Prediction of species specific forest inventory attributes using a nonparametric semi-individual tree crown approach based on fused airborne laser scanning and multispectral data

The estimation of forest biophysical attributes improves when airborne laser scanning (ALS) is integrated. Individual tree detection methods (ITD) and traditional area-based approaches (ABA) are the two main alternatives in ALS-based forest inventory. This study evaluated the performance of the enhanced area-based approach (EABA), an edge-correction method based on ALS data that combines ITD and ABA, at improving the estimation of forest biophysical attributes, while testing its efficiency when considering co-registration errors that bias remotely sensed predictor variables. The study was developed based on a stone pine forest (Pinus pinea L.) in Central Spain, in which tree spacing and scanning conditions were optimal for the ITD approach. Regression modeling was used to select the optimal predictor variables to estimate forest biophysical attributes. The accuracy of the models improved when using EABA, despite the low-density of the ALS data. The relative mean improvement of EABA in terms of root mean squared error was 15.2%, 17.3%, and 7.2% for growing stock volume, stand basal area, and dominant height, respectively. The impact of co-registration errors in the models was clear in the ABA, while the effect was minor and mitigated under EABA. The implementation of EABA can highly contribute to improve modern forest inventory applications.

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Species-specific combination and calibration between area-based and tree-based diameter distributions using airborne laser scanning

Canadian Journal of Forest Research ◽

10.1139/cjfr-2016-0032 ◽

2016 ◽

Vol 46 (6) ◽

pp. 753-765 ◽

Cited By ~ 9

Author(s):

Zhengyang Hou ◽

Qing Xu ◽

Jari Vauhkonen ◽

Matti Maltamo ◽

Timo Tokola

Keyword(s):

Laser Scanning ◽

Economic Value ◽

Airborne Laser Scanning ◽

Stem Volume ◽

Individual Tree ◽

Tree Detection ◽

Breast Height ◽

Airborne Laser ◽

Wood Procurement ◽

Species Specific

The planning of wood procurement requires reliable information about the species-specific timber assortments on which the economic value of a production forest depends. The timber assortments refer to the stem volumes of the sawlog and pulpwood fractions, specified in terms of both timber quality and allowable log dimensions, e.g., the stem diameter at breast height (dbh). We propose here an airborne laser scanning based calibration framework for generating species-specific dbh distributions that combines the area-based approach (ABA) and individual-tree detection (ITD), two established and independent approaches for retrieving forest attributes from airborne laser scanning data. Both ABA- and ITD-derived dbh distributions were generated nonparametrically for pine, spruce, coniferous, deciduous, and all species and assessed with respect to the plot-level species-specific total stem volume (m3·ha–1) and approximations of volume of timber assortments. Although after calibration, the total volume of all species and the volume approximations of coniferous sawlog and spruce pulpwood decreased in accuracy by 4%–7%, the calibration improved the accuracy of the other 12 species-specific estimates by 2%–17%, testifying to the general effectiveness of the proposed calibration framework.

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Agent-based region growing for individual tree crown delineation from airborne laser scanning (ALS) data

International Journal of Remote Sensing ◽

10.1080/01431161.2015.1030043 ◽

2015 ◽

Vol 36 (7) ◽

pp. 1965-1993 ◽

Cited By ~ 21

Author(s):

Zhen Zhen ◽

Lindi J. Quackenbush ◽

Stephen V. Stehman ◽

Lianjun Zhang

Keyword(s):

Laser Scanning ◽

Region Growing ◽

Airborne Laser Scanning ◽

Tree Crown ◽

Individual Tree ◽

Agent Based ◽

Airborne Laser ◽

Crown Delineation ◽

Tree Crown Delineation

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Large scale mapping of forest attributes using heterogeneous sets of airborne laser scanning and National Forest Inventory data

Forest Ecosystems ◽

10.1186/s40663-021-00338-4 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Marius Hauglin ◽

Johannes Rahlf ◽

Johannes Schumacher ◽

Rasmus Astrup ◽

Johannes Breidenbach

Keyword(s):

