Next Article in Journal
Three-Fold Urban Expansion in Saudi Arabia from 1992 to 2013 Observed Using Calibrated DMSP-OLS Night-Time Lights Imagery
Next Article in Special Issue
Asbestos—Cement Roofing Identification Using Remote Sensing and Convolutional Neural Networks (CNNs)
Previous Article in Journal
The Impact of the Radar-Sampling Volume on Multiwavelength Spaceborne Radar Measurements Using Airborne Radar Observations
Article

Multitemporal Hyperspectral Data Fusion with Topographic Indices—Improving Classification of Natura 2000 Grassland Habitats

1
Department of Geoinformatics, Cartography and Remote Sensing, Chair of Geomatics and Information Systems, Faculty of Geography and Regional Studies, University of Warsaw, 00-927 Warsaw, Poland
2
MGGP Aero sp. z o.o., 33-100 Tarnów, Poland
3
Department of Geobotany and Plant Ecology, Faculty of Biology and Environmental Protection, University of Lodz, 90-237 Łódź, Poland
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(19), 2264; https://doi.org/10.3390/rs11192264
Received: 1 August 2019 / Revised: 20 September 2019 / Accepted: 27 September 2019 / Published: 28 September 2019
(This article belongs to the Special Issue European Remote Sensing-New Solutions for Science and Practice)
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. View Full-Text
Keywords: classification; hyperspectral; topographic indices; multitemporal; Natura 2000 habitats; Random Forest classification; hyperspectral; topographic indices; multitemporal; Natura 2000 habitats; Random Forest
Show Figures

Graphical abstract

MDPI and ACS Style

Marcinkowska-Ochtyra, A.; Gryguc, K.; Ochtyra, A.; Kopeć, D.; Jarocińska, A.; Sławik, Ł. Multitemporal Hyperspectral Data Fusion with Topographic Indices—Improving Classification of Natura 2000 Grassland Habitats. Remote Sens. 2019, 11, 2264. https://doi.org/10.3390/rs11192264

AMA Style

Marcinkowska-Ochtyra A, Gryguc K, Ochtyra A, Kopeć D, Jarocińska A, Sławik Ł. Multitemporal Hyperspectral Data Fusion with Topographic Indices—Improving Classification of Natura 2000 Grassland Habitats. Remote Sensing. 2019; 11(19):2264. https://doi.org/10.3390/rs11192264

Chicago/Turabian Style

Marcinkowska-Ochtyra, Adriana, Krzysztof Gryguc, Adrian Ochtyra, Dominik Kopeć, Anna Jarocińska, and Łukasz Sławik. 2019. "Multitemporal Hyperspectral Data Fusion with Topographic Indices—Improving Classification of Natura 2000 Grassland Habitats" Remote Sensing 11, no. 19: 2264. https://doi.org/10.3390/rs11192264

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop