Exploring LiDAR–RaDAR synergy—predicting aboveground biomass in a southwestern ponderosa pine forest using LiDAR, SAR and InSAR

Hyde, Peter; Nelson, Ross; Kimes, D. S.; Levine, Elissa R.

doi:10.1016/j.rse.2006.07.017

Cited by 124 publications

(60 citation statements)

References 32 publications

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“…First, variance inflation factors (VIF) were used to eliminate models with multiple collinearity. A low VIF for all predictor variables was desirable and in all cases the maximum VIF was less than 10 (Hyde et al 2007). Second, all predictor variables should be statistically significant (preferably at 95% confidence or better).…”

Section: Multiple Regression Analysismentioning

confidence: 96%

LiDAR and Weibull modeling of diameter and basal area

Thomas¹,

Oliver²,

Lim³

et al. 2008

The Forestry Chronicle

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This study investigates the ability to predict forest diameter distributions from light detection and ranging (LiDAR) data using Weibull modelling for forest stands in central Ontario. Results suggest that the unimodal 2-parameter Weibull model is a promising technique for the prediction of diameter class distributions, with strong relationships evident for several subgroups (at 95% confidence, r 2 adj =0.83, 0.78, 0.88, 0.80, 0.83, and 0.65, with validation RMSE of 4.09 m 2 /ha, 0.61 stems/ha, 6.05, 0.64, 4.73, and 0.09 for basal area, stem density, and the Weibull a and b parameters for basal area and stem density, respectively). The unimodal models were found to be least effective for the irregularly shaped diameter distributions, particularly for low-density coniferous plots that have undergone shelterwood treatment. A significant improvement in results for these irregular plots was found with a finite mixture modelling approach, suggesting that finite mixture models may extend our ability to predict diameter distributions over large portions of the landscape.Key words: LiDAR, Weibull, finite mixture modeling, diameter class distributions, multiple linear regression RÉSUMÉCette étude porte sur la capacité de prédiction de la distribution des diamètres d'arbres à partir de données de détection de la lumière et de calcul de la distance (LiDAR) utilisées dans un modèle Weibull pour des peuplements forestiers du centre de l'Ontario. Les résultats laissent entendre que le modèle Weibull unimodal à 2 paramètres constitue une technique prometteuse de la prédiction de la distribution des classes de diamètre comportant de fortes corrélations évidentes pour certains sous-groupes (à un niveau de confiance 95%, r 2 adj =0.83, 0.78, 0.88, 0.80, 0.83 et 0.65, une variance de 4,09 m 2 /ha, 0,61 tiges/ha, 6,05, 0,64, 4,73 et de 0,09 respectivement pour la surface terrière, le nombre de tige par hectare et pour les paramètres a et b de Weibull portant sur la surface terrière et le nombre de tige). Les modèles unimodaux se sont avérés être moins efficaces dans le cas des distributions de forme irrégulière des diamètres, particulièrement dans le cas de parcelles de faible densité de résineux ayant subi des traitements de coupe progressive. Une amélioration significative des résultats de ces parcelles irrégulières est apparue à la suite d'une approche de modélisation pour peuplement mélangé spécifique, laissant entendre que les modèles pour peuplement mélangé spécifique pourrait accroître notre capacité de prédire la distribution des diamètres pour de grandes portions de l' écosystème.Mots clés : LiDAR, Weibull, modélisation de peuplement mélangé spécifique, distributions des classes de diamètre, régression linéaire multiple

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Section: Multiple Regression Analysismentioning

confidence: 96%

LiDAR and Weibull modeling of diameter and basal area

Thomas¹,

Oliver²,

Lim³

et al. 2008

The Forestry Chronicle

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“…These forests exhibit greater correlation between individual tree height and biomass (Popescu et al 2003). Hence lidar-based biomass estimates are highly accurate and precise in studies conducted in these type of forests (cross-validation R 2 = 93.3 and RMSE=33.9 tonnes/ha in Nelson et al 2007; crossvalidation R 2 =82.6 and RMSE=26.05 tonnes/ha in Hyde et al 2007), and little is gained by further adding radarderived variables. On the other hand, canopy height estimation in hardwood and mixed forests using small footprint lidar data is less accurate than in coniferous forests, mainly because of (1) increased understory and (2) failure to sample the tops of the relatively broad trees (Clark et al 2004;Lefsky et al 2002;Means et al 1999).…”

