scholarly journals Vegetation Cover Change Analysis of Phytogeographic Regions of Turkey Based on CORINE Land Cover Datasets from 1990 to 2018

2021 ◽  
Vol 21 (2) ◽  
pp. 150-164
Author(s):  
Emre AKTÜRK ◽  
Kerim GÜNEY
Keyword(s):  
Land Cover ◽  
Vegetation Cover ◽  
Change Analysis ◽  
Corine Land Cover ◽  
Vegetation Cover Change

scholarly journals LAND-COVER MODELLING USING CORINE LAND COVER DATA AND MULTI-LAYER PERCEPTRON

2014 ◽  
Vol 33 (1) ◽  
pp. 5-22 ◽  
Author(s):  
Piotr Dzieszko
Keyword(s):  
Land Cover ◽  
Change Process ◽  
Potential Model ◽  
Historical Change ◽  
Land Cover Changes ◽  
Multi Layer Perceptron ◽  
Change Analysis ◽  
Corine Land Cover ◽  
Rates Of Change ◽  
Land Cover Data

Abstract Last decades of research have revealed the environmental impacts of Land-Use/Cover Change (LUCC) throughout the globe. Human activities’ impact is becoming more and more pronounced on the natural environment. The key activity in the LUCC projects has been to simulate the syntheses of knowledge of LUCC processes, and in particular to advance understanding of the causes of land-cover change. Still, there is a need of developing case studies regional models to understand LUCC change patterns. The aim of this work is to reveal and describe the main changes in LUCC patterns occurring in Poznań Lakeland Mesoregion according to CORINE Land Cover database. Change analysis was the basis for the identification of the main drivers in land cover changes in the study area. The dominant transitions that can be grouped and modelled separately were identified. Each submodel was combined with all submodels in the final change prediction process. Driver variables were used to model the historical change process. Transitions were modelled using multi-layer perceptron (MLP) method. Using the historical rates of change and the transition potential model scenario for year 2006 was predicted. Corine Land Cover 2006 database was used for model validation.

scholarly journals Vegetation Cover Change and Its Diversity in Urban Areas of Medan

2021 ◽  
Author(s):  
Anita Zaitunah ◽  
Samsuri ◽  
Fauziah Sahara
Keyword(s):  
Land Cover ◽  
Environmental Quality ◽  
Vegetation Cover ◽  
Urban Areas ◽  
Open Space ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Change Analysis ◽  
Vegetation Class

Abstract Vegetation plays an important role in maintaining the environmental quality of urban areas. Increase in population and development of cities has led to land conversion with lesser vegetated areas. Land cover change analysis in urban areas is needed, especially for urban regional planning with green open space consideration. This research was conducted to analyze urban vegetation cover and its changes in two sub-districts of Medan between the years 1999 and 2019. Normalized difference vegetation index (NDVI) and change analysis were conducted in the research. The diversity of plant within this areas was observed. The results showed changes in vegetation cover areas in the mentioned years. In 1999, most of the areas were under a highly dense vegetation class while in 2019, they were under a low-density vegetation class. This indicates a decrease in vegetation cover due to changes to non-vegetation cover or land cover areas with less vegetation. There are a diverse of plants within the area such as paddy, cassava, corn etc and also many tree species. It is recommended to optimize the land by replanting in the area with no or less vegetation to maintain the environmental quality.

REVEALS-based reconstruction of Holocene vegetation abundance in temperate China: new insights on past human-induced land-cover change for climate modelling

2020 ◽  
Author(s):  
Furong Li ◽  
Marie-José Gaillard ◽  
Shinya Sugita ◽  
Xianyong Cao ◽  
Ulrike Herzschuh ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Vegetation Cover ◽  
Eastern China ◽  
Plant Cover ◽  
Climate Modelling ◽  
Important Region ◽  
Open Land ◽  
Vegetation Cover Change

<p>Quantification of the effects of human-induced vegetation-cover change on past (present and future) climate is still a subject of debate. Our understanding of these effects greatly depends on the availability of empirical reconstructions of past anthropogenic vegetation cover. Such reconstructions can be used to evaluate Anthropogenic Land-Cover Change (ALCC) scenarios for the past (such as HYDE and KK), and simulated past vegetation using dynamic vegetation models such as LPJGUESS. In this context, China is an important region given that agriculture started already in early Holocene, and expanded rapidly over large areas throughout the eastern part of the country. Quantitative reconstructions of plant cover based on pollen data has long been a challenge. The REVEALS model (Sugita, 2007) is one of the approaches for quantitative reconstruction of past plant cover that has been applied, tested, and validated in many regions of the world over the last years. Relative pollen productivity (RPP) of plant taxa is a key parameter required for REVEALS applications. A synthesis of all RPP estimates available in temperate China is published in Li et al. (2018). These RPPs were used with pollen records from lakes and bogs to produce REVEALS-based estimates of Holocene regional vegetation-cover change in temperate China. In order to interpret the REVEALS reconstructions in terms of climate or anthropogenic land-cover change, we compared the REVEALS estimates of vegetation-cover change with existing palaeoclimatic data and archaeological evidences on human history and past land-use change. We also compared the REVEALS estimates with fractions of plant functional types simulated by LPJGUESS and ALCC scenarios from HYDE and KK.</p><p>The results suggest that the REVEALS-based values of plant cover strongly differ from the pollen percentages and provide new insights on past changes in plant composition and vegetation dynamics over the Holocene. Human-induced deforestation is highest in eastern China with 3 major phases at ca. 5500, 3000 and 2000 calibrated years before present. Disentangling human-induced from climate-induced pollen-based open-land cover remains a challenge. However,  thorough comparison of the REVEALS reconstructions with historical and archaeological information on settlement and land-use history, and with palaeoclimate reconstructions provide important clues to the question. This study is a contribution to PAGES LandCover6k.</p><p><em>References: Li et al., 2018. Front Plant Sci; Sugita, 2007. Holocene.</em></p>

