Abstract
This paper compares a variety of classification tree-based approaches to map 10 vegetation cover classes and a single built-up class in the Kissimmee Prairie Ecosystem, an endangered grass-shrubland landscape in south-central Florida (USA). This comparison is provided to identify an effective and replicable mapping methodology and facilitate the ongoing regional-scale management and monitoring of grass-shrubland ecosystems. Results showed that the best-performing models included environmental variables, due to the ability of these variables to help distinguish spectrally similar classes. The highest overall proportional accuracy of 81% was the result of incorporating linear spectral mixture analysis and geo-environmental variables into the classification tree.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.