Yuhong He , Professor


Administrative Position:
Chair, Department of Geography, Geomatics and Environment, UTM

Home Campus:
U o f T Mississauga

University of Saskatchewan (2008)

Other Degrees:
MSc, Atmospheric Science, Nanjing Institute of Meteorology, China (2002)
BSc, Agrometeorology, Nanjing Institute of Meteorology, China (1999)

Cross Appointments:
Graduate Department of Physical and Environmental Sciences, University of Toronto Scarborough
Centre for Global Change Science, University of Toronto
School of the Environment, University of Toronto
Centre for Urban Environments, University of Toronto Mississauga

Honours & Awards:
2017 - The E. A. Robinson Teaching Excellence Award, University of Toronto Mississauga
2015 - Early researcher award, Ministry of Research and Innovation - Ontario
2011 - Early career award, Remote Sensing Specialty Group, AAG 2012 annual meeting

Contact Information:

Phone:    (905) 569-4679
Location:  DV3271 (UTM)
Downtown Office: SSH5027A (100 St. George Street)
Email:     yuhong.he@utoronto.ca

Personal Website: http://sites.utm.utoronto.ca/yuhong

Research Interests:

  • Remote sensing of terrestrial ecosystems
  • High spatial (UAV, Helicopter, & Satellite) thermal, multispectral and hyperspectral remote sensing
  • Radiative transfer modelling
  • Invasive and endangered species detection and mapping
  • Critical habitat monitoring for the endangered species
  • Climate Change

Teaching This Academic Year:

GGR335H5F – GIS and Remote Sensing Integration (UTM)
GGR1200H5F – Physical Geography Core Course


Call For Students:

Fully-funded MSc and Ph.D. positions are available at my Remote Sensing and Spatial Ecosystem Modeling laboratory (the RSSEM Lab). Candidates will develop new techniques for quantifying vegetation stress using the latest remote sensing technology. Some of the data will be collected using unmanned aerial vehicles or helicopters carrying hyperspectral sensors, multispectral sensors, as well as thermal sensors. I look for students with the following qualifications: 1) sincere interest in remote sensing and vegetation ecology; 2) strong quantitative skills; 3) remote sensing skills, or the ability to learn them quickly; and 4) excellent oral and written communication skills in English. Applicants should send me a letter of inquiry and curriculum vitae.

Current Students:


Selected Publications:


Book Edited:
He, Y.,
and Q., Weng. 2018. High Spatial Resolution Remote Sensing : Data, Analysis, and Applications. Boca Raton, FL: CRC Press/Taylor and Francis, pp. 381.

Refereed Journal Articles (Student’s names in bold):

Croft, H., J.M. Chen, G. Mo, S  Luo, X. Luo, L. He,  A. Gonsamo, J. Arabian, Y. Zhang, A. Simic, T.L. Noland, Y. He, L. Homolová, Z. Malenovský, Q. Yi, J. Beringer, R. Amiri, L. Hutley, P. Arellano, C. Stahl, and D. Bonal. 2020. The global distribution of leaf chlorophyll contentRemote Sensing of Environment. 236: 111479. https://doi.org/10.1016/j.rse.2019.111479 (IF: 8.218)

Kaluskar, S., A. Richards, C. Johnson, A. Langlois, Y. He, D. Kim, and G. Arhonditsis. 2019. Development of a model ensemble to predict Peary caribou populations in the Canadian Arctic Archipelago. Ecosphere. 10(12): e02976. https://doi.org/10.1002/ecs2.2976 (IF: 5.273)

Bonney, M. T., and Y. He. 2019. Attributing drivers to spatio-temporal changes in tree density across a suburbanizing landscape since 1944Landscape and Urban Planning. 192, 103652, https://doi.org/10.1016/j.landurbplan.2019.103652 (IF: 5.144)

He Y., J. Yang, J. Caspersen, and T. Jones. 2019. An operational workflow of deciduous-dominated forest species classification: crown delineation, gap elimination, and object-based classification.  Remote Sensing. 11(18), 2078. https://doi.org/10.3390/rs11182078. (IF: 3.406)

Lu, B., and Y. He. 2019. Evaluating Empirical Regression, Machine Learning, and Radiative Transfer Modelling for Estimating Vegetation Chlorophyll Content Using Bi-Seasonal Hyperspectral ImagesRemote Sensing. 11(17), 1979; https://doi.org/10.3390/rs11171979 (IF: 4.118)

Lu, B., and Y. He. 2019. Leaf area index estimation in a heterogeneous grassland using optical, SAR, and DEM dataCanadian Journal of Remote Sensing. https://doi.org/10.1080/07038992.2019.1641401(IF: 2.553)

