黑河下游胡杨季节尺度径向生长变化研究

利用半径型树木生长测量仪, 于2009-2010年生长季对西北内陆河-黑河流域下游荒漠河岸林优势树种-胡杨径向生长进行了监测, 结合环境气象、 水文因子同步监测资料, 对胡杨季节变化节律和环境影响因素进行了研究.结果表明: 按照胡杨径向生长日变化特征, 将其分为增长型(ΔR+)、 负增长型(ΔR-)和持续增长型(ΔR++)3种类型.在生长季, 胡杨径向生长季节变化呈"S"型, 可分为前期缓慢增长(P1)、 迅速增长(P2)和后期微弱增长(P3) 3个阶段; ΔR+/++类型在这3个阶段中所占比例分别为63.64%, 85.51%和48.61%; 5月末至8月初是胡杨年轮形成的主要阶段, 在该阶段气温和地下水位埋深均表现出显著的相关关系, 但地下水位埋深应是最根本的因素. 因此, 在树木年轮学应用方面, 胡杨可以用来反演区域水环境变化, 包括河道径流和地下水位变化等. 在荒漠河岸林管理方面, 满足春夏季地下水位条件和适度频率的春汛, 是保证该地区胡杨河岸林正常生长和保持合理种群结构的前提.     

Fifty-thousand-year vegetation and climate history of Noel Kempff Mercado National Park, Bolivian Amazon

Pollen and charcoal records from two large, shallow lakes reveal that throughout most of the past 50,000 yr Noel Kempff Mercado National Park, in northeastern lowland Bolivia (southwestern Amazon Basin), was predominantly covered by savannas and seasonally dry semideciduous forests. Lowered atmospheric CO₂ concentrations, in combination with a longer dry season, caused expansion of dry forests and savannas during the last glacial period, especially at the last glacial maximum. These ecosystems persisted until the mid-Holocene, although they underwent significant species reassortment. Forest communities containing a mixture of evergreen and semideciduous species began to expand between 6000 and 3000 ¹⁴C yr B.P. Humid evergreen rain forests expanded to cover most of the area within the past 2000 ¹⁴C yr B.P., coincident with a reduction in fire frequencies. Comparisons between modern pollen spectra and vegetation reveal that the Moraceae-dominated rain forest pollen spectra likely have a regional source area at least 2-3 km beyond the lake shore, whereas the grass- and sedge-dominated savanna pollen spectra likely have a predominantly local source area. The Holocene vegetation changes are consistent with independent paleoprecipitation records from the Bolivian Altiplano and paleovegetation records from other parts of southwestern Amazonia. The progressive expansion in rain forests through the Holocene can be largely attributed to enhanced convective activity over Amazonia, due to greater seasonality of insolation in the Southern Hemisphere tropics driven by the precession cycle according to the Milankovitch Astronomical Theory.     

Evaluation of classifiers for processing Hyperion (EO-1) data of tropical vegetation

There is an urgent necessity to monitor changes in the natural surface features of earth. Compared to broadband multispectral data, hyperspectral data provides a better option with high spectral resolution. Classification of vegetation with the use of hyperspectral remote sensing generates a classical problem of high dimensional inputs. Complexity gets compounded as we move from airborne hyperspectral to Spaceborne technology. It is unclear how different classification algorithms will perform on a complex scene of tropical forests collected by spaceborne hyperspectral sensor. The present study was carried out to evaluate the performance of three different classifiers (Artificial Neural Network, Spectral Angle Mapper, Support Vector Machine) over highly diverse tropical forest vegetation utilizing hyperspectral (EO-1) data. Appropriate band selection was done by Stepwise Discriminant Analysis. The Stepwise Discriminant Analysis resulted in identifying 22 best bands to discriminate the eight identified tropical vegetation classes. Maximum numbers of bands came from SWIR region. ANN classifier gave highest OAA values of 81% with the help of 22 selected bands from SDA. The image classified with the help SVM showed OAA of 71%, whereas the SAM showed the lowest OAA of 66%. All the three classifiers were also tested to check their efficiency in classifying spectra coming from 165 processed bands. SVM showed highest OAA of 80%. Classified subset images coming from ANN (from 22 bands) and SVM (from 165 bands) are quite similar in showing the distribution of eight vegetation classes. Both the images appeared close to the actual distribution of vegetation seen in the study area. OAA levels obtained in this study by ANN and SVM classifiers identify the suitability of these classifiers for tropical vegetation discrimination.     

