{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T22:43:10Z","timestamp":1774046590022,"version":"3.50.1"},"reference-count":244,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2023,7,14]],"date-time":"2023-07-14T00:00:00Z","timestamp":1689292800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"North Dakota Agricultural Experiment Station","award":["FARG080021"],"award-info":[{"award-number":["FARG080021"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Conventional measurement methods for above-ground biomass (AGB) are time-consuming, inaccurate, and labor-intensive. Unmanned aerial systems (UASs) have emerged as a promising solution, but a standardized procedure for UAS-based AGB estimation is lacking. This study reviews recent findings (2018\u20132022) on UAS applications for AGB estimation and develops a vegetation type-specific standard protocol. Analysis of 211 papers reveals the prevalence of rotary-wing UASs, especially quadcopters, in agricultural fields. Sensor selection varies by vegetation type, with LIDAR and RGB sensors in forests, and RGB, multispectral, and hyperspectral sensors in agricultural and grass fields. Flight altitudes and speeds depend on vegetation characteristics and sensor types, varying among crop groups. Ground control points (GCPs) needed for accurate AGB estimation differ based on vegetation type and topographic complexity. Optimal data collection during solar noon enhances accuracy, considering image quality, solar energy availability, and reduced atmospheric effects. Vegetation indices significantly affect AGB estimation in vertically growing crops, while their influence is comparatively less in forests, grasses, and horizontally growing crops. Plant height metrics differ across vegetation groups, with maximum height in forests and vertically growing crops, and central tendency metrics in grasses and horizontally growing crops. Linear regression and machine learning models perform similarly in forests, with machine learning outperforming in grasses; both yield comparable results for horizontally and vertically growing crops. Challenges include sensor limitations, environmental conditions, reflectance mixture, canopy complexity, water, cloud cover, dew, phenology, image artifacts, legal restrictions, computing power, battery capacity, optical saturation, and GPS errors. Addressing these requires careful sensor selection, timing, image processing, compliance with regulations, and overcoming technical limitations. Insights and guidelines provided enhance the precision and efficiency of UAS-based AGB estimation. Understanding vegetation requirements aids informed decisions on platform selection, sensor choice, flight parameters, and modeling approaches across different ecosystems. This study bridges the gap by providing a standardized protocol, facilitating widespread adoption of UAS technology for AGB estimation.<\/jats:p>","DOI":"10.3390\/rs15143543","type":"journal-article","created":{"date-parts":[[2023,7,14]],"date-time":"2023-07-14T08:40:06Z","timestamp":1689324006000},"page":"3543","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["An Overview of Using Unmanned Aerial System Mounted Sensors to Measure Plant Above-Ground Biomass"],"prefix":"10.3390","volume":"15","author":[{"given":"Aliasghar","family":"Bazrafkan","sequence":"first","affiliation":[{"name":"Department of Agricultural and Biosystems Engineering, North Dakota State University, Fargo, ND 58102, USA"}]},{"given":"Nadia","family":"Delavarpour","sequence":"additional","affiliation":[{"name":"Department of Agricultural and Biosystems Engineering, North Dakota State University, Fargo, ND 58102, USA"}]},{"given":"Peter G.","family":"Oduor","sequence":"additional","affiliation":[{"name":"Department of Earth, Environmental, and Geospatial Sciences, North Dakota State University, Fargo, ND 58102, USA"}]},{"given":"Nonoy","family":"Bandillo","sequence":"additional","affiliation":[{"name":"Department of Plant Sciences, North Dakota State University, Fargo, ND 58102, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3964-6904","authenticated-orcid":false,"given":"Paulo","family":"Flores","sequence":"additional","affiliation":[{"name":"Department of Agricultural and Biosystems Engineering, North Dakota State University, Fargo, ND 58102, USA"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1186\/s13021-018-0098-0","article-title":"Estimating Urban above Ground Biomass with Multi-Scale LiDAR","volume":"13","author":"Wilkes","year":"2018","journal-title":"Carbon Balance Manag."