基于RS/GIS的青藏高原冻土分布模拟研究
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摘要
青藏高原多年冻土是世界上中低纬度海拔最高,面积最大的多年冻土区。由于地域广阔,实测资料比较少,这就使依靠台站观测资料的传统制图方法受到很大限制,因此建立冻土空间模型非常必要。
本文基于GIS技术,以遥感数据为主要数据源,结合青藏高原冻土分布特征,构建了两个经验统计模型:基于RS/GIS的地温反演模型和多变量综合分析模型。模型中充分考虑了与青藏高原冻土分布有关的高程、地表温度、植被和土壤水分等环境因素,并选取青藏公路沿线范围作为试验区,利用所建立的模型模拟其冻土分布现状,在模拟结果能很好的反应公路沿线冻土分布的情况下,将其推广到青藏高原全区,模拟了青藏高原的冻土分布。同时,利用高分辨率的ASTER数据在青藏高原局部地区实现了高精度建模。
As an important part of cryosphere,frozen soil covers a large area of global land surface.And permafrost covers about 24%of the northern hemisphere.For its wide distribution and unique thermal characteristics of water,frozen soil is a very important factor of land surface process.
Permafrost mainly distributed in Qinghai-Tibet Plateau,Pamirs Plateau, Daxinganling and the top of some eastern mountains in China.And the permafrost mainly in Qinghai-Tibet Plateau at high altitude is the world's highest elevation and largest area of permafrost regions in the lower latitude.A clear understanding of the the laws of permafrost distribution in Qinghai-Tibet Plateau,which is not only conducive to better plan and resolve the actual engineering problems of permafrost regions,but also it will have a far-reaching significance for the study of permafrost changes,the regulation and contribution to regional water resources in Qinghai-Tibet under climate warming.
For the vast area,the measured data of frozen soil is relatively few in Qinghai-Tibet Plateau.In the western,the stations and data is not available,which makes restrict to the traditional method rely on observational data.The existence of frozen soil is bound to their surrounding environment for its wide range and unique thermal characteristics of water,and reflects on the ground information.However we can rapidly obtained a large area and multi-temporal ground information related to permafrost using RS,which makes it possible to study permafrost from the view of RS.
Based on GIS technology,using RS data as the main data sources and combined with the feature of permafrost distribution in Qinghai-Tibet Plateau,we build two models:Retrieval Model of Ground Temperature based on RS/GIS and Multi-variable Comprehensive Analysis Model.The Retrieval Model of Ground Temperature based on RS/GIS is on the foundation of MAGT,which is the key division indicator of permafrost in Qinghai-Tibet Plateau.Through the relations established between MAGT and RS information to obtain the MAGT indirectly,and achieved the purpose of permafrost division.While Multi-variable Comprehensive Analysis Model depends entirely on the ground data related to the permafrost distribution to instruct the occurrence of permafrost.
Considering the permafrost map used to verify the simulation results,which is based on the measured ground temperature along the Qinghai-Tibet road,so the status of permafrost distribution in the permafrost map should be more reliable along the Qinghai-Tibet road.Therefore we choose the scope along Qinghai-Tibet road as a test area to simulate the status of permafrost distribution using the established model, compared and analyzed the simulation results with the part of permafrost map along the Qinghai-Tibet road.On the premise of simulation results in a very good response to the permafrost distribution,extended the model to the Qinghai-Tibet Plateau.
In order to study the merits and demerits of various models,and evaluate the simulation results,we selected the Elevation Model and Frost Number Model to simulate the permafrost distribution along the Qinghai-Tibet road.Compared and analyzed the results with the simulation results of the two models created in this article.Obtained the following conclusion:
1.The frozen soil is divided based on the Retrieval Model of Ground Temperature based on RS/GIS,through setting up the relationship between MAGT and remote sensing variables,indirectly obtaining MAGT.The model precision depends on the quantity of the ground temperature observation points and distribution situation.The quality of regression equation is not ideal,because the ground temperature observation points used to set up model are small distribution in island permafrost zone.There are a certain differences between the simulation results in south island permafrost zone and the frozen soil distribution image.The simulation precision will be improved by increasing the ground temperature observation points' quantity in the island permafrost zone.If the quantity of the ground temperature observation points is enough,this method is still a feasible one which used to obtain the permafrost distribution.
2.Multi-variable Comprehensive Analysis Model uses the statistical relationship of surface in permafrost zone variables to indirectly infer the existence of permafrost. The sample points are representative.Because they were picked up along the road, and there were some points distributed on the island permafrost zone.Contrasted with DEM Model,Frost Number Model,Multi-variable Comprehensive Analysis Model shows the frozen soil distribution along the Qinghai-Tibet highway well.Using this model,the relative error of simulation area is less,the firing between the result and frozen soil distribution is best.
3.The Multi-variable Comprehensive Analysis Model is used in the whole Qinghai-Tibet Plateau in order to simulate the distribution of frozen soil.The simulation result basically shows the frozen soil distribution principle.The area of permafrost is 136.33 million square kilometers,and that is 52.7 percentage of the whole Qinghai-Tibetan Pateau area.
4.In the process of setting up the ground temperature model based on RS and GIS, the stability and prediction ability of model improved obviously,segmented considering the equivalent latitude.The determination coefficient R~2 is improved from 0.598 to 0.617 and 0.826.It shows that slope and aspect have important effect on the frozen soil distribution in Qinghai-Tibeta Plateau.
5.In the process of calculating the frost-number model,considering the hardly obtaining of ground temperature by traditional method,affected by the interpolation precision,the precision of simulation result is influenced.Improving the model by trying to use ground temperature,the simulation result is not very well.Therefore,the attempt of introducing the ground temperature retrieval from RS in Fost Number Model is not successful.
6.High resolution ASTER data are used in frozen soil simulation in partial area along the road.The result shows that,the precision of simulation result is improved. Especially,in the island permafrost zone,contrasted with the MODIS data simulation result,ASTER data simulation result has the better fitting with frozen soil distribution image.
Compared with previous studies,special features of this paper are as follows:
1.Based on GIS,and taken the remote sensing data as the main data source,we established the permafrost model suitable for Qinghai-Tibet Plateau,and simulated the distribution of permafrost in Qinghai-Tibet Plateau,which realized the application of remote sensing technology in the study of permafrost distribution in Qinghai-Tihet Plateau.
