dataesr/scientific-paragraphs-categorization huggingface dataset:
Specifically, LUC data can help, among other things, to characterize the land for disease control; monitor fires; assess the potential of land for biofuel production and wind or hydropower generation; and assess the role of LUC changes in the dynamics of hydrological systems and vegetation  (Giri 2016b ). Among scientists, LUC maps are frequently used as a basis for modelling exercises  (Tsendbazar et al. 2015; Herold et al. 2016). At a global scale, climate change models require global LUC maps  (Sophie et al. 2011 ). At regional and local scales, land use and cover change models have emerged as valuable tools for policy support  (Van Delden et al. 2011; White et al. 2015). These models are built on LUC datasets  (Sohl and Sleeter 2012). LUC information is also used for many other research activities, most of them related to the different policy fields mentioned above.
In recent years, it has been applied, for example, in studies analysing habitat distribution and ecosystem services  (Jacob et al. 2003; Brown 2013) , spatial patterns of biodiversity  (Zimmermann et al. 2010; Tuanmu and Jetz 2014) , and ecosystem status and biogeochemical cycling  (Johnson and Patil 1998; Lawrence et al. 2012) , etc. A wide variety of processes are also studied using LUC data. Bielecka (2019)  review some of the most common processes analysed through the CORINE Land Cover database. These include agricultural abandonment, urbanization, afforestation, deforestation, landscape fragmentation, etc.
Sohl et al. (2016)  consider the lack of information regarding uncertainty and the failure to quantify it as one of the reasons hampering the adoption of LUCC models in decision-making. The uncertainty of most of the available LUC datasets has been assessed in a large range of research studies  (Grekousis et al. 2015; Tsendbazar 2016). However, these studies do not usually address all possible sources of uncertainty. Some limitations have been reported regarding the validation of specific areas and categories, which are heterogenous and, therefore, more difficult to map  (Leyk et al. 2005; Fassnacht et al. 2006). The mapping accuracy of these categories and areas is not usually well characterized, as validation exercises only assess the general uncertainty or validity of the whole dataset  (Prestele et al.
2016). Moreover, the validity of a specific dataset will depend on how it is used  (Castilla and Hay 2007 ). An LUC map considered invalid for a specific type of study could be a reliable source of information for another study at another scale and with different aims. Maps like these are often described as "fit for use" or "fit for purpose" (Chrisman 2010). In addition, users often process the datasets in some way, so introducing sources of uncertainty that need to be evaluated  (Nienkemper and Menz 2016). When using a series of LUC maps, additional uncertainties may arise. As  Olofsson et al. (2013)  noted, even when two independent maps are both very accurate, it is possible that the accuracy of the change map obtained by post-classification comparison will be low due to error propagation.
Many users develop their own maps, given the increasing availability of free imagery and tools with which to process and classify the images easily  (Belward and Skøien 2015; Yuan et al. 2020). They need to validate the maps that they produce both for general purposes and for the specific use for which they were designed  (Chuvieco 2016). The LUCCM community also need to validate the results of their modelling exercises  (Paegelow and Camacho Olmedo 2008). To correctly interpret these results, they also need to understand the uncertainty of the LUC databases on which LUCC models are built  (Prestele et al. 2016; García-Álvarez 2018b) , given that input data and, specifically, input LUC maps, are considered one of the main sources of uncertainty in LUCCM  (Verburg et al. 2013; Houet et al. 2015).
Conclusions Many frequent users of LUC data and LUCC models are unaware of the latest developments in validation and uncertainty analysis of LUC data. It is also possible that they have limited knowledge of many of the datasets currently available for carrying out LUC exercises. Many of the recent advances in this field remain within closed scientific communities and are not disseminated among the wider LUC community outside the research arena. This book seeks to respond to their needs. It provides an overview of the state of the art on LUC datasets, including time series of LUC maps, and the tools and methods available for LUC map validation. It also presents and explains frequently used tools and guidelines for validating the results produced by LUCC models. As many of the tools and techniques reviewed here are used in both LUC mapping and LUCC modelling validation exercises, in this book we address these two analyses together.
A full validation exercise, characterizing all the uncertainties of a given dataset or model, is a complex task that requires a high level of expertise and a wide range of tools and strategies, each one addressing different sources of uncertainty. This is beyond the scope of this book. Here we focus on the quantitative validation of LUC maps and LUCC model results. For detailed information about qualitative analyses of uncertainty, we refer readers to more specialized bibliography, depending on the specific objectives of their research. Readers wishing to find out more about other important aspects of uncertainty and validation practice, such as uncertainty communication, are also referred to specific literature on this topic.
