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Recent Presentations

The Advantages of a Point Cloud Data Representation in the Distribution, Analysis and Application of Hyperspectral Imaging Data

Summary of the presentation given by Deep Inamdar at the 2021 EARSel Symposium, entitled: The Utility of Point Cloud and Rectangular Pixel Raster Data Representations in Hyperspectral Imaging

Abstract: Hyperspectral image pixels are not uniformly spaced across the imaged scene due to various factors such as lens distortion, rugged terrains and sensor design. To rectify this spatial non-uniformity while preserving the spectral integrity of the imagery, the data are often spatially resampled onto a pre-specified grid using a nearest neighbour methodology. However, nearest neighbour resampling compromises the spatial integrity of hyperspectral imaging (HSI) data by duplicating, removing and shifting pixels. The objective of this study was to develop a data representation that preserved the spatial and spectral integrity of HSI data more effectively than conventional square pixel rasters generated with a nearest neighbour resampling methodology. The study first investigated the manner in which nearest neighbour resampling compromises the spatial integrity of HSI data. Two different nearest neighbour resampling approaches were tested on HSI data collected at different spatial scales (~ 1.5 cm and ~50 cm) to generate end products with square pixels. The resampling approaches either oversampled or undersampled the data, depending on whether the grid resolution was set to the across track spacing or the along track spacing of the imagery in its raw sensor geometry. When oversampling the data, ~50-75% of all pixels were duplicates. Oppositely, ~50-75% of the collected pixels from the imagery pre-rasterization were lost all together when undersampling the data. Using a rectangular pixel resampling approach, it was possible to minimize pixel loss and duplication to <14%. In all resampled data products, the true spatial position of each measurement was shifted by an average of ~0.3-1.3 pixels. By using the developed Directly-Georeferenced Hyperspectral Point Cloud (DHPC), no pixels were shifted, duplicated or lost. Despite containing additional surface elevation data, the DHPC was up to 13 times smaller in file size than the corresponding rasterized datasets. In various applications (classification, spectra geo-location and target detection) the DHPC outperformed the rectangular pixel HSI data, which in turn outperformed the square pixel images. Overall, the presented rectangular pixel raster and DHPC data representations will push the limits of hyperspectral imaging data distribution, analysis, and application.

Read more about the Directly Georeferenced Hyperspectral Point Cloud in the two links below:

Determining the Spectral Separability of Submerged Aquatic Vegetation of the St. Lawrence River

Gillian Rowan presented a portion of her thesis research at the 42nd Canadian Symposium on Remote Sensing and was awarded 2nd place in the Student Oral Presentation competition. In this presentation, she discusses the leaf-level separability of freshwater submerged vegetation from the St. Lawrence River across multiple spectral scales.

Read more about remote sensing of submerged aquatic vegetation and the detection of SAV from airborne hyperspectral imagery in the articles below.

Quantifying Spectral Complexity Metrics Using Hyperspectral Imagery for Forest Ecosystem Assessment

Presentation by Patrick Osei Darko recorded during the 3rd CABO Annual Meeting (9th November 2020).

Abstract: Two well-known measures in statistical physics, Mean Information Gain (MIG) and Marginal Entropy (ME) have been adapted to high spatial resolution (2.5 m) full range (Visible - Shortwave-Infrared) airborne hyperspectral imagery. When MIG and ME measures of complexity are adapted to hyperspectral imagery, it is possible to quantify two spectral complexity metrics namely, spectral reflectance spatial heterogeneity (MIG) and spectral reflectance aspatial heterogeneity (ME). In this study, we compare the utility of spectral complexity metrics (MIG and ME) with spectral vegetation indices and spectral signatures for studying terrestrial and mangrove forest ecosystems.

Read more about the spectral complexity metrics as applied to airborne hyperspectral imagery of mangroves in the link below:

Characterizeing and Mitigating Sensor Generated Spatial Correlations in Airborne Hyperspectral Imaging Data

Presentation by Deep Inamdar recorded during the 3rd CABO Annual Meeting (9th November 2020).

