Article
Forestry
Alex Appiah Mensah, Jonas Jonen, Kenneth Nystrom, Jorgen Wallerman, Mats Nilsson
Summary: Recent advancements in remote sensing of forests have shown that bi-temporal ALS data and auxiliary information can accurately predict and map site productivity of Norway spruce and Scots pine populations. Multiple linear regression and non-parametric random forests models were used for prediction, and the predictors included height metrics, altitude, distance to coast, and soil moisture. These models and maps are valuable for operational forest management planning in Sweden.
FOREST ECOLOGY AND MANAGEMENT
(2023)
Article
Forestry
Xavier Gallagher-Duval, Olivier R. van Lier, Richard A. Fournier
Summary: This study aimed to determine the optimal approach for estimating stem diameter distributions (SDD) from airborne laser scanning (ALS) data using different metrics. The results showed that the performance of SDD modality classification models was consistent and the best SDD function parameter models were generally fitted with a combination of metrics. It was found that CHM texture metrics can improve the estimate of SDD parameters and differentiating for modality prior to estimating SSD is especially beneficial in stands with bimodal SDD.
Article
Environmental Sciences
Andre Beaudoin, Ronald J. J. Hall, Guillermo Castilla, Michelle Filiatrault, Philippe Villemaire, Rob Skakun, Luc Guindon
Summary: Satellite forest inventories using k-nearest neighbor algorithm combined with Landsat and SAR data can accurately map forest attributes in Canada's northern boreal forests. This study demonstrates the feasibility and effectiveness of optimizing k-NN parameters and feature space for inventory mapping.
Article
Environmental Sciences
Guillermo Castilla, Ronald J. Hall, Rob Skakun, Michelle Filiatrault, Andre Beaudoin, Michael Gartrell, Lisa Smith, Kathleen Groenewegen, Chris Hopkinson, Jurjen van der Sluijs
Summary: Sustainable forest management requires detailed information on the spatial distribution, composition, and structure of forests. However, in regions with large tracts of noncommercial forest, such as the Northwest Territories (NWT) of Canada, this information is often lacking. The Multisource Vegetation Inventory (MVI) project used a combination of field data and remote sensing data from multiple sources to create a large area forest inventory map that could support strategic forest management in the NWT. This project demonstrated that a reasonably accurate forest inventory map for large, remote, predominantly non-inventoried boreal regions can be obtained at a low cost.
Article
Remote Sensing
Mikko Kukkonen, Matti Maltamo, Lauri Korhonen, Petteri Packalen
Summary: The study introduces a method called hybrid tree detection (HTD) utilizing unmanned airborne laser scanning (UALS) point clouds for forest inventories. Through testing at various validation plots, it is concluded that the proposed HTD approach improves the accuracy of traditional individual tree crown delineation (ITC) methods.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2021)
Article
Engineering, Aerospace
Ulas Yunus Ozkan, Tufan Demirel, Ibrahim Ozdemir, Serhun Saglam, Ahmet Mert
Summary: This study examined the capability of combined LiDAR/WorldView-3 data in estimating plot-level stand attributes in a complex forest in northwest Turkey. Prediction models were developed at different levels, with multiple linear regression (MLR) and random forest (RF) modeling approaches tested. The results showed higher prediction accuracy for height at tree species level and forest types level, with homogeneous coniferous stands providing higher estimation accuracy. The combination of aerial laser scanning and high resolution satellite data has potential for predicting stand attributes in complex forest ecosystems.
ADVANCES IN SPACE RESEARCH
(2022)
Article
Forestry
Antoine Leboeuf, Martin Riopel, Dave Munger, Marie-Soleil Fradette, Jean Begin
Summary: This study developed and validated parametric merchantable wood volume estimation models for mapping forest attributes using LiDAR data. The models showed good accuracy rates when compared to validation zones.
