Examinando por Autor "Pizarro Carcausto, Samuel"

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Digital soil mapping of metals and metalloids in croplands using multiple geospatial data and machine learning, implemented in GEE, for the Peruvian Mantaro Valley
(Elsevier, 2024-03-29) Pizarro Carcausto, Samuel; Vera Vilchez, Jesús Emilio; Huamani, Joseph; Cruz, Juancarlos; Lastra, Sphyros; Solórzano Acosta, Richard; Verástegui Martínez, Patricia
Quality and safety of the soil are essential to ensure social and economic development and provides the supply of contaminant free food. With agriculture intensification, expansion of urban zones, construction of roads, and mining, some agricultural soils sites become polluted increasing environmental risks to ecosystems functions and human health. Hence the need know the spatial distribution of elements in soils, we mapped 25 elements, namely Ca, Mg, Sr, Ba, Be, K, Na, As, Sb, Se, Tl, Cd, Zn, Al, Pb, Hg, Cr, Ni, Cu, Mo, Ag, Fe, Co, Mn and V, using various geospatial datasets, such as remote sensing, climate, topography, soil data, and distance, to establish the spatial estimation models of spatial distribution trained trough machine learning model with a supervised dataset of 109 topsoil samples, into Google earth engine platform. Using R2, RMSE and MAE to assess the prediction accuracy. First Random Forest gave satisfactory results in predicting the distribution of analyzed elements in soil, being improved for some elements when adds more trees. Additionally, each element analyzed has a different combination of environmental covariates as predictor, mainly soil, climate, topographic and distance variables especially croplands close to rivers, with less importance for spectral variables. Our results suggest that is possible to identify polluted soils and improved regulations to minimize harm to environmental health and human health, for short-to-medium-term environmental risk control.
Estimation of forage biomass in oat (Avena sativa) using agronomic variables through UAV multispectral imaging
(MDPI, 2024-10-06) Urquizo Barrera, Julio Cesar; Ccopi Trucios, Dennis; Ortega Quispe, Kevin; Castañeda Tinco, Italo; Patricio Rosales, Solanch; Passuni Huayta, Jorge; Figueroa Venegas, Deyanira; Enriquez Pinedo, Lucia; Ore Aquino, Zoila; Pizarro Carcausto, Samuel
Accurate and timely estimation of oat biomass is crucial for the development of sustainable and efficient agricultural practices. This research focused on estimating and predicting forage oat biomass using UAV and agronomic variables. A Matrice 300 equipped with a multispectral camera was used for 14 flights, capturing 21 spectral indices per flight. Concurrently, agronomic data were collected at six stages synchronized with UAV flights. Data analysis involved correlations and Principal Component Analysis (PCA) to identify significant variables. Predictive models for forage biomass were developed using various machine learning techniques: linear regression, Random Forests (RFs), Support Vector Machines (SVMs), and Neural Networks (NNs). The Random Forest model showed the best performance, with a coefficient of determination R2 of 0.52 on the test set, followed by Support Vector Machines with an R2 of 0.50. Differences in root mean square error (RMSE) and mean absolute error (MAE) among the models highlighted variations in prediction accuracy. This study underscores the effectiveness of photogrammetry, UAV, and machine learning in estimating forage biomass, demonstrating that the proposed approach can provide relatively accurate estimations for this purpose.
Estimation of height and aerial biomass in Eucalyptus globulus plantations using UAV-LiDAR
(Elsevier B.V., 2024-12-22) Enriquez Pinedo, Lucía; Ortega Quispe, Kevin; Ccopi Trucios, Dennis; Urquizo Barrera, Julio; Rios Chavarría, Claudia; Pizarro Carcausto, Samuel; Matos Calderon, Diana; Patricio Rosales, Solanch; Rodríguez Cerrón, Mauro; Ore Aquino, Zoila; Paz Monge, Michel; Castañeda Tinco, Italo
The lack of precise methods for estimating forest biomass results in both economic losses and incorrect decisions in the management of forest plantations. In response to this issue, this study evaluated the effectiveness of using the DJI Zenmuse L1 LiDAR, mounted on a DJI Matrice 300 RTK UAV, to provide three-dimensional measurements of canopy structure and estimate the aboveground biomass of Eucalyptus globulus. Various LiDAR metrics were employed alongside field measurements to calibrate predictive models using multiple regression and machine learning algorithms. The results at the individual tree level show that RF is the most accurate model, with a coefficient of determination (R²) of 0.76 in the training set and 0.66 in the test set, outperforming Elastic Net (R² of 0.58 and 0.57, respectively). At the plot level, a multiple regression model achieved an R² of 0.647, highlighting LiDAR-derived metrics as key predictors. The findings revealed that the combination of LiDAR with advanced statistical techniques, such as multiple regression and Random Forest, significantly improves the accuracy of biomass estimation, surpassing traditional methods based on allometric equations. Therefore, the use of LiDAR in conjunction with machine learning represents an effective alternative for biomasss estimation, with great potential in such plantations and contribute to more sustainable exploitation of timber resources.