杨树林全生长期LAI遥感估算模型适用性

基于时间序列的植被叶面积指数(LAI)估算方法一直是遥感领域研究的热点,对植被全生长期LAI进行估算以跟踪其生长情况具有重要的实用意义。以此为出发点,以滁州地区杨树林为研究对象,获取多时相环境卫星CCD(简称HJ-CDD)遥感影像,并利用LAI-2000同步测量杨树林叶面积指数(LAI)。使用归一化植被指数(NDVI)分别建立展叶期、花果期、叶面积稳定期和落叶始期的LAI估算模型,通过对比分析得到了全生长期LAI估算模型,并利用实测LAI对估算LAI进行了验证。最后进一步对该模型的适用性进行了验证,结果表明,此模型对于各个时期LAI的估算具有一定的适用性和有效性,可用于全生育期的遥感LAI生成,从而为LAI的动态变化监测提供了一种有效的研究思路和方法途径。     

Environmental conditions at arrival to the wintering grounds and during spring migration affect population dynamics of barn swallows Hirundo rustica breeding in Northern Italy

Several populations of long-distance migratory birds are currently suffering steep demographic declines. The identification of the causes of such declines is difficult because population changes may be driven by events occurring in distant geographical areas during different phases of the annual life-cycle of migrants. Furthermore, wintering areas and migration routes of populations of small-sized species are still largely unknown, with few exceptions. In this paper we identified the critical phases of the annual life-cycle that most influence the population dynamics of a small passerine, the Barn Swallow Hirundo rustica. We used information on temporal dynamics of a population breeding in Northern Italy, whose wintering range and timing of migration have been recently described by miniaturised tracking dataloggers. Our results indicated that primary productivity in the wintering grounds in the month when most individuals arrive from autumn migration and primary productivity in an area that is probably a stopover site during spring migration, influenced population dynamics more than habitat conditions at the breeding grounds. By using annual variation in primary productivity at the wintering grounds and stopover sites as predictors, we replicated the observed interannual population changes with great accuracy. However, the steep decline recently suffered by the population could be replicated only by including a constant annual decline in the model, suggesting that changes in primary productivity only predicted the interannual variation around the long-term trend. Our study therefore suggests the existence of critical periods during wintering and migration that may have large impact on population fluctuations of migrant birds.     

Correlation analysis of Normalized Different Vegetation Index (NDVI) difference series and climate variables in the Xilingole steppe, China from 1983 to 1999

There is a crucial need in the study of global change to understand how terrestrial ecosystems respond to the climate system. It has been demonstrated by many researches that Normalized Different Vegetation Index (NDVI) time series from remotely sensed data, which provide effective information of vegetation conditions on a large scale with highly temporal resolution, have a good relation with meteorological factors. However, few of these studies have taken the cumulative property of NDVI time series into account. In this study, NDVI difference series were proposed to replace the original NDVI time series with NDVI difference series to reappraise the relationship between NDVI and meteorological factors. As a proxy of the vegetation growing process, NDVI difference represents net primary productivity of vegetation at a certain time interval under an environment controlled by certain climatic conditions and other factors. This data replacement is helpful to eliminate the cumulative effect that exist in original NDVI time series, and thus is more appropriate to understand how climate system affects vegetation growth in a short time scale. By using the correlation analysis method, we studied the relationship between NOAA/AVHRR ten-day NDVI difference series and corresponding meteorological data from 1983 to 1999 from 11 meteorological stations located in the Xilingole steppe in Inner Mongolia. The results show that: (1) meteorological factors are found to be more significantly correlation with NDVI difference at the biomass-rising phase than that at the falling phase; (2) the relationship between NDVI difference and climate variables varies with vegetation types and vegetation communities. In a typical steppe dominated by Leymus chinensis, temperature has higher correlation with NDVI difference than precipitation does, and in a typical steppe dominated by Stipa krylovii, the correlation between temperature and NDVI difference is lower than that between precipitation and NDVI difference. In a typical steppe dominated by Stipa grandis, there is no significant difference between the two correlations. Precipitation is the key factor influencing vegetation growth in a desert steppe, and temperature has poor correlation with NDVI difference; (3) the response of NDVI difference to precipitation is fast and almost simultaneous both in a typical steppe and desert steppe, however, mean temperature exhibits a time-lag effect especially in the desert steppe and some typical steppe dominated by Stipa krylovii; (4) the relationship between NDVI difference and temperature is becoming stronger with global warming. __________ Translated from Acta Phytoecologica Sinica, 2005, 29(5): 753–765 [译自: 植物生态学报]     

The urban fire ant paradox: native fire ants persist in an urban refuge while invasive fire ants dominate natural habitats

In contrast to the widespread extirpation of native fire ants (Solenopsis geminata) across southern US following the invasion by imported red fire ants (S. invicta), some residential areas of Austin form unexpected refuges for native fire ants. Ironically, these urban environments provide refuges for the native fire ants while adjacent natural habitats have been overrun by invasive fire ants. Resistance to invasive fire ants in these urban areas occurs mainly in older residential properties constructed prior to the S. invicta invasion, while more recent construction has allowed establishment by S. invicta. The invasive ability of S. invicta is often attributed to escape from parasitoids and efficient dispersal of polygyne multiple queen colonies. Here we also show the importance of landscape parameters in the invasion process, where low levels of disturbance and continuous plant cover in older residential areas form possible barriers to colonization. Dense leaf cover (high NDVI) was also found to be associated with native ant refuges. Long term residential land ownership may have resulted in lower recent disturbance levels and increased plant cover that support refuges of native fire ants.     

