Near Real-Time Browsable Landsat-8 Imagery

被引:2
|
作者
Liu, Cheng-Chien [1 ,2 ]
Nakamura, Ryosuke [3 ]
Ko, Ming-Hsun [1 ]
Matsuo, Tomoya [1 ]
Kato, Soushi [3 ]
Yin, Hsiao-Yuan [4 ]
Huang, Chung-Shiou [5 ]
机构
[1] Natl Cheng Kung Univ, Global Earth Observat & Data Anal Ctr, 1 Ta Hsueh Rd, Tainan 701, Taiwan
[2] Natl Cheng Kung Univ, Dept Earth Sci, 1 Ta Hsueh Rd, Tainan 701, Taiwan
[3] Natl Inst Adv Ind Sci & Technol, Informat Technol Res Inst, Tsukuba, Ibaraki 3058568, Japan
[4] Council Agr, Soil & Water Conservat Bur, Debris Flow Disaster Prevent Ctr, Nantou 54044, Taiwan
[5] Council Agr, Execut Yuan, Hsinchu Forest Dist Off, Hsinchu 30046, Taiwan
来源
REMOTE SENSING | 2017年 / 9卷 / 01期
关键词
Landsat-8; near real-time; browsable image; pan-sharpening; adaptive contrast enhancement; Openlayers; Google Maps; Google Earth; Taiwan; Formosat-2; CLOUD SHADOW; MISSION;
D O I
10.3390/rs9010079
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The successful launch and operation of Landsat-8 extends the remarkable 40-year acquisition of space-based land remote-sensing data. To respond quickly to emergency needs, real-time data are directly downlinked to 17 ground stations across the world on a routine basis. With a size of approximately 1 Gb per scene, however, the standard level-1 product provided by these stations is not able to serve the general public. Users would like to browse the most up-to-date and historical images of their regions of interest (ROI) at full-resolution from all kinds of devices without the need for tedious data downloading, decompressing, and processing. This paper reports on the Landsat-8 automatic image processing system (L-8 AIPS) that incorporates the function of mask developed by United States Geological Survey (USGS), the pan-sharpening technique of spectral summation intensity modulation, the adaptive contrast enhancement technique, as well as the Openlayers and Google Maps/Earth compatible superoverlay technique. Operation of L-8 AIPS enables the most up-to-date Landsat-8 images of Taiwan to be browsed with a clear contrast enhancement regardless of the cloud condition, and in only one hour's time after receiving the raw data from the USGS Level 1 Product Generation System (LPGS). For any ROI in Taiwan, all historical Landsat-8 images can also be quickly viewed in time series at full resolution (15 m). The debris flow triggered by Typhoon Soudelor (8 August 2015), as well as the barrier lake formed and the large-scale destruction of vegetation after Typhoon Nepartak (7 July 2016), are given as three examples of successful applications to demonstrate that the gap between the user's needs and the existing Level-1 product from LPGS can be bridged by providing browsable images in near real-time.
引用
收藏
页数:13
相关论文
共 50 条
  • [1] Analysis of Coastline Extraction from Landsat-8 OLI Imagery
    Liu, Yaolin
    Wang, Xia
    Ling, Feng
    Xu, Shuna
    Wang, Chengcheng
    WATER, 2017, 9 (11)
  • [2] Effects of Training Samples and Classifiers on Classification of Landsat-8 Imagery
    Shang, Ming
    Wang, Shi-Xin
    Zhou, Yi
    Du, Cong
    JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING, 2018, 46 (09) : 1333 - 1340
  • [3] VOLCANOSTRATIGRAPHY INTERPRETATION OF MAMUJU AREA BASED ON LANDSAT-8 IMAGERY ANALYSIS
    Indrastomo, Frederikus Dian
    Sukadana, I. Gde
    Saepuloh, Asep
    Harsolumakso, Agus Handoyo
    Kamajati, Dhatu
    EKSPLORIUM-BULETIN PUSAT TEKNOLOGI BAHAN GALIAN NUKLIR, 2015, 36 (02): : 71 - 88
  • [4] Using ResWnet for semantic segmentation of active wildfires from Landsat-8 imagery
    Afsar, Rayan
    Sultana, Aqsa
    Abouzahra, Shaik N.
    Aspiras, Theus
    Asari, Vijayan K.
    PATTERN RECOGNITION AND PREDICTION XXXV, 2024, 13040
  • [5] Monitoring Plastic-Mulched Farmland Using Landsat-8 OLI Imagery
    Hasituya
    Chen Zhong-xin
    Wu Wen-bin
    Qing Huang
    2015 FOURTH INTERNATIONAL CONFERENCE ON AGRO-GEOINFORMATICS, 2015,
  • [6] Monitoring policy-driven crop area adjustments in northeast China using Landsat-8 imagery
    Yang, Lingbo
    Wang, Limin
    Huang, Jingfeng
    Mansaray, Lamin R.
    Mijiti, Ruzemaimaiti
    INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2019, 82
  • [7] Object-based classification approach for greenhouse mapping using Landsat-8 imagery
    Wu Chaofan
    Deng Jinsong
    Wang Ke
    Ma Ligang
    Tahmassebi, Amir Reza Shah
    INTERNATIONAL JOURNAL OF AGRICULTURAL AND BIOLOGICAL ENGINEERING, 2016, 9 (01) : 79 - 88
  • [8] Alluvial fan surface ages recorded by Landsat-8 imagery in Owens Valley, California
    D'Arcy, Mitch
    Mason, Philippa J.
    Roda-Boluda, Duna C.
    Whittaker, Alexander C.
    Lewis, James M. T.
    Najorka, Jens
    REMOTE SENSING OF ENVIRONMENT, 2018, 216 : 401 - 414
  • [9] Disaggregation of Landsat-8 Thermal Data Using Guided SWIR Imagery on the Scene of a Wildfire
    Cho, Kangjoon
    Kim, Yonghyun
    Kim, Yongil
    REMOTE SENSING, 2018, 10 (01):
  • [10] Landsat-8 data processing evolution
    Morfitt, Ron A.
    Choate, Mike J.
    Barsi, Julia A.
    EARTH OBSERVING SYSTEMS XIX, 2014, 9218