地面反射波谱与卫星磁带记录数据间关系模拟的初步探讨──以冬小麦和土壤为例

根据冬小麦和土壤地面反射波谱测试数据，计算了在卫星高度上与卫星磁带数据相对应波段的辐亮度值，对ＮＯＡＡＡＶＨＲＲ和ＴＭ某些通道的差值绿度植被指数ＤＶＩ、归一化绿度植被指数ＮＤＶＩ和比值绿度植被指数ＲＶＩ的分析，从理论上证明了目前采用ＴＭＤＶＩ＿（４，３）提取冬小麦种植面积和ＮＯＡＡＮＤＶＩ＿（２，１）区分植被和土壤背景的有效性。同时在冬小麦种植面积和长势监测方面提出了一些新建议。     

新疆阜康县草地资源产量动态监测模型的研究

利用ＮＯＡＡ／ＡＶＨＨＲ数据和地面实测产量值，分析、模拟了鲜草重量和植被指数之间的数理关系，并对草地产量进行了模拟预报。结果表明，采用两种植被指数和七种经验公式所选出的最优预报模型，在地势比较平坦，草地类型变化不大的地区，可以较准确地反映草地产量的变化；但在地形复杂、草地类型变化较大的地区，模型稳定性变差，不适合于草地产量的预报。     

利用NOAA－AVHRR资料提取水体信息的初步研究

讨论了应用ＮＯＡＡ－ＡＶＨＲＲ资料来提取水体信，包的方法。采用一、二通道反射率数据（ＣＨ＿１、ＣＨ＿２）构成的归一化植被指数（ＮＤＶＩ）来识别水体，并初步提出了应用模糊数学的方法提取混合像元中的水体面积信息。     

单向总线LAN上语音与数据的综合

９４０２３单向总线ＬＡＮ上语音与数据的综合ＶｏｉｃｅａｎｄｄａｔａｉｎｔｅｇｒａｔｉｏｎｏｎａｕｎｉｄｉｒｅｃｔｉｏｎａｌｂｕｓＬＡＮ∥ｃｏｍｐｕｔｅｒｃｏｍｍｕｎｉｃａｔｉｏｎｓ，—１９９２，—１５（１），—３７～４４介绍在折叠式单向总线ＬＡＮ上实...     

退牧还草工程项目遥感分析与效益评价——以四川省阿坝县为例

根据对卫星遥感影像的判读解译,探讨了利用3S技术（遥感（RS）、全球定位系统（GPS）、地理信息系统（GIS）技术）监测四川省阿坝县的退牧还草工程现状。通过陆地卫星TM遥感影像数据和同期野外调查数据,分析了植被指数与草地植被生物量之间的相关关系,建立了不同植被指数与草地生物量之间的一元线性回归模型和非线性回归模型。结果表明,利用遥感卫星的植被指数可以较好反映牧草植被群落变化和不同草原类型的牧草产草量差异。在全年放牧草地中,地上总生物量、植被总覆盖度、植被平均高度等指标均低于围栏内的草地。因此,利用“3S”技术可以对全县草原地上生物量进行遥感估测并对草原基况做出评价,客观反映退牧还草工程实施后效果。同时,为推动高空间分辨率卫星影像在我国草业和生态环境建设中的应用打下了坚实基础。     

辽宁地区作物低温冷害的遥感监测和气象预报

农作物低温冷害是造成辽宁地区粮食产量大幅度减产的三大灾害（洪涝、干旱）之一。应用卫星遥感（ＮＯＡ／ＡＶＨＲＲ）手段监测低温的发生、强度及路经等，研究它的发生发展规律。通过综合分析定出主要作物受害指标，分析了５００ｈｐａ高度场、海温场及太阳黑子、极涡面积指数、海冰面积等与低温冷害的关系。为及时采取农技措施提供适时可靠情报，建立了气象预报的系列模式，进行低温冷害预报。为防灾减灾，为粮食的稳产高产提供气象理论依据。     

环境卫星一号(ENVISAT-1)卫星系统

环境卫星一号（ＥＮＶＩＳＡＴ－１）卫星系统分类号Ｖ４７．２欧空局（ＥＳＡ）于１９９１年７月成功地发射了第一颗欧洲遥感卫星（ＥＲＳ－１），该卫星的成功运行吸引了不断壮大的用户群的极大的兴趣。为保证数据提供的连续性，ＥＳＡ又于１９９５年４月成功地发射了Ｅ...     

