Using canopy heights from digital aerial photogrammetry to enable spatial transfer of forest attribute models: a case study in central Europe

This paper describes a workflow utilizing detailed canopy height information derived from digital airphotos combined with ground inventory information gathered in state-owned forests and regression modelling techniques to quantify forest-growing stocks in private woodlands, for which little information is generally available. Random forest models were trained to predict three different variables at the plot level: quadratic mean diameter of the 100 largest trees (d₁₀₀), basal area weighted mean height of the 100 largest trees (h₁₀₀), and gross volume (V). Two separate models were created – one for a spruce- and one for a beech-dominated test site. We examined the spatial portability of the models by using them to predict the aforementioned variables at actual inventory plots in nearby forests, in which simultaneous ground sampling took place. When data from the full set of available plots were used for training, the predictions for d₁₀₀, h₁₀₀, and V achieved out-of-bag model accuracies (scaled RMSEs) of 15.1%, 10.1%, and 35.3% for the spruce- and 15.9%, 9.7%, and 32.1% for the beech-dominated forest, respectively. The corresponding independent RMSEs for the nearby forests were 15.2%, 10.5%, and 33.6% for the spruce- and 15.5%, 8.9%, and 33.7% for the beech-dominated test site, respectively.     

Photogrammetric measurement of tree stems from vertical fisheye images

Forest variables are typically surveyed using sample plots, from which parameters for large areas are estimated. The diameter at breast height (DBH) is one of the main variables collected in the field and can be used with other forest measures. This study presents an automatic technique for the mapping and measurement of individual tree stems using vertical terrestrial images collected with a fisheye camera. Distinguishable points from the stem surface are automatically extracted in the images, and their 3D ground coordinates are determined by bundle adjustment. The XY coordinates of each stem define an arc shape, and these points are used as observations in a circle fitting by least squares. The circle centre determines the tree position in a local reference system, and the estimated radius is used to calculate the DBH. Experiments were performed in a sample plot to assess the approach and compare it with a technique based on terrestrial laser scanning. In the validation with measurements collected on the stems using a measuring tape, the discrepancies had an average error of 1.46?cm with a standard deviation of 1.09?cm. These results were comparable with the manual measurements and with the values generated from laser point clouds.     

智能手机单片摄影测树技术研究与试验

森林因子采集是林业观测的重中之重，智能手机为林业调查提供了新思路、新手段。为了更高效、精确的观测森林，以智能手机为载体，摄影测量学、测树学原理为理论基础，Java为开发语言，设计了一套单片摄影测树系统。系统仅利用智能手机的定焦镜头正直拍摄一张图像，即可进行单木的胸径、树高、材积的自动解算，同时，还可进行林分平均胸径、蓄积量和林分密度的自动解算。通过对200株立木进行胸径、树高、材积测量试验，对15块样地进行林分平均胸径、林分密度和林分蓄积量测量试验。结果表明，单木因子测量的相对偏差为4.95%～5.86%，相对均方根误差为6.09%～7.46%，林分因子测量的相对偏差为0.51%～1.46%，相对均方根误差为7.47%～8.6%。以上指标均高于传统林业测量的精度要求，该技术可以在林业调查中推广使用。     

基于航片建立数字高程模型及林分信息提取

以东北林业大学帽儿山实验林场2004年航片和同年森林经理调查数据为基础,在VirtuoZo软件平台上应用数字摄影测量方法与技术,完成了帽儿山航片的空三加密过程,在试验区生成立体像对、建立数字高程模型(DEM)及制作数字正射影像图(DOM).通过等高线回放目视检查法及检查点法验证了DEM的精度,并在数字测图模块(VirtuoZo IGS)下进行了林分平均高及郁闭度两种林分信息提取的研究,结果显示:林分平均高的精度为83.3%,郁闭度的精度为76.6%.将提取的结果与地面实测数据结合,建立了回归模型,通过计算验证,林分平均高、郁闭度精度都有相应的提高,其值分别为86.5%,87.4%.     

单株立木图像信息的提取与解算

该文采用数码相机获取立木的图像信息，对单株立木图像信息的提取分别运用近景摄影测量DLT模型和双目立体视觉技术进行解算，解决了立木图像信息与立木二维坐标之间的解算问题.基于近景摄影测量技术树木图像信息的二维处理方法，简化了解算方程，提高了求解的运算速度.在提取单株立木形状参数时, 采用计算机双目立体视觉技术存在着操作复杂、成本高的缺点.实验结果表明，应用近景摄影测量技术提取单株立木图像信息的方法有效、可行.      

航空摄影测量技术在高标准基本农田建设项目中的应用研究

高标准基本农田建设是我国“十二五”土地整治的重要目标,是提高耕地质量的重要措施.高标准基本农田建设项目要求质量高,时间紧,如何在短时间内完成规划设计工作是困扰设计单位的重要难题,尤其是项目区现状图的测绘和踏勘工作往往会消耗大量的时间.该研究介绍了高标准基本农田建设项目的地形图测绘要求,并详细探讨了航空摄影测量方法在重大土地整治项目中的应用.     

