About

Kenjin Chang is associated with the Bronfenbrenner Center for Translational Research at Cornell University. The center assists faculty in developing translational research projects by providing support such as proposal preparation assistance, training, technical support, and help in brokering collaborative relationships. The center also offers workshops, an intensive summer institute, and talks on current research topics related to translational research. While specific details about Professor Chang's individual research focus or background are not provided on the page, his affiliation with the BCTR indicates involvement in translational research efforts aimed at applying research findings to practical community and societal issues.

Research topics

• Engineering
• Computer Science
• Physics
• Mechanics
• Geology
• Materials science
• Geotechnical engineering
• Meteorology
• Acoustics
• Structural engineering
• Geomorphology
• Telecommunications
• Optics
• Classical mechanics
• Geodesy

Selected publications

• TGLO Establish Historical Long-term Wetland Boundary Evolution Through Satellite Imagery

  Zenodo (CERN European Organization for Nuclear Research) · 2026-05-20

  datasetOpen access

  This dataset includes wetland erosion rates for West Galveston, Matagorda, and San Antonio Bay. Long-term trends in wetland boundary changes are estimated using Landsat satellite imagery. Sections of the wetland experiencing the highest rates of erosion will be further investigated through CubeSat satellite observations. The format of the data is summarized as follows (1) Landsat-based wetland evolution results from 1984 to 2020 The Annual and seasonal water occurrence (in gif format) The wetland change map (in TIF format) (2) CubeSat-based wetland evolution results from 2009 to 2020 Water occurrence maps from 2009 to 2021 for the RapidEye-based bi-annual results (FSX-occurrence-yyy1-yyy2.tif) and the PlanetScope-based annual results (FSX-occurrence-yyy1.tif), where yyyy represents the given year. The legend image is 'occurrence-cbar.jpg.' Erosion maps based on the difference between water occurrence mapping in 2009 and 2021: ('FSX-occurrence-diff-2021-2009.tif'). The legend is 'occurrence-diff-cbar.jpg.' The 0.2-meter bed counter line images based on the water occurrence maps and the tide elevation threshold from 2017 to 2021: (FSX-bed-yyy1.tif). Again, yyy1 represents the given year. The legend is 'color-bed.jpg The difference between the beds in the 0.2-meter bed counter line images in 2017 and 2021 at FS-1, FS-2, FS-3, and FS-4: (FSX-bed-diff-2021-2017.tif). The legend is 'color-bed-dif.jpg.' (3) Analysis of wetland boundary evolution and erosion rate (data format in ArcGIS shapefile) Landsat-based wetland loss rate from 1984 to 2020 Landsat-based coastlines in 1984, 2000, 2010, and 2020 (4) Ratio of Sediment Erosion Rate to Sea-Level Rise (data format in ArcGIS shapefile) CubeSat-based wetland loss rate from 1984 to 2020 CubeSat-based bed erosion rate from 1984 to 2020 Sedimentary deficit: ratio of sediment erosion rate to sea-level rise rate

  PublisherDOI

• Comparison of Surface Current Measurement Techniques and Observations of Tidal Inlet Dynamics Using Unmanned Aerial Systems

  Journal of Atmospheric and Oceanic Technology · 2025-09-18

  article

  Abstract Understanding mixing dynamics through barrier island inlets requires large-scale observations of tidal exchange near the bay–ocean boundary, including reliable measurements of surface currents. In this paper, we evaluate the ocean surface currents at Galveston Bay inlet and Freeport Harbor inlet, Texas, from a video sequence of surface waves collected from a small, unmanned aerial system (UAS). Surface current measurements from three different measurement techniques (UAS wave-based current mapping, particle image velocimetry, and optical flow) were compared with and without the presence of artificial floating tracer particles. When used, tracer particles of packaging peanuts were released using a payload-capable UAS platform. Particle image velocimetry (PIV) and optical flow methods estimated coherent surface currents consistent with the motion of uniform, well-distributed seeding particles drifting upon the spectral surface waves and the near-surface currents. Without seeding particles, the UAS wave-based current mapping technique captured the near-surface currents at tidal inlets. The UAS wave-based current mapping technique (Streßer et al.), which does not require particle seeding, is further validated using in situ measurements of current velocity from an acoustic Doppler current profiler (ADCP). The UAS wave-based surface current measurements agreed with the ADCP measurements within a root-mean-square error (RMSE) of 0.06 m s −1 and 16° in magnitude and direction, respectively, and a normalized RMSE (NRMSE) of 0.26 in magnitude. The surface currents near the jetty at Galveston Bay inlet were visualized with the resulting UAS wave-based current maps.

