Remote detection of invasive plants: a review of spectral,textural and phenological approaches

Remote sensing image analysis is increasingly being used as a tool for mapping invasive plant species. Resulting distribution maps can be used to target management of early infestations and to model future invasion risk. Remote identification of invasive plants based on differences in spectral signatures is the most common approach, typically using hyperspectral data. But several studies have found that textural and phenological differences are also effective approaches for identifying invasive plants. I review examples of remote detection of invasive plants based on spectral, textural and phenological analysis and highlight circumstances where the different approaches are likely to be most effective. I also review sources and availability of remotely sensed data that could be used for mapping and suggest field data collection approaches that would support the analysis of remotely sensed data. Remote mapping of biological invasions remains a relatively specialized research topic, but the distinct cover, morphology and/or seasonality of many invaded versus native ecosystems suggests that more species could be detected remotely. Remote sensing can sometimes support early detection and rapid response directly, however, accurately detecting small, nascent populations is a challenge. However, even maps of heavily infested areas can provide a valuable tool for risk assessment by increasing knowledge about temporal and spatial patterns and predictors of invasion.     

Mapping an invasive bryophyte species using hyperspectral remote sensing data

Reliable distribution maps are crucial for the management of invasive plant species. An alternative to traditional field surveys is the use of remote sensing data, which allows coverage of large areas. However, most remote sensing studies on invasive plant species focus on mapping large stands of easily detectable study species. In this study, we used hyperspectral remote sensing data in combination with field data to derive a distribution map of an invasive bryophyte species, Campylopus introflexus, on the island of Sylt in Northern Germany. We collected plant cover data on 57 plots to calibrate the model and presence/absence data of C. introflexus on another 150 plots for independent validation. We simultaneously acquired airborne hyperspectral (APEX) images during summer 2014, providing 285 spectral bands. We used a Maxent modelling approach to map the distribution of C. introflexus. Although C. introflexus is a small and inconspicuous species, we were able to map its distribution with an overall accuracy of 75 %. Reducing the sampling effort from 57 to 7 plots, our models performed fairly well until sampling effort dropped below 12 plots. The model predicts that C. introflexus is present in about one quarter of the pixels in our study area. The highest percentage of C. introflexus is predicted in the dune grassland. Our findings suggest that hyperspectral remote sensing data have the potential to provide reliable information about the degree of bryophyte invasion, and thus provide an alternative to traditional field mapping approaches over large areas.     

Scaling up functional traits for ecosystem services with remote sensing: concepts and methods

Ecosystem service‐based management requires an accurate understanding of how human modification influences ecosystem processes and these relationships are most accurate when based on functional traits. Although trait variation is typically sampled at local scales, remote sensing methods can facilitate scaling up trait variation to regional scales needed for ecosystem service management. We review concepts and methods for scaling up plant and animal functional traits from local to regional spatial scales with the goal of assessing impacts of human modification on ecosystem processes and services. We focus our objectives on considerations and approaches for (1) conducting local plot‐level sampling of trait variation and (2) scaling up trait variation to regional spatial scales using remotely sensed data. We show that sampling methods for scaling up traits need to account for the modification of trait variation due to land cover change and species introductions. Sampling intraspecific variation, stratification by land cover type or landscape context, or inference of traits from published sources may be necessary depending on the traits of interest. Passive and active remote sensing are useful for mapping plant phenological, chemical, and structural traits. Combining these methods can significantly improve their capacity for mapping plant trait variation. These methods can also be used to map landscape and vegetation structure in order to infer animal trait variation. Due to high context dependency, relationships between trait variation and remotely sensed data are not directly transferable across regions. We end our review with a brief synthesis of issues to consider and outlook for the development of these approaches. Research that relates typical functional trait metrics, such as the community‐weighted mean, with remote sensing data and that relates variation in traits that cannot be remotely sensed to other proxies is needed. Our review narrows the gap between functional trait and remote sensing methods for ecosystem service management.     

