Stand Characterization of Eucalyptus spp. Plantations in Uruguay Using Airborne Lidar Scanner Technology

Hirigoyen, Andrés; Varo-Martínez, Ma Ángeles; Rachid-Casnati, Cecilia; Franco, Jorge; Navarro-Cerrillo, Rafael M.

doi:10.3390/rs12233947

Cited by 6 publications

(11 citation statements)

References 39 publications

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“…Among these variables, the height based metric zq95 and zmax emerged as the most critical features in the RF model, and these were consistent variables found in other studies in predicting AGB [74][75][76]. These variables significantly contributed to reducing errors and enhancing the purity of decision trees within RF models compared to other predictors [77,78]. The other LiDAR metrics, such as density and crown-based predictor variables, were found to be less essential variables in predicting AGB using the RF model.…”

Section: Discussionsupporting

confidence: 79%

Estimation of Above-Ground Forest Biomass in Nepal by the Use of Airborne LiDAR, and Forest Inventory Data

KC,

Liu,

Saud

et al. 2024

Land

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Forests play a significant role in sequestering carbon and regulating the global carbon and energy cycles. Accurately estimating forest biomass is crucial for understanding carbon stock and sequestration, forest degradation, and climate change mitigation. This study was conducted to estimate above-ground biomass (AGB) and compare the accuracy of the AGB estimating models using LiDAR (light detection and ranging) data and forest inventory data in the central Terai region of Nepal. Airborne LiDAR data were collected in 2021 and made available by Nepal Ban Nigam Limited, Government of Nepal. Thirty-two metrics derived from the laser-scanned LiDAR point cloud data were used as predictor variables (independent variables), while the AGB calculated from field data at the plot level served as the response variable (dependent variable). The predictor variables in this study were LiDAR-based height and canopy metrics. Two statistical methods, the stepwise linear regression (LR) and the random forest (RF) models, were used to estimate forest AGB. The output was an accurate map of AGB for each model. The RF method demonstrated better precision compared to the stepwise LR model, as the R2 metric increased from 0.65 to 0.85, while the RMSE values decreased correspondingly from 105.88 to 60.9 ton/ha. The estimated AGB density varies from 0 to 446 ton/ha among the sample plots. This study revealed that the height-based LiDAR metrics, such as height percentile or maximum height, can accurately and precisely predict AGB quantities in tropical forests. Consequently, we confidently assert that substantial potential exists to monitor AGB levels in forests effectively by employing airborne LiDAR technology in combination with field inventory data.

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Section: Discussionsupporting

confidence: 79%

Estimation of Above-Ground Forest Biomass in Nepal by the Use of Airborne LiDAR, and Forest Inventory Data

KC,

Liu,

Saud

et al. 2024

Land

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“…Simple linear regression related observed and predicted values were evaluated using the R 2 to assess the models selected [34]. After the best model was selected, it was validated using one cross-validation technique [17,35,36]. The R 2 , RMSE and Bias values were calculated for each fit model and were averaged to obtain the R 2 , RMSE and Bias from the cross-validation, which were compared with the values obtained for the model selected [34].…”

Section: Model Assessment and Validationmentioning

confidence: 99%

“…In a study conducted in Uruguay, ALS metrics have been used to improve inventories of forest stands of Eucalyptus spp. [17].…”

Section: Introductionmentioning

confidence: 99%

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Site Index Estimation Using Airborne Laser Scanner Data in Eucalyptus dunnii Maide Stands in Uruguay