  PublisherDOI

• High-Resolution Surface Current Mapping from UAS Video: A Continuous Flight Mission Approach

  2025-12-26

  articleOpen access

  Coastal surface currents influence processes like pollutant transport, sediment resuspension, and navigation safety, presenting the need for high-resolution monitoring tools for informed decision-making. We present a remote sensing approach based on unmanned aerial systems (UAS) that estimates surface currents using Doppler analysis of video collected from a continuous UAS flight transect over Freeport Harbor, Texas. This work extends the open-source MATLAB software CopterCurrents, originally developed for UAS videos collected by hovering at fixed station points (Streßer et al., 2017), to linear flights in which the continuous videos are spatially segmented into equivalent hovering videos. The segmentation is achieved by tracking and analyzing the motion of subwindows (240×240 pixels each) over the fixed ocean surface, each representing approximately 10×10 m 2 . Instead of relying on a fixed hover, each subwindow is tracked as it moves through the camera’s field of view during flight. For subwindows with sufficient temporal overlap (approximately 30 s duration, which is greater than 248 frames in this study), the mean 2D surface velocity field is extracted using three-dimensional fast Fourier transform and Doppler fitting to the linear, deepwater dispersion relation for surface waves (Streßer et al., 2017). Subwindow positions are geo-referenced in UTM coordinates based on UAS flight logs. To remove spurious current vectors, a signal-to-noise ratio threshold was applied to filter out noisy data, along with an upper velocity limit. We apply this method to a 10-minute UAS mission over Freeport Harbor, covering approximately 900 m and producing over 1000 geo-located subwindow measurements. The final output is a spatially continuous current map overlaid on satellite imagery, visualized through a vector field and a current-speed heatmap. This approach enables high-resolution current estimation without the need for hovering, thereby supporting efficient deployments for estuarine dynamics, rapid-response coastal monitoring, and operational nearshore oceanography. References Streßer, M., Carrasco, R., & Horstmann, J. (2017). Video-Based Estimation of Surface Currents Using a Low-Cost Quadcopter. IEEE Geoscience and Remote Sensing Letters, 14(11), 2027–2031. https://doi.org/10.1109/LGRS.2017.2749120

  PublisherOA PDFDOI

• OBSERVATION OF TIDAL EXCHANGE DYNAMICS FROM SURFACE CURRENT MEASUREMENTS USING UNMANNED AIRCRAFT SYSTEMS

  Coastal Engineering Proceedings · 2025-05-29

  articleOpen accessSenior author

  Coastal transport processes are fundamental to oceanic biogeochemical cycles, sediment dynamics, and pollution management. Surface-associated material transport in coastal settings, including the fate of floating marine contaminants such as oil spills and harmful algal blooms, is heavily controlled by ocean surface currents, together with wave action. During tidal exchange, ocean currents are one of the key parameters that affect whether these surface pollutants may be transported in or out of the bay and estuary through tidal inlets. Thus, practical measurements of ocean surface currents are important to predict many coastal transport processes effectively. However, observation of these transport processes requires large-scale current mapping in the spatial domain to fully resolve the governing mechanisms. In the present study, we utilize an unmanned aircraft systems (UAS) wave-based current mapping technique introduced by Streßer et al., 2017 to visualize the flow structures of the tidal exchange through the Galveston Bay inlets, TX, in high spatial resolution, using a consumer-grade UAS for imaging the ocean surface.

  PublisherDOI

• Characterization of Tidal Inlet Exchange Flows Using Satellite Imagery

  Journal of Geophysical Research Oceans · 2025-05-01

  articleOpen access

  Abstract We present a satellite‐based classification scheme for the main characteristics of large‐scale starting jets interacting with coastal currents at Galveston Bay inlet, Texas, using satellite imagery from Sentinel‐2 and the Moderate Resolution Imaging Spectroradiometer (MODIS). The Sentinel‐2 satellite image analysis identifies two types of tidal starting‐jet vortex structures: a shallow single or dipole vortex. The type of starting‐jet vortex that forms depends on the propagation path of the tidal jet, given by the angle between the jet and inlet axis, and the tidal dynamics, summarized by an inlet Strouhal number. We show a correlation between these metrics that predicts when the dynamics of vortex formation is dominantly governed by the offshore currents, causing a single‐vortex, or the ebbing fluid flow, forming a vortex dipole. By comparing the deflection angle of the tidal jets with local wind observations, we deduce that along‐shore currents are the dominant mechanism responsible for the jet deflection and vortex types. Validation of the classification scheme using MODIS satellite images reported up to 94% agreement between the classification scheme and the observed flow type. Comparison of the Sentinel‐2 satellite images with empirical equations from laboratory experiments showed good agreement for vortex spin‐up time and the diameter of the vortex core.

  PublisherOA PDFDOI

• Using unmanned aerial systems for observations of water wave characteristics

  Experiments in Fluids · 2024-12-04 · 1 citations

  articleOpen accessSenior author

  Dominant wave components within a wavefield play key hydrodynamic and morphodynamic processes. Herein, we present a method to detect and measure the parameters of these waves, such as their wavelength, propagation angle and period. Image sequences of the free surface are captured with the use of a commercial unmanned aerial system. A snapshot proper orthogonal decomposition analysis is then applied to the image sequence, and a 2D autocorrelation is performed on the resulting modes. By extracting the mode that is representative of the dominant wave signal, it is then possible to infer the wave properties of the dominant wave. The outlined procedure is applied to ocean swells, wind waves, free surface undulations along a river and propagating ship wakes. Our results demonstrate an improvement in the signal-to-noise ratio of the peak wave signal to ambient noise over the more widely used fast Fourier transform approach.

