基于NOAA-AVHRR数据的中国东部地区植被遥感分类研究

该文采用 19幅 (时间跨 8个月 ) 时间序列的NOAA_AVHRR的归一化植被指数 (NDVI) 最大值合成影像遥感数据, 经过主分量分析 (Principlecomponentanalysis, PCA) 处理后, 用非监督分类方法的ISODATA算法, 对中国东部地区的 (五省一市 ) 植被进行分类, 结果可以分出 2 8种土地覆盖类型, 除了两种类型为水体和城市或裸地外, 其余 2 6种类型均为植被类型, 根据中国植被分类系统, 这 2 6类可以归并为 6大植被类型 :1) 常绿阔叶林 ;2 ) 针叶林 ;3) 竹林 ;4 ) 灌草丛 ;5 ) 水生植被 ;6 ) 农业植被。用 1∶10 0 0 0 0 0数字化《中国植被图集》的植被类型检验遥感分类结果表明, 针叶林、灌草丛、常绿阔叶林和农业植被的分类具有较高的位置精度和面积精度, 位置精度分别为 79.2 %、91.3%、6 8.2 %和 95.9%, 面积精度分别达到 92.1%、95.9%、6 3.8%和 90.5 %。这 6大植被类型在地理空间上的分布规律与中国东部常绿阔叶林区植被的地带性分布基本一致。     

Vegetation classification of East China with multi-temporal NOAA-AVHRR data

East China lies in the subtropical monsoon climatic zone and is dominated by subtropical evergreen broad-leaved forests,a unique vegetation type mainly distributed in East Asia with the largest distnbution in China.It is important to be able to monitor and estimate forest biomass and production,regional carbon storage,and global climate change impacts on these important vegetation types.In this paper,we used coarse resolution remote sensing data to identify the vegetation types in East China and developed a map of the spatial distribution of vegetation types in this region.Nineteen maximum normalized difference vegetation index(NDVI)composite images(acquisition time span of 7 months from February to August),which were derived from 10 days National Oceanographic and Atmospheric Administration(NOAA)Advanced Very High Resolution Radiometer(AVHRR)channel 1 and channel 2 observations,an unsupervised classification method,and the ISODATA algorithm were employed to identify the vegetation types.To reduce the dimensions of the dataset resulted in a total of 28 spectral clusters of land-cover of which two clusters were urban/bare soil and water,the images were processed using principal component analysis(PCA).The 26 remaining spectral clusters were merged into six vegetation types using the Chinese vegetation taxonomy system:evergreen broad-leaved forest,coniferous forest,bamboo forest,shrub-grass,aquatic vegetation,and agricultural vegetation.The spatial distribution and areal extent for the coniferous forests,shrub-grass,evergreen broad-leaved forests,and agricultural vegetation were calculated and comscale.The spatial accuracy and the area accuracy for coniferous forests,shrub-grass,evergreen broad-leaved forests,and agricultural vegetation were 79.2%,91.3%,68.2% and 95.9% and 92.1%,95.9%,63.8% and 90.5%,respectively.The spatial accuracy and area accuracy of the bamboo forest were 28.7% and 96.5%,respectively;the spatial accuracy of aquatic vegetation was 69.6%,but there was a significant difference in its area accuracy because image acquisition did not cover the full year.Our study demonstrated the fea sibility of using NOAA-AVHRR to identify the different vegetation types in the subtropical evergreen broad-leaved forest zone in East China.The spatial location of the six identified vegetation types agreed with the actual geo graphical distribution of the vegetation types in East China.     

Montane forest ecotones moved downslope in northeastern USA in spite of warming between 1984 and 2011

Ecotones are transition zones that form, in forests, where distinct forest types meet across a climatic gradient. In mountains, ecotones are compressed and act as potential harbingers of species shifts that accompany climate change. As the climate warms in New England, USA, high‐elevation boreal forests are expected to recede upslope, with northern hardwood species moving up behind. Yet recent empirical studies present conflicting findings on this dynamic, reporting both rapid upward ecotonal shifts and concurrent increases in boreal species within the region. These discrepancies may result from the limited spatial extent of observations. We developed a method to model and map the montane forest ecotone using Landsat imagery to observe change at scales not possible for plot‐based studies, covering mountain peaks over 39 000 km². Our results show that ecotones shifted downward or stayed stable on most mountains between 1991 and 2010, but also shifted upward in some cases (13–15% slopes). On average, upper ecotone boundaries moved down ?1.5 m yr^?1 in the Green Mountains, VT, and ?1.3 m yr^?1 in the White Mountains, NH. These changes agree with remeasured forest inventory data from Hubbard Brook Experimental Forest, NH, and suggest that processes of boreal forest recovery from prior red spruce decline, or human land use and disturbance, may swamp out any signal of climate‐mediated migration in this ecosystem. This approach represents a powerful framework for evaluating similar ecotonal dynamics in other mountainous regions of the globe.     

