New publication: Towards an all-in-one sensor for forestry applications?

New publication: Towards an all-in-one sensor for forestry applications?

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 expert survey on the potenaitls and pitfalls of remote sensing-assisted forest inventory, in which internationally renowned peers from all over the world took part.

 

Area-based predictions of tree species, aboveground biomass and tree density based on WorldView-2 stereo data

 

The modeling/classification results were comparable to earlier studies in the same test site, obtained with more expensive airborne acquisitions. All in all, the study concludes that the simpler acquisition, reasonable price and the comparably easy data format and handling of VHRSI compared with other sensor types justifies further research on the application of these data for supporting operational forest inventories. The fulltext version of the paper together with the supplementary material can be found here.

Fassnacht, F.E., Mangold, D., Schäfer, J., Immitzer, M., Kattenborn, T., Koch, B and Latifi, H. 2017. Estimating stand density, biomass and tree species from very high resolution stereo-imagery – towards an all-in-one sensor for forestry applications? Forestry, DOI: 10.1093/forestry/cpx014

 

new article: Remote Sensing and New Generation SDMs

new article: Remote Sensing and New Generation SDMs

our new article “Will remote sensing shape the next generation of species distribution models?” in Remote Sensing in Ecology and Conservation is now online. Two prominent limitations of species distribution models (SDMs) are spatial biases in existing occurrence data and a lack of spatially explicit predictor variables to fully capture habitat characteristics of species. Can existing and emerging remote sensing technologies meet these challenges and improve future SDMs? We believe so. Novel products derived from multispectral and hyperspectral sensors, as well as future Light Detection and Ranging (LiDAR) and RADAR missions, may play a key role in improving model performance. In this perspective piece, we demonstrate how modern sensors onboard satellites, planes and unmanned aerial vehicles are revolutionizing the way we can detect and monitor both plant and animal species in terrestrial and aquatic ecosystems as well as allowing the emergence of novel predictor variables appropriate for species distribution modeling. We hope this interdisciplinary perspective will motivate ecologists, remote sensing experts and modelers to work together for developing a more refined SDM framework in the near future.

Kate S. He, Bethany A. Bradley, Anna F. Cord, Duccio Rocchini, Mao-Ning Tuanmu, Sebastian Schmidtlein, Woody Turner, Martin Wegmann andNathalie Pettorelli (2015) “Will remote sensing shape the next generation of species distribution models?”, Remote Sensing in Ecology and Conservation, DOI: 10.1002/rse2.7

New MSc. student – LiDAR for species distribution modeling

New MSc. student – LiDAR for species distribution modeling

Lidar_Bay_Forest_remote-sensing_euA new MSc. student started her thesis “Suitability of Light detection and ranging (LiDAR) data and texture measures of aerial images to model the species distribution of Glaucidium passerinum (Pygmy Owl) in Vercors, French Alps”. Wanda Graf is using LiDAR data to describe the three dimensional habitat structure of Glaucidium passerinum at different scales. Moreover texture measures of aerial images will be used to describe the habitat structure of Glaucidium passerinum at different scales. It is assumed that LiDAR data are more suitable to model the distribution of Glaucidium passerinum than texture measures of aerial images. The thesis is supervised by Dr. Björn Reineking and Dr. Martin Wegmann