Classification of Pinus massoniana and secondary deciduous tree species in northern subtropical region based on high resolution and hyperspectral remotely sensed data

Aims Using remote sensing data for tree species classification plays a key role in forestry resource monitoring, sustainable forest management and biodiversity research.Methods This study used integrated sensor LiCHy (LiDAR, CCD and Hyperspectral) to obtain both the high resolution imagery and the hyperspectral data at the same time for the natural secondary forest in south Jiangsu hilly region. The data were used to identify the crown and to classify tree species at multiple levels. Firstly, tree crowns were selected by segmenting high-resolution imagery at multiple scales based on edge detection; secondly, characteristic variables of hyperspectral images were extracted, then optimization variables were selected based on the theory of information entropy. Tree species and forest types were classified using either all characteristic variables or optimization variables only. Finally, tree species and forest types were reclassified along with the tree crowns information, and the accuracy of classification was discussed. Important findings Based on all available characteristic variables, the overall accuracy for four typical tree species classification was 64.6%, and the Kappa coefficient was 0.493. The overall accuracy for forest types classification was 81.1%, and the Kappa coefficient was 0.584. Based on optimization variables only, the overall accuracy for four typical tree species classification dropped to 62.9%, and the Kappa coefficient was 0.459. The overall accuracy for forest types classification was 77.7%, and the Kappa coefficient was 0.525. Obtaining both high resolution image and hyperspectral data at the same time by integrated sensor can increase overall accuracy in classifying forest types and tree species in northern subtropical forest.     

基于高分辨率与高光谱遥感影像的北亚热带马尾松及次生落叶树种的分类

利用遥感数据开展森林资源树种的分类对森林资源的监测、森林可持续经营及生物多样性研究都有重要意义。该文以江苏南部丘陵地区的北亚热带天然次生林为研究对象, 利用LiCHy (LiDAR、CCD、Hyperspectral)集成传感器同期获取的高分辨率和高光谱数据, 进行冠幅识别和多个层次的树种分类: 首先, 对高分辨率影像进行基于边缘检测的多尺度分割, 提取出单木冠幅; 其次, 对高光谱影像进行特征变量提取, 并对提取出的特征变量利用信息熵原理选取优化特征变量; 然后, 分别利用全部特征变量和经优化的重要特征变量对森林树种及森林类型进行预分类; 最后, 在预分类结果中加入单木冠幅信息对森林树种及森林类型进行重分类, 并分析分类结果的精度。研究表明: 1)利用全部特征变量进行4个典型树种分类时, 总体精度为64.6%, Kappa系数为0.493; 而针对森林类型的分类精度为81.1%, Kappa系数为0.584。2)利用选取的优化特征变量分类精度略低于利用全部特征变量的分类精度, 其中对4个典型树种分类时, 总体精度为62.9%, Kappa系数为0.459; 而针对森林类型的分类精度为77.7%, Kappa系数为0.525。通过集成传感器同期获取的高分辨率和高光谱数据可以有效地进行北亚热带森林的树种分类及森林类型的划分。     

A high‐resolution approach for the spatiotemporal analysis of forest canopy space using terrestrial laser scanning data

Forest canopies and tree crown structures are of high ecological importance. Measuring canopies and crowns by direct inventory methods is time‐consuming and of limited accuracy. High‐resolution inventory tools, in particular terrestrial laser scanning (TLS), is able to overcome these limitations and obtain three‐dimensional (3D) structural information about the canopy with a very high level of detail. The main objective of this study was to introduce a novel method to analyze spatiotemporal dynamics in canopy occupancy at the individual tree and local neighborhood level using high‐resolution 3D TLS data. For the analyses, a voxel grid approach was applied. The tree crowns were modeled through the combination of two approaches: the encasement of all crown points with a 3D α‐shape, which was then converted into a voxel grid, and the direct voxelization of the crown points. We show that canopy occupancy at individual tree level can be quantified as the crown volume occupied only by the respective tree or shared with neighboring trees. At the local neighborhood level, our method enables the precise determination of the extent of canopy space filling, the identification of tree–tree interactions, and the analysis of complementary space use. Using multitemporal TLS data recordings, this method allows the precise detection and quantification of changes in canopy occupancy through time. The method is applicable to a wide range of investigations in forest ecology research, including the study of tree diversity effects on forest productivity or growing space analyses for optimal tree growth. Due to the high accuracy of this novel method, it facilitates the precise analyses even of highly plastic individual tree crowns and, thus, the realistic representation of forest canopies. Moreover, our voxel grid framework is flexible enough to allow for the inclusion of further biotic and abiotic variables relevant to complex analyses of forest canopy dynamics.     

