Maps of social-ecological patches for study area 1 and 2.

<p>Urban land and water is a land cover, but not a social-ecological patch.</p

Schematic of the generalizability of the social-ecological patches.

<p>The dashed arrows indicate suggested new social-ecological patches that were not included by Sinare and colleagues [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0192019#pone.0192019.ref023" target="_blank">23</a>]. The crossed arrow connected to fallow indicates the failure to extrapolate this patch using remotely sensed data (for explanation see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0192019#sec008" target="_blank">Results</a>). Social-ecological patches in dashed boxes were not mapped in the current study, either because they are additions to the original categorization or because it was impossible with the available data.</p

Description of social-ecological patches and their contribution to livelihood benefits.

<p>a) Social-ecological patches and characteristics of their ecosystem services generation; b) Relative contribution of the social-ecological patches found in the current study to the five identified livelihood benefits. Original scores are means from 36 focus groups conducted by Sinare and colleagues [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0192019#pone.0192019.ref023" target="_blank">23</a>]. Scores here are adapted from the original work, based on feedback during fieldwork conducted for this paper, to better represent per unit area contribution of social-ecological patches (for detailed description, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0192019#pone.0192019.s005" target="_blank">S1 Text</a>). Figure partly based on Sinare and colleagues [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0192019#pone.0192019.ref023" target="_blank">23</a>] under a CC BY license, with permission from the authors, original copyright 2016.</p

Overview maps.

<p>Maps of the study areas and villages where fieldwork was conducted. Study areas indicate extent of analyzed remote sensing images. Points show location of villages included in this study (blue) and mapped by Sinare and colleagues [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0192019#pone.0192019.ref023" target="_blank">23</a>] (yellow). Boundary data is from Natural Earth [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0192019#pone.0192019.ref041" target="_blank">41</a>] and location of villages and towns collected with GPS during fieldwork.</p

The spatial distribution of livelihood benefits.

<p>Maps of the distribution of the five livelihood benefits (a-e) in study area 1 and study area 2 were generated using the social-ecological patch maps and the weighted livelihood benefit scores. The heat maps (f) were generated by adding all separate livelihood benefit maps into one composite map. The heat maps show the provision of multiple livelihood benefits, with high scores suggesting highly multifunctional parts of the landscape.</p

Percentage of landscape covered by each social-ecological patch and their relative accuracies¹.

<p>Percentage of landscape covered by each social-ecological patch and their relative accuracies<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0192019#t002fn001" target="_blank">¹</a>.</p

Schematic of work flow for the developed hybrid classification method.

<p>The data layers used include the red, near infrared and mid-infrared bands of the Landsat 8 OLI scenes (bands 4, 5 and 7); Normalized difference vegetation index (NDVI; [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0192019#pone.0192019.ref052" target="_blank">52</a>,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0192019#pone.0192019.ref053" target="_blank">53</a>]); and Tasseled Cap Transformation (TCT), an index that compresses multispectral Landsat data into the three bands brightness, greenness and wetness [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0192019#pone.0192019.ref054" target="_blank">54</a>,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0192019#pone.0192019.ref055" target="_blank">55</a>]. To define the spectral signatures of some of the social-ecological patches, we used mean and standard deviations (st.d.) as well as the M statistic (a measure of spectral separability between classes; [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0192019#pone.0192019.ref056" target="_blank">56</a>,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0192019#pone.0192019.ref057" target="_blank">57</a>]) for the different patch calibration points in all data layers. For implementation of the method, three GIS softwares were used: ArcMap [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0192019#pone.0192019.ref058" target="_blank">58</a>], ENVI [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0192019#pone.0192019.ref043" target="_blank">43</a>] and Google Earth.</p

Average correct identification of the blood parasites surveys for all sites.

<p>Average correct identification of the blood parasites surveys for all sites.</p

PT Performance in 2009–2010 for all centers.

<p>PT Performance in 2009–2010 for all centers.</p

Classification of errors.

<p>Classification of errors.</p