Mapping Cropland in Ethiopia Using Crowdsourcing

Abstract

<p class="MsoNormal"> <span lang="EN-US" style="font-family:Verdana;">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.</span> </p

Harnessing the power of volunteers, the internet and Google Earth to collect and validate global spatial information using Geo-Wiki

“…Although a high resolution land use map of Ethiopia exists through AFRICOVER, this map is not currently openly available so one must rely on global land cover maps for this data. A comparison of the interpolated crowdsourced map using standard performance measures showed that this map was better than any of the individual global land cover products for Ethiopia (See et al, 2013b).…”

“…To collect data on the degree of cultivation and the degree of human settlement in Ethiopia in the context of land grabbing See et al, 2013b) 36 participants 2278 points/ participant …”

Exploring Land Use and Land Cover of Geotagged Social-Sensing Images Using Naive Bayes Classifier

“…However, the urban areas have the highest distribution of the crowdsourced LULC map. This agreement could be due to the density and distribution of available images being sufficiently high in a particular tile [16].…”

“…However, the urban areas have the highest distribution of the crowdsourced LULC map. This agreement could be due to the density and distribution of available images being sufficiently high in a particular tile [16]. After filtering the individual tile, we performed supervised classification on the Landsat TM5 image using the majority filtered crowdsourced LULC tiles as training and validation datasets to acquire the LULC map.…”

Irrigation assessment via remote sensing and land surface model data

“…Despite of the existence of the drought, national crop production did not appear to be significantly affected as reported by the Food and Agriculture Organization of the United Nations (FAO, 2014b). This might be explained by the fact that the drought mainly affected the east and south of the country and the majority of croplands use rain-fed production systems and are located in the other part of the country (See et al, 2013).…”