Piotr Wężyk

Volumetric changes of a soft cliff coast 2008-2012 based on DTM from airborne laser scanning (Wolin Island, southern Baltic Sea)

ABSTRACT Dudzińska-Nowak, J., Wężyk, P., 2014. Volumetric changes of a soft cliff coast 2008–2012 based on DTM from airborne laser scanning (Wolin Island, southern Baltic Sea). In: Green, A.N. and Cooper, J.A.G. (eds.), Proceedings 13th International Coastal Symposium (Durban, South Africa), Journal of Coastal Research, Special Issue No. 70, pp. 59–64, ISSN 0749-0208. A comparison of DTMs off a 2 km-long section of the southern Baltic Sea cliff coast at Wolin Island, composed of unconsolidated Pleistocene sediments with NW exposure and maximum height of 93 m, prepared on the basis of airborne laser scanning (ALS) data collected in 2008, 2009, 2011 and 2012 allowed the magnitude and spatial distribution of changes to be determined. Morphodynamic processes were spatially and temporally diverse. The time period 2008–2012 is dominated by erosion expressed by a negative sediment balance of −33,000 m3. The volume of eroded material was 49,080 m3, while the volume of accumulated material − 15,678 m3. The largest changes were observed in the upper parts of active cliff as a result of mass wasting triggered by loss of the slope stability due to erosion of the lower part of the slope. Significant erosion also occurred on the lower part of the cliff and on the beach. The accumulation is a consequence of material deposition on the beach and at the cliff base. Erosion could be correlated with the number of storm events and with water levels. The results confirm previous studies on the role of factors that regulate the magnitude of coastal erosion, the first being water level rise during the storm events as well as the influence of a series of storms.

Estimating defoliation of Scots pine stands using machine learning methods and vegetation indices of Sentinel-2

ABSTRACT In the presented study, the Sentinel-2 vegetation indices (VIs) were evaluated in context of estimating defoliation of Scots pine stands in western Poland. Regression and classification models were built based on reference data from 50 field plots and Sentinel-2 satellite images from three acquisition dates. Three machine-learning (ML) methods were tested: k-nearest neighbors (kNN), random forest (RF), and support vector machines (SVM). Regression models predicted stands defoliation with moderate accuracy. R2 values for regression models amounted to 0.53, 0.57, 0.57 for kNN, RF and SVM, accordingly. Analogically, the following values of normalized root mean squared error were obtained: 12.2%, 11.9% and 11.6%. Overall accuracies for two-class classification models were 78%, 75%, 78% for kNN, RF and SVM methods. The Green Normalized Difference Vegetation Index and MERIS Terrestrial Chlorophyll Index VIs were found to be most robust defoliation predictors regardless of the ML method. We conclude that Sentinel-2 satellite images provide useful information about forest defoliation and may contribute to forest monitoring systems.

Measuring visual pollution by outdoor advertisements in an urban street using intervisibilty analysis and public surveys

ABSTRACT Debates on the encroaching commercialization of public space by outdoor advertising highlight its possible negative impact on local quality of life and enjoyment of public spaces. These overstimulating outdoor advertisements are often considered a source of visual pollution, but cities have no standard way of measuring where it exists and its local impact, and thus cannot regulate it effectively. This study illustrates that visual pollution can be measured in a useful way by relating public opinion to the number of visible advertisements (intervisibility analysis). Using a 2.5D outdoor advertisement (OA) dataset (location and height) of a busy urban street in Lublin, Poland, this preliminary experiment translates visibility into visual pollution. It was found that streetscape views with more than seven visible OAs created visual pollution in this case study. The GIS-based methodology proposed could provide Lublin officials with a basic tool to assess and manage visual pollution, by informing permitting decisions on OAs.

Using Geobia and Data Fusion Approach for Land use and Land Cover Mapping

Abstract Land Use and Land Cover (LULC) maps play an important role in an environmental modelling, and for many years efforts have been made to improve and streamline the expensive mapping process. The aim of the study was to create LULC maps of three selected water catchment areas in South Poland using a Geographic Object-Based Image Analysis (GEOBIA) in order to highlight the advantages of this innovative, semi-automatic method of image analysis. the classification workflow included: multi-stage and multi-scale analyses based on a data fusion approach. Input data consisted mainly of BlackBridge (RapidEye) high resolution satellite imagery, although for distinguishing particular LULC classes, additional satellite images (LANDSAT TM5) and GIS-vector data were used. Accuracy assessment of GEOBIA classification results varied from 0.83 to 0.87 (kappa), depending on the specific catchment area. The main recognized advantages of GEOBIA in the case study were: performing of multi-stage and multi-scale image classification using different features for specific LULC classes and the ability to using knowledge-based classification in conjunction with the data fusion approach in an efficient and reliable manner.

Forest cover changes in Gorce NP (Poland) using photointerpretation of analogue photographs and GEOBIA of orthophotos and nDSM based on image-matching based approach

ABSTRACT Forest cover change can be detected with high precision using 3D geospatial data and semi-automatic analyses of Remote Sensing data. The aim of our study, performed in Gorce National Park in Poland, was to generate a land use land cover (LULC) map and use it to analyse forest cover change. The study area is a subalpine forest region that has been affected by bark beetle and wind disturbances. The Geographic Object-Based Image Analysis approach was used for classification, with Colour Infrared orthophotos and normalized Digital Surface Models generated using image-matching approach. Gathered results showed that dominating LULC class is coniferous forests (3380 ha; 47% of study area), when second largest class is deciduous forests (2204 ha; 30%). The dead Norway spruce stands (465.5 ha; 6.5%) showed significant increase comparing to 114.1 ha mapped in 1997.