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Poley, L.G., and McDermid, G.J. (2020). A Systematic Review of the Factors Influencing the Estimation of Vegetation Aboveground Biomass Using Unmanned Aerial Systems. Remote Sens., 12.","DOI":"10.3390\/rs12071052"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Liu, K., Shen, X., Cao, L., Wang, G., and Cao, F. (2018, January 18\u201320). The Evaluation of Parametric and Non-Parametric Models for Total Forest Biomass Estimation Using UAS-LiDAR. Proceedings of the 5th International Workshop on Earth Observation and Remote Sensing Applications, EORSA 2018\u2014Proceedings, Xi\u2019an, China.","DOI":"10.1109\/EORSA.2018.8598572"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1602","DOI":"10.1080\/01431161.2020.1826057","article-title":"Estimation of Winter-Wheat above-Ground Biomass Using the Wavelet Analysis of Unmanned Aerial Vehicle-Based Digital Images and Hyperspectral Crop Canopy Images","volume":"42","author":"Yue","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"979","DOI":"10.1007\/s10712-019-09538-8","article-title":"The Importance of Consistent Global Forest Aboveground Biomass Product Validation","volume":"40","author":"Duncanson","year":"2019","journal-title":"Surv. Geophys."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wan, X., Li, Z., Chen, E., Zhao, L., Zhang, W., and Xu, K. (2021). Forest Aboveground Biomass Estimation Using Multi-Features Extracted by Fitting Vertical Backscattered Power Profile of Tomographic Sar. Remote Sens., 13.","DOI":"10.3390\/rs13020186"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"165","DOI":"10.15287\/afr.2022.2390","article-title":"Above-Ground Biomass Estimation in a Mediterranean Sparse Coppice Oak Forest Using Sentinel-2 Data","volume":"65","author":"Moradi","year":"2022","journal-title":"Ann. For. Res."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Khan, I.A., Khan, W.R., Ali, A., and Nazre, M. (2021). Assessment of Above-Ground Biomass in Pakistan Forest Ecosystem\u2019s Carbon Pool: A Review. Forests, 12.","DOI":"10.3390\/f12050586"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"14501","DOI":"10.1117\/1.JRS.16.014501","article-title":"Biomass Estimation of Crops and Natural Shrubs by Combining Red-Edge Ratio with Normalized Difference Vegetation Index","volume":"16","author":"Chang","year":"2022","journal-title":"J. Appl. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Xu, C., Ding, Y., Zheng, X., Wang, Y., Zhang, R., Zhang, H., and Dai, Z. (2022). A Comprehensive Comparison of Machine Learning and Feature Selection Methods for Maize Biomass Estimation Using Sentinel-1 SAR, Sentinel-2 Vegetation Indices, and Biophysical Variables. Remote Sens., 14.","DOI":"10.3390\/rs14164083"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"840","DOI":"10.1007\/s11119-018-9560-y","article-title":"Onion Biomass Monitoring Using UAV-Based RGB Imaging","volume":"19","author":"Ballesteros","year":"2018","journal-title":"Precis. Agric."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Alebele, Y., Zhang, X., Wang, W., Yang, G., Yao, X., Zheng, H., Zhu, Y., Cao, W., and Cheng, T. (2020). Estimation of Canopy Biomass Components in Paddy Rice from Combined Optical and SAR Data Using Multi-Target Gaussian Regressor Stacking. Remote Sens., 12.","DOI":"10.3390\/rs12162564"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"938216","DOI":"10.3389\/fpls.2022.938216","article-title":"Estimation of Potato Above-Ground Biomass Based on Unmanned Aerial Vehicle Red-Green-Blue Images with Different Texture Features and Crop Height","volume":"13","author":"Liu","year":"2022","journal-title":"Front. Plant Sci."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Vargas, J.J.Q., Zhang, C., Smitchger, J.A., McGee, R.J., and Sankaran, S. (2019). Phenotyping of Plant Biomass and Performance Traits Using Remote Sensing Techniques in Pea (Pisum sativum, L.). Sensors, 19.","DOI":"10.3390\/s19092031"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Acorsi, M.G., das Dores Abati Miranda, F., Martello, M., Smaniotto, D.A., and Sartor, L.R. (2019). Estimating Biomass of Black Oat Using UAV-Based RGB Imaging. Agronomy, 9.","DOI":"10.3390\/agronomy9070344"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"6506","DOI":"10.1073\/pnas.1711842115","article-title":"The Biomass Distribution on