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USDA's
research and operational programs used remotely sensed data and related
technologies to monitor, assess, and administer agricultural and forestry
resources. The Agriculture Research Service (ARS) enhanced remote-sensing
knowledge and developed productive applications at research facilities
located throughout the United States. At Weslaco, Texas, the ARS Integrated
Farming and Natural Resources Unit used airborne video data integrated
with Global Positioning System (GPS) and Geographic Information System
(GIS) technologies to detect and map the aquatic weeds, hydrilla, and
water hyacinth in the Rio Grande River in extreme southern Texas. The
unit also developed and delivered GIS maps to the Texas Parks and Wildlife
Department and the Lower Rio Grande River Water Districts, which can use
the maps to control the spread of these weeds. At Weslaco, aerial digital
images in conjunction with yield monitor data were collected from 20 grain
sorghum fields owned by Rio Farms, Inc., Monte Alto, Texas. Rio Farms
managers have used the aerial images and yield maps, produced from the
yield monitor data, to improve farm management.
The
ARS Hydrology Laboratory initiated work to develop methods for retrieving
soil moisture information from satellite-borne microwave sensors. Scientists
conducted experiments at ARS facilities in Oklahoma using aircraft prototypes
of these instruments. At the ARS Jornada Experimental Range, the Hydrology
Laboratory collected laser scanning data and visible, thermal infrared,
and video imagery to infer surface temperature, albedo, vegetation indices,
roughness, and other land-surface characteristics. These parameters are
to be used as inputs for land-surface models, coupled with atmospheric
models. In preparation for the launch of NASA's Earth Observing System
(EOS) AM1 satellite (now called Terra), the Hydrology Laboratory has flown
multispectral thermal infrared sensors (TIMS and MASTER) over the Jornada
Experimental Range to estimate surface emissivity and temperature. ARS
will use the latter to estimate the surface-sensible heat flux. This approach
has been successfully demonstrated with data acquired over the El Reno
Grazing Lands facility in Oklahoma as part of the Southern Great Plains
97 experiment.
At
ARS laboratories in Ames, Iowa, ARS researchers continued to develop methods
for detecting weeds within corn and soybean canopies. Scientists measured
and recorded the leaf spectra of weeds, corn, and soybeans grown in single
species plots and in competition. ARS made concurrent reflectance measurements
with a broadband radiometer of plots with different weed densities and
species composition. To discriminate nutrient stress and its potential
impact on yields, ARS measured the reflectance of corn produced from various
populations and nitrogen management strategies. To determine the effect
of soil background on crop detection, scientists developed a seasonal
reflectance library for corn, soybeans, and wheat with different tillage
and crop residue practices.
The
ARS Great Plains System Research Unit in Ft. Collins, Colorado, the ARS
Southwest Watershed Research Center in Tucson, Arizona, and Michigan State
University brought private, nonprofit, and public-sector groups together
in Arizona, Colorado, New Mexico, and Nebraska to develop techniques for
estimating the amount and spatial distribution of total standing, green,
and senescent biomass in grassland ecosystems. The project was designed
to demonstrate how remote sensing can be cost-effectively used in managing
these diverse and important natural resources.
In
Phoenix, Arizona, the U.S. Water Conservation Laboratory (USWCL), in cooperation
with the University of Arizona, designed and tested a system for acquiring
images from sensors mounted on agricultural implements. Scientists used
data from the system to verify a new chlorophyll index that shows promise
as an indicator of crop fertilizer needs. In a separate study using the
Free Air Carbon dioxide Enrichment (FACE) facility, USWCL used measurements
of plant reflectance and temperature to assess the response of wheat and
sorghum crops to increased atmospheric carbon dioxide. This research will
improve our ability to monitor and understand how various global change
scenarios will affect agricultural productivity and carbon sequestration.
USWCL scientists cooperated with NASA's Stennis Space Center, through
a Space Act Agreement, to develop products and applications to manage
crops and soils using data from multispectral airborne sensors. Scientists
have used the data to develop methods for detecting crop water stress
and improving irrigation scheduling with minimal ground-based inputs and
no image calibration. USWCL scientists, as members of the NASA Landsat
7 Science Team, combined imagery from the Landsat 7 Enhanced Thematic
Mapper-Plus (ETM+) sensor with a grassland growth model to produce daily
maps of grassland biomass, root biomass, and soil moisture over a semiarid
watershed. These maps allow for better rangeland management and provide
a greater understanding of rangeland ecology for addressing soil erosion
and drought.