基于CATV的NVOD系统结构分析与关键技术研究

作为视频点播（ＶＩＤＥＯＯＮＤＥＭＡＮＤ）的一种替代品一准视频点播（ＮＥＡＲＶＩＤＥＯＯＮＤＥＭＡＮＤ）随着有关领域的发展已逐渐成为研究的热点。本文对基于ＣＡＴＶ的ＮＶＯＤ系统结构进行分析,并对其中的关键技术进行了讨论。     

VLAN技术及其应用

虚网（ＶＬＡＮ）技术是目前较为热门的计算机网络技术,本文通过与传统的ＬＡＮ技术的对比,阐述了ＶＬＡＮ技术的基本概念、方法和技术标准,分析了ＶＬＡＮ技术的优点以及面临的主要问题,并介绍了ＶＬＡＮ技术在组网中的实际应用。     

BANYAN企业网络操作系统VINES

以Ｎｏｖｅｌｌ Ｎｅｔｗａｒｅ为代表的小型局域网已不能满足大型企业对网络的进一步需求，尤其在广域网方面，ＢＡＮＹＡＮ企业网络操作系统ＶＩＮＥＳ集成了一整套企业网络服务（ＥＮＳ），既非常适用于广域网（ＷＡＮ），又可用于局域网（ＬＡＮ）。ＶＩＮＥＳ有强大的通讯服务，网间互连及企业级信息传输功能，可将多厂家、多平台的异构网络集成为一个易于使用和管理、统一的虚拟网络。     

Comprehensive drought index as an indicator for use in drought monitoring integrating multi-source remote sensing data: a case study covering the Sichuan-Chongqing region

Numerous meteorological drought-monitoring indices and remote-sensing-based spatial drought monitoring indices have been developed and applied to monitor drought in different ways. However, individual indices have obvious deficiencies in terms of their responses to drought, and they do not comprehensively reflect the available information on drought. To overcome issues with the data themselves and improve drought monitoring techniques, we use a comprehensive drought index (CDI) derived from the vegetation condition index, the temperature condition index, and the precipitation condition index to monitor meteorological or agricultural drought for the Sichuan-Chongqing region. To assess CDI performance, monthly CDI values for Sichuan-Chongqing region were used to analyse the spatial and temporal variations of the 2006 drought. The results indicated that all aspects of the drought were monitored, and the results were in agreement with related research. Meanwhile, an extreme drought was accurately explored using the CDI in the Sichuan-Chongqing region from 2000 to 2011. Finally, a validation was performed, and the results show that the CDI is closely related with the standardized precipitation index calculated using a 3-month time scale (SPI3), as well as variations in crop yield and drought-affected crop area. These results provide further evidence that the CDI is an indicator that can be used in integrated drought monitoring and that it can simultaneously reflect meteorological and agricultural drought information.     

农业干旱遥感监测业务化运行方法研究

研究一种基于MODIS遥感数据的快速且易于实际应用的农业旱情监测方法及其系统实现。监测方法基于植被供水指数的思想,利用NDVI进行植被盖度分级来计算,标准化作物供水指数,再耦合近8旬的综合降水距平指数,由此实现对旱情的监测。在介绍监测方法的同时,又针对应用数据的特点,设计了有效的数据管理系统,为高效管理各种数据而服务。     

Developing the vegetation drought response index for South Korea (VegDRI-SKorea) to assess the vegetation condition during drought events

South Korea has experienced severe droughts and water scarcity problems that have influenced agriculture, food prices, and crop production in recent years. Traditionally, climate-based drought indices using point-based meteorological observations have been used to help quantify drought impacts on the vegetation in South Korea. However, these approaches have a limited spatial precision when mapping detailed vegetation stress caused by drought. For these reasons, the development of a drought index that provides detailed spatial-resolution information on drought-affected vegetation conditions is essential to improve the country’s drought monitoring capabilities, which are needed to help develop more effective adaptation and mitigation strategies. The objective of this study was to develop a satellite-based hybrid drought index called the vegetation drought response index for South Korea (VegDRI-SKorea) that could improve the spatial resolution of agricultural drought monitoring on a national scale. The VegDRI-SKorea was developed for South Korea, modifying the original VegDRI methodology (developed for the USA) by tailoring it to the available local data resources. The VegDRI-SKorea utilizes a classification and regression tree (CART) modelling approach that collectively analyses remote-sensing data (e.g. normalized difference vegetation index (NDVI)), climate-based drought indices (e.g. self-calibrated Palmer drought severity index (PDSI) and standardized precipitation index (SPI)), and biophysical variables (e.g. elevation and land cover) that influence the drought-related vegetation stress. This study evaluates the performance of the recently developed VegDRI-SKorea for severe and extreme drought events that occurred in South Korea in 2001, 2008, and 2012. The results demonstrated that the hybrid drought index improved the more spatially detailed drought patterns compared to the station-based drought indices and resulted in a better understanding of drought impacts on the vegetation conditions. The VegDRI-SKorea model is expected to contribute to the monitoring of drought conditions nationally. In addition, it will provide the necessary information on the spatial variations of those conditions to evaluate local and regional drought risk assessment across South Korea and assist local decision-makers in drought risk management.     

