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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

Satellite spectral data and archaeological reconnaissance in western Greece

A Macro-geographical reconnaissance of the Western Peloponnesos adopts spectral signatures taken by Landsat-5 Thematic Mapper as a new instrument of archaeological survey in Greece. Ancient records indicate that indigenous resources contributed to the prosperity of the region. Natural resources and Ancient, Medieval, and Pre-modern Folklife in the Western Peloponnesos describes the principal lines of research. For a supervised classification of attested ancient resources, a variety of biophysical surface features were pinpointed: stone quarries, coal mines, forests of oak and silver fir, terracotta-producing clay beds, crops, and various wild but exploited shrubs such as flax.

Cooper, Frederick A.↗
DOE OSTI2022

Evaluation of artificial neural network performance for classification of potato plants infected with potato virus Y using spectral data on multiple varieties and genotypes

Potato virus Y (Potyviridae, PVY) is a plant virus that poses a significant threat to potato producers on a global basis. The pathogen has disrupted seed potato supplies and negatively impacted yield and quality of commercial potato crops. The potato industry currently manages PVY infection levels via insecticide applications, regional seed certification programs that rely on field scouting to visually assess individual plants for infection status, and destructive and costly tissue sampling coupled with laboratory assays. Despite these efforts, PVY continues to confound potato industry stakeholders resulting in economic harm. Remote sensing and machine learning provide for the development of new tools to more accurately detect and spatially quantify PVY-infected plants versus the current state of the art. However, there is a need to understand how the occurrence of many different potato varieties impact the dynamics of developing models to detect potato plants impacted with PVY and their potential effectiveness. This study evaluates classification modelling outcomes using spectral datasets collected in different temporal and spatial environments (greenhouse and a production field) on multiple potato varieties consisting of labelled instances of plants infected with PVY and those not infected with the virus. A modelling framework was developed to support iterative modelling runs using artificial neural network (ANN) architectures configured as binary classifiers to develop sample populations to support statistical analysis on model performance using specific spectral subsets. When using spectral data to detect PVY-infected plants, ANN models achieved the highest mean accuracy of 0.894 on a single variety. Conversely, the same ANN model architecture only achieved a mean accuracy of 0.575 on a spectral data set representing 29 potato breeding lines. Additionally, statistical analysis indicates spectral regions including the red edge, near infrared and shortwave infrared contain more important spectral features for the ANN classifier introduced in this research.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1985

1985 International Geoscience and Remote Sensing Symposium (IGARSS '85), University of Massachusetts, Amherst, October 7-9, 1985, Digest. Volumes 1 & 2

The present conference on remote sensing instrumentation considers topics in water resources research, planetary remote sensing and mathematical geophysics, sea ice behavior in view of the Cold Regions Research and Engineering Laboratories pond measurements, Shuttle Imaging Radar-B (SIR-B) system performance and calibration results, geological applications of remote sensing, and microwave scattering from vegetation. Further attention is given to atmospheric probing with lasers, image processing for remote sensing, forest inventory and condition assessment, atmospheric remote sensing, SIR-B geological results, the reflectance, emission, and scattering characteristics of vegetation, sea ice sensing, multipolarization SAR results, the theoretical modeling of surfaces and volumes, crop condition assessment and productivity estimates, and SIR-B results for vegetation cover. Also considered are the microwave remote sensing of soil moisture, advanced sensors, advanced information extraction, wind and wave remote observations, results from the ESA Remote Sensing satellite, large area crop and land cover statistics, SIR-B results for directional ocean wave spectra, seasonal snow cover, ice sheets, and lake ice, fundamental research in terrain remote sensing, image classification methods, vegetation stress detection, remote sensing of coastal processes, SAR systems and calibration, electromagnetic geophysical methods and signal processing, image processing techniques, and SAR internal wave measurements.

Carver, K. R.↗
NASA NTRS1973

A study of the utilization of ERTS-1 data from the Wabash River Basin

The author has identified the following significant results. The crop identification effort produced results indicating difficulty in discriminating cotton and soybeans in the Missouri area in the late August, September-October period. The use of prior probabilities in classification was shown to increase performance significantly. The use of temporal data was observed to improve classification accuracy for September 14 and October 2 data but not for August 26 data. Geometric correction and temporal overlay processing of ERTS-1 data proved to be very valuable for field location and relationship of fields from one time to another. Precision geometric correction of ERTS-1 data was achieved using manually derived ground control checkpoints.

