2024-03-29T13:58:38Z
http://docs.lib.purdue.edu/do/oai/
oai:docs.lib.purdue.edu:jto-1023
2008-05-16T18:13:31Z
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The Characterization of Soil Properties to Develop “Soil Management/Mapping Units” Using High-Resolution Remotely Sensed Data Sets
Morris, D. Keith
Ross, Kenton W.
Johannsen, Christian J.
remote sensing
imagery
soil characterization
soil mapping
Article
The intent of this research was to assess the possible use of high resolution remotely sensed hyperspectral
and multispectral data to characterize soil types, specifically focusing on organic matter
content, in an associative manner with the results obtained from traditional Order 1 and Order 2 soil surveys. A chi-square analysis indicated a strong association between soil type and organic matter
content. A Cramer’s V analysis (of a supervised classification) indicated a stronger relationship between the Order 1 and organic matter. However, when an unsupervised classification scheme was applied
to the aerial imagery, again using Cramer’s analysis, the Order 2 out-performed the Order 1. This superior
performance was due in part to the grouping of multi-band spectral response patterns into statistically
separable clusters. A One-Way ANOVA analysis indicated that all soils were significantly different in the Order 2 survey for both the hyperspectral and the multispectral data sets. However, the Order 1 results show the ITD sensor more successfully grouping the darker soils than did the ATLAS which grouped the lighter soils. A linear discriminate analysis (LDA) demonstrates that the computer classification
of images more successfully assessed the Order 2 survey than the Order 1. Again it is worth noting that the LDA also grouped the soils in a similar manner as did the ANOVA in that the ITD sensor
grouped the darker soils and the ATLAS sensor grouped the lighter soils. This sensor preference is another significant secondary finding of this study. Despite the subjective nature of the soil mapping
exercise and the use of un-calibrated data sets, high resolution imagery was able to differentiate
different soil mapping scales. Even though associations were relatively low statistically, this study supports the hypothesis that high resolution imagery, although limited by its two-dimensional
capabilities, can be effectively used as a predictive tool, although with the current technological
limits, the imagery cannot serve as a surrogate for more traditional soil surveys.
1
https://docs.lib.purdue.edu/jto/vol1/iss1/art4
oai:docs.lib.purdue.edu:jto-1028
2008-05-20T18:30:09Z
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Contents 1:1
Article
1
https://docs.lib.purdue.edu/jto/vol1/iss1/art1
oai:docs.lib.purdue.edu:jto-1026
2008-05-20T18:32:45Z
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Simulation System for Investigation of the Aral-Caspian Water Regime
Krapivin, Vladimir F.
Shutko, Anatolij M.
Chukhlantsev, Alexander A.
Golovachev, Sergei P.
environmental modelling
water control
Arial-Caspian system
percipitation cycle
Article
Remote measurements of different environmental parameters received during last 30 years are used for the synthesis of a Complex Simulation Model (CSM) describing the combined water regimes of the Aral-Caspian System (ACS) including the consideration of aquatic and climatic natural processes. The problem of stabilizing the sea levels of these two water bodies is modelled and a solution obtained through implementation of a modeling system, including the CSM, remote measurements database, data processing sub-block, surface cover recognition sub-block, and user interface. A set of scenarios to control the ACS water regime is investigated. The role of remote sensing methods for the estimation
of water balance components and synoptic situations is also evaluated. The main purpose of the computer experiments is in the search of a scenario for control of the water regime in the ACS under the realization of which the Aral Sea level will increase and the Caspian Sea level will decrease. The results of this research indicate that there is a regime for ACS water control under which it is possible to stabilize the Aral and Caspian Sea levels at their 1960 level within twelve to fifteen years. This would be implemented by the transport of water from the open portion of the Caspian Sea into saline lowlands and the Kara-Bogaz-Gol Gulf, located on its eastern shore, thus facilitating rapid evaporation followed by the movement of atmospheric moisture into the Aral Sea basin. The scenario “evaporation/precipitation” when the Caspian Sea level is lowered by increasing the flow of its waters to other reservoirs/evaporators is evaluated. Such reservoirs are the area of saline lands and depressions
situated in the East Caspian Sea coast. Their absolute levels are below the recent Caspian Sea level. The results of this investigation show that the formation of an adaptive measurements system with the interchange of remote measurements and mathematical modeling provides a reliable evaluation of the ACS. This will lead to the creation of a system capable of predicting the dynamics of natural processes and assessing long-term consequences of large-scale global-change effects on the ACS.