Vegetation classification of East China with multi-temporal NOAA-AVHRR data

East China lies in the subtropical monsoon climatic zone and is dominated by subtropical evergreen broad-leaved forests, a unique vegetation type mainly distributed in East Asia with the largest distribution in China. It is important to be able to monitor and estimate forest biomass and production, regional carbon storage, and global climate change impacts on these important vegetation types. In this paper, we used coarse resolution remote sensing data to identify the vegetation types in East China and developed a map of the spatial distribution of vegetation types in this region. Nineteen maximum normalized difference vegetation index (NDVI) composite images (acquisition time span of 7 months from February to August), which were derived from 10 days National Oceanographic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) channel 1 and channel 2 observations, an unsupervised classification method, and the ISODATA algorithm were employed to identify the vegetation types. To reduce the dimensions of the dataset resulted in a total of 28 spectral clusters of land-cover of which two clusters were urban/bare soil and water, the images were processed using principal component analysis (PCA). The 26 remaining spectral clusters were merged into six vegetation types using the Chinese vegetation taxonomy system: evergreen broad-leaved forest, coniferous forest, bamboo forest, shrub-grass, aquatic vegetation, and agricultural vegetation. The spatial distribution and areal extent for the coniferous forests, shrub-grass, evergreen broad-leaved forests, and agricultural vegetation were calculated and compared with the Vegetation Atlas of China at a 1:1,000,000 scale. The spatial accuracy and the area accuracy for coniferous forests, shrub-grass, evergreen broad-leaved forests, and agricultural vegetation were 79.2%, 91.3%, 68.2% and 95.9% and 92.1%, 95.9%, 63.8% and 90.5%, respectively. The spatial accuracy and area accuracy of the bamboo forest were 28.7% and 96.5%, respectively; the spatial accuracy of aquatic vegetation was 69.6%, but there was a significant difference in its area accuracy because image acquisition did not cover the full year. Our study demonstrated the feasibility of using NOAA-AVHRR to identify the different vegetation types in the subtropical evergreen broad-leaved forest zone in East China. The spatial location of the six identified vegetation types agreed with the actual geographical distribution of the vegetation types in East China. __________ Translated from Acta Phytoecologica Sinica, 2005, 29(3): 436–443 [译自: 植物生态学报, 2005, 29(3): 436–443]     

Vegetation classification,mapping, and monitoring at Voyageurs National Park,Minnesota: An application of the U.S. National Vegetation Classification

Question: How can the U.S. National Vegetation Classification (USNVC) serve as an effective tool for classifying and mapping vegetation, and inform assessments and monitoring? Location: Voyageurs National Park, northern Minnesota, U.S.A and environs. The park contains 54 243 ha of terrestrial habitat in the sub-boreal region of North America. Methods: We classified and mapped the natural vegetation using the USNVC, with ‘alliance’and ‘association’as base units. We compiled 259 classification plots and 1251 accuracy assessment test plots. Both plot and type ordinations were used to analyse vegetation and environmental patterns. Color infrared aerial photography (1:15840 scale) was used for mapping. Polygons were manually drawn, then transferred into digital form. Classification and mapping products are stored in publicly available databases. Past fire and logging events were used to assess distribution of forest types. Results and Discussion: Ordination and cluster analyses confirmed 49 associations and 42 alliances, with three associations ranked as globally vulnerable to extirpation. Ordination provided a useful summary of vegetation and ecological gradients. Overall map accuracy was 82.4%. Pinus banksiana - Picea mariana forests were less frequent in areas unburned since the 1930s. Conclusion: The USNVC provides a consistent ecological tool for summarizing and mapping vegetation. The products provide a baseline for assessing forests and wetlands, including fire management. The standardized classification and map units provide local to continental perspectives on park resources through linkages to state, provincial, and national classifications in the U.S. and Canada, and to NatureServe's Ecological Systems classification.     

