Access the full text.
Sign up today, get DeepDyve free for 14 days.
Burak Güneralp, G. Gertner, G. Mendoza, A. Anderson (2003)
Spatial Simulation and Fuzzy Threshold Analyses for Allocating Restoration AreasTransactions in GIS, 7
F. Johnson (1982)
Effects of Tank Training Activities on Botanical Features at Fort Hood, TexasSouthwestern Naturalist, 27
G Wang, S Wente, GZ Gertner, AB Anderson (2002)
Improvement in mapping vegetation cover factor for universal soil loss equation by geostatistical methods with Landsat TM imagesInternational Journal of Remote Sensing, 23
A. Stein, F. Meer, B. Gorte (2002)
Spatial statistics for remote sensing, 1
D. Althoff, J. Rivers, J. Pontius, P. Gipson, P. Woodford (2004)
A Comprehensive Approach to Identifying Monitoring Priorities of Small Landbirds on Military InstallationsEnvironmental Management, 34
Guangxing Wang, Simo Poso, M. Waite, M. Holopainen (1998)
The Use of Digitized Aerial Photographs and Local Operation for Classification of Stand Development ClassesSilva Fennica, 32
V. Diersing, R. Shaw, D. Tazik (1992)
US army land condition-trend analysis (LCTA) programEnvironmental Management, 16
M. Braunack, B. Williams (1993)
The effect of initial soil water content and vegetative cover on surface soil disturbance by tracked vehiclesJournal of Terramechanics, 30
M. Braunack (1986)
The residual effects of tracked vehicles on soil surface propertiesJournal of Terramechanics, 23
V. Pawlowsky-Glahn, R. Olea (2004)
Geostatistical Analysis of Compositional Data
G. Gertner, Guangxing Wang, A. Anderson (2006)
Determination of Frequency for Remeasuring Ground and Vegetation Cover Factor Needed for Soil Erosion ModelingEnvironmental Management, 37
A. Anderson, Guangxing Wang, Shoufan Fang, G. Gertner, Burak Güneralp, Don Jones (2005)
Assessing and predicting changes in vegetation cover associated with military land use activities using field monitoring data at Fort Hood, TexasJournal of Terramechanics, 42
J. Dungan (1999)
Conditional Simulation: An alternative to estimation for achieving mapping objectives
RG Bailey (1976)
Ecoregions of the United States (map)
A. Anderson, A. Palazzo, P. Ayers, J. Fehmi, S. Shoop, P. Sullivan (2005)
Assessing the impacts of military vehicle traffic on natural areas. Introduction to the special issue and review of the relevant military vehicle impact literatureJournal of Terramechanics, 42
J. Carr, F. Miranda (1998)
The semivariogram in comparison to the co-occurrence matrix for classification of image textureIEEE Trans. Geosci. Remote. Sens., 36
D. Tazik, S. Warren, V. Diersing, R. Shaw, R. Brozka, C. Bagley, W. Whitworth (1992)
U.S. Army Land Condition-Trend Analysis (LCTA) Plot Inventory Field Methods
F. Hall, D. Strebel, J. Nickeson, S. Goetz (1991)
Radiometric rectification - Toward a common radiometric response among multidate, multisensor imagesRemote Sensing of Environment, 35
M. Chica-Olmo, F. Abarca-hernandez (2000)
Computing geostatistical image texture for remotely sensed data classificationComputers & Geosciences, 26
P. Atkinson, Philip Lewis (2000)
Geostatistical classification for remote sensing: an introductionComputers & Geosciences, 26
J. Daigle, W. Hudnall, Wayne Gabriel, E. Mersiovsky, R. Nielson (2005)
The National Soil Information System (NASIS): Designing soil interpretation classes for military land-use predictionsJournal of Terramechanics, 42
E. Ziegel, C. Deutsch, A. Journel (1998)
Geostatistical Software Library and User's GuideTechnometrics, 40
R. Jones, D. Horner, P. Sullivan, R. Ahlvin (2005)
A methodology for quantitatively assessing vehicular rutting on terrainsJournal of Terramechanics, 42
Guangxing Wang, S. Wente, G. Gertner, A. Anderson (2002)
Improvement in mapping vegetation cover factor for the universal soil loss equation by geostatistical methods with Landsat Thematic Mapper imagesInternational Journal of Remote Sensing, 23
Y. Pannatier (1996)
Variowin: Software for Spatial Data Analysis in 2D
P. Althoff, S. Thien (2005)
Impact of M1A1 main battle tank disturbance on soil quality, invertebrates, and vegetation characteristicsJournal of Terramechanics, 42
S. Wilson (1988)
The effects of military tank traffic on prairie: A management modelEnvironmental Management, 12
Guangxing Wang, G. Gertner, A. Anderson, H. Howard, D. Gebhart, D. Althoff, T. Davis, P. Woodford (2007)
Spatial variability and temporal dynamics analysis of soil erosion due to military land use activities: uncertainty and implications for land managementLand Degradation & Development, 18
G. Abele, J. Brown, M. Brewer (1984)
Long-term effects of off-road vehicle traffic on tundra terrainJournal of Terramechanics, 21
J. Cloudsley-Thompson, R. Webb, H. Wilshire (1984)
Environmental Effects of Off-Road Vehicles: Impacts and Management in Arid Regions.Journal of Ecology, 72
P. Ayers (1994)
Environmental damage from tracked vehicle operationJournal of Terramechanics, 31
A. Almeida, A. Journel (1994)
Joint simulation of multiple variables with a Markov-type coregionalization modelMathematical Geology, 26
C. Deutsch, A. Journel (1993)
GSLIB: Geostatistical Software Library and User's Guide
R. Haralick, K. Shanmugam, I. Dinstein (1973)
Textural Features for Image ClassificationIEEE Trans. Syst. Man Cybern., 3
R. Webb, H. Wilshire (1983)
Environmental effects of off-road vehicles.
