Use Of Earth Observation Data Research Articles

Data-push innovation beyond serendipity: The case of a digital platform making Earth Observation data fit into multiple use contexts

The potential of data to circulate across organizations and sectors and stimulate innovation in multiple contexts has been largely acknowledged by practitioners and researchers. This has given rise to a specific form of innovation strategy, “data-push innovation”, which consists of stimulating the use of existing data by third-party actors. However, how to manage such a strategy remains challenging. The paper explores this question by examining the longitudinal case study of an actor that has successfully stimulated the use of Earth observation data by multiple actors over the last 40 years. The paper offers several contributions to research in information systems and innovation management. First, the paper shows that data-push innovation can be fostered through the intentional design of a so-called “fit system” allowing data to be used in multiple contexts. Such a fit system can be built as a generic system, following similar “generification” strategies as those supporting platform or software development but with original patterns to adapt to the specificities of data-push innovation. Second, the paper characterizes the types of “boundary resources” needed to support this process. These boundary resources especially have a two-way resourcing function: they help third-party actors contribute to the fit system development, but they also allow the fit system owner to identify the knowledge boundaries preventing data from gaining meaning in new contexts. Third, the paper reveals an intriguing form of localized and nondominant platform leadership, focusing on gaining generative power rather than controlling power over the platform ecosystem.

A Review Of Perspectives From Earth Observation Data To Investigate The Effects Of COVID-19 On The Environment

Background: Earth observation data has established themselves as extremely useful and very diverse domains for research associated with space, spatio-temporal components, and geography. Following the outbreak of Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) last December 2019 in China. This study aims to systematically review and synthetize perspectives from earth observation data to investigate the effects of COVID-19 on the environment. Material and Methods: A total of 41articles were first collected from four main digital databases including Web of Science, SCOPUS, PubMed/MEDLINE, and Google scholar. It will go on comprehensively review and synthesize applications of earth observation data in studies the COVID-19 impacts on environment. Specifically, the content is presented under three sub-sections; namely the use of earth observation data in (i) studies of impacts on water quality, (ii) studies of impacts on air quality, and (iii) studies of other impacts on the environment, respectively. Results: It was found that change in the intensity of air and water pollution after reduced anthropogenic activities around the world were captured by remote sensing – supplying concrete evidence that can help inform improved environmental policy during the COVID-19 pandemic. Conclusion: It can be concluded that the varied use of remote sensing techniques affirms the value of earth observation data to studies of infectious diseases to environment, especially in times of such large-scale disasters as the COVID-19 pandemic.

Stakeholders’ composition, knowledge and use of earth observation data in wetland ecological assessments, Zimbabwe

The study analyses stakeholders’ knowledge and use of earth observation (EO) derived data in the assessment and monitoring of Driefontein and Intunjambili wetlands’ ecological conditions. Data was collected using a questionnaire survey administered to 282 randomly selected households and semi-structured interviews carried out with purposefully selected key informants representing institutional stakeholders. The results of this study show that the stakeholder categories identified in the two wetland areas are government departments, statutory bodies, non-governmental organisations, research institutions, academia and the local people. Most of the institutional stakeholders (55.6%) and household respondents (81.7%) do not have knowledge of EO data application in wetland conditions monitoring. Chi-Square test results further show that the households’ knowledge on use of remotely sensed derived products and services does not vary with age (p > 0.05), marital status (p > 0.05), level of education (p > 0.05), except gender in Driefontein (p < 0.05). However, the various stakeholder categories recommended wider application of earth observation products and services in the assessment of land use land cover changes, water quantity and quality, flood monitoring, fire outbreaks and the establishment of a wetland inventory to abate wetland degradation. The utilisation of EO data is constrained by lack of knowledge on the use of remotely sensed data in wetland ecological conditions assessment. Investment in capacity building initiatives for both technical and non-technical stakeholders involved in wetland management in Zimbabwe is required so as to enhance their skills in EO data utilisation.

The Effects of Meteorological Factors on Dengue Cases in Malaysia.

