Browsing by Author "Simms, Daniel M."
Now showing 1 - 15 of 15
Results Per Page
Sort Options
Item Open Access Adaptive vibration condition monitoring technology for local tooth damage in gearboxes.(Learned and Professional Society Publishers, 2005-08-01T00:00:00Z) Gelman, Leonid; Zimroz, R; Birkel, J; Leigh-Firbank, H; Simms, Daniel M.; Waterland, B; Whitehurst, GAn adaptive approach was applied for local tooth damage diagnostics in gearboxes. The expediency of adaptation was proved experimentally for the new diagnostic feature, the sum of normalized sideband amplitudes. The positive correlation between mesh amplitudes and their sideband amplitudes was found experimentally for the first time. Novel adaptive vibration condition monitoring technology for local tooth damage in gearboxes was developed and experimentally validated. The experimental results showed an increase in effectiveness of the diagnostics when the adaptive technology was used.Item Open Access The application of time-series MODIS NDVI profiles for the acquisition of crop information across Afghanistan(Taylor and Francis, 2014-08-26) Simms, Daniel M.; Waine, Toby W.; Taylor, John C.; Juniper, Graham R.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.Item Open Access Fully convolutional neural nets in-the-wild(Taylor and Francies, 2020-10-20) Simms, Daniel M.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.Item Open Access Improved estimates of opium cultivation in Afghanistan using imagery-based stratification(Taylor and Francis, 2017-03-30) Simms, Daniel M.; Waine, Toby W.; Taylor, John C.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.Item Open Access An integrated approach to control and manage potato black dot disease: a review(Springer, 2023-09-15) Sanzo-Miró, Marta; Simms, Daniel M.; Rezwan, Faisal I.; Terry, Leon A.; Alamar, M. CarmenPotato black dot is a foliar and tuber blemish disease that has become an increasingly economic problem in recent years. Black dot is caused by the fungus Colletotrichum coccodes and is characterised by silver/brown lesions on the tuber skin leading to lower aesthetic quality of potatoes destined for the pre-pack market. Given the consumers’ growing demand for washed and pre-packed potatoes, skin blemish diseases (such as black dot and silver scurf), once considered of minor importance, are now serious challenges for the fresh potato industry. The management of C. coccodes is far from satisfactory at either pre- or postharvest stages: firstly, the disease symptoms have not been consistently described on potato plant foliage; and secondly, black dot disease is often confounded with other tuber blemishes during postharvest storage. Good field managing practices in combination with improved postharvest strategies and an accurate detection support tool can be a useful integrated approach to manage potato black dot disease. This review aims to evaluate and critically discuss different novel approaches for better management and detection of potato black dot disease.Item Open Access Mapping agricultural land in Afghanistan’s opium provinces using a generalised deep learning model and medium resolution satellite imagery(MDPI, 2023-09-26) Simms, Daniel M.; Hamer, Alex M.; Zeiler, Irmgard; Vita, Lorenzo; Waine, Toby W.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.Item Open Access Mapping agricultural land in support of opium monitoring in Afghanistan with Convolutional Neural Networks (CNNs).(Cranfield University, 2021-12) Hamer, Alex Matthew; Waine, Toby W.; Simms, Daniel M.This work investigates the use of advanced image classification techniques for improving the accuracy and efficiency in determining agricultural areas from satellite images. The United Nations Office on Drugs and Crime (UNODC) need to accurately delineate the potential area under opium cultivation as part of their opium monitoring programme in Afghanistan. They currently use unsupervised image classification, but this is unable to separate some areas of agriculture from natural vegetation and requires time-consuming manual editing. This is a significant task as each image must be classified and interpreted separately. The aim of this research is to derive information about annual changes in land-use related to opium cultivation using convolutional neural networks with Earth observation data. Supervised machine learning techniques were investigated for agricultural land classification using training data from existing manual interpretations. Although pixel-based machine learning techniques achieved high overall classification accuracy (89%) they had difficulty separating between agriculture and natural vegetation at some locations. Convolutional Neural Networks (CNNs) have achieved ground-breaking performance in computer vision applications. They use localised image features and offer transfer learning to overcome the limitations of pixel-based methods. There are challenges related to training CNNs for land cover classification because of underlying radiometric and temporal variations in satellite image datasets. Optimisation of CNNs with a targeted sampling strategy focused on areas of known confusion (agricultural boundaries and natural vegetation). The results showed an improved overall classification accuracy of +6%. Localised differences in agricultural mapping were identified using a new tool called ‘localised intersection over union’. This provides greater insight than commonly used assessment techniques (overall accuracy and kappa statistic), that are not suitable for comparing smaller differences in mapping accuracy. A generalised fully convolutional model (FCN) was developed and evaluated using six years of data and transfer learning. Image datasets were standardised across image dates and different sensors (DMC, Landsat, and Sentinel-2), achieving high classification accuracy (up to 95%) with no additional training. Further fine-tuning with