The collection of ecological data in the field is essential to diagnose,
monitor and manage ecosystems in a sustainable way. Since acquisition of this
information through traditional methods are generally time-consuming, due to
the capability of recording large volumes of data in short time periods,
automation of data acquisition sees a growing trend. Terrestrial laser scanners
(TLS), particularly LiDAR sensors, have been used in ecology, allowing to
reconstruct the 3D structure of vegetation, and thus, infer ecosystem
characteristics based on the spatial variation of the density of points.
However, the low amount of information obtained per beam, lack of data analysis
tools and the high cost of the equipment limit their use. This way, a low-cost
TLS (<10k$) was developed along with data acquisition and processing mechanisms
applicable in two case studies: an urban garden and a target area for
ecological restoration. The orientation of LiDAR was modified to make
observations in the vertical plane and a motor was integrated for its rotation,
enabling the acquisition of 360 degree data with high resolution. Motion and
location sensors were also integrated for automatic error correction and
georeferencing. From the data generated, histograms of point density variation
along the vegetation height were created, where shrub stratum was easily
distinguishable from tree stratum, and maximum tree height and shrub cover were
calculated. These results agreed with the field data, whereby the developed TLS
has proved to be effective in calculating metrics of structural complexity of
vegetation.

采集生态数据，使用低成本 TLS、激光雷达传感器和数据处理机制，通过密度变化直方图计算植被结构复杂度指标，并与现场数据一致。