Conference Agenda

The US Geological Survey has a long land cover history, starting with the 1976 landmark A Land Use and Land Cover Classification System for use with Remote Sensor Data, and including global land cover mapping and the ongoing production of the National Land Cover Database. While these past projects have had a significant impact, land cover data needs are changing due to the demand for increasingly innovative and timely land cover products needed to meet the community’s insatiable appetite for science quality geospatial land cover and land change data. New strategies are needed that generate higher quality results that include additional land cover variables, more detailed legends, and more frequent land cover and land change geospatial and statistical information.

The USGS response to the growing requirements for land use, cover, and condition data, information, and knowledge, is the Land Change Monitoring, Assessment, and Projection (LCMAP) initiative. LCMAP is an end-to-end capability that uses the rich Landsat record to continuously track and characterize changes in land cover, use, and condition and translate such information into assessments of current and historical processes of cover and change. LCMAP aims to generate science-quality land cover and land change products from current and near-real time Landsat data. All available Landsat data for any given location are used to characterize land cover and change at any point across the full Landsat record and to detect and characterize land cover and land change as it occurs. The basis for this is the Continuous Change Detection and Classification (CCDC) algorithm developed by Zhu and Woodcock (2014). The initial LCMAP land cover and land change products are annual land cover maps, maps of key change attributes (e.g., date, type, and magnitude of change), corresponding accuracy assessments, and land cover and land change area estimates. This presentation focuses on the overall LCMAP strategy and reviews early results that demonstrates how an improved land and change information lead to a better understanding of the rates, causes, and consequences of land change.

Zhu, Z. and Woodcock, C.E., 2014. Continuous change detection and classification of land cover using all available Landsat data. Remote sensing of Environment, 144: 152-171.

The NASA Land-Cover/Land-Use Change (LCLUC) program is developing interdisciplinary research combining aspects of physical, social and economic sciences, with a high level of societal relevance, using remote sensing tools, methods and data.One of its stated goals is todevelop the capability for periodic satellite-based inventories of land cover and monitoring and characterizing land-cover and land-use change. Synergistic use of data from different satellites is an efficient way to get most out of current remote sensing capabilities to study land changes. This presentation will focus on the recent achievements in multi-source land imaging projects under LCLUC program, in which optical mid-resolution data from Landsat system and Sentinel-2, as well as radar data from Sentinel-1, are combined to develop higher-level medium spatial resolution (10-60m) satellite products in analyzing changes in land cover at the global and continental scales. This presentation will include illustrations of recent, significant results in the LCLUC program for various sectors, including forestry, agriculture and urban.

The Group on Earth Observations (GEO) is the organization building the Global Earth Observation Systems of Systems ( GEOSS). The organization is comprised of over 100 member countries and over 100 Participating Organizations that are coordinating their efforts to improve the use of Earth observations for decision making. GEO has eight Societal Benefit Areas (SBAs) and more than 70 different activities that are filling in observational gaps, increasing data sharing, and enhancing discovery, access and application of data contained in the GEOSS Common Infrastructure (GCI).

Land cover is one of GEO’s top-priority areas due to its importance to many SBAs. It is critically important for ecosystems, biodiversity, water and disasters, and plays a major role in climate change processes. Land cover and land use information is required by a variety of the Multilateral Environmental Agreements (MEAs) such as the Sustainable Development Goals (SDGs), the Convention on Biological Diversity (UNCBD), the Convention to Combat Desertification (UNCCD), the Ramsar Convention on Wetlands, and several others. GEO is actively involved with the SDGs, focused on providing the Earth observations and derived products that can support achieving them and generating the indicators that mark progress. And at the national level, governments need land cover and land use information, particularly change information, to meet their many internal needs.

Contributors to the GEO Land Cover (LC) and Land Cover Change (LCC) Task work to improve the availability and quality of LC and LCC data by helping to convene and coordinate various sectors of the LC community, including data providers and consumers. Stakeholders include environmental agencies, science communities, national mapping agencies, commercial users, and MEAs. Activities include work to improve access to existing LC and LCC information, development of shared tools to facilitate validation of LC datasets, and working towards sustainable systems for LC product generation.

Because LC products are needed for so many users and applications, meeting these varied needs is a big challenge. Most current LC product generation approaches are labour intensive and unable to meet the needs of all users. Consequently, GEO is facilitating discussions within the LC community on how to move towards automated, sustainable, operational systems that can support a wider range of user needs.

Land cover is one of the most easily detected indicators of human interventions on the land. Information on land cover is therefore critical for the implementation of environmental, food security and humanitarian programmes of UN, international and national institutions.

The Food and Agriculture Organisation (FAO) has long experiences in developing land cover data sets and in using space-technology-based data for: in situ monitoring, data collection, agriculture and environment monitoring and development of sustainable agriculture policy in the country members. During the last 3 decades FAO has devoted considerable attention to the development of techniques for land cover and land cover changes mapping using enhanced methodologies and tools underpinned by standards (LCML, LCCS).

FAO adopt two main approaches for producing estimates of land cover and land cover change: i) wall-to-wall approach, an analysis that covers the full spatial extent of the study area, derived from FAO GLCN programme; and ii) sample base approach derived from the methodology developed under the FAO’s EcoNet programme. Both methods take advantage of geospatial technology and FAO Land Cover Classification System/LCML language (ISO standard) for description of the land cover features by using a set of independent diagnostic criteria that allow correlation with existing classifications and legends. LCML provides a general framework of rules from which more exclusive conditions can be derived to create specific legends. The LCCS describes the “real world” according to specific needs of the end users, combining the LCML Elements, by means of a specific software, to form their own category (class).

The key application of FAO land cover mapping approach stand on the exploitation of the time series geospatial information for the assessment of land use and land cover change over time and generation of specific information in particular monitoring of changes on agriculture, forests, rural and urban communities. The assessment of the level of land with focus on crop changes in a long period of time can significantly support the analysing in the types, rates, and causes of change and the effect on the environment and climate.

The focus of this paper is to give an overview of the FAO commitment in developing state-of-the-art techniques and methods integrated with geospatial technology for the monitoring and analysing of the landscape evolution as well as the assessment of the impact of the land uses and land covers change in response to evolving economic, social, and biophysical conditions. Geospatial technology enables using the integrated assessment of biophysical and socioeconomic variables for a coherent, reliable science-based approach to address a number of related parameters that are key to food security and sustainable agricultural landscapes.

The latest, free or lower-cost generation of remote sensing data products (including thermal and radar imagery) has now achieved a level of spatial, temporal and spectral resolution that may be directly applied to tracking the location and performance of complex, fragmented, low-input smallholder farming systems and landscapes in various environments.