Estimation of aboveground biomass using in situ hyperspectral measurements in five major grassland ecosystems on the Tibetan Plateau

Aims There are numerous grassland ecosystem types on the Tibetan Plateau. These include the alpine meadow and steppe and degraded alpine meadow and steppe. This study aimed at developing a method to estimate aboveground biomass (AGB) for these grasslands from hyperspectral data and to explore the feasibility of applying air/satellite-borne remote sensing techniques to AGB estimation at larger scales.Methods We carried out a field survey to collect hyperspectral reflectance and AGB for five major grassland ecosystems on the Tibetan Plateau and calculated seven narrow-band vegetation indices and the vegetation index based on universal pattern decomposition (VIUPD) from the spectra to estimate AGB. First, we investigated correlations between AGB and each of these vegetation indices to identify the best estimator of AGB for each ecosystem type. Next, we estimated AGB for the five pooled ecosystem types by developing models containing dummy variables. At last, we compared the predictions of simple regression models and the models containing dummy variables to seek an ecosystem type-independent model to improve prediction of AGB for these various grassland ecosystems from hyperspectral measurements.Important findings When we considered each ecosystem type separately, all eight vegetation indices provided good estimates of AGB, with the best predictor of AGB varying among different ecosystems. When AGB of all the five ecosystems was estimated together using a simple linear model, VIUPD showed the lowest prediction error among the eight vegetation indices. The regression models containing dummy variables predicted AGB with higher accuracy than the simple models, which could be attributed to the dummy variables accounting for the effects of ecosystem type on the relationship between AGB and vegetation index (VI). These results suggest that VIUPD is the best predictor of AGB among simple regression models. Moreover, both VIUPD and the soil-adjusted VI could provide accurate estimates of AGB with dummy variables integrated in regression models. Therefore, ground-based hyperspectral measurements are useful for estimating AGB, which indicates the potential of applying satellite/airborne remote sensing techniques to AGB estimation of these grasslands on the Tibetan Plateau.     

Benefits of hyperspectral remote sensing for tracking plant invasions

Aim We aim to report what hyperspectral remote sensing can offer for invasion ecologists and review recent progress made in plant invasion research using hyperspectral remote sensing. Location United States. Methods We review the utility of hyperspectral remote sensing for detecting, mapping and predicting the spatial spread of invasive species. We cover a range of topics including the trade‐off between spatial and spectral resolutions and classification accuracy, the benefits of using time series to incorporate phenology in mapping species distribution, the potential of biochemical and physiological properties in hyperspectral spectral reflectance for tracking ecosystem changes caused by invasions, and the capacity of hyperspectral data as a valuable input for quantitative models developed for assessing the future spread of invasive species. Results Hyperspectral remote sensing holds great promise for invasion research. Spectral information provided by hyperspectral sensors can detect invaders at the species level across a range of community and ecosystem types. Furthermore, hyperspectral data can be used to assess habitat suitability and model the future spread of invasive species, thus providing timely information for invasion risk analysis. Main conclusions Our review suggests that hyperspectral remote sensing can effectively provide a baseline of invasive species distributions for future monitoring and control efforts. Furthermore, information on the spatial distribution of invasive species can help land managers to make long‐term constructive conservation plans for protecting and maintaining natural ecosystems.     

Species classification from hyperspectral leaf information using machine learning approaches

