Prediction of Aboveground Biomass from Low-Density LiDAR Data: Validation over P. radiata Data from a Region North of Spain

Tojal, Leyre-Torre; Bastarrika, Aitor; Barrett, Brian; Espeso, Javier María Sánchez; López-Guede, José Manuel; Graña, Manuel

doi:10.3390/f10090819

Cited by 13 publications

(7 citation statements)

References 49 publications

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“…Finally, two regression models between ALS-height data and dbh and SOC-S were generated (in concordance with results obtained in other studies [23,62,65]. The best accuracies were obtained for linear (dbh, R 2 adj = 0.64) and potential regression models (SOC, R 2 adj = 0.81).…”

Section: Allometric and Soc Stock Estimation From Als Datasupporting

confidence: 83%

Carbon Sequestration in Carob (Ceratonia siliqua L.) Plantations under the EU Afforestation Program in Southern Spain Using Low-Density Aerial Laser Scanning (ALS) Data

Palacios-Rodríguez

Quinto

Lara-Gómez³

et al. 2022

Forests

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Climate change is one of the environmental issues of global dominance and public opinion, becoming the greatest environmental challenge and of interest to researchers. In this context, planting trees on marginal agricultural land is considered a favourable measure to alleviate climate change, as they act as carbon sinks. Aerial laser scanning (ALS) data is an emerging technology for quantitative measures of C stocks. In this study, an estimation was made of the gains of C in biomass and soil in carob (Ceratonia siliqua L.) plantations established on agricultural land in southern Spain. The average above-ground biomass (AGB) corresponded to 85.5% of the total biomass (average 34.01 kg tree−1), and the root biomass (BGB) was 14.5% (6.96 kg tree−1), with a BGB/AGB ratio of 0.20. The total SOC stock in the top 20 cm of the soil (SOC-S20) was 60.70 Mg C ha−1 underneath the tree crown and 43.63 Mg C ha−1 on the non-cover (implantation) area for the C. siliqua plantations. The allometric equations correlating the biomass fractions with the dbh and Ht as independent variables showed an adequate fit for the foliage (Wf, R2adj = 0.70), whereas the fits were weaker for the rest of the fractions (R2adj < 0.60). The individual trees were detected using colour orthophotography and the tree height was estimated from 140 crowns previously delineated using the 95th percentile ALS-metric. The precision of the adjusted models was verified by plotting the correlation between the LiDAR-predicted height (HL) and the field data (R2adj = 0.80; RMSE = 0.53 m). Following the selection of the independent variable data, a linear regression model was selected for dbh estimation (R2adj = 0.64), and a potential regression model was selected for the SOC (R2adj = 0.81). Using the segmentation process, a total of 8324 trees were outlined in the study area, with an average height of 3.81 m. The biomass C stock, comprising both above- and below-ground biomass, was 4.30 Mg C ha−1 (50.67 kg tree−1), and the SOC20-S was 37.45 Mg C ha−1. The carbon accumulation rate in the biomass was 1.94 kg C tree−1 yr−1 for the plantation period. The total C stock (W-S and SOC20-S) reached 41.75 Mg ha−1 and a total of 4091.5 Mg C for the whole plantation. Gleaned from the synergy of tree cartography and these models, the distribution maps with foreseen values of average C stocks in the planted area illustrate a mosaic of C stock patterns in the carob tree plantation.

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Section: Allometric and Soc Stock Estimation From Als Datasupporting

confidence: 83%

Carbon Sequestration in Carob (Ceratonia siliqua L.) Plantations under the EU Afforestation Program in Southern Spain Using Low-Density Aerial Laser Scanning (ALS) Data

Palacios-Rodríguez

Quinto

Lara-Gómez³

et al. 2022

Forests

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“…Finally, two regression models between ALS-height data and dbh and SOC-S were generated (in concordance with results obtained in other studies (Castaño-Díaz, et al, 2017;Tojal et al, 2019;Lara et al, 2020). The best accuracies were obtained for linear (dbh, R 2 adj=0,64) and potential regression models (SOC, R 2 adj=0,81).…”

Section: Allometric and Soc Stock Estimation From Als Datasupporting

confidence: 83%

Sistemas agroforestales a partir de técnicas de forestación en zonas agrícolas degradadas bajo el contexto de cambio climático

