SkySat Data Quality Assessment within the EDAP Framework

Saunier, Sébastien; Karakas, Gizem; Yalcin, I.; Done, Fay; Mannan, Rubinder; Albinet, C.; Goryl, Philippe; Kocaman, Sultan

doi:10.3390/rs14071646

Cited by 12 publications

(13 citation statements)

References 24 publications

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“…Our approach enabled the derivation of the height information, normally only available from high-cost LiDAR data, from less expensive aerial imagery ( 62 , 63 , 71 ). While aerial images cannot be considered “low cost,” the availability of submeter resolution images from nanosatellites, such as SkySat ( 72 ), provides a comparable quality for a reasonable price ( 73 , 74 ). We propose such an individual tree localization and characterization approach as the means to produce a comprehensive tree database with a focus on nonforest trees, which are essential yet often not systematically investigated by conventional forest inventories ( 9 ).…”

Section: Discussionmentioning

confidence: 99%

Deep learning enables image-based tree counting, crown segmentation, and height prediction at national scale

Brandt

Fensholt

et al. 2023

PNAS Nexus

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Sustainable tree resource management is the key to mitigating climate warming, fostering a green economy, and protecting valuable habitats. Detailed knowledge about tree resources is a prerequisite for such management but is conventionally based on plot-scale data, which often neglects trees outside forests. Here, we present a deep learning-based framework that provides location, crown area and height for individual overstory trees from aerial images at country scale. We apply the framework on data covering Denmark and show that large trees (stem diameter > 10 cm) can be identified with a low bias (12.5%), and that trees outside forests contribute to 30% of the total tree cover, which is typically unrecognized in national inventories. The bias is high (46.6%) when our results are evaluated against all trees taller than 1.3 m, which involve undetectable small or understory trees. Furthermore, we demonstrate that only marginal effort is needed to transfer our framework to data from Finland, despite markedly dissimilar data sources. Our work lays the foundation for digitalized national databases, where large trees are spatially traceable and manageable.

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Section: Discussionmentioning

confidence: 99%

Deep learning enables image-based tree counting, crown segmentation, and height prediction at national scale

Brandt

Fensholt

et al. 2023

PNAS Nexus

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“…Additionally, both PlanetScope and SkySat satellite constellations are comprised of a fleet of spaceborne sensors, meaning that successive images over the same site are not necessarily acquired by the same sensor on each date. In an independent quality assessment of the SkySat fleet, Saunier et al report that although signal-to-noise ratios fall within the manufacturer-reported range, the radiometric quality of individual SkySat cameras can differ substantially (Saunier et al 2022). These sources of variation in geometric and radiometric quality impact the accuracy and reliability of any derived optical data and should be mitigated to the extent possible before imagery is used in decision support systems for crop protection.…”

Section: Discussionmentioning

confidence: 99%

“…Recent engineering innovations and advances in satellite constellation design have improved the temporal and radiometric consistency as well as scalability of spaceborne sensing platforms. These platforms include DigitalGlobe’s World View fleet (0.5-1.2 m spatial resolution, 8-38 spectral bands), the Pleiades constellation operated by AirBus Defense (2 m, 4 bands), and Planet Lab’s SuperDove (3 m, 8 bands) and SkySat-C (0.5 m, 4 bands) fleets (Yang 2018; Frazier and Hemingway 2021; Saunier et al 2022). The SuperDoves operate on a sun-synchronous orbit with a 24-hr revisit time and offer global coverage.…”

Section: Introductionmentioning

confidence: 99%

“…Imagery is available as a harmonized product that is radiometrically consistent with coincident Sentinel-2 acquisitions (Tu et al 2022; Kim et al 2022). Planet’s SkySat C fleet offers imagery at 50 cm spatial resolution in four multispectral bands (red, green, blue, NIR) and is currently available as a tasked product with revisit time dependent on the area of interest and contract specifications (Saunier et al 2022; “Planet Imagery Product Specifications” 2022).…”

