(Citations >9200, h-index=31: http://scholar.google.com/citations?user=SrrF3Y0AAAAJ&hl=en)
Selected Journal Publications:
Liu, L., Zhang, X., Yu, Y., Gao, F., Yang, Z., 2018, Real-time Monitoring of Crop Phenology in the Midwestern United States using VIIRS Observations, Remote Sensing, 10(10), 1540; https://doi.org/10.3390/rs10101540
Zhang, X., Liu, L., Liu, Y., Jayavelu, S., Wang, J., Moon, M., Henebry, G.M., Friedl, M.A., Schaaf, C.B., 2018, Generation and evaluation of the VIIRS land surface phenology product, Remote Sensing of Environment, 216, 212-229, https://doi.org/10.1016/j.rse.2018.06.047
Wang, J., Bhattacharjee, P.S., Tallapragada, V., Lu, C., Kondragunta, S., da Silva, A., Zhang, X., Chen, S., 2018, The implementation of NEMS GFS Aerosol Component (NGAC) Version 2.0 for global multispecies forecasting at NOAA/NCEP –Part 1: Model descriptions, Geoscientific Model Development, 11, 2315–2332, https://doi.org/10.5194/gmd-11-2315-2018
Donnelly, A., Liu, L., Zhang, X., and Wingler, A., 2018, Autumn leaf phenology: discrepancies between in situ observations and satellite data at urban and rural sites, International Journal of Remote Sensing, https://doi.org/10.1080/01431161.2018.1482021
An, S., Zhang, X., Chen, X., Dong Yan, D., and Henebry, G.M., 2018, An exploration of terrain effects on land surface phenology across the Qinghai–Tibet Plateau using Landsat ETM+ and OLI data, Remote Sensing, 10, 1069; https://doi.org/10.3390/rs10071069
Li, F., Zhang, X., Kondragunta, S., Csiszar, I., 2018, Comparison of fire radiative power estimates from VIIRS and MODIS observations. Journal of Geophysical Research-Atmosphere, https://doi.org/10.1029/2017JD027823.
Li, F., Zhang, X., Kondragunta, S., Roy, D.P., 2018, Investigation of the fire radiative energy biomass combustion coefficient - a comparison of polar and geostationary satellite retrievals over the Conterminous United States. Journal of Geophysical Research-Biogeoscience, 132, 722-739. https://doi.org/10.1002/2017JG004279.
Huang, R., Zhang, X., Chan, D., Kondragunta, S., Russell, A.G., Odman, M.T., 2018, urned Area Comparisons between Prescribed Burning Permits in Southeastern USA and two Satellite‐derived Products. Journal of Geophysical Research-Atmosphere, https://doi.org/10.1029/2017JD028217
Peng, D., Wu, C., Zhang, X., Yu, L., Huete, A.R., Wang, F., Luo, S., Liu, X., Zhang, H., 2018, Scaling up spring phenology derived from remote sensing images. Agricultural and Forest Meteorology, 256, 207-219. https://doi.org/10.1016/j.agrformet.2018.03.010.
Zhang, X., Jayavelu, S., Liu, L., Friedl, M.A., Henebry, G.M., Liu, Y., Schaaf, C.B., Richardson, A.D., and Gray, J., 2018, Evaluation of Land Surface Phenology from VIIRS Data using Time Series of PhenoCam Imagery, Agricultural and Forest Meteorology, 256–257, 137-149. https://doi.org/10.1016/j.agrformet.2018.03.003.
