5 of 21 Shuttle Radar Topography Mission (SRTM) (Farr et al. 2007), at 30-meter spatial resolution were used to define floodplains. Given its focus on agricultural landscapes, urban areas were not directly mapped. Instead, urban pixels were extracted from Landsat’s Global Human Built-up and Settlement Extent (HBASE) Dataset (Wang et al. 2017). The HBASE provides global 30-meter spatial resolution information on settlement extent for 2010.

Using these data, we developed an LCLU mapping framework adapted to the region’s agricultural landscapes to produce a 30-meter resolution map of northern Nigeria for the baseline year 2015. The framework implemented a knowledge-based expert system (KBES) that relied on dense stacks of Landsat 8 images and exploited inter- and intra-annual vegetation dynamics and contextual information to map the main components of the study region’s land surfaces (Srinivasan and Richards 1990). Recent research highlights the role of expert knowledge to advance remote sensing–based agricultural monitoring (Bégué et al. 2018), even if such experts lack remote sensing experience (for example, field extension agents). These systems constitute a useful alternative when the lack of consistent training and validation data limit the use of supervised learning systems. Expert knowledge is commonly stored as a set of rules in a knowledge base. Subsequently, the information is passed to an inference mechanism that interprets and assigns class memberships to pixels. A KBES has been successfully implemented in a number of applications, such as protected areas conservation (Szantoi et al. 2016), crop classification (Choudhury et al. 2012; Cohen and Shoshany 2002; El Hajj et al. 2009; Janssen and Middelkoop 1992), and urban mapping (Amarsaikhan et al. 2013).

We built a knowledge base that established production rules using spectral, temporal, and spatial constraints to identify the main agricultural systems and natural vegetation types in the study area (Cohen and Shoshany 2002). The production rules were not necessarily conclusive, but they provided a degree of evidence in favor of some class labels (Richards and Jia 2006, 342–55). These rules were defined from the analysis of the spectral profiles during the vegetative cycles of the different land-use types. Specific temporal windows were selected to maximize the separability between classes using expert knowledge in the seasonal dynamics of the land surfaces in the study area (figure 2, panel a). Similar strategies have been previously used for LCLU in tropical environments (Hansen et al. 2008; Sedano, Gong, and Ferrao 2005). The numerical values in the spectral rules were provided as thresholds that were empirically generated from observed data (Hodgson et al. 2003). These spectral thresholds were defined based on a training data set of 1,750 points of known land-use types randomly searched using visual interpretation of a combination of Landsat and very high-resolution imagery (Google Earth) for the period of study. The thresholds for each land-use class were calculated from the statistical distribution of pixel values in the training data set. The spectral rules were built based on Normalized Difference Vegetation Index (NDVI) images (Tucker 1985) calculated from each image in the original data set. The NDVI was chosen because, besides data compression, it facilitates the interpretation of land surface dynamics over time and the definition of spectral thresholds. The presence of burnt areas in natural vegetation surfaces within the three-year imagery epoch was interpreted as a sign of land-cover transition, supporting the labeling as nonstable natural vegetation pixels.