This paper presents a Spatial Decision Support System for local governments of developing countries.It allows municipality government,enterprises,scientific community and civil society to address decision problems usi...This paper presents a Spatial Decision Support System for local governments of developing countries.It allows municipality government,enterprises,scientific community and civil society to address decision problems using GIS.The framework is supported by four modules of information technologies:Environmental Decision Support Database,Data Manipulation,Decision Support,and Mapping.A case study is presented covering the implementation of this framework in one municipality of Cuba.An example of land suitability planning for coconut crops is used to evaluate the system performance and usability.Results show local municipalities are able to use this framework to solve local decision problems using state of the art decision making even with low infrastructure development.展开更多
A comprehensive understanding of spatial distribution and clustering patterns of gravels is of great significance for ecological restoration and monitoring.However,traditional methods for studying gravels are low-effi...A comprehensive understanding of spatial distribution and clustering patterns of gravels is of great significance for ecological restoration and monitoring.However,traditional methods for studying gravels are low-efficiency and have many errors.This study researched the spatial distribution and cluster characteristics of gravels based on digital image processing technology combined with a self-organizing map(SOM)and multivariate statistical methods in the grassland of northern Tibetan Plateau.Moreover,the correlation of morphological parameters of gravels between different cluster groups and the environmental factors affecting gravel distribution were analyzed.The results showed that the morphological characteristics of gravels in northern region(cluster C)and southern region(cluster B)of the Tibetan Plateau were similar,with a low gravel coverage,small gravel diameter,and elongated shape.These regions were mainly distributed in high mountainous areas with large topographic relief.The central region(cluster A)has high coverage of gravels with a larger diameter,mainly distributed in high-altitude plains with smaller undulation.Principal component analysis(PCA)results showed that the gravel distribution of cluster A may be mainly affected by vegetation,while those in clusters B and C could be mainly affected by topography,climate,and soil.The study confirmed that the combination of digital image processing technology and SOM could effectively analyzed the spatial distribution characteristics of gravels,providing a new mode for gravel research.展开更多
The increasing severity of ground subsidence,ground fissure and other disasters caused by the excessive exploitation of deep underground resources has highlighted the pressing need for effective management.A significa...The increasing severity of ground subsidence,ground fissure and other disasters caused by the excessive exploitation of deep underground resources has highlighted the pressing need for effective management.A significant contributing factor to the challenges faced is the inadequacy of existing soil mechanics experimental instruments in providing effective indicators,creating a bottleneck in comprehensively understanding the mechanisms of land subsidence.It is urgent to develop a multi-field and multi-functional soil mechanics experimental system to address this issue.Based soil mechanics theories,the existing manufacturing capabilities of triaxial apparatus and the practical demands of the test system,a set of multi-field coupled high-pressure triaxial system is developed tailored for testing deep soils(at depths of approximately 3000 m)and soft rock.This system incorporates specialized design elements such as high-pressure chamber and horizontal deformation testing devices.In addition to the conventional triaxial tester functions,its distinctive feature encompass a horizontal deformation tracking measuring device,a water release testing device and temperature control device for the sample.This ensemble facilitates testing of horizontal and vertical deformation water release and other parameters of samples under a specified stress conditions,at constant or varying temperature ranging from-40℃–90℃.The accuracy of the tested parameters meets the requirements of relevant current specifications.The test system not only provides scientifically robust data for revealing the deformation and failure mechanism of soil subjected to extreme temperature,but also offers critical data support for major engineering projects,deep exploration and mitigation efforts related to soil deformation-induced disaster.展开更多
Accurate mapping of loess waterworn gully(LWG)is essential to further study gully erosion and geomorphological evolution for the Chinese Loess Plateau(CLP).Due to the vertical joint and collapsibility of loess,LWGs ha...Accurate mapping of loess waterworn gully(LWG)is essential to further study gully erosion and geomorphological evolution for the Chinese Loess Plateau(CLP).Due to the vertical joint and collapsibility of loess,LWGs have the characteristics of zigzag and unique slope abruptness under synthetic action of hydraulic force and gravity.This forces existing LWG mapping methods to either focus on the improvement of mapping accuracy or center on the increase of mapping efficiency.However,simultaneously achieving accurate and efficient mapping of LWG is still in its infancy under complex topographic conditions.Here,we proposed a method that innovatively integrates the loess slope abruptness feature into an improved deep learning semantic segmentation framework for LWG mapping using 0.6 m Google imagery and 5 m DEM data.We selected four study areas representing typical loess landforms to test the performance of our method.The proposed method can achieve satisfactory mapping results,with the F1 score,mean Intersection-over-Union(mIoU),and overall accuracy of 90.5%,85.3%,and 92.3%,respectively.In addition,the proposed model also showed significant accuracy improvement by inputting additional topographic information(especially the slope of slope).Compared with existing algorithms(Random forests,original DeepLabV3+,and Unet),the proposed approach in this study achieved a better accuracy-efficiency trade-off.Overall,the method can ensure high accuracy and efficiency of the LWG mapping for different loess landform types and can be extended to study various loess gully mapping and water and soil conservation.展开更多
