More Like this

1. Archaeological Remote Sensing Using Multi-Temporal, Drone-Acquired Thermal and Near Infrared (NIR) Imagery: A Case Study at the Enfield Shaker Village, New Hampshire

   https://doi.org/10.3390/rs12040690  

   Hill, Austin Chad ; Laugier, Elise Jakoby ; Casana, Jesse ( February 2020 , Remote Sensing)

   While archaeologists have long understood that thermal and multi-spectral imagery can potentially reveal a wide range of ancient cultural landscape features, only recently have advances in drone and sensor technology enabled us to collect these data at sufficiently high spatial and temporal resolution for archaeological field settings. This paper presents results of a study at the Enfield Shaker Village, New Hampshire (USA), in which we collect a time-series of multi-spectral visible light, near-infrared (NIR), and thermal imagery in order to better understand the optimal contexts and environmental conditions for various sensors. We present new methods to remove noise from imagery and to combine multiple raster datasets in order to improve archaeological feature visibility. Analysis compares results of aerial imaging with ground-penetrating radar and magnetic gradiometry surveys, illustrating the complementary nature of these distinct remote sensing methods. Results demonstrate the value of high-resolution thermal and NIR imagery, as well as of multi-temporal image analysis, for the detection of archaeological features on and below the ground surface, offering an improved set of methods for the integration of these emerging technologies into archaeological field investigations. 

   more » « less
2. Using Object-Oriented Classification for Coastal Management in the East Central Coast of Florida: A Quantitative Comparison between UAV, Satellite, and Aerial Data

   https://doi.org/10.3390/drones3030060  

   Yang, Bo ; Hawthorne, Timothy L. ; Torres, Hannah ; Feinman, Michael ( September 2019 , Drones)

   High resolution mapping of coastal habitats is invaluable for resource inventory, change detection, and inventory of aquaculture applications. However, coastal areas, especially the interior of mangroves, are often difficult to access. An Unmanned Aerial Vehicle (UAV), equipped with a multispectral sensor, affords an opportunity to improve upon satellite imagery for coastal management because of the very high spatial resolution, multispectral capability, and opportunity to collect real-time observations. Despite the recent and rapid development of UAV mapping applications, few articles have quantitatively compared how much improvement there is of UAV multispectral mapping methods compared to more conventional remote sensing data such as satellite imagery. The objective of this paper is to quantitatively demonstrate the improvements of a multispectral UAV mapping technique for higher resolution images used for advanced mapping and assessing coastal land cover. We performed multispectral UAV mapping fieldwork trials over Indian River Lagoon along the central Atlantic coast of Florida. Ground Control Points (GCPs) were collected to generate a rigorous geo-referenced dataset of UAV imagery and support comparison to geo-referenced satellite and aerial imagery. Multi-spectral satellite imagery (Sentinel-2) was also acquired to map land cover for the same region. NDVI and object-oriented classification methods were used for comparison between UAV and satellite mapping capabilities. Compared with aerial images acquired from Florida Department of Environmental Protection, the UAV multi-spectral mapping method used in this study provided advanced information of the physical conditions of the study area, an improved land feature delineation, and a significantly better mapping product than satellite imagery with coarser resolution. The study demonstrates a replicable UAV multi-spectral mapping method useful for study sites that lack high quality data. 

   more » « less
3. Airborne Lidar Survey, Density-Based Clustering, and Ancient Maya Settlement in the Upper Usumacinta River Region of Mexico and Guatemala

   https://doi.org/10.3390/rs13204109  

   Golden, Charles ; Scherer, Andrew K. ; Schroder, Whittaker ; Murtha, Timothy ; Morell-Hart, Shanti ; Fernandez Diaz, Juan Carlos ; Jiménez Álvarez, Socorro del ; Alcover Firpi, Omar ; Agostini, Mark ; Bazarsky, Alexandra ; et al ( October 2021 , Remote Sensing)

