Efficiency Research of Planted Rice Area Estimation From NOAA一AVHRR Data in Taihu Domain

运用NOAA-AVHRR资料估算水稻种植面积，是遥感应用领域中一个新的研究方向，结合国家“八五”攻关项目“太湖地区遥感话产”的要求，在太湖地区进行了初步的尝试:(1)根据估产精度要求和NOAA一AVHRR资料校正精度，探讨了运用NOAA一AVHRR资料估产所需的最小区域范围。(2)针对太湖地区的具休地理环境设计了提取水稻种植曲积的技术方案，并在试验区取得了初步成果。     

基于NOAA／AVHRR数据的景观格局分析——以塔里木河干流区域为例

在对NOAA／AVHRR数据进行灰度拉伸,直方图均衡化,多通道合成等方法处理后,应用IDRISI软件的空间分析功能,对塔里木河流域进行了景观格局的宏观分析,结果表明在塔里木河干流区城景观类型相对简单,各景观自身破碎度低,连通性较好,景观间的均匀度差,是一种典型的脆弱的干旱区陆河流域生态环境景观。     

多时机NOAA—AVHRR数据主成分分析的生物学意义

利用多时上NOAA－AVHRR的中国归一化植被指数NDVI数据进行主成分分析,并与从NDVI派生的4个生物不数作相关分析,结果表明：主成分变换既压缩了信息,将21个月的信息主要压缩到前4个主分量,又提取了关键的变化信息,第一主分量反映基本植被覆信息,第二、第三和第四主分量反映植被季相变化信息,正是由于一年12个月的NDVI曲线反映了植被季相变化特征,使得主成分变换得到的各主分量具有一定的生物学意义,而且17种中国典型植被在这4个主分量图像上存在一定的差异性,使其具有进行较高精度土地覆盖分类的潜力。     

基于Landsat TM数据的潮白河流域植被覆盖变化研究

使用经严格配准的同一时间(1991年和2002年)Landsat TM图像数据，编制归一化植被指数(NDVI)图，进而计算生成植被覆盖度图像。通过掩膜技术和变化检测等提取了北京潮白河流域中上游地区从1991～2002年的植被覆盖变化信息。研究结果表明，北京潮白河流域中上游地区11年间植被退化的总面积为1635．3km^2，占该区域总面积的30．6％；其中植被覆盖度为40％～50％的类型退化的面积最多，为411．74km^2，变化率为66．0％，覆盖度为90％～100％的类型退化的面积最少，为14km^2，变化率为4．4％；覆盖度为30-40％的类型变化率最大，为100％，覆盖度为90％～100％的类型的变化率最小。为4．4％；从植被覆盖度变化的趋势来看，随着植被覆盖度的增加，变化率在逐渐降低；流域中游、密云水库北部和东北部以及上游的河谷地带由于受人类活动干扰的强度较大，植被退化较严重；而上游的山地区域由于人类活动干扰较少，再加上近年来采取封山育林、植树造林等措施，植被覆盖程度有所改善。     

基于NOAA/AVHRR热红外数据的城市热岛强度年内变化特征

采用ENVI/IDL编程技术,实现NOAA/AVHRR数据的校准、几何纠正、云污染识别与剔除、影像特征统计与输出等过程的批处理自动化操作。并以济南市中心城区为例,通过2005~2006年间获取的白天NOAA/AVHRR影像热红外波段调查了济南市区城市热岛强度的年内变化规律与过程。研究结果表明：① 济南市区全年大部分时间存在热岛现象,4~9月份城市热岛效应较为明显,尤以5、7、8月为甚。② 全年城市热岛平均强度2.77℃,最强的热岛效应出现于7月下旬至8月中旬间。③ 从季节分布来看,济南市区夏季热岛效应最明显,春季次之,秋、冬两季较弱。④ 城市热岛强度与城、郊地表温度存在正相关关系,但相关程度较差。     

NOAA/AVHRR数据的雪盖信息提取与复合

在对NOAA/AVHRR数据特征与雪冰波谱特性分析的基础上，对各种提取雪盖信息的方法进行了比较，指出了各种方法的优劣，认为在实时的雪灾监浏与评估系统中，直方图分割的方法快速有效。另一方面，通过雪盖影像与GIS中各种矢量图形的复合配准实验，指出宜先对AVHRR影像进行点位计算，然后利用控制点、进行精校正，所产生的图像才能达到与矢量图形的准确配准。     

森林过火面积的遥感测算方法

根据对近年来多次特大森林火灾和相应的气象卫星资料的分析,提出利用NOAA/AVHRR数据测算森林大火的过火面积的四种方法,即灰度修正像元法、植被修正像元法、坐标法和蔓延法。在GIS地面信息数据库支持下,利用这4种方法能准确、快速地计算出过火面积。经今春应急评估试运行验证,森林大火过火面积测算精度达90%。     