Laamrani, A., A. Berg, P. Voroney, H. Feilhauer, L. Blackburn, M. March, P.D. DaoY. He, and R.C. Martin. 2019. Ensemble identification of spectral bands related to soil organic carbon levels over an agricultural field in southern Ontario, CanadaRemote Sensing. 2019, 11(11), 1298; https://doi.org/10.3390/rs11111298 (IF: 4.118)

Lu, B., Y. He, and Dao, P.D. 2019. Comparing the Performance of Multispectral and Hyperspectral Images for Estimating Vegetation PropertiesIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. DOI: 10.1109/JSTARS.2019.2910558. (IF: 2.777)

Proctor, C., and, Y. He. 2019. Quantifying Peatland Plant Vertical Root Distribution for Estimating the Interface with the Anoxic ZonePlant and Soil, 1-18. https://doi.org/10.1007/s11104-019-04079-w (IF: 3.306)

Xu., J., Y. Zheng, Y. He, B. Mai, L. Wang, and L. Xu. 2019. Estimating stomatal conductance and partitioning total ozone uptake over a winter wheat field. Atmospheric Pollution Research, Available online 4 January 2019, https://doi.org/10.1016/j.apr.2018.12.018 (5-year IF: 2.299)

Dao, P.D., Y. He, and B. Lu. 2019. Maximizing the quantitative utility of airborne hyperspectral imagery for studying plant physiology: an optimal sensor exposure setting procedure and empirical line method for atmospheric correction. International Journal of Applied Earth Observations and Geoinformation. 77: 140-150. https://doi.org/10.1016/j.jag.2018.11.010 (IF: 4.003)

Lu, B., and Y. He. 2017. Optimal spatial resolution of UAV imagery for species classification in a heterogeneous ecosystem. GIScience and Remote Sensing.  1-16, https://doi.org/10.1080/15481603.2017.1408930 (IF: 3.049)

Proctor, C., B. Lu, and Y. He. 2017. Determining the absorption coefficients of decaying pigments in decaying monocot leaves. Remote Sensing of Environment. 199: 137-153. (5-year IF: 7.388)

Lu, B., Y. He, and H. Liu. 2017. Mapping vegetation biophysical and biochemical properties using Unmanned Aerial Vehicles (UAV)-acquired imageryInternational Journal of Remote Sensing. 1-23. http://dx.doi.org/10.1080/01431161.2017.1363441 (IF: 1.724)

Proctor, C., and Y. He. 2017. Quantifying root extracts and exudates of sedge and shrub in relation to root morphology. Soil Biology and Biochemistry. 114: 168-180, 2017. (5-year IF: 5.041)

Lu, B., and Y. He. 2017. Species classification using Unmanned Aerial Vehicle (UAV)-acquired high spatial resolution imagery in a heterogeneous grassland. ISPRS Journal of Photogrammetry and Remote Sensing. 128: 73-85. (5-year IF: 5.062)

Tong, A., and Y. He. 2017. Estimating and mapping chlorophyll content for a heterogeneous grassland: Comparing prediction power of a suite of vegetation indices across scales between years. ISPRS P&RS. 126: 146-167. (5-year IF: 5.062)

Yang, J., Y. He, and J. Caspersen. 2017. Region merging using local spectral angle thresholds: a more accurate method for hybrid segmentation of remote sensing images. Remote Sensing of Environment. 192: 137-148. (5-year IF: 7.388)

Yang, J. and Y. He. 2017. Automated mapping of impervious surfaces in urban and suburban areas: Linear spectral unmixing of high spatial resolution imagery. International Journal of Applied Earth Observation and Geoinformation; 54: 53-64.  (5-Year IF: 3.904)

Mui, A., B. Caverhill, M.J- Fortin, B. Johnson, and Y. He. 2017.  Using multiple metrics to estimate seasonal landscape connectivity for Blanding’s turtles (Emydoidea blandingii) in a fragmented landscape. Landscape Ecology. doi:10.1007/s10980-016-0456-9 (IF: 3.657)

Yang, J., Y. He, J. Caspersen, and T. Jones. 2016. Delineating individual tree crowns in an uneven-aged, mixed broadleaf forest using multi-spectral watershed segmentation and multi-scale fitting. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 99: 1-12. (IF: 2.145)

Murfitt, J., Y. He,  J. YangA. Mui, and K. Demille. 2016. Ash decline assessment in Emerald Ash Borer infested natural forests using high spatial resolution images. Remote Sensing; 8(3), 256; doi:10.3390/rs8030256. (IF: 3.180)

Xu, J., Y. Zheng, Y. He, and R. Wu. 2016. The effect of elevated ozone concentrations with varying shading on dry matter loss in a winter wheat-producing region in China. PLOS ONE; 11(1):e0145446. doi: 10.1371/journal.pone.0145446. (IF: 3.234)

