An Interdisciplinary Approach to the Sustainable Management of Territorial Resources in Hodh el Chargui, Mauritania

Assessment of Earth Observations Data Harmonization

Earth observation (EO) data are remote sensing image data obtained by air-borne or space-borne sensors, which has the characters of distribution and heterogeneity. Because these data are owned by different space agencies. With Petabyte-scale data volumes of Earth Observation (EO) obtained, the EO data has been one of the most important part of big data. The characters of distribution and heterogeneity of big EO data storage and processing systems owned by different space agencies brings challenges to integrate and use these data. To meet the big EO data challenges, the new generation Spatial data infrastructure is needed to provide the solution and implementation technique to big EO data storage, access, process and analysis. The concept and architecture of Next-generation national EO Spatial Data Infrastructure (EO-SDI) is proposed in the paper. Key techniques are introduced to solve the complexity of distributed and heterogeneous EO data integration and on-demand processing. An interoperable national EO data system of systems is developed to integrate distributed EO archives of eight EO data centers.

Earth Observation (EO) data have the macro-level decision and analysis capabilities, which plays a key role in Earth science and significant scientific discoveries [1]. With the rapid development of space technologies, more and more EO data are accessed freely and shared openly. However, it is increasingly difficult, also more complex, to mine the information and knowledge among these massive-temporal datasets. EO Data Cubes (DC) provides a best-of-breed technology to build a spatial data infrastructure to fill this gap [2]. Among them, Australian Geoscience Data Cube (AGDC) [3, 4], as the leader and contributor, has supported data processing and analytical capability by dividing and restructuring grids. Following this open source software and work experience, we design and develop China Data Cube (CDC) system in recent years. Based on the new OGC DGGS standard and cloud computing technologies, CDC has got better system performance, more EO data types and richer local application cases. According to current and future months of work, this paper will describe and share the lessons learned from design and implement of CDC system. It will include following several aspects: 1) Overview of CDC system; 2) parallel EO data ingestion technology; 3) Cloud based EO data storage strategy; 4) Extension of more EO data types, especially China EO data; 5) The future work for CDC.

When spatial land tenure relations are not available, the only effective alternative data method is to rely on the agricultural census at the regional or national scale, based on household surveys and a participatory mapping at the local scale. However, what if even these are not available, which is typical for conflict-affected countries, administrations suffering from a lack of data and resources, or agencies that produce a sub-standard quality. Would it, under such circumstances, be possible to rely on remotely sensed Earth Observation (EO) data? We hypothesize that it is possible to qualify and quantify certain types of unknown land tenure relations based on EO data. Therefore, this study aims to standardize the identification and categorization of certain objects, environments, and semantics visible in EO data that can (re-)interpret land tenure relations. The context of this study is the opportunity to mine data on North Korean land tenure, which would be needed in case of a Korean (re-)unification. Synthesizing land tenure data in conjunction with EO data would align land administration practices in the respective parts and could also derive reliable land tenure and governance variables. There are still many unanswered questions about workable EO data proxies, which can derive information about land tenure relations. However, this first exploration provides a relevant contribution to bridging the semantic gap between land tenure and EO data.

Development of satellite remote sensing platforms and sensors enables massive Earth Observation (EO) data available. Satellite earth observation data has been one of the most important scientific data for Global Change Research (GCR) studies. This paper focuses on developing a cloud computing infrastructure for environmental monitoring using inter-agency EO data. A test bed of collaborative computing platform based on cloud computing is introduced to coordinate EO data and computing resources to implement on-demand EO data discovery, access and data-intensive processing, which are time-consuming work for the GCR scientists to. EO data are commonly distributed archived and most of the non-commercial data can be accessed by internet. With more usage of EO data in the GCR, researchers must spend a lot of time to collect distributed data via limited internet access. And several steps such as data download, preprocessing, information extraction with specific algorithms or software, visualization and mapping should be done mostly by manual to get useful output for further applications, which has been the heavy burden for the research work. In this paper, a Geo-computing platform is introduced to harness multi-source EO data and coordinate computing resources for on-demand processing. Cloud computing and workflow technique are the key components of the platform. And several case studies such drought monitoring are used to test the performance of the platform.

