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吉林省积雪遥感信息时空变化研究

Study on Spatio-temporal Changes of Snow Cover by Remote Sensing in Jilin Province

【作者】 路鹏

【导师】 陈圣波; 周云轩;

【作者基本信息】 吉林大学 , 地学信息工程, 2011, 博士

【摘要】 积雪是地球表面重要的覆盖物,北半球的森林地区季节性积雪覆盖范围是全球陆地表面的8%。积雪是冰冻圈中分布最广泛、季节变化最显著因素,也是全球大气与地表相互作用的重要影响因素。因此基于气候模型,利用积雪遥感数据预测气候变化趋势预计雪融洪水可能造成的影响成为可能。积雪融水也是农田灌溉等的主要水源来源。中国稳定积雪覆盖区主要在新疆、青藏高原和东北地区。吉林省位于中国东北三省的中部,南邻辽宁省,西接内蒙古自治区,北与黑龙江省相连。吉林省地貌形态差异明显。地势由东南向西北倾斜,呈现明显的东南高、西北低的特征。吉林省属于温带大陆性季风气候,四季分明,冬季寒冷漫长。冬季平均气温在-11℃以下,降雪量大。吉林省是我国商品粮的主产区,担负着全国商品粮供给的重要责任。研究中以吉林省作为研究对象,对该区影响积雪信息提取的因素包括遥感影像上云覆盖、地形起伏和森林区对积雪信息提取的影响分别进行分析。由此分析吉林省2000年至2010年10年间的积雪覆盖时空变化及与研究区地形、气候和土地覆盖类型的关系。主要工作及其结论有:1.云覆盖区积雪信息提取研究。利用MODIS云掩膜产品,结合多时相数据融合恢复目标日期的云下地物。从恢复前后积雪提取结果看出,对云下地物的恢复不仅提取了云遮挡处的积雪信息,还对误判为雪的云信息进行有效的去除。因此在对遥感数据进行应用前,对云噪声的处理很必要。2.复杂地形区积雪信息提取研究。利用余弦校正算法完成研究区地形校正,校正后的雪盖指数值略低于校正前的值,并且阳坡校正后的雪盖指数降低,而阴坡的变化不显著。通过分析余弦校正模型中各参数对雪盖指数的影响,结果显示不同的太阳天顶角和坡度对积雪提取结果有很大的影响。坡度和坡向对积雪覆盖提取影响的分析表明,坡向对积雪覆盖影响不显著,坡度对积雪覆盖率呈正相关大。对比校正前后积雪提取结果,校正后积雪像元的数量要多于校正前。3.植被覆盖区积雪信息提取研究。根据几何光学模型,提出适合长白山地区的混合反射率模型。将植被与积雪的混合分为:树冠上有雪而下垫面完全积雪覆盖、树冠上无雪而下垫面完全积雪覆盖、树冠上有雪而下垫面为无雪。利用模型分别模拟混合反射率,结果与实测反射率曲线变化趋势吻合。研究表明,树上有雪、下垫面有雪的情况和树上有雪、下垫面无雪的情况变化趋势相同,而且雪盖指数的值较相近。树上无雪、下垫面有雪的情况与前两种情况相差较大,变化趋势也相反。选择对雪盖指数影响较大的树冠半径和叶面积指数对积雪信息提取影响的研究表明随着树冠半径的增加积雪覆盖率在半径为0.5到1米不变,1到1.5米降低后在1.5到2.5米又呈升高趋势。而叶面积指数与积雪覆盖率呈负相关关系,太阳天顶角与积雪覆盖率呈正相关关系。4.研究区积雪信息提取流程。通过不同参数对积雪信息提取的敏感度分析,确定积雪信息提取参数的阈值。由此,分别通过坡度和雪盖指数对研究区分区,根据区域特征分别提取积雪信息。结果表明,该方法提取的积雪像元更多,同时有效地减少了误判为积雪的森林植被像元。5.研究利用积雪覆盖指数对吉林省积雪覆盖2000年10月1日到2010年4月30日的时空变换特征。吉林省平均积雪持续天数(SCD)呈明显的分带,由西北向东南依次增加。对初始降雪时间和融雪时间的时间序列分析,吉林省大部分地区的初始降雪时间在11月中旬,在东部山区降雪时间较早,在11月初。而初始融雪时间为2月初,山区的融雪时间较晚,在2月末或者更晚。其中2000年至2001年、2004年至2005年、2006年至2007年冬季的融雪时间较晚,在2月下旬。6.地形、气候特征和土地覆盖率与积雪覆盖指数的影响研究。随着高程的升高,积雪覆盖持续时间呈升高趋势。气温对积雪覆盖持续时间的影响也较明显,气温低的年份,积雪持续时间较长,相反积雪覆盖持续时间短的年份,年平均气温相对较高。降水量的升高对积雪覆盖持续时间的影响比较显著,降水量多的年份,积雪覆盖持续时间较长,反之亦然。在对气温和降水量与初始融雪时间的影响研究发现,气温对初始融雪时间影响较大,而降水量变化时初始融雪时间没有规律的变化。积雪覆盖指数与降水量正相关,积雪覆盖率与降水量无关。年平均气温与积雪覆盖指数及积雪覆盖率都呈负相关。选取农田、草地、人工用地、裸地和森林研究土地覆盖类型与积雪覆盖指数的关系。结果表明,积雪持续时间在裸地和森林区最长,最低的是人工用地,初始降雪时间也是在裸地和森林区最早,但是初始融雪时间较晚。人工用地的初始降雪时间较晚,初始融雪时间较早。说明影响积雪覆盖分布的,不仅是地形和气候变化,还与人类活动密切相关。

