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by Jan Cees Venema

CLEARSITE Projects from CoE4UNSDI | SDI Magazine

The Centre of Excellence for UN Spatial Data Infrastructure (CoE4UNSDI) is undertaking a set of three projects, collectively called ‘ClearSite’. Find out more.

Geospatial data is any data which has embedded within it a location ‘tag’. Examples of geospatial data are post codes, words which refer to a location (e.g. Sudan), maps, satellite or aerial images, videos, pictures, spreadsheets with location tags and navigation system data logs.

Globally, the UN, partner agencies, member states and other relevant institutions are becoming increasingly involved in a host of vital services from disaster response and peacekeeping to environmental protection and economic development. In doing so, they produce geospatial data they need to share to raise operational effectiveness and coordinate efforts.

The global community of citizens is also becoming an increasingly valuable resource with the emergence of “crowd sourcing” of voluntarily contributed geospatial information.

Currently within the UN there is little capacity to leverage an individual organization’s investment in geospatial data obtained from a variety of sources for the benefit of all stakeholders. The Centre of Excellence for UN Spatial Data Infrastructure first projects will improve on this situation, in close collaboration with stakeholders, by using leading information and communications technology solutions.

The Office of Information and Communications Technology (OICT) of the UN Secretariat in New York has established the Centre of Excellence for UN Spatial Data Infrastructure (CoE4UNSDI) under the strategic direction of the UN Spatial Data Infrastructure (UNSDI) Steering Committee of the United Nations Geographic Information Working Group (UNGIWG). OICT, the Food and Agriculture Organization (FAO) and the United Nations Office in Geneva, Information and Communications Technology Service (UNOG/ICTS) have formed an inter-agency partnership to undertake an initial set of three CoE4UNSDI projects, collectively called ClearSite, as a UN System-wide harmonization initiative in reference to the UN ICT Strategy endorsed by the General Assembly in 2010 (Section 29 (A/64/6)).

ClearSite will provide the UN and its partners with a Web-based toolset to retrieve, combine and visualize the information needed to support their operations’ decision-making processes. This will create many new possibilities for the UN to “deliver as one” and to become more efficient and effective. ClearSite will directly benefit work being strategically accomplished in primary areas of operation and interest to the UN and its partners, such as Peace and Security, Social Protection, Food Security, Environment and Sustainable Development, Human Rights and Disaster Management. This harmonization initiative will also support and enhance initiatives in the fields of early intervention, conflict prevention, crisis response and management and strategic planning.

Major Project Stakeholders

Australia and the Federal Republic of Germany are the Founding Members of the CoE4UNSDI as the initial contributors to the CoE4UNSDI Trust Fund.

As a UN System-wide initiative, ClearSite is the result of a consultative process with extensive user input from key UN organizations. To promulgate existing international standards and to ensure broad adoption of its tools and guidelines, ClearSite partners are seeking to align with the activities of global and regional entities such as the Global Spatial Data Infrastructure (GSDI) Association, the Group on Earth Observation/Global Earth Observation System of Systems (GEO/GEOSS), the Infrastructure for Spatial Information in the European Community (INSPIRE), the Open Geospatial Consortium (OGC), the Open Source Geospatial Foundation (OSGeo) as well as leading geospatial information technology companies.

The UN Secretary-General’s Global Pulse Project, with its objective of supporting social protection policy formulations at Pulse Labs around the world (such as the ones being established in Indonesia and Uganda), is providing initial use cases. UN Environment Programme (UNEP) is another organization aligning its infrastructure development efforts with ClearSite. The Common and Fundamental Operational Datasets initiative of the humanitarian community lead by the Office for the Coordination of Humanitarian Affairs (OCHA) and the Department of Safety and Security (DSS) of the UN Secretariat are also early beneficiaries.

The ClearSite Projects

ClearSite projects are funded through voluntary contributions of UN Member States, technology companies, international organizations, foundations and industry associations to a Trust Fund established at the UN Secretariat.

The three ClearSite projects to be completed within 3 years are:

• Standards and Best Practices for Provisioning of Core Geospatial Datasets (OICT)
• Geospatial Data Warehouse (FAO)
• Visualization Facility (UNOG/ICTS)

Standards and Best Practices for Provisioning of Core Geospatial Datasets

The UN Spatial Data Infrastructure (UNSDI) Gazetteer Framework will deliver an infrastructure to enable access, management and cross-referencing of gazetteers (directories of place names), a core geospatial dataset of critical importance. The Framework will also establish a method for validating and incorporating crowd-sourced information to enhance authoritative source gazetteers.

Geospatial Data Warehouse

The Geospatial Data Warehouse will establish strong connections between the existing geospatial information systems of UN Agencies, Funds and Programmes. It will also build new connections, using widely available software and common, standardized data-sharing practices. Users will be able to easily locate, access and re-use UN geospatial content such as maps, Geographic Information System data, remote sensing imagery and Global Navigation Satellite System (GNSS) data logs.

