Remote sensing of opium poppy cultivation in Afghanistan

This work investigates differences in the survey methodologies of the monitoring programmes of the United Nations Office on Drugs and Crime (UNODC) and the US Government that lead to discrepancies in quantitative information about poppy cultivation. The aim of the research is to improve annual estimates of opium production. Scientific trials conducted for the UK Government (2006–2009) revealed differences between the two surveys that could account for the inconsistency in results. These related to the image interpretation of poppy from very high resolution satellite imagery, the mapping of the total area of agriculture and stratification using full coverage medium resolution imagery. MODIS time-series profiles of Normalised Difference Vegetation Index (NDVI), used to monitor Afghanistan’s agricultural system, revealed significant variation in the agriculture area between years caused by land management practices and expansion into new areas. Image interpretation of crops was investigated as a source of bias within the sample using increasing levels of generalisation in sample interpretations. Automatic segmentation and object-based classification were tested as methods to improve consistency. Generalisation was found to bias final estimates of poppy up to 14%. Segments were consistent with manual field delineations but object-based classification caused a systematic labelling error. The findings show differences in survey estimates based on interpretation keys and the resolution of imagery, which is compounded in areas of marginal agriculture or years with poor crop establishment. Stratified and unstratified poppy cultivation estimates were made using buffered and unbuffered agricultural masks at resolutions of 20, 30 and 60 m, resampled from SPOT-5 10 m data. The number of strata (1, 4, 8, 13, 23, 40) and sample fraction (0.2 to 2%) used in the estimate were also investigated. Decreasing the resolution of the imagery and buffering increased unstratified estimates. Stratified estimates were more robust to changes in sample size and distribution. The mapping of the agricultural area explained differences in cultivation figures of the opium monitoring programmes in Afghanistan. Supporting methods for yield estimation for opium poppy were investigated at field sites in the UK in 2004, 2005 and 2010. Good empirical relationships were found between NDVI and the yield indicators of mature capsule volume and dry capsule yield. The results suggested a generalised relationship across all sampled fields and years (R2 >0.70) during the 3–4 week period including poppy flowering. The application of this approach in Afghanistan was investigated using VHR satellite imagery and yield data from the UNODC’s annual survey. Initial results indicated the potential of improved yield estimates using a smaller and targeted collection of ground observations as an alternative to random sampling. The recommendations for poppy cultivation surveys are: the use of image-based stratification for improved precision and reducing differences in the agricultural mask, and use of automatic segmentation for improved consistency in field delineation of poppy crops. The findings have wider implications for improved confidence in statistical estimates from remote sensing methodologies

Opium yield estimates in Afghanistan using remote sensing

Accurate estimates of opium production are essential for informing counter-narcotics policy in Afghanistan. The cultivated area of opium poppy is estimated remotely by interpretation or digital classification of very high resolution (VHR) satellite imagery at sample locations. Obtaining an accurate estimate of average yield is more challenging as poor security prevents access to a sufficient number of field locations to collect a representative sample. Previous work carried out in the UK developed a regression estimator methodology using the empirical relationship between the remotely sensed normalised difference vegetation index (NDVI) and the yield indicator mature capsule volume. The application of the remote sensing approach was investigated in the context of the existing annual opium survey conducted by the United Nations Office on Drugs and Crime and Afghanistan’s Ministry of Counter Narcotics (UNODC/MCN) and indicated the potential for bias correction of yield estimates from a small targeted field sample. In this study we test the approach in Afghanistan using yield data and VHR satellite imagery collected by the UNODC/MCN surveys in 2013 and 2014. Field averaged measurements of capsule volume were compared to field averaged NDVI extracted using visual interpretation of poppy fields. The study compares the empirical relationships from the UK field trials with the Afghanistan data and discusses the challenges of developing an operational methodology for accurate opium yield estimation from the limited sample possible in Afghanistan

The application of time-series MODIS NDVI profiles for the acquisition of crop information across Afghanistan

