新疆喀纳斯国家自然保护区植被叶面积指数观测与遥感估算

叶面积指数(Leaf Area Index,LAI)是重要的植被结构参数,调控着植被与大气之间的物质与能量交换,在生态环境脆弱的我国西北部开展植被LAI的研究对阐明该地区植被对气候变化和人类活动的响应特征具有重要的科学意义.利用LAI-2200和TRAC仪器观测了新疆喀纳斯国家级自然保护区森林和草地的有效叶面积指数(LAIe)和真实LAI,构建了其遥感估算模型,生成了研究区LAIe和LAI的空间分布图.在此基础上,分析了LAI随地形因子(海拔、坡度、坡向)的变化特征,探讨了将其应用于估算研究区森林生物量密度的可行性,并评估了研究区MODIS LAI产品的精度.结果表明:研究区阔叶林、针阔混交林、针叶林、草地LAIe的平均值分别为4.40、3.18、2.57、1.76,LAI的平均值分别为4.76、3.93、3.27、2.30.LAIe和LAI的高值主要集中分布在湖泊和河流附近;植被LAI随海拔、坡度和坡向的变化表现出明显的垂直地带性的特点.LAI随海拔和坡度的增加呈现先增加后减小的变化趋势,坡向对针叶林和草地LAI的影响明显,但对阔叶林和针阔混交林LAI的影响较弱;森林生物量密度(BD)随LAI增加而线性增加(BD=44.396LAI-25.946,R2=0.83),研究区森林生物量密度平均值为120.3 t/hm2,估算的总生物量为5.0×l06 t;MODIS LAI产品与利用TM数据生成的LAI之间具有一定的相似性(森林R2=0.42,草地R2=0.53),但森林和草地的MODIS LAI产品分别比利用TM数据生成的LAI偏低16.5％和24.4％.     

林下植被对遥感估算马尾松LAI的影响

叶面积指数是一项极其重要的描述植被冠层结构的植被特征参量。根据植被物候规律,利用中国环境卫星CCD多光谱影像和野外马尾松样区调查数据,通过建立不同季节和不同郁闭度样区马尾松LAI和影像NDVI经验回归模型,并利用一个新的LAI观测方式定量比较乔木层LAI和生态系统总LAI(包括草本层、灌木层和乔木层)的差异,研究林下植被对马尾松反演的影响程度。结果表明:(1)由于林下植被的物候变化,冬季林下植被对马尾松LAI反演影响最小,马尾松NDVI和LAI线性关系R2维持在0.65;夏季林下植被影响最大,线性关系R2只有0.25;春季和秋季影响居中,NDVI和LAI线性关系R2在0.47附近。但是,受林下植被影响较小的A类样区4个季节内NDVI和LAI线性关系基本都在0.60以上(夏季略低于0.60);(2)乔木层LAI和总LAI差距非常大,最大差距达到2.93,相差的比例最大达到了2.45倍;(3)总LAI和NDVI相关关系显著,其中线性关系R2达到0.66,对数关系R2可达到0.68,而乔木层LAI和NDVI相关关系较差,线性关系R2只有0.30。分别建立冬季和其它季节实测总LAI和NDVI的关系,可以估算出林下植被对马尾松LAI反演的影响程度。     

中国区域植被叶面积指数时空分布——机理模型模拟与遥感反演比较

叶面积指数是表征植被冠层特征的重要参数,同时也是决定生态系统净初级生产力的重要因子,它对全球变化和生态系统碳循环研究具有重要意义。目前大范围的叶面积指数只能通过遥感反演和机理模型模拟获得,而通过这两种方法获取的叶面积指数都存在一定的不确定性。利用大气-植被相互作用模型(AVIM2)在0.1°×0.1°经纬度网格上模拟产生了中国区域叶面积指数并与两套使用不同遥感反演方法生成的叶面积指数在空间分布和季节变化特征方面进行了比较。通过比较说明中国区域植被叶面积指数分布主要受水分条件限制,整体呈现东南部高西北部低的趋势。中国区域植被生长的季节变化受季风影响显著,与气温及地表太阳辐射的季节变化趋势相一致。中国区域叶面积指数整体呈现夏季高、春秋季次之而冬季低的趋势。     

