RSSEMLabUniversity of Toronto Mississauga

Publications

Refereed articles, books and conference contributions from the Remote Sensing and Spatial Ecosystem Modeling Laboratory, spanning grassland, wetland and forest ecosystems.

Publications 133Conference presentations 144

Publications

133 records

IF: 2025 Journal Impact Factor (Clarivate).

2026

11 publications
  1. Zhang, Y., Chen, G., Hong, T., He, Y., & Zhou, Y. (2026). CausalSolar: Leveraging causal representations of residential solar adoption to enhance image-based detection of rooftop solar panels. Sustainable Cities and Society, 149, 107816. https://doi.org/10.1016/j.scs.2026.107816 (IF: 13.3)
  2. Qiang, C., Ruppert, J., Cartwright, L., & He, Y. (2026). Hierarchical classification of urban LULC using Sentinel-2 and WorldView imagery: A comparative study for conservation applications. Ecological Informatics, 98, 103907. https://doi.org/10.1016/j.ecoinf.2026.103907 (IF: 8.5)
  3. Fang, X., Cao, L., Pei, Z., Zhu, S., & He, Y. (2026). Future climate-driven changes in carbon stocks in the Yellow River Basin of China. Remote Sensing, 18(13), 2205. https://doi.org/10.3390/rs18132205 (IF: 4.3)
  4. Zhou, W., Guo, Y., Fu, Y. H., Hao, F., Zhang, X., Xu, L., & He, Y. (2026). Deep feature fusion with vegetation indices for wheat lodging monitoring using UAV multi-spectral imagery. Remote Sensing, 18(11), 1860. https://doi.org/10.3390/rs18111860 (IF: 4.3)
  5. Joshi, I., Ge, H., Zhu, T., He, Y., & Toh, S. M. (2026). Impact of geospatial training on key planning sustainability competencies. Sustainable Futures, 11, 101934. https://doi.org/10.1016/j.sftr.2026.101934 (IF: 6.7)
  6. Li, W., Chen, L., Liu, X., Zhang, J., Jia, Y., Zhao, L., & He, Y. (2026). SC-DRGAN: A cross-site transfer learning architecture for tropical forest structural parameters estimation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 19, 13097–13110. https://doi.org/10.1109/JSTARS.2026.3681078 (IF: 6.3)
  7. Yang, G., Jin, N., Ai, W., Zheng, Z., He, Y., & He, Y. (2026). Integrating remote sensing data assimilation, deep learning and large language model to interactive yield prediction for wheat breeding. Computers and Electronics in Agriculture, 244, 111495. https://doi.org/10.1016/j.compag.2026.111495 (IF: 10.3)
  8. Bonney, M. T., & He, Y. (2026). Leveraging the full potential of Landsat to investigate inequalities in post-development residential canopy cover (1972–2020). Canadian Journal of Remote Sensing, 52(1), 2622730. https://doi.org/10.1080/07038992.2026.2622730 (IF: 2.1)
  9. Kim, T., Banwait, P., Singhal, T., Ma, Q., Tate, J. E., He, Y., & Kwon, O.-S. (2026). Modularised framework for power network disaster resilience assessment under natural hazards. Structure and Infrastructure Engineering, 1–16. https://doi.org/10.1080/15732479.2026.2628858 (IF: 3.1)
  10. Zhang, J., Shang, J., Liu, J., Wang, L., Han, M., Chen, M., Chen, C., He, Y., & Yan, X. (2026). Genetic constraints on in-situ reflectance spectral variation in bermudagrass populations across Hainan Island. Plant Phenomics, 8(1), 100168. https://doi.org/10.1016/j.plaphe.2026.100168 (IF: 8.2)
  11. Nelson, D. M., He, Y., & Moore, G. W. K. (2026). Driver analysis of subarctic wildfire severity over a 35-year period. Big Earth Data, 10(1), 319–345. https://doi.org/10.1080/20964471.2025.2558408 (IF: 3.6)

2025

10 publications
  1. Li, F., Jiang, Y., Long, Y., Li, W., & He, Y. (2025). Forest height estimation in Jiangsu: Integrating dual-polarimetric SAR, InSAR, and optical remote sensing features. Remote Sensing, 17(21), 3620. https://doi.org/10.3390/rs17213620 (IF: 4.3)
  2. Noonan, M., He, Y., Nelson, D. M., Ge, H., Siu, R., & Duval, T. P. (2025). Estimating seasonal fractional green and dead vegetation cover in rehabilitated ecosystems using drone remote sensing. Ecological Informatics, 92, 103456. https://doi.org/10.1016/j.ecoinf.2025.103456 (IF: 8.5)
  3. Wan, Y., Tang, L., Hou, X., Lin, H., Cai, X., Huang, Z., Xu, Q., & He, Y. (2025). The role of wetland vegetation and water connectivity in shaping waterbird populations under human disturbance. Ecological Indicators, 178, 114014. https://doi.org/10.1016/j.ecolind.2025.114014 (IF: 8.7)
  4. Guo, Y., Zhou, W., Fu, Y. H., Hao, F., Zhang, X., Xu, L., Liu, J., & He, Y. (2025). SegNeXt-RCMSCA: An improved SegNeXt network for detecting winter wheat lodging from UAS RGB images. Smart Agricultural Technology, 12, 101230. https://doi.org/10.1016/j.atech.2025.101230 (IF: 7.1)
  5. Huda, T., Wang, A., Zhang, H., Gao, L., He, Y., & Zhu, T. (2025). Identifying food deserts in Mississauga: A comparative analysis of socioeconomic indicators. Urban Science, 9(7), 265. https://doi.org/10.3390/urbansci9070265 (IF: 3.2)
  6. Xi, B., Zhang, Y., Li, W., Zhao, L., Xu, K., Ma, Y., & He, Y. (2025). An advanced approach for understory terrain extraction utilizing TomoSAR and MCSF algorithm. IEEE Geoscience and Remote Sensing Letters, 22, 1–5. https://doi.org/10.1109/LGRS.2025.3565785 (IF: 4.8)
  7. Dao, P. D., He, Y., Lu, B., & Axiotis, A. (2025). Imaging spectroscopy reveals topographic variability effects on grassland functional traits and drought responses. Ecology, 106(3), e70006. https://doi.org/10.1002/ecy.70006 (IF: 4.1)
  8. Bonney, M. T., & He, Y. (2025). Using Landsat time-series to investigate nearly 50 years of tree canopy cover change across an urban-rural landscape in southern Ontario. Canadian Journal of Remote Sensing, 51(1), 2445836. https://doi.org/10.1080/07038992.2024.2445836 (IF: 2.1)
  9. Li, W., Xia, H., Xi, B., Wang, Y., Lu, J., & He, Y. (2025). SSL-MBC: Self-supervised learning with multibranch consistency for few-shot PolSAR image classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 18, 4696–4710. https://doi.org/10.1109/JSTARS.2025.3528529 (IF: 6.3)
  10. Zhang, J., Shang, J., He, Y., Liu, J., Fan, J., Zhang, C., Sun, S., Han, M., & Yan, X. (2025). Chloroplast DNA phylogeography reveals genetic divergence of bermudagrass along latitudinal and longitudinal gradients in China. Genetic Resources and Crop Evolution, 72(2), 2141–2155. https://doi.org/10.1007/s10722-024-02088-y (IF: 1.9)

2024

12 publications
  1. Bonney, M. T., He, Y., Vogeler, J., Conway, T., & Kaye, E. (2024). Mapping canopy cover for municipal forestry monitoring: Using free Landsat imagery and machine learning. Urban Forestry & Urban Greening, 100, 128490. https://doi.org/10.1016/j.ufug.2024.128490 (IF: 8.2)
  2. Li, W., Xia, H., Zhang, J., Wang, Y., Jia, Y., & He, Y. (2024). Complex-valued 2D-3D hybrid convolutional neural network with attention mechanism for PolSAR image classification. Remote Sensing, 16(16), 2908. https://doi.org/10.3390/rs16162908 (IF: 4.3)
  3. Lee, H., He, Y., & Isaac, M. E. (2024). Close-range imaging for green roofs: Feature detection, band matching, and image registration for mixed plant communities. Geomatica, 76(2), 100011. https://doi.org/10.1016/j.geomat.2024.100011
  4. Liu, W., Sun, S., He, Y., Zhang, Y., & Huang, C. (2024). Climatic characteristics of heat wave events (1959–2023) in Jiangxi Province, China. Geomatics, Natural Hazards and Risk, 15(1), 2379595. https://doi.org/10.1080/19475705.2024.2379595 (IF: 4.9)
  5. Nelson, D. M., He, Y., & Moore, G. W. K. (2024). Trends and applications in wildfire burned area mapping: Remote sensing data, cloud geoprocessing platforms, and emerging algorithms. Geomatica, 76(1), 100008. https://doi.org/10.1016/j.geomat.2024.100008
  6. Guo, Y., Fu, Y. H., Chen, S., Hao, F., Zhang, X., de Beurs, K., & He, Y. (2024). Predicting grain yield of maize using a new multispectral-based canopy volumetric vegetation index. Ecological Indicators, 166, 112295. https://doi.org/10.1016/j.ecolind.2024.112295 (IF: 8.7)
  7. Guo, Y., Hao, F., Zhang, X., He, Y., & Fu, Y. H. (2024). Improving maize yield estimation by assimilating UAV-based LAI into WOFOST model. Field Crops Research, 315, 109477. https://doi.org/10.1016/j.fcr.2024.109477 (IF: 7.9)
  8. Xu, E., Wei, M., Li, T., Lei, V., Gao, J., Wang, N., & He, Y. (2024). Assessing burn severity and vegetation restoration in Alberta’s boreal forests following the 2016 Fort McMurray wildfire – a remote sensing time-series study. Sustainable Environment, 10(1), 2361569. https://doi.org/10.1080/27658511.2024.2361569 (IF: 3.9)
  9. Zhang, J., He, Y., Liu, J., Fan, J., Shang, J., & Yan, X. (2024). Integrating spectral data and phylogeographic patterns to study plant genetic variation: a review. Grass Research, 4(1), e011. https://doi.org/10.48130/grares-0024-0009 (IF: 2.9)
  10. Shen, Y., Lu, X., Lyu, M., Zhou, H., Guan, W., Jiang, L., He, Y., & Cen, H. (2024). High-throughput phenotyping of individual plant height in an oilseed rape population based on Mask-RCNN and UAV images. Precision Agriculture, 25(2), 811–833. https://doi.org/10.1007/s11119-023-10095-9 (IF: 7.6)
  11. Lee, H., He, Y., Isaac, M. E., & Roberto, A. (2024). Investigating crop performance on urban green roofs using hyperspectral data. Ecological Informatics, 81, 102599. https://doi.org/10.1016/j.ecoinf.2024.102599 (IF: 8.5)
  12. Hisey, F., Zhu, T., & He, Y. (2024). Use of interactive storytelling trailers to engage students in an online learning environment. Active Learning in Higher Education, 25(1), 151–166. https://doi.org/10.1177/14697874221107574 (IF: 3.4)