<p>Geological and environmental conditions that influence local topography also affect indirectly the location of human settlement dynamics. Understanding those relationships plays an important role in archaeological research related to the evolution of settlement dynamics. In the lower Tyrrhenian Islands, an important parameter is also the volcanic landscape evolution. This work aims to study the patterns of Neolithic, Cooper and Bronze Age settlements, based on known archaeological sites at the Low Tyrrhenian Islands, and to generate hypotheses about the relations of settlement patterns with the volcanic landscape. To that end, a Web-GIS database was created, which was fed with topographic, geological, geomorphological data and Earth Observation data. Geomorphological analysis, derived from digital elevation models, and earth observation products such as the SENTINEL missions, can provide useful estimations into the processes shaping landscapes and insight into the location and evolution of settlements. The analysis includes a series of different data correlation, from geomorphologic to socioeconomic, integrated by an indicator analysis. A series of thematic maps were developed to interpret why areas were selected to host settlements. Through the use of the database that was developed during the project, a set of indexes have been applied. Those included exposure and vulnerability indices for the inland and coastal areas, but also location and defensibility indices for the archaeological sites. Moreover, baseline maps for future risk estimations through a Multi-Criteria Decision Analysis System (MCDA), have been produced. The Volcanic Islands of the lower Tyrrhenian coast have a volcanic origin and were influenced, and partly still are, by explosive and effusive eruptions of various energy and types, by more or less intense deformational events, often connected with the dynamics of the volcano, and quiescent periods of varying duration. The areas under investigation present different characteristics in their geomorphological but also their societal evolution. Geomorphological data further analyzed in a ternary diagram that indicated the relative influence of each of the parameters in each area. From the diagram, it can be seen that the locations of human activities are strongly affected by past and recent volcanic activity.</p><p>Acknowledgement: This work is part of the Brains2Islands “INDAGINE MULTIDISCIPLINARE NEI CONTESTI INSULARI BASSO TIRRENICI” project Funded by FONDAZIONE CON IL SUD project number 2015-0296</p>

Airborne light detection and range (LiDAR) with three-dimensional product acquisition capabilities has become an important research hot spot. The classification and information extraction of LiDAR were usually carried out with other earth observation (EO) data (VIR/SAR, spaceborne/airborne). However, if these EO data are not always available, a particularly interesting possibility is to use only the products of LiDAR including the digital elevation model, the digital surface model and the intensity image. In this article, at the study site of Beauport, around Lac-Saint-Charles, Québec, Canada, only airborne LiDAR products are applied to realise land cover interpretation and classification. A two-hierarchy decision-tree classification is suggested and carried out. Six land cover classes are extracted, including water bodies, bare soils, impervious areas, vegetation, buildings and high-voltage electric wires. All the reference and checking data are based on 1:20,000 topographic map of Québec. According to the accuracy statistics with 966 independent checking data, it is an efficient attempt in land cover classification using only airborne LiDAR products in this site. The feasibility of the methods and the classification data sources are verified for use in the scale of 1:25,000–50,000.

Climatic stress and anthropogenic disturbances have caused significant environmental changes in the Sahel. In this context, the importance of soil is often underrepresented. Thus, we analyze and discuss the interdependency of soil and vegetation by classifying soil types and its woody cover for a region in the Senegalese Ferlo. Clustering of 28 soil parameters led to four soil types which correspond with local Wolof denotations: Dek, Bowel, Dior and Bardial. The soil types were confirmed by a Non-metric Multidimensional-Scaling (NMDS) ordination and extrapolated via a Random Forest classifier using six significant variables derived from Landsat imagery and a digital elevation model (out-of-bag error rate: 7.3%). In addition, canopy cover was modeled using Landsat and a Reduced-Major-Axis (RMA) regression (R2 = 0.81). A woody vegetation survey showed that every soil type has its own species composition. However, 29% of Bowel regions are deforested (i.e., degraded) and interviews revealed extensive environmental changes and a strong decline and local extinction of woody species. The differences between the soil types are significant, showing that vegetation changes (i.e., degradation and greening), resilience to climatic stress and human activities largely depend on soil properties. We highlight that spatial heterogeneity is an important aspect when dealing with environmental changes in the Sahel, and local knowledge can be well used to classify spatial units by means of public Earth observation data.

The western part of Crete Island has undergone serious landslide events in the past. The intense rainfalls that took place in the September 2018 to February 2019 period provoked extensive landslide events at the northern part of Chania prefecture, along the motorway A90. Geospatial analysis methods and earth observation data were utilized to investigate the impact of the various physical and anthropogenic factors on landslides and to evaluate landslide susceptibility. The landslide inventory map was created based on literature, aerial photo analysis, satellite images, and field surveys. A very high-resolution Digital Elevation Model (DEM) and land cover map was produced from a dense point cloud and Earth Observation data (Landsat 8), accordingly. Sentinel-2 data were used for the detection of the recent landslide events and offered suitable information for two of them. Eight triggering factors were selected and manipulated in a GIS-based environment. A semi-quantitative method of Analytical Hierarchy Process (AHP) and Weighted Linear Combination (WLC) was applied to evaluate the landslide susceptibility index (LSI) both for Chania prefecture and the motorway A90 in Chania. The validation of the two LSI maps provided accurate results and, in addition, several susceptible points with high landslide hazards along the motorway A90 were detected.