Using landscape context to map invasive species with medium‐resolution satellite imagery

The spread of invasive species is a global problem of major ecological and economic concern. Landscape level assessment of invasive spread is critical, but remote sensing (RS) analyses are often complicated by the spectral similarity of species and the need to balance spatial resolution with data storage and analysis complexity. One example is the ridge and slough landscape (RSL) of the Florida Everglades, where inflowing nutrients have facilitated large‐scale cattail invasions. Hand delineation of aerial imagery has been successful in mapping cattail spread, but this technique requires considerable time and effort. Computerized classification of medium‐resolution imagery would increase the ability of scientists to provide up‐to‐date data for water management decisions. Advances in RS technologies have created opportunities that were not previously available in landscapes such as the RSL—to automatically classify sawgrass and cattail communities with medium‐resolution satellite imagery using knowledge of the invasion ecology of cattail and landscape context. We developed a computer‐classification technique that provided measure of cattail expansion that matched ground‐truthed data and show an increase in cattail area (similar to previous estimates), but a reduction in the rate of expansion over time. Although this technique can miss small patches of plants that might indicated new invasions, its rapid mapping can improve tracking of invasion fronts in the Everglades and other landscapes.     

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。     

航空航天遥感在物种多样性研究与保护中的应用

人类活动导致全球范围内生物多样性丧失日趋严重。物种多样性是研究最为深入以及最贴近生物多样性管理的层次。物种多样性的研究往往受到多时空尺度生态过程的影响, 传统物种多样性调查方法受到人力物力影响, 局限性大, 物种多样性的研究与管理亟需整合不同来源的数据。遥感技术从传统的光学遥感阶段发展到不同平台、不同维度相结合的多源遥感阶段, 并逐渐进入以高空间分辨率和高光谱为特征、以激光雷达为前沿发展方向的综合遥感阶段。遥感技术因为其监测范围广、能监测人迹罕至地区以及长期可重复等特性, 为研究不同时空尺度的生态学科学问题提供了更新更优的研究手段。本文围绕种群动态、种间关系与群落多样性、功能属性及功能多样性以及生物多样性保护管理等生物多样性研究热点问题, 系统地论述了航空航天遥感技术在物种多样性研究与保护领域的应用, 总结了航空航天遥感技术在研究与物种多样性有关的主要生态学问题中的机遇与挑战。我们认为航空航天遥感技术利用多光谱甚至高光谱与激光技术从空中监测物种多样性, 从不同视角、基于不同光源提供了物种多样性不同侧面的信息, 能够减小地面调查强度, 在大范围和边远地区的物种多样性调查研究中有着至关重要的作用。依据光谱特性的物种判别以及依据激光雷达的三维结构量测将促进生物多样性的研究与管理, 加强遥感学家和生物多样性研究者的沟通交流将有助于促进不同时空尺度的生物多样性与遥感技术的结合。     

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数据特征中选择适合于森林乔木物种多样性建模的少量特征。该研究在云南普洱开展对季风常绿阔叶林的遥感估测研究,可为森林生物多样性调查提供补充手段,有助于森林生物多样性大尺度、长期动态监测。     

Remote sensing and geographic information systems in the spatial temporal dynamics modeling of infectious diseases

Similar to species immigration or exotic species invasion, infectious disease transmission is strengthened due to the globalization of human activities. Using schistosomiasis as an example, we propose a conceptual model simulating the spatio-temporal dynamics of infectious diseases. We base the model on the knowledge of the interrelationship among the source, media, and the hosts of the disease. With the endemics data of schistosomiasis in Xichang, China, we demonstrate that the conceptual model is feasible; we introduce how remote sensing and geographic information systems techniques can be used in support of spatio-temporal modeling; we compare the different effects caused to the entire population when selecting different groups of people for schistosomiasis control. Our work illustrates the importance of such a modeling tool in supporting spatial decisions. Our modeling method can be directly applied to such infectious diseases as the plague, lyme disease, and hemorrhagic fever with renal syndrome. The application of remote sensing and geographic information systems can shed light on the modeling of other infectious disease and invasive species studies.     

