Remote Sensing Applications in Monitoring Poplars: A Review
Abstract
:1. Introduction
2. Materials and Methods
2.1. Database Search Process
2.2. Inclusion/Exclusion Criteria
2.3. Data Analysis Methods
3. Results and Discussion
3.1. Historical Research Trends
3.2. Geographical Distribution of Poplar Remote Sensing Studies
3.3. Characteristics of Methods and Materials
3.4. Co-Occurrence Words/Keywords
4. Limitations and Future Research
5. Conclusions and Recommendations
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Khayatinejad Aqgonbad, S.; Hossein Zadeh, O.; Hajjarian, M.; Abdi, M.R. Impact of Key Stakeholders’ Participation in Strategic Decision-Making Regarding Poplar Tree Farming Development. Small-Scale For. 2022, 21, 229–247. [Google Scholar] [CrossRef]
- Zhang, Y.; Thenkabail, P.S.; Wang, P. A Bibliometric Profile of the Remote Sensing Open Access Journal Published by MDPI between 2009 and 2018. Remote Sens. 2019, 11, 91. [Google Scholar] [CrossRef]
- Kota, N.L.; Landenberger, R.E.; McGraw, J.B. Germination and early growth of Ailanthus and tulip poplar in three levels of forest disturbance. Biol. Invasions 2007, 9, 197–211. [Google Scholar] [CrossRef]
- Sikorska, D.; Sikorski, P.; Archiciński, P.; Chormański, J.; Hopkins, R.J. You Can’t See the Woods for the Trees: Invasive Acer negundo L. in Urban Riparian Forests Harms Biodiversity and Limits Recreation Activity. Sustainability 2019, 11, 5838. [Google Scholar] [CrossRef]
- Richardson, D.M.; Rejmánek, M. Trees and shrubs as invasive alien species—A global review. Divers. Distrib. 2011, 17, 788–809. [Google Scholar] [CrossRef]
- González-Moreno, P.; Pino, J.; Carreras, D.; Basnou, C.; Fernández-Rebollar, I.; Vilà, M. Quantifying the landscape influence on plant invasions in Mediterranean coastal habitats. Landsc. Ecol. 2013, 28, 891–903. [Google Scholar] [CrossRef]
- Caudullo, G.; De Rigo, D. Ulmus-Elms in Europe: Distribution, Habitat, Usage and Threats. European Atlas of Forest Tree Species. 2016. Available online: https://www.researchgate.net/profile/Giovanni-Caudullo/publication/299472225_Ulmus_-_elms_in_Europe_distribution_habitat_usage_and_threats/links/61ee6a58c5e3103375b83367/Ulmus-elms-in-Europe-distribution-habitat-usage-and-threats.pdf (accessed on 27 November 2022).
- Danise, T.; Innangi, M.; Curcio, E.; Piccolella, S.; Fioretto, A.; Pacifico, S. White poplar (Populus alba L.) leaf waste recovery and intercropping outcome on its polyphenols. Ind. Crops Prod. 2021, 171, 113866. [Google Scholar] [CrossRef]
- Henderson, L. Invasive, naturalized and casual alien plants in southern Africa: A summary based on the Southern African Plant Invaders Atlas (SAPIA). Bothalia 2007, 37, 215–248. [Google Scholar] [CrossRef]
- Stettler, R.F. Cottonwood and the River of Time: On Trees, Evolution, and Society; University of Washington Press: Washington, DC, USA, 2011. [Google Scholar]
- Lechner, A.M.; Foody, G.M.; Boyd, D.S. Applications in remote sensing to forest ecology and management. One Earth 2020, 2, 405–412. [Google Scholar] [CrossRef]
- Szuster, B.W.; Chen, Q.; Borger, M. A comparison of classification techniques to support land cover and land use analysis in tropical coastal zones. Appl. Geogr. 2011, 31, 525–532. [Google Scholar] [CrossRef]
