A recently publisher 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… Read More


Our new publication “Open-access and open-source for remote sensing training in ecology” lead by Duccio Rocchini is now online covering the potential of open-access and open-source within training Earth Observation applications in other disciplines such as ecology. It is related to the special issue on remote sensing training for ecology and conservation published earlier this… Read More


Our new publication about the drivers of Asian elephant (Elephas maximus) abundance and distribution has been published in Biodiversity and Conservation. The influence of habitat- and governance-related drivers on elephant abundance across the 13 Asian elephant range countries has been analysed. Competing statistical models by integrating a binary index of elephant abundance (IEA) derived from… Read More


A recent paper published by Forestry and featuring Dr. Hooman Latifi from Dept. of Remote Sensing presents novel results of estimating most relevant forest inventory attributes from very high resolution stereoscopic satellite imagery.  The paper couples a systematic review of the state-of-the-art in photogrammetry-basad forest attribute estimation, a case study in southwestern Germany and an… Read More


A new published work featuring Hooman Latifi from Dept. of Remote Sensing and Siddhartha Khare from Indian Institute of Technology Roorkee presents a full remote sensing-based approach to assess the vegetation diversity across the areas affected and invaded by Lantana camara, an invasive plant species. The study comprises two main steps  utilizing  multi-source satellite earth… Read More


In a recently-published paper in Forestry featuring Hooman Latifi, Steven Hill and Stefan Dech from the Dept. of Remote Sensing, further advancements have been reported in developing unbiased statistical models for area-based estimation of forest understorey layers using LiDAR point cloud information. The study leveraged an original high-density LiDAR point cloud, which was further processed… Read More


pettorelli_et_al_2016_rs-ebv

Our article in the special issue on RS-EBVs is out on “framing the concept of remote sensing essential biodiversity variables”. From the abstract: Although satellite-based variables have for long been expected to be key components to a unified and global biodiversity monitoring strategy, a definitive and agreed list of these variables still remains elusive. The… Read More