天津地区冬季不同粒径大气颗粒物中的DDTs

为进一步探讨大气颗粒物中DDTs的环境危害和给风险评价提供数据支持,采用气溶胶粒度分布采样器(辽阳应用技术研究所,FA-3)采集并分析了天津13个样点冬季不同粒径大气颗粒物中DDTs残留量。结果表明,p,p'-DDT(1.071±0.736ng·m-3),p,p'-DDE(0.858±0.532ng·m-3)是主要污染物,p,p'-DDD(0.436±0.190ng·m-3)浓度相对较低。不同粒径颗粒物对DDTs的富集程度有差异,在4.7-5.8μm和<0.43μm范围内有较强的富集趋势。粒径<2.1μm的颗粒中p,p'-DDE/ΣDDT比值明显小于粒径>2.1μm的颗粒的该比值。不同样点大气颗粒物中DDTs浓度差异显著,可能与早期农业施用空间差异有关。     

基于激光点云的三维虚拟果园构建方法

针对果园管理数字化程度低、构建方法较为单一等问题,本研究提出了一种基于激光点云的三维虚拟果园构建方法。首先采用手持式三维点云采集设备（3D-BOX）结合即时定位与地图构建-激光测距与测绘（Simultaneous Localization and Mapping-Lidar Odometry and Mapping,SLAM-LOAM）算法获取果园点云数据集;然后通过统计滤波算法完成点云数据离群点与噪声点的去除,并结合布料模拟算法（Cloth Simulation Filtering,CSF）与DBSCAN（Density-Based Spatial Clustering of Applications with Noise）聚类算法,实现地面去除与果树聚类分割,进而使用VoxelGrid滤波器降采样;最后利用Unity3D引擎,构建虚拟果园漫游场景,将作业机械的实时GPS（Global Positioning System）数据从WGS-84坐标系转换为高斯投影平面坐标系,并通过LineRenderer显示实时轨迹,实现作业机械运动轨迹控制与作业轨迹的可视化展示。为验证虚拟果园构建方法的有效性,在海棠果园与芒果园开展果园构建方法测试。结果表明,所提出的点云数据处理方法对海棠果树与芒果树聚类分割的准确率分别达到了95.3%与98.2%;通过与实际芒果园的果树行距、株距对比,虚拟芒果园的平均行间误差约为3.5%,平均株间误差约为6.6%。并且将Unity3D构建出的虚拟果园与实际果园相比,该方法能够有效复现果园三维实际情况,得到了较好的可视化效果,为果园的数字化建模与管理提供了一种技术方案。     

Mapping of urban roadside trees – A case study in the tree register update process in Helsinki City

This case study describes a method for utilizing leaf-off airborne laser scanning (ALS) data for mapping characteristics of urban trees. ALS data were utilized to detect and update all street trees in the tree inventory of the City of Helsinki, Finland. The inventory consists of roughly 20,000 street trees with mean diameter at breast height (DBH) of 24 cm and mean height of 10.6 m. The large number of trees makes the manual updating process very laborious. The automatic mapping procedure presented in this paper detected 88.8% of all trees in the inventory. Tree height was predicted with root mean square error (RMSE) of 1.27 meters and tree DBH with RMSE of 6.9 cm. The presented method provides a practical and cost–effective tool for the mapping of urban tree characteristics. The cost–efficiency was further enhanced because the used ALS data were originally collected for other urban planning purposes.     

机载LiDAR和高光谱融合实现普洱山区树种分类

[目的]通过机载遥感影像对普洱山区进行植被分类研究,为山区森林经营规划与可持续经营方案的制图提供高效应用途径。[方法]将2014年4月航拍的机载AISA Eagle II高光谱和Li DAR同步数据融合,利用点云数据提取的数字冠层高度模型(CHM)得到树种的垂直结构信息,结合经过主成分分析(PCA)的高光谱降维影像,选用支持向量机(SVM)分类器进行分类。[结果]普洱市万掌山实验区主要树种分为思茅松、西南桦、刺栲、木荷等。融合影像数据分类的总体精度和Kappa系数分别为80.54%、0.78,比单一高光谱影像数据分类精度分别提高6.55%、0.08,其中主要经营树种思茅松的制图精度达到了90.24%。[结论]该方法对山区主要树种的识别是有效的,将机载Li DAR与高光谱影像融合可以有效改善分类精度。     