Earth","volume":"115","author":"Phillips","year":"2018","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhang, H., Sun, Y., Chang, L., Qin, Y., Chen, J., Qin, Y., Du, J., Yi, S., and Wang, Y. (2018). Estimation of Grassland Canopy Height and Aboveground Biomass at the Quadrat Scale Using Unmanned Aerial Vehicle. Remote Sens., 10.","DOI":"10.3390\/rs10060851"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wang, Z., Ma, Y., Zhang, Y., and Shang, J. (2022). Review of Remote Sensing Applications in Grassland Monitoring. Remote Sens., 14.","DOI":"10.3390\/rs14122903"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Clementini, C., Pomente, A., Latini, D., Kanamaru, H., Vuolo, M.R., Heureux, A., Fujisawa, M., Schiavon, G., and Del Frate, F. (2020). Long-Term Grass Biomass Estimation of Pastures from Satellite Data. Remote Sens., 12.","DOI":"10.3390\/rs12132160"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Muumbe, T.P., Baade, J., Singh, J., Schmullius, C., and Thau, C. (2021). Terrestrial Laser Scanning for Vegetation Analyses with a Special Focus on Savannas. Remote Sens., 13.","DOI":"10.3390\/rs13030507"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Grybas, H., and Congalton, R.G. (2022). Evaluating the Impacts of Flying Height and Forward Overlap on Tree Height Estimates Using Unmanned Aerial Systems. Forests, 13.","DOI":"10.3390\/f13091462"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Delavarpour, N., Koparan, C., Nowatzki, J., Bajwa, S., and Sun, X. (2021). A Technical Study on UAV Characteristics for Precision Agriculture Applications and Associated Practical Challenges. Remote Sens., 13.","DOI":"10.3390\/rs13061204"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1080\/10106049.2018.1552322","article-title":"Above-Ground Biomass Estimation of Arable Crops Using UAV-Based SfM Photogrammetry","volume":"35","year":"2020","journal-title":"Geocarto Int."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"106304","DOI":"10.1016\/j.compag.2021.106304","article-title":"Unmanned Aerial Vehicle-Based Field Phenotyping of Crop Biomass Using Growth Traits Retrieved from PROSAIL Model","volume":"187","author":"Wan","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, D., Gu, X., Pang, Y., Chen, B., and Liu, L. (2018). Estimation of Forest Aboveground Biomass and Leaf Area Index Based on Digital Aerial Photograph Data in Northeast China. Forests, 9.","DOI":"10.3390\/f9050275"},{"key":"ref_26","first-page":"102358","article-title":"UAV-Based Individual Shrub Aboveground Biomass Estimation Calibrated against Terrestrial LiDAR in a Shrub-Encroached Grassland","volume":"101","author":"Zhao","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Gano, B., Dembele, J.S.B., Ndour, A., Luquet, D., Beurier, G., Diouf, D., and Audebert, A. (2021). Using Uav Borne, Multi-Spectral Imaging for the Field Phenotyping of Shoot Biomass, Leaf Area Index and Height of West African Sorghum Varieties under Two Contrasted Water Conditions. Agronomy, 11.","DOI":"10.3390\/agronomy11050850"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"147335","DOI":"10.1016\/j.scitotenv.2021.147335","article-title":"Estimating the Aboveground Biomass of Coniferous Forest in Northeast China Using Spectral Variables, Land Surface Temperature and Soil Moisture","volume":"785","author":"Jiang","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/j.foreco.2018.12.019","article-title":"Comparison of Machine Learning Algorithms for Forest Parameter Estimations and Application for Forest Quality Assessments","volume":"434","author":"Zhao","year":"2019","journal-title":"For. Ecol. Manag."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1186\/s13007-020-00613-5","article-title":"Non-Destructive Estimation of Field Maize Biomass Using Terrestrial Lidar: An Evaluation from Plot Level to Individual Leaf Level","volume":"16","author":"Jin","year":"2020","journal-title":"Plant Methods"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.isprsjprs.2020.02.013","article-title":"Above-Ground Biomass Estimation and Yield Prediction in Potato by Using UAV-Based RGB and Hyperspectral Imaging","volume":"162","author":"Li","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Gr\u00fcner, E., Astor, T., and Wachendorf, M. (2019). Biomass Prediction of Heterogeneous Temperate Grasslands Using an SFM Approach Based on UAV Imaging. Agronomy, 9.","DOI":"10.3390\/agronomy9020054"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"111323","DOI":"10.1016\/j.rse.2019.111323","article-title":"Combining LiDAR and Hyperspectral Data for Aboveground Biomass Modeling in the Brazilian Amazon Using Different Regression Algorithms","volume":"232","author":"Ometto","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"126411","DOI":"10.1016\/j.eja.2021.126411","article-title":"Exploiting Centimetre Resolution of Drone-Mounted Sensors for Estimating Mid-Late Season above Ground Biomass in Rice","volume":"132","author":"Adeluyi","year":"2022","journal-title":"Eur. J. Agron."