Information on plant species is fundamental to forest ecosystems, in the context of biodiversity monitoring and forest management. Traditional methods for plant species inventories are generally inefficient, in terms of cost and performance, and there is a high demand for a quick and feasible approach to be developed. Of the various attempts, remote sensing has emerged as an active approach for plant species classification, but most studies have concentrated on image processing and only a few of them ever use hyperspectral information, despite the wealth of information it contains. In this study, plant species are classified from hyperspectral leaf information using different machine learning models, coupled with feature reduction and selection methods, and their performance is optimized through Bayesian optimization. The results show that including feature selection and Bayesian optimization increases the classification accuracy of machine learning models. Among these, the Bayesian optimization-based support vector machine (SVM) model, combined with the recursive feature elimination (RFE) feature selection method, yields the best output, with an overall accuracy of 86% and a kappa coefficient of 0.85. Furthermore, the confusion matrix revealed that the number of samples correlates with classification accuracy. The support vector machine with informative bands after Bayesian optimization outperformed in classing plant species. The results of this study facilitate a better understanding of spectral (phenotype) information with plant species (genotype) and help to bridge hyperspectral information with ecosystem functions.     

低空无人机遥感在草原监测评价中的应用进展

低空域无人机遥感技术具有高时效性、高分辨率、低成本、易操控等优势,作为地面与高空遥感（航天与航空遥感）间测量尺度空缺的有益补充,低空无人机遥感扩展了样地样方空间尺度,提高了中、小尺度遥感观测信息的精细化程度,实现了草原生境信息的快速采集、处理与分析应用,是草原"星-空-地"一体化监测的重要组成。针对草原监测评价,总结了国内外低空无人机遥感在草原基况调查（草原草层高度监测、草原植被覆盖度监测与草原地上生物量估算）、草原动态监测（草原植被长势监测、草原产草量估测与草畜平衡监测）和草原应急管理（草原火灾、雪灾与生物灾害监测）中的应用。结合大数据、人工智能、云计算与物联网等新型技术,分析了低空无人机遥感在草原生态监测领域存在的不足和未来的发展方向,以期为低空无人机遥感关于草原监测评价与智慧草原的后续研究提供参考。     

Developing and applying novel spectral feature parameters for classifying soil salt types in arid land

Soil salinization is a major desertification process that threatens especially the stability of arid ecosystems. There is an urgent need for intensive monitoring and quick assessment of salinization through remote sensing as a tool for combating desertification in such ecosystems. Recent researches have revealed that in order to retrieve soil salt contents accurately from hyperspectral reflectance, a pre-knowledge of salt types is required, which greatly outlines the spectral features of saline soil reflectance. In this study, a set of feature parameters have been developed after a thorough investigation of spectral responses to different soil salt types and salt contents for quick and accurate classification of soil salt types. The application has been validated using three independent datasets composed from: laboratory experiments (dataset I), in-situ field measurements (dataset II), and satellite-borne Hyperion image (dataset III). For comparison, four other common classification algorithms have been validated using the same datasets. The results showed that the new approach proposed in this study performed well with not only single-type but also multiple-type salts for which the four common algorithms performed rather fairly. Furthermore, validating using datasets II and III showed that the newly proposed approach had a stable performance while the other four failed, indicating the advantage of the new approach. The feature parameters developed in this study hence provide a novel and efficient approach for salt type classification from reflectance spectra, and we foresee its potential applications on large-scale soil salt type mapping towards better understanding soil salinity characterization from remote sensing data.     