2.Extracted the environment information related to permafrost distribution,such as elevation,land surface temperature,vegetation,soil moisture and so on,which make up for the lack of permafrost data in Qinghai-Tibet Plateau.Compared with the previous studies that only considered a single factor of elevation or air temperature, the model established in this paper is more reasonable.
3.Unfrozen regions reflected in the simulation results for the first time,and the simulation results are better fit to the actual distribution of permafrost.
Mapping permafrost based on remote sensing technologies is a meaningful work.It is a meaningful attempt to study the simulation of permafrost distribution from the view of RS.
本文基于GIS技术,以遥感数据为主要数据源,结合青藏高原冻土分布特征,构建了两个经验统计模型:基于RS/GIS的地温反演模型和多变量综合分析模型。模型中充分考虑了与青藏高原冻土分布有关的高程、地表温度、植被和土壤水分等环境因素,并选取青藏公路沿线范围作为试验区,利用所建立的模型模拟其冻土分布现状,在模拟结果能很好的反应公路沿线冻土分布的情况下,将其推广到青藏高原全区,模拟了青藏高原的冻土分布。同时,利用高分辨率的ASTER数据在青藏高原局部地区实现了高精度建模。
As an important part of cryosphere,frozen soil covers a large area of global land surface.And permafrost covers about 24%of the northern hemisphere.For its wide distribution and unique thermal characteristics of water,frozen soil is a very important factor of land surface process.
Permafrost mainly distributed in Qinghai-Tibet Plateau,Pamirs Plateau, Daxinganling and the top of some eastern mountains in China.And the permafrost mainly in Qinghai-Tibet Plateau at high altitude is the world's highest elevation and largest area of permafrost regions in the lower latitude.A clear understanding of the the laws of permafrost distribution in Qinghai-Tibet Plateau,which is not only conducive to better plan and resolve the actual engineering problems of permafrost regions,but also it will have a far-reaching significance for the study of permafrost changes,the regulation and contribution to regional water resources in Qinghai-Tibet under climate warming.
For the vast area,the measured data of frozen soil is relatively few in Qinghai-Tibet Plateau.In the western,the stations and data is not available,which makes restrict to the traditional method rely on observational data.The existence of frozen soil is bound to their surrounding environment for its wide range and unique thermal characteristics of water,and reflects on the ground information.However we can rapidly obtained a large area and multi-temporal ground information related to permafrost using RS,which makes it possible to study permafrost from the view of RS.
Based on GIS technology,using RS data as the main data sources and combined with the feature of permafrost distribution in Qinghai-Tibet Plateau,we build two models:Retrieval Model of Ground Temperature based on RS/GIS and Multi-variable Comprehensive Analysis Model.The Retrieval Model of Ground Temperature based on RS/GIS is on the foundation of MAGT,which is the key division indicator of permafrost in Qinghai-Tibet Plateau.Through the relations established between MAGT and RS information to obtain the MAGT indirectly,and achieved the purpose of permafrost division.While Multi-variable Comprehensive Analysis Model depends entirely on the ground data related to the permafrost distribution to instruct the occurrence of permafrost.
Considering the permafrost map used to verify the simulation results,which is based on the measured ground temperature along the Qinghai-Tibet road,so the status of permafrost distribution in the permafrost map should be more reliable along the Qinghai-Tibet road.Therefore we choose the scope along Qinghai-Tibet road as a test area to simulate the status of permafrost distribution using the established model, compared and analyzed the simulation results with the part of permafrost map along the Qinghai-Tibet road.On the premise of simulation results in a very good response to the permafrost distribution,extended the model to the Qinghai-Tibet Plateau.
In order to study the merits and demerits of various models,and evaluate the simulation results,we selected the Elevation Model and Frost Number Model to simulate the permafrost distribution along the Qinghai-Tibet road.Compared and analyzed the results with the simulation results of the two models created in this article.Obtained the following conclusion:
1.The frozen soil is divided based on the Retrieval Model of Ground Temperature based on RS/GIS,through setting up the relationship between MAGT and remote sensing variables,indirectly obtaining MAGT.The model precision depends on the quantity of the ground temperature observation points and distribution situation.The quality of regression equation is not ideal,because the ground temperature observation points used to set up model are small distribution in island permafrost zone.There are a certain differences between the simulation results in south island permafrost zone and the frozen soil distribution image.The simulation precision will be improved by increasing the ground temperature observation points' quantity in the island permafrost zone.If the quantity of the ground temperature observation points is enough,this method is still a feasible one which used to obtain the permafrost distribution.
2.Multi-variable Comprehensive Analysis Model uses the statistical relationship of surface in permafrost zone variables to indirectly infer the existence of permafrost. The sample points are representative.Because they were picked up along the road, and there were some points distributed on the island permafrost zone.Contrasted with DEM Model,Frost Number Model,Multi-variable Comprehensive Analysis Model shows the frozen soil distribution along the Qinghai-Tibet highway well.Using this model,the relative error of simulation area is less,the firing between the result and frozen soil distribution is best.
3.The Multi-variable Comprehensive Analysis Model is used in the whole Qinghai-Tibet Plateau in order to simulate the distribution of frozen soil.The simulation result basically shows the frozen soil distribution principle.The area of permafrost is 136.33 million square kilometers,and that is 52.7 percentage of the whole Qinghai-Tibetan Pateau area.
4.In the process of setting up the ground temperature model based on RS and GIS, the stability and prediction ability of model improved obviously,segmented considering the equivalent latitude.The determination coefficient R~2 is improved from 0.598 to 0.617 and 0.826.It shows that slope and aspect have important effect on the frozen soil distribution in Qinghai-Tibeta Plateau.
5.In the process of calculating the frost-number model,considering the hardly obtaining of ground temperature by traditional method,affected by the interpolation precision,the precision of simulation result is influenced.Improving the model by trying to use ground temperature,the simulation result is not very well.Therefore,the attempt of introducing the ground temperature retrieval from RS in Fost Number Model is not successful.
6.High resolution ASTER data are used in frozen soil simulation in partial area along the road.The result shows that,the precision of simulation result is improved. Especially,in the island permafrost zone,contrasted with the MODIS data simulation result,ASTER data simulation result has the better fitting with frozen soil distribution image.