2023

6 publications
  1. Li, W., Liu, Q., Zhang, Y., Wang, Y., Yuan, Y., Jia, Y., & He, Y. (2023). Few-shot hyperspectral image classification using meta learning and regularized finetuning. IEEE Transactions on Geoscience and Remote Sensing, 61, 1–14. https://doi.org/10.1109/tgrs.2023.3328263 (IF: 9.4)
  2. Guo, Y., Xiao, Y., Hao, F., Zhang, X., Chen, J., de Beurs, K., He, Y., & Fu, Y. H. (2023). Comparison of different machine learning algorithms for predicting maize grain yield using UAV-based hyperspectral images. International Journal of Applied Earth Observation and Geoinformation, 124, 103528. https://doi.org/10.1016/j.jag.2023.103528 (IF: 8.2)
  3. Lyu, M., Lu, X., Shen, Y., Tan, Y., Wan, L., Shu, Q., He, Y., He, Y., & Cen, H. (2023). UAV time-series imagery with novel machine learning to estimate heading dates of rice accessions for breeding. Agricultural and Forest Meteorology, 341, 109646. https://doi.org/10.1016/j.agrformet.2023.109646 (IF: 6.6)
  4. Cherif, E., Feilhauer, H., Berger, K., Dao, P. D., Ewald, M., Hank, T. B., He, Y., Kovach, K. R., Lu, B., Townsend, P. A., & Kattenborn, T. (2023). From spectra to plant functional traits: Transferable multi-trait models from heterogeneous and sparse data. Remote Sensing of Environment, 292, 113580. https://doi.org/10.1016/j.rse.2023.113580 (IF: 12.3)
  5. Qiang, C., Leydon, J., & He, Y. (2023). Impact of COVID-19 restrictions on the urban thermal environment of Edmonton, Canada. Environmental Management, 72(4), 862–882. https://doi.org/10.1007/s00267-023-01813-0 (IF: 3.4)
  6. Wang, Y., Ma, Z., He, Y., Yu, W., Chang, J., Peng, D., Min, X., Guo, H., Xiao, Y., Gao, L., & Shi, Z. (2023). Vegetation disturbances characterization in the Tibetan Plateau from 1986 to 2018 using Landsat time series and field observations. Environmental Research Letters, 18(1), 014016. https://doi.org/10.1088/1748-9326/acab1b (IF: 5.4)

2022

6 publications
  1. Guo, Y., Xiao, Y., Li, M., Hao, F., Zhang, X., Sun, H., de Beurs, K., Fu, Y. H., & He, Y. (2022). Identifying crop phenology using maize height constructed from multi-sources images. International Journal of Applied Earth Observation and Geoinformation, 115, 103121. https://doi.org/10.1016/j.jag.2022.103121 (IF: 8.2)
  2. Xu, M., Liu, R., Chen, J. M., Liu, Y., Wolanin, A., Croft, H., He, L., Shang, R., Ju, W., Zhang, Y., He, Y., & Wang, R. (2022). A 21-year time series of global leaf chlorophyll content maps from MODIS imagery. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–13. https://doi.org/10.1109/TGRS.2022.3204185 (IF: 9.4)
  3. Xu, M., Liu, R., Chen, J. M., Shang, R., Liu, Y., Qi, L., Croft, H., Ju, W., Zhang, Y., He, Y., Qiu, F., Li, J., & Lin, Q. (2022). Retrieving global leaf chlorophyll content from MERIS data using a neural network method. ISPRS Journal of Photogrammetry and Remote Sensing, 192, 66–82. https://doi.org/10.1016/j.isprsjprs.2022.08.003 (IF: 12.9)
  4. Mantripragada, K., Dao, P. D., He, Y., & Qureshi, F. Z. (2022). The effects of spectral dimensionality reduction on hyperspectral pixel classification: A case study. PLOS ONE, 17(7), e0269174. https://doi.org/10.1371/journal.pone.0269174
  5. Marajh, L., & He, Y. (2022). Temperature variation and climate resilience action within a changing landscape. Remote Sensing, 14(3), 701. https://doi.org/10.3390/rs14030701 (IF: 4.3)
  6. Proctor, C., Pereira, C., Jin, T., Lim, G., & He, Y. (2022). Linking the spectra of decomposing litter to ecosystem processes: Tandem close-range hyperspectral imagery and decomposition metrics. Remote Sensing, 14(2), 370. https://doi.org/10.3390/rs14020370 (IF: 4.3)

2021

9 publications
  1. He, Y., Dosanjh, M., Shang, J., Liu, J., Drury, C. F., Yang, X., Yang, J., Dao, P. D., Axiotis, A., & Proctor, C. (2021). Mapping crop fractional green canopy cover using high spatial resolution thermal and optical remote sensing in southern Ontario, Canada. Geomatica, 75(3), 116–129. https://www.sciencedirect.com/science/article/pii/S1195103624005512
  2. Lu, B., & He, Y. (2021). Assessing the impacts of species composition on the accuracy of mapping chlorophyll content in heterogeneous ecosystems. Remote Sensing, 13(22), 4671. https://doi.org/10.3390/rs13224671 (IF: 4.3)
  3. Dao, P. D., Axiotis, A., & He, Y. (2021). Mapping native and invasive grassland species and characterizing topography-driven species dynamics using high spatial resolution hyperspectral imagery. International Journal of Applied Earth Observation and Geoinformation, 104, 102542. https://doi.org/10.1016/j.jag.2021.102542 (IF: 8.2)
  4. Bonney, M. T., & He, Y. (2021). Temporal connections between long-term Landsat time-series and tree-rings in an urban–rural temperate forest. International Journal of Applied Earth Observation and Geoinformation, 103, 102523. https://doi.org/10.1016/j.jag.2021.102523 (IF: 8.2)
  5. Dao, P. D., He, Y., & Proctor, C. (2021). Plant drought impact detection using ultra-high spatial resolution hyperspectral images and machine learning. International Journal of Applied Earth Observation and Geoinformation, 102, 102364. https://doi.org/10.1016/j.jag.2021.102364 (IF: 8.2)
  6. Proctor, C., Dao, P. D., & He, Y. (2021). Close-range, heavy-duty hyperspectral imaging for tracking drought impacts using the PROCOSINE model. Journal of Quantitative Spectroscopy and Radiative Transfer, 263, 107528. https://doi.org/10.1016/j.jqsrt.2021.107528 (IF: 2.0)
  7. Lu, B., Proctor, C., & He, Y. (2021). Investigating different versions of PROSPECT and PROSAIL for estimating spectral and biophysical properties of photosynthetic and non-photosynthetic vegetation in mixed grasslands. GIScience & Remote Sensing, 58(3), 354–371. https://doi.org/10.1080/15481603.2021.1877435 (IF: 6.8)
  8. Proctor, C., & He, Y. (2021). Modeling root exudate accumulation gradients to estimate net exudation rates by peatland soil depth. Plants, 10(1), 106. https://doi.org/10.3390/plants10010106 (IF: 4.7)
  9. Dao, P. D., Mantripragada, K., He, Y., & Qureshi, F. Z. (2021). Improving hyperspectral image segmentation by applying inverse noise weighting and outlier removal for optimal scale selection. ISPRS Journal of Photogrammetry and Remote Sensing, 171, 348–366. https://doi.org/10.1016/j.isprsjprs.2020.11.013 (IF: 12.9)

2020

4 publications
  1. Bonney, M. T., He, Y., & Myint, S. W. (2020). Contextualizing the 2019–2020 Kangaroo Island bushfires: Quantifying landscape-level influences on past severity and recovery with Landsat and Google Earth Engine. Remote Sensing, 12(23), 3942. https://doi.org/10.3390/rs12233942 (IF: 4.3)
  2. He, Y., Yang, J., & Guo, X. (2020). Green vegetation cover dynamics in a heterogeneous grassland: Spectral unmixing of Landsat time series from 1999 to 2014. Remote Sensing, 12(22), 3826. https://doi.org/10.3390/rs12223826 (IF: 4.3)
  3. Lu, B., Dao, P. D., Liu, J., He, Y., & Shang, J. (2020). Recent advances of hyperspectral imaging technology and applications in agriculture. Remote Sensing, 12(16), 2659. https://doi.org/10.3390/rs12162659 (IF: 4.3)
  4. Croft, H., Chen, J. M., Wang, R., Mo, G., Luo, S., Luo, X., He, L., Gonsamo, A., Arabian, J., Zhang, Y., Simic-Milas, A., Noland, T. L., He, Y., Homolová, L., Malenovský, Z., Yi, Q., Beringer, J., Amiri, R., Hutley, L., Arellano, P., Stahl, C., & Bonal, D. (2020). The global distribution of leaf chlorophyll content. Remote Sensing of Environment, 236, 111479. https://doi.org/10.1016/j.rse.2019.111479 (IF: 12.3)

