黔西南日照时数对烟叶品质的影响分析

黔西南烟叶品质的好坏与气象条件密切相关,特别是日照条件对烟叶质量的影响较大,通过对1995～2006年烟叶的商品等级与日照时数的关系统计分析,探讨日照时数对黔西南烤烟质量的影响。     

经验模态分解方法在树木年轮宽度序列标准化中的应用

高分辨率的树木年轮是记录历史时期气候变化的良好生物载体,在古气候研究中被广泛应用。但年轮宽度与气候因子之间有着复杂的联系,这种关系受气候因子之间的相互制衡和因物种而异的树木生长节律的共同影响。在利用树木年轮开展历史时期气候变化的研究中,剔除树木年轮与年龄相关的生长趋势是准确获取气候信号的先决条件。然而,传统的和相对改进的一些树轮标准化方法在拟合并剔除树龄相关的趋势及非气候干扰信息方面仍存在一些问题。本文利用经验模态分解(EMD)方法进行树轮资料的标准化方法研究, 对已获得的树轮生长序列所记录的信息进行分解,得到一系列不同物理意义的本征模态分量,结合多样本信息的对比及生物学特性,深入解读各分量表征的气候变化、环境干扰及缓慢生长趋势项等不同物理意义,进而剔除非气候信息,得到可以准确反映气候变化的代用序列,并将该方法与目前广泛采用的标准化方法进行对比,分析不同方法的利弊所在,为进一步改进树轮标准化方法提供新思路。     

Deriving airborne laser scanning based computational canopy volume for forest biomass and allometry studies

A computational canopy volume (CCV) based on airborne laser scanning (ALS) data is proposed to improve predictions of forest biomass and other related attributes like stem volume and basal area. An approach to derive the CCV based on computational geometry, topological connectivity and numerical optimization was tested with sparse-density, plot-level ALS data acquired from 40 field sample plots of 500–1000 m² located in a boreal forest in Norway. The CCV had a high correspondence with the biomass attributes considered when derived from optimized filtrations, i.e. ordered sets of simplices belonging to the triangulations based on the point data. Coefficients of determination (R²) between the CCV and total above-ground biomass, canopy biomass, stem volume, and basal area were 0.88–0.89, 0.89, 0.83–0.97, and 0.88–0.92, respectively, depending on the applied filtration. The magnitude of the required filtration was found to increase according to an increasing basal area, which indicated a possibility to predict this magnitude by means of ALS-based height and density metrics. A simple prediction model provided CCVs which had R² of 0.77–0.90 with the aforementioned forest attributes. The derived CCVs always produced complementary information and were mainly able to improve the predictions of forest biomass relative to models based on the height and density metrics, yet only by 0–1.9 percentage points in terms of relative root mean squared error. Possibilities to improve the CCVs by a further analysis of topological persistence are discussed.     

A bottom-up approach to segment individual deciduous trees using leaf-off lidar point cloud data

Light Detection and Ranging (Lidar) can generate three-dimensional (3D) point cloud which can be used to characterize horizontal and vertical forest structure, so it has become a popular tool for forest research. Recently, various methods based on top-down scheme have been developed to segment individual tree from lidar data. Some of these methods, such as the one developed by Li et al. (2012), can obtain the accuracy up to 90% when applied in coniferous forests. However, the accuracy will decrease when they are applied in deciduous forest because the interlacing tree branches can increase the difficulty to determine the tree top. In order to solve challenges of the tree segmentation in deciduous forests, we develop a new bottom-up method based on the intensity and 3D structure of leaf-off lidar point cloud data in this study. We applied our algorithm to segment trees in a forest at the Shavers Creek Watershed in Pennsylvania. Three indices were used to assess the accuracy of our method: recall, precision and F-score. The results show that the algorithm can detect 84% of the tree (recall), 97% of the segmented trees are correct (precision) and the overall F-score is 90%. The result implies that our method has good potential for segmenting individual trees in deciduous broadleaf forest.     

PTrees: A point-based approach to forest tree extraction from lidar data

This paper introduces PTrees, a multi-scale dynamic point cloud segmentation dedicated to forest tree extraction from lidar point clouds. The method process the point data using the raw elevation values (Z) and compute height (H = Z − ground elevation) during post-processing using an innovative procedure allowing to preserve the geometry of crown points. Multiple segmentations are done at different scales. Segmentation criteria are then applied to dynamically select the best set of apices from the tree segments extracted at the various scales. The selected set of apices is then used to generate a final segmentation. PTrees has been tested in 3 different forest types, allowing to detect 82% of the trees with under 10% of false detection rate. Future development will integrate crown profile estimation during the segmentation process in order to both maximize the detection of suppressed trees and minimize false detections.     

Quantifying the variability and allocation patterns of aboveground carbon stocks across plantation forest types,structural attributes and age in sub-tropical coastal region of KwaZulu Natal,South Africa using remote sensing

Quantifying the variability and allocation patterns of aboveground carbon stocks across plantation forests is central in deriving accurate and reliable knowledge and understanding of the extent to which these species contribute to the global carbon cycle and towards minimizing climate change effects. The principal objective of this study was to quantify the variability and allocation patterns of aboveground carbon stocks across Pinus and Eucalyptus plantation forests, tree-structural attributes (i.e. stems, barks, branches and leaves) and age groups, using models developed based on remotely sensed data. The results of this study demonstrate that aboveground carbon stocks significantly (α = 0.05) vary across different plantation forest species types, structural attributes and age. Pinus taeda and Eucalyptus grandis species contained aboveground carbon stocks above 110 t C ha⁻¹, and Eucalyptus dunii had 20 t C ha⁻¹. Across plantation forest tree structural attributes, stems contained the highest aboveground carbon stocks, when compared to barks, branches and leaves. Aboveground carbon stock estimates also varied significantly (α = 0.05) with stand age. Mature plantation forest species (i.e. between 7 and 20 years) contained the highest aboveground carbon stock estimates of approximately 120 t C ha⁻¹, when compared to younger species (i.e. between 3 and 6 years), which had approximately 20 t C ha⁻¹. The map of aboveground carbon stocks showed distinct spatial patterns across the entire study area. The findings of this study are important for understanding the contribution of different plantation forest species, structural attributes and age in the global carbon cycle and possible climate change moderation measures. Also, this study demonstrates that data on vital tree structural attributes, previously difficult to obtain, can now be easily derived from cheap and readily-available satellite data for inventorying carbon stocks variability.     