Occurrence of microorganic pollutants in the atmosphere of Singapore during haze and non-haze periods

Microorganic pollutants such as polycylic aromatic hydrocarbons （PAHs）, organochlorine pesticides （OCPs）, polychlorinated biphenyls （PCBs） and total petroleum hydrocarbons （TPHs） are known to be present in urban ambient air. These organic compounds are prone to atmospheric transport and deposition over long distances, thus enabling them to accumulate even in regions remote from their sources. Deposition from the atmosphere can be via direct deposition and exchange with crops that may be directly or indirectly ingested by humans. It can also take place via wet and dry deposition and air-water exchange. Following their deposition, these microorganic pollutants tend to accumulate in soils, sediments and in human and ecological food chains. There are many reports in the literature on the atmospheric concentrations of microorganic pollutants, but there are few reports and data in Asia. This study was performed in Singapore to determine the relative amounts of persistent organic pollutants and TPHs in ambient aerosols. One of the important sources of these organic compounds in Southeast Asia is thought to be biomass burning （vegetation fires）. Hence, air sampling was conducted during both smoke haze and non-haze periods. The data obtained from this study will be presented and discussed.     

Distinguishing streamflow trends caused by changes in climate,forest cover,and permafrost in a large watershed in northeastern China

Understanding how rivers respond to changes in land cover, climate, and subsurface conditions is critical for sustainably managing water resources and ecosystems. In this study, long‐term hydrologic, climate, and satellite data (1973–2012) from the Upper Tahe River watershed (2359 km²) in the Da Hinggan Mountains of northeast China were analysed to quantify the relative hydrologic effects of climate variability (system input) and the combined influences of forest cover change and permafrost thaw (system characteristics) on average annual streamflow (system response) using 2 methods: the sensitivity‐based method and the Kendall–Theil robust line method. The study period was subdivided into a forest harvesting period (1973–1987), a forest stability period (1988–2001), and a forest recovery period (2002–2012). The results indicated that the combined effects of forest harvesting and permafrost thaw on streamflow (+ 47.0 mm) from the forest harvesting period to the forest stability period was approximately twice as large as the effect associated with climate variability (+20.2 mm). Similarly, from the forest stability period to the forest recovery period, the decrease in average annual streamflow attributed to the combined effects of forest recovery and permafrost thaw (?38.0 mm) was much greater than the decrease due to climate variability (?22.2 mm). A simple method was used to separate the distinct impacts of forest cover change and permafrost thaw, but distinguishing these influences is difficult due to changes in surface and subsurface hydrologic connectivity associated with permafrost thaw. The results highlight the need to consider multiple streamflow drivers in future watershed and aquatic ecosystem management. Due to the ecological and hydrological susceptibility to disturbances in the Da Hinggan Mountains, forest harvesting will likely negatively impact ecohydrological processes in this region, and the effects of forest species transition in the forest recovery process should be further investigated.     

机载LiDAR生成亚热带森林林下DTM的地面误差分析

目前LiDAR技术已经成为DTM的主要生产方法。地面误差对LiDAR生成DTM的精度影响比较明显,特别是由于亚热带森林植被覆盖区LiDAR激光点云少,生成的DTM更复杂,需要分析地面误差对LiDAR生成林下DTM的精度影响。本文以华南农业大学增城教学科研基地为研究对象,从森林郁闭度和坡度两个方面探讨了地面误差对机载LiDAR数据生成林下DTM精度的影响。研究结果发现高程误差会随郁闭度的增大而增大;而随坡度变化趋势不明显,但是坡度为15°时成为误差的分水岭,其前后误差差异比较明显。总体而言,郁闭度的影响更为明显。     

