Multispectral Remote Sensing from Unmanned Aircraft: Image Processing Workflows and Applications for Rangeland Environments

By Andrea S. Laliberte, Mark A. Goforth, Caitriana M. Steele, Albert Rango. Remote Sensing, 2011

327 citations recorded by the source corpus.

Using unmanned aircraft systems (UAS) as remote sensing platforms offers the unique ability for repeated deployment for acquisition of high temporal resolution data at very high spatial resolution. Multispectral remote sensing applications from UAS are reported in the literature less commonly than applications using visible bands, although light-weight multispectral sensors for UAS are being used increasingly. . In this paper, we describe challenges and solutions associated with efficient processing of multispectral imagery to obtain orthorectified, radiometrically calibrated image mosaics for the purpose of rangeland vegetation classification. We developed automated batch processing methods for file conversion, band-to-band registration, radiometric correction, and orthorectification. An object-based image analysis approach was used to derive a species-level vegetation classification for the image mosaic with an overall accuracy of 87%. We obtained good correlations between: (1) ground and airborne spectral reflectance (R 2 = 0.92); and (2) spectral reflectance derived from airborne and WorldView-2 satellite data for selected vegetation and soil targets. UAS-acquired multispectral imagery provides quality high resolution information for rangeland applications with the potential for upscaling the data to larger areas using high resolution satellite imagery.

Subfields: Ecology, Environmental Engineering, Media Technology

View Paper: https://www.semanticscholar.org/paper/4791f8ebf55af9ab63cc9c277d082c6c36321bb4

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Citations in the corpus

Vegetation monitoring using multispectral sensors – best practices and lessons learned from high latitudes (2018, background): “image DNs and surface reflectance under the specific light conditions of the survey (Laliberte et al. 2011; Turner et al. 2014; Aasen et al. 2015; Wang and Myint 2015; Wehrhan et al. 2016; Ahmed et al. 2017; Crusiol et al. 2017; Dash et al. 2017).”
On the Use of Unmanned Aerial Systems for Environmental Monitoring (2018, unknown): “Multispectral cameras commonly employ multiple lenses, which introduce band-to-band offsets that need to be adequately corrected in order to avoid artefacts introduced into the combined multiband product [48,49].”
Radiometric Calibration for Multispectral Camera of Different Imaging Conditions Mounted on a UAV Platform (2019, methodology): “Laliberte et al., 2011, adopted the object-based image analysis (OBIA) method to monitor and assess the rangeland using ultra-high-resolution multispectral images acquired from a fixed-wing UAV [1].”
An Automated Technique for Generating Georectified Mosaics from Ultra-High Resolution Unmanned Aerial Vehicle (UAV) Imagery, Based on Structure from Motion (SfM) Point Clouds (2012, background): “Laliberte [9] also collected imagery of the New Mexico rangelands, but also used a six band multispectral camera to capture high resolution data in the near infrared.”
Unmanned Aerial Vehicle (UAV) for Monitoring Soil Erosion in Morocco (2012, background): “[12] describe multispectral UAV-based remote sensing for rangeland environments including image processing workflows.”
Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives (2017, methodology): “The commonly used statistical reduction methods include principal component analysis, wavelet transform, manifold learning and supervised correlation vector machine, support vector machine and discriminant analysis (Laliberte et al., 2011).”
Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows (2018, methodology): “[112] used the dodging method to compensate for the uneven lighting conditions across a photo frame due to the BRDF effects, fragmented cloud cover, vignetting, and other factors.”
Algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning (2017, methodology): “In remote sensing, semantic segmentation is often referred to as image classification, and semantic segmentation of non-RGB imagery has numerous applications, such as land-cover classification (IEEE GRSS, 2017), vegetation classification (Laliberte et al., 2011), and urban planning (Rottensteiner et”