基于遥感生态指数的阜新市生态质量评估

城市化建设的不断推进及发展给生态环境带来了日益增加的压力,如何多方位、客观准确并且快速地对区域生态环境质量进行评估是目前研究的一个重要方向。以辽宁省阜新市为研究区域,选取2000年、2008年和2016年的遥感影像作为基础研究数据,借助ENVI5.1软件作为数据处理平台,再结合主成分分析方法,借助遥感生态环境质量评价指数RSEI对阜新市的生态质量进行评估。结果表明:2000年、2008年和2016年,阜新市的生态质量总体呈上升趋势;2000—2016年间,研究区RSEI主要由1、2级向3、4、5级转变,生态质量变好和变坏的面积分别占总面积的53.28%和2.38%,生态质量变差的区域主要集中在阜新市东北的风沙区和西南部盆地区;而阜新市西南部盆地区主要是市区,该区生态环境质量变差主要在于近年来的城市快速发展,东北部的风沙区与科尔沁沙地接壤,该区的环境变差在于所处地理位置以及防风固沙林中部分林木出现退化现象等,因此能否在快速城市化建设的同时重视生态环境的保护与修复,成为阜新市生态环境质量变化的重要决定因素。     

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。     

植被叶面积指数遥感监测模型

叶面积指数是植被定量遥感的重要参数,区域的时序列叶面积指数揭示了区域生态的演化过程,反演方法上主要是通过植被指数建立相关模型实现的,对于不同地区或不同气候带而言,模型的通用性以及各种植被指数在模型中的灵敏度都需做进一步的探讨。以江苏省宜兴市作为研究区,采用2002年8月22日获得的Landsat-5TM图像数据和2003年8月23～26日采用LAI-2000进行的野外实测植被叶面积指数(LAI)数据,分别探讨了植被指数(VI)与LAI的一元、多元线性回归模型和非线性回归模型,其中的非线性回归模型包括对数、指数、乘幂和多项式回归模型。结果表明,VI与LAI之间的最佳回归模型为多元线性回归模型,R2达0.864;采用逐步选择剔除法,遴选出了用于回归模型的植被指数为RVI、PVI、SAVIL=0.35、MSAVI、ARVIγ=1、ARVIγ=0.5和SARVI。经模型LAI=-ln((VI-VI∞)/(VIg-VI∞))/KVI检验,预测值(y)与实测值(x)的拟合度较好y=0.5345x 1.3304,R2为0.7379。RVI与LAI的三次多项式回归模型也较好,R2为0.7806。再次为RVI与LAI的一元线性回归模型,R2为0.7726,比值植被指数RVI在反演叶面积指数模型中具有较高的灵敏度。     

城市遥感生态指数的创建及其应用

城市生态与人类生活息息相关,快速、准确、客观地了解城市生态状况已成为生态领域的一个研究重点。基于遥感技术,提出一个完全基于遥感技术,以自然因子为主的遥感生态指数（RSEI）来对城市的生态状况进行快速监测与评价。该指数利用主成分分析技术集成了植被指数、湿度分量、地表温度和建筑指数等4个评价指标,它们分别代表了绿度、湿度、热度和干度等4大生态要素。通过在福州主城区的应用表明,RSEI指数可以定量地评价和对比城市的生态质量,方便地进行时空动态变化分析。由于所选的指标完全基于遥感信息,容易获得,且计算过程无需人工干预,因此结果客观可靠、可比性强。     

Global remote sensing of water–chlorophyll ratio in terrestrial plant leaves

I evaluated the use of global remote sensing techniques for estimating plant leaf chlorophyll a + b (C_ab; μg cm⁻²) and water (C_w; mg cm⁻²) concentrations as well as the ratio of C_w/C_ab with the PROSAIL model under possible distributions for leaf and soil spectra, leaf area index (LAI), canopy geometric structure, and leaf size. First, I estimated LAI from the normalized difference vegetation index. I found that, at LAI values <2, C_ab, C_w, and C_w/C_ab could not be reliably estimated. At LAI values >2, C_ab and C_w could be estimated for only restricted ranges of the canopy structure; however, the ratio of C_w/C_ab could be reliably estimated for a variety of possible canopy structures with coefficients of determination (R²) ranging from 0.56 to 0.90. The remote estimation of the C_w/C_ab ratio from satellites offers information on plant condition at a global scale.     

