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Lindi J. Quackenbush
Publications

Research Portfolios

Book Chapters

  • Im, J., L.J. Quackenbush, M. Li, and F.Fang, 2014. Optimum Scale in Object-Based Image Analysis, in Scale Issues in Remote Sensing, edited by Q. Weng, John Wiley & Sons, pp. 197-213.
  • Quackenbush, L.J., J. Im, and Y. Zuo, 2013. Road Extraction: A Review of LiDAR-Focused Studies, in Remote Sensing of Natural Resources, edited by G. Wang and Q. Weng, CRC Press, Boca Raton, Florida, pp. 155-169.
  • Quackenbush, L.J., 2007.  Separating Types of Impervious Land Cover Using Fractals, in Remote Sensing of Impervious Surfaces, editied by Q. Weng, CRC Press, Boca Raton, Florida, pp. 119–142.

Peer-reviewed Journal Publications

  • Xu, J., Quackenbush, L.J., Volk, T.A., & Stehman, S.V., 2022. Shrub willow canopy chlorophyll content estimation from unmanned aerial systems (UAS) data: Estimation and uncertainty analysis across time, space, and scales. International Journal of Applied Earth Observation and Geoinformation, 108, 102737. https://doi.org/10.1016/j.jag.2022.102737
  • Zhao, Y., Ma, Y., Quackenbush, L.J., & Zhen, Z., 2022. Estimation of Individual Tree Biomass in Natural Secondary Forests Based on ALS Data and WorldView-3 Imagery. Remote Sensing (MDPI), 14(271), 271. https://doi.org/10.3390/rs14020271
  • Adeli, S. B. Salehi, M. Mahidanpari, LJ Quackenbush, & B Chapman, 2021. Moving Towards L-Band NASA-ISRO SAR Mission (NISAR) Dense Time Series: Multi-Polarization Object-Based Wetland Classification in Yucatan Lake, Louisiana. Earth and Space Science Open Archive ESSOAr. DOI:10.1002/essoar.10506622.1.
  • Adeli, S., Salehi, B., Mahidanpari, M., & Quackenbush, L.J., 2021. Toward a multi-source remote sensing wetland inventory of the USA: Preliminary results on wetland inventory of Minnesota. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-3-2021(3), 97–100. https://doi.org/10.5194/isprs-annals-V-3-2021-97-2021
  • Khan, R.M., Salehi, B., Mahdianpari, M., Mohammadimanesh, F., Mountrakis, G., & Quackenbush, L.J., 2021. A meta-analysis on harmful algal bloom (HAB) detection and monitoring: A remote sensing perspective. Remote Sensing (MDPI), 13(21), 4347–. https://doi.org/10.3390/rs13214347
  • Pu, G., Quackenbush, L.J., & Stehman, S.V. (2021). Identifying Factors That Influence Accuracy of Riparian Vegetation Classification and River Channel Delineation Mapped Using 1 m Data. Remote Sensing (MDPI), 13(22), 4645–. https://doi.org/10.3390/rs13224645
  • Pu, G., Quackenbush, L.J., & Stehman, S.V. (2021). Using Google Earth Engine to Assess Temporal and Spatial Changes in River Geomorphology and Riparian Vegetation. Journal of the American Water Resources Association, 57(5), 789–806. https://doi.org/10.1111/1752-1688.12950
  • Xu, J., T.A. Volk, L.J. Quackenbush, & S.V. Stehman, 2021.  Estimation of shrub willow leaf chlorophyll concentration across different growth stages using a hand-held chlorophyll meter to monitor plant health and production. Biomass and Bioenergy, 150, 106132
  • Adeli, S. B. Salehi, M. Mahidanpari, LJ Quackenbush, & B Chapman, 2021. Moving Towards L-Band NASA-ISRO SAR Mission (NISAR) Dense Time Series: Multi-Polarization Object-Based Wetland Classification in Yucatan Lake, Louisiana. Earth and Space Science Open Archive ESSOAr. DOI:10.1002/essoar.10506622.1.
