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At least 19 records

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DOE OSTI2021

Secure LoRa Firmware Update with Adaptive Data Rate Techniques

Internet of Things (IoT) devices rely upon remote firmware updates to fix bugs, update embedded algorithms, and make security enhancements. Remote firmware updates are a significant burden to wireless IoT devices that operate using low-power wide-area network (LPWAN) technologies due to slow data rates. One LPWAN technology, Long Range (LoRa), has the ability to increase the data rate at the expense of range and noise immunity. The optimization of communications for maximum speed is known as adaptive data rate (ADR) techniques, which can be applied to accelerate the firmware update process for any LoRa-enabled IoT device. In this paper, we investigate ADR techniques in an application that provides remote monitoring of cattle using small, battery-powered devices that transmit data on cattle location and health using LoRa. In addition to issues related to firmware update speed, there are significant concerns regarding reliability and security when updating firmware on mobile, energy-constrained devices. A malicious actor could attempt to steal the firmware to gain access to embedded algorithms or enable faulty behavior by injecting their own code into the device. A firmware update could be subverted due to cattle moving out of the LPWAN range or the device battery not being sufficiently charged to complete the update process. To address these concerns, we propose a secure and reliable firmware update process using ADR techniques that is applicable to any mobile or energy-constrained LoRa device. The proposed system is simulated and then implemented to evaluate its performance and security properties.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

White Rock Canyon Riparian Monitoring

Lands at Los Alamos National Laboratory (LANL) are owned and managed by the Department of Energy, National Nuclear Security Administration (DOE/NNSA). The Laboratory’s eastern boundary is in White Rock Canyon (WRC) along the Rio Grande within Technical Areas (TAs) 70 and 33, with approximately 4 miles of property along the river (Figure 1). Feral cattle have been documented along the Rio Grande in WRC since the 1980s. Cattle activity is known to be especially harmful to riparian ecosystems, which are hubs for biodiversity and crucial habitat used by multiple endangered species in this region, including the Southwestern Willow Flycatcher and Yellow-billed Cuckoo on LANL property (Poessel 2020, Sanchez 2021). To gain a comprehensive understanding of the status of riparian ecosystems within WRC and the damage feral cattle populations are imposing, ecologists at LANL conducted a month-long vegetation monitoring project in September 2025. Riparian vegetation monitoring protocols and rapid assessments of cattle impacts were used to quantify the intensity and extent of cattle damage. The data collected will be used as a baseline for comparison following the removal of feral cattle from the canyon. Abundant cattle sign indicates current and heavy utilization of the riparian areas that were surveyed. There are multiple wallowing areas where soils are completely denuded of vegetation and highly compacted. Based on the severity and extent of impact observed in these areas, cattle use appears consistent and on-going over an extended period of years, supporting the need for removal and restoration management.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2022

Colorado Eastern Plains Agriculture: Rangeland Monitoring to Inform Grazing Management in Eastern Colorado

Adaptive management on cattle ranches requires rangeland managers to decide the location and duration of the cattle grazing activity across different pastures. Biodiversity, forage availability, and cattle health are all affected by rangeland management. Virtual fencing is a tool that rangeland managers can use to potentially increase biodiversity and improve ranching operations. NASA DEVELOP and Colorado State University (CSU) collaborated with the Nature Conservancy (TNC), and Red Top Ranch to demonstrate the efficacy of virtual fencing. We sought to identify annual and monthly biomass patterns on the ranch through the creation of monthly max biomass productivity maps. We utilized a dataset from the Agricultural Research Service (ARS) to calculate biomass on the ranch. To validate our remotely-sensed results, we compared model-predicted biomass values to field-collected biomass clipping data and an additional biomass dataset from the Rangeland Analysis Platform (RAP). We used satellite imagery from Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI), Landsat 9 –OLI-2, and Sentinel-2 MultiSpectral Instrument (MSI) for 2021 and 2022. We found that there was heterogeneity in biomass across the ranch, with higher biomass on the western side. The highest peak of biomass was in the summer months, with a smaller peak in mid-September. The ARS biomass dataset had a significant relationship with RAP for 2021. ARS biomass did not have a significant relationship with the biomass field data collected in 2022. The results of our study are aimed to support rotation management, ranch production, biodiversity, and conservation management.

cattle↗

NASA NTRS1971

A Pilot System for Environmental Monitoring Through Domestic Animals

A pilot system for environmental monitoring is in its early phases of development in Northern California. It is based upon the existing nation wide Federal-State Market Cattle Testing (14CT) program for brucellosis in cattle. This latter program depends upon the collection of blood program at the time of identified cattle. As the cattle Population of California is broadly distributed throughout the state, we intend to utilize these blood samples to biologically monitor the distribution and intensity of selected environmental pollutants. In a 2-year preliminary trial, the feasibility of retrieving, utilizing for a purpose similar to this, and tracing back to their geographic areas of origin of MCT samples have been demonstrated.

