Características morfológico-funcionales, diversidad arbórea, tasa de crecimiento y de secuestro de carbono en especies y ecosistemas de Polylepis del sur de Ecuador

The management and identification of forest species in a city are essential tasks for current administrations, particularly in planning urban green spaces. However, the cost and time required are typically high. This study evaluates the potential of RGB point clouds captured by unnamed aerial vehicles (UAVs) for automating tree species classification. A dataset of 809 trees (crowns) for eight species was analyzed using a random forest classifier and deep learning with PointNet and PointNet++. In the first case, eleven variables such as the normalized red–blue difference index (NRBDI), intensity, brightness (BI), Green Leaf Index (GLI), points density (normalized), and height (maximum and percentiles 10, 50, and 90), produced the highest reliability values, with an overall accuracy of 0.70 and a Kappa index of 0.65. In the second case, the PointNet model had an overall accuracy of 0.62, and 0.64 with PointNet++; using the features Z, red, green, blue, NRBDI, intensity, and BI. Likewise, there was a high accuracy in the identification of the species Populus alba L., and Melaleuca armillaris (Sol. ex Gaertn.) Sm. This work contributes to a cost-effective workflow for urban tree monitoring using UAV data, comparing classical machine learning with deep learning approaches and analyzing the trade-offs.

Building and updating tree inventories is a challenging task for city administrators, requiring significant costs and the expertise of tree identification specialists. In Ecuador, only the Trees Inventory of Cuenca (TIC) contains this information, geolocated and integrated with the taxonomy, origin, leaf, and crown structure, phenological problems, and tree images taken with smartphones of each tree. From this dataset, we selected the fourteen classes with the most information and used the images to train a model, using a Transfer Learning approach, that could be deployed on mobile devices. Our results showed that the model based on ResNet V2 101 performed best, achieving an accuracy of 0.83 and kappa of 0.81 using the TensorFlow Lite interpreter, performing better results using the original model, with an accuracy and kappa of 0.912 and 0.905, respectively. The classes with the best performance were Ramo de novia, Sauce, and Cepillo blanco, which had the highest values of Precision, Recall, and F1-Score. The classes Eucalipto, Capuli, and Urapan were the most difficult to classify. Our study provides a model that can be deployed on Android smartphones, being the beginning of future implementations.

Drones en espacios urbanos: Caso de estudio en parques, jardines y patrimonio edificado de Cuenca

La implementación de proyectos de transporte mejora el acceso y movilidad urbana, influenciando las decisiones de localización de las personas y las empresas. Así también, genera impactos en el entorno, los cuales pueden ser positivos o negativos. Entre los posibles impactos, se destacan las fluctuaciones en el valor del suelo. El objetivo de este artículo es relatar el proceso y los resultados de un estudio exploratorio realizado para recopilar datos acerca de los precios de oferta de venta y de alquiler de propiedades de uso residencial y comercial, en función de la distancia a un nuevo sistema de transporte de tranvía en Cuenca, Ecuador. Los datos se levantaron en noviembre de 2017 utilizando la herramienta Kobo Toolbox y llamadas telefónicas, luego se analizaron a través del modelo de precios hedónicos. Los resultados mostraron que los precios de alquiler de las viviendas aumentan a medida que su ubicación se aleja del eje principal del sistema (aún en construcción al momento de la toma de datos) y que, por el contrario, los arriendos para propiedades comerciales aumentan cuando hay con una mayor proximidad al eje. Con respecto al precio de venta de las propiedades residenciales o comerciales en función de esta distancia, no hubo evidencia de cambios. Estos resultados pueden deberse a las obras civiles, sin embargo, constituyen una interesante primera aproximación a la temática que deberá ser contrastada con estudios históricos y futuros (cuando el tranvía se encuentre operando).

GEOBIA is an alternative to create and update land cover maps. In this work we assessed the combination of geographic datasets of the Cajas National Park (Ecuador) to detect which is the appropriate dataset-algorithm combination for the classification tasks in the Ecuadorian Andean region. The datasets included high resolution data as photogrammetric orthomosaic, DEM and derivated slope. These data were compared with free Sentinel imagery to classify natural land covers. We evaluated two aspects of the classification problem: the appropriate algorithm and the dataset combination. We evaluated SMO, C4.5 and Random Forest algorithms for the selection of attributes and classification of objects. The best results of kappa in the comparison of algorithms of classification were obtained with SMO (0.8182) and Random Forest (0.8117). In the evaluation of datasets the kappa values of the photogrammetry orthomosaic and the combination of Sentinel 1 and 2 have similar values using the C4.5 algorithm.