Landgrebe, D. A.↗
NASA NTRS2021

Flood Monitoring and Crop Damage Assessment

In recent years, the occurrence and impact of inland and coastal flood events have become more frequent and damaging, especially within agricultural fields, due to the global climate change and consistent sea level rise. Monitoring and measuring the magnitude of flood events in a timely manner and assessing the subsequent crop damages accurately are precursors in minimizing detrimental consequences that could potentially lead to a global food security crisis. Traditional gauge-based measurements with sophisticated hydrological models are capable of monitoring flood events precisely but limited within the smaller spatial extent, time-consuming, and costly. In recent decades, advancement in airborne- and satellite-based remote sensing technologies offering products at a daily global spatial extent with various spectral resolution helps address the shortcomings of the traditional in situ approaches in flood monitoring. Furthermore, the methods such as classification and band ratioing using remote sensing products are simple and effective in assessing flood-induced agricultural damages. The combination of remote sensing products and geographic information systems along with the current development in web mapping, users now can get near real-time flood monitoring and crop damage assessments, albeit dependent upon the quality of available data. A case study to quantify the impact of the 2011 Missouri Mississippi River flooding on the surrounding cornfield was performed through a regression model. The model was trained using historical daily NDVI and corn yield across Nebraska and Missouri, and the overall accuracy in estimating corn yield was about 90%. The method implemented in this localized case study could be extended at a larger geographical scale.

Flood monitoring↗
NASA NTRS1975

Crop identification from radar imagery of the Huntington County, Indiana test site

The author has identified the following significant results. Like polarization was successful in discriminating corn and soybeans; however, pasture and woods were consistently confused as soybeans and corn, respectively. The probability of correct classification was about 65%. The cross polarization component (highest for woods and lowest for pasture) helped in separating the woods from corn, and pasture from soybeans, and when used with the like polarization component, the probability of correct classification increased to 74%.

Batlivala, P. P.↗
NASA NTRS1975

A scheme for the uniform mapping and monitoring of earth resources and environmental complexes: An assessment of natural vegetation, environmental, and crop analogs

The author has identified the following significant results. A study was performed to develop and test a procedure for the uniform mapping and monitoring of natural ecosystems in the semi-arid and wood regions of the Sierra-Lahontan and Colorado Plateau areas, and for the estimating of rice crop production in the Northern Great Valley (Ca.) and the Louisiana Coastal Plain. ERTS-1 and high flight and low flight aerial photos were used in a visual photointerpretation scheme to identify vegetation complexes, map acreages, and evaluate crop vigor and stress. Results indicated that the vegetation analog concept is valid; that depending on the kind of vegetation and its density, analogs are interpretable at different levels in the hierarchical classification from second to the fourth level. The second level uses physiognomic growth form-structural criteria, and the fourth level uses floristic or taxonomic criteria, usually at generic level. It is recommended that analog comparisons should be made in relatively small test areas where large homogeneous examples can be found of each analog.

Poulton, C. E.↗
NASA NTRS1980

Identification of USSR Indicator Regions

Potential indicator regions were determined by comparing the statistics for barley and wheat at the lowest administrative levels for which published statistics were available. Fourteen were selected for review based on their relative abundances of wheat and barely. These potential indicator regions were grouped according to three conditions that could affect labeling and classification accuracies: (1) high-barley content; (2) presence of barley and spring wheat; and (3) presence of barley and winter wheat. Each region was further evaluated based on the availability of crop calendars, LANDSAT acquisitions, and ancillary data. Based on the relative abundance of wheat and barley and the availability of data, three indicator regions were recommended. Within each region, individual oblasts and/or krays were selected according to segment availability and segment acquisition histories for potential barley separation.

Disler, J.↗
DOE OSTI2026

BiG-SCAPE 2.0 and BiG-SLiCE 2.0: scalable, accurate and interactive sequence clustering of metabolic gene clusters

Microbial metabolic gene clusters encode the biosynthesis or catabolism of metabolites that facilitate ecological specialization, mediate microbiome interactions and constitute a major source of medicines and crop protection agents. Here, we present BiG-SCAPE and BiG-SLiCE 2.0, next-generation methods that facilitate scalable, accurate and interactive gene cluster analyses. BiG-SCAPE 2.0 updates its classification, alignment methods, and visualizations, enabling more accurate analysis, up to 8x faster runtimes and halved memory requirements. BiG-SLiCE 2.0 updates its distance metric, pHMM database, and classification logic, resulting in increased sensitivity nearing that of BiG-SCAPE. Analysis of 260,630 biosynthetic gene clusters from publicly available genomes reveals that both tools generate concurring estimates of gene cluster diversity, thus providing significantly extended methodological support for recent evidence indicating that the vast majority of natural product diversity remains unexplored. Together, these updates will facilitate global genome mining efforts for natural product discovery and microbiome analyses scalable with current data sizes.