1
https://docs.lib.purdue.edu/jto/vol1/iss1/art7
oai:docs.lib.purdue.edu:jto-1030
2008-05-19T19:38:11Z
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Editors' Introduction
Rochon, Gilbert L.
Johannsen, Christian J.
Article
1
https://docs.lib.purdue.edu/jto/vol1/iss1/art2
oai:docs.lib.purdue.edu:jto-1024
2008-05-16T18:19:21Z
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publication:geopubs
Recognizing Patterns Within Cropland Vegetation: A Crop Anomaly Classification System
Carter, Paul G.
Johannsen, Christian J.
Engel, Bernard A.
classification systems
crops
anomalies
Article
The framework for a national classification system for agricultural cropland anomalies utilizing remote sensing information is presented. Cropland anomalies in Midwest USA have been identified in field crops of corn (Zea mays), soybeans (Glycine max), wheat (Triticum), and miscellaneous hay crops such as alfalfa (Medicago sativa). By identifying cropland anomalies through ground observations and describing the characteristics associated with them, it is possible to group them according to common casual properties such as water, nutrition, weeds, insects, disease, and management, leading to the development of a Cropland Anomaly Classification System. This system advances understanding of specific anomalies and provides an environment for standardization of anomaly characteristics while allowing the possibility for producers and managers to make sound economic decisions. The introductions
of new technologies in remote sensing such as increased spatial, spectral, and temporal resolution will cause continual development and improvement of the proposed Anomaly Classification System.
1
https://docs.lib.purdue.edu/jto/vol1/iss1/art5
oai:docs.lib.purdue.edu:jto-1029
2008-05-16T19:01:45Z
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Foreword
Landgrebe, David
Article
1
https://docs.lib.purdue.edu/jto/vol1/iss1/art3
oai:docs.lib.purdue.edu:jto-1025
2008-05-21T13:54:23Z
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On-Farm Profitability of Remote Sensing in Agriculture
Tenkorang, Frank
Lowenberg-DeBoer, James
farm profits
crop yields
statistics
Article
Remote sensing is used in agriculture to guide application of fertilizer, pesticides and other farm inputs. Its application in agriculture is well documented. However, evidence of profitability to farmers remains fuzzy. The objective of this study is to summarize publicly available information on the economic benefits
of remote sensing in agriculture. Out of the hundreds of agricultural remote sensing documents reviewed only a few reported economic benefit estimates. Many of those documents do not provide details on how the economic benefit was estimated. Clues in the reports and the fact that the numbers are often much larger than those for detailed studies suggest that the studies not reporting details are often reporting gross benefits without deducting the associated cost. Standardizing budgeting methods and using the reported changes in yield and input application in 12 studies, remote sensing is estimated to have the potential to improve average farm profits by about $31.74/ha Most of the studies based profit estimates on a single crop season of data. Key improvements needed for studies of the economics of remote sensing for field crops include: detailed reporting of budget assumptions, multiple year data sets in the same fields, and replication of studies of the same technology in different states.
1
https://docs.lib.purdue.edu/jto/vol1/iss1/art6
oai:docs.lib.purdue.edu:jto-1027
2008-05-16T18:56:48Z
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Improving the Accuracy of Historic Satellite Image Classification by Combining Low-Resolution Multispectral Data with High-Resolution Panchromatic Data
Getman, Daniel J.
Harbor, Jonathan M.
Johannsen, Chris J.
Engel, Bernard A.
