基于NDVI时间序列轨迹的草原露天矿区植被时空动态特征

采矿作用下草原露天矿区植被时空动态特征尚不明确.以胜利露天矿区为例,选取MODIS和Landsat影像,利用ESTARFM构建2001—2015年植被生长期一致的年际Landsat时间序列,以时间分割算法拟合像元NDVI时间序列轨迹,结合轨迹形态特征提取早期扰动型、持续扰动型、扰动稳定型、扰动稳定恢复型和扰动恢复型5种植被动态类型及各类型的时间特征.结果表明:胜利矿区植被动态类型以扰动恢复型为主,占各类型像元总数的55.2%,扰动稳定型和持续下降型次之,分别为25.6%和11.0%,早期扰动型和扰动稳定恢复型较小,分别为3.5%和4.7%.扰动多发于2004—2009年,稳定期多始于2008年,空间上多分布于露天采场和排土场,恢复期多始于2008年和2010年,其空间范围较小且集中于矿井外围和排土场.扰动持续时长以1年为主,稳定期持续时长以7年为主,恢复期持续时长中稳定恢复型为2～5年,扰动恢复型为8年.     

A multiscale analysis of ecosystem services supply in the NW Iberian Peninsula from a functional perspective

In recent years, the assessment of ecosystem services (ES) supply has been based on the use of Land Use/Land Cover (LULC) data as proxies for spatial representation of ecosystems. Nevertheless, some shortcomings of this method, such as uncertainties derived from generalization of the ecosystem types and assumptions of invariance across spatial scales, indicate the need for new approaches. Such approaches could be aimed at improving knowledge of the relationships between ecosystem services and landscape structure and the spatial characteristics of ES patterns. In this study, we propose an integrative approach that involves the generation and analysis of continuous maps representing the supply of five ES potentially related to the amount of biomass. Five remote sensing images of the Northwestern Iberian Peninsula, obtained with Landsat-5 TM, were used to generate a proxy for net primary production by combining the normalized difference vegetation index (NDVI) of each image to calculate a ΣNDVI index that could act as a potential indicator of some ecosystem services. This information was combined with three variables – terrain slope, population density and occurrence of protected areas – to produce spatial models for the five ES and eventually a series of five supply maps. Food, materials and energy provision services showed a clustered pattern, with high supply values in flat zones and areas with high population densities. In contrast, mass flow and climate regulation services were more widely distributed throughout the study area. The five ecosystem service patterns were analyzed at different scales by two methods: lacunarity and four term local quadrat variance (4TLQV) analysis. These methods revealed differences in the spatial pattern: lacunarity analysis was useful for detection of scale thresholds at the local level, whereas 4TLQV was more sensitive to scale thresholds at larger spatial levels. Thus, the variance analysis yielded higher values for larger windows sizes, particularly for provisioning services. The results demonstrated the suitability of the proposed approach for the spatially explicit modeling of ecosystem services, avoiding the uncertainty of other assessments such as those based on LULC data, and for the exploratory analysis of ES supply from a spatial point of view.     

Debating the greening vs. browning of the North American boreal forest: differences between satellite datasets

A number of remote sensing studies have evaluated the temporal trends of the normalized difference vegetation index (NDVI or vegetation greenness) in the North American boreal forest during the last two decades, often getting quite different results. To examine the effect that the use of different datasets might be having on the estimated trends, we compared the temporal trends of recently burned and unburned sites of boreal forest in central Canada calculated from two datasets: the Global Inventory, Monitoring, and Modeling Studies (GIMMS), which is the most commonly used 8 km dataset, and a new 1 km dataset developed by the Canadian Centre for Remote Sensing (CCRS). We compared the NDVI trends of both datasets along a fire severity gradient in order to evaluate the variance in regeneration rates. Temporal trends were calculated using the seasonal Mann–Kendall trend test, a rank‐based, nonparametric test, which is robust against seasonality, nonnormality, heteroscedasticity, missing values, and serial dependence. The results showed contrasting NDVI trends between the CCRS and the GIMMS datasets. The CCRS dataset showed NDVI increases in all recently burned sites and in 50% of the unburned sites. Surprisingly, the GIMMS dataset did not capture the NDVI recovery in most burned sites and even showed NDVI declines in some burned sites one decade after fire. Between 50% and 75% of GIMMS pixels showed NDVI decreases in the unburned forest compared with <1% of CCRS pixels. Being the most broadly used dataset for monitoring ecosystem and carbon balance changes, the bias towards negative trends in the GIMMS dataset in the North American boreal forest has broad implications for the evaluation of vegetation and carbon dynamics in this region and globally.     

