projects

USDA Forest Service BRASS: Burning Risk Advisory Support System

gabe.saldana · April 25, 2022 ·

USDA Forest Service BRASS: Burning Risk Advisory Support System

Developing the USDA Forest Service’s vegetation and fire monitoring system

Protecting National Forests and Grasslands from fire

Researchers taking samples in an open field — Researchers take samples in a national grassland

The U.S. Department of Agriculture Forest Service is a Federal agency that manages public lands in national forests and grasslands. Administering 193 million acres of land, an area equivalent to the size of Texas, the US Forest Service is divided into 9 regions, encompassing 155 National Forests and 20 National Grasslands. The natural resources on these lands are some of the Nation’s greatest assets and have major economic, environmental, and social significance for all Americans.

The mission of the USDA Forest Service is to sustain the health, diversity, and productivity of the Nation’s forests and grasslands to meet the needs of present and future generations.

Role of BRASS: Burning Risk Advisory Support System

In keeping with US Forest Service guiding principles of using an ecological approach to multiple-use management, using the best scientific knowledge in making decisions, and selecting the most appropriate technologies in the management of resources, the BRASS (Burning Risk Advisory Support System) decision support tool provides a continuous means for forest and grassland managers to assess vegetation and weather to support decisions related to prescribed burning and/or the risk of wildfire.

The objective of the vegetation and fire monitoring system is to inventory, monitor, evaluate, and integrate land condition trends and capabilities with Forest Service management and public use goals to enhance, improve, repair, and sustain national forests and grasslands. Texas Agrilife Research has a continuing agreement with the US Forest Service to develop this system using a viable Phytomass Plant Growth model (PHYGROW) and a Burning Risk Advisory Support System (BRASS).

History

Texas A&M Agrilife Research began its involvement with the US Forest Service in 2005 with a contract through the USDA Risk Management Agency (RMA) to develop BRASS for the Lincoln National Forest in New Mexico. An extension was received which also included the Prescott National Forest, and later in 2008, the Coconino National Forest in Arizona.

We are currently working on developing the BRASS model for other forests including the Kaibab, Carson, and Santa Fe, as well as creating a new scalable technology stack of the automation system that we can transfer to the Forest Service so that they can model their forests independent of CNRIT. Though our models and GIS applications will remain in C and C++, our handlers, web services, and other middleware applications are all being converted to PHP 5 and javascript for easy to maintain open source functionality.

Data Collection

Field collected vegetation data is necessary to parameterize and validate the PHYGROW growth model. The PHYGROW sample points have been distributed across the landscape based on unique plant communities. The plant communities were established using a combination of unique spectral characteristics of the vegetation obtained from satellite imagery, unique ecological sites, and major land use areas of the forests. For the initial field data collection at each sample point, the species composition, litter production, and herbaceous production parameters are determined along a permanent transect.

measuring tools on dirt floor — A 1-meter PHYGROW sampling frame (left) contains four equidistant points for measuring basal areas of perennial grasses and cover classes, with 5x5cm quadrants for frecuency of herbaceous vegetation. A 40x40cm frame (right) containes three sampling points at the top center endpoints, with 10 cm and 40cm frequency quadrants

Field sampling on the Lincoln and Prescott National forests followed the standard PHYGROW sampling procedure. In 2008, a method was developed on the Coconino National Forest integrating the Quadrat Frequency Method (QFM), an existing Forest Service sampling technique, with the PHYGROW sampling method. The resulting Enhanced quadrat Frequency Method (EFM) can be used by Forest Service personnel to collect data necessary for Forest Service purposes, while still collecting all data needed for the PHYGROW model. Pictured below are the PHYGROW sampling frame (left) and the EFM sampling frame (above left).