内蒙古不同类型草地叶面积指数遥感估算

叶面积指数(Leaf Area Index,LAI)是重要的植被结构参数,反演LAI是植被遥感的重要研究内容之一。根据在内蒙古呼伦贝尔和锡林浩特草原利用LAI 2000观测的草地LAI,比较了不同植被指数(SR、RSR、EVI、NDVI、SAVI和ARVI)估算不同类型草地LAI的能力,建立了基于Landsat-5 TM遥感数据的LAI估算模型,并利用LAI观测数据对模型进行了检验,生成了研究区内草地LAI分布图,据此对MODIS LAI产品一致性进行了评价。结果表明,在呼伦贝尔和锡林浩特两个研究区,RSR与LAI的相关性最高(R²分别为0.628、0.728,RMSE分别为0.512、0.490),在低密度草地,RSR的优势更为明显;验证表明,根据RSR建立的LAI估算模型的精度可达70%;利用TM数据生成的两个地区的LAI(TM LAI)空间变化明显,锡林浩特草地的LAI值整体上低于呼伦贝尔草地;在呼伦贝尔和锡林浩特,MODIS LAI产品与TM LAI一致性分别为0.566,0.323,MODIS LAI产品高估了呼伦贝尔草地LAI值,而在锡林浩特研究区则存在低估现象。     

Comparison of leaf area index inversion for grassland vegetation through remotely sensed spectra by unmanned aerial vehicle and field-based spectroradiometer

Aims Remote sensing technology has been proved useful in mapping grassland vegetation properties. Spectral features of vegetation cover can be recorded by optical sensors on board of different platforms. With increasing popularity of applying unmanned aerial vehicle (UAV) to mapping plant cover, the study aims to investigate the possible applications and potential issues related to mapping leaf area index (LAI) through integration of remote sensing imagery collected by multiple sensors.     

Exploring the influence of soil types underneath the canopy in winter wheat leaf area index remote estimating

Aims Remote sensing is an effective and nondestructive way to retrieve leaf area index (LAI) from plot, regional and global range. Soil background is one of the confounding factors limiting remotely estimating LAI. And soil type contains a large proportion of soil background information, which can influence the optical properties of vegetation canopy and soil. However, our knowledge on the effects stemmed from soil types underneath the canopy on LAI remote estimating have been in shortage. Thus, this study aims to explore the influences of soil types underneath the canopy on winter wheat LAI remote estimating. Methods We analyzed the sensitivity variation of eight spectral indices, named normalized difference vegetation index (NDVI), modified soil-adjusted vegetation index (MSAVI), modified chlorophyll absorption ratio index 2 (MCARI₂), red edge inflection point (REIP), red edge amplitude (Dr), red edge area (SDr), red edge symmetry (RES), normalized difference spectral index (NDSI), to LAI in different soil types, and then we identified some spectral intervals or parameters that were insensitive to soil type variations underneath the canopy. We also compared the accuracy of two commonly used regression models, partial least squares regression (PLSR) and random forest regression (RFR), in estimating LAI for different soil types. We also explored the problems arising from applying the regression model developed in single soil type area to complex soil types area in retrieving LAI. Important findings This paper demonstrates the effects of soil types underneath the canopy on LAI retrieving. 1) The sensitivity of spectral indices to LAI is significantly different due to the soil type variation, but REIP has the least effects from soil type variation among the eight spectral indices. Meanwhile, the band selection algorithm of lambda-by-lambda not only chooses the most sensitive spectral interval for LAI, but also provides a feasible way to construct the spectral index that exhibits strong resistances to the effects of soil types underneath the canopy. 2) The accuracy of LAI estimation by regression models differs under soil type considered or not. So we suggest that in small scale researches, especially in a field scale, the ability of regression models in explaining variables is the priority consideration, while the PLSR is superior to RFR in this respect. Under the premise of unknown priori knowledge of land surfaces, the RFR is more suitable for retrieving LAI than PLSR, but land surface priori knowledge is still necessary. These findings provide the theoretical basis and methods for developing remotely sensing estimating LAI models adapted to various land surfaces. Further analysis is needed in applying the findings in more crop types, cultivars and growth stages.     

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.     

