Spatial distribution and temporal variation of tropical mountaintop vegetation through images obtained by drones

Abstract

Modern UAS (Unmanned Aerial Vehicles) or just drones have emerged with the primary goal of producing maps and imagery with extremely high spatial resolution. The refined information provides a good opportunity to quantify the distribution of vegetation across heterogeneous landscapes, revealing an important strategy for biodiversity conservation. We investigate whether computer vision and machine learning techniques (Object-Based Image Analysis—OBIA method, associated with Random Forest classifier) are effective to classify heterogeneous vegetation arising from ultrahigh-resolution data generated by UAS images. We focus our fieldwork in a highly diverse, seasonally dry, complex mountaintop vegetation system, the campo rupestre or rupestrian grassland, located at Serra do Cipó, Espinhaço Range, Southeastern Brazil. According to our results, all classifications received general accuracy above 0.95, indicating that the methodological approach enabled the identification of subtle variations in species composition, the capture of detailed vegetation and landscape features, and the recognition of vegetation types’ phenophases. Therefore, our study demonstrated that the machine learning approach and combination between OBIA method and Random Forest classifier, generated extremely high accuracy classification, reducing the misclassified pixels, and providing valuable data for the classification of complex vegetation systems such as the campo rupestre mountaintop grassland.

Uso de drones en la agricultura de precisión

“…El trabajo reportado por Medeiros et al (2023) indica que los drones modernos han surgido con el objetivo principal de producir mapas e imágenes con una resolución espacial extremadamente alta. La información refinada proporciona una buena oportunidad para cuantificar la distribución de la vegetación a través de paisajes heterogéneos (Li et al, 2023), revelando una estrategia importante para la conservación de la biodiversidad.…”

Rainfall-Induced Shallow Landslide Recognition and Transferability Using Object-Based Image Analysis in Brazil

“…Although several studies have demonstrated the applicability of OBIA for landslide mapping in various environments, its application for landslide mapping in tropical environments in Brazil is still incipient. Brazilian studies which applied OBIA, for example, focused on urban infrastructure, coral reefs, gullies, and vegetation pattern mapping, and not on natural hazards [34][35][36][37][38]. This method considerably simulates human cognition and enables the transfer of existing knowledge into machine-executable rule sets [27].…”