Classification of vegetation above the tree line in the Krkonoše Mts. National Park using remote sensing multispectral data

Abstract

This paper compares suitability of multispectral data with different spatial and spectral resolutions for classifications of vegetation above the tree line in the Krkonoše Mts. National Park. Two legends were proposed: the detailed one with twelve classes, and simplified legend with eight classes. Aerial orthorectified images (orthoimages) with very high spatial resolution (12.5 cm) and four spectral bands have been examined using the object based classification. Satellite data WorldView-2 (WV-2) with high spatial resolution (2 metres) and eight spectral bands have been examined using object based classification and per-pixel classification. Per-pixel classification has been applied also to the freely available Landsat 8 data (spatial resolution 30 metres, seven spectral bands). Of the algorithms for per-pixel classification, the following classifiers were compared: maximum likelihood classification (MLC), support vector machine (SVM), and neural net (NN). The object based classification utilized the example-based approach and SVM algorithm (all available in ENVI 5.2). Both legends (simplified and detailed ones) show best results in the case of orthoimages (overall accuracy 83.56% and 71.96% respectively, Kappa coefficient 0.8 and 0.65 respectively). The WV-2 classification brought best results using the object based approach and simplified legend (68.4%); in the case of per-pixel classification it was the SVM method (RBF) and detailed legend (60.82%). Landsat data were best classified using the MLC (78.31%). Our research confirmed that Landsat data are sufficient to get a general overview of basic land cover classes above the tree line in the Krkonoše Mts. National Park. Based on the comparison of the data with different spectral and spatial resolution we can however conclude that very high spatial resolution is the decisive feature that is essential to reach high overall classification accuracy in the detailed level.

Classification of Tundra Vegetation in the Krkonoše Mts. National Park Using APEX, AISA Dual and Sentinel-2A Data

“…As already proved in our previous study [Suchá et al, 2016], the object-based classification can bring very good results for the data with very high spatial resolution (e.g. orthoimages).…”

“…According to some of them (Pal and Mather [2005]) SVM method works very well with low number of training data (from 20 -30% of) while this share is not ideal for other classification methods. Based on our experiences (Zagajewski [2010], Suchá et al [2016]) we used 40% of our data collected in the field for training and 60% for validation in the case of all data types' classifications based on the detailed legend in the Eastern Tundra (Figure 4). Fifty one polygons (11,388 m 2 ) were used as training dataset and the classification accuracies were assessed by seventy two validation polygons (17,129 m 2 ), both representing all eleven categories.…”

“…This detailed legend was used for classifications of all the image data (AISA Dual, APEX and Sentinel-2A) in Eastern tundra (see Figure 4). Moreover, the simplified legend with grouped categories specifically created for Landsat 8 classification in Suchá et al [2016] was used in this study for Sentinel-2A image classification of the whole Krkonoše tundra area (Figure 4). We wanted to check if simplified legend can lead to better classification results than the detailed one in the case of Sentinel-2A data that have lower spatial and spectral resolution in comparison with other used types of data.…”

“…To ensure maximal classification accuracy different NN architectures and full set of parameters in ENVI during the network training and optimization were tested. The tests were based mainly on our previous experiences [Suchá et al, 2016] and recommended settings from literature (for example Ndehedehe et al, [2013], Zhou and Yang, [2008], Wan-Kadir et al, [2011]). Finally, single layer feedforward architecture, logistic activation function and parameter configurations shown in Table 2 were used.…”

“…Our previous study [Suchá et al, 2016] compared suitability of multispectral data with different spatial and spectral resolutions (orthoimages with infrared band, WorldView-2 and Landsat 8) for classifications of vegetation above the tree line in the Krkonoše Mts. National Park.…”