Land cover change detection by integrating object-based data blending model of Landsat and MODIS
Land cover change detection by integrating object-based data blending model of Landsat and MODIS
复制标题
通过集成Landsat和MODIS基于对象的数据混合模型进行土地覆盖变化检测
DOI：
10.1016/j.rse.2016.07.028
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发表时间：
2016-10-01
影响因子：
13.5
通讯作者：
Wu, Wenbin
中科院分区：
文献类型：
--
作者：
Lu, Miao;Chen, Jun;Wu, Wenbin
准确的土地覆盖变化信息对于全球变化研究、土地覆盖制图和生态系统管理至关重要。虽然有许多变化检测方法，但如果从不同季节获取数据，可能会出现伪变化，这对土地覆盖变化检测提出了重大挑战。本文提出了基于Landsat和MODIS数据混合模型的土地覆盖变化检测方法。最小映射单元（MMU）约束下的尺度参数估计（ESP）工具，以确定最佳尺度的Landsat图像分割。基于对象的时空植被指数解混模型（OB-STVIUM）利用空间分析和线性混合理论将MODIS NDVI分解为Landsat对象。在此基础上，提出了基于NDVI梯度差的变化检测方法（NDVI-GD），同时考虑了NDVI的形状差异和数值差异，实现了变化目标和非变化目标的检测。研究结果表明，在这项研究中提出的方法可以有效地检测变化的区域时，从不同的季节获取的陆地卫星图像。与时空自适应反射率融合模型（STARFM）和归一化植被指数线性混合增长模型（NDVI-LMGM）相比，OB-STVIUM模型对遥感影像的数量和获取时间不敏感，更适合于变化检测应用。(C)2016 Elsevier Inc. All rights reserved.
Accurate information on land cover changes is critical for global change studies, land cover mapping and ecosystem management Although there are numerous change detection methods, pseudo changes can occur if data are acquired from different seasons, which presents a significant challenge for land cover change detection. In this study, land cover change detection by integrating object-based data blending model of Landsat and MODIS is proposed to solve this issue. The Estimation of Scale Parameter (ESP) tool under Minimum Mapping Unit (MMU) restriction is employed to identify the optimal scale for Landsat image segmentation. The Object Based Spatial and Temporal Vegetation Index Unmixing Model (OB-STVIUM) disaggregates MODIS NDVIs to Landsat objects using the spatial analysis and the linear mixing theory. Then, the change detection method of NDVI Gradient Difference (NDVI-GD) is developed to detect change and no-change objects considering the NDVI shape and value differences simultaneously. The results of the study indicate that the approach proposed in this study can effectively detect change areas when Landsat images are acquired from different seasons. OB-STVIUM is more suitable for change detection application compared with the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and NDVI Linear Mixing Growth Model (NDVI-LMGM), because it is less sensitive to the number and acquisition time of Landsat images. (C) 2016 Elsevier Inc. All rights reserved.