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

Thabeng, Olaotse Lokwalo; Merlo, Stefania; Adam, Elhadi

doi:10.1007/s10437-020-09372-z

Cited by 6 publications

(6 citation statements)

References 149 publications

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“…In recent years, advances in the technologies and datasets available for airborne remote sensing have expanded applications of spatial data for African archaeology. Airborne remote sensing (multi‐spectral imagery, Synthetic Aperture Radar [SAR] and Light Detection and Ranging [LiDAR]) alongside survey, near‐surface geophysics, and GIS analysis are increasingly used by archaeologists across the continent (Klehm & Gokee, 2020), often in combination with ground‐based techniques (Thabeng et al, 2020). Remote sensing for the identification of settlement distribution patterns based on aerial and satellite photos is common in open (vegetation) landscapes, especially in North Africa (Biagetti et al, 2017; Parcak et al, 2017) and southern Africa (Davis & Douglass, 2020; Sadr, 2016; Thabeng et al, 2019).…”

Section: Spatial Data and Remote Sensing In African Archaeologymentioning

confidence: 99%

“…By employing tools capable of recording the microtopography beneath foliage in a way that visible photography cannot, this methodology can be used to identify anomalous landforms and features related to human activity, such as house platforms, walls, and sunken or cut features like pits, ditches and quarries. Multispectral datasets are increasingly employed to identify archaeological sites and to build predictive models of site locations based on topographic and environmental contexts (Khalaf & Insoll, 2019;Reid, 2016Reid, , 2020Thabeng et al, 2020). Several of these have involved the use of high-resolution imagery to flag core settlement areas and related hinterlands based on the environmental impact of cultivation and activity on vegetative health.…”

Section: Spatial Data and Remote Sensing In African Archaeologymentioning

confidence: 99%

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Integrating spatial and legacy data to understand archaeological sites in their landscape. A case study from Unguja Ukuu, Zanzibar

Fitton

Sulas

Lisowski

et al. 2022

Archaeological Prospection

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Spatial analysis is paramount for understanding, monitoring, and conserving ancient settlements and cultural landscapes. Advancing remote sensing and prospection techniques are expanding the methodological frame of archaeological settlement analysis by enabling remote, landscape-scale approaches to mapping and investigation. Whilst particularly effective in arid lands and areas with sparse or open ground cover, such as vegetation and buildings, these approaches remain peripheral in tropical environments because of technical and contextual challenges. In tropical Eastern Africa, for example, scales, resolution and visibility are often compromised by thick vegetation cover, inadequate access to, if not lack of, imagery resources and technologies, and the availability of comparative archaeological data for interpretation. This

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Section: Spatial Data and Remote Sensing In African Archaeologymentioning

confidence: 99%

Section: Spatial Data and Remote Sensing In African Archaeologymentioning

confidence: 99%

Integrating spatial and legacy data to understand archaeological sites in their landscape. A case study from Unguja Ukuu, Zanzibar

Fitton

Sulas

Lisowski

et al. 2022

Archaeological Prospection

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“…Thabeng and colleagues further explore the possibilities of detecting these ancient farming sites using hyperspectral analysis (Thabeng et al, 2020;. They find that collecting hyperspectral measurements of archaeological deposits on the ground can be used to train machine learning algorithms to directly detect these features in satellites, even those with coarser spatial resolutions (e.g., Sentinel-2, Landsat, etc.).…”

Section: Machine Learning and The Direct Detection Of Non-structural Archaeological Elementsmentioning

confidence: 99%

“…Recently, there have been attempts to using machine learning for predictive modeling and the detection of subtle archaeological deposits with few -if any -structural remains. Some examples are "indirect" approaches (sensu Howey et al 2020) for locating archaeological materials (e.g., Davis et al 2020a), while others have successfully detected features like dung deposits and remnants of farming communities where no structures still stand (e.g., Thabeng et al, 2020;. For these "direct" approaches for archaeological prospection, the use of multi-and hyper-spectral sensors at extremely high spatial resolutions was key to their success.…”

