Participatory mapping of forest plantations with Open Foris and Google Earth Engine
| Autor: | Joni Koskinen, Ulpu Leinonen, Remi d'Annunzio, Andreas Vollrath, Niina Käyhkö, Erik Lindquist, Anssi Pekkarinen, Antonia Ortmann |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Earth observation
Resource (biology) Data collection 010504 meteorology & atmospheric sciences business.industry Environmental resource management ta1172 0211 other engineering and technologies Elevation ta1171 Cloud computing 02 engineering and technology Shuttle Radar Topography Mission 01 natural sciences Atomic and Molecular Physics and Optics Computer Science Applications Reference data Participatory GIS Geography Computers in Earth Sciences business Engineering (miscellaneous) 021101 geological & geomatics engineering 0105 earth and related environmental sciences |
| Zdroj: | ISPRS Journal of Photogrammetry and Remote Sensing. 148:63-74 |
| ISSN: | 0924-2716 |
| Popis: | 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 through 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 set of reference data, focusing on forest plantation coverage, species and age information distribution, was collected in a two-week participatory GIS campaign where 22 Tanzanian experts interpreted very high-resolution satellite images in Google Earth with the Open Foris Collect Earth tool developed by the Food and Agriculture Organization of the United Nations. The collected samples were used as training data to classify a multi-sensor image stack of Landsat 8 (2013–2015), Sentinel-2 (2015–2016), Sentinel-1 (2015), and SRTM derived elevation and slope data layers into a 30 m resolution forest plantation map in Google Earth Engine. The results show that the forest plantation area was estimated with high overall accuracy (85%). The interpretation accuracy of local experts was high considering general definition of forest 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 to 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. |
| Databáze: | OpenAIRE |
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