Sensitivity Analysis of the DART Model for Forest Mensuration with Airborne Laser Scanning

Airborne Laser Scanning (ALS) measurements are increasingly vital in forest management and national forest inventories. Despite the growing reliance on ALS data, comparatively little research has examined the sensitivity of ALS measurements to varying survey conditions over commercially important forests. This study investigated: (i) how accurately the Discrete Anisotropic Radiative Transfer (DART) model was able to replicate small-footprint ALS measurements collected over Irish conifer plantations, and (ii) how survey characteristics influenced the precision of discrete-return metrics. A variance-based global sensitivity analysis demonstrated that discrete-return height distributions were accurately and consistently simulated across 100 forest inventory plots with few perturbations induced by varying acquisition parameters or ground topography. In contrast, discrete return density, canopy cover and the proportion of multiple returns were sensitive to fluctuations in sensor altitude, scanning angle, pulse repetition frequency and pulse duration. Our findings corroborate previous studies indicating that discrete-return heights are robust to varying acquisition parameters and may be reliable predictors for the indirect retrieval of forest inventory measurements. However, canopy cover and density metrics are only comparable for ALS data collected under similar acquisition conditions, precluding their universal use across different ALS surveys. Our study demonstrates that DART is a robust model for simulating discrete-return measurements over structurally complex forests; however, the replication of foliage morphology, density and orientation are important considerations for radiative transfer simulations using synthetic trees with explicitly defined crown architectures.

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Mapping forest age using National Forest Inventory, airborne laser scanning, and Sentinel-2 data

Forest Ecosystems ◽

10.1186/s40663-020-00274-9 ◽

2020 ◽

Vol 7 (1) ◽

Author(s):

Johannes Schumacher ◽

Marius Hauglin ◽

Rasmus Astrup ◽

Johannes Breidenbach

Keyword(s):

Forest Inventory ◽

Regression Models ◽

Laser Scanning ◽

Site Index ◽

National Forest Inventory ◽

Airborne Laser Scanning ◽

National Forest ◽

Practical Applications ◽

Airborne Laser ◽

Sentinel 2

Abstract Background The age of forest stands is critical information for forest management and conservation, for example for growth modelling, timing of management activities and harvesting, or decisions about protection areas. However, area-wide information about forest stand age often does not exist. In this study, we developed regression models for large-scale area-wide prediction of age in Norwegian forests. For model development we used more than 4800 plots of the Norwegian National Forest Inventory (NFI) distributed over Norway between latitudes 58° and 65° N in an 18.2 Mha study area. Predictor variables were based on airborne laser scanning (ALS), Sentinel-2, and existing public map data. We performed model validation on an independent data set consisting of 63 spruce stands with known age. Results The best modelling strategy was to fit independent linear regression models to each observed site index (SI) level and using a SI prediction map in the application of the models. The most important predictor variable was an upper percentile of the ALS heights, and root mean squared errors (RMSEs) ranged between 3 and 31 years (6% to 26%) for SI-specific models, and 21 years (25%) on average. Mean deviance (MD) ranged between − 1 and 3 years. The models improved with increasing SI and the RMSEs were largest for low SI stands older than 100 years. Using a mapped SI, which is required for practical applications, RMSE and MD on plot level ranged from 19 to 56 years (29% to 53%), and 5 to 37 years (5% to 31%), respectively. For the validation stands, the RMSE and MD were 12 (22%) and 2 years (3%), respectively. Conclusions Tree height estimated from airborne laser scanning and predicted site index were the most important variables in the models describing age. Overall, we obtained good results, especially for stands with high SI. The models could be considered for practical applications, although we see considerable potential for improvements if better SI maps were available.

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The shelf-life of airborne laser scanning data for enhancing forest inventory inferences

Remote Sensing of Environment ◽

10.1016/j.rse.2017.12.017 ◽

2018 ◽

Vol 206 ◽

pp. 254-259 ◽

Cited By ~ 15

Author(s):

Ronald E. McRoberts ◽

Qi Chen ◽

Dale D. Gormanson ◽

Brian F. Walters

Keyword(s):

Shelf Life ◽

Forest Inventory ◽

Laser Scanning ◽

Airborne Laser Scanning ◽

Airborne Laser

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How much can airborne laser scanning based forest inventory by tree species benefit from auxiliary optical data?

