基于NDVI的风景区索道建设前后植被覆盖动态变化研究

基于归一化植被指数(NDVI)分析方法,利用黄山风景名胜区新云谷索道建设前(2004年)后(2008年)的2个时相的遥感数据对索道建设区进行植被覆盖动态变化分析。结果表明:新云谷索道建设后周边的植被覆盖和生长情况明显优于建设前,森林生态系统正向演替没有因为工程建设而受到限制,工程建设导致的人工干扰没有使植被遭受明显破坏,研究区植被生态系统的结构与功能没有因索道建设而受到影响。以遥感技术(Remote sensing,RS)为手段的风景区植被指数分析方法在生态环境研究中具有广阔的应用前景。     

基于PCA变换和神经元网络分类方法的中国森林制图研究

主要讨论基于NOAA AVHRR数据生成的NDVI时间序列数据与其他来源的地理数据结合进行中国森林细分类和制图的研究。首先利用ISODATA聚类方法对由NDVI时间序列数据派生的NDVI矩阵变量因子进行土地覆盖类型的分类，然后利用已有的植被类型图、TM影像以及地面样点数据作为参考依据, 进行类合并，获得中国森林边界。然后用PCA方法对NDVI时间序列数据进行信息增强与压缩处理，以排除各种干扰因素，提高分类精度。最后结合部分地理数据和地面样点调查数据，利用神经元网络方法进行中国森林分类，并依据种属和物候学特征、中国气候区划图以及国家植被分类二、三级分类系统，进行重新归类，得到最后的1 km中国森林分类图。分类结果表明，所用方法能够更加细致地划分森林类型，并且能保留一个相对较高的分类精度。     

不同土地利用类型的地表特征参量的遥感反演研究——以猫跳河流域为例

利用2007年5月Lansat5 TM数据,运用单通道算法,基于全国遥感监测土地利用/覆盖分类体系,对贵州省猫跳河流域的地表特征参量进行了反演,得到不同土地利用类型下的典型地表特征参数值。在3种主要的土地利用类型中,林地的植被指数最高、地表温度最低,农田的植被指数最低而地表温度最高,草地介乎林地和农田之间。城镇建设用地主要表现为热岛效应,而水域则主要表现为地表温度（LST）和归一化植被指数（NDVI）的双低特征。进一步对二级土地利用类型的分析表明,由于结构、功能的不同,土地利用二级类在地表特征参量方面也表现出了有规律地变化。有助于深入认识不同土地利用类型的物理特征,可以丰富主要土地利用类型的影像分类的先验知识,有望将不同土地利用方式的生态环境效应研究推进一步     

Analysis of Spatial and Temporal Evolution of Vegetation Cover in the Spanish Central Pyrenees: Role of Human Management

A vegetation cover increase has been identified at global scales using satellite images and vegetation indices. This fact is usually explained by global climatic change processes such as CO₂ and temperature increases. Nevertheless, although these causes can be important, the role of socioeconomic transformations must be considered in some places, since in several areas of Northern Hemisphere an important change in management practices has been detected. Rural depopulation and land abandonment have reactivated the natural vegetation regeneration processes. This work analyses the vegetation evolution in the central Spanish Pyrenees from 1982 to 2000. The analysis has been done by using calibrated-NDVI temporal series from NOAA-AVHRR images. A positive and significant trend in NDVI data has been identified from 1982 to 2000 coinciding with a temperature increase in the study area. However, the spatial differences in magnitude and the sign of NDVI trends are significant. The role of land management changes in the 20th century is considered as a hypothesis to explain the spatial differences in NDVI trends. The role of land-cover and human land-uses on this process has been analyzed. The highest increment of NDVI is detected in lands affected by abandonment and human extensification. The importance of management changes in vegetation growth is discussed, and we indicate that although climate has great importance in vegetal evolution, land-management changes can not be neglected in our study area.     

人口与经济发展对植被的影响研究——以重庆市为例

植被是陆地表面主要的覆盖物,也是许多地球系统过程的重要变量.人类活动对植被的影响在区域尺度上是显著的.以重庆市为例,使用滑动平均和SG滤波对SPOT/VGT-NDVI时间序列数据进行了平滑处理,结合GDP和人口栅格化数据,从时间序列和空间相关场两个方面分析了植被与人类活动因子之间的时空相关性.结果表明,从1998到2005年,植被的分布和生长状况并没有随GDP和人口的持续增加而增加.在时间序列上,植被覆盖与GDP和人口之间总体上呈显著的负相关关系;在空间上,这种相关关系表现出了较强的异质性.空间相关场表明:较强的负相关主要发生在相对发达的主城区周围,反映了快速的经济发展和城市化使得植被面积和生产力降低;然而在偏远的山区,由于经济落后同时植被覆盖较少,出现了正相关.     

Preliminary Evidence of a Barrier Effect of a Railroad on the Migration of Mongolian Gazelles

Abstract:  Much remains to be learned about how anthropogenic structures affect migration in large mammals. We examined the potential influence of the international railroad in Mongolia on migration in Mongolian gazelles (Procapra gutturosa). We examined gazelle movements and the normalized difference vegetation index (NDVI) on both sides of the railroad. The tracked gazelles never crossed the railroad, despite movements that mainly followed the railroad in winter and higher NDVI values on the opposite side. It is likely that the railroad had a barrier effect on gazelle migration because it split the gazelles' habitat. Our results, although based on an extremely small sample, have utility in indicating approaches that might be useful for examining migration in ungulates.     