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。     

新疆草地净初级生产力(NPP)空间分布格局及其对气候变化的响应

分析植被物候与净初级生产力对气候变化的响应一直是研究全球变化的核心内容之一。新疆草地生态系统极为脆弱,对气候和环境变化的影响十分敏感,在新疆地区开展草地物候和净初级生产力及其对气候变化的响应有着独特的意义。基于遥感数据和野外台站实测数据,利用CASA模型模拟了新疆草地植被净初级生产力(NPP),阐述了2001—2014年新疆地区草地的NPP的空间格局及与气象因子的关系。(1)通过实测生物量精度检验表明,CASA模型基本可以反映新疆地区草地植被NPP。(2)2001—2014年新疆草地NPP平均值为102.49 gC m~(-2) a~(-1)。不同草地类型的NPPA存在明显差异。其中,山地草甸平均NPP最高,达到252.37 gC m~(-2) a~(-1);温性草甸草原次之,为204.93 gC m~(-2) a~(-1)。高寒荒漠和温性荒漠的平均NPP最低,分别为43.94 gC m~(-2) a~(-1),53.11 gC m~(-2) a~(-1)。(3)新疆NPP的空间分布格局具有如下特点:山区NPP高于盆地NPP,北疆NPP高于南疆NPP;(4)降水能够促进新疆草地NPP增加,其中,夏季和秋季的降水对草地NPP的影响最为明显,温度对新疆地区草地NPP影响不大。降雨可以促进新疆草原NPP的增加。特别是在降水量较少但温度较高的草原,如温带荒漠草原、温带草原沙漠、温带沙漠、低地草甸等,年降水量和夏秋降水量对草地NPP有显著影响。温度对新疆草地NPP的影响不大。通过对新疆草地空间格局的分析,研究了草地NPP对气候变化的响应,为合理规划新疆草地的生产和利用,以及草地生态系统的健康发展和应对气候变化提供决策依据。     

围栏对青藏高原不同类型草地土壤原核微生物多样性的影响

草地退化是草地植被的倒退演替,导致生物多样性丧失和生态系统功能退化,围栏是恢复退化草地生态系统功能的有效管理措施。微生物是土壤中的重要组成部分,在维持草地生态系统稳定性和功能方面发挥着重要作用。然而,目前尚不清楚围栏如何影响不同类型草地土壤微生物群落。以青藏高原草甸、草原和荒漠草地三种草地类型的退化草地为研究对象,设置围栏和放牧两种处理,采用Illumina HiSeq高通量测序技术研究了围栏对土壤原核微生物群落多样性和群落结构的影响。结果表明：围栏未显著影响草甸土壤原核微生物的丰富度、Shannon多样性和均匀度,但显著增加了草原土壤的原核微生物的丰富度、Shannon多样性和均匀度（P<0.05）,稍降低了荒漠草地土壤原核微生物的丰富度、Shannon多样性和均匀度（P=0.086、0.072和0.099）。在围栏处理的草地中,土壤原核微生物丰富度、Shannon多样性和均匀度与年均温、干旱度和pH显著负相关（P<0.01）,与年平均降水量、溶解性有机碳、地上生物量和植物多样性显著正相关（P<0.01）。在放牧处理的草地中,土壤原核微生物丰富度、Shannon多样性和均匀度与年均温和干旱度显著负相关（P<0.05）,但原核微生物丰富度和Shannon多样性与所有土壤理化和植被因素均无显著相关性。冗余分析（RDA）表明,不同类型草地土壤原核微生物群落结构发生了显著的变化,并沿草甸、草原和荒漠草地的过渡逐渐转变（P<0.001）。方差分解分析（VPA）进一步表明,原核微生物群落结构变化主要受年均温、年平均降水量、干旱度和pH的驱动。围栏显著改变了不同类型草地中部分样点土壤原核微生物群落结构。三种草地类型的主要原核微生物优势门均为放线菌门（Actinobacteria）、变形菌门（Proteobacteria）和酸杆菌门（Acidobacteria）。放线菌门（Actinobacteria）的相对丰度在荒漠草地土壤中最高,而变形菌门（Proteobacteria）和酸杆菌门（Acidobacteria）的相对丰度在草甸土壤中最高。此外,不同类型围栏和放牧草地土壤原核微生物类群的相对丰度均无显著差异。研究表明不同类型草地土壤原核微生物群落对围栏的响应不同,这为因地制宜制定草地管理措施提供了数据支持,为草地退化的防治提供了理论支持。     

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.     

