Estimating forest biomass using small footprint LiDAR data: An individual tree-based approach that incorporates training data

Bortolot, Zachary J.; Wynne, Randolph H.

doi:10.1016/j.isprsjprs.2005.07.001

Cited by 148 publications

(86 citation statements)

References 32 publications

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“…With its ability to directly measure forest structure, including canopy height and crown dimensions, ALS is increasingly used for forest inventories at different levels. Previous studies have shown that ALS data can be used to estimate a variety of forest inventory attributes including tree, plot and stand level estimates for tree height [15][16][17][18], biomass [19][20][21], volume [22][23][24], basal area [20,25,26] and tree species [21,[27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Classification of Defoliated Trees Using Tree-Level Airborne Laser Scanning Data Combined with Aerial Images

Kantola

Vastaranta

et al. 2010

Remote Sensing

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Climate change and rising temperatures have been observed to be related to the increase of forest insect damage in the boreal zone. The common pine sawfly (Diprion pini L.) (Hymenoptera, Diprionidae) is regarded as a significant threat to boreal pine forests. Defoliation by D. pini can cause severe growth loss and tree mortality in Scots pine (Pinus sylvestris L.) (Pinaceae). In this study, logistic LASSO regression, Random Forest (RF) and Most Similar Neighbor method (MSN) were investigated for predicting the defoliation level of individual Scots pines using the features derived from airborne laser scanning (ALS) data and aerial images. Classification accuracies from 83.7% (kappa 0.67) to 88.1% (kappa 0.76) were obtained depending on the method. The most accurate result was produced using RF with a combination of data from the two sensors, while the accuracies when using ALS and image features separately were 80.7% and 87.4%, respectively. Evidently, the combination of ALS and aerial images in detecting needle losses is capable of providing satisfactory estimates for individual trees.

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Section: Introductionmentioning

confidence: 99%

Classification of Defoliated Trees Using Tree-Level Airborne Laser Scanning Data Combined with Aerial Images

Kantola

Vastaranta

et al. 2010

Remote Sensing

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“…Such metrics have also been used in [19], where different regression methods were tested to find the best combination of predictors avoiding collinearity. Non-tropical scenarios also give similar results when LiDAR metrics are used for AGB estimation; with a single-tree-based approach training areas in a pine plantation, [20] reported a coefficient of correlation that reached a value of 0.82 between actual and predicted AGB.…”

Section: Introductionmentioning

confidence: 65%

Small Footprint Full-Waveform Metrics Contribution to the Prediction of Biomass in Tropical Forests

et al. 2014

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Abstract:We tested metrics from full-waveform (FW) LiDAR (light detection and ranging) as predictors for forest basal area (BA) and aboveground biomass (AGB), in a tropical moist forest. Three levels of metrics are tested: (i) peak-level, based on each return echo; (ii) pulse-level, based on the whole return signal from each emitted pulse; and (iii) plot-level, simulating a large footprint LiDAR dataset. Several of the tested metrics have significant correlation, with two predictors, found by stepwise regression, in particular: median distribution of the height above ground (nZ median ) and fifth percentile of total pulse return intensity (i_tot 5th ). The former contained the most information and explained 58% and 62% of the variance in AGB and BA values; stepwise regression left us with two and four predictors, respectively, explaining 65% and 79% of the variance. For BA, the predictors were standard deviation, median and fifth percentile of total return pulse intensity (i_tot stdDev , i_tot median and i_tot 5th ) and nZ median , whereas for AGB, only the last two were used. The plot-based metric showed that the median height of echo count (HOMTC) performs best, with very similar results as nZ median , as expected. Cross-validation OPEN ACCESSRemote Sens. 2014, 6 9577 allowed the analysis of residuals and model robustness. We discuss our results considering our specific case scenario of a complex forest structure with a high degree of variability in terms of biomass.

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“…Computer-based algorithms have therefore successfully been applied to automatically identify tree crown structures and extract individual tree attributes, including total height, crown height, and crown diameter (Ziegler et al 2000;Persson et al 2002;Schardt et al 2002;Naesset et al 2004Naesset et al , 2005Bortolot and Wynne 2005;Falkowski et al 2006;Naesset and Nelson 2007). Fig.…”

Section: Individual Tree-based Assessmentmentioning

confidence: 99%

The role of LiDAR in sustainable forest management

Wulder¹,

Bater²,

Coops³

et al. 2008

The Forestry Chronicle

324

186

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Forest characterization with light detection and ranging (LiDAR) data has recently garnered much scientific and operational attention. The number of forest inventory attributes that may be directly measured with LiDAR is limited; however, when considered within the context of all the measured and derived attributes required to complete a forest inventory, LiDAR can be a valuable tool in the inventory process. In this paper, we present the status of LiDAR remote sensing of forests, including issues related to instrumentation, data collection, data processing, costs, and attribute estimation. The information needs of sustainable forest management provide the context within which we consider future opportunities for LiDAR and automated data processing.Key words: LiDAR, airborne laser altimetry, forest inventory, height, volume, biomass, update, remote sensing RÉSUMÉ La représentation des forêts à partir de données LiDAR (détection de la lumière et calcul de la distance) a attiré dernière-ment beaucoup d'attention tant scientifique qu' opérationnelle. Le nombre de variables d'inventaire forestier qui peuvent être mesurées directement par LiDAR est limité, mais lorsqu' on considère le contexte de toutes les variables mesurées et dérivées requises pour compléter un inventaire forestier, le LiDAR peut constituer un outil précieux du processus d'inventaire. Nous présentons dans cet article un portrait de la télédétection des forêts par LiDAR, ainsi que les questions portant sur l'appareillage, la collecte des données, les coûts et l' estimation des variables. Les besoins d'information en matière d'aménagement forestier durable constituent le contexte que nous retenons pour les possibilités d'application future du LiDAR et du traitement automatisé des données.

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Estimating forest biomass using small footprint LiDAR data: An individual tree-based approach that incorporates training data

Cited by 148 publications

References 32 publications

Classification of Defoliated Trees Using Tree-Level Airborne Laser Scanning Data Combined with Aerial Images

Classification of Defoliated Trees Using Tree-Level Airborne Laser Scanning Data Combined with Aerial Images

Small Footprint Full-Waveform Metrics Contribution to the Prediction of Biomass in Tropical Forests

The role of LiDAR in sustainable forest management

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