Detecting and mapping vegetation distribution on the Antarctic Peninsula from remote sensing data

We present the first regional map of vegetation of anywhere on the Antarctic continent based on remote sensing (RS) data. We have used a normalized difference vegetation index (NDVI) for the examination of Landsat ETM data on the Antarctic Peninsula. The results show that 44.6 km² (0.086%) of the study area (74,468 km²) is classed with a probability of vegetation of over 50%. The NDVI analysis is ground-truthed against vegetation surveys in Ryder Bay on the Antarctic Peninsula, and the results have been corrected for several factors influencing low NDVI readings in this environment. This methodology has been applied to 13 Landsat scenes covering Graham Land in the Northern part of the Antarctic Peninsula to examine the distribution of vegetation in the region. The Antarctic Peninsula region is important, as it has shown rapid warming of over 3°C during the past 50 years, and predictions indicate accelerated future warming. A baseline survey of the amount and distribution of vegetation is required against which to monitor future change. The results give a comprehensive coverage and allow us to present the first remote sensing-based vegetation map in Antarctica. However, initial results point to the need for further investigation of apparent errors resulting from geology on bare ground.     

林地叶面积指数遥感估算方法适用分析

叶面积指数是与森林冠层能量和CO2交换密切相关的一个重要植被结构参数,为了探讨估算林地叶面积指数LAI的遥感适用方法和提高精度的途径,利用TRAC仪器测定北京城区森林样地的LAI,从Landsat TM遥感图像计算NDVI、SR、RSR、SAVI植被指数,分别建立估算LAI的单植被指数统计模型、多植被指数组合的改进BP神经网络,获取最有效描述LAI与植被指数非线性关系的方法并应用到TM图像估算北京城区LAI。结果表明,单植被指数非线性统计模型估算LAI的精度高于线性统计模型;多植被指数组合神经网络中,以NDVI、RSR、SAVI组合估算LAI的精度最高,估算值与观测值线性回归方程的R2最高,为0.827,而RMSE最低,为0.189,神经网络解决了多植被指数组合统计模型非线性回归方程的系数较多、较难确定的问题,可较为有效的应用于遥感图像林地LAI的估算。     

Climatic and biotic extreme events moderate long‐term responses of above‐ and belowground sub‐Arctic heathland communities to climate change

Climate change impacts are not uniform across the Arctic region because interacting factors causes large variations in local ecosystem change. Extreme climatic events and population cycles of herbivores occur simultaneously against a background of gradual climate warming trends and can redirect ecosystem change along routes that are difficult to predict. Here, we present the results from sub‐Arctic heath vegetation and its belowground micro‐arthropod community in response to the two main drivers of vegetation damage in this region: extreme winter warming events and subsequent outbreaks of the defoliating autumnal moth caterpillar (Epirrita autumnata). Evergreen dwarf shrub biomass decreased (30%) following extreme winter warming events and again by moth caterpillar grazing. Deciduous shrubs that were previously exposed to an extreme winter warming event were not affected by the moth caterpillar grazing, while those that were not exposed to warming events (control plots) showed reduced (23%) biomass from grazing. Cryptogam cover increased irrespective of grazing or winter warming events. Micro‐arthropods declined (46%) following winter warming but did not respond to changes in plant community. Extreme winter warming and caterpillar grazing suppressed the CO₂ fluxes of the ecosystem. Evergreen dwarf shrubs are disadvantaged in a future sub‐Arctic with more stochastic climatic and biotic events. Given that summer warming may further benefit deciduous over evergreen shrubs, event and trend climate change may both act against evergreen shrubs and the ecosystem functions they provide. This is of particular concern given that Arctic heath vegetation is typically dominated by evergreen shrubs. Other components of the vegetation showed variable responses to abiotic and biotic events, and their interaction indicates that sub‐Arctic vegetation response to multiple pressures is not easy to predict from single‐factor responses. Therefore, while biotic and climatic events may have clear impacts, more work is needed to understand their net effect on Arctic ecosystems.     

