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At least 991 records · Page 55

Automated Subpixel Snow Parameter Mapping with AVIRIS Data

We describe an automated algorithm (MEMSCAG) for mapping subpixel snow covered area (SCA) and snow grain size with AVIRIS data. The algorithm is based on the multiple endmember approach to spectral mixture analysis in which the spectral endmembers and the number of endmembers can vary on a pixel-by-pixel basis. This approach accounts for surface cover heterogeneity within a scene. The mixture analysis runs on endmembers from a spectral library of snow, vegetation, rock, soil, and lake ice spectra. Snow endmembers of varying grain size were produced with a radiative transfer model. All non-snow endmembers were collected with a portable field spectrometer. Mapping is performed through sequential 2-endmember, 3-endmember, and 4- endmember mixture model runs, each subject to constraints on RMS, residuals, fractions and priority. Grain size is determined by the grain size of the snow endmember used in the optimal mixture model. We apply MEMSCAG to AVIRIS data collected over Mammoth Mountain, CA and the northern site of the BOREAS in Manitoba, Canada. MEMSCAG produces appropriate snow covered area estimates in all regions. A preliminary comparison of grain size estimates from MEMSCAG with field measurements demonstrates high accuracy.

Painter, Thomas H.↗

NASA'S Water Resources Element Within the Applied Sciences Program

The NASA Applied Sciences Program works within NASA Earth sciences to leverage investment of satellite and information systems to increase the benefits to society through the widest practical use of NASA research results. Such observations provide a huge volume of valuable data in both near-real-time and extended back nearly 50 years about the Earth's land surface conditions such as land cover type, vegetation type and health, precipitation, snow, soil moisture, and water levels and radiation. Observations of this type combined with models and analysis enable satellite-based assessment of numerous water resources management activities. The primary goal of the Earth Science Applied Science Program is to improve future and current operational systems by infusing them with scientific knowledge of the Earth system gained through space-based observation, model results, and development and deployment of enabling technologies, systems, and capabilities. Water resources is one of eight elements in the Applied Sciences Program and it addresses concerns and decision making related to water quantity and water quality. With increasing population pressure and water usage coupled with climate variability and change, water issues are being reported by numerous groups as the most critical environmental problems facing us in the 21st century. Competitive uses and the prevalence of river basins and aquifers that extend across boundaries engender political tensions between communities, stakeholders and countries. Mitigating these conflicts and meeting water demands requires using existing resources more efficiently. The potential crises and conflicts arise when water is competed among multiple uses. For example, urban areas, environmental and recreational uses, agriculture, and energy production compete for scarce resources, not only in the Western U.S. but throughout much of the U.S. but also in many parts of the world. In addition to water availability issues, water quality related problems are seriously affecting human health and our environment. The NASA Water Resources Program Element works to use NASA products to address these critical issues.

Toll, David↗

Multi-Frequency Radiometer-Based Soil Moisture Retrieval and Algorithm Parameterization Using In Situ Sites

L-band brightness temperature (TB) has been shown to provide the best sensitivity to soil moisture (SM) although C- and X-band based products offer a longer time-series from satellite-based measurements. Currently, global coverage SM is routinely produced from spaceborne measurements using all three frequency bands, but despite continued validation efforts of the products, the relative characteristics and performance of these observations have not been fully established. Therefore, this study focused on the parametrization of SM retrieval algorithms at L-, C- and X-bands using TB observations from the L-band radiometer on NASA's SM Active Passive (SMAP) mission and the C- and X-band channels of JAXA's Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the GCOM-W satellite. These can be applied in global SM retrieval algorithms using either one of the frequencies or a combination of them. The reference in situ SM data was obtained from 12 core validation sites across various land cover types around the world. The investigation highlighted the known challenges of retrieving SM from C- and X-band data compared to the higher sensitivity of the L-band data. Even with a site-specific retrieval algorithm parameterization, the mean correlation of the C- and X-band retrievals for the core validation site SM measurement were much lower than that for L-band, being 0.52 (0.54) and 0.45 (0.47) for vertical (horizontal) polarization, respectively, while for the L-band retrieval the corresponding values were 0.81 (0.77). The parameterization exercise showed that matching the C- and X-band TB measurements with an emission model was not difficult; the problem was relating the observations to SM under the influence of large roughness and vegetation effects. As a result, parameter optimization produced values for some sites that were not realistic or did not allow any practical sensitivity to SM at C- and X-band. Considering the L-band observations, the parameter optimization resulted in superior bias performance as compared to the operational SMAP product parameterization, but the sensitivity to SM changes (R and unbiased root mean square difference) did not improve markedly, or in some cases degraded at the expense of a smaller bias.