Compared with previous studies,special features of this paper are as follows:
1.Based on GIS,and taken the remote sensing data as the main data source,we established the permafrost model suitable for Qinghai-Tibet Plateau,and simulated the distribution of permafrost in Qinghai-Tibet Plateau,which realized the application of remote sensing technology in the study of permafrost distribution in Qinghai-Tihet Plateau.
2.Extracted the environment information related to permafrost distribution,such as elevation,land surface temperature,vegetation,soil moisture and so on,which make up for the lack of permafrost data in Qinghai-Tibet Plateau.Compared with the previous studies that only considered a single factor of elevation or air temperature, the model established in this paper is more reasonable.
3.Unfrozen regions reflected in the simulation results for the first time,and the simulation results are better fit to the actual distribution of permafrost.
Mapping permafrost based on remote sensing technologies is a meaningful work.It is a meaningful attempt to study the simulation of permafrost distribution from the view of RS.
引文
[1]周幼吾,郭东信,邱国庆,等.中国冻土[M].北京:科学出版社,2000:1-2.
[2]中国科学院兰州冰川冻土沙漠研究所.冻土[M].北京:科学出版社,1975:19-23.
[3]李树德.青藏公路沿线多年冻土的温度和厚度(摘要)[C].中国地理学会冰川冻土学术会议论文选集.北京:科学出版社,1982:7-10.
[4]曹梅盛,李新,陈贤章等.冰冻圈遥感[M].北京:科学出版社,2006:1-10.
[5]施雅风.第四纪中期青藏高原冰冻圈的演化及其与全球变化的联系[J].冰川冻土.1998,20(3):197-208.
[6]Zhang T,Barry R G,Knowles K,et al.Statistics and characteristics of permafrost and ground ice distribution in the Northern Hemisphere[J].Polar Geography.1999,23(2):147-169.
[7]IPCC.Climate Change 2001:Scientific Basis[M].Camvridge,UK:Cambridge Unversity Press,2001:1-79.
[8]南卓铜.青藏高原冻土分布研究及青藏铁路数字路基建设[D].兰州:中国科学院寒区旱区环境与工程研究所,2003:3-9.
[9]Etzelmüller B,Hoelzle M,Heggem E S F,et al.Mapping and modelling the occurrence and distribution of mountain permafrost[J].Norwegian Journal of Geography.2001,55(4):186-194.
[10]徐敩祖,王家澄.中国冻土分布及其地带性规律的初步探讨[C].第二届全国冻土学术会议论文选集.兰州:甘肃人民出版社,1982:3-12.
[11]周幼吾,郭东信.我国多年冻土的主要特征[J].冰川冻土.1982,4(1):1-19.
[12]吴青柏,朱元林,刘勇智.人类工程活动下冻土环境变化评价模型[J].中国科学(D辑).2002,32(2):141-148.
[13]Nelson F E,Anisimov O A,Shiklomanov N I,et al.Climate change and hazard zonation in the circum-Arctic permafrost regions[J].Nature Hazards.2002,26:203-225.
[14]刘志飞,王成善.青藏高原隆升对新生代全球气候变化的影响[J].大自然探索.1998,17(65):30-33.
[15]姚檀栋.青藏高原中部冰冻圈动态特征[M].北京:地质出版社,2002:234-275.
[16]李新,程国栋.高海拔多年冻土对全球变化的响应模型[J].中国科学(D辑).1999,29(2):185-192.
[17]Heginbottom J A.Permafrost mapping:a review[J].Progress in Physical Geography.2002,26(4):623-642.
[18]Halsey L A,Vitt D H,Zoltai S C.Disequilibrium response of permafrost in boreal Continental western canada to climate change[J].Climate Change.1995,30:57-73.
[19]Laberge M J,Payette S.Long-term monitoring of permafrost change in a Paisa Peatland in Northern Quebec,Canada[J].Arctic and Alpine Research.1995,27(2):167-171.
[20]李新,程国栋.冻土.气候关系模型评述[J].冰川冻土.2002,24(3):315-321.
[21]Lunardini V J.Climatic warming and the degradation of warm permafrost[J].Permafrost and Periglacial Process.1996,7(4):311-320.
[22]Smith M W,Riseborough D W.Permafrost monitoring and detection of climate change[J].Permafrost and Periglacial Process.1996,7(4):301-309.
[23]Hoelzle M,Mittaz C,Etzelmüller B.Surface energy fluxes and distribution models of permafrost in European mountain areas:an overview of current developments[J].Permafrost and Periglacial Process.2001,12:53-68.
[24]Keller F.Automated mapping of mountain permafrost using the program PERMAKART within the geographic information system ARC/INFO[J].Permafrost and Periglacial Process.1992,3(2):139-142.
[25]Imhof M.Modelling and verification of the permafrost distribution in the Bernese Alps(Western Switzerland)[J].Permafrost and Periglacial Process.1996,7:267-280.
[26]Frauenfelder R,Allgower B,Haeberli W.Permafrost investingations with GIS-A case study in the Fletschhorn Area,Wallis,Swiss Alps[C]. Proceedings of Seventh International Conference on Permafrost. Yellowknife, Canada: 1998: 291-295.
[27] Funk M, Hoelzle M. A model of potential direct solar radiation for investigation occurrence of mountain permafrost[J]. Permafrost and Periglacial Process. 1992, 3(2): 139-142.
[28] Jorgenson M T, Kreig R A. A model for mapping permafrost distribution based on landscape components and climatic variables[C]. Proceedings Volume 1 of Fifth International Conference on Permafrost. Trondheim, Norway: Tapir Publishers, 1988: 176-182.
[29] Haugen R K, Greeley N H, Collins C M. Permafrost mapping using GRASS[C]. Proceedings Volume 2 of Sixth International Conference on Permafrost. South China University of Technology Press, 1993: 1128-1131.
[30] Nelson F E, Outcalt S I. A frost index number for spatial prediction of ground-frost zones[C]. Permafrost-Fourth International Conference Proceedings, vol 1. Washington, DC: National Academy Press, 1983: 907-911.
[31] Nelson F E, Outealt S I. A computational method for prediction and regionalization of permafrost[J]. Arctic and Alpine Research. 1987, 19(3): 279-288.
