CORINE Land Cover Map Research Articles

What is the actual composition of specific land cover? An evaluation of the accuracy at a national scale – Remote sensing in comparison to topographic land cover

Satellite imagery allows us to capture and collect land cover information for increasingly large areas. This allows us to represent current land cover on maps in a simple and standardized way; however, any land cover determined in this way is subject to some algorithmic uncertainty. This paper aims, for the first time, to indicate the magnitude of this uncertainty through the empirical probability distribution of a given land cover at a given location. By analyzing 3 data sources, i.e. the Corine Land Cover map, the POLSA land cover map and the classic map - the BDOT10k database of topographic objects. Empirical distributions of the occurrence of land cover class data in areas with a given land use on a topographic map were determined. The work was carried out on a large scale, i.e. on the maximum possible sample for Poland, i.e. on the area of the whole country. This makes it possible to introduce and quantify uncertainties. Spatial analyses were carried out using satellite-based methods to determine land cover or using a topographic map. This work and its results will be useful to all users who want to assess the occurrence of a phenomenon in a given area, taking into account the uncertainty of the land cover, and thus obtain more accurate and reliable results. It also provides, for the first time, a methodology for verifying such map correspondences, which can be replicated in work by other researchers, using the confusion matrix and as evaluation metrics the true positive rate (TPR) and weighted accuracy have been adopted. The paper proposes a link between land cover classes in all databases. It was shown that the TPR for BDOT10k was higher than 50% only with CLC Level 1 (72.0%) and POLSA Land Cover (61%), while the TPR for RS classes for each remote sensing data was always higher than 60% with BDOT10k. The class with the highest remote sensing classes was related to water, especially marine (92.0% for POLSA and 85.3% for CLC level 3), arable land (98% for POLSA, lowest for CLC level 3 (80%), and forests (coniferous POLSA – 89%, CLC level 1 and 2–85%), while low values were obtained for wetlands, peatbogs. The authors do not state which land cover approach is better, as each may have multiple uses, but the values presented in this work must raise awareness of uncertainties in land cover and critical implementation in decision-making processes for multiple areas of human activity. The study provides ready-to-use values of the probability of a given land cover class being present on a topographic map, given that remote sensing has classified it as such. These functions can also be used in reverse, to determine the probability of a given land cover class being present in remote sensing, given that a specific class has been identified on a topographic map. The results of the consistency assessment, with the composition structure, can be used by a wide range of users, including public administration, land managers, land architects, public services, academia and individuals.

Assessing Mineral Content and Heavy Metal Exposure in Abruzzo Honey and Bee Pollen from Different Anthropic Areas.

Honey and bee pollen offer potential health benefits due to their nutrient and bioactive molecules, but they may also harbor contaminants such as heavy metals. This study aimed to assess the content of different metals, including Mg, Al, K, Ca, V, Cr, Mn, Fe, Co, Ni, Zn, Cu, As, Rb, Sr, Cd, Cs, Tl, Pb and U, in honey and bee pollen collected from different Abruzzo region (Italy) areas (A1, A2, A3, A4), characterized by different anthropic influences described by Corine Land Cover maps. Differences were observed in the mineral and heavy metal content associated with the influence of biotic and abiotic factors. Honeys were found to be safe in regard to non-carcinogenic risk in all the consumer categories (THQm < 1). A particular carcinogenic risk concern was identified for toddlers associated with Cr (LCTR > 1 × 10-4) in A1, A2 and A3 apiaries. Pb and Ni represent potential non-carcinogenic and carcinogenic health risks in children and adults due to bee pollen consumption, showing high values of THQm and LCTR. The results suggest the advantages of utilizing bee products to screen mineral and heavy metal content, providing valuable insights into environmental quality and potential health risks.