  • Son, B., S. Park, J. Im, S. Park, Y. Ke, L.J. Quackenbush, 2021.  A new drought monitoring approach: Vector Projection Analysis (VPA). Remote Sensing of Environment, 252, 112145
  • Tamiminia, H., B. Salehi, M. Mahdianpari, L. Quackenbush, S. Adeli, & B. Brisco, 2020. Google Earth Engine for geo-big data applications: A meta-analysis and systematic review. ISPRS Journal of Photogrammetry and Remote Sensing, 164, 152-170
  • Park, S., J. Lee, J. Im, C.K. Song, M. Choi, J. Kim, S. Lee, R. Park, S.M. Kim, J. Yoon, D.-W. Lee & L.J. Quackenbush, 2020. Estimation of spatially continuous daytime particulate matter concentrations under all sky conditions through the synergistic use of satellite-based AOD and numerical models. Science of The Total Environment, 713, 136516
  • Shin, M., Y. Kang, S. Park, J. Im, C. Yoo, & L.J. Quackenbush, 2020. Estimating ground-level particulate matter concentrations using satellite-based data: a review. GIScience & Remote Sensing, 57 (2), 174-189
  • Xu, J., L.J. Quackenbush, T.A. Volk, & J. Im, 2020. Forest and Crop Leaf Area Index Estimation Using Remote Sensing: Research Trends and Future Directions, Remote Sensing 12 (18), 2934.
  • Adeli, S., B. Salehi, M. Mahdianpari, L.J. Quackenbush, B. Brisco, H. Tamiminia, & S. Shaw. Wetland monitoring using SAR data: A meta-analysis and comprehensive review. Remote Sensing, 12 (14), 2190
  • Lee, J., D. Han, M. Shin, J. Im, J. Lee, & L.J. Quackenbush, 2020. Different Spectral Domain Transformation for Land Cover Classification Using Convolutional Neural Networks with Multi-Temporal Satellite Imagery. Remote Sensing, 12 (7), 1097.
  • Kyaw, T.Y., R.H. Germain, S.V. Stehman, & L.J. Quackenbush, 2020. Quantifying forest loss and forest degradation in Myanmar’s “home of teak”. Canadian Journal of Forest Research 50 (2), 89-101.
  • Xu, J., Meng, J., & L.J. Quackenbush, 2019. Use of remote sensing to predict the optimal harvest date of corn. Field Crops Research, 236, 1-13.
  • Li, S., L.J. Quackenbush, & J. Im, 2019. Airborne Lidar Sampling Strategies to Enhance Forest Aboveground Biomass Estimation from Landsat Imagery. Remote Sensing 11 (16), 1906.
  • Kim, M., Lee, J., Han, D., Shin, M., Im, J., Lee, J., Quackenbush, L.J. & Gu, Z., 2018. Convolutional Neural Network-Based Land Cover Classification Using 2-D Spectral Reflectance Curve Graphs With Multitemporal Satellite Imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(12), 4604-4617.
  • Lee, J., Im, J., Kim, K., Quackenbush, L.J, 2018. Machine Learning Approaches for Estimating Forest Stand Height Using Plot-Based Observations and Airborne LiDAR Data. Forests, 9(5): 268.
  • Yoo, C., Im, J., Park, S., & Quackenbush, L.J., 2018. Estimation of daily maximum and minimum air temperatures in urban landscapes using MODIS time series satellite data. ISPRS Journal of Photogrammetry and Remote Sensing, 137, 149-162.
  • Bhattarai, N., Quackenbush, L. J., Im, J., & Shaw, S. B., 2017. A new optimized algorithm for automating endmember pixel selection in the SEBAL and METRIC models. Remote Sensing of Environment, 196, 178-192. https://doi.org/10.1016/j.rse.2017.05.009
  • Xu, Z., Mountrakis, G., & Quackenbush, L.J., 2017. Impervious surface extraction in imbalanced datasets: integrating partial results and multi-temporal information in an iterative one-class classifier. International Journal of Remote Sensing, 38(1), 43-63. http://dx.doi.org/10.1080/01431161.2016.1259677
  • Bhattarai, N., S.B. Shaw, L.J. Quackenbush, J. Im, and R. Niraula, 2016.  Evaluating five remote sensing based single-source surface energy balance models for estimating daily evapotranspiration in a humid subtropical climate.  International Journal of Applied Earth Observation and Geoinformation, 49: 75–86, http://dx.doi.org/10.1016/j.jag.2016.01.010.
  • Lee, S., Im, J., Kim, J., Kim, M., Shin, M., Kim, H. C., & Quackenbush, L. J., 2016. Arctic Sea Ice Thickness Estimation from CryoSat-2 Satellite Data Using Machine Learning-Based Lead Detection. Remote Sensing, 8(9), 698 https://doi.org/10.3390/rs8090698.