[32] Nelson F E, Anisimov 0 A. Permafrost zonation in Russia under anthropogenic climate change[J]. Permafrost and Periglacial Process. 1993,4(2): 137-148.
[33] Anisimov O A, Nelson F E. Permafrost distribution in the Northern Hemisphere under scenarios of climate change [J]. Global and Planetary Change. 1996, 14: 59-72.
[34] Smith M W, Riseborough D W. Permafrost monitoring and detection of climate change [J]. Permafrost and Periglacial Process. 1996,7(4): 301-309.
[35] Smith M W, Riseborough D W. Climate and the limits of permafrost:a zonal analysis[J]. Permafrost and Periglacial Process. 2002, 7: 301-309.
[36] Henry K A, Smith M W. A model-based map of ground temperatures for the permafrost regions of Canada[J]. Permafrost and Periglacial Process. 2001, 12: 389-398.
[37] Riseborough D W. The mean annual temperature at the top of permafrost, the TTOP model,and the effect of unfrozen water[J]. Permafrost and Periglacial Process. 2002,13: 137-143.
[38] Juliussen H, Humlum O. Towards a TTOP ground temperature model for mountain terrain in Central-Eastern Norway[J], Permafrost and Periglacial Process. 2007,18: 161-184.
[39] Riseborough D, Shiklomanov N, Etzelmiiller B, et al. Recent advances in permafrost modelling[J]. Permafrost and Periglacial Process. 2008,19: 137-156.
[40] Hoelzle M. Permafrost occurence from BTS measurements and climatic parameters in the Eastern Swiss Alps[J]. Permafrost and Periglacial Process. 1992, 3(2): 143-147.
[41] Hoelzle M, Mittaz C, Etzelmuller B. Surface energy fluxes and distribution models of permafrost in European mountain areas:an overview of current developments[J]. Permafrost and Periglacial Process. 2001,12(1): 53-68.
[42] Antoni G, Lewkowicz, Ednie M. Probability and mapping of mountain permafrost using the BTS method, Wolf Creek, Yukon Territory, Canada [J]. Permafrost and Periglacial Process. 2004, 15: 67-80.
[43] Brenning A, Gruber S, Hoelzle M. Sampling and statistical analyses of BTS Measurements[J]. Permafrost and Periglacial Process. 2005,16: 383-393.
[44] Gruber S, Hoelzle M. Statistical modelling of mountain permafrost distribution: Local calibration and incorporation of remotely sensed data[J]. Permafrost and Periglacial Process. 2001,12(1): 69-73.
[45] Etzelmuller B, Odegard R, Berthling I. Terrain parameters and remote sensing data in the analysis of permafrost distribution and periglacial processes: principles and examples from southern Norway [J]. Permafrost and Periglacial Process. 2001,12(1): 79-92.
[46] David W, Leverington, Claude R D. A neural network method to determine the presence or absence of permafrost near Mayo, Yukon Territory, Canada[J]. Permafrost and Periglacial Process. 1997, 8:205-215.
[47] Etzelmuller B, Heggem E S, Sharkhuu N, et al. Mountain permafrost distribution modelling using a multi-criteria approach in Hovsgol area,Northern Mongolia[J]. Permafrost and Periglacial Process.2006,17:91-104.
[48]郭东信.东北大小兴安岭多年冻土区[J].冰川冻土.1981,3(3):1-9.
[49]中国科学院兰州冰川冻土研究所.青藏冻土研究论文集[M].北京:科学出版社,1983:1-30.
[50]徐斅祖,郭东信.1:400万中国冻土分布图的编制[J].冰川冻土.1982,4(2):18-25.
[51]李树德,程国栋.青藏高原冻土图[Z].兰州:甘肃文化出版社,1996.
[52]中国科学院寒区旱区环境与工程研究所.1:4000000中国冰川冻土沙漠图[Z].北京:中国地图出版社,2006.
[53]南卓铜,李述训,程国栋.未来50与100 a青藏高原多年冻土变化情景预测[J].中国科学(D辑).2004,34(6):528-534.
[54]李述训,程国栋,郭东信.气候持续变暖条件下青藏高原多年冻土变化趋势数值模拟[J].中国科学(D辑).1996,26(4):342-347.
[55]吴青柏,李新,李文君.青藏公路沿线冻土区域分布计算机模拟与制图[J].冰川冻土.1999,22(4):323-326.
[56]吴青柏,朱元林,刘永智.青藏高原多年冻土顶板温度和温度位移预报模型的应用[J].冰川冻土.2002,24(5):614-617.
[57]南卓铜,李述训,刘永智等.基于年平均地温的青藏高原冻土分布制图及应用[J].冰川冻土.2002,24(2):142-148.
[58]Chen S B.Permafrost classification on the Tibet Plateau based on surface emissivity retrieval from Terra-MODIS data[C].IEEE International Geoscience and Remote Sensing Symposium.Anchorage,Alaska:2004.
[59]陈圣波.青藏高原天然气水合物环境效应可能性研究[D].北京:中国科学院地理科学与资源研究所,2002:62-86.
[60]张勇,蔡石泉.青藏高原多年冻土分布的特点及与高纬度地区对比[C].北京:地质出版社,1984.273-282.
[61]程国栋.中国青藏高原多年冻土与加拿大北部多年冻土的一些差别[J].冰 川冻土.1979(2):39-43.
[62]童伯良,李树德.青藏高原多年冻土的某些特征及其影响因素[C].青藏冻土研究论文集.北京:科学出版社,1983:1-11.
[63]汤懋苍,程国栋,林振耀.青藏高原近代气候变化及对环境的影响[M].广州:广东科技出版社,1998:237-259.
[64]王家澄,王绍令,邱国庆.青藏公路沿线的多年冻土[J].地理学报.1979,34(1):1-3.
[65]周幼吾,杜榕桓.青藏高原冻土初步考察[J].科学通报.1963(2):60-63.
[66]程国栋.中国区域冻土研究的回归和展望[J].冰川冻土.1988,10(3):296-299.
[67]曾群柱,寇有观,谢维荣,等.青藏高原辐射平衡的研究[C].中国科学院兰州冰川冻土研究集刊(3号刊).北京:科学出版社,1982:32-51.