Section: Introductionmentioning

confidence: 99%

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Assessing the capacity of high-resolution commercial satellite imagery for grapevine downy mildew detection and surveillance in New York state

Kanaley,

Combs,

Paul

et al. 2023

Preprint

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Grapevine downy mildew (GDM), caused by the oomycetePlasmopara viticola,can cause 100% yield loss and vine death under conducive conditions. Growers currently rely on frequent fungicide applications for control, but this practice has led to widespread resistance. Rapid remote detection and surveillance of GDM outbreaks would enable precision pesticide applications to target effective but resistance-prone fungicides where and when most needed, while relying on less resistance-prone protectants elsewhere. High resolution commercial satellite platforms offer the opportunity to track rapidly spreading diseases like GDM over large, heterogeneous fields. Here, we investigate the capacity of PlanetScope (3 m) and SkySat (50 cm) imagery for season-long GDM detection and surveillance. A team of trained scouts rated GDM severity and incidence in two acres of Chardonnay grapevines in Geneva, NY, USA in June-August of 2020, 2021, and 2022. Satellite imagery acquired within 72 hours of scouting was processed to extract single-band reflectance and vegetation indices (VIs). Random forest models trained on spectral bands and VIs derived from both image datasets could classify areas of high and low GDM incidence and severity with maximum accuracies of 0.88 (SkySat) and 0.94 (PlanetScope). However, we do not observe significant differences between VIs of high and low damage classes until late July-early August. We identify cloud cover, image co-registration, and low spectral resolution as key challenges to operationalizing satellite-based GDM surveillance. This work establishes the capacity of spaceborne multispectral sensors to detect late-stage GDM and outlines steps towards incorporating satellite remote sensing in grapevine disease surveillance systems.

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“…EDAP has established standards for performing early quality assessments on the data of national or commercial satellites. In this context, quality assessment procedures have been applied to several satellites such as Third Party Missions (TPM), such as SkySat (Saunier et al, 2022). The MAXAR HD images were also evaluated within the EDAP context (e.g., see Yalcin et al, 2021).…”

Section: Introductonmentioning

confidence: 99%

Investigations on the Effect of Hd Processing in Land Cover Classification

Yalcin

Karakas

Kocaman

et al. 2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The requirement of automated Land Use/Land Cover (LULC) classification has arisen in ecosystem related applications, such as natural hazard assessments, urban and rural area planning, natural resource management, etc. The data source and the classification method used in the production of LULC maps depend on the study area size and the location, and also determined by taking the time and cost into account. Recently, MAXAR Technologies announced a new product, High Definition (HD) with 15 cm resolution, which is obtained by post-processing of images with 30 cm Ground Sampling Distance (GSD). The post-processing employs machine learning methods. On the other side, the effect of HD processing on the image quality, and the usability of such products in various applications are still needed to be investigated. In this study, the influence of HD processing algorithm on LULC classification results was investigated by using 15 cm HD and 30 cm resolution images provided by MAXAR. By using the Random Forest (RF) and Support Vector Machine (SVM) methods in two different study areas, image classification was performed to detect water, vegetation, asphalt road, building, shadow, agriculture and barren land classes. The results show that in HD products, the edges of objects were sharper, whereas the classification noise was higher inside agricultural fields. Considering the overall results, it can be concluded that with the use of HD products in urban areas, improved LULC maps can be obtained.

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SkySat Data Quality Assessment within the EDAP Framework

Cited by 12 publications

References 24 publications

Deep learning enables image-based tree counting, crown segmentation, and height prediction at national scale

Deep learning enables image-based tree counting, crown segmentation, and height prediction at national scale

Assessing the capacity of high-resolution commercial satellite imagery for grapevine downy mildew detection and surveillance in New York state

Investigations on the Effect of Hd Processing in Land Cover Classification

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