- Turner, W.; Spector, S.; Gardiner, N.; Fladeland, M.; Sterling, E.; Steininger, M. Remote sensing for biodiversity science and conservation. Trends Ecol. Evol. 2003, 18, 306–314. [Google Scholar] [CrossRef]
- Tonbul, H.; Colkesen, I.; Kavzoglu, T. Classification of poplar trees with object-based ensemble learning algorithms using Sentinel-2A imagery. J. Geod. Sci. 2020, 10, 14–22. [Google Scholar] [CrossRef]
- Eslami, A.; Zahedi, S.S. Providing poplar plantation map by Indian remote sensing (IRS) satellite imagery in Northern Iran. Afr. J. Agric. Res. 2011, 6, 4769–4774. [Google Scholar] [CrossRef]
- Hamrouni, Y.; Paillassa, E.; Chéret, V.; Monteil, C.; Sheeren, D. From local to global: A transfer learning-based approach for mapping poplar plantations at national scale using Sentinel-2. ISPRS J. Photogramm. Remote Sens. 2021, 171, 76–100. [Google Scholar] [CrossRef]
- D’Amico, G.; Francini, S.; Giannetti, F.; Vangi, E.; Travaglini, D.; Chianucci, F.; Mattioli, W.; Grotti, M.; Puletti, N.; Corona, P.; et al. A deep learning approach for automatic mapping of poplar plantations using Sentinel-2 imagery. GISci. Remote Sens. 2021, 58, 1352–1368. [Google Scholar] [CrossRef]
- Laslier, M.; Hubert-Moy, L.; Dufour, S. Mapping Riparian Vegetation Functions Using 3D Bispectral LiDAR Data. Water 2019, 11, 483. [Google Scholar] [CrossRef]
- Moher, D.; Shamseer, L.; Clarke, M.; Ghersi, D.; Liberati, A.; Petticrew, M.; Shekelle, P.; Stewart, L.A.; PRISMA—P Group. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst. Rev. 2015, 4, 1. [Google Scholar] [CrossRef]
- Javanmardi, E.; Liu, S.; Xie, N. Exploring Grey Systems Theory-Based Methods and Applications in Sustainability Studies: A Systematic Review Approach. Sustainability 2020, 12, 4437. [Google Scholar] [CrossRef]
- Van Eck, N.J.; Waltman, L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 2010, 84, 523–538. [Google Scholar] [CrossRef] [PubMed]
- R Core Team. R: A Language and Environment for Statistical Computing. 2010. Available online: https://cir.nii.ac.jp/crid/1370294721063650048 (accessed on 19 April 2022).
- Man, Q.; Dong, P.; Yang, X.; Wu, Q.; Han, R. Automatic Extraction of Grasses and Individual Trees in Urban Areas Based on Airborne Hyperspectral and LiDAR Data. Remote Sens. 2020, 12, 2725. [Google Scholar] [CrossRef]
- Chen, L.; Ren, C.; Zhang, B.; Wang, Z.; Liu, M.; Man, W.; Liu, J. Improved estimation of forest stand volume by the integration of GEDI LiDAR data and multi-sensor imagery in the Changbai Mountains Mixed forests Ecoregion (CMMFE), northeast China. Int. J. Appl. Earth Obs. Geoinf. 2021, 100, 102326. [Google Scholar] [CrossRef]
- Xu, D.; Wang, H.; Xu, W.; Luan, Z.; Xu, X. LiDAR Applications to Estimate Forest Biomass at Individual Tree Scale: Opportunities, Challenges and Future Perspectives. Forests 2021, 12, 550. [Google Scholar] [CrossRef]
- Wang, K.; Wang, T.; Liu, X. A Review: Individual Tree Species Classification Using Integrated Airborne LiDAR and Optical Imagery with a Focus on the Urban Environment. Forests 2018, 10, 1. [Google Scholar] [CrossRef]
- Marrs, J.; Ni-Meister, W. Machine Learning Techniques for Tree Species Classification Using Co-Registered LiDAR and Hyperspectral Data. Remote Sens. 2019, 11, 819. [Google Scholar] [CrossRef]
- Khorrami, R.A.; Naeimi, Z.; Wing, M.; Ahani, H.; Bateni, S.M. A new multistep approach to identify leaf-off poplar plantations using airborne imagery. J. Geogr. Inf. Syst. 2022, 14, 634–651. [Google Scholar] [CrossRef]
