Maximum likelihood methods and their application in neighbourhood models

Aparicio, Lorena Gómez; Castuera, José Manuel Ávila; Cayuela, Luis

doi:10.7818/ecos.2013.22-3.03

Cited by 3 publications

(1 citation statement)

References 36 publications

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“…Our selection of these algorithms is based on their popularity in solving classification problems, their ability to enhance weaker methods, and their widespread application in ecology, including neighborhood models [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Using UAVs and Machine Learning for Nothofagus alessandrii Species Identification in Mediterranean Forests

Cabrera-Ariza,

Peralta-Aguilera,

Henríquez-Hernández

et al. 2023

Drones

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This study explores the use of unmanned aerial vehicles (UAVs) and machine learning algorithms for the identification of Nothofagus alessandrii (ruil) species in the Mediterranean forests of Chile. The endangered nature of this species, coupled with habitat loss and environmental stressors, necessitates efficient monitoring and conservation efforts. UAVs equipped with high-resolution sensors capture orthophotos, enabling the development of classification models using supervised machine learning techniques. Three classification algorithms—Random Forest (RF), Support Vector Machine (SVM), and Maximum Likelihood (ML)—are evaluated, both at the Pixel- and Object-Based levels, across three study areas. The results reveal that RF consistently demonstrates strong classification performance, followed by SVM and ML. The choice of algorithm and training approach significantly impacts the outcomes, highlighting the importance of tailored selection based on project requirements. These findings contribute to enhancing species identification accuracy in remote sensing applications, supporting biodiversity conservation and ecological research efforts.

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Section: Introductionmentioning

confidence: 99%