Modeling Stand Height, Volume, and Biomass from Very High Spatial Resolution Satellite Imagery and Samples of Airborne LiDAR

Mora, B.; Wulder, Michael A.; White, Joanne C.; Hobart, Geordie W.

doi:10.3390/rs5052308

Cited by 52 publications

(35 citation statements)

References 35 publications

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“…In terms of the forest stand variables to be predicted, most studies focused on the estimation of conventional forest stand variables such as SV, NT, BA, and QMD using remotely sensed data [35,55,74,75]. Only a few studies have investigated the extraction of the more complex structural variables such as tree size diversity and tree position diversity [13,76,77].…”

Section: Discussionmentioning

confidence: 99%

Estimation of Forest Structural Diversity Using the Spectral and Textural Information Derived from SPOT-5 Satellite Images

Meng

Wang

et al. 2016

Remote Sensing

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Uneven-aged forest management has received increasing attention in the past few years. Compared with even-aged plantations, the complex structure of uneven-aged forests complicates the formulation of management strategies. Forest structural diversity is expected to provide considerable significant information for uneven-aged forest management planning. In the present study, we investigated the potential of using SPOT-5 satellite images for extracting forest structural diversity. Forest stand variables were calculated from the field plots, whereas spectral and textural measures were derived from the corresponding satellite images. We firstly employed Pearson's correlation analysis to examine the relationship between the forest stand variables and the image-derived measures. Secondly, we performed all possible subsets multiple linear regression to produce models by including the image-derived measures, which showed significant correlations with the forest stand variables, used as independent variables. The produced models were evaluated with the adjusted coefficient of determination (R 2 adj ) and the root mean square error (RMSE). Furthermore, a ten-fold cross-validation approach was used to validate the best-fitting models (R 2 adj > 0.5). The results indicated that basal area, stand volume, the Shannon index, Simpson index, Pielou index, standard deviation of DBHs, diameter differentiation index and species intermingling index could be reliably predicted using the spectral or textural measures extracted from SPOT-5 satellite images.

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Section: Discussionmentioning

confidence: 99%

Estimation of Forest Structural Diversity Using the Spectral and Textural Information Derived from SPOT-5 Satellite Images

Meng

Wang

et al. 2016

Remote Sensing

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“…In Mora et al [25], only stand height was modeled, and RMSE accuracy of 2.3 m (21%) was obtained in British Columbia, Canada, using a k-nearest neighbor (k-NN) approach. Mora et al [26] obtained an RMSE accuracy of 1.95 m (11.6%) for stand height, 9.6 m 3 /ha (12.8%) for stand volume and 22.2 t/ha (15.8%) for AGB. In both of these studies, accuracies were reported at the stand level (mean size 9.6 ha in [26]).…”

Section: Introductionmentioning

confidence: 99%

“…3 m RMS-accuracy [22,23]. Recently, ALS has been used for calibration and validation of forest characteristic predictions using optical imagery [24][25][26]. Chen et al [24] tested integration of Landsat imagery and ALS to estimate tree height variables.…”

Section: Introductionmentioning

confidence: 99%

“…The estimation errors for mean, dominant and Lorey's height were 4.9 m, 4.1 m and 4.7 m, respectively, validated at the plot level (625 m 2 ). Mora et al [25,26] used very high spatial resolution (VHSR, <1 m) optical imagery calibrated and validated with ALS to estimate forest characteristics. Stand and tree objects were delineated, followed by modeling of stand height, stem volume, and AGB using metrics derived from the stand and tree crown objects.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Forest Stand Attributes Using TerraSAR-X Stereo Imagery

et al. 2014

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Consistent, detailed and up-to-date forest resource information is required for allocation of forestry activities and national and international reporting obligations. We evaluated the forest stand attribute prediction accuracy when radargrammetry was used to derive height information from TerraSAR-X stereo imagery. Radargrammetric elevations were normalized to heights above ground using an airborne laser scanning (ALS)-derived digital terrain model (DTM). Derived height metrics were used as predictors in the most similar neighbor (MSN) estimation approach. In total, 207 field measured plots were used in MSN estimation, and the obtained results were validated using 94 stands with an average area of 4.1 ha. The relative root mean square errors for Lorey's height, basal area, stem volume, and above-ground biomass were 6.7% (1.1 m), 12.0% (2.9 m 2 /ha), 16.3% (31.1 m 3 /ha), and 16.1% (15.6 t/ha). Although the prediction accuracies were promising, it should be noted that the predictions included bias. The respective biases were −4.6% (−0.7 m), −6.4% (−1.6 m 2 /ha), −9.3% (−17.8 m 3 /ha), and −9.5% (−9.1 t/ha).With detailed DTM, TerraSAR-X stereo radargrammetry-derived forest information OPEN ACCESSRemote Sens. 2014, 6 3228 appears to be suitable for providing consistent forest resource information over large areas.

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“…Wulder and Seemann [22] used an empirical model to estimate airborne LiDAR-derived stand heights from Landsat5 TM data after delineating forest stands with an image segmentation. Mora et al [23,24] estimated LiDAR-based stand heights with high resolution satellite images (i.e., QuickBird-2, Worldview-1) using diverse regression techniques including linear regression, k-nearest neighbor, regression trees, and random forest. However, prior studies were generally carried out on non-rugged terrain with relatively analogous tree species composition, which makes it difficult to apply to mountainous forests in South Korea.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches for Estimating Forest Stand Height Using Plot-Based Observations and Airborne LiDAR Data

Lee

Kim

et al. 2018

Forests

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Effective sustainable forest management for broad areas needs consistent country-wide forest inventory data. A stand-level inventory is appropriate as a minimum unit for local and regional forest management. South Korea currently produces a forest type map that contains only four categorical parameters. Stand height is a crucial forest attribute for understanding forest ecosystems that is currently missing and should be included in future forest type maps. Estimation of forest stand height is challenging in South Korea because stands exist in small and irregular patches on highly rugged terrain. In this study, we proposed stand height estimation models suitable for rugged terrain with highly mixed tree species. An arithmetic mean height was used as a target variable. Plot-level height estimation models were first developed using 20 descriptive statistics from airborne Light Detection and Ranging (LiDAR) data and three machine learning approaches-support vector regression (SVR), modified regression trees (RT) and random forest (RF). Two schemes (i.e., central plot-based (Scheme 1) and stand-based (Scheme 2)) for expanding from the plot level to the stand level were then investigated. The results showed varied performance metrics (i.e., coefficient of determination, root mean square error, and mean bias) by model for forest height estimation at the plot level. There was no statistically significant difference among the three mean plot height models (i.e., SVR, RT and RF) in terms of estimated heights and bias (p-values > 0.05). The stand-level validation based on all tree measurements for three selected stands produced varied results by scheme and machine learning used. It implies that additional reference data should be used for a more thorough stand-level validation to identify statistically robust approaches in the future. Nonetheless, the research findings from this study can be used as a guide for estimating stand heights for forests in rugged terrain and with complex composition of tree species.

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Modeling Stand Height, Volume, and Biomass from Very High Spatial Resolution Satellite Imagery and Samples of Airborne LiDAR

Cited by 52 publications

References 35 publications

Estimation of Forest Structural Diversity Using the Spectral and Textural Information Derived from SPOT-5 Satellite Images

Estimation of Forest Structural Diversity Using the Spectral and Textural Information Derived from SPOT-5 Satellite Images

Prediction of Forest Stand Attributes Using TerraSAR-X Stereo Imagery

Machine Learning Approaches for Estimating Forest Stand Height Using Plot-Based Observations and Airborne LiDAR Data

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