Estimating aboveground herbaceous plant biomass via proxies: The confounding effects of sampling year and precipitation

Direct measurements of aboveground plant biomass are often not feasible, thus various biomass proxies are in use. To obtain biomass estimates, these proxies are calibrated against actual biomass, and the resulting proxy-biomass relationship is often used across multiple years and experimental treatments within a study. We investigated how the proxy-biomass relationship varied across years and considered interannual precipitation variability as a contributing factor.We sampled a perennial grassland for ten consecutive years (2003–2012) in central Hungary and estimated vegetation cover and Normalized Difference Vegetation Index (NDVI); two frequently used biomass proxies representing two contrasting methods. Aboveground live herbaceous plant biomass was harvested from each plot after sampling, and regression models were used to assess the relationship between biomass proxies and actual aboveground biomass.We found that cover and NDVI were equally effective at estimating biomass. However, the relationship between either biomass proxy and actual biomass varied amongst years, and this was related to the amount of precipitation. In wetter years, proxy-biomass relationships were steeper than in drier years.These results indicate that using the same proxy-biomass relationship across different years or precipitation regimes may not be valid and may introduce systematic error into biomass estimations in long-term studies or precipitation manipulation experiments.     

Giant tortoise habitats under increasing drought conditions on Aldabra Atoll—Ecological indicators to monitor rainfall anomalies and related vegetation activity

Aldabra Atoll has the largest population of giant tortoises (Aldabrachelys gigantea) in the world. As such an important biological resource, it is necessary to understand how the effects of climate change will impact this keystone species; in particular the frequency of drought, which is likely to affect tortoise habitat. To assess whether drought frequency has changed over the last 50 years on Aldabra, we calculated the standardized precipitation index (SPI) to identify drought periods using monthly rainfall data collected during 1969–2013. We found that drought frequency has increased to more than six drought months per year today compared with about two months per year in the 1970s (t = 2.884, p = 0.006). We used MODIS normalized difference vegetation index (NDVI) as a proxy for vegetation activity, to determine how vegetation has responded to the changing drought frequency between 2000 and 2013. We found that Aldabra’s vegetation is highly responsive to changes in rainfall: anomalies in long-term mean monthly NDVI across Aldabra were found to decrease below the mean during most drought periods and increase above the mean during most non-drought periods. To investigate the response of tortoise habitat to rainfall, we extracted mean NDVI anomalies for three key habitat types. Open mixed scrub and grasslands, the preferred habitat of tortoises, showed the greatest decrease in vegetation activity during drought periods, and the greatest increase in average greenness during non-drought periods. Recent analysis has shown vegetation changes on Aldabra in recent decades. If these changes are caused by decreased precipitation, then the increased frequency of drought could impact the tortoise population, in both the short and long term, by limiting the quality and quantity of forage and/or shade availability within favoured habitats, and by changing the habitat composition across the atoll.     

黄河三角洲植被空间分布特征及其环境解释

为了解黄河三角洲地区植被空间分布与环境因子之间的关系,通过局地植被样方调查、区域遥感影像提取归一化植被指数(NDVI)及地形高度、地下水位埋深、表层土壤Cl~-含量等环境数据采集,综合样地植被与环境数据进行了除趋势对应分析(DCA)和除趋势典范对应分析(DCCA),并对区域NDVI与主要环境变量进行了单因子相关性分析和多元逐步回归分析。结果显示:DCA排序可将黄河三角洲植被分为翅碱蓬、柽柳-翅碱蓬、芦苇-柽柳、芦苇4个主要群落类型(群丛),DCCA与DCA排序图总体相似,但DCCA更清晰地表明其第一轴主要代表的是潜水Cl~-浓度等关键水盐因子,且随着水土环境系统盐分含量的减小,群落由翅碱蓬逐渐向芦苇演变。区域典型植被群落和NDVI分布格局与变化趋势受地下水位埋深和潜水Cl~-浓度2个环境因素影响较大(NDVI与2个环境变量间建立的二元回归方程R~2=0.57),而土壤Cl~-含量的植被效应实际上受地下水位埋深和潜水Cl~-浓度的影响。在区域地下水普遍浅埋条件下,地下水成为影响植被生长与分布的生态环境最敏感要素,而地下水位埋深和潜水Cl~-浓度是这一要素中的2个关键因子,尤其是后者梯度变化对天然植被分布格局起重要的控制作用。     