The impact of ecological variability on the reproductive endocrinology of wild female African elephants

Non-invasive endocrine methods enable investigation of the relationship between ecological variation and ovarian activity and how this impacts on demographic processes. The underlying physiological factors driving high variation in inter-calving intervals among multi-parous African elephants offer an interesting system for such an investigation. This study investigates the relationship between Normalized Differential Vegetation Index (NDVI), an ecosystem surrogate measure of primary productivity, and fecal progestin concentrations among wild female elephants. Matched fecal samples and behavioral data on reproductive activity were collected from 37 focal individuals during the two-year study. Linear mixed models were used to explore the relationship between fecal 5alpha-pregnane-3-ol-20-one concentrations and the independent variables of NDVI, calf sex, female age, gestation day, and time since last parturition. Among both non-pregnant and pregnant females, fecal 5alpha-pregnane-3-ol-20-one concentrations were significantly correlated with time-specific NDVI indicating a strong relationship between ecological conditions and endocrine activity regulating reproduction. In addition, the age of a female and time since her last parturition impacted hormone concentrations. These results indicate that the identification of an individual's reproductive status from a single hormone sample is possible, but difficult to achieve in practice since numerous independent factors, particularly season, impact fecal hormone concentrations. Regardless of season, however, fecal 5alpha-pregnane-3-ol-20-one concentrations below 1 microg/g were exclusively collected from non-pregnant females, which could be used as a threshold value to identify non-pregnant individuals. Collectively the information generated contributes to a better understanding of environmental regulation of reproductive endocrinology in wild elephant populations, information salient to the management and manipulation of population dynamics in this species.     

Anopheles atroparvus density modeling using MODIS NDVI in a former malarious area in Portugal

Malaria is dependent on environmental factors and considered as potentially re-emerging in temperate regions. Remote sensing data have been used successfully for monitoring environmental conditions that influence the patterns of such arthropod vector-borne diseases. Anopheles atroparvus density data were collected from 2002 to 2005, on a bimonthly basis, at three sites in a former malarial area in Southern Portugal. The development of the Remote Vector Model (RVM) was based upon two main variables: temperature and the Normalized Differential Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra satellite. Temperature influences the mosquito life cycle and affects its intra-annual prevalence, and MODIS NDVI was used as a proxy for suitable habitat conditions. Mosquito data were used for calibration and validation of the model. For areas with high mosquito density, the model validation demonstrated a Pearson correlation of 0.68 (p<0.05) and a modelling efficiency/Nash-Sutcliffe of 0.44 representing the model's ability to predict intra- and inter-annual vector density trends. RVM estimates the density of the former malarial vector An. atroparvus as a function of temperature and of MODIS NDVI. RVM is a satellite data-based assimilation algorithm that uses temperature fields to predict the intra- and inter-annual densities of this mosquito species using MODIS NDVI. RVM is a relevant tool for vector density estimation, contributing to the risk assessment of transmission of mosquito-borne diseases and can be part of the early warning system and contingency plans providing support to the decision making process of relevant authorities.     

基于NDVI的三江源地区植被生长对气候变化和人类活动的响应研究

采用Spot VEGETATION 逐旬NDVI数据、1 ∶ 100万植被类型图和气象站资料,在掌握近10a三江源地区植被变化趋势基础上,分不同植被类型探讨植被生长对气候变化的响应机制,并通过分离气候要素与人类活动对NDVI的贡献,定量评估生态保护与建设工程的实施效果。结果表明,区域尺度上,三江源地区2001-2010年植被生长呈好转趋势,植被增长从东南向西北递减;在10a时间尺度上,气候变化是影响植被生长的决定性因素,但人类活动可在短期内加快植被变化速率,气候要素和人类活动对植被生长的贡献分别为79.32%和20.68%;降水和气温对植被生长的影响程度相当,其中受春季和秋季的降水和气温影响最大,尤其是植被生长季前后一个月(4月份和10月份)的气候条件;与林地和灌丛相比,高寒草地受气候条件的抑制作用更为明显,其中高寒草甸受气候变化的影响最大,NDVI与降水和气温均具有较高相关性,高寒草原受气温的影响比较大,而高山植被受降水的抑制作用更为明显;在气候条件利于植被生长的趋势下,2001-2010年三江源地区的人类活动对生态环境表现出正影响,实测NDVI_max与模拟NDVI_max之间的残差为0.0863,表明生态保护与建设行动取得初步成效,其中黄河源区东部和长江源区通天河两侧的生态恢复效益最为明显,而在唐古拉山、昆仑山、布青山、阿尼玛卿山等山脉的周边地区,人类活动对生态环境仍表现为负影响;时间尺度上人类活动对植被的正影响呈现出下降趋势,2001-2010年NDVI_max残差的回归斜率为-0.0039,表明生态项目实施的短期行为严重,生态建设的效果缺乏长效性。     

