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

Abstract

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.

Classification of Expansive Grassland Species in Different Growth Stages Based on Hyperspectral and LiDAR Data

“…The usefulness of only spectral information to discrimination of M. caerulea is supported by other studies [20,68]. In classification of Natura 2000 heathlands in Kalmthoutse Heide in Belgium [69] the best mean accuracies were obtained for heathlands with Molinia (80.7% using SVM classifier, 69.7% using RF) on CHRIS data from July.…”

“…Remote sensing offers many possibilities for vegetation research, from condition analysis [13][14][15][16][17] to land use/land cover mapping including plant species or community identification [18][19][20]. The electromagnetic spectrum covering the visible (VIS) and near infrared (NIR) ranges is the most commonly used one for the analysis of vegetation [21].…”

“…Often, remote sensing vegetation indicators, such as normalized difference vegetation index (NDVI), are included in the classification, especially in multi-temporal analyses, insofar as they are good indicators for reflecting periodically dynamic changes of vegetation groups [32,33]. Machine learning methods, such as Random Forest and Support Vector Machines (SVM), are successfully used to identify both communities and species [19,20,34,35]. Comparative analysis of methods used to classify particular species presented higher accuracies reached for a Random Forest algorithm [36][37][38], which is better than SVM because of processing time.…”

“…The vast majority of classifications present single training and validation data split but they can induce biased results [68]. Iterative accuracy assessment was proposed by several authors in the classification of trees [39,40,69] and non-forest vegetation [20,27]. This approach allows for more objective conclusions, avoiding very poor or very good results obtained by chance.…”

Mapping Functional Urban Green Types Using High Resolution Remote Sensing Data

“…Both the adoption of a hierarchical classification approach and the application of dimensionality reduction techniques (in this case MNF) on the hyperspectral dataset improved classification accuracies for detailed urban green types (Table A3). These results agree with earlier findings regarding hierarchical classification of urban green [49] and relating to the added value of dimensionality reduction techniques for detailed vegetation classification [29,105]. Despite the use of spatially and spectrally detailed data sources and advanced analysis techniques (i.e., OBIA and Random Forest classification), uncertainties of detailed urban green types still remained high, particularly for shrub and herbaceous vegetation types (Table 3).…”