基于无人机多源遥感数据的亚热带森林树种分类

树种多样性是生态学研究的重要内容,树木的种类和空间分布信息可有效服务于可持续森林管理。但在复杂林分条件下,获取高精度分类结果的难度大。而无人机遥感可获取局域超精细数据,为树种分类精度的提高提供了可能。基于可见光、高光谱、激光雷达等多源无人机遥感数据,探究其在亚热带林分条件下的树种分类潜力。研究发现:(1)随机森林分类器总体精度和各树种的F1分数最高,适合亚热带多树种的分类制图,其区分13种类别(8乔木,4草本)的总体精度为95.63%,Kappa系数为0.948;(2)多源数据的使用可以显著提高分类精度,全特征模型精度最高,且高光谱和激光雷达数据显著影响全特征模型分类精度,可见光纹理数据作用较小;(3)分类特征重要性从大到小排序为结构信息,植被指数,纹理信息,最小噪声变换分量。     

黄土高原不同植被覆被类型NDVI对气候变化的响应

植被与气候是目前研究生态与环境的重要内容。为探究黄土高原地区植被与气候因子之间的响应机制,利用线性趋势分析、Pearson相关分析、多元线性回归模型以及通径分析的方法,对黄土高原2000—2015年全区和不同植被覆被类型区内NDVI与气候因子的变化趋势以及相互作用关系进行分析。植被覆被分类数据和植被指数数据分别来源于ESA CCI-LC(The European Space Agency Climate Change Initiative Land Cover)以及MODND1T/NDVI(Normalized Difference Vegetation Index)。结果表明:(1) 2000—2015年黄土高原全区植被年NDVI_(max)显著增加的区域占总面积的74.25%,不同植被覆被类型年NDVI_(max)分别为常绿阔叶林常绿针叶林落叶阔叶林落叶针叶林镶嵌草地农田镶嵌林地草地灌木,并且都呈显著增加趋势,其中常绿阔叶林和农田增加幅度最大,为0.012/a。(2)黄土高原全区NDVI与气温、日照、降水和相对湿度等气候因子之间没有显著相关性,但在不同植被覆被类型区,气候因子对NDVI存在显著作用,且不同植被覆被类型差异明显。(3)在全区和不同植被覆被类型区NDVI仅对降水的响应比较一致,气温无论在整个区域尺度还是不同植被覆被类型区对植被的影响均不显著。(4)常绿阔叶林、落叶阔叶林、常绿针叶林及镶嵌林地等以乔木为主的植被覆被类型受年均相对湿度和年总日照时数的显著负效应驱动,草地、镶嵌草地等以草本为主的植被覆被类型则受到年总降水量的显著正效应影响。这说明对植被类型进行区分,更有利于揭示气候对植被的作用机制。     

Landscape characteristics of non‐native pine plantations and invasions in Southern Chile

The spread of non‐native conifers into areas naturally dominated by other vegetation types is a growing problem in South America. This process results in a landscape transformation as the conifers suppress native vegetation leading to reduced biodiversity, lower water availability and altered nutrient dynamics. Previous research highlights the broad spatial extents of land cover change in parts of Chile. However, in Southern Chile, the extent of plantations and the landscape characteristics associated with plantations and ongoing pine invasions are poorly understood. Here, we characterised non‐native pine land cover within one Landsat scene (World Reference System 2 Path 232/Row 92; ~34 000 km²) in Southern Chile. We created training data based on historical high‐resolution imagery, derived land cover predictors from time series of Landsat observations and used a Random Forest classifier to map the distribution of non‐native pines. The overall classification accuracy was 88%, and the accuracy of the non‐native pine class exceeded 90%. Although 71% of non‐native pine patches were within 500 m of other non‐native pine patches, isolated non‐native pine patches were found to occur up to 55 km from the nearest neighbour. These distant plantations could exacerbate invasion risk by creating propagule sources for novel invasion fronts. In relation to landscape characteristics, non‐native pines were found to be more likely to occur in low slope and mid‐elevation areas. Because most of the study area is native forest, most non‐native pine patches border native forest. However, non‐native pine patches were almost three times more likely than random patches to border grass/agriculture. This suggests that grasslands and disturbed sites, which have low resistance to non‐native pine invasion, are disproportionately exposed to pine propagules. Our results indicate that non‐native pine plantations are extensive across Southern Chile, and well poised to cause future invasion.     

