Data on the mass density and carbon content of tree organs, and in particular stem wood, are essential for accurate assessments of forest carbon sequestration. However most available data, including that for East Asia, has neglected the volatile C fraction. Wood samples were collected and assayed for C content from 14 native tree species in Jilin Province, NE China. C content showed statistically significant variation among species, ranging from 48.4% to 51.0%. The volatile C fraction was non-negligible, averaging 2.2%, and showed high variation among species. As found in prior studies, wood C content was appreciably higher in conifer than hardwood (angiosperm) species (50.8+/-0.1% vs. 49.5+/-0.2%, respectively). Wood carbon density (gC/cm(3)) showed very high inter-specific variation, due mainly to differences in wood specific gravity. Our analyses, in conjunction with recently published data from North America, indicate a global mean value of 47.5+/-0.5% wood C content exclusive of volatile C; the widely used 50% figure corresponds more closely to total wood C inclusive of the volatile fraction. Failure to include volatile C or to use species- or higher-taxon-specific C content values in forest C assessments is likely to introduce biases on the order approximately 4-6%. In addition, the stocks and flows of the volatile C fraction in wood are in themselves an important and sorely neglected aspect of forest C processes likely to be strongly impacted by harvests and other management practices.

Deep neural networks are currently the focus of many remote sensing approaches related to forest management. Although they return satisfactory results in most tasks, some challenges related to hyperspectral data remain, like the curse of data dimensionality. In forested areas, another common problem is the highly-dense distribution of trees. In this paper, we propose a novel deep learning approach for hyperspectral imagery to identify single-tree species in highly-dense areas. We evaluated images with 25 spectral bands ranging from 506 to 820 nm taken over a semideciduous forest of the Brazilian Atlantic biome. We included in our network’s architecture a band combination selection phase. This phase learns from multiple combinations between bands which contributed the most for the tree identification task. This is followed by a feature map extraction and a multi-stage model refinement of the confidence map to produce accurate results of a highly-dense target. Our method returned an f-measure, precision and recall values of 0.959, 0.973, and 0.945, respectively. The results were superior when compared with a principal component analysis (PCA) approach. Compared to other learning methods, ours estimate a combination of hyperspectral bands that most contribute to the mentioned task within the network’s architecture. With this, the proposed method achieved state-of-the-art performance for detecting and geolocating individual tree-species in UAV-based hyperspectral images in a complex forest.

森林是陆地生态系统的主体,森林生态系统监测技术是实现森林可持续利用与全球变化研究的重要支撑与信息保障。从4个方面概述了遥感技术在森林生态系统监测中的应用研究进展：森林遥感分类及变化监测、森林植被参数遥感反演、森林蓄积量与生物量遥感估算、森林干扰遥感监测等。结合遥感技术的发展,总结了森林生态系统监测中使用的多源遥感数据和各类模型,提出集成地面调查数据、高分地-空雷达扫描监测技术,以及多源光学遥感建模技术和生态系统过程模型,构建多维度、多尺度、高时间密度的森林生态系统监测平台的研究展望。

Forest is a main component of the terrestrial ecosystem, and its monitoring is the vital support for sustainable utilization of forest and global change researches. The home and aboard progress of remote sensing techniques application on monitoring forest ecosystems was concluded in this paper from four respects: classification and changes detection, the retrieval of vital ecological parameters of forest ecosystems including tree height, leaf area index, canopy density, etc., the estimation of stand volume and biomass, and disturbance monitoring. After summarized remote sensing data and models used in forest ecosystems monitoring, research prospect of establishment of integrated forest ecosystems monitoring platform with multi-dimension, multi-scale and high time density were put forward, which synthesized filed data, land-to-air high-resolution radar scanning techniques, multi-source optical remote sensing modeling and process models.

以白桦、日阴菅及其它主要草本植物的个体数量为指标,分析了各植物种群在土壤有机质、速效P和pH值3个资源维上的生态位宽度和生态位重叠及其在不同海拔条件下的变化规律.日阴菅生态位宽度随海拔升高而增大,其余植物种类在有机质资源维上的生态位宽度,大都是以中等海拔(800m)的样带最宽,而在速效P资源维上,又以中等海拔的样带为最窄.由于高海拔及相应低气温在某种程度上限制了植物种群对土壤有机质和速效P的利用,主要植物种对之间在这两个资源维上的生态位重叠以高海拔(950m)样带为最小.在土壤pH值资源维上,溪荪与其它主要植物种间的生态位重叠皆以低海拔(650m)样带为最小,可能是其特殊的环境组合迫使溪荪发生了生态位移动.大多数种对在土壤有机质、速效P和pH值3个资源维上都以海拔800m的样带生态位重叠最大.