2019

10 publications
  1. Bonney, M. T., & He, Y. (2019). Attributing drivers to spatio-temporal changes in tree density across a suburbanizing landscape since 1944. Landscape and Urban Planning, 192, 103652. https://doi.org/10.1016/j.landurbplan.2019.103652 Awarded first paper award in Geography at UTM. (IF: 11.4)
  2. He, Y., Yang, J., Caspersen, J. P., & Jones, T. A. (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: 4.3)
  3. Lu, B., & He, Y. (2019). Evaluating empirical regression, machine learning, and radiative transfer modelling for estimating vegetation chlorophyll content using bi-seasonal hyperspectral images. Remote Sensing, 11(17), 1979. https://doi.org/10.3390/rs11171979 (IF: 4.3)
  4. Kaluskar, S., Blukacz-Richards, E. A., Johnson, C. A., He, Y., Langlois, A., Kim, D.-K., & Arhonditsis, G. (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: 3.0)
  5. Lu, B., & He, Y. (2019). Leaf area index estimation in a heterogeneous grassland using optical, SAR, and DEM data. Canadian Journal of Remote Sensing, 45(5), 618–633. https://doi.org/10.1080/07038992.2019.1641401 (IF: 2.1)
  6. Laamrani, A., Berg, A. A., Voroney, P., Feilhauer, H., Blackburn, L., March, M., Dao, P. D., He, Y., & Martin, R. C. (2019). Ensemble identification of spectral bands related to soil organic carbon levels over an agricultural field in southern Ontario, Canada. Remote Sensing, 11(11), 1298. https://doi.org/10.3390/rs11111298 (IF: 4.3)
  7. Lu, B., He, Y., & Dao, P. D. (2019). Comparing the performance of multispectral and hyperspectral images for estimating vegetation properties. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(6), 1784–1797. https://doi.org/10.1109/JSTARS.2019.2910558 (IF: 6.3)
  8. Proctor, C., & He, Y. (2019). Quantifying wetland plant vertical root distribution for estimating the interface with the anoxic zone. Plant and Soil, 440(1-2), 381–398. https://doi.org/10.1007/s11104-019-04079-w (IF: 4.8)
  9. Xu, J., Zheng, Y., He, Y., Zhu, F., Mai, B., Wang, S., Zhang, M., Zhao, X., Wang, L., Xu, L., Ding, L., & Guo, Z. (2019). Estimating stomatal conductance and partitioning total ozone uptake over a winter wheat field. Atmospheric Pollution Research, 10(3), 904–912. https://doi.org/10.1016/j.apr.2018.12.018 (IF: 3.9)
  10. Dao, P. D., He, Y., & Lu, B. (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 Observation and Geoinformation, 77, 140–150. https://doi.org/10.1016/j.jag.2018.11.010 Awarded first paper award in Geography at UTM. (IF: 8.2)

2018

6 publications
  1. Lu, B., & He, Y. (2018). Optimal spatial resolution of unmanned aerial vehicle (UAV)-acquired imagery for species classification in a heterogeneous grassland ecosystem. GIScience & Remote Sensing, 55(2), 205–220. https://doi.org/10.1080/15481603.2017.1408930 (IF: 6.8)
  2. Lu, B., He, Y., & Liu, H. H. T. (2018). Mapping vegetation biophysical and biochemical properties using unmanned aerial vehicles-acquired imagery. International Journal of Remote Sensing, 39(15-16), 5265–5287. https://doi.org/10.1080/01431161.2017.1363441 (IF: 2.6)
  3. Qi, X., Zhang, C., He, Y., & Wang, K. (2018). Fraction vegetation cover extraction using high spatial resolution imagery in karst areas. In Y. He & Q. Weng (Eds.), High spatial resolution remote sensing: Data, analysis, and applications (pp. 347–359). CRC Press. https://doi.org/10.1201/9780429470196-16
  4. Lu, B., & He, Y. (2018). UAV-based multispectral images for investigating grassland biophysical and biochemical properties. In Y. He & Q. Weng (Eds.), High spatial resolution remote sensing: Data, analysis, and applications (pp. 245–259). CRC Press. https://doi.org/10.1201/9780429470196-12
  5. Tong, A., Lu, B., & He, Y. (2018). Inversion of a radiative transfer model using hyperspectral data for deriving grassland leaf chlorophyll. In Y. He & Q. Weng (Eds.), High spatial resolution remote sensing: Data, analysis, and applications (pp. 261–282). CRC Press. https://doi.org/10.1201/9780429470196-13
  6. He, Y., & Weng, Q. (Eds.). (2018). High spatial resolution remote sensing: Data, analysis, and applications. CRC Press. https://doi.org/10.1201/9780429470196

2017

9 publications
  1. Proctor, C., Lu, B., & He, Y. (2017). Determining the absorption coefficients of decay pigments in decomposing monocots. Remote Sensing of Environment, 199, 137–153. https://doi.org/10.1016/j.rse.2017.07.007 (IF: 12.3)
  2. Proctor, C., & He, Y. (2017). Quantifying root extracts and exudates of sedge and shrub in relation to root morphology. Soil Biology and Biochemistry, 114, 168–180. https://doi.org/10.1016/j.soilbio.2017.07.006 (IF: 12.2)
  3. Lu, B., & He, Y. (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. https://doi.org/10.1016/j.isprsjprs.2017.03.011 (IF: 12.9)
  4. Yang, J., He, Y., & Caspersen, J. P. (2017). Region merging using local spectral angle thresholds: A more accurate method for hybrid segmentation of remote sensing images. Remote Sensing of Environment, 190, 137–148. https://doi.org/10.1016/j.rse.2016.12.011 (IF: 12.3)
  5. Tong, A., & He, Y. (2017). Estimating and mapping chlorophyll content for a heterogeneous grassland: Comparing prediction power of a suite of vegetation indices across scales between years. ISPRS Journal of Photogrammetry and Remote Sensing, 126, 146–167. https://doi.org/10.1016/j.isprsjprs.2017.02.010 (IF: 12.9)
  6. Yang, J., & He, Y. (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. https://doi.org/10.1016/j.jag.2016.09.006 (IF: 8.2)
  7. Mui, A. B., Caverhill, B., Johnson, B., Fortin, M.-J., & He, Y. (2017). Using multiple metrics to estimate seasonal landscape connectivity for Blanding’s turtles (Emydoidea blandingii) in a fragmented landscape. Landscape Ecology, 32(3), 531–546. https://doi.org/10.1007/s10980-016-0456-9 (IF: 4.7)
  8. Yang, J., He, Y., Caspersen, J. P., & Jones, T. A. (2017). Delineating individual tree crowns in an uneven-aged, mixed broadleaf forest using multispectral watershed segmentation and multiscale fitting. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(4), 1390–1401. https://doi.org/10.1109/JSTARS.2016.2638822 (IF: 6.3)
  9. Yang, J., He, Y., & Caspersen, J. P. (2017). Individual tree-based species classification for uneven-aged, mixed-deciduous forests using multi-seasonal WorldView-3 images. In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 827–830). Fort Worth, TX. IEEE. https://doi.org/10.1109/IGARSS.2017.8127080

2016

7 publications
  1. Murfitt, J., He, Y., Yang, J., Mui, A. B., & De Mille, K. (2016). Ash decline assessment in Emerald Ash Borer infested natural forests using high spatial resolution images. Remote Sensing, 8(3), 256. https://doi.org/10.3390/rs8030256 (IF: 4.3)
  2. Xu, J., Zheng, Y., He, Y., Wu, R., Mai, B., & Kang, H. (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. https://doi.org/10.1371/journal.pone.0145446
  3. Mui, A. B., Edge, C. B., Paterson, J. E., Caverhill, B., Johnson, B., Litzgus, J. D., & He, Y. (2016). Nesting sites in agricultural landscapes may reduce the reproductive success of populations of Blanding’s turtles (Emydoidea blandingii). Canadian Journal of Zoology, 94(1), 61–67. https://doi.org/10.1139/cjz-2015-0154 (IF: 1.4)
  4. Lu, B., He, Y., & Tong, A. (2016). Evaluation of spectral indices for estimating burn severity in semiarid grasslands. International Journal of Wildland Fire, 25(2), 147–157. https://doi.org/10.1071/WF15098 (IF: 3.0)
  5. Yang, J., He, Y., & Caspersen, J. P. (2016). A self-adapted threshold-based region merging method for remote sensing image segmentation. In 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 6320–6323). Beijing, China. IEEE. https://doi.org/10.1109/IGARSS.2016.7730652
  6. Lu, B., He, Y., & Liu, H. H. T. (2016). Investigating species composition in a temperate grassland using unmanned aerial vehicle-acquired imagery. In 2016 4th International Workshop on Earth Observation and Remote Sensing Applications (EORSA) (pp. 107–111). Guangzhou, China. IEEE. https://doi.org/10.1109/EORSA.2016.7552776
  7. Tong, A., Lu, B., He, Y., & Guo, X. (2016). Role of remote sensing in sustainable grassland management: A review and case studies for a mixed-grass prairie ecosystem. In Q. Weng (Ed.), Remote sensing for sustainability (pp. 103–120). CRC Press.

