A Comparison of Modeling Methods for Predicting Forest Attributes Using Lidar Metrics

Adhikari, Angel; Montes, Cristián R.; Peduzzi, Alicia

doi:10.3390/rs15051284

Cited by 15 publications

(12 citation statements)

References 59 publications

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“…As machine learning technologies evolve, an increasing number of researchers explore employing machine learning algorithms for tree DBH prediction. Remote sensing data [31] also significantly influence DBH prediction. By using high-resolution remote sensing images and lidar data from forest areas, relevant geographical, morphological, and structural features can be extracted and used as input variables for constructing and optimizing tree DBH prediction models.…”

Section: Research Status Of Dbh Prediction Of Treesmentioning

confidence: 99%

Tree Crown Segmentation and Diameter at Breast Height Prediction Based on BlendMask in Unmanned Aerial Vehicle Imagery

Xu,

Su,

Sun

et al. 2024

Remote Sensing

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The surveying of forestry resources has recently shifted toward precision and real-time monitoring. This study utilized the BlendMask algorithm for accurately outlining tree crowns and introduced a Bayesian neural network to create a model linking individual tree crown size with diameter at breast height (DBH). BlendMask accurately outlines tree crown shapes and contours, outperforming traditional watershed algorithms in segmentation accuracy while preserving edge details across different scales. Subsequently, the Bayesian neural network constructs a model predicting DBH from the measured crown area, providing essential data for managing forest resources and conducting biodiversity research. Evaluation metrics like precision rate, recall rate, F1-score, and mAP index comprehensively assess the method’s performance regarding tree density. BlendMask demonstrated higher accuracy at 0.893 compared to the traditional watershed algorithm’s 0.721 accuracy based on experimental results. Importantly, BlendMask effectively handles over-segmentation problems while preserving edge details across different scales. Moreover, adjusting parameters during execution allows for flexibility in achieving diverse image segmentation effects. This study addresses image segmentation challenges and builds a model linking crown area to DBH using the BlendMask algorithm and a Bayesian neural network. The average discrepancies between calculated and measured DBH for Ginkgo biloba, Pinus tabuliformis, and Populus nigra varitalica were 0.15 cm, 0.29 cm, and 0.49cm, respectively, all within the acceptable forestry error margin of 1 cm. BlendMask, besides its effectiveness in crown segmentation, proves useful for various vegetation classification tasks like broad-leaved forests, coniferous forests, and grasslands. With abundant training data and ongoing parameter adjustments, BlendMask attains improved classification accuracy. This new approach shows great potential for real-world use, offering crucial data for managing forest resources, biodiversity research, and related fields, aiding decision-making processes.

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Section: Research Status Of Dbh Prediction Of Treesmentioning

confidence: 99%

Tree Crown Segmentation and Diameter at Breast Height Prediction Based on BlendMask in Unmanned Aerial Vehicle Imagery

Xu,

Su,

Sun

et al. 2024

Remote Sensing

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“…A map of forest characteristics for an extensive forest area is an ideal outcome of remote sensing methods, yet this outcome is complicated by two underlying factors: the multitude of potential independent variables that can be derived from remotely sensed data, and the potential correlation amongst these which can induce a multicollinearity problem [35,36]. Furthermore, a large number of independent variables within predictive models can challenge the application of these models for developing broad scale GIS databases.…”

Section: Preprintsmentioning

confidence: 99%

Estimating Forest Inventory Information for the Talladega National Forest Using Airborne Laser Scanning Systems

Lee,

Vatandaslar,

Merry

et al. 2024

Preprint

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Accurately assessing forest structure and maintaining up-to-date information about forest structure is crucial for various forest planning efforts, including the development of reliable forest plans and assessments of the sustainable management of natural resources. Field measurements traditionally applied to acquire forest inventory information (e.g., basal area, tree volume, and aboveground biomass) are labor-intensive and time-consuming. To address this limitation, remote sensing tech-nology has been widely applied in modeling efforts to help estimate forest inventory information. Among various remotely sensed data, LiDAR can potentially help describe forest structure. This study was conducted to estimate and map forest inventory information across the Talladega Na-tional Forest by employing ALS-derived data and aerial photography. The quality of predictive models was evaluated to determine whether additional remotely sensed data can help improve forest structure estimates. Additionally, the quality of general predictive models was compared to that of species-group models. This study confirms that quality level 2 LiDAR data was sufficient for developing adequate predictive models (R2adj. ranging between 0.71 and 0.82) when compared to the predictive models based on LiDAR and aerial imagery. Additionally, this study suggests that species-group predictive models were of higher quality than general predictive models. Lastly, landscape-level maps were created from the predictive models, and these may be helpful to plan-ners, forest managers, and landowners in their management efforts.

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“…Examples of LIDAR-based metrics retrieved from relevant literature for this review paper are presented in Table 5. These metrics retrieved from point cloud data are used to estimate stand-level attributes via comparison with plot-level data [45]. For instance, Pascual et al [36] sampled LIDAR-based metrics such as standard deviation, mean, and median of airborne laser scanning return heights obtained from grid cells which match the pixel dimensions of the remotely sensed data over a test plot in central Spain.…”

Section: Height Metrics For Biomass Modelmentioning

confidence: 99%

LIDAR-Based Forest Biomass Remote Sensing: A Review of Metrics, Methods, and Assessment Criteria for the Selection of Allometric Equations

Borsah,

Nazeer,

Wong

2023

Forests

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The increasing level of atmospheric carbon dioxide and its effects on our climate system has become a global environment issue. The forest ecosystem is essential for the stability of carbon in the atmosphere as it operates as a carbon sink and provides a habitat for numerous species. Therefore, our understanding of the structural elements of the forest ecosystem is vital for the estimation of forest biomass or terrestrial carbon stocks. Over the last two decades, light detection and ranging (LIDAR) technology has significantly revolutionized our understanding of forest structures and enhanced our ability to monitor forest biomass. This paper presents a review of metrics for forest biomass estimation, outlines metrics selection methods for biomass modeling, and addresses various assessment criteria for the selection of allometric equations for the aboveground forest biomass estimations, using LIDAR data. After examining one hundred publications written by different authors between 1999 and 2023, it was observed that LIDAR technology has become a dominant data collection tool for aboveground biomass estimation with most studies focusing on the use of airborne LIDAR data for the plot-level analysis on a local scale. Parametric-based models dominated in most studies with coefficient of determination (R2) and root mean square error (RMSE) as assessment criteria. In addition, mean top canopy height (MCH) and quadratic mean height (QMH) were reported as strong predictors for aboveground biomass (AGB) estimation. Pixel-based uncertainty analysis was found to be a reliable method for assessing spatial variations in uncertainties.

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A Comparison of Modeling Methods for Predicting Forest Attributes Using Lidar Metrics

Cited by 15 publications

References 59 publications

Tree Crown Segmentation and Diameter at Breast Height Prediction Based on BlendMask in Unmanned Aerial Vehicle Imagery

Tree Crown Segmentation and Diameter at Breast Height Prediction Based on BlendMask in Unmanned Aerial Vehicle Imagery

Estimating Forest Inventory Information for the Talladega National Forest Using Airborne Laser Scanning Systems

LIDAR-Based Forest Biomass Remote Sensing: A Review of Metrics, Methods, and Assessment Criteria for the Selection of Allometric Equations

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