Beyond “greening” and “browning”: Trends in grassland ground cover fractions across Eurasia that account for spatial and temporal autocorrelation

Grassland ecosystems cover up to 40% of the global land area and provide many ecosystem services directly supporting the livelihoods of over 1 billion people. Monitoring long-term changes in grasslands is crucial for food security, biodiversity conservation, achieving Land Degradation Neutrality goals, and modeling the global carbon budget. Although long-term grassland monitoring using remote sensing is extensive, it is typically based on a single vegetation index and does not account for temporal and spatial autocorrelation, which means that some trends are falsely identified while others are missed. Our goal was to analyze trends in grasslands in Eurasia, the largest continuous grassland ecosystems on Earth. To do so, we calculated Cumulative Endmember Fractions (annual sums of monthly ground cover fractions) derived from MODIS 2002–2020 time series, and applied a new statistical approach PARTS that explicitly accounts for temporal and spatial autocorrelation in trends. We examined trends in green vegetation, non-photosynthetic vegetation, and soil ground cover fractions considering their independent change trajectories and relations among fractions over time. We derived temporally uncorrelated pixel-based trend maps and statistically tested whether observed trends could be explained by elevation, land cover, SPEI3, climate, country, and their combinations, all while accounting for spatial autocorrelation. We found no statistical evidence for a decrease in vegetation cover in grasslands in Eurasia. Instead, there was a significant map-level increase in non-photosynthetic vegetation across the region and local increases in green vegetation with a concomitant decrease in soil fraction. Independent environmental variables affected trends significantly, but effects varied by region. Overall, our analyses show in a statistically robust manner that Eurasian grasslands have changed considerably over the past two decades. Our approach enhances remote sensing-based monitoring of trends in grasslands so that underlying processes can be discerned.     

基于生态风险评估的锡林河流域退化草地优化管理

利用遥感监测和空间信息分析技术,通过对退化草地的退化等级评估、风险度评估以及易恢复度评估,构建了草地生态优化管理指数(EMI),探讨了退化草地生态优化管理的实践途径;并以锡林河流域为例, 分析了退化草地生态优化管理指数.结果表明,通过构建退化草地优化管理模型,定量分析流域内不同优化管理等级退化草地的退化程度、危害性和易恢复性,可以有针对性地采取合理的治理措施,有利于退化草地治理中的资源优化配置.该模型能整合退化草地的各种相关信息,具有较强的普适性.     

大气氮沉降影响草地植物物种多样性机制研究综述

大气氮沉降对草地生态系统结构和功能的影响已成为全球变化生物学研究重点。大气氮沉降导致草地群落物种多样性降低已成为全球普遍现象,但其生物学机制还不清楚,因此有必要系统梳理大气氮沉降对全球不同草地生态系统的研究结果,以便在氮沉降背景下为我国草地生态系统的研究和管理制定科学决策。系统综述了氮沉降降低草地群落物种多样性的可能机制,主要包括资源竞争排斥、群落更新限制、土壤酸化及其离子毒害、养分失衡、氮素本身的毒害、次生胁迫。氮沉降导致草地物种多样性降低是多种机制综合作用的结果,每种机制在不同时空具有不同的相对贡献。同时,与欧洲酸性土壤草地和美国高草草原相比,我国草地土壤类型和植被属性具有明显差异。因此,应根据我国草地生态系统的特征、不同植物功能利用养分策略,从土壤养分变化、根系养分吸收转运、叶片生理过程等方面的整合研究思路,探讨氮沉降影响我国草地群落物种多样性的生物学机制,为我国草地生态系统的科学管理提供理论依据。     

遥感反演植被含氮量研究进展

植被含氮量表征植被氮素状态。它作为植被生长状况的重要指标,在生态系统健康状况检测、生态系统生产估测、精准农业、生态系统干扰评估等方面均有重要意义。遥感监测植被含氮量主要基于高光谱和多光谱数据,采用的算法包括经验方法(波谱指数与回归分析)及物理方法(辐射传输模型法)。但受数据源和研究方法的局限,目前植被氮含量遥感监测局限于区域范围较小且内部植被类型与环境条件(气候、地形等)基本一致的情形,而对复杂生态系统的监测能力不足。未来的研究需针对氮沉降和人类活动的生态系统响应这一重大研究需求,发展和改进现有植被含氮量遥感反演方法。可考虑开展对不同环境条件下、不同类型植被光谱曲线进行标准化的研究,以形成普适的植被含氮量反演方法。并考虑综合运用多种数据(如微波遥感、无人机遥感),形成多尺度同步监测,以提高遥感对区域乃至全球范围内植被氮含量常规监测的能力。     

