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

A multi sensor data fusion approach for creating variable depth tillage zones

Efficiency of tillage depends largely on the nature of the field, soil type, spatial distribution of soil properties and the correct setting of the tillage implement. However, current tillage practice is often implemented without full understanding of machine design and capability leading to lowered efficiency and further potential damage to the soil structure. By modifying the physical properties of soil only where the tillage is needed for optimum crop growth, variable depth tillage (VDT) has been shown to reduce costs, labour, fuel consumption and energy requirements. To implement VDT it is necessary to determine and map soil physical properties, spatially and with depth through the soil profile. In this research a multi-sensor and data fusion approach was developed that augmented data collected with an electromagnetic sensor with a standard penetrometer, and conventional methods for the measurement of bulk density (BD) and moisture content (MC). Packing density values were recorded for eight soil layers of 0-5, 5-10, 10-15, 15-20, 20-25, 25-30 30-35 and 35-40 cm. From the results only 62% of the site required the deepest tillage at 38 cm, 16% required tillage at 33 cm and 22% required no tillage at all. The resultant maps of packing density were shown to be a useful tool to guide VDT operations. The results provided in this study indicate that the new multi-sensor and data fusion approach introduced is a useful approach to map layered soil compaction to guide VDT operations

Maritime vessel classification to monitor fisheries with SAR: demonstration in the North Sea

Integration of methods based on satellite remote sensing into current maritime monitoring strategies could help tackle the problem of global overfishing. Operational software is now available to perform vessel detection on satellite imagery, but research on vessel classification has mainly focused on bulk carriers, container ships, and oil tankers, using high-resolution commercial Synthetic Aperture Radar (SAR) imagery. Here, we present a method based on Random Forest (RF) to distinguish fishing and non-fishing vessels, and apply it to an area in the North Sea. The RF classifier takes as input the vessel’s length, longitude, and latitude, its distance to the nearest shore, and the time of the measurement (am or pm). The classifier is trained and tested on data from the Automatic Identification System (AIS). The overall classification accuracy is 91%, but the precision for the fishing class is only 58% because of specific regions in the study area where activities of fishing and non-fishing vessels overlap. We then apply the classifier to a collection of vessel detections obtained by applying the Search for Unidentified Maritime Objects (SUMO) vessel detector to the 2017 Sentinel-1 SAR images of the North Sea. The trend in our monthly fishing-vessel count agrees with data from Global Fishing Watch on fishing-vessel presence. These initial results suggest that our approach could help monitor intensification or reduction of fishing activity, which is critical in the context of the global overfishing problem

A multi sensor data fusion approach for creating variable depth tillage zones

In this research a multi-sensor and data fusion approach was developed to create variable depth tillage zones. Data collected with an electromagnetic sensor was fused with measurements taken with a hydraulic penetrometer and conventionally acquired soil bulk density (BD) and moisture content (MC) measurements. Packing density values were then calculated for eight soil layers to determine the need to cultivate or not. From the results 62% of the site required the deepest tillage at 38 cm, 16% required tillage at 33 cm and 22% required no tillage at all. The resultant maps of packing density were shown to be a useful approach to map layered soil compaction and guide VDT operations

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

A method to assess the performance of SAR-derived surface soil moisture products

Synthetic aperture radar (SAR) is a remote sensing technique for mapping of soil moisture with high spatial resolution. C -band SAR can resolve features at field scale, or better, but responds to moisture only within the top 1 to 2 cm of the soil. When validating SAR-derived soil moisture products against standard in situ measurements at 5 to 10 cm depth, the greater moisture variability at the soil surface may be inaccurately categorized as measurement error. An alternative method was developed where the C -band SAR product is validated against soil moisture simulated at 2 cm depth by the HYDRUS-1D model. This reproduces soil moisture depth profiles from daily meteorological observations, leaf area index, and soil hydraulic parameters. The model was fitted at 13 COSMOS-UK sites so that the model output at 10 cm depth closely reproduced the cosmic ray neutron sensor data. At ten of the sites studied, there was an improvement of up to 8% in root-mean-squared difference by validating the Copernicus surface soil moisture (SSM) product at 2 cm compared to 10 cm. This suggests that Copernicus SSM and other C -band SAR surface soil moisture algorithms may be more accurate than have hitherto been acknowledged

Optimising configuration of a hyperspectral imager for on-line field measurement of wheat canopy

There is a lack of information on optimal measurement configuration of hyperspectral imagers for on-line measurement of a wheat canopy. This paper aims at identifying this configuration using a passive sensor (400–750 nm). The individual and interaction effects of camera height and angle, sensor integration time and light source distance and height on the spectra's signal-to-noise ratio (SNR) were evaluated under laboratory scanning conditions, from which an optimal configuration was defined and tested under on-line field measurement conditions. The influences of soil total nitrogen (TN) and moisture content (MC) measured with an on-line visible and near infrared (vis-NIR) spectroscopy sensor on SNR were also studied. Analysis of variance and principal component analysis (PCA) were applied to understand the effects of the laboratory considered factors and to identify the most influencing components on SNR. Results showed that integration time and camera height and angle are highly influential factors affecting SNR. Among integration times of 10, 20 and 50 ms, the highest SNR was obtained with 1.2 m, 1.2 m and 10° values of light height, light distance and camera angle, respectively. The optimum integration time for on-line field measurement was 50 ms, obtained at an optimal camera height of 0.3 m. On-line measured soil TN and MC were found to have significant effects on the SNR with Kappa values of 0.56 and 0.75, respectively. In conclusion, an optimal configuration for a tractor mounted hyperspectral imager was established for the best quality of on-line spectra collected for wheat canopy

Evaluating MODIS dust-detection indices over the Arabian Peninsula

Sand and dust storm events (SDEs), which result from strong surface winds in arid and semi-arid areas, exhibiting loose dry soil surfaces are detrimental to human health, agricultural land, infrastructure, and transport. The accurate detection of near-surface dust is crucial for quantifying the spatial and temporal occurrence of SDEs globally. The Arabian Peninsula is an important source region for global dust due to the presence of extensive deserts. This paper evaluates the suitability of five different MODIS-based methods for detecting airborne dust over the Arabian Peninsula: (a) Normalized Difference Dust Index (NDDI); (b) Brightness Temperature Difference (BTD) (31–32); (c) BTD (20–31); (d) Middle East Dust Index (MEDI) and (e) Reflective Solar Band (RSB). We derive detection thresholds for each index by comparing observed values for ‘dust-present’ versus ‘dust-free’ conditions, taking into account various land cover settings and analyzing associated temporal trends. Our results suggest that the BTD (31–32) method and the RSB index are the most suitable indices for detecting dust storms over different land-cover types across the Arabian Peninsula. The NDDI and BTD (20–31) methods have limitations in identifying dust over multiple land-cover types. Furthermore, the MEDI has been found to be unsuitable for detecting dust in the study area across all land-cover types

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