Amazon Forest Structure from IKONOS Satellite Data and the Automated Characterization of Forest Canopy Properties

We developed an automated tree crown analysis algorithm using 1-m panchromatic IKONOS satellite images to examine forest canopy structure in the Brazilian Amazon. The algorithm was calibrated on the landscape level with tree geometry and forest stand data at the Fazenda Cauaxi (3.75° S, 48.37° W) in the eastern Amazon, and then compared with forest stand data at Tapajos National Forest (3.08° S, 54.94° W) in the central Amazon. The average remotely sensed crown width (mean ± SE) was 12.7 ± 0.1 m (range: 2.0–34.0 m) and frequency of trees was 76.6 trees/ha at Cauaxi. At Tapajos, remotely sensed crown width was 13.1 ± 0.1 m (range: 2.0–38.0 m) and frequency of trees was 76.4 trees/ha. At both Cauaxi and Tapajos, the remotely sensed average crown widths were within 3 percent of the crown widths derived from field measurements, although crown distributions showed significant differences between field-measured and automated methods. We used the remote sensing algorithm to estimate crown dimensions and forest structural properties in 51 forest stands (1 km²) throughout the Brazilian Amazon. The estimated crown widths, tree diameters (dbh), and stem frequencies differed widely among sites, while estimated biomass was similar among most sites. Sources of observed errors included an inability to detect understory crowns and to separate adjacent, intermingled crowns. Nonetheless, our technique can serve to provide information about structural characteristics of large areas of unsurveyed forest throughout Amazonia.     

Vertical distribution of epiphytic bryophytes in an Indonesian rainforest

We studied species richness, composition and vertical distribution of epiphytic bryophytes in submontane rainforest of Central Sulawesi. Bryophytes were sampled on eight canopy trees and on eight trees in the forest understorey. Microclimate was measured at trunk bases and at crown bases. The total recorded number of 146 epiphytic bryophyte species is among the highest ever reported for tropical forests and underlines the importance of the Malesian region as a global biodiversity hotspot. Species composition differed significantly between understorey trees and canopy tree trunks on the one hand, and the forest canopy on the other. Fourty-five percent of the bryophyte species were restricted to canopy tree crowns, 12% to the understorey. Dendroid and fan-like species mainly occurred in the forest understorey whereas tufts were most species rich in the tree crowns. The findings reflect the different microclimatic regimes and substrates found in the understorey and in the forest canopy. The results indicate that assessments of the bryophyte diversity of tropical forests are inadequate when understorey trees and tree crowns are excluded.     

Terrestrial laser scanner based 3D reconstruction of trees and retrieval of leaf area index in a forest environment

Three-dimensional reconstruction of trees and the estimation of biophysical parameters is significant for the management of forest resources, ecological studies carbon cycle and biodiversity. Terrestrial LiDAR data provides detailed, objective and three-dimensional measurement of forest structure and exact metrics of the tree canopies. Several methods for tree detection including canopy height models and raster interpolation models are based on commercial software and huge data processing. The objective of the given study is the three-dimensional reconstruction of trees by implementing segmentation algorithms and thereby estimating the Leaf Area Index of individual tree segments by terrestrial laser scanned data in the Mudumalai forests of Western Ghats, India. The hierarchical minimum cut segmentation method is used for the three-dimensional reconstruction of the individual trees by tracking cylinders along individual branches and trees in a hierarchical order. Super voxel clustering method is also implemented in the study for tree reconstruction and estimating the tree parameters. Leaf area index is calculated by applying a multivariate regression technique for the heights and the diameter obtained from both the segmentation methods. Results obtained indicated a strong correlation with the in-situ measurements which are obtained from the instruments. The approach addresses the applicability of segmentation algorithms which can be run fully automatically. The approach successfully reconstructed a high precision and realistic model of trees in the Western Ghats region which failed in the case of traditional tree modeling methods which requires multiple instruments operating simultaneously for extracting each parameter. The method proved that using TLS; multiple forest parameters can be estimated simultaneously.     