2015

7 publications
  1. Yang, J., Jones, T. A., Caspersen, J. P., & He, Y. (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. https://doi.org/10.3390/rs71215811 (IF: 4.3)
  2. Mui, A. B., He, Y., & Weng, Q. (2015). An object-based approach to delineate wetlands across landscapes of varied disturbance with high spatial resolution satellite imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 109, 30–46. https://doi.org/10.1016/j.isprsjprs.2015.08.005 (IF: 12.9)
  3. Mui, A. B., Nelson, S., Huang, B., He, Y., & Wilson, K. (2015). Development of a web-enabled learning platform for geospatial laboratories: improving the undergraduate learning experience. Journal of Geography in Higher Education, 39(3), 356–368. https://doi.org/10.1080/03098265.2015.1039503 (IF: 1.4)
  4. Yang, J., He, Y., & Weng, Q. (2015). An automated method to parameterize segmentation scale by enhancing intrasegment homogeneity and intersegment heterogeneity. IEEE Geoscience and Remote Sensing Letters, 12(6), 1282–1286. https://doi.org/10.1109/lgrs.2015.2393255 (IF: 4.8)
  5. Yang, J., He, Y., Caspersen, J. P., & Jones, T. A. (2015). A discrepancy measure for segmentation evaluation from the perspective of object recognition. ISPRS Journal of Photogrammetry and Remote Sensing, 101, 186–192. https://doi.org/10.1016/j.isprsjprs.2014.12.015 (IF: 12.9)
  6. Yang, J., He, Y., & Caspersen, J. P. (2015). Fully constrained linear spectral unmixing based global shadow compensation for high resolution satellite imagery of urban areas. International Journal of Applied Earth Observation and Geoinformation, 38, 88–98. https://doi.org/10.1016/j.jag.2014.12.005 (IF: 8.2)
  7. He, Y., Ma, Z., & Guo, X. (2015). Grassland productivity simulation: Integrating remote sensing and an ecosystem process model. In J. Li & X. Yang (Eds.), Monitoring and modeling of global changes: A geomatics perspective (pp. 155–169). Springer. https://doi.org/10.1007/978-94-017-9813-6_8

2014

8 publications
  1. 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. https://doi.org/10.1016/j.jaridenv.2014.05.022 (IF: 2.5)
  2. He, Y. (2014). The relationship between an invasive shrub and soil moisture: Seasonal interactions and spatially covarying relations. ISPRS International Journal of Geo-Information, 3(3), 1139–1153. https://doi.org/10.3390/ijgi3031139 (IF: 3.2)
  3. Yang, J., Li, P., & He, Y. (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. https://doi.org/10.1016/j.isprsjprs.2014.04.008 (IF: 12.9)
  4. Yang, J., He, Y., & Oguchi, T. (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. https://doi.org/10.1016/j.jag.2013.09.013 (IF: 8.2)
  5. Yang, J., He, Y., & Caspersen, J. P. (2014). A multi-band watershed segmentation method for individual tree crown delineation from high resolution multispectral aerial image. In 2014 IEEE Geoscience and Remote Sensing Symposium (IGARSS) (pp. 1588–1591). Quebec City, QC. IEEE. https://doi.org/10.1109/IGARSS.2014.6945949
  6. Tong, A., & He, Y. (2014). Remote sensing of grassland chlorophyll content: Assessing the spatial-temporal performance of spectral indices. In 2014 IEEE Geoscience and Remote Sensing Symposium (IGARSS) (pp. 2846–2849). Quebec City, QC. IEEE. https://doi.org/10.1109/IGARSS.2014.6947069
  7. Lu, B., & He, Y. (2014). Analyzing a North American prairie wildfire using remote sensing imagery. In 2014 IEEE Geoscience and Remote Sensing Symposium (IGARSS) (pp. 832–835). Quebec City, QC. IEEE. https://doi.org/10.1109/IGARSS.2014.6946553
  8. He, Y. (2014). Estimating grassland chlorophyll content from leaf to landscape level: Bridging the gap in spatial scales. In Q. Weng (Ed.), Scale issues in remote sensing (pp. 126–138). John Wiley & Sons. https://doi.org/10.1002/9781118801628.ch07

2013

7 publications
  1. Proctor, C., & He, Y. (2013). Estimation of foliar pigment concentration in floating macrophytes using hyperspectral vegetation indices. International Journal of Remote Sensing, 34(22), 8011–8027. https://doi.org/10.1080/01431161.2013.828183 (IF: 2.6)
  2. Jiang, X., Lu, B., & He, Y. (2013). Response of the turbidity maximum zone to fluctuations in sediment discharge from river to estuary in the Changjiang Estuary (China). Estuarine, Coastal and Shelf Science, 131, 24–30. https://doi.org/10.1016/j.ecss.2013.07.003 (IF: 2.8)
  3. Wong, K. K., & He, Y. (2013). Estimating grassland chlorophyll content using remote sensing data at leaf, canopy, and landscape scales. Canadian Journal of Remote Sensing, 39(2), 155–166. https://doi.org/10.5589/m13-021 (IF: 2.1)
  4. Proctor, C., He, Y., & Robinson, V. (2013). Texture augmented detection of macrophyte species using decision trees. ISPRS Journal of Photogrammetry and Remote Sensing, 80, 10–20. https://doi.org/10.1016/j.isprsjprs.2013.02.022 (IF: 12.9)
  5. Tong, A., & He, Y. (2013). Comparative analysis of SPOT, Landsat, MODIS, and AVHRR normalized difference vegetation index data on the estimation of leaf area index in a mixed grassland ecosystem. Journal of Applied Remote Sensing, 7(1), 073599. https://doi.org/10.1117/1.JRS.7.073599 (IF: 1.8)
  6. Shen, L., He, Y., & Guo, X. (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. https://doi.org/10.1117/1.JRS.7.073574 (IF: 1.8)
  7. Shen, L., He, Y., & Guo, X. (2013). Exploration of loggerhead shrike habitats in Grassland National Park of Canada based on in situ measurements and satellite-derived adjusted transformed soil-adjusted vegetation index (ATSAVI). Remote Sensing, 5(1), 432–453. https://doi.org/10.3390/rs5010432 (IF: 4.3)

2012

4 publications
  1. He, Y., Dixon, P., Wilmshurst, J. F., & Guo, X. (2012). AVHRR NDVI baseline for natural vegetation ecosystems in northern Canadian national parks. Journal of Geophysics & Remote Sensing, 1(1), 103. https://doi.org/10.4172/2169-0049.1000103
  2. He, Y., Khan, A., & Mui, A. B. (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. https://doi.org/10.14358/PERS.78.2.161 AAG Remote Sensing Specialty Group 2010 Early Career Award winner. (IF: 3.0)
  3. Proctor, C., Robinson, V., & He, Y. (2012). Multispectral detection of European frog-bit in the South Nation River using QuickBird imagery. Canadian Journal of Remote Sensing, 38(4), 476–486. (IF: 2.1)
  4. He, Y., Guo, X., Dixon, P., & Wilmshurst, J. F. (2012). NDVI variation and its relation to climate in Canadian ecozones. The Canadian Geographer, 56(4), 492–507. https://doi.org/10.1111/j.1541-0064.2012.00441.x (IF: 1.1)

2011

4 publications
  1. Banerjee, S., He, Y., Guo, X., & Si, B. C. (2011). Spatial relationships between leaf area index and topographic factors in a semiarid grassland: Joint multifractal analysis. Australian Journal of Crop Science, 5(6), 756–763. http://www.cropj.com/banerjee_5_6_2011_756_763.pdf
  2. Franklin, S. E., He, Y., Pape, A., Guo, X., & McDermid, G. J. (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. https://doi.org/10.1080/01431161003674642 (IF: 2.6)
  3. Guo, X., Black, S. C., & He, Y. (2011). Estimation of leaf CO2 exchange rates using a SPOT image. International Journal of Remote Sensing, 32(2), 353–366. https://doi.org/10.1080/01431160903464161 (IF: 2.6)
  4. He, Y., Franklin, S. E., Guo, X., & Stenhouse, G. B. (2011). Object-oriented classification of multi-resolution images for the extraction of narrow linear forest disturbance. Remote Sensing Letters, 2(2), 147–155. https://doi.org/10.1080/01431161.2010.504755 (IF: 1.5)

2010

1 publication
  1. He, Y., & Mui, A. B. (2010). Scaling up semi-arid grassland biochemical content from the leaf to the canopy level: Challenges and opportunities. Sensors, 10(12), 11072–11087. https://doi.org/10.3390/s101211072 (IF: 4.0)

2009

3 publications
  1. He, Y., Franklin, S. E., Guo, X., & Stenhouse, G. B. (2009). Narrow-linear and small-area forest disturbance detection and mapping from high spatial resolution imagery. Journal of Applied Remote Sensing, 3(1), 033570. https://doi.org/10.1117/1.3283905 (IF: 1.8)
  2. He, Y., Guo, X., & Wilmshurst, J. F. (2009). Reflectance measures of grassland biophysical structure. International Journal of Remote Sensing, 30(10), 2509–2521. https://doi.org/10.1080/01431160802552751 (IF: 2.6)
  3. Wang, K., Franklin, S. E., Guo, X., He, Y., & McDermid, G. J. (2009). Problems in remote sensing of landscapes and habitats. Progress in Physical Geography, 33(6), 747–768. https://doi.org/10.1177/0309133309350121 (IF: 3.5)

2008

3 publications
  1. Dixon, P., He, Y., & Guo, X. (2008). Satellite monitoring of northern ecosystems. Geomatica, 62(2), 151–158. https://hdl.handle.net/1807/18049
  2. Guo, X., & He, Y. (2008). Mismatch of band sequences between an image and header file: a potential error in SPOT L1a products. Canadian Journal of Remote Sensing, 34(1), 1–4. https://doi.org/10.5589/m08-003 (IF: 2.1)
  3. Li, Z., Guo, X., Dixon, P., & He, Y. (2008). Applicability of land surface temperature (LST) estimates from AVHRR satellite image composites in northern Canada. Prairie Perspectives, 11, 119–130. https://hdl.handle.net/1807/69334

