Detecting gas flares and estimating flaring volumes at individual flow stations using MODIS data.

Gas flaring has gained global recognition as a prominent agent of pollution, leading to the establishment of the Global Gas Flaring Reduction (GGFR) initiative, which requires an objective means of monitoring flaring activity. Because auditable information on flaring activity is difficult to obtain there have recently been attempts to detect flares using satellite imagery, typically at global scales. However, to adequately assess the environmental and health impacts of flaring from local to regional scales, it is important that we have a means of acquiring information on the location of individual active flaring sites and the volume of gas combusted at these sites. In this study we developed an approach to the retrieval of such information using nighttime MODIS thermal imagery. The MODIS flare detection technique (MODET) and the MODIS flare volume estimation technique (MOVET) both exploit the absolute and contextual radiometric response of flare sites. The levels of detection accuracy and estimation error were quantified using independent observations of flare location and volume. The MODET and MOVET were applied to an archive of MODIS data spanning 2000–2014 covering the Niger Delta, Nigeria, a significant global hotspot of flaring activity. The results demonstrate the substantial spatial and temporal variability in gas flaring across the region, between states and between onshore and offshore sites. Thus, whilst the estimated total volume of gas flared in the region over the study period is large (350 Billion Cubic Metres), the heterogeneity in the flaring indicates that the impacts of such flares will be highly variable in space and time. In this context, the MODET and MOVET offer a consistent and objective means of monitoring flaring activity over an appropriate range of scales and it is now important that their robustness and transferability is tested in other oil-producing regions of the world

Forest disturbance and regeneration: a mosaic of discrete gap dynamics and open matrix regimes?

Question: Recent research in boreal forest suggests that an ‘open matrix’ model may be more appropriate than the traditional model of spatially discrete gap dynamics for describing forest disturbance and regeneration, but what is the evidence from temperate broad-leaved deciduous forests concerning the prevalence of these alternative models? Location: Semi-natural temperate broad-leaved deciduous forest in southern England. Methods: Multi-temporal LiDAR data were used to monitor the changes in tree canopy height and canopy gaps over a 10-yr period for a 130-ha area of forest. Gap dynamics were characterized by quantifying gap creation, expansion, contraction and closure. By identifying the types and rates of canopy height transitions, areas of gap contraction and closure were attributed to the processes of lateral crown growth or vertical regeneration. Results: Across the study site there was a zonation in canopy and gap properties and their dynamics. Many areas of the forest had the characteristics of open wood-pasture dominated by large, complex gaps being maintained under a regime of chronic disturbance. In these areas, several characteristics of the gap dynamics indicated that regeneration was restricted and this may be attributable to spatially-focused overgrazing by large herbivores. In contrast, other areas were characterized by high, closed canopy forest with small, discrete gaps where gap creation and infill were balanced. Conclusions: At the landscape-scale broad-leaved deciduous forests contain a spatial mosaic of zones, which conform to different models of disturbance and regeneration dynamics; discrete gap dynamics and open matrix regimes are juxtaposed. It is now important to elucidate the abiotic factors and biotic interactions that determine the spatio-temporal distribution of the different regimes and to examine whether such a ‘regime mosaic’ model is applicable in other forest types

How well is current plant trait composition predicted by modern and historical forest spatial configuration?

There is increasing evidence to suggest that a delayed response of many forest species to habitat loss and fragmentation leads to the development of extinction debts and immigration credits in affected forest habitat. These time lags result in plant communities which are not well predicted by present day landscape structure, reducing the accuracy of biodiversity assessments and predictions for future change. Here, species richness data and mean values for five life history characteristics within deciduous broadleaved forest habitat across Great Britain were used to quantify the degree to which aspects of present day forest plant composition are best explained by modern or historical forest patch area. Ancient forest specialist richness, mean rarity and mean seed terminal velocity were not well predicted by modern patch area, implying the existence of a degree of lag in British forest patches. Mean seedbank persistence values were more closely related to modern patch area than historical, particularly in larger patches. The variation in response for different mean trait values suggests that species respond to landscape change at different rates depending upon their combinations of different trait states. Current forest understorey communities are therefore likely to consist of a mixture of declining species whose extinction debt is still to be paid, and faster colonising immigrant species. These results indicate that without management action, rare and threatened species of plant are likely to be lost in the future as a result of changes in forest spatial configuration that have already taken place. The lag seen here for rare specialist plants suggests however that there may still be scope to protect such species before they are lost from forest patches

Dynamics and controls of urban heat sink and island phenomena in a desert city:development of a local climate zone scheme using remotely-sensed inputs

