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

Łukasz Sławik,Hubert Piórkowski,Adam Kania,Jan Niedzielko,Dominik Kopeć

doi:10.3390/rs11080970

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.

Highlights

Data fusion is a collection of methods and tools for combining data collected from various sources
It is noticeable that classification accuracy results were always better when using data from multiple sensors (MFDF, Instrument Fusion (IF)) than SS classification results
The highest accuracy was when the classification was based on input data with the highest individual accuracy Multiple Flights Data Fusion (MFDF) (ALS15HS17 0.82 ± 0.02)

Summary

Introduction

Data fusion is a collection of methods and tools for combining data collected from various sources. In order to evaluate the influence of each remote sensing modality and the way in which remote sensing data were collected, the common study area (Figure 1) was classified using ALS, hyperspectral and data combined using the MFDF and IF methods. Protected at the European level, i.e., Xeric sand calcareous grasslands (code: 6120), alluvial meadows of river valleys of the Cnidion dubii (code: 6440), and species-rich Nardus grasslands (code: 6230) The majority of this valuable conservation study area is used for agriculture. The most common type of agriculture is grasslands, mowed once or twice a year Both the phenology of diagnostic RsepmeocteieSsenas.n2d019m, 1o1w, 9i7n0g schedule were considered to determine optimum time to collect airborn7eoafn17d ground data. This is a very short window, around two weeks, starting in early July

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