Updating the Grassland Vegetation Inventory Using Change Vector Analysis and Functionally-Based Vegetation Indices

REFERENCES

• Abutaleb, A.S. 1989. “Automatic thresholding of grey-level pictures using two-dimensional entropy.” Computer Vision, Graphics, and Image Processing, Vol. 47: pp. 22–32.
   Google Scholar
• Ahmadizadeh, S., Yousefi, M., and Saghafi, M. 2014. “Land use change detection using remote sensing and artificial neural network: application to Birjand, Iran.” Computational Ecology and Software, Vol. 4: pp. 276–288.
   Google Scholar
• Alberta Environment Protection, Natural Resources Service, Recreation and Protected Areas Division, and Natural Heritage Protection and Education Branch (ANRN). 1997. “The grassland natural region of Alberta: one of a series of reports prepared for the special places 2000 provincial coordinating committee.” Edmonton, Alberta, Canada: Alberta Environment Protection.
   Google Scholar
• Barrett, B., Nitze, I., Green, S., and Cawkwell, F. 2014. “Assessment of multi-temporal, multi-sensor radar and ancillary spatial data for grasslands monitoring in Ireland using machine learning approaches.” Remote Sensing of Environment, Vol. 152: pp. 109–124.
   Web of Science ®Google Scholar
• Bella, D., Faivre, R., Ruget, F., Seguin, B., and Guérif, B. 2004. “Remote sensing capabilities to estimate pasture production in France.” International Journal of Remote Sensing, Vol. 25: pp. 5359–5372.
   Web of Science ®Google Scholar
• Bradley, B., Jacob, R., Hermance, J., and Mustard, J. 2007. “A curve fitting procedure to derive inter-annual phenologies from time series of noisy satellite NDVI data.” Remote Sensing of Environment, Vol. 106(No.2): pp. 137–145.
   Web of Science ®Google Scholar
• Brodsky, L., Gangkofner, U., Jacob, P., Keil, M., and Soukup, T. 2011. “Geoland2-technical note on HR grassland layer product specification.” Accessed January 2016, http://www.gmes-geoland.info/fileadmin/geoland2/redakteur/pdf/project_Documentation/Service_Specification/Techical_ProductSpecification_HR_Grassland_Layer_12.pdf.
   Google Scholar
• Chen, X., Chen, J., Shi, Y., and Yamaguchi, Y. 2012. “An automated approach for updating land cover maps based on integrated change detection and classification methods.” ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 71: pp. 86–95.
   Web of Science ®Google Scholar
• Esch, T., Metz, A., Marconcini, M., and Keil, M. 2014. “Combined use of multi-seasonal high and medium resolution satellite imagery for parcel-related mapping of cropland and grassland.” International Journal of Applied Earth Observation and Geoinformation, Vol. 28: pp. 230–237.
   Web of Science ®Google Scholar
• Gao, B. 1996. “NDWI- A normalized difference water index for remote sensing of vegetation liquid water from space.” Remote Sensing of Environment. Vol. 58: pp. 257–266.
   Web of Science ®Google Scholar
• Gao, F., Masek, J., Schwaller, M., and Hall, F. 2006. “On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 44: pp. 2207–2218.
   Web of Science ®Google Scholar
• Gauthier, D.A., and Wiken, E.D.B. 2003. “Monitoring the conservation of grassland habitats, Prairie Ecozone, Canada.” Environmental Monitoring and Assessment, Vol. 88: pp. 343–364.
   Google Scholar
• Gitelson, A.A., and Merzlyak, M.N. 1997. “Remote estimation of chlorophyll content in higher plant leaves.” International Journal of Remote Sensing, Vol. 18: pp. 2691–2697.
   Google Scholar
• Gong, P., Mahler, S.A., Biging, G.S., and Newburn, D.A. 2003. “Vineyard identification in an oak woodland landscape with airborne digital camera imagery.” International Journal of Remote Sensing, Vol. 24: pp. 1303–1315.
   Web of Science ®Google Scholar
• Government of Alberta, 2010. “Grassland Vegetation Inventory (GVI) Specifications.” Alberta Sustainable resource Development, Government of Alberta, pp. 101.
   Google Scholar
• Guerschman, J.P., Hill, M.J., Barrett, D.J., Renzullo, L., Marks, A., and Botha, E. 2009. “Estimating fractional cover of photosynthetic vegetation non-photosynthetic vegetation and soil in mixed tree-grass vegetation using the EO-1 and MODIS sensors.” Remote Sensing of Environment, Vol. 113: pp. 928–945.