Deep learning ancient map segmentation to assess historical landscape changes

ABSTRACT Ancient geographical maps are our window into the past for understanding the spatial dynamics of last centuries. This paper proposes a novel approach to address this problem using deep learning. Convolutional neural networks (CNNs) are today the state-of-the-art methods in handling a variety of problems in the fields of image processing. The Cassini map, created in the eighteenth century, is used to illustrate our methodology. This approach enables us to extract the surfaces of classes of lands in the Cassini map: forests, heaths, arboricultural, and hydrological. The evolution of land use between the end of the eighteenth century andtoday was quantified by comparison with Corine Land Cover (CLC) database. For the Rhone watershed, the results show that forests, arboriculture, and heaths are more extensive on the CLC map, in contrast to the hydrological network. These unprecedented results are new findings that reveal the major anthropo-climatic changes.

Pixel-based mapping of open field and protected agriculture using constrained Sentinel-2 data

Protected agriculture boosts the production of vegetables, berries and fruits, and it plays a pivotal role in guaranteeing food security globally in the face of climate change. Remote sensing is proven to be useful for identifying the presence of (low-tech) plastic greenhouses and plastic mulches. However, the classification accuracy notoriously decreases in the presence of small-scale farming, heterogeneous land cover and unaccounted seasonal management of protected agriculture. Here, we present the random forest-based pixel-level Open field and Protected Agriculture land cover Classifier (OPAC) developed using Sentinel-2 L2A data. OPAC is trained using tiles from Switzerland over 2 years and the Almeria region in Spain over 1 acquisition day. OPAC classifies eight land covers typical of open field and protected agriculture (plastic mulches, low-tech greenhouses and for the first time high-tech greenhouses). Finally, we assess (1) how the land covers in OPAC are labelled in the Sentinel-2 Scene Classification Layer (SCL) and (2) the correspondence between pixels classified as protected agriculture by OPAC and by the best performing Advanced Plastic Greenhouse Index (APGI). To reduce anthropogenic land covers, we constrain the classification task to agricultural areas retrieved from cadastral data or the Corine Land Cover map. The 5-fold cross-validation reveals an overall accuracy of 92% but other classification scores are moderate when keeping the separation among the three classes of protected agriculture. However, all scores substantially improve upon grouping the three classes into one (with an Intersection Over Union of 0.58 as an average among the scores of the three classes and of 0.98 for one single class). Given the recently acknowledged importance of Sentinel-2 Band 1 (central wavelength of 443 nm), the classification accuracy of OPAC for the Swiss small-scale farming is mostly limited by the band's reduced spatial accuracy (60 m). A careful visual assessment indicates that OPAC achieves satisfactory generalization capabilities also in North European (the Netherlands) and four Mediterranean areas (Spain, Italy, Crete and Turkey) without the need of adding location and temporal specific information. There is good agreement among natural land covers classified by OPAC and the SCL. However, the SCL does not have a class for protected agriculture, the latter being often classified as clouds. APGI achieved similar to lower classification accuracies than OPAC. Importantly, the APGI classification task depends on a user-defined space- and time-specific threshold, whereas OPAC does not. Therefore, OPAC paves the way for rapid mapping of protected agriculture at continental scale.

Abiotic and biotic correlates of the occurrence, extent and cover of invasive aquatic Elodea nuttallii.

Biological invasions, especially invasive alien aquatic plants, are a major and growing ecological and socioeconomic problem worldwide. Freshwater systems are particularly vulnerable to invasion, where impacts of invasive alien species can damage ecological structure and function. Identifying abiotic and biotic factors that mediate successful invasions is a management priority. Our aim was to determine the environmental correlates of Elodea nuttallii; a globally significant invasive aquatic species. Elodea nuttallii presence/absence (occurrence), extent (patch area) and percentage cover (density) was visually assessed from a boat throughout Lough Erne (approximately 144 km2), County Fermanagh, Northern Ireland during the active summer growth season (July–September). In addition, substrate type and zebra mussel Dreissena polymorpha occurrence was recorded. Fourteen water chemistry variables were collected monthly from 12 recording stations throughout the lake during the 9 years before the survey to spatially interpolate values and establish temporal trajectories in their change. Shoreline land use was derived from CORINE land cover maps. Environmental associations between E. nuttallii, substrate, D. polymorpha, water chemistry and land use were assessed. Elodea nuttallii occurrence was positively associated with water conductivity, alkalinity, suspended solids, phosphorus (both total and soluble) and chlorophyll‐a concentrations, but negatively associated with pH and total oxidised nitrogen. E. nuttallii patch extent and proportional cover were positively associated, to varying degrees, with the presence of D. polymorpha, biological oxygen demand, water clarity and soft substrate, but negatively associated with urban development and ammonium. Elodea nuttallii displayed high levels of phenotypic plasticity in response to environmental variation, allowing it to adapt to a wide range of conditions and potentially gain competitive advantage over native or other invasive macrophytes.It is evident that multiple abiotic and biotic factors, including facilitation by co‐occurring invasive dreissenid mussels, interact to influence the distribution and abundance of E. nuttallii. Thus, it is necessary to consider a more comprehensive environmental context when planning Elodea management strategies.

