moer_mpg_upscaling_unit04_Overview

[utterances-bot]

Overview | Upscaling Biodiversity

LiDAR or Light Detection And Ranging, is a very promising remote sensing method that uses lasers to record distances which when combined with complimentary data can be used to generated 3D models of a surface. With its 3D approach we can measure canopy height and density along with many environmental parameters. In remote areas as well as areas with steep slopes, Airborne LiDAR has proved very effective in monitoring vegetation structure (Getzin et al 2017). In this unit we explore the potential of Airborne LiDAR data in understanding the forest structure and derive different LiDAR metrics useful for upscaling.

https://geomoer.github.io/moer-mpg-upscaling/unit04/unit04-01_overview.html

[ecoinfo-netra]

Dear All, For the task 2, here is the way how you can use a sentinel scene to make the dtm

Choose one band from sentinel 2 and crop it with your lidar area sentinel_scene <- rast("./T37MCS_20220103T074321_B02_10m.jp2") cropped_sentinel <- crop(sentinel_scene, flm1_clipped)

make your dtm using the cropped sentinel as the base grid upon which the dtm will be based dtm <- rasterize_terrain(flm1_clipped,cropped_sentinel, knnidw()) plot(dtm)

normalize your point cloud nlas <- normalize_height(flm1_clipped, dtm)

now you can calculate your mean vegetation height mvh <- pixel_metrics(nlas, ~mean(Z), cropped_sentinel)