Article
Environmental Sciences
Frederico Tupinamba-Simoes, Adrian Pascual, Juan Guerra-Hernandez, Cristobal Ordonez, Tiago de Conto, Felipe Bravo
Summary: The use of mobile laser scanning allows for high-resolution, 3D description of forest ecosystems. This study tested a mobile Handheld Laser Scanning (HLS) system in a structurally complex Mediterranean forest in Spain to estimate tree attributes. The HLS approach achieved a high tree detection rate and accurate estimation of diameter at breast height (DBH) and tree height. The study demonstrates the feasibility and efficiency of HLS in mapping trees in mixed forests, with potential application in forest monitoring programs.
Article
Environmental Sciences
Manuela Hirschmugl, Florian Lippl, Carina Sobe
Summary: This study examines the options for assessing vertical forest structure in a mountainous near-natural forest in the Austrian Alps using airborne and spaceborne LiDAR data. The indicators of foliage height diversity (FHD) and number of layers (NoL) are investigated. Expert-based assessment (EBA) outperforms break-detection algorithm (BDA) in terms of overall accuracy (OA) for estimating NoL. ALS data shows better OA for NoL than GEDI data. The usability of waveform-based structure parameters shows promise and should be further tested on larger areas.
Article
Ecology
Marc Fuhr, Etienne Lalechere, Jean-Matthieu Monnet, Laurent Berges
Summary: Building a network of interconnected overmature forests is crucial for biodiversity conservation. LiDAR technology can accurately assess forest structural parameters and identify overmature forest patches over large areas. In this study, an index combining forest structural maturity attributes was developed to characterize the maturity of field plots. LiDAR metrics, along with elevation, slope, and echo intensity distribution, were important for predicting forest maturity. The model showed a high correlation between observed and predicted maturity values, indicating accurate ranking of field plots.
REMOTE SENSING IN ECOLOGY AND CONSERVATION
(2022)
Article
Chemistry, Multidisciplinary
Fernando J. Aguilar, Abderrahim Nemmaoui, Manuel A. Aguilar, Alberto Penalver
Summary: This study explores the use of machine learning regression algorithms to model tree aboveground biomass estimation, finding that models using both tree height and crown diameter perform better than those using only tree height. Among the machine learning algorithms, boosting regression algorithms like CatBoost and GBoost outperformed traditional models such as random forest regression and linear regression in terms of goodness-of-fit and stability.
APPLIED SCIENCES-BASEL
(2021)
Article
Forestry
S. Peters, J. Liu, D. Bruce, J. Li, A. Finn, J. O'Hehir
Summary: This study investigates cost-efficient updates of timber yield estimates using multitemporal ALS and simulated UAV data. The findings show that combining newly acquired UAV-LiDAR data at the plot level with prior assimilated stand-wide ALS datasets can effectively predict wood volume changes.
AUSTRALIAN FORESTRY
(2021)
Article
Environmental Sciences
James E. Lamping, Harold S. J. Zald, Buddhika D. Madurapperuma, Jim Graham
Summary: This study evaluated the ability of low-cost UAS DAP and HPGPS to accurately predict key forest attributes in various forest conditions in California, USA. The results showed that UAS DAP models were comparable to lidar models, and when combined with low-cost HPGPS, could accurately predict key forest attributes across a range of forest types.
Article
Forestry
Hans Ole Orka, Endre Hansen, Michele Dalponte, Terje Gobakken, Erik Naesset
Summary: This study utilized airborne laser scanning and hyperspectral data along with field sample plots to provide species composition in forest management inventories. The results showed acceptable accuracy in estimating species-specific volume proportions, with improved results compared to traditional approaches. Dominant species were classified with an overall accuracy of 91%, indicating the potential of using remotely sensed data for estimating tree species composition and volumes.
Article
Environmental Sciences
Henrik J. Persson, Kenneth Olofsson, Johan Holmgren
Summary: This study compared a two-phase laser-scanning method with traditional field inventory for forest stands in Sweden, showing that laser-scanning provided significantly higher accuracy and efficiency in measuring tree height, classifying tree species, and estimating forest variables. The results demonstrated the potential for laser-scanning to replace manual field inventories in the future.
REMOTE SENSING OF ENVIRONMENT
(2022)