- Lu, S.; Lu, X.; Zhao, W.; Liu, Y.; Wang, Z.; Omasa, K. Comparing vegetation indices for remote chlorophyll measurement of white poplar and Chinese elm leaves with different adaxial and abaxial surfaces. J. Exp. Bot. 2015, 66, 5625–5637. [Google Scholar] [CrossRef] [PubMed]
- Pirotti, F. Assessing a Template Matching Approach for Tree Height and Position Extraction from Lidar-Derived Canopy Height Models of Pinus Pinaster Stands. Forests 2010, 1, 194–208. [Google Scholar] [CrossRef]
- Duncanson, L.I.; Dubayah, R.O.; Cook, B.D.; Rosette, J.; Parker, G. The importance of spatial detail: Assessing the utility of individual crown information and scaling approaches for lidar-based biomass density estimation. Remote Sens. Environ. 2015, 168, 102–112. [Google Scholar] [CrossRef]
- Cao, L.; Liu, H.; Fu, X.; Zhang, Z.; Shen, X.; Ruan, H. Comparison of UAV LiDAR and Digital Aerial Photogrammetry Point Clouds for Estimating Forest Structural Attributes in Subtropical Planted Forests. Forests 2019, 10, 145. [Google Scholar] [CrossRef]
- Gao, L.; Zhang, X. Above-Ground Biomass Estimation of Plantation with Complex Forest Stand Structure Using Multiple Features from Airborne Laser Scanning Point Cloud Data. Forests 2021, 12, 1713. [Google Scholar] [CrossRef]
- Lin, W.; Lu, Y.; Li, G.; Jiang, X.; Lu, D. A comparative analysis of modeling approaches and canopy height-based data sources for mapping forest growing stock volume in a northern subtropical ecosystem of China. GISci. Remote Sens. 2022, 59, 568–589. [Google Scholar] [CrossRef]
- Leite, R.V.; Silva, C.A.; Mohan, M.; Cardil, A.; Almeida, D.R.A.; Carvalho, S.P.C.; Jaafar, W.S.W.M.; Guerra-Hernández, J.; Weiskittel, A.; Klauberg, C.; et al. Individual Tree Attribute Estimation and Uniformity Assessment in Fast-Growing Eucalyptus spp. Forest Plantations Using Lidar and Linear Mixed-Effects Models. Remote Sens. 2020, 12, 3599. [Google Scholar] [CrossRef]
- Capolupo, A.; Nasta, P.; Palladino, M.; Cervelli, E.; Boccia, L.; Romano, N. Assessing the ability of hybrid poplar for in-situ phytoextraction of cadmium by using UAV-photogrammetry and 3D flow simulator. Int. J. Remote Sens. 2018, 39, 5175–5194. [Google Scholar] [CrossRef]
- Huang, C.Y.; Asner, G.P. Applications of remote sensing to alien invasive plant studies. Sensors 2009, 9, 4869–4889. [Google Scholar] [CrossRef] [PubMed]
- Colombo, R.; Meroni, M.; Marchesi, A.; Busetto, L.; Rossini, M.; Giardino, C.; Panigada, C. Estimation of leaf and canopy water content in poplar plantations by means of hyperspectral indices and inverse modeling. Remote Sens. Environ. 2008, 112, 1820–1834. [Google Scholar] [CrossRef]
- Barnes, M.L.; Breshears, D.D.; Law, D.J.; van Leeuwen, W.J.D.; Monson, R.K.; Fojtik, A.C.; Barron-Gafford, G.A.; Moore, D.J.P. Beyond greenness: Detecting temporal changes in photosynthetic capacity with hyperspectral reflectance data. PLoS ONE 2017, 12, e0189539. [Google Scholar] [CrossRef]
- Huang, T.; Ding, X.; Zhu, X.; Chen, S.; Chen, M.; Jia, X.; Lai, F.; Zhang, X. Assessment of Poplar Looper (Apocheima cinerarius Erschoff) Infestation on Euphrates (Populus euphratica) Using Time-Series MODIS NDVI Data Based on the Wavelet Transform and Discriminant Analysis. Remote Sens. 2021, 13, 2345. [Google Scholar] [CrossRef]