International Journal of Applied Earth Observation and Geoinformation ◽

10.1016/j.jag.2018.06.017 ◽

2018 ◽

Vol 72 ◽

pp. 91-98 ◽

Cited By ~ 4

Author(s):

Mikael Kukkonen ◽

Lauri Korhonen ◽

Matti Maltamo ◽

Aki Suvanto ◽

Petteri Packalen

Keyword(s):

Tree Species ◽

Forest Inventory ◽

Laser Scanning ◽

Airborne Laser Scanning ◽

Optical Data ◽

Airborne Laser

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Effect of scanning angle on vegetation metrics derived from a nationwide Airborne Laser Scanning acquisition

Canadian Journal of Remote Sensing ◽

10.5589/m13-052 ◽

2013 ◽

Vol 39 (sup1) ◽

pp. S152-S173 ◽

Cited By ~ 10

Author(s):

Alessandro Montaghi

Keyword(s):

Laser Scanning ◽

Airborne Laser Scanning ◽

Scanning Angle ◽

Airborne Laser

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Augmentation of Traditional Forest Inventory and Airborne Laser Scanning with Unmanned Aerial Systems and Photogrammetry for Forest Monitoring

Remote Sensing ◽

10.3390/rs10101562 ◽

2018 ◽

Vol 10 (10) ◽

pp. 1562 ◽

Cited By ~ 17

Author(s):

Kathryn Fankhauser ◽

Nikolay Strigul ◽

Demetrios Gatziolis

Keyword(s):

Laser Scanning ◽

Canopy Cover ◽

Tree Height ◽

Forest Monitoring ◽

Unmanned Aerial Systems ◽

Aerial Laser Scanning ◽

Forest Inventories ◽

Airborne Laser ◽

Aerial Systems

Forest inventories are constrained by resource-intensive fieldwork, while unmanned aerial systems (UASs) offer rapid, reliable, and replicable data collection and processing. This research leverages advancements in photogrammetry and market sensors and platforms to incorporate a UAS-based approach into existing forestry monitoring schemes. Digital imagery from a UAS was collected, photogrammetrically processed, and compared to in situ and aerial laser scanning (ALS)-derived plot tree counts and heights on a subsample of national forest plots in Oregon. UAS- and ALS-estimated tree counts agreed with each other (r2 = 0.96) and with field data (ALS r2 = 0.93, UAS r2 = 0.84). UAS photogrammetry also reasonably approximated mean plot tree height achieved by the field inventory (r2 = 0.82, RMSE = 2.92 m) and by ALS (r2 = 0.97, RMSE = 1.04 m). The use of both nadir-oriented and oblique UAS imagery as well as the availability of ALS-derived terrain descriptions likely sustain a robust performance of our approach across classes of canopy cover and tree height. It is possible to draw similar conclusions from any of the methods, suggesting that the efficient and responsive UAS method can enhance field measurement and ALS in longitudinal inventories. Additionally, advancing UAS technology and photogrammetry allows diverse users access to forest data and integrates updated methodologies with traditional forest monitoring.

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Evaluating the accuracy of ALS-based removal estimates against actual logging data

Annals of Forest Science ◽

10.1007/s13595-020-00985-7 ◽

2020 ◽

Vol 77 (3) ◽

Author(s):

Ville Vähä-Konka ◽

Matti Maltamo ◽

Timo Pukkala ◽

Kalle Kärhä

Keyword(s):

Model Prediction ◽

Forest Inventory ◽

Boreal Forests ◽

Laser Scanning ◽

Growth Models ◽

Airborne Laser Scanning ◽

Inventory Data ◽

Attribute Data ◽

Forest Inventory Data ◽

Airborne Laser

Abstract Key message We examined the accuracy of the stand attribute data based on airborne laser scanning (ALS) provided by the Finnish Forest Centre. The precision of forest inventory data was compared for the first time with operative logging data measured by the harvester. Context Airborne laser scanning (ALS) is increasingly used together with models to predict the stand attributes of boreal forests. The information is updated by growth models. Information produced by remote sensing, model prediction, and growth simulation needs field verification. The data collected by harvesters on logging sites provide a means to evaluate and verify the accuracy of the ALS-based data. Aims This study investigated the accuracy of ALS-based forest inventory data provided by the Finnish Forest Centre at the stand level, using harvester data as the reference. Special interest was on timber assortment volumes where the quality reductions of sawlog are model predictions in ALS-based data and true realized reductions in the logging data. Methods We examined the accuracy of total volume and timber assortment volumes by comparing ALS-based data and operative logging data measured by a harvester. This was done both for clear cuttings and thinning sites. Accuracy of the identification of the dominant tree species of the stand was examined using the Kappa coefficient. Results In clear-felling sites, the total harvest removals based on ALS and model prediction had a RMSE% of 26.0%. In thinning, the corresponding difference in the total harvested removal was 42.4%. Compared to logged volume, ALS-based prediction overestimated sawlog removals in clear cuttings and underestimated pulpwood removals. Conclusion The study provided valuable information on the accuracy of ALS-based stand attribute data. Our results showed that ALS-based data need better methods to predict the technical quality of harvested trees, to avoid systematic overestimates of sawlog volume. We also found that the ALS-based estimates do not accurately predict the volume of trees removed in actual thinnings.

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