skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: Archaeology in the Fourth Dimension: Studying Landscapes with Multitemporal PlanetScope Satellite Data
For the last seven years, PlanetScope satellites have started near-daily imaging of parts of the Earth’s surface, making high-density multitemporal, multispectral, 3-m pixel imagery accessible to researchers. Multitemporal satellite data enables landscape archaeologists to examine changes in environmental conditions at time scales ranging from daily to decadal. This kind of temporal resolution can accentuate landscape features on the ground by de-emphasizing non-permanent signatures caused by seasonal or even daily changes in vegetation. We argue that the availability of high spatial and temporal resolution multispectral imagery from Planet Inc. will enable new approaches to studying archaeological visibility in landscapes. While palimpsests are discrete overlapping layers of material accumulation, multitemporal composites capture cyclical and seasonal time and can be used to interpret past landscape histories at multiple scales. To illustrate this perspective, we present three case studies using PlanetScope imagery in tropical environments on the Indian Ocean islands of Madagascar, Mauritius, and Zanzibar.  more » « less
Award ID(s):
2203789 2012590
PAR ID:
10504429
Author(s) / Creator(s):
; ;
Publisher / Repository:
Springer Nature
Date Published:
Journal Name:
Journal of Archaeological Method and Theory
ISSN:
1072-5369
Subject(s) / Keyword(s):
Multitemporal resolution Satellite remote sensing Landscape archaeology Socioecological systems Indian Ocean Africa
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; ; ; ; ; ; ; et al (, Journal of Social Archaeology)
    Many communities in southwestern Madagascar rely on a mix of foraging, fishing, farming, and herding, with cattle central to local cultures, rituals, and intergenerational wealth transfer. Today these livelihoods are critically threatened by the intensifying effects of climate change and biodiversity loss. Improved understanding of ancient community-environment dynamics can help identify pathways to livelihood sustainability. Multidisciplinary approaches have great potential to improve our understanding of human-environment interactions across spatio-temporal scales. We combine archaeological survey data, oral history interviews, and high-resolution multispectral PlanetScope imagery to explore 400 years of human-environment interaction in the Namonte Basin. Our analysis reveals that settlement and land-use led to significant changes in the region’s ecology, both during periods of occupation and after settlement abandonment. Human activity over this period may have stabilized vegetative systems, whereby seasonal changes in vegetative health were reduced compared to surrounding locations. These ecological legacies may have buffered communities against unpredictable climate challenges. 
    more » « less
  2. ; ; ; ; ; ; ; ; ; (, NSF Arctic Data Center)
    This dataset, called DARTS, comprises footprints of retrogressive thaw slump (RTS) identified and quantified using an automated deep learning approach in RTS hotspots across the Arctic and Subarctic permafrost regions. We utilized multispectral PlanetScope imagery with a spatial resolution of ~3 meters (m), complemented by ArcticDEM (Digital Elevation Model) and derived datasets, including slope, relative elevation, and Landsat-derived change trends. The dataset covers an area of 1.6 million square-kilometers (km²), with at least one coverage between 2021 and 2023, and provides annual coverage for approximately 900,000 km². In several highly active key sites, such as Banks Island, Peel Plateau, and Novaya Zemlya, we extended the data frequency and temporal coverage to 2018-2023. We mapped a total of more than 43,000 individual RTS and ALD, many of them multiple times. We offer two levels of datasets; Level 1: RTS footprints per image with timestamps; and Level 2: annually aggregated RTS footprints. Essential metadata includes image footprints, dataset coverage, timestamps, and model-specific information. To enhance reproducibility and further use, the training labels, processing code, and model checkpoints are publicly available. This version, v1.1, is the revised first openly accessible release. The dataset will be maintained and continuously updated in both spatial and temporal extent. It can be used for mapping and quantifying RTS, analyzing spatio-temporal patterns of RTS dynamics, or serving as input for landscape dynamics models. 
