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...     

中国植被生长的最适温度估算与分析

利用中国区域的GIMMS NDVI数据集和相应时期气象站点实测的温度数据集,分别在时间尺度和空间地域上对比分析植被生长过程与环境温度变化的关系,并由此提取区域植被生长关键阶段对应的温度,最后得到了中国各生态地理区植被生长的最适温度及其变化区间。结果显示:①研究首次给出了不同生态区植被生长的最适温度,其中青藏高原的参考最适温度最低,在10 ℃左右,而长江中下游和华南地区的较高,很多区域的值都超过了25 ℃,说明植被生长的最适温度具有很强的地域分异性;②根据论文结果,通过海拔高度和纬度两个地理因子,可快速拟合得到中国陆地植被生长的近似最适温度。研究结果可以为生态系统模型的参数本地化和空间化提供参考。     

东北多年冻土区植被NDVI变化及其对气候变化和土地覆被变化的响应

利用1982~2006年的NOAA AVHRR-GIMMS和MODIS 2种数据集的归一化植被指数(NDVI)数据对东北多年冻土区植被NDVI年际动态和空间差异进行分析,并结合气象数据和土地利用/覆被数据分析了植被NDVI对气候变化和土地利用/覆被变化的响应.研究表明,东北多年冻土区植被NDVI值较高,且空间差异明显;森林为该区主要植被类型,NDVI值较高,主要分布于大小兴安岭和伊春地区;草地集中分布于西南部, NDVI值相对较低.东北多年冻土区过去25a间植被生长的变化趋势为:伴随着气温的显著升高和降水量减少,植被NDVI显著下降.较气温而言,降水量是影响植被NDVI的主要因子(r = 0.77, P < 0.01).在气候变化和人类活动的双重作用下,东北多年冻土区植被NDVI在1982~2006年间表现为4个阶段:1982~1990年,植被NDVI虽有小幅波动,但整体上呈持续增加的趋势;1990~1993年,植被NDVI呈迅速下降趋势; 1993~1997年,植被NDVI呈现回升态势,表现出缓慢上升的趋势;1998~2006年,植被NDVI呈现总体下降趋势.不同植被类型表现出不同的NDVI年际变化规律,尤以草地NDVI值波动最大.植被NDVI变化空间异质性显著.气候变化和多年冻土退化影响了东北多年冻土区植被NDVI动态.年均气温升高和年降水量降低影响了植被的生长.从像元尺度来看,研究区植被NDVI与气温和降水均具有较显著的相关性.研究区土地利用/覆被变化的分析结果表明,不同的土地利用类型间的转变对植被NDVI的大小和空间分布产生了重要影响.     

西藏当雄县高寒草地NDVI的海拔分异特征及其指示

通过提取西藏当雄县Landsat ETM影像NDVI以及DEM的海拔要素,在坡面的小尺度水平上分析了NDVI的海拔格局,并利用沿海拔梯度的草地地上生物量实测数据进行验证,进一步在全县尺度上分析了当雄NDVI >0.2 的区域内草地NDVI 的海拔分异特征.主要结果如下:①在坡面尺度上,随海拔的增加,不同计算方式的NDVI均表现为先增加后降低的单峰格局,与沿海拔梯度实测的地上生物量格局一致,NDVI最大值与围栏外生物量最大值均出现在4 950 m左右;②在全县尺度上,当雄草地NDVI随海拔的变化略为复杂:在4 300 m以上,NDVI随海拔的增加表现为先升高后降低,最高值出现在4 700 m左右;在4 300 m以下植被可能以湿地草甸为主,形成另一个NDVI高值区,但其分布面积仅为总草场面积的10%左右.上述结果表明,海拔是影响当雄县NDVI 空间分布的重要地形因子,NDVI 的海拔格局体现了较大尺度上草地植被对不同水热组合环境的适应特征,这对于在气候变化背景下实施高寒地区生态保护工程以及制定合理的草场资源配置方案具有重要指导意义.     

Post-fire vegetation regrowth detection in the Deiva Marina region (Liguria-Italy) using Landsat TM and ETM+ data

Obtaining quantitative information about the recovery of fire-affected ecosystems is of utmost importance from the management and decision-making point of view. Nowadays the concern about natural environment protection and recovery is much greater than in the past. However, the resources and tools available for its management are still not sufficient. Thus, attention and precision is needed when decisions must be taken. Quantitative estimates on how the vegetation is recovering after a fire can be of help for evaluating the necessity of human intervention on the fire-affected ecosystem, and their importance will grow as the problem of forest fires, climate change and desertification increases.This article performs a comparison of methods to extract quantitative estimates of vegetation cover regrowth with Landsat TM and ETM+ data in an area that burned during the summer of 1998 in the Liguria region (Italy). In order to eliminate possible sources of error, a thorough pre-processing was carried out, including a careful geometric correction (reaching RMSE lower than 0.3 pixels), a topographic correction by means of a constrained Minnaert model and a combination of absolute and relative atmospheric correction methods. Pseudo Invariant Features (PIF) were identified by implementing an automated selection method based in temporal Principal Component Analysis (PCA), which has been called multi-Temporal n-Dimensional Principal Component Analysis (mT-nD-PCA).Spectral Mixture Analysis (SMA) was compared against quantitative vegetation indices which are based on well known traditional vegetation indices like Normalized Difference Vegetation Index (NDVI) and Modified Soil Adjusted Vegetation Index (MSAVI). Accuracy assessment was performed by regressing vegetation cover results obtained with each method against field data gathered during the fieldwork campaign carried out in the study area. Results obtained showed how vegetation cover fractions extracted from the NDVI based quantitative index were the most accurate, being superior to the rest of the techniques applied, including SMA.     

