Abstract The change detection via remote sensing offers a cost-effective solution to monitor the earth surface variations and manage natural resources. Change vector analysis (CVA) is one of the most appropriate suitable amongst various change detection techniques that attract the interest of researchers due to its potential and overall applicability. However, the effectiveness of recently developed CVA methods is yet to be explored over the mountainous region like Himalayas with most commonly used satellite sensors such as MODIS and Landsat-8. In the present analysis, we have performed the qualitative and quantitative analysis of advanced CVA algorithms such as improved CVA (ICVA), posterior probability-based CVA (CVAPS), median CVA (MCVA) and fuzzy-based CVA (FCVA). Two experiments were conducted on different study sites of western Himalayas (Himachal Pradesh, India) using topographically corrected MODIS and Landsat-8 datasets. The experimental outcomes of change maps confirm the effectiveness of FCVA (86.80–87.6%) as compared to MCVA (78.4–83.6%), CVAPS (82.8–84%) and ICVA (73.6–80.4%). This study provides a comprehensive framework to explore the potential of different CVA algorithms to detect the land use and land cover changes especially over rugged terrain region.

中文翻译：

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使用印度喜马拉雅西部的 MODIS 和 Landsat-8 数据对地形衍生的 CVA 算法进行定性和定量分析

摘要 遥感变化检测为监测地表变化和管理自然资源提供了一种经济有效的解决方案。变化向量分析 (CVA) 是各种变化检测技术中最合适的一种，因其潜力和整体适用性而吸引了研究人员的兴趣。然而，最近开发的 CVA 方法的有效性尚未在喜马拉雅山等山区使用最常用的卫星传感器（如 MODIS 和 Landsat-8）进行探索。在目前的分析中，我们对先进的 CVA 算法进行了定性和定量分析，例如改进的 CVA (ICVA)、基于后验概率的 CVA (CVAPS)、中值 CVA (MCVA) 和基于模糊的 CVA (FCVA)。使用经地形校正的 MODIS 和 Landsat-8 数据集在喜马拉雅西部（印度喜马偕尔邦）的不同研究地点进行了两项实验。变化图的实验结果证实了 FCVA (86.80–87.6%) 与 MCVA (78.4–83.6%)、CVAPS (82.8–84%) 和 ICVA (73.6–80.4%) 相比的有效性。本研究提供了一个综合框架来探索不同 CVA 算法在检测土地利用和土地覆盖变化方面的潜力，尤其是在崎岖地形区域。