Introduction Validation is a required step prior to the effective use of any Land Use Cover (LUC) dataset or of the results of a Land Use Cover Change Modelling (LUCCM) exercise. We need to understand to what extent these datasets and results are uncertain in order to be able to assess the limits that these uncertainties may impose on the conclusions of our analyses and studies. There are many methods, tools and strategies currently available for validating LUC data and LUCCM exercises. However, comprehensive guidelines providing users with clear instructions and recommendations about how to carry out this validation are scarce. Olofsson et al. (2013 Olofsson et al. ( , 2014) )  review the validation of land change maps and offer a series of recommendations as to how to perform a credible scientific validation, accepting that other recommendations or good practice guidelines could be equally valid and perhaps even more so.
Now, several decades later, the validation of single LUC maps is a very common practice, and although new methods and tools have been developed over the years, the original ones remain popular. These are based above all on the comparison of the assessed LUC map with reference datasets through cross-tabulation  (Foody 2002; Strahler et al. 2006). In recent years, the use of pattern analysis and other validation methods has become increasingly common. The reference datasets for validating single LUC maps may be obtained from different sources of LUC data. These can be classified into two main groups: ground samples and reference LUC maps. However, in the validation exercises, other reference spatial data can also be used, such as the raw imagery used in the classification process or the soft maps obtained as a result.
The ground samples collected through field surveys provide highly accurate, detailed data. However, this information is very expensive to obtain and fieldwork is not an option when working with large study areas. This is why most reference LUC samples are obtained by photointerpretation or classification of satellite imagery. The data obtained via photointerpretation must be of higher quality that the data being validated. This usually involves careful interpretation of a set of samples using imagery with a higher spatial resolution than the images used to create the map. Another option is photointerpretation of the same imagery used to obtain the dataset, applying a different workflow and methods or techniques that guarantee better quality. Those using these methods to obtain LUC samples for validation purposes should provide information about their accuracy or uncertainty.
When obtaining reference data by field surveys or photointerpretation, users must take particular care when selecting the sampling strategy they will apply during the collection of this information, as it can have an important impact on the results of the validation exercise and on their validity (see chapter "Visualization and Communication of LUC Data"). LUC maps can also be validated against other LUC maps. In these cases, the reference LUC map must have a higher spatial resolution and greater detail that the map being assessed. They must also be of proven quality, i. e. maps or datasets with verified accuracy and uncertainty. Although less precise, validation exercises carried out by comparing the evaluated map with other LUC maps are quick and very cheap, hence their popularity. This also allows a wider set of methods and techniques to be used compared to the possibilities offered by reference datasets other than maps.
Users can also validate their LUC maps against additional sources of information other than reference datasets, in order to characterize the maps in more detail and gain a clearer picture of their uncertainty. Such sources include raw imagery, which is often used in the classification process, or the soft maps obtained from it, which are used to assess the characteristics of the pixels that make up each class. Raw imagery can be used to evaluate the reflectance value for all the pixels belonging to a particular class and how close it is to the reference reflectance value used in the classification process. When available, users can also compare each category pixel with soft maps showing the percentage of each pixel belonging to each of the LUC categories under consideration. Similar insights into the accuracy of LUC maps can be obtained by comparing them with continuous LUC If we focus on validation tools (Fig.
1 ), the agreement between the reference data/map (t 1 ) and the LUC map under evaluation (t 1 )-the two maps should have the same date t 1 -can be assessed using the cross-tabulation matrix 1  (see Sect. 1 in chapter "Basic and Multiple-Resolution Cross Tabulation to Validate Land Use Cover Maps" ). This is also referred to in the literature as the confusion or error matrix, or as the contingency table. Cross tabulation is usually the first step in any validation exercise, as the raw matrix provides plenty of information regarding the spatial agreement between the LUC map being validated and the reference dataset. In some cases, the level of agreement may vary at different levels of spatial detail. For example, when spatially aggregated and simplified, the LUC map being evaluated may show more agreement with the reference dataset.
The choice of spatial resolution is therefore a source of uncertainty. To account for this uncertainty, we can cross-tabulate the assessed and reference datasets at multiple spatial resolutions (see Sect. 2 in chapter "Basic and Multiple-Resolution Cross Tabulation to Validate Land Use Cover Maps"), i. e. the original resolution and other coarser ones. Different metrics are calculated from the confusion matrix (see chapters "Metrics Based on a Cross-Tabulation Matrix to Validate Land Use Cover Maps" and "Pontius Jr. Methods Based on a Cross-Tabulation Matrix to Validate Land Use Cover Maps"). These metrics summarize the agreement between reference and validated datasets in a single value and are therefore very easy to interpret. As a result, they have been widely used in LUC validation. The most common metrics are the accuracy assessment statistics (see Sect.
