The Silent Crisis: Unravelling the complexities of biodiversity loss

In the intricate dance of life on Earth, human activities have emerged as a formidable force, reshaping landscapes, economies, and ecosystems. The Silent Crisis gigamap delves into the paradoxical interplay between human endeavours and the subtle yet profound crisis of biodiversity loss that has been set in motion. While our pursuits have ushered in remarkable progress and technological advancements, they have also triggered a silent and pervasive crisis, the consequences of which may only become apparent when biodiversity loss reaches a critical crescendo. At this pivotal juncture, humanity stands at a precipice, facing the sobering reality that urgent action and unwavering dedication to biodiversity conservation are no longer lofty ideals but imperatives for the survival of our species and the preservation of the intricate web of life that blankets our planet. This brief is an accompaniment to the gigamap and serves to explore the multifaceted dimensions of human-induced biodiversity loss, investigating its causes, manifestations, and potentially far-reaching consequences. The project adopted an interdisciplinary approach, drawing on ecological, economic, political, sociocultural, and emotional perspectives to provide a holistic understanding of the crisis of biodiversity loss. By synthesising existing research and presenting novel insights, it aims to inform policymakers, researchers, and various stakeholders by drawing a parallel between the cost of neglecting and the cost of conserving biodiversity. The urgency of the matter is emphasised, highlighting the need for a paradigm shift in societal values and policies to foster sustainable coexistence with the natural world. Our research underscores the necessity of immediate and concerted efforts to address the silent crisis of biodiversity loss, urging humanity to transcend its current trajectory and embrace a harmonious coexistence with the intricate tapestry of life that envelops our world, focusing on the lack of conversation around the subject weighing out the costs of neglecting vs conserving biodiversity. The gigamap presented here serves as a metaphorical narrative, illustrating the vastness and complexity of the system being studied to facilitate a deeper understanding and aid in the development of strategies and solutions

Monitoring cotton crop condition through synergy of optical and radar remote sensing

Synergistic use of Optical and Microwave remotely sensed data for cotton condition based on biophysical traits, indices from ground measurements and satellite derived reflectance were assessed. Regression models derived for estimating LAI, biomass and VWC using ground based indices were applied to satellite products. LAI, biomass and VWC of cotton with optical VIs as input (R2= 0.42, 0.51 and 0.52) respectively were estimated. Correlation between RVI and plant height, crop age and VWC were found between 0.4 and 0.6. Fresh biomass from RVI ranged 100 –4000 gm−2, dry biomass from NDVI ranged 50–950 gm−2 and VWC 65–85%. Correlation between VIs and RVI was found to be non- significant. A multiple linear regression model using NDVI vs. LSWI and RVI were developed to assess LAI, biomass and VWC. The model predicted LAI with R2 of 0.5 but failed to estimate biomass and VWC

Evaluation of modified Dubois model for estimating surface soil moisture using dual polarization RISAT-1 C-band SAR data

The present study focuses on the soil moisture estimation using dual polarimetric RISAT-1, C-band SAR data in HH and HV polarization. The semi empirical approach derived by Dubois and the same modified and proposed as modified Dubois model (MDM) is worked for winter wheat crop from initial vegetative to maturity stage. Surface roughness model derived roughness is replaced with ‘s’ parameter for retrieval of dielectric constant which is further used in Topp’s model to derive soil moisture. Model derived soil moisture and ground measured soil moisture demonstrates pragmatic correlation after performing linear regression. The behaviour of inversion model varies at different crop stages depending on the soil exposure and crop cover. Normalized difference vegetation index from moderate resolution imaging spectroradiometer has also been evaluated to monitor crop status. Performance of MDM is promising for early and maturity stages of crop for soil moisture estimation

Surface soil moisture estimation in bare agricultural soil using modified Dubois model for Sentinel-1 C-band SAR data

Surface soil moisture has vital role in water energy balance, climate change and agriculture mainly for crop water requirements and irrigation scheduling. Microwave remote sensing with its unique characteristics of high penetration and sensitivity towards dielectric constant, has enabled the researchers to explore various techniques for soil moisture estimation. With the launch of Sentinel-1 (A&B) Synthetic Aperture Radar (SAR) satellites, the hindrance in accessing high spatial and temporal resolution data is eliminated. The current study focuses on surface soil moisture estimation for bare agricultural fields in the semi-arid region. Field soil moisture up to 5 cm depth using HydraGo Probe sensor and surface roughness synchronizing with satellite pass dates were collected from total 102 locations spanning four dates. Volumetric and sensor-based soil moisture are well correlated with R2 = 0.85. The Modified Dubois Model (MDM) was applied to obtain the relative permittivity of the soil for the backscattering coefficient (σ◦) for VV polarization, which is used as one of the inputs in universal Topp’s model for soil moisture calculation. Model derived soil moisture is well correlated with ground-based soil moisture for the entire range of the soil moisture (0.02-0.18 m3m-3) with R2 = 0.85 and RMSE=0.005. The entire soil moisture was categorized in three soil moisture ranges to evaluate the sensitivity. The highest correlation was observed for 0.06-0.1 m3m-3 with R2 = 0.73 and RMSE=0.003 followed by 0.015-0.6 m3m-3 with R2 = 0.81 and RMSE=0.001 and 0.11-0.18 m3m-3 with R2 = 0.48 and RMSE=0.019 which is significantly low. Performance accuracy of MDM is encouraging for bare soil moisture estimation for even the lower range of surface soil moisture

Abstracts of Scientifica 2022

This book contains the abstracts of the papers presented at Scientifica 2022, Organized by the Sancheti Institute College of Physiotherapy, Pune, Maharashtra, India, held on 12–13 March 2022. This conference helps bring researchers together across the globe on one platform to help benefit the young researchers. There were six invited talks from different fields of Physiotherapy and seven panel discussions including over thirty speakers across the globe which made the conference interesting due to the diversity of topics covered during the conference. Conference Title:  Scientifica 2022Conference Date: 12–13 March 2022Conference Location: Sancheti Institute College of PhysiotherapyConference Organizer: Sancheti Institute College of Physiotherapy, Pune, Maharashtra, Indi