scholarly journals Comparative Assessment of the Built-Up Area Expansion Based on Corine Land Cover and Landsat Datasets: A Case Study of a Post-Socialist City

2020 ◽  
Vol 12 (13) ◽  
pp. 2137 ◽  
Author(s):  
Ilinca-Valentina Stoica ◽  
Marina Vîrghileanu ◽  
Daniela Zamfir ◽  
Bogdan-Andrei Mihai ◽  
Ionuț Săvulescu
Keyword(s):  
Land Cover ◽  
Urban Expansion ◽  
Ground Truth ◽  
Cost Effective ◽  
Planning System ◽  
The European Union ◽  
Scale Level ◽  
Corine Land Cover ◽  
Macro Scale ◽  
Area Expansion

Monitoring uncontained built-up area expansion remains a complex challenge for the development and implementation of a sustainable planning system. In this regard, proper planning requires accurate monitoring tools and up-to-date information on rapid territorial transformations. The purpose of the study was to assess built-up area expansion, comparing two freely available and widely used datasets, respectively, Corine Land Cover and Landsat, to each other, as well as the ground truth, with the goal of identifying the most cost-effective and reliable tool. The analysis was based on the largest post-socialist city in the European Union, the capital of Romania, Bucharest, and its neighboring Ilfov County, from 1990 to 2018. This study generally represents a new approach to measuring the process of urban expansion, offering insights about the strengths and limitations of the two datasets through a multi-level territorial perspective. The results point out discrepancies between the datasets, both at the macro-scale level and at the administrative unit’s level. On the macro-scale level, despite the noticeable differences, the two datasets revealed the spatiotemporal magnitude of the expansion of the built-up area and can be a useful tool for supporting the decision-making process. On the smaller territorial scale, detailed comparative analyses through five case-studies were conducted, indicating that, if used alone, limitations on the information that can be derived from the datasets would lead to inaccuracies, thus significantly limiting their potential to be used in the development of enforceable regulation in urban planning.

scholarly journals Цифровізація розвитку міст: Urban Atlas на основі відкритих даних для міст України

2021 ◽  
pp. 19-28
Author(s):  
Андрій Юрійович Шелестов ◽  
Алла Миколаївна Лавренюк ◽  
Богдан Ялкапович Яйлимов ◽  
Ганна Олексіївна Яйлимова
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Urban Growth ◽  
Urban Expansion ◽  
The European Union ◽  
Project Smart ◽  
Unified Algorithm ◽  
High Level ◽  
The Impact ◽  
The City

Ukraine is an associate member of the European Union and in the coming years it is expected that all data and services already used by EU countries will be available to Ukraine. The lack of quality national products for assessing the development and planning of urban growth makes it impossible to assess the impact of cities on the environment and human health. The first steps to create such products for the cities of Ukraine were initiated within the European project "SMart URBan Solutions for air quality, disasters and city growth" (SMURBS), in which specialists from the Space Research Institute of NAS of Ukraine and SSA of Ukraine received the first city atlas for the Kyiv city, which was similar to the European one. However, the resulting product had significantly fewer types of land use than the European one and therefore the question of improving the developed technology arose. The main purpose of the work is to analyze the existing technology of European service Urban Atlas creation and its improvement by developing a unified algorithm for building an urban atlas using all available open geospatial and satellite data for the cities of Ukraine. The development of such technology is based on our own technology for classifying satellite time series with a spatial resolution of 10 meters to build a land cover map, as well as an algorithm for unifying open geospatial data to urban atlases Copernicus. The technology of construction of the city atlas developed in work, based on the intellectual model of classification of a land cover, can be extended to other cities of Ukraine. In the future, the creation of such a product on the basis of data for different years will allow to assess changes in land use and make a forecast for further urban expansion. The proposed information technology for constructing the city atlas will be useful for assessing the dynamics of urban growth and closely related social and economic indicators of their development. Based on it, it is also possible to assess indicators of achieving the goals of sustainable development, such as 11.3.1 "The ratio of land consumption and population growth." The study shows that the city atlas obtained for the Kyiv city has a high level of quality and has comparable land use classes with European products. It indicates that such a product can be used in government decision-making services.

scholarly journals Integrating C- and L-Band SAR Imagery for Detailed Flood Monitoring of Remote Vegetated Areas

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2745 ◽  
Author(s):  
Alberto Refice ◽  
Marina Zingaro ◽  
Annarita D’Addabbo ◽  
Marco Chini
Keyword(s):  
Land Cover ◽  
Visual Analysis ◽  
Ground Cover ◽  
Ground Truth ◽  
Integrated Analysis ◽  
Corine Land Cover ◽  
Multi Temporal ◽  
L Band ◽  
Flood Detection ◽  
Sar Imagery

