Although studies on species-level classification and mapping using multisource data and machine learning approaches are plenty, the use of data with ideal placement of central wavelength and bandwidth at appropriate spatial resolution, for the classification of mangrove species is underreported. The species composition of a mangrove forest has been estimated utilising the red-edge spectral bands and chlorophyll absorption information from AVIRIS-NG and Sentinel-2 data. In this study, three dominant species, Heritiera fomes, Excoecaria agallocha and Avicennia officinalis, have been classified using the random forest (RF) model for a mangrove forest in Bhitarkanika Wildlife Sanctuary, India. Various combinations of reflectance/backscatter bands and vegetation indices derived from Sentinel-2, AVIRIS-NG, and Sentinel-1 were used for species-level discrimination and mapping. The RF model showed maximum accuracy using Sentinel-2, followed by the AVIRIS-NG, in discriminating three dominant species and two mixed compositions. This study indicates the potential of Sentinel-2 data for discriminating various mangrove species owing to the appropriate placement of central wavelength and bandwidth in Sentinel-2 at ≥10 m spatial resolution. The variable importance plots proved that species-level classification could be attempted using red edge and chlorophyll absorption information. This study has wider applicability in other mangrove forests around the world.
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.
The west part of Ganga River Basin (WGRB) has experienced continuous land transformation since the Indus Valley Civilisation shifted from the Indus basin to the Ganga basin. Particularly in the last few decades the land transformation has increased many-folds due to the changing climate and rapid increase in population. In this paper, we assessed land transformation and associated degradation in the WGRB based on the forest cover land use (FCLU) mapping and residual trend analysis (RTA). The FCLU maps for 1975 and 2010 were generated using 216 Landsat satellite images and validated using 1509 ground points. We mapped 29 forest and 18 non-forest types and estimated a total loss of 5571 km 2 forest cover and expansion in settlement areas (5396 km 2 ). Other major changes mapped include a decrease in wetlands and water bodies, while an increase in agriculture and barren lands with an overall mapping accuracy of 85.3% (kappa, 0.82) and 88.43% (kappa, 0.84) for 1975 and 2010, respectively. We also performed the RTA analysis using GIMMS-NDVI3g to identify areas of significant negative vegetative photosynthetic change as an indicator for land degradation. All the RTA models showed monotonic nature of the residual trends and resulted as moderately positive but highly significant (P<0.001). Land degradation in the form of barren land accompanied by a decline in vegetation quality and coverage was found prominent in the basin with a possibility of an accelerated rate of land degradation in future due to the rapid loss of permanent forest cover.
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