This study aims to determine the dynamics and controls of Surface Urban Heat Sinks (SUHS) and Surface Urban Heat Islands (SUHI) in desert cities, using Dubai as a case study. A Local Climate Zone (LCZ) schema was developed to subdivide the city into different zones based on similarities in land cover and urban geometry. Proximity to the Gulf Coast was also determined for each LCZ. The LCZs were then used to sample seasonal and daily imagery from the MODIS thermal sensor to determine Land Surface Temperature (LST) variations relative to desert sand. Canonical correlation techniques were then applied to determine which factors explained the variability between urban and desert LST. Our results indicate that the daytime SUHS effect is greatest during the summer months (typically ∼3.0 °C) with the strongest cooling effects in open high-rise zones of the city. In contrast, the night-time SUHI effect is greatest during the winter months (typically ∼3.5 °C) with the strongest warming effects in compact mid-rise zones of the city. Proximity to the Arabian Gulf had the largest influence on both SUHS and SUHI phenomena, promoting daytime cooling in the summer months and night-time warming in the winter months. However, other parameters associated with the urban environment such as building height had an influence on daytime cooling, with larger buildings promoting shade and variations in airflow. Likewise, other parameters such as sky view factor contributed to night-time warming, with higher temperatures associated with limited views of the sky

Contributions of gas flaring to a global air pollution hotspot:spatial and temporal variations, impacts and alleviation

Studies of environmental impacts of gas flaring in the Niger Delta are hindered by limited access to official flaring emissions records and a paucity of reliable ambient monitoring data. This study uses a combination of geospatial technologies and dispersion modelling techniques to evaluate air pollution impacts of gas flaring on human health and natural ecosystems in the region. Results indicate that gas flaring is a major contributor to air pollution across the region, with concentrations exceeding WHO limits in some locations over certain time periods. Due to the predominant south-westerly wind, concentrations are higher in some states with little flaring activity than in others with significant flaring activity. Twenty million people inhabit areas of high flare-associated air pollution, which include all of the main ecological zones of the region, indicating that flaring poses a substantial threat to human health and the environment. Model scenarios demonstrated that substantial reductions in pollution could be achieved by stopping flaring at a small number of the most active sites and by improving overall flaring efficiency

Linking species thermal tolerance to elevational range shifts in upland dung beetles

Climate warming has been proposed as the main cause of the recent range shifts seen in many species. Although species’ thermal tolerances are thought to play a key role in determining responses to climate change, especially in ectotherms, empirical evidence is still limited. We investigate the connection between species’ thermal tolerances, elevational range and shifts in the lower elevational limit of dung beetle species (Coleoptera, Aphodiidea) in an upland region in the northwest of England. We measured thermal tolerances in the laboratory, and used current and historical distribution data to test specific hypotheses about the area’s three dominant species, particularly the species most likely to suffer from warming: Agollinus lapponum. We found marked differences between species in their minimum and maximum thermal tolerance and in their elevational range and patterns of abundance. Overall, differences in thermal limits among species matched the abundance patterns along the elevation gradient expected if distributions were constrained by climate. A. lapponum abundance increased with elevation and this species showed lower maximum and minimum thermal limits than Acrossus depressus, for which abundance declined with elevation. Consistent with lower tolerance to high temperature, we recorded an uphill retreat of the low elevation limit of A. lapponum (177 m over 57 years) in line with the increase in summer temperature observed in the region over the same period. Moreover, this species has been replaced at low and mid-elevations by the other two warm-tolerant species (A. depressus and Agrilinus ater). Our results provide empirical evidence that species’ thermal tolerance constrains elevational ranges and contributes to explain the observed responses to climate warming. A mechanistic understanding of how climate change directly affects species, such as the one presented here, will provide a robust base to inform predictions of how individual species and whole assemblages may change in the future

How well does random forest analysis model deforestation and forest fragmentation in the Brazilian Atlantic forest?

We assessed the value of applying random forest analysis (RF) to relating metrics of deforestation (DF) and forest fragmentation (FF) to socio-economic (S-E) and bio-geophysical (BGP) factors, in the Brazilian Atlantic Forest of Minas Gerais, Brazil. A vegetation-monitoring project provided land cover maps, from which we derived DF and FF metrics. An ecologic-economical zoning project provided more than 300 S-E and BGP factors. We used random forest analysis (RF) to identify relationships between these sets of variables, and compared its performance in this task to that of a more traditional multiple linear regression approach. We found that RF modelled relatively-well variance in all metrics used (the rate of deforestation, the amount of forest, and the density and isolation of forest patches), presenting a better performance when compared to the classical approach. RF also identified geographical location and topographic factors as being most closely associated with patterns of DF and FF. Both analyses found factors associated with economic productivity, social institutions, accessibility and exploration to have little relationship with metrics. RF was better at explaining variations in rates of deforestation, remaining forest and patch patterns, than the multiple linear regression approach. We conclude that RF provides a promising methodology for elucidating the relationships between land use and cover changes with potential drivers

