The identification of tree species can provide a useful and efficient tool for forest managers for planning and monitoring purposes. Hyperspectral data provide sufficient spectral information to classify individual tree species. Two non-parametric classifiers, support vector machines (SVM) and random forest (RF), have resulted in high accuracies in previous classification studies. This research takes a comparative classification approach to examine the SVM and RF classifiers in the complex and heterogeneous forests of Muir Woods National Monument and Kent Creek Canyon in Marin County, California. The influence of object- or pixel-based training samples and segmentation size on the object-oriented classification is also explored. To reduce the data dimensionality, a minimum noise fraction transform was applied to the mosaicked hyperspectral image, resulting in the selection of 27 bands for the final classification. Each classifier was also assessed individually to identify any advantage related to an increase in training sample size or an increase in object segmentation size. All classifications resulted in overall accuracies above 90%. No difference was found between classifiers when using object-based training samples. SVM outperformed RF when additional training samples were used. An increase in training samples was also found to improve the individual performance of the SVM classifier.

The information acquired in the narrow bands of hyperspectral remote sensing data has potential to capture plant species spectral variability, thereby improving forest tree species mapping. This study assessed the utility of spaceborne EO-1 Hyperion data in discrimination and classification of broadleaved evergreen and conifer forest tree species in western Himalaya. The pre-processing of 242 bands of Hyperion data resulted into 160 noise-free and vertical stripe corrected reflectance bands. Of these, 29 bands were selected through step-wise exclusion of bands (Wilk's Lambda). Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) algorithms were applied to the selected bands to assess their effectiveness in classification. SVM was also applied to broadband data (Landsat TM) to compare the variation in classification accuracy. All commonly occurring six gregarious tree species, viz., white oak, brown oak, chir pine, blue pine, cedar and fir in western Himalaya could be effectively discriminated. SVM produced a better species classification (overall accuracy 82.27%, kappa statistic 0.79) than SAM (overall accuracy 74.68%, kappa statistic 0.70). It was noticed that classification accuracy achieved with Hyperion bands was significantly higher than Landsat TM bands (overall accuracy 69.62%, kappa statistic 0.65). Study demonstrated the potential utility of narrow spectral bands of Hyperion data in discriminating tree species in a hilly terrain.

In this work, many of the fundamental and advanced spectral processing methods available to geologic remote sensing are reviewed. A novel categorization scheme is proposed that groups the techniques into knowledge-based and data-driven approaches, according to the type and availability of reference data. The two categories are compared and their characteristics and geologic outcomes are contrasted. Using an oil-sand sample scanned through the sisuCHEMA hyperspectral imaging system as a case study, the effectiveness of selected processing techniques from each category is demonstrated. The techniques used to bridge between the spectral data and other geoscience products are then discussed. Subsequently, the hybridization of the two approaches is shown to yield some of the most robust processing techniques available to multi-, and hyperspectral remote sensing. Ultimately, current and future challenges that spectral analysis is expected to overcome and some potential trends are highlighted.

高光谱遥感技术的出现将为解决森林树种的精细识别难题提供有效的途径。 利用高光谱遥感技术进行树种鉴别时, 光谱特征的选择及提取是个非常重要的过程。 与多光谱数据相比, 高光谱数据具有波段多、 数据量大、 冗余度大等特点。 该文利用光谱微分法对原始光谱数据进行处理, 分析不同树种原始光谱、 光谱一阶微分和光谱二阶微分曲线图, 从中选择差异较大的波段用于鉴别不同树种。 最后利用欧氏距离对所选择的波段进行检验识别不同树种的效果, 检验的结果显示选择的波段能有效地区分不同树种。 区分不同树种的有效波段大都位于近红外波段, 并且差异最大的波段也是近红外波段, 其分别为1 657～1 666和1 868～1 877 nm。Characteristic Analysis

