Developing a site index model for P. Pinaster stands in NW Spain by combining bi-temporal ALS data and environmental data

Guerra-Hernández, Juan; Arellano-Pérez, Stéfano; González-Ferreiro, Eduardo; Pascual, Adrián; Altelarrea, Vicente Sandoval; Ruiz-González, Ana Daría; Álvarez‐González, Juan Gabriél

doi:10.1016/j.foreco.2020.118690

Cited by 32 publications

(18 citation statements)

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“…Spatially continuous information on forest 3D structure is essential to estimate AGB distribution [10]. During the past three decades, airborne laser scanning (ALS) has become an established solid method for accurately mapping key indicators for Mediterranean forests (e.g., canopy height [11], forest inventory variables [12], aboveground carbon [13], and canopy fuel characteristics [14]) at high spatial resolution and in a relatively short time compared to conventional methods. The high costs of surveying at local/regional scale using ALS technology can result in low temporal resolution of AGB estimates, narrowing the potential of ALS technology at vectorizing the estimation of AGB maps in many countries.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Accuracy of GEDI Data for Canopy Height and Aboveground Biomass Estimates in Mediterranean Forests

et al. 2021

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Global Ecosystem Dynamics Investigation (GEDI) satellite mission is expanding the spatial bounds and temporal resolution of large-scale mapping applications. Integrating the recent GEDI data into Airborne Laser Scanning (ALS)-derived estimations represents a global opportunity to update and extend forest models based on area based approaches (ABA) considering temporal and spatial dynamics. This study evaluates the effect of combining ALS-based aboveground biomass (AGB) estimates with GEDI-derived models by using temporally coincident datasets. A gradient of forest ecosystems, distributed through 21,766 km2 in the province of Badajoz (Spain), with different species and structural complexity, was used to: (i) assess the accuracy of GEDI canopy height in five Mediterranean Ecosystems and (ii) develop GEDI-based AGB models when using ALS-derived AGB estimates at GEDI footprint level. In terms of Pearson’s correlation (r) and rRMSE, the agreement between ALS and GEDI statistics on canopy height was stronger in the denser and homogeneous coniferous forest of P. pinaster and P. pinea than in sparse Quercus-dominated forests. The GEDI-derived AGB models using relative height and vertical canopy metrics yielded a model efficiency (Mef) ranging from 0.31 to 0.46, with a RMSE ranging from 14.13 to 32.16 Mg/ha and rRMSE from 38.17 to 84.74%, at GEDI footprint level by forest type. The impact of forest structure confirmed previous studies achievements, since GEDI data showed higher uncertainty in highly multilayered forests. In general, GEDI-derived models (GEDI-like Level4A) underestimated AGB over lower and higher ALS-derived AGB intervals. The proposed models could also be used to monitor biomass stocks at large-scale by using GEDI footprint level in Mediterranean areas, especially in remote and hard-to-reach areas for forest inventory. The findings from this study serve to provide an initial evaluation of GEDI data for estimating AGB in Mediterranean forest.

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Section: Introductionmentioning

confidence: 99%

Assessing the Accuracy of GEDI Data for Canopy Height and Aboveground Biomass Estimates in Mediterranean Forests

et al. 2021

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“…Only a few studies had used ecological variables to predict site productivity (Curt et al, 2001;Guerra-Hernández et al, 2021; R. P. Sharma et al, 2012); however these studies have also used climatic variables, where availability can be a limitation for extensive and remote areas.…”

Section: Resultsmentioning

confidence: 99%

“…Several age-independent SI models have been developed recently (Guerra-Hernández et al, 2021;Noordermeer et al, 2020;Solberg et al, 2019). These studies use low-density ALS data (<10 points/m 2 ) but rely on repeated data collection and an evaluation of the rate of tree growth.…”