Using an artificial neural network to characterize the relative suitability of environments for forest types in a complex tropical vegetation mosaic

A predictive understanding of the environmental controls on forest distributions is essential for the conservation of biodiversity and management of landscapes in the tropics. This is particularly true now because of potentially rapid climate change. The floristic complexity of tropical forests and the lack or absence of data severely limits the applicability of modelling methods based on the ecology or distribution of individual species. Here we present an artificial neural network (ANN) model using the information available in the humid tropics of North Queensland: a structural classification of forest types, maps of the forest mosaic, and estimates of spatial environmental variables. The ANN model characterizes the relative suitabilities of environments for 15 forest classes defined by their physiognomy and canopy structure. Inputs include seven climate variables, nine soil parent-material classes, and seven terrain variables. The data used to train the model consisted of a stratified random sample of 75000 points. Output of the model is used to measure the dissimilarity between the environment at each location and the environment that would be most suitable for the forest type that is mapped there. The model is highly successful at distinguishing the relative suitability of environments for the forest classes with 75% of the region's forest mosaic accurately predicted by the model at a one hectare resolution. In contrast, a comparable maximum likelihood classification has an accuracy of only 38%. In the remaining 25% of the region the environments are quite dissimilar to what would be expected for the forest types present there. This is especially the case at boundaries between forest classes and for a transitional forest class. Areas mapped as this disturbed, transitional class are generally classified by the model as having environments suitable to the forest type they are most likely to become. The approach has high potential for the analysis of climate change impacts as well as inferring vegetation patterns in the past and should be applicable wherever vegetation maps and spatial estimates of climate variables are available.     

Predictive modeling of the potential natural vegetation pattern in northeast China

Based on the characteristics of natural vegetation distribution in northeast China, using multivariate analysis and geographical information system technology, we established a regional ‘vegetation–environment’ model to simulate geographical distribution of 16 natural vegetation types under present environmental conditions, representing the potential natural vegetation (PNV) of northeast China, on the basis of digital maps of seven environmental variables including climate and topography. Comparison of simulated PNVs distributions with the actual natural vegetation distribution indicated a good agreement, with overall predictive accuracy of 66.9% and overall Kappa value of 0.67. The predictions of model, however, were poor, for only 0.62 of AUC value was yielded. The current resolution and accuracy of the model can be applied to simulate and map the natural vegetation pattern at the regional scale and also used to analyze the effect of climatic changes on natural vegetation.     

On the Evolution of the Palynoflora and Climatic Environment During Late Pleistocence in Tengger Desert,China

Palynoflora studies on the Duantouliang section, located at 39º40'N, 103º55'E in Northwestern Tengger Desert, China showed that, based on the spore-pollen assemblages, the major vegetation and climatic environment between 42 000 to 23 000 a BP could be divided into the following different periods: Ⅰ. From 42 000 to 38 000 a BP, the spore-pollen assemblages displayed that the mixed conifer/deciduous broad-leaved forests developed on the mountain and its foothill regions where the bare Gobi-desert are at present; At that time, Populus and Salix forests and grassland surrounded the Paleaolake, the climate condition was much warmer and humid than today; Ⅱ. From 38 000 to 37 000 a BP, the climate was warm and moist, it was the most suitable period for the plant growth, the studied area was dominated by the temperate and warm-temperate mixed broad-leaf deciduous and needleleaf forest, there was meadow spreaded on the river sides and lake beaches; Ⅲ. From 31 000 to 30000 a BP, the needleleaf forests and cold-temperate Salix oritrepha shrubs were flourishing, and the climate at that time was relatively cold;Ⅳ. From 30000 to 28000 a BP, the temperatures began increasing, the high lake levels was formed during this time, and the vegetations were meadows and swamps; V. From 28 000 to 23 000 a BP, temperate Cupressaceae and Betula mixed conifer/deciduous forests grew on mountain and foothill region, grassland developed on plain areas, Salix was on lake and river sides. This indicates a warm and moist climate condition but it was drier than the earliest period.     