Advances in computing technology have fostered the development of new and powerful deep learning (DL) techniques, which have demonstrated promising results in a wide range of applications. Particularly, DL methods have been successfully used to classify remotely sensed data collected by Earth Observation (EO) instruments. Hyperspectral imaging (HSI) is a hot topic in remote sensing data analysis due to the vast amount of information comprised by this kind of images, which allows for a better characterization and exploitation of the Earth surface by combining rich spectral and spatial information. However, HSI poses major challenges for supervised classification methods due to the high dimensionality of the data and the limited availability of training samples. These issues, together with the high intraclass variability (and interclass similarity) often present in HSI data-may hamper the effectiveness of classifiers. In order to solve these limitations, several DL-based architectures have been recently developed, exhibiting great potential in HSI data interpretation. This paper provides a comprehensive review of the current-state-of-the-art in DL for HSI classification, analyzing the strengths and weaknesses of the most widely used classifiers in the literature. For each discussed method, we provide quantitative results using several well-known and widely used HSI scenes, thus providing an exhaustive comparison of the discussed techniques. The paper concludes with some remarks and hints about future challenges in the application of DL techniques to HSI classification. The source codes of the methods discussed in this paper are available from: https://github.com/mhaut/hyperspectral_deeplearning_review.

Tree species classification at individual tree crowns (ITCs) level, using remote-sensing data, requires the availability of a sufficient number of reliable reference samples (i.e., training samples) to be used in the learning phase of the classifier. The classification performance of the tree species is mainly affected by two main issues: (i) an imbalanced distribution of the tree species classes, and (ii) the presence of unreliable samples due to field collection errors, coordinate misalignments, and ITCs delineation errors. To address these problems, in this paper, we present a weighted Support Vector Machine (wSVM)-based approach for the detection of tree species at ITC level. The proposed approach initially extracts (i) different weights associated to different classes of tree species, to mitigate the effect of the imbalanced distribution of the classes; and (ii) different weights associated to different training samples according to their importance for the classification problem, to reduce the effect of unreliable samples. Then, in order to exploit different weights in the learning phase of the classifier a wSVM algorithm is used. The features to characterize the tree species at ITC level are extracted from both the elevation and intensity of airborne light detection and ranging (LiDAR) data. Experimental results obtained on two study areas located in the Italian Alps show the effectiveness of the proposed approach.

Rapid and accurate identification of tree species and their distribution patterns is the basis and premise for forest resource management and biodiversity conservation. Compared with the traditional field investigation methods, the rapid development of the surface of remote sensing technology in recent years can obtain high resolution hyperspectral remote sensing images flexibly, efficiently, and conveniently. However, how to select features with large amount of information and low redundancy from many features containing rich information for automatic tree species identification is an urgent problem. We used spectral sensors carried by unmanned aerial vehicles to take hyperspectral images, which covered an area of 6 hm² of the 25 hm² temperate mixed conifer-broadleaf plot in Changbai Mountain. Six canopy tree species, including <em>Pinus koraiensis</em>, <em>Ulmus davidiana</em>, <em>Quercus mongolica</em>, <em>Fraxinus mandshurica</em>,<em>Populus ussuriensis</em>, and <em>Tilia amurensis</em>, were selected as the field labeled tree species. Realtime Kinematic Phase Difference (RTK) technology was used to accurately capture the position of those target tree species. In addition, visual interpretation of the image of the study area was performed using ArcGIS based on the forest re-inventory results in 2019. Three classification methods, including convolutional neural network, maximum likelihood and Mahalanobis distance, were used to analyze the automatic classification of canopy tree species. Our results showed that: (1) The overall accuracy and Kappa coefficient of tree species classification of convolutional neural network (99.85%, 0.998) were better than maximum likelihood (89.11%, 0.86) and Maharanobis distance method (79.65%, 0.75); (2) Among the three classification methods, the classification accuracy of single dominant tree species was the highest when using the convolve neural network, and the highest classification accuracy of <em>P. koraiensis</em>, <em>U. davidiana</em>, <em>Q. mongolica</em>, <em>F. mandshurica</em>, <em>P. ussuriensis</em> and <em>T. amurensis</em> were 100%, 99.9%, 99.9%, 99.8%, 99.8% and 99.5%, respectively. (3) Overall, convolutional neural network had the lowest degree of mixing, while Mahalanobis distance method had the most serious degree of mixing problem. This study indicated that the convolutional neural network model based on deep learning approach can obtain the accurate and efficient classification of canopy species in natural temperate forests, which could provide a great step forward into the species diversity monitoring and forestry resource survey.