Before 2008

6 publications
  1. He, Y., Guo, X., & Si, B. C. (2007). Detecting grassland spatial variation by a wavelet approach. International Journal of Remote Sensing, 28(7), 1527–1545. https://doi.org/10.1080/01431160600794621 (IF: 2.6)
  2. He, Y., Guo, X., & Wilmshurst, J. F. (2007). Comparison of different methods for measuring leaf area index in a mixed grassland. Canadian Journal of Plant Science, 87(4), 803–813. https://doi.org/10.4141/CJPS07024 (IF: 1.7)
  3. He, Y., Guo, X., Wilmshurst, J. F., & Si, B. C. (2006). Studying mixed grassland ecosystems II: optimum pixel size. Canadian Journal of Remote Sensing, 32(2), 108–115. https://doi.org/10.5589/m06-018 (IF: 2.1)
  4. He, Y., Guo, X., & Wilmshurst, J. F. (2006). Studying mixed grassland ecosystems I: suitable hyperspectral vegetation indices. Canadian Journal of Remote Sensing, 32(2), 98–107. https://doi.org/10.5589/m06-009 (IF: 2.1)
  5. He, Y., & Guo, X. (2006). Leaf area index estimation using remotely sensed data for Grasslands National Park. Prairie Perspectives, 9(1), 105–117. https://hdl.handle.net/1807/69335
  6. He, Y., Guo, X., & Zheng, Y. (2005). Impact of climate change with enhanced UV-B radiation on China’s agricultural NPP. Prairie Perspectives, 8, 50–60. https://hdl.handle.net/1807/69336

Conference presentations

144 records

2025

5 presentations
  1. He, Y. (2025). Automating land use and land cover mapping across TRCA. Toronto and Region Conservation Authority (TRCA) Research and Science Meeting. October 30, 2025. Toronto, Canada. Oral Presentation.
  2. He, Y. (2025). Agriculture land inventory at 30-m scale from 1984 to 2023. TOsustain Phase 2 Scientific Meeting. June 6, 2025. Toronto, Canada. Oral Presentation.
  3. Leung, F., He, Y., & Martin, A. (2025). Investigating the spatial-temporal patterns of green roofs with remote sensing. Livable Cities, July 16–18, 2025. Barcelona, Spain. Virtual Oral Presentation.
  4. Zhang, Y., He, Y., Ruppert, J., Cartwright, L., & Chen, G. (2025). Urban vegetation cover mapping via a semantic adaptive model across multiple spatial resolution satellite imagery. 46th Canadian Symposium on Remote Sensing, June 16–19, 2025. Lethbridge, Alberta, Canada. Oral Presentation.
  5. He, Y. (2025). Drone remote sensing: Opportunities, challenges, and transformative applications in ecosystem science. Inaugural Caribbean Drone Symposium, February 10–12, 2025, UWI Cave Hill Campus, Barbados. Invited Keynote Speaker, Virtual.

2024

10 presentations
  1. He, Y. (2024). Mapping changes in vegetative ecosystems using remote sensing. The 31st International Conference on Geoinformatics (Geoinformatics’2024), August 14–16, 2024, Toronto, Canada. Oral Presentation.
  2. Zhu, D., He, Y., et al. (2024). GIS-based platform for resilience assessment of a power grid subjected to natural hazards. The 31st International Conference on Geoinformatics (Geoinformatics’2024), August 14–16, 2024, Toronto, Canada. Oral Presentation.
  3. Yang, F., Li, W., & He, Y. (2024). Multi-source remote sensing image classification based on multi-modal fusion transformer. The 31st International Conference on Geoinformatics (Geoinformatics’2024), August 14–16, 2024, Toronto, Canada. Oral Presentation.
  4. Hashim, S., & He, Y. (2024). Monitoring post-rehabilitation changes in pits and quarries in southern Ontario using field surveys. The 31st International Conference on Geoinformatics (Geoinformatics’2024), August 14–16, 2024, Toronto, Canada. Oral Presentation.
  5. He, Y. (2024). Teaching for tomorrow: Navigating the future of remote sensing. AAG 2024. April 16–20, 2024, Honolulu, Hawaii, USA. In-Person Oral Presentation.
  6. Zhu, D., He, Y., & Moore, G. W. K. (2024). Novel environmental big data grid integration and interoperability model. EGU General Assembly 2024. April 14–19, 2024, Vienna, Austria. In-Person Poster Presentation. https://doi.org/10.5194/egusphere-egu24-6798
  7. Cheng, H., & He, Y. (2024). Distinguishing the relative contribution of climate change and human activities on global urban vegetation dynamics at 30 m resolution using Google Earth Engine. AAG 2024. April 16–20, 2024, Honolulu, Hawaii, USA. In-Person Oral Presentation.
  8. Noonan, M., & He, Y. (2024). Investigating ecological rehabilitation using UAV imagery. AAG 2024. April 16–20, 2024, Honolulu, Hawaii, USA. In-Person Oral Presentation.
  9. De Braga, F., & He, Y. (2024). Quantifying agricultural land loss in Ontario, Canada from 1984 to 2022. AAG 2024. April 16–20, 2024, Honolulu, Hawaii, USA. In-Person Oral Presentation.
  10. Qiang, C., & He, Y. (2024). Innovating urban ecosystem mapping: Leveraging high-resolution satellite imagery and advanced learning technologies for LULC classification. AAG 2024. April 16–20, 2024, Honolulu, Hawaii, USA. In-Person Oral Presentation.

2023

12 presentations
  1. Xu, S., & He, Y. (2023). Urban/suburban vegetative productivity estimation based on high-resolution plant functional type in Toronto, Canada. AGU 2023. December 11–15, 2023. Virtual Oral Presentation.
  2. Cheng, H., & He, Y. (2023). Distinguishing the relative contribution of climate change and human activity on urban vegetation dynamics in the Greater Toronto-Hamilton Area using Google Earth Engine. The Sustainability: Transdisciplinary Theory, Practice, and Action (STTPA) 2023 Conference. October 11, 2023. Mississauga, Canada. In-Person Oral Presentation.
  3. Zhang, J., & He, Y. (2023). Remote sensing of landscape genetics across large-scale spatial scales. The Sustainability: Transdisciplinary Theory, Practice, and Action (STTPA) 2023 Conference. October 11, 2023. Mississauga, Canada. In-Person Oral Presentation.
  4. Nelson, D. M., & He, Y. (2023). Deep learning driver analysis of wildfires in the Central Yukon. The Sustainability: Transdisciplinary Theory, Practice, and Action (STTPA) 2023 Conference. October 11, 2023. Mississauga, Canada. In-Person Oral Presentation.
  5. Wang, Y., & He, Y. (2023). Mapping the vegetation disturbance regimes of Tibetan Plateau mapping using Landsat time series. The Sustainability: Transdisciplinary Theory, Practice, and Action (STTPA) 2023 Conference. October 11, 2023. Mississauga, Canada. In-Person Oral Presentation.
  6. Lee, H., He, Y., Isaac, M. E., & Roberto, A. (2023). Assessing the spectral response of the red-edge region between healthy and water-stressed beans on urban greenroofs using remote sensing. The Sustainability: Transdisciplinary Theory, Practice, and Action (STTPA) 2023 Conference. October 11, 2023. Mississauga, Canada. In-Person Oral Presentation.
  7. He, Y., et al. (2023). Understanding burn severity and post fire recovery: A remote sensing time series study following the 2016 Fort McMurray wildfire. The Sustainability: Transdisciplinary Theory, Practice, and Action (STTPA) 2023 Conference. October 11, 2023. Mississauga, Canada. In-Person Oral Presentation.
  8. He, Y., Zhang, Y., Zhang, Y., & Chen, G. (2023). Mapping human-induced fine-scale forest disturbance features using deep learning and high-resolution imagery. 44th Canadian Symposium on Remote Sensing. June 19–22, 2023. Yellowknife, NT, Canada. In-Person Oral Presentation.
  9. Qiang, C., & He, Y. (2023). Automated delineating urban land use land cover change using high-resolution images. 44th Canadian Symposium on Remote Sensing. June 19–22, 2023. Yellowknife, NT, Canada. In-Person Presentation.
  10. De Braga, F., He, Y., Maddalena, D., & Zhang, Y. (2023). Using Google Earth Engine to quantify prime agricultural land loss in southern Ontario from 1982 to 2022. 44th Canadian Symposium on Remote Sensing. June 19–22, 2023. Yellowknife, NT, Canada. In-Person Presentation.
  11. Zhang, J., Shang, J., He, Y., Liu, J., & Yan, X. (2023). Detection of phenotypic variations in Bermuda grass using remote sensing along the longitudinal and latitudinal. 44th Canadian Symposium on Remote Sensing. June 19–22, 2023. Yellowknife, NT, Canada. In-Person Poster Presentation.
  12. Nelson, D. M., & He, Y. (2023). Machine learning analysis of forest fire severity and recovery in the Yukon Plateau. AAG 2023. March 23–27, 2023. Denver, USA. In-Person Presentation.

2022

2 presentations
  1. Lee, H., He, Y., Isaac, M. E., & Roberto, A. (2022). Assessing the impact of intercropping and drought on crop response using leaf-level hyperspectral data. ICAG-CSRS 2022. July 11–14, 2022. Quebec City, Canada. In-Person Presentation.
  2. He, Y., & Axiotis, A. (2022). Hyperspectral remote sensing for detecting Emerald Ash Borer infestation in a mixed wood forest. ICAG-CSRS 2022. July 11–14, 2022. Quebec City, Canada. Virtual Oral Presentation.