Dengue is a vector-borne disease affected by meteorological factors and is commonly recorded from ground stations. Data from ground station have limited spatial representation and accuracy, which can be overcome using satellite-based Earth Observation (EO) recordings instead. EO-based meteorological recordings can help to provide a better understanding of the correlations between meteorological variables and dengue cases. This paper aimed to first validate the satellite-based (EO) data of temperature, wind speed, and rainfall using ground station data. Subsequently, we aimed to determine if the spatially matched EO data correlated with dengue fever cases from 2011 to 2019 in Malaysia. EO data were spatially matched with the data from four ground stations located at states and districts in the central (Selangor, Petaling) and east coast (Kelantan, Kota Baharu) geographical regions of Peninsular Malaysia. Spearman’s rank-order correlation coefficient (ρ) was performed to examine the correlation between EO and ground station data. A cross-correlation analysis with an eight-week lag period was performed to examine the magnitude of correlation between EO data and dengue case across the three time periods (2011–2019, 2015–2019, 2011–2014). The highest correlation between the ground-based stations and corresponding EO data were reported for temperature (mean ρ = 0.779), followed by rainfall (mean ρ = 0.687) and wind speed (mean ρ = 0.639). Overall, positive correlations were observed between weekly dengue cases and rainfall for Selangor and Petaling across all time periods with significant correlations being observed for the period from 2011 to 2019 and 2015 to 2019. In addition, positive significant correlations were also observed between weekly dengue cases and temperature for Kelantan and Kota Baharu across all time periods, while negative significant correlations between weekly dengue cases and temperature were observed in Selangor and Petaling across all time periods. Overall negative correlations were observed between weekly dengue cases and wind speed in all areas from 2011 to 2019 and 2015 to 2019, with significant correlations being observed for the period from 2015 to 2019. EO-derived meteorological variables explained 48.2% of the variation in dengue cases in Selangor. Moderate to strong correlations were observed between meteorological variables recorded from EO data derived from satellites and ground stations, thereby justifying the use of EO data as a viable alternative to ground stations for recording meteorological variables. Both rainfall and temperature were found to be positively correlated with weekly dengue cases; however, wind speed was negatively correlated with dengue cases.

Citizen science for Earth Observation (Citzens4EO): understanding current use in the UK

ABSTRACT The role of Earth observation (EO) data in addressing societal problems from environmental through to humanitarian should not be understated. Recent innovation in EO means provision of analysis ready data and data cubes, which allows for rapid use of EO data. This in combination with processing technologies, such as Google Earth Engine and open source algorithms/software for EO data integration and analyses, has afforded an explosion of information to answer research questions and/or inform policy making. However, there is still a need for both training and validation data within EO projects – often this can be challenging to obtain. It has been suggested that citizen science can help here to provide these data, yet there is some perceived hesitancy in using citizen science within EO projects. This paper reports on the Citizen Science 4 EO (Citizens4EO) project that aimed to obtain an in-depth understanding of researchers’ and practitioners’ experiences with citizen science data in EO within the UK. Through a mixed methods approach (online and in-depth surveys and a spotlight case study) it was found that although the benefits of using citizen science data in EO projects were many (and highlighted in the spotlighted “Slavery from Space” case study), there were a number of common concerns around using citizen science. These were around the mechanics of deploying citizen science and the unreliability of a potentially misinformed or undertrained citizen base. As such, comparing the results of this study with those of a similar survey undertaken in 2016, it is apparent that progress towards optimizing citizen science use in EO has been incremental but positive with evidence of the realization of the benefits of citizen science for EO (Citizens4EO). As such, we conclude by offering priority action areas to support further use of citizen science by the EO community within the UK, which ultimately should be adopted further afield.

Landsat Data Ecosystem Case Study: Actor Perceptions of the Use and Value of Landsat

It is well-known that Earth observation (EO) data plays a critical role in scientific understanding about the global environment. There is also growing support for the use of EO data to provide context-specific insights, with significant implications for their use in decision support systems. Technological development over recent years, including cloud computing infrastructure, machine learning techniques, and rapid expansion of the velocity, volume, and variety of space-borne data sources, offer huge potential to provide solutions to the myriad environmental problems facing society and the planet. The USGS/NASA Landsat Program, the longest continuously gathered source of land surface data, has played a central role in our understanding of environmental change, particularly for its contribution of longitudinal products that offer greater context for present research and decision support activities. The challenge facing the Landsat and EO data community, however, now lies in moving beyond context-specific knowledge generation to translating such knowledge into tangible value for society. Drawing from an open data ecosystem framework and qualitative social science methods, we map the Landsat data ecosystem (LDE) and the relationships linking multiple actors responsible for processing, indexing, analyzing, synthesizing, and translating raw Landsat data into information that is useful, useable, and used by end users in particular social-environmental contexts. Both the role of Big Data and associated technologies are discussed as they relate to the ultimate use of Landsat-derived information products to guide decision-making, and key data ecosystem characteristics that shape the likelihood of these products’ use are highlighted.

