Advances in remote sensing of freshwater fish habitat: A systematic review to identify current approaches,strengths and challenges

Remote sensing technology offers the ability to derive information on freshwater fish habitats across broad geographic areas and has the potential to transform approaches to monitoring. However, the numerous platforms, sensors and analytical software that are available may overwhelm those interested in utilizing this important technology and thus limit its broad application and uptake. Our review is intended to shed light on the capacity of this technology to transform freshwater fish habitat monitoring by examining the fundamental characteristics of the major remote sensing technologies that have been used for characterizing freshwater habitats, conducting a systematic literature review of studies that have used remote sensing technologies to characterize freshwater fish habitats and, highlighting some of the key habitat features, fish species and regions, that have been examined. Lastly, we identify the relative strengths and weaknesses of the various remote sensing technologies that can be used, recommend future research that could help improve the use of these technologies, and, provide a series of important considerations for those who are interested in utilizing these technologies for freshwater fish habitat characterization.     

Poisson Voronoï tiling for finding clusters in spatial point patterns

Abstract

In forest stand mapping a delineation of spatial compact clusters of trees with similar attributes can improve inventory accuracy and growth and yield predictions. To this end a Poisson Voronoï tiling (PVT) for identifying and delineating clusters (features) in spatial point patterns is proposed. PVT operates on the assumption that the point density in clusters is higher than that outside the clusters. A spatial domain of an observed point pattern is tessellated repeatedly into k (random) Poisson Voronoï cells. An average EM-based likelihood of feature based on observed cell point densities is computed for each point and location of interest. Points and locations of interest are then classified by maximizing a classification likelihood. PVT avoids the need to specify the number of clusters. In a direct comparison with a non-parametric maximum profile likelihood procedure, and a smoothed version of the same, PVT performed well on two artificial point patterns with known feature domain and points, and on two spatial point patterns of first returns from a forest lidar survey on Vancouver Island, British Columbia, Canada.     

A comparative study of the use of laser scanner data and field measurements in the prediction of crown height in boreal forests

Abstract

This study compares the results of the prediction of crown height characteristics using airborne laser scanner (ALS) data and intensive field measurements in boreal forests. The data consisted of 31 sample plots located in Kalkkinen, southern Finland. Crown height models were constructed at both the tree and plot level. Scots pine, Norway spruce and birches were used. The models included independent variables of tree levels, such as tree height, crown area and independent plot-level variables, i.e. canopy height and density quantiles and proportion of vegetation hits. Field measurement-based models used tree height and diameter at breast height as the independent tree-level variables, whereas basal area, mean diameter and height were used as the plot-level variables. The results indicated that the ALS-based crown height models were more accurate than the field measurement-based models when plot-level information was used as independent variables. However, the field measurement-based tree-level models for Scots pine and Norway spruce were more accurate than the ALS-based models. Even so, the accuracy of the different models was very similar and the study data set was quite small. The results of this study can be used for different tree growth studies and for the assessment of tree stock quality in boreal forests.     

基于机载激光雷达数据的天山云杉林蓄积量反演模型构建

构建多变量的天山云杉林Schumacher蓄积收获模型,提高林分蓄积量反演精度,获得便捷、快速提取森林蓄积信息的技术方法,为探索山地天然林精准监测与评价提供技术途径。以2020年激光雷达影像与样地实测数据为研究材料,在激光雷达影像图中提取遥感因子,代入Schumacher蓄积收获模型中,通过再参数化来构建适用于天山云杉林的可变密度收获预估模型,并进行精度检验。结果表明,激光雷达影像分辨率较高,进行点云数据处理后,树高提取精度为89.64%,每公顷株数提取精度为85.13%,坡度提取精度为84.26%,坡向提取精度为84.26%,海拔提取精度为97.25%。结合Schumacher蓄积收获模型构建天山云杉蓄积量反演模型,R²=0.80,将检验数据代入模型中,估测蓄积量与实测蓄积量平均精度为90.22%,模型的拟合度较好。研究将立地因子、林分密度、林龄等变量引入Schumacher蓄积收获模型,对于天山云杉的蓄积估测精度有较大提高,优于以往经验模型,满足新疆山地天然林数字经营管理的标准。     

雷达昆虫学研究进展及应用前景

雷达昆虫学经过近50年的发展,取得一系列的重要成果。本文系统介绍了各类昆虫雷达的体制和功能,总结了相似体制天气雷达与昆虫雷达的主要区别,回顾了各相关国家雷达昆虫学的实践情况,归纳了昆虫雷达建制技术取得的进步,并展望了雷达应用的前景及其今后昆虫雷达建制技术的发展方向,以期促进我国昆虫雷达事业的发展。     