Dynamic Landscapes in the UK Driven by Pressures from Energy Production and Forestry—Results of the CORINE Land Cover Map 2018

The CORINE Land Cover (CLC) map was established in 1985 and is now one of the most widely used products from the Copernicus Land Monitoring Service. As the world’s longest consistent operational land cover monitoring product, CLC maps have been produced for reference years 1990, 2000, 2006, 2012 and now for 2018. This paper presents the results from the CLC2018 mapping project in the UK and analyses the results of the land cover status layer and the change layer from the period 2012–2018. It sets this change in context with the change results from the period 2006–2012 and finds that the rate of change between the subsequent CORINE land cover maps is continuing to increase. Changes mapped for the period 2012–2018 covered 76,032 ha greater than the change mapped between 2006 and 2012, an increase of 26% of mapped change. The area of changes mapped covered an area equivalent to 1.16% of the total land area of the UK. The number of different types of changes also continue to diversify; however, the dominance of rotational forestry is consistent with the previous map. The process of urban land take has been highlighted in the results between 2012 and 2018 and is a trend identified in previous iterations of the CLC inventories. The largest gain is in industrial or commercial units (an increase of 14.4%). This growth is mainly attributed to renewable energy infrastructure. As well as the descriptive analysis, the results have been analysed to identify the likely pressures being experienced on the land in the UK. Although the CLC mapping approach is consistent, there have been improvements to the input EO data used to map the changes. For 2018, the Copernicus Sentinel-2 system offered a consistent and reliable image source for the first time. This increased the spatial resolution of the source datasets to 10 m, allowing for more accurate identification of small features and those with fine spatial textures such as suburban, road networks and windfarms. We also look forward to the development of CLC+, the new generation of CORINE land mapping, and the improvements it could make.

Assessment of the Pressure Level over Lentic Waterbodies through the Estimation of Land Uses in the Catchment and Hydro-Morphological Alterations: The LUPLES Method

The features of lentic waterbodies largely depend on the surrounding environment. Mediterranean coastal lagoons have been historically altered, with their catchment being highly modified for agricultural, livestock, or urban uses. Changes in land uses induce pressures that impact the waterbodies and alter their ecological status. The objectives of this paper were: to develop a methodological approach (LUPLES: Land Uses for estimating Pressure Levels to approach the Ecological Status), to quantify the main pressures on the waterbodies and to forecast the possible impacts of these pressures on their ecological status. Corine-Land Cover maps and Geographic Information System technics were used to delineate and identify land uses in the catchments. Specific algorithms were created to quantify the main pressures from land uses in the catchment and hydro-morphological alterations in the immediate basin. The values of the estimated pressure levels were correlated with ecological status indicators using metrics developed for the European Water Framework Directive. Data were obtained from European and River Basin Authorities databases. Results showed statistically significant correlations between the pressure levels quantified by the LUPLES method and the impact level detected by biological, physical and chemical metrics. This method provides a useful approach to estimate the pressure levels affecting lentic waterbodies and could be applied to approach how they could affect their ecological status.