Using
remote-sensing technology, the ARS Remote Sensing and Modeling Laboratory
(RSML) and the Hydrology Laboratory initiated a long-term experiment to
evaluate the economic and environmental impact of four alternative farming
practices on surface and subsurface water quality. They mapped subsurface
flow patterns with Ground Penetrating Radar and linked them to crop yields
and remotely sensed hyperspectral data. Their remote-sensing assessment
of the spatial and temporal variability of crops will benefit farmers
and various agricultural industries by providing a watershed-scale demonstration
site at which crop yields, profitability, and environmental impact can
be compared under identical hydrogeological setting and climatic conditions.
In
a field study cosponsored by Stennis Space Center and ARS, RSML investigated
the use of Airborne Visible and Infrared Imaging Spectrometer (AVIRIS)
hyperspectral data to detect nitrogen deficiency and water stress in crops.
Scientists conducted the study at Shelton, Nebraska, over an irrigated
corn field with 20 variable-rate nitrogen plots. RSML acquired coverage
from three NASA AVIRIS flights during the growing season and made extensive
ground measurements of the agronomic and spectral characteristics to facilitate
the development of models to identify crop stress. Over spring wheat fields
in Montana, North Dakota, South Dakota, and Minnesota, RSML identified
and monitored crop stress throughout the growing season. Through cooperative
research with the USDA's Foreign Agricultural Service (FAS), scientists
evaluated models for assessing spring wheat production in Siberia and
Kazakhstan. Also, RSML used fluorescent sensing to detect ozone damage,
ultraviolet radiation damage to vegetation, and the effect of increased
carbon dioxide. RSML demonstrated that remote sensing can accurately assess
tropospheric environmental problems.
Researchers
at the ARS Western Integrated Cropping Systems Research Unit in Shafter,
California, continued to develop procedures for detecting and managing
water stress and pest infestations. The Shafter Airborne Multispectral
Remote Sensing System (SAMRSS), which acquires high-resolution imagery
in the visible, near-infrared, and thermal infrared, was flown on 30 missions.
Using this imagery and data from ground-based collections, researchers
developed procedures for detecting early infestations of spider mites
in cotton and the onset of water stress. Researchers at Shafter cooperated
with colleagues from Opto-Knowledge Systems, Inc., and NASA in studying
the use of hyperspectral imagery from AVIRIS in agricultural applications.
In cooperation with Cotton, Inc., researchers purchased a cotton yield
monitor and installed it on a cotton picker to determine spatial relationships
between yields and remotely sensed variables. They determined that the
spatial variability in cotton yields across a field can best be estimated
using midseason multispectral imagery.
The
ARS Rangeland Resources Research Unit at the High Plains Grasslands Research
Station in Cheyenne, Wyoming, collected Very Large Scale Aerial (VLSA)
images of soil erosion plots at the Central Plains Experimental Range.
To improve rangeland management, researchers there compared measurements
made from this aerial imagery with hand-collected data of bare ground,
plant cover, microtopography and microflow patterns. In cooperation with
the Department of the Interior's Bureau of Land Management, the State
of Wyoming, and private ranchers, the unit has used VLSA and space imagery
for rangeland monitoring that is low-cost and defendable in court. An
additional critical component of this research will relate important aspects
of rangelands like leaf area of dominant functional plant types to carbon
dioxide fluxes and carbon cycling.
At
Reynolds Creek Experimental Watershed, the ARS Northwest Watershed Research
Center (NWRC) used satellite imageryLandsat 5 Thematic Mapper and
Satellite Pour l'Observation de la Terre (SPOT) 3HRVto classify
accurately shrub steppe and subalpine plant communities. A practical procedure
for classifying and mapping intermountain plant communities using satellite
imagery was provided to the Bureau of Land Management's Snake River District
in Idaho. NWRC also has evaluated multispectral digital aerial photography
as a tool to determine stream shading by riparian vegetation and its effects
on surface water quality in rangelands. Using Landsat imagery, NWRC quantified
leaf area indices for rangeland plant communities for input to water and
energy balance models that can be used to estimate rangeland plant production.