Using fractal analysis of crown images to measure the structural condition of trees

Observations of tree canopy structure are routinely used as an indicator of tree condition for the purposes of monitoring tree health, assessing habitat characteristics or evaluating the potential risk of tree failure. Trees are assigned to broad categories of structural condition using largely subjective methods based upon ground-based, visual observations by a surveyor. Such approaches can suffer from a lack of consistency between surveyors; are qualitative in nature and have low precision. In this study, a technique is developed for acquiring, processing and analysing hemispherical images of sessile oak (Quercus petraea (Matt.) Liebl.) tree crowns. We demonstrate that by calculating the fractal dimensions of tree crown images it is possible to define a continuous measurement scale of structural condition and to be able to quantify intra-category variance of tree crown structure. This approach corresponds with traditional categorical methods; however, we recognize that further work is required to precisely define interspecies thresholds. Our study demonstrates that this approach has the potential to form the basis of a new, transferable and objective methodology that can support a wide range of uses in arboriculture, ecology and forest science

Quantifying the exposure of humans and the environment to oil pollution in the Niger Delta using advanced geostatistical techniques

The Niger Delta is one of the largest oil producing regions of the world. Large numbers and volumes of oil spills have been reported in this region. What has not been quantified is the putative exposure of humans and/or the environment to this hydrocarbon pollution. In this novel study, advanced geostatistical techniques were applied to an extensive database of oil spill incidents from 2007 to 2015. The aims were to (i) identify and analyse spill hotspots along the oil pipeline network and (ii) estimate the exposure of the hydrocarbon pollution to the human population and the environment within the Niger Delta. Over the study period almost 90 million litres of oil were released. Approximately 29% of the human population living in proximity to the pipeline network has been potentially exposed to oil contamination, of which 565,000 people live within high or very high spill intensity sectors. Over 1000 km2 of land has been contaminated by oil pollution, with broadleaved forest, mangroves and agricultural land the most heavily impacted land cover types. Proximity to the coast, roads and cities are the strongest spatial factors contributing to spill occurrence, which largely determine the accessibility of sites for pipeline sabotage and oil theft. Overall, the findings demonstrate the high levels of environmental and human exposure to hydrocarbon pollutants in the Niger Delta. These results provide evidence with which to spatially target interventions to reduce future spill incidents and mitigate the impacts of previous spills on human communities and ecosystem health

Applications of satellite ‘hyper-sensing’ in Chinese agriculture:Challenges and opportunities

Ensuring adequate food supplies to a large and increasing population continues to be the key challenge for China. Given the increasing integration of China within global markets for agricultural products, this issue is of considerable significance for global food security. Over the last 50 years, China has increased the production of its staple crops mainly by increasing yield per unit land area. However, this has largely been achieved through inappropriate agricultural practices, which have caused environmental degradation, with deleterious consequences for future agricultural productivity. Hence, there is now a pressing need to intensify agriculture in China using practices that are environmentally and economically sustainable. Given the dynamic nature of crops over space and time, the use of remote sensing technology has proven to be a valuable asset providing end-users in many countries with information to guide sustainable agricultural practices. Recently, the field has experienced considerable technological advancements reflected in the availability of ‘hyper-sensing’ (high spectral, spatial and temporal) satellite imagery useful for monitoring, modelling and mapping of agricultural crops. However, there still remains a significant challenge in fully exploiting such technologies for addressing agricultural problems in China. This review paper evaluates the potential contributions of satellite ‘hyper-sensing’ to agriculture in China and identifies the opportunities and challenges for future work. We perform a critical evaluation of current capabilities in satellite ‘hyper-sensing’ in agriculture with an emphasis on Chinese sensors. Our analysis draws on a series of in-depth examples based on recent and on-going projects in China that are developing ‘hyper-sensing’ approaches for (i) measuring crop phenology parameters and predicting yields; (ii) specifying crop fertiliser requirements; (iii) optimising management responses to abiotic and biotic stress in crops; (iv) maximising yields while minimising water use in arid regions; (v) large-scale crop/cropland mapping; and (vi) management zone delineation. The paper concludes with a synthesis of these application areas in order to define the requirements for future research, technological innovation and knowledge exchange in order to deliver yield sustainability in China