利用坐标增量解直接线性变换的探讨

推导出利用坐标增量解直接线性变换的公式，适用于近景摄影测量内业解析处理．经模拟实验验证正确、实用．     

Comparison of UAV-based LiDAR and digital aerial photogrammetry for measuring crown-level canopy height in the urban environment

Spatial information on urban forest canopy height (FCH) is fundamental for urban forest monitoring and assisting urban planning and management. Traditionally, ground-based canopy height measurements are time-consuming and laborious, making it challenging for periodic inventory of urban FCH at crown level. Airborne-light detection and ranging (LiDAR) sensor can efficiently measure crown-level FCH; however, the high cost of airborne-LiDAR data collection over large scales hinders its wide applications at a high temporal resolution. Previous studies have shown that in some cases, the Unmanned Aerial Vehicle (UAV)-digital aerial photogrammetry (DAP) approach (i.e., UAV-based structure from motion algorithm) is equivalent to or even outperform airborne-LiDAR in measuring forest structure, but few studies have evaluated their performances in measuring FCH in more complex urban environment, across non-ground coverage (including both canopy and building coverage) and topographical slope gradients. Also, the contribution of multi-angle measurement technique from UAV-DAP to FCH estimation accuracy has rarely been explored in the urban environment. Here, we compared the performances of UAV-LiDAR and UAV-DAP approaches on measuring thousands of crown-level FCH at different non-ground coverage and topographical slope areas in an urban environment. Specifically, UAV-LiDAR-based spatial measurements of crown-level FCH were used as the reference after ground-based validation (R² = 0.88, RMSE = 2.36 m). The accuracy of UAV-DAP approach with/without multi-angle measurement in different non-ground coverage and topographical slope areas were then analyzed. The results showed that although the DAP multi-angle-based approach can improve the accuracy of spatial measurement for crown-level FCH in some cases, non-ground coverage (including both canopy and building coverage) was still the main factor affecting the broad applications of DAP approach in measuring urban FCH: at areas where non-ground coverage < 0.95, no matter how topographical slope varied, the accuracy of DAP approach was high (R² = 0.86∼0.94, RMSE = 1.56∼2.93 m); at areas where non-ground coverage > 0.95, except for the case of flat areas (i.e., topographical slope <= 2°), the accuracy of DAP was poor (R² = 0.20, RMSE = 12.34 m). However, using LiDAR-digital terrain model (DTM) instead of DAP-DTM, at areas where non-ground coverage > 0.95, can significantly improve the accuracy of UAV-DAP approach in measuring crown-level FCH (R² = 0.91, RMSE =1.61 m). Our study thus provides a complete guidance on the usage of cost-effective UAV-DAP approach for measuring crown-level FCH in the urban environment, which will be helpful for precise urban forest management and improving the efficiency of urban environmental planning.     

手杖式测树仪立木树高量测研究

【目的】树高是森林经营决策中最重要的一个参量,常用于估计森林生长、年龄、材积、生物量和碳储量等立木参数,其精度对立木质量的评价及森林生长的预测分析影响重大。为解决传统的树高量测仪器移动不便,测量周期长,人力耗损大等问题。【方法】以近景摄影测量为基础,构建了一种以登山杖绑定安卓智能手机为测量平台的便携、快捷的树高测量装备;针对立木生长环境有无坡度,拍摄是否产生倾角等情况建立了树高量测模型。在手机环境下,研制了立木树高测量软件APP。APP采用上下分屏技术,利用手机成像系统以及坐标系转换,使屏幕与待测立木建立关联,从而自动解算立木的深度信息;结合手机内部方向传感器的强大性能,单站作业可实时获取树高估计值。在北京市平谷区红石门村选取不同坡度的307株立木作为研究对象,使用该装备分别在上坡位和下坡位对其进行量测,将测定结果与全站仪多次量测求得的平均值进行对比分析。【结果】结果表明,树高估计值平均绝对误差为0.21 m,平均相对误差为2.11%,整体精度达到97.89%。当处于下坡位观测时,树高估计值平均绝对误差为0.11 m,平均相对误差为1.16%,树高精度高达98.84%。当处于上坡位观测时,树高估计值平均绝对误差为0.32 m,平均相对误差为3.07%,树高精度达到96.93%。智能手机倾斜角较大时,精度达到95.19%,智能手机倾斜角较小时,精度高达99.03%,说明手杖式测树仪观测时所产生倾角越小,精度越高。量测同一株立木时,下坡位观测的精度优于上坡位。【结论】此装备的研发满足国家森林资源连续清查中的测量精度要求,且装备成本低、灵活性强、不依赖其他设备获取深度信息、携带方便、具有较高利用价值,未来可作为森林资源调查树高测量装备。     

倾斜摄影测量技术在生产建设项目水土保持监管中的应用

为探索和总结单个生产建设项目水土保持监管的工作流程和技术支撑模式,结合无人机倾斜摄影测量技术优势,对照生产建设项目监管工作要求,梳理了各项监管指标对应的信息获取方法,建立了利用无人机倾斜摄影测量技术开展生产建设项目水土保持信息化监管指标提取的技术流程。在成都市的应用结果表明:无人机倾斜摄影测量技术具备信息获取丰富、实现真三维量测、综合成本低等优势,可以完成大部分项目监管指标的提取工作,其技术流程包括资料收集和预处理、无人机倾斜摄影测量、合规性详查3个阶段,相较于传统监管手段,其信息提取成本更低、效率更高、比对方式更灵活。无人机倾斜摄影测量技术在生产建设项目精细化监管工作中具备广阔的应用前景。