Adrian Ochtyra

The application of APEX images in the assessment of the state of non-forest vegetation in the Karkonosze Mountains

Abstract Information about vegetation condition is needed for the effective management of natural resources and the estimation of the effectiveness of nature conservation. The aim of the study was to analyse the condition of non-forest mountain communities: synanthropic communities and natural grasslands. UNESCO’s M&B Karkonosze Transboundary Biosphere Reserve was selected as the research area. The analysis was based on 40 field test polygons and APEX hyperspectral images. The field measurements allowed the collection of biophysical parameters - Leaf Area Index (LAI), fraction of Absorbed Photosynthetically Active Radiation (fAPAR) and chlorophyll content - which were correlated with vegetation indices calculated using the APEX images. Correlations were observed between the vegetation indices (general condition, plant structure) and total area of leaves (LAI), as well as fraction of Absorbed Photosynthetically Active Radiation (fAPAR). The outcomes show that the non-forest communities in the Karkonosze are in good condition, with the synanthropic communities characterised by better condition compared to the natural communities.

Subalpine and alpine vegetation classification based on hyperspectral APEX and simulated EnMAP images

ABSTRACT The characterization of vegetation is a very important ecological task, especially in sensitive mountain areas, as alpine regions often respond to small short-term variations of abiotic and biotic components as well as long-term global changes. Spatial techniques, such as imaging spectroscopy, allow for detailed classification of different syntaxonomic categories of vegetation and their status. Based on the Airborne Prism Experiment (APEX) and simulated Environmental Mapping and Analysis Program (EnMAP) data, this study focused on subalpine and alpine vegetation mapping in the eastern part of the Polish Karkonosze National Park (KPN). The spatial resolution of APEX (3.12 m) enabled the classification of 21 vegetation communities, which was generalized into eight vegetation types. These types were identified on scaled-up APEX data, as both 252 bands from most of the spectral range and a spectrally reduced dataset of 30 minimum noise fraction (MNF) transforms, and compared to the simulated (30 m spatial resolution) EnMAP data using test areas extracted from the field survey derived reference non-forest vegetation map. After preprocessing, a pixel purity index (PPI) was calculated using the MNF image and then the training and validation pixels were selected with Support Vector Machine classification of vegetation communities carried out using different kernel functions: linear, polynomial, radial basis function, and sigmoid. The classification accuracy was obtained for 21 base classes, and the best result was achieved by using the linear function and 252 bands (overall accuracy (OA) of 74.39%). The next step was to classify the eight generalized vegetation types, and the OA for the APEX data reached 90.72% while EnMAP reached 78.25%. The results show the potential use of APEX and EnMAP imagery in mapping subalpine and alpine vegetation on a community and vegetation-type scales, within a diverse ecosystem such as the Karkonosze National Park.

Mapping vegetation communities of the Karkonosze National Park using APEX hyperspectral data and Support Vector Machines

Abstract This research aims to discover the potential of hyperspectral remote sensing data for mapping mountain vegetation ecosystems. First, the importance of mountain ecosystems to the global system should be stressed due to mountainous ecosystems forming a very sensitive indicator of global climate change. Furthermore, a variety of biotic and abiotic factors influence the spatial distribution of vegetation in the mountains, producing a diverse mosaic leading to high biodiversity. The research area covers the Szrenica Mount region on the border between Poland and the Czech Republic - the most important part of the Western Karkonosze and one of the main areas in the Karkonosze National Park (M&B Reserve of the UNESCO). The APEX hyperspectral data that was classified in this study was acquired on 10th September 2012 by the German Aerospace Center (DLR) in the framework of the EUFAR HyMountEcos project. This airborne scanner is a 288-channel imaging spectrometer operating in the wavelength range 0.4-2.5 μm. For reference patterns of forest and non-forest vegetation, maps (provided by the Polish Karkonosze National Park) were chosen. Terrain recognition was based on field walks with a Trimble GeoXT GPS receiver. It allowed test and validation dominant polygons of 15 classes of vegetation communities to be selected, which were used in the Support Vector Machines (SVM) classification. The SVM classifier is a type of machine used for pattern recognition. The result is a post classification map with statistics (total, user, producer accuracies, kappa coefficient and error matrix). Assessment of the statistics shows that almost all the classes were properly recognised, excluding the fern community. The overall classification accuracy is 79.13% and the kappa coefficient is 0.77. This shows that hyperspectral images and remote sensing methods can be support tools for the identification of the dominant plant communities of mountain areas.

Classification of High-Mountain Vegetation Communities within a Diverse Giant Mountains Ecosystem Using Airborne APEX Hyperspectral Imagery

Mapping plant communities is a difficult and time consuming endeavor. Methods relying on field surveys deliver high quality data but are usually limited to relatively small areas. In this paper we apply airborne hyperspectral data to vegetation mapping in remote and hard to reach areas. We classified 22 vegetation communities in the Giant Mountains on 3.12-m Airborne Prism Experiment (APEX) hyperspectral images, registered in 288 spectral bands (10 September 2012). As the classification algorithm, Support Vector Machines (SVM) was used. APEX data were corrected geometrically and atmospherically, and three dimensionality reduction methods were performed to select the best dataset. As reference we used a non-forest vegetation map containing vegetation communities of Polish Karkonosze National Park from 2002, orthophotomap and field surveys data from 2013 to 2014. We obtained the post-classification maps of 22 vegetation communities, lakes and areas without any vegetation. Iterative accuracy assessment repeated 100 times was used to obtain the most objective results for individual communities. The median value of overall accuracy (OA) was 84%. Fourteen out of twenty-four classes were classified of more than 80% of producer accuracy (PA) and sixteen out of twenty-four of user accuracy (UA). APEX data and SVM with the use of iterative accuracy assessment are useful for the mountain communities classification. This can support both Polish and Czech national parks management by giving the information about diversity of communities in the whole transboundary area, helping with identification especially in changing environment caused by humans.