Section: Discussionmentioning

confidence: 99%

“…In forested environments, these sensors measure reflected energy that is largely a function of canopy architecture (leaf area, leaf angle, and clumping), leaf pigments, soil background, and underground vegetation (Goel 1989). A direct physical relationship between aboveground biomass and optical remote sensing data does not exist; hence, the latter only provide indirect estimates of aboveground biomass (Chopping et al 2008;Hyde et al 2007). Different approaches have been utilized with varying degrees of success to estimate biomass from optical remote sensing data, for example, establishing the relationships between biomass and vegetation indices (Peddle et al 2001;Sader et al 1989), spectral bands (Boyd et al 1999;Foody et al 2003;Steininger 2000), image texture (Lu 2005), and combinations of texture and spectral information (Lu and Batistella 2005).…”

Section: Introductionmentioning

confidence: 99%

Synergistic use of very high-frequency radar and discrete-return lidar for estimating biomass in temperate hardwood and mixed forests

Banskota

Wynne

Johnson

et al. 2011

Annals of Forest Science

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Abstract• Introduction Accurate estimation of aboveground biomass is essential to better understand the carbon cycle in forest ecosystems.• Methods The objective of this study was to determine whether biomass in temperate hardwood forests is better estimated using very high-frequency radar data (from BioSAR) alone or in combination with small-footprint discrete-return lidar data (both profiling and scanning). The study area was in the Appomattox-Buckingham State Forest, Virginia, USA (78°41′W, 37°25′N). Aboveground biomass for 28 stands was estimated using 131 basal area factor 10 point samples. The resulting stand biomass estimates were used as the dependent variable in a multiple linear regression. Descriptors of the lidar distributions (both profiling and scanning) and averaged normalized radar cross-sections in each of these stands were used as independent variables.• Results Regression results revealed the following: (1) neither BioSAR nor scanning lidar data alone are good predictors of stand biomass (R 2 =0.57, root mean squared error (RMSE)= 31.0 tonnes/ha and R 2 =0.64, RMSE=28.5 tonnes/ha, respectively); (2) BioSAR data combined with small-footprint discrete lidar data (either profiling or scanning) are the best predictors of stand biomass (R 2 =0.80, RMSE=21.3 tonnes/ ha and R 2 =0.76, RMSE=24.2 tonnes/ha, respectively); and (3) when used with BioSAR data for stand biomass estimation, less costly profiling lidar data convey the same information as more costly scanning lidar data.• Conclusion Useful synergy can be realized by considering lidar and radar measurements jointly in estimating aboveground biomass in hardwood and mixed forests.

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“…Fusion of LiDAR, multispectral, and Interferometric Synthetic Aperture Radar (InSAR) data was tested to optimize prediction of variables relating to wildlife habitat and carbon stores [116]; results showed that the best combination of variables were from LiDAR and ETM+7, whereas InSAR and Quickbird did little to improve models. A similar study explored LiDAR-radar synergy for predicting aboveground biomass, and found only negligible improvement by including radar [117]. LiDAR has also been found to be superior to radar for accurately detecting the height of individual trees and forest plots [118].…”

Section: Sensor Integrationmentioning

confidence: 99%

LiDAR Utility for Natural Resource Managers

2009

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Applications of LiDAR remote sensing are exploding, while moving from the research to the operational realm. Increasingly, natural resource managers are recognizing the tremendous utility of LiDAR-derived information to make improved decisions. This review provides a cross-section of studies, many recent, that demonstrate the relevance of LiDAR across a suite of terrestrial natural resource disciplines including forestry, fire and fuels, ecology, wildlife, geology, geomorphology, and surface hydrology. We anticipate that interest in and reliance upon LiDAR for natural resource management, both alone and in concert with other remote sensing data, will continue to rapidly expand for the foreseeable future.

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Exploring LiDAR–RaDAR synergy—predicting aboveground biomass in a southwestern ponderosa pine forest using LiDAR, SAR and InSAR

Cited by 124 publications

References 32 publications

LiDAR and Weibull modeling of diameter and basal area

LiDAR and Weibull modeling of diameter and basal area

Synergistic use of very high-frequency radar and discrete-return lidar for estimating biomass in temperate hardwood and mixed forests

LiDAR Utility for Natural Resource Managers

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