Correlating species and spectral diversities using hyperspectral remote sensing in early‐successional fields

Advances in remote sensing technology can help estimate biodiversity at large spatial extents. To assess whether we could use hyperspectral visible near‐infrared (VNIR) spectra to estimate species diversity, we examined the correlations between species diversity and spectral diversity in early‐successional abandoned agricultural fields in the Ridge and Valley ecoregion of north‐central Virginia at the Blandy Experimental Farm. We established plant community plots and collected vegetation surveys and ground‐level hyperspectral data from 350 to 1,025 nm wavelengths. We related spectral diversity (standard deviations across spectra) with species diversity (Shannon–Weiner index) and evaluated whether these correlations differed among spectral regions throughout the visible and near‐infrared wavelength regions, and across different spectral transformation techniques. We found positive correlations in the visible regions using band depth data, positive correlations in the near‐infrared region using first derivatives of spectra, and weak to no correlations in the red‐edge region using either of the two spectral transformation techniques. To investigate the role of pigment variability in these correlations, we estimated chlorophyll, carotenoid, and anthocyanin concentrations of five dominant species in the plots using spectral vegetation indices. Although interspecific variability in pigment levels exceeded intraspecific variability, chlorophyll was more varied within species than carotenoids and anthocyanins, contributing to the lack of correlation between species diversity and spectral diversity in the red‐edge region. Interspecific differences in pigment levels, however, made it possible to differentiate these species remotely, contributing to the species‐spectral diversity correlations. VNIR spectra can be used to estimate species diversity, but the relationships depend on the spectral region examined and the spectral transformation technique used.     

遥感用于森林生物多样性监测的进展

随着物种和栖息地的丧失,全球范围的生物多样性保护已经成为迫切的需要。航空航天技术的迅猛发展使遥感成为能提供跨越不同时空尺度监测陆地生态系统生物多样性的重要工具,这方面的研究在欧美等国已经有了小范围的开展,在国内刚刚起步。国外关于生物多样性遥感探测的方法基本有3种:1.利用遥感数据直接对物种或生境制图,进而估算生物多样性;2 .建立遥感数据的光谱反射率与地面观测物种多样性的关系模型;3.与野外调查数据结合直接在遥感数据上进行生物多样性指数制图。研究表明,物种直接制图法只能应用于较小的范围;生境制图的方法,应用广泛,技术相对成熟,研究范围局限于几百公里的范畴,但不能获取生境内部的多样性信息。光谱模型技术目前正处于探索阶段,对于植被复杂、生物多样性高的地域,具有较大的应用潜力。在遥感数据上直接进行生物多样性制图在加拿大已经得到了应用。     

Microphytobenthos assemblage mapping by spatial visible-infrared remote sensing in a shellfish ecosystem

The aim of this work is to assess the use of (SPOT) multispectral visible infrared remote sensing to study microphytobentos assemblages in a shellfish ecosystem (Bay of Bourgneuf, France). SPOT satellite images (acquired at low tide in spring or autumn between 1986 and 1998) were calibrated using in situ radiometric data, and the normalised vegetation index (NDVI) obtained from these images showed microphytobenthos on bay mudflats. Proliferation was mainly along a north-south strip, essentially localised around the +2 m isobath and covering a surface area of 19 to 25% of the total mudflat area studied (420 to 550 ha). Three factors seem to be responsible for the spatial structure of the assemblages: bathymetry, nutrient input from the Falleron River and its channel, and the location of oyster-farming areas. Although spatial and spectral resolutions of multispectral remote sensing data have certain limitations, this approach opens up a new field of application for hyperspectral remote sensing, particularly for synoptic mapping of biomass distribution.     

Effects of microtopography and hydrology on phenology of an invasive herb

Phenological traits may influence invasion success via effects on invasiveness of the colonizing species and invasibility of the receiving ecosystems. Many species exhibit substantial fine-scaled spatial variation in phenology and interannual differences in phenological timing in response to environmental variation. Yet describing and understanding this variation is limited by the availability of appropriate spatial and temporal datasets. Remote sensing provides such datasets, but has primarily been used to monitor broad-scale phenological patterns at coarse resolutions, necessarily missing fine spatial detail and intraspecies variation. We used hyperspectral remote sensing to characterize the spatial and temporal phenological variation of the invasive species Lepidium latifolium (perennial pepperweed) at two sites in California's San Francisco Bay/Sacramento–San Joaquin River Delta. Considerable phenological variation was detected: L. latifolium was simultaneously present in vegetative, early flowering, peak flowering, fruiting, and senescent stages in late June; the relative dominance and distribution of these stages varied interannually. Environmental determinants of phenology were investigated with variables derived from the hyperspectral image data, from a high resolution LiDAR (light detection and ranging) digital elevation model (DEM), and from local precipitation and streamflow data. Lepidium latifolium phenology was found to track water availability, and may also be influenced by intraspecific competition and edaphic stress. Lepidium latifolium has a unique phenology (summer flowering) relative to the communities it invades, which may allow invasion of an empty niche. Furthermore, many habitats are invaded by L. latifolium, which occurs in locally appropriate phenologies under the different environmental conditions. The environmental responsiveness of L. latifolium phenology may mediate the wide breadth of invasible habitats.     

