Malaria incidence in Nairobi,Kenya and dekadal trends in NDVI and climatic variables

The primary objective of this research was to determine if the remotely-sensed metric, Normalised Difference Vegetation Index (NDVI) and ground-collected dekadal climatological variables were useful predictors of future malaria outbreaks in an epidemic-prone area of Nairobi, Kenya. Data collected consisted of 36 dekadal (10-day) periods for the variables rainfall, temperature and NDVI along with yearly documented malaria admissions in 2003 for Nairobi, Kenya. Linear regression models were built for malaria cases reported in Nairobi, Kenya, as the dependent variable and various time-based groupings of temperature, rainfall and NDVI data from the dekads in both the current and the previous month as the independent variables. Data from 2003 show that malaria incidence in any given month is best predicted (R²  = 0.881, p < 0.001) by the average NDVI for the 30 days including the final two dekads of the previous month and first dekad of the current month, and by the average rainfall for the 30 days including the three dekads of rainfall data from the prior month. Forecasting an outbreak in an epidemic zone would allow public health entities to plan for and disseminate resources to the general public such as antimalarials and insecticide impregnated bed nets. In addition, vector control measures could be implemented to slow the rate of transmission in the impacted population.     

Satellite-derived variability of the Aegean Sea ecohydrodynamics

Eight years of AVHRR-derived sea surface temperature (SST) and SeaWiFS-derived surface chlorophyll (Chl) data (1998–2005) are used to investigate key processes affecting the spatial and temporal variability of the two parameters in the Aegean Sea. Seasonal mean SST and Chl maps are constructed using daily data to study seasonal dynamics whereas empirical orthogonal function (EOF) and correlational analysis is applied to the 8-day composite SST and Chl anomaly time-series in order to study the variability and co-variability of the two parameters from subseasonal to interannual time-scales. The seasonal mean fields show that Black Sea cold and chlorophyll-rich waters enter through the Dardanelles Strait and they are accumulated in the north-eastern part of the Aegean Sea, steered by the Samothraki anticyclone. Large chlorophyll concentrations are encountered in the hydrological front off the Dardanelles Strait as well as in coastal areas affected by large riverine/anthropogenic nutrient loads. The SST seasonal mean patterns reveal strong cooling that is associated with upwelling along the eastern boundary of the basin during summer due to strong northerly winds, a process which is not present in the surface chlorophyll climatology. The Chl dataset presents much stronger sub-seasonal variability than SST, with large variations in the phase and strength of the phytoplankton seasonal cycles. EOF analysis of the anomaly time-series shows that SST non-seasonal variability is controlled by synoptic weather variations and anomalies in the north–south wind-stress component regulating the summer coastal upwelling regime. Mean SST and Chl patterns, and their associated variations, are not closely linked implying that Black Sea and riverine inputs mainly control the intra-annual and interannual variability of the surface chlorophyll in the Aegean Sea rather than mixing and/or upwelling processes.     

The estimate of sea ice resources quantity in the Bohai Sea based on NOAA/AVHRR data

The research on sea ice resources is the academic base of sea ice exploitation in the Bohai Sea. According to the ice-water spectrum differences and the correlation between ice thickness and albedo, this paper comes up with a sea ice thickness inversion model based on the NOAA/AVHRR data. And then a sea ice resources quantity (SIQ) time series of Bohai Sea is established from 1987 to 2009. The results indicate that the average error of inversion sea ice thickness is below 30%. The maximum sea ice resources quantity is about 6 × 10 9 m 3 and the minimum is 1.3 × 10 9 m 3 . And a preliminary analysis has been made on the errors of the estimate of sea ice resources quantity (SIQ).     

基于RS与GIS技术的南中国海湿地保护区选址研究

作者选取植被指数和温度作为湿地保护区划定的基本依据,采用感测范围广、获取信息方便的NOAA／AVHRR影像作为主要数据源,反演南中国海区域的归一化植被指数NDVI、海面温度和陆地表面温度。对反演结果进行分析,确定湿地保护区的NDVI阈值和温度阈值,再对各个参数的阈值进行空间分析,最终确定南中国海湿地保护区地址和范围。最后把确定的保护区域与联合国环境署／全球环境基金确定的湿地示范区作对比研究,发现根据NDVI与温度确定的湿地保护区包括了6个重要湿地示范区中的5个。     

