[1] Ahmadi, M. and Narangifard, M. (2015). Quality assessment and detection of forest area changes using satellite images (Case study: Rustam, Fars). Journal of RS and GIS for Natural, 6(3), 87-100.
[2] Atak, B.K. and Tonyaloglu, E.E. (2020). Evaluating spectral indices for estimating burned areas in the case of Izmir/Turkey. Eurasian Journal of Forest Science, 8(1), 63-73.
[3] Baig, M.H.A., Zhang, L., Shuai, T. and Tong, Q. (2014). Derivation of a tasselled cap transformation based on Landsat 8 at-satellite reflectance. Remote Sensing Letters, 5(5), 423-431.
[4] Bing, L.u., Yuhong, He. and Alexander, T. (2016). Evaluation of spectral indices for estimating burn severity in semiarid grasslands. International Journal of Wildland Fire, 25, 147-157.
[5] Broich, M., Tulbure, M.G., Verbesselt, J., Xin, Q. and Wearne, J. (2018). Quantifying Australias dryland vegetation response to flooding and drought at sub-continental scale. Remote Sensing of Environment, 212(1), 60-78.
[6] Chen X., Vogelmann J. E., Rollins M., Ohlen D., Key C. H., Yang L., Huang C. and Shi, H. (2011). Detecting post-fire burn severity and vegetation recovery using multitemporal remote sensing spectral indices and field-collected composite burn index data in a ponderosa pine forest. International Journal of Remote Sensing, 32(23), 7905-7927.
[7] Chuvieco E., Martin M. P. and Palacios, A. (2002). Assessment of different spectral indices in the red-near-infrared spectral domain for burned land discrimination. International Journal of Remote Sensing, 23(23), 5103-5110.
[8] Fornaca, D., Ren, G. and Xiao, W. (2018). Evaluating the Best Spectral Indices for the Detection of Burn Scars at Several Post-Fire Dates in a Mountainous Region of Northwest Yunnan, China. Journal of Remote Sens, 10(8), 4-21.
[9] Gerard, F., Plummer, S., Wadsworth, R., Sanfeliu, A.F., Iliffe, L., Balzter, H. and Wyatt, B. (2003). Forest fire scar detection in the boreal forest with multitemporal spot-vegetation data. IEEE Transactions on Geoscience and Remote Sensing, 41(11), 2575–2585.
[10] Gu, Y. and Wylie, B.K. (2015). Developing a 30-m grassland productivity estimation map for central Nebraska using 250-m MODIS and 30-m Landsat-8 observations. Remote Sensing of Environment, 171, 291-298.
[11] Higginbottom, T.P., Symeonakis, E., Meyer, H. and Van Der Linden, S. (2018). Mapping fractional woody cover in semi-arid savannahs using multi-seasonal composites from Landsat data. ISPRS Journal of Photogrammetry and Remote Sensing, 139, 88-102.
[12] Hill, M.J. (2013). Vegetation index suites as indicators of vegetation state in grassland and savanna: An analysis with simulated SENTINEL 2 data for a North American transect. Remote Sensing of Environment, 137, 94-111.
[13] Hislop, S., Jones, S., Soto-Berelov, M., Skidmore, A.K., Haywood, A. and Nguyen, T. (2018). Using Landsat Spectral Indices in Time-Series to Assess Wildfire Disturbance and Recovery. Remote Sensing, 10, 2-17.
[14] Holden, Z., Smith, A., Morgan, P., Rollins, M. and Gessler, P. (2005). Evaluation of novel thermally enhanced spectral indices for mapping fire perimeters and comparisons with fire atlas data. International Journal of Remote Sensing, 26, 4801–4808.
[15] Iranmehr, M., Pourmanafi, S. and Soffianian, A. (2015). Ecological monitoring and assessment of spatial-temporal changes in land cover with an emphasis on agricultural water consumption in Zayandeh Rood Region. Iranian Journal of Eco Hydrology, 2(1), 23-28.
