Estimación de biomasa aérea de &lt;i&gt;Eucalyptus grandis&lt;/i&gt; y &lt;i&gt;Pinus&lt;/i&gt; spp. usando imágenes Sentinel1A y Sentinel2A en Colombia

Blanco, Adriana Lizeth Tovar; Salcedo, Iván Alberto Lizarazo; Eraso, Nelly Rodríguez

doi:10.14483/2256201x.14854

Cited by 4 publications

(2 citation statements)

References 42 publications

(46 reference statements)

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“…The different species could have caused differences in spectral responses of the tree canopies associated with the variation in the growth rate of each species. This fact was also observed by Blanco et al 64 . estimating biomass in Eucalyptus sp.…”

Section: Discussionsupporting

confidence: 83%

Estimating stem volume of Eucalyptus sp. and Pinus sp. plantations in Brazil, using Sentinel-1B and ALOS-2/PALSAR-2 data

Diniz

Gama

Reis

et al. 2023

J. Appl. Rem. Sens.

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.Multifrequency synthetic aperture radar (SAR) data have been applied to discriminate subtle differences in the vegetation and to better characterize its structural properties, since each SAR frequency will interact with the different sections of the vegetation canopy. In this study, our main objective was to evaluate the use of multifrequency Sentinel-1 and ALOS-2/PALSAR-2 data for stem volume estimations in Eucalyptus sp. and Pinus sp. plantations using three different machine learning algorithms: random forest (RF), support vector regression (SVR), and extreme gradient boosting (XGB). Different experiments were carried out using combinations of predictor variables derived from both SAR sensors: backscattering, polarimetric decompositions, and interferometry data, and field data considering specific models for Eucalyptus sp. and Pinus sp. and a generic model comprising all forest plantations data. The machine learning models using predictor variables derived from SAR data achieved moderately high accuracy to predict stem volume, mainly when SAR data were used in combination with stand age (Experiment iv). In the best prediction scenario (Experiment iv), the RF, SVR, and XGB models were able to explain 81.7%, 68.5%, and 81.8% [coefficient of variation (R2) values] of stem volume variability considering the generic models, respectively. Our results pointed out that the RF algorithm showed the best performance in predicting stem volume with significant good results and easier implementation in comparison with the other two algorithms (SVR and XGB).

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Section: Discussionsupporting

confidence: 83%

Estimating stem volume of Eucalyptus sp. and Pinus sp. plantations in Brazil, using Sentinel-1B and ALOS-2/PALSAR-2 data

Diniz

Gama

Reis

et al. 2023

J. Appl. Rem. Sens.

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“…Therefore, the sensitivity of SAR and optical remote sensing to AGB may be affected by climatic and geographical conditions, leading to diverse outcomes. Numerous studies found that integrating SAR with optical sensing could enhance AGB estimation accuracy to a certain degree [13,17,65]. In this study, the combination of S-1 and S-2 improved the estimation accuracy compared to S-2; compared to S-1, only the combination of S-1 and S-2 in spring or autumn could improve the estimation accuracy.…”

Section: Effect Of Seasonality On Data Selectionmentioning

confidence: 61%

Estimation of Above-Ground Biomass for Pinus densata Using Multi-Source Time Series in Shangri-La Considering Seasonal Effects

Chen,

He,

Zhang

et al. 2023

Forests

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Forest above-ground biomass (AGB) is the basis of terrestrial carbon storage estimation, and making full use of the seasonal characteristics of remote sensing imagery can improve the estimation accuracy. In this study, we used multi-source time series and sample plots with the Random Forest (RF) model to estimate the AGB. The sources included Sentinel-1 (S-1), Sentinel-2 (S-2), and the S-1 and S-2 combination (S-1S-2). Time series included single season, annual, and multi-season. This study aims to (1) explore the optimal image acquisition season to estimate AGB; (2) determine whether the ability to estimate the AGB of multi-seasonal imagery exceeded that of annual and single-season imagery; (3) discover the sensitivity of different data to AGB according to phenological conditions. The results showed that: (1) images acquired in autumn were more useful for AGB estimation than spring, summer, and winter; (2) the S-1 multi-seasonal AGB model had higher accuracy than the annual or single-season one; (3) in autumn and spring, S-1 had higher estimation accuracy than S-2, and in autumn and spring, estimation accuracy from S-1S-2 was higher than that from S-1 and S-2; (4) in 16 AGB estimation models, the best estimation accuracy was achieved by the autumn AGB model from S-1S-2 (R2 = 0.90, RMSE = 16.26 t/ha, p = 0.82, and rRMSE = 18.97). This study could be useful to identify the optimal image acquisition season for AGB estimation, thus reducing the economic cost of image acquisition and improving the estimation accuracy.

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Machine-Learning model for estimating sugarcane production at crop level

Lárraga-Altamirano,

Hernández-López,

Piedad-Rubio

et al. 2024

JTI

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Yield maps provide essential information for those who manage the field. The anticipated production data will be able to make better decisions on how resources should be used in harvesting, define market strategies and, above all, it will help evaluate treatments used on the crop. Sugar cane is the predominant crop in Huasteca Potosina, Mexico. The proposed Machine Learning model based on Random Forest Regressor integrates time series of vegetation indices extracted from Sentinel-2 images and meteorological data. The R2 and RMSE metrics (0.84 y 0.034) show the effectiveness of the model for prediction.

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Estimación de biomasa aérea de <i>Eucalyptus grandis</i> y <i>Pinus</i> spp. usando imágenes Sentinel1A y Sentinel2A en Colombia

Cited by 4 publications

References 42 publications

Estimating stem volume of Eucalyptus sp. and Pinus sp. plantations in Brazil, using Sentinel-1B and ALOS-2/PALSAR-2 data

Estimating stem volume of Eucalyptus sp. and Pinus sp. plantations in Brazil, using Sentinel-1B and ALOS-2/PALSAR-2 data

Estimation of Above-Ground Biomass for Pinus densata Using Multi-Source Time Series in Shangri-La Considering Seasonal Effects

Machine-Learning model for estimating sugarcane production at crop level

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