Browsing by Author "Snapir, Boris"
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Item Open Access Bias correction of high-resolution regional climate model precipitation output gives the best estimates of precipitation in Himalayan catchments(American Geophysical Union (AGU), 2019-12-14) Bannister, Daniel; Orr, Andrew; Jain, Sanjay K.; Holman, Ian P.; Momblanch, Andrea; Phillips, Tony; Adeloye, Adebayo J.; Snapir, Boris; Waine, Toby W.; Hosking, J. Scott; Allen‐Sader, ClareThe need to provide accurate estimates of precipitation over catchments in the Hindu Kush, Karakoram, and Himalaya mountain ranges for hydrological and water resource systems assessments is widely recognised, as is identifying precipitation extremes for assessing hydro‐meteorological hazards. Here, we investigate the ability of bias‐corrected Weather Research and Forecasting model output at 5 km grid spacing to reproduce the spatiotemporal variability of precipitation for the Beas and Sutlej river basins in the Himalaya, measured by 44 stations spread over the period 1980 to 2012. For the Sutlej basin, we find that the raw (uncorrected) model output generally underestimated annual, monthly, and (particularly low‐intensity) daily precipitation amounts. For the Beas basin, the model performance was better, although biases still existed. It is speculated that the cause of the dry bias over the Sutlej basin is a failure of the model to represent an early‐morning maximum in precipitation during the monsoon period, which is related to excessive precipitation falling upwind. However, applying a non‐linear bias‐correction method to the model output resulted in much better results, which were superior to precipitation estimates from reanalysis and two gridded datasets. These findings highlight the difficulty in using current gridded datasets as input for hydrological modelling in Himalayan catchments, suggesting that bias‐corrected high‐resolution regional climate model output is in fact necessary. Moreover, precipitation extremes over the Beas and Sutlej basins were considerably under‐represented in the gridded datasets, suggesting that bias‐corrected regional climate model output is also necessary for hydro‐meteorological risk assessments in Himalayan catchments.Item Open Access Harvest monitoring of Kenyan tea plantations with X-band SAR(IEEE, 2018-02-23) Snapir, Boris; Waine, Toby W.; Corstanje, Ronald; Redfern, Sally P.; De Silva, Jacquie; Kirui, CharlesTea is an important cash crop in Kenya, grown in a climatically restricted geographic area where climatic variability is starting to affect yield productivity levels. This paper assesses the feasibility of monitoring tea growth between, but also within fields, using X-band COSMO-SkyMed SAR images (five images at VV polarization and five images at HH polarization). We detect the harvested and nonharvested areas for each field, based on the loss of interferometric coherence between two images, with an accuracy of 52% at VV polarization and 74% at HH polarization. We then implement a normalization method to isolate the scattering component related to shoot growth and eliminate the effects of moisture and local incidence angle. After normalization, we analyze the difference in backscatter between harvested and nonharvested areas. At HH polarization, our backscatter normalization reveals a small decrease (∼0.1 dB) in HH backscatter after harvest. However, this decrease is too small for monitoring shoot growth. The decrease is not clear at VV polarization. This is attributed to the predominantly horizontal orientation of the harvested leaves.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 Maritime vessel classification to monitor fisheries with SAR: demonstration in the North Sea(MDPI, 2019-02-11) Snapir, Boris; Waine, Toby; Biermann, LaurenIntegration 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.Item Open Access A method for monthly mapping of wet and dry snow using Sentinel-1 and MODIS: Application to a Himalayan river basin(Elsevier, 2018-10-01) Snapir, Boris; Momblanch, Andrea; Jain, Sanjay K.; Waine, Toby; Holman, Ian P.Satellite Remote Sensing, with both optical and SAR instruments, can provide distributed observations of snow cover over extended and inaccessible areas. Both instruments are complementary, but there have been limited attempts at combining their measurements. We describe a novel approach to produce monthly maps of dry and wet snow areas through application of data fusion techniques to MODIS fractional snow cover and Sentinel-1 wet snow mask, facilitated by Google Earth Engine. The method is demonstrated in a 55,000 km2 river basin in the