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From the Bottom Up: Assessing the Spectral Ability of Common Multispectral Sensors to Detect Surface Archaeological Deposits Using Field Spectrometry and Advanced Classifiers in the Shashi-Limpopo Confluence Area

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Abstract

This paper investigates the ability of six common multispectral sensors (GeoEye, Landsat 8 OLI, RapidEye, Sentinel-2, SPOT 5, and WorldView-2) to map archaeological sites typically inhabited by the farming communities of Southern Africa and characterized by surface features such as middens, non-vitrified dung, and vitrified dung. To achieve this, hyperspectral data collected in the field using a GER-1500 field spectroradiometer were resampled to the spectral resolutions of the selected sensors using the spectral library resampling tool in ENVI. Mean decrease in accuracy was used to assess the importance of both hyperspectral wavelengths and each band allocated to a multispectral sensor in discriminating the selected archaeological classes. Two predictive models based on the resampled hyperspectral data were developed in R using algorithms for support vector machine (SVM) and random forest (RF) classifiers. The results demonstrate that data resampled to the resolution of common multispectral sensors have the ability to predict surface archaeological features using RF and SVM classifiers. Important bands for predicting sites are mostly in the visible and shortwave infrared regions of the electromagnetic spectrum. The best performance was achieved with data resampled to the resolution of the Sentinel-2 sensor, which attained 81.90% and 92.38% accuracy in both RF and SVM classifiers respectively. The predictions indicate the relevance of field spectroscopy studies to better understand the spectral models critical for archaeological sites detection.

Résumé

Cet article étudie la capacité de six courants capteurs multispectraux (GeoEye, Landsat 8 OLI, RapidEye, Sentinel-2, SPOT 5 et WorldView-2) les plus appropriés pour la cartographie des sites archéologiques habités par les communautés agricoles de l’Afrique australe. Ces sites ont des caractéristiques de surface spécifiques, telles que des amas, de bouse non vitrifiée et de bouse vitrifiée. Pour y parvenir, des données hyperspectrales ont été recueillies sur le terrain à l’aide d’un spectroradiomètre de champ GER-1500. Les données ont ensuite été rééchantillonnées aux résolutions spectrales de les capteurs sélectionnés. Cela s’est fait à l’aide de l’outil de rééchantillonnage des bibliothèques spectrales integré dans l’ENVI. La diminution moyenne de la precision a été utilisée pour évaluer l’importance des longueurs d’onde hyperspectrales et de chaque bande attribuée à un capteur multispectral, afin de distinguer les classes archéologiques susmentionnées. Deux modèles prédictifs basés sur les données hyperspectrales rééchantillonnées ont été développés dans le R, en utilisant des algorithmes classificateurs « support vector machine » (SVM) et « random forest » (RF). Les résultats ont montré que les données rééchantillonnées à la résolution des capteurs multispectraux courants permettent de prédire les caractéristiques archéologiques de surface à l’aide de classificateurs RF et SVM. Les bandes importantes pour la prédiction des sites étaient principalement dans les régions de l’infrarouge visible et à ondes courtes du spectre électromagnétique. Les meilleures performances ont été obtenues avec des données rééchantillonnées à la résolution du capteur Sentinel-2, qui ont atteint une précision de 81,90% et 92,38% dans les classificateurs RF et SVM. Les prédictions indiquent la pertinence des études de spectroscopie de terrain pour la compréhension des modèles spectraux les plus importants pour la détection des sites archéologiques.

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Acknowledgments

We would like to thank Inos Dhau, Tshekiso Kgosietsile, and Andani Gangashe for their assistance during data collection. We are also grateful to SANParks for allowing access to the Mapungubwe National Park and the DeBeers Group (through Duncan MacFadyen) for allowing access to the Venetia Nature Reserve and use of the research facility. The authors are also grateful to Prof. Thomas Huffman for availing his data and devoting his time to take us through the study area. Special thanks to Lesego Madisha (former archaeologist at SANParks, Mapungubwe) for her kind assistance and SANParks Cultural Heritage Manager Crispen Chauke and the Venetia Nature Reserve staff for their help.

Funding

This study was funded by the University of Botswana training department and the University of the Witwatersrand.

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  1. Archaeology Unit, University of Botswana, Plot 4775 Notwane Rd, Gaborone, Botswana

    Olaotse L. Thabeng

  2. School of Geography, Archaeology and Environmental Studies, University of the Witwatersrand, Johannesburg, 2050, South Africa

    Olaotse L. Thabeng, Stefania Merlo & Elhadi Adam

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  1. Olaotse L. Thabeng
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Archaeological time period: Tenth to nineteenth century AD

Country and region discussed: Botswana, South Africa, and Zimbabwe, Southern Africa

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Thabeng, O.L., Merlo, S. & Adam, E. From the Bottom Up: Assessing the Spectral Ability of Common Multispectral Sensors to Detect Surface Archaeological Deposits Using Field Spectrometry and Advanced Classifiers in the Shashi-Limpopo Confluence Area. Afr Archaeol Rev 37, 25–49 (2020). https://doi.org/10.1007/s10437-020-09372-z

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