Vegetation gross primary production(GPP)is an important variable for the carbon cycle on the Qinghai-Tibetan Plateau(QTP).Based on the measurements from 12 eddy covariance flux sites,we validated a light use efficienc...Vegetation gross primary production(GPP)is an important variable for the carbon cycle on the Qinghai-Tibetan Plateau(QTP).Based on the measurements from 12 eddy covariance flux sites,we validated a light use efficiency model(i.e.EC-LUE)to evaluate the spatial-temporal patterns of GPP and the effect of environmental variables on QTP.In general,EC-LUE model performed well in predicting GPP at different time scale over QTP.Annual GPP over the entire QTP ranged from 575 to 703 Tg C,and showed a significantly increasing trend from 1982 to 2013.However,there were large spatial heterogeneities in long-term trends of GPP.Throughout the entire QTP,air temperature increase had a greater influence than solar radiation and precipitation(PREC)changes on productivity.Moreover,our results highlight the large uncertainties of previous GPP estimates due to insufficient parameterization and validations.When compared with GPP estimates of the EC-LUE model,most Coupled Model Intercomparison Project(CMIP5)GPP products overestimate the magnitude and increasing trends of regional GPP,which potentially impact the feedback of ecosystems to regional climate changes.展开更多
This paper uses the expected utility under risk hypothesis to develop a new approach to GIS modeling for land use suitability analysis with competitive learning algorithms （CLG-LUSA）. It uses Kohonen＇s Self Organ- ...This paper uses the expected utility under risk hypothesis to develop a new approach to GIS modeling for land use suitability analysis with competitive learning algorithms （CLG-LUSA）. It uses Kohonen＇s Self Organ- ized Maps （SOM） and Linear Vector Quantization （LVQ） among other tools to create comprehensive ordering of high number of options. The model uses decision makers preferred locations and environmental data to construct a manifold of the decision＇s attribute space. Then, decision and uncertainty maps are derived from this manifold. An application example is provided using the selection of suitable environments for coconut development in a mu- nicipality of Cuba. CLG-LUSA model was able to provide accurate visual feedback of key aspects of the decision process, making the methodology suitable for personal or group decision making.展开更多
基金This paper has been supported by the project 2009DFA13000 funded by the Ministry of Science and Technology of the People’s Republic of China.The authors want to thank the researchers from Instituto de Investigaciones en Fruticultura Tropical,Republic of Cuba,in special Dr Jorge Cuetothe staff of Nipe-Sagua-Baracoa mountain office,and the government of Baracoa for their kind support and advice.
文摘This paper presents a Spatial Decision Support System for local governments of developing countries.It allows municipality government,enterprises,scientific community and civil society to address decision problems using GIS.The framework is supported by four modules of information technologies:Environmental Decision Support Database,Data Manipulation,Decision Support,and Mapping.A case study is presented covering the implementation of this framework in one municipality of Cuba.An example of land suitability planning for coconut crops is used to evaluate the system performance and usability.Results show local municipalities are able to use this framework to solve local decision problems using state of the art decision making even with low infrastructure development.
基金funded by the National Natural Science Foundation of China(41971226,41871357)the Major Research and Development and Achievement Transformation Projects of Qinghai,China(2022-QY-224)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA28110502,XDA19030303).
文摘A comprehensive understanding of spatial distribution and clustering patterns of gravels is of great significance for ecological restoration and monitoring.However,traditional methods for studying gravels are low-efficiency and have many errors.This study researched the spatial distribution and cluster characteristics of gravels based on digital image processing technology combined with a self-organizing map(SOM)and multivariate statistical methods in the grassland of northern Tibetan Plateau.Moreover,the correlation of morphological parameters of gravels between different cluster groups and the environmental factors affecting gravel distribution were analyzed.The results showed that the morphological characteristics of gravels in northern region(cluster C)and southern region(cluster B)of the Tibetan Plateau were similar,with a low gravel coverage,small gravel diameter,and elongated shape.These regions were mainly distributed in high mountainous areas with large topographic relief.The central region(cluster A)has high coverage of gravels with a larger diameter,mainly distributed in high-altitude plains with smaller undulation.Principal component analysis(PCA)results showed that the gravel distribution of cluster A may be mainly affected by vegetation,while those in clusters B and C could be mainly affected by topography,climate,and soil.The study confirmed that the combination of digital image processing technology and SOM could effectively analyzed the spatial distribution characteristics of gravels,providing a new mode for gravel research.
基金supported by National Natural Science Foundation(No.41272301 and No.42007171)Nature Fund of Hebei(No.D2021504034)Chinese Academy of Geological Sciences(No.YYWF201628).
