Geographical patterns of Corine land cover diversity across Europe

2012 ◽  
Vol 37 (2) ◽  
pp. 161-177 ◽  
Author(s):  
Athanasios S. Kallimanis ◽  
Nikos Koutsias
Keyword(s):  
Land Cover ◽  
Spatial Pattern ◽  
Spatial Autocorrelation ◽  
Latitudinal Gradients ◽  
Limited Attention ◽  
Corine Land Cover ◽  
Geographical Patterns ◽  
Grain Sizes ◽  
Fine Grain ◽  
Thematic Resolution

Land cover diversity is often used as a surrogate of habitat heterogeneity. Nevertheless, its spatial pattern has received limited attention. Here, we examine Corine land cover diversity patterns across Europe, and test (1) if geographical (longitudinal, latitudinal) gradients exist, (2) if the scale of analysis (and specifically the grain of analysis) influences the patterns, and (3) if the thematic resolution affects the results. We estimated diversity landscape metrics for 2818 locations throughout Europe. We analysed the spatial pattern at five grains (0.25, 1, 25, 100 and 625 km2), and for three hierarchical levels of the Corine Land Cover 2000 classification scheme. To account for spatial autocorrelation, we used Clifford’s test. Latitude was significantly correlated with land cover diversity (once spatial autocorrelation was taken into account) only at large grain sizes. Longitude, with a few exceptions at fine grain, was not correlated to land cover diversity. The spatial pattern of land cover diversity is scale-dependent, with spatial pattern at fine grain (<1 km2) being statistically independent of the pattern at large grain (625 km2). Also the grain of the analysis affected the spatial autocorrelation of land cover diversity. Fine grain analysis displayed autocorrelation, but over short (hundreds of kilometres) distances, while large grain analysis displayed autocorrelation over longer distances (thousands of kilometres). Increasing the detail of the thematic resolution seems to have effects similar to increasing the grain size. The thematic resolution in certain cases influenced the results qualitatively and thus inference from low-resolution landscape analysis should be done with caution.

scholarly journals Land cover changes in Poland between 1990 and 2012

Geografie ◽  
2018 ◽  
Vol 123 (1) ◽  
pp. 63-83 ◽  
Author(s):  
Marta Borowska-Stefańska ◽  
Katarzyna Leśniewska-Napierała ◽  
Szymon Wiśniewski
Keyword(s):  
Land Cover ◽  
Spatial Autocorrelation ◽  
Land Surface ◽  
Matrix Form ◽  
Land Cover Changes ◽  
Corine Land Cover ◽  
Artificial Surfaces ◽  
The Matrix

The aim of the article is to assess the intensity and directions of land cover changes in Poland between 1990 and 2012. To achieve this goal, the authors used data from databases such as the CORINE Land Cover (CLC). The changes were analyzed for the first level of data and then presented in the matrix form both as absolute values (ha) and as percentages referring to the total aggregate land surface subject to land cover changes in this period. At the following stage of the analysis attention shifted solely to those fluctuations which referred to artificial surfaces in relation to the municipality or the cadastral unit. Subsequently, a spatial autocorrelation of land cover changes in municipalities in Poland was defined.

scholarly journals Cropland Abandonment in Slovakia: Analysis and Comparison of Different Data Sources

Land ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 334
Author(s):  
Juraj Lieskovský ◽  
Dana Lieskovská
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Spatial Data ◽  
Data Sources ◽  
Communist Regime ◽  
Corine Land Cover ◽  
Cropland Area ◽  
Cropland Abandonment ◽  
Multi Temporal

This study compares different nationwide multi-temporal spatial data sources and analyzes the cropland area, cropland abandonment rates and transformation of cropland to other land cover/land use categories in Slovakia. Four multi-temporal land cover/land use data sources were used: The Historic Land Dynamics Assessment (HILDA), the Carpathian Historical Land Use Dataset (CHLUD), CORINE Land Cover (CLC) data and Landsat images classification. We hypothesized that because of the different spatial, temporal and thematic resolution of the datasets, there would be differences in the resulting cropland abandonment rates. We validated the datasets, compared the differences, interpreted the results and combined the information from the different datasets to form an overall picture of long-term cropland abandonment in Slovakia. The cropland area increased until the Second World War, but then decreased after transition to the communist regime and sharply declined following the 1989 transition to an open market economy. A total of 49% of cropland area has been transformed to grassland, 34% to forest and 15% to urban areas. The Historical Carpathian dataset is the more reliable long-term dataset, and it records 19.65 km2/year average cropland abandonment for 1836–1937, 154.44 km2/year for 1938–1955 and 140.21 km2/year for 1956–2012. In comparison, the Landsat, as a recent data source, records 142.02 km2/year abandonment for 1985–2000 and 89.42 km2/year for 2000–2010. These rates, however, would be higher if the dataset contained urbanisation data and more precise information on afforestation. The CORINE Land Cover reflects changes larger than 5 ha, and therefore the reported cropland abandonment rates are lower.