Modelling potential impacts of climate change on the spatial distribution of zonal forest communities in Switzerland

Abstract. A spatially explicit, climate-sensitive vegetation model is presented to simulate both present and future distribution of potential natural vegetation types in Switzerland at the level of zonal forest communities. The model has two versions: (1) a ‘basic’ version using geographical region, aspect, bedrock (represented by soil pH), and elevation, and (2) a ‘climate-sensitive’ version obtained by replacing elevation (complex environmental gradient) with temperature (climatic factor). Version 2 is used to predict vegetation response under different (today's and projected) climatic conditions. Two regional climate scenarios are applied: (1) assuming an annual mean temperature increase of 1.1 — 1.4 °C, and (2) assuming an increase of 2.2 — 2.75 °C. Both scenarios result in significant changes of the spatial vegetation patterns as compared with today's climatic conditions. In scenario 1, ca. 33 % of the sample points remain unchanged in terms of the simulated zonal forest community; in scenario 2, virtually all sample points change. The most noticeable changes occur on the Swiss Plateau with Carpinion forests (zonal vegetation of present colline belt) expanding to areas that are occupied today by submontane and low-montane Fagus forests. To estimate the reliability of the simulation, quantitative (comparison with field mapping) and qualitative (comparison with climate types in the Alpine region) tests are performed and the main limitations of the approach are evaluated.     

A simulated map of the potential natural forest vegetation of Switzerland

Using empirical data (ca. 7500 phytosociological releves), a simple, probabilistic ‘vegetation-site’ model was developed, to simulate geographical distribution of 71 forest community types, representing the potential natural vegetation (PNV) of Switzerland. The model was interfaced to a geographic information system (GIS) and used to generate a numerical vegetation map, on the basis of digital maps of 12 environmental variables including climatic conditions (temperature and precipitation), topography (elevation, slope, aspect), and soil parameters (soil pH and physical soil parameters). The predicted distribution of forest communities was compared with several vegetation maps, prepared for some subregions of Switzerland by means of traditional field methods. Similarity ranged from 50 to 80 %, depending on the community type, level of vegetational hierarchy and the geographical region. The current resolution and accuracy of the simulated vegetation map allows us to study the vegetational patterns on the level of the entire country or its major geographical and climatic regions. The simulated vegetation map is potentially an important tool in ecological risk assessment studies concerning the possible impacts of climate change on the ecological potential of forest sites and biological diversity of forest communities.     

Forest history and the development of old‐growth characteristics in fragmented boreal forests

Questions: Can small and isolated high‐conservation value forests (e.g. designated woodland key habitats) maintain old‐growth forest characteristics and functionality in fragmented landscapes? To what extent have past disturbances (natural and anthropogenic) influenced the development of old‐growth characteristics of these forests? How long does it take for selectively cut stands to attain conditions resembling old‐growth forests? Location: Southern boreal zone of central Sweden. Methods: We linked multiple lines of evidence from historical records, biological archives, and analyses of current forest structure to reconstruct the forest history of a boreal landscape, with special emphasis on six remaining core localities of high‐conservation value forest stands. Results: Our reconstructions revealed that several of these stands experienced wildfires up to the 1890s; all had been selectively harvested in the late 1800s; and all underwent substantial structural and compositional reorganization over the following 100‐150 years. This time interval was sufficient to recover considerable amounts of standing and downed dead wood (mean 60.3 m³ ha^?1), a range of tree ages and sizes (mean basal area 32.6 m² ha^?1), and dominance of shade‐tolerant spruce. It was insufficient to obtain clearly uneven tree age structures and large (>45 cm diameter) living and dead trees. Thus, these forests contain some, but not all, important compositional and structural attributes of old‐growth forests, their abundance being dependent on the timing and magnitude of past natural and anthropogenic disturbances. Our landscape‐level analysis showed marked compositional and structural differences between the historical forest landscape and the present landscape, with the latter having a greater proportion of young forests, introduction of non‐native species, and lack of large trees and dead wood. Conclusions: The remnant high‐conservation value stands were not true representatives of the pre‐industrial forests, but represent the last vestige of forests that have regenerated naturally and maintained a continuous tree cover. These traits, coupled with their capacity for old‐growth recovery, make them valuable focal areas for conservation.     