There has been great debate in the literature about the threshold above which the Overall accuracy of a map can be considered acceptable. The 85% threshold proposed by  Anderson (1971)  was the common reference for many years and continues to be applied by a lot of users nowadays  (Wulder et al. 2006; Foody 2008 ). However, there is no specific accuracy threshold regarded as valid for all study cases and datasets. The acceptable level of accuracy will depend on the intended application of the dataset and the characteristics of the area being mapped. As regards different scales and spatial resolution, we cannot compare the accuracy of global or supra-national LUC maps with that of regional and local ones, which are not subject to the same level of simplification or abstraction as the global or supra-national maps.
As pointed out by  Olofsson et al. (2013) , even when both maps are highly accurate, it is possible that the change map accuracy will be low and the estimated change area strongly biased. One of the main limitations when it comes to validating LUC changes and LUC map series is the lack of reference data. We could obtain reference datasets via photointerpretation or field surveys. However, it is difficult to guess where the LUC changes will take place, as they may happen at different places and with different intensities and patterns over space and time. In addition, there is a clear lack of LUC map series showing accurate, validated LUC change that could be used as reference data. Another option would be to validate the LUC changes against other types of reference data. This could be done for example by comparing the LUC changes measured over a time series of LUC maps against the difference in reflectance between two satellite images for the same time period.
This is because when LUC change takes place, there is a significant change in the reflectance value registered by the satellite capturing the images. Nevertheless, as commented earlier, the most common situation is that there are no reference datasets available. In these cases, the uncertainty of the LUC map series must be assessed by evaluating the consistency and the logic of the measured LUC change. The tools and techniques recommended here provide a great deal of information to the user. However, the final interpretation of the measured LUC change will be subjective, based on the user's expertise and understanding of the study area. In this situation, visual inspection can be very useful for quickly understanding many of the uncertainties in the time series of LUC maps that cannot be measured using quantitative metrics.
When this happens, the errors and inconsistencies probably arise due to the level of detail at which the dataset was created. The cross-tabulation matrix is an excellent source of information, which we can easily summarize using other tools and metrics. As commented in Sect. 3, Areal and spatial agreement metrics (see Sect. 2 in chapter "Metrics Based on a Cross-Tabulation Matrix to Validate Land Use Cover Maps") and Kappa Indices (see Sect. 3 in chapter "Metrics Based on a Cross-Tabulation Matrix to Validate Land Use Cover Maps") are used to assess the agreement between two maps. Despite their limitations, these metrics can be used to chart, in a generic way, the persistence or changes between two dates. If two maps in a series undergo the normal rate of change that we associate with any landscape, the differences between them should be slight, which means that the Kappa and agreement metrics should reflect high levels of coincidence between the maps being compared.
Once this has been done, we can validate the changes against a reference dataset of changes through cross-tabulation, obtaining from the resulting table the overall, producer's and user's accuracy metrics. Pouliot and Latifovic (2013)  coined the term Update Accuracy (UA) to refer to the accuracy of the measured changes. They refer to the accuracy of the base map as the Base Map Accuracy (BMA). They also propose a metric called Time Series Accuracy (TSA) as the mean accuracy of all the LUC maps that make up the series, individually validated through a specific reference LUC dataset for each case. Change statistics (see Sect. 1 in chapter "Metrics Based on a Cross-Tabulation Matrix to Validate Land Use Cover Maps")  (FAO 1995; Puyravaud 2003)  are widely used to assess land use and cover changes.
Introduction Nowadays, there are many sources of Land Use Cover (LUC) data. The availability of LUC data has been increasing since the end of the last century, in line with the development of remote sensing techniques and easier access to aerial and satellite imagery. LUC data is available at all spatial scales, from local to global. Access to spatial information, including LUC datasets, has also improved in the last decade with the development of the open access culture. Most of the LUC data being produced today refers to LUC maps, which are either single, one-off maps or form part of a time series. These maps provide layers of spatial data with LUC information for each part of the area being mapped at one (single maps) or several points in time (series of maps). Other spatial sources of LUC information include reference datasets used to validate LUC maps or train remote sensing classifiers.
Although datasets of this kind have been produced since the beginning of the satellite remote sensing era, they have only recently become widely available for general purposes. In this chapter, we review the main producers of LUC maps and the most relevant LUC datasets currently available -both LUC maps and data packages with reference data. Although this aspires to be a comprehensive review, some LUC products may be missing. We focus on the datasets that are available for download and can be used in practice. When relevant, we also mention others that are currently unavailable for download. Many older LUC maps are not included, because they were drawn at very coarse resolution using old-fashioned production methods and therefore cannot meet the demands of modern users. Because of the scope and extent of the book, we focus exclusively on datasets at global and supra-national levels.