Developing capacity for impactful use of Earth Observation data: Lessons from the AfriCultuReS project

An increasing number of products and services based on satellite Earth Observation (EO) data are being developed for use by decision-makers in African agricultural contexts, providing information such as weather and climate forecasts, crop yields and water availability. Capacity development to support impactful use of EO data is a key component of many EO-for-development initiatives, but there is little consensus over where or how capacity should be developed. Our goal in this piece is to provide a critical perspective on the capacity development required to support the creation of more impactful EO data services. Drawing on a capacity needs assessment carried out as part of the AfriCultuReS project (a major EO-for-development initiative), we identify proximate factors which inhibit the success of EO data services such as flawed communication strategies, low relevance in African agricultural contexts, duplication of existing products, and lack of financial sustainability. We link these proximate challenges to deeper issues such as unequal access to funding and resources, fragmentation in the EO field, and relational asymmetries of power, all of which combine to exclude important forms of knowledge from decision-making. Based on this needs assessment, we argue that capacity development requires broader systems-based approaches which develop the capacities of all actors (including those in the Global North) to respect different forms of knowledge, use and participate in co-design approaches, and recognise and challenge the asymmetries of power which currently limit the involvement of certain groups in processes of EO data service design.

Synergistic Use of Multispectral Data and Crop Growth Modelling for Spatial and Temporal Evapotranspiration Estimations

The aim of this research is to explore the analysis of methods allowing a synergetic use of information exchange between Earth Observation (EO) data and growth models in order to provide high spatial and temporal resolution actual evapotranspiration predictions. An assimilation method based on the Ensemble Kalman Filter algorithm allows for combining Sentinel-2 data with a new version of Simple Algorithm For Yield (SAFY_swb) that considers the effect of the water balance on yield and estimates the daily trend of evapotranspiration (ET). Our study is relevant in the context of demonstrating the effectiveness and necessity of satellite missions such as Land Surface Temperature Monitoring (LSTM), to provide high spatial and temporal resolution data for agriculture. The proposed method addresses the problem both from a spatial point of view, providing maps of the areas of interest of the main biophysical quantities of vegetation (LAI, biomass, yield and actual Evapotranspiration), and from a temporal point of view, providing a simulation on a daily basis of the aforementioned variables. The assimilation efficiency was initially evaluated with a synthetic, large and heterogeneous dataset, reaching values of 70% even for high measurement errors of the assimilated variable. Subsequently, the method was tested in a case study in central Italy, allowing estimates of the daily Actual Evapotranspiration with a relative RMSE of 18%. The novelty of this research is in proposing a solution that partially solves the main problems related to the synergistic use of EO data with crop growth models, such as the difficult calibration of initial parameters, the lack of frequent high-resolution data or the high computational cost of data assimilation methods. It opens the way to future developments, such as the use of simultaneous assimilation of multiple variables, to deeper investigations using more specific datasets and exploiting the advanced tools.

Trends in use of remotely sensed data in wetlands assessment and monitoring in Zimbabwe

AbstractThe paper assesses trends in use of earth observation data in wetland conditions monitoring and assessment in Zimbabwe from 1980 to 2019. Reviewed literature shows academia and research institutions (69.8%), government agencies (14%) and international development partners (16.3%) as the main users of remotely sensed data in wetland ecological assessments and monitoring. There is more reliance by the abovementioned stakeholders on freely available low‐cost resolution imagery from Landsat (62.9%) and Moderate Resolution Imaging Spectroradiometer (14.3%). Other stakeholders, however, are reliant on high‐resolution imagery like Rapid Eye (5.7%) and aerial photography (11.4%). Satellite images in wetland management in Zimbabwe are used for land use land cover change detection (42.1%), vegetation health monitoring (21.1%), water quantity monitoring (5.3%), water quality monitoring (13.2%) and wetland mapping (18.4%). The identified challenges faced by different stakeholders to effectively utilise EO data include high cost of high‐resolution imageries, limited expertise, inadequate equipment and software. Since the cost of high‐resolution satellite imagery mainly constraints the acquisition of suitable satellite data to assess the small wetlands that dominate Zimbabwe's landscape, there is need to promote use of recently launched freely available high‐resolution Sentinel data to improve the ecological assessment of wetland conditions.