  PublisherOA PDFDOI

• Experimental study on flow kinematics of breaking wave impinging and overtopping on a deck structure

  Ocean Engineering · 2024-03-28 · 3 citations

  articleSenior author
  PublisherDOI

• Turbulence over young wind waves dominated by capillaries and micro-breakers

  Journal of Fluid Mechanics · 2024-04-19 · 4 citations

  articleOpen accessSenior authorCorresponding

  We conducted experiments in a laboratory to study turbulent flow over wind generated water waves. The experiments were performed in a wind-wave-current flume with three free stream wind speeds of U ref = 6.0, 8.0 and 10.0 m s −1 , corresponding to 10 m equivalent wind speed of U 10 = 10.2, 12.2 and 14.1 m s −1 and the root-mean-square wave height of 0.7, 1.1 and 1.7 cm, respectively, at a fetch of 6.2 m. The instantaneous velocity fields above the waves were obtained by using a particle image velocimetry (PIV) technique. The velocity fields were decomposed into the mean, wave-induced and turbulent velocity components. The tested wind waves were primarily dissipated by capillaries and microscale breaking waves. The Bond number and the shear velocity-fetch based Reynolds number were found to correlate with the wind wave regimes well. The turbulent dissipation rates above the water surface were determined based on resolved spatial gradient of instantaneous velocities, where the time-averaged dissipation rate values were calibrated using those estimated from the one-dimensional velocity spectrum in the temporal space. Subsequently, the turbulent kinetic energy (TKE) budget including its production, dissipation, advection and turbulent transport was presented. In addition, conditional averaging analysis of the TKE budgets over leeward, windward sides and all phases was performed. The results showed a strong dependency with the wave phase in the TKE budget terms except for the dissipation. The production-dissipation ratio increased significantly as the wind speed increased, likely attributed to the increased roughness over the substantial coverage of micro-breaking waves.

  PublisherOA PDFDOI

• Using unmanned aerial systems for observations of water wave characteristics 

  Research Square · 2024-05-29

  preprintOpen accessSenior author
  PublisherOA PDFDOI

• TGLO Establish Historical Long-term Wetland Boundary Evolution Through Satellite Imagery

  Zenodo (CERN European Organization for Nuclear Research) · 2023-10-23

  datasetOpen access

  This dataset includes wetland erosion rates for West Galveston, Matagorda, and San Antonio Bay. Long-term trends in wetland boundary changes are estimated using Landsat satellite imagery. Sections of the wetland experiencing the highest rates of erosion will be further investigated through CubeSat satellite observations. The format of the data is summarized as follows (1) Landsat based wetland evolution results from 1984 to 2020 The Annual and seasonal water occurrence (in gif format) The wetland change map (in TIF format) (2) CubeSat based wetland evolution results from 2009 to 2020 Water occurrence maps from 2009 to 2021 for the RapidEye based bi-annual results (FSX-occurrence-yyy1-yyy2.tif) and the PlanetScope based annual results (FSX-occurrence-yyy1.tif), where yyyy represents the given year. The legend image is 'occurrence-cbar.jpg' Erosion maps based on the difference between water occurrence mapping in 2009 and 2021: ('FSX-occurrence-diff-2021-2009.tif'). The legend is 'occurrence-diff-cbar.jpg' The 0.2-meter bed counter line images based on the water occurrence maps and the tide elevation threshold from 2017 to 2021: (FSX-bed-yyy1.tif). Again, yyy1 represents the given year. The legend is 'color-bed.jpg The difference between the beds in the 0.2-meter bed counter line images in 2017 and 2021 at FS-1, FS-2, FS-3, and FS-4: (FSX-bed-diff-2021-2017.tif). The legend is 'color-bed-dif.jpg' (3) Analysis of wetland boundary evolution and erosion rate Landsat based Wetland erosion rate from 1984 to 2020 and CubeSat erosion rate from 2009 to 2021 (data format in ArcGIS shapefile)* Landsat based coastlines in 1984, 2000, 2010, and 2020** * *The dataset also shown on the Coast Atlas website (this link)

  PublisherDOI

Frequent coauthors

• Yonguk Ryu

  Chonnam National University

  36 shared

• James M. Kaihatu

  Texas A&M University

  35 shared

• Jin Young Kim

  Korea Basic Science Institute

  32 shared

• Richard Mercier

  Texas A&M University

  31 shared

• Philip L.‐F. Liu

  National University of Singapore

  31 shared

• Scott A. Socolofsky

  Texas A&M University

  31 shared

• Pengzhi Lin

  20 shared

• Chang Lin

  National Chung Hsing University

  20 shared