Tree Species ◽

Forest Inventory ◽

Laser Scanning ◽

National Forest Inventory ◽

Mixed Effects ◽

Airborne Laser Scanning ◽

National Forest ◽

Airborne Laser ◽

Maturity Class ◽

Species Specific

Abstract Background The Norwegian forest resource map (SR16) maps forest attributes by combining national forest inventory (NFI), airborne laser scanning (ALS) and other remotely sensed data. While the ALS data were acquired over a time interval of 10 years using various sensors and settings, the NFI data are continuously collected. Aims of this study were to analyze the effects of stratification on models linking remotely sensed and field data, and assess the accuracy overall and at the ALS project level. Materials and methods The model dataset consisted of 9203 NFI field plots and data from 367 ALS projects, covering 17 Mha and 2/3 of the productive forest in Norway. Mixed-effects regression models were used to account for differences among ALS projects. Two types of stratification were used to fit models: 1) stratification by the three main tree species groups spruce, pine and deciduous resulted in species-specific models that can utilize a satellite-based species map for improving predictions, and 2) stratification by species and maturity class resulted in stratum-specific models that can be used in forest management inventories where each stand regularly is visually stratified accordingly. Stratified models were compared to general models that were fit without stratifying the data. Results The species-specific models had relative root-mean-squared errors (RMSEs) of 35%, 34%, 31%, and 12% for volume, aboveground biomass, basal area, and Lorey’s height, respectively. These RMSEs were 2–7 percentage points (pp) smaller than those of general models. When validating using predicted species, RMSEs were 0–4 pp. smaller than those of general models. Models stratified by main species and maturity class further improved RMSEs compared to species-specific models by up to 1.8 pp. Using mixed-effects models over ordinary least squares models resulted in a decrease of RMSE for timber volume of 1.0–3.9 pp., depending on the main tree species. RMSEs for timber volume ranged between 19%–59% among individual ALS projects. Conclusions The stratification by tree species considerably improved models of forest structural variables. A further stratification by maturity class improved these models only moderately. The accuracy of the models utilized in SR16 were within the range reported from other ALS-based forest inventories, but local variations are apparent.

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

Remote Sensing ◽

10.3390/rs9050505 ◽

2017 ◽

Vol 9 (5) ◽

pp. 505 ◽

Cited By ~ 6

Author(s):

Sebastian Lamprecht ◽

Andreas Hill ◽

Johannes Stoffels ◽

Thomas Udelhoven

Keyword(s):

Machine Learning ◽

Forest Inventory ◽

Laser Scanning ◽

Airborne Laser Scanning ◽

Machine Learning Method ◽

Learning Method ◽

Individual Tree ◽

Airborne Laser

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Erratum: A Machine Learning Method for Co-Registration and Individual Tree Matching of Forest Inventory and Airborne Laser Scanning Data. Remote Sens. 2017, 9, 505

Remote Sensing ◽

10.3390/rs9070692 ◽

2017 ◽

Vol 9 (7) ◽

pp. 692

Author(s):

Sebastian Lamprecht ◽

Andreas Hill ◽

Johannes Stoffels ◽

Thomas Udelhoven

Keyword(s):

Machine Learning ◽

Forest Inventory ◽

Laser Scanning ◽

Airborne Laser Scanning ◽

Machine Learning Method ◽

Learning Method ◽

Individual Tree ◽

Airborne Laser

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A mixed pixel- and region-based approach for using airborne laser scanning data for individual tree crown delineation in Pinus radiata D. Don plantations

International Journal of Remote Sensing ◽

10.1080/01431161.2013.823523 ◽

2013 ◽

Vol 34 (21) ◽

pp. 7671-7690 ◽

Cited By ~ 21

Author(s):

Eduardo González-Ferreiro ◽

Ulises Diéguez-Aranda ◽

Laura Barreiro-Fernández ◽

Sandra Buján ◽

Miguel Barbosa ◽

...

Keyword(s):

Pinus Radiata ◽

Laser Scanning ◽

Airborne Laser Scanning ◽

Tree Crown ◽

Individual Tree ◽

Mixed Pixel ◽

Airborne Laser ◽

Crown Delineation ◽

Tree Crown Delineation

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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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