Upscaling leaf area index in an Arctic landscape through multiscale observations

Monitoring and understanding global change requires a detailed focus on upscaling, the process for extrapolating from the site‐specific scale to the smallest scale resolved in regional or global models or earth observing systems. Leaf area index (LAI) is one of the most sensitive determinants of plant production and can vary by an order of magnitude over short distances. The landscape distribution of LAI is generally determined by remote sensing of surface reflectance (e.g. normalized difference vegetation index, NDVI) but the mismatch in scales between ground and satellite measurements complicates LAI upscaling. Here, we describe a series of measurements to quantify the spatial distribution of LAI in a sub‐Arctic landscape and then describe the upscaling process and its associated errors. Working from a fine‐scale harvest LAI–NDVI relationship, we collected NDVI data over a 500 m × 500 m catchment in the Swedish Arctic, at resolutions from 0.2 to 9.0 m in a nested sampling design. NDVI scaled linearly, so that NDVI at any scale was a simple average of multiple NDVI measurements taken at finer scales. The LAI–NDVI relationship was scale invariant from 1.5 to 9.0 m resolution. Thus, a single exponential LAI–NDVI relationship was valid at all these scales, with similar prediction errors. Vegetation patches were of a scale of ～0.5 m and at measurement scales coarser than this, there was a sharp drop in LAI variance. Landsat NDVI data for the study catchment correlated significantly, but poorly, with ground‐based measurements. A variety of techniques were used to construct LAI maps, including interpolation by inverse distance weighting, ordinary Kriging, External Drift Kriging using Landsat data, and direct estimation from a Landsat NDVI–LAI calibration. All methods produced similar LAI estimates and overall errors. However, Kriging approaches also generated maps of LAI estimation error based on semivariograms. The spatial variability of this Arctic landscape was such that local measurements assimilated by Kriging approaches had a limited spatial influence. Over scales >50 m, interpolation error was of similar magnitude to the error in the Landsat NDVI calibration. The characterisation of LAI spatial error in this study is a key step towards developing spatio‐temporal data assimilation systems for assessing C cycling in terrestrial ecosystems by combining models with field and remotely sensed data.     

The greening and browning of Alaska based on 1982–2003 satellite data

Aim To examine the trends of 1982–2003 satellite‐derived normalized difference vegetation index (NDVI) values at several spatial scales within tundra and boreal forest areas of Alaska. Location Arctic and subarctic Alaska. Methods Annual maximum NDVI data from the twice monthly Global Inventory Modelling and Mapping Studies (GIMMS) NDVI 1982–2003 data set with 64‐km² pixels were extracted from a spatial hierarchy including three large regions: ecoregion polygons within regions, ecozone polygons within boreal ecoregions and 100‐km climate station buffers. The 1982–2003 trends of mean annual maximum NDVI values within each area, and within individual pixels, were computed using simple linear regression. The relationship between NDVI and temperature and precipitation was investigated within climate station buffers. Results At the largest spatial scale of polar, boreal and maritime regions, the strongest trend was a negative trend in NDVI within the boreal region. At a finer scale of ecoregion polygons, there was a strong positive NDVI trend in cold arctic tundra areas, and a strong negative trend in interior boreal forest areas. Within boreal ecozone polygons, the weakest negative trends were from areas with a maritime climate or colder mountainous ecozones, while the strongest negative trends were from warmer basin ecozones. The trends from climate station buffers were similar to ecoregion trends, with no significant trends from Bering tundra buffers, significant increasing trends among arctic tundra buffers and significant decreasing trends among interior boreal forest buffers. The interannual variability of NDVI among the arctic tundra buffers was related to the previous summer warmth index. The spatial pattern of increasing tundra NDVI at the pixel level was related to the west‐to‐east spatial pattern in changing climate across arctic Alaska. There was no significant relationship between interannual NDVI and precipitation or temperature among the boreal forest buffers. The decreasing NDVI trend in interior boreal forests may be due to several factors including increased insect/disease infestations, reduced photosynthesis and a change in root/leaf carbon allocation in response to warmer and drier growing season climate. Main conclusions There was a contrast in trends of 1982–2003 annual maximum NDVI, with cold arctic tundra significantly increasing in NDVI and relatively warm and dry interior boreal forest areas consistently decreasing in NDVI. The annual maximum NDVI from arctic tundra areas was strongly related to a summer warmth index, while there were no significant relationships in boreal areas between annual maximum NDVI and precipitation or temperature. Annual maximum NDVI was not related to spring NDVI in either arctic tundra or boreal buffers.     

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.     