Wide-Area Land Cover Mapping With Sentinel-1 Imagery Using Deep Learning Semantic Segmentation Models

Land cover (LC) mapping is essential for monitoring the environment and understanding the effects of human activities on it. Recent studies demonstrated successful applications of specific deep learning models to small-scale LC mapping tasks (e.g., wetland mapping). However, it is not readily clear which of the existing state-of-the-art models for natural images are the best candidates to be taken for the particular remote sensing task and data. In this article, we answer that question for mapping the fundamental LC classes using the satellite imaging radar data. We took ESA Sentinel-1 C-band SAR images acquired during the whole summer season of 2018 in Finland, which are representative of the land cover in the country. CORINE LC map was used as a reference, and the models were trained to distinguish between the five major CORINE-based classes. We selected seven among the state-of-the-art semantic segmentation models so that they cover a diverse set of approaches: U-Net, DeepLabV3+, PSPNet, BiSeNet, SegNet, FC-DenseNet, and FRRN-B, and further fine-tuned them. Upon evaluation and benchmarking, all the models demonstrated solid performance with overall accuracy between 87.9% and 93.1%, with good to a very good agreement (Kappa statistic between 0.75 and 0.86). The two best models were fully convolutional DenseNets (FC-DenseNet) and SegNet (encoder-decoder-skip), with the latter having a much shorter inference time. Overall, our results indicate that the semantic segmentation models are suitable for efficient wide-area mapping using satellite SAR imagery and provide baseline accuracy against which the newly proposed models should be evaluated.

Geomatics and EO Data to Support Wildlife Diseases Assessment at Landscape Level: A Pilot Experience to Map Infectious Keratoconjunctivitis in Chamois and Phenological Trends in Aosta Valley (NW Italy)

Geomatics and satellite remote sensing offer useful analysis tools for several technical-scientific fields. This work, with reference to a regional case of study, investigates remote sensing potentialities for describing relationships between environment and diseases affecting wildlife at landscape level in the light of climate change effects onto vegetation. Specifically, the infectious keratoconjunctivitis (IKC) of chamois (Rupicapra rupicapra L.) in Aosta Valley (NW Italy) was investigated at the regional level. IKC (Mycoplasma conjunctivae) is a contagious disease for domestic and wild ruminants (Caprinae and Ovinae). Two types of analysis were performed: one aimed at exploring by remotely sensed data phenological metrics (PMs) and evapotranspiration (ET) trends of vegetation in the area; one investigating the correlation between PMs and ET, versus IKC prevalence. The analysis was based on TERRA MODIS image time series ranging from 2000 to 2019. Ground data about IKC were available for a shorter time range: 2009–2019. Consequently, PMs and ET trend investigations were focused on the whole times range (2000–2019); conversely, correlation analysis was achieved with reference to the reduced 2009–2019 period. The whole study was based on freely available data from public archives. MODIS products, namely MOD13Q1 v.6 and MOD16A2, were used to derive PM and ET trends, respectively. Shuttle Radar Topography Mission (SRTM) Digital Terrain Model (DTM) was used to describe local topography; CORINE Land Cover map was adopted to describe land use classes. PMs and ET (as derivable from EO data) proved to significantly changed their values in the last 20 years, with a continuous progressive trend. As far as correlation analysis was concerned, ET and some PMs (specifically, End of Season (EOS) and Length of Season (LOS) proved significantly condition IKC prevalence. According to results, the proposed methodology can be retained as an effective tool for supporting public health and eco-pathological sectors. Specifically, it can be intended for a continuous monitoring of effects that climatic dynamics determine onto wild animals in the Alpine area, included diseases and zoonosis, moving future environmental management and planning towards the One Health perspective.