The Producers of LUC Data We have classified LUC data producers into four main groups (Fig. 1 ): (i) Individual users and small actors; (ii) Research projects; (iii) Governmental and other organizations; and (iv) citizens producing LUC information through Volunteering Geographic Information (VGI) initiatives. The type of LUC data produced by each group varies. At local and detailed scales, many organizations and users create their own LUC datasets. The fact that they have easy access to aerial/satellite imagery and to software for processing, photointerpreting and classifying these images has facilitated this process. This allows users to obtain very specific datasets that match their particular requirements. The datasets created for small projects and for specific purposes are not usually disseminated and remain the property of the communities or users that produce them.
When these datasets are made available, they are often provided without the necessary technical information and general metadata. At regional, national, supra-national and global scales, an increasing number of LUC databases are being produced for a broad range of users. Often these databases are specially designed for specific communities, such as the climate change research community. In other cases, they provide more general LUC information for a wide range of research fields and as support for policy decisions. There are two main producers of LUC datasets. Firstly, nationally or internationally funded research projects, which Fig. 1  Classification and characterization of LUC data producers produce the datasets in collaboration with different universities and research institutions. The limited timeframe of these projects often affects the continuity of the mapping work they perform, and the datasets are not usually improved or updated once the project has come to an end.
Dissemination of the data may also be affected by the end of funding. The Global Land Cover Facility, a reference initiative in the field of LUC research, which recently went offline, 1  is a perfect example of this problem. Depending on the specific objectives of the projects and the institutions involved, these datasets may or may not be available for download. The quality of metadata and auxiliary information can also vary a lot from one project to the next. In some cases, a lot of technical and auxiliary information is provided, while in others users can only access the dataset itself and the research paper in which it is presented. Governmental and other organizations are the other big producers of LUC data. In these cases, the objective is to provide information about the areas for which the organization is responsible or the areas affected by its policies and/or decisions.
This data is a useful source of information for the policymaking process and is usually part of wider cartographic efforts by national and regional governments, and sometimes by international organizations, to provide geographic information of reference. As these projects are part of official mapping work conducted by nations, regions and other large organizations, they are usually backed by significant long-term funding. These databases are therefore more likely to be updated or improved in the future. Another advantage is that they usually provide highly detailed, accurate information. They are also quite flexible. As a result, these databases are widely used by the whole scientific community, public and private sector professionals and many other users. In recent years, there has been an increase in the data produced by members of the public through crowdsourcing or similar practices.
This kind of information is known as Volunteered Geographic Information (VGI) and is part of a movement called 'citizen science', in which private citizens participate in scientific research, either by gathering or validating data or by assisting in any of the other phases of the scientific process. Approaches of this kind allow local knowledge and expertise to be incorporated into data production. Highly detailed, up-to-date datasets can be produced easily and cheaply. Nevertheless, important issues can arise in terms of data quality and uncertainty, due to possible inconsistencies in the methods and procedures followed by the contributors, their different levels of expertise, etc.
Land Use Cover Maps Reviewing all the LUC maps currently available is a daunting task, which perhaps explains why it has rarely been attempted. To our knowledge, the only researchers to carry out an extensive review of LUC maps at global and regional scales were  Grekousis et al. (2015). They focused on general LUC products synthetizing all the land uses and land covers on Earth, so overlooking the increasing trend towards thematic LUC datasets that provide detailed mapping of a specific land use or land cover (e. g. forest, crop areas…). The dividing line between general and thematic LUC products is not always clear. Some LUC maps, for example, provide general information on several different land covers (e. g. artificial, vegetation, water) while providing a detailed study of just one of them, thereby adopting a thematic approach.
Although, in our review, we classify LUC maps as either general or thematic, readers should be aware of these possible inconsistencies. Both types of LUC maps, general and thematic, can also be classified according to the extent they cover, differentiating between global, supranational, national, regional and local LUC maps. However, a comprehensive review of national, regional and local maps would be a huge task that is beyond the scope of this book. We will therefore be focusing exclusively on global and supranational LUC maps. LUC maps for national and, especially, for regional and local areas, are usually only available for developed countries, or even highly developed countries, which can afford to invest in the production of spatial information and in research programmes. The most developed nations of the European Union, Australia and the United States usually have detailed LUC datasets, not only at a national level but also for specific regions.