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blog

letters

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2021

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projects

Fix the Jet

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Make images and figures colorblind friendly by swapping their colormaps

publications

Satellite-based vegetation optical depth as an indicator of drought-driven tree mortality

Published in Remote Sensing of Environment, 2019

This paper is about developing a scalable plant drought stress indicator using vegetation optical depth.

Recommended citation: Rao, K., Anderegg, W.R.L., Sala, A., Martínez-Vilalta, J. & Konings, A.G. (2019). Satellite-based vegetation optical depth as an indicator of drought-driven tree mortality. Remote Sens. Environ., 227, 125–136. https://www.sciencedirect.com/science/article/pii/S0034425719301208

Drainage canals in Southeast Asian peatlands increase carbon emissions

Published in AGU Advances, 2021

This paper presents a novel convolutional neural network to detect canals and study the effect of canals on land subsidence.

Recommended citation: Dadap, N. C., Hoyt, A. M., Cobb, A. R., Oner, D., Kozinski, M., Fua, P. V., Rao, K., Harvey, C. F., & Konings, A. G. (2021). Drainage Canals in Southeast Asian Peatlands Increase Carbon Emissions. AGU Advances, 2(1), e2020AV000321. https://doi.org/10.1029/2020AV000321 https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020AV000321

Interannual Variations of Vegetation Optical Depth Are Due to Both Water Stress and Biomass Changes

Published in Geophysical Research Letters, 2021

This paper re-examines the commonly held assumption that VOD interannual variations are proportional to interannual variations in biomass, and shows that the assumption for the most part, is invalid.

Recommended citation: Konings, A. G., Holtzman, N. M., Rao, K., Xu, L., & Saatchi, S. S. (2021). Interannual Variations of Vegetation Optical Depth are Due to Both Water Stress and Biomass Changes. Geophysical Research Letters, 48(16), e2021GL095267. https://doi.org/10.1029/2021gl095267 https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2021GL095267

DamageMap: A post-wildfire damaged buildings classifier

Published in International Journal of Disaster Risk Reduction, 2021

This paper presents an AI-based method to classify damaged buildings using post-wildfire images only.

Recommended citation: Galanis, M., Rao, K., Yao, X., Tsai, Y.-L., Ventura, J., & Fricker, G. A. (2021). DamageMap: A post-wildfire damaged buildings classifier. International Journal of Disaster Risk Reduction, 102540. https://doi.org/10.1016/j.ijdrr.2021.102540 https://www.sciencedirect.com/science/article/pii/S221242092100501X

talks

Stormwater drainage

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This talk presented retrofitting measures for the storwater drainage system of the IIT Madras campus. The 650-acre campus’ stormwater drainage network is more than 50 years old fails frequently during big storms. The talk focussed on presenting a 3-phase expansion plan to include-

  1. New culverts at frequently-flooded areas with high traffic
  2. Stormwater drainage basin to control overflow from the campus lake
  3. Sustainable stormwater drainage elements such as swales, greenroofs, etc.