Estimation of stand variables in Pinus radiata D. Don plantations using different LiDAR pulse densities

González-Ferreiro, Eduardo; Diéguez-Aranda, Ulises; Miranda, David

doi:10.1093/forestry/cps002

Cited by 99 publications

(115 citation statements)

References 38 publications

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“…In this case, the study site was defined as a 4.130 × 8.787 km 2 rectangle (586,315;4783,000 and 595,102;4787,130 UTM). The forest type in this area was P. radiata stands, which were also characterized by low-intensity silvicultural treatments and by the presence of tall shrubs (Supplementary Information, Gonç alves-Seco et al, 2011 andGonzalez-Ferreiro et al, 2012).…”

Section: Study Sitesmentioning

confidence: 99%

“…The whole study area was flown over in 18 strips and each strip was flown over three times, which gave an average measurement density of about 4 pulses m −2 . To obtain two different resolutions, an artificial reduction based on a random selection of LiDAR returns in a grid cell of 1 m 2 was carried out and resulted in LiDAR data with a return density of 0.5 returns m −2 (Gonzalez-Ferreiro et al, 2012).…”

Section: Lidar Datamentioning

confidence: 99%

“…Moreover, problems related to multiple returns (Höfle and Pfeifer, 2007), which are very common in forest areas, were not taken into account either. Although the problems associated with the use of intensity might have suggested not to include it into the models, for the sake of comparison, we decided to follow the same methodology as in the previously published works for the study sites which did use intensity statistics (Gonç alves- Seco et al, 2011;Gonzalez-Ferreiro et al, 2012).…”

Section: Lidar Datamentioning

confidence: 99%

“…Only models whose parameters were significant at a given level (in our case, 5%) were taken into account (Gonzalez-Ferreiro et al, 2012).…”

Section: Best-subset Selectionmentioning

confidence: 99%

“…Although BIC was used as the final criterion for best model selection because of its better suitability for following the parsimony principle (Gonzalez-Ferreiro et al, 2012), it does not provide an intuitive idea of model precision. R 2 and RMSE were therefore calculated to provide additional information.…”

Section: Regression Modelsmentioning

confidence: 99%

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Evolutionary feature selection to estimate forest stand variables using LiDAR

García-Gutiérrez

González-Ferreiro

Santos

et al. 2014

International Journal of Applied Earth Observation and Geoinfor

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a b s t r a c tLight detection and ranging (LiDAR) has become an important tool in forestry. LiDAR-derived models are mostly developed by means of multiple linear regression (MLR) after stepwise selection of predictors. An increasing interest in machine learning and evolutionary computation has recently arisen to improve regression use in LiDAR data processing. Although evolutionary machine learning has already proven to be suitable for regression, evolutionary computation may also be applied to improve parametric models such as MLR. This paper provides a hybrid approach based on joint use of MLR and a novel genetic algorithm for the estimation of the main forest stand variables. We show a comparison between our genetic approach and other common methods of selecting predictors. The results obtained from several LiDAR datasets with different pulse densities in two areas of the Iberian Peninsula indicate that genetic algorithms perform better than the other methods statistically. Preliminary studies suggest that a lack of parametric conditions in field data and possible misuse of parametric tests may be the main reasons for the better performance of the genetic algorithm. This research confirms the findings of previous studies that outline the importance of evolutionary computation in the context of LiDAR analisys of forest data, especially when the size of fieldwork datatasets is reduced.

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Section: Study Sitesmentioning

confidence: 99%

Section: Lidar Datamentioning

confidence: 99%

Section: Lidar Datamentioning

confidence: 99%

“…Only models whose parameters were significant at a given level (in our case, 5%) were taken into account (Gonzalez-Ferreiro et al, 2012).…”

Section: Best-subset Selectionmentioning

confidence: 99%

Section: Regression Modelsmentioning

confidence: 99%

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Evolutionary feature selection to estimate forest stand variables using LiDAR

García-Gutiérrez

González-Ferreiro

Santos

et al. 2014

International Journal of Applied Earth Observation and Geoinfor

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Light Detection and Ranging for Implementing Water‐Oriented Forest Management in a Semiarid Sub‐Catchment (Valencia, Spain)

Manrique‐Alba

Campo

González-Sanchis

2015

CLEAN Soil Air Water

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Diminishing rainfall interception in semiarid forests may offset reductions of available water either for ecosystem or human needs under the climate change predictions. Forest managers should be aware of this issue and develop specific hydrology‐oriented silvicultural treatments. In this sense, relating forest–water processes to detailed forest structure information, as provided by light detection and ranging (LiDAR), is essential. In this study, low‐density LiDAR data are used to characterize forest structure and throughfall water. First, LiDAR was validated as an estimator of the forest structure variables (canopy cover, canopy height, and leaf area index) in a semiarid forest, with acceptable root mean square errors (6.2% cover, 0.4 m2 m−2 LAI and 0.7 m height). Secondly, the LiDAR‐derived variables were related to the measured throughfall by means of simple and multiple regression models. The best model included canopy cover and stand height, and improved upon previous results based on field measures from r2 = 0.76–0.95. Finally, the model was used to estimate the throughfall in the whole sub‐catchment, where very low or low throughfall (<74%) was identified in 47.5% of the area. Low‐density LiDAR was proved to be useful in guiding hydrology‐oriented silviculture to increase the amount of throughfall water.

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Evaluating the sensitivity of forest structural diversity characterization to LiDAR point density

et al. 2022

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Recent expansion in data sharing has created unprecedented opportunities to explore structure-function linkages in ecosystems across spatial and temporal scales. However, characteristics of the same data product, such as resolution, can change over time or spatial locations, as protocols are adapted to new technology or conditions, which may impact the data's potential utility and accuracy for addressing end user scientific questions. The National Ecological Observatory Network (NEON) provides data products for users from 81 sites and over a planned 30-year time frame, including discrete-return light detection and ranging (LiDAR) from an airborne observation platform. LiDAR is a well-established and increasingly available remote sensing technology for measuring three-dimensional characteristics of ecosystem and landscape structure, including forest structural diversity. The LiDAR product that NEON provides can vary in point density from 2 to 25+ pt/m 2 depending on the instrument and acquisition date. We used NEON LiDAR from five forested sites to (1) identify the minimum point density at which structural diversity metrics can be robustly estimated across forested sites from different ecoclimatic zones in the United States and (2) to test the effects of variable point density on the estimation of a suite of structural diversity metrics and multivariate structural complexity types within and across forested sites. Twelve of 16 structural diversity metrics were sensitive to LiDAR point density in at least one of the five NEON forested sites. The minimum point density to reliably estimate the metrics ranged from 2.0 to 7.5 pt/m 2 , but our results indicate that point densities above 7-8 pt/m 2 should provide robust measurements of structural diversity in forests for temporal or spatial comparisons. The delineation of multivariate structural complexity types from a suite of 16 structural diversity metrics was robust within sites and across forest types for a LiDAR point density of 4 pt/m 2 and above. This study shows that different metrics of structural diversity can vary in their sensitivity to the resolution of LiDAR data and that users of these

show abstract

Estimation of stand variables in Pinus radiata D. Don plantations using different LiDAR pulse densities

Cited by 99 publications

References 38 publications

Evolutionary feature selection to estimate forest stand variables using LiDAR

Evolutionary feature selection to estimate forest stand variables using LiDAR

Light Detection and Ranging for Implementing Water‐Oriented Forest Management in a Semiarid Sub‐Catchment (Valencia, Spain)

Evaluating the sensitivity of forest structural diversity characterization to LiDAR point density

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