Identifying potential areas of Cannabis sativa plantations using object-based image analysis of SPOT-5 satellite data

• Published in: International journal of remote sensing 2013, Vol.34 (15), p.5409-5428
• Main Authors: Lisita, Alessandra, Sano, Edson E, Durieux, Laurent
• Format: Article
• Language: English
• Subjects: algorithms; Animal, plant and microbial ecology; Applied geophysics; Biological and medical sciences; Cannabis; Cannabis sativa; computer software; crops; Cultivation; Density; Earth sciences; Earth, ocean, space; Exact sciences and technology; Fundamental and applied biological sciences. Psychology; fuzzy logic; General aspects. Techniques; Illicit; illicit drugs; image analysis; Internal geophysics; irrigation; land cover; Plantations; satellites; semiarid zones; Spatial resolution; Teledetection and vegetation maps; topography; Vegetation; water content; water supply; weight-of-evidence
• ISSN: 1366-5901; 0143-1161; 1366-5901
• DOI: 10.1080/01431161.2013.790574

The rapid and efficient detection of illicit drug cultivation, such as that of Cannabis sativa, is important in reducing consumption. The objective of this study was to identify potential sites of illicit C. sativa plantations located in the semi-arid, southern part of Pernambuco State, Brazil. The study was conducted using an object-based image analysis (OBIA) of Système Pour l'Observation de la Terre high-resolution geometric (SPOT-5 HRG) images (overpass: 31 May, 2007). OBIA considers the target's contextual and geometrical attributes to overcome the difficulties inherent in detecting illicit crops associated with the grower's strategies to conceal their fields and optimizes the spectral information extracted to generate land-cover maps. The capabilities of the SPOT-5 near-infrared and shortwave infrared bands to discriminate herbaceous vegetation with high water content, and employment of the support vector machine classifier, contributed to accomplishing this task. Image classification included multiresolution segmentation with an algorithm available in the eCognition Developer software package. In addition to a SPOT-5 HRG multispectral image with 10 m spatial resolution and a panchromatic image with 2.5 m spatial resolution, first-order indices such as the normalized difference vegetation index and ancillary data including land-cover classes, anthropogenic areas, slope, and distance to water sources were also employed in the OBIA. The classification of segments (objects) related to illegal cultivation employed fuzzy logic and fixed-threshold membership functions to describe the following spectral, geometrical, and contextual properties of targets: vegetation density, topography, neighbourhood, and presence of water supplies for irrigation. The results of OBIA were verified from a weight of evidence analysis. Among 15 previously known C. sativa sites identified during police operations conducted on 5–17 June 2007, eight sites were classified as maximum-alert areas (total area of 22.54 km² within a total area of object-oriented image classification of ∼1800 km²). The approach proposed in this study is feasible for reducing the area to be searched for illicit cannabis cultivation in semi-arid regions.