Shao, Goufan
image classification
multispectral data
panchromatic data
data accuracy
remote sensing
archival data
Article
Many attempts to observe changes in terrestrial systems over time would be significantly enhanced if it were possible to improve the accuracy of classifications of low-resolution historic satellite data. In an effort to examine improving the accuracy of historic satellite image classification by combining satellite and air photo data, two experiments were undertaken in which low-resolution multispectral data and high-resolution panchromatic data were combined and then classified using the ECHO spectral-spatial image classification algorithm and the Maximum Likelihood technique. The multispectral data consisted of 6 multispectral channels (30-meter pixel resolution) from Landsat 7. These data were augmented with panchromatic data (15m pixel resolution) from Landsat 7 in the first experiment, and with a mosaic of digital aerial photography (1m pixel resolution) in the second. The addition of the Landsat 7 panchromatic data provided a significant improvement in the accuracy of classifications made using the ECHO algorithm. Although the inclusion of aerial photography provided an improvement in accuracy, this improvement was only statistically significant at a 40-60% level. These results suggest that once error levels associated with combining aerial photography and multispectral satellite data are reduced, this approach has the potential to significantly enhance the precision and accuracy of classifications made using historic remotely sensed data, as a way to extend the time range of efforts to track temporal changes in terrestrial systems.
1
https://docs.lib.purdue.edu/jto/vol1/iss1/art8
oai:docs.lib.purdue.edu:jto-1039
2009-03-04T15:41:07Z
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Contents
Article
1
https://docs.lib.purdue.edu/jto/vol1/iss2/art1
oai:docs.lib.purdue.edu:jto-1040
2009-03-04T15:48:12Z
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publication:jto
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Editors’ Introduction
Rochon, Gilbert L.
Johannsen, Chris J.
Article
1
https://docs.lib.purdue.edu/jto/vol1/iss2/art2
oai:docs.lib.purdue.edu:jto-1042
2009-03-04T15:52:24Z
publication:sps
publication:jto
publication:eas
publication:sci
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publication:libraries
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A Review of NEXRAD Level II: Data, Distribution, and Applications
Huber, Matthew
Trapp, Jeff
Article
1
https://docs.lib.purdue.edu/jto/vol1/iss2/art4
oai:docs.lib.purdue.edu:jto-1045
2009-03-04T15:57:18Z
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A Community-Based Information Technology Services Determination of GIS User Information Needs
Caldwell, Barrett S.
Article
1
https://docs.lib.purdue.edu/jto/vol1/iss2/art7
oai:docs.lib.purdue.edu:jto-1043
2009-03-04T15:54:11Z
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Application of Geo-informatics to Transboundary Biodiversity Conservation across Thailand, Lao PDR, and Cambodia
Trisurat, Yongyut
Article
1
https://docs.lib.purdue.edu/jto/vol1/iss2/art5
oai:docs.lib.purdue.edu:jto-1044
2009-03-04T15:55:55Z
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Earth Observations in Social Science Research for Management of Natural Resources and the Environment: Identifying the Landsat Contribution
Macauley, Molly K.
Article
1
https://docs.lib.purdue.edu/jto/vol1/iss2/art6
oai:docs.lib.purdue.edu:jto-1046
2009-03-04T15:59:25Z
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Remote Sensing and Geographic Information Systems in Developing Countries: Case of the United Arab Emirates (UAE)
Yagoub, M. M.
Engel, Bernard
Article
1
https://docs.lib.purdue.edu/jto/vol1/iss2/art8
oai:docs.lib.purdue.edu:jto-1041
2009-03-04T15:50:32Z
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publication:jto
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Foreword
Baumgardner, Marion F.
Article
1
https://docs.lib.purdue.edu/jto/vol1/iss2/art3
oai:docs.lib.purdue.edu:jto-1061
2010-03-11T17:42:12Z
publication:sps
publication:jto
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Contents
Article
1
https://docs.lib.purdue.edu/jto/vol2/iss1/art1
oai:docs.lib.purdue.edu:jto-1062
2010-03-11T17:45:55Z
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Editors’ Introduction
Rochon, Gilbert L.
Johannsen, Chris J.