Retrievals of key biophysical parameters at mesoscale from the Ts/VI scatterplot domain

Earth Observation (EO) is often combined with simulated process models to gain insight on key parameters for the physical mechanisms and interactions of the Earth's system. The objective of this study is two-fold: First, it explores the combined use of EO data from the Advanced Along-Track Scanning Radiometer (AATSR) with the SimSphere land biosphere model via the “triangle” to derive latent (LE) and sensible heat (H) fluxes and soil moisture content (SMC) for diverse European ecosystems. Secondly, it investigates the influence of atmospheric correction on the “triangle”-derived retrievals. Both non-atmospherically (1P) and atmospherically corrected (2P) AATSR data products are used. The data cover selected days from 2007 to 2011 at 12 sites of a European ground monitoring network. For all the predicted parameters, the 2P product leads to improved statistical validation measures. The present study for the first time: (1) assesses the triangle technique at 1km mesoscale resolution using AATSR data; (2) investigates the effects of atmospheric correction and surface heterogeneity on prediction accuracy. The findings provide important information regarding potential operational use of the technique.

Earth Observation Data Supporting Non-Communicable Disease Research: A Review

A disease is non-communicable when it is not transferred from one person to another. Typical examples include all types of cancer, diabetes, stroke, or allergies, as well as mental diseases. Non-communicable diseases have at least two things in common—environmental impact and chronicity. These diseases are often associated with reduced quality of life, a higher rate of premature deaths, and negative impacts on a countries’ economy due to healthcare costs and missing work force. Additionally, they affect the individual’s immune system, which increases susceptibility toward communicable diseases, such as the flu or other viral and bacterial infections. Thus, mitigating the effects of non-communicable diseases is one of the most pressing issues of modern medicine, healthcare, and governments in general. Apart from the predisposition toward such diseases (the genome), their occurrence is associated with environmental parameters that people are exposed to (the exposome). Exposure to stressors such as bad air or water quality, noise, extreme heat, or an overall unnatural surrounding all impact the susceptibility to non-communicable diseases. In the identification of such environmental parameters, geoinformation products derived from Earth Observation data acquired by satellites play an increasingly important role. In this paper, we present a review on the joint use of Earth Observation data and public health data for research on non-communicable diseases. We analyzed 146 articles from peer-reviewed journals (Impact Factor ≥ 2) from all over the world that included Earth Observation data and public health data for their assessments. Our results show that this field of synergistic geohealth analyses is still relatively young, with most studies published within the last five years and within national boundaries. While the contribution of Earth Observation, and especially remote sensing-derived geoinformation products on land surface dynamics is on the rise, there is still a huge potential for transdisciplinary integration into studies. We see the necessity for future research and advocate for the increased incorporation of thematically profound remote sensing products with high spatial and temporal resolution into the mapping of exposomes and thus the vulnerability and resilience assessment of a population regarding non-communicable diseases.

Crops monitoring and yield estimation using sentinel products in semi-arid smallholder irrigation schemes

ABSTRACT The use of earth observation data for crop mapping and monitoring in West Africa has concentrated on rainfed systems due to its pre-dominance in the sub-region. However, irrigated systems, though of limited extent, provide critical livelihood support to many. Accurate statistics on irrigated crops are, thus, needed for effective management and decision making. This study explored the use of Sentinel 1 (S-1) and Sentinel 2 (S-2) data to map the extent and yield of irrigated crops in an informal irrigation scheme in Burkina Faso. Random Forest classification and regression were used together with an extensive field data comprising 842 polygons. Four irrigated crops (tomoto, onion, green bean and other) were classified while the yield of tomatoes was modelled through regression analysis. Apart from spectral bands, derivatives (e.g. biophysical parameters and vegetation indices) from S-2 were used. Different data configuration of S-1, S-2 and their derivatives were tested to ascertain optimal temporal windows for accurate irrigated crop mapping and yield estimation. Results of the crop classification revealed a greater overall accuracy (76.3%) for S-2 compared to S-1 (69.4%), with S-2 biophysical parameters (especially the fraction of absorbed photosynthetic active radiation i.e fAPAR) being prominent. For yield prediction, however, S-1 VV polarization came up as the most prominent predictor in the regression analysis (= 0.63), while the addition of S-2 fAPAR marginally improved the fit (= 0.64). Tomato yield in the study area was found to range from 1 to 16 kg m−2, although about 83% of the area have yields of less than 10 kg m−2. Our study revealed that early season images (acquired in December) perform better in classifying irrigated crop compared to mid or late season. On the other hand, the use of early to mid-season (December to February) images for yield modelling produced reasonable prediction accuracy. This indicates the possibility of using S-1 and S-2 data to predict crop yield prior to harvest season for efficient planning and food security attainment.

