京津唐地区城市化对植被物候的影响研究

城市化过程使得土地利用、地表植被覆盖发生着显著的变化,如何定量化描述城市化对植被物候的影响越来越受到各方的关注。基于京津唐地区2001~2006年NDVI时间序列影像,得出了京津唐地区植被物候空间分布格局,计算出北京、天津、唐山3个核心城市城区的距离变量与平均植被物候,并分析了城市化对植被物候指标的影响趋势。结果表明:①2001~2006年,北京城市化使得城区及离城区较近的地方植被生长开始时间提前、结束时间推后、生长季周期变长、NDVI振幅减小;②天津和唐山的城市化使得城区及离城区较近的地方植被生长开始时间延后、结束时间提前、生长季周期变短\,NDVI振幅减小;③城市化对植被物候的影响与该地区城市扩张类型存在相关性关系。     

HJ-1A NDVI与MODIS NDVI时间序列提取植被物候特征对比研究

以HJ-1A和MODIS为数据源,通过动态阈值法提取物候特征参数,对HJ-1A NDVI和MODIS NDVI时间序列进行植被物候特征提取进行定性和定量比较,通过比较结果,提出HJ-1A NDVI数据在该应用中存在的问题,促进国产中空间高时间分辨率影像数据在植被物候信息提取研究中的应用,提高其在生态系统研究中的应用价值。结果表明:在SOS、EOS和LOS以及TOMS几个主要的物候时间点上,MODIS NDVI时间序列的标准差较小,所得物候数据更为集中,偏离度较小,所得物候数据较稳定;而HJ-1A NDVI时间序列所得物候数据的标准差较大,数据偏离程度较大,而在POS、BOS和AOS等表征植被生命周期中生长幅度数据上,其标准差较小,离散程度小。     

21世纪初中国农作物与自然植被物候时空差异

利用离散小波方法对2001~2012年MODIS EVI时序数据进行平滑,基于动态阈值法提取我国植被物候信息,探讨农作物和自然植被物候的时空变化特征。结果表明:(1)我国第一季农作物开始、峰值和结束日期主要以华北平原为中心随海拔的上升而推迟,而自然植被物候更早20d左右,且随海拔的上升先推迟后提前;(2)物候在时序上有显著变化的第一季区域,43.98%开始日期、52.83%峰值日期呈现提前趋势,多在开始晚、结束早的西南区及东北与内蒙古交界处,其余区域开始、峰值日期及81.80%结束日期呈推迟趋势,发生在开始早、结束晚的黄土高原及双季农作区;农作物物候推迟幅度小于自然植被。     

Morlet 小波在地表物候遥感识别中尺度的选取与应用

地表物候是人类了解地球生态系统的必要参数,也是动植物保护、农耕等活动的重要依据。使用遥感数据进行地表物候提取为大尺度的地表物候变化监测提供了一种有效途径。研究发现目前应用最广泛的非对称性高斯函数拟合法和双 Logistic 函数拟合法存在一定的缺陷,尤其是在提取 NDVI 峰值谷值和物候周期不明显区域 (如干旱地区、沙地) 的物候参数时存在严重的误差,而 morlet 小波在分析地表年际变化的周期特征方面表现良好。本文使用 morlet 小波对青海湖流域 2003-2014 年的 MODIS 数据进行分析,得到地表物候在该时间段内的年度变化与趋势,进行不同区域、不同时间的差异性分析,发现青海湖流域的地表物候期整体都略有提前,但生长季的长度呈延长趋势,认为青海湖流域的地表物候整体上响应全球变暖的趋势。中部地区的生长季长度大于高海拔、高纬度区域和沙地区域,认为青海湖流域的中部地区是最适合动植物生长、活动的范围。     

Our current understanding of lake ecosystem response to climate change: What have we really learned from the north temperate deep lakes?

