Adriana Marcinkowska-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.

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.

Application of Sentinel-2 and EnMAP new satellite data to the mapping of alpine vegetation of the Karkonosze Mountains

Abstract Effective assessment of environmental changes requires an update of vegetation maps as it is an indicator of both local and global development. It is therefore important to formulate methods which would ensure constant monitoring. It can be achieved with the use of satellite data which makes the analysis of hard-to-reach areas such as alpine ecosystems easier. Every year, more new satellite data is available. Its spatial, spectral, time, and radiometric resolution is improving as well. Despite significant achievements in terms of the methodology of image classification, there is still the need to improve it. It results from the changing needs of spatial data users, availability of new kinds of satellite sensors, and development of classification algorithms. The article focuses on the application of Sentinel-2 and hyperspectral EnMAP images to the classification of alpine plants of the Karkonosze (Giant) Mountains according to the: Support Vector Machine (SVM), Random Forest (RF), and Maximum Likelihood (ML) algorithms. The effects of their work is a set of maps of alpine and subalpine vegetation as well as classification error matrices. The achieved results are satisfactory as the overall accuracy of classification with the SVM method has reached 82% for Sentinel-2 data and 83% for EnMAP data, which confirms the applicability of image data to the monitoring of alpine plants.

THE USE OF VEGETATION INDICES IN THE EVALUATION OF VEGETATION PHENOLOGY BASED ON MERIS DATA: THE CZECH REPUBLIC CASE STUDY

This article focuses on utilization of vegetation indices for vegetation phenology analysis based on multitemporal MERIS data. The model data set contained imagery acquired during the vegetation season of the year 2009 and it covered most of the area of the Czech Republic. Databases LPIS and GlobCover were used for spatial delimitation of the observed vegetation types. Firstly, a methodology of processing multitemporal MERIS data for atmospheric and geometric corrections is presented. The main part deals with the evaluation of spectral characteristic of the forest species and agricultural crops by means of vegetation indices. Results showed that the MTCI index is well related to the changes of chlorophyll concentration and it is a suitable measure for chlorophyll estimation from MERIS data. Indices fCover and LAI are very sensitive to the quantity of vegetation cover (biomass). Perspectives of the research regarding the planned missions of the satellites Sentinel 2 and Sentinel 3 are given in conclusion.