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应用NOAA/AVHRR数据测算局地水稻种植面积方法研究 总被引:29,自引:3,他引:29
本文采用在模糊监督分类中增加迭代过程的方法--模糊监督分类一迭代法,在分解混合像元的基础上,利用AVHRR数据求算水稻种植面积。根据稻田与旱地存在温度差异的特点,在分析AVHRR数据统计特征的基础上,增加了第3和4两通道参加模糊监督分类,从而增加了分类橡元值矢量维数,增强了对水稻的鉴别能力。反采用的迭代法是收敛具有效的,经本方法输出的水稻种植面积百分含量图与实际水稻分布十分吻合,测算出的面积值与T 相似文献
3.
NOAA卫星沙尘暴光谱特征分析及信息提取研究 总被引:12,自引:5,他引:7
通过对2000—2002年多次沙尘暴过程NOAA卫星AVHRR资料的分析,研究了沙尘、云、沙漠、戈壁、积雪、裸地、植被等不同目标物的光谱特性,发现沙尘暴在AVHRR-2中各通道均有不同程度的反映。1,2通道中沙尘的反射率较高(介于云和沙漠之间);4,5通道的亮温低于晴空地表高于云;在3通道中沙尘表现的很独特,其亮度温度为所有研究目标物中最高的,表明通道3包含较多的沙尘信息,AVHRR-3取代AVHRR-2后对监测沙尘有不利影响。在此基础上提出定量提取沙尘信息的两种沙尘判识指数,并利用沙尘判识指数成功地提取多次沙尘暴过程的沙尘信息。结果表明:利用多通道组合沙尘判识指数能够对沙尘信息进行有效提取。 相似文献
4.
黄淮海平原耕地转移对植被碳储量的影响 总被引:8,自引:0,他引:8
应用GLO-PEM模型和1988年NOAA/AVHRR遥感数据,估算了黄淮海平原植被净初级生产力(NPP),并根据NPP和植被凋落物产生量计算了不同土地利用类型的植被碳密度。通过对1988年与2000年土地利用图的叠置分析,统计了耕地与其他土地利用类型之间的转移量,并估算了耕地转移对植被碳储量的影响。研究发现,耕地转移对植被碳储量变化的影响在不同区域与不同土地利用类型上存在显著差异。1988~2000年间,耕地转移导致全区植被碳储量下降了0.24%,其中耕地转为建设用地是植被碳储量减少的主要原因。这一研究结论为正确把握耕地转移对区域植被碳储量的影响,并据此制订碳汇管理措施具有重要的参考价值。 相似文献
5.
References: 《极地研究(英文版)》2007,18(1):54-62
Chinese meteorological satellite FY-1D can obtain global data from four spectral channels which include visible channel(0.58-0.68 μm) and infrared channels(0.84-0.89 μm,10.3-11.3 μm,11.5-12.5 μm).2366 snow and ice samples,2024 cloud samples,1602 land samples and 1648 water samples were selected randomly from Arctic imageries.Land and water can be detected by spectral features.Snow-ice and cloud can be classified by textural features.The classifier is Bayes classifier.By synthesizing five d ays classifying result of Arctic snow and ice cover area,complete Arctic snow and ice cover area can be obtained.The result agrees with NOAA/NESDIS IMS products up to 70%. 相似文献
6.
Potential evapotranspiration (PET) is a key input to hydrological models. Its estimation has often been via the Penman–Monteith (P–M) equation, most recently in the form of an estimate of reference evapotranspiration (RET) as recommended by FAO‐56. In this paper the Shuttleworth–Wallace (S–W) model is implemented to estimate PET directly in a form that recognizes vegetation diversity and temporal change without reference to experimental measurements and without calibration. The threshold values of vegetation parameters are drawn from the literature based on the International Geosphere–Biosphere Programme land cover classification. The spatial and temporal variation of the LAI of vegetation is derived from the composite NOAA‐AVHRR normalized difference vegetation index (NDVI) using a method based on the SiB2 model, and the Climate Research Unit database is used to provide the required meteorological data. All these data inputs are publicly and globally available. Consequently, the implementation of the S–W model developed in this study is applicable at the global scale, an essential requirement if it is to be applied in data‐poor or ungauged large basins. A comparison is made between the FAO‐56 method and the S–W model when applied to the Yellow River basin for the whole of the last century. The resulting estimates of RET and PET and their association with vegetation types and leaf area index (LAI) are examined over the whole basin both annual and monthly and at six specific points. The effect of NDVI on the PET estimate is further evaluated by replacing the monthly NDVI product with the 10‐day product. Multiple regression relationships between monthly PET, RET, LAI, and climatic variables are explored for categories of vegetation types. The estimated RET is a good climatic index that adequately reflects the temporal change and spatial distribution of climate over the basin, but the PET estimated using the S–W model not only reflects the changes in climate, but also the vegetation distribution and the development of vegetation in response to climate. Although good statistical relationships can be established between PET, RET and/or climatic variables, applying these relationships likely will result in large errors because of the strong non‐linearity and scatter between the PET and the LAI of vegetation. It is concluded that use of the implementation of the S–W model described in this study results in a physically sound estimate of PET that accounts for changing land surface conditions. Copyright © 2006 John Wiley & Sons, Ltd. 相似文献
7.