Abstract: In hyperspectral imaging (HSI), the spatial contribution to each pixel is non-uniform and extends past the traditionally square spatial boundaries designated by the pixel resolution, resulting in sensor-generated blurring effects. The spatial contribution to each pixel can be characterized by the net point spread function, which is overlooked in many airborne HSI applications. The objective of this study was to characterize and mitigate sensor blurring effects in airborne HSI data with simple tools, emphasizing the importance of point spread functions. Two algorithms were developed to (1) quantify spatial correlations and (2) use a theoretically derived point spread function to perform deconvolution. Both algorithms were used to characterize and mitigate sensor blurring effects on a simulated scene with known spectral and spatial variability. The first algorithm showed that sensor blurring modified the spatial correlation structure in the simulated scene, removing 54.0%–75.4% of the known spatial variability. Sensor blurring effects were also shown to remove 31.1%–38.9% of the known spectral variability. The second algorithm mitigated sensor-generated spatial correlations. After deconvolution, the spatial variability of the image was within 23.3% of the known value. Similarly, the deconvolved image was within 6.8% of the known spectral variability. When tested on real-world HSI data, the algorithms sharpened the imagery while characterizing the spatial correlation structure of the dataset, showing the implications of sensor blurring. This study substantiates the importance of point spread functions in the assessment and application of airborne HSI data, providing simple tools that are approachable for all end-users.

Read more about characterizing and mitigating the sensor generated spatial correlations using the link below:

Remote Sensing of Invasive Phragmites within Îles-de-Boucherville National Park

Presentation by Kathryn Elmer recorded during the 3rd CABO Annual Meeting (9th November 2020).

Abstract: Invasive species currently pose one of the greatest threats to global biodiversity, therefore the early detection and identification of species of concern is essential for the creation of prevention and management strategies. By utilizing GNSS field data and hyperspectral imagery (HSI) from an airborne platform, a methodology was established to identify and map the prolific invasive reed Phragmites australis subsp. australis throughout Iles-de-Boucherville National Park located outside of Montreal, Quebec.

Read more about the GNSS ground data of Phragmites stands throughout the Iles-de-Boucherville National park and the detection of Phragmites stands from airborne hyperspectral imagery using the links below:

Data Hosting, Distribution and Metadata of CABO Hyperspectral Imagery

Presentation by Dr. Margaret Kalacska recorded during the 3rd CABO Annual Meeting (9th November 2020).

This presentation summarizes the processing of the approximately 200 lines of hyperspectral airborne and UAV imagery acquired in 2018 and 2019 for CABO. Examples of the geospatial portal and secure FTP are presented as well as an overview of the metadata provided with the imagery.

Exploring the application constraints of optical remote sensing to the study of inland submerged aquatic vegetation

Presentation by Gillian Rowan recorded during the 3rd CABO Annual Meeting (9th November 2020).

Abstract: The viability of applying optical remote sensing to monitor submerged aquatic vegetation (SAV) depends on five factors: the optical separability of the plants, the depth of the plants, the water type and quality, a sensor’s spectral resolution, and the imagery’s spatial resolution. While most previous aquatic applications of optical remote sensing have focused on clear coastal waters, inland waters have received much less attention due to their comparative complexity. It is therefore largely unknown how well optical remote sensing would perform in inland waters according to the five factors above. This work begins the process of evaluating the strengths and constraints of optical remote sensing in these complex systems. Data was collected on the spectral reflectance of various SAV species, and the reflectance of the water column at various depths for two water types. Imagery over the field site is available from 6 different sensors including UAV, airborne, and satellite platforms. Using these data sets, the spectral separability according to SAV taxonomy has been evaluated and work to explain their spectral diversity is underway. The effect of the water column was found to vary according to depth, wavelength, and water type, with similar patterns present between the two water types. In work to come, the species’ classification accuracy will be examined when subjected to the effects of water column attenuation, spectral resampling according to various sensors, and differences in spatial resolution.

Read more about remote sensing of submerged aquatic vegetation and the detection of SAV from airborne hyperspectral imagery in the articles below.

CABO Hyperspectral Imagery Processing Workshop

Presentation by Deep Inamdar given as a special CABO image processing workshop given October 8, 2020

This presentation provides an overview of the pre-processing steps applied by ARSL to prepare airborne and UAV hyperspectral imagery for CABO users. Topics include radiometric, atmospheric and geometric corrections. The Directly Georeferenced Hyperspectral Point Cloud is presented as an alternative to the conventional raster end products commonly seen with remotely sensed data.



Read more about the benefits of the Directly Georeferenced Hyperspectral Point Cloud in the two links below:

Videos from the field

360° video from one of the upper tributaries of the Xingu river, Brazil

See one of the upper tributaries of the Xingu River in virtual reality 360° . Filmed during the 2017 Fish+Forest Expedition to Brazil. Look closely at the water and you might even see a stingray! Video by M. Kalacska

This video is best viewed with a headset such as the Oculus Quest 2, HTC Vive Pro, or other virtual reality headset. If viewing this video on a computer monitor use the mouse to change the 360° view.