   Web of Science ®Google Scholar
• Haboudane, D., Miller, J.R., Tremblay, N., Zarco-Tejada, P.J., Dextraze, L. 2002. “Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture.” Remote Sensing of Environment, Vol. 81: pp. 416–426.
   Web of Science ®Google Scholar
• Haboudane, D., Miller, J.R., Pattey, E., Zarco-Tejada, P.J., Strachan, I. 2004. “Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modelling and validation in the context of precision agriculture.” Remote Sensing of Environment, Vol. 90: pp. 337–352.
   Web of Science ®Google Scholar
• Hatfield, J.L., and Prueger, J.H. 2010. “Value of using different vegetative indices to quantify agricultural crop characteristics at different growth stages under varying management practices.” Remote Sensing, Vol. 2: pp. 562–578.
   Web of Science ®Google Scholar
• Hardisky, M.A., Klemas, V., and Smart, R.M. 1983. “The influences of soil salinity, growth form, and leaf moisture on the spectral reflectance of Spartina alterniflora canopies.” Photogrammetric Engineering and Remote Sensing, Vol. 49: pp. 77–83.
   Web of Science ®Google Scholar
• Hazaymeh, K., and Hassan, Q.K. 2015. “Spatiotemporal image-fusion model for enhancing the temporal resolution of Landsat-8 surface reflectance images using MODIS images.” Journal of Applied Remote Sensing, Vol. 9: pp. 1–14.
   Web of Science ®Google Scholar
• Helmholz, P., Rottensteiner, F., and Heipke, C. 2014. “Semi-automatic verification of cropland and grassland using very high resolution mono-temporal satellite images.” ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 97: pp. 204–218.
   Web of Science ®Google Scholar
• Hill, M. J. 2013. “Vegetation index suites as indicators of vegetation state in grassland and savana: An analysis with simulated SENTINEL 2 data for a North American transect.” Remote Sensing of Environment, Vol. 137: pp. 94–111.
   Web of Science ®Google Scholar
• Hilker, T., Wulder, M.A., Coops, N.C., Sritz, N., White, J.C., Gao, F., Masek, J.G., and Stenhouse, G. 2009. “Generation of dense time series synthetic Landsat data through data blending with MODIS using a spatial and temporal adaptive reflectance fusion model.” Remote Sensing of Environment, Vol. 113: pp.1988–1999.
   Web of Science ®Google Scholar
• Huang, X., Lu, Q., and Zhang, L. 2014. “New postprocessing methods for remote sensing image classification: a systematic study.” IEEE Transactions on Geoscience and Remote Sensing.” Vol. 52: pp. 7140–7159.
   Web of Science ®Google Scholar
• Huete, A., Dian, K., Miura, T., Rodriguez, E.P., Gao, X., and Ferreira, L.G. 2002. “Overview of the radiometric and biophysical performance of the MODIS vegetation indices.” Remote Sensing of Environment, Vol. 83: pp. 195–213.
   Web of Science ®Google Scholar
• Jin, S., Yang, L., Danielson, P., Homer, C., Fry, J., and Xian, G. 2013. “A comprehensive change detection method for updating the national land cover database to circa 2011.” Remote Sensing of Environment, Vol. 132: pp. 159–175.
   Web of Science ®Google Scholar
• Kapur, J.N., Sahoo, P.K., Wong, A.K.C. 1985. “A new method for grey-level picture thresholding using entropy of the histogram.” Computer Vision, Graphics, and Image Processing, Vol. 29: pp. 273–285.
   Google Scholar
• Kittler, J., and Illingworth, J. 1986. “Minimum error thresholding.” Pattern Recognition, Vol.19: pp. 41–47.
   Web of Science ®Google Scholar
• Linke, J., and McDermid, G.J. 2012. “Monitoring landscape change in multi-use west-central Alberta, Canada using the disturbance-inventory framework.” Remote Sensing of Environment, Vol. 125: pp. 112–124.
   Web of Science ®Google Scholar
• Lobo, A., Chic, O., and Casterad, A. 1996. “Classification of Mediterranean crops with multisensory data: per-pixel versus per-object statistics and image segmentation.” International Journal of Remote Sensing, Vol. 17: pp. 2385–2400.