Leaf phenology amplitude derived from MODIS NDVI and EVI: Maps of leaf phenology synchrony for Meso‐ and South America

AbstractThe leaf phenology (i.e. the seasonality of leaf amount and leaf demography) of ecosystems can be characterized through the use of Earth observation data using a variety of different approaches. The most common approach is to derive time series of vegetation indices (VIs) which are related to the temporal evolution of FPAR, LAI and GPP or alternatively used to derive phenology metrics that quantify the growing season. The product presented here shows a map of average ‘amplitude’ (i.e. maximum minus minimum) of annual cycles observed in MODIS‐derived NDVI and EVI from 2000 to 2013 for Meso‐ and South America. It is a robust determination of the amplitude of annual cycles of vegetation greenness derived from a Lomb–Scargle spectral analysis of unevenly spaced data. VI time series pre‐processing was used to eliminate measurement outliers, and the outputs of the spectral analysis were screened for statistically significant annual signals. Amplitude maps provide an indication of net ecosystem phenology since the satellite observations integrate the greenness variations across the plant individuals within each pixel. The average amplitude values can be interpreted as indicating the degree to which the leaf life cycles of individual plants and species are synchronized. Areas without statistically significant annual variations in greenness may still consist of individuals that show a well‐defined annual leaf phenology. In such cases, the timing of the phenology events will vary strongly within the year between individuals. Alternatively, such areas may consist mainly of plants with leaf turnover strategies that maintain a constant canopy of leaves of different ages. Comparison with in situ observations confirms our interpretation of the average amplitude measure. VI amplitude interpreted as leaf life cycle synchrony can support model evaluation by informing on the likely leaf turn over rates and seasonal variation in ecosystem leaf age distribution.

Information from Earth Observation for the Management of Sustainable Land Use and Land Cover in Brazil: An Analysis of User Needs

Brazil has some of the world’s most important forest and natural ecosystem resources and their sustainability is of global importance. The expansion of agriculture for livestock, the extractive industries, illegal logging, land conflicts, fire and deforestation are pressures on land use and drivers of land use change in many regions of Brazil. While different institutions in Brazil have sought to use Earth Observation (EO) data to support better land use management and conservation projects, several problems remain at the national and state level in the implementation of EO to support environmental policies and services provided to Brazilian society. This paper presents the results of a systematic analysis of the key challenges in using EO data in land management in Brazil and summarises them in a conceptual model of the factors influencing EO data use for assessing sustainable land use and land cover in Brazil. The research was based on a series of in-depth, semi-structured interviews (43) and structured interviews (53) with key stakeholders who make use of EO data across different locations in Brazil. The major challenges identified in the complex and multifaceted aspects of using this information were associated with access to, and with the processing of, raw data into usable information. The analysis also revealed novel insights on a lack of inter-institutional communication, adequate office infrastructure and personnel, availability of the right type of EO data and funding restrictions, political instability and bureaucracy as factors that limit more effective use of EO data in Brazil at present. We close this analysis by considering how EO information for the sustainable management of land use and land cover can assist institutions as they respond to the varied political and economic instabilities affecting environmental governance and deforestation levels.

Detection of mineral alteration induced by hydrocarbon microseepages by using remotely sensed data in the Fateh Jang area of the Northern Potwar region of Pakistan

