Detecting the Distribution of Callery Pear (<i>Pyrus calleryana</i>) in an Urban U.S. Landscape Using High Spatial Resolution Satellite Imagery and Machine Learning

Detecting the Distribution of Callery Pear (<i>Pyrus calleryana</i>) in an Urban U.S. Landscape Using High Spatial Resolution Satellite Imagery and Machine Learning

Using Planetscope imagery, we trained a random forest model to detect Callery pear (<i>Pyrus calleryana</i>) throughout a diverse urban landscape in Columbia, Missouri. The random forest model had a classification accuracy of 89.78%, a recall score of 0.693, and an F1 score of 0.819. The...

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Bibliographic Details
Main Authors: Justin Krohn, Hong He, Timothy C. Matisziw, Lauren S. Pile Knapp, Jacob S. Fraser, Michael Sunde
Format: Article
Language:English
Published: MDPI AG 2025-04-01
Series:Remote Sensing
Subjects:
<i>Pyrus calleryana</i> Decne
digital image processing
spatial clustering
species inventory
Planetscope
Online Access:https://www.mdpi.com/2072-4292/17/8/1453
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