黄河上游玛曲县气候变化对植被的影响研究

利用1982年至2003年NASAGIMMS逐月归一化植被指数（NDVI）数据集和玛曲县气温、降水资料,对玛曲县近22年来NDVI变化和气候变化特征及其相互关系进行分析,以揭示黄河上游地区植被对全球变暖的区域响应.结果表明：（1）玛曲县植被变化在不同时段表现出较大差异,NDVI年际变化略有增加.（2）夏季是NDVI增长最快的季节,春季NDVI在20世纪90年代后期到本世纪初呈下降趋势,秋、冬季NDVI呈下降趋势.（3）返青期和NDVI值在春季达到同一水平值的时间及夏季达到峰值的时间逐年提前,说明生长季提前是该地区植被对全球变暖的主要响应表现.（4）玛曲县近22年来植被的NDVI变化在中等盖度水平（0.3～0.5）呈增加趋势,高盖度水平（≥0.7）的植被呈下降趋势,而在年代间变化水平上,气温和降水对植被生长都有影响,其中气温要比降水更显著;玛曲县降水和气温在季节尺度上对NDVI的影响不明显,除夏季气温与夏季NDVI关系密切之外,其他季节的关系均不明显.（5）按植被变化特点,将NDVI变化斜率最大值0.005定义为返青期指标,发现该地区牧草返青期的变化主要受温度条件影响,随气候变暖,返青期提前,其变化规律为4～5月份平均气温每10年升高0.6℃,返青期每10年提前3 d.     

A global prognostic scheme of leaf onset using satellite data

Leaf phenology describes the seasonal cycle of leaf functioning. Although it is essential for understanding the interactions between the biosphere, the climate, and biogeochemical cycles, it has received little attention in the modelling community at global scale. This article focuses on the prediction of spatial patterns of the climatological onset date of leaf growth for the decade 1983–93. It examines the possibility of extrapolating existing local models of leaf onset date to the global scale. Climate is the main variable that controls leaf phenology for a given biome at this scale, and satellite observations provide a unique means to study the seasonal cycle of canopies. We combine leaf onset dates retrieved from NOAA/AVHRR satellite NDVI with climate data and the DISCover land‐cover map to identify appropriate models, and determine their new parameters at a 0.5° spatial resolution. We define two main regions: at temperate and high latitudes leaf onset models are mainly dependent on temperature; at low latitudes they are controlled by water availability. Some local leaf onset models are no longer relevant at the global scale making their calibration impossible. Nevertheless, we define our unified model by retaining the model that best reproduced the spatial distribution of leaf onset dates for each biome. The main spatial patterns of leaf onset date are well simulated, such as the Sahelian gradient due to aridity and the high latitude gradient due to frost. At temperate and high latitudes, simulated onset dates are in good agreement with climatological observations; 62% of treated grid‐cells have a simulated leaf onset date within 10 days of the satellite observed onset date (which is also the temporal resolution of the NDVI data). In tropical areas, the subgrid heterogeneity of the phenology is larger and our model's predictive power is diminished. The difficulties encountered in the tropics are due to the ambiguity of the satellite signal interpretation and the low reliability of rainfall and soil moisture fields.     

Application of a geographically-weighted regression analysis to estimate net primary production of Chinese forest ecosystems

Aim The objective of this paper is to obtain a net primary production (NPP) regression model based on the geographically weighted regression (GWR) method, which includes spatial non‐stationarity in the parameters estimated for forest ecosystems in China. Location We used data across China. Methods We examine the relationships between NPP of Chinese forest ecosystems and environmental variables, specifically altitude, temperature, precipitation and time‐integrated normalized difference vegetation index (TINDVI) based on the ordinary least squares (OLS) regression, the spatial lag model and GWR methods. Results The GWR method made significantly better predictions of NPP in simulations than did OLS, as indicated both by corrected Akaike Information Criterion (AIC_c) and R². GWR provided a value of 4891 for AIC_c and 0.66 for R², compared with 5036 and 0.58, respectively, by OLS. GWR has the potential to reveal local patterns in the spatial distribution of a parameter, which would be ignored by the OLS approach. Furthermore, OLS may provide a false general relationship between spatially non‐stationary variables. Spatial autocorrelation violates a basic assumption of the OLS method. The spatial lag model with the consideration of spatial autocorrelation had improved performance in the NPP simulation as compared with OLS (5001 for AIC_c and 0.60 for R²), but it was still not as good as that via the GWR method. Moreover, statistically significant positive spatial autocorrelation remained in the NPP residuals with the spatial lag model at small spatial scales, while no positive spatial autocorrelation across spatial scales can be found in the GWR residuals. Conclusions We conclude that the regression analysis for Chinese forest NPP with respect to environmental factors and based alternatively on OLS, the spatial lag model, and GWR methods indicated that there was a significant improvement in model performance of GWR over OLS and the spatial lag model.