TITLE:
Mapping Cropland in Ethiopia Using Crowdsourcing
AUTHORS:
Linda See, Ian McCallum, Steffen Fritz, Christoph Perger, Florian Kraxner, Michael Obersteiner, Ujjal Deka Baruah, Nitashree Mili, Nripen Ram Kalita
KEYWORDS:
Cropland Mapping; Crowdsourcing; Interpolation; Validation
JOURNAL NAME:
International Journal of Geosciences,
Vol.4 No.6A,
August
8,
2013
ABSTRACT:
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.