passive microwave↗

Multi-Frequency NMM3D Simulations of Wave Propagation in Vegetation for Remote Sensing of Soil Moisture

To investigate the feasibility of using multi-frequencies to fur- ther improve the soil moisture retrieval, the recently devel- oped hybrid method is used to perform full-wave scattering simulations of a wheat field at three different frequencies of L-, S- and C- bands. In the hybrid method, the multiple scattering within a single wheat plant is first captured using the T-matrix of vec- tor cylindrical waves calculated from the full-wave solutions of HFSS. In the second step, the scattering among different plants is considered using the Foldy-Lax equations of multiple scattering theory (MST) and the extracted T-matrix. The transmission of microwaves through wheat field at the L-, S- and C-bands are calculated using the hybrid method. Results show that: (1) the transmission obtained from the full-wave simulations is much larger than those computed from the radiative transfer equations (RTE) model, (2) the hybrid method transmission has a weaker frequency dependence than those of the RTE, and (3) the attenuation caused by the vegetation layer would saturate with an increase in frequency.

Yueh, Simon↗

Evaluating the Contribution of NASA Remotely-Sensed Data Sets on a Convection-Allowing Forecast Model

The Short-term Prediction Research and Transition (SPoRT) Center is a collaborative partnership between NASA and operational forecasting partners, including a number of National Weather Service forecast offices. SPoRT provides real-time NASA products and capabilities to help its partners address specific operational forecast challenges. One challenge that forecasters face is using guidance from local and regional deterministic numerical models configured at convection-allowing resolution to help assess a variety of mesoscale/convective-scale phenomena such as sea-breezes, local wind circulations, and mesoscale convective weather potential on a given day. While guidance from convection-allowing models has proven valuable in many circumstances, the potential exists for model improvements by incorporating more representative land-water surface datasets, and by assimilating retrieved temperature and moisture profiles from hyper-spectral sounders. In order to help increase the accuracy of deterministic convection-allowing models, SPoRT produces real-time, 4-km CONUS forecasts using a configuration of the Weather Research and Forecasting (WRF) model (hereafter SPoRT-WRF) that includes unique NASA products and capabilities including 4-km resolution soil initialization data from the Land Information System (LIS), 2-km resolution SPoRT SST composites over oceans and large water bodies, high-resolution real-time Green Vegetation Fraction (GVF) composites derived from the Moderate-resolution Imaging Spectroradiometer (MODIS) instrument, and retrieved temperature and moisture profiles from the Atmospheric Infrared Sounder (AIRS) and Infrared Atmospheric Sounding Interferometer (IASI). NCAR's Model Evaluation Tools (MET) verification package is used to generate statistics of model performance compared to in situ observations and rainfall analyses for three months during the summer of 2012 (June-August). Detailed analyses of specific severe weather outbreaks during the summer will be presented to assess the potential added-value of the SPoRT datasets and data assimilation methodology compared to a WRF configuration without the unique datasets and data assimilation.

Zavodsky, Bradley T.↗

How Universal is the Relationship Between Remotely Sensed Vegetation Indices and Crop Leaf Area Index? A Global Assessment

Leaf Area Index (LAI) is a key variable that bridges remote sensing observations to the quantification of agroecosystem processes. In this study, we assessed the universality of the relationships between crop LAI and remotely sensed Vegetation Indices (VIs). We first compiled a global dataset of 1459 in situ quality-controlled crop LAI measurements and collected Landsat satellite images to derive five different VIs including Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI), two versions of the Enhanced Vegetation Index (EVI and EVI2), and Green Chlorophyll Index (CI(sub Green)). Based on this dataset, we developed global LAI-VI relationships for each crop type and VI using symbolic regression and Theil-Sen (TS) robust estimator. Results suggest that the global LAI-VI relationships are statistically significant, crop-specific, and mostly non-linear. These relationships explain more than half of the total variance in ground LAI observations (R2 greater than 0.5), and provide LAI estimates with RMSE below 1.2 m2/m2. Among the five VIs, EVI/EVI2 are the most effective, and the crop-specific LAI-EVI and LAI-EVI2 relationships constructed by TS, are robust when tested by three independent validation datasets of varied spatial scales. While the heterogeneity of agricultural landscapes leads to a diverse set of local LAI-VI relationships, the relationships provided here represent global universality on an average basis, allowing the generation of large-scale spatial-explicit LAI maps. This study contributes to the operationalization of large-area crop modeling and, by extension, has relevance to both fundamental and applied agroecosystem research.

