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Remote Sensing
Volume 14
Issue 23
10.3390/rs14235968
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Article

Predicting the Forest Canopy Height from LiDAR and Multi-Sensor Data Using Machine Learning over India

by
Sujit M. Ghosh
1,
Mukunda D. Behera
2,*,
Subham Kumar
2,
Pulakesh Das
3,
Ambadipudi J. Prakash
2,
Prasad K. Bhaskaran
4,
Parth S. Roy
3,
Saroj K. Barik
5,
Chockalingam Jeganathan
6,
Prashant K. Srivastava
7 and
Soumit K. Behera
5
1
Solid World DAO, Pärnu mnt 15 // Tatari tn 2, 10141 Tallinn, Estonia
2
Centre for Oceans, Rivers, Atmosphere and Land Sciences, IIT Kharagpur, Kharagpur 721302, India
3
Sustainable Landscapes and Restoration, World Resources Institute India, New Delhi 110016, India
4
Ocean Engineering and Naval Architecture, IIT Kharagpur, Kharagpur 721302, India
5
CSIR-National Botanical Research Institute, Lucknow 226001, India
6
Department of Remote Sensing, Birla Institute of Technology (BIT), Mesra, Ranchi 835215, India
7
Institute of Environment & Sustainable Development, Banaras Hindu University, Varanasi 221005, India
*
Author to whom correspondence should be addressed.
Remote Sens. 2022, 14(23), 5968; https://doi.org/10.3390/rs14235968
Submission received: 30 September 2022 / Revised: 19 November 2022 / Accepted: 21 November 2022 / Published: 25 November 2022
(This article belongs to the Special Issue Geostatistics and Spatial Data Mining for Ecological Climatology)
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Abstract

Forest canopy height estimates, at a regional scale, help understand the forest carbon storage, ecosystem processes, the development of forest management and the restoration policies to mitigate global climate change, etc. The recent availability of the NASA’s Global Ecosystem Dynamics Investigation (GEDI) LiDAR data has opened up new avenues to assess the plant canopy height at a footprint level. Here, we present a novel approach using the random forest (RF) for the wall-to-wall canopy height estimation over India’s forests (i.e., evergreen forest, deciduous forest, mixed forest, plantation, and shrubland) by employing the high-resolution top-of-the-atmosphere (TOA) reflectance and vegetation indices, the synthetic aperture radar (SAR) backscatters, the topography and tree canopy density, as the proxy variables. The variable importance plot indicated that the SAR backscatters, tree canopy density and the topography are the most influential height predictors. 33.15% of India’s forest cover demonstrated the canopy height <10 m, while 44.51% accounted for 10–20 m and 22.34% of forests demonstrated a higher canopy height (>20 m). This study advocates the importance and use of GEDI data for estimating the canopy height, preferably in data-deficit mountainous regions, where most of India’s natural forest vegetation exists.
Keywords: GEDI; vegetation type; SAR backscatters; topography; canopy height GEDI; vegetation type; SAR backscatters; topography; canopy height
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MDPI and ACS Style

Ghosh, S.M.; Behera, M.D.; Kumar, S.; Das, P.; Prakash, A.J.; Bhaskaran, P.K.; Roy, P.S.; Barik, S.K.; Jeganathan, C.; Srivastava, P.K.; et al. Predicting the Forest Canopy Height from LiDAR and Multi-Sensor Data Using Machine Learning over India. Remote Sens. 2022, 14, 5968. https://doi.org/10.3390/rs14235968

AMA Style

Ghosh SM, Behera MD, Kumar S, Das P, Prakash AJ, Bhaskaran PK, Roy PS, Barik SK, Jeganathan C, Srivastava PK, et al. Predicting the Forest Canopy Height from LiDAR and Multi-Sensor Data Using Machine Learning over India. Remote Sensing. 2022; 14(23):5968. https://doi.org/10.3390/rs14235968

Chicago/Turabian Style

Ghosh, Sujit M., Mukunda D. Behera, Subham Kumar, Pulakesh Das, Ambadipudi J. Prakash, Prasad K. Bhaskaran, Parth S. Roy, Saroj K. Barik, Chockalingam Jeganathan, Prashant K. Srivastava, and et al. 2022. "Predicting the Forest Canopy Height from LiDAR and Multi-Sensor Data Using Machine Learning over India" Remote Sensing 14, no. 23: 5968. https://doi.org/10.3390/rs14235968

APA Style

Ghosh, S. M., Behera, M. D., Kumar, S., Das, P., Prakash, A. J., Bhaskaran, P. K., Roy, P. S., Barik, S. K., Jeganathan, C., Srivastava, P. K., & Behera, S. K. (2022). Predicting the Forest Canopy Height from LiDAR and Multi-Sensor Data Using Machine Learning over India. Remote Sensing, 14(23), 5968. https://doi.org/10.3390/rs14235968

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