Mineral alteration can be induced by the hydrocarbon seepages at the earth surface that can be detected by the use of earth observation data. The long-term seepage of hydrocarbons in the subsurface can induce different types of chemical and mineralogical alterations in the rosk and soil. Mapping of these mineral alterion is important to explore hydrocarbon. In this study, satellite images were used to identify the surface mineral alterations due to hydrocarbon microseepages in the Fateh Jang area of the Northern Potwar region, Islamabad, Pakistan. Landsat TM band ratios of 7/5, 3/1, and 5/4 are used to detect ferric minerals, clay minerals, and ferrous iron minerals bearing sedimentary rocks, respectively, which are applied to distinguish the altered and unaltered rocks. Such rocks have specific spectral responses in various bands of the electromagnetic spectrum and thus give rise to a number of anomalies indicating the presence of hydrocarbons. This study uses enhancement techniques of principal component analysis (PCA), band ratio, false color composite (FCC), and thermal anomaly composition for surface expressions caused by microseeps. It is revealed that spectral reflectance signify that the ferrous iron and red bed bleaching have reflection band and absorption band in TM bands 1, 3, and 4, respectively. Clay minerals and kaolinite have enormous reflection in band 5 and absorption capacity in TM band 7. These results can be used as a model to identify the alteration induced by hydrocarbon seepages. The study area is categorized into northern, eastern, southern, and western zones on the basis mineral alteration. The northern zone is characterized by strong anomalies and is relatively rich in clay alteration and ferrous as compared to other zones. Two types of hydrocarbon microseepages were observed in the Fateh Jang area which includes active seepages (gas microseepages and oil seepage) and passive seepage (bleaching effects).

The use of earth observation methods for estimating regional crop evapotranspiration and yield for water footprint accounting

AbstractRemote sensing can efficiently support the quantification of crop water requirements included in the goal of assessing water footprints, which is to analyse how human activities or specific products relate to issues of water scarcity and pollution and identify how activities and products can become more sustainable from a water perspective. Remote sensing techniques have become popular in estimating actual crop evapotranspiration and hence crop water requirements in recent decades due to the advantages they offer to users, e.g. low cost, regional data and use of maps instead of point measurements as well as saving time. The use of earth observation data supports models’ accuracy in the procedure for assessing water footprint, since no average values are used: instead, users have real values for the specific parameters.The present study provides two examples of how remote sensing techniques are used essentially for estimating evapotranspiration along with crop yield, two basic parameters, for assessing water footprint. Two different case studies have been illustrated to define the methodology proposed, which refers to Mediterranean conditions and can be applied after inferring the necessary field data of each crop. The first case study refers to the application of Surface Energy Balance Algorithm for Land (SEBAL) for estimating evapotranspiration, while the second refers to the Crop Yield prediction. Both elements, such as evapotranspiration and crop yield, are vital for water footprint accounting. Firstly, the SEBAL was adopted, under the essential adaptations for local soil and meteorological conditions for estimating groundnut water requirements. Landsat-5 TM, Landsat-7 Enhanced Thematic Mapper+ and Landsat 8 OLI images were used to retrieve the required spectral data. The SEBAL model is enhanced with empirical equations regarding crop canopy factors, in order to increase the accuracy of crop evapotranspiration estimation. Maps were created for evapotranspiration (ET) using the SEBAL modified model for the area of interest. The results were compared with measurements from an evaporation pan, used as a reference. Statistical comparisons showed that the modified SEBAL can predict ETc in a very effective and accurate way and provide water footprint modellers with high-level crop water data. Yield prediction plays a vital role in calculating water footprint. Having real values rather than taking reference (or averaged) values from FAO is an advantage that Earth Observation means can provide. This is very important in econometric or any other prediction models used for estimating water footprint because using average data reduces accuracy. In this context, crop and soil parameters along with remotely sensed data can be used to develop models that can provide users with accurate yield estimations. In a second step, crop and soil parameters along with the normalized difference vegetation index were correlated to examine whether crop yield can be predicted and to define the actual time-window to predict the yield. Statistical and remote sensing techniques were then applied to derive and map a model that can predict crop yield. The algorithm developed for this purpose indicates that remote sensing observations can predict crop yields effectively and accurately. Using the statistical Student's t test, it was found that there was no statistically significant difference between predicted and real values for crop yield.

Potentials of Active and Passive Geospatial Crowdsourcing in Complementing Sentinel Data and Supporting Copernicus Service Portfolio