2021

8 presentations
  1. Qiang, C., & He, Y. (2021). Impact of COVID-19 induced lockdown on urban thermal environment of city of Edmonton. CAGONT. November 14, 2021. Virtual Oral Presentation.
  2. Bonney, M. T., & He, Y. (2021). Long-term Landsat time-series (1972–2020) reveal historical inequalities in residential canopy cover. IUFRO WorldDay session. September 28, 2021. Virtual Oral Presentation.
  3. Bonney, M. T., & He, Y. (2021). Leveraging the full potential of Landsat to observe post-development suburban canopy cover change (1972–2020). 42nd Canadian Symposium on Remote Sensing. June 21–24, 2021. Virtual Oral Presentation.
  4. Lu, B., Proctor, C., Dao, P. D., Axiotis, A., & He, Y. (2021). Examining the performance of PROSAIL model in estimating vegetation properties for different ecosystems using hyperspectral images. 42nd Canadian Symposium on Remote Sensing. June 21–24, 2021. Virtual Oral Presentation.
  5. Marajh, L., & He, Y. (2021). Examining local temperature through people and sensors in the Greater Angkor Region of Cambodia. 71st CAG Annual Meeting. June 7–11, 2021. Virtual Oral Presentation (1st Place Presentation Award Winner).
  6. He, Y., & Bonney, M. T. (2021). On the capability of the nearly 50-year Landsat archive for quantifying development and post-development vegetation change in urban-rural landscapes. AAG Annual Meeting 2021. April 9, 2021. Virtual Presentation.
  7. Bonney, M. T., & He, Y. (2021). On the relationship between Landsat-derived canopy cover and tree ring time-series across an urban-rural temperate forest region. AAG Annual Meeting 2021. April 9, 2021. Virtual Presentation.
  8. Marajh, L., & He, Y. (2021). Examining land-use and land-cover change with local perceptions in the Greater Angkor Region of Cambodia. AAG Annual Meeting 2021. April 9, 2021. Virtual Presentation.

2020

7 presentations
  1. He, Y., Bonney, M. T., & Myint, S. W. (2020). Rapid mapping of bushfire hazard using Landsat images and Google Earth Engine. 2020 IEEE International Geoscience and Remote Sensing Symposium. September 26 – October 2, 2020. Virtual Presentation made by Mitchell Bonney.
  2. Bonney, M. T., & He, Y. (2020). Dendrochronology vs. remote sensing: a time-series comparison of tree rings and Landsat in a temperate forest context. 1st Tree Ring Society Virtual Symposium. May 29, 2020. Virtual Oral Presentation.
  3. Dao, P. D., Mantripragada, K., He, Y., & Qureshi, F. Z. (2020). Improving hyperspectral image segmentation by applying inverse noise weighting and outlier removal for optimal scale selection. AAG Annual Meeting 2020. April 6–10, in Colorado, USA. Oral Presentation (Virtual session, Won Second Place for the presentation in the Student Honors Paper Competition of the Remote Sensing Specialty Group).
  4. He, Y., & Bonney, M. T. (2020). Using Landsat time series and Google Earth Engine to examine bushfires in Australia. AAG Annual Meeting 2020. April 6–10, in Colorado, USA. Oral Presentation. Cancelled due to COVID-19.
  5. Axiotis, A., & He, Y. (2020). High resolution disturbance monitoring of the Emerald Ash Borer: the future of ash tree health in North America. AAG Annual Meeting 2020. April 6–10, in Colorado, USA. Oral Presentation. Cancelled due to COVID-19.
  6. Wafi, A., & He, Y. (2020). Crop leaf area estimation using hemispherical photos and hyperspectral images. AAG Annual Meeting 2020. April 6–10, in Colorado, USA. Oral Presentation. Cancelled due to COVID-19.
  7. Lu, B., He, Y., Shang, J., Liu, J., Proctor, C., Dao, P. D., & Wafi, A. (2020). Monitoring crop physiological status using airborne hyperspectral and thermal images. AAG Annual Meeting 2020. April 6–10, in Colorado, USA. Oral Presentation. Cancelled due to COVID-19.

2019

18 presentations
  1. He, Y., & Proctor, C. (2019). Can remote sensing detect changes in the decomposition process? Implications for carbon and nitrogen cycling. STTPA (Sustainability: Transdisciplinary Theory, Practice, and Action) 2019. October 16 in Mississauga, Canada. Oral Presentation.
  2. Bedford, Z. E., Dosanjh, M., & He, Y. (2019). Mapping spatial and temporal trends in lake bathymetry in the Credit Valley Watershed using Landsat images. CVC Research Colloquium. November 29, in Mississauga, Canada. Oral Presentation.
  3. Bonney, M. T., & He, Y. (2019). Insight on long-term forest change in the Credit Valley Watershed: Connecting satellites and field data. CVC Research Colloquium. November 29 in Mississauga, Canada. Oral Presentation.
  4. Bedford, Z. E., Dosanjh, M., & He, Y. (2019). Using Landsat images to map spatial and temporal trends in lake bathymetry in the Credit Valley Watershed. CAGONT Annual Meeting 2019. October 26, in Guelph, Canada. Poster Presentation.
  5. Wafi, A., & He, Y. (2019). Using airborne hyperspectral and thermal remote sensing in precision agriculture. CAGONT Annual Meeting 2019. October 26, in Guelph, Canada. Poster Presentation.
  6. Axiotis, A., & He, Y. (2019). Using high spatial and spectral resolution remote sensing for disturbance detection. CAGONT Annual Meeting 2019. October 26, 2019 in Guelph, Ontario, Canada. Oral Presentation.
  7. Bedford, Z. E., Dosanjh, M., & He, Y. (2019). Mapping wetland bathymetry in the Credit Valley Watershed. STTPA 2019. October 16, in Mississauga, Canada. Oral Presentation.
  8. Dosanjh, M., Bedford, Z. E., & He, Y. (2019). Spatial and temporal trends in lake bathymetry in the Credit Valley Watershed. STTPA 2019. October 16, in Mississauga, Canada. Oral Presentation.
  9. Bonney, M. T., & He, Y. (2019). Monitoring changes in forest growth-decline across a suburbanizing landscape: Extracting useful information from nearly 50 years of Landsat imagery. STTPA 2019. October 16 in Mississauga, Canada. Oral Presentation.
  10. Bedford, Z. E., Dosanjh, M., & He, Y. (2019). Using satellite remote sensing to determine spatial and temporal trends in lake bathymetry. UTM Smarti Gras 2019. August 14, in Mississauga, Canada. Oral Presentation.
  11. Dosanjh, M., Bedford, Z. E., & He, Y. (2019). Using satellite remote sensing to map lake bathymetry in the Credit Valley Watershed. UTM Smarti Gras 2019. August 14, in Mississauga, Canada. Poster Presentation.
  12. Xu, B., Axiotis, A., & He, Y. (2019). Airborne hyperspectral image for tree species identification. UTM Smarti Gras 2019. August 14 in Mississauga, Canada. Poster Presentation.
  13. Bonney, M. T., & He, Y. (2019). Ground validation of long-term Landsat time-series across an urban-rural forest landscape. UTM Graduate Research Colloquium 2019. May 1 in Mississauga, Canada. Oral Presentation.
  14. He, Y., & Bonney, M. T. (2019). Quantifying fine-scale changes in tree distribution across a suburbanizing landscape: 1944 to 2017. AAG Annual Meeting 2019. April 4, in Washington, DC, USA. Oral Presentation.
  15. He, Y. (2019). Machine learning in remote sensing: History, challenges, opportunities. Digital Humanities and Computational Sciences (DHCS 2019). February 26, at UTM, Mississauga, Canada. Oral Presentation.
  16. Bonney, M. T., & He, Y. (2019). Understanding long-term changes in urban-rural forest communities using Landsat trajectories and hemispherical photography. AAG Annual Meeting. April 7, in Washington, DC, USA. Oral Presentation.
  17. Dao, P. D., & He, Y. (2019). Drought-induced plant pigment dynamics and light use efficiency of photosynthesis: the role of remote sensing in early stress detection. The American Association of Geographers Annual Meeting, April 3, Washington, DC, USA. Oral Presentation.
  18. Axiotis, A., & He, Y. (2019). Assessing the effects of Emerald Ash Borer on ash trees in a mixed forest stand using high spatial hyperspectral remote sensing. American Association of Geographers 115th Annual Meeting. April 3 in Washington, DC, USA. Oral Presentation.