Aerial video of the Rio Iriri rapids, at the confluence of the Xingu

Filmed by UAV in 2016 by the Fish+Forest project- see the Rio Iriri, just upstream of the confluence with the Rio Xingu in Brazil. Tonino, our boat pilot is catching a very large cachorro, Hydrolycus armatus by hook and line! Check out our 3D models of a Hydrolycus skull and the Iriri rapids. Video by O. Lucanus

Rare aerial view from 2016 of Arapuja island before flooding by the Belo Monte dam

UAV video from Brazil by the Fish+Forest project filmed in 2016 showing the forest clearing on the Arapuja island in the Xingu river across from the city of Altamira. Video by O. Lucanus

Underwater virtual reality 360°

Underwater 360° video from one of the Xingu river's tributaries. The headwaters of the Rio Xingu in Brazil's Mato Grosso state drain small valleys in a region devastated by industrial agriculture from soy bean production and cattle farming. Where forest remains the rivers have clear water and many fish school in deep water. In this location an old bridge has collapsed, leaving behind timbers and structures in deep water (10 m/30 ft) below the new bridge. Video by O. Lucanus

This video is best viewed with a headset such as the Oculus Quest 2, HTC Vive Pro, or other virtual reality headset. If viewing this video on a computer monitor use the mouse to change the 360° view.

Lake Natron cichlids feeding in nature

Endangered Alcolapia latilabris, Alcolapia ndalalani and Alcolapia alcalica from the very shallow freshwater springs around Lake Natron, Tanzania. The natural range of these fish is very limited and they are highly vulnerable to changes in their environment. Read about their habitat in our publication in Remote Sensing Video by M. Kalacska

Cowichan Garry Oak Preserve, British Columbia

Video showcasing the endangered Cowichan Garry Oak Preserve on Vancouver Island, British Columbia, Canada. Filmed in 2019 during the Canadian Airborne Biodiversity Observatory (CABO) hyperspectral imaging campaign. Video by O. Lucanus.

Early morning flight over the Mer Bleue beaver pond

Early morning test fight with with a DJI Inspire 1, filmed in 2017 at the Mer Blue peatland, Ontario, Canada. Video by O. Lucanus

Aerial video from Olho d'Agua, Mato Grosso do Sul, Brazil

Crystal clear waters filled with aquatic vegetation. Many large fish such as the golden dorado (Salminus brasiliensis), Piraputango (Brycon hilarii) and Curimbata (Prochilodus lineatus) can be seen. Filmed in 2019 by the Fish + Forest Project. Video by O. Lucanus

Sunrise at the Mer Bleue peatland

UAV video at sunrise over the Mer Bleue peatland, near Ottawa, ON, Canada. Video by O. Lucanus

Close up view of Garry Oaks

UAV video of Garry Oak trees at the Cowichan Garry Oak Preserve, British Columbia, Canada. Filmed for the Canadian Airborne Biodiversity Observatory (CABO). Video by O. Lucanus

Fly through from Ile Grosbois, Montreal, QC

UAV fly through of one CABO's focal study sites on Ile Gros Bois in Parc national des Îles-de-Boucherville. Video by O. Lucanus

First FPV video from the Mer Bleue peatland

Experience a fly through of the Mer Bleue peatland in first person view (FPV) mode. Video by O. Lucanus

Recording of an early morning aerial live-stream from Mer Bleue

Check out the Mer Bleue peatland at 5 in the morning. A unique aerial perspective of the peatland not many have seen in person. Video by O. Lucanus

Animated Models

Fly through of the Cowichan Garry Oak meadow reconstructed in 3D

Structure from Motion photogrammetry reconstruction of part of the Cowichan Garry Oak Preserve meadow, British Columbia, Canada. Photographs acquired in 2018 for CABO with a DJI Inspire 2.

3D SfM model from the Cowichan Garry Oak Preserve

Structure from Motion photogrammetry reconstruction of part of the encroachment upon the Cowichan Garry Oak Preserve, British Columbia, Canada. Photographs acquired in 2018 for CABO with a DJI Inspire 2.

Tutorials

How to view SfM or LiDAR point clouds in Augmented Reality via Sketchfab app

3D point clouds, or mesh models hosted on Sketchfab can be viewed in Augmented Reality on a smartphone (iOS or Android) using the Sketchfab app. This video shows how to find the ARSL models in the library.

How to interact with SfM or LiDAR point clouds in VR

Steps to launch VRifier via the Oculus Rift link on the Oculus Quest 2 headset. Once SfM or LiDAR point clouds have been converted to .ply format they can be viewed and interacted with in virtual reality.

