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Dr. Jayan Wijesingha

Postdoctoral Researcher

Site
University of Kassel
Faculty of Organic Agricultural Sciences
Section of Grassland Science and Renewable Plant Resources
Steinstr. 19
37213 Witzenhausen
Room
Lecture hall building Steinstrasse

Brief introduction  (Dr. Jayan Wijesingha)

Jayan Wijesingha is a postdoctoral researcher at the section of Grassland Science and Renewable Plant Resources at the Universität Kassel. His research interests are the Unmanned Aerial Vehicle (UAV) borne remote sensing for grassland and agriculture monitoring. He studied Surveying Sciences in Sri Lanka and Geomatics at the Lund University, Sweden specialised in remote sensing. His core area is to develop models to quantify biophysical and biochemical characteristics of grassland and agricultural crops utilising UAV-borne hyperspectral image data. The second focus of his research is using the 3D point cloud data from remote sensing for vegetation monitoring.


Research interests  (Dr. Jayan Wijesingha)

  • Application of satellite-borne and UAV-borne remote sensing data (RGB, multi-/hyperspectral, LiDAR) models for plant biophysical and biochemical parameter estimation
  • Analysis of Satellite remote sensing data for agricultural land-cover/land-use mapping
  • Evaluate spatial-temporal land-use/land-cover pattern and changes due to agricultural intensification, urbanisation, bioeconomic activities etc.
  • Vegetation cover mapping using high-resolution image semantic segmentation with deep learning


Current research projects  (Dr. Jayan Wijesingha)

DFG FOR2432/2 "Social-Ecological Systems in the Indian Rural-Urban Interface: Functions, Scales, and Dynamics of Transition"

RE-DIRECT II - EU Interreg NWE "REgional Development and Integration of unused biomass wastes as REsources for Circular products and economic Transformation"


Completed research projects  (Dr. Jayan Wijesingha)

RE-DIRECT I - EU Interreg NWE "REgional Development and Integration of unused biomass wastes as REsources for Circular products and economic Transformation"

DBU Lupine - Conservation and Restoration of Species Diversity in the Mountain Meadows of the Rhön Biosphere Reserve - Management of the Invasive Perennial Lupine (Lupinus polyphyllus Lindl.) in a Complex System of Protected Areas


Publications  (Dr. Jayan Wijesingha)

2022

Wengert, M., Wijesingha, J., Schulze-Brüninghoff, D., Wachendorf, M., Astor, T., 2022. Multisite and Multitemporal Grassland Yield Estimation Using UAV-Borne Hyperspectral Data. Remote Sensing 14, 2068. https://doi.org/10.3390/rs14092068

2021

Wengert, M., Piepho, H.-P., Astor, T., Graß, R., Wijesingha, J., Wachendorf, M., 2021. Assessing spatial variability of barley whole crop biomass yield and leaf area index in silvoarable agroforestry systems using UAV-borne remote sensing. Remote Sensing 13, 2751. https://doi.org/10.3390/rs13142751
Wijesingha, J., Dayananda, S., Wachendorf, M., Astor, T., 2021. Comparison of spaceborne and UAV‐borne remote sensing spectral data for estimating monsoon crop vegetation parameters. Sensors 21, TBD. https://doi.org/10.3390/s21082886

2020

Karunaratne, S., Thomson, A., Morse-McNabb, E., Wijesingha, J., Stayches, D., Copland, A., Jacobs, J., 2020. The fusion of spectral and structural datasets derived from an airborne multispectral sensor for estimation of pasture dry matter yield at paddock scale with time. Remote Sensing 12, TBD. https://doi.org/10.3390/rs12122017
Wijesingha, J., Astor, T., Schulze-Brüninghoff, D., Wachendorf, M., 2020. Mapping Invasive Lupinus polyphyllus Lindl. in Semi-natural Grasslands Using Object-Based Image Analysis of UAV-borne Images. Journal of Photogrammetry, Remote Sensing and Geoinformation Science 20, 391–406. https://doi.org/10.1007/s41064-020-00121-0
Wijesingha, J., Astor, T., Schulze-Brüninghoff, D., Wengert, M., Wachendorf, M., 2020. Predicting Forage Quality of Grasslands Using UAV-Borne Imaging Spectroscopy. Remote Sensing 12, 126. https://doi.org/10.3390/rs12010126

2019

Wijesingha, J., Astor, T., Hensgen, F., Wachendorf, M., 2019. Evaluation of 3D point cloud-based models for the prediction of grassland biomass. International Journal of Applied Earth Observation and Geoinformation 78, 352–359. https://doi.org/10.1016/j.jag.2018.10.006
Wijesingha, J., Astor, T., Hensgen, F., Wachendorf, M., 2019. Hyperspectral ortho-mosaic from UAV-borne images for discriminating different grassland management regimes, in: EARSeL (Ed.), 11th EARSel Sig Imaging Spectroscopy Workshop. EARSeL, Brno, Czech Republic, p. NN.
Dayananda, S., Astor, T., Wijesingha, J., Chickadibburahalli Thimappa, S., Dimba Chowdappa, H., Nidamanuri, R.R., Nautiyal, S., Wachendorf, M., 2019. Multi-Temporal Monsoon Crop Biomass Estimation Using Hyperspectral Imaging. Remote Sensing 11, 1771. https://doi.org/10.3390/rs11151771

2018

Wijesingha, J., Astor, T., Hensgen, F., Wachendorf, M., 2018. Grassland biomass modelling from remote sensing 3D point clouds, in: Stützel, H., Fricke, A., Francke-Weltmann, L. (Eds.), 61. Jahrestagung Der Gesellschaft Für Pflanzenbauwissenschaften - From Big Data to Smart Farming. Verlag Liddy Halm, Göttingen, pp. 37–38.

Vita  (Dr. Jayan Wijesingha)

2007-2011

Studies in "Surveying Sciences" with specialisation in Photogrammetry and Remote Sensing at Sabaragamuwa University of Sri Lanka, B.Sc., Topic of the Bachelor-Thesis: "Automatic road feature extraction from high-resolution satellite images using LVQ neural networks."

2014-2016

Studies in "Geomatics" at Lund University, Sweden, M.Sc., Topic of the Master-Thesis: "Geometric quality assessment of multi-rotor unmanned aerial vehicle-borne remote sensing products for precision agriculture."

2017-2020

Doktor der Agrarwissenschaften (Dr. agr.), Thesis: "Fine-scale grassland monitoring using unmanned aerial vehicle-borne remote sensing"

 

 

2012-2013

Academic Demonstrator at Sabaragamuwa University of Sri Lanka.

2013-2014

Research Associate at Geoinformatics Center, Asian Institute of Technology, Bangkok, Thailand.

2016-2017

Freelance GIS Consultant in Land Copernicus project at European Environment Agency, Copenhagen, Denmark.

Anwendung von Modellen aus satelliten- und UAV-gestützten Fernerkundungsdaten (RGB, multi-/hyperspektral, LiDAR) zur Schätzung biophysikalischer und biochemischer Parameter von Pflanzen
Analyse von Satelliten-Fernerkundungsdaten für die Kartierung der landwirtschaftlichen Bodenbedeckung/Landnutzung
Auswertung von räumlich-zeitlichen Landnutzungs-/Landbedeckungsmustern und Veränderungen u.a. aufgrund von landwirtschaftlicher Intensivierung, Urbanisierung, bioökonomischen Aktivitäten etc.
Kartierung der Vegetationsbedeckung durch semantische Segmentierung hochauflösender Bilder mittels Deep Learning