Section: Introductionmentioning

confidence: 99%

Theoretical Repositioning of Automated Remote Sensing Archaeology: Shifting from Features to Ephemeral Landscapes

Davis

2021

Journal of Computer Applications in Archaeology

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Automated remote sensing has made substantial breakthroughs for archaeological investigation. Over the past 20 years, the reliability of these methods has vastly improved, and the total number of practitioners has been increasing. Nonetheless, much of the work conducted, to date, focuses almost exclusively on specific topographic features and monumental architecture, ignoring the potential of automation to readily assess more ephemeral components of the archaeological record. Likewise, the emphasis on specific feature types overlooks broader landscape patterns, thus delegating automated remote sensing analysis as a method in and of itself, mostly disconnected from larger archaeological and anthropological investigations. Here, I review recent attempts to rectify this shortcoming by using automated analysis methods to record and explain ephemeral archaeological material distributions. While such research is limited, I argue that the successes achieved in these recent studies offer a pathway forward for automated remote sensing to become more fully integrated with archaeological work beyond the detection of specific topographically distinct features.

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“…We compiled 5 years of Sentinel-2 imagery by month using GEE (Gorelick et al, 2017; also see Supplementary File). SWIR has increased sensitivity to moisture content and can be used to distinguish mineral compositions of soils (Davis, 2017;Thabeng et al, 2020). One limitation of the Sentinel-2 data is that it has much lower spatial resolution (20 m).…”

Section: Vegetative Indicesmentioning

confidence: 99%

Remote Sensing Reveals Lasting Legacies of Land-Use by Small-Scale Foraging Communities in the Southwestern Indian Ocean

Davis

Douglass

2021

Front. Ecol. Evol.

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Archaeologists interested in the evolution of anthropogenic landscapes have productively adopted Niche Construction Theory (NCT), in order to assess long-term legacies of human-environment interactions. Applications of NCT have especially been used to elucidate co-evolutionary dynamics in agricultural and pastoral systems. Meanwhile, foraging and/or highly mobile small-scale communities, often thought of as less intensive in terms of land-use than agropastoral economies, have received less theoretical and analytical attention from a landscape perspective. Here we address this lacuna by contributing a novel remote sensing approach for investigating legacies of human-environment interaction on landscapes that have a long history of co-evolution with highly mobile foraging communities. Our study is centered on coastal southwest Madagascar, a region inhabited by foraging and fishing communities for close to two millennia. Despite significant environmental changes in southwest Madagascar’s environment following human settlement, including a wave of faunal extinctions, little is known about the scale, pace and nature of anthropogenic landscape modification. Archaeological deposits in this area generally bear ephemeral traces of past human activity and do not exhibit readily visible signatures of intensive land-use and landscape modification (e.g., agricultural modifications, monumental architecture, etc.). In this paper we use high-resolution satellite imagery and vegetative indices to reveal a legacy of human-landscape co-evolution by comparing the characteristics – vegetative productivity and geochemical properties – of archaeological sites to those of locations with no documented archaeological materials. Then, we use a random forest (RF) algorithm and spatial statistics to quantify the extent of archaeological activity and use this analysis to contextualize modern-day human-environment dynamics. Our results demonstrate that coastal foraging communities in southwest Madagascar over the past 1,000 years have extensively altered the landscape. Our study thus expands the temporal and spatial scales at which we can evaluate human-environment dynamics on Madagascar, providing new opportunities to study early periods of the island’s human history when mobile foraging communities were the dominant drivers of landscape change.

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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

Cited by 6 publications

References 149 publications

Integrating spatial and legacy data to understand archaeological sites in their landscape. A case study from Unguja Ukuu, Zanzibar

Integrating spatial and legacy data to understand archaeological sites in their landscape. A case study from Unguja Ukuu, Zanzibar

Theoretical Repositioning of Automated Remote Sensing Archaeology: Shifting from Features to Ephemeral Landscapes

Remote Sensing Reveals Lasting Legacies of Land-Use by Small-Scale Foraging Communities in the Southwestern Indian Ocean

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