短花针茅荒漠草原土壤微生物群落组成及结构

为详细了解内蒙古短花针茅荒漠草原生态系统中土壤微生物群落组成与结构。对其土壤中微生物的总DNA提取后,采用高通量测序技术对土壤中细菌的16Sr DNA和真菌的ITS基因进行了序列测定,分析了短花针茅荒漠草原土壤中微生物群落结构特征。结果表明,共获得细菌OTUs13711个,真菌OTUs 5929个。物种分类显示,细菌种类隶属于29门57纲111目191科485属,其中优势类群为Gammaproteobacteria和Thermoleophilia,它们的相对丰度分别为32.68%和26.83%。真菌隶属于4门16纲45目78科105属,优势类群为Ascomycota和Basidiomycota,它们的相对丰度分别为35.76%和25.90%。土壤中细菌群落的多样性和丰富度均高于真菌。     

喷灌对藏北高寒草地生产力和物种多样性的影响

通过3a(2008—2010年)的藏北高寒草地喷灌试验,研究了不同喷灌量对草地群落生产力和物种多样性的影响。结果表明,丰水年灌溉对藏北高寒草地的影响较小;而在相对干旱年份灌溉对高寒草地生产力和物种多样性影响显著。喷灌条件下高寒草地生物量显著提高,最高增幅出现在高水(GS)样地中,达到116%。喷灌明显促进物种重要值提高,其中灌木和阔叶杂草比例增加趋势更为明显。不同喷灌条件下优势物种相对重要值均有不同程度的降低,高水处理降低幅度最大。物种多样性方面,喷灌措施能够明显促进高寒草地Simpson指数和Shannon-weiner指数增加(P0.05),E.Pielou均匀度指数无显著变化(P0.05)。Shannon-weiner指数与生物量之间存在显著正相关关系(P0.05)。未来降水增多的气候条件可以减少干旱对高寒草地带来的负面影响,有利于提高草地生产力和维持草地物种多样性,促进高寒草地畜牧业健康发展。     

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...     

青藏高原高寒草地生物多样性与生态系统功能的关系

生物多样性和生态系统功能(BEF)之间的关系是目前陆地生态系统生态学研究的热点, 对于生态系统的高效利用与管理意义重大, 而且对于退化生态系统功能的恢复及生物多样性的保护有重要的指导作用。高寒草地是青藏高原生态系统的主体, 近年来, 在气候变化与人为干扰等因素的驱动下, 高寒草地生态系统功能严重衰退。为此, 本文在综述物种多样性和生态系统功能及其相互关系研究进展的基础上, 首先从地下生态学过程研究、全球变化对生态系统多功能性的影响等方面解析了目前关于草地生物多样性和生态系统功能研究中存在的问题。继而, 从不同草地类型、草地退化程度、放牧、模拟气候变化、刈割、施肥、封育和补播等干扰利用方式对高寒草地物种多样性与生态系统功能的影响进行了全面的评述。并指出了高寒草地BEF研究中存在的不足, 今后应基于物种功能多样性开展高寒草地BEF研究, 全面且综合地考虑非生物因子(养分资源、外界干扰、环境波动等)对生物多样性与生态系统功能之间关系的影响, 关注尺度效应和要素耦合在全球气候变化对高寒草地BEF研究中的作用。最后, 以高寒草地BEF研究进展和结论为支撑依据, 综合提出了高寒草地资源利用和生物多样性保护的措施与建议: 加强放牧管理, 保护生物多样性; 治理退化草地, 维持生物多样性功能; 加强创新保护理念, 增强生态系统功能。     