Vegetation Index↗

Measurement of aerodynamic roughness using radar backscatter over vegetated surfaces

The importance of the measurement of wind fields is discussed. Wind regime data can be used to infer the amount and type of wind induced (aerolian) transport of sand and dust, or to establish global circulation models, for example on other planets. Since local measurements are costly and often impossible, it is desired to infer such data from remotely sensed information. A potential mechanism for remotely inferring the wind regime by using synthetic aperture radar data to describe the roughness of the surface is described. A project to estimate the practicality of using such a mechanism is described. An experiment that extends the mechanism to vegetated sites, where the goal is to measure potential for erosion, is reported.

Wall, S. D.↗

Preliminary Results of a U.S. Deep South Modeling Experiment Using NASA SPoRT Initialization Datasets for Operational National Weather Service Local Model Runs

A joint collaborative modeling effort among the NWS offices in Mobile, AL, and Houston, TX, and NASA Short-term Prediction Research and Transition (SPoRT) Center began during the 2011-2012 cold season, and continued into the 2012 warm season. The focus was on two frequent U.S. Deep South forecast challenges: the initiation of deep convection during the warm season; and heavy precipitation during the cold season. We wanted to examine the impact of certain NASA produced products on the Weather Research and Forecasting Environmental Modeling System in improving the model representation of mesoscale boundaries such as the local sea-, bay- and land-breezes (which often leads to warm season convective initiation); and improving the model representation of slow moving, or quasi-stationary frontal boundaries (which focus cold season storm cell training and heavy precipitation). The NASA products were: the 4-km Land Information System, a 1-km sea surface temperature analysis, and a 4-km greenness vegetation fraction analysis. Similar domains were established over the southeast Texas and Alabama coastlines, each with an outer grid with a 9 km spacing and an inner nest with a 3 km grid spacing. The model was run at each NWS office once per day out to 24 hours from 0600 UTC, using the NCEP Global Forecast System for initial and boundary conditions. Control runs without the NASA products were made at the NASA SPoRT Center. The NCAR Model Evaluation Tools verification package was used to evaluate both the positive and negative impacts of the NASA products on the model forecasts. Select case studies will be presented to highlight the influence of the products.

Wood, Lance↗

Evapotranspiration-Based Irrigation Scheduling in Cool-Season Vegetables

Crop evapotranspiration (ETc) is strongly linked with photosynthetically active vegetation fraction (Fc). Estimation of ETc may support efficiency gains in irrigation water management, which in turn can mitigate nitrate leaching, promote water supply sustainability, and reduce energy costs associated with water pumping or transport. The University of California Cooperative Extension operates the CropManage (CM) model as a freely-available web-application for growers and consultants to support irrigation and nitrogen scheduling decisions. CM accounts for the rapid growth and typically brief cycle of cool-season vegetables, where Fc and crop coefficient (fraction of reference ET) can change daily during canopy development. Daily weather conditions are inherently accounted for by use of grass reference ETo data imported from the California Dept. Water Resources. Crop water requirement calculations are output in terms of irrigation system runtime. Empirical equations are used to estimate daily Fc time-series for a given crop type, primarily as a function of planting date and expected harvest. An applications programming interface (API) enables CM to import satellite-based Fc observations from NASA's Satellite Irrigation Management Support, which uses Landsat imagery to monitor about eight million irrigation acres statewide. The API is intended to provide a check on internal CM predictions of Fc and to facilitate expansion of the web-app to new crops and regions. A replicated irrigation trial was performed on cauliflower during spring/summer 2018 at the USDA Agricultural Research Station in Salinas, CA. The crop was established by sprinkler irrigation, and CropManage was then used to guide a series of drip irrigation treatments at 50%, 75%, 100%, and 150% of ETc replacement levels. Results will be presented with respect to water use efficiency, nitrogen use efficiency, biomass yield, and marketable yield. Additional findings will be presented for a celery trial harvested during autumn 2018.