Using multi-source data to map and model the predisposition of forests to wind disturbance

Natural disturbances such as wind are known to cause threats to ecosystem services as well as sustainable forest ecosystem management. The objective of this research was to better understand and quantify drivers of predisposition to wind disturbance, and to model and map the probability of wind-induced forest disturbances (P_DIS) in order to support forest management planning. To accomplish this, we used open-access airborne light detection and ranging (LiDAR) data as well as multi-source National Forest Inventory (NFI) data to model P_DIS in southern Finland. A strong winter storm occurred in the study area in December 2011. High spatial resolution aerial images, acquired after the disturbance event, were used as reference data. Potential drivers associated with P_DIS were examined using a multivariate logistic regression model. The model based on LiDAR provided good agreement with detected areas susceptible to wind disturbance (73%); however, when LiDAR was combined with multi-source NFI data, the results were more promising: prediction accuracy increased to 81%. The strongest predictors in the model were mean canopy height, mean elevation, and stem volume of the main tree species (Norway spruce and Scots pine). Our results indicate that open-access LiDAR data can be used to model and map the probability of predisposition to wind disturbance, providing spatially detailed, valuable information for planning and mitigation purposes.     

Multiple attribute decision making for individual tree detection using high-resolution laser scanning

A canopy height model (CHM) is a standard LiDAR-derived product for deriving relevant forest inventory information, including individual tree positions, crown boundaries and plant density. Several image-processing techniques for individual tree detection from LiDAR data have been extensively described in literature. Such methods show significant performance variability depending on the vegetation characteristics of the monitored forest. Moreover, over regions of high vegetation density, existing algorithms for individual tree detection do not perform well for overlapping crowns and multi-layered forests. This study presents a new time and cost-efficient procedure to automatically detect the best combination of the morphological analysis for reproducing the monitored forest by estimating tree positions, crown boundaries and plant density from LiDAR data. The method needs an initial calibration phase based on multi attribute decision making-simple additive weighting (MADM-SAW). The model is tested over three different vegetation patterns: two riparian ecosystems and a small watershed with sparse vegetation. The proposed approach allows exploring the dependences between CHM filtering and segmentation procedures and vegetation patterns. The MADM architecture is able to self calibrate, automatically finding the most accurate de-noising and segmentation processes over any forest type. The results show that the model performances are strongly related to the vegetation characteristics. Good results are achieved over areas with a ratio between the average plant spacing and the average crown diameter (TCI) greater than 0.59, and plant spacing larger than the remote sensing data spatial resolution. The proposed algorithm is thus shown a cost effective tool for forest monitoring using LiDAR data that is able to detect canopy parameters in complex broadleaves forests with high vegetation density and overlapping crowns and with consequent significant reduction of the field surveys, limiting them over only the calibration site.     

Ultra low-level airborne (ULLA) sensing of crop canopy reflectance: A case study using a CropCircle™ sensor

Operational airborne and satellite remote sensing in agriculture remains constrained by matching platform availability to suitable daytime weather and illumination conditions, crop development, and availability of ground staff. An ultra low-level aircraft carrying an active NIR/Red CropCircle™ sensor was successfully deployed to record and subsequently map crop vigour via the simple ratio (SR) index over a field of sorghum. Given the logging frequency of ≈20 Hz and the presence of alternate rows of bare soil, the Moiré effect reduced the contrast between crop and bare soil skip-rows. Such effects would not be expected to occur in non-skip-row crops. The ultra low-level airborne (ULLA)-SR map derived from the 20 m transect records compared favorably with the SR map derived from a meter-resolution airborne digital multispectral image that was re-sampled to a similar spatial resolution. This case study, involving a CropCircle™ sensor mounted in a low-level aircraft demonstrates another deployment option for users of this class of sensor. Moreover, an ULLA configuration offers the potential for greater flexibility in scheduling compared to airborne imaging, given it can be flown at any sun-angle, under cloud, at night, and may easily be incorporated into aircraft already conducting low-level operations, for example crop dusting and reconnaissance, over agricultural fields.     