  • Zhen, Z., L.J. Quackenbush, and L. Zhang, 2016. Trends in Automatic Individual Tree Crown Detection and Delineation—Evolution of LiDAR Data. Remote Sensing 8(4): 333, http://dx.doi.org/10.3390/rs8040333.
  • Bhattarai, N., L.J. Quackenbush, M. Dougherty and L.J. Marzen, 2015. A simple Landsat–MODIS fusion approach for monitoring seasonal evapotranspiration at 30 m spatial resolution.  International Journal of Remote Sensing, 36(1): 115–143, http://dx.doi.org/10.1080/01431161.2014.990645.
  • Liu, T., J. Im., L.J. Quackenbush, 2015. A novel transferable individual tree crown delineation model based on Fishing Net Dragging and boundary classification.  ISPRS Journal of Photogrammetry and Remote Sensing, 110: 34 – 47, http://dx.doi.org/10.1016/j.isprsjprs.2015.10.002.
  • Zhen, Z., L.J. Quackenbush, S.V. Stehman, and L. Zhang, 2015. Agent-based region growing for individual tree crown delineation from airborne laser scanning (ALS) data, ISPRS Journal of Photogrammetry and Remote Sensing, 36(7): 1965-1993, http://dx.doi.org/10.1080/01431161.2015.1030043.
  • Kang, D., J. Im, M.-I. Lee, and L.J. Quackenbush, 2014. The MODIS ice surface temperature product as an indicator of sea ice minimum over the Arctic Ocean, Remote Sensing of Environment, 153(9): 99–108, http://dx.doi.org/10.1016/j.rse.2014.05.012.
  • Li, M., J. Im, L.J. Quackenbush, T. Liu, 2014. Forest Biomass and Carbon Stock Quantification Using Full Waveform LiDAR data: A Comparison of Statistical and Machine Learning Approaches, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(7): 3143–3156; http://dx.doi.org/10.1109/JSTARS.2014.2304642.
  • Shaw, S.B., J. Marrs, N. Bhattarai, and L.J. Quackenbush, 2014. Longitudinal Study of the Impacts of Land Cover Change on Hydrologic Response in Four Mesoscale Watersheds in New York State, USA, Journal of Hydrology 519(A): 12-22; http://dx.doi.org/10.1016/j.jhydrol.2014.06.055.
  • Zhen, Z., L.J. Quackenbush, and L. Zhang, 2014. Impact of Tree-Oriented Growth Order in Marker-Controlled Region Growing for Individual Tree Crown Delineation Using Airborne Laser Scanner (ALS) Data, Remote Sensing, 6, 555-579; http://dx.doi.org/10.3390/rs6010555.
  • Zhen, Z., L.J. Quackenbush, S.V. Stehman, and L. Zhang, 2013. Impact of training and validation sample selection on classification accuracy and accuracy assessment when using reference polygons in object-based classification, International Journal of Remote Sensing, 34(19): 6914-6930, http://dx.doi.org/10.1080/01431161.2013.810822
  • Lu, Z., Im, J. , L.J. Quackenbush, and S. Yoo, 2013.  Remote sensing based house value estimation using an optimized regional regression model, Photogrammetric Engineering and Remote Sensing, 79(9): 809-820, http://dx.doi.org/10.14358/PERS.79.9.809.
  • Im, J., Z. Lu, J. Rhee, and L.J. Quackenbush, 2012. Impervious surface quantification using a synthesis of artificial immune networks and decision/regression trees from multi-sensor data, Remote Sensing of Environment, 117: 102–113, http://dx.doi.org/10.1016/j.rse.2011.06.024.
  • Zhang, W., L.J. Quackenbush, J. Im, and L. Zhang, 2012. Indicators for separating undesirable and well-delineated tree crowns from high spatial resolution imagery, International Journal of Remote Sensing, 33(17): 5451–5472, http://dx.doi.org/10.1080/01431161.2012.663109.
  • Ke, Y., and L.J. Quackenbush, 2011. A review of methods for automatic individual tree crown detection and delineation. International Journal of Remote Sensing, 32(13): 3625–3647, http://dx.doi.org/10.1080/01431161.2010.494184.