We investigated and developed a prototype crop information system integrating 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data with other available remotely sensed imagery, field data, and knowledge as part of a wider project monitoring opium and cereal crops. NDVI profiles exhibited large geographical variations in timing, height, shape, and number of peaks, with characteristics determined by underlying crop mixes, growth cycles, and agricultural practices. MODIS pixels were typically bigger than the field sizes, but profiles were indicators of crop phenology as the growth stages of the main first-cycle crops (opium poppy and cereals) were in phase. Profiles were used to investigate crop rotations, areas of newly exploited agriculture, localized variation in land management, and environmental factors such as water availability and disease. Near-real-time tracking of the current years’ profile provided forecasts of crop growth stages, early warning of drought, and mapping of affected areas. Derived data products and bulletins provided timely crop information to the UK Government and other international stakeholders to assist the development of counter-narcotic policy, plan activity, and measure progress. Results show the potential for transferring these techniques to other agricultural systems

Survey and monitoring of opium poppy and wheat in Afghanistan: 2003-2009

An integrated application of remote-sensing technology was devised and applied in Afghanistan during 2003–2009 providing critical information on cereal and poppy cultivation and poppy eradication. The results influenced UK and international policy and counter-narcotics actions in Afghanistan

Fully convolutional neural nets in-the-wild

The ground breaking performance of fully convolutional neural nets (FCNs) for semantic segmentation tasks has yet to be achieved for landcover classification, partly because a lack of suitable training data. Here the FCN8 model is trained and evaluated in real-world conditions, so called in-the-wild, for the classification of opium poppy and cereal crops at very high resolution (1 m). Densely labelled image samples from 74 Ikonos scenes were taken from 3 years of opium cultivation surveys for Helmand Province, Afghanistan. Models were trained using 1 km2 samples, subsampled patches and transfer learning. Overall accuracy was 88% for a FCN8 model transfer-trained on all three years of data and complex features were successfully grouped into distinct field parcels from the training data alone. FCNs can be trained end-to-end using variable sized input images for pixel-level classification that combines the spatial and spectral properties of target objects in a single operation. Transfer learning improves classifier performance and can be used to share information between FCNs, demonstrating their potential to significantly improve land cover classification more generally

Mapping the expansion of galamsey gold mines in the cocoa growing area of Ghana using optical remote sensing

Artisanal gold mining (galamsey) and cocoa farming are essential sources of income for local populations in Ghana. Unfortunately the former poses serious threats to the environment and human health, and conflicts with cocoa farming and other livelihoods. Timely and spatially referenced information on the extent of galamsey is needed to understand and limit the negative impacts of mining. To address this, we use multi-date UK-DMC2 satellite images to map the extent and expansion of galamsey from 2011 to 2015. We map the total area of galamsey in 2013 over the cocoa growing area, using k-means clustering on a cloud-free 2013 image with strong spectral contrast between galamsey and the surrounding vegetation. We also process a pair of hazy images from 2011 and 2015 with Multivariate Alteration Detection to map the 2011–2015 galamsey expansion in a subset, labelled the change area. We use a set of visually interpreted random sample points to compute bias-corrected area estimates. We also delineate an indicative impact zone of pollution proportional to the density of galamsey, assuming a maximum radius of 10 km. In the cocoa growing area of Ghana, the estimated total area of galamsey in 2013 is 27,839 ha with an impact zone of 551,496 ha. In the change area, galamsey has more than tripled between 2011 and 2015, resulting in 603 ha of direct encroachment into protected forest reserves. Assuming the same growth rate for the rest of the cocoa growing area, the total area of galamsey in 2015 is estimated at 43,879 ha. Galamsey is developing along most of the river network (Offin, Ankobra, Birim, Anum, Tano), with downstream pollution affecting both land and water

Improved estimates of opium cultivation in Afghanistan using imagery-based stratification

