El presente proyecto de "Estudio de la contaminación del río Monzón y afluentes por efecto de la elaboración de clorhidrato de cocaína en la provincia de Huamalies - Huánuco", tiene como objetivos: el identificar las fuentes potenciales de contaminación a través de la cuenca del Monzón, determinar los contaminantes del río Monzón y afluentes e identificar el impacto ambiental al río Monzón y afluentes por efecto dela elaboración de clorhidrato de cocaína; considerando que los reactivos usados para la elaboración de clorhidrato de cocaína son desechados a las orillas de los ríos aledaños. El estudio se realizó muestreando las aguas de afluentes, aguas arriba y aguas bajo del río Monzón, de acuerdo a un diagnóstico previo de la situación en el valle, en dos periodos climáticos húmedo (Enero) y seco (Setiembre), en 5 puntos de muestreo los cuales son: punto A: Río...

Remote sensing is essential in precision agriculture as this approach provides high-resolution information on the soil's physical and chemical parameters for detailed decision making. Globally, technologies such as remote sensing and machine learning are increasingly being used to infer these parameters. This study evaluates soil fertility changes and compares them with previous fertilization inputs using high-resolution multispectral imagery and in situ measurements. A UAV-captured image was used to predict the spatial distribution of soil parameters, generating fourteen spectral indices and a digital surface model (DSM) from 103 soil plots across 49.83 hectares. Machine learning algorithms, including classification and regression trees (CART) and random forest (RF), modeled the soil parameters (N-ppm, P-ppm, K-ppm, OM%, and EC-mS/m). The RF model outperformed others, with R² values of...

The spatial heterogeneity of soil properties has a significant impact on crop growth, making it difficult to adopt site-specific crop management practices. Traditional laboratory-based analyses are costly, and data extrapolation for mapping soil properties using high-resolution imagery becomes a computationally expensive procedure, taking days or weeks to obtain accurate results using a desktop workstation. To overcome these challenges, cloud-based solutions such as Google Earth Engine (GEE) have been used to analyze complex data with machine learning algorithms. In this study, we explored the feasibility of designing and implementing a digital soil mapping approach in the GEE platform using high-resolution reflectance imagery derived from a thermal infrared and multispectral camera Altum (MicaSense, Seattle, WA, USA). We compared a suite of multispectral-derived soil and vegetation indi...