B11F-01 INVITED
The National Land Imaging Program
On August 14, 2007, the Administration issued a report calling for the United States to establish a National Land Imaging Program (NLIP). The NLIP is a framework for continuing collection of moderate-resolution, multispectral, remotely sensed data for the globe. The report calls for continued U.S. commitment to moderate-resolution land imagery, recommends the United States maintain a core operational capability for land imagery while supplementing its data with similar data, and designates the Department of the Interior (DOI) as the leader of the new program. The DOI would provide focused leadership and management for the Nation's civil operational land imaging efforts. This focused leadership would achieve a stable and sustainable operational space-based land imaging capability and ensure continued U.S. scientific, technological, and policy leadership in civil land imaging and the scientific disciplines it supports. Through this new program, the land imaging data acquisition needs of Federal agencies, States, local land management officials, scientists, and geographic researchers will be coordinated and more effectively managed. With a single agency providing the technical leadership, gathering user requirements, and translating imaging needs into technical capabilities, the broad interests of the user community can be more efficiently met. This discussion will describe the details of the report and possible future activities of the NLIP.
B11F-02 INVITED
Remote Estimation of Crop Biophysical Characteristics: Problems and Solutions
Characterization of crop physiological and phenological status, or crop condition, requires robust retrievals of important crop biophysical properties, preferably using non-destructive methods. The sensitivity of the widely used Normalized Difference Vegetation Index (NDVI) saturates at moderate levels of aboveground biomass; i.e., when leaf area index (LAI) increases above about 2. We report the results of our investigation of the performance of an advanced suite of four vegetation indices that expand the dynamic ranges of canopy biophysical properties over high biomass surfaces. The indices are: 1) the Visible Atmospherically Resistant Vegetation Index (VARI), for retrieving the fractional cover of green vegetation; 2) the Wide Dynamic Range Vegetation Index (WDRVI), which allows retrieval of LAI: 3) the Green NDVI and Red Edge NDVI, designed to yield the photosynthetically active component of total absorbed photosynthetically active radiation (fAPAR); and 4) Green and Red Edge Chlorophyll Indices for measuring the total chlorophyll content in a vegetation canopy. We discuss the results of estimating the biophysical characteristics noted above using close range sensing (reflectance taken 6 meters above the canopy), an airborne imaging spectrometer and satellite observations. The techniques were tested for maize and soybean in agricultural fields under irrigated and rainfed conditions. It is possible to accurately estimate the fractional cover of green vegetation, the photosynthetically active component of absorbed PAR, green LAI and chlorophyll content in crops with different canopy architectures and leaf structures with green leaf area indices ranging from 0 to more than 6.
B11F-03
Risk Mitigation as a Metric for Evaluating Decision Support System Inputs and Function
Defect Detection and Prevention (DDP) software, a risk management tool designed for spacefaring mission planning, was applied in a novel way to evaluate the efficacy of remote sensing and other inputs to the decision support system used to produce global crop yield estimates by the USDA's Office of Global Analysis/International Production Assessment Branch (IPA). IPA's activities contribute directly to the competitiveness and thus the sustainability of American agriculture. The DDP tool provides an explicit accounting of institutional objectives (e.g., accurate monthly crop estimates, dissemination of information), the risks to meeting those objectives that exist in the decision making system (inadequate timeliness or quality of data), and the effect of various mitigations that act to reduce risk and promote attainment of objectives (new remote sensing data streams, better crop models). Periodic reassessment of IPA's&pdecision support system has provided a view of its evolution over time, through enhancement with NASA science results and other inputs, and through major institutional change.
B11F-04
Meeting Challenges for a Sustainable Future
Achieving sustainability, i.e., meeting the needs and values of the present generation without compromising the ability of future generations to meet theirs, is the biggest challenge facing humanity in the 21st century. While solid science is necessary, it is not by itself sufficient to introduce the changes in lifestyles and business practices that a sustainable future will require: meeting the sustainability challenge is inherently multi- disciplinary. In addition, sustainability is only achieved through actions. Researchers and educators must engage with the public for three purposes: (1) learn what information is most needed, (2) share what is known about Earth's environment, (3) provide real applications that grant both economic and environmental benefits. With these purposes in mind, a consortium involving seven universities in five northern tier states has been providing applications of remote sensing to agriculture, forestry, and tribal cultural values. In addition, it has conducted both informal and formal education, the latter both K-12 and higher education. The work of the Upper Midwest Aerospace Consortium will be described. http://www.umac.org
B11F-05
Nitrogen and Water Stress Impact on Hard Red Spring Wheat Crop Reflectance, Yield and Grain Quality
Water and nitrogen stress impact hard red spring wheat (Triticum aestivum) crop reflectance, yield and grain quality. To minimize yield losses from nitrogen (N) and water stress, it is essential to apply appropriate N in relation to water stress. The objective of this experiment was to determine the influence of N and water stress on hard red spring wheat crop reflectance, yield, and grain quality. Complete randomized block experiments were conducted in 2003, 2004 and 2004 in dryland and irrigated fields at three locations in central South Dakota. Treatments consisted of N rates and N application at different growth stages. Nitrogen fertilizer rates ranged from 0 to 200 kg ha-1. Nitrogen fertilizer application times were (1) planting; (2) planting and tillering (Feekes 2 -3) or (3) tillering (Feekes 2 -3). Reflectance data was collected using a Cropscan and a CropCircle radiometer. Reflectance data was collected at bare soil, tillering (Feekes 2-3) and flag leaf (Feekes 9-10). Carbon 13 isotopic discrimination (Ä) was used to determine yield loss to nitrogen or water stress. Reflectance data was compared to yield and Ä values or grain quality and Ä values. Correlation of crop reflectance (measured at the different growth stages and by the different radiometers) with yield loss to nitrogen or water and grain quality will be presented. Information presented will be used to make corrective nitrogen treatments and improve marketing decisions as related to grain quality.