大气散射对采用归一化植被指数进行赤潮遥感监测的影响研究 总被引:3,自引:0,他引:3
1 引言近年来赤潮灾害在我国海域频繁发生,对沿海环境、经济和人民生产生活都造成一定的影响,因此对赤潮进行实时监测并业务化运行就显得尤其重要.赤潮遥感监测是目前常用手段之一,在实践中取得了一定的效果,但到目前为止还没有一种成熟的算法能用于业务化监测赤潮的发生.很多研究人员借鉴归一化植被指数(NDVI)阈值法、海表温度(SST)异常等来判断赤潮的发生,但归一化植被指数阈值法经常会出现误判或无法区分正常与异常水体的情况.该阈值范围由先验知识得到,没有定量的理论依据.归一化植被指数是陆地遥感中用以表征地表植物覆盖程度和健… 相似文献
8.
The U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center routinely produces and distributes a remote sensing phenology (RSP) dataset derived from the Advanced Very High Resolution Radiometer (AVHRR) 1-km data compiled from a series of National Oceanic and Atmospheric Administration (NOAA) satellites (NOAA-11, −14, −16, −17, −18, and −19). Each NOAA satellite experienced orbital drift during its duty period, which influenced the AVHRR reflectance measurements. To understand the effect of the orbital drift on the AVHRR-derived RSP dataset, we analyzed the impact of solar zenith angle (SZA) on the RSP metrics in the conterminous United States (CONUS). The AVHRR weekly composites were used to calculate the growing-season median SZA at the pixel level for each year from 1989 to 2014. The results showed that the SZA increased towards the end of each NOAA satellite mission with the highest increasing rate occurring during NOAA-11 (1989–1994) and NOAA-14 (1995–2000) missions. The growing-season median SZA values (44°–60°) in 1992, 1993, 1994, 1999, and 2000 were substantially higher than those in other years (28°–40°). The high SZA in those years caused negative trends in the SZA time series, that were statistically significant (at α = 0.05 level) in 76.9% of the CONUS area. A pixel-based temporal correlation analysis showed that the phenological metrics and SZA were significantly correlated (at α = 0.05 level) in 4.1–20.4% of the CONUS area. After excluding the 5 years with high SZA (>40°) from the analysis, the temporal SZA trend was largely reduced, significantly affecting less than 2% of the study area. Additionally, significant correlation between the phenological metrics and SZA was observed in less than 7% of the study area. Our study concluded that the NOAA satellite orbital drift increased SZA, and in turn, influenced the phenological metrics. Elimination of the years with high median SZA reduced the influence of orbital drift on the RSP time series. 相似文献
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10.
应用遥感数据研究中国植被生态系统与气候的关系 总被引:48,自引:2,他引:48
应用1982-1994年NOAA/AVHRR的归一化植被指数(NDVI)资料和587个气象台站的数据对我国不同类型植被生态系统和气候的关系进行研究,首先将我国的植被类型划分为21类,在此基础上分别研究了不同时间尺度下我国不同区域,不同植被类型和气候的关系。结果表明:在多年平均状态下,植被生态系统NDVI水平主要受水分条件的影响;年内变化上,温度对植被生态系统季相变化化起着比降水略大的作用,年降水量造成了植被季相响应的差异,在年际变化上,分别研究了4个季节和整个生长期尺度上的关系,一般情形为温度和降水对植被的年际波动起着大致相反的作用,不同植被类型在不同的生长时期(季节)对气候的变化响应方式也不同,发现在植被的生长期,我国南方和北方的植被生态系统对温度和降水的响应方式相反;同时存在2个植被-气候敏感区,分别为我国北方的典型草原到森林的过渡区和云南中部部分区域。 相似文献