锡林河流域温带草地植物VOC释放及其对草地生态系统碳循环的贡献

植物源挥发性有机碳化合物（Volitale organic compounds, VOC）是大气VOC的主要来源，与对流层大气质量、大气化学密切相关。鉴于温带草地的分布范围很广，草地植物VOC释放潜力某种程度上影响植物源VOC的总释放量。另外，植物源VOC也是光合作用固定碳素的损失方式之一，可能在特定区域或生态系统中具有重要意义。基于上述想法，本文设计了四个方面的实验作为研究内容：1） 温带草地物种水平VOC释放潜力、及其与植物功能群的关系？2) 沙地植物物种水平VOC释放潜力、及其与植物功能群的关系？3) 沙地植物-草地植物VOC释放潜力存在显著性差异吗？4) 温带典型草地和退化草地的VOC释放速率如何？在生态系统水平，植物源VOC对温带草地碳循环的贡献多大？ 在所测定的175种温带草地植物中，不同植物间异戊二烯和单萜释放潜力差异很大;除少数物种外，大多数植物的异戊二烯和单萜释放潜力都较低，尤其是典型草地的优势物种。在此基础上，作者探讨了分类学赋值方法对温带草地植被的可行性，并初步建立了锡林河流域温带草地植物的VOC释放目录（共277种植物）。另外，温带草地植物的异戊二烯和单萜释放潜力与植物功能群（植物生活型和水分功能群）具有一定的联系，尤其是植物生活型。总的来说，温带草原的优势生活型（物种），即多年生根茎禾草（或多年生丛生禾草），具有较低的异戊二烯和单萜释放潜力。各水分功能群间差异不显著，但中旱生植物、旱中生植物 (温带草原的优势功能群)，具有较低的异戊二烯、单萜释放潜力。因此，温带草原退化过程中，那些具有较高VOC释放潜力的植物，重要性将会增加。 沙地植物种类组成非常丰富，不同物种间的异戊二烯和单萜释放潜力变异也很大。另外，沙地植物的异戊二烯和单萜释放潜力与其功能群间关系较密切，不同植物生活型间差异显著;其中也以多年生根茎禾草、多年生从生禾草的释放潜力最低，而乔木的释放潜力相对最高;该结论基本与草地的研究结论一致。然而，沙地植物的异戊二烯和单萜释放潜力与其水分功能群的关系比较模糊，中生植物具有更高的释放潜力，湿生植物的释放潜力较小。 通过对比沙地植物和草地植物的释放潜力，发现沙地植物的异戊二烯和单萜释放潜力比草地植物高，且这种差异整体上显著。另外，这种显著性差异，在不同植物生活型、水分功能群间也同样存在。沙地植物比对应的草地植物具有更高的异戊二烯和单萜释放潜力，最可能的原因：沙地正午的温度明显比草地温度高，前者实测温度可超过 45 ℃,这种经常性、周期性高温，促使沙地植物采用与草地植物不同的适应策略，即沙地植物通过释放更多的异戊二烯或单萜来减少其可能遭的热胁迫或热伤害，这种长期适应策略，使沙地植物具有更高的萜类化合物释放潜力。 本文还调查了温带典型草地生态系统和退化草地生态系统的异戊二烯和单萜释放速率，结果表明典型草地的标准异戊二烯和单萜释放速率分别为0.50 μgC g-1 h-1和0.69 μgC g-1 h-1;退化草地的标准异戊二烯和单萜释放潜力分别为0.32 μgC g-1 h-1和1.59 μgC g-1 h-1。总的来说，温带草地的异戊二烯和单萜释放速率都比较低，尤其是典型草地。整个生长季，典型草地释放的异戊二烯和单萜分别为31.6 mgC•m-2　和 70.4 mgC•m-2;退化草地的异戊二烯和单萜释放量分别为20.8 mgC•m-2 和 168.8 mgC•m-2。退化草地萜类化合物总释放速率远高于典型草地，尤其是单萜释放能力。过度放牧引起的草地退化，通过改变植被种类组成，对温带草地的异戊二烯和单萜释放速率产生显著影响;总体而言，温带草地退化将会使草地释放更多萜类化合物。 在温带草地生态系统中，Clost as VOC相对其NPP而言很小，在环境PAR和温度高时,它的贡献率相对较大;Clost as VOC约占典型草地生态系统NEP的5.32 %，退化草地生态系统NEP的0.23 %。植物源VOC释放所损失的碳素相对草地生态系统NPP而言几乎可以忽略不计;但是，相对其NEP，Clost as VOC还是具有一定的相关性。虽然，草地生态系统Clost as VOC对NPP或NEP的贡献率较小，但考虑到全球尺度植物源VOC的巨大释放速率，它在碳循环中的贡献率仍然不容忽视;在某些特殊的生态系统中，仍可能扮演重要角色。

Evaluation of aerial remote sensing techniques for vegetation management in power line corridors