Schwabe, Calvin W.↗

NASA NTRS2012

Remote sensing analysis of forest disturbances

The present invention provides systems and methods to automatically analyze Landsat satellite data of forests. The present invention can easily be used to monitor any type of forest disturbance such as from selective logging, agriculture, cattle ranching, natural hazards (fire, wind events, storms), etc. The present invention provides a large-scale, high-resolution, automated remote sensing analysis of such disturbances.

Asner, Gregory P.↗

NASA NTRS2015

Remote Sensing Analysis of Forest Disturbances

Asner, Gregory P.↗

DOE OSTI2024

Vesicular Stomatitis Virus Transmission Dynamics Within Its Endemic Range in Chiapas, Mexico

Vesicular stomatitis virus (VSV), comprising vesicular stomatitis New Jersey virus (VSNJV) and vesicular stomatitis Indiana virus (VSIV), emerges from its focus of endemic transmission in Southern Mexico to cause sporadic livestock epizootics in the Western United States. A dearth of information on the role of potential arthropod vectors in the endemic region hampers efforts to identify factors that enable endemicity and predict outbreaks. In a two-year, longitudinal study at five cattle ranches in Chiapas, Mexico, insect taxa implicated as VSV vectors (blackflies, sandflies, biting midges, and mosquitoes) were collected and screened for VSV RNA, livestock vesicular stomatitis (VS) cases were monitored, and serum samples were screened for neutralizing antibodies. VS cases were reported during the rainy (n = 20) and post-rainy (n = 2) seasons. Seroprevalence against VSNJV in adult cattle was very high (75–100% per ranch) compared with VSIV (0.6%, all ranches). All four potential vector taxa were sampled, and VSNJV RNA was detected in each of them (11% VSNJV-positive of 874 total pools), while VSIV RNA was only detected in four pools of mosquitoes. Our findings indicate that VSNJV is the dominant serotype across our sampling sites with a variety of potential insect vectors involved in its transmission throughout the year. Although no livestock cases were reported in Chiapas during the dry season, VSNJV was detected in insects during this period, suggesting that mechanisms other than transmission from livestock support VSV endemicity.

Virology↗

DOE OSTI2026

AmeriFlux US-NC5 NC Butner Farm

This is the AmeriFlux version of the carbon flux data for the site US-NC5 NC Butner Farm. Site Description - The US-NC5 flux tower is located within an 80-year-old mixed pine-hardwood forest at the Umstead Research Farm in Butner, North Carolina. The northern section of this 20-hectare Fall Lake Watershed of the Neuse River Basin in the Piedmont of North Carolina, USA. The Northern portion is currently a managed cattle farm, which is slated for expansion—necessitating forest clearing in the flux site. To establish a reference baseline, a year-long, all-season eddy covariance flux monitoring campaign will be conducted from April 2025 to March 2026. This effort aims to capture the carbon flux dynamics of the mature forest ecosystem prior to a planned land-use conversion. The site will be transitioned into a silvopasture, maintained through prescribed burning and cattle grazing to promote an open-canopy watershed structure. Flux measurements will continue after the conversion.

Sun, Ge [USDA Forest Service]↗

DOE OSTI2026

AmeriFlux FLUXNET-1F US-NC5 NC Butner Farm

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-NC5 NC Butner Farm. This is the FLUXNET version of the carbon flux data for the site US-NC5 NC Butner Farm produced by applying the standard ONEFlux (1F) software. Site Description - The US-NC5 flux tower is located within an 80-year-old mixed pine-hardwood forest at the Umstead Research Farm in Butner, North Carolina. The northern section of this 20-hectare Fall Lake Watershed of the Neuse River Basin in the Piedmont of North Carolina, USA. The Northern portion is currently a managed cattle farm, which is slated for expansion—necessitating forest clearing in the flux site. To establish a reference baseline, a year-long, all-season eddy covariance flux monitoring campaign will be conducted from April 2025 to March 2026. This effort aims to capture the carbon flux dynamics of the mature forest ecosystem prior to a planned land-use conversion. The site will be transitioned into a silvopasture, maintained through prescribed burning and cattle grazing to promote an open-canopy watershed structure. Flux measurements will continue after the conversion.