Visualization Facility

Using the authoritative directory of place names and the aggregated geographic data of various UN organizations, the visualization component of the UNSDI project will provide a holistic, common view of that information in a consumable and visually intuitive manner. The base layer of authoritative maps will include overlays of thematic information so that the various mandated tasks being undertaken by the UN and partner organizations can be viewed through the standard facility, or, if necessary, by the UN Agencies, Funds and Programmes as well as partners through their facilities.

via SDI Magazine (Roger Longhorn)..

by Jan Cees Venema

Crop monitoring and forecasting

Analysis of meteorological and climatic data allows to provide near real-time information about the crop state, in quality and quantity, with the possibility of early warning on alarm/alert situations so that timely interventions can be planned and undertaken. Crop forecasting philosophy is based on various kind of data collected from different sources: meteorological data, agrometeorological phenology, yield, soil water holding capacity, remotely sensed, agricultural statistics. Based on meteorological and agronomic data, several indices are derived which are deemed to be relevant variables in determining crop yield, for instance crop water satisfaction, surplus and excess moisture, average soil moisture, etc.

Crop forecasting is the art of predicting crop yields tons/ha and production before the harvest actually takes place, typically a couple of months in advance. Crop forecasting relies on computer programmes that describe the plant-environment interactions in quantitative terms. Such programmes are called “models”, and they attempt to simulate plant-weather-soil interactions. They need, therefore, information and data on the most important factors that affect crop yields – the model inputs. After passing “through” the model, the inputs are converted to a number of outputs, such as maps of crop conditions and yields.

via CLIMPAG: Climate Impact on Agriculture | ADVICE and WARNINGS.

by Jan Cees Venema

OGC Sensor Observation Service Standard Version 2.0 Adopted

The Open Geospatial Consortium (OGC®) membership has adopted the OGC Sensor Observation Service (SOS) Interface Standard Version 2.0.

Whether from in-situ sensors (e.g., water monitoring) or remote sensors (e.g., satellite imaging), observations made from sensor systems contribute most of the geospatial data by volume used in geospatial systems today. The OGC® Sensor Observation Service Interface Standard (SOS) provides an open, well-defined API for managing measured data as well as metadata from deployed sensors. The SOS is one standard in the OGC Sensor Web Enablement (SWE) suite of standards.

SOS 2.0 includes a modular restructuring of the document, a new and easy to use key-value-pair binding, a new SOAP binding, a redesign of the observation offering concept, and it now relies on the common OGC Sensor Web Enablement Service Model. SOS 2.0 is highly modular and follows the OGC core/extension design pattern. The main SOS 2.0 document incorporates the core as well as the transactional extension, result handling extension, enhanced operations extension, binding extension, and a profile for spatial filtering of observations. Further extensions can be built upon this framework in the future.

The SOS 2.0 standard is available at: http://www.opengeospatial.org/standards/sos.

The OGC is an international consortium of more than 435 companies, government agencies, research organizations, and universities participating in a consensus process to develop publicly available geospatial standards. OGC Standards support interoperable solutions that “geo-enable” the Web, wireless and location-based services, and mainstream IT. OGC Standards empower technology developers to make geospatial information and services accessible and useful with any application that needs to be geospatially enabled. Visit the OGC website at http://www.opengeospatial.org.

 

via GeoCommunity SpatialNews.

by Jan Cees Venema

Integrating crop growth simulation and remote sensing to improve resource use efficiency in farming systems

Wageningen University dissertation no. 3951

This study investigated the scope and constraints for integrated use of mechanistic crop growth simulation models and earth observation techniques. Integration of high-quality crop growth models and information derived from earth observations can contribute to improved use of resources, reduced crop production risks, reduced environmental degradation, and increased farm income. In the past, both, simulation modelling and remote sensing have been shown to be valuable tools in separate applications in agriculture. Crop growth simulation has made valuable contributions to yield forecasting, proto-typing crop varieties, generation of input-output coefficients for improved agricultural production technologies and to management decision support systems at field level. Likewise, remote sensing techniques have been successfully applied in classification of arable crops and in quantification of vegetation characteristics at different spatial and temporal scales. The starting point of this study was the hypothesis that integration of both techniques would lead to improvements in the dynamic simulation of the crop-soil system and thus contribute to improvements in management decision support systems for environmentally sound agricultural production.