Climatic change is recognized as an important factor capable of influencing the structural properties of aquatic ecosystems. Lake ecosystems are particularly sensitive to climate change. Several long time-series studies have shown close coupling between climate, lake thermal properties and individual organism physiology, population abundance, community structure, and food-web structure. Understanding the complex interplay between climate, hydrological variability, and ecosystem structure and functioning is essential to inform water resources risk assessment and fisheries management. The purpose of this paper is to present the current understanding of climate-induced changes on lake ecosystem phenology. We first review the ability of climate to modulate the interactions among lake hydrodynamics, chemical factors, and food-web structure in several north temperate deep lakes (e.g., Lake Washington, Lake Tahoe, Lake Constance, Lake Geneva, Lake Baikal, and Lake Zurich). Our aim is to assess long-term trends in the physical (e.g., temperature, timing of stratification, and duration of ice cover), chemical (e.g., nutrient concentrations), and biological (e.g., timing of the spring bloom, phytoplankton composition, and zooplankton abundance) characteristics of the lakes and to examine the signature of local weather conditions (e.g., air temperature and rainfall) and large-scale climatic variability (e.g., ENSO and PDO) on the lake physics, chemistry and biology. We also conducted modeling experiments to quantify the relative effect of climate change and nutrient loading on lake phenology. These modeling experiments focused on the relative changes to the major causal associations underlying plankton dynamics during the spring bloom and the summer stratified period. To further understand the importance of climate change on lakes, we propose two complementary directions of future research. First, additional research is needed to elucidate the wide array of in-lake processes that are likely to be affected by the climate change. Second, it is essential to examine the heterogeneity in responses among different water bodies. The rationale of this approach and its significance for dealing with the uncertainty that the climate signals cascade through lake ecosystems and shape abiotic variability and/or biotic responses have been recently advocated by several other synthesis papers.     

Attraction ofCarpophilus spp. (Coleoptera: Nitidulidae) to synthetic aggregation pheromones and host-related coattractants in Australian stone fruit orchards: Beetle phenology and pheromone dose studies

Synthetic aggregation pheromones ofCarpophilus hemipterus (L.) andCarpophilus mutilatus Erichson were field tested during a 10-month period in southern New South Wales stone fruit orchards to determineCarpophilus spp. phenology and the effect of two pheromone doses on attraction. Aggregation pheromones synergize the attraction of host volatiles toCarpophilus spp. Four major species,C. hemipterus, C. mutilatus, C. davidsoni Dobson andC. (Urophorus) humeralis (F.), were trapped, with greater numbers of each species inC. hemipterus pheromone/fermenting whole-wheat breaddough-baited traps, than in dough-only-traps. InC. mutilatus pheromone/ fermenting-dough-baited traps, onlyC. mutilatus andC. davidsoni responded in greater numbers than to dough-only traps. Beetles first appeared in traps in late September (early spring) when daily maximum temperatures averaged 17.5C. Trappings reached a peak during October and declined to very low levels in November–December (late spring-early summer). Numbers trapped of all species increased during February–March (late summer–early autumn), presumably due to the presence of abundant host resources (ripening and fallen fruit), and continued at high levels until May (late autumn). An 18-week study demonstrated significantly greater responses byCarpophilus spp. to 5000-g than to 500-g doses of C.hemipterus andC. mutilatus pheromones. Greatest responses to 5000g were recorded forC. hemipterus andC. mutilatus responding to their own pheromones (increased attraction over dough alone of 259x and 21.2x respectively). Implications of the study and the potential for using synthetic aggregation pheromones for managingCarpophilus spp. populations in Australian stone fruit are discussed.     

A Two-Step Filtering approach for detecting maize and soybean phenology with time-series MODIS data

The crop developmental stage represents essential information for irrigation scheduling/fertilizer management, understanding seasonal ecosystem carbon dioxide (CO₂) exchange, and evaluating crop productivity. In this study, we devised an approach called the Two-Step Filtering (TSF) for detecting the phenological stages of maize and soybean from time-series Wide Dynamic Range Vegetation Index (WDRVI) data derived from Moderate Resolution Imaging Spectroradiometer (MODIS) 250-m observations. The TSF method consists of a Two-Step Filtering scheme that includes: (i) smoothing the temporal WDRVI data with a wavelet-based filter and (ii) deriving the optimum scaling parameters from shape-model fitting procedure. The date of key crop development stages are then estimated by using the optimum scaling parameters and an initial value of the specific phenological date on the shape model, which are preliminary defined in reference to ground-based crop growth stage observations. The shape model is a crop-specific WDRVI curve with typical seasonal features, which were defined by averaging smoothed, multi-year WDRVI profiles from MODIS 250-m data collected over irrigated maize and soybean study sites.In this study, the TSF method was applied to MODIS-derived WDRVI data over a 6-year period (2003 to 2008) for two irrigated sites and one rainfed site planted to either maize or soybean as part of the Carbon Sequestration Program (CSP) at the University of Nebraska-Lincoln. A comparison of satellite-based retrievals with ground-based crop growth stage observations collected by the CSP over the six growing seasons for these three sites showed that the TSF method can accurately estimate the date of four key phenological stages of maize (V2.5: early vegetative stage, R1: silking stage, R5: dent stage and R6: maturity) and soybean (V1: early vegetative stage, R5: beginning seed, R6: full seed and R7: beginning maturity). The root mean square error (RMSE) of phenological-stage estimation for maize ranged from 2.9 [R1] to 7.0 [R5] days and from 3.2 [R6] to 6.9 [R7] days for soybean, respectively. In addition, the TSF method was also applied for two years (2001 and 2002) over eastern Nebraska to test its ability to characterize the spatio-temporal patterns of these key phenological stages over a larger geographic area. The MODIS-derived crop phenological stage dates agreed well with the statistical crop progress data reported by the United State Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) for eastern Nebraska's three crop agricultural statistic districts (ASDs). At the ASD-level, the RMSE of phenological-stage estimation ranged from 1.6 [R1] to 5.6 [R5] days for maize and from 2.5 [R7] to 5.3 [R5] days for soybean.     