Sun, Ge [USDA Forest Service]↗

NASA NTRS1999

Environmental and Archaeological Research in the Peten, Guatemala

The Peten, Northern Guatemala, was once inhabited by a population of several million Maya before their collapse in the 9th century A.D.. The seventh and eight centuries were a time of crowning glory four millions of Maya; by 930 A.D. only a few scattered houses remained, testifying to the greatest disaster in human history. What is known is that at the time of their collapse the Maya had cut down most of their trees. After centuries of regeneration the Peten now represent the largest remaining tropical forest in Central America but is experiencing rapid deforestation in the wake of an invasion of settlers. The successful adaptive techniques of the indigenous population are being abandoned in favor of the destructive techniques of monoculture and cattle raising. Remote sensing and Geographic Information System (GIS) analysis are being used to address issues in Maya archeology as well as monitor the effects of increasing deforestation in the area today. One thousand years ago the forests of the Peten were nearly destroyed by the ancient Maya who after centuries of successful adaptation finally overused their resources. Current inhabitants are threatening to do the same thing today in a shorter time period with a lesser population. Through the use of remote sensing/GIS analysis we are attempting to answer questions about the past in order to protect the resources of the future.

Sever, Thomas L.↗

DOE OSTI2024

Multi-Scale Integrated Monitoring System for Enhancing Methane Emission Detection, Quantification & Prediction

This report details the progress and findings of a comprehensive study on reviewing existing solutions, identifying technology gaps, and formulating an “all-in-one” integrated strategy for developing the next-generation multiscale methane monitoring and modeling platform, conducted under grant number DE-FE0032292. Co-led by Dr. David Ebert, Dr. Binbin Weng, and Dr. Chenghao Wang at the University of Oklahoma, the project’s goal was to develop an integrated approach for building this engineering platform to detect, quantify, and mitigate methane emissions across various temporal scale, spatial scales, and sectors. The planning grant study began with an extensive review of various methane sensing and monitoring technologies and systems, surveying over 100 technology providers globally. This review revealed the prevalence of optical methods over chemical methods in commercially available sensors, with Non-Dispersive Infrared (NDIR), Tunable Diode Laser Absorption Spectroscopy (TDLAS), and Optical Gas Imaging (OGI) cameras being the most prevalent options. A trend towards more advanced optical techniques was observed, driven by increased regulatory focus and technological advancements. The technical evaluation of these sensing technologies provided crucial insights into their capabilities and limitations. The study examined emerging technologies such as Differential Absorption LiDAR (DIAL), which show promise for high-precision and long-range detection. The team then investigated the features and application bandwidth of various sensing platforms, including handheld, fixed/stationary, mobile, aerials, and spaceborne monitors. Pilot field studies were conducted to assess the capabilities of solutions for different emission scenarios. Field work with sensor deployments was conducted at three distinct site types: an oil & gas industry site, a cattle ranching operation, and a waste processing facility. The team also conducted a thorough review of methane flux inverse modeling approaches, focused on physically based methods. These approaches were categorized into simple, intermediate, and advanced methods. A realtime WRF-GHG (Weather Research and Forecasting-Greenhouse Gas) modeling system was developed and applied, incorporating multiple data sources to guide field experiments and inform methane plume detection. The project identified and analyzed numerous categories of methane data sources, including satellite measurements, ground-based sensors, and inventory databases. Key platforms examined include EDGAR, EPA GHGI, NASA TROPOMI, Carbon Mapper, and Climate TRACE, among others. The team proposed an architecture for a comprehensive methane monitoring platform. This system incorporates multi-source data acquisition, advanced data processing and assimilation, interactive visualization tools, and analytical capabilities for emissions forecasting and scenario analysis. The proposed platform aims to provide a user-friendly interface catering to various stakeholders, from researchers to policymakers. The architecture includes sophisticated data ingestion methods, a centralized data warehouse, and advanced analytical tools for data fusion and interpretation. To ensure the relevance and effectiveness of the proposed system, a comprehensive survey was conducted to gather stakeholder input on system requirements. Key findings include a strong need for integrating various data types and formats, a preference for real-time data updates and advanced visualization tools, and a demand for user-friendly interfaces catering to different expertise levels.