scholarly journals Spatiotemporal water yield variations and influencing factors in the Lhasa River Basin, Tibetan Plateau

2020 ◽  
Author(s):  
Huiting Lu ◽  
Yan Yan ◽  
Jieyuan Zhu ◽  
Tiantian Jin ◽  
Guohua Liu ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Influencing Factors ◽  
River Basin ◽  
Vegetation Cover ◽  
Water Yield ◽  
Lhasa River Basin ◽  
Lhasa River ◽  
Vegetation Cover Change

<p>Climate and land use/cover changes are widely recognized as two main drivers of variations in ecosystem services including water yield. However, vegetation cover condition, which can also influence the hydrological cycle through evapotranspiration process, is seldom considered. In this study, we used the Seasonal Water Yield Model (SWYM) to assess the spatiotemporal water yield changes of Lhasa River Basin from 1990 to 2015, and analysed its influencing factors by focusing on precipitation change, land cover change, and vegetation cover change (indexed by Normalized Difference Vegetation Index, i.e. NDVI). We first examined the model through Morris Screening sensitivity analysis and validated it with observed flow data. Spatiotemporal variation of three indices of water yield, baseflow, quick flow and local recharge, were then assessed. To analyse the contribution of each factor to water yield change, three scenarios were built in which one factor was altered at a time. Results showed that, the precipitation and vegetation cover change were substantial during the study period, while land cover change was quite small. From 1990 to 2015, the baseflow, local recharge and quick flow decreased by 67.03%, 80.21% and 37.03% respectively, with the change mainly occurring during 2000-2010. The spatial pattern of water yield remained mostly unchanged. The upstream area had relatively high baseflow and local recharge, and was the main contributor of quick flow. The downstream area had relatively low or even zero baseflow, and most of its local recharge was negative due to high evapotranspiration. According to contribution analysis, precipitation and vegetation cover change were the main factors affecting water yield in the Lhasa River Basin. For baseflow, the influence of precipitation change was, on average, 7.98 times as big as vegetation cover change, and the influence of vegetation cover change was, on average, 115.45 times as big as land cover change. However, land cover change began to exert greater influence after 2010. We suggest that besides climate and land use/cover change, vegetation cover change should also be studied in greater depth to fully understand its effect on regional hydrological process and ecosystem service provision.</p>

scholarly journals Pixel- vs. Object-Based Landsat 8 Data Classification in Google Earth Engine Using Random Forest: The Case Study of Maiella National Park

2021 ◽  
Vol 13 (12) ◽  
pp. 2299
Author(s):  
Andrea Tassi ◽  
Daniela Gigante ◽  
Giuseppe Modica ◽  
Luciano Di Martino ◽  
Marco Vizzari
Keyword(s):  
Land Cover ◽  
National Park ◽  
Time Window ◽  
Central Italy ◽  
Google Earth ◽  
Data Cube ◽  
Landsat 8 ◽  
Change Analysis ◽  
Object Based ◽  
Google Earth Engine

With the general objective of producing a 2018–2020 Land Use/Land Cover (LULC) map of the Maiella National Park (central Italy), useful for a future long-term LULC change analysis, this research aimed to develop a Landsat 8 (L8) data composition and classification process using Google Earth Engine (GEE). In this process, we compared two pixel-based (PB) and two object-based (OB) approaches, assessing the advantages of integrating the textural information in the PB approach. Moreover, we tested the possibility of using the L8 panchromatic band to improve the segmentation step and the object’s textural analysis of the OB approach and produce a 15-m resolution LULC map. After selecting the best time window of the year to compose the base data cube, we applied a cloud-filtering and a topography-correction process on the 32 available L8 surface reflectance images. On this basis, we calculated five spectral indices, some of them on an interannual basis, to account for vegetation seasonality. We added an elevation, an aspect, a slope layer, and the 2018 CORINE Land Cover classification layer to improve the available information. We applied the Gray-Level Co-Occurrence Matrix (GLCM) algorithm to calculate the image’s textural information and, in the OB approaches, the Simple Non-Iterative Clustering (SNIC) algorithm for the image segmentation step. We performed an initial RF optimization process finding the optimal number of decision trees through out-of-bag error analysis. We randomly distributed 1200 ground truth points and used 70% to train the RF classifier and 30% for the validation phase. This subdivision was randomly and recursively redefined to evaluate the performance of the tested approaches more robustly. The OB approaches performed better than the PB ones when using the 15 m L8 panchromatic band, while the addition of textural information did not improve the PB approach. Using the panchromatic band within an OB approach, we produced a detailed, 15-m resolution LULC map of the study area.

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 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.

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