Landsat approaches to map agro-pastoral farming in the wetlands of southern Sudan

ABSTRACT

Machine-learning algorithms (MLA) are coming of age within satellite remote sensing (SRS). This study compares the performance of a number of MLAs with more traditional indices and algorithms to map annual agro-pastoralist farming activity in southern Sudan. Two Landsat images from the early dry season 2014 and 2015 were analysed thoroughly and evaluated by interpretation of farming cover from very high resolution (VHR) images on Google Earth (GE). Traditional SRS indices based upon red and near infrared (NIR) bands used for monitoring rangelands did not perform well for the wet rangeland conditions compared to the use of blue and shortwave infrared (SWIR) bands. The species distribution model programme, MaxEnt, was used to produce a continuous farming activity indices using only Landsat-derived variables. Compared to other SRS classification approaches, maximum entropy (MaxEnt) showed the best overall performance to map farming activity followed by classification tree analysis (CTA). Overall mapping agreement >95.0% was reached for most methodologies, with MaxEnt showing very high mapping agreement (≥98.5%) for both years. When the result of MaxEnt’s good performance is put together in a 2014–15 or a 1999–2002 change detection scenario, it corroborates ground reports on massive human abuses that have taken place in Unity state of southern Sudan.

Disclosure statement

No potential conflict of interest was reported by the author.