Imaging spectroscopy links aspen genotype with below-ground processes at landscape scales

Fine-scale biodiversity is increasingly recognized as important to ecosystem-level processes. Remote sensing technologies have great potential to estimate both biodiversity and ecosystem function over large spatial scales. Here, we demonstrate the capacity of imaging spectroscopy to discriminate among genotypes of Populus tremuloides (trembling aspen), one of the most genetically diverse and widespread forest species in North America. We combine imaging spectroscopy (AVIRIS) data with genetic, phytochemical, microbial and biogeochemical data to determine how intraspecific plant genetic variation influences below-ground processes at landscape scales. We demonstrate that both canopy chemistry and below-ground processes vary over large spatial scales (continental) according to aspen genotype. Imaging spectrometer data distinguish aspen genotypes through variation in canopy spectral signature. In addition, foliar spectral variation correlates well with variation in canopy chemistry, especially condensed tannins. Variation in aspen canopy chemistry, in turn, is correlated with variation in below-ground processes. Variation in spectra also correlates well with variation in soil traits. These findings indicate that forest tree species can create spatial mosaics of ecosystem functioning across large spatial scales and that these patterns can be quantified via remote sensing techniques. Moreover, they demonstrate the utility of using optical properties as proxies for fine-scale measurements of biodiversity over large spatial scales.     

遥感在林冠动态监测研究中的应用

 林冠动态大致包括三方面的内容,即由病虫害、林火等引起的林冠变化、由大风等灾害引起的林隙动态、以及树冠和林冠的正常变化等。遥感在林冠动态研究中的地位和作用已被广泛认知,国内外在此方面的研究已积累了丰富经验。进行林冠动态研究所利用的遥感数据主要有Landsat TM卫     

Predicting Species Cover of Marine Macrophyte and Invertebrate Species Combining Hyperspectral Remote Sensing,Machine Learning and Regression Techniques

In order to understand biotic patterns and their changes in nature there is an obvious need for high-quality seamless measurements of such patterns. If remote sensing methods have been applied with reasonable success in terrestrial environment, their use in aquatic ecosystems still remained challenging. In the present study we combined hyperspectral remote sensing and boosted regression tree modelling (BTR), an ensemble method for statistical techniques and machine learning, in order to test their applicability in predicting macrophyte and invertebrate species cover in the optically complex seawater of the Baltic Sea. The BRT technique combined with remote sensing and traditional spatial modelling succeeded in identifying, constructing and testing functionality of abiotic environmental predictors on the coverage of benthic macrophyte and invertebrate species. Our models easily predicted a large quantity of macrophyte and invertebrate species cover and recaptured multitude of interactions between environment and biota indicating a strong potential of the method in the modelling of aquatic species in the large variety of ecosystems.     

Multiscale modeling of spring phenology across Deciduous Forests in the Eastern United States

Phenological events, such as bud burst, are strongly linked to ecosystem processes in temperate deciduous forests. However, the exact nature and magnitude of how seasonal and interannual variation in air temperatures influence phenology is poorly understood, and model‐based phenology representations fail to capture local‐ to regional‐scale variability arising from differences in species composition. In this paper, we use a combination of surface meteorological data, species composition maps, remote sensing, and ground‐based observations to estimate models that better represent how community‐level species composition affects the phenological response of deciduous broadleaf forests to climate forcing at spatial scales that are typically used in ecosystem models. Using time series of canopy greenness from repeat digital photography, citizen science data from the USA National Phenology Network, and satellite remote sensing‐based observations of phenology, we estimated and tested models that predict the timing of spring leaf emergence across five different deciduous broadleaf forest types in the eastern United States. Specifically, we evaluated two different approaches: (i) using species‐specific models in combination with species composition information to ‘upscale’ model predictions and (ii) using repeat digital photography of forest canopies that observe and integrate the phenological behavior of multiple representative species at each camera site to calibrate a single model for all deciduous broadleaf forests. Our results demonstrate variability in cumulative forcing requirements and photoperiod cues across species and forest types, and show how community composition influences phenological dynamics over large areas. At the same time, the response of different species to spatial and interannual variation in weather is, under the current climate regime, sufficiently similar that the generic deciduous forest model based on repeat digital photography performed comparably to the upscaled species‐specific models. More generally, results from this analysis demonstrate how in situ observation networks and remote sensing data can be used to synergistically calibrate and assess regional parameterizations of phenology in models.     