Landscape Modeling

The BRASS decision support tool provides a continuous means for the US Forest Service resource managers to assess vegetation and weather to support decisions related to prescribed burning and/or the risk of wildfire. The BRASS system is composed of two main components, the PHYGROW growth model and the PHYRESIM burning model. PHYRESIM was developed from a software toolkit called Firelib, which is the same toolkit that drives the highly respected BEHAVE burning application. Firelib was developed by the US Forest Service as a toolkit to build custom BEHAVE type applications.

elevation map — A didgital elevation model is a type of imagery used to enhance fire modeling capabilities. It is used to monitor watersheda and to calculate slope, aspect and elevation — factors that affect a fire’s direction and spread rate.

The PHYGROW model is a near-real-time plant growth model that is updated daily utilizing current and forecasted weather conditions from the National Oceanic and Atmospheric Administration (NOAA). A PHYGROW model has been calibrated for each of the major plant communities and ecological sites within the base, which will continuously monitor vegetation production and fuel load conditions.

In order to distribute the modeled point data across the landscape, a methodology developed by the US Forest Service has been implemented called Most Similar Neighbor (MSN). First, a landscape map of plant communities is developed within a Geographic Information System (GIS) using available resources such as ecological site maps derived by the Natural Resource Conservation Service (NRCS), plant communities derived from classification of remotely sensed satellite imagery, and supplemental field collected data. Advanced image processing software (i.e. ERDAS, ENVI, and IDRISI) has facilitated the development of plant community polygons from multispectral satellite imagery. Next, the necessary PHYGROW and BRASS field sample data is collected for a minimum of one polygon within each unique plant community. The field-collected dataset is then distributed across the landscape by matching similar non-sampled plant community polygons as determined by the MSN analysis with field sample data collected within the sampled polygons.

The PHYGROW output is integrated with the fire behavior model, PHYRESIM, to provide a continuously updated fire risk map for an area. PHYGROW outputs current live herbaceous moisture, live herbaceous production, 1-hr. fuel accumulation, live wood moisture, and live wood production to the PHYRESIM subsystem on a daily basis. PHYRESIM coordinates the fuel moisture stick model and PHYGROW outputs with NOAA current and forecasted weather data to produce a 7-day forecast updated at 6-hour intervals. Changing weather conditions and fluctuating plant communities create dynamic BRASS 30-minute burn area, flame length, spread rate, and fuel moisture outputs. This data can be used to select areas beforehand with adequate fuel-load and appropriate weather conditions for a prescribed burn, as well as, determine wildfire risk conditions.

Product Delivery

The final delivery for these multi-forest projects is the BRASS (Burning Risk Advisory Support System) software and configuration database. Fire conditions can be assessed for any point on a forest via the internet to assist controlled burn crews, firefighters, and other groups associated with fire management in assessing conditions in the field. Additional range information such as vegetation production, drought prediction, and historical ranking is also delivered through the internet. The US Forest Service will establish it’s own data center for running the BRASS software and will begin using their own system by the end of 2012.

Publications

Rhodes, E.C., D. Tolleson, W. Shaw, E. Twombly, J. Kava, and T. Brown. 2009. Comparing herbaceous vegetation sampling methods on the Coconino National Forest, AZ, USA. Society for Range Management 62nd Annual Meeting, Albuquerque, NM.

Mali Livestock and Pastoralist Initiative

gabe.saldana · April 25, 2022 ·

Mali Livestock and Pastoralist Initiative

Access to technology, capacity building for a stronger livestock system in Mali

Mali livestock and pastoralist initiative logo

A mission to improve the Mali livestock system

USAID-Mali has identified an overall goal to “improve the productivity and income of the producers in Mali by enabling them to access technologies and build the capacity of all actors involved in the development of an extensive livestock system”. To meet this goal, USAID-Mali has outlined these specific objectives:

Promote the development of the extensive livestock sector,
Empower pastoralists and improve their capacity for risk management,
Create equitable livestock information and communication systems that provide monitoring and analysis technology to foster strategic partnerships between pastoral communities, markets, and policy,
Markets development and integration,
Build capacity of Mali to sustain the new techniques and technologies.