中国不同植被区对极端气候的响应差异

分析不同区域植被对极端气候的响应对于加深对植被与气候之间关系的理解以及制定应对极端气候条件的措施尤为重要。基于2001—2020年气候数据和归一化植被指数(NDVI)数据,以植被区划为分析单元,分析中国8个植被区的NDVI和27个极端气候指数的时空变化趋势,探究各植被区植被NDVI对极端气候的响应特征与差异性。结果表明：(1)整个研究区及各植被区的平均NDVI年最大值呈显著增加趋势,其中,温带针叶、落叶阔叶混交林区增加趋势最明显,青藏高原高寒植被区增加趋势最弱。(2)极端高温指数多呈升高趋势。极端降水指数在研究区东部呈升高趋势,在西南部呈减少趋势。(3)在不同植被区对NDVI影响最大的极端气候指数不同,其中在寒温带针叶林区影响最大的指数为温暖时间持续指数(WSDI);在温带针叶、落叶阔叶混交林区和热带季风雨林、雨林区影响最大的指数为最高低温(TNx);在暖温带落叶阔叶林区和亚热带常绿阔叶林区为简单降水强度指数(SDII);在温带草原区为最高高温(TXx);在温带荒漠区为年总降水量(PRCPTOT);在青藏高原高寒植被区为结冰天数(ID)。     

Environmental Influences on Dall's Sheep Survival

Understanding how species respond to environmental conditions can assist with conservation strategies and harvest management, especially in arctic and boreal regions that are experiencing rapid climate change. Although climatic influences on species distributions have been studied, broad-scale effects of climate on survival are less well known. We examined the interactive effects of meteorological and remotely sensed environmental variables on survival of Dall's sheep (Ovis dalli dalli) lambs and adults by synthesizing radio-telemetry data across their range. We used data from 9 studies of adult sheep and 2 studies of lambs that were conducted between 1997 and 2012 at sites spanning the species' range in Alaska, USA, and northwestern Canada. We obtained environmental variables throughout the range of Dall's sheep, including the normalized difference vegetation index (NDVI) from optical remote sensing, freeze-thaw frequency (FTF) from passive microwave remote sensing, and gridded climate variables such as snow water equivalent, temperature, and precipitation. We used Cox proportional hazard regression to investigate the effects of environmental variables recorded during summer, winter, and the previous winter on annual survival rates of Dall's sheep lambs and adults. Summer NDVI was the most influential environmental factor affecting lamb survival, with improved lamb survival occurring in years with a high maximum NDVI. Also, lamb predation by coyotes (Canis latrans) and golden eagles (Aquila chrysaetos) decreased substantially with increasing NDVI. The previous winter FTF had the strongest effect on adult survival, with decreased survival occurring after winters with high FTF. In addition, these remotely sensed environmental factors interacted with meteorological factors to affect survival, such that effects of winter temperature depended on summer NDVI and winter FTF. Warm winters increased lamb survival only when preceded by summers with high NDVI, and warm winters increased adult survival only when winter FTF was low. Thus, potential benefits of climate warming may be counteracted if wintertime freeze-thaw events markedly increase. Correlations among environmental variables across sites were low, and regional climate cycles such as the Pacific Decadal Oscillation (PDO) had weak effects, indicating substantial local variability in climatic conditions experienced by Dall's sheep across their range. These findings can help managers anticipate how Dall's sheep populations will respond to changes in local environmental conditions. Our results also highlight the utility of multiple remotely sensed environmental conditions for ungulate management, especially passive microwave products that provide valuable information on winter icing events. © 2020 The Wildlife Society.     

青藏高原植被生长对PDO响应的季节分异

利用卫星遥感观测的区域尺度归一化植被指数(NDVI)和格点气候数据,借助Spearman相关分析及基于多变量回归分析的结构方程模型,研究了1982—2015年青藏高原植被生长季节变化对太平洋10年际涛动(PDO)的响应格局及机理过程.结果表明:青藏高原生长季(4—10月)平均NDVI与PDO指数存在显著的负相关关系,但是PDO与不同季节NDVI之间的关系呈现出明显的季节分异,具体表现为PDO与秋季NDVI的负相关关系强于夏季,且冬季PDO显著影响次年青藏高原夏季植被生长.另外,PDO对青藏高原植被生长的调控过程在季节间存在明显分异,夏季表现为PDO对温度和降水的共同调控,而秋季则以对温度调控为主.     

