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

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

The insertion/deletion variation in the α(2B)-adrenoceptor does not seem to modify the risk for acute myocardial infarction, but may modify the risk for hypertension in sib-pairs from families with type 2 diabetes

BACKGROUND: An insertion/deletion polymorphism in the α(2B)-adrenoceptor (AR) has been associated with the risk for acute myocardial infarction (AMI) and sudden cardiac death. In this study we tested whether this polymorphism is associated with the risk for AMI among members of families with type 2 diabetes. METHODS: 154 subjects with a history of AMI were matched for age and sex with one of their siblings who did not have a history of AMI. The prevalence of the genotypes of the α(2B)-AR insertion/deletion polymorphism was compared between the siblings using McNemar's test. We also explored the data to see whether this genetic variation affects the risk for hypertension by using logistic regression models in the two subpopulations of subjects, with and without a history of AMI. RESULTS: Among all study subjects, 73 (24%) carried the α(2B)-AR deletion/deletion genotype, 103 (33%) carried the insertion/insertion genotype, and 132 (43%) were heterozygous. The distribution of genotypes of the α(2B)-AR insertion/deletion variation in the group of subjects with a history of AMI and their phenotype-discordant siblings did not statistically significantly differ from that expected by random distribution (p = 0.52): the deletion/deletion genotype was carried by 34 subjects with AMI (22%), and by 39 subjects without AMI (25%). Neither did we observe any significant difference in deletion allele frequencies of the α(2B)-AR insertion/deletion polymorphism between patients with a history of AMI (0.44) and their sib-pair controls (0.46, p = 0.65). In an exploratory analysis, the α(2B)-AR deletion/deletion genotype was associated with increased odds for hypertension compared with subjects carrying any of the other genotypes. CONCLUSIONS: The deletion/deletion genotype of the α(2B)-AR does not emerge in this study as a risk factor for AMI among members of families with type 2 diabetes; however, it might be involved in the development of hypertension

The influence of soil properties in estimating soil moisture from satellite C-band synthetic aperture radar

Regularly updated soil moisture maps are needed that combine wide scale for regional and national studies, whilstcapturing the field-scale variability that is needed by many applications in agriculture, hydrology and meteorology.C-band SAR satellites, such as Sentinel-1, offer the high spatial and temporal resolution required, but the estimationof soil moisture from SAR requires correction for many contributing factors including vegetation, soil roughness,soil texture and temperature. This paper reviews and predicts the significance of soil texture and organic mattercontent to the errors that may be present in any estimation that is made using default assumptions. We showthat each factor may contribute to a 10% error if an incorrect assumption is made. Soil moisture retrieval overagricultural fields in northern latitudes requires any algorithm to account for rapid and large changes in SARbackscatter due to crop growth and harvesting, tillage operations and freezing of the soil surface. This has particularsignificance for the extending the use of change detection approaches into arable farming areas. We discuss theprospect for developing a model to guide the setting of soil roughness parameters based on land use, soil textureand tillage, and for automatic correction for frozen soil. Successful implementation will improve the accuracy andvalidity of estimating soil moisture from C-band SAR satellite data, at the field scale

A method for monthly mapping of wet and dry snow using Sentinel-1 and MODIS: Application to a Himalayan river basin

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

Insights into mixed contaminants interactions and its implication for heavy metals and metalloids mobility, bioavailability and risk assessment

Mobility of heavy metals at contaminated sites is mainly influenced by the soil physicochemical properties and environmental conditions, therefore assessing heavy metals (HMs) and metalloids fractionation can provide insights into their potential risk and the mechanisms that regulate bioavailability. A 12-months mesocosms experiment was setup to investigate the effect of physicochemical factors (pH, moisture, and temperature) and weathering (time) on HMs and metalloids fractionation in three different multi-contaminated soil matrices (low, medium, and high contamination) collected from a soil treatment facility located in the United Kingdom, and two rural contaminated soil samples. The study demonstrates that even though Pb and Zn were found associated with the exchangeable fraction in the soil with the highest contamination (total average Pb 3400 mg/kg, and total average Zn 2100 mg/kg in Soil C), neither the condition applied nor the weathering caused an increase in their mobility. Although it was expected that lower pH (4.5) would favours the dissociation of HMs and metalloids, no significant differences were observed, potentially due to the initial alkaline pH of the genuine-contaminated soil samples. The results show that even though total concentration of Pb, Cu, and Zn exceed the soil standards and guideline values, HMs were predominantly associated with the non-exchangeable fraction, while only 5% were dissolved in the pore water fraction (potentially bioavailable). In addition, the mobility and bioavailability of HMs remained constant over the 12 months monitoring, suggesting that these soils pose negligible risk to the environment

Prediction of bioavailability and toxicity of complex chemical mixtures through machine learning models

Empirical 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

Harvest monitoring of Kenyan tea plantations with X-band SAR

Tea 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