基于背景知识的全球长时间序列反照率反演

全球范围时空连续的长时间序列地表反照率,对气候模拟与陆面过程研究具有重要意义。针对现有地表反照率产品普遍存在大量的数据缺失、有效反演比例低和时间序列短的问题,本文以多年MODIS和AVHRR数据,通过构建背景知识库进行高时间分辨率的AVHRR和MODIS数据的BRDF参数反演,实现MODIS与AVHRR数据在像元尺度上的定量融合,生成了全球时空连续长时间序列的地表反照率产品。首先,通过假设不同年份同一时期的地表状态不变,利用多年同一时期的MODIS和AVHRR观测数据构造多角度方向反射率,基于BRDF模型反演得到窄波段反照率;然后,通过宽波-窄波转换,得到MODIS的宽波段反照率;最后,结合AVHRR长时间序列优势及MODIS数据多光谱的特点,对二者进行定量融合,生成具有高度一致性长时间序列地表反照率产品。验证结果表明,本文地表反照率产品在地表异质性较小时与SURFRAD地面实测反照率之间具有非常好的一致性,在无积雪覆盖时与MODIS反照率产品之间吻合良好。本文的地表反照率产品无时空缺失,且时间覆盖率得到了极大的提高,能支持气候模式模拟与陆面过程模型进行近30 a来的地气系统模拟研究。     

中国北部地区卫星积雪产品数据集检验

同其他卫星相比,NOAA卫星搭载的AVHRR积雪产品,具有长达10 a的长时间序列数据集,能够应用于长时间、较大区域范围的积雪覆盖变化分析。由于不同卫星使用的反演算法,波谱宽度和大气订正等不完全相同,故需对不同卫星积雪产品数据集进行一致性检验,将卫星积雪产品更好地应用于气候分析研究。本文采用一种新的评估方法,对空间分辨率为0.05^o×0.05^o的AVHRR积雪产品与IMS和MOD10A1积雪产品,分别在空间和时间变化上进行对比分析,对AVHRR积雪产品数据集进行检验,发现AVHRR与MODIS积雪产品具有较好的一致性。     

Study of 19-year Satellite Sea Surface Temperature Variability along the West Coast of India

The seasonal and interannual variation of upwelling along the west coast of India between 8°N and 24°N was studied for the period 1985 through 2003 using NOAA-AVHRR sea surface temperature data. The seasonal migration of pronounced upwelling, which follows the seasonal shift of the winds in southwest monsoon period and northeast monsoon, was confined. The temporal mean sea surface temperature images clearly show the upwelling season, as does the seasonal sea surface temperature anomaly. These dominate features of the upwelling system are also the most variable, with most of the variance being explained by the seasonal cycle. Quasi-cyclic behavior of seas surface temperature on interannual scales has also been observed.     

1982～2010年中国东北地区植被NPP时空格局及驱动因子分析

应用逐像元线性回归模型方法,整合应用MODIS和AVHRR NDVI数据集,构建1982~2010年覆盖东北地区的8 km空间分辨率的NDVI数据集,进而应用CASA模型估算得到东北地区29 a NPP数据集,模拟精度在75%以上。29 a平均的东北地区植被NPP总量为6.5×108tC/a。植被NPP的分布受植被类型、气候、地形因素的综合影响。NPP地域差异明显,山地区植被>平原区植被>高原区植被,变化最大的植被类型为草地植被。过去29 a间,植被NPP呈显著上升趋势(P<0.01)。气候变化和土地利用变化均是影响植被时空格局的重要因素。     

基于动态阈值法的NOAA系列卫星云检测结果检验

采用我国地面报资料、国际船舶报资料和云卫星(CloudSat)产品,分别对多通道动态阈值云检测方法的云检测结果进行了检验。结果表明,动态阈值云检测方法可用于长时间序列卫星观测数据的云检测,不仅运行速度较快,可实现全自动,且精度较高,陆地上空云检测准确率为82.8%(地面报资料),海洋上空云检测准确率为91.5%(船舶报资料)。此外,还利用CloudSat资料检验不同观测时间差对NOAA系列卫星云检测结果检验的影响。     

利用NOAA—AVHRR资料估算积雪参量

本文介绍了利用气象卫星AVHRR资料对青海省青南高原看牧区冬春（10月－4月）积雪进行监测的原理，提出了利用AVHRR资料估算积雪深度和面积的方法，并对其精度进行了检验。