    more » « less
  3. ; ; ; (, NSF Arctic Data Center)
    This dataset describes measurements of inter-annual to sub-seasonal riverbank erosion rates on the Koyukuk River, Alaska, over the period 2016-2022. The data are used in the paper: “Geyman, E., Douglas, M., Avouac, J.-P. and Lamb, M. Permafrost slows Arctic riverbank erosion, in review (2024).” The dataset contains two sets of measurements: (1) riverbank displacement estimated from Sentinel-2 optical satellite imagery (10 meter (m) resolution) over the period 30-Aug-2016 to 13-Jul-2022, and (2) riverbank displacement estimated from Planet optical satellite imagery (3 m resolution) over the period 31-Aug-2016 to 01-Oct-2022. The first dataset is based on comparison of Sentinel-2 satellite acquisitions from the start and end of the study interval. The second dataset analyzes 65 PlanetScope image mosaics (for an average of 9 observations per year). The Matlab code used to analyze the Sentinel-2 and PlanetScope imagery, as well as to process the sub-seasonal displacement estimates, is included in the file “Code.zip”. 
    more » « less
  4. ; ; ; ; ; ; ; ; ; (, NSF Arctic Data Center)
    This dataset, called DARTS, comprises footprints of retrogressive thaw slump (RTS) and active layer detachments slides (ALD) identified and quantified using an automated deep learning approach in RTS hotspots across the Arctic and Subarctic permafrost regions. We utilized multispectral PlanetScope imagery with a spatial resolution of 3 meters (m), complemented by ArcticDEM (Digital Elevation Models) and derived datasets, including slope, relative elevation, and Landsat-derived change trends. The dataset covers an area of 1.6 million square-kilometers (km²), with at least one coverage between 2021 and 2023, and provides annual coverage for approximately 900,000 km². In several highly active key sites, such as Banks Island, Peel Plateau, and Novaya Zemlya, we extended the data frequency and temporal coverage to 2018-2023. We mapped a total of more than 43,000 individual RTS and ALD, many of them multiple times. We offer two levels of datasets; Level 1: RTS footprints per image with timestamps; and Level 2: annually aggregated RTS footprints. Essential metadata includes image footprints, dataset coverage, timestamps, and model-specific information. To enhance reproducibility and further use, the training labels, processing code, and model checkpoints are publicly available. This version, v1, is the first openly accessible release. The dataset will be maintained and continuously updated in both spatial and temporal extent. It can be used for mapping and quantifying RTS, analyzing spatio-temporal patterns of RTS dynamics, or serving as input for landscape dynamics models. 
    more » « less
  5. ; ; ; ; ; ; ; ; ; (, Frontiers in plant science)
    Unmanned aerial vehicles (UAVs) equipped with multispectral sensors offer high spatial and temporal resolution imagery for monitoring crop stress at early stages of development. Analysis of UAV-derived data with advanced machine learning models could improve real-time management in agricultural systems, but guidance for this integration is currently limited. Here we compare two deep learning-based strategies for early warning detection of crop stress, using multitemporal imagery throughout the growing season to predict field-scale yield in irrigated rice in eastern Arkansas. Both deep learning strategies showed improvements upon traditional statistical learning approaches including linear regression and gradient boosted decision trees. First, we explicitly accounted for variation across developmental stages using a 3D convolutional neural network (CNN) architecture that captures both spatial and temporal dimensions of UAV images from multiple time points throughout one growing season. 3D-CNNs achieved low prediction error on the test set, with a Root Mean Squared Error (RMSE) of 8.8% of the mean yield. For the second strategy, a 2D-CNN, we considered only spatial relationships among pixels for image features acquired during a single flyover. 2D-CNNs trained on images from a single day were most accurate when images were taken during booting stage or later, with RMSE ranging from 7.4 to 8.2% of the mean yield. A primary benefit of convolutional autoencoder-like models (based on analyses of prediction maps and feature importance) is the spatial denoising effect that corrects yield predictions for individual pixels based on the values of vegetation index and thermal features for nearby pixels. Our results highlight the promise of convolutional autoencoders for UAV-based yield prediction in rice. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email