes,is approximately 115.02(105.47–129.59)×10^4 km^2.The areas corresponding to the very stable,stable,semi-stable,transitional,and unstable types are 0.86×10^4,9.62×10^4,38.45×10^4,42.29×10^4,and 23.80×10^4 km^2,respectively.This new map is of fundamental importance for engineering planning and design,ecosystem management,and evaluation of the permafrost change in the future on the TP as a baseline.
基金This study is financially supported by the program of National Natural Science Foundation of China(41401081,41871052,41771074)Joint Key Program of NSFC‒Heilongjiang Province for Regional Development(U20A2082)the Research Project of the State Key Laboratory of Frozen Soil Engineering(SKLFSE-ZT-41,SKLFSE-ZY-20).
文摘The Nanwenghe Wetlands Reserve in the Yile'huli Mountains is a representative region of the Xing'an permafrost.The response of permafrost to climate change remains unclear due to limited field investigations.Thus,longer-term responses of the ground thermal state to climate change since 2011 have been monitored at four sites with varied surface characteristics:Carex tato wetland(P1)and shrub-C.tato wetland(P2)with a multi-year average temperatures at the depth of zero annual amplitude(T_(ZAA))of−0.52 and−1.19℃,respectively;Betula platyphylla-Larix gmelinii(Rupr.)Kuzen mixed forest(P3)with T_(ZAA) of 0.17℃,and;the forest of L.gmelinii(Rupr.)Kuzen(P4)with T_(ZAA) of 1.65℃.Continuous observations demonstrate that the ecosystem-protected Xing'an permafrost experienced a cooling under a warming climate.The temperature at the top of permafrost(TTOP)rose(1.8℃ per decade)but the TZAA declined(−0.14℃ per decade),while the active layer thickness(ALT)thinned from 0.9 m in 2012 to 0.8 m in 2014 at P1.Both the TTOP and TZAA increased(0.89 and 0.06℃ per decade,respectively),but the ALT thinned from 1.4 m in 2012 to 0.7 m in 2016 at P2.Vertically detached permafrost at P3 disappeared in summer 2012,with warming rates of+0.42 and+0.17℃ per decade for TTOP and T_(ZAA),respectively.However,up to date,the ground thermal state has remained stable at P4.We conclude that the thermal offset is crucial for the preservation and persistence of the Xing'an permafrost at the southern fringe.
基金the National Natural Science Foundation of China(grant numbers 41601426 and 41771462)the Hunan Provincial Natural Science Foundation(grant number 2018JJ3155)+1 种基金the Open Foundation of Key Laboratory of Digital Mapping and Land Information Application of National Administration of Surveying,Map-ping and Geoinformation,Wuhan University(grant number GCWD201806)the China Scholarship Council(grant number 201708430040).
文摘The automatic classification of power lines from airborne light detection and ranging(LiDAR)data is a crucial task for power supply management.The methods for power line classification can be either supervised or unsupervised.Supervised methods might achieve high accuracy for small areas,but it is time consuming to collect training data over areas of different conditions and complexity.Therefore,unsupervised methods that can automatically work over different areas without sophisticated parameter tuning are in great demand.In this paper,we presented a hierarchical unsupervised LiDAR-based power line classification method that first screened the power line candidate points(including the power line corridor direction detection based on a layered Hough transform,connectivity analysis,and Douglas–Peucker simplification algorithm),followed by the extraction of contextual linear and angular features for each candidate laser points,and finally by setting the feature threshold values to identify the power line points.We tested the method over both forest and urban areas and found that the precision,recall and quality rates were up to 96.7%,88.8%and 78.3%,respectively,for the test datasets and were higher than the ones from a previously developed supervised classification method.Overall,our approach has the advantages of achieving relatively high accuracy and being relatively fast.
基金This work was supported by the National Key Research and Development plan[#2017YFB0504204]“100 Talents Project”of CAS[Y938091&Y674141001]+2 种基金Hunan NSF[#2018JJ2116&2018JJ3151]open funding of state key laboratory of Remote Sensing Science[#OFSLRSS201102]Liaoning Revitalization Talents Program[#XLYC1802027].
文摘Land surface albedo is a critical variable in determining surface energy balance,and regulating climate and ecosystem processes through feedback mechanisms.Therefore,climatic modelers and radiative monitoring require accurate estimates of land surface albedo.With the instrument development,algorithm upgrade,spectral-band-adjustment in wavelength center or band width,and the increasing distinct requirement from diversified communities,various albedo terms have been generated in related satellite-based products.The lack of understanding on the divergence of these terminologies can introduce potential considerable errors in the subsequent applications,or an elevated probability to invert the deduced conclusion.We surveyed the basic concepts of reflectance quantities,retrieval strategies,and models developed since the 1970s,and discuss both strength and opportunity for improvements on land surface albedo extraction,and product generation.In addition,we exemplified the difference of albedo terms using the daily MODIS product(MCD43A)to emphasize the potential risk of the ambiguous usage,over typical IGBP land covers in Northern Kazakhstan.Our investigation shows that relative differences among various albedo terms can reach up to 181%and 50%,while 0.266 and 0.118 of absolute variance respectively in the narrow and broad-band surface albedo,which illuminated cautions against the ambiguous understanding of albedo terminologies or erroneous usage of albedo products.