Yang, J., T. Jones, J. Caspersen, and Y. He. 2015. Object-based canopy gap segmentation and classification: quantifying the pros and cons of integrating optical and LiDAR data. Remote Sensing; 7 (12): 15917-15932. (IF: 3.180)

Mui, A., C.B. Edge, J. Paterson, B. Caverhill, B. Johnson, J.D. Litzgus, and Y. He. 2015. Nesting sites in agricultural landscapes are potential sinks for turtle populations. Canadian Journal of Zoology; 2016, 94(1): 61-67. 10.1139/cjz-2015-0154. (IF: 1.303)

Lu, B., Y. He, and A. Tong. 2015. Evaluation of spectral indices for estimating burn severity in semi-arid grasslands. International Journal of Wildland Fire; 25(2) 147-157. http://dx.doi.org/10.1071/WF15098. ( IF: 2.506)

Mui, A., Y. He, and Q. Weng. 2015. An object-based approach to delineate wetlands across landscapes of varied disturbance with high spatial resolution satellite imagery. ISPRS P&RS; 109: 30-46. (5-Year IF: 4.652)

He, Y., Z. Ma, X. Guo. 2015. Grassland productivity simulation: integrating remote sensing and an ecosystem process model. In Li J. and Yang X. (eds): Monitoring and Modeling of Global Changes: A Geomatics Perspective, Springer Remote Sensing/Photogrammetry, DOI 10.1007/978-94-017-9813-6_8.

Yang, J., Y. He, ​and Q. Weng. 2015. An automated method to parameterize segmentation scale by enhancing intra-segment homogeneity and inter-segment heterogeneity​. IEEE Geoscience and Remote Sensing Letters; 12(6): 1282-1286​. (5-Year IF: 1.98)

Yang, J., Y. He, J. Caspersen, and T. Jones. 2015. A discrepancy measure for segmentation evaluation from the perspective of object recognitionISPRS Journal of Photogrammetry and Remote Sensing101: 186-192. (5-Year IF: 4.202)

Yang, J., Y. He, and J. Caspersen. 2015. Fully constrained linear spectral unmixing based global shadow compensation for high resolution satellite imagery of urban areasInternational Journal of Applied Earth Observation and Geoinformation; 38: 88-98. (5-Year IF: 2.809)

He, Y. 2014. The effect of precipitation on vegetation cover over three landscape units in a protected semi-arid grassland: temporal dynamics and suitable climatic index. Journal of Arid Environments; 109:74-82. (5-Year IF: 2.120)

He, Y. 2014. The relationship between an invasive shrub and soil moisture: seasonal interactions and spatially covarying relations. ISPRS Int. J. Geo-Inf. 3: 1139-1153. (5-Year IF: 1.960)

Yang, J., P. Li, and Y. He. 2014. A multi-band approach to unsupervised scale parameter selection for multi-scale image segmentation. ISPRS Journal of Photogrammetry and Remote Sensing; 94:13-24. (5-Year IF: 4.202)

Yang, J., Y. He, and T. Oguchi. 2014. An endmember optimization approach for linear spectral unmixing of fine-scale urban imagery. International Journal of Applied Earth Observation and Geoinformation; 27: 137-146. (5-Year IF: 2.809)

Proctor, C, and Y. He. 2013. Estimation of foliar pigment concentration in floating Macrophytes using hyperspectral vegetation indices. International Journal of Remote Sensing. 34(22): 8011-8027. (5-Year IF: 1.67)

He, Y. 2013. Estimating grassland chlorophyll content from leaf to landscape level: bridging the gap in spatial scales. In Q. Weng (ed.) Scale Issues in Remote Sensing. John Wiley and Sons. Pg 127-138.

Jiang, X., B. Lu, and Y. He. 2013. Response of the turbidity maximum zone to fluctuations of sediment discharge from river to estuary in the Changjiang Estuary (China). Journal of Estuarine, Coastal and Shelf Science. 131: 24-30. (5-Year IF: 2.804)

Wong, K. and Y. He. 2013. Estimating grassland chlorophyll content using remote sensing data at the species, canopy, and landscape scales. Canadian Journal of Remote Sensing, 39(2): 155-166. (5-Year IF: 1.27)

Proctor, C., Y. He, and V. Robinson. 2013. Texture augmented detection of Macrophyte species using Decision Trees. ISPRS Journal of Photogrammetry and Remote Sensing. 80, 10-20. (5-Year IF: 4.026)

Tong, A., and Y. He, 2013. Remote sensing of leaf area index in a mixed grassland ecosystem: comparing SPOT, Landsat, MODIS, and AVHRR data. Journal of Applied Remote Sensing. 7 (1), 073599; doi: 10.1117/1.JRS.7.073599. (5-Year IF: 1.33)

