An efficient means to map tree plantations is needed to detect tropical land use change and evaluate reforestation projects. To analyze recent tree plantation expansion in northeastern Costa Rica, we examined the potential of combining moderate-resolution hyperspectral imagery (2005 HyMap mosaic) with multitemporal, multispectral data (Landsat) to accurately classify (1) general forest types and (2) tree plantations by species composition. Following a linear discriminant analysis to reduce data dimensionality, we compared four Random Forest classification models: hyperspectral data (HD) alone; HD plus interannual spectral metrics; HD plus a multitemporal forest regrowth classification; and all three models combined. The fourth, combined model achieved overall accuracy of 88.5%. Adding multitemporal data significantly improved classification accuracy (p < 0.0001) of all forest types, although the effect on tree plantation accuracy was modest. The hyperspectral data alone classified six species of tree plantations with 75% to 93% producer’s accuracy; adding multitemporal spectral data increased accuracy only for two species with dense canopies. Non-native tree species had higher classification accuracy overall and made up the majority of tree plantations in this landscape. Our results indicate that combining occasionally acquired hyperspectral data with widely available multitemporal satellite imagery enhances mapping and monitoring of reforestation in tropical landscapes.

Introducción. Es necesario implementar y validar tecnologías de agricultura de precisión (AP) en las regiones tropicales. Objetivo. El objetivo de este estudio fue calibrar y validar el equipo Veris MSP3 en dos terrenos distintos ubicados en la provincia de Guanacaste, Costa Rica. Materiales y métodos. Se correlacionaron los datos obtenidos por los sensores integrados en el equipo Veris MSP3 con análisis de laboratorio de muestras de suelo, y así desarrollar regresiones lineales simples o múltiples para predecir: textura del suelo, materia orgánica, nitrógeno en suelo, capacidad de intercambio catiónico total (CIC total) y pH, para obtener el modelo que mejor se adapte a cada parámetro. Resultados. Las regresiones que resultaron con los mejores modelos fueron: i) conductividad eléctrica aparente (CEa) con textura del suelo, ii) radio óptico y pendiente con materia orgánica, iii) radio óptico con N, iv) CEa con CIC total y v) pH Veris con pH en agua y KCl. El coeficiente de determinación más alto se obtuvo entre CEa y porcentaje de arena con un r2 de 0,82. El r2 para los parámetros evaluados varió de 0,28 a 0,82. Conclusión. El método de calibración utilizado obtuvo correlaciones razonablemente precisas (r2≥0,55) para las variables textura de suelo en la profundidad de 0 a 30 cm y materia orgánica. Sin embargo, para textura de suelo de 30 a 90 cm, N, CIC total y pH, se debe considerar otra metodología para la calibración, debido a que se obtuvieron correlaciones no precisas (r2≤0,55).

Isla del Caño is a marine protected area on the south Pacific coast of Costa Rica, surrounded by coral reefs and coral communities. The ecology of these coral reefs has been studied for over 20 years, but they have not been mapped. Maps are considered a great research, planning, management and monitoring tool. Medium to high resolution images (HyMap 2005 and Quickbird 2007 respectively) were processed and classified in order to test and compare their accuracy in producing a marine habitat map. Manta tow surveys were performed in the field for identification of 7 marine habitats 1. Coral community; 2. Coral reef; 3. Bed rock; 4. Sand; 5. Sand with boulders; 6. Sand with rodolyths; and 7. Deep water. The overall accuracy was slightly higher using Quickbird (87%) than using Hymap (60%), however the difference was not significant. The map produced using Quickbird was selected to represent the marine habitats of Isla del Caño. This map will help to analyze the adequate size and zoning of the marine protected area.

Estimating nitrogen (N) and the corresponding crude protein (CP) content in forage crops is essential for optimizing fertilization and livestock nutrition. However, standard methods such as the Dumas and Kjeldahl techniques are destructive, costly, and impractical for field use in certain regions of developing countries. This study evaluated four non-destructive approaches—morphometric measurements, Pantone^® color scales, smartphone-based RGB analysis (ColorDetector app), and SPAD chlorophyll readings—for predicting N and CP in Megathyrsus maximus (Mombasa grass). A total of 120 samples were collected under three nitrogen fertilization levels and assessed using linear mixed-effects models with cross-validation. Morphometric variables showed poor performance (R² < 0.01), indicating low correlation with nutrient content. Pantone-based RGB models provided slightly better predictions (R² ≈ 0.30) but were limited by subjectivity and discrete data. SPAD-based models demonstrated moderate predictive accuracy (R² ≈ 0.53; RMSE ≈ 0.46%). The highest accuracy was achieved with smartphone-derived RGB data, where full RGB models reached R² = 0.60 and RMSE = 0.45%. Based on these results, a practical green color scale was developed from RGB values to support real-time, in-field nitrogen and crude protein assessment. This study highlights smartphone imaging as a scalable, low-cost, and accurate tool for non-destructive estimation of nitrogen and crude protein in tropical forages, offering an accessible alternative to laboratory methods for producers and field technicians.