Monitoring forest landscape restoration through Light Detection and Ranging (LIDAR)
| Grant number: | 17/03867-6 |
| Support type: | Scholarships abroad - Research Internship - Doctorate |
| Effective date (Start): | September 03, 2017 |
| Effective date (End): | March 02, 2018 |
| Field of knowledge: | Agronomical Sciences - Forestry Resources and Forestry Engineering |
| Principal Investigator: | Pedro Henrique Santin Brancalion |
| Grantee: | Danilo Roberti Alves de Almeida |
| Supervisor abroad: | Scott Christopher Stark |
| Home Institution: | Escola Superior de Agricultura Luiz de Queiroz (ESALQ). Universidade de São Paulo (USP). Piracicaba , SP, Brazil |
| Local de pesquisa : | Michigan State University (MSU), United States |
| Associated to the scholarship: | 16/05219-9 - Monitoring forest landscape restoration through Light Detection and Ranging (LIDAR), BP.DR |
Abstract Many studies use small footprint LiDAR technology to estimate canopy structure variables,such as forest height, leaf area vertical profiles and biomass stocks, for use in ecologicalmonitoring and modeling. Thus, it is critical to develop universal approaches to estimate theseforest structural parameters from LiDAR to allow comparison among LiDAR devices, surveysconducted under different conditions, and different vegetation types. However, universal modeldevelopment is difficult because of the fundamentals of LiDAR data, which consist of a 'pointcloud' of accurate laser pulse reflection locations in space, with the density of pulses varyingfrom a few to hundreds per horizontal meter-squared. The fundamental challenge lies inrecovering structural variable estimates over variation in pulse density (and potentiallyadditional survey characteristics); many current approaches do not fully address this challenge,instead relying on survey or device-specific metrics that are not intuitive and difficult tobiologically interpret as proxies for biological structural parameters. I propose to investigatehow this key element of variation in LiDAR survey impacts our estimation of biologicalvariables of forest canopy structure, such as the leaf area index and leaf area density along the2vertical profile. My project will improve understanding of how to statistically model the linksbetween biological forest structure and LiDAR point clouds, and improve the accuracy andcomparability of important fundamental LiDAR forest monitoring approaches, particularlyrelated to leaf are and stand structure. | |