Mapping three invasive weeds using airborne hyperspectral imagery

Invasive plant species present a serious problem to the natural environment and have adverse ecological and economic impacts on both terrestrial and aquatic ecosystems they invade. This article presents three case studies on the use of hyperspectral remote sensing for mapping invasive plant species in both terrestrial and aquatic environments. Methods and procedures for acquisition, processing and classification of airborne hyperspectral imagery as well as accuracy assessment are presented. Examples are excerpted and adapted from published work to illustrate how airborne hyperspectral imagery has been used to map two terrestrial weeds, Ashe juniper (Juniperus ashei Buchholz) and Broom snakeweed [Gutierrezia sarothrae (Pursh.) Britt. and Rusby], and one aquatic weed, waterhyacinth [Eichhornia crassipes (Mart.) Solms], in Texas. In addition to the standard classification methods used in the previous studies, a spectral unmixing technique, mixture tuned matched filtering (MTMF), was applied to the three study cases and the classification results are reported in this paper. A brief discussion is provided on the considerations of different types of remote sensing imagery for mapping invasive weeds.     

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。     

塔里木河中游绿洲盐漠带典型盐生植物光谱特征

盐生植物作为干旱区绿洲重要的生命支持系统, 对于维持绿洲生态系统平衡起着核心的作用。很多学者对此展开了大量研究, 但是对盐生植物的光谱特征研究较少, 因此, 该文以渭干河-库车河三角洲绿洲盐漠带典型盐生植物为研究对象, 利用FieldSpec Pro FR便携式地物波谱仪, 2010年10月通过对盐穗木(Halostachys caspica)、盐节木(Halocnemum strobilaceum)、骆驼刺(Alhagi sparsifolia)、白刺(Nitrarria sibirica)、柽柳(Tamarix ramosissima)、花花柴(Karelinia caspia)及芦苇(Phragmites australis)野外光谱数据采集和处理, 获得了这7种盐生植物的高光谱数据, 然后采用光谱学分析方法分析其特征变化, 结果表明: (1)采用Percentile Filter平滑方法进行光谱噪声去除, 有很好的去除噪音效果。(2)对3种类型盐生植物样本的原始光谱曲线提取光谱吸收特征参数, 结果发现稀盐盐生植物、泌盐盐生植物、拒盐盐生植物的波谷波长位置接近, 说明盐生植物的吸收波段特征有一定的相似性。(3)对盐生植物光谱反射曲线进行归一化处理, 发现在消除土壤等背景影响、突出目标及消除同物异谱现象上具有很好的效果, 能够提高盐生植被的识别精度。(4)应用二阶导数的方法得到盐生植物识别的9个最佳波段: 510、550、690、730、950、1150、1210、1290和1310 nm。研究成果不仅可以为盐漠带植物的高光谱遥感数据处理提供一定的科学依据, 而且可以为盐漠带盐生植物的遥感识别和分类提供一定的参考。     

The perfect storm: Weed invasion and intense storms in tropical forests

Climate change scientists predict an increased intensity of storms (cyclones, hurricanes and typhoons) in the future. Intense storms facilitate plant invasion by increasing resource availability, reducing competition and increasing opportunities for propagule dispersal. We document here the state of current understanding about the response of invasive plant species to intense storms and suggest that the structure and function of forests in storm‐prone regions may be much altered in the future as a result of weed invasion. Intense storms provide a large spatial and temporal window of opportunity for invasion and empirical research demonstrates growth and recruitment rates of invasive species increase following such events, and they spread readily. In particular, lianas and woody invasive species that are shade tolerant and recruit from the seedling layer may constitute the greatest threat to tropical forests following storm events. Forests persisting in fragmented landscapes will be exposed to some of the most severe consequences of intense storms and subsequent weed invasion. In storm‐prone regions, forests of the future are likely to experience a decrease in diversity of native species and homogenization of communities at landscape and regional scales, slower rates of forest succession, increasing degradation of forest fragments and ultimately a decrease in ecosystem function.     

