Mapping Fragmented Agricultural Systems in the Sudano-Sahelian Environments of Africa Using Random Forest and Ensemble Metrics of Coarse Resolution MODIS Imagery
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04-GC-005.qxd 7/13/12 8:49 PM Page 839 Mapping Fragmented Agricultural Systems in the Sudano-Sahelian Environments of Africa Using Random Forest and Ensemble Metrics of Coarse Resolution MODIS Imagery Elodie Vintrou, Mamy Soumaré, Simon Bernard, Agnès Bégué, Christian Baron, and Danny Lo Seen Abstract Key deliverables of food security systems in terms of We worked on the assumption that agricultural systems crop monitoring consist of early estimates of cultivated area shaped the landscape through human cropping practices, and crop-type distribution, cropping practices (cropping and that the resulting landscape can be described with a intensity, crop calendar, water, and fertilizer inputs), detec- set of coarse resolution satellite-derived metrics (spectral, tion of growth anomalies, and crop yield estimates. Remote textural, temporal, and spatial metrics). A Random Forest sensing plays an important role in delivering information for classification model was developed at the village scale in crop monitoring in insecure countries (e.g., Thenkabail et al., South Mali, based on 100 samples, with data on the main 2009a; Thenkabail et al., 2011; Thenkabail et al., 2009c). type of agricultural system in each village (three-class Satellites help locate and estimate areas of crop types and typology), and 30 MODIS-derived and socio-environmental crop potential yield at local scales (e.g., Bruzzone et al., metrics calculated on agricultural areas. The model was 1997). However, remote sensing techniques face numerous found to perform well (overall accuracy of 60 percent) and challenges for crop mapping of larger areas such as the was stable. Class A (food crops) and B (intensive agricul- continents and the globe (Thenkabail, 2010). Global monitor- ture) displayed good producer’s accuracy (70 percent and ing typically resorts to using free low- and medium-resolu- 67 percent, respectively), while class C (mixed agriculture) tion images (Hountondji et al., 2006; Justice et al., 1985), but was less accurate (50 percent). The most important metrics these images do not enable precise localization of the crops. were shown to be the annual mean of NDVI, followed by the This is particularly true in regions with fragmented cropped phenology transition dates and texture metrics. However, areas, like West Africa, where 20 percent to 40 percent of when considering each set of metrics separately, texture crop classification error using MODIS is inherent to the emerged as the most discriminating factor (with 53 percent structure of the landscape (Vintrou et al., 2011). One way to of global accuracy). This result, i.e., that even coarse get around this problem is to develop sub-pixel approaches resolution imagery contains textural information that can (e.g., Ichokua and Karnieli, 1996) or to increase the scale of be used for crop mapping, is new. Such maps could be the observed objects. used in food security systems as an indicator of system Many papers deal with the sub-pixel approach, which vulnerability, or as spatial inputs for crop yield models. gives correct results in the case of simple landscapes (e.g., De Fries et al., 1997), but is not ideal for the complex landscapes of West Africa. In the Sudano-Sahelian region, Introduction peasant farming is the dominant production system with Sub-Saharan Africa is the only region in the world where low cropland intensification, small fields, and marked per capita food production is declining, making continued between-field and within-field variability of vegetation. The investment in monitoring and aid program mandatory presence of trees in the field and the rainfall regime make it (Brown et al., 2007). Different early warning systems, such even more difficult to distinguish between the natural the Famine Early Warning System Network (FEWS NET - vegetation and the cropped areas (Fritz and See, 2008). USAID) or the Global Information and Early Warning System In this context, we tested an approach that consisted in (GIEWS - FAO), use methods that can identify food security changing the observation scale, and consequently in chang- hazards early enough so that political and budgetary deci- ing the nature of the observed object. We chose to work at a sions can be made in a timely manner. local scale (a village) and with one object (the agricultural system) compatible with the existing coarse resolution imagery. The agricultural system is described by cropping practices (cultivated species and level of intensification) and can be used as the basis for crop monitoring at large scales. Elodie Vintrou, Agnès Bégué, Christian Baron, and Danny There is a real need for better discrimination and identifica- Lo Seen are with CIRAD UMR TETIS, Maison de la Télédé- tion of the main agricultural systems in many regions of the tection, 500 rue J.F. Breton, Montpellier, F-34093 France ([email protected]). Photogrammetric Engineering & Remote