Johnson, Lee↗

Intercomparison of Soil Moisture, Evaporative Stress, and Vegetation Indices for Estimating Corn and Soybean Yields Over the U.S.

This paper presents an intercomparative study of 12 operationally produced large-scale datasets describing soil moisture, evapotranspiration (ET), and or vegetation characteristics within agricultural regions of the contiguous United States (CONUS). These datasets have been developed using a variety of techniques, including, hydrologic modeling, satellite-based retrievals, data assimilation, and survey in-field data collection. The objectives are to assess the relative utility of each dataset for monitoring crop yield variability, to quantitatively assess their capacity for predicting end-of-season corn and soybean yields, and to examine the evolution of the yield-index correlations during the growing season. This analysis is unique both with regards to the number and variety of examined yield predictor datasets and the detailed assessment of the water availability timing on the end-of-season crop production during the growing season. Correlation results indicate that over CONUS, at state-level soil moisture and ET indices can provide better information for forecasting corn and soybean yields than vegetation-based indices such as normalized difference vegetation index. The strength of correlation with corn and soybean yields strongly depends on the interannual variability in yield measured at a given location. In this case study, some of the remotely derived datasets examined provide skill comparable to that of in situ field survey-based data further demonstrating the utility of these remote sensing-based approaches for estimating crop yield.

evapotranspiration↗

Satellite-assisted Evaluation of Irrigation Application Efficiency for Lettuce in the Salinas Valley

The Salinas Valley region of California’s Central Coast is a majorU.S. vegetable producer. Lettuce is a leading commodity, grown on about 100,000 acres with annual value near $1.4B (2022 Monterey County Ag Report). Vegetables crops in the area aretypically well watered and fertilized in pursuit of commercial yield and quality standards. Information on irrigation application efficiency, meanwhile, can enhance waterresource sustainability and mitigate impacts of nitrate infiltrationon groundwater quality. The Crop Consumptive Use Fraction (CCUF), previously recommended by the California Department of Water Resources, is a metric that expresses evapotranspiration (ET) of applied water relative to total applied water. More recently under Assembly Bill 1668 addressing water conservation and drought planning, the metric can be used to help quantify application efficiency to support preparation of agricultural water management plans in the state. This study will report CCUF evaluations from several commercial lettuce plantings in the Salinas Valley during the 2022 dry season (late spring-early fall). The evaluations were derived from satellite-based observations of cropET from the OpenETcloud application, and applied water monitored by in-line flowmetersand supplemented by grower reports of irrigation system runtime. The study will also compare observed (satellite-based) ET with modeled ET from the CropManage irrigationand nutrient management decision-support system operated by Cooperative Extension. Additional study in cool-season vegetables is ongoing in 2023.

Satellite-assisted↗

Impacts of 1.5 °C and 2.0 °C global warming above pre-industrial on potential winter wheat production of China

Keeping global temperatures below 2.0 °C above pre-industrial condition and pursuing efforts toward the more ambitious 1.5 °C goal in the late 21st century was the main target from the Paris Agreement in 2015. Here we assessed the likely challenges for the China’s winter wheat production under 1.5 °C and 2.0 °C increase of global temperature, with four wheat crop models (CERES-Wheat, Nwheat, WheatGrow, and APSIM-Wheat) and the latest climate projections from the Half a degree Additional warming, Prognosis and Projected Impacts project (HAPPI). Instead of using average “winter type” wheat cultivar, and same management and soil inputs for whole region, location-specific winter wheat cultivars with local agronomic information were calibrated for each of the representative wheat growing area of China, allowing a better spatial agronomic representation of the whole wheat planting area. The mean growing season temperature (GST) during the winter wheat vegetative stage was projected to increase by 0.6 to 1.4 °C for the 1.5 °C scenario, and 0.9 to 1.8 °C for the 2.0 °C scenario, while during the reproductive stage was decreased between 0 and 0.9 °C for the 1.5 °C scenario and -0.3 and 1.1 °C for the 2.0 °C scenario. Growing season duration (GSD) for the whole period was shortened by 6 to 15 days for the 1.5 °C scenario and 8 to 18 days for the 2.0 °C scenario, as a result of higher GST under global warming. Increase in GST and decrease in GSD was more obvious in the Southwest Subregion (SWS) than subregions in the north. The shortening GSD for the whole wheat growth period was mostly from the shortening vegetative period, as no appreciable difference in number of days from anthesis to maturity was found for the whole regions. Although there is variability among models, the indication is that wheat yields were projected to increase in the North Subregion (NS), the Huang-Huai Subregion (HHS), and the Middle-lower Researches of Yangzi River Subregion (MYS), but to decrease in the SWS under two warming scenarios. The effects of elevated CO2 concentration were mostly beneficial and tended to offset the negative impacts of increasing temperature at both global warming scenarios, with a rate of 7-14% yield increase per 100-ppm, except for locations with GST of baseline higher than 11 °C. Aggregating to regional wheat production, the total winter wheat production of China was projected to increase by 2.8% (1.6% to 3.0%, 25th percentile to 75th percentile) and 8.3% (7.0% to 9.6%, 25th percentile to 75th percentile) under 1.5 °C and 2.0 °C scenarios, and most of increase was observed in the north subregions due to the largest wheat planting area. Our results will lay the foundation for developing adaptation strategies to future climate change to ensure China and global wheat supply and food security.