Indian Himalayan region over a period of ∼2.5 years, although it can be applied to any areas of the world where Sentinel-1 data are routinely available. The typical underestimation of wet snow area by SAR is corrected using a digital elevation model to estimate the average melting altitude. We also present an empirical model to derive the fractional cover of wet snow from Sentinel-1. Finally, we demonstrate that Sentinel-1 effectively complements MODIS as it highlights a snowmelt phase which occurs with a decrease in snow depth but no/little decrease in snowpack area. Further developments are now needed to incorporate these high resolution observations of snow areas as inputs to hydrological models for better runoff analysis and improved management of water resources and flood risk.Item Open Access Prediction of bioavailability and toxicity of complex chemical mixtures through machine learning models(Elsevier, 2018-09-11) Cipullo, Sabrina; Snapir, Boris; Prpich, George; Campo Moreno, Pablo; Coulon, FredericEmpirical data from a 6-month mesocosms experiment were used to assess the ability and performance of two machine learning (ML) models, including artificial neural network (NN) and random forest (RF), to predict temporal bioavailability changes of complex chemical mixtures in contaminated soils amended with compost or biochar. From the predicted bioavailability data, toxicity response for relevant ecological receptors was then forecasted to establish environmental risk implications and determine acceptable end-point remediation. The dataset corresponds to replicate samples collected over 180 days and analysed for total and bioavailable petroleum hydrocarbons and heavy metals/metalloids content. Further to this, a range of biological indicators including bacteria count, soil respiration, microbial community fingerprint, seeds germination, earthworm's lethality, and bioluminescent bacteria were evaluated to inform the environmental risk assessment. Parameters such as soil type, amendment (biochar and compost), initial concentration of individual compounds, and incubation time were used as inputs of the ML models. The relative importance of the input variables was also analysed to better understand the drivers of temporal changes in bioavailability and toxicity. It showed that toxicity changes can be driven by multiple factors (combined effects), which may not be accounted for in classical linear regression analysis (correlation). The use of ML models could improve our understanding of rate-limiting processes affecting the freely available fraction (bioavailable) of contaminants in soil, therefore contributing to mitigate potential risks and to inform appropriate response and recovery methods.Item Open Access SAR remote sensing of soil Moisture(Cranfield University, 2014-12) Snapir, Boris; Hobbs, S. E.Synthetic Aperture Radar (SAR) has been identified as a good candidate to provide high-resolution soil moisture information over extended areas. SAR data could be used as observations within a global Data Assimilation (DA) approach to benefit applications such as hydrology and agriculture. Prior to developing an operational DA system, one must tackle the following challenges of soil moisture estimation with SAR: (1) the dependency of the measured radar signal on both soil moisture and soil surface roughness which leads to an ill-conditioned inverse problem, and (2) the difficulty in characterizing spatially/temporally surface roughness of natural soils and its scattering contribution. The objectives of this project are (1) to develop a roughness measurement method to improve the spatial/temporal characterization of soil surface roughness, and (2) to investigate to what extent the inverse problem can be solved by combining multipolarization, multi-incidence, and/or multi-frequency radar measurements. The first objective is achieved with a measurement method based on Structure from Motion (SfM). It is tailored to monitor natural surface roughness changes which have often been assumed negligible although without evidence. The measurement method is flexible, a.ordable, straightforward and generates Digital Elevation Models (DEMs) for a SAR-pixel-size plot with mm accuracy. A new processing method based on band-filtering of the DEM and its 2D Power Spectral Density (PSD) is proposed to compute the classical roughness parameters. Time series of DEMs show that non-negligible changes in surface roughness can happen within two months at scales relevant for microwave scattering. The second objective is achieved using maximum likelihood fitting of the Oh backscattering model to (1) full-polarimetric Radarsat-2 