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Swayze, N.C., Tinkham, W.T., Creasy, M.B., Vogeler, J.C., Hoffman, C.M., and Hudak, A.T. (2022). Influence of UAS Flight Altitude and Speed on Aboveground Biomass Prediction. Remote Sens., 14.","DOI":"10.3390\/rs14091989"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"106585","DOI":"10.1016\/j.ecss.2020.106585","article-title":"Improving Mangrove Above-Ground Biomass Estimates Using LiDAR","volume":"236","author":"Salum","year":"2020","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Domingo, D., \u00d8rka, H.O., N\u00e6sset, E., Kachamba, D., and Gobakken, T. (2019). Effects of UAV Image Resolution, Camera Type, and Image Overlap on Accuracy of Biomass Predictions in a Tropical Woodland. Remote Sens., 11.","DOI":"10.3390\/rs11080948"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Viljanen, N., Honkavaara, E., N\u00e4si, R., Hakala, T., Niemel\u00e4inen, O., and Kaivosoja, J. (2018). A Novel Machine Learning Method for Estimating Biomass of Grass Swards Using a Photogrammetric Canopy Height Model, Images and Vegetation Indices Captured by a Drone. Agriculture, 8.","DOI":"10.3390\/agriculture8050070"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"112540","DOI":"10.1016\/j.rse.2021.112540","article-title":"Influence of Flight Parameters on UAS-Based Monitoring of Tree Height, Diameter, and Density","volume":"263","author":"Swayze","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_40","first-page":"47","article-title":"Analysing the Potential of UAV Point Cloud as Input in Quantitative Structure Modelling for Assessment of Woody Biomass of Single Trees","volume":"81","author":"Ye","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhang, H., Wang, C., Zhu, J., Fu, H., Xie, Q., and Shen, P. (2018). Forest Above-Ground Biomass Estimation Using Single-Baseline Polarization Coherence Tomography with P-Band PolInSAR Data. Forests, 9.","DOI":"10.3390\/f9040163"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"N\u00e4si, R., Viljanen, N., Kaivosoja, J., Alhonoja, K., Hakala, T., Markelin, L., and Honkavaara, E. (2018). Estimating Biomass and Nitrogen Amount of Barley and Grass Using UAV and Aircraft Based Spectral and Photogrammetric 3D Features. Remote Sens., 10.","DOI":"10.3390\/rs10071082"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"d\u2019Oliveira, M.V.N., Broadbent, E.N., Oliveira, L.C., Almeida, D.R.A., Papa, D.A., Ferreira, M.E., Zambrano, A.M.A., Silva, C.A., Avino, F.S., and Prata, G.A. (2020). Aboveground Biomass Estimation in Amazonian Tropical Forests: A Comparison of Aircraft- and GatorEye UAV-Borne LiDAR Data in the Chico Mendes Extractive Reserve in Acre, Brazil. Remote Sens., 12.","DOI":"10.3390\/rs12111754"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Guascal, E., Rojas, S., Kirby, E., Toulkeridis, T., Fuertes, W., and Heredia, M. (2020, January 22\u201324). Application of Remote Sensing Techniques in the Estimation of Forest Biomass of a Recreation Area by UAV and RADAR Images in Ecuador. Proceedings of the 2020 Seventh International Conference on eDemocracy & eGovernment (ICEDEG), Buenos Aires, Argentina.","DOI":"10.1109\/ICEDEG48599.2020.9096880"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Jiang, Q., Fang, S., Peng, Y., Gong, Y., Zhu, R., Wu, X., Ma, Y., Duan, B., and Liu, J. (2019). UAV-Based Biomass Estimation for Rice-Combining Spectral, TIN-Based Structural and Meteorological Features. Remote Sens., 11.","DOI":"10.3390\/rs11070890"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"4175","DOI":"10.1109\/JSTARS.2019.2918572","article-title":"Estimation of Forest Structural Parameters Using UAV-LiDAR Data and a Process-Based Model in Ginkgo Planted Forests","volume":"12","author":"Cao","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Niu, Y., Zhang, L., Zhang, H., Han, W., and Peng, X. (2019). Estimating Above-Ground Biomass of Maize Using Features Derived from UAV-Based