[16] Kavzoglu, T., Erdemir, M.Y. and Tonbul, H. (2016). Evaluating performances of spectral indices for burned area mapping using object-based image analysis. Proceedings of Spatial Accurac, 366, 162-168.
[17] Kawamura, K., Akiyama, T., Yokota, H.O., Tsutsumi, M., Yasuda, T., Watanabe, O. and Wang, S. (2005). Quantifying grazing intensities using geographic information systems and satellite remote sensing in the Xilingol steppe region, Inner Mongolia, China. Agriculture, Ecosystems & Environment, 107(1), 83-93.
[18] Kaufman, Y.J. and Remer, L.A. (1994). Detection of Forests Using Mid-IR Reflectance: An Application for Aerosol Studies. IEEE transactions on geoscience and Sensing, 32, 672–683.
[19] Key, C.H. and Benson, N.C. (2006). Landscape assessment: remote sensing of severity, the Normalized Burn Ratio. In: Lutes, D.C. (Ed.), FIREMON: Fire Effects Monitoring and Inventory System. USDA Forest Service, Rocky Mountain Research Station, Ogden,UT, USA General Technical Report, RMRS-GTR-164-CD:LA1-LA51.
[20] Kennedy, R.E., Yang, Z. and Cohen, W.B. (2010). Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr-Temporal segmentation algorithms. Remote Sensing of Environment, 114, 2897–2910.
[21] Lambin, E.F., Goyvaerts, K. and Petit, C. (2003). Remotely-sensed indicators of burning efficiency of savannah and forest fires. International Journal of Remote Sensing, 24, 3105–3118.
[22] Lasaponara, R. (2006). Estimating spectral separability of satellite derived parameters for burned areas mapping in the Calabria region by using SPOT-Vegetation data. Ecological Modelling, 196, 265–270.
[23] Libonati, R., DaCamara, C.C., Pereira, J.M.C. and Peres, L.F. (2011). On a new coordinate system for improved discrimination of vegetation and burned areas using MIR/NIR information. Remote Sensing of Environment, 115, 1464–1477.
[24] Llamas, P.G., Seoane, S.S., Guisuraga, J.M.F., Garcia, V.F., Manso, A.F., Quintano, C., Taboada, A., Marcos, E. and Calvo, L. (2019). Evaluation and comparison of Landsat 8, Sentinel-2 and Deimos-1 remote sensing indices for assessing burn severity in Mediterranean fire-prone ecosystems ,
International Journal of Applied Earth Observation and Geoinformation, 80, 137–144.
[25] Martin, M.P., Gomez, I. and Chuvieco, E. (2005). Performance of a burned-area index (BAIM) for mapping Mediterranean burned scars from MODIS data. In: Riva J, Perez-Cabello F, Chuvieco E (eds) Proceedings of the 5th international workshop on remote sensing and GIS applications to forest fire management: fire effects assessment, pp 193–198.
[26] Marshall, G.S. (2005). Drought detection and quantification using field-based spectral measurements of vegetation in semi-arid regions.
[27] Meng, R., Dennison, P.E., D’Antonio, C.M. and Moritz, M.A. (2014). Remote Sensing Analysis of vegetation recovery following short-interval fires in southern California shrublands. Journal PLOS ONE 9(10), e110637.
[28] Melchiori, A.E., Candido, P., Libonati, R., Morelli, F., Setzer, A., de Jesus, S.C., Garcia Fonseca, L.M. and Korting, T.S. (2015). Spectral indices and multi-temporal change image detection algorithms for burned area extraction in the Brazilian Cerrado. In Proceedings of the Anais XVII Simposio Brasileiro de Sensoriamento Remoto—SBSR, Joao Pessoa-PB, Brasil, 25–29 April 2015, pp. 643–650.
[29] Mohammadian, A., Asadi borujeni, E., Ebrahimi, A.A., Tahmasebi, P. and Naghipour, A.A. (2020). Effect of intraction fire period and intensity grazing on plant species diversity in the semi-steppe rangeland of chaharmahal and bakhtiari province, Iranian Journal of Range and Desert Research, 27(1), 84-97.
