Venkatesh Kolluru
@venkateshkolluru.bsky.social
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.
Interests: vegetation dynamics, coupled natural and human systems, and climate-vegetation interactions applying geospatial technologies.
Thank you so much🤗
December 8, 2024 at 7:18 PM
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. 🌎🛰️
December 8, 2024 at 6:56 PM
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. 🌎🛰️
Could you please add me to the list if possible...thanq
November 24, 2024 at 6:08 AM
Could you please add me to the list if possible...thanq
Please add me to the starter pack @eor-wuerzburg.bsky.social
November 24, 2024 at 12:56 AM
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.
sq.km
November 24, 2024 at 12:24 AM
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.
November 24, 2024 at 12:24 AM
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.