Mapping cropland in Ethiopia using crowdsourcing

10293 10 70940 2 196469 document 70940 IJG_2013080810294253.pdf application/pdf 7daddc66ee57622d16b0f7886ec7f849 MD5 5509090 2016-09-12 15:03:04 https://pure.iiasa.ac.at/id/eprint/10293/1/IJG_2013080810294253.pdf 10293 1 1 application/pdf text en public cc_by

IJG_2013080810294253.pdf

published 70941 2 196472 document 70941 lightbox.jpg image/png 071d3d8add97e2ef602083636538bb98 MD5 60824 2016-09-12 15:03:19 https://pure.iiasa.ac.at/id/eprint/10293/2/lightbox.jpg 10293 2 2 image/png other Thumbnails conversion from text to thumbnail_lightbox en public

lightbox.jpg

preview.jpg

medium.jpg

small.jpg

indexcodes.txt

http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/70940 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/70940 http://eprints.org/relation/isIndexCodesVersionOf https://pure.iiasa.ac.at/id/document/70940 archive 351 disk0/00/01/02/93 2016-01-15 08:48:26 2022-10-19 05:00:18 2016-01-15 08:48:26 article show See L. 8571 0000-0002-2665-7065 McCallum I. 1731 0000-0002-5812-9988 Fritz S. 8130 0000-0003-0420-8549 Perger C. 1899 Kraxner F. 7904 Obersteiner M. 7255 0000-0001-6981-2769 Deka Baruah U. Mili N. Ram Kalita N. Mapping cropland in Ethiopia using crowdsourcing pub iis_lca iis_mod prog_esm Cropland Mapping; Crowdsourcing; Interpolation; Validation The spatial distribution of cropland is an important input to many applications including food security monitoring and economic land use modeling. Global land cover maps derived from remote sensing are one source of cropland but they are currently not accurate enough in the cropland domain to meet the needs of the user community. Moreover, when compared with one another, these land cover products show large areas of spatial disagreement, which makes the choice very difficult regarding which land cover product to use. This paper takes an entirely different approach to mapping cropland, using crowdsourcing of Google Earth imagery via tools in Geo-Wiki. Using sample data generated by a crowdsourcing campaign for the collection of the degree of cultivation and settlement in Ethiopia, a cropland map was created using simple inverse distance weighted interpolation. The map was validated using data from the GOFC-GOLD validation portal and an independent crowdsourced dataset from Geo-Wiki. The results show that the crowdsourced cropland map for Ethiopia has a higher overall accuracy than the individual global land cover products for this country. Such an approach has great potential for mapping cropland in other countries where such data do not currently exist. Not only is the approach inexpensive but the data can be collected over a very short period of time using an existing network of volunteers. 2013-08 published Scientific Research Publishing 10.4236/ijg.2013.46A1002 XJ-13-255 International Journal of Geosciences; 4(6):6-13 (August 2013) [doi:10.4236/ijg.2013.46A1002] 276 202 98 228 165 219 public International Journal of Geosciences 4 6 6-13 TRUE 2156-8359 FALSE info:eu-repo/semantics/article