Geology Reports⌕ Search

Geology topics

Pete G. Chirico

Publications and source records attributed to Pete G. Chirico.

12 recordsLinked to original sources
USGS2026

Creating 3D point clouds, digital elevation models, and orthomosaics from historical aerial imagery through structure from motion aided photogrammetry

Detailed and accurate historical data are crucial to characterizing landscape change, whether it is caused by natural phenomena or of anthropogenic origin. Aerial imagery has been periodically collected by government and nongovernment agencies, for a broad range of purposes, since the early 20th century. The acquisition of aerial images involves using standardized equipment with large-format, carefully calibrated cameras and lens assemblies. Historical archives of aerial images preserve a record of the landscape and are invaluable sources of information that predate satellite and lidar. Structure from motion (SfM) aided photogrammetry provides an efficient means for creating digital three-dimensional data from the vast archives of historical aerial imagery. This report presents techniques, methods, best practices, and a standard workflow for creating point clouds, digital elevation models, and orthomosaic digital scans of overlapping (stereographic) vertical aerial photographs using photogrammetry software. A list of critical issues relevant to SfM-aided photogrammetry of historical aerial imagery is provided, along with potential contingencies and solutions. The workflow was tested in many geographic study areas and with historical imagery datasets of different scales and sources. Three case studies are included. By integrating known SfM methods, these procedures enable the consistent and optimal extraction of historical quantitative data from archives like the U.S. Geological Survey (USGS) Aerial Photo Single Frame archive in the USGS EarthExplorer data portal.

Techniques and Methods↗
USGS2023

Artisanal mining river dredge detection using SAR: A method comparison

Challenges exist in monitoring artisanal and small-scale mining (ASM) activities, given their dynamic and often informal nature. ASM takes form through various techniques and scales, including riverine dredging, which often targets the abundant alluvial gold deposits in South America. Remote sensing offers a solution to improve data collection, regulation, and monitoring of the more mobile and elusive ASM activities and their impacts. Mapping ASM riverine dredges using Synthetic Aperture Radar (SAR) is one of the application areas least explored. Three semi-automated detection approaches using Sentinel-1 SAR are compared on their ability to identify dredges with minimal false positives. The methods are: (i) Search for Unidentified Maritime Objects (SUMO), an established method for large ocean ship detection; and two techniques specifically developed for riverine environments that are introduced in this paper: (ii) a local detection method; and (iii) a global threshold method. A visual interpretation of SAR data with the inclusion of optical high-resolution data are used to generate a validation dataset. Results show it is possible to semi-automatically detect riverine dredge using SAR and that a local detection method provides the best balance between sensitivity and precision and has the lowest risk of error. Future improvements may consider further automation, more discriminatory variables, and analyzing the methods in different environments and at higher spatial resolutions.

Remote Sensing↗
USGS2023

Detection and monitoring of small-scale diamond and gold mining dredges using synthetic aperture radar on the Kadéï (Sangha) River, Central African Republic

Diamond and gold mining has been practiced by artisanal miners in the Central African Republic (CAR) for decades. The recent introduction of riverine dredges indicates a transition from artisanal/manual digging and sorting techniques to small-scale mining methods. This study implements a remote sensing analysis of Synthetic Aperture Radar (SAR) data to map gold and diamond dredges operating on the Kadéï (Sangha) river in the CAR. Riverine vessels are identified in Sentinel-1 SAR data between 2015 and 2019, and their activity levels are mapped over time. The number of active dredges identified on the river increased over the five years studied, with the largest increase occurring between 2016 and 2017. Detailing a method for mapping and monitoring riverine diamond and gold dredge mining is an important step in keeping up with evolving technologies and new areas of mineral exploitation and in helping address concerns over resource governance in remote and conflict-prone terrain. The use of SAR technology, with its weather-independence, broad coverage, and available wavelength combinations, allows for higher temporal resolution and improved vessel detection in the monitoring of small-scale mining (SSM) dredges.

Remote Sensing↗
USGS2022

Quantifying and visualizing 32 years of agricultural land use change in Kabul, Afghanistan

Agriculture is a key element of Afghanistan’s economy and plays an essential role supporting the expanding population and urban development of Kabul, the country’s capital. Over the past decades the urban landscape has changed substantially and agricultural land use has shifted in its extent, location, and density. Identifying trends in the amount of agricultural area, as an indication of food production, is important for city planning and humanitarian efforts. While many studies have investigated Afghanistan's agriculture, most are conducted at scales that preclude their use for local-scale decision-making. This study quantifies agricultural extent across 32 years from 1988 to 2020 at local scale using simple and repeatable Landsat multispectral image analysis. The volume of data in time-series analysis complicatesvisualization of key findings and long-term trends. This study also explored visualization methods such as zonal mapping, animations, and the isolation of key themes in a 2D static map.