We investigated the utility of high spectral and spatial resolution imagery for the automated species-level classification of individual tree crowns (ITCs) in a tropical rain forest (TRF). Laboratory spectrometer and airborne reflectance spectra (161 bands, 437–2434 nm) were acquired from seven species of emergent trees. Analyses focused on leaf-, pixel- and crown-scale spectra. We first described the spectral regions and factors that most influence spectral separability among species. Next, spectral-based species classification was performed using linear discriminant analysis (LDA), maximum likelihood (ML) and spectral angle mapper (SAM) classifiers applied to combinations of bands from a stepwise-selection procedure. Optimal regions of the spectrum for species discrimination varied with scale. However, near-infrared (700–1327 nm) bands were consistently important regions across all scales. Bands in the visible region (437–700 nm) and shortwave infrared (1994–2435 nm) were more important at pixel and crown scales. Overall classification accuracy decreased from leaf scales measured in the laboratory to pixel and crown scales measured from the airborne sensor. Leaf-scale classification using LDA and 40 bands had 100% overall accuracy. Pixel-scale spectra from sunlit regions of crowns were classified with 88% overall accuracy using a ML classifier and 60 bands. The highest crown-scale (ITC) accuracy was 92% with LDA and 30 bands. Producer's accuracies ranged from 70% to 100% and User's accuracies ranged from 81% to 100%. The SAM classifier performed poorly at all scales and spectral regions of analysis. ITCs were also classified using an object-based approach in which crown species labels were assigned according to the majority class of classified pixels within a crown. An overall accuracy of 86% was achieved with an object-based LDA classifier applied to 30 bands of data. Object-based and crown-scale ITC classifications were significantly more accurate with 10 narrow-bands relative to accuracies achieved with simulated multispectral, broadband data. We concluded that high spectral and spatial resolution imagery acquired over TRF canopy has substantial potential for automated ITC species discrimination.

Discriminating commercial tree species using hyperspectral remote sensing techniques is critical in monitoring the spatial distributions and compositions of commercial forests. However, issues related to data dimensionality and multicollinearity limit the successful application of the technology. The aim of this study was to examine the utility of the partial least squares discriminant analysis (PLS-DA) technique in accurately classifying six exotic commercial forest species (Eucalyptus grandis, Eucalyptus nitens, Eucalyptus smithii, Pinus patula, Pinus elliotii and Acacia mearnsii) using airborne AISA Eagle hyperspectral imagery (393–900nm). Additionally, the variable importance in the projection (VIP) method was used to identify subsets of bands that could successfully discriminate the forest species. Results indicated that the PLS-DA model that used all the AISA Eagle bands (n=230) produced an overall accuracy of 80.61% and a kappa value of 0.77, with user’s and producer’s accuracies ranging from 50% to 100%. In comparison, incorporating the optimal subset of VIP selected wavebands (n=78) in the PLS-DA model resulted in an improved overall accuracy of 88.78% and a kappa value of 0.87, with user’s and producer’s accuracies ranging from 70% to 100%. Bands located predominantly within the visible region of the electromagnetic spectrum (393–723nm) showed the most capability in terms of discriminating between the six commercial forest species. Overall, the research has demonstrated the potential of using PLS-DA for reducing the dimensionality of hyperspectral datasets as well as determining the optimal subset of bands to produce the highest classification accuracies.

Despite numerous studies existing for tree species classification the difficult situation in dense and mixed temperate forest is still a challenging task. This study attempts to extend the existing limitations by investigating comprehensive sets of different types of features derived from multiple data sources. These sets include features from full-waveform LiDAR, LiDAR height metrics, texture, hyperspectral data and colour infrared (CIR) images. Support vector machines (SVM) are used as an appropriate classifier to handle the high dimensional feature space and an internal ranking method allows the determination of the most important parameters. In addition, for species discrimination, focus is put on single tree applicable scale. While most experiences within these scales derive from boreal forests and are often restricted to two or three species, we concentrate on more complex temperate forests. The four main species pine (Pinus sylvestris), spruce (Picea abies), oak (Quercus petraea) and beech (Fagus sylvatica) are classified with an accuracy of 89.7%, 88.7%, 83.1% and 90.7%, respectively. Instead of directly classifying delineated single trees a raster cell based classification is conducted. This overcomes problems with erroneous polygons of merged tree crowns, which occur frequently within dense deciduous or mixed canopies. Lastly, we further test the possibility to correct these failures by combining species classification with single tree delineation.

采用ASD公司生产的FieldSpec HandHeldTM地物光谱仪,分别于2005、2006、2008年冬季跟踪观测杉木、马尾松、黑松、雪松等针叶树种的高光谱数据,经筛选后获取有效观测数据160条,其中120条作为训练集,40条作为测试集。将平滑去噪的一阶微分高光谱数据进行PCA方法和GA方法降维,然后利用BP神经网络和支持向量机（SVM）对降维后的测试集数据进行分类。结果表明：PCA—BP神经网络模型分类准确率95%,PCA—SVM分类准确率97.5%,GA和BP分类准确率92.5%,GA-SVM分类准确率100%。这说明两种降维方式结合支持向量机的分类均优于其与BP神经网络结合的分类,基于GA的降维方法对高光谱波段的选择更有效率,具有较好的应用前景。