Sensing Mamy Soumaré is with IER, Rue Mohamed V, BP 258, Vol. 78, No. 8, August 2012, pp. 839–848. Bamako, Mali. 0099-1112/12/7808–839/$3.00/0 Simon Bernard is with the University of Rouen, LITIS EA © 2012 American Society for Photogrammetry 4108, BP 12 - 76801 Saint-Etienne du Rouvray, France. and Remote Sensing PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING August 2012 839 04-GC-005.qxd 7/13/12 8:49 PM Page 840 world, because they indicate how the system will interact To investigate the applicability of moderate-resolution with the local environment and water conditions and thus imagery for the classification of agricultural systems, we influence yields and productions. These interactions can be chose to use the example of Mali and, on the crop mask, estimated empirically or by using agro-meteorological calculated spectral, spatial, textural, and temporal metrics models, so as to propose forecast scenarios of crop yield derived from MODIS time-series images. We used a training estimates. data set of observations collected in 100 villages in Mali and Thus, in the context of fragmented landscapes, and due the remotely-sensed metrics associated with each village to to the absence of adequate Earth Observing Systems, the build a Random Forest (RF) statistical model, capable of objective of this study was to develop a new approach to predicting the main type of agricultural system in every classifying and mapping the main agricultural systems in village in South Mali. Our paper is organized as follows: West Africa using coarse resolution imagery. The method is after a brief description of the study area, we present the based on the general assumption that human practices shape training data set and the RF algorithm we built. We then the landscape. We adapted this assumption to the cultivated investigate the importance of satellite-derived metrics for domain by claiming that agricultural systems shape the mapping agricultural systems, and present the resulting cultivated part of the landscape, and as a result, these classification of the villages of South Mali. We conclude by systems could be captured through a set of landscape discussing the accuracy and relevance of the method and attributes, such as (a) vegetation biomass, (b) organization of the possible use of the resulting map in food security the landscape components, and (c) vegetation seasonality systems. (phenology and crop calendar). These attributes are known to be accessible using time series of multispectral images. Among these characteristics, biomass estimation is certainly Study Area and Data Sets the most frequently investigated (e.g., Hutchinson, 1991; Prince and Goward, 1995). Concerning landscape organiza- Study Area tion, classical pixel-based classification methods do not Mali is a land-locked country covering of 1,240,192 km2 in make sufficient use of spatial concepts of neighborhood, West Africa between latitudes 10°N and 24°N (Figure 1). proximity, or homogeneity (Burnett and Blaschke, 2003). The topography consists mostly of low plateaus and basins A number of authors used texture analysis to classify high with occasional rocky hills. The plateaus never exceed 300 and very high resolution images (e.g., Kayitakire et al., 2006; or 400 m. Mali has a latitudinal climatic gradient ranging Peddle and Franklin, 1991), but very few studied possibility from sub-humid to semi-arid that, further north, extends to of using the texture of coarse resolution images in land arid and desertic. Like other West African countries along classification (see Tsaneva et al. (2010) for one example). the same latitudinal belt, food security relies on an adequate Concerning vegetation seasonality, recent multitemporal supply of rainfall during the monsoon season. Mali encom- studies demonstrated that MODIS data can be used to detect passes the Sudano-Sahelian zone, where strong dependence unique multi-temporal signatures for each of the major crop on rain-fed agriculture implies exposure to climate variabil- types (e.g., Wardlow et al., 2007), to map double cropping ity, in addition to the impacts population growth have on (e.g., Arvor et al., 2012), or to map irrigated areas (Thenk- food security. Farming is the main source of income for abail et al., 2009b). many people in the Sudano-Sahelian region, where millet Figure 1. Map of Mali showing the geographic location of the 100 villages sampled along the rainfall gradient (1971-2000 year average). 840 August 2012 PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING 04-GC-005.qxd 7/13/12 8:49 PM Page 841 and sorghum are the main food crops. The vast majority of • Class C villages represent 34 percent of the data set, and the population (80 percent) consists of subsistence farmers. have less intensive practices, as they cultivate both coarse A few larger farms produce crops for sale (cash crops), grain (sorghum) and a cash crop (cotton). Class C represents mainly cotton and peanuts. In this study, we did not “mixed agriculture” neither completely dedicated to grains, include the Saharan zone in the north of the country where nor to cotton.