Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Prediction of Resin Production in Copal Trees (Bursera spp.) Using a Random Forest Model
Non-timber forest products (NTFPs) are essential for community development, but their enormous demand has posed a serious threat to trees growing in their natural habitat. Copal resin is one of these products, which has a great deal of religious and ceremonial significance in Mexico and around the world. Resin extraction from a tree depends on its morphological and physiological characteristics, as well as its physical and sanitary condition. In this study, a methodology was proposed for determining the yield and health status of Copal trees, and a random forest (RF) model was developed to explain their resin production based on their morphological and condition characteristics. The experiment was conducted in the Agua Escondida watershed in Puebla, Mexico. With the training data, the average accuracy of the model was 99%, with a Kappa index of 98%, which is considered an excellent level of agreement beyond chance, and with the validation data, the average accuracy was 71% and 47%, which is considered a good level of agreement beyond chance. Tree condition was the most important factor affecting resin production in Copal trees, followed by stem diameter (33 and 38 cm), height (2 and 2.5 m), and diameter of secondary branches (from 8 to 15, 22 and 32 cm).
Prediction of Resin Production in Copal Trees (Bursera spp.) Using a Random Forest Model
Non-timber forest products (NTFPs) are essential for community development, but their enormous demand has posed a serious threat to trees growing in their natural habitat. Copal resin is one of these products, which has a great deal of religious and ceremonial significance in Mexico and around the world. Resin extraction from a tree depends on its morphological and physiological characteristics, as well as its physical and sanitary condition. In this study, a methodology was proposed for determining the yield and health status of Copal trees, and a random forest (RF) model was developed to explain their resin production based on their morphological and condition characteristics. The experiment was conducted in the Agua Escondida watershed in Puebla, Mexico. With the training data, the average accuracy of the model was 99%, with a Kappa index of 98%, which is considered an excellent level of agreement beyond chance, and with the validation data, the average accuracy was 71% and 47%, which is considered a good level of agreement beyond chance. Tree condition was the most important factor affecting resin production in Copal trees, followed by stem diameter (33 and 38 cm), height (2 and 2.5 m), and diameter of secondary branches (from 8 to 15, 22 and 32 cm).
Prediction of Resin Production in Copal Trees (Bursera spp.) Using a Random Forest Model
Julio César Buendía-Espinoza (Autor:in) / Elisa del Carmen Martínez-Ochoa (Autor:in) / Rosa María García-Nuñez (Autor:in) / Selene del Carmen Arrazate-Jiménez (Autor:in) / Alejandro Sánchez-Vélez (Autor:in)
2022
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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