北京海淀区植被覆盖的遥感动态研究

 植被覆盖度fg人(植被的垂直投影面积与单位面积之比)是一个十分重要的生态气候参数。为了有效地从遥感资料中提取植被覆盖度,发展了一套计算区域植被覆盖度的亚象元分解模型法。运用该方法对北京市海淀区1975、1991和1997年的植被覆盖度进行了计算,并在此基础上,求得研究区不同植被覆盖等级的变化转移矩阵,分析了海淀区22年来植被覆盖等级变化的空间过程和变化趋势。     

基于无人机低空遥感的荒漠植被覆盖度与归一化植被指数验证及其对水热梯度的响应

为了验证在荒漠地区MODIS-NDVI产品的精度以及为在气候变化背景下荒漠草地的科学管理提供依据,本文利用无人机低空遥感研究了干旱荒漠地区植被覆盖度(FVC)和归一化植被指数(NDVI)对水、热梯度的响应规律。在内蒙古阿拉善荒漠地区的100个样点采用GreenSeeker手持光谱仪获得NDVI值(NDVI_R),通过MODIS-NDVI数据产品提取每个样点的NDVI(NDVI_M),借助NDVI_R验证NDVI_M的精确度;通过无人机遥感手段获得每个采样点的FVC(FVC_U),利用像元二分模型反演每个样点的FVC(FVC_M),借助FVC_U验证FVC_M的精确度;并结合气象数据探讨基于无人机低空遥感的荒漠地区FVC和NDVI对水热梯度的响应。结果表明: MODIS-NDVI数据产品能够反映阿拉善地区的NDVI,精确度为84.2%,但比真实值高15.7%;FVC_M能够反映阿拉善地区的FVC状况,精确度为83.1%,但比真实值低14.8%;不同采集方式获得的NDVI受气象因子的影响程度不同,NDVI不仅受气温和降雨的影响,也受地温、蒸发量以及两者相互作用的影响,由于受大气影响程度不同, NDVI_M受地温、蒸发量、降水量的影响比NDVI_R大,NDVI_R受气温的影响比NDVI_M大。在阿拉善地区研究FVC随水热梯度的变化不仅要考虑降水量和气温,还应考虑蒸发量、地温以及气象因子之间相互作用的影响,其中,气温与降雨、蒸发量与地温以及气温与蒸发量之间相互作用对FVC_U的影响较大。     

基于多源遥感数据的大豆叶面积指数估测精度对比

近年来遥感技术的革新促使遥感源越来越丰富.为分析多源遥感数据的叶面积指数(LAI)估测精度,本文以大豆为研究对象,利用比值植被指数(RVI)、归一化植被指数(NDVI)、土壤调整植被指数(SAVI)、差值植被指数(DVI)、三角植被指数(TVI)5种植被指数,结合地面实测LAI构建经验回归模型,比较3类遥感数据(地面高光谱数据、无人机多光谱影像以及高分一号WFV影像)对大豆LAI的估测能力,并从传感器几何位置和光谱响应特性以及像元空间分辨率三方面分析讨论了3类遥感数据的LAI反演差异.结果表明： 地面高光谱数据模型和无人机多光谱数据模型都可以准确预测大豆LAI(在α=0.01显著水平下,R²均>0.69,RMSE均<0.40);地面高光谱RVI对数模型的LAI预测能力优于无人机多光谱NDVI线性模型,但两者差异不大(E_A相差0.3%,R²相差0.04,RMSE相差0.006);高分一号WFV数据模型对研究区内大豆LAI的预测效果不理想(R²<0.30,RMSE>0.70).针对星、机、地三类遥感信息源,地面高光谱数据在反演LAI方面较传统多光谱数据有优势但不突出;16 m空间分辨率的高分一号WFV影像无法满足田块尺度作物长势监测的需求;在保证获得高精度大豆LAI预测值和高工作效率的前提条件下,基于无人机遥感的农情信息获取技术不失为一种最佳试验方案.在当今可用遥感信息源越来越多的情况下,农业无人机遥感信息可成为指导田块精细尺度作物管理的重要依据,为精准农业研究提供更科学准确的信息.     