Liu, Y., Wang, Z., Sun, Q., Erb, A.M., Li, Z., Schaaf, C.B., Zhang, X., Román, M.O., Scott, R.L., Zhang, Q., Novick, K.A., Bret-Harte, S., Petroy, S., SanClements, M., 2017, Evaluation of the VIIRS BRDF, Albedo and NBAR products suite and an assessment of continuity with the long term MODIS record, Remote Sensing of Environment, 201, 256-274. http://dx.doi.org/10.1016/j.rse.2017.09.020
Peng, D., Zhang, X., Zhang, B., Liu, L., Liu, X., Huete, A.R., Huang, W., Wang, S., Luo, S., Zhang, X., Zhang, H., 2017, Scaling effects on spring phenology detections from MODIS data at multiple spatial resolutions over the contiguous United States, ISPRS Journal of Photogrammetry and Remote Sensing, 132:185-198. https://doi.org/10.1016/j.isprsjprs.2017.09.002
Wang, J., Zhang, X., 2017, Impacts of wildfires on interannual trends in land surface phenology: an investigation of the Hayman Fire, Environmental Research Letters, 12: 05400. https://doi.org/10.1088/1748-9326/aa6ad9 (news http://environmentalresearchweb.org/cws/article/news/69988)
Zhang, X., Liu, L., and Yan, D., 2017, Comparisons of global land surface seasonality and phenology derived from AVHRR, MODIS, and VIIRS data, Journal of Geophysical Research-Biogeoscience, 122, https://doi.org/10.1002/2017JG003811. (Highlighted by the Journal)
Krehbiel, C., Zhang, X., and Henebry, G.M., 2017, Impacts of Thermal Time on Land Surface Phenology in Urban Areas, Remote Sensing, 9, 499, https://doi.org/10.3390/rs9050499
Yan, D., Zhang, X., Yu, Y., and Guo, W., 2017, Characterizing land cover impacts on the responses of land surface phenology to the rainy season in the Congo Basin, Remote Sensing, 9(5), 461; https://doi.org/10.3390/rs9050461
Peng, D., Zhang, X., Wu, C., Huang, W., Gonsamo, A., Huete, A.R. Didan, K., Tang, B.,Liu, X., Zhang, B., 2017, Intercomparison and evaluation of spring phenology products using National Phenology Network and AmeriFlux observations in the contiguous United States, Agricultural and Forest Meteorology, 242: 33–46. https://doi.org/10.1016/j.agrformet.2017.04.009
Wang, Z., Schaaf, C.B., Sun, Q., Kim, J., Erb, A.M., Gao, F., Román, M.O., Yang, Y., Petroy, S., Taylor, J.R., Masek, J.G., Morisette, J.T., Zhang, X., Papuga, S.A., 2017, Monitoring land surface albedo and vegetation dynamics using high spatial and temporal resolution synthetic time series from Landsatand the MODIS BRDF/NBAR/albedo product, International Journal of Applied Earth Observation and Geoinformation, 59, 104–117. http://dx.doi.org/10.1016/j.jag.2017.03.008
Liu, L., Zhang, X., Yu, Y., Guo, W., 2017, Real-time and short-term predictions of spring phenology in North America from VIIRS data, Remote Sensing of Environment, 194, 89–99, http://dx.doi.org/10.1016/j.rse.2017.03.009
Peng, D., Wu, C., Li, C., Zhang, X., Liu, Z., Ye, H., Luo, S., Liu, X., Hu, Y. Fang, B., 2017, Spring green-up phenology products derived from MODIS NDVI and EVI: Intercomparison, interpretation and validation using National Phenology Network and AmeriFlux observations, Ecological Indictors, 77, 323-336, http://dx.doi.org/10.1016/j.ecolind.2017.02.024
Liu , Y., Hill, M.J., Zhang, X., Wang, Z., Richardson, A., Hufkens, K., Filippa, G., Baldocchi, D. D., Ma, S., Verfaillie, J., Schaaf, C.B., 2017, Using data from Landsat, MODIS, VIIRS and PhenoCams to monitor the phenology of California oak/grass savanna and open grassland across spatial scales, Agricultural and Forest Meteorology, 237–238, 311–325, http://dx.doi.org/10.1016/j.agrformet.2017.02.026
Zhang, X., Wang, J., Gao, F., Liu, Y., Schaaf, C.B., Friedl, M.A., Yu, Y., Jayavelu, S., Gray, J., Liu, L., Yan, D., and Henebry, G.M., 2017, Exploration of Scaling Effects on Coarse Resolution Land Surface Phenology, Remote Sensing of Environment, 190, 318-330, http://dx.doi.org/10.1016/j.rse.2017.01.001
Liu, L. Zhang, X., Yu, Y., and Donnelly, A., 2017. Detecting spatiotemporal changes of peak foliage coloration in deciduous and mixedforests across the Central and Eastern United States, Environmental Research Letters, 12 024013, https://doi.org/10.1088/1748-9326/aa5b3a (news http://environmentalresearchweb.org/cws/article/news/68993
Gao F., Anderson, M.C., Zhang, X., Yang, Z., Alfieri, J.G., Kustas, W.P., Mueller, R., Johnson, D.M., Prueger, J.H., 2017, Toward mapping crop progress at field scales using Landsat and MODIS imagery, Remote Sensing of Environment, 188, 9–25, http://dx.doi.org/10.1016/j.rse.2016.11.004.