A protocol for measuring abundance and size of a neotropical liana,Desmoncus polyacanthos (Palmae), in relation to forest structure

This study investigated the relationship between the abundance and size ofD. polyacanthos and measures of forest canopy structure. Várzea and terra firme forest were selected for study at the mouth of the Amazon estuary, Pará, Brazil. The forest canopy characteristics investigated included individual tree height and height to base of the live portion of the crown, forest canopy depth (the distance from the lowest foliage in the understory to the top of the canopy including all free space between top and bottom), forest canopy thickness (the portion of the forest canopy depth occupied only by foliage) and forest canopy density (the sum of lengths of each of the individual crowns that make up forest canopy depth) (see Fig. 2b). The attributes ofD. polyacanthos measured included leaf number, stem height and percent coverage within the stands. Results from this study suggest thatD. polyacanthos can be encouraged to grow through selective cutting of canopy trees and promotion of regenerating forest stands on terra firme fallow. The protocol used to relateD. polyacanthos abundance and size to kind of forest canopy structure can be used for evaluation of other climbing palms.     

Consensus framework for exploring microarray data using multiple clustering methods

The large variety of clustering algorithms and their variants can be daunting to researchers wishing to explore patterns within their microarray datasets. Furthermore, each clustering method has distinct biases in finding patterns within the data, and clusterings may not be reproducible across different algorithms. A consensus approach utilizing multiple algorithms can show where the various methods agree and expose robust patterns within the data. In this paper, we present a software package - Consense, written for R/Bioconductor - that utilizes such an approach to explore microarray datasets. Consense produces clustering results for each of the clustering methods and produces a report of metrics comparing the individual clusterings. A feature of Consense is identification of genes that cluster consistently with an index gene across methods. Utilizing simulated microarray data, sensitivity of the metrics to the biases of the different clustering algorithms is explored. The framework is easily extensible, allowing this tool to be used by other functional genomic data types, as well as other high-throughput OMICS data types generated from metabolomic and proteomic experiments. It also provides a flexible environment to benchmark new clustering algorithms. Consense is currently available as an installable R/Bioconductor package (http://www.ohsucancer.com/isrdev/consense/).     

Crown typology and the identification of rain forest trees on large-scale aerial photographs

This paper assesses the potential of large-scale aerial photographs for the identification of rain forest trees. Colour slides at 1: 3000 scale were acquired in French Guiana, above the canopy of a 25 ha study site where trees had already been identified. Firstly, the stereoscopic analysis of photographic prints served to establish a typology of the crowns, based on seven main classes of criteria: crown size, crown status, crown contour, crown architecture, foliage cover, foliage texture and colour, completed where possible by information on phenology. The terminology chosen was based on those proposed in previous studies. Secondly, a smaller area of 5 ha was delimited in the field, on which 15 tree categories (vernacular names) represented by 5 crowns or more on the photographs were selected. For each category, a standard crown was described using terminology previously defined. Twelve tree categories, including first and second class commercial timbers, displayed specific characteristics allowing them to be identified on aerial photographs. Further analyses will be undertaken in the future to measure the success of this identification method. This method may be applied for the recognition of particular species of interest: commercial, rare, endemic or key-stone species. Phenological data, as well as information on crown architectural development, can also be retrieved from aerial photographs, bringing new prospects for a better knowledge of crowns biology and their functional role in the forest ecosystem.     