Integration of LiDAR and QuickBird imagery for mapping riparian biophysical parameters and land cover types in Australian tropical savannas

Riparian zones are exposed to increasing pressures because of disturbance from agricultural and urban expansion and overgrazing. Accurate and cost-effective mapping of riparian environments is important for baseline inventories and monitoring and managing their functions associated with water quality, biodiversity, and wildlife habitats. In this study, we integrate remotely sensed light detection and ranging (LiDAR) data and high spatial resolution satellite imagery (QuickBird-2) to estimate riparian biophysical parameters and land cover types in the Fitzroy catchment in Queensland, Australia. An object based image analysis (OBIA) was adopted for the study. A digital terrain model (DTM), a tree canopy model (TCM) and a plant projective cover (PPC) map were first derived from the LiDAR data. A map of the streambed was then produced using the DTM information. Finally, all the LiDAR-derived biophysical map products and the QuickBird image bands were combined in an OBIA to (1) map the following land cover types: riparian vegetation, streambed, bare ground, woodlands and rangelands; (2) determine the distribution of overhang vegetation within the streambed; and (3) measure the width of both the riparian zone and the streambed. The combined use of both datasets allowed accurate land cover mapping, with an overall accuracy of 85.6%. The estimated widths of the riparian zone and the streambed showed strong correlation with the actual field measurements (r = 0.82 and 0.98 respectively). Our results show that the combined use of LiDAR and high spatial resolution imagery can potentially be used for the assessment of the riparian condition in a tropical savanna woodland riparian environment. This work also shows the capacity of OBIA to assist in the assessment of the composition of the riparian environment from multiple image datasets.     

基于ICESat-GLAS的坡地最大树高估测模型比较研究

地形坡度对星载LiDAR(lightdetection and ranging)估测最大树高具有较大的影响。为了提高坡度条件下树高的反演精度,通过建立坡地条件下5种不同的最大树高估测模型,前3个模型分别使用不同DEM(digital elevation model)数据的地形指数来量化地形坡度的Xing模型,第4个模型使用波形参数-未改进边缘长度来量化地形坡度,第5个模型与第4个模型类似,用改进边缘长度来替换未改进边缘长度。结果可知,波形参数模型的精度要高于使用DEM数据的地形指数的Xing模型的精度,第5个模型的精度要高于第4个模型的精度。表明波形参数量化地形坡度的能力要优于DEM数据的地形指数,而改进边缘长度模型更适合估测坡地的最大树高。     

2020 -01期目录

  目的  探讨纹理变量及其相应参数配置范围,阐明各纹理变量随输入参数的变化规律,以便指导高分光学影像纹理在林业上的应用。  方法  以福建省将乐国有林场不同坡向不同龄组的杉木人工林为例,基于QuickBird影像的全色波段进行灰度共生矩阵（GLCM）纹理的计算与分析。  结果  结果表明:（1）除均值外的所有GLCM纹理变量对阴坡的3个龄组的区分能力均强于阳坡,且纹理变量优选需同时考虑衡量指标和纹理变量之间的相关程度。（2）窗口大小是统计组和有序组纹理最关键的输入参数,合适的窗口大小与影像的分辨率以及研究对象的空间尺度有关,对比度组纹理与窗口大小无关,可随意设置。（3）应用统计组和有序组纹理,无需关注像元间距,而应用对比度组纹理,不可忽视像元间距。（4）应用统计组纹理,像元间距越大越需关注计算方向;而对比度组和有序组纹理则相反,即像元间距越小越需关注计算方向。（5）作为最不受研究人员重视的灰度量化等级,推荐采用32或者64。  结论  高分光学影像的纹理信息对光谱重叠度较高的地物具有一定的区分能力,能部分“弥补”阴影导致的光谱信号损失,但在应用中需对纹理变量及其输入参数进行优化选择和配置。该文的研究结论能够为高分光学影像纹理信息的优化应用提供实用的参考借鉴。