Predicting species distributions across the Amazonian and Andean regions using remote sensing data

Aim We explore the utility of newly available optical and microwave remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and QuikSCAT (QSCAT) instruments for species distribution modelling at regional to continental scales. Using eight Neotropical species from three taxonomic groups, we assess the extent to which remote sensing data can improve predictions of their geographic distributions. For two bird species, we investigate the specific contributions of different types of remote sensing variables to the predictions and model accuracy at the regional scale, where the benefits of the MODIS and QSCAT satellite data are expected to be most significant. Location South America, with a focus on the tropical and subtropical Andes and the Amazon Basin. Methods Potential geographic distributions of eight species, namely two birds, two mammals and four trees, were modelled with the maxent algorithm at 1‐km resolution over the South American continent using climatic and remote sensing data separately and combined. For each species and model scenario, we assess model performance by testing the agreement between observed and simulated distributions across all thresholds and, in the case of the two focal bird species, at selected thresholds. Results Quantitative performance tests showed that models built with remote sensing and climatic layers in isolation performed well in predicting species distributions, suggesting that each of these data sets contains useful information. However, predictions created with a combination of remote sensing and climatic layers generally resulted in the best model performance across the three taxonomic groups. In Ecuador, the inclusion of remote sensing data was critical in resolving the known geographically isolated populations of the two focal bird species along the steep Amazonian–Andean elevational gradients. Within remote sensing subsets, microwave‐based data were more important than optical data in the predictions of the two bird species. Main conclusions Our results suggest that the newly available remote sensing data (MODIS and QSCAT) have considerable utility in modelling the contemporary geographical distributions of species at both regional and continental scales and in predicting range shifts as a result of large‐scale land‐use change.     

Radiation measurement for plant ecophysiology

The principles of radiation physics for plant ecophysiological studies are outlined with an emphasis on choosing appropriate sensors for specific purposes such as for studies of photosynthesis, UV-B damage or canopy energy balance. Remote sensing, both from the ground and from aircraft or satellites, is increasingly being used as a tool for the study of plant canopies. Therefore, relevant terminology and applications are discussed, including the use of remote sensing for the determination of canopy structural properties and the use of thermal remote sensing for the measurement of canopy temperature, for example, in energy balance studies.     

近地遥感在森林冠层物候动态监测中的应用

近地遥感技术是原位观测森林冠层物候的重要手段,具有高时间分辨率的优点,而且空间尺度适中,是实现物候尺度推绎的有力工具.本研究首先评述了利用3种光学传感器(辐射表、光谱仪和数码相机)监测森林物候的近地遥感方法;结合帽儿山通量观测站的实测数据分析识别物候期的不确定性来源,发现最重要的误差来自物候提取方法;剖析近地遥感与其他物候观测方法的衔接以及该技术自身存在的问题.最后提出该领域的重点研究方向: 加强冠层光学(或冠层结构)物候与功能(生理、生态过程)物候的联系;整合各区域冠层物候观测网络,实现冠层尺度的全球物候联网观测与数据共享;充分发挥近地遥感的优势,整合多源多尺度物候数据;发展近地遥感物候模型,改进动态全球植被模型中物候模拟.     