To meet this overall goal and the specific objectives, the Mali Livestock and Pastoralist Initiative project was initiated in 2008. The project is led by Texas AgriLife Research with US partners that include Syracuse University, University of Arizona, University of Wisconsin, and South Dakota State University. Government and educational partner organizations in Mali include Observatoire du Marche Agricole (OMA), Direction Nationale des Productions et des Industries Animales (DNPIA), Institut d’Economie Rurale (IER), and l’Institut Polytechnique Rural (IPR).

Mongolia LEWS: Livestock Early Warning System

gabe.saldana · April 25, 2022 ·

Mongolia LEWS: Livestock Early Warning System

Preparing pastoralists and decision-makers with critical, timely information in the face of drought

Early warning for livestock in Mongolia’s Gobi Region

Mongolia pastoralists and horses near lake

During the period from 1999 to 2002, Mongolia experienced a series of droughts and severe winters that lowered livestock numbers by approximately 30% countrywide. In the Gobi region, livestock mortality reached as much as 50% with many households losing entire herds. Due to these extreme losses of livestock and its impact on pastoral livelihoods, the USAID mission in Mongolia and the Global Livestock-CRSP (GL-CRSP) initiated the Gobi Forage program with the goal of transferring Livestock Early Warning System (LEWS) technology developed by the GL-CRSP in East Africa to Mongolia. The Livestock Early Warning System technology combines near real-time weather, computer modeling, and satellite imagery to monitor and forecast livestock forage conditions so that pastoralists and other decision-makers have needed information for timely decision making in the face of drought. Under the Gobi Forage program, three major activities have been conducted:

infusion of forage monitoring technology to assess regional forage quantity.
development of nutritional profiling technology to assess forage quality.
information delivery and outreach.

Implementing forage monitoring

Through the GL-CRSP program, Texas A&M University and Mercy Corps partnered to implement the forage monitoring technology in 8 aimags (provinces) that encompass the area where previous drought impacts were greatest. A series of 297 monitoring sites have been established across the region to provide inputs to the computer modeling and ground-truth data. Computer model simulations are developed for each monitoring site and are driven by climate data (rainfall and temperature) provided by the US National Oceanic and Atmospheric Administration (NOAA) to predict forage availability. This information is then combined with satellite greenness imagery (Normalized Difference Vegetation Index) provided by the National Aeronautics and Space Administration (NASA) to produce regional maps of forage production. These regional maps provide a spatial and temporal assessment of forage conditions and can highlight areas of significantly low forage availability. A sophisticated statistical forecasting technique is used to predict forage conditions for 60 days into the future. The current and forecast forage maps can be compared to long-term average maps to allow herders and decision makers to assess how bad or good conditions are compared to the average, and the level of risk they are willing to accept based on projected forage conditions. Map validation has indicated an overall accuracy level of 70% and successful identification of drought-stricken areas in the Dundgobi and Gobi Altai aimags in 2007.

Nutritional profiling with Near-Infrared Reflectance Spectroscopy, NIRS

The nutritional profiling technology has been implemented to allow near-real-time assessment of forage quality. Knowledge of forage quality is especially important in Mongolia given the short growing season and the extremely cold winter months that can lead to loss of body condition if animals have poor quality forages. The nutritional profiling technology uses Near-Infrared Reflectance Spectroscopy (NIRS) to scan the manure of livestock. These scans examine the reflectance of near-infrared wavelengths and compare them to wavelengths from known diets of livestock, therefore allowing a prediction to be made about the amount of protein and digestibility of the forage. This information can then be used in a computer model to determine whether the animal is losing or gaining weight and what forage or supplements can be fed to the animal to allow it to maintain weight or nurse offspring. As part of Gobi Forage, a laboratory has been established in Mongolia for conducting nutritional profiling and a mobile laboratory is being tested to allow this technology to be brought directly to herders. The NIRS technology is also being examined for several value-added analyses such as feedstuff quality, and cashmere and wool grading.

