Remote sensing based estimation of forest biophysical variables using machine learning algorithm

Leaf Area Index (LAI), Fraction of Intercepted Photosynthetically Active Radiation (fIPAR) and forest Aboveground Biomass (AGB) are important regulatory parameters for several functions of the forest canopy. An accurate information about the spatial variability of these biophysical variables is vital to capture the variability in estimates of gross primary productivity, carbon exchange and microclimate in terrestrial ecosystems. The present study aims at developing predictive models for generating spatial distribution of LAI, fIPAR and AGB by integrating remote sensing imagery and field data using random forest (RF) regression algorithm. The study was carried out in a tropical moist deciduous forest of Uttarakhand, India. Various spectral and texture variables were derived using Sentinel-2 data of 10 April 2017. In-situ measurements of LAI, incident Photosynthetically Active Radiation (PAR) above canopy (I_o), below canopy (I), and diameter at breast height (dbh) were taken. fIPAR and AGB were calculated. RF regression algorithm was used to optimize the variables to select the best predictor variables. Three models, using only spectral variables, only texture variables and both spectral and texture variables were tested. For all three biophysical variables, the models using both spectral and texture variables gave better results. The best predictor variables were used to map the spatial distribution of LAI, fIPAR and AGB. On validation, the models were able to predict LAI with R^2=0.83, %RMSE = 13.25%, fIPAR with R^2=0.87, %RMSE = 13.24%, and AGB with R^2=0.85, %RMSE = 12.17%. The estimated biophysical parameters showed high interdependence (LAI-fIPAR R2= 0.71, LAI-AGB R^2=0.75 and fIPAR-AGB R^2= 0.74). The results showed that RF can be effectively applied to predict the spatial distribution of forest biophysical variables like LAI, fIPAR and AGB with adequate accuracy

Coupling Earth observation and eddy covariance data in light-use efficiency based model for estimation of forest productivity

The light use efficiency (LUE) approach is a well-established method for estimating gross primary productivity (GPP) over large areas using Earth observation data. The present study aims to determine maximum light use efficiency (LUEmax) values specific to the northwest Himalayan foothills of India. It also aims to estimate the spatio-temporal variability of GPP from 2001 to 2020 using remote sensing data in combination with eddy covariance data in the LUE-based model. The model was parameterized using different sets of default and calculated parameters. The study showed that the use of PFT-specific LUEmax and temperatures increased the accuracy of the model predictions. On validation, the LUE-based model predicted GPP showed R2 = 0.82 for moist deciduous and R2 = 0.83 for dry deciduous PFTs. The study revealed that with rigorous model parameterization, RS data can be used in an LUE-based model to achieve accurate spatio-temporal estimates of GPP

Spatio-temporal variability of gross primary productivity in moist and dry deciduous plant functional types of Northwest Himalayan foothills of India using temperature-greenness model

The present study aims to estimate the spatio-temporal variability of gross primary productivity (GPP) in moist and dry deciduous plant functional types (PFTs) of northwest Himalayan foothills of India using remote sensing-based Temperature-Greenness (TG) model and to study the response of GPP to environmental variables. TG model was implemented in Google Earth Engine platform using Moderate Resolution Imaging Spectroradiometer enhanced vegetation index (MOD13A2) and land surface temperature (MOD11A2) from 2001 to 2018. The mean monthly GPP ranged from 1.80 to 18.57 gCm−2day−1 in moist deciduous and from 0.20 to 12.06 gCm−2day−1 in dry deciduous PFTs. On site-scale validation with eddy covariance flux tower GPP, the modelled GPP showed R2=0.79 for moist deciduous and R2=0.77 for dry deciduous PFT. Leaf area index showed the highest correlation with the predicted GPP (r = 0.74 for moist and 0.83 for dry deciduous PFTs). The study revealed that TG model could predict the long-term forest GPP with minimum in-situ inputs