Yan, D., Zhang, X., Yu, Y., Guo, W. and Hanan, N. P., 2016, Characterizing land surface phenology and responses to rainfall in the Sahara Desert, Journal of Geophysical Research- Biogeosciences, 121, http://dx.doi.org/10.1002/2016JG003441.
Peng, D., Wu, C., Zhang, B., Huete, A., Zhang, X., Sun, R., Lei, L., Huang, W., Liu, L., Liu, X., Li, J., Luo, S., Fang, B., 2016, The Influences of Drought and Land-Cover Conversion on Inter-Annual Variation of NPP in the Three-North Shelterbelt Program Zone of China Based on MODIS Data. PloS one, 11(6), http://dx.doi.org/10.1371/journal.pone.0158173.
Liang, L., Schwartz, M., Zhang, X., 2016, Mapping Temperate Vegetation Climate Adaptation Variability Using Normalized Land Surface Phenology, Climate, 4(2), 24; http://dx.doi.org/10.3390/cli4020024
Yan, D., Zhang, X., Yu, Y., and Guo, W., 2016, A comparison of tropical rainforest phenology retrieved from geostationary (SEVIRI) and polar-orbiting (MODIS) sensors across the Congo Basin, IEEE Transactions On Geoscience and Remote Sensing, http://doi.org/10.1109/TGRS.2016.2552462.
Zhang, X., and Zhang, Q. 2016, Monitoring interannual variation in global crop yield using long-term AVHRR and MODIS observations, ISPRS Journal of Photogrammetry and Remote Sensing 114, 191-205, http://dx.doi.org/10.1016/j.isprsjprs.2016.02.010.
Liu, L., Zhang, X., Donnelly, A. and Liu, X. ,2016, Interannual variations in spring phenology and their response to climate change across the Tibetan Plateau from 1982 to 2013, Int. J. Biometeorol., doi:10.1007/s00484-016-1147-6
Wu, M., Zhang, X, Huang, W., Niu, Z., Wang,C., Li, W. and Hao, P., 2015, Reconstruction of Daily 30 m Data from HJ CCD, GF-1 WFV, Landsat, and MODIS Data for Crop Monitoring, Remote Sensing, http://dx.doi.org/10.3390/rs71215826.
Liang, L., Zhang, X., 2015. Coupled Spatiotemporal Variability of Temperature and Spring Phenology in the Eastern U.S., International Journal of Climatology, http://dx.doi.org/10.1002/joc.4456.
Yue, X., Unger, N., Keenan, T. F., Zhang, X., and Vogel, C. S. 2015. Probing the past 30-year phenology trend of US deciduous forests. Biogeosciences, 12, 4693–4709, http://dx.doi.org/10.5194/bg-12-4693-2015.
Senthilnath, J., Kumar, D., Benediktsson, J.A., Zhang, X., 2015. A novel hierarchical clustering technique based on splitting and merging. International Journal of Image and Data Fusion, http://dx.doi.org/10.1080/19479832.2015.1053995.
Zhang, X., 2015. Reconstruction of a Complete Global Time Series of Daily Vegetation Index Trajectory from Long-term AVHRR Data. Remote Sensing of Environment, http://dx.doi.org/10.1016/j.rse.2014.10.012.
Zhang, Q., Cheng, Y.B., Lyapustin, A.I., Wang, Y., Zhang, X., Suyker, A., Verma, S., Shuai, Y., Middleton, E.M., 2015. Estimation of crop gross primary production (GPP): II. Do scaled MODIS vegetation indices improve performance? Agricultural and Forest Meteorology, 200, 1–8, http://dx.doi.org/10.1016/j.agrformet.2014.09.003.
Fan, B., Guo, L., Li, N., Chen, J., Lin, H., Zhang, X., Shen, M., Rao, Y., Wang, C., Ma, L., 2014. Earlier vegetation green-up has reduced spring dust storms. Scientific Reports, 4 : 6749, http://dx.doi.org/10.1038/srep06749.