minimal training data and a targeted training strategy further increased model performance between years (up to +5%). The annual changes in agricultural area from 2010 to 2019 were mapped using the generalised FCN model in Helmand Province, Afghanistan. This provided new insight into the expansion of agriculture into marginal areas in response to counter-narcotic and alternative livelihoods policy. New areas of cultivation were found to contribute to the expansion of opium cultivation in Helmand Province. The approach demonstrates the use of FCNs for fully automated land cover classification. They are fast and efficient, can be used to classify satellite imagery from different sensors and can be continually refined using transfer learning. The proposed method overcomes the manual effort associated with mapping agricultural areas within the opium survey while improving accuracy. These findings have wider implications for improving land cover classification using legacy data on scalable cloud-based platforms.Item Open Access Mapping the expansion of galamsey gold mines in the cocoa growing area of Ghana using optical remote sensing(Elsevier, 2017-02-23) Snapir, Boris; Simms, Daniel M.; Waine, Toby W.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.Item Open Access Modeling the spatial-spectral characteristics of plants for nutrient status identification using hyperspectral data and deep learning methods(Frontiers, 2023-10-16) Okyere, Frank Gyan; Cudjoe, Daniel; Sadeghi-Tehran, Pouria; Virlet, Nicolas; Riche, Andrew B.; Castle, March; Greche, Latifa; Simms, Daniel M.; Mhada, Manal; Mohareb, Fady; Hawkesford, Malcolm JohnSustainable fertilizer management in precision agriculture is essential for both economic and environmental reasons. To effectively manage fertilizer input, various methods are employed to monitor and track plant nutrient status. One such method is hyperspectral imaging, which has been on the rise in recent times. It is a remote sensing tool used to monitor plant physiological changes in response to environmental conditions and nutrient availability. However, conventional hyperspectral processing mainly focuses on either the spectral or spatial information of plants. This study aims to develop a hybrid convolution neural network (CNN) capable of simultaneously extracting spatial and spectral information from quinoa and cowpea plants to identify their nutrient status at different growth stages. To achieve this, a nutrient experiment with four treatments (high and low levels of nitrogen and phosphorus) was conducted in a glasshouse. A hybrid CNN model comprising a 3D CNN (extracts joint spectral-spatial information) and a 2D CNN (for abstract spatial information extraction) was proposed. Three pre-processing techniques, including second-order derivative, standard normal variate, and linear discriminant analysis, were applied to selected regions of interest within the plant spectral hypercube. Together with the raw data, these datasets were used as inputs to train the proposed model. This was done to assess the impact of different pre-processing techniques on hyperspectral-based nutrient phenotyping. The performance of the proposed model was compared with a 3D CNN, a 2D CNN, and a Hybrid Spectral Network (HybridSN) model. Effective wavebands were selected from the best-performing dataset using a greedy stepwise-based correlation feature selection (CFS) technique. The selected wavebands were then used to retrain the models to identify the nutrient status at five selected plant growth stages. From the results, the proposed hybrid model achieved a classification accuracy of over 94% on the test dataset, demonstrating its potential for identifying nitrogen and phosphorus status in cowpea and quinoa at different growth stages.Item Open Access Multi-stage semantic segmentation quantifies fragmentation of small habitats at a landscape scale(MDPI, 2023-11-07) van der Plas, Thijs L.; Geikie, Simon T.; Alexander, David G.; Simms, Daniel M.Land cover (LC) maps are used extensively for nature conservation and landscape planning, but low spatial resolution and coarse LC schemas typically limit their applicability to large, broadly defined habitats. In order to target smaller and more-specific habitats, LC maps must be developed at high resolution and fine class detail using automated methods that can efficiently scale to large areas of interest. In this work, we present a Machine Learning approach that addresses this challenge. First, we developed a multi-stage semantic segmentation approach that uses Convolutional Neural Networks (CNNs) to classify LC across the Peak District National Park (PDNP, 1439 km2) in the UK using a detailed, hierarchical LC schema. High-level classes were predicted with 95% accuracy and were subsequently used as masks to predict low-level classes with 72% to 92% accuracy. Next, we used these predictions to analyse the degree and distribution of fragmentation of one specific habitat—wet grassland and rush pasture—at the landscape scale in the PDNP. We found that fragmentation varied across areas designated as primary habitat, highlighting the importance of high-resolution LC maps provided by CNN-powered analysis for nature conservation.Item Open Access Opium yield estimates in Afghanistan using remote sensing(International Institute for Sustainable Development, 2016-10-31) Simms, Daniel M.; Waine, TobyAccurate 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.Item Open Access Remote sensing of opium poppy cultivation in Afghanistan(Cranfield University, 2016-01-04) Simms, Daniel M.; Waine, Toby W.; Taylor, J. C.