Dataset.
2023
An algorithm to detect endangered Cultural Heritage by agricultural expansion at a global scale
CORA.Repositori de Dades de Recerca
doi:10.34810/data600
CORA.Repositori de Dades de Recerca
- Conesa, Francesc C.
- Orengo Romeu, Hèctor A.
An algorithm that combines Big Earth Data and geospatial analysis in Google Earth Engine for the automated detection of archaeological mounds and Cultural Heritage sites that are potentially endangered by new agricultural developments.<br>
<br>The dataset contains supplementary materials to accompany the paper “Conesa, F. C., Orengo, H. A., Lobo, A., & Petrie, C. A. (2022). An Algorithm to Detect Endangered Cultural Heritage by Agricultural Expansion in Drylands at a Global Scale. Remote Sensing, 15(1), 53. MDPI AG. <a href="http://doi.org/10.3390/rs15010053">http://doi.org/10.3390/rs15010053</a>".<br>
<br>It includes the JavaScript code to be implemented in Google Earth Engine(c) and the R script for vector output visualisation.
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1 Documentos relacionados
1 Documentos relacionados
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/347478
Artículo científico (article). 2024
AN ALGORITHM TO DETECT ENDANGERED CULTURAL HERITAGE BY AGRICULTURAL EXPANSION IN DRYLANDS AT A GLOBAL SCALE
Digital.CSIC. Repositorio Institucional del CSIC
- Conesa, Francesc C.
- Orengo, Hèctor A.
- Lobo, Agustín
- Petrie, Cameron A.
This article presents AgriExp, a remote-based workflow for the rapid mapping and monitoring of archaeological and cultural heritage locations endangered by new agricultural expansion and encroachment. Our approach is powered by the cloud-computing data cataloguing and processing capabilities of Google Earth Engine and it uses all the available scenes from the Sentinel-2 image collection to map index-based multi-aggregate yearly vegetation changes. A user-defined index threshold maps the first per-pixel occurrence of an abrupt vegetation change and returns an updated and classified multi-temporal image aggregate in almost-real-time. The algorithm requires an input vector table such as data gazetteers or heritage inventories, and it performs buffer zonal statistics for each site to return a series of spatial indicators of potential site disturbance. It also returns time series charts for the evaluation and validation of the local to regional vegetation trends and the seasonal phenology. Additionally, we used multi-temporal MODIS, Sentinel-2 and high-resolution Planet imagery for further photo-interpretation of critically endangered sites. AgriExp was first tested in the arid region of the Cholistan Desert in eastern Pakistan. Here, hundreds of archaeological mound surfaces are threatened by the accelerated transformation of barren lands into new irrigated agricultural lands. We have provided the algorithm code with the article to ensure that AgriExp can be exported and implemented with little computational cost by academics and heritage practitioners alike to monitor critically endangered archaeological and cultural landscapes elsewhere., F.C.C. is a Beatriu de Pinós Fellow (2020-BP-00203) and, together with H.A.O., he conceived this research as a Juan de la Cierva-Incorporación Fellow (IJC2018-038319-I, Spanish Ministry of Science, Innovation and Universities) as a result of his Marie Sklodowska-Curie Action fellowship held at the University of Cambridge (MarginScapes, no. 794711). C.A.P. coordinates the Arcadia Foundation-funded project Mapping Archaeological Heritage in South Asia (MAHSA, University of Cambridge) and was also the PI on the ERC-funded TwoRains project (no. 648609).
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1 Versiones
1 Versiones
CORA.Repositori de Dades de Recerca
doi:10.34810/data600
Dataset. 2023
AN ALGORITHM TO DETECT ENDANGERED CULTURAL HERITAGE BY AGRICULTURAL EXPANSION AT A GLOBAL SCALE
CORA.Repositori de Dades de Recerca
- Conesa, Francesc C.
- Orengo Romeu, Hèctor A.
An algorithm that combines Big Earth Data and geospatial analysis in Google Earth Engine for the automated detection of archaeological mounds and Cultural Heritage sites that are potentially endangered by new agricultural developments.<br>
<br>The dataset contains supplementary materials to accompany the paper “Conesa, F. C., Orengo, H. A., Lobo, A., & Petrie, C. A. (2022). An Algorithm to Detect Endangered Cultural Heritage by Agricultural Expansion in Drylands at a Global Scale. Remote Sensing, 15(1), 53. MDPI AG. <a href="http://doi.org/10.3390/rs15010053">http://doi.org/10.3390/rs15010053</a>".<br>
<br>It includes the JavaScript code to be implemented in Google Earth Engine(c) and the R script for vector output visualisation.
No hay resultados en la búsqueda