2018

15 presentations
  1. Dao, P. D., & He, Y. (2018). Observing drought-induced changes in light use efficiency and photosynthetic performance in grassland ecosystems: the use of hyperspectral remote sensing. The American Geophysical Union Fall Meeting, December 10–14, Washington, DC, USA. Poster Presentation.
  2. Proctor, C., He, Y., & Dao, P. D. (2018). Close range hyperspectral imaging of vegetation decomposition. The Canadian Association of Geographers – Ontario Division’s Annual Meeting, October 19–20, Toronto, Ontario, Canada. Oral Presentation.
  3. Bonney, M. T., & He, Y. (2018). Attributing drivers to fine-scale changes in tree distribution across a suburbanizing landscape since 1944 using aerial photography. CAGONT 2018. October 20 in Toronto, Canada. Oral Presentation.
  4. Axiotis, A., & He, Y. (2018). Exploring the effects of Emerald Ash Borer on ash trees in a natural forest stand using hyperspectral imaging. Canadian Association of Geographers Ontario (CAGONT) 2018. October 20 in Toronto, Ontario, Canada. Oral Presentation.
  5. Dao, P. D., & He, Y. (2018). Hyperspectral remote sensing for ecosystem health monitoring and assessment: image spectral analysis and numerical modelling. The Canadian Association of Geographers – Ontario Division’s Annual Meeting, October 19–20, Toronto, Ontario, Canada. Oral Presentation.
  6. Shadrova, A., & He, Y. (2018). Investigating burn severity and post-fire regrowth in Alberta Canada following the 2016 Fort McMurray wildfire using Landsat 8. The Canadian Association of Geographers – Ontario Division’s Annual Meeting, October 19–20, Toronto, Ontario, Canada. Oral Presentation.
  7. He, Y., Yang, J., & Guo, X. (2018). Quantifying green vegetation cover dynamics in a heterogeneous grassland using Landsat time series. 39th Canadian Symposium on Remote Sensing. June 20 in Saskatoon, SK, Canada. Oral Presentation. http://programme.exordo.com/csrs2018/delegates/presentation/47/
  8. Bonney, M. T., Roslihuddin, M., & He, Y. (2018). The “urban” forest of yesterday: Mississauga’s changing forested landscape since 1944. The Urban Forest of Tomorrow: Strategies for a Changing Environment. June 14 in Mississauga, Canada. Poster Presentation.
  9. Dao, P. D., Bonney, M. T., & He, Y. (2018). Observing the changes in plant pigment and water contents under drought stress by means of hyperspectral remote sensing and model inversion. 39th Canadian Symposium on Remote Sensing. June 20 in Saskatoon, SK, Canada. Oral Presentation. http://programme.exordo.com/csrs2018/delegates/presentation/77/
  10. Bonney, M. T., & He, Y. (2018). Monitoring changes in forest health across the urban-rural transition using multi-source data. UTM Graduate Research Colloquium 2018. May 1 in Mississauga, Canada. Oral Presentation. (Best Oral Presentation Award).
  11. Lu, B., Dao, P. D., & He, Y. (2018). UAV-acquired imagery to study the effects of environmental factors on vegetation growth at plant functional group level. AAG 2018. April 13 in New Orleans, USA. Oral Presentation.
  12. He, Y., Lu, B., Yang, J., & Proctor, C. (2018). High spatial resolution optical remote sensing and its application. AAG 2018. April 11 in New Orleans, USA. Oral Presentation.
  13. Wrathall, D., & He, Y. (2018). Tree species classification using airborne hyperspectral imagery. AAG 2018. April 11 in New Orleans, USA. Oral Presentation.
  14. Bonney, M. T., & He, Y. (2018). Monitoring changes in forest health across an urban-natural transition using multiple scales of analysis. AAG 2018. April 11 in New Orleans, USA. Oral Presentation.
  15. Dao, P. D., Lu, B., & He, Y. (2018). Detecting changes in grass physiological properties under drought stress using hyperspectral remote sensing. AAG 2018. April 11 in New Orleans, USA. Oral Presentation.

2017

8 presentations
  1. Proctor, C., & He, Y. (2017). Do vertical gradients in soil environmental conditions regulate exudation rates from peatland vegetation? AGU 2017. December 11 in New Orleans, USA. Oral Presentation.
  2. He, Y., Mui, A. B., Huang, B., & Wilson, K. (2017). An effective web-based platform for geospatial learning. GIS in Education and Research Conference. October 11 in Toronto, Canada. Oral Presentation.
  3. Lu, B., Dao, P. D., & He, Y. (2017). Spatial analysis influences of environmental factors on vegetation growth using UAV-acquired imagery. GIS in Education and Research Conference. October 11 in Toronto, Canada. Oral Presentation.
  4. Shadrova, A., Dauginis, A., & He, Y. (2017). Remote sensing of fire severity and vegetation recovery: A case study for the Fort McMurray wildfire. GIS in Education and Research Conference. October 11 in Toronto, Canada. Oral Presentation.
  5. Lu, B., & He, Y. (2017). Optimal spatial resolution of UAV imagery for species classification in a heterogeneous ecosystem. UnmannedCanada 2017. November 1–3 in Toronto, Canada.
  6. Perreault, G., Wrathall, D., & He, Y. (2017). Emerald ash borer infestation in urban forest of Mississauga. Undergraduate Summer Research Celebration. August 16 in Mississauga, Canada. Poster Presentation.
  7. He, Y., & Yang, J. (2017). Suitable spectral mixing space selection for linear spectral unmixing of fine-scale urban imagery. AAG 2017. April 7 in Boston, USA. Oral Presentation.
  8. Yang, J., & He, Y. (2017). Individual tree-based species classification for uneven-aged, mixed-deciduous forests using multi-seasonal WorldView-3 images. ASPRS 2017 Annual Conference, March 16 in Baltimore, MD, USA. Oral Presentation.

2016

6 presentations
  1. Yang, J., He, Y., & Caspersen, J. P. (2016). A self-adapted threshold-based region merging method for remote sensing image segmentation. International Geoscience and Remote Sensing Symposium (IGARSS) 2016. July 14 in Beijing, China. Oral Presentation. https://doi.org/10.1109/IGARSS.2016.7730652
  2. Lu, B., & He, Y. (2016). Estimating vegetation biophysical and biochemical properties using remote sensing and modeling in a semi-arid grassland. 10th International Rangeland Congress. July 16–22 in Saskatoon, Saskatchewan, Canada. Oral Presentation.
  3. He, Y., Yang, J., & Lu, B. (2016). Green vegetation cover dynamics in a heterogeneous grassland: Spectral unmixing of Landsat time series. EORSA 2016. July 4 in Guangzhou, China. Oral Presentation.
  4. Lu, B., & He, Y. (2016). Investigating spatial and temporal variations of species composition in a North American grassland using unmanned aerial vehicles (UAV)-acquired imagery. EORSA 2016. July 4 in Guangzhou, China. Oral Presentation. (3rd place winner of Best Student Paper Awards).
  5. He, Y., & Yang, J. (2016). Automated mapping of impervious surface in urban and sub-urban areas: linear spectral unmixing of high spatial resolution imagery. AAG 2016. March 30 in San Francisco, USA. Oral Presentation.
  6. Lu, B., He, Y., & Liu, H. H. T. (2016). Investigating spatio-temporal variations of grassland biophysical and biochemical properties using unmanned aerial vehicle (UAV)-acquired imagery. AAG 2016. April 2 in San Francisco, USA. Oral Presentation.

2015

8 presentations
  1. Lu, B., & He, Y. (2015). Investigating grassland biochemical and biophysical properties using unmanned aerial vehicle (UAV)-acquired imagery. CAGONT 2015. October 24 in Ottawa, Canada. Oral Presentation.
  2. Strzelczyk, J., He, Y., Mcneill, K., & Bavrlic, K. (2015). Monitoring forest decline in southern Ontario using field observations and Landsat imagery. CAGONT 2015. October 24 in Ottawa, Canada. Oral Presentation.
  3. Proctor, C., & He, Y. (2015). Workflow for building a hyperspectral UAV: challenges and opportunities. UAV-g 2015 Conference – International Conference on Unmanned Aerial Vehicles in Geomatics. August 31 in Toronto, Ontario, Canada. Oral Presentation. https://doi.org/10.5194/isprsarchives-XL-1-W4-415-2015
  4. Mishra, S., Murfitt, J., & He, Y. (2015). Mapping Emerald Ash Borer (EAB) infested trees using multispectral UAV technology. The CGCS summer intern presentation day, Toronto, ON, Canada. Oral Presentation.
  5. Mui, A. B., He, Y., Fortin, M.-J., & Johnson, B. (2015). Seasonal modeling of turtle habitat across two landscapes using remote sensing imagery. The International Association for Landscape Ecology World Congress, July 5–10 in Portland, Oregon, USA. Oral Presentation (Awarded NASA-MSU Professional Enhancement Award).
  6. Murfitt, J., He, Y., Mui, A. B., Yang, J., & De Mille, K. (2015). Ash decline assessment in Emerald Ash Borer infested forests using high spatial remote sensing technologies. CSRS Annual Conference. June 9 in St. John’s, Newfoundland, Canada. Oral Presentation (Won a CRSS-SCT Travel grant for this presentation).
  7. Yang, J., He, Y., Caspersen, J. P., & Jones, T. A. (2015). An automated multi-scale integration method to map individual tree crowns for an uneven-aged deciduous-dominated forest using high spatial resolution infrared color aerial images. ASPRS 2015 Annual Meeting, May 6 in Tampa, FL, USA. Oral Presentation.
  8. Lu, B., Proctor, C., & He, Y. (2015). Evaluating PROSPECT and soil-leaf-canopy model for estimating chlorophyll content in a mixed grassland prairie. AAG 2015 Annual Conference. April 23 in Chicago, Illinois, USA. Oral Presentation.

2014

12 presentations
  1. Tariq, F., & He, Y. (2014). Peat incubation to analyze CH4 flux from root exudates. The CGCS summer intern presentation day, Toronto, ON, Canada. Oral Presentation.
  2. Mui, A. B., He, Y., Fortin, M.-J., & Johnson, B. (2014). Modeling seasonal landscape connectivity for turtles in a fragmented landscape. 2014 International Association for Landscape Ecology, May 18–22 in Anchorage, Alaska, USA. Oral Presentation (Awarded the Best Student Presentation Award).
  3. He, Y., Murfitt, J., Mui, A. B., & De Mille, K. (2014). Ash decline assessment in Emerald Ash Borer-infested areas: A case study using UAV imaging technologies. Insect Biotech Conference, June 4–6, 2014 in Niagara-on-the-Lake, Ontario, Canada. Invited Oral Presentation.
  4. Proctor, C., & He, Y. (2014). Calibration of the PROSPECT radiative transfer model for non-photosynthetic vegetation. IGARSS 2014. July 18, 2014. Quebec City, Quebec, Canada. Oral Presentation.
  5. Mui, A. B., He, Y., & Johnson, B. (2014). Remote sensing data for mapping seasonally changing wetland habitat of a threatened turtle species in Ontario. IGARSS 2014. July 17, 2014. Quebec City, Quebec, Canada. Poster Presentation.
  6. Yang, J., He, Y., & Caspersen, J. P. (2014). A multi-band watershed segmentation method for individual tree crown delineation from high resolution multispectral aerial image. IGARSS 2014. July 17, 2014. Quebec City, Quebec, Canada. Oral Presentation. https://doi.org/10.1109/IGARSS.2014.6945949
  7. Tong, A., & He, Y. (2014). Remote sensing of grassland chlorophyll content: Assessing the spatial-temporal performance of spectral indices. IGARSS 2014. July 16, 2014. Quebec City, Quebec, Canada. Oral Presentation. https://doi.org/10.1109/IGARSS.2014.6947069
  8. Lu, B., & He, Y. (2014). Analyzing a North American prairie wildfire using remote sensing imagery. IGARSS 2014. July 14, 2014. Quebec City, Quebec, Canada. Oral Presentation. https://doi.org/10.1109/IGARSS.2014.6946553
  9. He, Y. (2014). Temporal variation of an invasive shrub in a native old field: potential colonization advantage in terms of water use. The American Association of Geographers 2014 annual meeting, April 8–12 in Tampa, Florida, USA. Oral Presentation.
  10. Mui, A. B., Markle, C., He, Y., & Chow-Fraser, P. (2014). Mapping habitat and travel corridors for Blanding's turtles in Rouge Park using remote sensing imagery and geospatial analysis. Ontario Turtle Conservation Conference, April 8–9 in Algonquin Park, Ontario, Canada. Oral Presentation.
  11. Proctor, C., & He, Y. (2014). SLC radiative transfer model for estimating LAI in wetland ecosystems. ASPRS 2014 Annual Conference. March 23–28 in Louisville, Kentucky, USA. Oral Presentation.
  12. Mui, A. B., He, Y., & Johnson, R. (2014). Seasonal changes in habitat preference by threatened turtles in a natural and disturbed wetland landscape. Ontario Wetlands Conference, February 10 in Toronto, Ontario, Canada. Poster Presentation.

