A long-term volcanic hazard event tree for Teide-Pico Viejo stratovolcanoes (Tenerife,Canary Islands)

We propose a long-term volcanic hazards event tree for Teide-Pico Viejo stratovolcanoes, two complex alkaline composite volcanoes that have erupted 1.8–3 km³ of mafic and felsic magmas from different vent sites during the last 35 ka. This is the maximum period that can be investigated from surface geology and also represents an upper time limit for the appearance of the first phonolites on that volcano. The whole process of the event tree construction was divided into three stages. The first stage included the determination of the spatial probability of vent opening for basaltic and phonolitic eruptions, based on the available geological and geophysical data. The second, involved the analysis of the different eruption types that have characterised the volcanic activity from Teide during this period. The third stage focussed on the generation of the event tree from the information obtained in the two previous steps and from the application of a probabilistic analysis on the occurrence of each possible eruption type. As for other volcanoes, the structure of the Teide-Pico Viejo Event Tree was subdivided into several steps of eruptive progression from general to more specific events. The precursory phase was assumed as an unrest episode of any geologic origin (magmatic, hydrothermal or tectonic), which could be responsible for a clear increase of volcanic activity revealed by geophysical and geochemical monitoring. According to the present characteristics of Teide-Pico Viejo and their past history, we started by considering whether the unrest episode would lead to a sector collapse or not. If the sector collapse does not occur but an eruption is expected, this could be either from the central vents or from any of the volcanoes' flanks. In any of these cases, there are several possibilities according to what has been observed in the period considered in our study. In the case that a sector collapse occurs and is followed by an eruption we considered it as a flank eruption. We conducted an experts elicitation judgement to assign probabilities to the different possibilities indicated in the event tree. We assumed long term estimations based on existing geological and historical data for the last 35 Ka, which gave us a minimum estimate as the geological record for such a long period is incomplete. However, to estimate probabilities for a short term forecast, for example during an unrest episode, we would need to include in the event tree additional information from the monitoring networks, as any possible precursors that may be identified could tell us in which direction the system will evolve. Therefore, we propose to develop future versions of the event tree to include also the precursors that might be expected on each path during the initial stages of a new eruptive event.     

Relationship between tree height and landslide characteristics obtained by GIS assessment

Landslides in forested landscapes have far-reaching implications, beyond that of just destroying the forest itself, sometimes initiating large-scale sediment disasters. Although vegetation increases slope stability through its root network, it is hard to evaluate its contribution to slope stability over a wide area. In this study, the relationship between tree height and landslide characteristics in the Ikawa catchment, central Japan, was investigated to develop a method for evaluating the effects of forest cover on slope stability over a regional extent. Catchment-wide tree height was obtained using airborne LiDAR point cloud data and used in conjunction with the root depth profile, measured for trees of various height by digging trenches. Root tensile strength per unit area of soil was calculated from individual root diameters and empirical power law equations on the relationship between root diameter and root tensile force in order to better understand the effect that tree height has on slope stability. Landslide density in the Ikawa catchment shows that landslides occur more frequently in forests with shorter trees, with occurrence decreasing as tree height increases. This is likely due to the stabilizing features of larger trees having a greater network of roots, which is supported by the general increase in total root area and the deeper penetration of root biomass into the soil as the height of trees surveyed increases. Landslide density was not solely affected by tree height, but also by slope gradient and plane curvature. Decreasing landslide occurrence and landslide area as tree height increases suggests that slope stability increases with tree height, while the random distribution of results when comparing landslide depth to tree height suggests that while tree height has an impact on relative slope stability, the landslide failure depth is independent of tree height, and thus controlled by other factors. © 2020 John Wiley & Sons, Ltd.     

Mapping global forest biomass and its changes over the first decade of the 21st century

Forests played an important role in carbon sequestration during the past two decades. Using a model tree ensemble method(MTE) to regress the seven reflectance bands of EOS-Terra-MODIS satellite data against country level forest biomass carbon density(BCD) of 2001–2005 provided by United Nations' s Forest Resource Assessment(FRA), we developed a global map of forest BCD at 1 km×1 km resolution for both 2001–2005 and 2006–2010. For 2006–2010, the total global forest biomass carbon stock is estimated as 279.6±7.1 Pg C, and the tropical forest biomass carbon stock is estimated as 174.4±5.4 Pg C. During the first decade of the 21 st century, we estimated an increase of global forest biomass of 0.28±0.75 Pg C yr^-1. Tropical forest biomass carbon stock slightly decreased(-0.31±0.60 Pg C yr^-1); by contrast, temperate and boreal forest biomass increased(0.58±0.28 Pg C yr^-1) during the same period. Our estimation of the global forest biomass carbon stock and its changes is subject to uncertainties due to lack of extensive ground measurements in the tropics, spatial heterogeneity in large countries, and different definitions of forest. The continuously monitoring of forest biomass carbon stock with MODIS satellite data will provide useful information for detecting forest changes.     

