Classification of Expansive Grassland Species in Different Growth Stages Based on Hyperspectral and LiDAR Data
PBN-AR
Instytucja
Wydział Geografii i Studiów Regionalnych (Uniwersytet Warszawski)
Informacje podstawowe
Główny język publikacji
en
Czasopismo
Remote Sensing (35pkt w roku publikacji)
ISSN
2072-4292
EISSN
Wydawca
DOI
URL
Rok publikacji
2018
Numer zeszytu
10
Strony od-do
1-22
Numer tomu
12
Identyfikator DOI
Liczba arkuszy
2.05
Słowa kluczowe
en
mapping; expansive grass species; hyperspectral; LiDAR; Natura 2000; Random Forest
Open access
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Otwarte czasopismo
Wersja tekstu w otwartym dostępie
Wersja opublikowana
Licencja otwartego dostępu
Creative Commons — Uznanie autorstwa
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Po publikacji
Ilość miesięcy od publikacji
1
Data udostępnienia w sposób otwarty
Streszczenia
Język
en
Treść
Expansive species classification with remote sensing techniques offers great support for botanical field works aimed at detection of their distribution within areas of conservation value and assessment of the threat caused to natural habitats. Large number of spectral bands and high spatial resolution allows for identification of particular species. LiDAR (Light Detection and Ranging) data provide information about areas such as vegetation structure. Because the species differ in terms of features during the growing season, it is important to know when their spectral responses are unique in the background of the surrounding vegetation. The aim of the study was to identify two expansive grass species: Molinia caerulea and Calamagrostis epigejos in the Natura 2000 area in Poland depending on the period and dataset used. Field work was carried out during late spring, summer and early autumn, in parallel with remote sensing data acquisition. Airborne 1-m resolution HySpex images and LiDAR data were used. HySpex images were corrected geometrically and atmospherically before Minimum Noise Fraction (MNF) transformation and vegetation indices calculation. Based on a LiDAR point cloud generated Canopy Height Model, vegetation structure from discrete and full-waveform data and topographic indexes were generated. Classifications were performed using a Random Forest algorithm. The results show post-classification maps and their accuracies: Kappa value and F1 score being the harmonic mean of producer (PA) and user (UA) accuracy, calculated iteratively. Based on these accuracies and botanical knowledge, it was possible to assess the best identification date and dataset used for analysing both species. For M. caerulea the highest median Kappa was 0.85 (F1 = 0.89) in August and for C. epigejos 0.65 (F1 = 0.73) in September. For both species, adding discrete or full-waveform LiDAR data improved the results. We conclude that hyperspectral (HS) and LiDAR airborne data could be useful to identify grassland species encroaching into Natura 2000 habitats and for supporting their monitoring
Cechy publikacji
discipline:Biologia
discipline:Ekologia
discipline:Geografia
discipline:Biology
discipline:Ecology
discipline:Geography
Original article
Original article presents the results of original research or experiment.
Oryginalny artykuł naukowy
Oryginalny artykuł naukowy przedstawia rezultaty oryginalnych badań naukowych lub eksperymentu.
Inne
System-identifier
PBN-R:903505
CrossrefMetadata from Crossref logo
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