wiesehahn / lidar-forestry

Personal space to organize information on the topic of lidar (mainly airborne) for forestry

Lidar for Forestry

This repository is meant as a personal space to gather and organize information about research and applications of lidar data for forestry.

Information is collected via Github issues and rudimentally organized by Tags. For example its possible to filter recent literature about lidar applications for forestry.

This information is further summarized as a website (updated irregularly from time to time).

lidar-forestry's Issues

Capturing seasonal radial growth of boreal trees with terrestrial laser scanning

https://doi.org/10.1016/j.foreco.2022.120733

EcoDes-DK15: high-resolution ecological descriptors of vegetation and terrain derived from Denmark's national airborne laser scanning data set

https://doi.org/10.5194/essd-14-823-2022

Integration of Photo Interpreted and LIDAR Attributes Into a Polygonal Forest Inventory Framework

https://bermane.github.io/ontario-inventory/

Harmonizing multi-temporal airborne laser scanning point clouds to derive periodic annual height increments in temperate mixedwood forests

https://doi.org/10.1139/cjfr-2022-0055

Tree species classification in Norway from airborne hyperspectral and airborne laser scanning data

https://doi.org/10.1080/22797254.2018.1434424

Developing a forest inventory approach using airborne single photon lidar data: from ground plot selection to forest attribute prediction

https://doi.org/10.1093/forestry/cpab051

Digital Aerial Photogrammetry for Updating Area-Based Forest Inventories: A Review of Opportunities, Challenges, and Future Directions

https://link.springer.com/article/10.1007/s40725-019-00087-2

Evaluating Factors Impacting Fallen Tree Detection from Airborne Laser Scanning Point Clouds

https://doi.org/10.3390/rs15020382

2023 EFI Cross-Country Checkup

Gain insight on recent research developments and future applications of LiDAR/Enhanced Forest Inventory from coast to coast.

Use of multiple LIDAR-derived digital terrain indices and machine learning for high-resolution national-scale soil moisture mapping of the Swedish forest landscape

https://doi.org/10.1016/j.geoderma.2021.115280

Mapping Drainage Ditches in Forested Landscapes Using Deep Learning and Aerial Laser Scanning

https://doi.org/10.1061/JIDEDH.IRENG-9796

Zwillinge groß gedacht – BUSINESS GEOMATICS

https://www.business-geomatics.com/2022/11/17/zwillinge-gross-gedacht/

Mapping individual trees with airborne laser scanning data in an European lowland forest using a self-calibration algorithm

https://doi.org/10.1016/j.jag.2020.102191

Individual tree point clouds and tree measurements from multi-platform laser scanning in German forests

https://doi.org/10.5194/essd-14-2989-2022

Watch "3DForEcoTech" on YouTube

https://youtube.com/@3dforecotech

Estimation of periodic annual increment of tree ring widths by airborne laser scanning

https://doi.org/10.1139/cjfr-2021-0267

Peering through the thicket: Effects of UAV LiDAR scanner settings and flight planning on canopy volume discovery

https://doi.org/10.1016/j.jag.2022.103056

Segmentation of conifer tree crowns from terrestrial laser scanning point clouds in mixed stands of Scots pine and Norway spruce

https://doi.org/10.1007/s10342-022-01481-5

Projekt TreeDigitalTwins

https://www.atb-potsdam.de/de/forschung/forschungsprojekte/projektsuche/projekt/projekt/treedigitaltwins

IntelliWay – Intelligente Wege – Condition Monitoring und Predictive Maintenance für Forstwege

https://kwf2020.kwf-online.de/portfolio/intelliway/

Forest carbon mapping with lidar and landsat

https://m.youtube.com/watch?v=neG_xmBn3f8

Fine-resolution landscape-scale biomass mapping using a spatiotemporal patchwork of LiDAR coverages

https://doi.org/10.1016/j.jag.2022.103059

lidaRtRee r-package

https://gitlab.irstea.fr/jean-matthieu.monnet/lidaRtRee

Correction, update, and enhancement of vectorial forestry road maps using ALS data, a pathfinder, and seven metrics

https://doi.org/10.1016/j.jag.2022.103020

Single photon lidar in canada

https://www.cif-ifc.org/wp-content/uploads/2021/04/AWARE_CIF_eLecture_New_LIDAR_Technologies_on_the_Horizon.pdf