Xiao J., Ollinger, S.V., Frolking, S., Hurtt, G.C., Hollinger, D.Y., Davis, K.J., Pan, Y., Zhang, X., Deng, F., Chen, J., Baldocchi, D.D., Law, B.E., Arain, M.A., Desai, A.R., Richardson, A.D., Sun, G., Amiro, B., Margolis, H., Gu, L., Scott, R.L., Blanken, P.S., Suyker, A.E., 2014. Data-driven diagnostics of terrestrial carbon dynamics over North America. Agricultural and Forest Meteorology, 197, 142–157, http://dx.doi.org/10.1016/j.agrformet.2014.06.013.
Zhang, X., Kondragunta, S., and Roy, D.P., 2014. Interannual variation in biomass burning and fire seasonality derived from geostationary satellite data across the contiguous United States from 1995 to 2011. Journal of Geophysical Research-Biogeosciences, http://dx.doi.org/10.1002/2013JG002518.
Zhang, F., Wang, J. Ichoku, C., Hyer, E., Yang, Z., Ge, C., Su, S., Zhang, X., Kondragunta, S., Kaiser, J., Wiedinmyer, C., and da Silva, A., 2014. Sensitivity of mesoscale modeling of smoke direct radiative effect to the emission inventory: A case study in northern sub-Saharan African region. Environmental Research Letters, 9, 075002, http://dx.doi.org/10.1088/1748-9326/9/7/075002.
Zhang, X., Tan, B., and Yu, Y. 2014. Interannual variation and trends in global land surface phenology derived from enhanced vegetation index during 1982-2010. International Journal of Biometeorology, http://dx.doi.org/10.1007/s00484-014-0802-z
Liang, L., Schwartz, M.D., Wang, Z., Gao, F., Schaaf, C.B., Tan, B., Morisette, J.T., and Zhang, X., 2014. A cross comparison of spatiotemporally enhanced springtime phenological measurements from satellites and ground in a northern U.S. mixed forest. IEEE Transactions On Geoscience And Remote Sensing, http://dx.doi.org/10.1109/TGRS.2014.2313558.
Yanmin, S., Schaaf, C., Zhang, X. Xiaoyang; Strahler, A., Roy, D., Morisette, J., Wang, Z., Nightingale, J., Nickeson, J., Richardson, A.D., Xie, D., Wang, J., Li, X., Strabala, K., Davies, J.E., 2013. Daily MODIS 500 m reflectance anisotropy direct broadcast (DB) products for monitoring vegetation phenology dynamics. International Journal of Remote Sensing, 34(16): 5997-6016.
Zhang, X., Kondragunta, S., Ram, J., Schmidt, C., Huang,H-C, 2012. Near Real Time Global Biomass Burning Emissions Product from Geostationary Satellite Constellation. Journal of Geophysical Research, 117, doi:10.1029/2012JD017459.
Zhang, X., Mitchell D. Goldberg, M.D., Yu, Y., 2012. Prototype for monitoring and forecasting fall foliage coloration in real time from satellite data. Agricultural and Forest Meteorology, 158:21-29.
Kovalskyy, V., Roy, D. P., Zhang, X., Ju, J., 2012. The suitability of multi-temporal Web-Enabled Landsat Data (WELD) NDVI for phenological monitoring – a comparison with flux tower and MODIS NDVI. Remote Sensing Letters, 3(4): 325–334.
Zhang, X. Kondragunta, S., and Quayle, B., 2011. Estimation of biomass burned areas using multiple-satellite-observed active fires. IEEE Transactions on Geosciences and Remote Sensing, 49: 4469-4482, 10.1109/TGRS.2011.2149535.
Zhang, X. and Goldberg, M, 2011. Monitoring Fall Foliage Coloration Dynamics Using Time-Series Satellite Data. Remote Sensing of Environment, 115 (2): 382-391.
Yang, E.S., Christopher, S.A., Kondragunta, S., and Zhang, X., 2010. Use of hourly GOES fire emissions in a Community Multiscale Air Quality (CMAQ) model for improving surface particulate matter predictions. Journal of Geophysical Research, 116, D04303, doi:10.1029/2010JD014482.
Zhang, X., Goldberg, M., Tarpley, D., Friedl, M., Morisette, J., Kongan, F., Yu, Y., 2010. Drought-induced Vegetation Reduction in Southwestern North America. Environmental Research Letters, 5 (2010) 024008, doi:10.1088/1748-9326/5/2/024008.
