New publication: vegetation response to environmental variables in the mountainous forests of Western Himalaya

New publication: vegetation response to environmental variables in the mountainous forests of Western Himalaya

May 23, 2017

A recently published paper featuring Hooman Latifi and Thorsten Dahms from Dept. of Remote Sensing presents novel results on phenological behaviour of the moist deciduous forests Hymalayan foothills in India during 2013–2015 using Landsat 8 time series data. The paper has been published in International Journal of Remote Sensing, and additionally suggests a new vegetation index called the temporal normalized phenology index (TNPI) to quantify the change in trajectories of Landsat 8 OLIderived normalized difference vegetation index (NDVI) during two time steps of the vegetation growth cycle.

 

Mean NDVI values from April 2014 to June 2015 plotted for study site along with SAL tree
phenology.

 

Based on cross-validated statistics the paper concludes that  TNPI is a superior alternative for the analysis of temporal phenology cycle between two time steps of maximum and minimum vegetation growth periods. This could, in turn, reduce the requirement of large time-series remote-sensing data sets for studies on long-term vegetation phenology. The paper can be retrieved here.

Bibliography:

Khare, S., Ghosh, S.K., Latifi, H., Vijay,S., Dahms,T. 2017. Seasonal-based analysis of vegetation response to environmental variables in the mountainous forests of Western Himalaya using Landsat 8 data. International Journal of Remote Sensing 38(15), 4418-4442.

you may also like:

Science Slam in Rosenheim: Hannes Taubenböck wins again

Science Slam in Rosenheim: Hannes Taubenböck wins again

After the completely surprising win of the Science Slam in Würzburg in November 2024 (we reported about it: https://remote-sensing.org/hannes-taubenbock-represents-eorc-at-the-science-slam-and-wins/), Hannes Taubenböck was invited to the Science Slam in Rosenheim. The...

Deep learning course by Thorsten Hoeser

Deep learning course by Thorsten Hoeser

This week Thorsten Hoeser, an expert in deep learning and data science, taught AI methods in remote sensing at our International EAGLE Earth Observation MSc Program. In this special module, Thorsten covered essential topics on the cutting-edge techniques for...

New Team Member: Sofia Haag

New Team Member: Sofia Haag

Sofia Haag joined the EORC in February 2025 as a research assistant for the EO4CAM project. After completing her Bachelor's degree in Geography at the University of Heidelberg, she pursued her Master's in Applied Physical Geography at the University of Würzburg. Sofia...