基于多种遥感植被指数、叶绿素荧光与CO2通量数据的温带针阔混交林物候特征对比分析

植被物候学作为研究植被与环境条件相互作用的科学,在全球气候变化的大背景下已成为国际热点研究领域,其中森林植被在调节全球碳平衡、维护全球气候稳定的过程中有着至关重要的作用。随着遥感技术的发展,多种遥感指数被应用到森林植被物候研究中,其中以MODIS NDVI和EVI应用最为广泛,而叶绿素荧光(SIF)作为植被光合作用的"探针"也被广泛应用于森林植被物候研究中。为了探究3种指数在森林植被物候研究中的差异与特性,本文以长白山温带红松阔叶林通量观测站为研究区域,采用模型拟合结合动态阈值法提取2007—2013森林物候特征参数,并使用通量数据(总初级生产力GPP)进行验证。结果表明:NDVI与EVI、SIF相比,表现为生长季开始时间与结束时间的明显提前和滞后,与GPP数据偏差较大,且夏季生长季峰期曲线形态过宽且平坦,无法较好反映生长季变化特征;EVI相较于NDVI有所改善,整体变化趋势与SIF、GPP基本吻合,但依然存在秋季衰减时间稍迟于SIF与GPP的问题;SIF虽然存在夏季骤降现象,但依然与GPP数据一致性最好,可以较好反映出森林植被季节变化特征。SIF数据与植被光合作用的紧密关联使其在植被物候研究中具有优于植被指数的准确性,并随着遥感平台的增加和反演方法的改善,将会在多尺度、多类型的植被物候监测中发挥更加重要的作用。     

陕西省植被覆盖时空变化及其对极端气候的响应

基于2001—2018年MODIS NDVI数据,从生态分区视角分析陕西省归一化植被指数(NDVI)的时空变化特征,并结合该地区31个气象站点日值数据,探讨NDVI对极端气温和极端降水指数的响应特征。结果表明：(1)陕西省及其各生态区的NDVI变化均显著上升,整体呈南高北低的分布特点,其中秦巴山地落叶与阔叶林生态区(IV)NDVI值最高为0.86,陕北北部典型草原生态区(I)NDVI值最低为0.38。(2)年际尺度上,陕西省NDVI与极端气温暖极值(暖夜日数)和极端降水指数总体呈显著正相关(P<0.05),在陕西省北部NDVI变化主要受极端降水的影响,南部则对极端气温的敏感度更高。(3)多年月尺度上,各生态区NDVI对极端气温冷极值(最低气温、日最低气温的极低值和日最高气温的极低值)和极端气温暖极值(最高气温、日最低气温的极高值和日最高气温的极高值)存在明显的滞后性,滞后时间多为3个月;与极端降水指数(单日最大降水量和连续5日最大降水量)的滞后时间为2个月,说明陕西省内NDVI对极端气候的响应具有显著的滞后效应。     

Probabilistic Downscaling of Remote Sensing Data with Applications for Multi-Scale Biogeochemical Flux Modeling

Upscaling ecological information to larger scales in space and downscaling remote sensing observations or model simulations to finer scales remain grand challenges in Earth system science. Downscaling often involves inferring subgrid information from coarse-scale data, and such ill-posed problems are classically addressed using regularization. Here, we apply two-dimensional Tikhonov Regularization (2DTR) to simulate subgrid surface patterns for ecological applications. Specifically, we test the ability of 2DTR to simulate the spatial statistics of high-resolution (4 m) remote sensing observations of the normalized difference vegetation index (NDVI) in a tundra landscape. We find that the 2DTR approach as applied here can capture the major mode of spatial variability of the high-resolution information, but not multiple modes of spatial variability, and that the Lagrange multiplier (γ) used to impose the condition of smoothness across space is related to the range of the experimental semivariogram. We used observed and 2DTR-simulated maps of NDVI to estimate landscape-level leaf area index (LAI) and gross primary productivity (GPP). NDVI maps simulated using a γ value that approximates the range of observed NDVI result in a landscape-level GPP estimate that differs by ca 2% from those created using observed NDVI. Following findings that GPP per unit LAI is lower near vegetation patch edges, we simulated vegetation patch edges using multiple approaches and found that simulated GPP declined by up to 12% as a result. 2DTR can generate random landscapes rapidly and can be applied to disaggregate ecological information and compare of spatial observations against simulated landscapes.     