- Colkesen, I.; Kavzoglu, T.; Tonbul, H.; Ozturk, M.Y.; Yilmaz, E.O. Poplar Tree Index (PTI): A New Vegetation Index for Monitoring Poplar Cultivated Areas. In Proceedings of the IGARSS 2022—2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 17–22 July 2022; pp. 5567–5570. [Google Scholar]
- Maleki, M.; Arriga, N.; Barrios, J.M.; Wieneke, S.; Liu, Q.; Peñuelas, J.; Janssens, I.A.; Balzarolo, M. Estimation of Gross Primary Productivity (GPP) Phenology of a Short-Rotation Plantation Using Remotely Sensed Indices Derived from Sentinel-2 Images. Remote Sens. 2020, 12, 2104. [Google Scholar] [CrossRef]
- Chianucci, F.; Puletti, N.; Grotti, M.; Ferrara, C.; Giorcelli, A.; Coaloa, D.; Tattoni, C. Nondestructive Tree Stem and Crown Volume Allometry in Hybrid Poplar Plantations Derived from Terrestrial Laser Scanning. For. Sci. 2020, 66, 737–746. [Google Scholar] [CrossRef]
- Bonan, G. Importance of leaf area index and forest type when estimating photosynthesis in boreal forests. Remote Sens. Environ. 1993, 43, 303–314. [Google Scholar] [CrossRef]
- Zande, D.V.; Jonckheere, I.; Stuckens, J.; Verstraeten, W.W.; Coppin, P. Sampling design of ground-based lidar measurements of forest canopy structure and its effect on shadowing. Can. J. Remote Sens. 2008, 34, 526–538. [Google Scholar] [CrossRef]
- Santi, E.; Paloscia, S.; Pampaloni, P.; Pettinato, S. Ground-Based Microwave Investigations of Forest Plots in Italy. IEEE Trans. Geosci. Remote Sens. 2009, 47, 3016–3025. [Google Scholar] [CrossRef]
- Ko, C.; Sohn, G.; Remmel, T.K.; Miller, J.R. Maximizing the Diversity of Ensemble Random Forests for Tree Genera Classification Using High Density LiDAR Data. Remote Sens. 2016, 8, 646. [Google Scholar] [CrossRef]
- Moffiet, T.; Mengersen, K.; Witte, C.; King, R.; Denham, R. Airborne laser scanning: Exploratory data analysis indicates potential variables for classification of individual trees or forest stands according to species. ISPRS J. Photogramm. Remote Sens. 2005, 59, 289–309. [Google Scholar] [CrossRef]
- Warner, T.A.; McGraw, J.B.; Landenberger, R. Segmentation and classification of high resolution imagery for mapping individual species in a closed canopy, deciduous forest. Sci. China Technol. Sci. 2006, 49, 128–139. [Google Scholar] [CrossRef]
- Brandtberg, T. Classifying individual tree species under leaf-off and leaf-on conditions using airborne lidar. ISPRS J. Photogramm. Remote Sens. 2007, 61, 325–340. [Google Scholar] [CrossRef]
- Rupasinghe, P.A.; Chow-Fraser, P. Relating pre-fire canopy species, fire season, and proximity to surface waters to burn severity of boreal wildfires in Alberta, Canada. For. Ecol. Manag. 2021, 496, 119386. [Google Scholar] [CrossRef]
- Li, L.; Hao, D.; Li, X.; Chen, M.; Zhou, Y.; Jurgens, D.; Asrar, G.; Sapkota, A. Satellite-based phenology products and in-situ pollen dynamics: A comparative assessment. Environ. Res. 2022, 204, 111937. [Google Scholar] [CrossRef]
- Hall, R.J.; Davidson, D.P.; Peddle, D.R. Ground and remote estimation of leaf area index in Rocky Mountain forest stands, Kananaskis, Alberta. Can. J. Remote Sens. 2003, 29, 411–427. [Google Scholar] [CrossRef]
- Schweier, J.; Schnitzler, J.P.; Becker, G. Selected environmental impacts of the technical production of wood chips from poplar short rotation coppice on marginal land. Biomass Bioenergy 2016, 85, 235–242. [Google Scholar] [CrossRef]
