Global land cover uncovered

In order to fully understand how land is being used for food production and foresee how land use will change in the future, reliable crop maps are essential. Not only can crop maps help identify yield gaps and monitor crops affected by drought, they can also help tackle environmental issues. As agricultural expansion is a major cause of deforestation, knowing where new crops are grown could assist calculations of additional greenhouse gas emissions, useful for initiatives to reduce emissions from deforestation, or determining the implications of climate change on crop production. But at present, there is no single global land cover product available that accurately displays where crops are grown. Some land cover maps even disagree over vast areas of the Earth’s surface. With the ambition to improve the quality of global land cover maps, a team based at the International Institute for Applied Systems Analysis (IIASA) started the pioneering Geo- Wiki project, a geospatial Wikipedia that uses the growing body of satellite imagery, Google Earth as a platform and crowdsourcing as the mechanism for collecting and verifying data

Comparing global land cover datasets through the Eagle matrix land cover components for continental Portugal

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesGlobal land cover maps play an important role in the understanding of the Earth's ecosystem dynamic. Several global land cover maps have been produced recently namely, Global Land Cover Share (GLC-Share) and GlobeLand30. These datasets are very useful sources of land cover information and potential users and producers are many times interested in comparing these datasets. However these global land cover maps are produced based on different techniques and using different classification schemes making their interoperability in a standardized way a challenge. The Environmental Information and Observation Network (EIONET) Action Group on Land Monitoring in Europe (EAGLE) concept was developed in order to translate the differences in the classification schemes into a standardized format which allows a comparison between class definitions. This is done by elaborating an EAGLE matrix for each classification scheme, where a bar code is assigned to each class definition that compose a certain land cover class. Ahlqvist (2005) developed an overlap metric to cope with semantic uncertainty of geographical concepts, providing this way a measure of how geographical concepts are more related to each other. In this paper, the comparison of global land cover datasets is done by translating each land cover legend into the EAGLE bar coding for the Land Cover Components of the EAGLE matrix. The bar coding values assigned to each class definition are transformed in a fuzzy function that is used to compute the overlap metric proposed by Ahlqvist (2005) and overlap matrices between land cover legends are elaborated. The overlap matrices allow the semantic comparison between the classification schemes of each global land cover map. The proposed methodology is tested on a case study where the overlap metric proposed by Ahlqvist (2005) is computed in the comparison of two global land cover maps for Continental Portugal. The study resulted with the overlap spatial distribution among the two global land cover maps, Globeland30 and GLC-Share. These results shows that Globeland30 product overlap with a degree of 77% with GLC-Share product in Continental Portugal

Copernicus Global Land Cover Layers—Collection 2

In May 2019, Collection 2 of the Copernicus Global Land Cover layers was released. Next to a global discrete land cover map at 100 m resolution, a set of cover fraction layers is provided depicting the percentual cover of the main land cover types in a pixel. This additional continuous classification scheme represents areas of heterogeneous land cover better than the standard discrete classification scheme. Overall, 20 layers are provided which allow customization of land cover maps to specific user needs or applications (e.g., forest monitoring, crop monitoring, biodiversity and conservation, climate modeling, etc.). However, Collection 2 was not just a global up-scaling, but also includes major improvements in the map quality, reaching around 80% or more overall accuracy. The processing system went into operational status allowing annual updates on a global scale with an additional implemented training and validation data collection system. In this paper, we provide an overview of the major changes in the production of the land cover maps, that have led to this increased accuracy, including aligning with the Sentinel 2 satellite system in the grid and coordinate system, improving the metric extraction, adding better auxiliary data, improving the biome delineations, as well as enhancing the expert rules. An independent validation exercise confirmed the improved classification results. In addition to the methodological improvements, this paper also provides an overview of where the different resources can be found, including access channels to the product layer as well as the detailed peer-review product documentation

Uncertainties in classification system conversion and an analysis of inconsistencies in global land cover products