乌鲁木齐山地草地生态系统服务价值变化评估——基于遥感与GIS

草地生态系统服务功能是指草地生态系统及其生态过程所形成和维持的人类赖以生存的自然环境及其有效性,中国干旱半干旱地区的草地退化严重制约着生态系统服务功能的。以乌鲁木齐市山地草地为研究靶区,利用遥感图像(Landsat TM/OLI)划分出7种不同草地类型并计算其生态系统服务价值(Ecosystem Service Values, ESV)。使用空间统计方法、网格分析、空间自相关等方法进一步分析研究区山地草地退化程度和ESV变化。结果表明:21年中,乌鲁木齐市草地生态系统服务价值呈现波动性下降趋势,年均减少0.27×10~7元。生态损失主要是由山地荒漠草地(7.44%)、山地荒漠草原草地(10.71%)、山地草甸草原草地(18.32%)和高寒草甸草地(40.69%)退化造成。单项生态系统服务价值中,草地对调节服务(土壤形成)和供给服务的贡献率较高。草地ESVs Moran平均值为0.869(1994—2015年),并且草地ESV与相邻网格(2 km×2 km)具有更高的空间相关性。草地ESV具有明显的空间分布特征,高ESV区基本上分布在南山山区,低ESV区主要分布在平原草地区,博格达山草地亚区呈现随机分布格局。研究区气候变化对草地的影响十分显著,平均气温、平均地表温度、湿度、蒸散量、降水量和风速对草地ESV变化均有一定的影响,过度放牧、不合理垦荒、药用植物过度开发、城市化等人为活动仍然是驱动草地退化的主要因子,因此亟需采取措施保育乌鲁木齐市山地草原生态系统。     

基于RS、GIS的岷县草场退化的时空特征研究

本文运用遥感手段,对2000年6月份的TM影像解译得到岷县草场资源分布图,对比分析1981年的岷县草场资源分布,对岷县草场退化的时空特征进行研究。经过Moran'I的全局空间自相关计算、局域空间自相关计算-G^*统计的Z(G_i^*)值的计算,发现岷县的草场退化重心位于县域的西北部;20年来草场资源一直呈现东多西少、南好北差之势,并且这种空间格局愈加明显。表现为县域的西北部和中部的草场呈现大面积的集中退化的趋势,而东部的几个乡镇的草场面积还有增加的现象。说明县域的西北部和中部的科技水平以及草场保护投入的资金相对不足,抗干扰能力差。     

Characterisation of a Tunisian coastal lagoon through hyperspectral underwater irradiance

North African coastal lagoons are unique ecosystems that often suffer degradation due to human activities. Therefore, monitoring methods are required to identify stressors and assist with the management of these valuable and often understudied ecosystems. A synthetic indicator of water ecological quality would be desirable for regular monitoring of these ecosystems under pressure. In 2008 an optical procedure was developed and applied in Ghar El Melh, a Tunisian lagoon which has been increasingly impacted by pollutant loading, especially from agriculture. In situ hyperspectral irradiance was measured at several stations, from which the apparent optical properties (AOPs), namely the irradiance attenuation coefficient K(λ) and the reflectance ratio R(λ), were obtained in order to relate them to water composition, in terms of light-attenuating substances (LASs). The significant relationships observed between R and LAS values enabled the application of a hyperspectral optical classification, which effectively highlighted threatened sectors of the lagoon. The pattern of differing water quality across the lagoon system that was derived from the hyperspectral classification agreed well with that obtained from a conventional optical classification that included AOPs and LASs. We suggest that hyperspectral analysis and classification is a useful monitoring tool for the assessment of change in coastal lagoons, and perhaps also in other shallow-water ecosystems.