Variation in crown light utilization characteristics among tropical canopy trees

BACKGROUND AND AIMS: Light extinction through crowns of canopy trees determines light availability at lower levels within forests. The goal of this paper is the exploration of foliage distribution and light extinction in crowns of five canopy tree species in relation to their shoot architecture, leaf traits (mean leaf angle, life span, photosynthetic characteristics) and successional status (from pioneers to persistent). METHODS: Light extinction was examined at three hierarchical levels of foliage organization, the whole crown, the outermost canopy and the individual shoots, in a tropical moist forest with direct canopy access with a tower crane. Photon flux density and cumulative leaf area index (LAI) were measured at intervals of 0.25-1 m along multiple vertical transects through three to five mature tree crowns of each species to estimate light extinction coefficients (K). RESULTS: Cecropia longipes, a pioneer species with the shortest leaf life span, had crown LAI <0.5. Among the remaining four species, crown LAI ranged from 2 to 8, and species with orthotropic terminal shoots exhibited lower light extinction coefficients (0.35) than those with plagiotropic shoots (0.53-0.80). Within each type, later successional species exhibited greater maximum LAI and total light extinction. A dense layer of leaves at the outermost crown of a late successional species resulted in an average light extinction of 61% within 0.5 m from the surface. In late successional species, leaf position within individual shoots does not predict the light availability at the individual leaf surface, which may explain their slow decline of photosynthetic capacity with leaf age and weak differentiation of sun and shade leaves. CONCLUSION: Later-successional tree crowns, especially those with orthotropic branches, exhibit lower light extinction coefficients, but greater total LAI and total light extinction, which contribute to their efficient use of light and competitive dominance.     

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     

Mapping Tropical Forest Trees Using High‐Resolution Aerial Digital Photographs

The spatial arrangement of tree species is a key aspect of community ecology. Because tree species in tropical forests occur at low densities, it is logistically challenging to measure distributions across large areas. In this study, we evaluated the potential use of canopy tree crown maps, derived from high‐resolution aerial digital photographs, as a relatively simple method for measuring large‐scale tree distributions. At Barro Colorado Island, Panama, we used high‐resolution aerial digital photographs (~0.129 m/pixel) to identify tree species and map crown distributions of four target tree species. We determined crown mapping accuracy by comparing aerial and ground‐mapped distributions and tested whether the spatial characteristics of the crown maps reflect those of the ground‐mapped trees. Nearly a quarter (22%) of the common canopy species had sufficiently distinctive crowns to be good candidates for reliable mapping. The errors of commission (crowns misidentified as a target species) were relatively low, but the errors of omission (missed canopy trees of the target species) were high. Only 40 percent of canopy individuals were mapped on the air photographs. Despite failing to accurately predict exact abundances of canopy trees, crown distributions accurately reproduced the clumping patterns and spatial autocorrelation features of three of four tree species and predicted areas of high and low abundance. We discuss a range of ecological and forest management applications for which this method can be useful.     

Methods for estimating understory light conditions using crown projection maps and topographic data

Four types of indices of canopy characteristics were compared to estimate understory light conditions using crown projection maps and topographic data. Crown area (CA) was formulated from the crowns in a focal sub-block (20 m×20 m). Canopy shade index (CSI) was formulated from the crowns in a focal sub-block and the surrounding sub-blocks with topographic effect. Site shade index (SSI) incorporated the shade of the slopes into CSI and represented the total physical closure of the site. Site light index (SLI) incorporated the azimuth of the site into SSI and evaluated the light condition of the understory. We tested the methods using the data on steep old-growth Cryptomeria japonica forests on Yakushima Island and the three-dimensional canopy structures of the forests were digitized on a geographic information system. The results were validated by the basic indices obtained by hemispherical photographs: canopy openness (CO) and gap light index (GLI). The three-dimensional structure of the surrounding canopies and topography had significant effects on the understory light conditions. CA had no significant correlation with CO, whereas CSI and SSI had significant negative correlations with CO. Although SLI had the highest negative correlation with GLI (R²=0.69), CSI had practical utility in the estimation of GLI (R²= 0.65). Our method is applicable to past crown projection maps that have been recorded at many forest research sites, thus making it possible to reconstruct past understory light conditions and to compare them with present conditions.     

