Supplementary Information: Cropland/pastureland dynamics and the slowdown of deforestation in Latin America

Size: px

Start display at page:

Download "Supplementary Information: Cropland/pastureland dynamics and the slowdown of deforestation in Latin America"

Mercy Cobb
7 years ago
Views:

1 Supplementary Information: Cropland/pastureland dynamics and the slowdown of deforestation in Latin America Jordan Graesser*, T. Mitchell Aide, H. Ricardo Grau, Navin Ramankutty *Corresponding author: 1. SI Materials & Methods 1.1. Land cover classification Annual statistics generated from the MODIS satellite were used as predictive variables in the Random Forest classifier. Latin America was divided into 24 mapping zones, and for each zone a region-specific Random Forest model was trained with samples from that zone. Each Random Forest zone model was then applied to 13 years of MODIS data. Land cover samples were manually collected from multi-temporal high-resolution imagery in Google Earth (GE) at the same scale as the MODIS image data. A team of four experts collected 60,014 (35,463 in SA, 24,551 in Mex/CA and the CB) land cover samples from visual interpretation of high-resolution imagery on GE that spanned 2001 to 2013 (Table S1). We overlaid a 250m x 250m grid onto the GE interface. Each user then assigned samples from the grid to one of the land cover classes of interest. The reference GE high-resolution image acquisition date was recorded for each sample. Then, a Random Forest classification model was trained for each mapping zone using a method similar to [1]. The mapping zone boundaries followed ecoregion and biome delineations [2]. To train a zone-specific Random Forest model, land cover samples within the mapping zone of interest and the samples GE high-resolution image acquisition date were paired with MODIS time series variables. For example, samples collected from 2005 GE high-resolution imagery were paired with 2005 MODIS time series variables. The zone-specific Random Forest models were then applied annually to produce 13 land cover maps for each mapping region Agricultural mapping & trends This study focused on the land cover trends of cropland and pastureland, and the land cover sources that these land covers replaced over time. Experts, using image interpretation, field analysis, and knowledge of the landscape identified each class. The cropland class represents row crop agriculture (e.g., maize, soy, wheat). It is characterized by seasonal variations in vegetation greenness, seasonal or annual rotations, and windrows from harvest detectable in high-resolution imagery. The pastureland class consists mainly of grasslands and savannas, and is predominately pastures for livestock grazing (observation from high-resolution imagery and field analysis). The class is characterized by less seasonal and annual variation than row crops, and contextual information such as watering holes, livestock trails, and in some cases, livestock. We chose to map pasturelands rather than grasslands although pastureland is a land use, whereas grasslands or savannas are a land cover. However, most grassland or savanna land cover in Latin America is used for pastureland. The employment of the MODIS sensor in this study allowed us to analyze agriculture on a regional scale. The high-temporal sensor revisit time is a key element for agricultural remote sensing [3, 4]. Annual crop characteristics, for example, can be similar to other vegetation at certain periods of the year, with a window of high variance (e.g., early growing season or harvest). The tradeo with MODIS is spatial resolution. At 250m, it is often too coarse for heterogeneous landscapes. Small-scale agriculture, in the Brazilian Caatinga or throughout the Andes, for example, is di cult to capture at the MODIS pixel scale. Future research could employ higher resolution imagery (e.g., Landsat), particularly with recent studies [5, 6] opening doors for broader scale use. The large-scale nature of agricultural expansion in Latin America, though, allowed us to capture much of the major changes since We conducted a per pixel map accuracy assessment, independent of samples used to develop classification models (Table S10). We used GE high-resolution imagery for the year 2013 to assess our 2013 MODIS map because highresolution imagery are more widely available for more recent years. Pixels were randomly sampled throughout Latin America. Then, the dominant land cover (i.e., most abundant) within the 250m x 250m MODIS pixel was recorded if a 2013 high-resolution image overlapped the pixel. At the sub-national scale, the majority of zonal trends were non-linear (Fig. S11). 1

2 Table S1: Land cover classification scheme used to map broad land cover categories across Latin America using MODIS satellite imagery. Class Description Cropland Row crop agriculture (e.g., maize, soy, wheat, sugarcane) Pastureland Herbaceous vegetation (grasslands, savannas) dominated by pastures for livestock grazing Forest Natural tree cover and tree plantations (e.g., pine, eucalyptus, banana, citrus, olives, palms) Shrub Sparse vegetation <2 meters, typically in dry habitats Other Bare soil, ice, snow, rock, sand dunes, built-up structures, and water Figure S2: Examples of polynomial selection, where each inset shows an example of a least squares fit, chosen for an intercept only model (top left), a linear model (top right), a quadratic model (bottom left), and a cubic model (bottom right). The black dots illustrate the endpoints used to calculate annual land cover changes in the examples shown. The bottom left inset includes examples of changes calculated for the 2001 to 2007 and 2007 to 2013 periods. *The linear, quadratic, and cubic models shown here are significant at p < 0.01 and have r 2 values of 0.9 or higher. 2

Figure S3: Cropland and pastureland change dynamics in Latin America (2001-2013) at the municipality scale.

