LOESS radiometric correction for contiguous scenes (LORACCS): Improving the consistency of radiometry in high-resolution satellite image mosaics

Wegmueller, Sarah A.; Leach, Nicholas R.; Townsend, Philip A.

doi:10.1016/j.jag.2020.102290

Cited by 8 publications

(6 citation statements)

References 17 publications

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“…Astrape Ground Producer's Sentinel-2 images were obtained from 12 June and 30 September 2020 (Table 8), while the Dove imagery was collected from 19 August and 29 September (Table 9). As in Case Study 1, Dove mosaics for this event were also corrected using LORACCS [63]. In both AOIs, images were topographically corrected and masked using the Tree Canopy Cover data.…”

Section: Resultsmentioning

confidence: 99%

“…Sentinel-2 imagery used to produce the map of damage caused by Hurricane Laura in Figure 6. * Denotes mosaic used as reference for radiometric correction using LORACCS [63].…”

Section: Resultsmentioning

confidence: 99%

“…We used Dove imagery in this Case Study (note: no clear Sentinel-2 data were available following the storm) (Table 4). Images were masked using Planet's usable data mask (UDM2) and mosaics (same sensor and cross-sensor) were created using the LOESS Radiometric Correction for Contiguous Scenes (LORACCS) [63]. A final radiometric correction was done by normalizing the pre-and post-event mosaics to Harmonized Landsat-Sentinel-2 images following Leach et al [64,65].…”

Section: Introduction To the Case Studiesmentioning

confidence: 99%

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Astrape: A System for Mapping Severe Abiotic Forest Disturbances Using High Spatial Resolution Satellite Imagery and Unsupervised Classification

Wegmueller

Townsend

2021

Remote Sensing

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Severe forest disturbance events are becoming more common due to climate change and many forest managers rely heavily upon airborne surveys to map damage. However, when the damage is extensive, airborne assets are in high demand and it can take managers several weeks to account for the damage, delaying important management actions. While some satellite-based systems exist to help with this process, their spatial resolution or latency can be too large for the needs of managers, as evidenced by the continued use of airborne imaging. Here, we present a new, operational-focused system capable of leveraging high spatial and temporal resolution Sentinel-2 and Planet Dove imagery to support the mapping process. This system, which we have named Astrape (“ah-STRAH-pee”), uses recently developed techniques in image segmentation and machine learning to produce maps of damage in different forest types and regions without requiring ground data, greatly reducing the need for potentially dangerous airborne surveys and ground sampling needed to accurately quantify severe damage. Although some limited field work is required to verify results, similar to current operational systems, Astrape-produced maps achieved 78–86% accuracy with respect to damage severity when evaluated against reference data. We present the Astrape framework and demonstrate its flexibility and potential with four case studies depicting four different disturbance types—fire, hurricane, derecho and tornado—in three disparate regions of the United States. Astrape is capable of leveraging various sources of satellite imagery and offers an efficient, flexible and economical option for mapping severe damage in forests.

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Section: Resultsmentioning

confidence: 99%

“…Sentinel-2 imagery used to produce the map of damage caused by Hurricane Laura in Figure 6. * Denotes mosaic used as reference for radiometric correction using LORACCS [63].…”

Section: Resultsmentioning

confidence: 99%

Section: Introduction To the Case Studiesmentioning

confidence: 99%

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Astrape: A System for Mapping Severe Abiotic Forest Disturbances Using High Spatial Resolution Satellite Imagery and Unsupervised Classification

Wegmueller

Townsend

2021

Remote Sensing

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“…LOESS is a nonparametric regression method that uses weights to adjust the fit in a localized fashion along points in a scatterplot, and reduces the effects of outliers while retaining general trends, thus it more flexible to fitting a line to data. Additionally, the objective of LOESS is to confirm the best fits for segments of the data, which generally utilizes a parabola or higher-order polynomial instead of a straight line to fit data, thus LOESS is the best way to described the data structure ( Wegmueller et al, 2021 ). Then, the relative influence of the environmental variables on the dominant microbial community was determined using the aggregated boosted tree (ABT) method ( Death, 2007 ).…”

Section: Methodsmentioning

confidence: 99%

Long-Term Chili Monoculture Alters Environmental Variables Affecting the Dominant Microbial Community in Rhizosphere Soil

Chen

Guo

et al. 2021

Front. Microbiol.

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“…As reported in previous studies, the acquired Planet-Scope surface reflectance scenes were subject to slight temporal radiometric inconsistency (Csillik et al, 2019;Leach et al, 2019;Wegmueller et al, 2021). We located two targets that appeared unchanged during the study period (a cemetery and a grassland area) in the south of Lambir Hills National Park.…”

Section: Planetscope Datamentioning

confidence: 97%

Utility of commercial high‐resolution satellite imagery for monitoring general flowering in Sarawak, Borneo

Miura

Tokumoto

Nagai

et al. 2023

Ecological Research

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General flowering (GF), irregular synchronous mass flowering of multiple tree species across multiple families, is a unique biological phenomenon of the mixed lowland dipterocarp forest in Southeast Asia. Characterizing the spatial extent and temporal dynamics of GF is essential for an improved understanding of climate-vegetation interactions and the potential climate change impact on this species-rich rainforest. We investigated the utility of newly available high-temporal (daily) and high-spatial (3-4 m) resolution remote sensing by the PlanetScope commercial satellite constellation for detecting flowering trees

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LOESS radiometric correction for contiguous scenes (LORACCS): Improving the consistency of radiometry in high-resolution satellite image mosaics

Cited by 8 publications

References 17 publications

Astrape: A System for Mapping Severe Abiotic Forest Disturbances Using High Spatial Resolution Satellite Imagery and Unsupervised Classification

Astrape: A System for Mapping Severe Abiotic Forest Disturbances Using High Spatial Resolution Satellite Imagery and Unsupervised Classification

Long-Term Chili Monoculture Alters Environmental Variables Affecting the Dominant Microbial Community in Rhizosphere Soil

Utility of commercial high‐resolution satellite imagery for monitoring general flowering in Sarawak, Borneo

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