Defining leaf area index for non-flat leaves

To eliminate the confusion in the definition of leaf area index (L) for non-flat leaves, the projection coefficients of several objects including spheres, cylinders, hemicircular cylinders, and triangular and square bars are investigated through mathematical derivation and numerical calculation for a range of ellipsoidal angular distributions. It is shown that the projection coefficient calculated based on half the total intercepting area is close to a constant of 0.5 when the inclination angle of the objects is randomly (spherically) distributed, whereas the calculated results based on the object's largest projected area are strongly dependent on the shape of the objects. Therefore, it is suggested that the leaf area index of non-flat leaves be defined as half the total intercepting area per unit ground surface area and that the definition of L based on the projected leaf area be abandoned.     

定量遥感在生态学研究中的基础应用

生态学问题 ,特别是近来兴起的全球变化问题 ,是存在于不同时空尺度的生物与环境互作的格局和动态变化 ,对它的研究需要较大时空尺度的数据支撑 ,因此不同时空分辩率的遥感影象图就成为了这一重要的数据源。从遥感的功能出发 ,介绍了遥感应用于植被覆盖分类、生态系统参数提取及生态系统模型等方面的基础研究情况 ,试图为生态学研究提供应用遥感的思路 ,为进一步应用遥感解决生态学问题提供基础。     

基于遥感生态指数的雄安新区生态质量评估

城镇化建设的持续推进给生态环境带来的压力日益增加,多方位、客观、准确、快速测算区域生态环境质量是生态学研究的一个重点.本研究从绿度、湿度、热度、干度4方面分别提取了归一化植被指数(NDVI)、湿度指数(WET)、地表温度(LST)、归一化建筑-土壤指数(NDBSI)4个分量指标,采用基于ENVI平台的主成分分析技术集成所选指标,基于新型遥感生态指数(RSEI)对1995—2015年雄安新区生态质量进行评估.结果表明: 1995、2004和2015年,雄安新区的RSEI均值分别为0.724、0.710、0.682,生态质量总体呈下降趋势;1995—2015年间,研究区RSEI主要由4、5级向1、2、3级转变,生态质量改善和恶化的面积分别占总面积的8.9%和20.9%,生态改善区域主要位于雄县东部和南部,主要原因在于该区域大面积的林地和园地受到当地政府的高度重视和严格保护,生态质量恶化区域主要位于城镇外围以及白洋淀周边,原因在于白洋淀水域面积的锐减和城市化的不断推进;RSEI与各分量指标的平均相关系数为0.804,均高于各分量指标间的平均相关系数,表明RSEI能较好地综合各分量指标信息,全面准确反映研究区生态质量状况.     

利用冠层光谱估测烟草叶面积指数和地上生物量

综合多种烟草类型、品种及肥料处理因素,分析了17种光谱参数与烟草叶面积指数（LAI）、地上鲜生物重（AFW）、地上干生物重（ADW）的关系,建立逐步回归模型对烟草LAI、AFW、ADW进行估测并结合相关分析筛选出相应的特征变量。结果表明：5个回归方程的复确定系数R^2、回归系数相伴概率均达到显著水平。包含17个光谱参量的逐步回归方程筛选出的第一自变量均为Rg／Rr,相关分析及散点图分析亦得出Rg／Rr与LAI、AFW、ADW相关系数分别为0．759、0．611、0．647,R^2为0．576、0．3727、0．4184,均达到极显著水平,证明烟草LAI、AFW、ADW的特征变量为Rg／Rr。仅采用8种植被指数建立模型,证明利用比值植被指数（RVI）估测LAI、ADW亦是可行的。经过统计检验,建立的模型估测效果均较好,估测值与实测值的相关性均达到显著水平,其中包含特征变量Rg／Rr的回归模型估测效果优于RVI构建的模型。表明采用高分辨率光谱或宽波段光谱提取光谱变量可对烟草LAI、AFW、ADW进行监测,并可根据数据条件选择有效的估测模型,为烟草遥感数据分析提供方法。     

Amazon forest carbon dynamics predicted by profiles of canopy leaf area and light environment

Tropical forest structural variation across heterogeneous landscapes may control above‐ground carbon dynamics. We tested the hypothesis that canopy structure (leaf area and light availability) – remotely estimated from LiDAR – control variation in above‐ground coarse wood production (biomass growth). Using a statistical model, these factors predicted biomass growth across tree size classes in forest near Manaus, Brazil. The same statistical model, with no parameterisation change but driven by different observed canopy structure, predicted the higher productivity of a site 500 km east. Gap fraction and a metric of vegetation vertical extent and evenness also predicted biomass gains and losses for one‐hectare plots. Despite significant site differences in canopy structure and carbon dynamics, the relation between biomass growth and light fell on a unifying curve. This supported our hypothesis, suggesting that knowledge of canopy structure can explain variation in biomass growth over tropical landscapes and improve understanding of ecosystem function.     