3 Figure S3: Cropland and pastureland change dynamics in Latin America ( ) at the municipality scale. The colors illustrate the change direction. Zones shown in black had either insu cient cropland or pastureland, or the 13 year, least squares polynomial trends were non-significant (p <.01). 3

Figure S4: Proportion of new cropland and pastureland (2001 2013) from di erent sources, by

4 Figure S4: Proportion of new cropland and pastureland ( ) from di erent sources, by selected ecoregions. The letters in the far left column correspond to the ecoregions in the map. 4

5 Figure S5: Cropland and pastureland change dynamics in Latin America ( ) at the ecoregion scale. The colors illustrate the change direction. Zones shown in black had either insu cient cropland or pastureland, or the 13 year, least squares polynomial trends were non-significant (p <.01). 5

Figure S6: Net (2001 2013) percentage change of cropland and pastureland

6 Figure S6: Net ( ) percentage change of cropland and pastureland at the: A) hexagon scale B) municipality scale and C) ecoregion scale. 6

7 Figure S7: National and selected ecoregion level changes: 2001 to The dashed black line (MODIS-derived estimates from this study) and the dashed gray line (FAOSTAT) values reflect the left y-axis. The colored lines match selected ecoregions in the maps directly right of the national plots, and the values reflect the right y-axis. The FAO data are harvested cropland area taken from the same major crop groups mentioned in the main article. 7

8 Figure S8: Net expansion and sources of new agriculture from 2001 to Figure S9: Net percentage change of cropland and pastureland: , , and

9 Table S10: Confusion matrix for 2013, assessed independently of model training samples. Map label Column Producer s High-res. label Cropland Pastureland Forest Shrub Other total accuracy Cropland % Pastureland % Forest % Shrub % Other % Row total 538 1, User s accuracy 63% 62% 83% 44% 76% Total pixels assessed: 2,642 Overall accuracy: 67% Figure S11: Polynomial order chosen for cropland and pastureland trends at the: A) hexagon scale B) municipality scale and C) ecoregion scale. 9

10 2. References [1] M. L. Clark, T. M. Aide, G. Riner, Land change for all municipalities in Latin America and the Caribbean assessed from 250-m MODIS imagery ( ), Remote Sensing of Environment 126 (2012) [2] D. M. Olson, E. Dinerstein, E. D. Wikramanayake, N. D. Burgess, G. V. Powell, E. C. Underwood, J. A. D amico, I. Itoua, H. E. Strand, J. C. Morrison, C. J. Loucks, T. F. Allnutt, T. H. Ricketts, Y. Kura, J. F. Lamoreux, W. W. Wettengel, P. Hedao, K. R. Kassem, Terrestrial ecoregions of the world: A new map of life, BioScience 51 (11) (2001) [3] D. Arvor, M. Jonathan, M. S. P. Meirelles, V. Dubreuil, L. Durieux, Classification of MODIS EVI time series for crop mapping in the state of Mato Grosso, Brazil, International Journal of Remote Sensing 32 (22) (2011) [4] G. L. Galford, J. F. Mustard, J. Melillo, A. Gendrin, C. C. Cerri, C. E. Cerri, Wavelet analysis of MODIS time series to detect expansion and intensification of row-crop agriculture in Brazil, Remote Sensing of Environment 112 (2) (2008) [5] M. Hansen, P. Potapov, R. Moore, M. Hancher, S. Turubanova, A. Tyukavina, D. Thau, S. Stehman, S. Goetz, T. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. Justice, J. Townshend, High-resolution global maps of 21st-century forest cover change, Science 342 (6160) (2013) [6] D. P. Roy, J. Ju, K. Kline, P. L. Scaramuzza, V. Kovalskyy, M. Hansen, T. R. Loveland, E. Vermote, C. Zhang, Web-enabled Landsat data (WELD): Landsat ETM+ composited mosaics of the conterminous United States, Remote Sensing of Environment 114 (1) (2010)

Supporting Online Material for Achard (RE 1070656) scheduled for 8/9/02 issue of Science

Supporting Online Material for Achard (RE 1070656) scheduled for 8/9/02 issue of Science Materials and Methods Overview Forest cover change is calculated using a sample of 102 observations distributed