RGB Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11111261"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Zhu, W., Sun, Z., Peng, J., Huang, Y., Li, J., Zhang, J., Yang, B., and Liao, X. (2019). Estimating Maize Above-Ground Biomass Using 3D Point Clouds of Multi-Source Unmanned Aerial Vehicle Data at Multi-Spatial Scales. Remote Sens., 11.","DOI":"10.3390\/rs11222678"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1016\/j.isprsjprs.2018.11.001","article-title":"Estimating Forest Structural Attributes Using UAV-LiDAR Data in Ginkgo Plantations","volume":"146","author":"Liu","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1016\/j.biombioe.2019.01.045","article-title":"A Comparison of Multiple Methods for Mapping Local-Scale Mesquite Tree Aboveground Biomass with Remotely Sensed Data","volume":"122","author":"Ku","year":"2019","journal-title":"Biomass Bioenergy"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Dorado-Roda, I., Pascual, A., Godinho, S., Silva, C.A., Botequim, B., Rodr\u00edguez-Gonz\u00e1lvez, P., Gonz\u00e1lez-Ferreiro, E., and Guerra-Hern\u00e1ndez, J. (2021). Assessing the Accuracy of Gedi Data for Canopy Height and Aboveground Biomass Estimates in Mediterranean Forests. Remote Sens., 13.","DOI":"10.3390\/rs13122279"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"111747","DOI":"10.1016\/j.rse.2020.111747","article-title":"The Application of Unmanned Aerial Vehicles (UAVs) to Estimate above-Ground Biomass of Mangrove Ecosystems","volume":"242","author":"Navarro","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Zheng, L., Tao, J., Bao, Q., Weng, S., Zhang, Y., Zhao, J., and Huang, L. (2022, January 23). Combining Spectral and Textures of Digital Imagery for Wheat Aboveground Biomass Estimation. Proceedings of the International Conference on Electronic Information Technology (EIT 2022), Chengdu, China.","DOI":"10.1117\/12.2639118"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Herwitz, S., Johnson, L., Arvesen, J., Higgins, R., Leung, J., and Dunagan, S. (2002, January 20\u201323). Precision Agriculture as a Commercial Application for Solar-Powered Unmanned Aerial Vehicles. Proceedings of the 1st UAV Conference, Infotech@Aerospace Conferences, Portsmouth, VA, USA.","DOI":"10.2514\/6.2002-3404"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"616689","DOI":"10.3389\/fpls.2021.616689","article-title":"Applications of UAS in Crop Biomass Monitoring: A Review","volume":"12","author":"Wang","year":"2021","journal-title":"Front. Plant Sci."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1016\/j.biombioe.2019.02.002","article-title":"Estimation Methods Developing with Remote Sensing Information for Energy Crop Biomass: A Comparative Review","volume":"122","author":"Chao","year":"2019","journal-title":"Biomass Bioenergy"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Panday, U.S., Pratihast, A.K., Aryal, J., and Kayastha, R.B. (2020). A Review on Drone-Based Data Solutions for Cereal Crops. Drones, 4.","DOI":"10.3390\/drones4030041"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1002\/agj2.20595","article-title":"Review on Unmanned Aerial Vehicles, Remote Sensors, Imagery Processing, and Their Applications in Agriculture","volume":"113","author":"Olson","year":"2021","journal-title":"Agron. J."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"105731","DOI":"10.1016\/j.compag.2020.105731","article-title":"A Review on Plant High-Throughput Phenotyping Traits Using UAV-Based Sensors","volume":"178","author":"Xie","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"106033","DOI":"10.1016\/j.compag.2021.106033","article-title":"A Comprehensive Review on Recent Applications of Unmanned Aerial Vehicle Remote Sensing with Various Sensors for High-Throughput Plant Phenotyping","volume":"182","author":"Feng","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Dat Pham, T., Xia, J., Thang Ha, N., Tien Bui, D., Nhu Le, N., and Tekeuchi, W. (2019). A Review of Remote Sensing Approaches for Monitoring Blue Carbon Ecosystems: Mangroves, Sea Grasses and Salt Marshes during 2010\u20132018. Sensors, 19.","DOI":"10.3390\/s19081933"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1007\/s40725-019-00087-2","article-title":"Digital Aerial Photogrammetry for Updating Area-Based Forest Inventories: A Review of Opportunities, Challenges, and Future Directions","volume":"5","author":"Goodbody","year":"2019","journal-title":"Curr. For. Rep."},{"key":"ref_63","unstructured":"Armi, L., and Fekri-Ershad, S. (2019). Texture Image Analysis and Texture Classification Methods\u2014A Review. arXiv."