Current and Future Fire Regimes and Their Influence on Natural Vegetation in Ethiopia

Fire is a major factor shaping the distribution of vegetation types. In this study, we used a recent high resolution map of potential natural vegetation (PNV) types and MODIS fire products to model and investigate the importance of fire as driver of vegetation distribution patterns in Ethiopia. We employed statistical modeling techniques to estimate the distribution of fire and the PNVs under current climatic conditions, and used the calibrated models to project distributions for different climate change scenarios. Results show a clear congruence between distribution patterns of fire and major vegetation types. The effect of climate change varies considerably between climate change models and scenarios, but as general trend expansions of moist Afromontane forest and Combretum–Terminalia woodlands were predicted. Fire-prone areas were also predicted to increase, and including this factor in vegetation distribution models resulted in stronger expansion of Combretum–Terminalia woodlands and a more limited increase of moist Afromontane forests. These results underline the importance of fire as a regulating factor of vegetation distribution patterns, and how fire needs to be factored into predict the possible effects of climate change. For conservation strategies to effectively address conservation challenges caused by rapid climate shifts, it is imperative that they not only consider the direct influence of climate changes on the vegetation, species species, or biodiversity patterns, but also the influence of future fire regimes.     

Evidence of a Cooler Continental Climate in East China during the Warm Early Cenozoic

The early Cenozoic was characterized by a very warm climate especially during the Early Eocene. To understand climatic changes in eastern Asia, we reconstructed the Early Eocene vegetation and climate based on palynological data of a borehole from Wutu coal mine, East China and evaluated the climatic differences between eastern Asia and Central Europe. The Wutu palynological assemblages indicated a warm temperate vegetation succession comprising mixed needle- and broad-leaved forests. Three periods of vegetation succession over time were recognized. The changes of palynomorph relative abundance indicated that period 1 was warm and humid, period 2 was relatively warmer and wetter, and period 3 was cooler and drier again. The climatic parameters estimated by the coexistence approach (CA) suggested that the Early Eocene climate in Wutu was warmer and wetter. Mean annual temperature (MAT) was approximately 16°C and mean annual precipitation (MAP) was 800–1400 mm. Comparison of the Early Eocene climatic parameters of Wutu with those of 39 other fossil floras of different age in East China, reveals that 1) the climate became gradually cooler during the last 65 million years, with MAT dropping by 9.3°C. This cooling trend coincided with the ocean temperature changes but with weaker amplitude; 2) the Early Eocene climate was cooler in East China than in Central Europe; 3) the cooling trend in East China (MAT dropped by 6.9°C) was gentler than in Central Europe (MAT dropped by 13°C) during the last 45 million years.     

Management oriented forest map of South India: thematic derivations

The principles and methods of the vegetation mapping undertaken at the French Institute, Pondicherry, are dealt with herein. Particularly, the characterisation of the different types of vegetation and especially the originality of the method: the dynamic interpretation of the vegetation and the depiction of the bioclimatic conditions. The programme of the forest map of South India at scale 1:250 000, undertaken in collaboration with the forest departments of the concerned states, is then described with special attention given to the source and the collection of data. This map has been conceived to serve as a basic document for the sustainable management of the forests. Three examples of its application are given. They concern the detection of anomalies between the existing forest cover and the prevalent climatic environment; the detection of areas for which protection is urgently needed; the selection of regions showing a particular interest in the field of nature conservation or as gene pool reserve. Finally, an example of a thematic map of sensibility of the forests is given, using the vegetation map as a basis.     

Short‐term propagation of rainfall perturbations on terrestrial ecosystems in central California

Question: Does vegetation buffer or amplify rainfall perturbations, and is it possible to forecast rainfall using mesoscale climatic signals? Location: Central California (USA). Methods: The risk of dry or wet rainfall events was evaluated using conditional probabilities of rainfall depending on El Niño Southern Oscillation (ENSO) events. The propagation of rainfall perturbations on vegetation was calculated using cross‐correlations between monthly seasonally adjusted (SA) normalized difference vegetation index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR), and SA antecedent rainfall at different time‐scales. Results: In this region, El Niño events are associated with higher than normal winter precipitation (probability of 73%). Opposite but more predictable effects are found for La Niña events (89% probability of dry events). Chaparral and evergreen forests showed the longest persistence of rainfall effects (0‐8 months). Grasslands and wetlands showed low persistence (0‐2 months), with wetlands dominated by non‐stationary patterns. Within the region, the NDVI spatial patterns associated with higher (lower) rainfall are homogeneous (heterogeneous), with the exception of evergreen forests. Conclusions: Knowledge of the time‐scale of lagged effects of the non‐seasonal component of rainfall on vegetation greenness, and the risk of winter rainfall anomalies lays the foundation for developing a forecasting model for vegetation greenness. Our results also suggest greater competitive advantage for perennial vegetation in response to potential rainfall increases in the region associated with climate change predictions, provided that the soil allows storing extra rainfall.     

