珍贵树种林下套种广东紫珠的密度效应研究

为了评价珍贵树种人工林下不同套种密度对广东紫珠 Callicarpa kwangtungensis 生长的影响， 于 7 a 生降香黄檀 Dalbergia odorifera- 檀香 Santalum album 混交林下设置 3 个密度处理种植广东紫珠，即 在行间套种 1 行、2 行、3 行，株距为 0.8 m，分别于定植后 6、12 和 18 个月调查其地径、树高和生物 量。结果表明：3 个套种密度处理间广东紫珠地径差异不显著 (P ≥ 0.05)，而高生长在各时间段均差异显 著 (P ＜ 0.05)，单株生物量差异极显著 (P ＜ 0.01)，表现出随密度增加而升高的趋势。本研究筛选出降香 黄檀 - 檀香混交林下套种广东紫珠的适宜密度为行间栽种 3 行 (12 500 株 · hm-2)，6 个月收获 1 次，每年 可采收广东紫珠 1 479.08 kg · hm-2( 鲜重 )，收入可达 5 916.33 元 · hm-2，可显著增加林农短期收益，为珍 贵树种规模发展提供了一种实用可行的经营模式。     

运用植被指数时序特征对落叶松人工林分类

为探究植被指数时序特征是否有利于落叶松人工林提取,以孟家岗林场为研究试验区域,根据落叶松人工林季相和物候特征,利用Landsat8OLI影像数据提取研究区内5种植被的归一化植被指数(I NDV)、差值植被指数(I DV)、比值植被指数(I RV)、增强型植被指数(I EV),构建相应的植被指数时序特征。采用最大似然和随机森林两种方法对单一时相影像和加入植被指数时序特征的影像进行对比试验。结果表明:影像中加入植被指数时序特征后,最大似然算法的分类总体精度为89.53%,Kappa系数为0.87,比单一时序特征的影像分类精度提高了13.35%;随机森林算法的森林类型分类总体精度为93.22%,Kappa系数为0.92,比单一时序特征的影像分类精度提高了19.8%。因此,加入植被指数时序特征后能得到更高的落叶松人工林提取精度。     

建德市公益林生态系统服务价值评价

对公益林生态系统服务功能和价值评估进行研究,为建德市公益林保护、规划和管理提供科学指导和理论依据。本文依据《生态系统服务功能评估规范》,结合涵养水源、保育土壤等8项指标,评价建德市公益林生态系统服务价值。结果表明,建德市公益林生态系统服务总价值81.64亿元,其中涵养水源效益25.22亿元,固土保肥效益4.43亿元,固碳释氧效益12.53亿元,积累营养效益0.48亿元,森林游憩效益22.19亿元,净化大气效益3.21亿元,生物多样性保护效益12.13亿元,森林防护效益1.45亿元。公益林生态系统服务价值远高于其直接利用价值,表明应继续实施生态补偿政策,努力提高生态补偿标准。     

基于机器学习算法的土壤有机质 质量比估算

为快速高效地估测干旱、半干旱地区土壤有机质(soil organic matter, SOM)质量比,提出了一种结合竞争适应重加权法(CARS)和随机森林(RF)的估测模型.以内陆干旱区艾比湖流域为研究区,测定土壤高光谱反射率和SOM质量比,经预处理后,利用CARS对原始光谱(R)、一阶导数(R′)、吸光度(log(1/R))及吸光度一阶导数［log(1/R)］′4种光谱变量的可见-近红外光谱进行筛选,并结合RF算法,建立全谱段RF模型与CARS-RF模型.结果表明,基于CARS方法对光谱进行变量筛选后,得出4种光谱变量的优选变量集个数分别为35,26,34和121;在4种光谱变量中,R′和［log(1/R)］′的SOM估测模型精度较高,以［log(1/R)］′为基础数据获得的模型精度最高;CARS-RF模型精度优于全谱段RF模型,模型验证集决定系数(R²)、均方根误差(RMSE)、相对分析误差(RPD)分别为0.881,6.438 g/kg和2.177.该研究在预处理的基础上通过变量优选,应用较少的变量个数获得较高的估测精度,为干旱、半干旱区SOM高光谱估测提供了适宜高效的方法.     

杨树低效林下土壤粒径分布与分形特征

[目的]研究鄂尔多斯地区杨树低效林下土壤粒径分布与分形特征,为该地区杨树低效林的成因分析及改造提供基础依据,并为杨树人工林的科学管理提供参考与决策支持。[方法]使用Mastersizer3000激光衍射粒度分析仪测量土壤粒径,采用土壤颗粒体积分形维数模型计算分形维数D值,并测定土壤养分。[结果]①鄂尔多斯地区杨树低效林下土壤颗粒组成主要以细砂粒(42.33%)、极细砂粒(15.72%)、中砂粒(19.77%)为主;主要土壤质地类型为砂土;土壤颗粒的分形维数D值在1.222 5～2.204 5范围内,处于较低水平。②土壤颗粒分形维数D值与粉粒、极细砂粒、细砂粒含量呈显著正相关关系,与中砂粒、粗砂粒、极粗砂粒呈显著负相关关系。③随着土层加深,粒径250μm的土壤颗粒含量增加,粒径250μm的土壤颗粒含量减少;土壤颗粒分形维数D值随土层加深而增大,增大幅度不大。④土壤颗粒分形维数D值与土壤有机质、全氮、速效氮、速效磷含量的关系均未达到显著水平,土壤各养分含量的变化对分形维数D值没有显著影响。[结论]鄂尔多斯地区杨树低效林下土壤颗粒组成以砂粒为主,土壤分形维数较低,土壤结构极不均匀,这样的土壤结构对杨树的正常生长发育极为不利,不良的土壤结构也在一定程度上导致了低效林的形成,因此在对杨树低效林进行预防和改造时也要改善林下土壤结构。     

