Machine Learning Approach for LiDAR-based Tree Species Classification in Forest Ecosystem Mapping

Authors

  • Gudimilla Pallavi, Gurrala Nagini, Katukuri Ranjith Reddy, Kolipaka Tony Babu, Theegala Pramatha, Kondra Chnadrabose

Keywords:

Keywords: Tree species classification, Forest mapping, Machine Learning, LiDAR Data, Logistic Regression.

Abstract

Accurate tree species classification in forest ecosystems is crucial for biodiversity conservation, forestmanagement, and ecological research. Traditionally, forests have been mapped through labour-intensivefield surveys and the visual interpretation of aerial images, methods prone to human error andinefficiencies. Studies show that error rates in manual mapping can exceed 15%, with variability inexpertise and limited scalability contributing to inconsistencies

References

Colgan, M. S., Baldeck, C. A., Féret, J. B., and Asner, G. P., "Mapping savanna tree species at ecosystem scales using support vector machine classification and BRDF correction on airborne hyperspectral and LiDAR data," Remote Sensing, 3462-3480.

George, R., Padalia, H., and Kushwaha, S. P. S., "Forest tree species discrimination in western Himalaya using EO-cedar cypresscedar 6897989.7%cypress 86 68288.8%precision88.9% 89.6% avg. f-value 89.3%classification resultrecallactual85.3 91.4 89.5 91.6 85.9 80.085.090.095.00 3 3 6 6 9 9 12 12 15accuracy(%)distance(m)Classification accuracy - distance," International Journal of Applied Earth observation and Geoinformation,

-149.

Downloads

Published

2025-01-02

How to Cite

Gudimilla Pallavi, Gurrala Nagini, Katukuri Ranjith Reddy, Kolipaka Tony Babu, Theegala Pramatha, Kondra Chnadrabose. (2025). Machine Learning Approach for LiDAR-based Tree Species Classification in Forest Ecosystem Mapping. Journal of Computational Analysis and Applications (JoCAAA), 34(4), 22–29. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/2269

Issue

Section

Articles

Similar Articles

1 2 3 4 5 6 7 8 9 10 > >> 

You may also start an advanced similarity search for this article.