Janet Tucker, D. Rideout, R. Shaw (1998)
Using linear programming to optimize rehabilitation and restoration of injured land: an application to US army training sitesJournal of Environmental Management, 52
AB Anderson, P Ayers, A Palazzo, J Fehmi, S Shoop, P Sullivan (2005)
Assessing the impacts of military vehicle traffic on natural areas: introduction to a special issue of the Journal of Terramechanics and Review of the Relevant Military Vehicle Impact LiteratureJournal of Terramechanics, 42
T. Greene, T. Nichols (1996)
Effects of Long Term Military Training Traffic on Forest Vegetation in Central MinnesotaNorthern Journal of Applied Forestry, 13
T. Coburn (2000)
Geostatistics for Natural Resources EvaluationTechnometrics, 42
Larry Sample, Jamie Steichen, J. Kelley (1998)
WATER QUALITY IMPACTS FROM LOW WATER FORDS ON MILITARY TRAINING LANDS 1JAWRA Journal of the American Water Resources Association, 34
B. Horne, Peter Sharpe (1998)
Effects of Tracking by Armored Vehicles on Townsend's Ground Squirrels in the Orchard Training Area, Idaho, USAEnvironmental Management, 22
D. Gebhart, S. Warren (1995)
Regional cost estimates for rehabilitation and maintenance practices on Army training lands
The land management of US Army installations requires information on land conditions and their history for planning future military training activities and allocation of land repair. There is thus a strong need for methodology development to estimate the land conditions and cumulative military training impacts for the purpose of repair and restoration. In this study, we simulated at Fort Riley, USA, spatial patterns and temporal dynamics of military training impacts on land conditions quantified as percent ground cover using an image-aided spatial conditional co-simulation algorithm. Moreover, we estimated the historical percent ground cover as a measure of the cumulative impacts, and then calculated the allocation of land repair and restoration based on both current and historical land conditions. In addition, we developed a loss function method for allocation of land repair and restoration. The results showed: (1) this co-simulation algorithm reproduced spatial and temporal variability of percent ground cover and provided estimates of uncertainties with the correlation coefficients and root mean square errors between the simulated and observed values varying from 0.63 to 0.88 and from 23% to 78%, respectively; (2) with and without the cumulative impacts, the obtained spatial patterns of the land repair categories were similar, but their land areas differed by 5% to 40% in some years; (3) the combination of the loss function with the co-simulation made it possible to estimate and computationally propagate the uncertainties of land conditions into the uncertainties of expected cost loss for misallocation of land repair and restoration; and (4) the loss function, physical threshold, and probability threshold methods led to similar spatial patterns and temporal dynamics of the land repair categories, however, the loss function increased the land area by 5% to 30% for intense and moderate repairs and decreased the area by 5% to 30% for no repairs and light repairs for most of the years. This approach provided the potential to improve and automate the existing land rehabilitation and maintenance (LRAM) system used for the land management of the U.S. Army installations, and it can be applied to the management of other civil lands and environments. In conclusion, this study overcame the important gaps that exist in the methodological development and application for simulating land conditions and cumulative impacts due to human activities, and also in the methods for the allocation of land for repair and restoration.
Environmental Management – Springer Journals
Published: Aug 26, 2009
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.