03 NATURAL GAS↗

NASA NTRS2000

Archaeological and Environmental Research of the Peten, Guatemala, Using Remote Sensing/GIS Research

The Peten, northern Guatemala, was once inhabited by a population of several million Maya before their collapse in the 9th century AD. Although the seventh and eight centuries were a time of crowning glory for millions of Maya; by 930 A.D. only a few scattered houses remained. What is known, is that at the time of their collapse, the Maya had cut down most of their trees. After centuries of regeneration the Peten now represent the largest remaining tropical forest in Central America but is experiencing rapid deforestation in the wake of an invasion of settlers. The successful adaptive techniques of the indigenous population are being abandoned in favor of the destructive techniques of monoculture and cattle raising. These techniques also contribute to the destruction and looting of unrecorded archeological sites. Remote sensing and GIS analysis are being used to address issues in Maya archeology as well as monitor the effects of increasing deforestation in the area today. One thousand years ago the forests of the Peten were nearly destroyed by the ancient Maya, who, after centuries of successful adaptation, finally overused their resources. Current inhabitants are threatening to do the same thing today in a shorter time period with a lesser population. Through the use of remote sensing/GIS analysis we are attempting to answer questions about the past in order to protect the resources of the future.

Sever, Thomas L.↗

NASA NTRS1999

The Ancient Maya Landscape from Space

The Peten, once inhabited by a population of several million before the collapse of the ancient Maya in the 10th and 11th centuries, is being repopulated toward its former demographic peak. Environmental dynamics, however, impose severe constraints to further development. Current practices in subsistence, commercial agriculture, and cattle raising are causing rapid deforestation resulting in the destruction of environmental and archeological resources. The use of remote sensing and Geographic Information Systems (GIS) technology is a cost-effective methodology for addressing issues in Maya archeology as well as monitoring the environmental impacts being experienced by the current population.

Sever, T.↗

NASA NTRS1978

Deforestation planning for cattle grazing in Amazon Basin using LANDSAT data

The author has identified the following significant results. This research did not show the total potential of the LANDSAT system, but tried to open up new research aspects for the utilization of LANDSAT data in natural resource control. Results obtained through this research showed that LANDSAT data can be used to develop monitoring programs in the tropical forest areas of Brazil.

Dejesusparada, N.↗

DOE OSTI2022

Using dorsal surface for individual identification of dairy calves through 3D deep learning algorithms

Advances in machine learning techniques have allowed the development of computer vision systems (CVS) that can accurately predict several phenotypes of interest for livestock operations. In this context, 3D images taken from a top-down view are particularly useful for estimating body condition score, growth development, and body biometrics in cattle. Frequently, such CVS rely on identification (ID) systems, such as electronic tags, as a way to match animal ID and the predicted phenotype. However, the same 3D images used to predict body weight and other animal biometrics could be adopted for animal recognition as well. Such alternative would optimize CVS to recognize animal ID and monitor growth development simultaneously while leveraging the same hardware infrastructure. Furthermore, this strategy could be used to recognize animals with similar color patterns. Nonetheless, growing animals are continuously changing body shape, which could limit its use as an invariant feature for pattern recognition. Thus, the objectives of this study were: (1) to compare algorithms for different 3D object representations to identify individual animals; and (2) to evaluate how short-term changes in body shape due to animal growth affect the predictive performance of these algorithms. For objective 1, the algorithms were trained (n = 4,558) and tested (n = 1,139) using images from 38 Holstein calves. For objective 2, we designed three different experiments using images (n = 2,347) from five Holstein calves taken over six weeks during their growing period, always training and testing on different weeks. Each experiment evaluated how changing a different parameter of the image capturing procedure affected the predictive ability of the trained algorithms. In the first experiment, we varied the total number of images per animal in the training set; in the second experiment, we varied the number of weeks while keeping a fixed number of images in the training set; and in the third experiment, we skipped weeks between images in the training and test sets. The F 1 score for objective (1) was up to 0.804 when testing with the last frames of each video, and up to 0.959 when using random frames for testing. For objective (2), the F 1 score was up to 0.947 for the first experiment when using 130 images per animal; up to 0.979 for the second experiment when using all five weeks; and up to 0.917 when not skipping weeks between training and testing. In conclusion, these results show that deep learning algorithms can be used to identify individual animals through their dorsal area 3D surfaces, and, from our experiments using calves in their growing period, that they are robust enough to account for changes in body shape and size, making them a promising tool for animal recognition during growth.