   Web of Science ®Google Scholar
• Lunettta, R.S., Knight, J.F., Ediriwickrema, J., Lyon, J.G., and Worthy, L.D. 2006. “Land-cover change detection using multi-temporal MODIS NDVI data.” Remote Sensing of Environment, Vol. 105: pp. 142–154.
   Web of Science ®Google Scholar
• Macleod, R.D, and Congalton, R.G. 1998. “A quantitative comparison of change-detection algorithms for monitoring eelgrass from remotely sensed data.” Photogrammetric Engineering and Remote Sensing, Vol. 64: pp. 207–216.
   Web of Science ®Google Scholar
• Masek, J.G., Vermote, E.F., Saleous, N.E., Wolfe, R., Hall, F.G., Huemmrich, K.F., Gao, F., Kutler, J., and Lim, T. 2006. “A Landsat surface reflectance dataset for North America, 1990–2000.” IEEE Geoscience and Remote Sensing Letters, Vol. 3: pp. 68–72.
   Web of Science ®Google Scholar
• McInnes, W.S., Smith, B., and McDermid, G.J. 2015. “Discriminating native and non-native grasses in the dry mixedgrass prairie with MODIS NDVI time series.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 8(No. 4): pp. 1395–1403.
   Web of Science ®Google Scholar
• Merzlyak, J.R., Gitelson, A.A., Chivkunova, O.B., and Rakitin, V.Y. 1999. “Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening.” Physiologia Plantarum, Vol. 106: pp. 135–141.
   Web of Science ®Google Scholar
• Müller, H., Rufin, P., Griffiths, P., Siqueira, A., and Hostert, P. 2015. “Mining dense Landsat time series for separating cropland and pasture in a heterogeneous Brazilian savanna landscape.” Remote Sensing of Environment, Vol. 156: pp. 490–499.
   Web of Science ®Google Scholar
• Naeth, M.A., Balley, A.W., Pluth, D.J., Chanasyk, D.S., and Hardin, R.T. 1991. “Grazing impacts on litter and soil organic matter in mixed prairie and fescue grassland ecosystems of Alberta.” Journal of Range Management, Vol. 44: pp. 7–12.
   Google Scholar
• Nagler, P.L., Inoue, Y., Glenn, E.P., Russ, A.L., and Daughtry, C.S.T. 2003. “Cellulose absorption index (CAI) to quantify mixed soil-plant litter scenes.” Remote Sensing of Environment, Vol. 87: pp. 310–325.
   Web of Science ®Google Scholar
• Peled, A., and Gilichinsky, M. 2010. “Knowledge-based classification of land covers for the quality assessment of GIS database.” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science, Vol. XXXVIII-4-8-2/W9: pp. 217–222.
   Google Scholar
• Peñuelas, J., Filella, I., Biel, C., Serrano, L., and Savé, R. 1993. “The reflectance at the 950-970 nm region as a indicator of plant water status.” International Journal of Remote Sensing, Vol. 14: pp. 1887–1905.
   Web of Science ®Google Scholar
• Price, P., Guo, X., and Stiles, J.M. 2002. “Optimal Landsat TM band combinations and vegetation indices for discrimination of six grassland types in eastern Kansas.” International Journal of Remote Sensing, Vol. 23: pp. 1–12.
   Web of Science ®Google Scholar
• Ruiz, L.A., Recio, J.A., and Hermosilla, T. 2007. “Methods for automatic extraction of regularity patterns and its application to object-oriented image classification.” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science, Vol. XXXVI-3/W49A5: pp. 117–121.
   Google Scholar
• Scurlock, J.M.O., and Hall, D.O. 1998. “The global carbon sink: a grassland perspective.” Global Change Biology, Vol. 4: pp. 229–233.
   Web of Science ®Google Scholar
• Sims, D.A., and Gamon, J.A. 2002. “Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages.” Remote Sensing of Environment, Vol. 81: pp. 337–354.
   Web of Science ®Google Scholar
• Smith, A.M., and Buckley, J.R. 2011. “Investigating RADARSAT-2 as a tool for monitoring grassland in western Canada.” Canadian Journal of Remote Sensing, Vol. 37: pp. 93–102.
   Web of Science ®Google Scholar
• Smith, A. M., Hill, M. J., and Zhang, Y. 2015. “Estimating ground cover in the mixed prairie grassland of Alberta using Landsat TM imagery.” Canadian Journal of Remote Sensing, Vol. 41: pp. 51–66.