南方丘陵地区水稻种植面积遥感信息提取的可行性分析

以浙江省为试验区，在地理信息系统支持下综合利用多种地理信息，探讨丘陵地区大面积提取水稻种植面积信息的可行性。开展了分类识别方法的比较试验及训练样点相对稳定性试验。针对丘陵地区的复杂地形，在数字化地形图的基础上，建立数字地形模型（ＤＴＭ），并衍生出地面坡度等地貌因子的数字化图像，结合ＮＯＡＡ／ＡＶＨＲＲ数据，进行分类。试验结果表明，传统的分类识别方法中，最大似然法的分类精度可满足业务化运行的要求；建立在混合像元分解基础上的模糊监督分类，有较高的分类精度和较好的稳定性，具有较强的适应性；地貌因子参与遥感影像的分类，不仅可以有效地提高丘陵地区水稻种植面积信息的提取精度，而且还可以使面积信息提取精度保持一定的稳定性，提高空间精度；为探讨丘陵地区水稻种植面积信息遥感提取的可靠性和客观性，在训练样点保持相对稳定的前提下，对１９９６年和１９９７年浙江省水稻种植面积进行测算，两年的数量精度均在９２％以上。     

基于Landsat TM/ETM+影像的武汉市冬季NPP估算及其时空变化分析

植被净第一生产力(NPP)作为反映植被固碳能力的重要指标,在全球CO2浓度上升的背景下,成为研究全球及区域生态系统对气候环境变化响应的热点之一。基于Landsat TM/ETM+遥感影像数据,采用改进的CASA模型,估算得到武汉市2001~2010年空间分辨率为30m的冬季NPP,并对其进行时空变化分析。研究结果表明:武汉市过去10a冬季平均NPP为8.55gC/m2·m。2001~2010年武汉市冬季NPP整体呈现波动上升的趋势,各区域具有不同的增长速率,其中以江夏区最快,而各植被类型中灌木林具有最快的增长速率和最高的平均NPP。武汉市冬季NPP均呈现从三环区域向四周增大的空间分布特征,过去10a武汉市冬季NPP最高的区域由黄陂区转移到了江夏区。     

Dryland observation at local and regional scale — Comparison of Landsat TM/ETM+ and NOAA AVHRR time series

During the last few decades, many regions have experienced major land use transformations, often driven by human activities. Assessing and evaluating these changes requires consistent data over time at appropriate scales as provided by remote sensing imagery. Given the availability of small and large-scale observation systems that provide the required long-term records, it is important to understand the specific characteristics associated with both observation scales. The aim of this study was to evaluate the potentials and limits of remote sensing time series for change analysis of drylands. We focussed on the assessment and monitoring of land change processes using two scales of remote sensing data. Special interest was given to the influence of the spatial and temporal resolution of different sensors on the derivation of enhanced vegetation related variables, such as trends in time and the shift of phenological cycles. Time series of Landsat TM/ETM+ and NOAA AVHRR covering the overlapping time period from 1990 to 2000 were compared for a study area in the Mediterranean. The test site is located in Central Macedonia (Greece) and represents a typical heterogeneous Mediterranean landscape. It is undergoing extensification and intensification processes such as long-term, gradual processes driven by changing rangeland management and the extension of irrigated arable land. Time series analysis of NOAA AVHRRR and Landsat TM/ETM+ data showed that both sensors are able to detect this kind of land cover change in complementary ways. Thereby, the high temporal resolution of NOAA AVHRR data can partially compensate for the coarse spatial resolution because it allows enhanced time series methods like frequency analysis that provide complementary information. In contrast, the analysis of Landsat data was able to reveal changes at a fine spatial scale, which are associated with shifts in land management practice.     

应用NOAA／AVHRR资料动态监测洪涝灾害的研究

以ＮＯＡＡ卫星为主要监测手段，对洪水、植被、土壤的光谱特征进行分析研究，提出了同时突出水体和植被的光谱分析思路。并根据客观分析需要，建立了洪涝灾情图像处理软件系统，对ＮＯＡＡ／ＡＶＨＲＲ图像资料进行投影、截取、放大、配准等一系列预处理。同时采用模糊非监督分类、比值、归一化植被指数方法对洪涝信息进行分析处理，确定受灾范围，量算受灾面积，划分受灾等级，提供灾情分布图，为防汛抗洪部门提供有力的科学依据。     

通过AVHRR数据研究中国陆面温度分异规律

近年来国内外利用遥感方法在陆面温度精确反演中开展了大量的研究工作,采用了一个在大区域上适用的由NOAA？AVHRR数据反演陆面温度的方法,反演中国晴空条件下各月和全年平均陆面温度,分析了中国陆面温度的分异规律,并与气温的分异规律作了对比,同时对中国土地利用／土地覆盖变化研究（LUCC）样带上的陆面温度变化进行了分析,这项工作从晴空条件重新认识了地面温度场的空间分异,对于研究中国陆地土壤蒸发,植物光合作用,土地覆盖的分布具有重要的指示意义。     