3D neural networks↗

DOE OSTI2021

AmeriFlux US-Jo1 Jornada Experimental Range Bajada Site

This is the AmeriFlux version of the carbon flux data for the site US-Jo1 Jornada Experimental Range Bajada Site. Site Description - The Jornada Basin Experimental Range (JER) covers 783 km2 in the La Jornada del Muerto Plain of the northern Chihuahuan Desert and is located 20km of Las Cruces, NM. Extensive livestock grazing at the JER and througout the US Southwest was coincident with large-scale grassland deterioration and transition to shrubland begining in the 1800s. The JER was established n 1912 to investigate these rangeland changes and has since become a central location for understanding dryland ecology. This flux tower monitors CO2 and H2O dynamics in a representative shrubland on the piedmont slope (bajada) of the San Andreas mountains. The dominant shrubs are evergreen Larrea tridentata (Creosote) and winter-deciduous Prosopis glandulosa (Honey Mesquite). Other cover types include Flourensia cernua (tarbush) and patchy occurences of the grasses Muhlenbergia porteri (Bush Muhly) and Dasyochloa pulchella (Fluff Grass). The site is occasionally visited by stray domestic cattle, free-ranging introduced Oryx, and other native herbivores (Jack Rabbits, Desert Pronghorn). Soils at the site are Ustic Calciargids and parent material consists of limestone, other sedimentary rock, and some igneous rock. Virtual Site Visit: https://youtu.be/v1uJCKuicqs

Tweedie, Craig↗

DOE OSTI2022

AmeriFlux FLUXNET-1F US-Jo1 Jornada Experimental Range Bajada Site

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-Jo1 Jornada Experimental Range Bajada Site. This is the FLUXNET version of the carbon flux data for the site US-Jo1 Jornada Experimental Range Bajada Site produced by applying the standard ONEFlux (1F) software. Site Description - The Jornada Basin Experimental Range (JER) covers 783 km2 in the La Jornada del Muerto Plain of the northern Chihuahuan Desert and is located 20km of Las Cruces, NM. Extensive livestock grazing at the JER and througout the US Southwest was coincident with large-scale grassland deterioration and transition to shrubland begining in the 1800s. The JER was established n 1912 to investigate these rangeland changes and has since become a central location for understanding dryland ecology. This flux tower monitors CO2 and H2O dynamics in a representative shrubland on the piedmont slope (bajada) of the San Andreas mountains. The dominant shrubs are evergreen Larrea tridentata (Creosote) and winter-deciduous Prosopis glandulosa (Honey Mesquite). Other cover types include Flourensia cernua (tarbush) and patchy occurences of the grasses Muhlenbergia porteri (Bush Muhly) and Dasyochloa pulchella (Fluff Grass). The site is occasionally visited by stray domestic cattle, free-ranging introduced Oryx, and other native herbivores (Jack Rabbits, Desert Pronghorn). Soils at the site are Ustic Calciargids and parent material consists of limestone, other sedimentary rock, and some igneous rock. Virtual Site Visit: https://youtu.be/v1uJCKuicqs

Tweedie, Craig↗

NASA NTRS1999

Spectrometry of Pasture Condition and Biogeochemistry in the Central Amazon

Regional analyses of Amazon cattle pasture biogeochemistry are difficult due to the complexity of human, edaphic, biotic and climatic factors and persistent cloud cover in satellite observations. We developed a method to estimate key biophysical properties of Amazon pastures using hyperspectral reflectance data and photon transport inverse modeling. Remote estimates of live and senescent biomass were strongly correlated with plant-available forms of soil phosphorus and calcium. These results provide a basis for monitoring pasture condition and biogeochemistry in the Amazon Basin using spaceborne hyperspectral sensors.

Asner, Gregory P.↗