Abstract
Abstract. Studying deforestation has been an important topic in forestry research. Especially, canopy classification using remotely sensed data plays an essential role in monitoring tree canopy on a large scale. As remote sensing technologies advance, the quality and resolution of satellite imagery have significantly improved. Oftentimes, leveraging high-resolution imagery such as the National Agriculture Imagery Program (NAIP) imagery requires proprietary software. However, the lack of insight into the inner workings of such software and the inability of modifying its code lead many researchers towards open-source solutions. In this research, we introduce CanoClass, an open-source cross-platform canopy classification system written in Python. CanoClass utilizes the Random Forest and Extra Trees algorithms provided by scikit-learn to classify canopy using remote sensing imagery. Based on our benchmark tests, this new canopy classification system was 283 % to 464 % faster than commercial Feature Analyst, but it produced comparable results with a similarity of 87.56 % to 87.62 %.
Highlights
Forested areas play an integral role in the maintenance of both local and global environments
As improvements are made in the fields of geospatial science and remote sensing, an increasing emphasis is put on accurate forest canopy detection, among other ecological factors, for the purpose of monitoring and predicting canopy change (Franklin, 2001)
CanoClass with both Extra Trees (ET) and Random Forest (RF) classifiers outperformed Feature Analyst except for one case where, for the Lookout Mountain district, Feature Analyst was faster than CanoClass with the RF classifier by 0.04 min
Summary
Forested areas play an integral role in the maintenance of both local and global environments. They are the bulk of Earth’s carbon sequestration for mitigating anthropogenic processes (Bala et al, 2007; Platz, 2015; Reed and Kaye, 2020; Shen et al, 2020), provide natural erosion and runoff control for flooding events, which have been growing in frequency because of climate change (Benito et al, 2003; Sriwongsitanon and Taesombat, 2011), and can offer respite for urban heat islands (Wong and Yu, 2005; Rani et al, 2018; Bosch et al, 2020). With the increase of high spatial resolution data available both publicly and commercially, a need arises for implementations capable of reproducible and efficient classification schemes designed for tree canopy detection
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