Artificial neural network modeling of microbial community structures in the Atlantic Forest of Brazil

Microbial communities vary across the landscape in forest soils, but prediction of their biomass and composition is a difficult challenge due to the large numbers of variables that influence their community structures. Here we examine the use of artificial neural network (ANN) models for extraction of patterns among soil chemical variables and microbial community structures in forest soils from three regions of the Atlantic Forest of Brazil. At each location, variations in soil chemical properties and FAME profiles of microbial community structures were mapped at 20 × 20 m intervals within 10 ha parcels. Geostatistical analyses showed that spatial variability in soil physical and chemical variables could be mapped at scale distances of 20 m, but that FAME profiles representing the microbial communities were highly variable and had no spatial dependence at the same scale in most cases. RDA analysis showed that FAME signatures representing different microbial groups were positively associated with soil pH, OM, P and base cations concentrations, whereas microbial biomass was negatively associated with the same environmental factors. In contrast, ANN models revealed clear relationships between microbial community structures at each parcel location, and generated verifiable predictions of variations in FAME profiles in relation to soil pH, texture, and the relative abundances of base cations. The results suggest that ANN modeling provides a useful approach for describing the relationships between microbial community structures and soil properties in tropical forest soils that were not able to be captured using geostatistical and RDA analyses.     

Brazilian savanna re-establishment in a monoculture forest:diversity and environmental relations of native regenerating understory in Pinus caribaea Morelet. stands

In this paper we analyze and compare natural regeneration in shrub-tree community in three areas below Pinus caribaea stands and a natural area (cerrado sensu stricto). We also analyze the influence of...     

Interaction between climate and management on beta diversity components of vegetation in relation to soil properties in arid and semi-arid oak forests,Iran

This study aimed to investigate the interaction between regions with different climatic conditions(arid vs. semi-arid) and management(protected vs. unprotected) on the turnover and nestedness of vegetation in relation to physical, chemical and biological properties of soils in the Ilam Province of Iran. In each of the two regions, we sampled 8 sites(4 managed and 4 unmanaged sites) within each of which we established 4 circular plots(1000 m~2) that were used to investigate woody species, while two micro-plots(1 m×1 m) were established in each 1000-m~2 plot to analyze herbaceous species. In each sample unit, we also extracted three soil samples(0–20 cm depth) for measuring soil properties. The results indicated that the interaction between region and conservational management significantly affected the percent of canopy cover of Persian oak(Quercus brantii Linddl), soil respiration, substrate-induced respiration, as well as beta and gamma diversities and turnover of plant species. The percent of oak canopy cover was positively correlated with soil silt, electrical conductivity, available potassium, and alpha diversity, whereas it was negatively correlated with plant turnover. In addition, plant turnover was positively related to available phosphorus, while nestedness of species was positively related to organic carbon and total nitrogen. According to these results, we concluded that physical, chemical, and biological characteristics of limited ecological niche generally influenced plant diversity. Also, this study demonstrated the major contribution of the beta diversity on gamma diversity, especially in semi-arid region, because of the higher heterogeneity of vegetation in this area.     

林地抚育对黔中地区杉木人工幼林生态系统碳储量的影响

林地抚育(松土、割灌、锄草)是提高人工林林分成活率，促进林木生长的重要措施，但对其固碳功能的影响研究仍鲜见报道。本研究以杉木人工林为研究对象，分析了林地抚育(松土、割灌、锄草)对黔中地区杉木人工幼林生态系统碳储量及其组分(植被层、枯落物层、作为主根系层的0~60 cm土壤层的碳储量)的影响。结果表明:林地抚育使得杉木人工林林木的保存率、林分郁闭度、林木胸径、树高等均显著高于对照林分，林木单株生长的固碳能力大幅提高，其碳储量是对照林分的4.93倍。抚育杉木人工幼林生态系统的总碳储量(106.37 t/hm2)显著高于对照(78.61 t/hm2)，其中植被碳库储量(26.07 t/hm2)是对照(4.64 t/hm2)的5.62倍，抚育后枯落物碳储量较对照高0.38 t/hm2。但是，林地抚育后表层土壤(0~10 cm)有机碳含量较对照下降5.44 g/kg，而10 cm以下土层较对照均表现为增加，土壤碳储量较对照总体增加3.30 t/hm2。因此，造林初期林地抚育可促进林木生长，提高植被、土壤和生态系统的碳储量，显著增强杉木幼龄林的碳汇功能。      

宜良县云南松林生物量遥感估测方法研究

利用landsat5影像和通过蓄积量-生物量转换方法获得的样地生物量数据,从宜良县云南松林遥感特征、地形特征、郁闭度等方面选取自变量,采用随机森林回归和偏最小二乘法进行生物量遥感估测建模。结果表明:随机森林回归估测模型调整决定系数R2为0.881,模型估测精度为82.3%;偏最小二乘模型的调整决定系数R2为0.753,模型估测精度为78.43%。随机森林回归在模型拟合效果和检验精度上均优于偏最小二乘模型。