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

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...

Full description

Saved in:
Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2022-12, Vol.14 (23), p.5968
Main Authors: Ghosh, Sujit M., Behera, Mukunda D., Kumar, Subham, Das, Pulakesh, Prakash, Ambadipudi J., Bhaskaran, Prasad K., Roy, Parth S., Barik, Saroj K., Jeganathan, Chockalingam, Srivastava, Prashant K., Behera, Soumit K.
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Backscattering
Bias
Biomass
Canopies
canopy height
Carbon sequestration
Climate change
Coniferous forests
Deciduous forests
Density
Ecosystem dynamics
Estimation
Forest management
GEDI
Global climate
Interferometry
Lasers
Lidar
Machine learning
Mixed forests
Mountain regions
Neural networks
Regional development
Remote sensing
SAR backscatters
Satellites
Sensors
Synthetic aperture radar
Topography
Vegetation
Vegetation index
vegetation type
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 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.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs14235968