【Abstract】 Snow is one of the essential land covers, which plays an important role in the global climate system and is also the main source of available water source. Especially in north Hemisphere 8 percent of the total terrain surface is covered by snow. Snow cover is one of the most significant factors to indicate the seasonal change and to influence the interaction between the global atmosphere and the ground surface. Thus, conbine with the climate model to the influence of the snowmelt flooding can be predicted, for the irrigatation in agriculture.In China, snow cover is distributed in Xinjiang province, Tibetan Plateau and the northeast region in the winter. Jilin province is located in northeast China, which south is locates Liaoning province, the west of Inner Mongolia Autonomous Region and Heilongjiang Province in the north. The elevation of the terrain is apparently higher in the southeast than that in the northwest. It is temperate continental monsoon climate and has the four distinct seasons. Especially in winter, it is cold and long, the average temperature is about-11℃.Furthermore, Jilin province is one of the main grain production area in our country and is vital important to the grain supply in our country. Thus, it is chosen as study area in our study. Several factors that can affect the snow cover extraction is firstly studied, including cloud cover in the remote sensing image, the terrain and the vegetation. Finally, the spatial and temporal changes of the snow cover in the year from 2000 to 2010 and its relationship with the temperature, precipitation and the terrain are analyzed in the paper. The results are as follows:1. Elimination of cloud cover in the remote sensing image. MODIS (Moderate Resolution Imaging Spectroradiometer) reflection data and the MOD35_L2 cloud mask data in Jilin Province on Nov.23, Nov.24 and Nov.25 in 2008 are selected to remove the cloud cover on Nov.24 data. Then the snow cover is extracted from cloud-reduced data by the Normalized Difference Snow Index (NDSI) algorithm. Finally, EOS Aqua (AMSR-E) snow water equivalent data from Advanced Microwave Scanning Radiometer (AMSR) is employed to verify the snow cover undercloud. The results indicate that snow cover fraction of cloud -reduced data is much closer to the AMSR-E snow cover fraction.2. The terrain effect is corrected by using the cosine topography correction method. Changbai mountain is taken as a topography test area due to its complex terrain and high altitude. After the adjusted snow cover index is lower than its original values on the sunny slope, the snow cover index is reduced after adjustment but the change of shady slope is not remarkable. The sensitive analysis indicates the different solar zenith angle and the slope has very tremendous influence to the extraction of snow. The aspect is not sensitive to the snow cover fraction but slop shows a good positive correlation. The comparation between snow cover derived from before and after correction shows the good result.3. The snow cover is derivation at a forest area.Based on GORT model asimulated hybrid relfection of plant canopy and snow. The result are as follows:The simulation results show an obvious hot spot and the trend is fit the measured curve. when the canopy has snow, the underlying surface is all snow cover and the underliying suface is snow free has the same sensitive and trend to the NDSI,but when the canopy has no snow, the underliying sucface is all snow cover appeared different character. The snow cover fraction isn’t increasing when the radius of plant canopy increase until the radius reach 1.5.The LAI and the snow cover fraction is negative related, but the solar zenith angle is opposite.4. In order to determine the threshold of the NDSI, under different parameters of the model in deferent situations extraction algorithm of the snow cover are simulated. Then the threshold of the snow cover can be verified through the analysis of the scatter map of the parameters. The algorithm separates the region to forest area and terrain area by NDVI and slop parameter. The new method not only derived more snow pixels, but also reduced the confused pixels of snow and forest. 5. Spatial-temporal changes of the snow cover in Jilin province are generated. The data used is from the day October 1st,2000 to April 30th,2010. The parameters include the average snow cover duration time (SCD), snow cover onset day (SCOD), snow cover melting day (SCMD),snow cover index(SCI) and snow cover ratio(SCR). The SCD presents the same obvious changes with altitude.The average SCOD in Jilin province is at mid-November, but it will be in the beginning of November in eastern mountain area. But the SCMD is in the early February, and the mountain area will be in the late February or later.The duration between 2006 to 2007 has the largest SCI, but the year 2001 to 2002 has minmum value.The largest SCR is appereant in 2000 to 2001,2001 to 2002 has the smallest SCR in the ten years.6. Relationships between the snow cover indexes and the terrain,the climate are analysised. With increasing of the elevation, the snow cover duration becomes longer and longer. The temperature also has an obvious impact on the snow cover duration. The snow cover duration will be long if the temperature is low in the year. While the snow cover duration will be short if the annual mean temperature is relatively high. The same correlation occurs to the relationship between the precipitation and the snow cover duration. The research on the influence of temperature and precipitation on the SCMD indicates that the temperature has apparently influence on the SCMD, while the influence from the precipitation is irregular. SCI and SCR have positive correlation to temperature, but the precipitation is only relative to SCI. The croplands, grass, artificial land, bare land and forest are use for this study. The SCD is long in the bare land and forest, the cropland is the lowest. The SCOD is earlier in bare land and forest, but SCMD in these two types are late. The SCOD of cropland is late, and SCMD is earlier. The result indicted the snow cover distribution is not only related to altitude and climate but also associated with the human activities.

  • 【网络出版投稿人】 吉林大学
  • 【网络出版年期】2011年 09期
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