Pettorelli N, Vik J O, Mysterud A, et al. Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends in Ecology & Evolution, 2005, 20(9): 503-510.
[2]
Qiao H B, Jiang J W, Cheng D F, et al. Comparison of hyperspectral characteristics in tobacco aphid damage. Chinese Bulletin of Entomology, 2007, 1: 015.
[3]
Wu T, Ni S X, Li Y M, et al. Monitoring of the damage intensity extent by oriental migration locust using of hyper-spectra data measured at ground surface. Journal of Remote Sensing, 2007, 11(1): 103-108.
[4]
Ni S, Wu T. Monitoring the intensity of locust damage to vegetation using hyper-spectra data obtained at ground surface. Optical Engineering Applications. International Society for Optics and Photonics, 2007: 66790B-66790B-9.
[5]
Lu H, Han J G, Zhang L D. Study on hyper-spectral remote sensing models for monitoring damage of Oedaleus asiaticus (Orthoptera: Acrididae). Spectroscope and Spectral Analysis, 2009, 29(3): 745-748.
[6]
Ji R, Xie B Y, Li D M, et al. Use of MODIS data to monitor the oriental migratory locust plague. Agriculture, Ecosystems & Environment, 2004, 104(3): 615-620.
[7]
Yang H S, Wang C B, Yu F, et al. Vegetation dynamics in locust occurrence area of Jeminay based on MODIS—NDVI. Jiangsu Agricultural Sciences, 2013, 41(5): 354-356.
[8]
Zhao F J, Wu H H, Liu Z Y, et al. Application of hyperspectral remote sensing in the biomass monitor of two grassland types in Xilinhot. Acta Agrestia Sinica, 2013, 21(6): 1059-1064.
[9]
Liang L, Zhang L P, Lin H, et al. Estimating canopy leaf water content in wheat based on derivative spectra. Scientia Agricultura Sinica, 2013, 46(1): 18-29.
[10]
Huang C Y, Wang D W, Huang D C, et al. Monitoring growing status of processing tomato based on hyperspectral vegetative index. Remote Sensing Information, 2012, 27(5):26-30.
[11]
Rouse J W. Monitoring vegetation systems in the Great Plains with ERTS.NASA. Goddard Space Flight Center 3 d ERTS-1 Symp, 1974, 309-317.
[12]
Ma J, Han X, Hasibagan, et al. Monitoring East Asian migratory locust plagues using remote sensing data and field investigations. International Journal of Remote Sensing, 2005, 26(3): 629-634.
[13]
Sugumaran R, Voss M. Object-oriented classification of LIDAR-fused hyperspectral imagery for tree species identification in an urban environment. Urban Remote Sensing Joint Event, IEEE, 2007: 1-6.
[14]
Boschetti M, Boschetti L, Oliveri S, et al. Tree species mapping with Airborne hyper-spectral MIVIS data: the Ticino Park study case. International Journal of Remote Sensing, 2007, 28(6): 1251-1261.
[15]
Vaiphasa C, Ongsomwang S, Vaiphasa T, et al. Tropical mangrove species discrimination using hyperspectral data: a laboratory study. Estuarine, Coastal and Shelf Science, 2005, 65(1): 371-379.
[16]
He K S, Rocchini D, Neteler M, et al. Benefits of hyperspectral remote sensing for tracking plant invasions. Diversity and Distributions, 2011, 17(3): 381-392.
[17]
Sheeren D, Fauvel M, Ladet S, et al. Mapping ash tree colonization in an agricultural mountain landscape: Investigating the potential of hyperspectral imagery. Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International, 2011: 3672-3675.
[18]
Salazar L, Kogan F, Roytman L. Use of remote sensing data for estimation of winter wheat yield in the United States. International Journal of Remote Sensing, 2007, 28(17): 3795-3811.
[19]
Becker-Reshef I, Vermote E, Lindeman M, et al. A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data. Remote Sensing of Environment, 2010, 114(6): 1312-1323.
[20]
Santin-Janin H, Garel M, Chapuis J L, et al. Assessing the performance of NDVI as a proxy for plant biomass using non-linear models: a case study on the Kerguelen archipelago. Polar Biology, 2009, 32(6): 861-871.
[21]
Verbesselt J, Somers B, van Aardt J A N, et al. Monitoring herbaceous biomass and water content with SPOT VEGETATION time-series to improve fire risk assessment in savanna ecosystems. Remote Sensing of Environment, 2006, 101(3): 399-414.