Rizzo-Martín

Hirigoyen

Arthus-Bacovich

et al. 2023

Forests

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Intensive silviculture demands new inventory tools for better forest management and planning. Airborne laser scanning (ALS) was shown to be one of the best alternatives for high-precision inventories applied to productive plantations. The aim of this study was to generate multiple stand-scale maps of the site index (SI) using ALS data in the intensive silviculture of Eucalyptus dunnii Maide plantations in Uruguay. Forty-three plots (314.16 m3) were established in intensive E. dunnii plantations in the departments of Río Negro and Paysandú (Uruguay). ALS data were obtained for an area of 1995 ha. Linear and Random Forest models were fitted to estimate the height and site index, and OrpheoToolBox (OTB) software was used for stand segmentation. Linear models for dominant height (DH) estimation had a better fit (R2 = 0.84, RMSE = 0.94 m, MAPE = 0.04, Bias = 0.002) than the Random Forest (R2 = 0.85, RMSE = 1.27 m, MAPE = 7.20, Bias=−0.173) model when including only the 99th percentile metric. The coefficient between RMSE values of the cross-validation and RMSE of the model had a higher value for the linear model (0.93) than the Random Forest (0.75). The SI was estimated by applying the RF model, which included the ALS metrics corresponding to the 99th height percentile and the 80th height bicentile (R2 = 0.65; RMSE = 1.62 m). OTB segmentation made it possible to define a minimum segment size of 2.03 ha (spatial radius = 30, range radius = 1 and minimum region size = 64). This study provides a new tool for better forest management and promotes the need for further progress in the application of ALS data in the intensive silviculture of Eucalyptus spp. plantations in Uruguay.

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“…Only above ground carbon (AGC) was considered, assuming that in short-rotation plantations of Eucalyptus, the soil organic C does not have significant variations [30]. The aboveground biomass (AGB) required to calculate AGC was obtained using a compatible system of equations developed by Hirigoyen et al (2020) [32] as functions of dg and Ho (Table 1). The rates of change in total AGB stocks were estimated by calculating the expected annual growth of the stand.…”

Section: Carbon Sequestration and Release Assumptionsmentioning

confidence: 99%

Use of Optimization Modeling to Assess the Effect of Timber and Carbon Pricing on Harvest Scheduling, Carbon Sequestration, and Net Present Value of Eucalyptus Plantations

Hirigoyen

Acuña

Rachid-Casnati

et al. 2021

Forests

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Quantifying the impact of carbon (C) and timber prices on harvest scheduling and economic returns is essential to define strategies for the sustainable management of short-rotation plantations so that they can provide timber products and contribute to C sequestration. In this paper, we present a mixed-integer linear programming model that optimizes harvest scheduling at the forest level, C sequestration, and Net Present Value (NPV) over a planning period of up to 15 years. The model included revenue from the sale of timber (pulplogs) and credits from the net C sequestered during the life of the stands. In addition, plantation establishment, management, harvesting, and transportation costs were included in the analysis. The study area comprised 88 Eucalyptus grandis W. Hill and Eucalyptus dunnii Maiden stands located in Uruguay, totaling a forest area of nearly 1,882 ha. The study investigated the impact of C and timber prices on NPV, harvest schedules, stands’ harvest age, timber flows to customers, and C sequestered per period. The maximum NPV among all the scenarios evaluated (USD 7.53 M) was calculated for a C price of 30 USD t−1, an interest rate of 6%, and a timber price of 75 USD m−3. This was USD 2.14 M higher than the scenario with the same parameters but that included only revenue from timber. C prices also impacted stands’ harvest age, C sequestration, and timber flows delivered to end customers. On average, in scenarios that included C prices, timber flows and C sequestration increased by 15.4 and 12.1%, respectively, when C price increased from 5 to 30 USD t−1. These results demonstrate that harvest scheduling, harvest age, and NPV are very sensitive to C and timber, and that the best economic returns are obtained when the stands are managed to maximize timber production and C sequestration.

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Stand Characterization of Eucalyptus spp. Plantations in Uruguay Using Airborne Lidar Scanner Technology

Cited by 6 publications

References 39 publications

Estimation of Above-Ground Forest Biomass in Nepal by the Use of Airborne LiDAR, and Forest Inventory Data

Estimation of Above-Ground Forest Biomass in Nepal by the Use of Airborne LiDAR, and Forest Inventory Data

Site Index Estimation Using Airborne Laser Scanner Data in Eucalyptus dunnii Maide Stands in Uruguay

Use of Optimization Modeling to Assess the Effect of Timber and Carbon Pricing on Harvest Scheduling, Carbon Sequestration, and Net Present Value of Eucalyptus Plantations

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