Winter wheat↗

Remote Sensing and halocene Vegetation: History of Global Change

Predictions of the future evolution of the earth's atmospheric chemistry and its impact on global circulation patterns are based on Global Climate Models (GCMs) that integrate the complex interactions of the biosphere, atmosphere and the oceans. Most of the available records of climate and environment are short-term records (from decades to a few hundred years) with convolved information of real trends and short-term fluctuations. GCMs must be tested beyond the short-term record of climate and environment to insure that predictions are based on trends and therefore are appropriate to support long term policy making. Unfortunately different parts of the world, weather stations are scattered, records extend over a period of only few years, and there are no systematic climate records for large portions of the globe.

D'Antoni, Hector L.↗

Remote Sensing and Holocene Vegetation: History of Global Change

Predictions of the future evolution of the earth's atmospheric chemistry and its impact on global circulation patterns are based on Global Climate Models (GCM's) that integrate the complex interactions of the biosphere, atmosphere and the oceans. Most of the available records of climate and environment are shortterm records (from decades to a few hundred years) with convolved information of real trends and short-term fluctuations. GCM's must be tested beyond the short-term record of climate and environment to insure that predictions are based on trends and therefore are appropriate to support long term policy making. An appropriate timeframe should extend over the Holocene period (the last 10,000 years) when most contemporary climate and environmental processes began. Since its inception in 1916, pollen analysis has successfully reconstructed the paleoecology of the last 10,000 years for many sites around the world, thus providing a powerful time-link between short- and long-term processes in the biosphere. However, pollen analytic results cannot be used in physiological models driven by remotely sensed data. Further, modern ecology and climate data are necessary to calibrate pollen analytical models. These are available for extensive regions in the northern hemisphere, particularly for eastern United States and Canada, and western Europe. In other parts of the world, weather stations are scattered, records extend over a period of only few years, and there are no systematic climate records for large portions of the globe. This is the case of Patagonia in Argentina where a few weather stations are located close to the Atlantic seaports, fewer stations are in towns located near the eastern Andean foothill, and fewer still are scattered on the extensive Patagonian plateau. This problem became evident after completion of the Argentine-German Program of Palynology (PROPAL), a cooperative effort of National University of Mar del Plata (Argentina) and University Bamberg (Germany) to produce a modern pollen database for the Pampa and Patagonia regions.

DAntoni, Hector↗

Land Boundary Conditions for the Goddard Earth Observing System Model Version 5 (GEOS-5) Climate Modeling System: Recent Updates and Data File Descriptions

The Earths land surface boundary conditions in the Goddard Earth Observing System version 5 (GEOS-5) modeling system were updated using recent high spatial and temporal resolution global data products. The updates include: (i) construction of a global 10-arcsec land-ocean lakes-ice mask; (ii) incorporation of a 10-arcsec Globcover 2009 land cover dataset; (iii) implementation of Level 12 Pfafstetter hydrologic catchments; (iv) use of hybridized SRTM global topography data; (v) construction of the HWSDv1.21-STATSGO2 merged global 30 arc second soil mineral and carbon data in conjunction with a highly-refined soil classification system; (vi) production of diffuse visible and near-infrared 8-day MODIS albedo climatologies at 30-arcsec from the period 2001-2011; and (vii) production of the GEOLAND2 and MODIS merged 8-day LAI climatology at 30-arcsec for GEOS-5. The global data sets were preprocessed and used to construct global raster data files for the software (mkCatchParam) that computes parameters on catchment-tiles for various atmospheric grids. The updates also include a few bug fixes in mkCatchParam, as well as changes (improvements in algorithms, etc.) to mkCatchParam that allow it to produce tile-space parameters efficiently for high resolution AGCM grids. The update process also includes the construction of data files describing the vegetation type fractions, soil background albedo, nitrogen deposition and mean annual 2m air temperature to be used with the future Catchment CN model and the global stream channel network to be used with the future global runoff routing model. This report provides detailed descriptions of the data production process and data file format of each updated data set.