Information delivery successes

The Gobi Forage program has made significant progress in the information delivery and outreach for Gobi Forage products. Current, forecast and long-term deviation forage maps are produced bi-monthly and are distributed via http://www.mongolialews.net/ and email. The maps are also printed in color and mailed to soum (district) governments for local government use and for posting on the local government bulletin boards. Radio bulletins are produced weekly and reported on Mongolian National Public Radio. A recent survey in the region has indicated that the program has been exceedingly well received, with over 70% of herders having some degree of familiarity with Gobi Forage products. Almost half of the surveyed herders reported that they had used Gobi Forage information to guide livestock movements (51%), provide supplemental feed (49%) or change their rotational grazing strategy (40%). Almost one-third reported a net profit resulting from these actions. An overwhelming majority (93%) of government officials found Gobi Forage products to be very useful in advising herders on grazing management and livestock movement. One provincial governor described how the system helped him manage the influx of almost 50,000 herders and their families from a neighboring drought-stricken aimag and prevent conflict with local herders.

The future

The Gobi Forage program is now in the process of becoming institutionalized in Mongolia to incorporate the program within a national government research and extension agency. Discussions are also underway with the World Bank to acquire funding to maintain the current system and expand it to the entire country. The expansion of the program, as well as institutionalization in a Mongolian government agency, will build the foundation for long-term sustainability of the system and make these GL-CRSP forage monitoring technologies available to all Mongolians.

East Africa LEWS: Livestock Early Warning System

gabe.saldana · April 25, 2022 ·

East Africa LEWS: Livestock Early Warning System

Understanding and communicating the emergence of drought — allowing pastoral communities to cope with a rapidly changing environment

Striking a balance

The delicate balance between selecting and maintaining a stocking rate that meets the short-term economic goals needed for ranch or pastoral household survival versus one that sustains long-term livestock carrying capacity has long dominated the decision-making process of livestock producers worldwide. This process is driven by the level of human needs of the decision-maker in relation to the level of risk an individual is willing to undertake under any livelihood.

With emerging problems associated with the increasing population, erratic climatic patterns with a higher frequency of drought, limited marketing opportunities, changing land tenure patterns, rising social conflict, limited water supply, and greater incidences of disease transmission, the traditional coping strategies of farmers, ranchers and pastoralists have become inappropriate. More uncertainties require new innovations in characterizing, monitoring, analyzing and communicating the emergence of drought to allow pastoral communities to cope with a rapidly changing environment.

East Africa LEWS: A collaborative solution

Map showing predicted quantity of forage in the East Africa LEWS program area

In collaboration with National Agricultural Research System in Kenya, Ethiopia, Uganda and Tanzania, scientists at Texas A&M University funded by USAID (1997-2003) through the Global Livestock Collaborative Research Support Program developed the Livestock Early Warning System (LEWS).
The LEWS was designed to provide an early warning system for monitoring rangeland forage conditions, livestock nutrition and health for maintaining the food security of pastoralists. The program framework is an integral part of the existing system for drought and famine in pastoral areas of Ethiopia, Djibouti, Somaliland, Kenya, Uganda, and Tanzania. The development and implementation of LEWS include spatial characterization, the establishment of monitoring sites, biophysical modeling, model analysis, and field verification and automation of information dissemination.

The central thrust of the LEWS project is to provide information on impending drought insufficient lead-time to allow the government, NGOs and pastoral communities to react to the conditions in a timely manner to prevent resource degradation and loss of assets. Timely decision making by livestock owners concerning the availability of forage supply, movement, destocking, and restocking of livestock will be valuable for sustainable livestock production in eastern Africa.

The indigenous knowledge of the pastoral societies regarding range and livestock is much more effective if they can have access to near real-time information on impending forage shortages for livestock and location of forage supplies. This minimizes conflict during periods of restrictive conditions. A combination of this indigenous knowledge and modern science is used by decision-makers to formulate clear mitigation strategies to reduce risk from extremes of weather conditions.

The project contributes to this noble venture by generating country-specific forage situation and deviation status reports updated every 10 days, a monthly advisory for Kenya, Tanzania, Ethiopia and Uganda, and a regional forecast report for the same countries. These reports are posted via e-mail to an array of users that include government agencies, NGOs, UN agencies, and livestock stakeholder groups.