...
首页> 外文期刊>International journal of remote sensing >Automatic unmixing of MODIS multi-temporal data for inter-annual monitoring of land use at a regional scale (Tensift, Morocco)
【24h】

Automatic unmixing of MODIS multi-temporal data for inter-annual monitoring of land use at a regional scale (Tensift, Morocco)

机译:自动分解MODIS多时相数据,以便在区域范围内对土地使用进行年度监视(摩洛哥,滕瑟夫特)

获取原文
获取原文并翻译 | 示例
           

摘要

The objective of this study is to develop an approach for monitoring land use over the semi-arid Tensift-Marrakech plain, a 3000 km~2 intensively cropped area in Morocco. In this objective, the linear unmixing method is adapted to process a 6-year archive of Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) 16-day composite data at 250 m spatial resolution. The result of the processing is a description of land use in terms of fractions of three predominant classes: orchard, non-cultivated area and annual crop. The typical signatures of land classes - endmembers - are retrieved on a yearly basis using an automated algorithm that detects the most pure pixels in the study area. The algorithm first extracts typical NDVI profiles as potential endmembers, then selects the profiles that have the best ability to reproduce the variability of MODIS NDVI time series over the study area. The endmembers appear stable over the 6 years of study and coherent with the vegetation seasonality of the three targeted land classes. Validation data allow us to quantify the error on land-use fractions to about 0.10 at 1 km resolution. Land-use estimates are consistent in space and time: the orchard class is stable, and differences in water availability (irri-gation and rainfall) partly explain a part of the inter-annual variations observed for the annual crop class. The advantages and drawbacks of the approach are discussed.
机译:这项研究的目的是开发一种方法来监测摩洛哥3000 km〜2的半干旱Tensift-Marrakech平原上的土地利用。在此目标中,线性分解方法适用于在250 m空间分辨率下处理为期6年的中分辨率成像光谱仪(MODIS)归一化植被指数(NDVI)16天复合数据的存档。处理的结果是按照三个主要类别的分数来描述土地利用:果园,非耕地面积和一年生作物。土地类别的典型特征-最终成员-每年使用自动算法进行检索,该算法可检测研究区域中最纯净的像素。该算法首先提取典型的NDVI轮廓作为潜在的末端成员,然后选择在研究区域内最能再现MODIS NDVI时间序列变异性的轮廓。最终成员在研究的6年中表现稳定,并且与三个目标土地类别的植被季节一致。验证数据使我们能够以1 km的分辨率将土地利用分数的误差量化为约0.10。土地利用的估计在空间和时间上是一致的:果园类别是稳定的,可用水量(灌溉和降雨)的差异部分解释了年度作物类别的年度间变化的一部分。讨论了该方法的优缺点。

著录项

  • 来源
    《International journal of remote sensing》 |2012年第6期|p.1325-1348|共24页
  • 作者单位

    CESBIO - Centre d'Etudes Spatiales de la Biosphere, 31401 Toulouse cedex 9, France;

    CESBIO - Centre d'Etudes Spatiales de la Biosphere, 31401 Toulouse cedex 9, France;

    CESBIO - Centre d'Etudes Spatiales de la Biosphere, 31401 Toulouse cedex 9, France;

    CESBIO - Centre d'Etudes Spatiales de la Biosphere, 31401 Toulouse cedex 9, France FSSM - Faculte des Sciences Semlalia, Universite Cadi Ayyad, BP 2390,Marrakech, Morocco;

    FSSM - Faculte des Sciences Semlalia, Universite Cadi Ayyad, BP 2390,Marrakech, Morocco;

    CESBIO - Centre d'Etudes Spatiales de la Biosphere, 31401 Toulouse cedex 9, France FSSM - Faculte des Sciences Semlalia, Universite Cadi Ayyad, BP 2390,Marrakech, Morocco;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

相似文献

  • 外文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号