Shen, L., Y. He, and X. Guo. 2013. Suitability of the normalized difference vegetation index and the adjusted transformed soil-adjusted vegetation index for spatially characterizing Loggerhead Shrike habitats in North American mixed prairie. Journal of Applied Remote sensing. 7 (1), 073574; doi: 10.1117/1.JRS.7.073574. (5-Year IF: 1.33)

Shen, L., Y. He, and X. Guo, 2013. Exploration of loggerhead shrike habitats in Grassland National Park based on in situ measurements and satellite-derived adjusted transformed soil-adjusted vegetation index. Remote sensing. 5: 452-453. (5-Year IF: 2.171)

He, Y., P. Dixon, J.F. Wilmshurst, and X. Guo.  2012. AVHRR NDVI baseline for natural vegetation ecosystems in Northern Canadian national parks. J Geophys Remote Sensing 1:103. doi:10.4172/jgrs.1000103.

He, Y., A. Khan, and A. Mui. 2012. Integrating remote sensing and wavelet analysis for studying fine-scaled vegetation spatial variation among three different ecosystems, Photogrammetric Engineering & Remote Sensing. 78(2): 161–168. (AAG REMOTE SENSING SPECIALTY GROUP 2010 Early Career Award Winner). (5-Year IF: 2.04)

Proctor, C., V. Robinson, and Y. He. 2012. Multispectral detection of European Frog-bit in the South Nation River using Quickbird imagery. Canadian Journal of Remote Sensing, 38(4): 1-11. (5-Year IF: 1.27)

He, Y., X. Guo, P., Dixon, and J. Wilmshurst. 2012. NDVI variation and its relations to climate in Canadian Ecozones. The Canadian Geographer. 56(4): 492-507.

Banerjee, S., Y. He, X. Guo, and B.C. Si. 2011. Spatial relationships between leaf area index and topographic factors in a semiarid grassland. Australian Journal of Crop Science. 5(6):756-763.

Franklin, S.E., Y. He, A.D. Pape, X. Guo and G.J. McDermid. 2011. Landsat-comparable land cover maps using ASTER and SPOT images: a case study for large-area mapping programmes. International Journal of Remote Sensing. 32(8): 2185-2205.

Guo, X., S. Black and Y. He. 2011. Estimation of leaf CO2 exchange rates using a SPOT image. International Journal of Remote Sensing. 32(2), 353-366.

He, Y., and A. Mui. 2010. Scaling up semi-arid grassland biochemical content from the leaf to the canopy level: challenges and opportunities. Sensors. 10: 11072-11087; doi:10.3390/s101211072. (5-Year IF: 2.395)

He, Y., S.E. Franklin, X. Guo, and G.B. Stenhouse. 2010. Object-oriented classification of multi-resolution images for the extraction of narrow linear forest disturbance. Remote Sensing Letters, 2(2): 147-155.

He, Y., S.E. Franklin, X. Guo, and G.B. Stenhouse. 2009. Narrow-linear and small-area forest disturbance detection and mapping from high spatial resolution Imagery. Journal of Applied Remote Sensing, 3:033570.

He, Y., X. Guo, and J. Wilmshurst. 2009. Reflectance measures of grassland biophysical structure. International Journal of Remote Sensing. 30 (10): 2509-2521.

Wang, K., S.E. Franklin, X. Guo, Y. He and G. J. McDermid. 2009. Problems in remote sensing of landscapes and habitats. Progress in Physical Geography. 33 (6): 747-768.

Dixon, P., Y. He, and X. Guo. 2008. Satellite monitoring of Northern ecosystems. Geomatica. 62 (2):151-158.

Guo, X., and Y. He. 2008. Mismatch of band sequence between image and header file: a potential error in SPOT L1A Products. Canadian Journal of Remote Sensing. 34(1): 1-4.

He, Y., X. Guo, and B.C. Si. 2007. Detecting grassland spatial variation by a wavelet approach. International Journal of Remote Sensing. 28 (7): 1527 – 1545.

He, Y., X. Guo, and J. Wilmshurst. 2007. Comparison of different methods for measuring LAI in a mixed grassland. Canadian Journal of Plant Science. 87: 803-813.

He, Y., X. Guo, and J. Wilmshurst. 2006. Studying mixed grassland ecosystems II: optimum pixel size. Canadian Journal of Remote Sensing. 32(2): 108-115.

He, Y., X. Guo, and J. Wilmshurst. 2006. Studying mixed grassland ecosystems I: suitable hyperspectral vegetation indices. Canadian Journal of Remote Sensing. 32(2): 98-107.

Research Clusters:
Nature, Society and Environmental Change