Misganu Debella-Gilo

List of publications from my research. (Cristin)

Selected publications:

Debella-Gilo, Misganu. "Detecting built-up areas in agricultural fields using deep learning on Sentinel-2 Satellite Image Time Series." Kart og Plan 115.2 (2022): 136-153.Doi: https://doi.org/10.18261/kp.115.2.4

Debella-Gilo, M., & Jonathan, R. (2021). Computing remote sensing big data using local hardware and open-source software packages. Kart og Plan, 13, 289. Doi: https://doi.org/10.18261/issn.2535-6003-2021-03-04-09 

Debella-Gilo, M., & Gjertsen, A.K. (2021). Mapping Seasonal Agricultural Land Use Types Using Deep Learning on Sentinel-2 Image Time Series. Remote Sensing, 114, 3-4, 254-273. Doi: https://doi.org/10.3390/rs13020289

Breidenbach, J., Waser, L.T., Debella-Gilo, M., Schumacher, J., Rahlf, J., Hauglin, M., Puliti, S., & Astrup, R. (2021). National mapping and estimation of forest area by dominant tree species using Sentinel-2 data. Canadian Journal of Forest Research. 51 (3),365-379. Doi: https://doi.org/10.1139/cjfr-2020-0170   

Debella-Gilo, M., Borchsenius, B.T., Bjørkelo, K., & Breidenbach, J. (2020). Automated Updating of Forest Cover Maps from Cloud-Free Sentinel-2 Mosaic Images Using Object-Based Image Analysis and Machine Learning Methods. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 803-807. Doi: https://doi.org/10.5194/isprs-annals-V-3-2020-803-2020

Vibeke Nørstebø, Svein Olav Krøgli, Misganu Debella-Gilo, Geraldo A. Perez-Valdes, Kristin T. Uggen & Wenche E. Dramstad (2020). "Identifying Suitable Bioeconomic Cluster Sites—Combining GIS-MCDA and Operational Research Methods." Environmental Modeling & Assessment 25 (2020): 689-703. Doi: https://doi.org/10.1007/s10666-020-09694-x

Astrup, R., Rahlf, J., Bjørkelo, K., Debella-Gilo, M., Gjertsen, A. K., & Breidenbach, J. (2019). Forest information at multiple scales: development, evaluation and application of the Norwegian forest resources map SR16. Scandinavian Journal of Forest Research, 34(6), 484-496.Doi: https://doi.org/10.1080/02827581.2019.1588989

Debella-Gilo, M. (2016) Bare-earth extraction and DTM generation from photogrammetric point clouds including the use of an existing lower-resolution DTM. International Journal of Remote Sensing  ; Volume  37. (13) s. 3104-3124. Doi: https://doi.org/10.1080/01431161.2016.1194543

Debella-Gilo, M., Kääb, A. (2013). Monitoring Slow-Moving Landslides Using Spatially Adaptive Least Squares Image Matching. In: Margottini, C., Canuti, P., Sassa, K. (eds) Landslide Science and Practice. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31445-2_39

Debella-Gilo, M., Bjørkelo, K., Breidenbach, and  J. Rahlf, J. (2013) Object-based analysis of aerial photogrammetric point cloud and spectral data for land cover mapping. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-1/W1, ISPRS Hannover Workshop 2013, 21 – 24 May 2013, Hannover, Germany. Doi: https://doi.org/10.5194/isprsarchives-XL-1-W1-63-2013

Debella-Gilo, M., and  A. Kääb. (2012) Measurement of surface displacement and deformation of mass movements using least squares matching of repeat high resolution satellite and aerial images. Remote Sensing, 4 (1): 43-67. Doi: https://doi.org/10.3390/rs4010043

Debella-Gilo, M. and A. Kääb. (2012)  Locally adaptive template sizes for matching repeat images of Earth surface mass movements. ISPRS J Photogramm Remote Sens. 69(1): 10–28. Doi: https://doi.org/10.1016/j.isprsjprs.2012.02.002

Debella-Gilo, M. and A. Kääb(2011). Sub-pixel precision image matching for measuring surface displacements on mass movements using normalized cross-correlation. Remote Sensing of Environment 115(1): 130-142. Doi: https://doi.org/10.1016/j.rse.2010.08.012