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.
official invitation of the dean of the faculty (extract)
On Friday, June 23rd, three talks will be given within the appointment procedure for the assignment of the professorship for Remote Sensing of Land Surface Dynamics.
„Anwendung von Satelliten-gestuetzten Erdbeobachtungen in der Kohlenstoffkreislauf und Artenvielfalt Forschung“
„Die Dynamik der Landoberfläche: Potentiale und Herausforderungen der Fernerkundung“
„Environmental Monitoring with Earth Observation Data – from Research to Operational Use“
We invite all students and colleagues to join the procedure in lecture room 4, Philosophy Building, starting at 8 a.m.
More information (only in German) can be found on the homepage of the Faculty of Philosophy.
42 young master and PhD students as well as junior university teachers and specialists from government agencies and research institutions participated into the two-week 4th CAWa Summer School “Methods and Tools for the Assessment and Monitoring of Central Asian Water and Land Resources” (June 5-17, 2017) that was hosted by the German-Kazakh University in Almaty, Kazakhstan. Participants came from Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan as well as from Afghanistan and Pakistan.
During the first week (June 5-8, 2017), trainers from the Department of Remote Sensing at the University of Würzburg (Lucia Morper-Busch and Dimo Dimov) together with two co-trainers from Uzbekistan (Sherzod Zaitov, SIC ICWC) and Kazakhstan (Almas Kitapbayev, DKU) trained the participants in the use of open-source GIS software (QGIS Desktop-2.18 with Orfeo Toolbox and several plugins) for spatial analyses and in the processing, analyses, and interpretation of satellite images (e.g., raster image analysis and land use classification). Furthermore, they provided an overview of remote sensing applications for water and land resource monitoring and introduced the participants into the online information tool WUEMoCA (Water Use Efficiency Monitor in Central Asia) that is developed at the Department of Remote Sensing in Würzburg together with Central Asian partners.
Participants and trainers in the 4th CAWa Summer School (module ‘Introduction to GIS and Remote Sensing’ in the first week of summer school) in front of the German-Kazakh University in Almaty, Kazakhstan.
This year’s CAWa Summer School was already the fourth in a row. It was organized by the CAWa Project (funded by the German Federal Foreign Office; http://www.cawa-project.net) and by the German-Kazakh University in Almaty (Kazakhstan) in cooperation with the Nazarbayev University (Department of Civil Engineering, School of Engineering) in Astana (Kazakhstan) and the Fribourg University (Physical Geography) in Fribourg (Switzerland). Focus was set on innovative methods and tools for the analysis and monitoring of water and land resources in Central Asia that are of great value in integrated water and land resource management. Therefore, theoretical lectures and practical exercises were combined with discussion sessions on the implementation of new methods and instruments for managing the water and land resources. The program included an in-depth introduction to GIS, an overview of remote sensing applications for water and land resource monitoring, an introduction to climatological data analysis, and an introduction to glaciology.
The r.pi GRASS extension is now published and available through the GRASS extension repository. It provides a range of functionality and allows to easily analyze spatial patch attributes in GRASS. Patches are expected to be raster objects derived from remote sensing data and their spatial characteristics is written into a new spatial file for further analysis.
The list of functions outlined in our recent publication in MEE can be seen here. More functions are available in the r.pi package but not specifically mentioned.
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
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.