• 主题
高级搜索  >
历史搜索 清空历史
首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >UAV-Based Digital Terrain Model Generation under Leaf-Off Conditions to Support Teak Plantations Inventories in Tropical Dry Forests. A Case of the Coastal Region of Ecuador
【2h】

UAV-Based Digital Terrain Model Generation under Leaf-Off Conditions to Support Teak Plantations Inventories in Tropical Dry Forests. A Case of the Coastal Region of Ecuador

机译：在脱叶条件下基于无人机的数字地形模型生成以支持热带干旱森林中的柚木人工林清单。厄瓜多尔沿海地区的一个案例

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Remote sensing is revolutionizing the way in which forests studies are conducted, and recent technological advances, such as Structure from Motion (SfM) photogrammetry from Unmanned Aerial Vehicle (UAV), are providing more efficient methods to assist in REDD (Reducing Emissions from Deforestation and forest Degradation) monitoring and forest sustainable management. The aim of this work was to develop and test a methodology based on SfM from UAV to generate high quality Digital Terrain Models (DTMs) on teak plantations (Tectona grandis Linn. F.) situated in the Coastal Region of Ecuador (dry tropical forest). UAV overlapping images were collected using a DJI Phantom 4 Advanced© quadcopter during the dry season (leaf-off phenological stage) over 58 teak square plots of 36 m side belonging to three different plantations located in the province of Guayas (Ecuador). A workflow consisting of SfM absolute image alignment based on field surveyed ground control points, very dense point cloud generation, ground points filtering and outlier removal, and DTM interpolation from labeled ground points, was accomplished. A very accurate Terrestrial Laser Scanning (TLS) derived ground points were employed as ground reference to estimate the UAV-SfM DTM vertical error in each reference plot. The plot-level obtained DTMs presented low vertical bias and random error (−3.1 cm and 11.9 cm on average, respectively), showing statistically significant greater error in those reference plots with basal area and estimated vegetation coverage above 15 m2/ha and 60%, respectively. To the best of the authors’ knowledge, this is the first study aimed at monitoring of teak plantations located in dry tropical forests from UAV images. It provides valuable information that recommends carrying out the UAV image capture during the leaf-off season to obtain UAV-SfM derived DTMs suitable to serve as ground reference in supporting teak plantations inventories.
机译：遥感正在彻底改变进行森林研究的方式，近来的技术进步，例如无人飞行器（UAV）的运动结构（SfM）摄影测量法，正在提供更有效的方法来协助REDD（减少森林砍伐和森林砍伐造成的排放）。森林退化）监测和森林可持续管理。这项工作的目的是开发和测试基于无人机的SfM的方法，以在位于厄瓜多尔沿海地区（热带热带森林）的柚木种植园（Tectona grandis Linn。F.）上生成高质量的数字地形模型（DTM）。 。在干旱季节（离开物候期），使用DJI Phantom 4 Advanced ©四旋翼飞机收集了58幅柚木正方形地块的无人机图像，这些柚子正方形地块位于36个侧面，分别位于加拿大的三个不同的种植园。瓜亚斯（厄瓜多尔）。完成了一个工作流程，该工作流程由基于实地调查的地面控制点的SfM绝对图像对齐，非常密集的点云生成，地面点过滤和离群值去除以及从标记的地面点进行DTM插值组成。一个非常精确的地面激光扫描（TLS）衍生的地面点被用作地面参考，以估计每个参考图中UAV-SfM DTM的垂直误差。在地块级别获得的DTM呈现出较低的垂直偏差和随机误差（分别为平均-3.1 cm和11.9 cm），在那些具有基础面积和估计植被覆盖度高于15 m 2 < / sup> / ha和60％。据作者所知，这是第一项旨在通过无人机图像监测位于干燥热带森林中的柚木人工林的研究。它提供了宝贵的信息，建议您在休假季节进行UAV图像捕获以获得适合于作为支持柚木种植清单的地面参考的UAV-SfM衍生的DTM。

著录项

相似文献

  • 外文文献
代理获取

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号