落叶阔叶林冠层光合有效辐射分量的遥感模拟与分析

冠层绿色叶片(光合组分)的光合有效辐射分量(绿色FPAR)真实地反映了植被与外界进行物质和能量交换的能力,获取冠层光合组分吸收的太阳光合有效辐射,对生态系统生产力的遥感估算精度的提高具有重要的意义。研究以落叶阔叶林为例,基于SAIL模型模拟森林冠层光合组分和非光合组分吸收的光合有效辐射,研究冠层FPAR变化规律以及与植被指数的相关关系。结果表明,冠层结构的改变会影响冠层对PAR的吸收能力,冠层绿色FPAR的大小与植被面积指数及光合组分面积比相关;在高覆盖度植被区,冠层绿色FPAR占冠层总FPAR的80%以上,非光合组分的贡献较小,但在低植被覆盖区,当光合组分和非光合组分面积相同时,绿色FPAR不及冠层总FPAR的50%;相比于NDVI,北方落叶阔叶林冠层EVI与绿色FPAR存在更为显著的线性相关关系(R~20.99)。     

1982-2012年中亚地区植被时空变化特征及其与气候变化的相关分析

干旱区植被生态系统对气候变化极为敏感,并且干旱区的植被变化研究对全球碳循环具有重要意义。然而近几十年来,中亚干旱区植被对气候变化的响应机制尚不甚明朗。利用归一化植被指数NDVI数据集和MERRA（Modern-Era Retrospective Analysis for Research and Applications）气象数据,采用经验正交函数（EOF,Empirical Orthogonal Function）和最小二乘法等方法系统分析了31a（1982-2012年）来中亚地区NDVI在不同时间尺度的时空变化特征。进一步分析和研究NDVI与气温和降水的相关性,结果表明：1982-2012年,中亚地区年NDVI总体呈现缓慢增长趋势,而1994年以后年NDVI呈现明显下降趋势,尤其在哈萨克斯坦北部草原地区下降趋势尤为突出。这可能是由于过去30年间,中亚地区降水累计量的持续减少造成的。NDVI的季节变化表明春季NDVI增长最为明显,冬季则显著下降。与平原区相比,中亚山区的NDVI值增长幅度最大,并且山区年NDVI与季节NDVI呈现显著增加趋势（P < 0.05）。中亚地区年NDVI与年降水量正相关,而年NDVI与气温变化存在弱负相关。年NDVI和气温的正相关中心在中亚南部地区,负相关中心则出现在哈萨克斯坦的西部和北部地区;NDVI和降水的相关性中心刚好与气温相反。此外,在近30年间的每年6月至9月,中亚地区NDVI与气温存在近一个月的时间延迟现象。本研究为中亚干旱区生态系统变化和中亚地区碳循环的估算提供科学依据。     

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.     

退耕还林(草)过程中陕北地区NDVI时空变异及其驱动因子的地理探测

地表植被作为生态环境变化的敏感因子,对维持区域生态稳定性具有重要作用.基于退耕还林(草)生态工程实施过程中2000-2019年陕北地区的MODIS NDVI数据,结合地形、地貌、气候、土壤和植被等环境因子,探究NDVI时空变异特征,并运用地理探测器模型对植被NDVI影响因子及其影响程度进行探测,最终确定主要环境因子对N...     

Influence of Density and Climate on Population Dynamics of a Large Herbivore Under Harsh Environmental Conditions

ABSTRACT Dynamics of herbivore populations can be influenced both by density-dependent processes and climate. We used age-at-harvest data for adult female white-tailed deer (Odocoileus virginianus) collected over 23 years to estimate survival and reproduction by age class and to identify effects of environmental factors. The study population was located on Anticosti Island (QC, Canada), at the northern limit of the species' range; the population was at high density, and the landscape had scarce forage and abundant snow during winter. Despite severe environmental conditions, population growth apparently increased during the study; adult survival was similar to other populations, although reproduction appeared lower. Winter severity was not related to survival, but density affected adult female survival. Density at estrus was the main factor influencing reproduction of 2- and 3–4-year-olds and also affected reproduction of prime-aged females (5–9-yr-olds), but not of older females. Reproductive rate of younger females was influenced by environmental conditions in autumn, such as high density or snow conditions that limited forage availability. Reproductive success of 5–9- and ≥10-year-old females appeared dependent on spring conditions favoring high-quality forage, probably through effects on neonatal survival. Relative to other studies on northern ungulates, demographic processes in our study appeared to be more affected by autumn and spring climate, in addition to population density, than by winter climate. We thus propose that population density, as well as autumn and spring climate, should be considered in management strategies. Harvest data offered a unique opportunity to study forest ungulates, for which individual monitoring is rarely possible.     

The responses of Moso bamboo (Phyllostachys heterocycla var. pubescens) forest aboveground biomass to Landsat TM spectral reflectance and NDVI

Bamboo is a special forest type in subtropical regions of china. Its huge biomass and carbon storage and its ecological function play an important role in global carbon sink. In this paper, Moso bamboo (Phyllostachys heterocycla var. pubescens) forest in Anji county, Zhejiang province, China was selected to examine the relationships between Landsat Thematic Mapper (TM) data, and aboveground biomass (AGB, Kg), This research indicates that (1) near infrared (TM4) and mid-infrared bands (TM5,TM7) are more important in explaining Moso bamboo AGB than the visible bands. In particular, TM band5 has higher path coefficient with AGB than any other TM bands, implying its important role in explaining Moso bamboo AGB; (2) the Normalized Difference Vegetation Index (NDVI) has weak correlation with Moso bamboo AGB, however, other vegetable indices such as Perpendicular Vegetation Index (PVI), Enhanced Vegetation Index(EVI), and Soil Adjust Vegetation Index (SAVI) which are related to soil adjustment parameters are significantly correlated with Moso bamboo AGB; (3) the new vegetation index developed in this paper is significantly correlated with Moso bamboo AGB (correlation coefficient is 0.48), and has higher correlation coefficient (R) than any other selected vegetation indices, implying that this new vegetation index can better explain Moso bamboo AGB than any other vegetation indices.