Integrating rapid assessment, variable probability sampling, and machine learning to improve accuracy and consistency in mapping local spatial distribution of plant species richness.

MLA

Perng, Bo-Hao, et al. “Integrating Rapid Assessment, Variable Probability Sampling, and Machine Learning to Improve Accuracy and Consistency in Mapping Local Spatial Distribution of Plant Species Richness.” Forestry: An International Journal of Forest Research, vol. 97, no. 2, Apr. 2024, pp. 282–94. EBSCOhost, https://doi.org/10.1093/forestry/cpad041.

APA

Perng, B.-H., Lam, T. Y., Su, S.-H., Sabri, M. D. B. M., Burslem, D., Cardenas, D., Duque, Á., Ediriweera, S., Gunatilleke, N., Novotny, V., O’Brien, M. J., & Reynolds, G. (2024). Integrating rapid assessment, variable probability sampling, and machine learning to improve accuracy and consistency in mapping local spatial distribution of plant species richness. Forestry: An International Journal of Forest Research, 97(2), 282–294. https://doi.org/10.1093/forestry/cpad041

Chicago

Perng, Bo-Hao, Tzeng Yih Lam, Sheng-Hsin Su, Mohamad Danial Bin Md Sabri, David Burslem, Dairon Cardenas, Álvaro Duque, et al. 2024. “Integrating Rapid Assessment, Variable Probability Sampling, and Machine Learning to Improve Accuracy and Consistency in Mapping Local Spatial Distribution of Plant Species Richness.” Forestry: An International Journal of Forest Research 97 (2): 282–94. doi:10.1093/forestry/cpad041.