Laguette, Soizik
Article
1
https://docs.lib.purdue.edu/jto/vol2/iss1/art2
oai:docs.lib.purdue.edu:jto-1063
2010-03-11T17:46:55Z
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Foreword
Shan, Jie
Article
1
https://docs.lib.purdue.edu/jto/vol2/iss1/art3
oai:docs.lib.purdue.edu:jto-1064
2010-03-11T17:49:02Z
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From Science to Applications: Determinants of Diffusion in the Use of Earth Observations
Macauley, Molly
Maher, Joe
Shih, Jhih-Shyang
Article
1
https://docs.lib.purdue.edu/jto/vol2/iss1/art4
oai:docs.lib.purdue.edu:jto-1065
2010-03-24T14:33:29Z
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Assessing Tree Cover in Agricultural Landscapes Using High-Resolution Aerial Imagery
Liknes, Greg C.
Perry, Charles H.
Meneguzzo, Dacia M.
Article
1
https://docs.lib.purdue.edu/jto/vol2/iss1/art5
oai:docs.lib.purdue.edu:jto-1067
2010-03-11T17:56:42Z
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Nitrogen and Water Stress Impacts Hard Red Spring Wheat (Triticum aestivum) Canopy Reflectance
Reese, Cheryl L.
Long, Daniel
Clay, Sharon
Clay, David
Beck, Dwayne
Article
1
https://docs.lib.purdue.edu/jto/vol2/iss1/art7
oai:docs.lib.purdue.edu:jto-1066
2010-03-11T17:53:13Z
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Automated Georeferencing of Historic Aerial Photography
Kim, Jae Sung
Miller, Christopher C.
Bethel, James
Article
1
https://docs.lib.purdue.edu/jto/vol2/iss1/art6
oai:docs.lib.purdue.edu:jto-1068
2010-05-24T19:16:26Z
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Contents
Article
1
https://docs.lib.purdue.edu/jto/vol2/iss2/art1
oai:docs.lib.purdue.edu:jto-1069
2010-05-24T19:18:00Z
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publication:jto
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Editors’ Introduction
Rochon, Gilbert L.
Johannsen, Chris J.
Laguette, Soizik
Article
1
https://docs.lib.purdue.edu/jto/vol2/iss2/art2
oai:docs.lib.purdue.edu:jto-1070
2010-05-24T19:18:39Z
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Foreword
Johannsen, Chris
Article
1
https://docs.lib.purdue.edu/jto/vol2/iss2/art3
oai:docs.lib.purdue.edu:jto-1072
2010-05-24T19:21:59Z
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Early Warning Systems: A Review
Quansah, Joseph E.
Engel, Bernard
Rochon, Gilbert L.
Article
1
https://docs.lib.purdue.edu/jto/vol2/iss2/art5
oai:docs.lib.purdue.edu:jto-1074
2010-05-24T19:25:49Z
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Refinement of Digital Elevation Models in Urban Areas Using Breaklines Via a Multi-Photo Least Squares Matching Algorithm
Elaksher, Ahmed F.
Bethel, James
Article
1
https://docs.lib.purdue.edu/jto/vol2/iss2/art7
oai:docs.lib.purdue.edu:jto-1073
2010-05-24T19:24:49Z
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The Relationship Between Urban Land Cover And Surface Kinetic Temperature: A Case Study In Terre Haute, Indiana
Jensen, Ryan R.
Hardin, Perry J.
Curran, Richard
Hardin, Thomas
Article
1
https://docs.lib.purdue.edu/jto/vol2/iss2/art6
oai:docs.lib.purdue.edu:jto-1071
2010-05-24T19:20:44Z
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A Flexible Approach to Help Overcome Limitations of Moderate Resolution Satellite Imagery for Mapping Invasive Saltcedar on the Bighorn River, Montanas
Powell, Scott L.
Lawrence, Rick L.
Austin, Christine Sommers
Wood, Shana
A Flexible Approach
Article
1
https://docs.lib.purdue.edu/jto/vol2/iss2/art4