2.3: Classification Systems
3:20pm - 3:40pm
Assessing and modelling a functional relationship of L.C. and L.U. a possible new path forward. The LCHML (Land Characterization Metal-Language) a new proposed FAO UML schema.
Land-cover and Land Use information are important parameters in most of the studies related to natural environment, ecosystem services and many other important disciplines. However despite its importance and the many efforts toward data harmonization (especially for LC) do not exist an accepted model on how to link and functionally correlate those two information. On the contrary there is often a contamination of LCLU terms in many LC nomenclatures (Anderson, Corine, etc) and surprising also in some LU classifications (UNFCC, NLUD, etc). Even when the two information are keep correctly clearly distinct (E.U. Inspire spatial data infrastructure) no effort is made to model/describe their functional relationship. FAO (and UNEP) have a long prominent role on the efforts to develop standardized LCLU classification and data harmonization. Especially in LC the development of the LCCS parametric method and subsequently the “object oriented” approach underlining the LCML (Land Cover Meta-Language) model (ISO Standard 19144-2) has open a new path forward for the representation/harmonization of LC information. Based on this experience and using as base part of the original LCML UML schema, a new model is under development, the LCHML (Land Characterization Meta-Language). LCHML not only propose a revised LC and a new LU model but also try to create a comprehensive standardized framework were is possible to create an exhaustive and functional correlation of both biophysical and human related activities. LCHML therefore try to integrate in a unique model both LC and LU. The objective is to create a standardized framework were it is possible to describe any geographic area from different perspectives: pure LC, pure LU, or a functional combination of the two called (tentatively) LCH (Land Characterization).
3:40pm - 4:00pm
Advances in Copernicus High-Resolution Land Monitoring
1GAF AG; 2GeoVille Information Systems GmbH; 3Systèmes d’Information à Référence Spatiale (SIRS) SAS
The Copernicus Programme, headed by the European Commission (EC) in partnership with the European Space Agency (ESA), offers Earth observation-based services for six core thematic areas: Land, Atmosphere, Oceans, Climate Change, Emergency and Security. Among these services – mainly based on Earth Observation (EO) data provided by ESA through the Copernicus Space Component – the Copernicus Land Monitoring Service delivers products on local, continental and global levels. As part of the pan-European Copernicus Land Service, coordinated by the European Environment Agency (EEA), the High Resolution Layers (HRLs) map multi-temporal land cover characteristics for five thematic areas (Imperviousness, Forest, Grassland, Water/Wetness, Small Woody Features) in 20 meters spatial resolution and in a consistent manner for 39 European countries. All thematic HRLs contain specific information on current environmental conditions and temporal variance of major land cover types with thematic accuracies exceeding 80–90% (depending on the product). The HRL products are tailored towards a multi-user community and are freely provided for download on the Copernicus website.
With the current production for the 2015 reference year, the HRLs are entering the era of big data multi-temporal image processing, incorporating large data volumes from different sensors in a decentralized processing framework in a network of industrial service providers. Our contribution will describe the framework, methodology and first results of the current HRL 2015 production comprising the update of the existing (2012) pan-European HRLs Imperviousness and Forest, including 2012–2015 change products, as well as 2015 mapping of other newly defined HRLs (Grassland, Water/Wetness, Small Woody Features).
Primary information source are multi-temporal, high-resolution satellite images from Sentinel-1 and -2, as well as data from SPOT, Resourcesat and Landsat contributing missions. Whereas the 2015 Forest and Imperviousness HRLs will be produced based on optical time series imagery, the newly defined Grassland and Water/Wetness products will benefit from innovative approaches on the basis of a fusion of optical and synthetic aperture radar (SAR) time series data. The novel HRL on Small Woody Features is the HRL using very high resolution (VHR) data as primary input. The VHR data sets will also be used for reference data collection and validation alongside national and pan-European in-situ data sets.
The full chain of image and in-situ data acquisition, pre-processing, generation of biophysical variables, multi-temporal image classification and validation will be demonstrated, and first results will be presented for all five HRLs. Semi-automatic classification techniques based on multi-temporal pixel-based as well as segment-based approaches are applied specifically tailored towards each HRL, resulting in raster products in full 20m resolution, as well as vector products on 1:5000 scale for the Small Woody Features.
The Copernicus HRLs are designed for a broad user community as basis for environmental and regional geo-spatial analyses as well as for supporting political decision-making. With future updates, the HRLs will significantly benefit from ESA’s growing Sentinel-1/-2 archive, further improving the products’ consistency, timeliness and accuracy. An outlook concludes on the potential usability of the presented methods and products for future European to global LC/LU applications on a HR scale.
