Transilvania University of Brasov, Brasov, Romania,
Environ Monit Assess. 2014 May;186(5):2685-99. doi: 10.1007/s10661-013-3571-y. Epub 2013 Dec 10.
This paper assesses the image differencing technique for the Normalized Difference Vegetation Index (NDVI), the second principal component (PC2), and the TM 4 band (TM 4), as well as the post-classification comparison (PCC) in order to analyze the land use/land cover changes in the South-East Transilvania, Romania. The analysis was performed using two frames from Landsat 5 TM satellite images acquired on August 5, 1993 and July 24, 2009. After applying the NDVI, PC2, and TM 4 image differencing techniques, the images obtained were transformed into change/no change maps. The thresholds identified to highlight the changes were set at 0.6 s for NDVI and 0.7 s for PC2 and TM 4. Before applying the PCC technique, the satellite images were classified through the supervised classification method. The overall accuracy obtained was 85.91 % and the kappa statistics 0.8249 for 1993, 88.18 % and 0.8497 for 2009, respectively. The assessment of the changes detection methods in the studied area shows that the first place is occupied by NDVI image differencing with an overall accuracy of 83.80 %, followed by PCC method with 83.20 %, PC2 difference with an overall accuracy of 81.60 %, and TM 4 difference with an overall accuracy of 79.40 %.
本文评估了归一化差异植被指数 (NDVI)、第二主成分 (PC2) 和 TM 4 波段 (TM 4) 的图像差值技术，以及后分类比较 (PCC)，以分析罗马尼亚东南部特兰西瓦尼亚的土地利用/土地覆盖变化。分析使用了 1993 年 8 月 5 日和 2009 年 7 月 24 日获取的 Landsat 5 TM 卫星图像的两个帧。在应用 NDVI、PC2 和 TM 4 图像差值技术后，获得的图像被转换为变化/无变化地图。为突出变化而确定的阈值分别设定为 NDVI 的 0.6 s 和 PC2 和 TM 4 的 0.7 s。在应用 PCC 技术之前，通过监督分类方法对卫星图像进行分类。1993 年获得的总体精度为 85.91%，kappa 统计量为 0.8249，2009 年分别为 88.18%和 0.8497。对研究区域变化检测方法的评估表明，NDVI 图像差值以 83.80%的总体精度位居首位，其次是 PCC 方法（83.20%）、PC2 差值（81.60%）和 TM 4 差值（79.40%）。
