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

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.

Mapping Forest Height and Aboveground Biomass by Integrating ICESat‐2, Sentinel‐1 and Sentinel‐2 Data Using Random Forest Algorithm in Northwest Himalayan Foothills of India

“…Studies have demonstrated that the use of multi‐sensor data is better than single sensor data for accurate prediction of forest biomass (Li, Li, et al., 2020; Liu et al., 2019). However, improved methods like machine learning algorithms (MLAs), are also necessary to integrate the multi‐source data (Ghosh et al., 2020; Koch, 2010; Li, Li, et al., 2020, Li, Niu, et al., 2020; Srinet et al., 2019; Vasudeva et al., 2021) for improved estimation of forest AGB (Dang et al., 2019; Dhanda et al., 2017; Pandit et al., 2018). Hence, it is in this context, the present study attempts to map the forest canopy height by integrating ICESat‐2 and Sentinel‐1 data and investigate the effect of integrating forest canopy height information with Sentinel‐2 data‐derived spectral variables on the prediction of spatial distribution of forest AGB.…”

Estimation of the Canopy Height Model From Multispectral Satellite Imagery With Convolutional Neural Networks

“…The same height data source was mentioned in [30]. In [31], they used Sentinel-2 images that were resampled to a 20 m pixel size to predict Mangrove forest canopy height. Other studies involving Sentinel-2 data are reported in [32]- [34].…”

Measuring Vegetation Heights and Their Seasonal Changes in the Western Namibian Savanna Using Spaceborne Lidars

“…The use of field measurements to estimate canopy height is not feasible at regional or global scales, as it is often too expensive and time-consuming. Active remote sensing methods such as Light Detection and Ranging (lidar) and Synthetic Aperture Radar (SAR) combined with optical remote sensing data [5][6][7][8][9][10][11][12] have shown their effectiveness in measuring vegetation height and estimating AGB at the regional scale. Airborne and terrestrial lidar are widely used as a reliable technique to accurately map canopy height over small-to-medium scales (10 s of km 2 ) [13][14][15].…”