Spatial distribution of young forests and carbon fluxes within recent disturbances in Russia

Forest stand age plays a major role in regulating carbon fluxes in boreal and temperate ecosystems. Young boreal forests represent a relatively small but persistent source of carbon to the atmosphere over 30 years after disturbance, while temperate forests switch from a substantial source over the first 10 years to a notable sink until they reach maturity. Russian forests are the largest contiguous forest belt in the world that accounts for 17% of the global forest cover; however, despite its critical role in controlling global carbon cycle, little is known about spatial patterns of young forest distribution across Russia as a whole, particularly before the year 2000. Here, we present a map of young (0–27 years of age) forests, where 12‐ to 27‐year‐old forests were modeled from the single‐date 500 m satellite record and augmented with the 0‐ to 11‐year‐old forest map aggregated from the 30 m resolution contemporary record between 2001 and 2012. The map captures the distribution of forests with the overall accuracy exceeding 85% within three largest bioclimatic vegetation zones (northern, middle, and southern taiga), although mapping accuracy for disturbed classes was generally low (the highest of 31% for user's and producer's accuracy for the 12–27 age class and the maximum of 74% for user's and 32% for producer's accuracy for the 0–11 age class). The results show that 75.5 ± 17.6 Mha (roughly 9%) of Russian forests were younger than 30 years of age at the end of 2012. The majority of these 47 ± 4.7 Mha (62%) were distributed across the middle taiga bioclimatic zone. Based on the published estimates of net ecosystem production (NEP) and the produced map of young forests, this study estimates that young Russian forests represent a total sink of carbon at the rate of 1.26 Tg C yr^?1.     

Climate and satellite-derived land cover for predicting breeding bird distribution in the Great Lakes Basin

Aim We examined relationships between breeding bird distribution of 10 forest songbirds in the Great Lakes Basin, large‐scale climate and the distribution of land cover types as estimated by advanced very high resolution radiometer (AVHRR) and multi‐spectral scanner (MSS) land cover classifications. Our objective was to examine the ability of regional climate, AVHRR (1 km resolution) land cover and MSS (200 m resolution) land cover to predict the distribution of breeding forest birds at the scale of the Great Lakes Basin and at the resolution of Breeding Bird Atlas data (5–10 km²). Specifically we addressed the following questions. (1) How well do AVHRR or MSS classifications capture the variation in distribution of bird species? (2) Is one land cover classification more useful than the other for predicting distribution? (3) How do models based on climate compare with models based on land cover? (4) Can the combination of both climate and land cover improve the predictive ability of these models. Location Modelling was conducted over the area of the Great Lakes Basin including parts of Ontario, Canada and parts of Illinois, Indiana, Michigan, New York, Ohio, Pennsylvania Wisconsin, and Minnesota, USA. Methods We conducted single variable logistic regression with the forest classes of AVHRR and MSS land cover using evidence of breeding as the response variable. We conducted multiple logistic regression with stepwise selection to select models from five sets of explanatory variables (AVHRR, MSS, climate, AVHRR + climate, MSS + climate). Results Generally, species were related to both AVHRR and MSS land cover types in the direction expected based on the known local habitat use of the species. Neither land cover classification appeared to produce consistently more intuitive results. Good models were generated using each of the explanatory data sets examined here. And at least one but usually all five variable sets produced acceptable or excellent models for each species. Main conclusions Both climate and large scale land cover were effective predictors of the distribution of the 10 forest bird species examined here. Models generated from these data had good classification accuracy of independent validation data. Good models were produced from all explanatory data sets or combinations suggesting that the distribution of climate, AVHRR land cover, and MSS land cover all captured similar variance in the distribution of the birds. It is difficult to separate the effects of climate and vegetation on the species’ distributions at this scale.