Drought assessment and monitoring through remote sensing and GIS in western tracts of Tamil Nadu,India

Drought is an insidious hazard of nature and is considered to be the most complex but least understood of all natural hazards. Large historical datasets are required to study drought and these involve complex interrelationships between climatological and meteorological data. Rainfall is an important meteorological parameter; the amount and distribution influence the type of vegetation in a region. To analyse the changes in vegetation cover due to variation in rainfall and identify the land-use areas facing drought risk, rainfall data from 1981 to 2003 were categorized into excess, normal, deficit and drought years. The Advanced Very High Resolution Radiometer (AVHRR) sensor's composite dataset was used for analysing the temporal and interannual behaviour of surface vegetation. The various land-use classes – crop land (annual, perennial crops), scrub land, barren land, forest land, degraded pasture and grassland – were identified using satellite data for excess, normal, deficit and drought years. Normalized Difference Vegetation Indices (NDVIs) were derived from satellite data for each land-use class and the highest NDVI mean values were 0.515, 0.436 and 0.385 for the tapioca crop in excess, normal and deficit years, respectively, whereas in the drought year, the groundnut crop (0.267) showed the maximum. Grassland recorded the lowest value of NDVI in all years except for the excess year. Annual crops, such as groundnut (0.398), pulses (0.313), sorghum (0.120), tapioca (0.436) and horse gram (0.259), registered comparatively higher NDVI values than the perennial crops for the normal year. The Vegetation Condition Index (VCI) was used to estimate vegetation health and monitor drought. Among land-use classes, the maximum VCI value of 92.1% was observed in onions for the excess year, whereas groundnut witnessed the maximum values of 78.2, 64.5 and 55.2% for normal, deficit and drought years, respectively. Based on the VCI classification, all land-use classes fall into the optimal or normal vegetation category in excess and normal years, whereas in drought years most of the land-use classes fall into the drought category except for sorghum, groundnut, pulses and grasses. These crops (sorghum 39.7%, groundnut 55.2%, pulses 38.5% and grassland 38.6%) registered maximum VCI values, revealing that they were sustained under drought conditions. It is suggested that the existing crop pattern be modified in drought periods by selecting the suitable crops of sorghum, groundnut and pulses and avoiding the cultivation of onion, rice and tapioca.     

New spectral vegetation indices based on the near-infrared shoulder wavelengths for remote detection of grassland phytomass

This article examines the possibility of exploiting ground reflectance in the near-infrared (NIR) for monitoring grassland phytomass on a temporal basis. Three new spectral vegetation indices (infrared slope index, ISI; normalized infrared difference index, NIDI; and normalized difference structural index, NDSI), which are based on the reflectance values in the H25 (863–881 nm) and the H18 (745–751 nm) Chris Proba (mode 5) bands, are proposed. Ground measurements of hyperspectral reflectance and phytomass were made at six grassland sites in the Italian and Austrian mountains using a hand-held spectroradiometer. At full canopy cover, strong saturation was observed for many traditional vegetation indices (normalized difference vegetation index (NDVI), modified simple ratio (MSR), enhanced vegetation index (EVI), enhanced vegetation index 2 (EVI 2), renormalized difference vegetation index (RDVI), wide dynamic range vegetation index (WDRVI)). Conversely, ISI and NDSI were linearly related to grassland phytomass with negligible inter-annual variability. The relationships between both ISI and NDSI and phytomass were however site specific. The WinSail model indicated that this was mostly due to grassland species composition and background reflectance. Further studies are needed to confirm the usefulness of these indices (e.g. using multispectral specific sensors) for monitoring vegetation structural biophysical variables in other ecosystem types and to test these relationships with aircraft and satellite sensors data. For grassland ecosystems, we conclude that ISI and NDSI hold great promise for non-destructively monitoring the temporal variability of grassland phytomass.     