Crown asymmetry in high latitude forests: disentangling the directional effects of tree competition and solar radiation

Light foraging by trees is a fundamental process shaping forest communities. In heterogeneous light environments this behavior is expressed as plasticity of tree growth and the development of structural asymmetries. We studied the relative influence of neighborhood structure and directional solar radiation on horizontal asymmetry of tree crowns in late‐successional high latitude (67–68°N) forests in northern Fennoscandia. We described crown asymmetries as crown vectors (i.e. horizontal vectors from stem center to crown center), which we obtained from canopy maps based on crown perimeter measurements in the field. To disentangle the influence of the two main determinants, inter‐tree competition and directionality of above‐canopy solar radiation at high latitudes, we applied circular statistical models, utilizing cylindrical distributions, to these data consisting of orientations and intensities of crown asymmetry. At the individual tree level, our model predicted crown asymmetry vectors from the current stand structure, and the predictions became better when the intensity of asymmetry (i.e. crown vector length) was higher. Competition was the main determinant of crown asymmetry for 2/3 of trees, and the model predictions improved when we incorporated the directionality of solar radiation. At the stand‐level, these asymmetries had resulted in a small increment of the projected canopy area and an increased regularity of spatial structure. Our circular statistical modelling approach provided a quantitative evaluation of the relative importance of directionality of solar radiation and neighborhood stand structure, showing how both of these factors play a role in formation of crown asymmetries in high latitude forests. This approach further demonstrated the applicability of circular statistical modeling in ecological studies where the response variable has both orientation and intensity.     

Canopy tree mortality depends on the proportion of crown exposed to sunlight,but this effect varies with species' wood density

Understanding what drives changes in tree mortality as well as the covariates influencing trees' response is a research priority to predict forest responses to global change. Here, we combined drone photogrammetry and ground-based data to assess the influence of crown exposure to light (relative to total crown area), growth deviations (relative to conspecifics), tree size, and species' wood density (as a surrogate for light-demanding and shade-tolerant life-history strategies) on the mortality of 984 canopy trees in an Amazon terra firme forest. Trees with lower wood density were less prone to die when their proportion of crown was more exposed to sunlight, but this relationship with relative crown exposure weakened and slightly reversed as wood density increased. Trees growing less than their species average had higher mortality, especially when the species' wood density decreased. The role of wood density in determining the survival of canopy trees under varying light conditions indicates differential responses of light-demanding versus shade-tolerant species. Our results highlight the importance of accounting for life-history strategies, via plant functional types, in vegetation dynamic models aiming to predict forest demography under a rapidly changing climate. Abstract in Spanish is available with online material.     

Estimating Canopy Structure in an Amazon Forest from Laser Range Finder and IKONOS Satellite Observations1

Canopy structural data can be used for biomass estimation and studies of carbon cycling, disturbance, energy balance, and hydrological processes in tropical forest ecosystems. Scarce information on canopy dimensions reflects the difficulties associated with measuring crown height, width, depth, and area in tall, humid tropical forests. New field and spaceborne observations provide an opportunity to acquire these measurements, but the accuracy and reliability of the methods are unknown. We used a handheld laser range finder to estimate tree crown height, diameter, and depth in a lowland tropical forest in the eastern Amazon, Brazil, for a sampling of 300 trees stratified by diameter at breast height (DBH). We found significant relationships between DBH and both tree height and crown diameter derived from the laser measurements. We also quantified changes in crown shape between tree height classes, finding a significant but weak positive trend between crown depth and width. We then compared the field‐based measurements of crown diameter and area to estimates derived manually from panchromatic 0.8 m spatial resolution IKONOS satellite imagery. Median crown diameter derived from satellite observations was 78 percent greater than that derived from field‐based laser measurements. The statistical distribution of crown diameters from IKONOS was biased toward larger trees, probably due to merging of smaller tree crowns, underestimation of understory trees, and overestimation of individual crown dimensions. The median crown area derived from IKONOS was 65 percent higher than the value modeled from field‐based measurements. We conclude that manual interpretation of IKONOS satellite data did not accurately estimate distributions of tree crown dimensions in a tall tropical forest of eastern Amazonia. Other methods will be needed to more accurately estimate crown dimensions from high spatial resolution satellite imagery.     