Quinto¹

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The main objective of this research work was to evaluate the current and future geographical environmental space of Q. ilex and P. halepensis plantations, as well as to analyze the use of agroforestry components based on afforestation techniques in degraded agricultural areas in the context of climate change in Andalusia Spain. Current survival was evaluated under current and foreseeable future climate change scenarios and by using Species Distribution Models (SDMs).For this purpose, the Geographic Information System, and the presence/absence database ofQ. ilex and P. halepensis, obtained from the Andalusian Forest Surveillance Network (RED SEDA), and the third National Forest Inventory of Spain were used. (IFN3); data on the presence and survival of afforestation with Holm oak and Aleppo pine in agricultural lands and environmental information in the form of a "raster" at a spatial resolution of 2000 and 200 m 2 were used. It was found that 25-38% of the Q. ilexandP. halepensis plantations planted between 1993 and 2000 were established in the optimal area of occurrence (probability of occurrence> 70%), but only 12,3% (Q. ilex) and 22,9% (P. halepensis) simultaneously presented an acceptable survival rate (> 50%).Furthermore, the volume of the environmental space defined by Q. ilex decreased, while that defined by P. halepensis remained constant in future climate change projections. The potential of SDMs to predict the survival rate distribution of Q. ilex and P. halepensis and to assess the future stability of each of these species has been confirmed. In the worst case, ~ 5% of Q. ilex and ~ 33% of P. halepensis in the planted area would withstand climate change.On the other hand, robust allometric models were determined to estimate the general carbon biomass and SOC stocks as a function of the height and diameter of Ceratonia siliqua trees obtained from field data. Measurements of individual trees were obtained derived from Low density ALS and dbh and SOC were also quantified, since dbh is the most reliable variable for estimating biomass; finally the total C stocks in the carob tree plantation were estimated and mapped. The values obtained for tree biomass in foliage, roots, branches, and stems for carob trees were 5,35, 17,06, 14,53 and 9,52%, respectively. The combination of allometric models allowed calculating the total C stock in the Ceratonia siliqua plantation with precision and at a lower cost than with field inventories.Finally, the landscape fragmentation processes associated with afforestation in agricultural lands between 1990 and 2018 in two localities in southern Spain (Andevalo and Guadix) were studied using the land cover databases of Andalusia (1990 and, The PatchAnalyst-ARCgis and Getis-Ord Gi analysis fragmentation metrics were calculated to quantify changes in ecosystem

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“…The suitable remote sensing variables that had a strong correlation with AGB were identified by the means of backward elimination and stepwise selection procedures [69]. Before implementing the MR, the normality of the dataset was evaluated using the Kolmogorov-Smirnov test [70].…”

Section: Methodsmentioning

confidence: 99%

Estimating Aboveground Biomass in Dense Hyrcanian Forests by the Use of Sentinel-2 Data

Moradi

Darvishsefat

Pourrahmati

et al. 2022

Forests

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Due to the challenges brought by field measurements to estimate the aboveground biomass (AGB), such as the remote locations and difficulties in walking in these areas, more accurate and cost-effective methods are required, by the use of remote sensing. In this study, Sentinel-2 data were used for estimating the AGB in pure stands of Carpinus betulus (L., common hornbeam) located in the Hyrcanian forests, northern Iran. For this purpose, the diameter at breast height (DBH) of all trees thicker than 7.5 cm was measured in 55 square plots (45 × 45 m). In situ AGB was estimated using a local volume table and the specific density of wood. To estimate the AGB from remotely sensed data, parametric and nonparametric methods, including Multiple Regression (MR), Artificial Neural Network (ANN), k-Nearest Neighbor (kNN), and Random Forest (RF), were applied to a single image of the Sentinel-2, having as a reference the estimations produced by in situ measurements and their corresponding spectral values of the original spectral (B2, B3, B4, B5, B6, B7, B8, B8a, B11, and B12) and derived synthetic (IPVI, IRECI, GEMI, GNDVI, NDVI, DVI, PSSRA, and RVI) bands. Band 6 located in the red-edge region (0.740 nm) showed the highest correlation with AGB (r = −0.723). A comparison of the machine learning methods indicated that the ANN algorithm returned the best ABG-estimating performance (%RMSE = 19.9). This study demonstrates that simple vegetation indices extracted from Sentinel-2 multispectral imagery can provide good results in the AGB estimation of C. betulus trees of the Hyrcanian forests. The approach used in this study may be extended to similar areas located in temperate forests.

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Prediction of Aboveground Biomass from Low-Density LiDAR Data: Validation over P. radiata Data from a Region North of Spain

Cited by 13 publications

References 49 publications

Carbon Sequestration in Carob (Ceratonia siliqua L.) Plantations under the EU Afforestation Program in Southern Spain Using Low-Density Aerial Laser Scanning (ALS) Data

Carbon Sequestration in Carob (Ceratonia siliqua L.) Plantations under the EU Afforestation Program in Southern Spain Using Low-Density Aerial Laser Scanning (ALS) Data

Sistemas agroforestales a partir de técnicas de forestación en zonas agrícolas degradadas bajo el contexto de cambio climático

Estimating Aboveground Biomass in Dense Hyrcanian Forests by the Use of Sentinel-2 Data

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