Abstract. Earth observation (EO) data cubes have revolutionized the way large volumes of EO data can be stored, accessed, and processed. However, users coming from application domains outside of traditional EO research still face some significant technical barriers when querying an EO data cube with the aim to infer knew knowledge about real world entities and events. They have to interpret EO data in order to give them meaning, which is an ill-posed problem that requires advanced expertise in the field of EO analytics. We propose a semantic querying framework in which users query the EO data cube through an ontology, rather than accessing the data values themselves. The ontology formalizes symbolic representations of real-world entities and events, which are mapped to data values in the EO data cube through a mapping component formulated by an EO expert. This takes away the need for users to be aware of the EO data and how to interpret them, and therefore lowers the technical barriers to extract valuable information from EO data. We implemented a proof-of-concept of our approach as an open-source Python package.

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.

This paper presents the results of research designed to explore the challenges involved in the use of Earth Observation (EO) data to support environmental management Brazil. While much has been written about the technology and applications of EO, the perspective of end-users of EO data and their needs has been under-explored in the literature. A total of 53 key informants in Brasilia and the cities of Rio Branco and Cuiaba were interviewed regarding their current use and experience of EO data and the expressed challenges that they face. The research builds upon a conceptual model which illustrates the main steps and limitations in the flow of EO data and information for use in the management of land use and land cover (LULC) in Brazil. The current paper analyzes and ranks, by relative importance, the factors that users identify as limiting their use of EO. The most important limiting factor for the end-user was the lack of personnel, followed by political and economic context, data management, innovation, infrastructure and IT, technical capacity to use and process EO data, bureaucracy, limitations associated with access to high-resolution data, and access to ready-to-use product. In general, users expect to access a ready-to-use product, transformed from the raw EO data into usable information. Related to this is the question of whether this processing is best done within an organization or sourced from outside. Our results suggest that, despite the potential of EO data for informing environmental management in Brazil, its use remains constrained by its lack of suitably trained personnel and financial resources, as well as the poor communication between institutions.

As the capabilities of remote sensing devices keep improving, large amounts of Earth Observation (EO) data are being generated by satellites every day, recording the status of environment. The EO data produced are drastically expanding in precision, quantity and size. However, the capacity of managing, retrieving and reasoning the big EO data from heterogeneous EO data sources across the world is very limited. In this paper, we propose a data model to develop mechanisms of describing geographical resources using properties, expressing the relations in EO data, and capturing the implicit knowledge in the given facts. We formalize the model using first order logic and demonstrate the implementation of the model using OWL and Jena.

Agricultural producers require knowledge of soil water at plant rooting depths,while many remote sensing studies have focused on surface soil water or mechanisticmodels that are not easily parameterized. We developed site-specific empirical models topredict spring soil water content for two Montana ranches. Calibration data sample sizeswere based on the estimated variability of soil water and the desired level of precision forthe soil water estimates. Models used Landsat imagery, a digital elevation model, and asoil survey as predictor variables. Our objectives were to see whether soil water could bepredicted accurately with easily obtainable calibration data and predictor variables and toconsider the relative influence of the three sources of predictor variables. Independentvalidation showed that multiple regression models predicted soil water with average error(RMSD) within 0.04 mass water content. This was similar to the accuracy expected basedon a statistical power test based on our sample size (n = 41 and n = 50). Improvedprediction precision could be achieved with additional calibration samples, and rangemanagers can readily balance the desired level of precision with the amount of effort tocollect calibration data. Spring soil water prediction effectively utilized a combination ofland surface imagery, terrain data, and subsurface soil characterization data. Rancherscould use accurate spring soil water content predictions to set stocking rates. Suchmanagement can help ensure that water, soil, and vegetation resources are usedconservatively in irrigated and non-irrigated rangeland systems.

There is an increasing amount of free and open Earth observation (EO) data, yet more information is not necessarily being generated from them at the same rate despite high information potential. The main challenge in the big EO analysis domain is producing information from EO data, because numerical, sensory data have no semantic meaning; they lack semantics. We are introducing the concept of a semantic EO data cube as an advancement of state-of-the-art EO data cubes. We define a semantic EO data cube as a spatio-temporal data cube containing EO data, where for each observation at least one nominal (i.e., categorical) interpretation is available and can be queried in the same instance. Here we clarify and share our definition of semantic EO data cubes, demonstrating how they enable different possibilities for data retrieval, semantic queries based on EO data content and semantically enabled analysis. Semantic EO data cubes are the foundation for EO data expert systems, where new information can be inferred automatically in a machine-based way using semantic queries that humans understand. We argue that semantic EO data cubes are better positioned to handle current and upcoming big EO data challenges than non-semantic EO data cubes, while facilitating an ever-diversifying user-base to produce their own information and harness the immense potential of big EO data.