- Hamrouni, Y.; Paillassa, E.; Chéret, V.; Monteil, C.; Sheeren, D. Sentinel-2 Poplar Index for Operational Mapping of Poplar Plantations over Large Areas. Remote Sens. 2022, 14, 3975. [Google Scholar] [CrossRef]
- Adagbasa, E.G.; Adelabu, S.A.; Okello, T.W. Application of deep learning with stratified K-fold for vegetation species discrimation in a protected mountainous region using Sentinel-2 image. Geocarto Int. 2022, 37, 142–162. [Google Scholar] [CrossRef]
- Adepoju, K.A.; Adelabu, S.A. Improving accuracy of Landsat-8 OLI classification using image composite and multisource data with Google Earth Engine. Remote Sens. Lett. 2020, 11, 107–116. [Google Scholar] [CrossRef]
- Cañete-Salinas, P.; Zamudio, F.; Yáñez, M.; Gajardo, J.; Valdés, H.; Espinosa, C.; Venegas, J.; Retamal, L.; Ortega-Farias, S.; Acevedo-Opazo, C. Evaluation of models to determine LAI on poplar stands using spectral indices from Sentinel-2 satellite images. Ecol. Modell. 2020, 428, 109058. [Google Scholar] [CrossRef]
- Simović, I.; Šikoparija, B.; Panić, M.; Radulović, M.; Lugonja, P. Remote Sensing of Poplar Phenophase and Leaf Miner Attack in Urban Forests. Remote Sens. 2022, 14, 6331. [Google Scholar] [CrossRef]
- Baird, R.A.; Verbyla, D.; Hollingsworth, T.N. Browning of the landscape of interior Alaska based on 1986–2009 Landsat sensor NDVI. Can. J. For. Res. 2012, 42, 1371–1382. [Google Scholar] [CrossRef]
- Mansfield, S.D.; Kang, K.Y.; Iliadis, L.; Tachos, S.; Avramidis, S. Predicting the strength of Populus spp. clones using artificial neural networks and ε-regression support vector machines (ε-rSVM). Holzforschung 2011, 65, 855–863. [Google Scholar] [CrossRef]
- Linhui, L.; Weipeng, J.; Huihui, W. Extracting the Forest Type From Remote Sensing Images by Random Forest. IEEE Sens. J. 2021, 21, 17447–17454. [Google Scholar] [CrossRef]
- Gao, W.; Zhou, L.; Liu, S.; Guan, Y.; Gao, H.; Hu, J. Machine learning algorithms for rapid estimation of holocellulose content of poplar clones based on Raman spectroscopy. Carbohydr. Polym. 2022, 292, 119635. [Google Scholar] [CrossRef]
- Adam, E.; Mutanga, O.; Odindi, J.; Abdel-Rahman, E.M. Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: Evaluating the performance of random forest and support vector machines classifiers. Int. J. Remote Sens. 2014, 35, 3440–3458. [Google Scholar] [CrossRef]
- Zhu, L.; Liu, X.; Wu, L.; Tang, Y.; Meng, Y. Long-Term Monitoring of Cropland Change near Dongting Lake, China, Using the LandTrendr Algorithm with Landsat Imagery. Remote Sens. 2019, 11, 1234. [Google Scholar] [CrossRef]
- Petráš, R.; Mecko, J.; Nociar, V. Diameter structure of the stands of poplar clones. J. Forensic Sci. 2010, 56, 165–170. [Google Scholar] [CrossRef]
- Ceylan, İ. Determination of Drying Characteristics of Timber by Using Artificial Neural Networks and Mathematical Models. Dry. Technol. 2008, 26, 1469–1476. [Google Scholar] [CrossRef]
- Franco-Lopez, H.; Ek, A.R.; Bauer, M.E. Estimation and mapping of forest stand density, volume, and cover type using the k-nearest neighbors method. Remote Sens. Environ. 2001, 77, 251–274. [Google Scholar] [CrossRef]
- Shafri, H.Z.M.; Suhaili, A.; Mansor, S. The performance of maximum likelihood, spectral angle mapper, neural network and decision tree classifiers in hyperspectral image analysis. J. Comput. Sci. 2007, 3, 419–423. [Google Scholar] [CrossRef]
