Canopy Height Estimation Using Sentinel Series Images through Machine Learning Models in a Mangrove Forest

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@Article{ghosh:2020:RS,

• author = "Sujit Madhab Ghosh and Mukunda Dev Behera and Somnath Paramanik",
• title = "Canopy Height Estimation Using Sentinel Series Images through Machine Learning Models in a Mangrove Forest",
• journal = "Remote Sensing",
• year = "2020",
• volume = "12",
• number = "9",
• month = "1 " # may,
• keywords = "genetic algorithms, genetic programming, symbolic regression, random forest, Sentinel-1, Sentinel-2, ICESat, Bhitarkanika",
• ISSN = "2072-4292",
• URL = "https://www.mdpi.com/2072-4292/12/9/1519",
• DOI = "doi:10.3390/rs12091519",
• abstract = "Canopy height serves as a good indicator of forest carbon content. Remote sensing-based direct estimations of canopy height are usually based on Light Detection and Ranging (LiDAR) or Synthetic Aperture Radar (SAR) interferometric data. LiDAR data is scarcely available for the Indian tropics, while Interferometric SAR data from commercial satellites are costly. High temporal decorrelation makes freely available Sentinel-1 interferometric data mostly unsuitable for tropical forests. Alternatively, other remote sensing and biophysical parameters have shown good correlation with forest canopy height. The study objective was to establish and validate a methodology by which forest canopy height can be estimated from SAR and optical remote sensing data using machine learning models i.e., Random Forest (RF) and Symbolic Regression (SR). Here, we analysed the potential of Sentinel-1 interferometric coherence and Sentinel-2 biophysical parameters to propose a new method for estimating canopy height in the study site of the Bhitarkanika wildlife sanctuary, which has mangrove forests. The results showed that interferometric coherence, and biophysical variables (Leaf Area Index (LAI) and Fraction of Vegetation Cover (FVC)) have reasonable correlation with canopy height. The RF model showed a Root Mean Squared Error (RMSE) of 1.57 m and R2 value of 0.60 between observed and predicted canopy heights; whereas, the SR model through genetic programming demonstrated better RMSE and R2 values of 1.48 and 0.62 m, respectively. The SR also established an interpretable model, which is not possible via any other machine learning algorithms. The FVC was found to be an essential variable for predicting forest canopy height. The canopy height maps correlated with ICESat-2 estimated canopy height, albeit modestly. The study demonstrated the effectiveness of Sentinel series data and the machine learning models in predicting canopy height. Therefore, in the absence of commercial and rare data sources, the methodology demonstrated here offers a plausible alternative for forest canopy height estimation.",
• notes = "also known as \cite{rs12091519}",

}

Genetic Programming entries for Sujit Madhab Ghosh Mukunda Dev Behera Somnath Paramanik

Citations