Tree Aboveground Carbon Mapping in an Indian Tropical Moist Deciduous Forest Using Object-Based Image Analysis and Very High Resolution Satellite Imagery

Forests’ capability to sequester and store a large amount of carbon makes it imperative to assess the carbon stocked in them. The present study aimed to map the tree aboveground carbon stock of sal (Shorea robusta) forests of Doon valley, India using object-based image analysis (OBIA) of WorldView-2, a very high resolution satellite imagery (VHRS). The study evaluated different pan-sharpening techniques for improving the spatial resolution of WorldView-2 multispectral imagery and found that the high pass filter resolution merge technique was better compared to others. OBIA was used for image segmentation and classification. It enabled the delineation of tree crowns and canopy projection area (CPA) calculation. The overall accuracy of image segmentation and classification were found to be 72.12% and 84.82% respectively. The study unveiled that there exists a strong relationship between diameter at breast height and the CPA of trees as well as CPA and tree carbon. The average forest carbon density in the study area was found to be 108 Mg ha−1. The study highlighted that OBIA of VHRS imagery coupled with field inventory can be efficiently used to quantify and map the tree carbon stock.</p

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

Simple guide to digital electronics

This book is written for all the students who want to study Digital Electronics. The circuits and the description are made very simple so that apart from technical students, whoever is interested in Digital Electronics can study this book

Application of remote sensing-based spectral variability hypothesis to improve tree diversity estimation of seasonal tropical forest considering phenological variations

Global decline in biodiversity warrants its systematic monitoring in space and time. Remote sensing derived Rao’s Q index has been proposed as a proxy for species diversity yet its scope for seasonal tropical forest is untested. The study assessed the influence of phenology on Rao’s Q index derived using multi-date Sentinel-2 NDVI to estimate tree diversity. Plot level vegetation inventory data (n = 61) was used to estimate tree diversity (Shannon-Wiener index (H')) of Nandhaur landscape in North-West Himalayan foothills. Rao’s Q index and H' showed lower correlation at the landscape level than individual forest types. Rao’s Q index based on NDVI observed higher correlation with H', especially during the leaf flushing period. NDVI-based multi-dimensional Rao’s Q index offered better performance for dry deciduous (R2 =0.69) followed by moist deciduous forest. The present approach can be used for estimating tree diversity, especially in seasonal tropical forests

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

Aboveground Forest Biomass Estimation by the Integration of TLS and ALOS PALSAR Data Using Machine Learning

Forest inventory parameters play an important role in understanding various biophysical processes of forest ecosystems. The present study aims at integrating Terrestrial Laser Scanner (TLS) and ALOS PALSAR L-band Synthetic Aperture Radar (SAR) data to assess Aboveground Biomass (AGB) in the Barkot Forest Range, Uttarakhand, India. The integration was performed to overcome the AGB saturation issue in ALOS PALSAR L-band SAR data for the high biomass density forest of the study area using 13 plots. Various parameters, namely, Gray-Level Co-Occurrence Matrix (GLCM) texture measures, Yamaguchi decomposition components, polarimetric parameters, and backscatter values of HH and HV band intensity, were derived from the ALOS SAR data. However, TLS was used to obtain the diameter at breast height (dbh) and tree height for the sample plots. A total of 23 parameters was retrieved using TLS and SAR data for integration with the LiDAR footprint. The integration was performed using Random Forest (RF) and Artificial Neural Network (ANN). The statistical measures for RF were found to be promising compared with ANN for AGB estimation. The R2 value obtained for the RF was 0.94, with an RMSE of 59.72 ton ha−1 for the predicted biomass value. The RMSE% was 15.92, while the RMSECV was 0.15. The R2 value for ANN was 0.77, with an RMSE of 98.46 ton ha−1. The RMSE% was 26.0, while the RMSECV was 0.26. RF performed better in estimating the biomass, which ranged from 122.46 to 581.89 ton ha−1, while uncertainty ranged from 15.75 to 85.14 ton ha−1. The integration of SAR and LiDAR data using machine learning shows great potential in overcoming AGB saturation of SAR data

In situ synthesis and antibacterial activity of copper nanoparticle loaded natural montmorillonite clay based on contact inhibition and ion release

Copper nanoparticle based clay composite has been synthesized by in situ reduction of a copper ammonium complex ion and characterized by different analytical instruments. The copper nanoparticles were both intercalated and adsorbed on the surface with diameters of 90% after 12 h. Cellular membrane damage permeated by direct attachment of the composite and indirect damage caused by released copper ion are the primary sources of antibacterial action. Cytotoxicity measurements showed minimal adverse effect on the two human cell lines beyond the M.B.C. value for the microorganisms studied. In the present form the clay composite shows good promise for use in therapeutic applications. (c) 2013 Elsevier B.V. All rights reserved

Neural network-based modelling for forest biomass assessment

Forest biomass is an important parameter for assessing the status of forest ecosystems. In the present study, forest biomass was assessed by integrating remotely-sensed satellite data and field inventory data using an artificial neural network (ANN) technique in Barkot forest, Uttarakhand, India. Spectral and texture variables were derived from Resourcesat-1 (RS1) LISS-III (Linear Imaging Self-Scanning Sensor) data of April 24, 2013. ANN was used for finding the relation of spectral and texture variables to field-measured biomass. The top 10 variables, namely shortwave infrared (SWIR) band reflectance, near infrared (NIR) band reflectance, normalized difference vegetation index (NDVI), difference vegetation index (DVI), green band contrast, green band variance, SWIR band contrast, NIR band dissimilarity, SWIR band second angular moment, and red band mean, were selected for generating a multiple linear regression model to predict the biomass. The predicted biomass showed a good relationship (R2 = 0.75 and root mean square error (RMSE) = 85.32 Mg ha−1) with field-measured biomass. The model was validated yielding R2 = 0.74 and RMSE = 93.41 Mg ha−1. The results showed that RS1 LISS-III satellite data have good capability to estimate forest biomass, and the ANN technique can be used to enhance the scope of biomass estimation with a minimum number of spectral and texture variables

Characterization and haemocompatibility of Aurum metallicum for its potential therapeutic application

Background: The objective of the study was to characterize homoeopathic nanomedicine Aurum metallicum and evaluate its biocompatibility, to explore its possible application as injectables. Metal-based homoeopathic medicine, Aurum metallicum, was chosen as a model drug and the haemocompatibility of the drug at three different potencies 6C, 30C, and 200C were studied to find the justification of the drug as an injectable candidate for clinical application. Methods: The model drug Aurum metallicum at the three potencies was characterized by dynamic light scattering (DLS), zeta potential, field emission scanning electron microscopy (FESEM), and energy dispersive X-ray analysis. Hemocompatibility of the homoeopathic medicine was performed by haemolysis assay. Red blood cell obtained from fresh human blood by centrifugation was incubated with Aurum metallicum. Haemoglobin release was measured using UV-vis spectrophotometer at 540 nm. Results: The DLS and FESEM studies show a decrease of particle size with increasing potency. The zeta potential values show a fairly constant value measured at an interval of 10 days. The haemolysis percentage for 6C, 30C, and 200C was 9.73%, 8.16%, and 0.73%, respectively. Conclusion: The nanomedicine Aurum metallicum was nontoxic at all doses of 6C, 30C, and 200C. The haemolytic percentage also shows that 200C is nonhemolytic, showing haemolysis <2% as per the American Society for Testing and Materials guidelines. The undertaking of larger controlled and in-depth qualitative studies is warranted