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.Item Open Access Replacing human interpretation of agricultural land in Afghanistan with a deep convolutional neural network(Taylor and Francis, 2021-01-18) Hamer, A. M.; Simms, Daniel M.; Waine, Toby WilliamAfghanistan’s annual opium survey relies upon time-consuming human interpretation of satellite images to map the area of potential poppy cultivation for statistical sample design. Deep Convolutional Neural Networks (CNNs) have shown ground-breaking performance for image classification tasks by encoding local contextual information, in some cases outperforming trained analysts. In this study, we investigate the development of a CNN to automate the classification of agriculture from medium-resolution satellite imagery as an alternative to manual interpretation. The residual network (ResNet50) CNN architecture was trained and validated for delineating the agricultural area using labelled multi-seasonal Disaster Monitoring Constellation (DMC) satellite imagery (32 m) of Helmand and Kandahar provinces. The effect of input image chip size, training sampling strategy, elevation data, and multi-seasonal imagery were investigated. The best-performing single-year classification used an input chip size of 33 × 33 pixels, a targeted sampling strategy and transfer learning, resulting in high overall accuracy (94%). The inclusion of elevation data marginally lowered performance (93%). Multi-seasonal classification achieved an overall accuracy of 89% using the previous two years’ data. Only 25% of the target year’s training samples were necessary to update the model to achieve >94% overall accuracy. A data-driven approach to automate agricultural mask production using CNNs is proposed to reduce the burden of human interpretation. The ability to continually update CNN models with new data has the potential to significantly improve automatic classification of vegetation across yearsItem Open Access Using Near-Surface Photogrammetry Assessment of Surface Roughness (NSPAS) to assess the effectiveness of erosion control treatments applied to slope forming materials from a mine site in West Africa(Elsevier, 2018-08-30) Campbell, Stephanie; Simmons, Robert W.; Rickson, R. Jane; Waine, Toby; Simms, Daniel M.Geo-spatial studies are increasingly using photogrammetry technology because the cost of the equipment is becoming cheaper, the techniques are accessible to non-experts and can generate better quality topographic data than traditional approaches. NSPAS (Near-Surface Photogrammetry Assessment of Surface Roughness) was developed to quantify the micro-topographic changes in ground surface roughness caused by simulated rainfall, to better understand the comparative erodibility of two non-soil and one soil slope forming materials from a mine in West Africa. This innovative approach creates DEMs (digital elevation models) using image pairs acquired by near-surface stereo photogrammetry (<300 m), to measure surface roughness within Leica Photogrammetry Suite 2011 (LPS) in ERDAS Imagine software and ESRI Arc-GIS. NSPAS can readily quantify aggregate breakdown processes across a 0.02 m2 surface by accurately detecting 0.84 mm to 2.49 mm changes in surface topography. The methodology is advantageous to micro-scale (<1 cm2) studies that require a high number of accurate DEMs, because it will produce image pairs even when the target does not have contrasting surface features in shot, which can be a constraint for the automated technique Structure from Motion. This paper demonstrates how NSPAS is more suitable to assess erosion from slope forming materials that do not have a high content of large rocks (>2 mm) at the surface. With further development NSPASS has the capability to be used in many other types of geospatial investigations.Item Open Access Vegetation cover dynamics along two Himalayan rivers: drivers and implications of change(Elsevier, 2022-08-18) Beale, John; Grabowski, Robert C.; Lokidor, Pauline Long'or; Vercruysse, Kim; Simms, Daniel M.Rivers are dynamic landscape features that change in response to natural and anthropogenic factors through hydrological, geomorphic and ecological processes. The severity and magnitude of human impacts on river system and riparian vegetation has dramatically increased over the last century with the proliferation of valley-spanning dams, intensification of agriculture, urbanization, and more widespread channel engineering. This study aims to determine how changes in geomorphic form and dynamics caused by these human alterations relate to changes in channels and riparian vegetation in the lower Beas and Sutlej Rivers. These rivers are tributaries of the Indus that drain the Western Himalayas but differ in the type and magnitude of geomorphic change in recent decades. Winter season vegetation was analysed over 30 years, revealing increasing trends in vegetated land cover in the valleys of both rivers, consistent with large-scale drivers of change. Greater trends within the active channels indicate upstream drivers are influencing river flow and geomorphology, vegetation growth and human exploitation. The spatial patterns of vegetation change differ between the rivers, emphasizing how upstream human activities (dams and abstraction) control geomorphic and vegetation community response within the landscape context of the river. The increasing area of vegetated land is reinforcing the local evolutionary trajectory of the river planform from wide-braided wandering to single thread meandering. Narrowing of the active channels is altering the balance of resource provision and risk exposure to people. New areas being exploited for agriculture are exposed to greater risk from river erosion, inundation, and sediment deposition. Moreover, the change from braided to meandering planform has concentrated erosion on riverbanks, placing communities and infrastructure at risk. By quantifying and evaluating the spatial variations in vegetation cover around these rivers, we can better understand the interaction of vegetation and geomorphology alongside the impacts of human activity and climate change in these, and many similar, large systems, which can inform sustainable development.