A critical assessment of two species distribution models: a case study of the vervet monkey (Cercopithecus aethiops)

Aim Species distribution models are invaluable tools in biogeographical, ecological and applied biological research, but specific concerns have been raised in relation to different modelling techniques in terms of their validity. Here we compare two fundamentally different approaches to species distribution modelling, one based on simple occurrence data where the lack of an ecological framework has been criticized, and the other firmly based in socio‐ecological theory but requiring highly detailed behavioural information that is often limited in availability. Location (Sub‐Saharan) Africa. Methods We used two distinct techniques to predict the realized distribution of a model species, the vervet monkey (Cercopithecus aethiops Linnaeus, 1758). A maximum entropy model was produced taking 13 environmental variables and presence‐only data from 174 sites throughout Africa as input, with an additional 58 sites retained to test the model. A time‐budget model considering the same environmental variables was constructed from detailed behavioural data on 20 groups representing 14 populations, with presence‐only data from the remaining 218 sites reserved to test model predictions on vervet monkey occurrence. Both models were further validated against a reference species distribution map as drawn up by the African Mammals Databank. Results Both models performed well, with the time budget and maximum entropy algorithms correctly predicting vervet monkey presence at 78.4% and 91.4% of their respective test sites. Similarly, the time‐budget model correctly predicted presence and absence at 87.4% of map pixels against the reference distribution map, and the maximum entropy model achieved a success rate of 81.8%. Finally, there was a high level of agreement (81.6%) between the presence–absence maps produced by the two models, and the environmental variables identified as most strongly driving vervet monkey distribution were the same in both models. Main conclusions The time‐budget and maximum entropy models produced accurate and remarkably similar species distribution maps, despite fundamental differences in their conceptual and methodological approaches. Such strong convergence not only provides support for the credibility of current results, but also relieves concerns about the validity of the two modelling approaches.     

Estimating switchgrass productivity in the Great Plains using satellite vegetation index and site environmental variables

Switchgrass is being evaluated as a potential feedstock source for cellulosic biofuels and is being cultivated in several regions of the United States. The recent availability of switchgrass land cover maps derived from the National Agricultural Statistics Service cropland data layer for the conterminous United States provides an opportunity to assess the environmental conditions of switchgrass over large areas and across different geographic locations. The main goal of this study is to develop a data-driven multiple regression switchgrass productivity model and identify the optimal climate and environment conditions for the highly productive switchgrass in the Great Plains (GP). Environmental and climate variables used in the study include elevation, soil organic carbon, available water capacity, climate, and seasonal weather. Satellite-derived growing season averaged Normalized Difference Vegetation Index (GSN) was used as a proxy for switchgrass productivity. Multiple regression analyses indicate that there are strong correlations between site environmental variables and switchgrass productivity (r = 0.95). Sufficient precipitation and suitable temperature during the growing season (i.e., not too hot or too cold) are favorable for switchgrass growth. Elevation and soil characteristics (e.g., soil available water capacity) are also an important factor impacting switchgrass productivity. An anticipated switchgrass biomass productivity map for the entire GP based on site environmental and climate conditions and switchgrass productivity model was generated. Highly productive switchgrass areas are mainly located in the eastern part of the GP. Results from this study can help land managers and biofuel plant investors better understand the general environmental and climate conditions influencing switchgrass growth and make optimal land use decisions regarding switchgrass development in the GP.     

中国北方草原对气候干旱的响应

草原生长动态受气候条件的影响和制约,在很大程度上取决于水分条件.为了较好阐明草原生长与干旱气候关系,利用表征草原生长变化的NDVI(Normalized Difference Vegetation Index)指数和表征干旱的SPI(Standardized Precipitation Index)指数研究了荒漠草原、典型草原、草甸草原与干旱气候的线性关系,表明荒漠草原的生长动态受季节性干旱影响很大,短期、中长期和长期干旱对荒漠草原影响较小.典型草原对季节性干旱响应较强,而对短期、中长期和长期的干旱响应较弱.草甸草原对季节性和长期干旱响应较强.并且草原对降雨量的响应具有时滞效应,水分盈亏对草原的影响是累积效应.利用基于虚拟变量的回归模型和简单回归模型模拟了草原NDVI对SPI指数的响应关系,基于虚拟变量的回归模型显示出对草原NDVI与SPI关系的较优拟合度.表明了草原生长动态对干旱气候响应具有季节性效应.