Long-term trend of Ulva prolifera blooms in the western Yellow Sea

Blooms of the green macroalga Ulva prolifera in the western Yellow Sea occurred every year since 2008, and they have been reported and studied extensively using a variety of means including remote sensing. However, to date, long-term bloom patterns have not been reported except for a few case studies showing examples in different years. Here, using MODIS observations and an objective method to perform statistical analysis, mean Ulva coverage in the western Yellow Sea has been derived and analyzed between 2007 and 2015 at both monthly and annual scales. On annual scale, mean Ulva coverage decreased after 2008, but increased rapidly after 2012 from 8 km² in 2012 to 116 km² in 2015 (the largest ever reported in history for this region). In the month of June the mean coverage increased from 18 km² in 2012 to 363 km² in 2015. Other than 2009 and 2010, the month of June showed maximum Ulva coverage in every year. These coverage estimates are significantly lower than previously reported values as they represent “pure” algae coverage after taking into account of partial pixel coverage. Several environmental factors were examined in an attempt to determine the reasons behind such long-term changes, yet the results are inconclusive, suggesting a strong necessity of further coordinated and multi-disciplinary researches.     

A Temporally Explicit Production Efficiency Model for Fuel Load Allocation in Southern Africa

Abstract We present a regional fuel load model (1 km² spatial resolution) applied in the southern African savanna region. The model is based on a patch-scale production efficiency model (PEM) scaled up to the regional level using empirical relationships between patch-scale behavior and multi-source remote sensing data (spatio-temporal variability of vegetation and climatic variables). The model requires the spatial distribution of woody vegetation cover, which is used to determine separate respiration rates for tree and grass. Net primary production, grass and tree leaf death, and herbivory are also taken into account in this mechanistic modeling approach. The fuel load model has been calibrated and validated from independent measurements taken from savanna vegetation in Africa southward from the equator. A sensitivity analysis on the effect of climate variables (incoming radiation, air temperature, and precipitation) has been conducted to demonstrate the strong role that water availability has in determining productivity and subsequent fuel load over the southern African region. The model performance has been tested in four different areas representative of a regional increasing rainfall gradient—Etosha National Park, Namibia, Mongu and Kasama, Zambia, as well as in Kruger National Park, South Africa. Within each area, we analyze model output from three different magnitudes of canopy coverage (<5, 30, and 50%). We find that fuel load ranges predicted by the model are globally in agreement with field measurements for the same year. High rainfall sustains green herbaceous production late in the dry season and delays tree leaf litter production. Effect of water on production varies across the rainfall gradient with delayed start of green material production in more arid regions.     

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.     

Expanded thermal niche for a diving vertebrate: A leatherback turtle diving into near-freezing water

The global distribution of extant reptiles is more limited than that of mammals or birds, with low reptilian species diversity at high latitudes. Central to this limited geographical distribution is the ectothermic nature of reptiles, which means that they generally become torpid at cold temperatures. However, here we report the first detailed telemetry from a leatherback turtle (Dermochelys coriacea) diving in cold water at high latitude. An individual equipped with a satellite tag that relayed temperature-depth profiles dived continuously for many weeks into sub-surface waters as cold as 0.4 °C. Global warming will likely increase the foraging range of leatherback turtles further into temperate and boreal waters.     

Land surface phenology as an indicator of biodiversity patterns

With the rapid decline in biodiversity worldwide it is imperative to develop procedures for assessing changes in biodiversity across space. The synoptic view provided by imaging remote sensors constitutes a suitable approach for analyzing biodiversity from local to regional scales. A procedure based on the close relationship between floristic similarity and the similarity in land surface phenology was recently developed and successfully applied to assess diversity patterns using time series imagery acquired by the Moderate Resolution Imaging Spectro-radiometer (MODIS). However, as it depends on high temporal resolution remotely sensed data (e.g., MODIS), the procedure is constrained by the coarse spatial resolution characterizing these high temporal resolution data. Using an optimized technique for image fusion, we combined high temporal resolution data acquired by the MODIS sensor system with moderate spatial resolution data acquired by the Landsat TM/ETM+ sensor systems. Our results show that the MODIS/Landsat data fusion allows the characterization of land surface phenology at higher spatial resolutions, which better corresponded with information acquired within vegetation survey plots established in temperate montane forests located in Wolong Nature Reserve, Sichuan Province, China. As such, the procedure is useful for capturing changes in biodiversity induced by disturbances operating at large spatial scales and constitutes a suitable tool for monitoring and managing biodiversity.     

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.     

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.     

基于MODIS NDVI的长江中游区域植被动态及与气候因子的关系

基于1999-2015年的MODIS NDVI时间序列遥感数据，应用趋势分析、变异系数、重标极差分析和偏相关分析等方法，分析了长江中游的植被时空变化特征及其与气象因子的关系。结果表明，长江中游地区NDVI均值总体上呈上升趋势（从0.72增加到0.80）。从空间分布来看，NDVI低值区域（0.1-0.5）占1.40%，高值区域（>0.7）占87.15%；NDVI空间格局呈"西高东低、北高南低"的分布特征，低值区域表现为以三省省会城市为中心向外辐射。Hurst指数显示，研究区大部分区域（60.54%）的NDVI变化趋势具有不确定性，持续性改善区域（34.78%）主要分布在西部山地区，持续性退化区域（3.26%）主要分布在人类活动频繁的较发达城市区域。在年际尺度上，研究区NDVI与各气象因子关系均不显著；月际尺度上，NDVI与降水、相对湿度和日照时数显著相关，降水和日照时数有明显的时滞性。区域内NDVI动态趋势以不确定性发展为主，城市群周边NDVI呈现持续退化的区域应该引起关注。     

基于耦合协调度的黄土高原地区NDVI与降水关系的变异诊断

植被是地表生态系统的重要"指示器"，在能量交换、水循环、碳循环、生物地球化学循环和维持中发挥着重要作用，降水是影响植被变化的主要气候因子，研究两者之间的作用关系具有重要的意义和价值。利用Mann-Kendall趋势检验法和Hust指数分析了黄土高原地区归一化植被指数（NDVI）的变化趋势，使用相对发展率（RDR）指数和重心转移模型分析了NDVI变化的时空差异，并构建了基于耦合协调度理论和Pettitt检验方法的NDVI与降水关系的变异诊断方法，识别了黄土高原地区NDVI与降水关系的突变点，探讨了降水对NDVI变化的影响以及造成NDVI与降水关系变化的原因。结果表明：（1）黄土高原地区73.49%面积的NDVI在1998-2017年有呈现显著增加趋势（P<0.05），大部分地区NDVI在未来依旧呈现增加趋势；（2）黄土高原地区丘陵沟壑区与高原沟壑区的NDVI增加幅度大于黄土高原地区整体的增加幅度，而北部风沙区和农灌区以及黄土高原地区边界区域的NDVI增加滞后于区域整体变化；（3） NDVI与降水耦合协调程度逐年增强，两者关系在2006年发生显著突变（P<0.05）；（4） NDVI呈现显著增加区域降水明显高于不显著变化区域（P<0.05），降水对NDVI变化存在一定影响，在丘陵沟壑区、高原沟壑区北部和东部河谷及土石山区北部NDVI和降水存在显著正相关关系（P<0.05），然而黄土高原地区大部分区域的降水并不存在显著变化趋势（P>0.05），因此造成黄土高原地区NDVI与降水关系在2006年发生显著突变的主要原因应该是人类活动（P<0.05）。研究成果有助于进一步理解黄土高原植被变化与降水的相互作用，为黄土高原生态建设和水土流失治理提供一定的科学支撑。     

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.