   Web of Science ®Google Scholar
• Song, H., and Huang, B. 2013. “Spatiotemporal satellite image fusion through one-pair image learning.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 51(No. 4): pp. 1883–1896.
   Web of Science ®Google Scholar
• Tarantino, C., Adamo, M., Lucas, R., and Blonda, P. 2016. “Detection of changes in semi-natural grasslands by cross correlation analysis with WorldView-2 images and new Landsat 8 data.” Remote Sensing of Environment, Vol.176: pp. 65–72.
   Web of Science ®Google Scholar
• Toscani, P., Immitzer, M., and Atzberger, C. 2013. “Wavelet-based texture measures for object-based classification of aerial images.” Photogramm Fernerkund Geoinformation, Vol. 2: pp. 105–121.
   Web of Science ®Google Scholar
• Tucker, C.J. 1979. “Red and photographic infrared linear combinations for monitoring vegetation.” Remote Sensing of Environment, Vol. 8: pp. 127–150.
   Web of Science ®Google Scholar
• Wang, W., and Fang, J. 2009. “Soil respiration and human effects on global grasslands.” Global and Planetary Change, Vol. 67: pp. 20–28.
   Web of Science ®Google Scholar
• Wang, L., and Qu, J. 2007. “NMDI: a normalized multi-band drought index for monitoring soil and vegetation moisture with satellite remote sensing.” Geophysical Research Letters, Vol. 34: pp. 1–5.
   Web of Science ®Google Scholar
• Wang, C., Jamison, B.E., and Spicci, A.A. 2010. “Trajectory-based warm season grassland mapping in Missouri prairies with multi-temporal ASTER imagery.” Remote Sensing of Environment, Vol. 114: pp.531–539.
   Web of Science ®Google Scholar
• Wardlow, B.D., and Egbert, S.L. 2008. “Large-area crop mapping using time-series MODIS 250 m NDVI data: An assessment for the US central great plains.” Remote Sensing of Environment, Vol. 112: pp. 1096–1116.
   Web of Science ®Google Scholar
• Wardlow, B.D., Egbert, S.L., and Kastens, J.H. 2007. “Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. central Great Plains.” Remote Sensing of Environment, Vol. 108: pp. 290–310.
   Web of Science ®Google Scholar
• Weeks, E.S., Ausseil, A.E., Shepherd, J.D., and Dymond, J.R. 2013. “Remote sensing methods to detect land-use/cover changes in New Zealand's ‘indigenous’ grasslands. New Zealand Geographer, Vol. 69: pp.1–13.
   Web of Science ®Google Scholar
• Xian, G., and Homer, C. 2010. “Updating the 2001 national land cover database impervious surface products to 2006 using Landsat imagery change detection methods.” Remote Sensing of Environment, Vol. 114: pp. 1676–1686.
   Web of Science ®Google Scholar
• Xian, G., Homer, C., and Fry, J. 2009. “Updating the 2001 national land cover database land cover classification to 2006 by using Landsat imagery change detection methods.” Remote Sensing of Environment, Vol. 113: pp. 1133–1147.
   Web of Science ®Google Scholar
• Yin, D., Gao, X., Chen, X., Shao, Y., and Chen, J. 2013. “Comparison of automatic thresholding methods for snow-cover mapping using Landsat TM imagery.” International Journal of Remote Sensing, Vol. 34: pp. 6529–6538.
   Web of Science ®Google Scholar
• Zhong, L., Gong, P., and Biging, G.S. 2014. “Efficient corn and soybean mapping with temporal extendability: a multi-year experiment using Landsat imagery.” Remote Sensing of Environment, Vol. 140: pp. 1–13.
   Web of Science ®Google Scholar
• Zhu, X., Chen, J., Gao, F., Chen, X., and Masek, J.G. 2010. “An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions.” Remote Sensing of Environment, Vol. 114: pp. 2610–2623.
   Web of Science ®Google Scholar
• Zhu, Z., Woodcock, C.E., and Olofsson, P. 2012. “Continuous monitoring of forest disturbance using all available Landsat imagery.” Remote Sensing of Environment, Vol. 122: pp. 75–91.
   Web of Science ®Google Scholar
• Zhu, Z., Woodcock, C.E., Holden, C., and Yang, Z. 2015. “Generating synthetic Landsat images based on all available Landsat data: predicting Landsat surface reflectance at any given time.” Remote Sensing of Environment, Vol. 162: pp. 67–83.
   Web of Science ®Google Scholar