Monitoring vegetation change and dynamics on U.S. Army training lands using satellite image time series analysis
Tài liệu tham khảo
Ahl, 2006, Monitoring spring canopy phenology of a deciduous broadleaf forest using MODIS, Remote Sens. Environ., 104, 88, 10.1016/j.rse.2006.05.003
Althoff, 2006, Plant community and bare ground trends on Fort Riley, Kansas: Implications for monitoring of a highly disturbed landscape, Trans. Kans. Acad. Sci., 109, 101, 10.1660/0022-8443(2006)109[101:PCABGT]2.0.CO;2
Bai, 2003, Computation and analysis of multiple structural change models, J. Appl. Econ., 18, 1, 10.1002/jae.659
1994
Beck, 2006, Improved monitoring of vegetation dynamics at very high latitudes: a new method using MODIS NDVI, Remote Sens. Environ., 100, 321, 10.1016/j.rse.2005.10.021
Borak, 2000, The use of temporal metrics for land-cover change detection at coarse spatial scales, Int. J. Remote Sens., 21, 1415, 10.1080/014311600210245
Brockwell, 1996
Chancellor, 1977
Cleveland, 1979, Robust locally weighted regression and smoothing scatterplots, J. Am. Stat. Assoc., 74, 829, 10.1080/01621459.1979.10481038
Cleveland, 1988, Locally-Weighted regression: an approach to regression analysis by local fitting, J. Am. Stat. Assoc., 83, 596, 10.1080/01621459.1988.10478639
Cleveland, 1990, STL: a seasonal-trend decomposition procedure based on loess, J. Off. Stat., 6, 3
Cohn, 1996, New defenders of wildlife, Bioscience, 46, 11, 10.2307/1312649
Congressional Research Service, 2012
Dash, 2010, The use of MERIS terrestrial chlorophyll index to study spatio-temporal variation in vegetation phenology over India, Remote Sens. Environ., 114, 1388, 10.1016/j.rse.2010.01.021
de Beurs, 2005, A statistical framework for the analysis of long image time series, Int. J. Remote Sens., 26, 1551, 10.1080/01431160512331326657
de Jong, 2011, Analysis of monotonic greening and browning trends from global NDVI time-series, Remote Sens. Environ., 115, 692, 10.1016/j.rse.2010.10.011
de Jong, 2013, Shifts in global vegetation activity trends, Remote Sens., 5, 1117, 10.3390/rs5031117
Department of Defense, 2010
Department of Defense, 2011
Department of the Army, 1988
Department of the Army, 2005
Egbert, 2001
Fensholt, 2009, Evaluation of earth observation based long term vegetation trends intercomparing NDVI time series trend analysis consistency of Sahel from AVHRR GIMMS, Terra MODIS and SPOT VGT data, Remote Sens. Environ., 113, 1886, 10.1016/j.rse.2009.04.004
Forkel, 2013, Trend change detection in NDVI time series: effects of inter-annual variability and methodology, Remote Sens., 5, 2113, 10.3390/rs5052113
Houston, 2001, Environmental risk of Army ranges and impact areas: an ecological framework for assessment, Fed. Facil. Environ. J., 11, 93, 10.1002/ffej.3330120110
Huete, 2002, Overview of the radiometric and biophysical performance of the MODIS vegetation indices, Remote Sens. Environ., 83, 195, 10.1016/S0034-4257(02)00096-2
Jacquin, 2010
Jacquin, 2010, Vegetation cover degradation assessment in Madagascar savanna based on trend analysis of MODIS NDVI time series, Int. J. Appl. Earth Observ. Geoinform., 12S, S3, 10.1016/j.jag.2009.11.004
Jacquin, 2013, Using spatial statistics tools on remote-sensing data to identify fire regime linked with savanna vegetation degradation, Int. J. Agric. Environ. Inform. Syst., 4, 69
Johnson, 2008
Jönsson, 2002, Seasonality extraction by function fitting to time-series of satellite sensor data, IEEE Trans. Geosci. Remote Sens., 40, 1824, 10.1109/TGRS.2002.802519
Julien, 2009, Global land surface phenology trends from GIMMS database, Int. J. Remote Sens., 30, 3495, 10.1080/01431160802562255
Knapp, 1998
Lambert, 2011, Comparison of two remote sensing time series analysis methods for monitoring forest decline, 93
Lambert, 2013, Monitoring forest decline through remote sensing time series analysis, GISci. Remote Sens., 1
Liu, 2009, Influence of turning radius on wheeled military vehicle induced rut formation, J. Terramech., 46, 49, 10.1016/j.jterra.2009.02.004
Lu, 2001
Meigs, 2011, A Landsat time series approach to characterize bark beetle and defoliator impacts on tree mortality and surface fuels in conifer forests, Remote Sens. Environ., 115, 3707, 10.1016/j.rse.2011.09.009
Milchunas, 1999, Plant community response to disturbance by mechanized military maneuvers, J. Environ. Qual., 28, 1533, 10.2134/jeq1999.00472425002800050019x
Millward, 2006, Time series analysis of medium-resolution, multisensor satellite data for identifying landscape change, Photogram. Eng. Remote Sens., 72, 653, 10.14358/PERS.72.6.653
Multi-Resolution Land Characteristics Consortium (MLRC), 2013
PRISM Climate Group, 2012
Quist, 2003, Military training effects on terrestrial and aquatic communities on a grassland military installation, Ecol. Appl., 13, 432, 10.1890/1051-0761(2003)013[0432:MTEOTA]2.0.CO;2
Serneels, 2001, Land-cover changes around a major East African wildlife reserve: the Mara ecosystem, Int. J. Remote Sens., 22, 3397, 10.1080/01431160152609236
Slayback, 2003, Northern hemisphere photosynthetic trends 1982–99, Glob. Change Biol., 9, 1, 10.1046/j.1365-2486.2003.00507.x
Sonnenschein, 2011, Differences in Landsat-based trend analyses in drylands due to the choice of vegetation estimate, Remote Sens. Environ., 115, 1408, 10.1016/j.rse.2011.01.021
Thurow, 1991, Hydrology and erosion (p. 141–159)
Thurow, 1993, Tracked vehicle traffic effects on the hydrologic characteristics of central texas rangeland, Trans. ASAE, 36, 1645, 10.13031/2013.28507
U.S Department of Agriculture, Soil Conservation Service, 1975
U.S. Department of Agriculture, 2012
Verbesselt, 2010, Detecting trend and seasonal changes in satellite image time series, Remote Sens. Environ., 114, 106, 10.1016/j.rse.2009.08.014
Verbesselt, 2010, Phenological change detection while accounting for abrupt and gradual trends in satellite image time series, Remote Sens. Environ., 114, 2970, 10.1016/j.rse.2010.08.003
Zeileis, 2005, Validating multiple structural change models-a case study, J. Appl. Econ., 20, 685, 10.1002/jae.856
Zhang, 2003, Monitoring vegetation phenology using MODIS, Remote Sens. Environ., 84, 471, 10.1016/S0034-4257(02)00135-9