Ganguly, S., Friedl, M.A., Tan, B., Zhang, X., and Verma, M., 2010. Land surface phenology from MODIS: Characterization of the Collection 5 global land cover dynamics product. Remote Sensing of Environment, 114(8), 1805-1816.
Christopher, S.A., Gupta, P., Nair, U., Jones, T.A., Kondragunta, S., Wu, Y.L, Hand, J., Zhang, X, 2009. Satellite Remote Sensing and Mesoscale Modeling of the 2007 Georgia/Florida Fires. Journal of Selected Topics in Earth Observations and Remote Sensing, 2:163 – 175, DOI:10.1109/JSTARS.2009.2026626.
Zhang, X., Friedl, M.A., Schaaf, C.B., 2009. Sensitivity of vegetation phenology detection to the temporal resolution of satellite data. International Journal of Remote Sensing, 30(8): 2061 – 2074, DOI: 10.1080/01431160802549237.
Zhang, X., Kondragunta, S., Schmidt, C., Kogan, F., 2008. Near real-time monitoring of biomass burning particulate emissions (PM2.5) using multiple satellite instruments. Atmospheric Environment, doi:10.1016/j.atmosenv.2008.04.060.
Al-Saadi, J., Soja, A., Pierce, B., Kittaka, C., Emmons, L., Kondragunta, S., Zhang, X., Wiedinmyer, C., Schaack, T. Szykman, J., 2008. Evaluation of Near-Real-Time Biomass Burning Emissions Estimates Constrained by Satellite Active Fire Detections. Journal of Applied Remote Sensing, v2, DOI: 10.1117/1.2948785.
Zhang, X., Kondragunta, S., 2008. Temporal and spatial variability in biomass burned areas across the USA derived from the GOES fire product. Remote Sensing of Environment, 112, doi:10.1016/j.rse.2008.02.006.
Zhang, X., Tarpley, D., Sullivan, J. 2007. Diverse responses of vegetation phenology to a warming climate. Geophysical Research Letters, 34, L19405, doi:10.1029/2007GL031447.
Zhang, X., Friedl, M.A., Schaaf, C.B., 2006. Global vegetation phenology from MODIS: evaluation of global patterns and comparison with in situ measurements. Journal of Geophysical Research, Vol. 111, G04017, doi:10.1029/2006JG000217.
Zhang X., Kondragunta, S., 2006. Estimating forest biomass in the USA using generalized allometric model and MODIS product data. Geophysical Research Letters, 33: L09402, doi:10.1029/2006GL025879.
Wiedinmyer, C., Quayle, B., Geron, C., Belote, A., McKenzie, Zhang, X., O’Neil, S., and Wynne, K.K., 2006. Estimating emissions from fires in North America for air quality modeling. Atmospheric Environment, 40: 3419-3432.
Zhang, X., Friedl, M.A., Schaaf, C.B., and Strahler, A.H., Liu, Z., 2005. Monitoring the response of vegetation phenology to precipitation in Africa by coupling MODIS and TRMM instruments. Journal of Geophysical Research-Atmospheres, 110, D12103.
Zhang, X., Friedl, M.A., Schaaf, C. B., Strahler, A.H., and Schneider, A., 2004. The footprint of urban climates on vegetation phenology. Geophysical Research Letter, Vol. 31, L12209, doi:10.1029/2004GL020137.
Zhang, X., Friedl, M.A., Schaaf, C.B., Strahler, A.H., 2004. Climate controls on vegetation phenological patterns in northern mid- and high latitudes inferred from MODIS data. Global Change Biology, 10:1133-1145.
Tian Y, Dickinson, R.E., Zhou, L., Zeng, X., Dai, Y., Myneni, R.B., Knyazikhin, Y., Zhang, X., Friedl, M., Yu, II., Wu, W., Shaikh, M. 2004. Comparison of seasonal and spatial variations of leaf area index and fraction of absorbed photosynthetically active radiation from Moderate Resolution Imaging Spectroradiometer (MODIS) and Common Land Model. Journal of Geophysical Research-Atmospheres, 109 (D1): Art. No. D01103.
Penuelas, J., Filella, I., Zhang, X., LLorens, L., Ogaya, R., Lloret, F., Comas, P., Estiarte, M., Terradas, J., 2004. Complex spatiotemporal phenological shifts as a response to rainfall changes. New Phytologist, 161(3): 837-846.