Thus far, mutually beneficial linkages have been limited to land use classification via remote sensing (choice of adequate model) and quantification of crop growth and development curves using e.g. estimates of leaf area indices derived from remote sensing images for model calibration under (usually) favourable growth conditions. Only a few studies have considered the potentials of remote sensing for model initialization of growth and development characteristics of a specific crop. In this thesis these potentials have been extended to a more continuous approach, in which remote sensing information is not only used in model initialization, but also in model calibration in the course of the simulation run, so-called run-time calibration. During such a run-time calibration procedure, simulated values of e.g. leaf area index (LAI) and canopy nitrogen status (CNS) are replaced by values estimated from remote sensing images acquired at different stages in the course of the growing period. LAI and CNS are important controlling variables in models for arable crops such as wheat, potato and maize. This run-time calibration procedure has been performed for a full crop growth cycle, for optimal as well as sub-optimal growth conditions. This approach enables spatial differentiation in crop growth simulation, as variations in crop status, resulting from differences in growth conditions, lead to differences in remote sensing signals. The relationships between near and remote sensing observations at leaf, plant and canopy level have been investigated and the effects of variations in estimated values of LAI and CNS used in run-time calibration of dynamic crop growth simulation models on final model results (e.g. crop yield) have been analyzed.

Results from potato trials in the Netherlands show that leaf nitrogen contents derived from near sensing observations can be up-scaled to plant and canopy nitrogen status by taking into account the vertical nitrogen distribution in the crop. A vertical nitrogen extinction coefficient (kn) of 0.41 resulted in an accuracy increase of the relation between leaf nitrogen (g N m-2 leaf) and SPAD readings (a near sensing technique at leaf level), with a correlation coefficient (r2) of 0.91. Remote sensing observations integrate nitrogen contents over canopy depth and do not require adjustment for vertical nitrogen gradients, if canopy nitrogen status is expressed in total nitrogen content per unit of soil surface. The red edge position (an index derived from remote sensing observations) could be related to canopy nitrogen content (g N m-2 soil) with a correlation coefficient (r2) of 0.82. Leaf area indices of potato (Netherlands) and maize (Argentina, France, USA) crops, for use in run-time calibration, were also accurately derived from field, airborne and spaceborne remote sensing platforms. Introducing LAI values derived from RS in the simulation model and concurrently adjusting CNS by retaining leaf N-concentrations, led to more accurate simulation results for CNS than without such adjustment. The different crops, and the range in environmental conditions, soil fertility status and management practices that were examined in the different case-studies in this thesis, have demonstrated the broad applicability of mechanistic simulation models integrated with remote sensing information

Winter wheat fields, wheat phenological stages (emergence, flowering) and management operations (harvest) were successfully identified on the basis of information from optical and radar remote sensing data in a case-study in South-eastern France. Timing of these phenological stages and management operations is important in model calibration as they mark the length of the crop growth period and of the grain-filling period, which are co-determinants of grain yield. At flowering, C-band radar backscatter from the soil is maximally reduced by canopy moisture content. This characteristic was successfully used to estimate regional wheat flowering dates. Integration of RS data in the (point-based) crop growth simulation model allowed its spatial application for prediction of wheat production at regional scale. The estimated value was in agreement with regional yield statistics. This integration thus allows expansion of the application area of valuable research tools, as up-scaling has become feasible.

Introduction of remote sensing-based estimates of LAI and CNS in the course of the growing seasons into dynamic simulation of the growth of potato and maize resulted in improved simulation accuracy for aerial crop characteristics, as well as for variables that could not be directly observed by remote sensing, such as soil inorganic nitrogen contents. The degree of success and robustness of the integrated approach depends on the timing, accuracy and number of remote sensing observations available for re-setting the relevant state variables in the course of the simulation period. Simulation accuracy was positively correlated with the number of observation dates from remote sensing. Remote sensing observations around flowering had more impact on calculated final grain yield (FGY) for maize than earlier or later observations.

The investigations reported in this thesis have shown that the accuracy of predictions of dynamic and mechanistic crop growth simulation models significantly improves through integrating earth observation-derived information as input for the models and for their run-time calibration. Such integration not only yields more accurate estimates of crop bio-physical variables, such as leaf area index and canopy nitrogen status, but also contributes to improved prediction at regional scales. Such models, producing reliable, site-specific predictions of crop performance and crop requirements are thus effective tools in the development of environmentally-friendly production methods and in optimizing the use of our natural resources.

Further research should focus on the scope for estimating additional crop variables of interest for integration in simulation modelling through remote sensing. Management interventions may be triggered by various crop characteristics, such as: 1) canopy temperatures derived from thermal remote sensing systems as an indicator for water stress, 2) canopy discolouring derived from optical remote sensing systems as an indicator for nutrient shortages and 3) canopy architecture derived from radar remote sensing images as an indicator for water and nutrient supply. Remote sensing is also a valuable technique to identify spatial patterns of crop performance and crop status within arable fields. Moreover, remote sensing allows identification of patterns that may be related to specific diseases or special events, such as outbreaks of phytophtera in potato, or lodging in grain crops.

This study has demonstrated that a decision support system for crop and soil management based on the integration of crop growth simulation modelling and remotely sensed data is within reach. In addition, nitrogen uptake, its vertical distribution within the crop, and the inorganic nitrogen content of the soil can be simulated more accurately with such an integrated system. Such a decision support system can be used for fine-tuning of fertilizer regimes thus contributing to more environmentally sound and sustained agricultural production.

ref. Plant Production Systems Group – Wageningen UR – Wageningen University.