Flood detection and monitoring is increasingly important, especially on remote areas such as African tropical river basins, where ground investigations are difficult. We present an experiment aimed at integrating multi-temporal and multi-source data from the Sentinel-1 and ALOS 2 synthetic aperture radar (SAR) sensors, operating in C band, VV polarization, and L band, HH and HV polarizations, respectively. Information from the globally available CORINE land cover dataset, derived over Africa from the Proba V satellite, and available publicly at the resolution of 100 m, is also exploited. Integrated multi-frequency, multi-temporal, and multi-polarizations analysis allows highlighting different drying dynamics for floodwater over various land cover classes, such as herbaceous vegetation, wetlands, and forests. They also enable detection of different scattering mechanisms, such as double bounce interaction of vegetation stems and trunks with underlying floodwater, giving precious information about the distribution of flooded areas among the different ground cover types present on the site. The approach is validated through visual analysis from Google EarthTM imagery. This kind of integrated analysis, exploiting multi-source remote sensing to partially make up for the unavailability of reliable ground truth, is expected to assume increasing importance as constellations of satellites, observing the Earth in different electromagnetic radiation bands, will be available.

scholarly journals Harmonised LUCAS in-situ land cover and use database for field surveys from 2006 to 2018 in the European Union

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Raphaël d’Andrimont ◽  
Momchil Yordanov ◽  
Laura Martinez-Sanchez ◽  
Beatrice Eiselt ◽  
Alessandra Palmieri ◽  
...  
Keyword(s):  
Land Use ◽  
European Union ◽  
Land Cover ◽  
Land Surface ◽  
Environmental Parameters ◽  
Ground Truth ◽  
The European Union ◽  
Full Resolution ◽  
Multi Temporal

Abstract Accurately characterizing land surface changes with Earth Observation requires geo-located ground truth. In the European Union (EU), a tri-annual surveyed sample of land cover and land use has been collected since 2006 under the Land Use/Cover Area frame Survey (LUCAS). A total of 1351293 observations at 651780 unique locations for 106 variables along with 5.4 million photos were collected during five LUCAS surveys. Until now, these data have never been harmonised into one database, limiting full exploitation of the information. This paper describes the LUCAS point sampling/surveying methodology, including collection of standard variables such as land cover, environmental parameters, and full resolution landscape and point photos, and then describes the harmonisation process. The resulting harmonised database is the most comprehensive in-situ dataset on land cover and use in the EU. The database is valuable for geo-spatial and statistical analysis of land use and land cover change. Furthermore, its potential to provide multi-temporal in-situ data will be enhanced by recent computational advances such as deep learning.

scholarly journals PEMANFAATAN CITRA LANDSAT 8 OPERATIONAL LAND IMAGER (OLI) UNTUK KLASIFIKASI TUTUPAN LAHAN DI TAMAN NASIONAL GUNUNG MERBABU

2020 ◽  
Vol 15 (2) ◽  
pp. 28-39
Author(s):  
Diyanti Isnani Siregar ◽  
Adnin Musadri Asbi
Keyword(s):  
Land Cover ◽  
Reference Data ◽  
Ground Truth ◽  
Cost Effective ◽  
Land Cover Classification ◽  
Google Earth ◽  
Training Data ◽  
Landsat 8 ◽  
Conservation Area ◽  
Effective Manner

Gunung Merbabu National Park (TNGMb) is a conservation area with a high level of biodiversity. Information on land cover is very important in making ecological management policies in conservation areas. Proven Remote Sensing technology produces precise information on land cover in a time and cost-effective manner. This study uses Landsat 8 imagery in TNGMb land cover classification process. Maximum Likelihood approach is used because it uses a probability calculation basis. A configuration matrix table between training data and reference data is made to test the accuracy of land cover classification. Reference data refers to Google Earth Pro high-resolution imagery. Results showed that the most extensive land cover type was secondary dryland forest with total of 23393 pixels classified as equivalent to 2113.54 hectares (34.5% of the total classification area. The open area, built-up area, and rice field/vegetable garden each have an area of ​​12.08 Ha; 11.02 Ha; and 170.96 Ha, of which part of the area is in enclaved areas within the TNGMb area. The accuracy test shows the Kappa Coefficient of 86.25%, User's Accuracy Average, Ground Truth Average, and Overall Accuracy respectively 89.62%; 85.42%; and 88.33%. Overall Accuracy shows that 88.33% of the total pixels represent each classification correctly.

scholarly journals Validation of Visually Interpreted Corine Land Cover Classes with Spectral Values of Satellite Images and Machine Learning

2021 ◽  
Vol 13 (5) ◽  
pp. 857
Author(s):  
Orsolya Gyöngyi Varga ◽  
Zoltán Kovács ◽  
László Bekő ◽  
Péter Burai ◽  
Zsuzsanna Csatáriné Szabó ◽  
...  
Keyword(s):  
Land Cover ◽  
Statistical Tests ◽  
Ground Truth ◽  
Relevant Information ◽  
Landsat 8 ◽  
Visual Interpretation ◽  
Accurate Analysis ◽  
Ground Truth Data ◽  
Corine Land Cover ◽  
Linear Discriminant

