Multiple Flights or Single Flight Instrument Fusion of Hyperspectral and ALS Data? A Comparison of their Performance for Vegetation Mapping

Abstract

Fusion of remote sensing data often improves vegetation mapping, compared to using data from only a single source. The effectiveness of this fusion is subject to many factors, including the type of data, collection method, and purpose of the analysis. In this study, we compare the usefulness of hyperspectral (HS) and Airborne Laser System (ALS) data fusion acquired in separate flights, Multiple Flights Data Fusion (MFDF), and during a single flight through Instrument Fusion (IF) for the classification of non-forest vegetation. An area of 6.75 km2 was selected, where hyperspectral and ALS data was collected during two flights in 2015 and one flight in 2017. This data was used to classify three non-forest Natura 2000 habitats i.e., Xeric sand calcareous grasslands (code 6120), alluvial meadows of river valleys of the Cnidion dubii (code 6440), species-rich Nardus grasslands (code 6230) using a Random Forest classifier. Our findings show that it is not possible to determine which sensor, HS, or ALS used independently leads to a higher classification accuracy for investigated Natura 2000 habitats. Concurrently, increased stability and consistency of classification results was confirmed, regardless of the type of fusion used; IF, MFDF and varied information relevance of single sensor data. The research shows that the manner of data collection, using MFDF or IF, does not determine the level of relevance of ALS or HS data. The analysis of fusion effectiveness, gauged as the accuracy of the classification result and time consumed for data collection, has shown a superiority of IF over MFDF. IF delivered classification results that are more accurate compared to MFDF. IF is always cheaper than MFDF and the difference in effectiveness of both methods becomes more pronounced when the area of aerial data collection becomes larger.

The t-SNE Algorithm as a Tool to Improve the Quality of Reference Data Used in Accurate Mapping of Heterogeneous Non-Forest Vegetation

“…Lower F1 values (0.69) were also obtained using full-waveform airborne laser scanning [41]. The obtained results clearly indicate that the improvement of the on-ground reference dataset using the t-SNE algorithm coupled with classification using a single hyperspectral mosaic is an alternative to the use of other approaches, including single sources (e.g., airborne laser scanning (ALS) [31]), multiple sources and using multi-temporal data fusion.…”

“…The hyperspectral data were obtained on 7 July 2017 with a HySpex sensor developed by the Norwegian Norsk Elektro Optikk (NEO) company. It is part of a remote sensing platform built within the framework of the HabitARS project by the MGGP Aero Company [31]. The data were acquired for an area of 40.59 km 2 .…”

“…In the next stage, the acquired raw data were preprocessed in accordance with the methodology presented in Sławik et. al 2019 (Subdivision 2.3.1) [31]. The hyperspectral mosaics were subjected to a minimum noise fraction transformation (MNF).…”

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

“…The bands covering wavelengths longer than 2.35 μm contained high noise, were cropped, leaving 430 bands that were subjected to the spectral polishing process using the Savitzky-Golay algorithm with a range of 13 bands. 57 In the last step, the single corrected images were merged to produce a mosaic. The mosaicking process was carried out in PARGE (ReSe Applications GmbH, Wil, Switzerland), 55 using the "center cropped" option that finds the middle of the overlapping areas between the images as a cut line.…”

A Hierarchical Classification Framework of Satellite Multispectral/Hyperspectral Images for Mapping Coastal Wetlands

“…Accordingly, it is a big challenge to achieve a fine classification map of coastal wetlands. Combing multispectral sensors with other sensors like SAR and Lidar might integrate respective advantages of these sensors and it has shown some promising results in mapping coastal wetlands [24][25][26]. On the other hand, the imaging spectrometer could provide numerous narrow spectral bands at each pixel [27].…”