The following paper presents an evaluation of airborne sensors for use in vegetation management in powerline corridors. Three integral stages in the management process are addressed including, the detection of trees, relative positioning with respect to the nearest powerline and vegetation height estimation. Image data, including multi-spectral and high resolution, are analyzed along with LiDAR data captured from fixed wing aircraft. Ground truth data is then used to establish the accuracy and reliability of each sensor thus providing a quantitative comparison of sensor options. Tree detection was achieved through crown delineation using a Pulse-Coupled Neural Network (PCNN) and morphologic reconstruction applied to multi-spectral imagery. Through testing it was shown to achieve a detection rate of 96%, while the accuracy in segmenting groups of trees and single trees correctly was shown to be 75%. Relative positioning using LiDAR achieved a RMSE of 1.4m and 2.1m for cross track distance and along track position respectively, while Direct Georeferencing achieved RMSE of 3.1m in both instances. The estimation of pole and tree heights measured with LiDAR had a RMSE of 0.4m and 0.9m respectively, while Stereo Matching achieved 1.5m and 2.9m. Overall a small number of poles were missed with detection rates of 98% and 95% for LiDAR and Stereo Matching.

Evaluation of spectral and texture features for object-based vegetation species classification using Support Vector Machines

The use of appropriate features to characterize an output class or object is critical for all classification problems. This paper evaluates the capability of several spectral and texture features for object-based vegetation classification at the species level using airborne high resolution multispectral imagery. Image-objects as the basic classification unit were generated through image segmentation. Statistical moments extracted from original spectral bands and vegetation index image are used as feature descriptors for image objects (i.e. tree crowns). Several state-of-art texture descriptors such as Gray-Level Co-Occurrence Matrix (GLCM), Local Binary Patterns (LBP) and its extensions are also extracted for comparison purpose. Support Vector Machine (SVM) is employed for classification in the object-feature space. The experimental results showed that incorporating spectral vegetation indices can improve the classification accuracy and obtained better results than in original spectral bands, and using moments of Ratio Vegetation Index obtained the highest average classification accuracy in our experiment. The experiments also indicate that the spectral moment features also outperform or can at least compare with the state-of-art texture descriptors in terms of classification accuracy.

Empirical comparison of machine learning algorithms for image texture classification with application to vegetation management in power line corridors

This paper reports on the empirical comparison of seven machine learning algorithms in texture classification with application to vegetation management in power line corridors. Aiming at classifying tree species in power line corridors, object-based method is employed. Individual tree crowns are segmented as the basic classification units and three classic texture features are extracted as the input to the classification algorithms. Several widely used performance metrics are used to evaluate the classification algorithms. The experimental results demonstrate that the classification performance depends on the performance matrix, the characteristics of datasets and the feature used.

Advances in vegetation management for power line corridor monitoring using aerial remote sensing techniques

This paper presents a comprehensive discussion of vegetation management approaches in power line corridors based on aerial remote sensing techniques. We address three issues 1) strategies for risk management in power line corridors, 2) selection of suitable platforms and sensor suite for data collection and 3) the progress in automated data processing techniques for vegetation management. We present initial results from a series of experiments and, challenges and lessons learnt from our project.

Color and texture feature fusion using kernel PCA with application to object-based vegetation species classification

A good object representation or object descriptor is one of the key issues in object based image analysis. To effectively fuse color and texture as a unified descriptor at object level, this paper presents a novel method for feature fusion. Color histogram and the uniform local binary patterns are extracted from arbitrary-shaped image-objects, and kernel principal component analysis (kernel PCA) is employed to find nonlinear relationships of the extracted color and texture features. The maximum likelihood approach is used to estimate the intrinsic dimensionality, which is then used as a criterion for automatic selection of optimal feature set from the fused feature. The proposed method is evaluated using SVM as the benchmark classifier and is applied to object-based vegetation species classification using high spatial resolution aerial imagery. Experimental results demonstrate that great improvement can be achieved by using proposed feature fusion method.

A biologically inspired object spectral-texture descriptor and its application to vegetation classification in power-line corridors

The use of appropriate features to represent an output class or object is critical for all classification problems. In this paper, we propose a biologically inspired object descriptor to represent the spectral-texture patterns of image-objects. The proposed feature descriptor is generated from the pulse spectral frequencies (PSF) of a pulse coupled neural network (PCNN), which is invariant to rotation, translation and small scale changes. The proposed method is first evaluated in a rotation and scale invariant texture classification using USC-SIPI texture database. It is further evaluated in an application of vegetation species classification in power line corridor monitoring using airborne multi-spectral aerial imagery. The results from the two experiments demonstrate that the PSF feature is effective to represent spectral-texture patterns of objects and it shows better results than classic color histogram and texture features.

