Additional file 2: of Local discrepancies in continental scale biomass maps: a case study over forested and non-forested landscapes in Maryland, USA

Figure S2. Histograms showing the distribution of forest biomass from old Saatchi (v1), new Saatchi (v2), and CMS_RF maps

Additional file 1: of Local discrepancies in continental scale biomass maps: a case study over forested and non-forested landscapes in Maryland, USA

Figure S1. Scatter plots of biomass at 250 m resolution from old Saatchi (v1) and new Saatchi (v2) maps versus CMS_RF product

Additional file 3: of Local discrepancies in continental scale biomass maps: a case study over forested and non-forested landscapes in Maryland, USA

Table S1. Total Maryland biomass, in Tg, for CMS_RF at 30 m resolution and FIA calculations (2008–2012 cycle) by forest and non-forest classification. FIA forest and non-forest definitions do not follow NLCD landcover classes, rather by on the ground plot conditions. Non-forest biomass in the FIA dataset was calculated by methods described in [28]

Butler (1980) map of areas where land use includes “primitive subsistence agriculture,” which in the humid tropics largely consists of shifting cultivation (reproduction by first author using ArcGIS 10.4 based on Hurtt et al. [2]).

<p>Butler (1980) map of areas where land use includes “primitive subsistence agriculture,” which in the humid tropics largely consists of shifting cultivation (reproduction by first author using ArcGIS 10.4 based on Hurtt et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0184479#pone.0184479.ref002" target="_blank">2</a>]).</p

Numbers and percentages of one-degree cells studied that showed signs of shifting cultivation (SC) or not (No SC), as well as percentages of cells showing signs of shifting cultivation in the various occurrence classes, per region.

<p>The area of interest ranges from 30°S and 30°N (6,704 one-degree cells on landmass), while the area investigated includes 2,817 cells. The remaining cells (3,887) were excluded from the analysis as shifting cultivation can be assumed to have never existed or disappeared decades ago (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0184479#pone.0184479.g005" target="_blank">Fig 5</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0184479#sec002" target="_blank">Method</a> section).</p

Preliminary estimates of changes in the occurrence of shifting cultivation between today and 2030, 2060 and 2090.

<p>This visualization is based on the estimation of landscapes showing signs of shifting cultivation around 2010 (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0184479#pone.0184479.g005" target="_blank">Fig 5</a>) as base year and estimated decreases of shifting cultivation (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0184479#pone.0184479.t003" target="_blank">Table 3</a>) based on the expert surveys and observed trend between the Butler map and our 2010. This figure was elaborated by the first author using ArcGIS 10.4.</p

Creation of validation dataset.

<p>(Fig 2A): the global distribution of the stratified sample of the 328 one-degree cells used in the validation data set. (Fig 2B): Location of the one-degree cell of Fig 2C - 2E. (Fig 2C): One-degree cell with a mesh of 1/100 degree cells as a basic unit for the validation data set, green cells having a shifting cultivation occurrence class of >1% in our global classification. The red box marks the extent of Fig 2D and Fig 2E. (Fig 2DA) and (Fig 2E): The white line grid marks the 1/100 degree cells used as basic unit for the validation data. Based on the spatio-temporal pattern of the GFC data (different colours denoted different year of clearings) and the patterns of clearing and regrowth in the very high resolution imagery (here Bing), a 1/100-degree cell is being classified as showing shifting cultivation or not. The red hatching in (B) indicates the 1/100 degree cells that were classified as having shifting cultivation. (Source of imagery in 2D and 2E: Pansharpened Landsat 8 image, acquisition date January 5 2014, available from the U.S. Geological Survey.). Maps created in QGIS 2.16.</p

Estimation of landscapes showing signs of shifting cultivation around 2010 between 30°S to 30°N.

<p>Based on visual inspection of annual global deforestation data [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0184479#pone.0184479.ref008" target="_blank">8</a>] from 2000 to 2014 and very high-resolution satellite imagery. Areas in which shifting cultivation can be assumed to have never existed or disappeared decades ago have been excluded from the analysis (dark gray). This figure was elaborated by the first author using ArcGIS 10.4.</p

Survey responses received, by country of study.

<p>This figure was elaborated by the first author using ArcGIS 10.4.</p

Identification of spatio-temporal pattern based on GFC global annual deforestation data [8] and very high–resolution satellite imagery.

<p>Fig 1A shows a one-degree square of northern Laos. The colored pixels indicate clearings in different years between 2000 and 2014 as recorded in the GFC data set [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0184479#pone.0184479.ref008" target="_blank">8</a>]. Fig 1B to Fig 1E show examples of different zoom levels used to decide whether the pattern in the GFC data is indeed related to shifting cultivation Fig 1E (showing pattern of clearing for the current year of cultivation and different stages of fallow) or not Fig 1D (larger scale clearings with young rubber). The imagery used for illustrative purpose in Fig 1 is based on Copernicus Sentinel 2 data from 2016. Maps created in QGIS 2.16.</p