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Your search keyword '"Mahajan, Seema"' showing total 4 results
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4 results on '"Mahajan, Seema"'

3. Cloud detection methodologies: variants and development—a review

Author

Mahajan, Seema and Fataniya, Bhavin

Abstract

Cloud detection is an essential and important process in satellite remote sensing. Researchers proposed various methods for cloud detection. This paper reviews recent literature (2004–2018) on cloud detection. Literature reported various techniques to detect the cloud using remote-sensing satellite imagery. Researchers explored various forms of Cloud detection like Cloud/No cloud, Snow/Cloud, and Thin Cloud/Thick Cloud using various approaches of machine learning and classical algorithms. Machine learning methods learn from training data and classical algorithm approaches are implemented using a threshold of different image parameters. Threshold-based methods have poor universality as the values change as per the location. Validation on ground-based estimates is not included in many models. The hybrid approach using machine learning, physical parameter retrieval, and ground-based validation is recommended for model improvement.

Published
2024
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4. Estimation of above-ground biomass and delineation of vegetation of tropical forests using EOS-04 data.

Author

Das, Anup K., Patnaik, C., Maity, Saroj, Praveen, M. S. S., Reddy, R. Suraj, Rajashekar, G., Chaube, Nilima R., Mahajan, Seema, Jain, Yashraj, Bhavsar, Dhruval, Chakraborty, Kasturi, and Putrevu, Deepak

Subjects
*TROPICAL forests, *BIOMASS estimation, *MANGROVE plants, *TROPICAL dry forests, *FOREST plants, *FOREST biomass, *BIOMASS conversion
Abstract

The C-band SAR on-board EOS-04 mission provides unique opportunities to characterize forest vegetation through its sensitiveness to vegetation structure and allweather imaging capability over regions of perpetual cloud cover. The present study has brought out the applications of EOS-04 data for estimation of aboveground biomass (AGB) of tropical deciduous forests and scrublands, mapping of forest cover and delineation of mangroves vegetation. The study suggested that EOS04 data can be used for mapping AGB of tropical scrublands and low density forests of AGB ≤ 80 t/ha. The overall RMSE for all vegetation with AGB ≤ 80 t/ha was 15.3 t/ha (R² – 0.49). It was shown that the integration of EOS-04 and Sentinel-2 data improved AGB estimates across biomass ranges of 0–245 t/ha (RMSE – 21.60 t/ha and 0.81). EOS-04 data was also found to be useful for the delineation of mangroves and forest vegetation using machine-learning algorithms. The study supports operational use of EOS-04 data for estimation of AGB over low biomass tropical forests and scrublands. [ABSTRACT FROM AUTHOR]

Published
2024
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