The recent trend toward open Earth observation (EO) data has revived a general interest in satellite-based monitoring and mapping of the Earth surface. The open policy now applied to LANDSAT data, and the starting of Sentinel operations, whose data are freely distributed even for commercial purposes, tore down a financial barrier to wider use of EO data in business activities, especially those with a narrow financial margin. Notwithstanding the flood of open data, some aspects of the Earth surface still escape satisfactory monitoring from space, especially in complex, anthropic areas. Due to insufficient spatial resolution, lack of visibility, or unsuitable revisit times, important pieces of information may not emerge from spaceborne data. In situ sensing can represent a vital source of integrative information to fill the aforementioned gaps and build a more complete and accurate picture of the situation and trends in the observed area. The contribution of in situ sensing was envisaged quite early in the Earth observation history, but for a long time it remained limited to tailored sensors displaced in strategic locations. With the increasing circulation of smartphones, a new opportunity has recently opened for a different paradigm of in situ sensing, offering a huge mass of additional data by tapping on data generated by mobile devices. Even if such data may be less specialized and less usable for various reasons, the sheer size of the data flow ensures that statistical analysis will pick possible useful clues. The increasing availability of mobile connections has indeed revived the concept of “crowdsourcing,” i.e., entrusting a pool of actors with problem solution or information collection tasks. In our scenario, individuals carrying mobile devices can become “citizen sensors” on a voluntary basis by contributing data through their connected terminals. Even considering the typical issues of crowdsourced data, like quality and reliability, the balance remains definitely positive. This paper provides an overview of the theme and discusses how it relates to an important, coordinated EO initiative like Copernicus. It finally presents a specific example realized in the framework of a recent research project under the Copernicus aegis.

Towards a standard licensing scheme for the access and use of satellite earth observation data for disaster management

This paper explores from the view of the data recipient and user the complexities of creating a common licensing scheme for the access and use of satellite earth observation (EO) data in international disaster management (DM) activities. EO data contributions in major disaster events often involve numerous data providers with separate licensing mechanisms for controlling the access, uses, and distribution of data by the end users. A lack of standardization among the terminology, wording, and conditions within these licenses creates a complex legal environment for users, and often prevents them from using, sharing and combining datasets in an effective and timely manner. It also creates uncertainty among data providers as to the types of licensing controls that should be applied in disaster scenarios. This paper builds from an ongoing comparative analysis of the common and conflicting conditions among data licenses that must be addressed in order to facilitate easier access and use of EO data within the DM sector and offers recommendations towards the alignment of the structural and technical aspects of licenses among data providers.

EXPOSURE AND VULNERABILITY GEOSPATIAL ANALYSIS USING EARTH OBSERVATION DATA IN THE CITY OF LIEGE, BELGIUM

Abstract. Risk situation can be mitigated by prevention measures, early warning tools and adequate monitoring of past experiences where Earth Observation and geospatial analysis have an adding value. This paper discusses the potential use of Earth Observation data and especially Land Cover / Land Use map in addressing within the three aspects of the risk assessment: danger, exposure and vulnerability. Evidences of the harmful effects of air pollution or heat waves are widely admitted and should increase in the context of global warming. Moreover, urban areas are generally warmer than rural surroundings, the so-called urban heat island. Combined with in-situ measurements, this paper presents models of city or local climate (air pollution and urban heat island), with a resolution of less than one kilometer, developed by integrating several sources of information including Earth Observation data and in particular Land Cover / Land Use. This assessment of the danger is then be related to a map of exposure and vulnerable people. Using dasymetric method to disaggregate statistical information on Land Cover / Land Use data, the SmartPop project analyzes in parallel the map of danger with the maps of people exposure A special focus on some categories at risk such as the elderly has been proposed by Aubrecht and Ozceylan (2013). Perspectives of the project includes the integration of a new Land Cover / Land Use map in the danger, exposure and vulnerability models and proposition of several aspects of risk assessment with the stakeholders of Wallonia.

GEOCAB Portal: A gateway for discovering and accessing capacity building resources in Earth Observation

The discovery of and access to capacity building resources are often essential to conduct environmental projects based on Earth Observation (EO) resources, whether they are Earth Observation products, methodological tools, techniques, organizations that impart training in these techniques or even projects that have shown practical achievements. Recognizing this opportunity and need, the European Commission through two FP7 projects jointly with the Group on Earth Observations (GEO) teamed up with the Committee on Earth observation Satellites (CEOS). The Global Earth Observation CApacity Building (GEOCAB) portal aims at compiling all current capacity building efforts on the use of EO data for societal benefits into an easily updateable and user-friendly portal. GEOCAB offers a faceted search to improve user discovery experience with a fully interactive world map with all inventoried projects and activities. This paper focuses on the conceptual framework used to implement the underlying platform. An ISO19115 metadata model associated with a terminological repository are the core elements that provide a semantic search application and an interoperable discovery service. The organization and the contribution of different user communities to ensure the management and the update of the content of GEOCAB are addressed.