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Mohsan, S.A.H., Khan, M.A., Noor, F., Ullah, I., and Alsharif, M.H. (2022). Towards the Unmanned Aerial Vehicles (UAVs): A Comprehensive Review. Drones, 6.","DOI":"10.3390\/drones6060147"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1080\/19475683.2022.2026476","article-title":"Unmanned Aerial Remote Sensing of Coastal Vegetation: A Review","volume":"28","author":"Morgan","year":"2022","journal-title":"Ann. GIS"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Ji, G., Shi, C., and Xue, M. (2022, January 11\u201312). The Application of Unmanned Aerial Vehicles Data Communication in Agriculture. Proceedings of the 2022 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS), Dalian, China.","DOI":"10.1109\/TOCS56154.2022.10015987"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Jo\u0144ca, J., Pawnuk, M., Bezyk, Y., Arsen, A., and S\u00f3wka, I. (2022). Drone-Assisted Monitoring of Atmospheric Pollution\u2014A Comprehensive Review. Sustainability, 14.","DOI":"10.3390\/su141811516"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Yu, Y., Pan, Y., Yang, X., and Fan, W. (2022). Spatial Scale Effect and Correction of Forest Aboveground Biomass Estimation Using Remote Sensing. Remote Sens., 14.","DOI":"10.3390\/rs14122828"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1007\/s10462-018-9653-z","article-title":"Towards the Use of Fuzzy Logic Systems in Rotary Wing Unmanned Aerial Vehicle: A Review","volume":"53","author":"Ferdaus","year":"2020","journal-title":"Artif. Intell. Rev."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"715983","DOI":"10.3389\/fpls.2021.715983","article-title":"High-Throughput Phenotyping and Random Regression Models Reveal Temporal Genetic Control of Soybean Biomass Production","volume":"12","author":"Lopez","year":"2021","journal-title":"Front. Plant Sci."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Holzhauser, K., R\u00e4biger, T., Rose, T., Kage, H., and K\u00fchling, I. (2022). Estimation of Biomass and N Uptake in Different Winter Cover Crops from UAV-Based Multispectral Canopy Reflectance Data. Remote Sens., 14.","DOI":"10.3390\/rs14184525"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"2699","DOI":"10.5194\/bg-19-2699-2022","article-title":"Estimating Dry Biomass and Plant Nitrogen Concentration in Pre-Alpine Grasslands with Low-Cost UAS-Borne Multispectral Data\u2014A Comparison of Sensors, Algorithms, and Predictor Sets","volume":"19","author":"Schucknecht","year":"2022","journal-title":"Biogeosciences"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"107985","DOI":"10.1016\/j.ecolind.2021.107985","article-title":"Estimating the Maize Biomass by Crop Height and Narrowband Vegetation Indices Derived from UAV-Based Hyperspectral Images","volume":"129","author":"Zhang","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Gr\u00fcner, E., Wachendorf, M., and Astor, T. (2020). The Potential of UAV-Borne Spectral and Textural Information for Predicting Aboveground Biomass and N Fixation in Legume-Grass Mixtures. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0234703"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Choudhury, M.R., Das, S., Christopher, J., Apan, A., Chapman, S., Menzies, N.W., and Dang, Y.P. (2021). Improving Biomass and Grain Yield Prediction of Wheat Genotypes on Sodic Soil Using Integrated High-Resolution Multispectral, Hyperspectral, 3d Point Cloud, and Machine Learning Techniques. Remote Sens., 13.","DOI":"10.3390\/rs13173482"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"21666","DOI":"10.1007\/s11356-020-08695-3","article-title":"Land Damage Assessment Using Maize Aboveground Biomass Estimated from Unmanned Aerial Vehicle in High Groundwater Level Regions Affected by Underground Coal Mining","volume":"27","author":"Ren","year":"2020","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"1406","DOI":"10.3389\/fpls.2018.01406","article-title":"Elucidating Sorghum Biomass, Nitrogen and Chlorophyll Contents with Spectral and Morphological Traits Derived from Unmanned Aircraft System","volume":"9","author":"Li","year":"2018","journal-title":"Front. Plant Sci."