Marta Szostak

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.

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).

Using of Sentinel-2 images for automation of the forest succession detection

ABSTRACT The study was performed for the part of the administrative district Milicz. The authors analysed the parcels where the changes in land use, compared to the cadastral data, were found. The areas of interest were the parcels, where agricultural use was abandoned and the forest succession progressed. This paper investigates the possibility of applying satellite images Sentinel-2A for the automation of land use/land cover change detection, mainly in the aspect of monitoring uncontrolled forest succession. The results of the supervised classification of images Sentinel-2A were referred to the results of the traditionally applied manual vectorization of aerial orthophotomap. The difference for area covered by trees or shrub was 3.85% of the analysed parcels area. Analysing the results for each parcel in which the process of succession occurred, the mean difference is on average 2.25% for one parcel. The mean difference in the absolute value of the total area of participation in individual land use plots was about 1.54% of the analysed area.

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.