The role of vegetation in enhancing radon concentration and ion production in the atmosphere

The role of ions in the production of atmospheric particles has gained wide interest due to their profound impact on climate. Away from anthropogenic sources, molecules are ionized by alpha radiation from radon exhaled from the ground and cosmic gamma radiation from space. These molecular ions quickly form into ‘cluster ions’, typically smaller than about 1.5 nm. Using our measurements and the published literature, we present evidence to show that cluster ion concentrations in forest areas are consistently higher than outside. Since alpha radiation cannot penetrate more than a few centimetres of soil, radon present deep in the ground cannot directly contribute to the measured cluster ion concentrations. We propose an additional mechanism whereby radon, which is water soluble, is brought up by trees and plants through the uptake of groundwater and released into the atmosphere by transpiration. We estimate that, in a forest comprising eucalyptus trees spaced 4m apart, approximately 28% of the radon in the air may be released by transpiration. Considering that 24% of the earth’s land area is still covered in forests; these findings have potentially important implications for atmospheric aerosol formation and climate.

Spectral–texture feature extraction using statistical moments with application to object-based vegetation species classification

The use of appropriate features to characterise an output class or object is critical for all classification problems. In order to find optimal feature descriptors for vegetation species classification in a power line corridor monitoring application, this article evaluates the capability of several spectral and texture features. A new idea of spectral–texture feature descriptor is proposed by incorporating spectral vegetation indices in statistical moment features. The proposed method is evaluated against several classic texture feature descriptors. Object-based classification method is used and a support vector machine is employed as the benchmark classifier. Individual tree crowns are first detected and segmented from aerial images and different feature vectors are extracted to represent each tree crown. The experimental results showed that the proposed spectral moment features outperform or can at least compare with the state-of-the-art texture descriptors in terms of classification accuracy. A comprehensive quantitative evaluation using receiver operating characteristic space analysis further demonstrates the strength of the proposed feature descriptors.

Importance of soil ecology in vegetation rehabilitation on a North Stradbroke Island sand mine

Sibelco Australia Limited (SAL), a mineral sand mining operation on North Stradbroke Island, undertakes progressive rehabilitation of mined areas. Initial investigations have found that some areas at SAL’s Yarraman Mine have failed to redevelop towards approved criteria. This study, undertaken in 2010, examined ground cover rehabilitation of different aged plots at the Yarraman Mine to determine if there was a relationship between key soil and vegetation attributes. Vegetation and soil data were collected from five plots rehabilitated in 2003, 2006, 2008, 2009 and 2010, and one unmined plot. Cluster (PATN) analysis revealed that vegetation species composition, species richness and ground cover differed between plots. Principal component analysis (PCA) extracted ten soil attributes that were then correlated with vegetation data. The attributes extracted by PCA, in order of most common variance, were: water content, pH, terrolas depth, elevation, slope angle, leaf litter depth, total organic carbon, and counts of macrofauna, fungi and bacteria. All extracted attributes differed between plots, and all except bacteria correlated with at least one vegetation attribute. Water content and pH correlated most strongly with vegetation cover suggesting an increase in soil moisture and a reduction in pH are required in order to improve vegetation rehabilitation at Yarraman Mine. Further study is recommended to confirm these results using controlled experiments and to test potential solutions, such as organic amendments.

The influence of vegetation and soil type on the speciation of iron in soil water

Soluble organic matter derived from exotic Pinus species has been shown to form stronger complexes with iron (Fe) than that derived from most native Australian species. It has also been proposed that the establishment of exotic Pinus plantations in coastal southeast Queensland may have enhanced the solubility of Fe in soils by increasing the amount of organically complexed Fe, but this remains inconclusive. In this study we test whether the concentration and speciation of Fe in soil water from Pinus plantations differs significantly from soil water from native vegetation areas. Both Fe redox speciation and the interaction between Fe and dissolved organic matter (DOM) were considered; Fe - DOM interaction was assessed using the Stockholm Humic Model. Iron concentrations (mainly Fe 2+) were greatest in the soil waters with the greatest DOM content collected from sandy podosols (Podzols), where they are largely controlled by redox potential. Iron concentrations were small in soil waters from clay and iron oxide-rich soils, in spite of similar redox potentials. This condition is related to stronger sorption on to the reactive clay and iron oxide mineral surfaces in these soils, which reduces the amount of DOM available for electron shuttling and microbial metabolism, restricting reductive dissolution of Fe. Vegetation type had no significant influence on the concentration and speciation of iron in soil waters, although DOM from Pinus sites had greater acidic functional group site densities than DOM from native vegetation sites. This is because Fe is mainly in the ferrous form, even in samples from the relatively well-drained podosols. However, modelling suggests that Pinus DOM can significantly increase the amount of truly dissolved ferric iron remaining in solution in oxic conditions. Therefore, the input of ferrous iron together with Pinus DOM to surface waters may reduce precipitation of hydrous ferric oxides (ferrihydrite) and increase the flux of dissolved Fe out of the catchment. Such inputs of iron are most probably derived from podosols planted with Pinus.