The United Nations O ce on Drugs and Crime and the US Government make extensive use of remote sensing to quantify and monitor trends in Afghanistans illicit opium production. Cultivation gures from their independent annual surveys can vary because of systematic di erences in survey methodologies relating to spectral strati cation and the addition of a pixel bu er to the agricultural area. We investigated the e ect of strati cation and bu ering on area estimates of opium poppy using SPOT5 imagery covering the main opium cultivation area of Helmand province and sample data of poppy elds interpreted from very high resolution satellite imagery. The e ect of resolution was investigated by resampling the original 10 m pixels to 20, 30 and 60 m, representing the range of available imagery. The number of strata (1, 4, 8, 13, 23, 40) and sample fraction (0.2 to 2%) used in the estimate were also investigated. Strati cation reduced the con dence interval by improving the precision of estimates. Cultivation estimates of poppy using 40 spectral strata and a sample fraction of 1.1% had a similar precision to direct expansion estimates using a 2% sample fraction. Strati ed estimates were more robust to changes in sample size and distribution. The mapping of the agricultural area had a signi cant e ect on poppy cultivation estimates in Afghanistan, where the area of total agricultural production can vary signi cantly between years. The ndings of this research explain di erences in cultivation gures of the opium monitoring programmes in Afghanistan and recommendations can be applied to improve resource monitoring in other geographic areas

Image segmentation for improved consistency in image-interpretation of opium poppy

The image-interpretation of opium poppy crops from very high resolution satellite imagery forms part of the annual Afghanistan opium surveys conducted by the United Nations Office on Drugs and Crime and the United States Government. We tested the effect of generalization of field delineations on the final estimates of poppy cultivation using survey data from Helmand province in 2009 and an area frame sampling approach. The sample data was reinterpreted from pan-sharpened IKONOS scenes using two increasing levels of generalization consistent with observed practice. Samples were also generated from manual labelling of image segmentation and from a digital object classification. Generalization was found to bias the cultivation estimate between 6.6% and 13.9%, which is greater than the sample error for the highest level. Object classification of image-segmented samples increased the cultivation estimate by 30.2% because of systematic labelling error. Manual labelling of image-segmented samples gave a similar estimate to the original interpretation. The research demonstrates that small changes in poppy interpretation can result in systematic differences in final estimates that are not included within confidence intervals. Segmented parcels were similar to manually digitized fields and could provide increased consistency in field delineation at a reduced cost. The results are significant for Afghanistan’s opium monitoring programmes and other surveys where sample data are collected by remote sensing

Mapping agricultural land in Afghanistan’s opium provinces using a generalised deep learning model and medium resolution satellite imagery

Understanding the relationship between land use and opium production is critical for monitoring the dynamics of poppy cultivation and developing an effective counter narcotics policy in Afghanistan. However, mapping agricultural land accurately and rapidly is challenging, as current methods require resource-intensive and time consuming manual image-interpretation. Deep convolutional neural nets have been shown to greatly reduce the manual effort in mapping agriculture from satellite imagery but require large amounts of densely labelled training data for model training. Here we develop a generalised model using past images and labels from different medium resolution satellite sensors for fully automatic agricultural land classification using the latest medium resolution satellite imagery. The model (FCN-8) is first trained on Disaster Monitoring Constellation (DMC) satellite images from 2007 to 2009. The effect of shape, texture and spectral features on model performance are investigated along with normalisation in order to standardise input medium resolution imagery from DMC, Landsat-5, Landsat-8, and Sentinel-2 for transfer learning between sensors and across years. Textural features make the highest contribution to overall accuracy (∼73%) while the effect of shape is minimal. The model accuracy on new images, with no additional training, is comparable to visual image interpretation (overall > 95%, user accuracy > 91%, producer accuracy > 85%, and frequency weighted intersection over union > 67%). The model is robust and was used to map agriculture from archive images (1990) and can be used in other areas with similar landscapes. The model can be updated by fine tuning using smaller, sparsely labelled datasets in the future. The generalised model was used to map the change in agricultural area in Helmand Province, showing the expansion of agricultural land into former desert areas. Training generalised deep learning models using data from both new and long-term EO programmes, with little or no requirement for fine tuning, is an exciting opportunity for automating image classification across datasets and through time that can improve our understanding of the environment.Natural Environment Research Council (NERC): NE/M009009/