scholarly journals Statistical Modeling of Fine Sediments Dredged Using a Variable Area Dredging Suction Head to Improve Water Quality

Hydrology ◽  
2021 ◽  
Vol 8 (3) ◽  
pp. 98
Author(s):  
Leigh A. Provost ◽  
Robert Weaver ◽  
Nezamoddin N. Kachouie
Keyword(s):  
Climate Change ◽  
Water Quality ◽  
Sedimentation Rates ◽  
Agricultural Lands ◽  
Design Efficiency ◽  
Fine Sediments ◽  
Grain Sizes ◽  
Fine Grain ◽  
Sediment Sampling ◽  
Improve Water Quality

The changing climate affects the agricultural lands, and, in turn, the changes in agricultural lands alter the watershed. A major concern regarding waterbodies is the increased sedimentation rates due to climate change. To improve the water quality, it is crucial to remove fine sediments. Using current environmental dredging methods is challenging because of the sediment volumes that must be dredged, the absence of nearby disposal sites, and the shoreline infrastructure at the dredging locations. To address these issues, we used a surgical dredging method with a variable area suction head that can easily maneuver around the docks, pilings, and other infrastructures. It can also isolate the fine grain material to better manage the dredged volumes in the seabed where nutrients are typically adhered. To this end, a statistical analysis of the dredged samples is essential to improve the design efficiency. In this work, we collected several samples using a variable area suction head with different design settings. The collected samples using each design setting were then used to model the distributions of the different grain sizes in the dredged sediments. The proposed statistical model can be effectively used for the prediction of sediment sampling outcomes to improve the gradation of the fine sediments.

scholarly journals Toward a Yearly Country-Scale CORINE Land-Cover Map without Using Images: A Map Translation Approach

2021 ◽  
Vol 13 (6) ◽  
pp. 1060
Author(s):  
Luc Baudoux ◽  
Jordi Inglada ◽  
Clément Mallet
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Time Frame ◽  
Superior Performance ◽  
Land Cover Mapping ◽  
Visual Interpretation ◽  
Remote Sensing Images ◽  
Corine Land Cover ◽  
Land Cover Map ◽  
By Products

CORINE Land-Cover (CLC) and its by-products are considered as a reference baseline for land-cover mapping over Europe and subsequent applications. CLC is currently tediously produced each six years from both the visual interpretation and the automatic analysis of a large amount of remote sensing images. Observing that various European countries regularly produce in parallel their own land-cover country-scaled maps with their own specifications, we propose to directly infer CORINE Land-Cover from an existing map, therefore steadily decreasing the updating time-frame. No additional remote sensing image is required. In this paper, we focus more specifically on translating a country-scale remote sensed map, OSO (France), into CORINE Land Cover, in a supervised way. OSO and CLC not only differ in nomenclature but also in spatial resolution. We jointly harmonize both dimensions using a contextual and asymmetrical Convolution Neural Network with positional encoding. We show for various use cases that our method achieves a superior performance than the traditional semantic-based translation approach, achieving an 81% accuracy over all of France, close to the targeted 85% accuracy of CLC.

scholarly journals Spatial Pattern Analysis of the Ecosystem Services in the Guangdong-Hong Kong-Macao Greater Bay Area Using Sentinel-1 and Sentinel-2 Imagery Based on Deep Learning Method

2021 ◽  
Vol 13 (13) ◽  
pp. 7044
Author(s):  
Dawei Wen ◽  
Song Ma ◽  
Anlu Zhang ◽  
Xinli Ke
Keyword(s):  
Hong Kong ◽  
Deep Learning ◽  
Ecosystem Services ◽  
Land Cover ◽  
Spatial Pattern ◽  
Sustainable Urban Development ◽  
Spatial Pattern Analysis ◽  
Bay Area ◽  
Urban Rural ◽  
Sentinel 2

Assessment of ecosystem services supply, demand, and budgets can help to achieve sustainable urban development. The Guangdong-Hong Kong-Macao Greater Bay Area, as one of the most developed megacities in China, sets up a goal of high-quality development while fostering ecosystem services. Therefore, assessing the ecosystem services in this study area is very important to guide further development. However, the spatial pattern of ecosystem services, especially at local scales, is not well understood. Using the available 2017 land cover product, Sentinel-1 SAR and Sentinel-2 optical images, a deep learning land cover mapping framework integrating deep change vector analysis and the ResUnet model was proposed. Based on the produced 10 m land cover map for the year 2020, recent spatial patterns of the ecosystem services at different scales (i.e., the GBA, 11 cities, urban–rural gradient, and pixel) were analyzed. The results showed that: (1) Forest was the primary land cover in Guangzhou, Huizhou, Shenzhen, Zhuhai, Jiangmen, Zhaoqing, and Hong Kong, and an impervious surface was the main land cover in the other four cities. (2) Although ecosystem services in the GBA were sufficient to meet their demand, there was undersupply for all the three general services in Macao and for the provision services in Zhongshan, Dongguan, Shenzhen, and Foshan. (3) Along the urban–rural gradient in the GBA, supply and demand capacity showed an increasing and decreasing trend, respectively. As for the city-level analysis, Huizhou and Zhuhai showed a fluctuation pattern while Jiangmen, Zhaoqing, and Hong Kong presented a decreasing pattern along the gradient. (4) Inclusion of neighborhood landscape led to increased demand scores in a small proportion of impervious areas and oversupply for a very large percent of bare land.