Imaging spectroscopy, also known as hyperspectral imaging, has been transformed in less than 30 years from being a sparse research tool into a commodity product available to a broad user community. Currently, there is a need for standardized data processing techniques able to take into account the special properties of hyperspectral data. In this paper, we provide a seminal view on recent advances in techniques for hyperspectral image processing. Our main focus is on the design of techniques able to deal with the high-dimensional nature of the data, and to integrate the spatial and spectral information. Performance of the discussed techniques is evaluated in different analysis scenarios. To satisfy time-critical constraints in specific applications, we also develop efficient parallel implementations of some of the discussed algorithms. Combined, these parts provide an excellent snapshot of the state-of-the-art in those areas, and offer a thoughtful perspective on future potentials and emerging challenges in the design of robust hyperspectral imaging algorithms.

黄山是我国东部高山之一,处于亚热带季风气候区,属南北植物区系交替的过渡带,是第四纪冰期动植物的避难所。其地带性植被为常绿阔叶林,植被垂直分布明显,是中国生物多样性保护优先区域,也是世界文化与自然遗产地以及享誉全球的风景名胜区。2014年,我们在黄山建立了10.24 ha的森林动态监测样地,并完成了首次调查。本文从物种组成、区系特征、径级结构和空间分布格局等方面分析了样地中植物的群落特征。结果表明：样地内有维管植物59科129属191种,其中乔木层内胸径≥1 cm的木本植物46科97属153种;热带性质的科、属分别占总科、属数的65.79%和45.36%,温带性质的科、属分别占34.21%和51.55%。样地内珍稀濒危物种较多,其中国家Ⅱ级重点保护野生植物6种、《中国生物多样性红色名录——高等植物卷》中的近危物种7种、《濒危野生动植物种国际贸易公约》（CITES）附录Ⅱ物种1种以及64种中国特有种,这些物种具有较高的保护和研究价值。当取样面积小于2,150 m~2时,物种数随着面积的增加而急剧增加;其后增加速率明显降低;但大于57,950 m~2时,增加速率又略变大。稀有种69种,占总树种数的45.10%。壳斗科和杜鹃花科的重要值占一半以上。建群种甜槠（Castanopsis eyrei）的重要值达26.25%,其次分别为细齿叶柃（Eurya nitida）（7.63%）、马银花（Rhododendron ovatum）（7.60%）、马尾松（Pinus massoniana）（6.29%）和檵木（Loropetalum chinense）（4.83%）。样地平均胸径为4.10 cm,小径木的数量占较大优势。乔木层可分为两个亚层,甜槠在两个亚层的个体数量均最多,马尾松数量也比较多。甜槠、细齿叶柃、马银花、马尾松等均呈较显著的聚集分布。

Abstract The objective of the present study is the comparison of the combined use of Earth Observation-1 (EO-1) Hyperion Hyperspectral images with the Random Forest (RF), Support Vector Machines (SVM) and Multivariate Adaptive Regression Splines (MARS) classifiers for discriminating forest cover groups, namely broadleaved and coniferous forests. Statistics derived from classification confusion matrix were used to assess the accuracy of the derived thematic maps. We demonstrated that Hyperion data can be effectively used to obtain rapid and accurate large-scale mapping of main forest types (conifers-broadleaved). We also verified higher capability of Hyperion imagery with respect to Landsat data to such an end. Results demonstrate the ability of the three tested classification methods, with small improvements given by SVM in terms of overall accuracy and kappa statistic.

To study the reflectance spectra of ground vegetation in forests a series of field measurements was performed by a GER-2600 spectrometer in Estonian and Swedish sub-boreal forests. The measured reflectance spectra of grasses, regenerating trees, mosses and dwarf shrubs were analysed with a two-layer model of vegetation canopy reflectance. By the inversion of the two-layer model a set of model parameters was estimated for several types of understorey vegetation. The estimated sets of understorey parameters can be used to reproduce the spectral signatures of the respective types of ground vegetation which are needed in simulating the reflectance spectra of sub-boreal and boreal forests. Principal component analysis showed that at least six biochemical components are needed to reproduce the spectral signatures of understorey vegetation in the spectral range of 400-2400 nm. Some vegetation indices (VI) which are in use in remote sensing are rather well correlated with canopy parameters estimated in the inversion. At the same time several VIs have very low sensitivity and/or almost random behavior.