  • Ke, Y., and L.J. Quackenbush, 2011.  A comparison of three methods for automatic tree crown detection and delineation from high spatial resolution imagery. International Journal of Remote Sensing, 32(13): 3625–3647, http://dx.doi.org/10.1080/01431161003762355.
  • Lu, Z., J. Im, and L.J. Quackenbush, 2011. A volumetric approach to population estimation using LiDAR remote sensing, Photogrammetric Engineering & Remote Sensing, 77(11): 1145–1156.
  • Gunson, K.E., G. Mountrakis, and L.J. Quackenbush, 2010. Spatial wildlife-vehicle collision models: A review of current work and their application to transportation mitigation projects.  Journal of Environmental Management, 92(4): 1074–1082, http://dx.doi.org/10.1016/j.jenvman.2010.11.027 .
  • Ke, Y., L.J. Quackenbush, and J. Im, 2010.  Synergistic use of QuickBird multispectral imagery and LIDAR data for object-based forest species classification.  Remote Sensing of Environment, 14(6): 1141–1154, http://dx.doi.org/10.1016/j.rse.2010.01.002.
  • Ke, Y., W. Zhang, and L.J. Quackenbush, 2010.  Active contour and hill-climbing for tree crown detection and delineation.  Photogrammetric Engineering and Remote Sensing, 76(10): 1169–1181.
  • Lu, Z., J. Im, L.J. Quackenbush, and K. Halligan, 2010. Population estimation based on multi-sensor data fusion. International Journal of Remote Sensing, 31(21): 5587–5604, http://dx.doi.org/10.1080/01431161.2010.496801.
  • Zhang, W., Y. Ke, L.J. Quackenbush, and L. Zhang, 2010. Using Error-in-Variable Regression to Predict Tree Diameter and Crown Width from Remotely Sensed Imagery.  Canadian Journal of Forest Research, 40(6): 1095–1108, http://dx.doi.org/10.1139/X10-073.
  • Doucette, J.S., W.M Stiteler, L.J. Quackenbush, and J.T. Walton, 2009.  A Rules-Based Approach for Predicting the Eastern Hemlock Component of Forests in the Northeastern United States.  Canadian Journal of Forest Research, 39: 1453–1464, http://dx.doi.org/10.1139/X09-060.
  • Wu, W., C.A. Hall, F.N. Scatena, L.J. Quackenbush, 2006.  Spatial modelling of evapotranspiration in the Luquillo Experimental Forest of Puerto Rico using remotely-sensed data.  Journal of Hydrology, 328(3-4): 733–752, http://dx.doi.org/10.1016/j.jhydrol.2006.01.020.
  • Quackenbush, L. J., 2004.  A Review of Techniques for Extracting Linear Features from Imagery.  Photogrammetric Engineering and Remote Sensing, 70(12): 1383–1392.
  • Quackenbush, L.J., P.F. Hopkins, and G.J. Kinn, 2000.  Developing Forestry Products from High Resolution Digital Aerial Imagery.  Photogrammetric Engineering and Remote Sensing, 66(11): 1337–1346.

Conference Proceedings

  • Adeli, Salehi, B., Mahidanpari, M., Quackenbush, L. J., & Chapman, B., 2021. Wetland classification using simulated NISAR data: a case study in Louisiana. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 100–103. https://doi.org/10.1109/IGARSS47720.2021.9553878
  • Adeli, S.., Quackenbush, L.J., Salehi, B., Mahdianpari, M., Beier, C. M., Johnson, L., & Phoenix, D. B., 2022. The importance of seasonal textural features for object-based classification of wetlands: New York State case study. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences., XLIII-B3-2022, 471–477. https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-471-2022
  • Bhattarai, N.,Quackenbush, L.J., Calandra, L.N., Im, J., and S.A. Teale, 2012.  An automated object-based analysis of Sirex infestation in pines. Proceedings of the 2012 ASPRS Conference, Sacramento, CA, March 19-23, Unpaginated CD-ROM.
  • Quackenbush, L.J., N. Bhattarai, L.N. Calandra, J. Im, and S.A. Teale, 2011. Spectral analysis of scotch pine infested by Sirex noctilio. Proceedings of 2011 ASPRS Annual Conference, Milwaukee, WI, 1–5 May.
