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

Abstract

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.

Acquisition of Forest Attributes for Decision Support at the Forest Enterprise Level Using Remote-Sensing Techniques—A Review

“…From trees detected in CHM derived from high-resolution RGB data, 90%-97% were correctly classified as pine, spruce, birch, or larch. The use of hyperspectral imagery for identification of deciduous tree species reports also Mozgeris et al [181]. The study compared several machine learning classifiers, the best of which achieved the accuracy of 51%-72% for six deciduous species.…”

Open geo-spatial data for sustainable forest management: Lithuanian case

“…It is obvious, that the private forest owner possessing several hectares of forest may not pay for remotely sensed data acquisition missions, even though they are very cheap solution based on e.g. the use of unmanned aviation vehicles or ultra-light aviation (Mozgeris & Augustaitis, 2013;Mozgeris et al, 2018a;Mozgeris et al, 2018b). Thus, availability of open source data opens additional opportunities for private forest inventories and management planning, even though the data may be not perfectly emulating the conventional orthophotos.…”

Scaling Up Sagebrush Chemistry With Near-Infrared Spectroscopy and Uas-Acquired Hyperspectral Imagery

“…We collected hyperspectral imagery of each flight area using the Rikola HSI (Senop Oy, Oulu, Finland) hyperspectral camera. The Rikola HSI camera collects spectra for each pixel within the range of 500-900 nm with 16 programmable bands for any increment within that range (Mozgeris et al, 2018a). For this study, we used a band combination from 550-849 nm (~20 nm increments).…”

“…Images acquired from the flights were pre-processed using the camera manufacturer software. Noise and vignetting were removed for image clarity, and digital number values (DN) were converted to radiance (W/(m 2 x srad x µm)) (Jakob et al, 2017;Mozgeris et al, 2018a). The Rikola HSI software aligns each image, but we found that the imagery had too much shift in between each band for the images to align properly (Mozgeris et al, 2018b).…”