   We present results from the archaeological analysis of 331 km2 of high-resolution airborne lidar data collected in the Upper Usumacinta River basin of Mexico and Guatemala. Multiple visualizations of the DEM and multi-spectral data from four lidar transects crossing the Classic period (AD 350–900) Maya kingdoms centered on the sites of Piedras Negras, La Mar, and Lacanja Tzeltal permitted the identification of ancient settlement and associated features of agricultural infrastructure. HDBSCAN (hierarchical density-based clustering of applications with noise) cluster analysis was applied to the distribution of ancient structures to define urban, peri-urban, sub-urban, and rural settlement zones. Interpretations of these remotely sensed data are informed by decades of ground-based archaeological survey and excavations, as well as a rich historical record drawn from inscribed stone monuments. Our results demonstrate that these neighboring kingdoms in three adjacent valleys exhibit divergent patterns of structure clustering and low-density urbanism, distributions of agricultural infrastructure, and economic practices during the Classic period. Beyond meeting basic subsistence needs, agricultural production in multiple areas permitted surpluses likely for the purposes of tribute, taxation, and marketing. More broadly, this research highlights the strengths of HDBSCAN to the archaeological study of settlement distributions when compared to more commonly applied methods of density-based cluster analysis. 

   more » « less
4. Analyzing Changing Thermal Features at Puyehue-Cordon Caulle, Chile Using Satellite, Field, and Drone Observations

   Andrea Gomez-Patron, Diego Aron ( January 2022 , AGU Fall Meeting, Chicago, IL & Online Everywhere, 12-16 December 2022)

   The Puyehue-Cordon Caulle (PCC) volcanic complex, Chile, hosts numerous thermal features, including a ~0.8 km3 laccolith formed during the 2011-2012 eruption. Laccoliths are large intrusions that form between country rock layers that have been rarely observed during the process of formation. We use medium-spatial resolution (90 m/pixel) satellite data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), to identify changes at the laccolith and other thermal features within PCC between 2000 and 2022. Previous studies have analyzed thermal behavior using MODIS images, which have low spatial resolution but high temporal resolution, and Landsat images, which have medium spatial resolution, but were only examined during the eruption (2011-2012). Prior research using ASTER data have only recorded the maximum temperature at PCC, while this study analyzes all of the individual thermal features and records both temperature and area for each feature identified in all 41 cloud-free, nighttime ASTER images available over the last 22 years. We focus on changes to seven features observed by satellite with temperatures at least 2 K above background (Trahuilco, Las Sopas, Los Venados, Los Baños/El Azufral, Puyehue, Laccolith, and a new unnamned feature). We create time series for each feature in order to: (1) evaluate temporal changes in area and temperature, (2) detect significant deviations from standard seasonality in non-eruptive periods, and (3) test for statistically significant precursors to the 2011 eruption. We identify both seasonal temperature variation and a general subtle increase in temperature over time at the laccolith. Furthermore, we find growth in the area of the laccolith with temperatures above background since 2016 including two periods of sudden increase in area between 11/2017 - 9/2018 and in mid 2020. We compare the ASTER observations with higher spatial resolution observations of fissures, craters, and fumaroles identified from field observations, drone thermal and optical imagery and high spatial resolution (~1 m/pixel) satellite SAR and optical data. We interpret the thermal changes at the laccolith to be related to fractures and craters in the laccolith exposing hot regions. 

   more » « less
5. DETECTION OF CLOUDS IN MEDIUM-RESOLUTION SATELLITE IMAGERY USING DEEP CONVOLUTIONAL NEURAL NETS

   https://doi.org/10.5194/isprs-archives-XLVI-M-2-2022-103-2022  

   Hasan, A. ; Witharana, C. ; Udawalpola, M. R. ; Liljedahl, A. K. ( July 2022 , The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences)

   Cloud detection is an inextricable pre-processing step in remote sensing image analysis workflows. Most of the traditional rule-based and machine-learning-based algorithms utilize low-level features of the clouds and classify individual cloud pixels based on their spectral signatures. Cloud detection using such approaches can be challenging due to a multitude of factors including harsh lighting conditions, the presence of thin clouds, the context of surrounding pixels, and complex spatial patterns. In recent studies, deep convolutional neural networks (CNNs) have shown outstanding results in the computer vision domain. These methods are practiced for better capturing the texture, shape as well as context of images. In this study, we propose a deep learning CNN approach to detect cloud pixels from medium-resolution satellite imagery. The proposed CNN accounts for both the low-level features, such as color and texture information as well as high-level features extracted from successive convolutions of the input image. We prepared a cloud-pixel dataset of approximately 7273 randomly sampled 320 by 320 pixels image patches taken from a total of 121 Landsat-8 (30m) and Sentinel-2 (20m) image scenes. These satellite images come with cloud masks. From the available data channels, only blue, green, red, and NIR bands are fed into the model. The CNN model was trained on 5300 image patches and validated on 1973 independent image patches. As the final output from our model, we extract a binary mask of cloud pixels and non-cloud pixels. The results are benchmarked against established cloud detection methods using standard accuracy metrics.

    

   more » « less