ASDToolkit

Demonstration on the use of the ASDToolkit to process field spectral from an ASD spectroradiometer to generate Estimated Absolute Reflectance. See Elmer et al. 2020 for details.

Videos from Publications

Cowichan Garry Oak Preserve

Supplemental figure 1: Video acquired May 16, 2019 illustrating the vegetation at the Cowichan Garry Oak Preserve.

From Arroyo-Mora J.P., Kalacska M., Løke T., Schläpfer D., Lucanus O., Leblanc G. 2021. Assessing the impact of illumination on UAV pushbroom hyperspectral imagery collected under various cloud cover conditions. Remote Sensing of Environment 258(1):112396

3D SfM model from the Cowichan Garry Oak Preserve

Supplemental figure 2: Fly-through animation of the CGOP meadow dense 3D point cloud which was used to generate the DSM through IDW interpolation. The location of the reference panels is shown as well as the extent of the UAV-HSI imagery.

From Arroyo-Mora J.P., Kalacska M., Løke T., Schläpfer D., Lucanus O., Leblanc G. 2021. Assessing the impact of illumination on UAV pushbroom hyperspectral imagery collected under various cloud cover conditions. Remote Sensing of Environment 258(1):112396

3D environment surrounding the reference panels for UAV-HSI

Supplemental figure 3: 360 degree animation of the 3D environment extracted from the dense point cloud surrounding the location of the reference panels for May 16th and 17th, 2019.

From Arroyo-Mora J.P., Kalacska M., Løke T., Schläpfer D., Lucanus O., Leblanc G. 2021. Assessing the impact of illumination on UAV pushbroom hyperspectral imagery collected under various cloud cover conditions. Remote Sensing of Environment 258(1):112396

Fisheye photographs of sky conditions during UAV HSI acquisition at CGOP in 2019

Supplemental figure 4: Animation of fisheye photographs of the sky acquired May 15th and 16th 2019.

From Arroyo-Mora J.P., Kalacska M., Løke T., Schläpfer D., Lucanus O., Leblanc G. 2021. Assessing the impact of illumination on UAV pushbroom hyperspectral imagery collected under various cloud cover conditions. Remote Sensing of Environment 258(1):112396

Directly georeference hyperspectral point cloud (DHPC) animation from Mer Bleue

Supplemental video: DHPC in a 12 × 12 m region (R = 639.6 nm, G = 550.3 nm, B = 459.0 nm, linearly stretched from a reflectance value of 0–12% for display purposes)

From Inamdar D., Kalacska M., Leblanc G., Arroyo-Mora J.P. 2021 Implementation of the directly-georeferenced hyperspectral point cloud. MethodsX 8:101429

Hyperspectral point cloud from UAV data - Mer Bleue peatland

Supplemental video 1: Animation of the DHPC generated from UAV imagery from the Mer Bleue peatland

From Inamdar D, Kalacska M, Arroyo-Mora J.P., Leblanc G. 2021. The Directly-Georeferenced Hyperspectral Point Cloud: Preserving the Integrity of Hyperspectral Imaging Data Frontiers in Remote Sensing 2:675323

Hyperspectral point cloud from airborne data - Mer Bleue peatland

Supplemental video 2: Animation of the DHPC generated from airborne imagery from the Mer Bleue peatland

From Inamdar D, Kalacska M, Arroyo-Mora J.P., Leblanc G. 2021. The Directly-Georeferenced Hyperspectral Point Cloud: Preserving the Integrity of Hyperspectral Imaging Data Frontiers in Remote Sensing 2:675323

Hyperspectral point cloud from UAV data - Cowichan Garry Oak Preserve

Supplemental video 3: Animation of the DHPC generated from UAV imagery from the Cowichan Garry Oak Preserve

From Inamdar D, Kalacska M, Arroyo-Mora J.P., Leblanc G. 2021. The Directly-Georeferenced Hyperspectral Point Cloud: Preserving the Integrity of Hyperspectral Imaging Data Frontiers in Remote Sensing 2:675323

Supplemental video 4: Hyperspectral point cloud from airborne data - Mont Mégantic

Supplemental video 4: Animation of the DHPC generated from airborne imagery from Mont Mégantic

From Inamdar D, Kalacska M, Arroyo-Mora J.P., Leblanc G. 2021. The Directly-Georeferenced Hyperspectral Point Cloud: Preserving the Integrity of Hyperspectral Imaging Data Frontiers in Remote Sensing 2:675323

Timeseries of landcover change - Sinop, Mato Grosso, Brazil

Video figure 16a: Screen recording illustrating the land cover change animation from the Sinop hydropower plant lentic zone from 1984–2020 from Google Earth Timelapse.