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"112967","DOI":"10.1016\/j.rse.2022.112967","article-title":"Comparison and Transferability of Thermal, Temporal and Phenological-Based in-Season Predictions of above-Ground Biomass in Wheat Crops from Proximal Crop Reflectance Data","volume":"273","author":"Li","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Barnetson, J., Phinn, S., and Scarth, P. (2020). Estimating Plant Pasture Biomass and Quality from UAV Imaging across Queensland\u2019s Rangelands. AgriEngineering, 2.","DOI":"10.20944\/preprints202009.0697.v1"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"3001","DOI":"10.1007\/s00122-020-03651-8","article-title":"Integration of Genotypic, Hyperspectral, and Phenotypic Data to Improve Biomass Yield Prediction in Hybrid Rye","volume":"133","author":"Jebsen","year":"2020","journal-title":"Theor. Appl. Genet."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"23","DOI":"10.5194\/agile-giss-3-23-2022","article-title":"Machine Learning with UAS LiDAR for Winter Wheat Biomass Estimations","volume":"3","author":"Bates","year":"2022","journal-title":"AGILE GIScience Ser."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Bates, J.S., Montzka, C., Schmidt, M., and Jonard, F. (2021). Estimating Canopy Density Parameters Time-Series for Winter Wheat Using Uas Mounted Lidar. Remote Sens., 13.","DOI":"10.3390\/rs13040710"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Zhang, X., Bao, Y., Wang, D., Xin, X., Ding, L., Xu, D., Hou, L., and Shen, J. (2021). Using UAV LiDAR to Extract Vegetation Parameters of Inner Mongolian Grassland. Remote Sens., 13.","DOI":"10.3390\/rs13040656"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"423","DOI":"10.13031\/aea.13941","article-title":"A Decade of Unmanned Aerial Systems in Irrigated Agriculture in the Western US","volume":"36","author":"Chavez","year":"2020","journal-title":"Appl. Eng. Agric."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Turton, A.E., Augustin, N.H., and Mitchard, E.T.A. (2022). Improving Estimates and Change Detection of Forest Above-Ground Biomass Using Statistical Methods. Remote Sens., 14.","DOI":"10.3390\/rs14194911"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"1046","DOI":"10.3390\/heritage3040057","article-title":"Small Multispectral UAV Sensor and Its Image Fusion Capability in Cultural Heritage Applications","volume":"3","author":"Kaimaris","year":"2020","journal-title":"Heritage"},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Maesano, M., Khoury, S., Nakhle, F., Firrincieli, A., Gay, A., Tauro, F., and Harfouche, A. (2020). UAV-Based LiDAR for High-Throughput Determination of Plant Height and Above-Ground Biomass of the Bioenergy Grass Arundo Donax. Remote Sens., 12.","DOI":"10.3390\/rs12203464"},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Zhang, L., Shao, Z., Liu, J., and Cheng, Q. (2019). Deep Learning Based Retrieval of Forest Aboveground Biomass from Combined LiDAR and Landsat 8 Data. Remote Sens., 11.","DOI":"10.3390\/rs11121459"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"2621","DOI":"10.1007\/s13157-020-01373-7","article-title":"Unmanned Aircraft System Photogrammetry for Mapping Diverse Vegetation Species in a Heterogeneous Coastal Wetland","volume":"40","author":"Durgan","year":"2020","journal-title":"Wetlands"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"3008","DOI":"10.1080\/01431161.2018.1539267","article-title":"Classification of Shoreline Vegetation in the Western Basin of Lake Erie Using Airborne Hyperspectral Imager HSI2, Pleiades and UAV Data","volume":"40","author":"Rupasinghe","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1111\/gfs.12439","article-title":"Canopy Height Measurements and Non-Destructive Biomass Estimation of Lolium Perenne Swards Using UAV Imagery","volume":"74","author":"Muylle","year":"2019","journal-title":"Grass Forage Sci."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"113319","DOI":"10.1016\/j.jenvman.2021.113319","article-title":"Scots Pine Stands Biomass Assessment Using 3D Data from Unmanned Aerial Vehicle Imagery in the Chernobyl Exclusion Zone","volume":"295","author":"Holiaka","year":"2021","journal-title":"J. Environ. Manag."