Abstract
Accurately identifying Natura 2000 habitat areas with the support of remote sensing techniques is becoming increasingly feasible. Various data types and methods are used for this purpose, and the fusion of data from various sensors and temporal periods (terms) within the phenological cycle allows natural habitats to be precisely identified. This research was aimed at selecting optimal datasets to classify three grassland Natura 2000 habitats (codes 6210, 6410 and 6510) in the Ostoja Nidziańska Natura 2000 site in Poland based on hyperspectral imagery and botanical on-ground reference data acquired in three terms during one vegetative period in 2017 (May, July and September), as well as a digital terrain model (DTM) obtained by airborne laser scanning (ALS). The classifications were carried out using a random forest (RF) algorithm on minimum noise fraction (MNF) transform output bands obtained for single terms, as well as data fusion combining the topographic indices (TOPO) calculated from the DTM, multitemporal hyperspectral data, or a combination of the two. The classification accuracy statistics were analysed in various combinations based on the datasets and their terms of acquisition. Topographic indices improved the classification accuracy of habitats 6210 and 6410, with the greatest impact noted in increased classification accuracy of xerothermic grasslands. The best terms for identifying specific habitats were autumn for 6510 and summer for 6210 and 6410, while the best results overall were obtained by combining data from all terms. The highest obtained values of the F1 coefficient were 84.5% for habitat 6210, 83.2% for habitat 6410, and 69.9% for habitat 6510. Comparing the data fusion results for habitats 6210 and 6410, greater accuracy was obtained by adding topographic indices to multitemporal hyperspectral data, while for habitat 6510, greater accuracy was obtained by fusing only multitemporal hyperspectral data.
Highlights
Human activity causes constant transformation of the natural environment and affects biodiversity at all levels of its organization [1]
This study aims to assess the impact on accuracy of the classification of three Natura 2000 habitats and (a) aerial hyperspectral data and their fusion with topographic indices, and (b) the fusion of multitemporal hyperspectral data acquired in three different terms of the phenological period and their fusion with topographic indices
Single date minimum noise fraction (MNF) + topographic indices (TOPO) and multitemporal MNF classification results were first analysed in comparison to a reference set of one-date MNF, showing increased accuracy with the application of each fusion
Summary
Human activity causes constant transformation of the natural environment and affects biodiversity at all levels of its organization [1]. The basic, widely used technique for identifying Natura 2000 habitats is on-ground measurement [3]. In contrast to on-ground methods, remote sensing methods provide significant savings in the time and human resources needed to identify natural vegetation with suitable precision. As a consequence, they enable changes in extent or state of preservation to be determined [4]. When using multiple data sources and types, an important consideration is the principled selection of data processing and analysis solutions specific to the purpose, in order to preserve one of the main advantages of using remote sensing methods: saving time [7]
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