[68]寇有观,曾群柱,谢维荣,等.青藏高原总辐射研究[C].北京:科学出版社,1982:1-17.
[69]邱国庆,程国栋.中国的多年冻土.过去与现在[J].第四纪研究.1995(1):13-22.
[70]张文纲,李述训,吴通华等.青藏高原地气温差变化分析[J].地理学报.2006,61(9):899-909.
[71]郭东信.地质构造对多年冻土的影响[J].地理科学.1985,5(2):97-105.
[72]Morrissery L A,Strong L L.Mapping permafrost in the Boreal forest with Thematic Mapper Satellite data[J].Photogrammetric Engineering & Remote Sensing.1986,52:1513-1519.
[73]韦志刚,罗四维,董文杰等.青藏高原积雪资料分析及其与我国夏季降水的关系[J].应用气象学报.1998,9(S1):39-46.
[74]梁风仙,罗祥瑞,王绍令.航空像片在冰缘地貌动态研究中的应用[J].冰川冻土.1980(S1):72-73.
[75]罗祥瑞,梁风仙,王绍令.沱沱河北多年冻土综合影像图的制作[J].冰川冻 土.1980(S1):70-71.
[76]卓宝熙.高原多年冻土地区遥感图像工程地质分区的探讨[J].工程地质学报.2003,11(3):225-229.
[77]赵尚民,程维明,柴慧霞,等.基于遥感与SRTM的青藏高原冰缘地貌信息提取方法-以1:100万标准分幅拉萨幅(H46)为例[J].地理研究.2007,26(6):1175-1185.
[78]赵英时.遥感应用分析原理与方法[M].北京:科学出版社,2003:194-195.
[79]梁凤仙,曾群柱,冯学智,等.冻土地区红外遥感最佳时段和多光谱可分性研究-以青海热水为例[C].中国科学院兰州冰川冻土研究所集刊(第6号).北京:科学出版社,1988:106-119.
[80]薛薇.SPSS统计分析方法及应用[M].北京:电子工业出版社,2004:281-299.
[81]李登科,张树誉.EOS/MODIS遥感数据的接受与处理[J].陕西气象.2003(1):34-36.
[82]李登科,张树誉.EOS/MODIS遥感数据与应用前景[J].陕西气象.2003(2):37-40.
[83]吴青柏.人类工程活动下冻土环境变化和工程适应性研究[D].兰州:中国科学院寒区旱区环境与工程研究所,2000:20-24.
[84]刘海昆,黄树祥,王慧.论冻土对土壤水分动态的影响[J].黑龙江水利科技.2002(3):87.
[85]马龙,刘闯.MODIS在三江平原湿地分布研究中的应用[J].地理与地理信息科学.2006,22(3):57-60.
[86]邢宇,姜琦刚,王坤.青藏高原DEM的生成与应用[J].世界地质.2007,26(4):479-483.
[87]邱玉宝,施建成,蒋玲梅等.AMSR-E被动微波土壤水分与降雨率时空相关性分析研究[J].北京师范大学学报(自然科学版).2007,43(3):350-355.
[88]刘闯,葛成辉.美国地球观测系统AQUA卫星数据政策、主要技术指标与数据本土化共享问题[J].遥感信息.2002(2):38-42.
[89]祝汉昌,朱福康,刘玉洁.青藏高原行星反照率与地面反照率关系的研究[J].科学通报.1990(20):1563-1565.
[90]焦子锑,王锦地,谢里欧等.地面和机载多角度观测数据的反照率反演及对MODIS反照率产品的初步验证[J].遥感学报.2005,9(1):64-72.
[91]中国科学院寒区旱区环境与工程研究所.1:4000000中国冰川冻土沙漠图说明书[M].北京:中国地图出版社,2006:7-12.
[92]余建英,何旭宏.数据统计分析与SPSS应用[M].北京:人民邮电出版社,2003:189-245.
[93]张文彤.世界优秀统计工具SPSS11.0统计分析教程高级篇[M].北京:北京电子希望出版社,2002:91-99.
[94]邱国庆,郭东信.论青藏公路沿线的融区[C].青藏冻土研究论文集.北京:科学出版社,1983:30-37.
[95]李新.冰冻圈信息系统及其应用研究[D].中国科学院兰州冰川冻土研究所.兰州:1998:65-78.
[96]程国栋,王绍令.试论中国高海拔多年冻土带的划分[J].冰川冻土.1982,4(2):1-17.
[97]李健.长白山地区地表温度反演研究[D].长春:吉林大学,2004:2-3.
[98]李海涛,田庆久.ASTER数据产品的特性及其计划介绍[J].遥感信息.2004(3):53-55.
[99]朱黎江,秦其明,陈思锦.ASTER遥感数据解读与应用[J].国土资源遥感.2003,56(2):59-63.
[100]毛克彪.用于MODIS数据的地表温度反演方法研究[D].南京:南京大学,2004:35-58.
[101]毛克彪,覃志豪,徐斌.针对ASTER数据的单窗算法[J].测绘学院学报.2005(1):40-42.
[102]毛克彪,施建成,覃志豪.一个针对ASTER数据同时反演地表温度和比辐射率的四通道算法[J].遥感学报.2006(4):593-599.
[103]毛克彪,唐华俊.一个从ASTER数据中反演地表温度的劈窗算法[J].遥感信息.2006(5):7-11.
[104]刘志武,党安荣.利用ASTER遥感数据反演陆面温度的算法及应用研究[J].地理科学进展.2003(5):507-513.
[105]唐世浩,朱启疆,苏理宏.基于订正ALPHA差值谱的热红外温度与发射率分离算法[J].红外与毫米波学报.2005(4):286-290.
[106]唐世浩,李小文,王锦地,等.改进的基于订正ALPHA差值谱的TES算法[J].中国科学(D辑).2006(7):663-671.
[107]李小文,汪骏发,王锦地等.多角度与热红外对地遥感[M].北京:科学出版社,200l:84-127.
[108]王凤敏,田庆久,虢建宏.基于ASTER数据反演我国南方山地陆表温度[J].国土资源遥感.2005(1):30-33.
[109]Gillespie A R,Rokugawa S,Matsunaga T.A temperature and emissivity separation algorithm for Advanced Spaceborne Thermal Emission and Reflection Radiometer(ASTER) images[J].IEEE Transactions on Geoscience and Remote Sensing.1998,36(4):1113-1126.
