@venkateshkolluru
PhD candidate (NASA LCLUC project) 🎓earth observation🌎 landscape ecology⛰️ environmental monitoring. 🛰️ Interests: vegetation dynamics, coupled natural and human systems, and climate-vegetation interactions applying geospatial technologies.
📢 Excited to share our paper on cross-scale mapping (field --> drone --> sentinel) of grassland biomass and canopy cover across 120+ million hectares in Mongolia and Kazakhstan has been published in Remote Sensing of Environment.
@elsevierconnect.bsky.social
authors.elsevier.com/a/1meLB7qzTA...
Thanks so much @obebo.bsky.social
Thank you so much🤗
I am also thrilled to share that I will be joining the University of Alabama in Huntsville as a Research Scientist for the NASA-IMPACT project! I feel incredibly fortunate about the opportunity to be part of this fantastic team and looking forward to contributing to many interesting EO projects. 🌎🛰️
I am delighted to share that I have successfully defended my PhD dissertation! 🎓
This journey has been both challenging and rewarding. I am humbled by the contributions of countless individuals who supported and inspired me along this journey.
Coming to #AGU24? Don't miss all the exciting works from our group led by Dr Ranjeet John. Lots of great work on grassland biomass, invasive species🌱, agriculture🌽, machine learning 🖥️ and earth observation 🌎🛰️
See you all in DC! @agu.org
Could you please add me to the list if possible...thanq
https://rdcu.be/dshsa
Anyone working on livestock monitoring or modeling?
I have developed the first-ever high-resolution (1km) longitudinal (two decades) gridded livestock (🐐🐑🐎) density database for Kazakhstan. Find out more details on methods and results here: rdcu.be/dshsa
@natureportfolio.bsky.social
Please add me to the starter pack @eor-wuerzburg.bsky.social
The proposed framework when tested in Kazakhstan (KZ) revealed that ~1.14 million sq.km are undergoing vegetation degradation, with Grazing and Snow cover variability playing a dominant role in about 44% of these degraded areas.
The proposed multi-stage multi-model approach — combining TSS-RESTREND, Granger Causality and Random Forest models — enables the detection of vegetation trends, quantification of driver contributions and attribution to key climate and land use forcings at spatial/regional scales.
Are you working on vegetation trend detection and aiming to identify the potential spatiotemporal drivers influencing these trends? Check out my new paper out in @CommsEarth that introduces a Detection, Causation and Attribution (DCA) framework.
doi.org/10.1038/s432...