  • Zhang, W., L.N. Calandra, L.J. Quackenbush, J. Im, and S.A. Teale, 2010.  Monitoring Scotch pine infested by Sirex noctilio using hyperspectral data: a laboratory study.  Proceedings of 2010 ASPRS Annual Conference. San Diego, CA.
  • Zuo, Y., and L.J. Quackenbush, 2010.  Road extraction from lidar data in residential and commercial areas of Oneida County, New York.  Proceedings of 2010 ASPRS Annual Conference. San Diego, CA.
  • Ke, Y., and L.J. Quackenbush, 2009.  Individual tree crown detection and delineation from high spatial resolution imagery using active contour and hill-climbing methods.  Proceedings of 2009 ASPRS Annual Conference, Baltimore, MD.
  • Ke, Y., and L.J. Quackenbush, 2008.  Comparison of Individual Tree Crown Detection and Delineation Methods.  Proceedings of 2008 ASPRS Annual Conference, Portland, OR. 
  • Shah, C.A., and L.J. Quackenbush, 2007.  Analyzing multi-sensor data fusion techniques: a multi-temporal change detection approach.  Proceedings of 2007 ASPRS Annual Conference, Tampa, FL.
  • Doucette, J.S., W.M. Stiteler, L.J. Quackenbush, 2006.  Combining multispectral imagery with species specific habitat elements to locate hemlock.  Proceedings, 2006 ASPRS Annual Conference, Reno, Nevada, 1–5 May.
  • Quackenbush, L.J., C.N. Kroll, 2006.  Investigating New Advances in Forest Species Classification.  Proceedings, 2006 ASPRS Annual Conference, Reno, Nevada, 1–5 May.
  • Quackenbush, L. J., 2005.  Calculating fractal dimension using the triangular prism method.  Proceedings, 2005 ASPRS Annual Conference, Baltimore, Maryland, 9–11 March.
  • Quackenbush, L.J., 2004.  Calculating fractal dimension using the triangular prism method.  Accepted for inclusion in Proceedings, 2004 ASPRS Annual Conference, Baltimore, Maryland, 8–11 March, 2005.
  • Quackenbush, L. J., 2004.  Classification of Impervious Land Cover Using Fractals.  Proceedings, 2004 ASPRS Annual Conference, Denver, Colorado, 24–28 May.
  • Quackenbush, L. J. and P. F. Hopkins, 2003.  Using Fractal Dimension to Separate Types of Impervious Land-Cover.  Proceedings, 2003 ASPRS Annual Conference, Anchorage, Alaska, 7–9 May.
  • Riordan, K. D., R. J. Turner, S. Chen, P. F. Hopkins, and L. J. Quackenbush, 2001.  Processing High Resolution, Digital Imagery to Efficiently Derive Forest Stand Information.  Presented at 2001 ASAE Annual International Meeting, Sacramento, California, Paper No. 01-8078, 29 July – 1 August.
  • Quackenbush, L. J., D. G. Murdock, K. D. Riordan, P. F. Hopkins, S. Chen, 2001.  Analysis of Fusion Techniques for Forestry Applications.  Proceedings, 2001 ASPRS Annual Conference, St. Louis, Missouri, 23–27 August.
  • Brock, R. H., E. Karakurt, L. J. Quackenbush, P. J. Szemkow, R. P. Aicher, J. T. Walton, S. Whisenand, 2000.  The Accuracy of Single Frequency Code-based GPS Positioning when used in Forest Conditions.  Presented at the 2000 ASAE Annual International Meeting, Milwaukee, Wisconsin, Paper No. 005012, 9–12 July.
  • Quackenbush, L. J., P. F. Hopkins and G. J. Kinn, 2000.  Using Template Correlation to Identify Individual Trees in High Resolution Imagery.  Proceedings, 2000 ASPRS Annual Conference, Washington DC, 23–26 May.
  • Quackenbush, L. J., P. F. Hopkins and G. J. Kinn, 1999.  Deriving Tree Crown Delineations and Counts from High Resolution Digital Aerial Imagery.  Presented at the 1999 ASAE/CSAE-SCGR Annual International Meeting, Toronto, Ontario, Paper No. 99-5034, 18–22 July.
  • Quackenbush, L. J., P. F. Hopkins and G. J. Kinn, 1999.  Developing Derivative Products from High Resolution Digital Aerial Imagery.  Proceedings, 1999 ASPRS Annual Conference, Portland, OR.