Distinguishing between live and dead standing tree biomass on the North Rim of Grand Canyon National Park, USA using small-footprint lidar data

Kim, Yunsuk; Yang, Zhiqiang; Cohen, Warren B.; Pflugmacher, Dirk; Lauver, Chris L.; Vankat, John L.

doi:10.1016/j.rse.2009.07.010

Cited by 114 publications

(108 citation statements)

References 39 publications

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“…From each point cloud data, the various height distribution related features were calculated and used as predictors in the regression for developing prediction models because they have been proved to be effective in predicting forest attributes [4,[37][38][39]. Most of the features were calculated from normalized points above the 2 m threshold.…”

Section: Plot Feature Derivationmentioning

confidence: 99%

Comparison of Laser and Stereo Optical, SAR and InSAR Point Clouds from Air- and Space-Borne Sources in the Retrieval of Forest Inventory Attributes

et al. 2015

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Abstract:It is anticipated that many of the future forest mapping applications will be based on three-dimensional (3D) point clouds. A comparison study was conducted to verify the explanatory power and information contents of several 3D remote sensing data sources on the retrieval of above ground biomass (AGB), stem volume (VOL), basal area (G), basal-area weighted mean diameter (D g ) and Lorey's mean height (H g ) at the plot level, utilizing the following data: synthetic aperture radar (SAR) Interferometry, SAR radargrammetry, satellite-imagery having stereo viewing capability, airborne laser scanning (ALS) with various densities (0.8-6 pulses/m 2 ) and aerial stereo imagery. Laser scanning is generally known as the primary source providing a 3D point cloud. However, photogrammetric, radargrammetric and interferometric techniques can be used to produce 3D point clouds from space-and air-borne stereo images. Such an image-based point cloud could be utilized in a similar manner as ALS providing that accurate digital terrain model is available. In this study, the performance of these data sources for providing point cloud data was evaluated with 91 sample plots that were established in Evo, southern Finland within a boreal forest zone and surveyed in 2014 for this comparison. The prediction models were built using random forests technique with features derived from each data sources as independent variables and field measurements of forest attributes as response variable. The relative root mean square errors (RMSEs) varied in the ranges of 4.6% (0.97 m)-13.4% (2.83 m) for H g , 11.7% (3.0 cm)-20.6% (5.3 cm) for D g , 14.8% (4.0 m 2 /ha)-25.8% (6.9 m 2 /ha) for G, 15.9% (43.0 m 3 /ha)-31.2% (84.2 m 3 /ha) for VOL and 14.3% (19.2 Mg/ha)-27.5% (37.0 Mg/ha) for AGB, respectively, depending on the data used. Results indicate that ALS data achieved the most accurate estimates for all forest inventory attributes. For image-based 3D data, high-altitude aerial images and WorldView-2 satellite optical image gave similar results for H g and D g , which were only slightly worse than those of ALS data. As expected, spaceborne SAR data produced the worst estimates. WorldView-2 satellite data performed well, achieving accuracy comparable to the one with ALS data for G, VOL and AGB estimation. SAR interferometry data 2015,7, seems to contain more information for forest inventory than SAR radargrammetry and reach a better accuracy (relative RMSE decreased from 13.4% to 9.5% for H g , 20.6% to 19.2% for D g , 25.8% to 20.9% for G, 31.2% to 22.0% for VOL and 27.5% to 20.7% for AGB, respectively). However, the availability of interferometry data is limited. The results confirmed the high potential of all 3D remote sensing data sources for forest inventory purposes. However, the assumption of using other than ALS data is that there exist a high quality digital terrain model, in our case it was derived from ALS.

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Section: Plot Feature Derivationmentioning

confidence: 99%

Comparison of Laser and Stereo Optical, SAR and InSAR Point Clouds from Air- and Space-Borne Sources in the Retrieval of Forest Inventory Attributes

et al. 2015

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“…Moreover, because these data have not been collected on a systematic, routine basis, they have limited use for widespread forest monitoring. Data from lidar sensors have great potential for forest monitoring because of their highly relevant quality (Kim et al, 2009), but repeated lidar coverage is only now becoming available . Consequently, for most of the Earth's forests, lidar will remain a sampling and target-of-opportunity instrument for the foreseeable future owing to cost and the current lack of a suitable space-based platform.…”

Section: Sensor Considerationsmentioning

confidence: 99%

Using remotely sensed data to construct and assess forest attribute maps and related spatial products

McRoberts

Cohen

Næsset

et al. 2010

Scandinavian Journal of Forest Research

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111

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Tremendous advances in the construction and assessment of forest attribute maps and related spatial products have been realized in recent years, partly as a result of the use of remotely sensed data as an information source. This review focuses on the current state of techniques for the construction and assessment of remote sensing-based maps and addresses five topic areas: statistical classification and prediction techniques used to construct maps and related spatial products, accuracy assessment methods, map-based statistical inference, and two emerging topics, change detection and use of lidar data. Multiple general conclusions were drawn from the review: (1) remotely sensed data greatly contribute to the construction of forest attribute maps and related spatial products and to the reduction of inventory costs; (2) parametric prediction techniques, accuracy assessment methods and probability-based (design-based) inferential methods are generally familiar and mature, although inference is surprisingly seldom addressed; (3) non-parametric prediction techniques and modelbased inferential methods lack maturity and merit additional research; (4) change detection methods, with their great potential for adding a spatial component to change estimates, will mature rapidly; and (5) lidar applications, although currently immature, add an entirely new dimension to remote sensing research and will also mature rapidly. Crucial forest sustainability and climate change applications will continue to push all aspects of remote sensing to the forefront of forest research and operations.

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“…However, the relevance of this type of variables was reported in forest studies to estimate tree volume and biomass by plots (Naesset, 2004;Li et al, 2008;Kim et al, 2009). In our study it was observed a trend in which the higher values of CH 1.5-3.5 , the larger values of volume were estimated.…”

Section: Models For Tree Volume Calculationmentioning

confidence: 99%

Estimation of wood volume and height of olive tree plantations using airborne discrete-return LiDAR data

Estornell

Velázquez‐Martí

Cortés

et al. 2014

GIScience & Remote Sensing

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The aim of this study is to analyze methodologies based on airborne LiDAR technology of low pulse density points (0.5 m -2 ) for height and volume quantification of olive trees in Viver (Spain). A total of 29 circular plots of radius 20 m were sampled and their volumes and height were obtained by dendrometric methods. For these estimations several statistics derived from LiDAR data were calculated in each plot. Regression models were calculated to predict volume and height. The results showed a good performance for estimating volume (R 2 = 0.70) and for total height estimations (R 2 = 0.67).

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Distinguishing between live and dead standing tree biomass on the North Rim of Grand Canyon National Park, USA using small-footprint lidar data

Cited by 114 publications

References 39 publications

Comparison of Laser and Stereo Optical, SAR and InSAR Point Clouds from Air- and Space-Borne Sources in the Retrieval of Forest Inventory Attributes

Comparison of Laser and Stereo Optical, SAR and InSAR Point Clouds from Air- and Space-Borne Sources in the Retrieval of Forest Inventory Attributes

Using remotely sensed data to construct and assess forest attribute maps and related spatial products

Estimation of wood volume and height of olive tree plantations using airborne discrete-return LiDAR data

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