考虑下垫面类型的干旱指数比较研究

干旱作为常见的自然灾害，在世界各地发生的频率日渐增加，已对经济发展、农业生产和人类生活等方面产生了严重影响。但是干旱的类型较多，包括气象干旱、土壤干旱、水文干旱、农田干旱等，无法用单个干旱指数对不同类型的干旱进行监测。按照干旱发生类型，利用气象干旱指数（Standardized Precipitation Index SPI）、土壤水分干旱指数（Soil Moisture Index, SMI）和蒸发压力干旱指数（Evaporative Stress Index, ESI）对美国的旱情进行监测。研究结果表明：不同干旱指数之间呈显著相关，相关系数R在0.7以上。ESI整体监测精度较高，它能够真实反映地表水分盈亏状况，同时与遥感数据结合，可以实现从田块到全球不同尺度干旱实时监测。不同植被类型覆盖下垫面对不同类型干旱响应存在较大差异，草地下垫面对不同类型的干旱响应较为一致，但是随着地上生物量的增加，不同干旱指数监测结果之间差异逐渐增大。因此，在干旱监测时需要考虑植被的结构特征，植被与气候之间的相互作用，才能具体分析不同下垫面的受灾情况，进一步考虑更适合的方法以及干旱指数监测不同下垫面的干旱情况。     

Monitoring regional drought using the Vegetation Condition Index

NDVI (Normalized Difference Vegetation Index) images generated from NOAA AVHRR GVI data were recently used to monitor large scale drought patterns and their climatic impact on vegetation. The purpose of this study is to use the Vegetation Condition Index (VCI) to further separate regional NDVI variation from geographical contributions in order to assess regional drought impacts. Weekly NDVI data for the period of July 1985 to June 1992 were used to produce NDVI and VCI images for the South American continent. NDVI data were smoothed with a median filtering technique for each year. Drought areas were delineated with certain threshold values of the NDVI and VCI. Drought patterns delineated by the NDVI and VCI agreed quite well with rainfall anomalies observed from rainfall maps of Brazil. NDVI values reflected the different geographical conditions quite well. Seasonal and interannual comparisons of drought areas delineated by the VCI provided a useful tool to analyse temporal and spatial evolution of regional drought as well as to estimate crop production qualitatively. It is suggested that VCI data besides NDVI may be used to construct a large scale crop yield prediction model.     

考虑下垫面类型的干旱指数比较研究

干旱作为常见的自然灾害，在世界各地发生的频率日渐增加，已对经济发展、农业生产和人类生活等方面产生了严重影响。但是干旱的类型较多，包括气象干旱、土壤干旱、水文干旱、农田干旱等，无法用单个干旱指数对不同类型的干旱进行监测。按照干旱发生类型，利用气象干旱指数（Standardized Precipitation Index SPI）、土壤水分干旱指数（Soil Moisture Index, SMI）和蒸发压力干旱指数（Evaporative Stress Index, ESI）对美国的旱情进行监测。研究结果表明：不同干旱指数之间呈显著相关，相关系数R在0.7以上。ESI整体监测精度较高，它能够真实反映地表水分盈亏状况，同时与遥感数据结合，可以实现从田块到全球不同尺度干旱实时监测。不同植被类型覆盖下垫面对不同类型干旱响应存在较大差异，草地下垫面对不同类型的干旱响应较为一致，但是随着地上生物量的增加，不同干旱指数监测结果之间差异逐渐增大。因此，在干旱监测时需要考虑植被的结构特征，植被与气候之间的相互作用，才能具体分析不同下垫面的受灾情况，进一步考虑更适合的方法以及干旱指数监测不同下垫面的干旱情况。     

基于植被指数的若尔盖高原湿地光合有效辐射吸收比例估算研究

植被光合有效辐射吸收比例（FPAR）是湿地生态系统碳收支和气候变化的关键参量,直接反映湿地植被生长发育状况。基于植被指数的经验统计方法简单高效,被广泛运用于草原、森林及作物等植被FPAR的模拟,却较少用于湿地,缺乏不同植被指数对湿地FPAR估算适应性的系统研究。研究对比了14种常见的植被指数,选出最优植被指数用于反演若尔盖高原湿地生长季FPAR。结果表明：常见的植被指数中,MSAVI指数动态考虑了土壤信息,能较好地适应湿地植被FPAR的估算,误差和R²均优于其他植被指数。若尔盖高原湿地生长季FPAR取值在0.22—0.80之间,整体分布较为均匀,泥炭湿地、湿草甸及沼泽湿地平均FPAR分别为0.46、0.63和0.58;生长季期间若尔盖高原不同类型湿地FPAR随时间呈现先增加后降低趋势。