2017年阿拉斯加与加拿大AirSWOT水体监测数据概览

102 浏览量更新于2024-08-03 收藏 785KB PDF 举报

"ABoVE-AirSWOT-Water-Masks数据介绍文件.pdf" 是一个关于2017年阿拉斯加和加拿大上空的AirSWOT彩色红外图像水体数据集的详细文档，该数据集包含了从7月9日至8月17日收集的1米分辨率的绿光、红光和近红外(NIR)三波段图像。这些正射影像图由NASA的Beechcraft Super King Air B200飞机上的Cirrus Designs数字摄像系统(DCS)在大约8-11千米的高度拍摄，覆盖了包括萨斯喀彻温河、萨斯卡通、伊努维克、育空河等在内的多个地点。此数据集的主要目的是为了验证地表水范围，为NASA的Arctic-Boreal Vulnerability Experiment (ABoVE)项目的AirSWOT Ka波段雷达回波提供辅助。AirSWOT是一种先进的遥感技术，能够精确测量地表水的分布和变化，对于理解和研究极地地区的气候变化尤其重要。数据集使用了303个地面控制点(GCP)进行地理校准，确保了数据的准确性。数据集中提供的内容包括： 1. 一个保守的开放水面掩模，用于从同步注册的AirSWOT Ka波段干涉数据中提取水面高度信息。 2. 高分辨率(1米)的水体分布地图，用于识别大于40平方米的水体。这涵盖了ABoVE基础飞行线上的水体分布。通过一种基于归一化差分水指数(NDWI)的半自动化水体分类算法，从飞行线路覆盖的23,380平方公里区域中，总共映射出了3,167平方公里的开放水面。检测到的水体大小范围从40平方米到15平方公里不等。这些数据集和图像块采用手动选择的地面控制点进行地理定位，确保了空间参考的精确性。它们为科学家和研究人员提供了详细的信息，以研究这些地区的水体动态，监测气候变化的影响，以及评估阿拉斯加和加拿大地表水资源的脆弱性。这个数据集是NASA地球科学领域的一个重要贡献，它为研究极地地区水体变化、冰川消融、湿地演变以及环境变迁提供了宝贵的数据支持。用户可以通过ORNL DAAC (Oak Ridge National Laboratory Distributed Active Archive Center)获取这些数据，并利用它们进行气候模型验证、环境变化分析和其他相关科研工作。

Feedback

Search ORNL DAAC

EARTHDATA

Other DAACs ▾

DAAC Home > Get Data > NASA Projects > Arctic-Boreal Vulnerability Experiment (ABoVE) > User guide

ABoVE: AirSWOT Water Masks from Color-Infrared

Imagery over Alaska and Canada, 2017

Get Data

Documentation Revision Date: 2020-03-02

Dataset Version: 1

Summary

This dataset provides 1) a conservative open water mask for future water surface elevation (WSE) extraction from the co-registered

AirSWOT Ka-band interferometry data, and 2) high-resolution (1 m) water body distribution maps for water bodies greater than 40 m2

along the NASA Arctic-Boreal Vulnerability Experiment (ABoVE) foundational flight lines. The masks and maps were derived from

georeferenced three-band orthomosaics generated from individual images collected during the flights and a semi-automated water

classification algorithm based on the Normalized Difference Water Index (NDWI). In total, 3,167 km2 of open water were mapped from

23,380 km2 of flight lines spanning 23 degrees of latitude. Detected water body sizes range from 40 m2 to 15 km2. The image tiles

were georeferenced using manually selected ground control points (GCPs). Comparison with manually digitized open water boundaries

yields an overall open-water classification accuracy of 98.0%.

Green, red, and near-infrared (NIR) digital imagery was collected with a Cirrus Designs Digital Camera System (DCS) mounted on a

B200 aircraft from approximately 8-11 km altitude over the following areas: Saskatchewan River, Saskatoon, Inuvik, Yukon River

including Yukon Flats, Sagavanirktok River, Arctic Coastal Plain, Old Crow Flats, Peace-Athabasca Delta, Slave River, Athabasca River,

Yellowknife, Great Slave Lake, Mackenzie River and Delta, Daring Lake, and other selected locations. Most locations were imaged twice

during two flight campaigns in Canada and Alaska extending roughly SE-NW then NW-SE up to a month apart.

Water masks are provided for 330 individual ABoVE grid cells (ChxxCvxx) in three data file formats: 1-m resolution GeoTIFF (*.tif) files,

as converted to Shapefiles (*.shp), and as converted to Google Earth format (*.kml). Shapefiles are provided in compressed format

(*.zip). There are four value-added water mask shapefiles (*.shp) produced by combining all of the shapefiles and with additional

processing and filtering of water bodies. These four files are also provided in Google Earth (*.kml) format. Characteristics of the original

orthomosaic images (coverage, quality and cloud flags, georeferencing data) and image processing metadata are provided in a

comma-separated (.csv) file, as converted to a Shapefile (.shp), and as converted to Google Earth format (.kml). There are 1,001 data

files in total.

Figure 1: Example of color-infrared imagery shown alongside the corresponding semi-automated open water classification, location:

Old Crow Flats, Alaska.

Citation

Kyzivat, E.D., L.C. Smith, L.H. Pitcher, J.V. Fayne, S.W. Cooley, M.G. Cooper, S. Topp, T. Langhorst, M.E. Harlan, C.J. Gleason, and T.M.

Pavelsky. 2019. ABoVE: AirSWOT Water Masks from Color-Infrared Imagery over Alaska and Canada, 2017. ORNL DAAC, Oak Ridge,

Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1707

Table of Contents

1. Dataset Overview

About Us Get Data Submit Data Tools Resources Help

ORNL DAAC Home NASA Home

下载后可阅读完整内容，剩余7页未读，立即下载

此星光明

粉丝: 7w+
资源: 1028

2017年阿拉斯加与加拿大AirSWOT水体监测数据概览

气象大数据资料.pdf

Learning-to-correct-segmentation-masks

前端项目-angular-input-masks.zip

face-id-with-medical-masks:带医用口罩的人脸ID识别

lbp代码matlab-Spoofing-Face-Recognition-With-3D-Masks:MatLab项目使用3D打印蒙版识别真

11k-hands-masks.7z

msm-rng.rar_Masks

operamasks-ui-2.0-demo--.zip

EDGE DETECTION OF BINARY IMAGES USING THE METHOD OF MASKS.pdf

msm-rng.rar_Masks_msm

最新资源