[110]吕一峰.基于ASTER影像的DEM与数字地貌制图研究[D].福建师范大学,2008:16-23.
[111]秦鹏,陈健飞.ASTER影像提取植被信息的NDVI与SAVI法比较—以广州花都区为例[J].热带地理.2008,28(5):419-422.
[112]卢远,华璀,杨小雄,等.基于ASTER数据的城市热环境效应定量分析[J].武汉大学学报(信息科学版).2008,33(3):297-301.
[2]中国科学院兰州冰川冻土沙漠研究所.冻土[M].北京:科学出版社,1975:19-23.
[3]李树德.青藏公路沿线多年冻土的温度和厚度(摘要)[C].中国地理学会冰川冻土学术会议论文选集.北京:科学出版社,1982:7-10.
[4]曹梅盛,李新,陈贤章等.冰冻圈遥感[M].北京:科学出版社,2006:1-10.
[5]施雅风.第四纪中期青藏高原冰冻圈的演化及其与全球变化的联系[J].冰川冻土.1998,20(3):197-208.
[6]Zhang T,Barry R G,Knowles K,et al.Statistics and characteristics of permafrost and ground ice distribution in the Northern Hemisphere[J].Polar Geography.1999,23(2):147-169.
[7]IPCC.Climate Change 2001:Scientific Basis[M].Camvridge,UK:Cambridge Unversity Press,2001:1-79.
[8]南卓铜.青藏高原冻土分布研究及青藏铁路数字路基建设[D].兰州:中国科学院寒区旱区环境与工程研究所,2003:3-9.
[9]Etzelmüller B,Hoelzle M,Heggem E S F,et al.Mapping and modelling the occurrence and distribution of mountain permafrost[J].Norwegian Journal of Geography.2001,55(4):186-194.
[10]徐敩祖,王家澄.中国冻土分布及其地带性规律的初步探讨[C].第二届全国冻土学术会议论文选集.兰州:甘肃人民出版社,1982:3-12.
[11]周幼吾,郭东信.我国多年冻土的主要特征[J].冰川冻土.1982,4(1):1-19.
[12]吴青柏,朱元林,刘勇智.人类工程活动下冻土环境变化评价模型[J].中国科学(D辑).2002,32(2):141-148.
[13]Nelson F E,Anisimov O A,Shiklomanov N I,et al.Climate change and hazard zonation in the circum-Arctic permafrost regions[J].Nature Hazards.2002,26:203-225.
[14]刘志飞,王成善.青藏高原隆升对新生代全球气候变化的影响[J].大自然探索.1998,17(65):30-33.
[15]姚檀栋.青藏高原中部冰冻圈动态特征[M].北京:地质出版社,2002:234-275.
[16]李新,程国栋.高海拔多年冻土对全球变化的响应模型[J].中国科学(D辑).1999,29(2):185-192.
[17]Heginbottom J A.Permafrost mapping:a review[J].Progress in Physical Geography.2002,26(4):623-642.
[18]Halsey L A,Vitt D H,Zoltai S C.Disequilibrium response of permafrost in boreal Continental western canada to climate change[J].Climate Change.1995,30:57-73.
[19]Laberge M J,Payette S.Long-term monitoring of permafrost change in a Paisa Peatland in Northern Quebec,Canada[J].Arctic and Alpine Research.1995,27(2):167-171.
[20]李新,程国栋.冻土.气候关系模型评述[J].冰川冻土.2002,24(3):315-321.
[21]Lunardini V J.Climatic warming and the degradation of warm permafrost[J].Permafrost and Periglacial Process.1996,7(4):311-320.
[22]Smith M W,Riseborough D W.Permafrost monitoring and detection of climate change[J].Permafrost and Periglacial Process.1996,7(4):301-309.
[23]Hoelzle M,Mittaz C,Etzelmüller B.Surface energy fluxes and distribution models of permafrost in European mountain areas:an overview of current developments[J].Permafrost and Periglacial Process.2001,12:53-68.
[24]Keller F.Automated mapping of mountain permafrost using the program PERMAKART within the geographic information system ARC/INFO[J].Permafrost and Periglacial Process.1992,3(2):139-142.
[25]Imhof M.Modelling and verification of the permafrost distribution in the Bernese Alps(Western Switzerland)[J].Permafrost and Periglacial Process.1996,7:267-280.
[26]Frauenfelder R,Allgower B,Haeberli W.Permafrost investingations with GIS-A case study in the Fletschhorn Area,Wallis,Swiss Alps[C]. Proceedings of Seventh International Conference on Permafrost. Yellowknife, Canada: 1998: 291-295.
[27] Funk M, Hoelzle M. A model of potential direct solar radiation for investigation occurrence of mountain permafrost[J]. Permafrost and Periglacial Process. 1992, 3(2): 139-142.
[28] Jorgenson M T, Kreig R A. A model for mapping permafrost distribution based on landscape components and climatic variables[C]. Proceedings Volume 1 of Fifth International Conference on Permafrost. Trondheim, Norway: Tapir Publishers, 1988: 176-182.
[29] Haugen R K, Greeley N H, Collins C M. Permafrost mapping using GRASS[C]. Proceedings Volume 2 of Sixth International Conference on Permafrost. South China University of Technology Press, 1993: 1128-1131.
[30] Nelson F E, Outcalt S I. A frost index number for spatial prediction of ground-frost zones[C]. Permafrost-Fourth International Conference Proceedings, vol 1. Washington, DC: National Academy Press, 1983: 907-911.
[31] Nelson F E, Outealt S I. A computational method for prediction and regionalization of permafrost[J]. Arctic and Alpine Research. 1987, 19(3): 279-288.
[32] Nelson F E, Anisimov 0 A. Permafrost zonation in Russia under anthropogenic climate change[J]. Permafrost and Periglacial Process. 1993,4(2): 137-148.
[33] Anisimov O A, Nelson F E. Permafrost distribution in the Northern Hemisphere under scenarios of climate change [J]. Global and Planetary Change. 1996, 14: 59-72.
[34] Smith M W, Riseborough D W. Permafrost monitoring and detection of climate change [J]. Permafrost and Periglacial Process. 1996,7(4): 301-309.