ASSESSING THE ACCURACY OF LAND USE LAND COVER (LULC) MAPS USING CLASS PROPORTIONS IN THE REFERENCE DATA

Abstract. Traditionally the accuracy assessment of a hard raster-based land use land cover (LULC) map uses a reference data set that contains one LULC class per pixel, which is the class that has the largest area in each pixel. However, when mixed pixels exist in the reference data, this is a simplification of reality that has implications for both the accuracy assessment and subsequent applications of LULC maps, such as area estimation. This paper demonstrates how the use of class proportions in the reference data set can be used easily within regular accuracy assessment procedures and how the use of class proportions can affect the final accuracy assessment. Using the CORINE land cover map (CLC) and the more detailed Urban Atlas (UA), two accuracy assessments of the raster version of CLC were undertaken using UA as the reference and considering for each pixel: (i) the class proportions retained from the UA; and (ii) the class with the majority area. The results show that for the study area and the classes considered here, all accuracy indices decrease when the class proportions are considered in the reference database, achieving a maximum difference of 16% between the two approaches. This demonstrates that if the UA is considered as representing reality, then the true accuracy of CLC is lower than the value obtained when using the reference data set that assigns only one class to each pixel. Arguments for and against using class proportions in reference data sets are then provided and discussed.

Multi-scale remote sensing to support insurance policies in agriculture: from mid-term to instantaneous deductions

ABSTRACT Climate change is today one of the biggest issues for farmers. The increasing number of natural disasters and change of seasonal trends is making insurance companies more interested in new technologies that can somehow support them in quantifying and mapping risks. Remotely sensed data, with special focus on free ones, can certainly provide the most of information they need, making possible to better calibrate insurance fees in space and time. In this work, a prototype of service based on free remotely sensed data is proposed with the aim of supporting insurance companies’ strategies. The service is thought to calibrate annual insurance rates, longing for their reduction at such level that new customers could be attracted. The study moves from the entire Piemonte region (NW Italy), to specifically focus onto the Cuneo province (Southern Piemonte), which is mainly devoted to agriculture. MODIS MOD13Q1-v6 and Sentinel-2 L2A image time series were jointly used. NDVI maps from MODIS data were useful to describe the midterm phenological trends of main crops at regional level in the period 2000–2018; differently, Sentinel-2 data permitted to map local crop differences at field level in 2016 and 2017 years. With reference to MODIS data, the average phenological behavior of main crop classes in the area, obtained from the CORINE Land Cover map Level 3, was considered using a time series decomposition approach. Trend analyses showed that the most of the crop classes alternated three phases (about 7 years) suggesting that, presently, this is probably the time horizon to be considered to tune mid-term algorithms for risk estimates in the agricultural context. Crop classes trends were consequently split into three phases and each of them modeled by a first-order polynomial function used to update correspondent insurance risk rate. Sentinel-2 data were used to map phenological anomalies at field level for the 2016 and 2017 growing seasons; shifts from class average behavior were considered to locally and temporarily tune insurance premium around its average trend as described at the previous step. Synthesizing, one can say that this approach, integrating MODIS and Sentnel-2 data, makes possible to locally and temporarily calibrate premiums of indexed insurance policies by describing the average trends of crop performance (NDVI) at regional level by MODIS data and refining it at field and specific crop level by Sentinel-2 data.

Monitoring the spatiotemporal changes in regional ecosystem health: a case study in Izmir, Turkey.

The maintenance of human well-being and development is dependent on ecosystem goods and services. However, natural ecosystems and biodiversity have been disturbed by human activities. Therefore, assessment of ecosystem health has become one of the main topics in landscape management. The aim of this study is to reveal the effects of land use/land cover (LULC) change on regional ecosystem health (REH) in Izmir, Turkey. The study used the vigour, organization and resilience model together with potential ecosystem services to understand the relationships between LULC structure, ecosystems and human activities using the most populous and urbanized parts of Izmir metropolitan city. For this purpose, the CORINE land cover maps and Landsat satellite images for 1990 and 2018 were used. Our results showed that there was an increase in the mean regional ecosystem health value (from 48.06 to 49.32) during 1990-2018, but it changed in an inconsistent way in the different parts of the study area. The urban sprawl type and the presence of vegetation were important factors affecting the overall REH value. The spatiotemporal LULC changes were effective on all the ecosystem health indicators. Among all the ecosystem health indicators, ecosystem vigour and resilience were the most important factors positively affected the REH level in highly urbanized districts. Overall, our study showed that whilst the gains in vegetation between urban and rural areas enhanced the ecosystem vigour and resilience during 1990-2018, they have also improved the regional health of districts located along the coastal parts of the study area.

