| Autor: |
Mehrotra, Sonakshi, Kumar, Anil, Roy, Arijit, Upadhyay, Priyadarshi |
| Zdroj: |
IEEE Geoscience & Remote Sensing Letters; 2023, Vol. 20, p1-3, 3p |
| Abstrakt: |
The use of semi-hypertemporal (SH) satellite data can play an important role in species differentiation based on the physiological and phenological characteristics of different plant species in a forest. Mapping of plant species is essential for addressing problems related to their management and conservation. This study has been conducted to map Acacia catechu from a heterogeneous forest area using SH images in fuzzy-based classifiers. This study has claimed that an individual sample as mean (ISM) training approach outperforms the conventional mean-based approach for selecting the training samples for fuzzy-based possibilistic $c$ means (PCM), noise clustering (NC), and modified PCM (MPCM) algorithms. Furthermore, the SH dataset is found to be better for a particular species extraction in contrast to the single date image, using both the conventional and ISM approaches. Moreover, an increasing number of images in the SH dataset produces significantly better classification outputs. This study can be used to map the endangered species with improved classification accuracy and for better management practices. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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