18 research outputs found
Spatial modelling of congruence of native biodiversity and potential hotspots of forest invasive species (FIS) in central Indian landscape
No full textCharacterization and classification of freshwater marshy wetland using synthetic aperture radar polarimetry: a case study from Loktak wetland, Northeast India
No full textAUTHORSHIP PATTERN AND COLLABORATIVE RESEARCH IN ASTROPHYSICS
No full textThe study presents the trends in authorship pattern and authors collaborative research in Astrophysics with a sample of 411 articles during the period 2008-2017. The study found single author has contributed 133 with (32.3 %), Multi authored articles are dominant i.e. 278 (67.6 %). The mean value for the overall degree of collaboration for the 2008-2017 is found to be 0.67, the collaboration index increased from 5.18 in 2008 to 8.22 in 2017 with an average of 4.00. The collaborative co-efficient for the year 2008 is 0.51 which increased gradually to 0.54 in 2017 with an average of 0.49. The most prolific author is Del Zanna, Giulio who published 15 publications followed by Spitaleri, C. published 10 publications. Without Collaboration stood first place in levels of collaboration with 133 (32.3%)
Space-Borne Sun-Induced Fluorescence:An Advanced Probe to Monitor Seasonality of Dry and Moist Tropical Forest Sites
No full textImpact of tsunami on the forest and biodiversity richness in Nicobar Islands (Andaman and Nicobar Islands), India
No full textSpatio-temporal variability of gross primary productivity in moist and dry deciduous plant functional types of Northwest Himalayan foothills of India using temperature-greenness model
No full textThe 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
Application of remote sensing-based spectral variability hypothesis to improve tree diversity estimation of seasonal tropical forest considering phenological variations
No full textGlobal 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
No full textThe 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
Coupling Earth observation and eddy covariance data in light-use efficiency based model for estimation of forest productivity
No full textThe 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.</p
