Land Use Cover Datasets and Validation Tools

This open access book represents a comprehensive review of available land-use cover data and techniques to validate and analyze this type of spatial information. The book provides the basic theory needed to understand the progress of LUCC mapping/modeling validation practice. It makes accessible to any interested user most of the research community's methods and techniques to validate LUC maps and models. Besides, this book is enriched with practical exercises to be applied with QGIS. The book includes a description of relevant global and supra-national LUC datasets currently available. Finally, the book provides the user with all the information required to manage and download these datasets

Expanding the conceptual, mathematical and practical methods for map comparison

Conventional methods of map comparison frequently produce unhelpful results for a variety of reasons. In particular, conventional methods usually analyze pixels at a single default scale and frequently insist that each pixel belongs to exactly one category. The purpose of this paper is to offer improved methods so that scientists can obtain more helpful results by performing multiple resolution analysis on pixels that belong simultaneously to several categories. This paper examines the fundamentals of map comparison beginning from the elementary comparison between two pixels that have partial membership to multiple categories. We examine the conceptual foundation of three methods to create a crosstabulation matrix for a single pair of pixels, and then show how to extend those concepts to compare entire maps at multiple spatial resolutions. This approach is important because the crosstabulation matrix is the basis for numerous popular measurements of spatial accuracy. The three methods show the range of possibilities for constructing a crosstabulation matrix based on possible variations in the spatial arrangement of the categories within a single pixel. A smaller range in the possible spatial distribution of categories within the pixel corresponds to more certainty in the crosstabulation matrix. The quantity of each category within each pixel constrains the range for possible arrangements in subpixel mapping, since there is more certainty for pixels that are dominated by a single category. In this respect, the proposed approach is placed in the context of a philosophy of map comparison that focuses on two separable components of information in a map: 1) information concerning the proportional distribution of the quantity of categories, and 2) information concerning the spatial distribution of the location of categories. The methods apply to cases where a scientist needs to compare two maps that show categories, even when the categories in one map are different from the categories in the other map. We offer a fourth method that is designed for the common case where a scientist needs to compare two maps that show the same set of categories. Results show that the methods can produce extremely different measurements, and that it is possible to interpret the differences at multiple resolutions in a manner that reveals patterns in the maps. The method is designed to present the results graphically in order to facilitate communication. We describe the concepts using simplified examples, and then apply the methods to characterize the change in land cover between 1971 and 1999 in Massachusetts

Urban land use and land cover change analysis and modeling a case study area Malatya, Turkey

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.This research was conducted to analyze the land use and land cover changes and to model the changes for the case study area Malatya, Turkey. The first step of the study was acquisition of multi temporal data in order to detect the changes over the time. For this purpose satellite images (Landsat 1990-2000-2010) have been used. In order to acquire data from satellite images object oriented image classification method have been used. To observe the success of the classification accuracy assessment has been done by comparing the control points with the classification results and measured with kappa. According to results of accuracy assessment the overall kappa value found around 75%. The second step was to perform the suitability analysis for the urban category to use in modeling process and it has been done using the Multi Criteria Evaluation method. The third step was to observe the changes between the defined years in the study area. In order to observe the changes land use/cover maps belongs to different years compared with cross tabulation and overlay methods, according to the results it has been observed that the main changes in the study area were the transformation of agricultural lands and orchards to urban areas. Every ten years around 1000ha area of agricultural land and orchards were transformed to urban. After detecting the changes in the study area simulation for the future has been performed. For the simulation two different methods have been used which are; the combination of Cellular Automata and Markov Chain methods and the combination of Multilayer Perceptron and Markov Chain methods with the support of the suitability analysis. In order to validate the models; both of them has been used to simulate the year 2010 land categories using the 1990 and 2000 data. Simulation results compared with the existing 2010 map for the accuracy assessment (validation). For accuracy assessment the quantity and allocation based disagreements and location and quantity based kappa agreements has been calculated. According to the results it has been observed that the combination of Multilayer Perceptron and Markov Chain methods had a higher accuracy in overall, so that this combination used for predicting the year 2020 land categories in the study area. According to the result of simulation it has been found that; the urban area would increase 1575ha in total and ~936ha of agricultural lands and orchards would be transformed to the urban area if the existing trend continued

Thematic Comparison between ESA WorldCover 2020 Land Cover Product and a National Land Use Land Cover Map