In this study, we automate tree species classification and mapping using field-based training data, high spatial resolution airborne hyperspectral imagery, and a convolutional neural network classifier (CNN). We tested our methods by identifying seven dominant trees species as well as dead standing trees in a mixed-conifer forest in the Southern Sierra Nevada Mountains, CA (USA) using training, validation, and testing datasets composed of spatially-explicit transects and plots sampled across a single strip of imaging spectroscopy. We also used a three-band ‘Red-Green-Blue’ pseudo true-color subset of the hyperspectral imagery strip to test the classification accuracy of a CNN model without the additional non-visible spectral data provided in the hyperspectral imagery. Our classifier is pixel-based rather than object based, although we use three-dimensional structural information from airborne Light Detection and Ranging (LiDAR) to identify trees (points &gt; 5 m above the ground) and the classifier was applied to image pixels that were thus identified as tree crowns. By training a CNN classifier using field data and hyperspectral imagery, we were able to accurately identify tree species and predict their distribution, as well as the distribution of tree mortality, across the landscape. Using a window size of 15 pixels and eight hidden convolutional layers, a CNN model classified the correct species of 713 individual trees from hyperspectral imagery with an average F-score of 0.87 and F-scores ranging from 0.67–0.95 depending on species. The CNN classification model performance increased from a combined F-score of 0.64 for the Red-Green-Blue model to a combined F-score of 0.87 for the hyperspectral model. The hyperspectral CNN model captures the species composition changes across ~700 meters (1935 to 2630 m) of elevation from a lower-elevation mixed oak conifer forest to a higher-elevation fir-dominated coniferous forest. High resolution tree species maps can support forest ecosystem monitoring and management, and identifying dead trees aids landscape assessment of forest mortality resulting from drought, insects and pathogens. We publicly provide our code to apply deep learning classifiers to tree species identification from geospatial imagery and field training data.

Urban areas feature complex and heterogeneous land covers which create challenging issues for tree species classification. The increased availability of high spatial resolution multispectral satellite imagery and LiDAR datasets combined with the recent evolution of deep learning within remote sensing for object detection and scene classification, provide promising opportunities to map individual tree species with greater accuracy and resolution. However, there are knowledge gaps that are related to the contribution of Worldview-3 SWIR bands, very high resolution PAN band and LiDAR data in detailed tree species mapping. Additionally, contemporary deep learning methods are hampered by lack of training samples and difficulties of preparing training data. The objective of this study was to examine the potential of a novel deep learning method, Dense Convolutional Network (DenseNet), to identify dominant individual tree species in a complex urban environment within a fused image of WorldView-2 VNIR, Worldview-3 SWIR and LiDAR datasets. DenseNet results were compared against two popular machine classifiers in remote sensing image analysis, Random Forest (RF) and Support Vector Machine (SVM). Our results demonstrated that: (1) utilizing a data fusion approach beginning with VNIR and adding SWIR, LiDAR, and panchromatic (PAN) bands increased the overall accuracy of the DenseNet classifier from 75.9% to 76.8%, 81.1% and 82.6%, respectively. (2) DenseNet significantly outperformed RF and SVM for the classification of eight dominant tree species with an overall accuracy of 82.6%, compared to 51.8% and 52% for SVM and RF classifiers, respectively. (3) DenseNet maintained superior performance over RF and SVM classifiers under restricted training sample quantities which is a major limiting factor for deep learning techniques. Overall, the study reveals that DenseNet is more effective for urban tree species classification as it outperforms the popular RF and SVM techniques when working with highly complex image scenes regardless of training sample size.