运用改进的地震定位交切法重定位2001—2010年云南地震

传统地震定位方法利用震源轨迹确定震源位置,但基于均匀或横向均匀介质模型必然导致定位误差。为此对传统方法进行改进,发展适用于三维复杂地壳速度模型的地震定位交切法。利用最小走时树射线追踪技术,以离散方式准确计算三维复杂地壳速度模型中的震源轨迹,将震源定位于震源轨迹交汇的密集点。将该方法应用于云南地区地震重定位,得到较高定位精度。     

Rainfall Algorithms Using Oceanic SatelliteObservations from MWHS-2

正1EarthEnvironmental Sciences Department, Vanderbilt University, Nashville, TN 37235, USA2Space ScienceEngineering Center, University of Wisconsin-Madison, Madison, WI 53706, USA     

结合多光谱影像降维与深度学习的城市单木树冠检测

多光谱数据的降维处理对基于深度学习的单木树冠检测研究有重要意义,如何使用合适的降维方法以提高单木检测的精度却少有研究讨论。本文使用无人机搭载多光谱相机进行航拍作业,采集研究区内银杏树种多光谱影像。将原始多光谱影像通过特征波段选择、特征提取、波段组合的方法生成5种不同的数据集用于训练3种经典的深度学习网络FPN-Faster-R-CNN,YOLOv3,Faster R-CNN。其中由波段组合方法得到的近红外、红色、绿色波段组合在不同类型的目标检测网络中都有最好的检测结果,其中FPN-Faster-R-CNN网络对银杏树冠的检测精度最高为88.4%,由OIF指标得到的蓝色、红色、近红外波段组合信息量最高,但在所有网络中的平均检测精度最低,仅为79.3%。实验结果表明：在不同波段降维方法中,若降维后的影像中目标物体的色彩与背景差异较明显,且轮廓清晰,则深度学习网络对树冠的检测可获得较好的结果。而影像自身的信息量则对深度学习网络的树冠检测能力的提升作用有限。本研究中针对多光谱影像的降维方法分析,为基于深度学习的单木树冠检测研究提供了重要的实验参考。     

河西走廊园林绿化树种选择及综合性能数量化评价

树种选取是城镇实现"绿化、美化、净化、香化、彩化"和持续发展的前提, 对于干旱沙区的城镇更为重要。现依据"适地适树、结构自然、突出特色、丰富多彩、功能优化"的原则, 采用层次分析法综合评判和选择确定甘肃河西城镇绿化的基调树种12种、骨干树种41种、一般树种54种、边缘树种47种。基调树种中有常绿乔木2种、落叶乔木7种、落叶灌木1种, 属于乡土树种的7种, 外来树种近占一半; 骨干树种中有常绿树种3种(乔木1种)、落叶乔木24种、落叶灌木14种, 属于乡土树种的14种, 外引树种占其3/4。     

Reconstruction of the Past Climate From Tree Rings Over Western Himalaya, India

RECONSTRUCTION OF THE PAST CLIMATE FROM TREE RINGS OVER WESTERN HIMALAYA, INDIA     

Understanding grower demographics,motivations and management practices to improve engagement,extension and industry resilience: a case study of the macadamia industry in the Northern Rivers,Australia

Research and development programs for improved productivity and profitability in horticulture have focused largely on understanding biophysical factors and production and harvest technologies. By contrast, relatively few studies focus upon the existing status of a rural community in terms of demographics, rationales and motivations underlying current farm management practices. Understanding such factors is increasingly important as pressures from climate change, globalised market forces, land-use competition and ageing workforces intersect on farms and for farmers. This paper explores experiences among macadamia growers in the Northern Rivers region of New South Wales, Australia, with the aim to better understand and improve the targeting of development programs to meet grower needs and aspirations, for improved industry resilience. Results suggest that growers are a diverse, ageing demographic who came into the industry with a variety of skill sets. Noting a lack of previous farming experience for many growers, we highlight the need to consider information transfer and succession planning, along with aspirations to consolidate properties and skills. New engagement approaches are recommended, with tailored extension to different grower groups. To aid with this process, a classification tree was developed for the purposes of separating growers into groups with different support needs.     

应用Sentinel-2A NDVI时间序列和面向对象决策树方法的农作物分类

农作物种植结构是农业生产活动对土地利用的表现形式。及时精确地获取农作物的空间分布信息对指导农业生产、合理分配资源以及解决粮食安全问题等具有重要意义。目前农作物信息提取研究大多局限于中低分辨率遥感影像的NDVI时间序列,影响了作物空间分布信息提取的准确性。随着Sentinel-2A卫星成功发射,为高分辨率NDVI时间序列的构建提供了可能。本文以黑龙江省北安市为研究区,基于覆盖完整生育期的Sentinel-2A多光谱数据,构建10 m分辨率的NDVI时间序列数据集,利用 Savitzky Golay (S-G) 滤波器对 Sentinel-2A NDVI时间序列数据进行平滑。基于典型时相的多光谱数据和NDVI时间序列构建面向对象决策树分类模型进行作物类型遥感识别。通过对样本的NDVI时间序列曲线分析,可以得出NDVI时间序列能够清晰地区分作物物候差异。此外,本文还利用面向对象分类和支持向量机（Support Vector Machine, SVM）分类两种方法,对典型时相的多光谱数据进行了作物分类对比实验,并对结果进行了对比分析。研究结果表明：① 典型时相多光谱数据引入平滑重构后的NDVI时间序列能够更好地描述作物的物候特性,能够准确刻画研究区作物发育情况,有效区分各类作物;② 通过对比分类实验发现,典型时相多光谱数据引入NDVI时间序列特征,增强了不同作物之间的光谱差异,提高了作物分类精度,总体精度和kappa系数较典型时相多光谱数据进行分类的结果分别提高了7.7% 和0.055;③ 基于面向对象的决策树分类模型在作物分类的结果中精度最高,总体精度为96.2%,kappa系数为0.892。本研究的方法为其他大区域农作物的分类提供了重要参考和借鉴价值。