data and (2) simulated multi-polarization / multi-incidence / multi-frequency radar data. Model fitting with the Radarsat-2 images leads to poor soil moisture retrieval which is related to inaccuracy of the Oh model. Model fitting with the simulated data quantifies the amount of multilooking for di.erent combinations of measurements needed to mitigate the critical e.ect of speckle on soil moisture uncertainty. Results also suggest that dual-polarization measurements at L- and C-bands are a promising combination to achieve the observation requirements of soil moisture. In conclusion, the SfM method along with the recommended processing techniques are good candidates to improve the characterization of surface roughness. A combination of multi-polarization and multi-frequency radar measurements appears to be a robust basis for a future Data Assimilation system for global soil moisture monitoring.Item Open Access SusHi-Wat - Monthly maps of snow cover(Cranfield University, 2018-02-06 09:13) Snapir, Boris; Waine, Toby; Momblanch Benavent, Andrea; Holman, IanThese data were generated for the project Sustaining Himalayan Water Resources in a Changing Climate (SusHi-Wat), which aims at improving our understanding on how water is stored in, and moves through, a Himalayan river system in northern India. The data set contains a list of images (GeoTIFF format) corresponding to monthly maps of dry snow and wet snow for a Himalayan river basin. The maps were obtained by combining satellite remote sensing data from Sentinel-1 and the Moderate Resolution Imaging Spectroradiometer (MODIS). The image resolution is about 500m. The coordinate system is EPSG:4326 The possible pixel values are: 0: no snow 1-100: wet snow cover fraction 101-200: dry snow cover fraction with an offset of 100 240: missing Sentinel-1 data 250: pixel wrongly identified as wet snow by sentinel-1 (false positives) 255: fill valueItem Open Access System design for geosynchronous synthetic aperture radar missions(Institute of Electrical and Electronics Engineers, 2014-06-12) Hobbs, Stephen; Mitchell, C.; Forte, B.; Holley, R.; Snapir, Boris; Whittaker, P.Geosynchronous synthetic aperture radar (GEO SAR) has been studied for several decades but has not yet been implemented. This paper provides an overview of mission design, describing significant constraints (atmosphere, orbit, temporal stability of the surface and atmosphere, measurement physics, and radar performance) and then uses these to propose an approach to initial system design. The methodology encompasses all GEO SAR mission concepts proposed to date. Important classifications of missions are: 1) those that require atmospheric phase compensation to achieve their design spatial resolution; and 2) those that achieve full spatial resolution without phase compensation. Means of estimating the atmospheric phase screen are noted, including a novel measurement of the mean rate of change of the atmospheric phase delay, which GEO SAR enables. Candidate mission concepts are described. It seems likely that GEO SAR will be feasible in a wide range of situations, although extreme weather and unstable surfaces (e.g., water, tall vegetation) prevent 100% coverage. GEO SAR offers an exciting imaging capability that powerfully complements existing systems.Item Open Access System design for geosynchronous synthetic aperture radar missions(IEEE, 2014-05-08) Hobbs, Stephen; Mitchell, Cathryn; Forte, Biagio; Holley, Rachel; Snapir, Boris; Whittaker, PhilipGeosynchronous synthetic aperture radar (GEO SAR) has been studied for several decades but has not yet been implemented. This paper provides an overview of mission design, describing significant constraints (atmosphere, orbit, temporal stability of the surface and atmosphere, measurement physics, and radar performance) and then uses these to propose an approach to initial system design. The methodology encompasses all GEO SAR mission concepts proposed to date. Important classifications of missions are: 1) those that require atmospheric phase compensation to achieve their design spatial resolution; and 2) those that achieve full spatial resolution without phase compensation. Means of estimating the atmospheric phase screen are noted, including a novel measurement of the mean rate of change of the atmospheric phase delay, which GEO SAR enables. Candidate mission concepts are described. It seems likely that GEO SAR will be feasible in a wide range of situations, although extreme weather and unstable surfaces (e.g., water, tall vegetation) prevent 100% coverage. GEO SAR offers an exciting imaging capability that powerfully complements existing systems.