Zhang, X., Schaaf, C. B., Friedl, M. A., Strahler, A. H., Gao F., Hodges, J. F., Reed, B. C., Huete, A., 2003. Monitoring vegetation phenology using MODIS. Remote Sensing of Environment, 84(3), 471-475.
Zhang, X., Drake, N. A., and Wainwright, J. 2002. Scaling land-surface parameters for global scale soil-erosion estimation. Water Resources Research, 38(9), 191-199.
Schaaf, C. B., Gao, F., Strahler, A. H., Lucht, W., Li, X., Tsang, T., Strugnell, N. C., Zhang, X., Jin, Y., Muller, J. P. et al. 2002. First operational BRDF, albedo nadir reflectance products from MODIS. Remote Sensing of Environment, 83(1-2), 135-148.
Friedl, M. A, McIver, D. K, Hodges, J. C., Zhang, X., Muchoney, D., Strahler, A. H., Woodcock, C. E., Gopal, S., Schnieder, A., Cooper, A., Baccini, A., Gao, F., and Schaaf, C. B. 2002. Global land cover mapping from MODIS: algorithms and early results. Remote Sensing of Environment, 83(1-2), 287-302.
Zhang, X., Drake, N. A., Wainwright, J. and Mulligan, M. 1999. Comparison of slope estimates from low resolution DEMs: scaling issues and a fractal method for their solution. Earth Surface Processes and Landforms, 24(9), 763-779.
Zhang, X. 1998. On the estimation of biomass of submerged vegetation using Landsat thematic mapper (TM) imagery: case study of the Honghu Lake, PR China. International Journal of Remote Sensing, 19(1), 11-20.
Selected Book Chapters
Zhang, X., 2018. Land Surface Phenology: Climate Data Record and Real-Time Monitoring. In Liang, S. (ed), Comprehensive Remote sensing: Terrestrial ecosystems, ELSE, https://doi.org/10.1016/B978-0-12-409548-9.10351-3
Zhang, X., Ni-meister, W., 2014. Remote sensing of Forest biomass. In Hanes, J. (ed), Biophysical Applications of Satellite Remote Sensing, Springer, New York, pp 63-98.
Zhang, X., Drake, N. A., and Wainwright, J., 2013. Spatial Modelling and Scaling Issues. In Wainwright, J. and Mulligan, M. (eds.), Environmental Modeling: Finding Simplicity in Complexity (Second Edition), John Wiley and Sons, Chichester, pp 69-90.
Zhang, X., Friedl, M.A., Tan, B., Goldberg, M.D., and Yu, Y., 2012, Long-Term Detection of Global Vegetation Phenology from Satellite Instruments. In Zhang, X. (ed), Phenology and Climate Change, InTec, pp 297-320.
Friedl, M.A., Zhang, X., and Strahler, A.H, 2011. Characterizing global land cover type and seasonal land cover dynamics at moderate spatial resolution using MODIS. In Ramachandran, B., Justice, C., and Abrams, M. (eds), Land Remote Sensing and Global Environmental Change: NASA’s Earth Observing System and the Science of ASTER and MODIS, Springer, pp709-721.
Drake, N.A., Zhang, X., Symeonakis, E., Patterson, G., and Bryant, A.R. 2004. Near Real-time Modeling of Regional scale soil erosion using AVHRR and METEOSAT data: a tool for monitoring the impact of sediment yield on the biodiversity of Lake Tanganyika. In Kelly, R., Drake, N., and Barr, S. (eds.), Spatial Modelling of the Terrestrial Environment. John Wiley and Sons, Chichester, pp. 157-174.
Zhang, X., Drake, N. A., and Wainwright, J. 2004. Scaling issues in environmental modeling. In Wainwright, J. and Mulligan, M. (eds.), Environmental Modeling: Finding Simplicity in Complexity, John Wiley and Sons, Chichester, pp. 319-334.
Drake, N. A., Zhang, X., Berkhout, E., Bonifacio, R., Grimes, D., Wainwright, J. and Mulligan, M. 1999. Modeling soil erosion at global and regional scales using remote sensing and GIS techniques. In Atkinson, P. M. and Tate, N. J. (eds.), Advances in Remote Sensing and GIS Analysis, John Wiley and Sons, Chichester, pp. 241-261.
Refereed Journal Papers and Book Chapters in Chinese (>20)