Monitoring an invasive perennial at the landscape scale with remote sensing

Summary   Worldwide, invasive weeds threaten agricultural, natural and urban ecosystems. In Australia's agricultural and grazing regions, invasive species often establish across extensive areas where weed management is hampered by an inability to detect the location and timing of an outbreak. In these vast landscapes, an effective detection and monitoring system is required to delineate the extent of the invasion and identify spatial and temporal factors associated with weed establishment and thickening. In this study, we utilize a time series of remote sensing imagery to detect the spatial and temporal patterns of Prickly Acacia ( Acacia nilotica ) invasion in the Mitchell grass plains of North Queensland. We develop a spectral index from Landsat images which is applied to images from 1989 to 2004, in combination with a classification mask, to identify locations and monitor changes in Prickly Acacia density across 29 000 km² of Mitchell grass plains. The approach identified spectral and temporal signatures consistent with Prickly Acacia infestation on 1.9% of this landscape. Field checking of results confirmed presence of the weed in previously unrecorded locations. The approach may be used to evaluate future spread, or outcomes of management strategies for Prickly Acacia in this landscape and could be employed to detect and monitor invasions in other extensive landscapes.     

基于“珠海一号”高光谱遥感影像的薇甘菊入侵监测

采用“珠海一号”卫星高光谱遥感影像,结合薇甘菊(Mikania micrantha)地面光谱数据,利用决策树分层分类提取方法,实现大范围薇甘菊入侵信息识别提取。结果表明,“珠海一号”OHS高光谱数据能准确反映薇甘菊精细光谱特征;在OHS高光谱数据的32个波段中,Band13 (665 nm)、Band22 (780 nm)、Band25 (833 nm)、Band26 (850 nm)、Band27 (865 nm)、Band28 (880 nm)是识别提取薇甘菊的最佳波段;利用OHS高光谱数据监测薇甘菊分布提取精度达82.6 %,可满足薇甘菊入侵监测日常业务需要,为薇甘菊精准防治提供有力支撑。     

Simple and robust methods for remote sensing of canopy chlorophyll content: a comparative analysis of hyperspectral data for different types of vegetation

Canopy chlorophyll content (CCC) is an essential ecophysiological variable for photosynthetic functioning. Remote sensing of CCC is vital for a wide range of ecological and agricultural applications. The objectives of this study were to explore simple and robust algorithms for spectral assessment of CCC. Hyperspectral datasets for six vegetation types (rice, wheat, corn, soybean, sugar beet and natural grass) acquired in four locations (Japan, France, Italy and USA) were analysed. To explore the best predictive model, spectral index approaches using the entire wavebands and multivariable regression approaches were employed. The comprehensive analysis elucidated the accuracy, linearity, sensitivity and applicability of various spectral models. Multivariable regression models using many wavebands proved inferior in applicability to different datasets. A simple model using the ratio spectral index (RSI; R815, R704) with the reflectance at 815 and 704 nm showed the highest accuracy and applicability. Simulation analysis using a physically based reflectance model suggested the biophysical soundness of the results. The model would work as a robust algorithm for canopy‐chlorophyll‐metre and/or remote sensing of CCC in ecosystem and regional scales. The predictive‐ability maps using hyperspectral data allow not only evaluation of the relative significance of wavebands in various sensors but also selection of the optimal wavelengths and effective bandwidths.     

Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review

Wetland vegetation plays a key role in the ecological functions of wetland environments. Remote sensing techniques offer timely, up-to-date, and relatively accurate information for sustainable and effective management of wetland vegetation. This article provides an overview on the status of remote sensing applications in discriminating and mapping wetland vegetation, and estimating some of the biochemical and biophysical parameters of wetland vegetation. Research needs for successful applications of remote sensing in wetland vegetation mapping and the major challenges are also discussed. The review focuses on providing fundamental information relating to the spectral characteristics of wetland vegetation, discriminating wetland vegetation using broad- and narrow-bands, as well as estimating water content, biomass, and leaf area index. It can be concluded that the remote sensing of wetland vegetation has some particular challenges that require careful consideration in order to obtain successful results. These include an in-depth understanding of the factors affecting the interaction between electromagnetic radiation and wetland vegetation in a particular environment, selecting appropriate spatial and spectral resolution as well as suitable processing techniques for extracting spectral information of wetland vegetation.