new publication: r.pi a GRASS package for semi-automatic spatial pattern analysis

new publication: r.pi a GRASS package for semi-automatic spatial pattern analysis

Our MEE paper on the r.pi GRASS package is now available online: “r.pi: a GRASS GIS package for semi-automatic spatial pattern analysis of remotely sensed land cover data”. This package allows a wide range of spatial pattern analysis from individual based dispersal models to graph theory or omni-directional connectivity metrics. It is part of the GRASS software and all outputs are provided in spatial formats and can be used for further processing in any spatial software such as GRASS, QGIS or R.

The full publication can be accessed here:

Wegmann, M., Leutner, B. F., Metz, M., Neteler, M., Dech, S. and Rocchini, D. (), r.pi: a GRASS GIS package for semi-automatic spatial pattern analysis of remotely sensed land cover data. Methods Ecol Evol.

 

 

new publication: land conversion in and around a transboundary protected area

new publication: land conversion in and around a transboundary protected area

Our former M.Sc. student Henrike published her work “Protection status and national socio-economic context shape land conversion in and around a key transboundary protected area complex in West Africa” where she outlined the capabilities of remotely sensed land cover information and its change over time to inform conservation activities. From the abstract: “ransboundary cooperation is being promoted as an effective way to conserve biodiversity that straddles national borders. However, monitoring the ecological outcomes of these large-scale endeavours is challenging, and as a result, the factors and processes likely to shape their effectiveness remain poorly identified and understood. To address this knowledge gap, we tested three hypotheses pertaining to natural vegetation loss across the W-Arly-Pendjari protected area complex, a key biodiversity hotspot in West Africa. Using a new methodology to compare land cover change across large remote areas where independent validation data is unevenly distributed across time, we demonstrate widespread agricultural expansion outside protected areas over the past 13 years.”

read more here:

Schulte to Bühne, H., Wegmann, M., Durant, S. M., Ransom, C., de Ornellas, P., Grange, S., Beatty, H., Pettorelli, N. (2017), Protection status and national socio-economic context shape land conversion in and around a key transboundary protected area complex in West Africa. Remote Sensing in Ecology and Conservation. doi: 10.1002/rse2.47

article accepted: remote sensing spatial pattern analysis

article accepted: remote sensing spatial pattern analysis

Our article in MEE got accepted “r.pi: a GRASS GIS package for semi-automatic spatial pattern analysis of remotely sensed land cover data” by Martin Wegmann, Benjamin Leutner, Markus Metz, Markus Neteler, Stefan Dech, Stefan and Duccio Rocchini. It outlines the capabilities of the r.pi package to analyze spatial patterns derived from remote sensing land cover data to inform about landscape conditions and changes. Such fragmentation measures are relevant for ecology or conservation as well as for remote sensing to produce value-added landcover maps that provide details on the spatial structure of the landscape.

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

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 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.

new publication: Open data and open source for remote sensing training in ecology

new publication: Open data and open source for remote sensing training in ecology

Our new publication “Open data 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 year and highlights the importance to embrace open-access and -source in remote sensing training.

 

read the full article here:

Duccio Rocchini, Vaclav Petras, Anna Petrasova, Ned Horning, Ludmila Furtkevicova, Markus Neteler, Benjamin Leutner, Martin Wegmann (2017) Open-access and open-source for remote sensing training in ecology, Ecological Informatics

 

 

new publication: Conservation status of Asian elephants

new publication: Conservation status of Asian elephants

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 expert knowledge with different predictor variables including habitat, human population, socioeconomics, and governance data were tested. Read the full article here:

Calabrese, A., Calabrese, J.M., Songer, M., Wegmann, M., Hedges, S., Rose, R. and P. Leimgruber (2017)  Biodiversity and Conservation doi:10.1007/s10531-017-1345-5