[30] Mitri G. and Gitas, I. (2004). A performance evaluation of a burned area object-based classification model when applied to topographically and non-topographically corrected TM imagery. International Journal of Remote Sensing, 25, 2863-2870.
[31] Morrison, I.M. (1980). Changes in the ligninand hemicellulose concentrations of ten varieties of temperate grasses with increasing maturity. Grass Forage Science, 35, 93-287.
[32] Mouillot, F., Schultz, M. G., Yue, C., Cadule, P., Tansey, K., Ciais, P. and Chuvieco, E. (2014). Ten years of global burned area products from spaceborne remote sensing—A review: Analysis of user needs and recommendations for future developments. International Journal of Applied Earth Observation and Geoinformation, 26, 64-79.
[33] Rahmani, SH., Ebrahimi, A. and Davoudian, A.R. (2013). Generating a Vegetation Map in Mountainous Region of Sabzkouh Using a Digital Elevation Model. Journal of Rangeland and Watershed Management, 69(3): 621-631.
[34] Rahman, A.F. and Gamon, J.A. (2004). Detecting biophysical properties of a semiarid grassland and distinguishing burned from unburned areas with hyperspectral reflectance. Journal of Arid Environments, 58, 597–610.
[35] Roy, D.P., Giglio, L., Kendall, J.D. and Justice, C.O. (1999). Multi-temporal activefire based burn scar detection algorithm. International Journal of Remote Sensing, 20, 1031–1038.
[36] Sharifi, J. and Akbarzadeh, M. (2010). Investigation of vegetation changes under precipitation in semi-steppic rangelands of Ardebil province (Case study: Arshagh Rangeland Research Site). Journal of Range and Watershed Management (Iranian Journal of Natural Resources), 65(4): 507-516.
[37] Smith, A.M.S., Drake, N.A., Wooster, M.J., Hudak, A.T., Holden, Z.A. and Gibbons, C.J. (2007). Production of Landsat ETM+ reference imagery of burned areas within Southern African savannahs: Comparison of methods and application to MODIS. International Journal of Remote Sensing, 28, 2753–2775.
[38]
Taddeoa, S.,
Dronovaa, I. and
Depsky, N. (2019). Spectral vegetation indices of wetland greenness: Responses to vegetation structure, composition, and spatial distribution. Remote Sensing of Environment, 234: 1-13.
[39] Trager, M., Wilson, G.W.T. and Hartnett, D.C. (2004). Concurrent effects of fire regime, grazing and bison wallowing on tallgrass prairie vegetation. American Midland Naturalist, 152, 237–247.
[40] Trigg, S. and Flasse, S. (2001). An evaluation of different bi-spectral spaces for discriminating burned shrub-savannah. International Journal of Remote Sensing, 22, 2641–2647.
[41] Tucker, C. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8, 127–150.
[42] USGS. (2019). Landsat Surface Reflectance-Derived Spectral Indices. https://www.usgs.gov/land-resources/nli/landsat/landsat-normalized-burn-ratio.
[43] Veraverbeke, S., Harris, S. and Hook, S. (2011). Evaluating spectral indices for burned area discrimination using MODIS/ASTER (MASTER) airborne simulator data. Remote Sensing of Environment, 115(10), 2702-2709.
[44] Veraverbeke, S., Dennison, P., Gitas, I., Hulley, G., Kalashnikova, O., Katagis, T., Kuai, L., Meng, R., Roberts, D. and Stavros, N. (2018). Hyperspectral remote sensing of fire: state-of-the-art and future perspectives. Remote Sensing of Environment, 216, 105-121.
[45] White, J.D., Ryan, K.C., Key, C.C. and Running, S.W. (1996). Remote sensing of forest fire severity and vegetation recovery. International Journal of Wildland Fire, 6, 125–136.
[46] Wilson, E.H. and Sader, S.A. (2002). Detection of forest harvest type using multiple dates of Landsat TM imagery. Remote Sensing of Environment, 80, 385–396.
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