Draisma, Arjan [Wageningen University & Research (↗
NASA NTRS1975

An interactive method for digitizing zone maps

A method is presented for digitizing maps that consist of zones, such as contour or climatic zone maps. A color-coded map is prepared by any convenient process. The map is then read into memory of an Image 100 computer by means of its table scanner, using colored filters. Zones are separated and stored in themes, using standard classification procedures. Thematic data are written on magnetic tape and these data, appropriately coded, are combined to make a digitized image on tape. Step-by-step procedures are given for digitization of crop moisture index maps with this procedure. In addition, a complete example of the digitization of a climatic zone map is given.

Giddings, L. E.↗
NASA NTRS2010

Exploiting the Free Landsat Archive for Operational Monitoring of Ecosystem Condition and Change Across the Chesapeake Bay Watershed

For the first time, all imagery acquired by the Landsat series of satellites is being made available by the USGS to users at no cost. This represents a key opportunity to use Landsat in a truly operational monitoring framework: large regions of the U.S. such as the Chesapeake Bay Watershed can now be analyzed using "wall-to-wall" imagery at timescales from approximately 1 month to several years. With the future launch of the Landsat Data Continuity Mission (LDCM) and Decadal Survey missions such as the hyperspectral HyspIRI, it is imperative to develop robust processing systems to perform annual ecosystem assessments over large regions such as the Chesapeake Bay. We have been working at NASA's Goddard Space Flight Center (GSFC) to develop an integrative framework for inserting 30m, annual, Landsat based data and derived products into the existing decision support system for the Bay, with a particular focus on ecosystem condition and changes over the entire watershed. The basic goal is to use a 'stack' of Landsat imagery with 40% or less cloud cover to produce multi-date (2005-2009 period), cloud/shadow/gap-free composited surface reflectance products that will support the creation of watershed scale land cover/ use products and the monitoring of ecosystem change across the Bay. Our scientific focus extends beyond the conventional definition of land cover (i.e. a classification of vegetation type) as we propose to monitor both changes in surface type (e.g. forest to urban), vegetation structure (e.g. forest disturbance due to logging or insect damage), as well as winter crop cover. These processes represent a continuum from large, interannual changes in land cover type, to subtler, intra-annual changes associated with short-term disturbance. The free Landsat data are being processed to surface reflectance and composited using the existing Landsat Ecosystem Disturbance Adaptive Processing System here at NASA/ GSFC, and land cover products (type, tree cover, impervious cover, winter cover) are being produced using well-established decision tree and regression tree algorithms. The goal of this session is to present the data products that we have been developing to the Bay science community and to discuss potential avenues for improvements and usage of the products for decision support.

BrowndeColstoun, Eric↗
NASA NTRS2021

Bhutan Agriculture: Developing a Crop Mask for Rice and Creating a Data Collection Protocol Utilizing Remotely Sensed Data in Bhutan

Rice cultivation in Bhutan has been increasingly threatened by deteriorating soil health and outbreaks of diseases and pests associated with the global change in climate patterns. Field surveys, which the national government of Bhutan has relied on to monitor remote agricultural lands, are becoming increasingly overwhelmed by growing threats to agricultural health. To address these concerns, NASA DEVELOP partnered with the Department of Agriculture of Bhutan, the Bhutan Foundation, and the Ugyen Wangchuck Institute of Conservation and Environmental Research (UWICER) and worked to increase the government of Bhutan’s agricultural monitoring capacity. Utilizing Earth observations including Landsat 8 Operational Land Imager (OLI), Sentinel-1 C-band Synthetic Aperture Radar (C-SAR), Shuttle Radar Topography Mission (SRTM), and Planet imagery, the DEVELOP team worked with NASA SERVIR and created a sampling protocol to identify rice plantations and supplement field surveys for more efficient agriculture monitoring. The analysis focused on districts Paro, Punakha, Samtse, Sarpang, Trongsa, Zhemgang, Wangdue Phodrang, and Samdrup Jongkhar in the year 2020 during the period of transplantation (June) to harvesting of rice (November). The team provided the partners with a sampling protocol for integrating NASA Earth observations into their crop monitoring methods, as well as a crop mask for rice identification and to aid crop management. The crop mask for rice was developed using the Random Forest (RF) classifier for the eight districts of Bhutan. Visually, the random forest model has proved to be more accurate and precise than the classification and Regression Tree model. Statistically, the Random Forest model was 91.8% accurate in identifying rice in Bhutan.