NWRC also has worked with NASA to develop the use of synthetic aperture
radar to map the distribution of frozen soil and soil-water content in
rugged terrain.
The
ARS laboratory in Sidney, Montana, conducted remote-sensing research for
noxious weed identification, mapping, and monitoring crop management.
The laboratory used color aerial photography to map leafy spurge infestations
in Theodore Roosevelt National Park and the Sheyenne National Grasslands
of North Dakota. It subsequently interpreted, digitized, and incorporated
the national park's imagery with similar data collected in 1993. The 5-year
comparison has provided a valuable evaluation of leafy spurge growth,
distribution, and dynamics within the park. The laboratory also acquired
normal color aerial photography of salt cedar stands in Wyoming, Utah,
Nevada, and California to develop baseline population levels for studies
involving releases of biological control agents. Research continued on
the use of aerial videography for monitoring crop development and yield
prediction in western North Dakota and eastern Montana. The Montana research
project used the remote imagery to develop and assess the success of precision
agriculture cropping strategies. Researchers have initiated the same technologies
in North Dakota to monitor potato crop development and production. A new
hyperspectral radiometer and imaging system will be used to improve weed
identification and mapping and provide calibrated data for the multitemporal
evaluation of crop development.
The
satellite remote-sensing program of FAS, operated by the Production Estimates
and Crop Assessment Division (PECAD), remained a critical element in USDA's
analysis of global agricultural production and crop conditions by providing
timely, accurate, and unbiased estimates of global area, yield, and production.
Satellite-derived early warning of unusual crop conditions and production
anomalies enabled more rapid and precise determinations of global supply
conditions. The FAS/PECAD analysts employed a proven "convergence of evidence"
approach to crop assessmentincorporating NOAA Advanced Very High
Resolution Radiometer (AVHRR), Landsat, and SPOT imagery; crop models;
weather data; U.S. agricultural attaché reports; field travel; and
ancillary data to forecast foreign grain, oilseed, and cotton production.
FAS/PECAD
accurately forecast 1999-2000 global grain production to within roughly
3 percent of final output. Visual interpretation of high-resolution SPOT
satellite imagery provided early warning of significant crop stress in
key Russian winter-wheat-producing regions. An analysis of yield-simulation
models and vegetative indices derived from AVHRR satellite data indicated
where drought impact was most severe and identified crop areas that escaped
damage. Subsequent harvest reports verified the FAS/PECAD early-season
analysis.
FAS/PECAD
personnel have participated in international agricultural research studies
in Russia and Kazakhstan, in conjunction with USDA/ARS researchers, to
expand and improve crop assessment resources for estimating wheat production
in the former Soviet Union. The team has worked closely with agricultural
and remote-sensing researchers from the Kazakhstan Space Research Institute
and the Institute for Environmental Monitoring in Western Siberia to evaluate
and refine the use of yield- simulation models and new satellite sensors.
This cooperative research resulted in a valuable exchange of crop-forecasting
technology.
FAS
remote sensing supported Department of State assessments of food needs
in the former Soviet Union, Indonesia, and North Korea. Also, FAS prepared
detailed analyses of the record Argentine soybean crop, the bumper wheat
crop in Australia, the bumper soybean crop in Brazil, drought in the Ukraine,
dryness in China and North Korea, and flooding in Mexico.
The
Farm Service Agency (FSA) continued to share with FAS the cost of analyzing
imagery of the United States. A timely analysis of U.S. crop conditions,
combined with weather data, crop model results, and GIS products, made
possible the development of accurate and timely projections and comprehensive
evaluations of crop disaster situations. During the 1999 growing season
in the United States, the domestic analysts of FAS/PECAD provided early
warning on anomalous crop conditions, including severe droughts in the
Mid-Atlantic States and Eastern Corn Belt, as well as flooding from hurricanes
and subsequent rainfall in North Carolina and southern Virginia. The impact
of the sixth consecutive wet spring and early summer on agricultural interests
in North and South Dakota was determined and reported in interagency briefings
and published on the internal FSA/FAS Web site. FSA continued to be a
partner in the National Aerial Photography Program (NAPP) and the National
Digital Orthoquad Program (NDOP). FSA started field-reengineered business
processes that combine the use of digital orthophotography, GIS, GPS,
and satellite imagery to replace the use of hard-copy NAPP aerial photography
and 35-millimeter slides.
