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.     

Using NDVI to Assess Vegetative Land Cover Change in Central Puget Sound

We used the Normalized Difference Vegetation Index (NDVI) in the rapidly growing Puget Sound region over three 5-year time blocks between 1986–1999 at three spatial scales in 42 Watershed Administrative Units (WAUs) to assess changes in the amounts and patterns of green vegetation. On average, approximately 20% of the area in each WAU experienced significant NDVI change over each 5-year time block. Cumulative NDVI change over 15 years (summing change over each 5-year time block) was an average of approximately 60% of each WAU, but was as high as 100% in some. At the regional scale, seasonal weather patterns and green-up from logging were the primary drivers of observed increases in NDVI values. At the WAU scale, anthropogenic factors were important drivers of both positive and negative NDVI change. For example, population density was highly correlated with negative NDVI change over 15 years (r = 0.66, P < 0.01), as was road density (r = 0.71, P < 0.01). At the smallest scale (within 3 case study WAUs) land use differences such as preserving versus harvesting forest lands drove vegetation change. We conclude that large areas within most watersheds are continually and heavily impacted by the high levels of human use and development over short time periods. Our results indicate that varying patterns and processes can be detected at multiple scales using changes in NDVIa values.     

广东省NDVI时空变化特征及其对气候因子的响应

广东省植被类型丰富、气候条件复杂,开展气候因子对植被的影响研究,对于全省保护生态环境、应对气候变化具有重要意义.利用MODIS NDVI数据和地面气象观测数据,基于变化趋势分析、空间自相关分析、相关性分析等方法,研究广东省NDVI时空分布特征及其对气温、降水、日照时数等气候因子的响应.结果表明,2000—2018年广东...     

鄱阳湖流域叶面积指数时空变化特征及其与气候因子的关系

利用1982~2001年NOAA/AVHRR NDVI数据,根据简单生物模型SiB2的方法计算鄱阳湖流域叶面积指数LAI,分析不同植被类型LAI年内和年际变化及其与降水、气温的关系。结果显示：在年内,LAI从1月开始减小,至3月降到最小,之后开始迅速增大,7月达到最大值,然后又开始减小;各植被覆盖类型LAI与前3月降水和前1月平均气温相关性较强,并且全部通过95％的显著性检验。在年际上,各植被覆盖类型LAI在20 a间无明显整体增大或减小趋势,但每隔2~3 a呈锯齿状增大减小交替变化,其中常绿针叶林LAI变幅较大,在23~35之间,而林地草原LAI变化较平缓,在05~09之间;植被LAI年际变化受流域内5~7月降水年际变化的影响较大。在空间上,植被LAI在春、冬季整体较小,空间分布差异也较小,仅在流域边缘山区林地覆盖区稍大,其余大片区域LAI值很低,且分布比较均一;夏、秋季LAI较大,空间分布差异也较大,其空间分布主要与流域内土地覆盖类型有关     

基于MODIS数据火点监测指数研究

根据火点辐射与背景辐射的差异及MODIS数据火灾监测原理,利用MODIS数据22通道(火点辐射)和31通道(背景辐射)亮温构建了一个火点指数FPI.根据只有当植被覆盖率达到一定程度时才会发生火灾的情况,利用归一化植被指数(NDVI)对其火点提取精度进行了修正,去除了地表不是植被、或植被稀少的不足以引起火灾的区域,有效减少了因裸土或城市热岛引起的误判.就湖南2008年3月1日和黑龙江2004年10月15日特大森林火灾的情况,提取了几种地物地FPI、NDVI值,分析了FPI、NDVI值与不同地表类型的关系.其中,云是干扰火点FPI值的最大因素,地表植被是干扰燃烧植被的最大因素,并确定了FPI和NDVI的阈值.经过分析,相对于MODIS绝对火点识别算法,其精度及提取速度上都有提高.