Inconsistencies of interannual variability and trends in long‐term satellite leaf area index products

Understanding the long‐term performance of global satellite leaf area index (LAI) products is important for global change research. However, few effort has been devoted to evaluating the long‐term time‐series consistencies of LAI products. This study compared four long‐term LAI products (GLASS, GLOBMAP, LAI3g, and TCDR) in terms of trends, interannual variabilities, and uncertainty variations from 1982 through 2011. This study also used four ancillary LAI products (GEOV1, MERIS, MODIS C5, and MODIS C6) from 2003 through 2011 to help clarify the performances of the four long‐term LAI products. In general, there were marked discrepancies between the four long‐term LAI products. During the pre‐MODIS period (1982–1999), both linear trends and interannual variabilities of global mean LAI followed the order GLASS>LAI3g>TCDR>GLOBMAP. The GLASS linear trend and interannual variability were almost 4.5 times those of GLOBMAP. During the overlap period (2003–2011), GLASS and GLOBMAP exhibited a decreasing trend, TCDR no trend, and LAI3g an increasing trend. GEOV1, MERIS, and MODIS C6 also exhibited an increasing trend, but to a much smaller extent than that from LAI3g. During both periods, the R² of detrended anomalies between the four long‐term LAI products was smaller than 0.4 for most regions. Interannual variabilities of the four long‐term LAI products were considerably different over the two periods, and the differences followed the order GLASS>LAI3g>TCDR>GLOBMAP. Uncertainty variations quantified by a collocation error model followed the same order. Our results indicate that the four long‐term LAI products were neither intraconsistent over time nor interconsistent with each other. These inconsistencies may be due to NOAA satellite orbit changes and MODIS sensor degradation. Caution should be used in the interpretation of global changes derived from the four long‐term LAI products.     

基于PROSAIL辐射传输模型的毛竹林叶面积旨数遥感反演

采用PROSAIL辐射传输模型建立毛竹林叶面积指数(LAI)-冠层反射率查找表,并结合Landsat TM卫星遥感数据,实现了毛竹林LAI的定量反演.结果表明:PROSAIL模型各输入参数的敏感性由高到低依次为LAI＞叶绿素含量(Cab)＞叶片结构参数(N)＞平均叶倾角(ALA)＞等效水厚度(Cw)＞干物质含量(Cm),并以LAI、Cab两个主要敏感因子用于构建毛竹林LAI-冠层反射率查找表;基于PROSAIL模型的毛竹林LAI遥感反演结果与实测LAI具有很好的一致性,二者相关系数为0.90,均方根误差和相关的均方根误差也较小,分别为0.58和13.0％,但也存在反演LAI平均值高于实际值的问题.     

Leaf area index (LAI) map of a protected area within the caldera of Vico Lake (Italy)

ABSTRACT

Structural traits of the vegetation types and plantations occurring in a protected area within the caldera of Vico Lake (Italy) were analysed. There were significant correlations among structural traits, at leaf and stand level. Leaf area index (LAI) and specific leaf area (SLA) were the most significantly correlated traits. LAI rose according to stand plant density, tree size and SLA; the highest LAI value monitored in the Fagus sylvatica L. forest was justified by the largest tree size (28.9±2.8 m height and 53±15 cm diameter) and the highest SLA (212±23 cm² g^-1). The main traits determining the variations in leaf structure among species were analysed by Principal Component Analysis (PCA). The LAI values were used to realise a map allowing us to delimit high LAI values (4.1–5.0), corresponding to the F. sylvatica forest and to the F. sylvatica forest with the sporadic presence of Quercus cerris L. and Castanea sativa Miller, mean LAI values (classes 3.1–4.0) corresponding to Corylus avellana L. plantations and to the Phragmites australis (Cav.) Trin. vegetation type, low LAI values (classes 2.6–3.0) corresponding to Q. cerris forests and C. sativa plantations.