Ecological quality status of the Turkish coastal waters by using marine macrophytes (macroalgae and angiosperms)

Marine macrophytes (macroalgae and angiosperms) were defined as one of the biological quality elements to assess the ecological quality status of transitional and coastal waters by the European Water Framework Directive (WFD, 2000/60/EC). In the present study, the Ecological Evaluation Index (EEI-c) was tested to assess the anthropogenic impact by using marine benthic macrophytes (macroalgae and angiosperms) and pressures by using MA-LUSI in the Turkish marine waters. Macrophyte samples were collected from 56 stations on the coasts of Turkey (at 17 Black Sea, 15 Marmara Sea, 13 Aegean Sea and 11 Mediterranean coastal water bodies) in the summer period between 2014 and 2016. In total, 209 marine macrophytes (macroalgae and angiosperms) taxa were found in all stations. The study revealed high ecological status class for 15 sites, good for 21 sites, moderate for 6 sites, poor for 10 sites, and bad for 4 sites. The MA-LUSI index was tested from the Corine land cover map which affects 1.5 km buffer zone around the sampling sites. The relationship between the pressure index MA-LUSI and EEI-ceqr (eqr: ecological quality ratio) was also tested, and a negative linear relationship was found between MA-LUSI and EEI-ceqr in the Turkish sites. The present study supports the use of the EEI-c index for ecological quality state classification in a wide area of Turkish coastal waters.

Spectral Index-Based Monitoring (2000–2017) in Lowland Forests to Evaluate the Effects of Climate Change

In the next decades, climate change will put forests in the Hungarian Great Plain in the Carpathian Basin to the test, e.g., changing seasonal patterns, more intense storms, longer dry periods, and pests are expected to occur. To aid in the decision-making process for the conservation of ecosystems depending on forestry, how woods could adapt to changing meso- and microclimatic conditions in the near future needs to be defined. In addition to trendlike warming processes, calculations show an increase in climate extremes, which need to be monitored in accordance with spatial planning, at least for medium-scale mappings. We can use the MODIS sensor dataset if up-to-date terrestrial conditions and multi-decadal geographical processes are of interest. For geographic evaluations of changes, we used vegetation spectral indices; Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI), based on the summer half year, 16-day MODIS data composites between 2000 and 2017 in an intensively forested study area in the Hungarian Great Plain. We delineated forest areas on the Danube–Tisza Interfluve using Corine Land Cover maps (2000, 2006, and 2012). Mid-year changes over the nearly two-decade-long period are currently in balance; however, based on their reactions, forests are highly sensitive to abrupt changes caused by extreme climatic events. The higher occurrence of years or periods with extreme water shortages marks an observable decrease in biomass production, even in shorter index time series, such as that between 2004 and 2012. In the drought-stricken July-August periods, the effect of a dry year, subsequent to years with more precipitation, immediately pushes back the green mass and the reduction in the biomass production could become persistent, according to climatology predictions. The changes of specific sub-periods in the vegetation period can be evaluated even in a relatively short, 18-year data series, including the change in the growing values of the vegetative growth in spring or the increase in the summertime biomass production. Standardized differences highlight spatial differences in the biomass production; in response to years with the highest (negative) biomass difference; typically, the northern and southwestern parts of the Danube–Tisza Interfluve in the study area have longer lasting losses in biomass production. A comparison of NDVI and EVI values with the PaDI drought index and the vegetation indices of LANDSAT Operational Land Imager sensor respectively confirms our results.

Repeat-pass SAR interferometry for land cover classification: A methodology using Sentinel-1 Short-Time-Series

In this paper we explore the potential of repeat-pass interferometric SAR (InSAR) for land cover classification purposes. We introduce a novel approach for the generation of large-scale thematic maps, based on the use of multi-temporal data, acquired over short observation intervals (short-time-series). A larger interferometric coherence loss is expected with the increasing time difference between two interferometric acquisitions. This phenomenon is normally indicated as temporal decorrelation whose amount differs depending on the type of imaged target on ground. The basic idea is therefore to accurately model the evolution in time of the temporal decorrelation and use the estimated parameters, together with backscatter, as input features for the Random Forest machine learning classification algorithm. The work has been carried out on the case study of land cover mapping over central Europe, considering Sentinel-1 C-band interferometric stacks, acquired over a time span of about one month. Three different land cover classes have been considered: artificial surfaces as e.g. urban areas, forests, and non-forested areas as the ensemble of low vegetation, bare soil, and agricultural areas. The results show a level of agreement above 91%, when compared to the CORINE land cover map product of 2012, which has been used as external reference for both training and testing of the classification algorithm.