4:00pm - 4:20pm
The National Land Cover Database (NLCD): A Successful National Land Change Monitoring System
United States Geological Survey, United States of America
The National Land Cover Database (NLCD) is an example of a national land cover change monitoring system that incorporates user requirements, scientific advances and the results from rigorous accuracy assessments to provide accurate and current data products that are useful to land managers and the public. It is managed by a consortium of United States governmental agencies, the Multi-Resolution Land Characteristics (MRLC) consortium, that require land cover information to assess environmental quality and promote the sustainable use of natural resources. This consortium is a collaborative forum, where members share research, methodological approaches, and data to establish protocols promoting the development and use of integrated land cover data products. The NLCD began as a one-time land cover thematic mapping effort of the conterminous US in 1992 and now encompasses four epochs (1992, 2001, 2006 and 2011) of thematic land cover data, as well as continuous field datasets such as percent impervious surface, required for water quality assessments and percent canopy cover, required for biodiversity, biomass and carbon sequestration assessments. All datasets are derived from Landsat imagery and so have a spatial resolution of 30 metres by 30 metres. Monitoring land cover change is accomplished by integrating a remote sensing image change analysis with a knowledge-based system on land change. This system consists of rules and/or attributes derived from the spectral, spatial, and temporal characteristics of remote sensing data and historical knowledge on land cover change and its trajectories. All of the datasets have undergone rigorous accuracy assessments that have been used to guide continuous improvements in the data, as well as advancing remote sensing science. The consortium has stressed advancing the technological aspects of land cover data production, including data preprocessing, classification methodologies and advancing an integrated database paradigm that enables change monitoring over time. The result has been a dramatic decrease in the amount of time required to create a dataset and its cost, as well as the identification of new compatible datasets such as the newly formulated percent bareground and shrub cover. The results of the consortium’s efforts have been a suite of land cover data products that provide valuable, tangible societal benefits. These benefits include: water quality monitoring and assessment; identifying potential relationships between land cover patterns and human well-being; assessing the impacts of natural disasters on ecosystem services; and influencing federal energy policies on land use change and potential environmental degradation that may arise from land use change.
4:20pm - 4:40pm
Global to Local Land Cover and Habitat Mapping: The Ecopotential Approach
1University of New South Wales, Australia; 2Consiglio Nazionale delle Ricerche, Italy; 3Centre for Research and Technology Hellas, Greece; 4Universitat Autonoma de Barcelona, Spain
A component of the EU-funded ECOPOTENTIAL project funded under the Horizon 2020 Program (Reference 641762) has been the development of the EO Data for Ecosystem Monitoring (EODESM) system. This system allows classification of land covers according to the Food and Agricultural Organisation (FAO) Land Cover Classification System (LCCS-2) taxonomy from EO-derived biophysical and thematic information. The presentation will a) demonstrate the EODESM system, giving examples from protected areas in Europe, Africa and the Middle East, and its wider application (including at the global level), and b) convey the uncertainty associated with including layers generated at different scales within the classification.
The EODESM system has been developed such that LCCS classifications can be generated from pre-prepared (including global) data layers as well as satellite data (e.g., as acquired by the Sentinel’s and Landsat). These layers relate to the broader LCCS Level 3 categories (natural/semi-natural and cultivated/managed landscapes, natural and artificial bare areas, and water) and include additional information (including lifeform, structure, phenology, hydroperiod, and urban density). Inputs can also include existing thematic information (e.g., cadastral and infrastructure maps), modeled outputs (e.g., hydrological) or knowledge. These layers are used to generate the component LCCS codes (e.g., A3 or A4 for trees or shrubs, D1 or D2 for broadleaved or evergreen), which are then combined. The system has also been adapted to consider changes in these codes but also biophysical and other attributes (including those unrelated to the classification; e.g., above ground biomass), and can attribute changes to specific causes and indicate or suggest consequences and the impacts on, for example, protected areas. An advantage is that it can be applied at any scale (local to global)and also integrate data from sensors operating in different modes (lidar, radar, optical) and spatial and temporal resolutions. Furthermore, consistent and highly detailed land cover classifications are provided together with a diverse range of information describing landscapes (e.g., biomass, hydrology, Leaf Area Index, soil moisture). The system is also well suited to follow the LCCS-3 or Land Cover Macro Language (LCML) and is being modified accordingly. In the context of ECOPOTENTIAL, the EODESM approach has been applied to a wide range of protected areas and their surrounds with the intention of providing timely and historical information to ensure protection and facilitate restoration of ecosystem services. The EODESM system has potential for global application and this will be conveyed within the presentation with reference to other land cover classification schemes. The EODESM system will be made available through a Virtual Laboratory being prepared by the ECOPOTENTIAL project and integrated within GEOSS. Land cover maps and change maps will also be accompanied by comprehensive metadata including data quality documentation.
4:40pm - 5:00pm
Assessment of Trends in Ecosystem Health and Condition
Boston University, United States of America
Traditionally remote sensing of land cover has focused on mapping land cover types, with monitoring being devoted to finding the conversions between land cover types. As time series of observations have become available, it is now possible to detect more subtle characteristics about landscapes, including trends in ecosystem health and condition. Using many observations it is possible to separate effects related to seasonality from those providing longer term indications of the condition of ecosystems. For example, it is possible to observe trends indicative of growth in forests, decline related to pests or degradation, and interannual variability related to climate. One particularly interesting case is to monitor the recovery of ecosystems following disturbance. Additionally, it is possible to begin to identify events that influence ecosystems, such as extreme weather. The net effect is the ability to derive more subtle information about land cover that will prove helpful in both ecosystem modeling efforts and land management.