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首页> 外文会议>Remote sensing for environmental monitoring, GIS applications, and geology VIII >Removing Long-Term Errors from the AVHRR Observation Based on Normalized Difference Vegetation Index (NDVI)
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Removing Long-Term Errors from the AVHRR Observation Based on Normalized Difference Vegetation Index (NDVI)

机译：基于归一化植被指数（NDVI）的AVHRR观测值消除长期误差

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摘要

This paper investigates Normalized Difference Vegetation Index (NDVI) stability in the NOAA/NESDIS Global Vegetation Index (GVI) data during 1982-2003. Advanced Very High Resolution Radiometer (AVHRR) weekly data for the five NOAA afternoon satellites for the China dataset is studied, for it includes a wide variety of different ecosystems represented globally. It was found that data for the years 1988, 1992, 1993, 1994, 1995 and 2000 are not stable enough compared to other years because of satellite orbit drift, and AVHRR sensor degradation. It is assumed that data from NOAA-7(1982, 1983), NOAA-9 (1985, 1986), NOAA-11 (1989, 1990), NOAA-14 (1996, 1997), and NOAA-16 (2001, 2002) to be standard because these satellite's equator crossing time fall within 1330 and 1500, and hence maximizing the value of coefficients. The crux of the proposed correction procedure consists of dividing standard year's data sets into two subsets. The subset 1 (standard data correction sets) is used for correcting unstable years and then corrected data for this years compared with the standard data in the subset 2 (standard data validation sets). In this paper, we apply empirical distribution function (EDF) to correct this deficiency of data for the affected years. We normalize or correct NDVI data by the method of EDF compared with the standard. Using these normalized values, we estimate new NDVI time series which provides NDVI data for these years that match in subset 2 that is used for data validation.
机译：本文研究了1982-2003年NOAA / NESDIS全球植被指数（GVI）数据中的归一化植被指数（NDVI）稳定性。研究了中国数据集的五颗NOAA下午卫星的高级超高分辨率辐射计（AVHRR）每周数据，因为它包含了全球范围内的各种不同生态系统。结果发现，由于卫星轨道漂移和AVHRR传感器退化，1988、1992、1993、1994、1995和2000年的数据与其他年份相比不够稳定。假定数据来自NOAA-7（1982，1983），NOAA-9（1985，1986），NOAA-11（1989，1990），NOAA-14（1996，1997）和NOAA-16（2001，2002） ）作为标准，因为这些卫星的赤道穿越时间在1330和1500之间，从而使系数的值最大化。提出的更正程序的症结在于将标准年的数据集分为两个子集。子集1（标准数据校正集）用于校正不稳定年份，然后将本年的校正数据与子集2（标准数据验证集）中的标准数据进行比较。在本文中，我们应用经验分布函数（EDF）来纠正受影响年份的数据不足。与标准相比，我们采用EDF方法对NDVI数据进行归一化或校正。使用这些归一化的值，我们估计新的NDVI时间序列，该序列提供了这些年份的NDVI数据，这些数据与用于数据验证的子集2相匹配。

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  • 来源
  • 会议地点 Cardiff(GB)
  • 作者

    Md Z Rahman; Leonid Roytman; Atiqur Rahman; Felix Kogan; Runa Jesmin;

  • 作者单位

    Department of Mathematics, LaGuardia Community College of the City University of New York,31-10 Thomson Avenue, Long Island City, NY-11101;

    Department of Electrical Engineering, The City College of the City University of New York, 138 street convent Avenue, New York, NY-10031;

    Department of Electrical Engineering, The City College of the City University of New York, 138 street convent Avenue, New York, NY-10031;

    National Oceanic and Atmospheric Administration (NOAA) Silver Spring, MD 20910;

    Department of Computer Science, King's College London (University of London), Strand London WC2R 2LS, London, UK;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 环境遥感;
  • 关键词

    vegetation; data; stability; satellite; ecosystem;

    机译：植被;数据;稳定性;卫星;生态系统;

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