Applications of Satellite Earth Observations section - NEODAAS: Providing satellite data for efficient research

The NERC Earth Observation Data Acquisition and Analysis Service (NEODAAS) provides a central point of Earth Observation (EO) satellite data access and expertise for UK researchers. The service is tailored to individual users’ requirements to ensure that researchers can focus effort on their science, rather than struggling with correct use of unfamiliar satellite data

Satellite monitoring of harmful algal blooms (HABs) to protect the aquaculture industry

Harmful algal blooms (HABs) can cause sudden and considerable losses to fish farms, for example 500,000 salmon during one bloom in Shetland, and also present a threat to human health. Early warning allows the industry to take protective measures. PML's satellite monitoring of HABs is now funded by the Scottish aquaculture industry. The service involves processing EO ocean colour data from NASA and ESA in near-real time, and applying novel techniques for discriminating certain harmful blooms from harmless algae. Within the AQUA-USERS project we are extending this capability to further HAB species within several European countries

Forest fire pattern extraction and rule generation using sliding window technique

The sliding window technique is being used to extract patterns of forest fire which consists of burnt area size, temperature, relative humidity, wind speed and rainfall.The initial data is being transformed by changing the continuous values of the attributes into categorical value. Extracted patterns are then being grouped based on the size of burnt are.Rules are then generated by transforming the categorical values into intervals and the merging different records into the same rules.The rule generation stage produces eight distinct patterns of meteorological conditions that could predict the size of forest fire

Uncertainty in the modeling of spatial big data on a pattern of bushfires holes

This paper focuses on the presence of vegetation patches, called holes remaining after forest fires. Holes are of interest to explore because their vegetation is affected by severe temperature stress nearby, although they can serve as an agent to regenerate a forest after the burn. Further, it is interesting to know why holes emerge at all, while little if anything is known about their structure and distribution in space. A statistical analysis of their presence and abundance and a spatial statistical analysis to analyze their positions was done within four forest fire footprints. Fractal dimension of the holes was compared to that of the forest fire footprint, whereas remote sensing imagery was used to identify the normalized difference vegetation index (NDVI) of the patches before and after the fire. Results showed that the fractal dimension of the holes is lower than that of the forest fire footprint, and that the NDVI is slowly recovering to the original NDVI. Differences with the NDVI of the surrounding areas remain large. We concluded that patches of vegetation after a forest fire are interesting to study, providing clues of why unburned patches occur despite the fire presence nearby, how they can be characterized spatially and how the vegetation composition responds to such nearby fire. The Recommendations for Resource Managers: Forest fires affect the forests, and have an effect on the population living within the forest and close to it. A forest fire commonly leaves behind a large number of unburnt vegetation patches. In this study we call them holes. These holes have been under severe heat and smoke pressure, but have similar tree species and forest structure as the original forest. They serve as the starting point to regenerate the forest. The primary implications for resource management are as follows: A better understanding of where they are, and how they are composed may help to understand the behavior of a fire. Their characterization may help to better understand the relation between vegetation as a fuel for forest fire. Their biodiversity will improve the fire spread modeling of burns that are carried out for management of a forest stand

Managing big, linked, and open earth-observation data: Using the TELEIOS/LEO software stack

Big Earth-observation (EO) data that are made freely available by space agencies come from various archives. Therefore, users trying to develop an application need to search within these archives, discover the needed data, and integrate them into their application. In this article, we argue that if EO data are published using the linked data paradigm, then the data discovery, data integration, and development of applications becomes easier. We present the life cycle of big, linked, and open EO data and show how to support their various stages using the software stack developed by the European Union (EU) research projects TELEIOS and the Linked Open EO Data for Precision Farming (LEO). We also show how this stack of tools can be used to implement an operational wildfire-monitoring service

The identification of determinant parameter in forest fire based on feature selection algorithms

This research conducts studies of the use of the Sequential Forward Floating Selection (SFFS) Algorithm and Sequential Backward Floating Selection (SBFS) Algorithm as the feature selection algorithms in the Forest Fire case study. With the supporting data that become the features of the forest fire case, we obtained information regarding the kinds of features that are very significant and influential in the event of a forest fire. Data used are weather data and land coverage of each area where the forest fire occurs. Based on the existing data, ten features were included in selecting the features using both feature selection methods. The result of the Sequential Forward Floating Selection method shows that earth surface temperature is the most significant and influential feature in regards to forest fire, while, based on the result of the Sequential Backward Feature Selection method, cloud coverage, is the most significant. Referring to the results from a total of 100 tests, the average accuracy of the Sequential Forward Floating Selection method is 96.23%. It surpassed the 82.41% average accuracy percentage of the Sequential Backward Floating Selection method

A review of approaches to estimate wildfire plume injection height within large-scale atmospheric chemical transport models

Landscape fires produce smoke containing a very wide variety of chemical species, both gases and aerosols. For larger, more intense fires that produce the greatest amounts of emissions per unit time, the smoke tends initially to be transported vertically or semi-vertically close by the source region, driven by the intense heat and convective energy released by the burning vegetation. The column of hot smoke rapidly entrains cooler ambient air, forming a rising plume within which the fire emissions are transported. The characteristics of this plume, and in particular the height to which it rises before releasing the majority of the smoke burden into the wider atmosphere, are important in terms of how the fire emissions are ultimately transported, since for example winds at different altitudes may be quite different. This difference in atmospheric transport then may also affect the longevity, chemical conversion, and fate of the plumes chemical constituents, with for example very high plume injection heights being associated with extreme long-range atmospheric transport. Here we review how such landscape-scale fire smoke plume injection heights are represented in larger-scale atmospheric transport models aiming to represent the impacts of wildfire emissions on component of the Earth system. In particular we detail (i) satellite Earth observation data sets capable of being used to remotely assess wildfire plume height distributions and (ii) the driving characteristics of the causal fires. We also discuss both the physical mechanisms and dynamics taking place in fire plumes and investigate the efficiency and limitations of currently available injection height parameterizations. Finally, we conclude by suggesting some future parameterization developments and ideas on Earth observation data selection that may be relevant to the instigation of enhanced methodologies aimed at injection height representation