UAV-derived data for mapping change on a swidden agriculture plot: preliminary results from a pilot study

Swidden agriculture, or the continuing agricultural system in which clearings are cropped for shorter periods than they are fallowed, landscapes have been described as 'difficult-to-map' because they host a high variety of land-cover types. Consequently, satellite-borne remotely sensed data have not proven overwhelmingly successful in detecting and measuring change within these landscapes. We utilize data derived from an optical sensor carried on-board an unmanned aerial vehicles (UAVs) to measure the change on a swidden agriculture plot over a 4 month period in Guyana. UAVs were built with indigenous farmers who were trained in their operation to collect data over the swidden plot. A two-class classification was developed to quantify the change in both cultivated and naturally occurring vegetation. We found that non-vegetation surfaces rapidly decreased over the 4 months, declining from 79.42% in June to 69.69% in September. Vegetation recolonization of the swidden crop was particularly the cassava crop planted by the Makushi farmer. While our analysis was completed over a single swidden plot, our work demonstrated that UAVs could play a role in mapping swidden landscapes and change the perception that they are difficult to map. Local people involvement was critical to mapping their landscapes.