We analyzed the Corine Land Cover 2018 (CLC2018) dataset to reveal the correspondence between land cover categories of the CLC and the spectral information of Landsat-8, Sentinel-2 and PlanetScope images. Level 1 categories of the CLC2018 were analyzed in a 25 km × 25 km study area in Hungary. Spectral data were summarized by land cover polygons, and the dataset was evaluated with statistical tests. We then performed Linear Discriminant Analysis (LDA) and Random Forest classifications to reveal if CLC L1 level categories were confirmed by spectral values. Wetlands and water bodies were the most likely to be confused with other categories. The least mixture was observed when we applied the median to quantify the pixel variance of CLC polygons. RF outperformed the LDA’s accuracy, and PlanetScope’s data were the most accurate. Analysis of class level accuracies showed that agricultural areas and wetlands had the most issues with misclassification. We proved the representativeness of the results with a repeated randomized test, and only PlanetScope seemed to be ungeneralizable. Results showed that CLC polygons, as basic units of land cover, can ensure 71.1–78.5% OAs for the three satellite sensors; higher geometric resolution resulted in better accuracy. These results justified CLC polygons, in spite of visual interpretation, can hold relevant information about land cover considering the surface reflectance values of satellites. However, using CLC as ground truth data for land cover classifications can be questionable, at least in the L1 nomenclature.

scholarly journals Transformation Through CLC with the Continuous Research Techniques - GIS (Open Code) and RS (Geo-Web Services)

2018 ◽  
Vol 12 (2) ◽  
pp. 147-154
Author(s):  
Florim Isufi ◽  
Shpejtim Bulliqi ◽  
Ardita Hajredini
Keyword(s):  
Land Cover ◽  
Satellite Images ◽  
Research Area ◽  
The European Union ◽  
Practical Application ◽  
Coverage Area ◽  
Corine Land Cover ◽  
Open Source Gis ◽  
Gis Software ◽  
Random Method

Abstract Land cover has always been and still it is one of the main challenges in the field of geography. This study will be focused on “experimentation” of one of the most modern techniques of our time, becoming irreplaceable standard for decision-making in matters of land cover and the square method. Here we are talking about the standard named: CORINE Land Cover, a technique for describing the land cover, initiated by the European Union in 1985. More precisely part of this paper will be the principles of this technique and their practical application, by doing a research through these techniques for specific areas. CORINE Land Cover will be used to explain the coverage area while the square method will be used for the division of the research area. The research area has been designed through random method. In this study are given three study areas along the coastline with an area of 100 km2, by making the entire research area of 300 km2. Each “main” area is divided in sub-areas of 100 ha, while each of these sub-areas is divided into smaller squares with equal area of 1 ha. There are two “main experiments” in this paper: 1. Land cover technique – to design the minimum research area we used the method of square, while for explaining the coverage we used CORINE Land Cover nomenclature. 2. Technology to implement the technique – we used the so called open source GIS software and for satellite images we used Google geo-web service.

scholarly journals Comparison of Support Vector Machines and Random Forests for Corine Land Cover Mapping

2021 ◽  
Vol 13 (4) ◽  
pp. 777
Author(s):  
Anca Dabija ◽  
Marcin Kluczek ◽  
Bogdan Zagajewski ◽  
Edwin Raczko ◽  
Marlena Kycko ◽  
...  
Keyword(s):  
European Union ◽  
Land Cover ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Landsat 8 ◽  
The European Union ◽  
Natural Habitats ◽  
Corine Land Cover ◽  
Economic Diversity ◽  
Spatial Management

Land cover information is essential in European Union spatial management, particularly that of invasive species, natural habitats, urbanization, and deforestation; therefore, the need for accurate and objective data and tools is critical. For this purpose, the European Union’s flagship program, the Corine Land Cover (CLC), was created. Intensive works are currently being carried out to prepare a new version of CLC+ by 2024. The geographical, climatic, and economic diversity of the European Union raises the challenge to verify various test areas’ methods and algorithms. Based on the Corine program’s precise guidelines, Sentinel-2 and Landsat 8 satellite images were tested to assess classification accuracy and regional and spatial development in three varied areas of Catalonia, Poland, and Romania. The method is dependent on two machine learning algorithms, Random Forest (RF) and Support Vector Machine (SVM). The bias of classifications was reduced using an iterative of randomized training, test, and verification pixels. The ease of the implementation of the used algorithms makes reproducing the results possible and comparable. The results show that an SVM with a radial kernel is the best classifier, followed by RF. The high accuracy classes that can be updated and classes that should be redefined are specified. The methodology’s potential can be used by developers of CLC+ products as a guideline for algorithms, sensors, and the possibilities and difficulties of classifying different CLC classes.

Sign in / Sign up

Export Citation Format

Share Document