2013

4 presentations
  1. He, Y. (2013). Fine-scale forest feature mapping using multisource remote sensing data. GLSL Biomass Impacts and Opportunities Study 2013 Partnership Meeting, October 28–30, 2013 in Petawawa Research Forest, Ontario, Canada. Oral Presentation.
  2. He, Y. (2013). The invasive shrub - soil moisture feedbacks revealed by remote sensing and wavelet analysis. The Canadian Association of Geographers 2013 annual meeting, August 10–16 in St. John’s, Newfoundland, Canada. Oral Presentation.
  3. He, Y. (2013). Temporal dynamics of vegetation cover in response to precipitation: a comparison over three plant functional groups in a protected semi-arid grassland. The Seventh International Conference and Workshop on the Analysis of Multi-temporal Remote Sensing Images (MultiTemp-2013), June 24–28 in Banff, Alberta, Canada. Oral Presentation.
  4. He, Y., & Proctor, C. (2013). Estimating grassland chlorophyll content from leaf to landscape level: bridging the gap in spatial scales. The American Association of Geographers 2013 annual meeting. April 9–13 in Los Angeles, USA. Oral Presentation.

2012

9 presentations
  1. Mui, A. B., & He, Y. (2012). Using remote sensing and GIS to extract and model critical habitat for the Blanding's turtle (Emydoidea blandingii) in southern Ontario, Canada. 2012 Society for Conservation GIS Conference. July 19 – 22, 2012 in Monterey, California, USA.
  2. He, Y., & Guo, X. (2012). Estimating leaf area index using remote sensing data in two grassland ecosystems. Grassland Remote Sensing International Conference 2012. June 28–29 in Saskatoon, SK, Canada. Oral Presentation.
  3. Tong, A., & He, Y. (2012). A comparative study of SPOT, Landsat, MODIS, and AVHRR data for leaf area index estimation in Canadian grasslands. Grassland Remote Sensing International Conference 2012. June 28–29 in Saskatoon, SK, Canada. Oral Presentation.
  4. Wong, K. K., & He, Y. (2012). Estimation of grassland biochemical content through remote sensing data. Grassland Remote Sensing International Conference 2012. June 28–29 in Saskatoon, SK, Canada. Oral Presentation.
  5. Saliola, A., He, Y., & Caspersen, J. P. (2012). Object-based image segmentation for the delineation of individual tree crowns and canopy gaps. The Canadian Association of Geographers 2012 annual meeting, May 28–June 2 in Waterloo, ON, Canada. Oral Presentation.
  6. He, Y., Ma, Z., & Guo, X. (2012). Integrating remote sensing data with an ecosystem model to estimate spatially explicit net primary productivity in a heterogeneous grassland. AAG 2012. New York, USA. Oral Presentation.
  7. Wong, K. K., & He, Y. (2012). Estimation of grassland biochemical content through remote sensing derived vegetation indices. AAG 2012. New York, USA. Oral Presentation.
  8. Lu, B., Jiang, X., & He, Y. (2012). The impacts of human activities on tidal flat evolution on the eastern beach of Chongming Island in the Yangtze estuary, China. AAG 2012. New York, USA. Oral Presentation.
  9. Mui, A. B., He, Y., & Johnson, R. (2012). Habitat modeling for Blanding's turtles (Emydoidea blandingii) in Ontario wetlands: The role of remote sensing. Ontario Wetlands Conference. Toronto, Ontario, Canada. Poster Presentation.

2011

4 presentations
  1. He, Y. (2011). Remote sensing and GIS integration for environmental applications. 5th Multi-disciplinary Symposium of Ontario Chinese Professors, Muskoka, ON, Canada. Oral Presentation.
  2. Proctor, C., Robinson, V., & He, Y. (2011). Mapping of European frog-bit (Hydrocharis morsus-ranae) using QuickBird imagery. The Canadian Association of Geographers 2011 annual meeting, Sherbrooke, Québec, Canada. Oral Presentation.
  3. Mui, A. B., He, Y., Johnson, B., & Caverhill, B. (2011). Fragmentation of turtle habitat in southern Ontario. The Canadian Association of Geographers 2011 annual meeting, Calgary, AB, Canada. (awarded best presentation award in Geographic Information Science Study Group).
  4. Proctor, C., Robinson, V., & He, Y. (2011). Mapping of European frog-bit (Hydrocharis morsus-ranae) using QuickBird imagery. The Canadian Association of Geographers 2011 annual meeting, Calgary, AB, Canada. Oral Presentation. (awarded best presentation award in Geographic Information Science Study Group).

2010

3 presentations
  1. He, Y. (2010). Remote sensing in biodiversity monitoring. Biodiversity in the Credit River Watershed Stewardship Forum. Mississauga, ON, Canada. Oral Presentation.
  2. Khan, A., & He, Y. (2010). Integrating remote sensing and GIS for studying spatial variation among three different ecosystems. The CGCS summer intern presentation day, Toronto, ON, Canada. Oral Presentation.
  3. He, Y., & Guo, X. (2010). Object-based grassland ecosystem modelling. The Prairie Summit 2010, Regina, SK, Canada. Oral Presentation.

2009

3 presentations
  1. He, Y., Franklin, S. E., Guo, X., & Stenhouse, G. B. (2009). Object-oriented classification of high spatial resolution images for the extraction of narrow linear forest disturbance. The 30th Canadian Symposium on Remote Sensing. Lethbridge, AB, Canada. Oral Presentation.
  2. He, Y., Franklin, S. E., Guo, X., McDermid, G. J., & Stenhouse, G. B. (2009). Linear forest disturbance recognition and mapping from high spatial resolution multispectral imagery. American Society for Photogrammetry and Remote Sensing. Baltimore, Maryland, USA. Oral Presentation.
  3. Guo, X., He, Y., & Wilmshurst, J. F. (2009). Managing loggerhead shrike habitat using remote sensing products. American Society for Photogrammetry and Remote Sensing. Baltimore, Maryland, USA. Oral Presentation.

Before 2008

10 presentations
  1. He, Y., Guo, X., Wilmshurst, J. F., & Dixon, P. (2007). Interannual variation of NDVI and its relation to climate change. The Prairie division of the Canadian Association of Geographers annual conference, Weyburn, SK, Canada. Oral Presentation.
  2. He, Y., & Guo, X. (2007). Comparison of ecosystem models and development of new model frame for mixed grassland prairie. The Canadian Association of Geographers 2007 annual meeting, Saskatoon, SK, Canada. Oral Presentation.
  3. He, Y., Guo, X., Wilmshurst, J. F., Sissons, R. A., & Fargey, P. (2007). Using logistic regression to model loggerhead shrike habitat suitability in Grasslands National Park of Canada. PCESW, Regina, SK, Canada. Oral Presentation.
  4. He, Y., Guo, X., & Wilmshurst, J. F. (2006). Comparison of different methods for measuring LAI in a mixed grassland. SWAAG, Norman, OK, USA. Oral Presentation.
  5. He, Y., Guo, X., & Wilmshurst, J. F. (2006). Analysis of temporal vegetation dynamics in northern parks using NOAA-AVHRR NDVI and climate data. The Canadian Association of Geographers 2006 annual meeting, Thunder Bay, ON, Canada. Oral Presentation.
  6. He, Y., Guo, X., & Wilmshurst, J. F. (2006). Assessing and predicting vegetation productivity in Canada’s national parks. The 3rd International Conference on Environmental Enhancement and Sustainable Development, Hohhot, Inner Mongolia, China. Oral Presentation.
  7. He, Y., & Guo, X. (2006). Discriminating landcover types and treatments using SPOT 4 single and multitemporal data in the mixed grassland ecosystem. Soil and Crops annual conference, Saskatoon, SK, Canada. Oral Presentation.
  8. He, Y., Guo, X., & Zheng, Y. (2005). Leaf area index estimation using remotely sensed data in a mixed grassland ecosystem. The Prairie division of the Canadian Association of Geographers annual conference, Winnipeg, MB, Canada. Oral Presentation.
  9. He, Y., Guo, X., & Si, B. C. (2005). Topography affects grassland heterogeneity. Soil and Crops annual conference, Saskatoon, SK, Canada. Oral Presentation.
  10. He, Y., Guo, X., & Zheng, Y. (2004). Impact of climate change with enhanced UV-B radiation on Chinese agricultural NPP. The Prairie division of the Canadian Association of Geographers annual conference, Saskatoon, SK, Canada. Oral Presentation.