4.7 Article

Development and Performance Evaluation of a Very Low-Cost UAV-Lidar System for Forestry Applications

期刊

REMOTE SENSING
卷 13, 期 1, 页码 -

出版社

MDPI
DOI: 10.3390/rs13010077

关键词

unmanned aerial vehicle (UAV); lidar; DJI Livox; low cost; forest inventory

资金

  1. National Key R&D Program of China [2017YFC0503905]
  2. National Natural Science Foundation of China [41901358, 41871332, 31971575]
  3. Beijing Municipal Science and Technology Project [Z191100007419004]
  4. Chinese Academy of Sciences President's International Fellowship Initiative [2019VTA0007]

向作者/读者索取更多资源

A low-cost UAV lidar system was developed and demonstrated effectiveness in estimating forest inventory attributes comparable to high-end systems. This study provides guidance on selecting appropriate UAV lidar systems and flight specifications for forest research and management.
Accurate and repeated forest inventory data are critical to understand forest ecosystem processes and manage forest resources. In recent years, unmanned aerial vehicle (UAV)-borne light detection and ranging (lidar) systems have demonstrated effectiveness at deriving forest inventory attributes. However, their high cost has largely prevented them from being used in large-scale forest applications. Here, we developed a very low-cost UAV lidar system that integrates a recently emerged DJI Livox MID40 laser scanner (similar to$600 USD) and evaluated its capability in estimating both individual tree-level (i.e., tree height) and plot-level forest inventory attributes (i.e., canopy cover, gap fraction, and leaf area index (LAI)). Moreover, a comprehensive comparison was conducted between the developed DJI Livox system and four other UAV lidar systems equipped with high-end laser scanners (i.e., RIEGL VUX-1 UAV, RIEGL miniVUX-1 UAV, HESAI Pandar40, and Velodyne Puck LITE). Using these instruments, we surveyed a coniferous forest site and a broadleaved forest site, with tree densities ranging from 500 trees/ha to 3000 trees/ha, with 52 UAV flights at different flying height and speed combinations. The developed DJI Livox MID40 system effectively captured the upper canopy structure and terrain surface information at both forest sites. The estimated individual tree height was highly correlated with field measurements (coniferous site: R-2 = 0.96, root mean squared error/RMSE = 0.59 m; broadleaved site: R-2 = 0.70, RMSE = 1.63 m). The plot-level estimates of canopy cover, gap fraction, and LAI corresponded well with those derived from the high-end RIEGL VUX-1 UAV system but tended to have systematic biases in areas with medium to high canopy densities. Overall, the DJI Livox MID40 system performed comparably to the RIEGL miniVUX-1 UAV, HESAI Pandar40, and Velodyne Puck LITE systems in the coniferous site and to the Velodyne Puck LITE system in the broadleaved forest. Despite its apparent weaknesses of limited sensitivity to low-intensity returns and narrow field of view, we believe that the very low-cost system developed by this study can largely broaden the potential use of UAV lidar in forest inventory applications. This study also provides guidance for the selection of the appropriate UAV lidar system and flight specifications for forest research and management.

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