Department of Civil Engineering and Geomatics, Faculty of Engineering and Technology, Cyprus University of Technology, Saripolou 2-8, 3036 Limassol, Cyprus.
Eratosthenes Centre of Excellence, Saripolou 2-8, 3036 Limassol, Cyprus.
Sensors (Basel). 2021 Mar 8;21(5):1884. doi: 10.3390/s21051884.
Urban sprawl can negatively impact the archaeological record of an area. In order to study the urbanisation process and its patterns, satellite images were used in the past to identify land-use changes and detect individual buildings and constructions. However, this approach involves the acquisition of high-resolution satellite images, the cost of which is increases according to the size of the area under study, as well as the time interval of the analysis. In this paper, we implemented a quick, automatic and low-cost exploration of large areas, for addressing this purpose, aiming to provide at a medium resolution of an overview of the landscape changes. This study focuses on using radar Sentinel-1 images to monitor and detect multi-temporal changes during the period 2015-2020 in Limassol, Cyprus. In addition, the big data cloud platform, Google Earth Engine, was used to process the data. Three different change detection methods were implemented in this platform as follow: (a) vertical transmit, vertical receive (VV) and vertical transmit, horizontal receive (VH) polarisations pseudo-colour composites; (b) the Rapid and Easy Change Detection in Radar Time-Series by Variation Coefficient (REACTIV) Google Earth Engine algorithm; and (c) a multi-temporal Wishart-based change detection algorithm. The overall findings are presented for the wider area of the Limassol city, with special focus on the archaeological site of "Amathus" and the city centre of Limassol. For validation purposes, satellite images from the multi-temporal archive from the Google Earth platform were used. The methods mentioned above were able to capture the urbanization process of the city that has been initiated during this period due to recent large construction projects.
城市扩张可能会对一个地区的考古记录产生负面影响。为了研究城市化进程及其模式，过去曾使用卫星图像来识别土地利用变化，并检测单个建筑物和构筑物。然而，这种方法涉及获取高分辨率卫星图像，其成本随着研究区域的大小以及分析的时间间隔而增加。在本文中，我们实施了一种快速、自动且低成本的大面积探索方法，旨在提供中等分辨率的景观变化概览。本研究侧重于使用雷达 Sentinel-1 图像来监测和检测塞浦路斯拉纳卡地区在 2015-2020 年期间的多时相变化。此外，还使用了大数据云平台谷歌地球引擎来处理数据。该平台实施了三种不同的变化检测方法，如下所示：（a）垂直发射、垂直接收（VV）和垂直发射、水平接收（VH）极化假彩色合成；（b）谷歌地球引擎中的快速和简单雷达时间序列变化检测算法（REACTIV）；以及（c）基于 Wishart 的多时相变化检测算法。总体结果展示了拉纳卡市的更大范围，特别关注“阿玛图斯”考古遗址和拉纳卡市中心。为了验证目的，使用了谷歌地球平台多时相档案中的卫星图像。上述方法能够捕捉到由于最近的大型建设项目而在这段时间开始的城市城市化进程。