[22]
Sims D A, Rahman A F, Cordova V D, et al. On the use of MODIS EVI to assess gross primary productivity of North American ecosystems. Journal of Geophysical Research: Biogeosciences (2005-2012), 2006, 111(G4). DOI: 10.1029/2006JG000162.
Rhee J, Im J, Carbone G J. Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data. Remote Sensing of Environment, 2010, 114(12): 2875-2887.
[35]
Caccamo G, Chisholm L A, Bradstock R A, et al. Assessing the sensitivity of MODIS to monitor drought in high biomass ecosystems. Remote Sensing of Environment, 2011, 115(10): 2626-2639.
[36]
Rojas O, Vrieling A, Rembold F. Assessing drought probability for agricultural areas in Africa with coarse resolution remote sensing imagery. Remote Sensing of Environment, 2011, 115(2): 343-352.
[37]
Huete A, Didan K. MODIS seasonal and inter-annual responses of semiarid ecosystems to drought in the Southwest USA. Geoscience and Remote Sensing Symposium, 2004.IGARSS’04.Proceedings.2004 IEEE International. IEEE, 2004: 1538-1541.
[38]
Deshayes M, Guyon D, Jeanjean H, et al. The contribution of remote sensing to the assessment of drought effects in forest ecosystems. Annals of Forest Science, 2006, 63(6): 579-595.
[39]
Carro H, Sepulcre G, Horion S, et al. A multitemporal and non-parametric approach for assessing the impacts of drought on vegetation greenness: A case study for Latin America. EARSeL eProceedings, 2013, 12(1): 8.
[40]
Zhang C B, Li J L, Zhang Y, et al. A quantitative analysis method for measuring grassland coverage based on the RGB model. Acta Prataculturae Sinica, 2013, 22(4): 220-226.
[41]
Yang H F, Li J L, Mu S J, et al. Analysis of hyperspectral reflectance characteristics of three main grassland types in Xinjiang. Acta Prataculturae Sinica, 2012, 21(6): 258-266.
[42]
Qian Y R, Yu J, Jia Z H, et al. Extraction and analysis of hyper-spectral data from typical desert grassland in Xinjiang. Acta Prataculturae Sinica, 2013, 22(1): 157-166.
[43]
McCulloch L, Hunter D M. Identification and monitoring of Australian plague locust habitats from Landsat. Remote Sensing of Environment, 1983, 13(2): 95-102.
[44]
Sivanpillai R, Latchininsky A V. Can late summer Landsat data be used for locating Asian migratory locust, Locusta migratoria migratoria, oviposition sites in the Amudarya River delta, Uzbekistan. Entomologia Experimentalis et Applicata, 2008, 128(2): 346-353.
[45]
Tratalos J A, Cheke R A. Can NDVI GAC imagery be used to monitor desert locust breeding areas. Journal of Arid Environments, 2006, 64(2): 342-356.
[46]
Latchininsky A V, Sivanpillai R. Locust Habitat Monitoring and Risk Assessment Using Remote Sensing and GIS Technologies. Integrated Management of Arthropod Pests and Insect Borne Diseases. Netherlands: Springer, 2010: 163-188.
[47]
Despland E, Rosenberg J, Simpson S J. Landscape structure and locust swarming: a satellite’s eye view. Ecography, 2004, 27(3): 381-391.
[48]
Sivanpillai R, Latchininsky A V, Driese K L, et al. Mapping locust habitats in River Ili Delta, Kazakhstan, using LANDSAT imagery. Agriculture, Ecosystems &Environment, 2006, 117(2): 128-134.
[49]
Song K S, Zhang B, Wang Z M, et al. Inverse model for estimating soybean chlorophyII concentration using in-situ collected canopy hyperspectral data. Transactions of the CSAE, 2006, 22(8): 16-21.
[50]
Cho M A, Skidmore A, Corsi F, et al. Estimation of green grass/herb biomass from airborne hyperspectral imagery using spectral indices and partial least squares regression. International Journal of Applied Earth Observation and Geoinformation, 2007, 9(4): 414-424.
[51]
Mutanga O, Skidmore A K. Narrow band vegetation indices overcome the saturation problem in biomass estimation. International Journal of Remote Sensing, 2004, 25(19): 3999-4014.
[52]
Chen M D, Huang X D, Hou X M, et al. Dynamic monitoring of biomass and vegetation coverage in rodent damaged grassland regions of Qinghai province, China. Acta Prataculturae Sinica, 2013, 22(4): 247-256.