Knowing fire incidence through fuel phenology: A remotely sensed approach

Fire is a basic ecological factor that contributes to determine vegetation diversity and dynamics in time and space. Fuel characteristics play an essential role in fire ignition and propagation; at the landscape scale fuel availability and flammability are closely related to the vegetation phenology that directly affects wildfire pattern in time and space. In this view, the annual normalized difference vegetation index (NDVI) profiles derived from high temporal resolution satellites, like SPOT Vegetation, represent an effective tool for monitoring the coarse-scale vegetation seasonal timing. The objective of this study thus consists in quantifying the explanatory power of multitemporal NDVI profiles on the fire regime characteristics of the potential natural vegetation (PNV) types of Sardinia (Italy) over a 5-year period (2000-2004). The results obtained show a good association between the NDVI temporal dynamics of the PNV of Sardinia and the corresponding fire regime characteristics, emphasizing the role of the bioclimatic timing of the vegetation in controlling the coarse-scale wildfire spatio-temporal distribution of Sardinia. By providing a sound phytogeographical framework for describing different wildfire regimes, PNV maps can thus be considered helpful cartographic documents for fire management strategies at the landscape scale.     

基于NDVI指数的骆马湖水生植被分级研究

基于典型时段骆马湖LandsatTM遥感影像,利用归一化植被指数（NDVI）法对骆马湖水生植被进行分级研究.采用标准差分级方法,将骆马湖水生植被分为5个等级.研究结果表明,利用这种分级方法处理的图像能够较准确地反映骆马湖水生植被区域分布和等级分区,能够更准确地给出采砂区和生态保护区域的位置,增强湖区水生态环境保护工作的有效性和针对性.     

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.     

Impact of the December 2004 tsunami on soil, groundwater and vegetation in the Nagapattinam district, India

The tsunami of 26 December 2004 struck the Nagapattinam District, Tamil Nadu, India. Sea water inundation from the tsunami caused salinization problems for soil and groundwater in coastal areas of the district, and also induced salt injuries in crops. To document the recovery of the agricultural environment from the tsunami, we conducted observations of the soil, groundwater, and vegetation. Soil electrical conductivity increased sharply after the tsunami, but returned to pre-tsunami levels the following year. Groundwater salinity returned to pre-tsunami levels by 2006. These rapid rates of recovery were due to the monsoon rainfall leaching salt from the highly permeable soils in the area. MODIS NDVI values measured before and after the tsunami showed that vegetation damaged by the tsunami recovered to its pre-tsunami state by the next rice cropping season, called samba, which starts from August to February. From these results, we conclude that the agricultural environment of the district has now fully recovered from the tsunami. Based on the results, we have also identified important management implications for soil, groundwater, and vegetation as follows: 1) due to the heavy monsoon rainfall and the high permeability of soils in this region, anthropogenic inputs like fertilizers should be applied carefully to minimize pollution, and the use of green manure is recommended; 2) areas that were contaminated by sea water extended up to 1000 m from the sea shore and over pumping of groundwater should be carefully avoided to prevent inducing sea water intrusion; and 3) data from a moderate resolution sensor of 250 m, such as MODIS, can be applied to impact assessment in widespread paddy field areas like the Nagapattinam District.     

基于定量遥感技术的重庆市热岛效应

近年来,城市热岛效应不断增强,已经成为一个突出的城市环境问题。重庆市作为长江中上游特大型城市,工业发达,经济快速发展,市区热岛效应明显,但是很少对其研究。以2007年Landsat5 TM影像为数据源,利用单窗算法对重庆市城市地表温度进行反演,并进行城市地面亮温等级划分,在从城市土地利用类型、NDVI等方面对重庆市城市热岛效应强度和空间格局分布及其特征进行了探讨。结果表明：城区热岛现象显著,且分布不均匀;不同土地利用类型对热岛效应影响不同;城市地面亮温与NDVI呈明显的线性负相关关系。最后结合重庆市的特殊地形,提出了以下解决城市热岛效应的对策：加强城市规划,调整城市结构;构建最佳绿化结构,提高城市绿化率;改变能源结构,提高能源利用率,控制工厂废气排放量。