Correlation of rangeland cover measures to satellite‐imagery‐derived vegetation indices

Senseman, Gary M.; Bagley, Calvin F.; Tweddale, Scott A.

doi:10.1080/10106049609354546

Cited by 53 publications

(24 citation statements)

References 6 publications

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“…Unlike previous work, where combined field observations and remotely sensed imagery provided some predictive capabilities in semiarid ecosystems (Tueller and Yuan 1992, Tueller 1992, Basavaraju 1994, Keller-Hatzell 1992, Senseman 1996, the results of this study were not conclusive. The data sets studied were, in most cases, of high statistical significance and seemed to describe relationships of interest.…”

Section: Discussioncontrasting

confidence: 55%

“…Additional studies using more observations on north slopes and using all shrub dominated vegetation types might provide greater among-site variability and thus serve to improve the multiple linear regression models. It may also be that these particular ARTR/AGSP releves lacked the overall variation that were found in earlier studies (Tueller and Yuan 1992, Tueller 1992, Basavaraju 1994, Keller-Hatzel 1992, and Senseman 1996 and tended to reduce the R 2 values and thus reduce the predictability. Another factor is that hypotheses testing was conducted using either May or August radiance data from satellite images, while LCTA field measurements of vegetation were collected throughout the summer months.…”

Section: Discussionmentioning

confidence: 81%

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Identification and Mapping of Sagebrush/Grass Successional Stages with Landsat Thematic Mapper Data at Yakima Training Center, Washington

Leedy¹,

Tueller²,

Gao³

et al. 2004

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ABSTRACT:In the future, remote sensing technologies will become an increasingly important and valuable tool for military land managers in semiarid regions. These technologies, when combined with field samples, have the potential to accurately monitor rangeland trends from year to year with smaller monetary investments compared to field sampling exclusively. This research attempted to identify and map successional changes on semiarid rangelands at Yakima Training Center, WA, using remote sensing techniques by developing a model derived from analysis of dependent and independent variables chosen from field surveys of vegetation and geomorphic data, along with the interpretation of Landsat TM data.Preliminary results based on small data sets separated by elevation and slope direction showed both low and some reasonable R 2 values, including some R 2 near 0.70. The removal of elevation and slope direction and consideration of multicollinearity and outliers and influentials provided generally significant relationships among dependent and independent variables. Significant relationships between multiple dependent and independent variables were also identified using canonical correlation analysis. Variability among the releves, collection of field vegetation and soil data over the entire summer including many phenophases, and the correction of the raster radiance values for topography were assumed to be factors that may have reduced the predictive capabilities of the techniques investigated. DISCLAIMER:The contents of this report are not to be used for advertising, publication, or promotional purposes. Citation of trade names does not constitute an official endorsement or approval of the use of such commercial products. All product names and trademarks cited are the property of their respective owners. The findings of this report are not to be construed as an official Department of the Army position unless so designated by other authorized documents.

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Section: Discussioncontrasting

confidence: 55%

Section: Discussionmentioning

confidence: 81%

Identification and Mapping of Sagebrush/Grass Successional Stages with Landsat Thematic Mapper Data at Yakima Training Center, Washington

Leedy¹,

Tueller²,

Gao³

et al. 2004

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“…The statistical model relates point and image data to create the C Factor data layer. The statistical techniques used to relate point and image data could be found in Senseman et al (1996) and Wu and Westervelt (1994).…”

Section: Factormentioning

confidence: 99%

Army Training and Testing Area Carrying Capacity (ATTACC) Land Condition Module (LCM) User Manual, Version 1.00

Anderson¹,

Sydelko²,

Teachman³

2001

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“…This is especially problematic when comparisons are being made across different soil types that may refl ect different amounts of light in the red and near infrared wavelengths (i.e., soils with different brightness values). The soil-adjusted vegetation index was developed as a modifi cation of the Normalized Difference Vegetation Index to correct for the infl uence of soil brightness when vegetative cover is low (Richardson and Everitt, 1992;Lyon et al, 1998;Senseman et al, 1996).…”

Section: Soil Adjusted Vegetation Index(savi)mentioning

confidence: 99%

Earth Observation and Geospatial techniques for Soil Salinity and Land Capability Assessment over Sundarban Bay of Bengal Coast, India

Das¹,

Choudhury²,

Das³

et al. 2016

Geodesy and Cartography

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Abstract:To guarantee food security and job creation of small scale farmers to commercial farmers, unproductive farms in the South 24 PGS, West Bengal need land reform program to be restructured and evaluated for agricultural productivity. This study established a potential role of remote sensing and GIS for identifi cation and mapping of salinity zone and spatial planning of agricultural land over the Basanti and Gosaba Islands(808.314sq. km) of South 24 PGS. District of West Bengal. The primary data i.e. soil pH, Electrical Conductivity (EC) and Sodium Absorption ratio (SAR) were obtained from soil samples of various GCP (Ground Control Points) locations collected at 50 mts. intervals by handheld GPS from 0-100 cm depths. The secondary information is acquired from the remotely sensed satellite data (LANDSAT ETM + ) in different time scale and digital elevation model. The collected fi eld samples were tested in the laboratory and were validated with Remote Sensing based digital indices analysisover the temporal satellite data to assess the potential changes due to over salinization.Soil physical properties such as texture, structure, depth and drainage condition is stored as attributes in a geographical soil database and linked with the soil map units. The thematic maps are integrated with climatic and terrain conditions of the area to produce land capability maps for paddy. Finally, The weighted overlay analysis was performed to assign theweights according to the importance of parameters taken into account for salineareaidentifi cation and mapping to segregate higher, moderate, lower salinity zonesover the study area.

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Correlation of rangeland cover measures to satellite‐imagery‐derived vegetation indices

Cited by 53 publications

References 6 publications

Identification and Mapping of Sagebrush/Grass Successional Stages with Landsat Thematic Mapper Data at Yakima Training Center, Washington

Identification and Mapping of Sagebrush/Grass Successional Stages with Landsat Thematic Mapper Data at Yakima Training Center, Washington

Army Training and Testing Area Carrying Capacity (ATTACC) Land Condition Module (LCM) User Manual, Version 1.00

Earth Observation and Geospatial techniques for Soil Salinity and Land Capability Assessment over Sundarban Bay of Bengal Coast, India

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