Yeshey Seldon↗
NASA NTRS1977

The Maximum Likelihood Estimation of Signature Transformation /MLEST/ algorithm

The Maximum Likelihood Estimation of Signature Transformation (MLEST) algorithm is used to obtain maximum likelihood estimates (MLE) of affine transformation. The algorithm has been evaluated for three sets of data: simulated (training and recognition segment pairs), consecutive-day (data gathered from Landsat images), and geographical-extension (large-area crop inventory experiment) data sets. For each set, MLEST signature extension runs were made to determine MLE values and the affine-transformed training segment signatures were used to classify the recognition segments. The classification results were used to estimate wheat proportions at 0 and 1% threshold values.

Thadani, S. G.↗
NASA NTRS1976

Symposium on Machine Processing of Remotely Sensed Data, Purdue University, West Lafayette, Ind., June 29-July 1, 1976, Proceedings

Papers are presented on the applicability of Landsat data to water management and control needs, IBIS, a geographic information system based on digital image processing and image raster datatype, and the Image Data Access Method (IDAM) for the Earth Resources Interactive Processing System. Attention is also given to the Prototype Classification and Mensuration System (PROCAMS) applied to agricultural data, the use of Landsat for water quality monitoring in North Carolina, and the analysis of geophysical remote sensing data using multivariate pattern recognition. The Illinois crop-acreage estimation experiment, the Pacific Northwest Resources Inventory Demonstration, and the effects of spatial misregistration on multispectral recognition are also considered. Individual items are announced in this issue.

Source record↗
NASA NTRS1982

Development, implementation and evaluation of satellite-aided agricultural monitoring systems

Research supporting the use of remote sensing for inventory and assessment of agricultural commodities is summarized. Three task areas are described: (1) corn and soybean crop spectral/temporal signature characterization; (2) efficient area estimation technology development; and (3) advanced satellite and sensor system definition. Studies include an assessment of alternative green measures from MSS variables; the evaluation of alternative methods for identifying, labeling or classification targets in an automobile procedural context; a comparison of MSS, the advanced very high resolution radiometer and the coastal zone color scanner, as well as a critical assessment of thematic mapper dimensionally and spectral structure.

Cicone, R.↗
NASA NTRS1984

Simple descriptor for contextual classification of hyperdimensional remotely sensed spectral data

An extended CIE transformation was employed to deal with data reduction problems of hyperdimensional spectral data for NASA airborne and Shuttle imaging spectrometers. A simple descriptor was found to be very effective in analyzing spectral data covers from 0.4 to 2.4 microns. Results reveal contextual properties of minerals, vegetation, and crops. It provides a means to monitor seasonal growth variations for crops. It can also serve as pseudocolor indexing for imaging spectrometer imagery extended beyond the visible range.

Chiou, W. C.↗
NASA NTRS2019

Consistency Between NASS Surveyed Soil Moisture Conditions and SMAP Soil Moisture Observations

The USDA National Agricultural Statistics Survey (NASS) collects and publishes crop growth status and soil moisture conditions in major US agricultural regions. The operationally-produced weekly reports are based on survey information. The surveys are based on visual assessments and ? in the case of soil moisture ? report soil moisture levels in one of four categories (Very Short, Short, Adequate and Surplus). In this study, we show that these reports have remarkable correspondence with the NASA Soil Moisture Active Passive (SMAP) Level-4 Soil Moisture (L4SM) product. This consistency allows the combining the two distinct types of data to produce a value-added combination, which is mapped fields rather than State-by-State tables and it is refreshed daily rather than weekly. In this study classification thresholds are derived for L4SM by mapping cumulative distribution functions of L4SM surface and root-zone SM to the categorical NASS SM conditions. The results show that, year-over-year, the SMAP cumulative SM distributions are consistent with the NASS SM conditions and, furthermore, that the temporal evolution of the SMAP-derived thresholds is consistent with the seasonal crop growth cycles from year to year. The results signify that the SMAP SM retrievals are relatable to SM estimation conducted in agriculture by land managers and farmers, which underlines the general applicability of the SMAP data.

Andreas Colliander↗
NASA NTRS1976

Large Area Crop Inventory Experiment (LACIE). Accuracy assessment report phase 1A, November - December 1974

The author has identified the following significant results. Results of the accuracy assessment activity for Phase IA of LACIE indicated that (1) The 90/90 criteria could be reached if the degree of accuracy of the LACIE performance in Kansas could be equaled in other areas. (2) The classification of both wheat and nonwheat fields was significantly accurate for the three ITS segments analyzed. The wheat field classification accuracy varied for the segments. However, this was not so with respect to nonwheat fields. (3) Biophase as well as its interaction with segment location turned out to be an important factor for the classification performance. Analyst interpretation of segments for training the classifier was a significant error-contributing factor in the estimation of wheat acreage at both the field and the segment levels.

Source record↗