In this study, using the common classification systems of IGBP-17, IGBP-9, IPCC-5 and TC (vegetation, wetlands and others only), we studied spatial and areal inconsistencies in the three most recent multi-resource land cover products in a complex mountain-oasis-desert system and quantitatively discussed the uncertainties in classification system conversion. This is the first study to compare these products based on terrain and to quantitatively study the uncertainties in classification system conversion. The inconsistencies and uncertainties decreased from high to low levels of aggregation (IGBP-17 to TC) and from mountain to desert areas, indicating that the inconsistencies are not only influenced by the level of thematic detail and landscape complexity but also related to the conversion uncertainties. The overall areal inconsistency in the comparison of the FROM-GLC and GlobCover 2009 datasets is the smallest among the three pairs, but the smallest overall spatial inconsistency was observed between the FROM-GLC and MODISLC. The GlobCover 2009 had the largest conversion uncertainties due to mosaic land cover definition, with values up to 23.9%, 9.68% and 0.11% in mountainous, oasis and desert areas, respectively. The FROM-GLC had the smallest inconsistency, with values less than 4.58%, 1.89% and 1.2% in corresponding areas. Because the FROM-GLC dataset uses a hierarchical classification scheme with explicit attribution from the second level to the first, this system is suggested for producers of map land cover products in the future

Cropland Capture – A Game for Improving Global Cropland Maps

Current satellite-derived global land-cover products, which are crucial for many modelling and monitoring applications, show large disagreements when compared with each another. To help improve global land cover (in particular the cropland class), we developed a game called Cropland Capture. This is a simple cross-platform game for collecting image classifications that will be used to develop and validate global cropland maps in the future. In this paper, we describe the game design of Cropland Capture in detail, including aspects such as simplicity,efficiency in data collection and what mechanisms were implemented to ensure data quality.We also discuss the impact of incentives on attracting and sustaining players in the game

Cropland Capture: A gaming approach to improve global land cover

Ponencias, comunicaciones y pósters presentados en el 17th AGILE Conference on Geographic Information Science "Connecting a Digital Europe through Location and Place", celebrado en la Universitat Jaume I del 3 al 6 de junio de 2014.Accurate and reliable information on global cropland extent is needed for a number of applications, e g. to estimate potential yield losses in the wake of a drought or for assessing future scenarios of climate change on crop production. However, current global land cover and cropland products are not accurate enough for many of these applications. One way forward is to increase the amount of data that are used to create these maps as well as for validation purposes. One method for doing this is to involve citizens in the classification of satellite imagery as undertaken using the Geo-Wiki tool. This paper outlines Cropland Capture, which is simplified game version of Geo-Wiki in which players classify satellite imagery based on whether they can see evidence of cropland or not. On overview of the game is provided along with some initial results from the first 3 months of game play. The paper concludes with a discussion of the future steps in this research

Harmonisation, Mosaicing and Production of the Global Land Cover 2000 Database.

Abstract not availableJRC.H-Institute for environment and sustainability (Ispra

Global land cover trajectories and transitions

Global land cover (LC) changes threaten sustainability and yet we lack a comprehensive understanding of the gains and losses of LC types, including the magnitudes, locations and timings of transitions. We used a novel, fine-resolution and temporally consistent satellite-derived dataset covering the entire Earth annually from 1992 to 2018 to quantify LC changes across a range of scales. At global and continental scales, the observed trajectories of change for most LC types were fairly smooth and consistent in direction through time. We show these observed trajectories in the context of error margins produced by extrapolating previously published accuracy metrics associated with the LC dataset. For many LC classes the observed changes were found to be within the error margins. However, an important exception was the increase in urban land, which was consistently larger than the error margins, and for which the LC transition was unidirectional. An advantage of analysing the global, fine spatial resolution LC time-series dataset is the ability to identify where and when LC changes have taken place on the Earth. We present LC change maps and trajectories that identify locations with high dynamism, and which pose significant sustainability challenges. We focused on forest loss and urban growth at the national scale, identifying the top 10 countries with the largest percentages of forest loss and urban growth globally. Crucially, we found that most of these ‘worst-case’ countries have stabilized their forest losses, although urban expansion was monotonic in all cases. These findings provide crucial information to support progress towards the UN’s SDGs