Inferring Behavioral States of Grazing Livestock from High-Frequency Position Data Alone

Studies of animal behavior are crucial to understanding animal-ecosystem interactions, but require substantial efforts in visual observation or sensor measurement. We investigated how classifying behavioral states of grazing livestock using global positioning data alone depends on the classification approach, the preselection of training data, and the number and type of movement metrics. Positions of grazing cows were collected at intervals of 20 seconds in six upland areas in Switzerland along with visual observations of animal behavior for comparison. A total of 87 linear and cumulative distance metrics and 15 turning angle metrics across multiple time steps were used to classify position data into the behavioral states of walking, grazing, and resting. Five random forest classification models, a linear discriminant analysis, a support vector machine, and a state-space model were evaluated. The most accurate classification of the observed behavioral states in an independent validation dataset was 83%, obtained using random forest with all available movement metrics. However, the state-specific accuracy was highly unequal (walking: 36%, grazing: 95%, resting: 58%). Random undersampling led to a prediction accuracy of 77%, with more balanced state-specific accuracies (walking: 68%, grazing: 82%, resting: 68%). The other evaluated machine-learning approaches had lower classification accuracies. The state-space model, based on distance to the preceding position and turning angle, produced a relatively low accuracy of 64%, slightly lower than a random forest model with the same predictor variables. Given the successful classification of behavioral states, our study promotes the more frequent use of global positioning data alone for animal behavior studies under the condition that data is collected at high frequency and complemented by context-specific behavioral observations. Machine-learning algorithms, notably random forest, were found very useful for classification and easy to implement. Moreover, the use of measures across multiple time steps is clearly necessary for a satisfactory classification.     

Above-ground biomass estimation in closed canopy Neotropical forests using lidar remote sensing: factors affecting the generality of relationships

Aim Previous studies have developed strong, site‐specific relationships between canopy metrics from lidar (light detecting and ranging) remote sensing data and forest structural characteristics such as above‐ground biomass (AGBM), but the generality of these relationships is unknown. In this study, we examine the generality of relationships between lidar metrics and forest structural characteristics, including AGBM, from two study areas in Central America with different precipitation patterns. Location A series of tropical moist forest sites in Panama and a tropical wet forest in Costa Rica. Methods Canopy metrics (e.g. canopy height) were calculated from airborne lidar data. Basal area, mean stem diameter and AGBM were calculated from measurements taken as a part of ongoing forest dynamics studies in both areas. We examined the generality of relationship between lidar metrics and forest structure, and possible environmental effects (e.g. leaf phenology). Results We found that lidar metrics were strongly correlated (R²: 0.65–0.92) with mean stem diameter, basal area and AGBM in both regions. We also show that the relationships differed between these regions. Deciduousness of canopy trees in the tropical moist forest area accounted for the differences in predictive equations for stem diameter and basal area. The relationships between lidar metrics and AGBM, however, remained significantly different between the two study areas even after adjusting for leaf drop. We attribute this to significant differences in the underlying allometric relationships between stem diameter and AGBM in tropical wet and moist forests. Conclusions Important forest structural characteristics can be estimated reliably across a variety of conditions sampled in these closed‐canopy tropical forests. Environmental factors such as drought deciduousness have an important influence on these relationships. Future efforts should continue to examine climatic factors that may influence the generality of the relationships between lidar metrics and forest structural characteristics and assess more rigorously the generality of field‐derived allometric relationships.     

利用半球图像法提取植被冠层结构特征参数

植被冠层结构深刻地影响着植物群落与环境的相互作用,对植被冠层结构的研究是深入理解植被生态系统格局、过程及其运作机制的重要基础。冠层结构特征参数的快速测量方法是植被冠层结构研究的前提,目前测量方法主要是基于实际测量的地面法,地面法一般费时费力,受人为因素影响较大,因此本文探索利用半球图像法获取植被冠层结构特征参数。通过对半球图像进行几何纠正并建立参数图层,与分类后的植被冠层图层进行运算提取植被冠层结构特征参数。将该方法应用于祁连山旺腰沟流域青海云杉冠层结构特征参数的提取,包括植被冠幅、冠层面积、冠层周长等,结果显示：半球图像法能够较好的提取植被冠层结构特征参数,该方法具有简单、客观、可重复等优点,也可作为植被冠层结构变化的监测方法。