A GIS OPEN-SOURCE APPLICATION TO ENHANCE THE IDENTIFICATION OF ARCHAEOLOGICAL CROP MARKS USING REMOTE SENSING DATA

• Type: Article in International Conference Proceedings Book
• Date: 2021

Authors

Lia Duarte (Author) FCUP; Sanchez, JG (Author) Other; Fonte, J (Author) Other; Ana Teodoro (Author) FCUP

Conference proceedings International

Title: EARTH RESOURCES AND ENVIRONMENTAL REMOTE SENSING/GIS APPLICATIONS XII

Conference on Earth Resources and Environmental Remote Sensing/GIS Applications XII / SPIE Remote Sensing Conference

ELECTR NETWORK, SEP 13-18, 2021

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Other information

Authenticus ID: P-00V-PDR

DOI: 10.1117/12.2599743

Abstract (EN): The use of vegetation indices to highlight archaeological features from remote sensing (RS) sensors is increasingly in demand, especially due to the possibility of obtaining high-resolution multispectral images with drones. The objective of this work is divided in two steps: i) to test techniques for the semi-automatic identification of crop marks in a study zone, in Salvada, Portugal, and; ii) to develop a Geographical Information Systems (GIS) open-source application, named ArchMarks, under QGIS software, to enhance the identification of archaeological crop marks using RS data/techniques, integrating the automatic creation of indices and the identification techniques tested before. ArchMarks aims to compute several vegetation and soil indices from multispectral imagery considering four bands (Red, Green and Blue and Near InfraRed (NIR)) for enhancing the identification of archaeological crop marks. In order to define the best approach to implement in the plugin, some tests were performed with two algorithms available in QGIS: ContrastEnhancement and WatershedSegmentation. High-resolution aerial imagery (25-50 cm spatial resolution) was obtained from Web Map Service (WMS) services in addition to Sentinel-2 satellite image, whose spatial resolution is of 10 meters in the Red Green Blue-Near InfraRed bands. The application is free and can be adapted to other region of interest. In the future, the plugin will be improved with a methodology to download remote sensing data (aerial images and satellite data) from WMS sources, band stacking, and machine learning algorithms, such as Support Vector Machine (SVM) algorithm, to automatically classify archaeological traces on the vegetation or bare soil.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 9