Assessing the utility WorldView-2 imagery for tree species mapping in South African subtropical humid forest and the conservation implications: Dukuduku forest patch as case study

Indigenous forest biome in South Africa is highly fragmented into patches of various sizes (most patches < 1 km²). The utilization of timber and non-timber resources by poor rural communities living around protected forest patches produce subtle changes in the forest canopy which can be hardly detected on a timely manner using traditional field surveys. The aims of this study were to assess: (i) the utility of very high resolution (VHR) remote sensing imagery (WorldView-2, 0.5–2 m spatial resolution) for mapping tree species and canopy gaps in one of the protected subtropical coastal forests in South Africa (the Dukuduku forest patch (ca.3200 ha) located in the province of KwaZulu-Natal) and (ii) the implications of the map products to forest conservation. Three dominant canopy tree species namely, Albizia adianthifolia, Strychnos spp. and Acacia spp., and canopy gap types including bushes (grass/shrubby), bare soil and burnt patches were accurately mapped (overall accuracy = 89.3 ± 2.1%) using WorldView-2 image and support vector machine classifier. The maps revealed subtle forest disturbances such as bush encroachment and edge effects resulting from forest fragmentation by roads and a power-line. In two stakeholders’ workshops organised to assess the implications of the map products to conservation, participants generally agreed amongst others implications that the VHR maps provide valuable information that could be used for implementing and monitoring the effects of rehabilitation measures. The use of VHR imagery is recommended for timely inventorying and monitoring of the small and fragile patches of subtropical forests in Southern Africa.     

气候因素变化对许昌烟区的影响及适应性分析

分析了许昌烟区1971—2005年4个年代气候因素变化特点,采用隶属度函数模型和指数和法,估算了许昌各县（市）的气候适生性指数（CFI）,并对其变化原因进行了分析。结果表明：不同年代各县（市）的CFI均在0．80以上,表明各县（市）均属烤烟生产气候适宜区。从4个年代CFI的均值来看,许昌县〉长葛〉襄城〉禹州〉鄢陵;从4个年代CFI变异程度来看,禹州〉长葛〉襄城〉鄢陵〉许昌县。各县（市）不同年代的CFI均呈“低-高-高-低”变化趋势;20世纪80年代以来,许昌县、襄城、长葛、禹州的CFI值及变化趋势基本一致,均明显高于鄢陵。80年代至21世纪初鄢陵的气候适生性指数明显低于其他县（市）,与伸根期温度相对较低,旺长期和成熟期多雨、高湿有关。     

基于分类回归树分析的遥感影像土地利用／覆被分类研究

以专家知识和经验为基础,综合影像光谱信息和其他辅助信息进行分类的基于知识的遥感影像解译方法,是提高遥感影像分类精度,实现自动解译的有效途径之一。然而,知识的获取一直是其得以广泛应用的“瓶颈”问题。以江苏省江宁试验区土地利用/覆被分类为例,利用分类回归树分析(CART)从训练样本数据集中发现分类规则,集成遥感影像的光谱特征、纹理特征和空间分布特征进行分类实验,并与传统的监督分类和逻辑通道分类方法进行比较。结果表明,基于CART的分类方法的精度基本在80%以上,与另两种方法相比,有了较大的提高,而且该算法复杂性低,效率高。由此说明,利用CART算法构建决策树获取的分类规则是合理的。它可以快速、有效地获取大量分类规则,是促进基于知识的遥感影像分类方法在土地利用/覆被分类中广泛应用的一项有效手段。     

Spatial patterns of lost and remaining trees in the Iberian wooded rangelands

Spanish dehesas and Portuguese montados are similar Iberian silvopastoral systems that provide important environmental services and hold a high biodiversity. However, there is a raising concern for the sustainability of these cultural landscapes, as they suffer from tree recruitment failure. Consequently, trees in those systems are lessening due to tree ageing, diseases and other causes. A key to developing successful management strategies in dehesas is to understand the spatiotemporal behaviour of this process. We analysed the spatial pattern of lost and remaining trees in five dehesas in Extremadura (SW Spain) between 1956 and 2009 based on aerial images. We studied the possible spatial dependency (clustering or segregation) of the lost and remaining trees in areas with low or high land use intensity at different spatial distances. Analyses were performed under the random labelling hypothesis and the critical values were obtained by using 199 permutations of a Monte Carlo test. A linear mixed model was used to investigate the possible factors affecting tree loss. The results indicate a clear segregation between remaining and lost trees in areas of high intensity land use, while in areas with low intensity this pattern was less evident. Also, lost trees were more clustered than the remaining ones. Higher tree loss density occurred at lower slopes, the areas initially occupied by high or medium-density shrubs being less prone to undergo tree loss than those that appeared in 1956 like ploughed lands, grasslands or low-density shrubs. These results show a concentration of the risk of tree loss in certain areas where conservation efforts should be focused.