Using NOAA AVHRR data to assess flood damage in China

The article used two NOAA-14 Advanced Very High ResolutionRadiometer (AVHRR) datasets to assess flood damage in the middleand lower reaches of China's Changjiang River (Yangtze River) in 1998. As the AVHRR is an optical sensor, it cannot penetratethe clouds that frequently cover the land during the flood season, and this technology is greatly limited in flood monitoring. However the widely used normalized difference vegetation index (NDVI) can be used to monitor flooding, sincewater has a much lower NDVI value than other surface features.Though many factors other than flooding (e.g. atmospheric conditions, different sun-target-satellite angles, and cloud) can change NDVI values, inundated areas can be distinguished fromother types of ground cover by changes in the NDVI value beforeand after the flood after eliminating the effects of other factors on NDVI. AVHRR data from 26 May and 22 August, 1998 wereselected to represent the ground conditions before and after flooding. After accurate geometric correction by collecting GCPs,and atmospheric and angular corrections by using the 6S code, NDVI values for both days and their differences were calculatedfor cloud-free pixels. The difference in the NDVI values betweenthese two times, together with the NDVI values and a land-use map, were used to identify inundated areas and to assess the arealost to the flood. The results show a total of 358 867 ha, with 207 556 ha of cultivated fields (paddy and non-irrigated field) inundated during the flood of 1998 in the middle and lower reaches of the Changjiang River Catchment; comparing with the reported total of 321 000 and 197 000 ha, respectively. The discrimination accuracy of this method was tested by comparing the results from two nearly simultaneous sets of remote-sensingdata (NOAA's AVHRR data from 10 September, 1998, and JERS-1 synthetic aperture radar (SAR) data from 11 September, 1998, with a lag of about 18.5 hr) over a representative flooded regionin the study area. The results showed that 67.26% of the total area identified as inundated using the NOAA data was also identified as inundated using the SAR data.     

AVHRR遥感数据在海表赤潮细胞数探测中的应用

以1998年渤海赤潮过程中的现场实测资料和同步的NOAA－14的AVHRR数据,以植被指数（NDVI）为基础,建立了渤海叉角藻赤潮生物细胞数遥感探测模型,该模型使用简便,可以有效地利用目前正在运行的气象卫星数据进行赤潮的业务化监测,直接获取赤潮浮游植物细胞数这个指标,确定赤潮的发生范围,赤潮面积以及赤潮生物分布的密集区,并计算细胞分裂速度以判定赤潮的生消状况,为赤潮灾害的应急防治工作提供依据。同时可以区分赤潮水体与其他如何流或排污口排放的泥沙或悬浮物,以消除这些物质对赤潮识别带来的影响。     

岩溶区峰丛洼地植被指数的克里格分析

为了全面掌握岩溶区峰丛洼地植被指数(NDVI)的空间分布状况,解决岩溶区峰丛洼地遥感影像山体阴影区域NDVI信息"缺失"的问题,对广西平果县果化生态重建示范区内的典型峰丛洼地非阴影区域NDVI进行了提取.利用地统计学的方法对NDVI的空间结构特征进行了分析,并对山体阴影区的NDVI进行了预测与验证.结果表明,研究区域NDVI主要受到内在因子的作用,具有强烈的空间自相关性,自相关距离为300 m;Kriging插值结果表明,研究区域NDVI的均值为0.196,在空间分布上表现为条带状和斑块状分布,NDVI高值区域主要分布在峰丛坡度>25°的山体区域,低值区域主要分布在峰丛坡度<25°的山脚和洼地等区域;Kriging插值验证结果表明,研究区域NDVI具有非常高的预测精度,能很好地估计山体阴影区NDVI,为岩溶区生态环境监控、石漠化评估提供了新的思路与方法.     

基于时间序列谐波分析的东北地区耕地资源提取

耕地是人类社会赖以生存发展最重要的资源之一,及时获取其空间分布是国家农业决策的基础。论文利用2007年多时相的SPOT/VGT NDVI数据提取东北地区耕地资源信息。以NDVI时间序列数据年内变化振幅和周期差异性作为分类的依据,采用时间序列谐波分析法对全年时间谱NDVI数据进行重构,减少高频噪声对信息提取的影响,获得研究区地物信息在时间维度上的振幅、相位以及年均NDVI值影像图,然后将三者合成。应用神经网络分类方法,对合成后的影像选择训练样本,获取东北地区耕地资源的空间分布。实验中提取耕地的精度为83.26%,Kappa系数为0.732 4;该方法获取耕地资源空间分布的精度均高于GLC2000、UMD、IGBP和中科院1∶100万土地利用数据4种分类产品。研究表明,基于时间序列谐波分析法对NDVI数据重建,利用不同类型植被NDVI曲线在一年内振幅、相位特征的差异,采用神经网络分类的方法,可以精确地提取耕地资源信息,及时为农业和土地管理部门管理决策提供科学依据。