南方丘陵地区水稻种植面积遥感信息提取的可行性分析*

以浙江省为试验区,在地理信息系统支持下综合利用多种地理信息,探讨丘陵地区大面积提取水稻种植面积信息的可行性。开展了分类识别方法的比较试验及训练样点相对稳定性试验。针对丘陵地区的复杂地形,在数字化地形图的基础上,建立数字地形模型(DTM),并衍生出地面坡度等地貌因子的数字化图像,结合NOAA/AVHRR数据,进行分类。试验结果表明,传统的分类识别方法中,最大似然法的分类精度可满足业务化运行的要求;建立在混合像元分解基础上的模糊监督分类,有较高的分类精度和较好的稳定性,具有较强的适应性;地貌因子参与遥感影像的分类,不仅可以有效地提高丘陵地区水稻种植面积信息的提取精度,而且还可以使面积信息提取精度保持一定的稳定性,提高空间精度;为探讨丘陵地区水稻种植面积信息遥感提取的可靠性和客观性,在训练样点保持相对稳定的前提下,对1996年和1997年浙江省水稻种植面积进行测算,两年的数量精度均在92%以上。     

基于GIS的水稻遥感估产模型研究

以NOAA/AVHRR资料为主,利用GIS技术提取水稻可能种植区域,在此基础上计算各区和各县的比值植被指数和规一化植被指数,提出的水稻遥感估产比值模型和回归模型,预报浙江省的水稻总产,1998年的拟合精度和1999年的预报精度都达到95%以上.     

AVHRR数据小火点自动识别方法的研究

利用NOAA-AVHRR数据,采用多因子分析方法,通过建立小火点自动识别模型来提取小火点燃烧信息。经实验验证,该方法能较好地减少云体、裸地对火点判断的干扰,从而在一定程度上提高了对小火点的监测精度。     

Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions

The full realization of the potential of remote sensing as a source of environmental information requires an ability to generalize in space and time. Here, the ability to generalize in space was investigated through an analysis of the transferability of predictive relations for the estimation of tropical forest biomass from Landsat TM data between sites in Brazil, Malaysia and Thailand. The data sets for each test site were acquired and processed in a similar fashion to facilitate the analyses. Three types of predictive relation, based on vegetation indices, multiple regression and feedforward neural networks, were developed for biomass estimation at each site. For each site, the strongest relationships between the biomass predicted and that measured from field survey was obtained with a neural network developed specifically for the site (r>0.71, significant at the 99% level of confidence). However, with each type of approach problems in transferring a relation to another site were observed. In particular, it was apparent that the accuracy of prediction, as indicated by the correlation coefficient between predicted and measured biomass, declined when a relation was transferred to a site other than that upon which it was developed. Part of this problem lies with the observed variation in the relative contribution of the different spectral wavebands to predictive relations for biomass estimation between sites. It was, for example, apparent that the spectral composition of the vegetation indices most strongly related to biomass differed greatly between the sites. Consequently, the relationship between predicted and measured biomass derived from vegetation indices differed markedly in both strength and direction between sites. Although the incorporation of test site location information into an analysis resulted in an increase in the strength of the relationship between predicted and actual biomass, considerable further research is required on the problems associated with transferring predictive relations.     

Accuracy of forest mapping based on Landsat TM data and a kNN-based method

A multi-source forest inventory (MSFI) method has been developed for use in the Norwegian National Forest Inventory (NFI). The method is based on a k-nearest neighbour rule and uses field plots from the NFI, land cover maps, and satellite image data from Landsat Thematic Mapper. The inventory method is used to produce maps of selected forest variables and to estimate the selected forest variables for large areas such as municipalities. In this study, focus has been on the qualitative variables ‘dominating species group’ and ‘development class’ because these variables are of central interest to forest managers. A mid-summer Landsat 5 TM scene was used as image data, and all NFI plots inside the scene were used as a reference dataset. The relationship between the spectral bands and the forest variables was analysed, and it was found that the levels of association were low. A leave-one-out method based on the reference dataset was used to estimate the pixel-level accuracies. They were found to be relatively low with 63% agreement for species groups. An independent control survey was available for a municipality and estimates from the MSFI were compared to it. The levels of error were quite high. It was concluded that the large area estimates were biased by the reference dataset.     

利用陆地卫星TM数据评估森林病虫害

回顾和总结了近十多年来森林病虫害遥感监测的研究进展，介绍了“八五”攻关课题的最新研究成果，充分肯定了陆地卫星TM数据在森林资源质量监测和评估中的应用前景，指出了今后的发展方向。