Duarte, D., Fonte, C., Costa, H., & Caetano, M. (2023). Thematic Comparison between ESA WorldCover 2020 Land Cover Product and a National Land Use Land Cover Map. Land, 12(2), 1-16. [490]. https://doi.org/10.3390/land12020490 --- Funding: This work has been supported by projects foRESTER (PCIF/SSI/0102/2017), SCAPEFIRE (PCIF/MOS/0046/2017) and FireLoc (PCIF/MPG/0128/2017), by Centro de Investigação em Gestão de Informação (MagIC) and grant UIDB/00308/2020, all funded by the Portuguese Foundation for Science and Technology (FCT). It was also supported by Compete2020 (POCI-05-5762-FSE-000368), funded by the European Social Fund.This work presents a comparison between a global and a national land cover map, namely the ESA WorldCover 2020 (WC20) and the Portuguese use/land cover map (Carta de Uso e Ocupação do Solo 2018) (COS18). Such a comparison is relevant given the current amount of publicly available LULC products (either national or global) where such comparative studies enable a better understanding regarding different sets of LULC information and their production, focus and characteristics, especially when comparing authoritative maps built by national mapping agencies and global land cover focused products. Moreover, this comparison is also aimed at complementing the global validation report released with the WC20 product, which focused on global and continental level accuracy assessments, with no additional information for specific countries. The maps were compared by following a framework composed by four steps: (1) class nomenclature harmonization, (2) computing cross-tabulation matrices between WC20 and the Portuguese map, (3) determining the area occupied by each harmonized class in each data source, and (4) visual comparison between the maps to illustrate their differences focusing on Portuguese landscape details. Some of the differences were due to the different minimum mapping unit ofCOS18 and WC20, different nomenclatures and focuses on either land use or land cover. Overall, the results show that while WC20 detail is able to distinguish small occurrences of artificial surfaces and grasslands within an urban environment, WC20 is often not able to distinguish sparse/individual trees from the neighboring cover, which is a common occurrence in the Portuguese landscape. While selecting a map, users should be aware that differences between maps can have a range of causes, such as scale, temporal reference, nomenclature and errors.publishersversionpublishe

Determining the accuracy in image supervised classification problems

A large number of accuracy measures for crisp supervised classification have been developed in supervised image classification literature. Overall accuracy, Kappa index, Kappa location, Kappa histo and user accuracy are some well-known examples. In this work, we will extend and analyze some of these measures in a fuzzy framework to be able to measure the goodness of a given classifier in a supervised fuzzy classification system with fuzzy reference data. In addition with this, the measures here defined also take into account the preferences of the decision maker in order to differentiate some errors that must not be considered equal in the classification process

Validation of Soft Classification Models using Partial Class Memberships: An Extended Concept of Sensitivity & Co. applied to the Grading of Astrocytoma Tissues

We use partial class memberships in soft classification to model uncertain labelling and mixtures of classes. Partial class memberships are not restricted to predictions, but may also occur in reference labels (ground truth, gold standard diagnosis) for training and validation data. Classifier performance is usually expressed as fractions of the confusion matrix, such as sensitivity, specificity, negative and positive predictive values. We extend this concept to soft classification and discuss the bias and variance properties of the extended performance measures. Ambiguity in reference labels translates to differences between best-case, expected and worst-case performance. We show a second set of measures comparing expected and ideal performance which is closely related to regression performance, namely the root mean squared error RMSE and the mean absolute error MAE. All calculations apply to classical crisp classification as well as to soft classification (partial class memberships and/or one-class classifiers). The proposed performance measures allow to test classifiers with actual borderline cases. In addition, hardening of e.g. posterior probabilities into class labels is not necessary, avoiding the corresponding information loss and increase in variance. We implement the proposed performance measures in the R package "softclassval", which is available from CRAN and at http://softclassval.r-forge.r-project.org. Our reasoning as well as the importance of partial memberships for chemometric classification is illustrated by a real-word application: astrocytoma brain tumor tissue grading (80 patients, 37000 spectra) for finding surgical excision borders. As borderline cases are the actual target of the analytical technique, samples which are diagnosed to be borderline cases must be included in the validation.Comment: The manuscript is accepted for publication in Chemometrics and Intelligent Laboratory Systems. Supplementary figures and tables are at the end of the pd

Hotspots of uncertainty in land-use and land-cover change projections: a global-scale model comparison

