More than counting pixels - perspectives on the importance of remote sensing training in ecology and conservation

This is the final version of the article. It first appeared from Wiley via http://dx.doi.org/10.1002/rse2.27As remote sensing (RS) applications and resources continue to expand, their importance for ecology and conservation increases – and so does the need for effective and successful training of professionals working in those fields. Methodological and applied courses often form part of university curricula, but their practical and long-term benefits only become clear afterwards. Having recently received such training in an interdisciplinary master’s programme, we provide our perspectives on our shared education. Through an online survey we include experiences of students and professionals in different fields. Most participants perceive their RS education as useful for their career, but express a need for more training at university level. Hands-on projects are considered the most effective learning method. Besides methodological knowledge, soft skills are clear gains, including problem solving, self-learning and finding individual solutions, and the ability to work in interdisciplinary teams. The largest identified gaps in current RS training concern the application regarding policy making, methodology and conservation. To successfully prepare students for a career, study programmes need to provide RS courses based on state-of-the-art methods, including programming, and interdisciplinary projects linking research and practice supported by a sound technical background.German Research Foundation (DFG), University of Bayreut

More than counting pixels – perspectives on the importance of remote sensing training in ecology and conservation

This is the final version of the article. It first appeared from Wiley via http://dx.doi.org/10.1002/rse2.27As remote sensing (RS) applications and resources continue to expand, their importance for ecology and conservation increases – and so does the need for effective and successful training of professionals working in those fields. Methodological and applied courses often form part of university curricula, but their practical and long-term benefits only become clear afterwards. Having recently received such training in an interdisciplinary master’s programme, we provide our perspectives on our shared education. Through an online survey we include experiences of students and professionals in different fields. Most participants perceive their RS education as useful for their career, but express a need for more training at university level. Hands-on projects are considered the most effective learning method. Besides methodological knowledge, soft skills are clear gains, including problem solving, self-learning and finding individual solutions, and the ability to work in interdisciplinary teams. The largest identified gaps in current RS training concern the application regarding policy making, methodology and conservation. To successfully prepare students for a career, study programmes need to provide RS courses based on state-of-the-art methods, including programming, and interdisciplinary projects linking research and practice supported by a sound technical background.German Research Foundation (DFG), University of Bayreut

Scenarios of land use and land cover change and their multiple impacts on natural capital in Tanzania

REDD+ (reducing emissions from deforestation, and forest degradation, plus the conservation of forest carbon stocks, sustainable management of forests, and enhancement of forest carbon stocks, in developing countries) requires information on land use and land cover changes (LULCC) and carbon emissions trends from the past to the present and into the future. Here we use the results of participatory scenario development in Tanzania, to assess the potential interacting impacts on carbon stock, biodiversity and water yield of alternative scenarios where REDD+ is effectively implemented or not by 2025, the green economy (GE) and the business as usual (BAU) respectively. Under the BAU scenario, land use and land cover changes causes 296 MtC national stock loss by 2025, reduces the extent of suitable habitats for endemic and rare species, mainly in encroached protected mountain forests, and produce changes of water yields. In the GE scenario, national stock loss decreases to 133 MtC. In this scenario, consistent LULCC impacts occur within small forest patches with high carbon density, water catchment capacity and biodiversity richness. Opportunities for maximising carbon emissions reductions nationally are largely related to sustainable woodland management but also contain trade-offs with biodiversity conservation and changes in water availability

Forest plantation mapping in the Southern Highlands, Tanzania 2016

Recent years have witnessed the practical value of open-access Earth observation data catalogues and software in land and forest mapping. Combined with cloud-based computing resources, and data collection though the crowd, these solutions have substantially improved possibilities for monitoring changes in land resources, especially in areas with difficult accessibility and data scarcity. In this study, we developed and tested a participatory mapping methodology utilizing the open data catalogues and cloud computing capacity to map the previously unknown extent and species composition of forest plantations in the Southern Highlands area of Tanzania, a region experiencing a rapid growth of smallholder-owned woodlots. A large reference data, focusing on plantation coverage, species and age information, was collected in a two-week Participatory GIS campaign where 22 Tanzanian experts interpreted high-resolution satellite images in Google Earth with Open Foris Collect Earth tool developed by FAO. The collected samples were used as training data to classify a multi-sensor image stack of Landsat 8 OLI (2013-2015), Sentinel-2 (2015-2016), Sentinel-1 (2015), and SRTM derived elevation and slope data layers into 30m resolution plantation map. The results show that the plantation area was estimated with high overall accuracy (85%). The interpretation accuracy of local experts was high considering general definition of plantation declining with increased details in interpretation attributes. The results showcase the unique value of local expert participation, enabling the collection of thousands of reference samples over a large geographical area in a short period of time simultaneously building the capacity of the experts. However, sufficient training prior the data collection is crucial for the interpretation success especially when detailed interpretation is conducted in complex landscapes. Since the methodology is built on open-access data and software, it presents a highly feasible solution for repetitive land resource mapping applicable at different spatial scales globally

Forest plantation mapping in the Southern Highlands, Tanzania 2016: reference point data set

Reference point data set of Southern Highlands land use / land cover, collected in a participatory mapping event, Mapathon, in Tanzania, October 2016, using Google Earth / Collect Earth software, later used in the final forest plantation classificatio

Forest plantation mapping in the Southern Highlands, Tanzania 2016: validation dataset field

Validation data set collected by the author team in the field, November 201

Forest plantation mapping in the Southern Highlands, Tanzania 2016: validation dataset

Validation data set collected by the author team, November 2016, using Google Earth / Collect Earth softwar

Forest plantation mapping in the Southern Highlands, Tanzania 2016: species map, link to geotiff file

The resulting forest plantation species map (raster) for the Southern Highlands area, created using the collected reference data and different remote sensing data sets from 2013-2015 as listed in the abstract of the publication. In the raster, the values are as follows: 1=pine, 2=eucalyptus or wattle, 3= other (0=non-plantation)

Forest plantation mapping in the Southern Highlands, Tanzania 2016: validation dataset

Validation data set collected by the author team, November 2016, using Google Earth / Collect Earth softwar