基于机载激光雷达校正的ICESat/GLAS数据森林冠层高度估测

针对星载激光雷达(geoscience laser altimeter system,GLAS)大光斑属性,该文提出了一种改进后的光斑尺度森林冠层高度估测方法,并分析了复杂地表对其估测精度的影响.首先,对机载lidar点云分类出地面点,并利用地面点对点云数据进行高度归一化处理,提取点云局部最大值得到光斑范围内机载lidar最大冠层高度;以机载lidar最大冠层高度作为模型参数拟合因变量,同时以坡度作为模型的输入变量,结合光斑大小和地表粗糙度,进行参数拟合,得到改进后光斑尺度森林冠层高度估测模型;最后,利用实测样地数据对冠层高度估测模型进行验证.结果表明:机载点云数据可以准确地反映光斑范围内森林冠层的分布,受到树种类型和点云密度的影响,不同森林类型的点云冠层分布存在明显差异.坡度等级直接影响GLAS光斑尺度森林冠层高度的估测精度,改进后的估测模型可以减小坡度对GLAS光斑森林冠层高度估测的影响,模型估测均方根误差(root mean square error,RMSE)稳定在3.26～3.88 m.样地Lorey's高与估测结果拟合度较好,相关系数r=0.66,不同森林类型光斑尺度冠层高度估测精度存在差异,混交林估测精度最高,r和RMSE分别为0.84和1.06 m.该方法可以有效减少地形条件对光斑尺度森林冠层高度估测的影响,并为更大尺度的冠层高度制图提供了有效的参考.     

基于多雷达的超车预警系统设计

为减少超车过程中频发的刮擦、追尾事故,设计了一种可以向驾驶员预先发出视听警告的检测系统,并建立安全距离模型。该系统采用激光雷达和毫米波雷达作为探测元件,测出超车车辆周围车辆的车速、车间距等。中央处理单元对其进行预处理,判定超车场景,进而针对不同的超车场景,结合运动学模型作进一步分析计算,当车辆间的纵向距离始终大于或者等于临界值,则判断为可超车,否则预警功能开启,警示驾驶员中止当前的超车行为。     

Forest stand age classification using time series of photogrammetrically derived digital surface models

In this research, we developed and tested a remote sensing-based approach for stand age estimation. The approach is based on changes in the forest canopy height measured from a time series of photo-based digital surface models that were normalized to canopy height models using an airborne laser scanning derived digital terrain model (DTM). Representing the Karelian countryside, Finland, CHMs from 1944, 1959, 1965, 1977, 1983, 1991, 2003, and 2012 were generated and allow for characterization of forest structure over a 68-year period. To validate our method, we measured stand age from 90 plots (1256?m²) in 2014, whereby producer's accuracy ranged from 25.0% to 100.0% and user's accuracy from 16.7% to 100.0%. The wide range of accuracy found is largely attributable to the quality and characteristics of archival images and intrastand variation in stand age. The lowest classification accuracies were obtained for the images representing the earliest dates. For forest managers and agencies that have access to long-term photo archives and a detailed DTM, the estimation of stand age can be performed, improving the quality and completeness of forest inventory databases.     

激光雷达技术及其在林业上的应用

激光雷达是近年来国际上发展十分迅速的主动遥感技术,在森林参数的定量测量和反演上取得了成功的应用。激光雷达具有与被动光学遥感不同的成像机理,对植被空间结构和地形的探测能力很强,特别是对森林高度的探测能力,具有其他遥感数据无法比拟的优势。介绍激光雷达遥感的基本原理、大光斑和小光斑激光雷达系统的特点,对它们在林业上的应用现状进行评述,重点分析激光雷达反演森林参数的方法,对激光雷达的林业应用前景进行分析和展望。     

激光雷达技术在森林资源监测中的研究进展

对激光雷达技术在森林资源监测方面的运用前景、局限性和研究进展进行总结,并研究激光雷达技术在森林资源监测方面的成果.结果表明:激光雷达技术植物资源方面的监测主要为森林的整体结构高度、植被的具体高度及森林中植被的占有密度,生物密度方面的监测一般应用小光斑系统,温带气候的森林植被叶面积较为均衡,可采用常规的激光检测进行监测....