An observational study on the evaluation of snow-covered area by remotely-sensed data with different spatial resolutions and on the snowmelt-runoff analysis

不同空间分辨率遥感数据评价积雪面积及融雪径流分析观测研究

基本信息

批准号：
15300306
负责人：
MATSUYAMA Hiroshi
金额：
$ 6.78万
依托单位：
Tokyo Metropolitan University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B)
财政年份：
2003
资助国家：
日本
起止时间：
2003 至 2005
项目状态：
已结题

项目摘要

To accomplish the objectives of the present study, the concrete contents of the analyses and the results obtained are summarized as follows.1.The snow index recently proposed was calculated by Landsat/ETM+ and was applied to the coniferous forests in Tsunan-town in Niigata prefecture. The threshold of LAI (Leaf Area Index) to distinguish between snow and coniferous forests using the snow index was estimated based on in situ field survey. As a result, it was clarified that the snow index could discern snow from coniferous forests if LAI was smaller than 4.(Shimamura et al., 2006).2.Effects of differences of the processes of direct measurements of LAI and their combinations on the estimation of LAI for planted stands of Japanese cedar (Cryptomeria japonica D.Don) were estimated by in situ field survey in Hinohara-village in Tokyo and Tsunan-town. The processes consist of selection of sample tree(s), sampling of leaves, and planimetry of leaf area. Each process has several methods, and th … More e differences of estimated LAI in each process were not so large. However, the accumulation of these differences led to as large as 40% of the estimated LAI, even in the planted stands of Japanese cedar which were taken care of such as pruning or thinning (Fujiwara et al., 2005).3.Snow surveys in Mt.Makihata and synchronized observations of the existence of snow with the passage of Landsat satellite were carried out in springs which made it possible to estimate the snow water resources in the Uono river above Muikamachi river discharge station. The estimated amount of snowmelt as revealed by the analyses of two scenes of Landsat/ETM+ was larger than the observed runoff. This implied the importance of precise analyses of snowmelt-runoff processes (Shimamura et al., 2005).4.The effect of the dependence of the satellite data on sun/sensor geometry (BRDF) for monitoring vegetation was evaluated. The vegetation in the succession stages after forest fire was investigated. The difference of Normalized Difference Vegetation Index (NDVI) of the value around 0.1-0.2 was caused by BRDF in relation to the sun/sensor geometry. Against BRDF, the reflectance of the near-infrared band normalized by the sum of other bands was most robust in the site where vegetation existed. Global Environmental Monitoring Index was most robust in the site where vegetation was scarce, while NDVI was strongly affected by BRDF in these sites (Hasegawa et al., 2006).5.The spatial representativity of the annual mean temperature of AMeDAS, which was important for snowmelt-runoff analyses, was investigated using the variogram. In the variogram, the value called as "range" was regarded as the measure of the spatial representativity. It was found that the annual mean temperature had the spatial representativity of at least 100km in all regions in Japan (Hadano et al., 2004). Less

为完成本研究的目标，具体分析内容和所得结果总结如下：1.最近提出的降雪指数是利用Landsat/ETM+计算的，并应用于新泻县津南镇的针叶林。基于实地实地调查，估算了利用积雪指数区分积雪和针叶林的 LAI（叶面积指数）阈值，结果明确了积雪指数可以区分积雪和针叶林。如果 LAI 小于 4，则为森林。(Shimamura et al., 2006)。 2. LAI 直接测量过程及其组合的差异对日本雪松 (Cryptomeria japonica D.Don) 人工林 LAI 估算的影响）是通过在东京日野原村和津南镇进行实地实地调查估算的。过程包括样本树的选择、叶子的取样和叶子的面积测量。每个过程都有多种方法，每个过程中估算的 LAI 差异并不大，但这些差异的累积导致估算的 LAI 高达 40%，即使在种植林中也是如此。进行了修剪或间伐等处理的日本柳杉（Fujiwara et al., 2005）。 3. 牧旗山的积雪调查以及随着雪的经过同步观测积雪的存在Landsat 卫星在春季进行，可以估算出 Muikamachi 河排放站上方 Uono 河的雪水资源。 Landsat/ETM+ 的两个场景分析显示的估算融雪量大于观测到的径流。这意味着精确分析融雪径流过程的重要性（Shimamura et al., 2005）。4.卫星数据对太阳/传感器几何形状的依赖性的影响评估了森林火灾后演替阶段的植被分布（BRDF），其值在 0.1-0.2 左右是由 BRDF 与太阳/传感器几何关系引起的。相对于 BRDF，通过其他波段总和归一化的近红外波段的反射率在植被存在的地点最稳健，全球环境监测指数在植被稀缺的地点最稳健。这些地点的 NDVI 受到 BRDF 的强烈影响（Hasekawa 等，2006）。5. AMeDAS 年平均温度的空间代表性对于融雪径流分析很重要，使用称为变异函数的值进行了研究。以“范围”作为空间代表性的衡量标准，发现日本所有地区的年平均气温至少具有100km的空间代表性。（秦野等人，2004）。更少