Section: Discussionmentioning

confidence: 99%

“…For the Height derived SI model (SI_h) (Grubinger et al, 2020;Guerra-Hernández et al, 2021;Puliti et al, 2019;Tompalski et al, 2018), I extracted the canopy metrics from the normalized heights -previously processed from the high-density LiDAR point cloud ( 2016), at the plot level using the lidr package (Jean-Romain et al, 2020) in R (R Core Team, 2020).…”

Section: Height Model Developmentmentioning

confidence: 99%

“…Recent technological innovations in remote sensing offer the potential to evaluate growth and yield at a fine spatial scale over extensive areas, and with reduced cost (Hilker et al, 2008;Silva et al, 2016). Remotely sensed information has been used to improve estimates of forest productivity (Guerra-Hernández et al, 2021;Socha et al, 2017), as well as forest health, species composition (Räty et al, 2016) and forest structure (Coops, Hilker, et al, 2007;Lim et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

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Evaluating site index models derived from topography and predictive ecosystem models

Terezan¹

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Remote sensing techniques have been demonstrated to be efficient tools for producing reliable and accurate information regarding forest inventory. Airborne Laser Scanning (ALS) can be used to develop Predictive Ecosystem Mapping (PEM) models based on Digital Elevation Model (DEM) derived layers. These PEM models, and the variables that they are based on may be used to generate reliable information to predict forest growth potential using an age-independent approach. This study evaluated the efficacy of using ALS-derived attributes to infer Site Index (SI) in plantation areas up to 30 years old, and compares the results with the current Biogeoclimatic Ecosystem Classification (BEC) estimates. I used a machine learning approach and Random Forest techniques to develop an age-independent SI model (SI_pem) incorporating DEM-derived topographic layers. The resulting SI_pem model produces accurate SI estimates at a fine grain across different forest ecosystems. The model performed significantly better when compared to Site Index by Biogeoclimatic Ecosystem Classification site series (SIBEC) lookup tables. Microclimate variables associated with water process, such as Topographic Positioning Index, Diurnal Anisotropic Heating, Topographic Openness Dominance and Overland Flow Horizontal Distance had significant importance to predict SI across the three areas studied in this work.

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Stand delineation based on laser scanning data and simulated annealing

et al. 2021

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The use of airborne laser scanning (LS) is increasing in forestry. Scanning can be conducted from manned aircrafts or unmanned aerial vehicles (UAV). The scanning data are often used to calculate various attributes for small raster cells. These attributes can be used to segment the forest into homogeneous areas, called segments, micro-stands, or, like in this study, stands. Delineation of stands from raster data is equal to finding the most suitable stand number for each raster cell, which is a combinatorial optimization problem. This study tested the performance of the simulated annealing (SA) metaheuristic in the delineation of stands from grids of UAV-LS attributes. The objective function included three criteria: within-stand variation of the LS attributes, stand area, and stand shape. The purpose was to create delineations that consisted of homogeneous stands with a low number of small stands and a regular and roundish stand shape. The results showed that SA is capable of producing stand delineations that meet these criteria. However, the method tended to produce delineations where the stands often consisted of disconnected parts and the stand borders were jagged. These problems were mitigated by using a mode filter on the grid of stand numbers and giving unique numbers for all disconnected parts of a stand. Three LS attributes were used in the delineation. These attributes described the canopy height, the height of the bottom of the canopy and the variation of echo intensity within 1-m2 raster cells. Besides, a texture variable that described the spatial variation of canopy height in the proximity of a 1-m2 raster cell was found to be a useful variable. Stand delineations where the average stand area was about one hectare explained more than 80% of the variation in canopy height.

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Developing a site index model for P. Pinaster stands in NW Spain by combining bi-temporal ALS data and environmental data

Cited by 32 publications

References 64 publications

Assessing the Accuracy of GEDI Data for Canopy Height and Aboveground Biomass Estimates in Mediterranean Forests

Assessing the Accuracy of GEDI Data for Canopy Height and Aboveground Biomass Estimates in Mediterranean Forests

Evaluating site index models derived from topography and predictive ecosystem models

Stand delineation based on laser scanning data and simulated annealing

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