伏牛山地区森林生态系统服务权衡/协同效应多尺度分析

森林生态系统服务权衡与协同研究已成为当前相关学科的研究热点和前沿,对服务权衡与协同关系的多尺度分析有助于更加有效地实施森林资源管理。综合森林类型图、NDVI、气象和土壤等多源数据,借助CASA模型、InVEST 3.2模型和ArcGIS 10.2软件,开展伏牛山地区森林生态系统服务评估,运用空间叠置方法从多个空间尺度（区域、南北坡、垂直带）探讨服务权衡与协同效应。结果表明：① 研究区森林生态系统平均蓄积量为49.26 m ³/hm ²,碳密度为156.94 t/hm ²,供水深度为494.46 mm,土壤保持量为955.4 t/hm ²,生境质量指数为0.79。② 区域尺度上,28.79%的森林服务之间存在高协同效应,10.15%的森林存在低协同效应,61.06%的森林存在强权衡和弱权衡效应。③ 南北坡尺度上,南坡服务之间的协同关系优于北坡。垂直带尺度上,南坡中山落叶阔叶林带（SIII）服务之间协同关系最好,北坡低山落叶阔叶林带（NI）协同关系最差。     

Decline of the mid‐ to late Holocene forests in China: climatic change or human impact?

Fossil pollen data from China indicate continued forest decline during the mid‐ to late Holocene in most regions north of the Yangtze River. The earliest forest decline can be detected ca. 5000 yr BP in the middle and lower Yellow River regions. North, northeast and northwest from this region, forest decline became progressively later, and almost no decline took place in the northernmost part of northeast China and in the remote areas of west China during the last 5000 yr. Climate changes could hardly account for the temporal and spatial patterns of the forest decline. Instead, anthropogenic disturbance may have been of overwhelming importance. Ancient agriculture and high‐density settlement expanded outward from the middle and lower Yellow River regions in similar patterns to those of forest change. This study also indicates that land‐use and land‐cover changes may have started in the early stage of Chinese civilization in an extensive area of the country. Copyright © 2000 John Wiley & Sons, Ltd.     

路南石林喀斯特的形态特征及地质演化

石林喀期特是喀斯特地貌中的一种极端形式，主要由表面溶良发育，高达３０￣５０ｍ的剑状石峰、石柱组成。同世界其他一些著名的剑状喀期特相比，路南石林喀斯特具有最为复杂的地质演化历史和最为多样的形态。研究表明：自２亿５千万年前早二叠世末期首次出现石牙以来，石林的发育经历了四大演化阶段；石林的形成是有利的地质、水文、气候条件综合作用的结果，而土下溶蚀是主要形成机理。剑状、峰状、塔状、蘑茹状及其他不规则状为石     

基于机器学习的表层土壤成矿元素空间预测: 以稀有金属铷元素为例

近些年随着土地质量地球化学调查工作的开展,获取了大量表层土壤样品数据。然而,这些数据也存在一个明显的缺陷,即1∶50 000大比例尺表层土壤数据往往缺少成矿元素。鉴于土壤成矿元素含量对于矿产资源勘查的重要指示作用,尝试基于现有数据对大比例尺表层土壤成矿元素含量提供一个补全方案。以稀有金属铷元素为例,采用随机森林算法把同一区域2 548组1∶250 000小比例尺表层土壤数据按照8∶2的比例随机分为两组,用80%的数据进行训练建模,20%的数据对模型进行验证。采用变量重要性度量排序和构建学习曲线的组合方法优选了8种元素(K、B、Ni、V、Zn、As、Co、Cu)作为预测变量,模型对训练数据和测试数据的拟合优度R²分别达到0.983 2和0.895 6,说明预测变量的优选方法是有效的。随后将1∶50 000表层土壤的上述预测变量数据作为输入变量导入模型中,得到预测的Rb元素含量,预测结果比较符合实际特征。本研究表明将大数据机器学习随机森林算法引入表层土壤地球化学元素含量空间定量预测具有可行性,可进一步拓展土地质量地球化学数据的服务应用维度。