[35] Smith M W, Riseborough D W. Climate and the limits of permafrost:a zonal analysis[J]. Permafrost and Periglacial Process. 2002, 7: 301-309.
[36] Henry K A, Smith M W. A model-based map of ground temperatures for the permafrost regions of Canada[J]. Permafrost and Periglacial Process. 2001, 12: 389-398.
[37] Riseborough D W. The mean annual temperature at the top of permafrost, the TTOP model,and the effect of unfrozen water[J]. Permafrost and Periglacial Process. 2002,13: 137-143.
[38] Juliussen H, Humlum O. Towards a TTOP ground temperature model for mountain terrain in Central-Eastern Norway[J], Permafrost and Periglacial Process. 2007,18: 161-184.
[39] Riseborough D, Shiklomanov N, Etzelmiiller B, et al. Recent advances in permafrost modelling[J]. Permafrost and Periglacial Process. 2008,19: 137-156.
[40] Hoelzle M. Permafrost occurence from BTS measurements and climatic parameters in the Eastern Swiss Alps[J]. Permafrost and Periglacial Process. 1992, 3(2): 143-147.
[41] Hoelzle M, Mittaz C, Etzelmuller B. Surface energy fluxes and distribution models of permafrost in European mountain areas:an overview of current developments[J]. Permafrost and Periglacial Process. 2001,12(1): 53-68.
[42] Antoni G, Lewkowicz, Ednie M. Probability and mapping of mountain permafrost using the BTS method, Wolf Creek, Yukon Territory, Canada [J]. Permafrost and Periglacial Process. 2004, 15: 67-80.
[43] Brenning A, Gruber S, Hoelzle M. Sampling and statistical analyses of BTS Measurements[J]. Permafrost and Periglacial Process. 2005,16: 383-393.
[44] Gruber S, Hoelzle M. Statistical modelling of mountain permafrost distribution: Local calibration and incorporation of remotely sensed data[J]. Permafrost and Periglacial Process. 2001,12(1): 69-73.
[45] Etzelmuller B, Odegard R, Berthling I. Terrain parameters and remote sensing data in the analysis of permafrost distribution and periglacial processes: principles and examples from southern Norway [J]. Permafrost and Periglacial Process. 2001,12(1): 79-92.
[46] David W, Leverington, Claude R D. A neural network method to determine the presence or absence of permafrost near Mayo, Yukon Territory, Canada[J]. Permafrost and Periglacial Process. 1997, 8:205-215.
[47] Etzelmuller B, Heggem E S, Sharkhuu N, et al. Mountain permafrost distribution modelling using a multi-criteria approach in Hovsgol area,Northern Mongolia[J]. Permafrost and Periglacial Process.2006,17:91-104.
[48]郭东信.东北大小兴安岭多年冻土区[J].冰川冻土.1981,3(3):1-9.
[49]中国科学院兰州冰川冻土研究所.青藏冻土研究论文集[M].北京:科学出版社,1983:1-30.
[50]徐斅祖,郭东信.1:400万中国冻土分布图的编制[J].冰川冻土.1982,4(2):18-25.
[51]李树德,程国栋.青藏高原冻土图[Z].兰州:甘肃文化出版社,1996.
[52]中国科学院寒区旱区环境与工程研究所.1:4000000中国冰川冻土沙漠图[Z].北京:中国地图出版社,2006.
[53]南卓铜,李述训,程国栋.未来50与100 a青藏高原多年冻土变化情景预测[J].中国科学(D辑).2004,34(6):528-534.
[54]李述训,程国栋,郭东信.气候持续变暖条件下青藏高原多年冻土变化趋势数值模拟[J].中国科学(D辑).1996,26(4):342-347.
[55]吴青柏,李新,李文君.青藏公路沿线冻土区域分布计算机模拟与制图[J].冰川冻土.1999,22(4):323-326.
[56]吴青柏,朱元林,刘永智.青藏高原多年冻土顶板温度和温度位移预报模型的应用[J].冰川冻土.2002,24(5):614-617.
[57]南卓铜,李述训,刘永智等.基于年平均地温的青藏高原冻土分布制图及应用[J].冰川冻土.2002,24(2):142-148.
[58]Chen S B.Permafrost classification on the Tibet Plateau based on surface emissivity retrieval from Terra-MODIS data[C].IEEE International Geoscience and Remote Sensing Symposium.Anchorage,Alaska:2004.
[59]陈圣波.青藏高原天然气水合物环境效应可能性研究[D].北京:中国科学院地理科学与资源研究所,2002:62-86.
[60]张勇,蔡石泉.青藏高原多年冻土分布的特点及与高纬度地区对比[C].北京:地质出版社,1984.273-282.
[61]程国栋.中国青藏高原多年冻土与加拿大北部多年冻土的一些差别[J].冰 川冻土.1979(2):39-43.
[62]童伯良,李树德.青藏高原多年冻土的某些特征及其影响因素[C].青藏冻土研究论文集.北京:科学出版社,1983:1-11.
[63]汤懋苍,程国栋,林振耀.青藏高原近代气候变化及对环境的影响[M].广州:广东科技出版社,1998:237-259.
[64]王家澄,王绍令,邱国庆.青藏公路沿线的多年冻土[J].地理学报.1979,34(1):1-3.
[65]周幼吾,杜榕桓.青藏高原冻土初步考察[J].科学通报.1963(2):60-63.
[66]程国栋.中国区域冻土研究的回归和展望[J].冰川冻土.1988,10(3):296-299.
[67]曾群柱,寇有观,谢维荣,等.青藏高原辐射平衡的研究[C].中国科学院兰州冰川冻土研究集刊(3号刊).北京:科学出版社,1982:32-51.
[68]寇有观,曾群柱,谢维荣,等.青藏高原总辐射研究[C].北京:科学出版社,1982:1-17.
[69]邱国庆,程国栋.中国的多年冻土.过去与现在[J].第四纪研究.1995(1):13-22.
[70]张文纲,李述训,吴通华等.青藏高原地气温差变化分析[J].地理学报.2006,61(9):899-909.
[71]郭东信.地质构造对多年冻土的影响[J].地理科学.1985,5(2):97-105.
[72]Morrissery L A,Strong L L.Mapping permafrost in the Boreal forest with Thematic Mapper Satellite data[J].Photogrammetric Engineering & Remote Sensing.1986,52:1513-1519.