Hyperspectral indices based on first derivative spectra closely trace canopy transpiration in a desert plant

A clear understanding of transpiration of arid ecosystems and its underlying mechanisms is critical for accurate prediction of long-term water and energy fluxes in such ecosystems, which have gradually been recognized to play major roles on a global scale. Unlike traditional measurements of transpiration that are generally time-consuming, expensive, and often unfeasible, remote sensing techniques such as hyperspectral indices are widely utilized as the only approach to obtain such information on a large scale. However, compared with other biochemical and biophysical parameters, few studies on hyperspectral indices have been applied to estimate canopy transpiration. In this study, we focused on a native dominant plant in the arid land of central Asia, Haloxylon ammodendron, to explore the featured spectra and to develop proper hyperspectral indices for estimating transpiration. This was based on a simultaneous dataset of original canopy-reflectance spectra as well as its first derivatives with transpiration estimated from sap flow. The results indicated that the derivative spectra-based indices are more effective for tracing canopy transpiration compared with its counterpart that was based on the original reflectance. The identified best index for estimating canopy transpiration was dSR(660,1040) based on the first-derivative spectra, which had a coefficient of determination (R²) of 0.54. The index is also relatively stable concerning spectral resolutions. Results obtained in this study should help lay the basis for using remote sensing data to estimate transpiration.     

基于遥感的光合有效辐射吸收比率(FPAR) 估算方法综述

光合有效辐射吸收比率(FPAR)是反映植被生长过程的重要生理参数,是陆地生态系统模型的关键参数,是反映全球气候变化的重要因子。基于遥感的FPAR估算方法是获取区域乃至全球尺度FPAR的有效方法。目前,主要形成了植被指数法和机理法两类方法,植被指数法是建立FPAR与植被指数的经验统计模型,简单、计算效率高;机理法则从物理模型上进行FPAR的求解与反演,机理明晰、可行性强。然而,由于FPAR本身的复杂性以及环境因素、遥感数据质量的影响,导致了估算方法面临诸多不确定性问题。为了解决这些不确定性问题以及满足生态过程深入研究的需求,将进一步注重FPAR的机理研究、先验知识的获取与积累,构建长时间序列FPAR以及高时空的FPAR算法研究。     

Testing predictions of beetle community patterns derived empirically using remote sensing

Patterns generated from ecological surveys are rarely tested in similar habitats to assess the accuracy of predictions. Testing empirically derived predictions provides a strong tool for establishing the consistency of general patterns in ecology. We test the consistency of beetle community associations with habitat complexity in open canopy forests and make both community and morphospecies-level comparisons with results from a previous study. We use Normalized Difference Vegetation Indices (NDVIs) from remote sensing as a surrogate for habitat complexity. The positive relationships between NDVIs and site-based beetle species richness and abundance were consistent in open canopy forests both south and north of Sydney, Australia. NDVIs were also useful for predicting differences in beetle composition in open canopy forests. Taxon-specific responses to NDVI differences in 'southern forests' were very similar to responses in 'northern forests', most likely reflecting beetle trophic roles. This study shows that NDVIs can be used as rapid biodiversity indicators, when integrated with identified faunal responses to vegetation structure, provided that the lower vegetation strata may be measured by remote sensing.     

普洱季风常绿阔叶林乔木物种多样性高分辨率遥感估测

季风常绿阔叶林是我国南亚热带典型的地带性植被,也是云南省普洱地区重要森林类型。季风常绿阔叶林乔木物种多样性遥感估测对研究区域尺度生物多样性格局及其规律具有重要作用。根据光谱异质性假说和环境异质性假说,首先使用1m空间分辨率的机载高光谱数据和激光雷达数据提取了光谱多样性特征和垂直结构特征。然后利用基于随机森林算法的递归特征消除方法选择对研究区森林乔木物种多样性指数具有较好解释能力的遥感特征,并对Shannon-Winner物种多样性指数进行建模、制图。研究结果表明：(1)基于机载LiDAR数据提取的垂直结构特征和机载高光谱数据提取的光谱多样性特征均对研究区森林乔木物种多样性具有较好的解释能力,随机森林模型估测结果分别为R²=0.48,RMSE=0.46和R²=0.5,RMSE=0.45;两种数据源融合可以进一步提高遥感数据的森林乔木物种多样性估测精度,随机森林估测模型R²和RMSE分别为0.69和0.37。(2)机载激光雷达数据对研究区针阔混交林乔木物种多样性的估测能力优于机载高光谱数据。(3)机器学习方法有助于从高维遥感...     