Experimental icing affects growth,mortality, and flowering in a high Arctic dwarf shrub

Effects of climate change are predicted to be greatest at high latitudes, with more pronounced warming in winter than summer. Extreme mid‐winter warm spells and heavy rain‐on‐snow events are already increasing in frequency in the Arctic, with implications for snow‐pack and ground‐ice formation. These may in turn affect key components of Arctic ecosystems. However, the fitness consequences of extreme winter weather events for tundra plants are not well understood, especially in the high Arctic. We simulated an extreme mid‐winter rain‐on‐snow event at a field site in high Arctic Svalbard (78°N) by experimentally encasing tundra vegetation in ice. After the subsequent growing season, we measured the effects of icing on growth and fitness indices in the common tundra plant, Arctic bell‐heather (Cassiope tetragona). The suitability of this species for retrospective growth analysis enabled us to compare shoot growth in pre and postmanipulation years in icing treatment and control plants, as well as shoot survival and flowering. Plants from icing treatment plots had higher shoot mortality and lower flowering success than controls. At the individual sample level, heavily flowering plants invested less in shoot growth than nonflowering plants, while shoot growth was positively related to the degree of shoot mortality. Therefore, contrary to expectation, undamaged shoots showed enhanced growth in ice treatment plants. This suggests that following damage, aboveground resources were allocated to the few remaining undamaged meristems. The enhanced shoot growth measured in our icing treatment plants has implications for climate studies based on retrospective analyses of Cassiope. As shoot growth in this species responds positively to summer warming, it also highlights a potentially complex interaction between summer and winter conditions. By documenting strong effects of icing on growth and reproduction of a widespread tundra plant, our study contributes to an understanding of Arctic plant responses to projected changes in winter climatic conditions.     

近30年河北大海陀自然保护区山地草甸植被(NDVI)变化及其对气候的响应

河北大海陀自然保护区地处我国暖温带落叶阔叶林区,草甸植被多处于山地顶部,对外界干扰敏感。为了研究该地区的草甸植被变化以及其对气候变化的响应,收集了该地区近30年的TM遥感影像资料,1980—2015年的气候数据,包括年平均气温、7月份平均气温、1月份平均气温、年降水量、6—8月平均降水量等多个气候指标,以及大海陀自然保护区及其周边地区的地形数据等,用滑动平均法、M-K检验法、相关分析法及偏相关分析法等方法分析了大海陀自然保护区草甸植被NDVI及其与气候响应。结果表明:(1)近30年来,大海陀自然保护区草甸植被NDVI呈先上升后下降的趋势,在2004年NDVI达到最高,随后逐渐下降。(2)大海陀自然保护区草甸区域的年降水量变化整体表现为波动循环的趋势,总体略有上升,但没有达到显著水平;年均温变化表现为上升趋势,且达到了极显著水平,该地区的气温上升趋势主要由以1月为主的冬季温度升高而引起。(3)草甸植被NDVI与年均温成显著的负相关关系,与年降水量的关系不明显。     

Satellite‐based evidence for shrub and graminoid tundra expansion in northern Quebec from 1986 to 2010