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.

The Method of Eliminating Banded Structure and the Effects of Banded Structure on the Transverse Properties in 20G Steel

2013 ◽  
Vol 347-350 ◽  
pp. 1171-1175 ◽  
Author(s):  
Bin Wang ◽  
Hong Mei Hu ◽  
Cui Zhou
Keyword(s):  
Heat Treatment ◽  
Grain Size ◽  
Stress Concentration ◽  
Base Metal ◽  
Metallic Inclusion ◽  
Banded Structure ◽  
Grain Sizes ◽  
Fine Grain ◽  
Treatment Processes ◽  
Metallic Inclusions

The transverse properties were inferior to the longitudinal properties for the existence of banded structure in 20G steel. In order to eliminate the banded structure and improve the transverse performance of 20G steel, different heat treatment processes were adopted. The results showed that conventional normalizing could reduce the banded structure and refine the grain sizes. When 20G was heated with 10°C/min heating rated and then held at 920°C for 2h, the banded structure in the steel was almost eliminated and the microstructure was homogeneous with fine grain size, the strength increased by 14%. The non-metallic inclusion and carbide in the microstructure leaded to stress concentration and separation with the base metal. To some extent, heat treatment can improve the distribution and form of non-metallic inclusions.

Accuracy Analysis of DEM Based on Coons Surface

2011 ◽  
Vol 65 ◽  
pp. 214-217
Author(s):  
Yao Ge Wang ◽  
Peng Yuan Wang
Keyword(s):  
Spatial Pattern ◽  
Spatial Autocorrelation ◽  
Boundary Curve ◽  
Digital Elevation ◽  
Grid Points ◽  
Elevation Model ◽  
Global Spatial Autocorrelation ◽  
Core Problem ◽  
Dem Model ◽  
Better Than

Interpolation is the core problem of Digital Elevation Model (DEM). The Coons DEM model is better than bilinear interpolation and moving surface fitting. It is constructed by grid boundary curve, the curve interpolates by some adjoining grid points. Its spatial pattern of error is random in global area, there is no significant global spatial autocorrelation, but it is an increasing trend along with the terrain average gradient increases.There is significant local spatial autocorrelation, the spatial pattern of error converges strongly in local areas.

scholarly journals Land Cover-Specific Local Incidence Angle Correction: A Method for Time-Series Analysis of Forest Ecosystems

2021 ◽  
Vol 13 (9) ◽  
pp. 1743
Author(s):  
Daniel Paluba ◽  
Josef Laštovička ◽  
Antonios Mouratidis ◽  
Přemysl Štych
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Forest Type ◽  
Incidence Angle ◽  
Correction Method ◽  
Google Earth ◽  
Forest Change ◽  
Corine Land Cover ◽  
Angle Correction ◽  
Wide Range

This study deals with a local incidence angle correction method, i.e., the land cover-specific local incidence angle correction (LC-SLIAC), based on the linear relationship between the backscatter values and the local incidence angle (LIA) for a given land cover type in the monitored area. Using the combination of CORINE Land Cover and Hansen et al.’s Global Forest Change databases, a wide range of different LIAs for a specific forest type can be generated for each scene. The algorithm was developed and tested in the cloud-based platform Google Earth Engine (GEE) using Sentinel-1 open access data, Shuttle Radar Topography Mission (SRTM) digital elevation model, and CORINE Land Cover and Hansen et al.’s Global Forest Change databases. The developed method was created primarily for time-series analyses of forests in mountainous areas. LC-SLIAC was tested in 16 study areas over several protected areas in Central Europe. The results after correction by LC-SLIAC showed a reduction of variance and range of backscatter values. Statistically significant reduction in variance (of more than 40%) was achieved in areas with LIA range >50° and LIA interquartile range (IQR) >12°, while in areas with low LIA range and LIA IQR, the decrease in variance was very low and statistically not significant. Six case studies with different LIA ranges were further analyzed in pre- and post-correction time series. Time-series after the correction showed a reduced fluctuation of backscatter values caused by different LIAs in each acquisition path. This reduction was statistically significant (with up to 95% reduction of variance) in areas with a difference in LIA greater than or equal to 27°. LC-SLIAC is freely available on GitHub and GEE, making the method accessible to the wide remote sensing community.

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