植物性状研究的机遇与挑战:从器官到群落

植物性状(Plant trait)或植物功能性状(Plant functional trait)通常是指植物对外界环境长期适应与进化后所表现出的可量度、且与生产力优化或环境适应等密切相关的属性。近几十年来,植物性状研究在性状-生产力、性状-养分、性状间相互关系、性状-群落结构维持等方面取得了卓越成就。然而,由于大多数性状调查都是以植物群落内优势种或亚优势种为对象,使其在探讨群落尺度的性状-功能关系、性状数据如何用于改进或优化模型、性状数据如何与遥感连接等问题时,存在空间尺度和量纲不匹配的极大挑战。为了破解上述难题,亟需发展新的、基于单位土地面积的群落性状(Community trait)概念体系、数据源和计算方法等,推动植物性状数据与快速发展的宏观生态学新技术(遥感、模型和通量观测等)相结合,既拓展了植物性状研究范畴,又可推动其更好地服务于区域生态环境问题的解决。所定义的群落性状(如叶片氮含量、磷含量、比叶面积、气孔密度、叶绿素含量等),是在充分考虑群落内所有物种的性状实测数据,再结合比叶面积、生物量异速生长方程和群落结构数据等,推导而成的基于单位土地面积的群落性状。受测试方法的影响,传统的直接算术平均法或相对生物量加权平均法所获得的群落水平的植物性状(如叶片氮含量g/kg或%),虽然可以有效地探讨群落结构维持机制,由于无法实现对群落性状在量纲上向单位土地面积转换,使它很难与模型和遥感数据相匹配。基于单位土地面积的群落性状,可在空间尺度匹配(或量纲匹配)的前提下实现个体水平测定的植物性状数据与生态模型和遥感观测相联系,更好地探讨区域尺度下自然生态系统结构和功能的关系及其对全球变化的响应与适应。同时,它也可更好地建立群落水平的性状-功能的定量关系(非物种水平),为更好地探讨自然群落结构维持机制和生产力优化机制提供了新思路。     

Spatial heterogeneity of global forest aboveground carbon stocks and fluxes constrained by spaceborne lidar data and mechanistic modeling

Forest carbon is a large and uncertain component of the global carbon cycle. An important source of complexity is the spatial heterogeneity of vegetation vertical structure and extent, which results from variations in climate, soils, and disturbances and influences both contemporary carbon stocks and fluxes. Recent advances in remote sensing and ecosystem modeling have the potential to significantly improve the characterization of vegetation structure and its resulting influence on carbon. Here, we used novel remote sensing observations of tree canopy height collected by two NASA spaceborne lidar missions, Global Ecosystem Dynamics Investigation and ICE, Cloud, and Land Elevation Satellite 2, together with a newly developed global Ecosystem Demography model (v3.0) to characterize the spatial heterogeneity of global forest structure and quantify the corresponding implications for forest carbon stocks and fluxes. Multiple-scale evaluations suggested favorable results relative to other estimates including field inventory, remote sensing-based products, and national statistics. However, this approach utilized several orders of magnitude more data (3.77 billion lidar samples) on vegetation structure than used previously and enabled a qualitative increase in the spatial resolution of model estimates achievable (0.25° to 0.01°). At this resolution, process-based models are now able to capture detailed spatial patterns of forest structure previously unattainable, including patterns of natural and anthropogenic disturbance and recovery. Through the novel integration of new remote sensing data and ecosystem modeling, this study bridges the gap between existing empirically based remote sensing approaches and process-based modeling approaches. This study more generally demonstrates the promising value of spaceborne lidar observations for advancing carbon modeling at a global scale.     