Global vegetation models predict rapid poleward migration of tundra and boreal forest vegetation in response to climate warming. Local plot and air‐photo studies have documented recent changes in high‐latitude vegetation composition and structure, consistent with warming trends. To bridge these two scales of inference, we analyzed a 24‐year (1986–2010) Landsat time series in a latitudinal transect across the boreal forest‐tundra biome boundary in northern Quebec province, Canada. This region has experienced rapid warming during both winter and summer months during the last 40 years. Using a per‐pixel (30 m) trend analysis, 30% of the observable (cloud‐free) land area experienced a significant (P < 0.05) positive trend in the Normalized Difference Vegetation Index (NDVI). However, greening trends were not evenly split among cover types. Low shrub and graminoid tundra contributed preferentially to the greening trend, while forested areas were less likely to show significant trends in NDVI. These trends reflect increasing leaf area, rather than an increase in growing season length, because Landsat data were restricted to peak‐summer conditions. The average NDVI trend (0.007 yr^?1) corresponds to a leaf‐area index (LAI) increase of ~0.6 based on the regional relationship between LAI and NDVI from the Moderate Resolution Spectroradiometer. Across the entire transect, the area‐averaged LAI increase was ~0.2 during 1986–2010. A higher area‐averaged LAI change (~0.3) within the shrub‐tundra portion of the transect represents a 20–60% relative increase in LAI during the last two decades. Our Landsat‐based analysis subdivides the overall high‐latitude greening trend into changes in peak‐summer greenness by cover type. Different responses within and among shrub, graminoid, and tree‐dominated cover types in this study indicate important fine‐scale heterogeneity in vegetation growth. Although our findings are consistent with community shifts in low‐biomass vegetation types over multi‐decadal time scales, the response in tundra and forest ecosystems to recent warming was not uniform.     

The response of boreal peatland community composition and NDVI to hydrologic change,warming, and elevated carbon dioxide

Widespread changes in arctic and boreal Normalized Difference Vegetation Index (NDVI) values captured by satellite platforms indicate that northern ecosystems are experiencing rapid ecological change in response to climate warming. Increasing temperatures and altered hydrology are driving shifts in ecosystem biophysical properties that, observed by satellites, manifest as long‐term changes in regional NDVI. In an effort to examine the underlying ecological drivers of these changes, we used field‐scale remote sensing of NDVI to track peatland vegetation in experiments that manipulated hydrology, temperature, and carbon dioxide (CO₂) levels. In addition to NDVI, we measured percent cover by species and leaf area index (LAI). We monitored two peatland types broadly representative of the boreal region. One site was a rich fen located near Fairbanks, Alaska, at the Alaska Peatland Experiment (APEX), and the second site was a nutrient‐poor bog located in Northern Minnesota within the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experiment. We found that NDVI decreased with long‐term reductions in soil moisture at the APEX site, coincident with a decrease in photosynthetic leaf area and the relative abundance of sedges. We observed increasing NDVI with elevated temperature at the SPRUCE site, associated with an increase in the relative abundance of shrubs and a decrease in forb cover. Warming treatments at the SPRUCE site also led to increases in the LAI of the shrub layer. We found no strong effects of elevated CO₂ on community composition. Our findings support recent studies suggesting that changes in NDVI observed from satellite platforms may be the result of changes in community composition and ecosystem structure in response to climate warming.     

1982-2020年内蒙古地区极端气候变化及其对植被的影响

内蒙古地处生态环境脆弱区,对气候变化尤为敏感。在全球气候变暖背景下,探究极端气候变化及其影响显得尤为重要。基于内蒙古地区115个气象站点1982—2020年的逐日气象数据,从强度、持续时间、频率3个维度出发计算了18个极端气候指数,在综合分析极端气候的时空变化特征的基础上,运用地理探测器和皮尔逊相关分析方法,定量评估极端气候对该区植被的影响。结果表明：(1)极端暖指数均呈增加趋势,说明1982—2020年期间内蒙古地区极端偏暖现象增多。(2)持续干旱日数与持续湿润日数呈减少趋势,说明39年来内蒙古地区连续性无降水天数和降水天数均减少。(3)极端气候指数与归一化植被指数(NDVI)的相关关系表现出明显的空间异质性,表明内蒙古不同区域NDVI对各极端气候指数的响应程度不同。(4)因子探测器结果表明极端降水指数相对于极端气温指数来说,对内蒙古植被生长变化的影响较大。研究结果可为内蒙古地区防灾减灾与生态修复工程提供一定的科学依据。     