Monitoring forest biodiversity and the impact of climate on forest environment using high-resolution satellite images

ABSTRACT The main objectives of the research work were to determine the usefulness of Landsat and SPOT data for monitoring various forest parameters, and to assess the impact of changeable climatic conditions with the use of vegetation indices derived from remotely sensed data. Vegetation indices describing various aspects of plant condition and vegetation structure were derived from satellite images, and their values were analysed in a temporal profile in different vegetation seasons, in conjunction with meteorological parameters. The results of analyses proved that dedicated vegetation indices of water stress in plants – the disease water stress index (DSWI) and normalized difference infrared index (NDII) – are able to detect changeable climatic conditions, especially the impact of drought on forest ecosystems. The indices are also useful for characterising types of forest site and tree stand mixture, in particular differentiating dry, fresh and humid forest sites. Results of analysis of satellite-based indices were supported by conclusions drawn from a study of vegetation parameters obtained in the course of field campaigns.

Application of HySpex hyperspectral images for verification of a two-dimensional hydrodynamic model

ABSTRACT This research focuses on the use of HySpex hyperspectral images for verification of two-dimensional hydrodynamic modelling of open-channel flow over loose bed (CCHE2D) and assessment of water quality in the Zegrze Reservoir. The CCHE2D hydrodynamic model results show the distribution of hydraulic parameters of water flow and the sediment concentrations in the reservoir. HySpex images were used to obtain remote sensing indices of water quality. The images were compared to the hydrodynamic model results and field measurements. The analysis of hydrodynamic model results and hyperspectral image indices show the spatial distribution of the water’s physico-chemical properties in the reservoir, and poor mixing of the Bug River and the Narew River at their confluence. This study shows that there is synergy potential in using hydrodynamic modelling results and remote sensing indices of water quality for analysis of the reservoir’s water quality.

Estimation of Evapotranspiration Using SEBAL Algorithm and Landsat-8 Data—A Case Study: Tatra Mountains Region

ET (Evapotranspiration) is one of the climate elements, which plays an important role in water balance, and effects on the ecosystem of any region. Therefore, many mathematical equations and algorithms have been found and designed to calculate and estimate values of evapotranspiration. Calculation methods are either based on data from meteorological stations or using other sources of data where the area is lacking from meteorological stations. Remote sensing data are one of the important sources and techniques to estimate many climate elements including evapotranspiration. The selected study area is located in Tatra Mountains on the borders between Poland and Slovakia. Tatra Mountains are the most valuable areas in Poland and Slovakia. The main objective of current study is to estimate the spatial variation of ET using SEBAL algorithm and Landsat-8 imagery. The analysis is carried out using Landsat-8 (OLI/TIRS) data, ASTER GDEM and reference weather parameters. Sixteen ERDAS models are prepared to calculate the various parameters related to solar radiation. The models are prepared to calculate the values of surface radiance surface reflectance, surface albedo, NDVI, LAI, surface emissivity, surface temperature, net radiation, soil heat flux, sensible heat flux, latent heat flux, which are consequently used to calculate the hourly and daily evapotranspiration in study area. Results of pixel wise calculations show the values of surface temperature which are varied from 6.2 C at mountain shadow areas to 34.6 C at bare rocks and bare land area, while the spatial variation of ET at different land covers shows the hourly ET ranged from 0 to 0.72 mm/hr, while the daily ET varied from 0.0 to 17.0 mm/day. Results show clear relation between land use/land cover and solar radiation parameters and impact of vegetation cover on the ET values in pixel wise domain.

Semiautomatic land cover mapping according to the 2nd level of the CORINE Land Cover legend

Abstract Actual land cover maps are a very good source of information on present human activities. It increases value of actual spatial databases and it is a key element for decision makers. Therefore, it is important to develop fast and cheap algorithms and procedures of spatial data updating. Every day, satellite remote sensing deliver vast amount of new data, which can be semi-automatically classified. The paper presents a method of land cover classification based on a fuzzy artificial neural network simulator and Landsat TM satellite images. The latest CORINE Land Cover 2012 polygons were used as reference data. Three satellite images acquired 21 April 2011, 5 June 2010, 27 August 2011 over Warsaw and surrounding areas were processed. As an outcome of classification procedure, the maps, error matrices and a set of overall, producer and user accuracies and a kappa coefficient were achieved. The classification accuracy oscillates around 76% and confirms that artificial neural networks can be successfully used for forest, urban fabric, arable land, pastures, inland waters and permanent crops mapping. Low accuracies were obtained in case of heterogenic land cover units.

An assessment of remote sensing for the classification of sedimentary rocks – a case study of Tauron Group Quarry, Poland

An assessment of remote sensing for the classification of sedimentary rocks – a case study of Tauron Group Quarry, Poland