Kabul↗
USGS2021

Remote sensing inventory and geospatial analysis of brick kilns and clay quarrying in Kabul, Afghanistan

Reconstruction and urban development in Kabul, Afghanistan, has prompted vast expansion of the clay quarrying and brick making industry. This study identified the extent and distribution of clay quarrying and brick kilns in the greater Kabul area between 1965 and 2018. Very high-resolution satellite imagery was interpreted to quantify and characterize the type, number, and location of brick kilns for 1965, 2004, 2011, and 2018. Geospatial analysis of kilns together with geologic data and the results of hyperspectral image analysis yielded information regarding the extent of relevant mineral resources. Finally, kernel density analysis of kiln locations for each date called attention to their shifting spatial distribution. The study found that the clay quarrying and brick making industry has expanded exponentially. The type of kilns has transitioned from artisanal style clamp kilns to small-scale Bull’s Trench Kilns (BTK), and ultimately to Fixed Chimney Bull’s Trench Kilns (FCBTK). While quarrying has occurred entirely within quaternary windblown loess and clay deposits, artisanal clamp kilns were located in fine sediments containing montmorillonite and FCBTKs have developed in sediments containing calcite and muscovite. The study’s inventory of kilns was then used to estimate kiln workforce at 27,500 workers and production at 1.579 billion bricks per year.

Kabul↗
USGS2020

Investigation of suitable habitat for the endangered plant Ptilimnium nodosum (Rose) Mathias (harperella) using remote sensing and field analysis—Documentation of methods and results

Ptilimnium nodosum (Rose) Mathias (harperella) is an endangered plant species found in Maryland, Virginia, and West Virginia, as well as in other locations throughout the southeastern United States. The narrow range of habitat characteristics for areas in which harperella has been found makes locating potential occurrence sites difficult and attempts at reintroduction of the plant relatively unsuccessful. Sightings of harperella have been made along the banks and in-channel bars of the Potomac River, along the Chesapeake and Ohio Canal National Historic Park, and within the Sideling Hill Wildlife Refuge near Hancock, Md. The large area covered by these sightings presents logistical challenges for repeat studies of harperella growth within the Park and in nearby areas. This study developed a geospatial method for characterizing harperella habitat through remote sensing, geospatial analysis, and field investigation. A geospatial prediction model was developed to model the habitat characteristics discussed in literature and found at harperella field observation sites in order to narrow the potential area for observation of the plant and its habitat. Analysis of historical aerial imagery was conducted within the space of the Potomac River to observe the persistence and flooding conditions of in-channel bars. The products of the geospatial prediction model and the historical aerial image analysis are a geospatial description of where harperella habitat is most likely to be found, as well as a map of in-channel bar locations and their persistence through time. From these two analyses, areas were identified that merited detailed observation. Very high resolution, unmanned aerial systems imagery was collected for 10 sites within this area in the Potomac River in June 2019. Unmanned aerial systems imagery has the potential to greatly improve detailed study of the harperella plant, as it provides the spatial resolution necessary to catalog detailed vegetation conditions (and potentially species identification). More importantly, the timing of imagery collection can be aligned carefully with the plant’s phenological patterns and local weather conditions to maximize cost-effectiveness of repeated imaging for specific areas.

Maryland, Virginia↗
USGS2020

Evaluating elevation change thresholds between structure-from-motion DEMs derived from historical aerial photos and 3DEP LiDAR data

This study created digital terrain models (DTMs) from historical aerial images using Structure from Motion (SfM) for a variety of image dates, resolutions, and photo scales. Accuracy assessments were performed on the SfM DTMs, and they were compared to the United States Geological Survey’s three-dimensional digital elevation program (3DEP) light detection and ranging (LiDAR) DTMs to evaluate geomorphic change thresholds based on vertical accuracy assessments and elevation change methodologies. The results of this study document a relationship between historical aerial photo scales and predicted vertical accuracy of the resultant DTMs. The results may be used to assess geomorphic change thresholds over multi-decadal timescales depending on spatial scale, resolution, and accuracy requirements. This study shows that if elevation changes of approximately ±1 m are to be mapped, historical aerial photography collected at 1:20,000 scale or larger would be required for comparison to contemporary LiDAR derived DTMs.

Virginia↗
USGS2020

Geomorphological mapping and anthropogenic landform change in an urbanizing watershed using structure-from-motion photogrammetry and geospatial modeling techniques

Increasing urbanization and suburban growth in cities globally has highlighted the importance of land planning using detailed geomorphologic maps that depict anthropogenic landform changes. Such mapping provides information crucial for land management, hazard identification, and the management of the challenges arising from urbanization. The development and use of quantitative and repeatable methods to map anthropogenic and natural processes are required to advance the science of urban geomorphological mapping. This study investigated the application of geospatial modeling, structure-from-motion (SfM) photogrammetric methods and DEM differencing as means of quantifying anthropogenic landform changes from archival aerial imagery. Anthropogenic landforms were incorporated into a detailed geomorphologic map in an urbanizing watershed located in the Washington, D.C. metropolitan suburb of Vienna, Virginia.

Virginia↗
USGS2020

Mapping the extent and methods of small-scale emerald mining in the Panjshir Valley, Afghanistan

Emerald mining in the Panjshir Valley, Afghanistan, has occurred for thousands of years, yet few records exist documenting the detailed spatial extent, techniques, or productivity of small-scale miners. This study proposes new methods to map and monitor the extent and changes in small-scale mining in remote and inaccessible terrain by integrating multispectral remote sensing analysis with archival geologic data and three-dimensional topographic change detection to examine emerald deposit zones and mining activity in the Panjshir Valley. Specifically, previously mapped geologic units known to host emeralds were re-analyzed using Landsat multispectral analysis to investigate the potential distribution of mining activity. Interpretation of very fine-resolution satellite imagery showed that mining activity is becoming more concentrated and transitioning from traditional tunneling methods to mechanized surface excavation. Finally, topographic change analysis of mechanized mine sites was combined with archival grade data to estimate production and consider improved recovery methods by small-scale miners.

Panjshir Valley↗