Ultra-Light Aircraft-Based Hyperspectral and Colour-Infrared Imaging to Identify Deciduous Tree Species in an Urban Environment

• Published in: Remote sensing (Basel, Switzerland) Vol. 10; no. 10; p. 1668
• Main Authors: Mozgeris, Gintautas, Juodkienė, Vytautė, Jonikavičius, Donatas, Straigytė, Lina, Gadal, Sébastien, Ouerghemmi, Walid
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2018; MDPI
• Subjects: Aerial photography; Aircraft; Cameras; Cities; Classification; Color; colour infrared; Computer Science; Computer simulation; Cubes; Data processing; Deciduous trees; Design engineering; Environment and Society; Environmental Sciences; Forest management; Geography; Humanities and Social Sciences; hyperspectral; Hyperspectral imaging; Image classification; Image Processing; Infrared cameras; Infrared imagery; Infrared imaging; Learning algorithms; Light aircraft; Machine learning; Mapping; Methods and statistics; Missions; Modeling and Simulation; Multilayer perceptrons; Platforms; Pretreatment; Rainforests; Remote sensing; Remote sensors; Sensors; Species; Species classification; Spectral bands; Spectrum analysis; Trees; ultra-light aircraft; Ultralight aircraft; Urban environments; urban trees; Vegetation mapping; Lithuania; United States > US; Ultra-light aircraft; Colour infrared; Hyperspectral; Classification; Urban trees
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs10101668

One may consider the application of remote sensing as a trade-off between the imaging platforms, sensors, and data gathering and processing techniques. This study addresses the potential of hyperspectral imaging using ultra-light aircraft for vegetation species mapping in an urban environment, exploring both the engineering and scientific aspects related to imaging platform design and image classification methods. An imaging system based on simultaneous use of Rikola frame format hyperspectral and Nikon D800E adopted colour infrared cameras installed onboard a Bekas X32 manned ultra-light aircraft is introduced. Two test imaging flight missions were conducted in July of 2015 and September of 2016 over a 4000 ha area in Kaunas City, Lithuania. Sixteen and 64 spectral bands in 2015 and 2016, respectively, in a spectral range of 500–900 nm were recorded with colour infrared images. Three research questions were explored assessing the identification of six deciduous tree species: (1) Pre-treatment of spectral features for classification, (2) testing five conventional machine learning classifiers, and (3) fusion of hyperspectral and colour infrared images. Classification performance was assessed by applying leave-one-out cross-validation at the individual crown level and using as a reference at least 100 field inventoried trees for each species. The best-performing classification algorithm—multilayer perceptron, using all spectral properties extracted from the hyperspectral images—resulted in a moderate classification accuracy. The overall classification accuracy was 63%, Cohen’s Kappa was 0.54, and the species-specific classification accuracies were in the range of 51–72%. Hyperspectral images resulted in significantly better tree species classification ability than the colour infrared images and simultaneous use of spectral properties extracted from hyperspectral and colour infrared images improved slightly the accuracy over the 2015 image. Even though classifications using hyperspectral data cubes of 64 bands resulted in relatively larger accuracies than with 16 bands, classification error matrices were not statistically different. Alternative imaging platforms (like an unmanned aerial vehicle and a Cessna 172 aircraft) and settings of the flights were discussed using simulated imaging projects assuming the same study area and field of application. Ultra-light aircraft-based hyperspectral and colour-infrared imaging was considered to be a technically and economically sound solution for urban green space inventories to facilitate tree mapping, characterization, and monitoring.