...
首页> 外文期刊>Remote Sensing of Environment: An Interdisciplinary Journal >Monitoring shrubland habitat changes through object-based change identification with airborne multispectral imagery
【24h】

Monitoring shrubland habitat changes through object-based change identification with airborne multispectral imagery

机译:通过机载多光谱图像通过基于对象的变化识别来监测灌木丛生境变化

获取原文
获取原文并翻译 | 示例
           

摘要

An object-based approach to generating shrub cover change maps of potential use for monitoring shrubland habitat reserves was developed and tested. A high fidelity, bi-temporal airborne image data set was generated through frame-based image acquisition, precise image-to-image registration, radiometric normalization, and selection of near-anniversary image acquisition dates with similar precipitation conditions prior to both image acquisitions. Image segmentation and classification processes were applied to the bi-temporal layer stack of very high spatial resolution visible and near infrared (V/NIR) image data, such that shrub change objects were delineated and identified directly. Image segments derived from the bi-temporal V/NIR image data set having I in spatial resolution delineated most shrub change features in a qualitatively realistic manner. A Standard Nearest Neighbor classifier with segment mean and standard deviation measures of Red, NIR, and normalized difference vegetation index (NDVI) image features yielded the shrub change map that agreed more closely with reference data than the classifier based on fuzzy membership functions. The overall accuracy and kappa statistics for the optimal shrub change map were 0.83 and 0.64, respectively, with the predominant error being associated with "over-classification" of no-change objects as some type of shrub change. No statistical difference in accuracies of three- and five-class maps was found, suggesting that changes in true shrubs and sub-shrubs within coastal sage scrub vegetation communities can be differentiated reliably. A net 5% loss of shrub cover was determined for the 1998-2005 period from the shrub change map of the study area. The greatest decrease and net loss of shrub cover occurred within the urban edge zone and within flat-lying areas. Patterns of shrub loss appear to be more related to anthropogenic disturbance than effects of the severe seven-year drought that occurred between image acquisition dates. (C) 2007 Elsevier Inc. All rights reserved.
机译:开发并测试了一种基于对象的方法来生成灌木覆盖变化图的潜在用途,以用于监测灌木丛生境保护区。通过基于帧的图像采集,精确的图像间图像配准,放射线归一化以及在两个图像采集之前选择具有相似降水条件的近周年图像采集日期,来生成高保真度的双时空机载图像数据集。将图像分割和分类过程应用于具有非常高空间分辨率的可见光和近红外(V / NIR)图像数据的双时间层堆栈,以便直接描绘和识别灌木变化对象。从空间分辨率为I的双时态V / NIR图像数据集得出的图像片段以定性逼真的方式描绘了大多数灌木变化特征。使用具有Red,NIR和归一化植被指数(NDVI)图像特征的分段均值和标准差的标准最近邻分类器,可以得出灌木变化图,与基于模糊隶属函数的分类器相比,该灌木变化图与参考数据更加吻合。最佳灌木变化图的总体准确度和kappa统计分别为0.83和0.64,其中主要误差与无变化对象的“过度分类”有关,即某种灌木类型的变化。没有发现三级和五级图的准确性的统计差异,这表明可以可靠地区分沿海鼠尾草灌木丛植被群落中真实灌木和亚灌木的变化。根据研究区域的灌木变化图,确定1998年至2005年期间灌木覆盖净损失5%。灌木覆盖的最大减少和净损失发生在城市边缘地区和平坦地区。灌木丛丢失的模式似乎与人为干扰有关,而不是图像获取日期之间发生的七年严重干旱的影响。 (C)2007 Elsevier Inc.保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 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