Monitoring the secondary forest succession and land cover/use changes of the Błędów Desert (Poland) using geospatial analyses

Abstract The role of image classification based on multi-source, multi-temporal and multi-resolution remote sensed data is on the rise in the environmental studies due to the availability of new satellite sensors, easier access to aerial orthoimages and the automation of image analysis algorithms. The remote sensing technology provides accurate information on the spatial and temporal distribution of land use and land cover (LULC) classes. The presented study focuses on LULC change dynamics (especially secondary forest succession) that occurred between 1974 and 2010 in the Błędów Desert (an area of approx. 1210 ha; a unique refuge habitat – NATURA 2000; South Poland). The methods included: photointerpretation and on-screen digitalization of KH-9 CORONA (1974), aerial orthoimages (2009) and satellite images (LANDSAT 7 ETM+, 1999 and BlackBridge – RapidEye, 2010) and GIS spatial analyses. The results of the study have confirmed the high dynamic of the overgrowth process of the Błędów Desert by secondary forest and shrub vegetation. The bare soils covered 19.3% of the desert area in 1974, the initial vegetation and bush correspondingly 23.1% and 30.5%. In the years 2009/2010 the mentioned classes contained: the bare soils approx. 1.1%, the initial vegetation – 8.7% and bush – 15.8%. The performed classifications and GIS analyses confirmed a continuous increase in the area covered by forests, from 11.6% (KH-9) up to 24.2%, about 25 years later (LANDSAT 7) and in the following 11 years, has shown an increase up to 35.7% (RapidEye 2010).

Trophic conditions of forest soils of the Pieniny National Park, southern Poland

Abstract The primary objective of this study was to characterise the edaphic conditions of forest areas in the Pieniny National Park (PNP), and to describe the dependencies between properties of forest soils and types of forest plant communities. The “Soil Trophic Index” (SIGg) for mountainous areas was applied. The evaluation of the trophism for 74 forest monitoring employed the soil trophic index for mountainous areas SIGg or SIGgo. Plant communities in the forest monitoring areas were classified according to the Braun-Blanquet’s phytosociological method. Soils of PNP present in the forest monitoring areas were mostly classified as eutrophic brown soils (72.9%), rendzinas (10.8%), brown rendzinas (5.41%), and rubble initial soils (5.41%). Pararendzinas, dystrophic brown soils, and gley soils were less common (total below 5.5%). In the forest monitoring areas of PNP, eutrophic soils predominate over mesotrophic soils. High SIGg index of the soils is caused by high values of acidity and nitrogen content. The Carpathian beech forest Dentario glandulosae-Fagetum and thermophilic beech forest Carici albae-Fagetum associations are characterised by high naturalness and compatibility of theoretical habitats. The soils of the Carpathian fir forest Dentario glandulosae-Fagetum abietetosum subcommunity is characterised by a higher share of silt and clay particles and lower acidity as compared to the Carpathian beech forest Dentario glandulosae-Fagetum typicum subcommunity. The soils of the forest monitoring areas in PNP stand out in terms of their fertility against forest soils in other mountainous areas in Poland.

Applications of Sentinel-2 data for agriculture and forest monitoring using the absolute difference (ZABUD) index derived from the AgroEye software (ESA)

To convince new users of the advantages of the Sentinel_2 sensor, a simplification of classic remote sensing tools allows to create a platform of communication among domain specialists of agricultural analysis, visual image interpreters and remote sensing programmers. An index value, known in the remote sensing user domain as “Zabud” was selected to represent, in color, the essentials of a time series analysis. The color index used in a color atlas offers a working platform for an agricultural field control. This creates a database of test and training areas that enables rapid anomaly detection in the agricultural domain. The use cases and simplifications now function as an introduction to Sentinel_2 based remote sensing, in an area that before relies on VHR imagery and aerial data, to serve mainly the visual interpretation. The database extension with detected anomalies allows developers of open source software to design solutions for further agricultural control with remote sensing.

Using High Resolution LiDAR Data for Snow Avalanche Hazard Mapping

Each year in the Carpathian Mountains and the Sudety Mountains snow avalanches cause a great number of accidents. Avalanches also threaten buildings and affect the environment. The latest studies in Poland aim to implement advanced snow avalanche hazard mapping procedures, which would allow the creation of complex cartographic products for the location of avalanche hazard areas. These preliminary studies showed that results of these procedures strongly depend on the quality of the input digital surface data. The main goal of this study is to investigate this problem in detail through comparison of different types of Digital Elevation Models (DEMs), putting stress on high resolution DEMs generated from airborne and terrestrial laser scanning, in the context of estimating potential avalanche release areas and making run-out calculations. Test sites in the Tatra Mountains in the Carpathians and in the Karkonosze Mountains in the Sudety Mountains were selected for this study. The analysis was performed using Swiss Rapid Mass Movements (RAMMS) model and modified script on delineation automated release area. The study recognized that not only quality but also resolution of a digital surface models influence the accuracy of release area and volume estimation, calculated topography parameters, location of avalanche track and other parameters calculated by dynamic models.