Model-based global projections of future land use and land cover (LULC) change are frequently used in environmental assessments to study the impact of LULC change on environmental services and to provide decision support for policy. These projections are characterized by a high uncertainty in terms of quantity and allocation of projected changes, which can severely impact the results of environmental assessments. In this study, we identify hotspots of uncertainty, based on 43 simulations from 11 global-scale LULC change models representing a wide range of assumptions of future biophysical and socio-economic conditions. We attribute components of uncertainty to input data, model structure, scenario storyline and a residual term, based on a regression analysis and analysis of variance. From this diverse set of models and scenarios we find that the uncertainty varies, depending on the region and the LULC type under consideration. Hotspots of uncertainty appear mainly at the edges of globally important biomes (e.g. boreal and tropical forests). Our results indicate that an important source of uncertainty in forest and pasture areas originates from different input data applied in the models. Cropland, in contrast, is more consistent among the starting conditions, while variation in the projections gradually increases over time due to diverse scenario assumptions and different modeling approaches. Comparisons at the grid cell level indicate that disagreement is mainly related to LULC type definitions and the individual model allocation schemes. We conclude that improving the quality and consistency of observational data utilized in the modeling process as well as improving the allocation mechanisms of LULC change models remain important challenges. Current LULC representation in environmental assessments might miss the uncertainty arising from the diversity of LULC change modeling approaches and many studies ignore the uncertainty in LULC projections in assessments of LULC change impacts on climate, water resources or biodiversity

Object-Oriented Techniques for Land Use/Cover Classification:Application of Metaponto Area (Basilicata, Southern Italy)

Sustainable management of natural resources requires constant and detailed monitoring of various aspects of the environment. Land use/cover mapping is considered a key element for planning protection, management and monitoring of semi-natural areas in urban ecosystems. Hence the importance of the information acquired through Remote Sensing, airplane and satellite, has been recognized for decades. The Remote Sensing data offers notable advantages for territorial monitoring, particularly of the vegetated areas, in comparison with data collected on the ground. The study of the spectral response of vegetation gained from airplanes or satellites makes it possible to obtain useful information about plant species and their conditions (density, vegetative state, etc.) in repetitive synoptic images. The research was carried out over an area of study in southern Italy (Basilicata, Metaponto area) near the mouth of the Basento River. For this area, synchronous and geometrically corecorded aerial photographs and Landsat TM image covering the period May 2004, were developed. Firstly a preliminary analysis was carried out using unsupervised means of classification with the aim of grouping together clusters of multi-band spectral responses that are statistically distinctive. Following this and after having properly defined the levels of segmentation of Landsat images using aerial photographs as a reference, a supervised classification procedure was applied, first pixel-oriented and then object-oriented, obtaining a marked improvement both in accuracy and in the reduction of the “salt&papper” effect of the map obtained by the Maximum Likelihood classifier

A framework for scale-sensitive, spatially explicit accuracy assessment of binary built-up surface layers

To better understand the dynamics of human settlements, thorough knowledge of the uncertainty in geospatial built-up surface datasets is critical. While frameworks for localized accuracy assessments of categorical gridded data have been proposed to account for the spatial non-stationarity of classification accuracy, such approaches have not been applied to (binary) built-up land data. Such data differs from other data such as land cover data, due to considerable variations of built-up surface density across the rural-urban continuum resulting in switches of class imbalance, causing sparsely populated confusion matrices based on small underlying sample sizes. In this paper, we aim to fill this gap by testing common agreement measures for their suitability and plausibility to measure the localized accuracy of built-up surface data. We examine the sensitivity of localized accuracy to the assessment support, as well as to the unit of analysis, and analyze the relationships between local accuracy and density / structure-related properties of built-up areas, across rural-urban trajectories and over time. Our experiments are based on the multi-temporal Global Human Settlement Layer (GHSL) and a reference database for the state of Massachusetts (USA). We find strong variation of suitability among commonly used agreement measures, and varying levels of sensitivity to the assessment support. We then apply our framework to assess localized GHSL data accuracy over time from 1975 to 2014. Besides increasing accuracy along the rural-urban gradient, we find that accuracy generally increases over time, mainly driven by peri-urban densification processes in our study area. Moreover, we find that localized densification measures derived from the GHSL tend to overestimate peri-urban densification processes that occurred between 1975 and 2014, due to higher levels of omission errors in the GHSL epoch 1975.Comment: 28 pages, 17 figure