Mapping secondary succession species in agricultural landscape with the use of hyperspectral and airborne laser scanning data

Aleksandra Radecka , Dorota Michalska-Hejduk , Katarzyna Osińska-Skotak , Adam Kania , Konrad Górski , Wojciech Ostrowski

Abstract

Secondary succession is a process that is often observed taking place in former agricultural ecosystems. Its characteristics are especially important in protected areas, for the purposes of monitoring and protective measures. Effective mapping of succession is facilitated by the development of automated methodologies based on remote sensing data, which are capable of complementing traditional field research. The objective of this work is to determine whether the classification of high-resolution hyperspectral and light detection and ranging (LiDAR) data with the use of the random forest algorithm enables us to produce an accurate succession species map. First, feature extraction techniques are applied to 1-m hyperspectral images and a ∼7 point∕m2 dense point cloud. Minimum noise fraction layers and vegetation indices are calculated from the hyperspectral data and geometry related indices from the LiDAR data. Finally, the recursive feature elimination algorithm is applied to the combined dataset and the reference polygons to select the optimal set of features for subsequent classification. The results indicate that the proposed methodology has the potential to be used operationally. The final classification product is characterized by a relatively high Cohen’s kappa value of 0.68, with single species classified with various accuracies, expressed by F1 scores ranging from 0.45 to 0.87.
Author Aleksandra Radecka (FGC / DPTSIS)
Aleksandra Radecka,,
- Department of Photogrammetry, Teledetection and Spatial Information Systems
, Dorota Michalska-Hejduk
Dorota Michalska-Hejduk,,
-
, Katarzyna Osińska-Skotak (FGC / DPTSIS)
Katarzyna Osińska-Skotak,,
- Department of Photogrammetry, Teledetection and Spatial Information Systems
, Adam Kania
Adam Kania,,
-
, Konrad Górski (FGC / DPTSIS)
Konrad Górski,,
- Department of Photogrammetry, Teledetection and Spatial Information Systems
, Wojciech Ostrowski (FGC / DPTSIS)
Wojciech Ostrowski,,
- Department of Photogrammetry, Teledetection and Spatial Information Systems
Journal seriesJournal of Applied Remote Sensing, ISSN 1931-3195, (A 20 pkt)
Issue year2019
Vol13 (3)
Pages1-22
Publication size in sheets1.05
Keywords in Englishecological process; Natura 2000 monitoring; hyperspectral imaging; light detection and ranging data; data fusion; random forest
ASJC Classification1900 General Earth and Planetary Sciences
DOIDOI:10.1117/1.JRS.13.034502
Internal identifier30/2019
Languageen angielski
Score (nominal)20
ScoreMinisterial score = 20.0, 01-08-2019, ArticleFromJournal
Publication indicators Scopus SNIP (Source Normalised Impact per Paper): 2017 = 0.599; WoS Impact Factor: 2017 = 0.976 (2) - 2017=1.146 (5)
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