A scalable approach to mapping annual land cover at 250 m using MODIS time series data: A case study in the Dry Chaco ecoregion of South America

Land use and land cover (LULC) maps from remote sensing are vital for monitoring, understanding and predicting the effects of complex human-nature interactions that span local, regional and global scales. We present a method to map annual LULC at a regional spatial scale with source data and processing techniques that permit scaling to broader spatial and temporal scales, while maintaining a consistent classification scheme and accuracy. Using the Dry Chaco ecoregion in Argentina, Bolivia and Paraguay as a test site, we derived a suite of predictor variables from 2001 to 2007 from the MODIS 250 m vegetation index product (MOD13Q1). These variables included: annual statistics of red, near infrared, and enhanced vegetation index (EVI), phenological metrics derived from EVI time series data, and slope and elevation. For reference data, we visually interpreted percent cover of eight classes at locations with high-resolution QuickBird imagery in Google Earth. An adjustable majority cover threshold was used to assign samples to a dominant class. When compared to field data, we found this imagery to have georeferencing error < 5% the length of a MODIS pixel, while most class interpretation error was related to confusion between agriculture and herbaceous vegetation. We used the Random Forests classifier to identify the best sets of predictor variables and percent cover thresholds for discriminating our LULC classes. The best variable set included all predictor variables and a cover threshold of 80%. This optimal Random Forests was used to map LULC for each year between 2001 and 2007, followed by a per-pixel, 3-year temporal filter to remove disallowed LULC transitions. Our sequence of maps had an overall accuracy of 79.3%, producer accuracy from 51.4% (plantation) to 95.8% (woody vegetation), and user accuracy from 58.9% (herbaceous vegetation) to 100.0% (water). We attributed map class confusion to limited spectral information, sub-pixel spectral mixing, georeferencing error and human error in interpreting reference samples. We used our maps to assess woody vegetation change in the Dry Chaco from 2002 to 2006, which was characterized by rapid deforestation related to soybean and planted pasture expansion. This method can be easily applied to other regions or continents to produce spatially and temporally consistent information on annual LULC.     

GEO_BUG: a geographical modelling environment for assessing the likelihood of pest development

This paper describes software designed to explore pest phenology (development) over space and time. The framework presented links sequences of interpolated daily maximum and minimum temperatures with a variety of process-based phenology and accumulated temperature models. The flexibility offered by this approach is demonstrated using examples of gridded maps of pest phenology on target dates, graphs of the sequences of pest development at individual locations and assessments of error in the predicted dates over the course of a model run. Finally, the potential application of the software in support of agricultural management systems, policy development and integrated research is discussed.     

Climate and Bordeaux wine quality: identifying the key factors that differentiate vintages based on consensus rankings

The climate of Bordeaux, France, was examined to determine if climatic factors can distinguish between consensus vintage rankings, developed using eight ratings sources, of red and sweet white wines from 1961 to 2009. Climate variables were computed for the growing season and average plant phenological stages and were compared between the 10 highest and lowest ranked vintages. Good vintages exhibited higher heat accumulation during the growing season and a general lack of rainfall, particularly during veraison. Most climate factors were consistent for both red and sweet white wines. Mean maximum temperature during the growing season was an important discriminator between good and poor vintages for both reds and whites, although sweet white wines were also affected by growing season precipitation and temperatures during the vine's dormant period. In general, consensus vintage quality is consistent between reds and whites (Spearman's ρ?=?0.66, p?相似文献