A tale of two cities—From separation to common green connectivity for maintaining of biodiversity and well-being

The provision of health and well-being is one of the fundamental tasks of urban green infrastructure. This requires a new estimation of the strategic distribution and accessibility of green areas. Gorizia and Nova Gorica are two cities lying next to each other on either side of the border between Italy and Slovenia. Due to political circumstances, they developed independently, which applies to their green areas as well. Urban city areas and other land cover classes were extracted from the CORINE Land Cover map; the digital data were processed using ArcGIS. At the landscape hierarchical level, the importance of natural vegetation remnants was assessed based on current land use maps. Forest patches, hedgerows, remnants of natural vegetation, and spontaneous afforestation on abandoned agricultural lands were detected and delineated using CNES digital images and digital orthophoto images. The green areas positioned in the two interior city areas were delineated separately according to data on the two cities’ green areas. We estimated the forest continuum separately, based on the oldest cartographic sources from the end of the 18th century, the Franziscean cadastre from the early 19th century, and the Italian cadastre from 1940. We estimated the interior or core area of the forest patches based on studies of habitat fragmentation and the depth of the forest edge. Our research theory is based on the proven sustainability of indispensable spatial patterns emerging from landscape ecology that are especially necessary in altered (rural, urban) landscapes. Our results show that the most important pattern of green areas supporting the well-being of both cities’ inhabitants can be achieved by the mutual development of the two major urban forests, each situated in one of the cities, in addition to riparian corridors and bits of nature represented by public parks. Collaborative planning between the two cities would enhance common benefits, including future economic and social influences and innovations.

Determining the use of Sentinel-2A MSI for wildfire burning & severity detection

ABSTRACTAccurate, reliable, and timely burn severity maps are necessary for planning, managing and rehabilitation after wildfires. This study aimed at assessing the ability of the Sentinel-2A satellite to detect burnt areas and separate burning severity levels. It also attempted to measure the spectral separability of the different bands and derived indices commonly used to detect burnt areas. A short investigation into the associated environmental variables present in the burnt landscape was also performed to explore the presence of any correlation. As a case study, a wildfire occurred in the Sierra de Gata region of the province of Caceres in North-Eastern Spain was used. A range of spectral indices was computed, including the Normalized Difference Vegetation Index (NDVI) and the Normalized Burn Ratio (NBR). The potential added value of the three new Red Edge bands that come with the Sentinel-2A MSI sensor was also used. The slope, aspect, fractional vegetation cover and terrain roughness were all derived to produce environmental variables. The burning severity was tested using the Spectral Angle Mapper (SAM) classifier. European Environment Agency’s CORINE land cover map was also used to produce the land cover types found in the burned area. The Copernicus Emergency Management Service have produced a grading map for the fire using 0.5 m resolution Pleiades imagery, that was used as reference. Results showed a variable degree of correlation between the burning severity and the tested herein spectral indices. The visible part of the electromagnetic spectrum was not well suited to discern burned from unburned land cover. The NBRb12 (short-wave infrared 2 – SWIR2) produced the best results for detecting burnt areas. SAM resulted in a 73% overall accuracy in thematic mapping. None of the environmental variables appeared to have a significant impact on the burning severity. All in all, our study result showed that Sentinel-2 MSI sensor can be used to discern burnt areas and burning severity. However, further studies in different regions using the same dataset types and methods should be implemented before generalizing the results of the current study.