Logging leaves a fingerprint on the number,size, spatial configuration and geometry of tropical forest canopy gaps

Recent advances in remote sensing such as airborne laser scanning have revolutionized our ability to accurately map forest canopy gaps, with huge implications for tracking forest dynamics at scale. However, few studies have explored how canopy gaps vary among forests at different successional stages following disturbances, such as those caused by logging. Moreover, most studies have focused exclusively on the size distribution of gaps, ignoring other key features such as their spatial distribution and shape. Here, we test a series of hypotheses about how the number, size, spatial configuration, and geometry of gaps vary across a logging disturbance gradient in Malaysian Borneo. As predicted, we found that recently logged forests had much higher gap fraction compared to old-growth forests, a result of having both a greater total number of gaps and a higher proportion of large gaps. Regrowing forests, on the other hand, fell at the opposite end of the spectrum, being characterized by both fewer and smaller gaps compared to nearby old-growth forests. Across all successional stages gaps were found to be spatially clustered. However, logging significantly diluted the degree of spatial aggregation and led to the formation of gaps with much more complex geometries. Our results showcase how logging and subsequent regrowth substantially alter not just the number and size of gaps in a forest, but also their spatial arrangement and shape. Linking these emergent patterns to their underlying processes is key to better understanding the impacts of human disturbance on the structure and function of tropical forests.     

遥感在生物多样性研究中的应用进展

随着人口的持续增长, 人类经济活动对自然资源的利用强度不断升级以及全球气候变暖, 全球物种正以前所未有的速度丧失, 生物多样性成为了全球关注的热点问题。传统生物多样性研究以地面调查方法为主, 重点关注物种或样地水平, 但无法满足景观尺度、区域尺度以及全球尺度的生物多样性保护和评估需求。遥感作为获取生物多样性信息的另一种手段, 近年来在生物多样性领域发展迅速, 其覆盖广、序列性以及可重复性等特点使之在大尺度生物多样性监测和制图以及评估方面具有极大优势。本文主要通过文献收集整理, 从观测手段、研究尺度、观测对象和生物多样性关注点等方面综述了遥感在生物多样性研究中的应用现状, 重点分析不同遥感平台的技术优势和局限性, 并探讨了未来遥感在生物多样性研究的应用趋势。遥感平台按观测高度可分为近地面遥感、航空遥感和卫星遥感, 能够获取样地-景观-区域-洲际-全球尺度的生物多样性信息。星载平台在生物多样性研究中应用最多, 航空遥感的应用研究偏少主要受飞行成本限制。近地面遥感作为一个新兴平台, 能够直接观测到物种的个体, 获取生物多样性关注的物种和种群信息, 是未来遥感在生物多样性应用中的发展方向。虽然遥感技术在生物多样性研究中的应用存在一定的局限性, 未来随着传感器发展和多源数据融合技术的完善, 遥感能更好地从多个尺度、全方位地服务于生物多样性保护和评估。     

Measuring and monitoring restored ecosystems: can remote sensing be applied to the ecological recovery wheel to inform restoration success?

The commencement of the United Nations Decade on Ecosystem Restoration has highlighted the urgent need to improve restoration science and fast-track ecological outcomes. The application of remote sensing for monitoring purposes has increased over the past two decades providing a variety of image datasets and derived products suitable to map and measure ecosystem properties (e.g. vegetation species, community composition, and structural dimensions such as height and cover). However, the operational use of remote sensing data and derived products for ecosystem restoration monitoring in research, industry, and government has been relatively limited and underutilized. In this paper, we use the Society for Ecological Restoration (SER) ecological recovery wheel (ERW) to assess the current capacity of drone-airborne-satellite remote sensing datasets to measure each of the SER's recommended attributes and sub-attributes for terrestrial restoration projects. Based on our combined expertise in the areas of ecological monitoring and remote sensing, a total of 11 out of 18 sub-attributes received the highest feasibility score and show strong potential for remote sensing assessments; while sub-attributes such as gene flows, all trophic levels and chemical and physical substrates have a reduced capacity for monitoring. We argue that in the coming decade, ecologists can combine remote sensing with the ERW to monitor restoration recovery and reference ecosystems for improved restoration outcomes at the local, regional, and landscape scales. The ERW approach can be adapted as a monitoring framework for projects to utilize the benefits of remote sensing and inform management through scalable, operational, and meaningful outcomes.     

Are changes in remotely sensed canopy cover associated to changes in vegetation structure,diversity, and composition in recovered tropical shrublands?