GEOS-5↗

An Interplay between Photons, Canopy Structure, and Recollision Probability: A Review of the Spectral Invariants Theory of 3D Canopy Radiative Transfer Processes

Earth observations collected by remote sensors provide unique information to our ever-growing knowledge of the terrestrial biosphere. Yet, retrieving information from remote sensing data requires sophisticated processing and demands a better understanding of the underlying physics. This paper reviews research efforts that lead to the developments of the stochastic radiative transfer equation (RTE) and the spectral invariants theory. The former simplifies the characteristics of canopy structures with a pair-correlation function so that the 3D information can be succinctly packed into a 1D equation. The latter indicates that the interactions between photons and canopy elements converge to certain invariant patterns quantifiable by a few wavelength independent parameters, which satisfy the law of energy conservation. By revealing the connections between plant structural characteristics and photon recollision probability, these developments significantly advance our understanding of the transportation of radiation within vegetation canopies. They enable a novel physically-based algorithm to simulate the “hot-spot” phenomenon of canopy bidirectional reflectance while conserving energy, a challenge known to the classic radiative transfer models. Therefore, these theoretical developments have a far-reaching influence in optical remote sensing of the biosphere.

vegetation remote sensing; stochastic radiative tr↗

Studies of 21st-Century Precipitation Trends Over West Africa

West Africa includes a semi-arid zone between the Sahara Desert and the humid Gulf of Guinea coast, approximately between 10 N and 20 N, which is irrigated by summer monsoon rains. This article refers to the region as the Sahel. Rain-fed agriculture is the primary sustenance for Sahel populations, and severe droughts (in the 1970s and 1980s), therefore, have devastating negative societal impacts. The future frequency of Sahel droughts and the evolution of its hydrological balance are therefore of great interest. The article reviews 10 recent research studies that attempt to discover how climate changes will affect the hydrology of the Sahel throughout the 21st century. All 10 studies rely on atmosphere ocean global climate model (AOGCM) simulations based on a range of greenhouse gas emissions scenarios. Many of the simulations are contained in the Intergovernmental Panel on Climate Change archives for Assessment Reports #3 and #4. Two of the studies use AOGCM data to drive regional climate models. Seven studies make projections for the first half of the 21st century and eight studies make projections for the second half. Some studies make projections of wetter conditions and some predict more frequent droughts, and each describes the atmospheric processes associated with its prediction. Only one study projects more frequent droughts before 2050, and that is only for continent-wide degradation in vegetation cover. The challenge to correctly simulate Sahel rainfall decadal trends is particularly daunting because multiple physical mechanisms compete to drive the trend upwards or downwards. A variety of model deficiencies, regarding the simulation of one or more of these physical processes, taints models climate change projections. Consequently, no consensus emerges regarding the impact of anticipated greenhouse gas forcing on the hydrology of the Sahel in the second half of the 21st century.

Druyan, Leonard M.↗

An interdisciplinary analysis of ERTS data for Colorado mountain environments using ADP techniques. An early analysis of ERTS-1 data

There are no author-identified significant results in this report. The principal problem encountered has been the lack of good quality, small scale baseline photography for the test areas. Analysis of the ERTS-1 data for the San Juan Site will emphasize development of a preliminary spectral classification defining grass cover categories, and then selection of subframes for intensive investigation of the forestry, geologic, and hydrologic properties of the area. Primary work has been devoted to the selection and digitization of areas for topographic modeling, and compilation of ground based data maps necessary for computer analysis. Study effort has emphasized: geomorphic features; macro-vegetation; micro-vegetation; snow-hydrology; insect/disease damage; and blow-down. Analysis of a frame of the Lake Texoma area indicates a great deal of potential in the analysis and interpretation of ERTS imagery. Preliminary results of investigations of geologic, forest, range, cropland, and water resources of the area are summarized.

Hoffer, R. M.↗