基于NDVI的云南省2009-2010年干旱遗产效应研究

气候变化正导致干旱事件发生的强度、频度显著改变,极端气候事件发生的不确定性直接影响陆地生态系统关键生态过程。我国西南地区在2009-2010年发生百年一遇的极端干旱,目前关于植被生长在长时间尺度对此次干旱事件的响应尚不明确。以云南省为研究区,基于多年Normalized difference vegetation index(NDVI)影像数据以及长时间序列气象资料对此次极端事件的干旱遗产效应开展研究,分析了干旱遗产效应的持续时间以及不同植被类型的响应差异。结果表明:1)云南省植被生长在极端干旱事件发生后受到的抑制时间大约持续1-2年,受影响区域主要集中在遭遇降水严重减少的地区;2)海拔2000 m附近为植被对干旱响应最为敏感的区域,海拔高于4000 m的植被生长几乎未受到干旱影响;3)较之草地和农田,森林植被受到的抑制作用更为强烈。研究揭示了极端干旱对云南省植被生长造成的影响,为该地区未来应对极端干旱并有效开展植被恢复提供理论依据。     

Mapping gains and losses in woody vegetation across global tropical drylands

Woody vegetation in global tropical drylands is of significant importance for both the interannual variability of the carbon cycle and local livelihoods. Satellite observations over the past decades provide a unique way to assess the vegetation long‐term dynamics across biomes worldwide. Yet, the actual changes in the woody vegetation are always hidden by interannual fluctuations of the leaf density, because the most widely used remote sensing data are primarily related to the photosynthetically active vegetation components. Here, we quantify the temporal trends of the nonphotosynthetic woody components (i.e., stems and branches) in global tropical drylands during 2000–2012 using the vegetation optical depth (VOD), retrieved from passive microwave observations. This is achieved by a novel method focusing on the dry season period to minimize the influence of herbaceous vegetation and using MODerate resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data to remove the interannual fluctuations of the woody leaf component. We revealed significant trends (P < 0.05) in the woody component (VOD_wood) in 35% of the areas characterized by a nonsignificant trend in the leaf component (VOD_leaf modeled from NDVI), indicating pronounced gradual growth/decline in woody vegetation not captured by traditional assessments. The method is validated using a unique record of ground measurements from the semiarid Sahel and shows a strong agreement between changes in VOD_wood and changes in ground observed woody cover (r² = 0.78). Reliability of the obtained woody component trends is also supported by a review of relevant literatures for eight hot spot regions of change. The proposed approach is expected to contribute to an improved assessment of, for example, changes in dryland carbon pools.     

Greater phenological sensitivity to temperature on higher Scottish mountains: new insights from remote sensing

Mountain plants are considered among the species most vulnerable to climate change, especially at high latitudes where there is little potential for poleward or uphill dispersal. Satellite monitoring can reveal spatiotemporal variation in vegetation activity, offering a largely unexploited potential for studying responses of montane ecosystems to temperature and predicting phenological shifts driven by climate change. Here, a novel remote‐sensing phenology approach is developed that advances existing techniques by considering variation in vegetation activity across the whole year, rather than just focusing on event dates (e.g. start and end of season). Time series of two vegetation indices (VI), normalized difference VI (NDVI) and enhanced VI (EVI) were obtained from the moderate resolution imaging spectroradiometer MODIS satellite for 2786 Scottish mountain summits (600–1344 m elevation) in the years 2000–2011. NDVI and EVI time series were temporally interpolated to derive values on the first day of each month, for comparison with gridded monthly temperatures from the preceding period. These were regressed against temperature in the previous months, elevation and their interaction, showing significant variation in temperature sensitivity between months. Warm years were associated with high NDVI and EVI in spring and summer, whereas there was little effect of temperature in autumn and a negative effect in winter. Elevation was shown to mediate phenological change via a magnification of temperature responses on the highest mountains. Together, these predict that climate change will drive substantial changes in mountain summit phenology, especially by advancing spring growth at high elevations. The phenological plasticity underlying these temperature responses may allow long‐lived alpine plants to acclimate to warmer temperatures. Conversely, longer growing seasons may facilitate colonization and competitive exclusion by species currently restricted to lower elevations. In either case, these results show previously unreported seasonal and elevational variation in the temperature sensitivity of mountain vegetation activity.