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26 May 2022 Assessing the niche of Rhododendron arboreum using entropy and machine learning algorithms: role of atmospheric, ecological, and hydrological variables
Akash Anand, Prashant K. Srivastava, Prem Chandra Pandey, Mohammed L. Khan, Mukund Dev Behera
Author Affiliations +
Abstract

Species distribution models (SDMs) have been used extensively in the field of landscape ecology and conservation biology since its origin in the late 1980s. But there is still a void for a universal modeling approach for SDMs. With recent advancements in satellite data and machine learning algorithms, the prediction of species occurrence is more accurate and realistic. Presently, four machine learning and regression-based algorithms, namely, generalized linear model, maximum entropy, boosted regression tree, and random forest (RF) are used to model the geographical distribution of Rhododendron arboreum, which is economically and medicinally important species found in the fragile ecosystem of Himalayas. To establish complex relation between the occurrence data and regional climatic and land use parameters, several satellite products, namely, MODIS, Sentinel-5p, GPM, ECOSTRESS, and shuttle radar topography mission (SRTM), are acquired and used as predictor variables to the different SDM algorithms. The performance evaluation has been conducted using the area under curve (AUC), which showed the best result for Maxent (AUC  =  0.871) and poor result was observed for RF (AUC  =  0.755) among all. The overall prediction confirmed the distribution of Rhododendron arboreum in the mid to high altitudes of central and southern parts of the Garhwal Division. We provide crucial evidence that combining multisatellite products using machine learning algorithms can provide a much better understanding of species distribution that can eventually help the researchers and policymakers to take the necessary step toward its conservation.

© 2022 Society of Photo-Optical Instrumentation Engineers (SPIE) 1931-3195/2022/$28.00 © 2022 SPIE
Akash Anand, Prashant K. Srivastava, Prem Chandra Pandey, Mohammed L. Khan, and Mukund Dev Behera "Assessing the niche of Rhododendron arboreum using entropy and machine learning algorithms: role of atmospheric, ecological, and hydrological variables," Journal of Applied Remote Sensing 16(4), 042402 (26 May 2022). https://doi.org/10.1117/1.JRS.16.042402
Received: 30 June 2021; Accepted: 7 January 2022; Published: 26 May 2022
JOURNAL ARTICLE
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KEYWORDS
Data modeling

Machine learning

Atmospheric modeling

Climatology

Satellites

Climate change

Performance modeling

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