From: Lucanus, O.; Kalacska, M.; Arroyo-Mora, J.P.; Sousa, L.; Carvalho, L.N. Before and After: A Multiscale Remote Sensing Assessment of the Sinop Dam, Mato Grosso, Brazil. Earth 2021, 2, 303-330.

Lentic zone of the Sinop hydropower dam reservoir from Google Earth VR

Video figure 16b: Screen recording of the lentic zone of the Sinop hydropower reservoir from Google Earth VR as seen through the Oculus Quest 2.

From: Lucanus, O.; Kalacska, M.; Arroyo-Mora, J.P.; Sousa, L.; Carvalho, L.N. Before and After: A Multiscale Remote Sensing Assessment of the Sinop Dam, Mato Grosso, Brazil. Earth 2021, 2, 303-330.

Ilha do Lair point cloud viewed in VR with the Oculus Quest 2

Video figure 17: Video of the Ilha do Lair (Mato Grosso, Brazil) dataset being viewed in VR with the Oculus Quest 2 HMD in VRifier.

From: Lucanus, O.; Kalacska, M.; Arroyo-Mora, J.P.; Sousa, L.; Carvalho, L.N. Before and After: A Multiscale Remote Sensing Assessment of the Sinop Dam, Mato Grosso, Brazil. Earth 2021, 2, 303-330.

Navigating the SfM and LiDAR point clouds on the Oculus Quest 2

Video figure 11: Video from VRifier illustrating the experience navigating the SfM and LiDAR point clouds on the Oculus Quest 2 headset.

From: Kalacska, M.; Arroyo-Mora, J.P.; Lucanus, O. Comparing UAS LiDAR and Structure-from-Motion Photogrammetry for Peatland Mapping and Virtual Reality (VR) Visualization. Drones 2021, 5, 36.

360° panorama of the Mer Bleue UAV SfM point cloud

Video figure 12a: Video illustrating a 360° panorama of the Mer Bleue SfM point cloud within VRifier. This panorama is being viewed on the Insta360 Player but can be opened by most 360° photograph viewers.

From: Kalacska, M.; Arroyo-Mora, J.P.; Lucanus, O. Comparing UAS LiDAR and Structure-from-Motion Photogrammetry for Peatland Mapping and Virtual Reality (VR) Visualization. Drones 2021, 5, 36.

360° panorama of the Mer Bleue UAV LiDAR point cloud

Video figure 12b: Video illustrating a 360° panorama of the Mer Bleue LiDAR point cloud within VRifier. This panorama is being viewed on the Insta360 Player but can be opened by most 360° photograph viewers.

From: Kalacska, M.; Arroyo-Mora, J.P.; Lucanus, O. Comparing UAS LiDAR and Structure-from-Motion Photogrammetry for Peatland Mapping and Virtual Reality (VR) Visualization. Drones 2021, 5, 36.

Augmented reality visualization of the Mer Bleue SfM point cloud

Video figure 13a: Video illustrating a screen recording of the AR visualization of the Mre Bleue SfM point cloud through the iOS Sketchfab application. The model can be viewed in AR at https://skfb.ly/onuUs

From: Kalacska, M.; Arroyo-Mora, J.P.; Lucanus, O. Comparing UAS LiDAR and Structure-from-Motion Photogrammetry for Peatland Mapping and Virtual Reality (VR) Visualization. Drones 2021, 5, 36.

Augmented reality visualization of the Mer Bleue LiDAR point cloud

Video figure 13b: Video illustrating a screen recording of the AR visualization of the Mre Bleue LiDAR point cloud through the iOS Sketchfab application. The model can be viewed in AR at https://skfb.ly/onuU9

From: Kalacska, M.; Arroyo-Mora, J.P.; Lucanus, O. Comparing UAS LiDAR and Structure-from-Motion Photogrammetry for Peatland Mapping and Virtual Reality (VR) Visualization. Drones 2021, 5, 36.

Hypancistrus zebra habitat underwater (Xingu river, Brazil)

Video figure 1. Hypancistrus zebra (zebra pleco) filmed in its natural habitat in the Xingu river, Brazil.

From: Sousa L., Lucanus O., Arroyo-Mora J.P., Kalacska M. 2021. Conservation and trade of the endangered Hypancistrus zebra (Siluriformes, Loricariidae), one of the most trafficked Brazilian fish. Global Ecology and Conservation 27:e01570

Applied Remote Sensing Lab

Department of Geography
McGill University
805 Sherbrooke West
Burnside Hall 705
Montreal, QC H3A 0B9
Canada

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