气候变化领域集成服务门户(CCPortal): Performance across WorldView-2 and RapidEye for reproducible seagrass mapping

CCPortal

DOI	10.1016/j.rse.2020.112036
	Performance across WorldView-2 and RapidEye for reproducible seagrass mapping
	Coffer M.M.; Schaeffer B.A.; Zimmerman R.C.; Hill V.; Li J.; Islam K.A.; Whitman P.J.
发表日期	2020
ISSN	00344257
卷号	250
英文摘要	Satellite remote sensing offers an effective remedy to challenges in ground-based and aerial mapping that have previously impeded quantitative assessments of global seagrass extent. Commercial satellite platforms offer fine spatial resolution, an important consideration in patchy seagrass ecosystems. Currently, no consistent protocol exists for image processing of commercial data, limiting reproducibility and comparison across space and time. Additionally, the radiometric performance of commercial satellite sensors has not been assessed against the dark and variable targets characteristic of coastal waters. This study compared data products derived from two commercial satellites: DigitalGlobe's WorldView-2 and Planet's RapidEye. A single scene from each platform was obtained at St. Joseph Bay in Florida, USA, corresponding to a November 2010 field campaign. A reproducible processing regime was developed to transform imagery from basic products, as delivered from each company, into analysis-ready data usable for various scientific applications. Satellite-derived surface reflectances were compared against field measurements. WorldView-2 imagery exhibited high disagreement in the coastal blue and blue spectral bands, chronically overpredicting. RapidEye exhibited better agreement than WorldView-2, but overpredicted slightly across all spectral bands. A deep convolutional neural network was used to classify imagery into deep water, land, submerged sand, seagrass, and intertidal classes. Classification results were compared to seagrass maps derived from photointerpreted aerial imagery. This study offers the first radiometric assessment of WorldView-2 and RapidEye over a coastal system, revealing inherent calibration issues in shorter wavelengths of WorldView-2. Both platforms demonstrated as much as 97% agreement with aerial estimates, despite differing resolutions. Thus, calibration issues in WorldView-2 did not appear to interfere with classification accuracy, but could be problematic if estimating biomass. The image processing routine developed here offers a reproducible workflow for WorldView-2 and RapidEye imagery, which was tested in two additional coastal systems. This approach may become platform independent as more sensors become available. © 2020
英文关键词	Atmospheric correction; Image classification; RapidEye; Remote sensing; Seagrass; WorldView-2
语种	英语
scopus关键词	Aerial photography; Antennas; Calibration; Convolutional neural networks; Data handling; Deep neural networks; Mapping; Plants (botany); Radiometry; Remote sensing; Classification accuracy; Classification results; Commercial satellites; Platform independent; Quantitative assessments; Radiometric performance; Satellite remote sensing; Scientific applications; Satellites; aerial photography; image processing; imagery; radiometric method; RapidEye; satellite altimetry; seagrass; spectral analysis; WorldView; Bay; Florida [United States]; Somalia; St Joseph; St Joseph Bay; United States
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179147
作者单位	ORISE fellow, U.S. Environmental Protection Agency, Office of Research and Development, Durham, NC, United States; Center for Geospatial Analytics, North Carolina State University, Raleigh, NC, United States; U.S. Environmental Protection Agency, Office of Research and Development, Durham, NC, United States; Department of Ocean, Earth & Atmospheric Sciences, Old Dominion University, Norfolk, VA, United States; Department of Electrical & Computer Engineering, Old Dominion University, Norfolk, VA, United States
推荐引用方式 GB/T 7714	Coffer M.M.,Schaeffer B.A.,Zimmerman R.C.,et al. Performance across WorldView-2 and RapidEye for reproducible seagrass mapping[J],2020,250.
APA	Coffer M.M..,Schaeffer B.A..,Zimmerman R.C..,Hill V..,Li J..,...&Whitman P.J..(2020).Performance across WorldView-2 and RapidEye for reproducible seagrass mapping.Remote Sensing of Environment,250.
MLA	Coffer M.M.,et al."Performance across WorldView-2 and RapidEye for reproducible seagrass mapping".Remote Sensing of Environment 250(2020).