The USDA Forest Service provided support to the Selection Committee for
the NASA Research Announcement on Agriculture, Forestry, and Range Management
by contributing three members to the selection panel. A proposal from
the Forest Service's Fire Sciences Laboratory for Mapping Fire and Fuels
Characteristics Using Remote Sensing and Biophysical Modeling for Operational
Fire Management was selected by the committee. This project will provide
researchers at the Forest Service and universities with vegetation maps
of areas prone to wildfires, allowing firefighters to determine which
plants are more likely to fuel wildfires and better predict the paths
of such fires.
Project
Redsky, a DoD/Forest Service experiment to detect fires using DoD satellites,
continued in 1999. The Forest Service also has supported the implementation
and testing of the Hazard Support System at the U.S. Geological Survey's
Reston, Virginia, campus. This system, which warns of the outbreak of
wildfires and volcanic eruptions, is a joint program among the DoD, the
U.S. Geological Survey, NASA, and other Government agencies. The Langley
Research Center FireSat Team provided airborne measurements during a series
of controlled fires in Gila National Forest; the measurements were conducted
to validate the performance of the Hazard Support System.
The
Forest Service continued to study the use of Light Intersection Direction
and Ranging (LIDAR) data to create three-dimensional structure maps for
forested lands. A NASA C-130, carrying the Laser Vegetation Imaging Sensor
(LVIS), mapped a 200-square-mile forest area in northern California as
a precursor to the launch of the Vegetation Canopy Lidar (VCL) satellite
scheduled for launch in September 2000. The California flights will allow
scientists to acquire VCL-like data that will be used to fine-tune data
analysis methods.
The
National Agricultural Statistics Service (NASS) used remote-sensing data
to construct area frames for statistical sampling, to estimate crop area,
to create crop-specific land-cover data layers for GIS, and to assess
crop conditions. For area frame construction, NASS combined digital Landsat
and SPOT data with U.S. Geological Survey digital line-graph data, enabling
the user to assign each piece of land in a State to a category, based
on the percentage of cultivation or other variables. NASS implemented
a new remote-sensing based area frame and sample for Mississippi. The
remote-sensing acreage estimation project analyzed Landsat data of the
1998 crop season in Arkansas, North Dakota, and South Dakota to produce
crop acreage estimates for major crops at State and county levels, plus
a crop-specific categorization in the form of a digital mosaic of Thematic
Mapper scenes distributed to users on a CD-ROM. For the 1999 crop season,
NASS headquarters and several NASS field offices entered into partnership
agreements with State organizations to decentralize the Landsat processing
and analysis tasks. Technicians collected data for 1999 acreage estimation
analysis in Arkansas, Illinois, Mississippi, New Mexico, and North Dakota.
Vegetation condition images based on AVHRR data were used with conventional
survey data to assess crop conditions. NASS employed this imagery to monitor
the 1999 drought in Mid-Atlantic States.
The
Natural Resources Conservation Service (NRCS) continued its cooperative
partnership with Federal, State, and local agencies in developing 1-meter
digital ortho-imagery coverage of the Nation through both NDOP and NAPP.
By year's end, approximately 1,800 counties will have complete digital
orthoimagery coverage. NRCS and FSA jointly awarded an innovative contract
for the development of digital color infrared ortho-imagery for Hawaii.
Imagery acquired for Hawaii will be fully digital and integrated with
data collected by an onboard inertial measuring unit and dual-band GPS.
The inertial measuring unit and GPS data significantly reduce the need
for obtaining costly ground control to generate digital orthoimagery to
meet national map accuracy standards. NRCS continued to work with the
Massachusetts Institute of Technology to make seamless digital orthoimagery
data accessible over the Internet.
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