Inclusion Criteria | Exclusion Criteria |
---|---|
Studies conducted on poplar species | Studies conducted without poplar species |
Remote sensing-based data sources | Studies without the use of remote sensing data |
Peer-reviewed published articles | Fugitive literature and review articles |
Articles published in English | Articles not written in English |
Biophysical Indicators | N | Spectral Indices | N |
Canopy cover | 24 | Normalized difference vegetation index (NDVI) | 30 |
Poplar growth | 20 | Leaf area index (LAI) | 22 |
Poplar biomass | 18 | Diversity index | 15 |
Poplar distribution | 15 | Soil-adjusted vegetation index (SAVI) | 7 |
Poplar cover | 10 | Chlorophyll vegetation index (CVI) | 6 |
Poplar volume | 8 | Normalized difference water index (NDWI) | 6 |
Breast diameter | 7 | Difference normalized burn ratio index (dNBRI) | 5 |
Crown cover | 7 | Green-red normalized difference vegetation index (GR-NDVI) | 5 |
Crown diameter | 6 | Photochemical reflectance index (PRI) | 3 |
Canopy structure | 5 | Stress susceptibility index (SSI) | 2 |
Classifiers | N | Software | N |
Random forest | 24 | MATLAB v 5.3.1, 6.0. 7.1, 7.9, 8, 9.2 and 9.6 (MathWorks, Natick, MA, USA) | 17 |
Support vector machine | 15 | R studio v 2.7.1, 3.0.2, 3.5.0 and 4.1.0 (R Foundation for Statistical Computing, Vienna, Austria) | 14 |
Maximum likelihood | 10 | Python v 3.3.3, 3.6.7, 3.5.8 and 3.10.9 (Altoros labs, Pleasanton, CA, USA) | 13 |
Decision tree | 7 | ArcMap v 10.6.1 (ESRI, Redlands, CA, USA) | 9 |
Classification and regression Tree | 6 | ENVI v 5.0 (NV5 Geospatial, Hollywood, FL, USA) | 9 |
Object-based | 6 | LiDAR 360 v 4.0, 5.0 and 6.0 (Green valley International, Berkeley, CA, USA) | 8 |
Neural network | 5 | ENVI v 5.0 and 5.1 (NV5 Geospatial, Hollywood, FL, USA) | 8 |
k-nearest neighbor | 4 | ArcGIS 10.7 (ESRI, Redlands, CA, USA) | 7 |
Density-based clustering | 3 | QGIS v 3.14, 3.18 and 3.30 (GeoApt LLC., Anchorage, AK, USA) | 7 |
Rotation forest | 3 | SPSS v 22 and 25 (IBM Corp., Armonk, NY, USA) | 6 |
Journals | Funder |
---|---|
International Geosciences and Remote sensing Symposium (14) | G (14) |
Remote Sensing (8) | G (8) |
Remote Sensing of the Environment (8) | G (8) |
IEEE Transactions on Geoscience and Remote Sensing (7) | G (7) |
International Journal of Remote Sensing (7) | G (7) |
Proceedings of SPIE—the International Society for Optical Engineering (7) | G (7) |
Canadian Journal of Remote Sensing (4) | G (3); G and P (1) |
ISPRS Journal of Photogrammetry and Remote Sensing (4) | G (4) |
International Archives of the Photogrammetry Remote Sensing and Spatial Information Science (4) | G (3); P (1) |
Journal of Geophysical Research: Atmospheres (4) | G (3) and P (1) |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Mapuru, M.J.; Xulu, S.; Gebreslasie, M. Remote Sensing Applications in Monitoring Poplars: A Review. Forests 2023, 14, 2301. https://doi.org/10.3390/f14122301
Mapuru MJ, Xulu S, Gebreslasie M. Remote Sensing Applications in Monitoring Poplars: A Review. Forests. 2023; 14(12):2301. https://doi.org/10.3390/f14122301
Chicago/Turabian StyleMapuru, Morena J., Sifiso Xulu, and Michael Gebreslasie. 2023. "Remote Sensing Applications in Monitoring Poplars: A Review" Forests 14, no. 12: 2301. https://doi.org/10.3390/f14122301