LiDAR (ALS) im Wald Teil 1: Rückegassen sichtbar machen - OGF

https://drohnenbefliegungen.de/2022/07/08/lidar-als-im-wald-teil-1-rueckegassen-sichtbar-machen/

Hidden gaps under the canopy: LiDAR-based detection and quantification of porosity in tree belts

https://doi.org/10.1016/j.ecolind.2022.109243

Topographic Wetness Index calculation guidelines based on measured soil moisture and plant species composition

https://doi.org/10.1016/j.scitotenv.2020.143785

Using repeat airborne LiDAR to map the growth of individual oil palms in Malaysian Borneo during the 2015–16 El Niño

https://doi.org/10.1016/j.jag.2022.103117

Large-scale variations in the dynamics of Amazon forest canopy gaps from airborne lidar data and opportunities for tree mortality estimates

https://doi.org/10.1038/s41598-020-80809-w

Open Lidar arrives in Germany

https://12098601808889571736.googlegroups.com/attach/3ad87d3f736fd/gis.Business_5_S.28-33_S_D_OpenLiDAR.pdf?part=0.1&vt=ANaJVrFFTKVkTRIZY8582r-dkt8zHmtsWZutPeTaVP1QQTj_vKOsKooC-mNJtyEnAU9X9q74hCVagpmxuxGmeHY8cJhRUNySJzVqXNlyB_5ZiFS2FwMfygo

Quantifying the precision of forest stand height and canopy cover estimates derived from air photo interpretation

https://doi.org/10.1093/forestry/cpab022

Counting trees in Dutch forests: Using AHN LiDAR data for National Forest Inventory

https://www.wur.nl/en/show/Counting-trees-in-Dutch-forests-Using-AHN-LiDAR-data-for-National-Forest-Inventory-1.htm

Evaluating the capacity of single photon lidar for terrain characterization under a range of forest conditions

https://doi.org/10.1016/j.rse.2020.112169

Nationwide Point Cloud—The Future Topographic Core Data

https://doi.org/10.3390/ijgi6080243

A Machine Learning Method for Co-Registration and Individual Tree Matching of Forest Inventory and Airborne Laser Scanning Data

https://doi.org/10.3390/rs9050505

static vs. kinematic lidar

TLS vs. ALS, ULS, MLS,...

ForestGapR R-package

https://github.com/carlos-alberto-silva/ForestGapR

An improved area-based approach for estimating plot-level tree DBH from airborne LiDAR data

https://doi.org/10.1016/j.fecs.2023.100089

Estimation of forest resources from a country wide laser scanning survey and national forest inventory data

https://doi.org/10.1016/j.rse.2011.12.022

Detection of coarse woody debris using airborne light detection and ranging (LiDAR)

https://doi.org/10.1016/j.foreco.2018.11.049

Species-related single dead tree detection using multi-temporal ALS data and CIR imagery

https://doi.org/10.1016/j.rse.2018.10.005

Trafficability Prediction Using Depth-to-Water Maps: the Status of Application in Northern and Central European Forestry

https://doi.org/10.1007/s40725-021-00153-8

Large-Scale Mapping of Tree Species and Dead Trees in Šumava National Park and Bavarian Forest National Park Using Lidar and Multispectral Imagery

https://doi.org/10.3390/rs12040661

Mit LiDAR auf der Suche nach Rückegassen im Wald

https://doi.org/10.3188/szf.2022.0196

Vegetation structure derived from airborne laser scanning to assess species distribution and habitat suitability: The way forward

https://doi.org/10.1111/ddi.13644

Airborne laser scanning reveals large tree trunks on forest floor

https://doi.org/10.1016/j.foreco.2021.119225

Predicting Growth of Individual Trees Directly and Indirectly Using 20-Year Bitemporal Airborne Laser Scanning Point Cloud Data

https://doi.org/10.3390/f13122040

Detection and Quantification of Coarse Woody Debris in Natural Forest Stands Using Airborne LiDAR

https://doi.org/10.1093/forsci/fxab023

Terrestrial, UAV-borne, and airborne laser scanning point clouds of central European forest plots, Germany, with extracted individual trees and manual forest inventory measurements.

https://doi.org/10.1594/PANGAEA.942856