Evaluation of a segment-based LANDSAT full-frame approach to corp area estimation

As the registration of LANDSAT full frames enters the realm of current technology, sampling methods should be examined which utilize other than the segment data used for LACIE. The effect of separating the functions of sampling for training and sampling for area estimation. The frame selected for analysis was acquired over north central Iowa on August 9, 1978. A stratification of he full-frame was defined. Training data came from segments within the frame. Two classification and estimation procedures were compared: statistics developed on one segment were used to classify that segment, and pooled statistics from the segments were used to classify a systematic sample of pixels. Comparisons to USDA/ESCS estimates illustrate that the full-frame sampling approach can provide accurate and precise area estimates

Processing techniques development, volume 2. Part 1: Crop inventory techniques

There are no author-identified significant results in this report

Research in the application of spectral data to crop identification and assessment, volume 2

The development of spectrometry crop development stage models is discussed with emphasis on models for corn and soybeans. One photothermal and four thermal meteorological models are evaluated. Spectral data were investigated as a source of information for crop yield models. Intercepted solar radiation and soil productivity are identified as factors related to yield which can be estimated from spectral data. Several techniques for machine classification of remotely sensed data for crop inventory were evaluated. Early season estimation, training procedures, the relationship of scene characteristics to classification performance, and full frame classification methods were studied. The optimal level for combining area and yield estimates of corn and soybeans is assessed utilizing current technology: digital analysis of LANDSAT MSS data on sample segments to provide area estimates and regression models to provide yield estimates

Effects of management practices on reflectance of spring wheat canopies

The effects of available soil moisture, planting date, nitrogen fertilization, and cultivar on reflectance of spring wheat (Triticum aestivum L.) canopies were investigated. Spectral measurements were acquired on eight dates throughout the growing season, along with measurements of crop maturity stage, leaf area index, biomass, plant height, percent soil cover, and soil moisture. Planting date and available soil moisture were the primary agronomic factors which affected reflectance of spring wheat canopies from tillering to maturity. Comparisons of treatments indicated that during the seedling and tillering stages planting date was associated with 36 percent and 85 percent of variation in red and near infrared reflectances, respectively. As the wheat headed and matured, less of the variation in reflectance was associated with planting date and more with available soil moisture. By mid July, soil moisture accounted for 73 percent and 69 percent of the variation in reflectance in red and near infrared bands, respectively. Differences in spectral reflectance among treatments were attributed to changes in leaf area index, biomass, and percent soil cover. Cultivar and N fertilization rate were associated with very little of the variation in the reflectance of these canopies

Diagnostic development through the decades (mostly a Los Alamos perspective)

Author Institution: National Security Technologies, LLCSlides presented at the 2014 Photonic Doppler Velocimetry (PDV) Workshop Hosted by National Security Technologies, LLC, and the University of Nevada Las Vegas, June 24-26, 2014, Las Vegas, Nevada

Evaluation of a Segment-Based Landsat Full-Frame Approach to Crop Area Estimation

Accurate and timely crop production information is a critical need in today\u27s economy. During the past decade, satellite remote sensing has been increasingly recognized as a means for crop identification and estimation of crop areas. The Landsat multispectral scanner (MSS) records as a single data point (pixel) a region on the ground about one acre (0.5 ha) in size. When estimates of crop areas are desired for large regions, a statistical sampling scheme is required as it is not feasible to examine all of the pixels in the region of interest. The development of a sampling strategy which is both efficient and cost-effective is thus an important objective. An extensive experiment, the Large Area Crop Inventory Experiment (LACIE), was conducted by NASA, the USDA, and NOAA from 1974 through 1977 (1). Its data analysis objective was to distinguish small grains from nonsmall grains using Landsat MSS data. Several other investigations have shown that the potential exists for identification and area estimation of corn and soybeans as well. The LACIE area estimation system was based on analysis of sample segments or cluster samples (each 5 x 6 nm in size) extracted from multidate Landsat data. The selection of this sampling scheme was driven to a large degree by the data registration technology which was available at that time. Registration technology research has made considerable progress toward an operational registration capability for Landsat MSS full frames, and so we are no longer restricted to sampling small geographic regions, each of which has been separately registered. This allows us to examine the sampling efficiencies which may be introduced by using a smaller sampling unit size distributed over a larger geographic area. One such sampling scheme, described by Bauer et al., separates the functions of sampling for training and sampling for classification and area estimation. Training data were developed by photointerpretation of aerial photography taken along north-south flightlines located at intervals across the area of interest. For classification and crop area estimation, a systematic sample of pixels distributed throughout the region was used. The use of different sampling units for training and classification provides both convenience for the data analyst and high precision of the resulting area estimates

Agricultural scene understanding, volume 1

There are no author-identified significant results in this report