Optimizing large area coverage from multiple satellite-sensors

Liu, Shufan; Hodgson, Michael E.

doi:10.1080/15481603.2013.866782

Cited by 11 publications

(7 citation statements)

References 28 publications

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“…This approach can be modified so newly-acquired imagery can inform prebuilt models, allowing for the rapid assessment of forest damage following disturbances. This type of analysis should accommodate multiple imagery and data inputs at multiple spatial scales to maximize the amount of functional imagery available within days of a natural disaster [38]. We also foresee the potential to integrate other remotely-sensed imagery sources from aerial and drone-based platforms.…”

Section: Discussionmentioning

confidence: 99%

Spatially Quantifying Forest Loss at Landscape-scale Following a Major Storm Event

et al. 2020

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Large scale forest disturbances are becoming more frequent across the world, and remote sensing must play a role in informing and prioritizing immediate, short-term and long-term disaster response and recovery. However, such evaluations from remote sensing are currently limited (e.g., burned area severity and change NDVI) and do not always explicitly relate to change in resources of interest. Herein we demonstrate a novel method to predict basal area loss, validated by independent field evaluations. Hurricane Michael made landfall on Mexico Beach in the Florida panhandle as a Category 5 storm on October 10th, 2018. The storm affected roughly 2 million hectares of largely forested land in the area. In this study, we use Sentinel-2 imagery and 248 forest plots collected prior to landfall in 2018 in the forests impacted by Hurricane Michael to build a general linear model of tree basal area across the landscape. The basal area model was constrained to areas where trees were present using a tree presence model as a hurdle. We informed the model with post-hurricane Sentinel-2 imagery and compared the pre- and post- hurricane basal area maps to assess the loss of basal area following the hurricane. The basal area model had an r-squared value of 0.508. Plots were revisited to ground truth the modelled results; this showed that the model performed well at categorizing forest hurricane damage. Our results validate a novel method to create a landscape scale spatial dataset showing the location and intensity of basal area loss at 10-m spatial resolution which can be used for quantifying forest disturbances worldwide.

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Section: Discussionmentioning

confidence: 99%

Spatially Quantifying Forest Loss at Landscape-scale Following a Major Storm Event

et al. 2020

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“…To the best of our knowledge, relatively few studies have investigated a computational model for a problem similar to ours. In [6], the authors used the circle intersection point set (CIPS) to derive a set of key representative geographic locations, similar to the facility location problem, based on the characteristics of the most restrictive sensor. This approach hugely reduces the combinatorial complexity; however, they rely on a preexisting module which gives the best acquisition opportunities for each interpolated point.…”

Section: Literature Reviewmentioning

confidence: 99%

Mission planning for non-homogeneous Earth observation satellites constellation for disaster response

Holvoet¹,

Vongsantivanich

Chaimatanan³

et al. 2018

2018 SpaceOps Conference

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“…Murray and Tong (2007) extended this work to accommodate line- and polygon-based demand. Given such optimality advantages, the FDS has been extensively applied in Berman, Verter, and Kara (2007), Berman and Wang (2011), Liu and Hodgson (2013), Schobel et al (2009), Tong and Murray (2009), Tong (2012), and Wei and Murray (2013) to represent continuous potential facility locations. However, the optimality of the FDS is dependent upon discrete demand and binary coverage assumption.…”

Section: Continuous Space Discretizationmentioning

confidence: 99%

Coverage Location Models

Wei

2015

International Regional Science Review

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Achieving maximal coverage of service facilities has been of great interest to urban and regional planners. Examples include placing cellular towers, siting emergency response stations, and locating weather radars, among others. In some planning contexts, facilities could be sited almost anywhere in a region due to their small geographic footprints and demand is continuously distributed. This location problem has been represented as the continuous space maximal coverage problem (CSMCP). The CSMCP is widely acknowledged to be challenging to solve exactly. A broadly used solution approach for the CSMCP is to transform the problem into discrete maximal coverage models through continuous space discretization. A variety of discrete simplifications of CSMCP have been developed, attempting to address spatial representation issues that arise in the application of discrete models used as a continuous space approximation. However, the performance of applying these discrete coverage models to approximately solving the CSMCP has not been explicitly evaluated. It remains elusive as to which approach provides the best approximation for the CSMCP. This article therefore presents a comparative performance analysis of various discrete approximations for the CSMCP. Empirical results provide insights on how to achieve a balance between model representation detail and reasonable computation. Potential research directions are also suggested.

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Optimizing large area coverage from multiple satellite-sensors

Cited by 11 publications

References 28 publications

Spatially Quantifying Forest Loss at Landscape-scale Following a Major Storm Event

Spatially Quantifying Forest Loss at Landscape-scale Following a Major Storm Event

Mission planning for non-homogeneous Earth observation satellites constellation for disaster response

Coverage Location Models

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