B11F-06
Mapping Spatial Variability in Crop Growth Using High Resolution Aerial Photographs
To adopt sustainable farming practices, farmers need information about the spatial variability of their cropland. Using this information, they can generate crop management zones and vary resource input to meet specific requirements within each zone. This often reduces the amount of resource (e.g., fertilizer, water) added to fields, resulting in direct benefit to the economy and the environment. To map the spatial variability within cropland, high resolution remotely sensed data are required. In this study we examined the utility of images from the Airborne Environmental Research Observational Camera (AEROCam) for identifying management zones in Lingle, Wyoming. Specifically, we examined the relationship between vegetation indices derived from AEROCam images and sunflower and barley yields. Normalized Difference Vegetation Index (NDVI) values accounted for most of the measured variance in crop yield, and were used to identify anomalies within the field. Differences in crop growth identified by AEROCam were related to variation in electrical conductivity and water holding capacity of the soil. Our results indicate that brightness values recorded by AEROCam images can be used to generate crop management zones.
B11F-07 INVITED
NativeView: A Geospatial Curriculum for Native Nation Building
In the spirit of collaboration and reciprocity, James Rattling Leaf of Sinte Gleska University on the Rosebud Reservation of South Dakota will present recent developments, experiences, insights and a vision for education in Indian Country. As a thirty-year young institution, Sinte Gleska University is founded by a strong vision of ancestral leadership and the values of the Lakota Way of Life. Sinte Gleska University (SGU) has initiated the development of a Geospatial Education Curriculum project. NativeView: A Geospatial Curriculum for Native Nation Building is a two-year project that entails a disciplined approach towards the development of a relevant Geospatial academic curriculum. This project is designed to meet the educational and land management needs of the Rosebud Lakota Tribe through the utilization of Geographic Information Systems (GIS), Remote Sensing (RS) and Global Positioning Systems (GPS). In conjunction with the strategy and progress of this academic project, a formal presentation and demonstration of the SGU based Geospatial software RezMapper software will exemplify an innovative example of state of the art information technology. RezMapper is an interactive CD software package focused toward the 21 Lakota communities on the Rosebud Reservation that utilizes an ingenious concept of multimedia mapping and state of the art data compression and presentation. This ongoing development utilizes geographic data, imagery from space, historical aerial photography and cultural features such as historic Lakota documents, language, song, video and historical photographs in a multimedia fashion. As a tangible product, RezMapper will be a project deliverable tool for use in the classroom and to a broad range of learners.
B11F-08
Developing a Nationwide Early Warning System of Meteorological Disasters for the Mongolian Pastoralism
Among natural disasters, drought affected the most people worldwide during the past few decades. Since the late 1970s, there has been a shift in El Niño-Southern Oscillation toward more warm events, closely related to a worldwide trend for intensified drought. Pastoral livestock husbandry, a major industry in Mongolia, has repeatedly suffered from drought and dzud (anomalous climatic and/or land-surface conditions leading to significant livestock mortality in winter-spring) due to its dry, cold climate. Droughts and dzuds between 1999 and 2002 killed 8.2 million livestock, which accounts for about one forth of the total number of livestock in Mongolia, and 3.0 million female livestock miscarried. The present paper proposes an early warning system (EWS) of the Mongolian meteorological disasters that is suitable for the environment and socio-economy. Although the state-of-the-art long-range weather forecasting has not yet produced reliable quantitative information, timely and accurate monitoring of the climate memory of land-surface anomaly conditions (such as soil moisture, pasture, and livestock) that resulted, with a time lag, from summer deficit rainfall will enable us to deliver early warnings of possible drought and dzud and finally to mitigate their effects on livestock husbandry. With this background in mind, the first attempt has been made to integrate operationally observed ground data and newly introduced remote sensing data in the context of climate memory to be overlaid on a nationwide map. A regression tree model is being developed in order to predict livestock mortality; the predictor variables included two indices developed from remote sensing data—the Normalized Difference Vegetation Index (NDVI) and the Snow Water Equivalent (SWE) —as well as the previous year's livestock numbers and mortality. According to the regression tree model, the most serious livestock mortality in winter-spring was associated with low NDVI values in August of the previous year (i.e., poor vegetation conditions in late summer), high SWE values in December of the previous year (i.e., significant snow accumulation by mid-winter), and a high previous year's mortality.