The recovery of vegetation cover is a process that has important implications for the conservation of biodiversity and ecosystem services. Generally, the recovery of vegetation cover is documented over large areas using remote sensing, and it is often assumed that ecosystem properties and processes recover along with remotely sensed canopy cover. Here we analyze and compare the structure, composition, and diversity of trees and shrubs among plots established in a stratified random sampling design over four remotely sensed canopy cover change (CCC) categories defined according to a gradient in the percent of canopy cover. Plots were located in the Lake Cuitzeo basin (Mexico), where canopy recovery associated with agricultural abandonment has occurred in recent decades (1975–2000). We found that diversity measures, basal area, tree and shrub density, ground-truthed canopy cover, and mean plant height increased with increasing CCC category. However, Shannon index (H′) was lower in the CCC category with the most closed canopy cover category than in plots apparently not affected by agriculture. Furthermore, ordination analyses showed that composition of dominant species were not associated with CCC categories. Our results suggest that canopy closure in our study area is not associated with the recovery of species diversity, and does not result in similar species dominance as in sites not affected by agriculture.     

High resolution satellite imagery for tropical biodiversity studies: the devil is in the detail

While high resolution satellite remote sensing has been hailed as a very useful source of data for biodiversity assessment and monitoring, applications have been more developed in temperate areas. The biodiverse tropics offer a challenge of an altogether different magnitude for hyperspatial and hyperspectral remote sensing. This paper examines issues related to hyperspatial and hyperspectral remotely sensed imagery, which constitutes one of the most potentially powerful yet underutilized sources of for tropical research on biodiversity. Hyperspatial data with their increased pixel resolution are possibly best suited at facilitating the accurate location of features such as tree canopies, but less suited to the identification of aspects such as species identity, particularly when spatial resolution becomes too fine and pixels are smaller than the size of the object (e.g., tree canopy) being identified. Hyperspectral data on the other hand, with their high spectral resolution, can be used to record information pertaining to a range of critical plant properties related to species identity, and can be very effective used for discriminating tree species in tropical forests, despite the greater complexity of such environments. There remains a glaring gap in the easy availability of hyperspectral and hyperspatial satellite data in the tropics due to reasons of cost, data coverage, and security restrictions. Stimulating discussion on the applications of this powerful, but underutilized tool by ecologists, is the first step in promoting a more extensive use of such data for ecological studies in tropical biodiversity rich areas.     

Plant ecophysiological processes in spectral profiles: perspective from a deciduous broadleaf forest

The need for progress in satellite remote sensing of terrestrial ecosystems is intensifying under climate change. Further progress in Earth observations of photosynthetic activity and primary production from local to global scales is fundamental to the analysis of the current status and changes in the photosynthetic productivity of terrestrial ecosystems. In this paper, we review plant ecophysiological processes affecting optical properties of the forest canopy which can be measured with optical remote sensing by Earth-observation satellites. Spectral reflectance measured by optical remote sensing is utilized to estimate the temporal and spatial variations in the canopy structure and primary productivity. Optical information reflects the physical characteristics of the targeted vegetation; to use this information efficiently, mechanistic understanding of the basic consequences of plant ecophysiological and optical properties is essential over broad scales, from single leaf to canopy and landscape. In theory, canopy spectral reflectance is regulated by leaf optical properties (reflectance and transmittance spectra) and canopy structure (geometrical distributions of leaf area and angle). In a deciduous broadleaf forest, our measurements and modeling analysis of leaf-level characteristics showed that seasonal changes in chlorophyll content and mesophyll structure of deciduous tree species lead to a seasonal change in leaf optical properties. The canopy reflectance spectrum of the deciduous forest also changes with season. In particular, canopy reflectance in the green region showed a unique pattern in the early growing season: green reflectance increased rapidly after leaf emergence and decreased rapidly after canopy closure. Our model simulation showed that the seasonal change in the leaf optical properties and leaf area index caused this pattern. Based on this understanding we discuss how we can gain ecophysiological information from satellite images at the landscape level. Finally, we discuss the challenges and opportunities of ecophysiological remote sensing by satellites.