文摘The increasing severity of ground subsidence,ground fissure and other disasters caused by the excessive exploitation of deep underground resources has highlighted the pressing need for effective management.A significant contributing factor to the challenges faced is the inadequacy of existing soil mechanics experimental instruments in providing effective indicators,creating a bottleneck in comprehensively understanding the mechanisms of land subsidence.It is urgent to develop a multi-field and multi-functional soil mechanics experimental system to address this issue.Based soil mechanics theories,the existing manufacturing capabilities of triaxial apparatus and the practical demands of the test system,a set of multi-field coupled high-pressure triaxial system is developed tailored for testing deep soils(at depths of approximately 3000 m)and soft rock.This system incorporates specialized design elements such as high-pressure chamber and horizontal deformation testing devices.In addition to the conventional triaxial tester functions,its distinctive feature encompass a horizontal deformation tracking measuring device,a water release testing device and temperature control device for the sample.This ensemble facilitates testing of horizontal and vertical deformation water release and other parameters of samples under a specified stress conditions,at constant or varying temperature ranging from-40℃–90℃.The accuracy of the tested parameters meets the requirements of relevant current specifications.The test system not only provides scientifically robust data for revealing the deformation and failure mechanism of soil subjected to extreme temperature,but also offers critical data support for major engineering projects,deep exploration and mitigation efforts related to soil deformation-induced disaster.
基金This study was supported by the National Natural Science Foundation of China(grant no.41871288)the China Postdoctoral Science Foundation(grant no.2022M711472)Funds for the Central Universities(no.GK202003064).
文摘Accurate mapping of loess waterworn gully(LWG)is essential to further study gully erosion and geomorphological evolution for the Chinese Loess Plateau(CLP).Due to the vertical joint and collapsibility of loess,LWGs have the characteristics of zigzag and unique slope abruptness under synthetic action of hydraulic force and gravity.This forces existing LWG mapping methods to either focus on the improvement of mapping accuracy or center on the increase of mapping efficiency.However,simultaneously achieving accurate and efficient mapping of LWG is still in its infancy under complex topographic conditions.Here,we proposed a method that innovatively integrates the loess slope abruptness feature into an improved deep learning semantic segmentation framework for LWG mapping using 0.6 m Google imagery and 5 m DEM data.We selected four study areas representing typical loess landforms to test the performance of our method.The proposed method can achieve satisfactory mapping results,with the F1 score,mean Intersection-over-Union(mIoU),and overall accuracy of 90.5%,85.3%,and 92.3%,respectively.In addition,the proposed model also showed significant accuracy improvement by inputting additional topographic information(especially the slope of slope).Compared with existing algorithms(Random forests,original DeepLabV3+,and Unet),the proposed approach in this study achieved a better accuracy-efficiency trade-off.Overall,the method can ensure high accuracy and efficiency of the LWG mapping for different loess landform types and can be extended to study various loess gully mapping and water and soil conservation.
基金Key Project of Chinese Academy of Sciences(CAS)[grant number KJZD-EW-G03-04]National Key R&D Program of China[grant number 2017YFA0604801]+2 种基金One Hundred Person Project of CAS[grant number Y329k71002]National Science Foundation for Excellent Young Scholars of China[grant number 41322005]the CAS Interdisciplinary Innovation Team of the Chinese Academy of Sciences.
文摘Vegetation gross primary production(GPP)is an important variable for the carbon cycle on the Qinghai-Tibetan Plateau(QTP).Based on the measurements from 12 eddy covariance flux sites,we validated a light use efficiency model(i.e.EC-LUE)to evaluate the spatial-temporal patterns of GPP and the effect of environmental variables on QTP.In general,EC-LUE model performed well in predicting GPP at different time scale over QTP.Annual GPP over the entire QTP ranged from 575 to 703 Tg C,and showed a significantly increasing trend from 1982 to 2013.However,there were large spatial heterogeneities in long-term trends of GPP.Throughout the entire QTP,air temperature increase had a greater influence than solar radiation and precipitation(PREC)changes on productivity.Moreover,our results highlight the large uncertainties of previous GPP estimates due to insufficient parameterization and validations.When compared with GPP estimates of the EC-LUE model,most Coupled Model Intercomparison Project(CMIP5)GPP products overestimate the magnitude and increasing trends of regional GPP,which potentially impact the feedback of ecosystems to regional climate changes.
基金partially supported by project 2009DFA13000 funded by the Ministry of Science and Technology of the People’s Republic of ChinaBeijing science and technology projects(Z151100003615012,Z151100003115007)+4 种基金Independent research project of State Key Laboratory of Resources and Environmental Information System(088RAC00YA)Surveying and mapping project of public welfare(201512015)Project of Beijing Excellent Talents(201500002685XG242)National Postdoctoral International Exchange Program(Grant No.20150081)National Natural Science Foundation of China(Grant No.41101116,41271546)
文摘This paper uses the expected utility under risk hypothesis to develop a new approach to GIS modeling for land use suitability analysis with competitive learning algorithms （CLG-LUSA）. It uses Kohonen＇s Self Organ- ized Maps （SOM） and Linear Vector Quantization （LVQ） among other tools to create comprehensive ordering of high number of options. The model uses decision makers preferred locations and environmental data to construct a manifold of the decision＇s attribute space. Then, decision and uncertainty maps are derived from this manifold. An application example is provided using the selection of suitable environments for coconut development in a mu- nicipality of Cuba. CLG-LUSA model was able to provide accurate visual feedback of key aspects of the decision process, making the methodology suitable for personal or group decision making.