Exposure to human influence – a geographical field approximating intensity of human influence on landscape structure

ABSTRACTA new spatial variable for the land use and land cover change modelling is introduced, approximating the intensity of human influence on the landscape. The ‘exposure’ simulates the dilution of human activity from settlements (source points with information about population size or other human activity quantification) to landscape, based on the accessibility. Exposure to a settlement is directly proportional to its population size and inversely proportional to the cost distance from the settlement. Cost distance uses the sine of the slope angle as a cost raster to simulate a barrier effect of the terrain. Overall exposure to human influence summates exposure to all individual settlements in a region. The resultant raster field created for Slovakia achieves observable resemblance to the actual intensity of land use derived from Corine Land Cover map. The ArcGIS tool developed for the exposure calculation is supplemented.

Acceleration and fragmentation of CORINE land cover changes in the United Kingdom from 2006–2012 detected by Copernicus IMAGE2012 satellite data

The CORINE land cover maps present the longest series of land cover maps with a consistent class labelling system and date back to 1985. This paper presents the results of the CORINE land cover mapping of the United Kingdom for 2012 and the corresponding land cover change map from 2006 to 2012. It compares the rates of change with those of the preceding land cover change map 2000–2006 and finds that land cover change has become smaller in scale, more diverse in types of change and affects more land cover polygons than in the past reporting period. Land cover change from 2006 to 2012 affected almost 60% more land cover polygons than from 2000 to 2006. A greater variety of 165 types of land cover change was detected from 2006 to 2012 than the 67 types of change from 2000 to 2006. The total land cover change area increased by over 21,000 ha or 11% but remained at around 1% of the total land area of the UK. Rotation forestry mostly of conifer forests was a dominant type of land cover change in both periods (53% of overall change from 2000 to 2006 and 54% from 2006 to 2012), followed by growth and replanting of conifer forest. From 2006 to 2012 the replanting rate decreased by almost 15,000 ha compared to 2000–2006 and a smaller decrease in planting of broadleaf and mixed forests was also observed. Urban land take continued from 2006 to 2012 in the UK, with over 16,000 ha of increase in artificial surfaces. The rate of change from other land cover types to artificial surfaces accelerated from 2006 to 2012. However, we urge caution when interpreting the rate of land take, as it includes wind farms in forested areas which leave the forest largely intact apart from an access road and the wind turbine sites. We also found that the inference from the land cover change matrices is dependent on the level of class aggregation (level 1, 2 or 3).

Study on spatial-temporal variations of Meteorological-Agricultural droughts in Turkey

Abstract. In this study, the meteorological drought represented by Standardized Precipitation Evapotranspiration Index (SPEI) and agriculture drought represented by Vegetation Condition Index (VCI) are analysed in seven regions over Turkey. VCI calculated using the Normalized Difference Vegetation Index (NDVI) data obtained from NOAA AVHRR, SPEI obtained from the SPEI global database with the version (SPEI base v2.5), and Land use/cover obtained from CORINE datasets. The study covers the period from January 1982 to December 2015 due to the availability of NDVI data. The correlation between monthly and seasonal VCI and SPEI (lag months 1, 3, 6, 9, and 12) was investigated in a regional and provincial scale. Monthly correlation found to be the highest in the Central Anatolia, Aegean, Marmara and Mediterranean regions respectively, while other regions have lower and non-homogenous values. One lag time of the VCI with respect to SPEI 12 improves the correlation. The regional correlation showed that, the highest correlation between two parameters is obtained for all the regions with SPEI 12 during summer, then followed by Autumn, and Spring months, the maximum values are recorded for the Central Anatolia (0.656) and Mediterranean (0.625) in Summer, and Aegean (0.643) in Autumn respectively; rather lower correlation values did occur in Marmara (0.515) in Autumn, Eastern Anatolia (0.501), SE Anatolia (0.375) and Black Sea (0.297) regions in Summer. The provincial investigation between seasonal VCI and SPEI indicated that the presence of a positive correlation in general in most of the provinces in all seasons with several exceptions in the Eastern Anatolia, South eastern Anatolia, Black sea, and Marmara. The land cover types with high correlation coefficients are noticed to be covered by forest, agricultural lands, non-irrigable lands and mostly covered by fruits (grape, olive etc.) using CORINE land cover map.