[73]韦志刚,罗四维,董文杰等.青藏高原积雪资料分析及其与我国夏季降水的关系[J].应用气象学报.1998,9(S1):39-46.
[74]梁风仙,罗祥瑞,王绍令.航空像片在冰缘地貌动态研究中的应用[J].冰川冻土.1980(S1):72-73.
[75]罗祥瑞,梁风仙,王绍令.沱沱河北多年冻土综合影像图的制作[J].冰川冻 土.1980(S1):70-71.
[76]卓宝熙.高原多年冻土地区遥感图像工程地质分区的探讨[J].工程地质学报.2003,11(3):225-229.
[77]赵尚民,程维明,柴慧霞,等.基于遥感与SRTM的青藏高原冰缘地貌信息提取方法-以1:100万标准分幅拉萨幅(H46)为例[J].地理研究.2007,26(6):1175-1185.
[78]赵英时.遥感应用分析原理与方法[M].北京:科学出版社,2003:194-195.
[79]梁凤仙,曾群柱,冯学智,等.冻土地区红外遥感最佳时段和多光谱可分性研究-以青海热水为例[C].中国科学院兰州冰川冻土研究所集刊(第6号).北京:科学出版社,1988:106-119.
[80]薛薇.SPSS统计分析方法及应用[M].北京:电子工业出版社,2004:281-299.
[81]李登科,张树誉.EOS/MODIS遥感数据的接受与处理[J].陕西气象.2003(1):34-36.
[82]李登科,张树誉.EOS/MODIS遥感数据与应用前景[J].陕西气象.2003(2):37-40.
[83]吴青柏.人类工程活动下冻土环境变化和工程适应性研究[D].兰州:中国科学院寒区旱区环境与工程研究所,2000:20-24.
[84]刘海昆,黄树祥,王慧.论冻土对土壤水分动态的影响[J].黑龙江水利科技.2002(3):87.
[85]马龙,刘闯.MODIS在三江平原湿地分布研究中的应用[J].地理与地理信息科学.2006,22(3):57-60.
[86]邢宇,姜琦刚,王坤.青藏高原DEM的生成与应用[J].世界地质.2007,26(4):479-483.
[87]邱玉宝,施建成,蒋玲梅等.AMSR-E被动微波土壤水分与降雨率时空相关性分析研究[J].北京师范大学学报(自然科学版).2007,43(3):350-355.
[88]刘闯,葛成辉.美国地球观测系统AQUA卫星数据政策、主要技术指标与数据本土化共享问题[J].遥感信息.2002(2):38-42.
[89]祝汉昌,朱福康,刘玉洁.青藏高原行星反照率与地面反照率关系的研究[J].科学通报.1990(20):1563-1565.
[90]焦子锑,王锦地,谢里欧等.地面和机载多角度观测数据的反照率反演及对MODIS反照率产品的初步验证[J].遥感学报.2005,9(1):64-72.
[91]中国科学院寒区旱区环境与工程研究所.1:4000000中国冰川冻土沙漠图说明书[M].北京:中国地图出版社,2006:7-12.
[92]余建英,何旭宏.数据统计分析与SPSS应用[M].北京:人民邮电出版社,2003:189-245.
[93]张文彤.世界优秀统计工具SPSS11.0统计分析教程高级篇[M].北京:北京电子希望出版社,2002:91-99.
[94]邱国庆,郭东信.论青藏公路沿线的融区[C].青藏冻土研究论文集.北京:科学出版社,1983:30-37.
[95]李新.冰冻圈信息系统及其应用研究[D].中国科学院兰州冰川冻土研究所.兰州:1998:65-78.
[96]程国栋,王绍令.试论中国高海拔多年冻土带的划分[J].冰川冻土.1982,4(2):1-17.
[97]李健.长白山地区地表温度反演研究[D].长春:吉林大学,2004:2-3.
[98]李海涛,田庆久.ASTER数据产品的特性及其计划介绍[J].遥感信息.2004(3):53-55.
[99]朱黎江,秦其明,陈思锦.ASTER遥感数据解读与应用[J].国土资源遥感.2003,56(2):59-63.
[100]毛克彪.用于MODIS数据的地表温度反演方法研究[D].南京:南京大学,2004:35-58.
[101]毛克彪,覃志豪,徐斌.针对ASTER数据的单窗算法[J].测绘学院学报.2005(1):40-42.
[102]毛克彪,施建成,覃志豪.一个针对ASTER数据同时反演地表温度和比辐射率的四通道算法[J].遥感学报.2006(4):593-599.
[103]毛克彪,唐华俊.一个从ASTER数据中反演地表温度的劈窗算法[J].遥感信息.2006(5):7-11.
[104]刘志武,党安荣.利用ASTER遥感数据反演陆面温度的算法及应用研究[J].地理科学进展.2003(5):507-513.
[105]唐世浩,朱启疆,苏理宏.基于订正ALPHA差值谱的热红外温度与发射率分离算法[J].红外与毫米波学报.2005(4):286-290.
[106]唐世浩,李小文,王锦地,等.改进的基于订正ALPHA差值谱的TES算法[J].中国科学(D辑).2006(7):663-671.
[107]李小文,汪骏发,王锦地等.多角度与热红外对地遥感[M].北京:科学出版社,200l:84-127.
[108]王凤敏,田庆久,虢建宏.基于ASTER数据反演我国南方山地陆表温度[J].国土资源遥感.2005(1):30-33.
[109]Gillespie A R,Rokugawa S,Matsunaga T.A temperature and emissivity separation algorithm for Advanced Spaceborne Thermal Emission and Reflection Radiometer(ASTER) images[J].IEEE Transactions on Geoscience and Remote Sensing.1998,36(4):1113-1126.
[110]吕一峰.基于ASTER影像的DEM与数字地貌制图研究[D].福建师范大学,2008:16-23.
[111]秦鹏,陈健飞.ASTER影像提取植被信息的NDVI与SAVI法比较—以广州花都区为例[J].热带地理.2008,28(5):419-422.
[112]卢远,华璀,杨小雄,等.基于ASTER数据的城市热环境效应定量分析[J].武汉大学学报(信息科学版).2008,33(3):297-301.
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