| Google Earth Engine for geo-big data applications: A meta-analysis and systematic review H Tamiminia, B Salehi, M Mahdianpari, L Quackenbush, S Adeli, B Brisco ISPRS journal of photogrammetry and remote sensing 164, 152-170, 2020 | 781 | 2020 |
| Wetland monitoring using SAR data: A meta-analysis and comprehensive review S Adeli, B Salehi, M Mahdianpari, LJ Quackenbush, B Brisco, ... Remote Sensing 12 (14), 2190, 2020 | 88 | 2020 |
| A particle swarm optimized kernel-based clustering method for crop mapping from multi-temporal polarimetric L-band SAR observations H Tamiminia, S Homayouni, H McNairn, A Safari International journal of applied earth observation and geoinformation 58 …, 2017 | 42 | 2017 |
| comparison of machine and deep learning methods to estimate shrub willow biomass from UAS imagery H Tamiminia, B Salehi, M Mahdianpari, CM Beier, DJ Klimkowski, TA Volk Canadian journal of remote sensing 47 (2), 209-227, 2021 | 13 | 2021 |
| A comparison of decision tree-based models for forest above-ground biomass estimation using a combination of airborne lidar and landsat data H Tamiminia, B Salehi, M Mahdianpari, CM Beier, L Johnson, DB Phoenix ISPRS annals of the photogrammetry, remote sensing and spatial information …, 2021 | 8 | 2021 |
| Decision tree-based machine learning models for above-ground biomass estimation using multi-source remote sensing data and object-based image analysis H Tamiminia, B Salehi, M Mahdianpari, CM Beier, L Johnson, DB Phoenix, ... Geocarto international 37 (26), 12763-12791, 2022 | 5 | 2022 |
| Mapping two decades of New York State Forest aboveground biomass change using remote sensing H Tamiminia, B Salehi, M Mahdianpari, CM Beier, L Johnson Remote Sensing 14 (16), 4097, 2022 | 4 | 2022 |
| Random forest outperformed convolutional neural networks for shrub willow above ground biomass estimation using multi-spectral UAS imagery H Tamiminia, B Salehi, M Mahdianpari, CM Beier, DJ Klimkowski, TA Volk 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 8269 …, 2021 | 4 | 2021 |
| State-wide forest canopy height and aboveground biomass map for New York with 10 m resolution, integrating GEDI, Sentinel-1, and Sentinel-2 data H Tamiminia, B Salehi, M Mahdianpari, T Goulden Ecological informatics 79, 102404, 2024 | 3 | 2024 |
| Evaluating pixel-based and object-based approaches for forest above-ground biomass estimation using a combination of optical, Sar, and an extreme gradient boosting model H Tamiminia, B Salehi, M Mahdianpari, CM Beier, L Johnson ISPRS annals of the photogrammetry, remote sensing and spatial information …, 2022 | 3 | 2022 |
| A comparison of random forest and light gradient boosting machine for forest above-ground biomass estimation using a combination of landsat, alos palsar, and airborne lidar data H Tamiminia, B Salehi, M Mahdianpari, CM Beier, L Johnson, DB Phoenix The International Archives of the Photogrammetry, Remote Sensing and Spatial …, 2021 | 3 | 2021 |
| A Comparative Analysis of Pixel-Based and Object-Based Approaches for Forest Above-Ground Biomass Estimation Using Random Forest Model H Tamiminia, B Salehi, M Mahdianpari, CM Beier, L Johnson The International Archives of the Photogrammetry, Remote Sensing and Spatial …, 2022 | 1 | 2022 |
| Clustering of Multi-Temporal Fully Polarimetric L-Band SAR Data for Agricultural Land Cover Mapping H Tamiminia, S Homayouni, A Safari The International Archives of the Photogrammetry, Remote Sensing and Spatial …, 2015 | 1 | 2015 |
| Generating A 10 M Resolution Canopy Height Model Of New York State Using Gedi And Sentinel-2 Data H Tamiminia, B Salehi, M Mahdianpari IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium …, 2023 | | 2023 |
| Forest Aboveground Biomass Estimation and Change Monitoring Using Multi-Source Remote Sensing Data and Machine Learning Techniques H Tamiminia College of Environmental Science, 2023 | | 2023 |
Masoud MahdianpariCross-Appointed Professor, MUN, C-CORE,Verified email at c-core.ca
