MODIS NDVI和AVHRR NDVI 对草原植被变化监测差异   总被引：5，自引：0，他引：5  

陈燕丽  龙步菊  潘学标  钟仕全  莫伟华 《遥感学报》2011,15(4):831-845

以草地作为研究载体,对比分析草原植被AVHRR NDVI和MODIS NDVI两种NDVI序列的年内、年际变化特征,讨论两种NDVI序列对降水量、平均气温和水汽压3种气候因子的响应差异,为合理选择NDVI序列对植被进行监测研究提供参考。结果表明：(1)两种NDVI序列所反映的草原植被年内变化趋势相似,但MODIS NDVI对各类草原的区分度优于AVHRR NDVI;(2)两种NDVI序列所反映的2000年—2003年草原植被年际变化差异明显。较之于MODIS NDVI,AVHRR NDVI变化趋势分类图表现出更强的植被改善趋势,植被改善面积在AVHRR NDVI变化趋势分类图中占94.25%,在MODIS NDVI中为83.33%;两种NDVI变化趋势分类图反映的植被变化趋势吻合度为52.88%。(3)两种NDVI序列与水汽压、降水量相关性差异显著。MODIS NDVI与各站点平均气温的相关系数均大于GIMMS NDVI;而MODIS NDVI与水汽压的相关系数83%(10个站点)小于GIMMS NDVI,与降水量的相关系数67%(8个站点)小于GIMMS NDVI。  相似文献   

通用劈窗算法的NOAA-18(N)AVHRR/3数据地表温度遥感反演与验证   总被引：1，自引：0，他引：1  

孙志伟  唐伯惠  吴骅  程耀东 《地球信息科学学报》2013,15(3):431-439

本文以NOAA-18(N)AVHRR/3数据,运用通用劈窗技术获得地表温度。首先,利用MODTRAN 4模拟不同地表和大气状况下热红外通道(Ch4,10.3~11.3μm和Ch5,11.5~12.5μm)的星上亮温,并建立模拟数据库。其次,按照地表温度、大气可降水汽含量、地表比辐射率和观测天顶角,对模拟数据库分组,确定出各分组的通用劈窗算法系数。然后,将构建的地表温度反演模型应用到NOAA-18(N)AVHRR/3数据,模型所需的地表比辐射率由NDVI阈值法确定,大气可降水汽含量是利用Li等(2003)提出的一种劈窗的协方差与方差比的方法来估算。反演结果表明:在观测天顶角小于30°或者大气可降水汽含量小于3.5 g/cm2时,地表温度反演的均方根误差小于1.0K;在观测天顶角小于45°并且大气可降水汽含量小于5.5g/cm2情况下,均方根误差小于1.5K。最后,利用美国通量站的实测数据对地表温度反演结果进行了验证,结果表明均方根误差小于1.8K。  相似文献   

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  

姜群鸥  邓祥征  战金艳  刘兴权 《地理研究》2008,27(4):839-847

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   

Mediterranean desertification and land degradation: Mapping related land use change syndromes based on satellite observations   总被引：3，自引：0，他引：3  

J. Hill  M. Stellmes  Th. Udelhoven  A. Rder  S. Sommer 《Global and Planetary Change》2008,64(3-4):146

  相似文献   

Estimation of potential evapotranspiration over the Yellow River basin: reference crop evaporation or Shuttleworth–Wallace?     

M. C. Zhou  H. Ishidaira  K. Takeuchi 《水文研究》2007,21(14):1860-1874

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   

SST variations of the Kuroshio from AVHRR observation   总被引：1，自引：0，他引：1  

张彩云  陈戈 《中国海洋湖沼学报》2006,24(4):345-351

1 INTRODUCTION The Kuroshio Current (KC), being the western boundary current in the North Pacific subtropical gyre, is the second strongest current in the world af- ter the Gulf Stream and is famous as a strong and fast flow. KC plays an important role in…  相似文献   

应用NOAA/AVHRR数据测算局地水稻种植面积方法研究   总被引：32，自引：3，他引：29  

李郁竹  曾燕 《遥感学报》1998,2(2):125-130

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   

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  

唐军武  丁静  王其茂  马超飞 《海洋学报》2004,26(3):136-142

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   

遥感技术在我国海冰研究方面的进展   总被引：1，自引：0，他引：1  

国巧真  陈云浩  李京  格日乐  史晓霞 《海洋预报》2006,23(4):95-103

本文首先对遥感技术在我国海冰研究中的发展情况作了详细地论述,并介绍了几种常用于海冰研究的遥感数据;其次,从海冰厚度识别、海冰运动速度矢量计算、海冰资源量测算以及海冰灾害监测等四个方面,简单论述了遥感技术在海冰研究中的应用。最后,以渤海海冰为例,阐述了遥感技术的具体应用,同时指出了今后的发展方向。  相似文献   

Effect of NOAA satellite orbital drift on AVHRR-derived phenological metrics     

《International Journal of Applied Earth Observation and Geoinformation》2017

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