},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"de Alckmin, G.T., Kooistra, L., Rawnsley, R., de Bruin, S., and Lucieer, A. (2020). Retrieval of Hyperspectral Information from Multispectral Data for Perennial Ryegrass Biomass Estimation. Sensors, 20.","DOI":"10.3390\/s20247192"},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Yue, J., Feng, H., Jin, X., Yuan, H., Li, Z., Zhou, C., Yang, G., and Tian, Q. (2018). A Comparison of Crop Parameters Estimation Using Images from UAV-Mounted Snapshot Hyperspectral Sensor and High-Definition Digital Camera. Remote Sens., 10.","DOI":"10.3390\/rs10071138"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1016\/j.compag.2018.11.041","article-title":"Methods for LiDAR-Based Estimation of Extensive Grassland Biomass","volume":"156","author":"Hensgen","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1007\/s11427-017-9056-0","article-title":"Crop 3D\u2014A LiDAR Based Platform for 3D High-Throughput Crop Phenotyping","volume":"61","author":"Guo","year":"2018","journal-title":"Sci. China Life Sci."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"113232","DOI":"10.1016\/j.rse.2022.113232","article-title":"Improving above Ground Biomass Estimates of Southern Africa Dryland Forests by Combining Sentinel-1 SAR and Sentinel-2 Multispectral Imagery","volume":"282","author":"David","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Chen, L., Ren, C., Bao, G., Zhang, B., Wang, Z., Liu, M., Man, W., and Liu, J. (2022). Improved Object-Based Estimation of Forest Aboveground Biomass by Integrating LiDAR Data from GEDI and ICESat-2 with Multi-Sensor Images in a Heterogeneous Mountainous Region. Remote Sens., 14.","DOI":"10.3390\/rs14122743"},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"\u00c4n\u00e4kk\u00e4l\u00e4, M., Lajunen, A., Hakoj\u00e4rvi, M., and Alakukku, L. (2022). Evaluation of the Influence of Field Conditions on Aerial Multispectral Images and Vegetation Indices. Remote Sens., 14.","DOI":"10.3390\/rs14194792"},{"key":"ref_100","first-page":"407","article-title":"Monitoring Forage Mass with Low-Cost UAV Data: Case Study at the Rengen Grassland Experiment","volume":"88","author":"Lussem","year":"2020","journal-title":"PFG\u2014J. Photogramm. Remote Sens. Geoinf. Sci."},{"key":"ref_101","first-page":"101942","article-title":"Utility of Hyperspectral Compared to Multispectral Remote Sensing Data in Estimating Forest Biomass and Structure Variables in Finnish Boreal Forest","volume":"83","author":"Halme","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Sharma, P., Leigh, L., Chang, J., Maimaitijiang, M., and Caff\u00e9, M. (2022). Above-Ground Biomass Estimation in Oats Using UAV Remote Sensing and Machine Learning. Sensors, 22.","DOI":"10.3390\/s22020601"},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Jayathunga, S., Owari, T., and Tsuyuki, S. (2019). Digital Aerial Photogrammetry for Uneven-Aged Forest Management: Assessing the Potential to Reconstruct Canopy Structure and Estimate Living Biomass. Remote Sens., 11.","DOI":"10.3390\/rs11030338"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.isprsjprs.2020.10.016","article-title":"Individual Tree Detection and Crown Delineation from Unmanned Aircraft System (UAS) LiDAR in Structurally Complex Mixed Species Eucalypt Forests","volume":"171","author":"Jaskierniak","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"5078","DOI":"10.1080\/01431161.2017.1420941","article-title":"A Meta-Analysis and Review of Unmanned Aircraft System (UAS) Imagery for Terrestrial Applications","volume":"39","author":"Singh","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1186\/s13007-019-0394-z","article-title":"Modeling Maize Above-Ground Biomass Based on Machine Learning Approaches Using UAV Remote-Sensing Data","volume":"15","author":"Han","year":"2019","journal-title":"Plant Methods"},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Pang, H., Zhang, A., Kang, X., He, N., and Dong, G. (2020). Estimation of the Grassland Aboveground Biomass of the Inner Mongolia Plateau Using the Simulated Spectra of Sentinel-2 Images. Remote Sens., 12.","DOI":"10.3390\/rs12244155"},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Royo, S., and Ballesta-Garcia, M. (2019). An Overview of Lidar Imaging Systems for Autonomous Vehicles. Appl. Sci., 9.","DOI":"10.3390\/app9194093"},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Zhang, C., Ang, M.H., and Rus, D. (2018, January 1\u20135). Robust Lidar Localization for Autonomous Driving in Rain. Proceedings of the 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain.","DOI":"10.1109\/IROS.2018.8593703"},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Liu, P., Zheng, P., Yang, S., and Chen, Z. (2019). Modeling and Analysis of Spatial Inter-Symbol Interference for RGB Image Sensors Based on Visible Light Communication. Sensors, 19.","DOI":"10.3390\/s19224999"},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Seifert, E., Seifert, S., Vogt, H., Drew, D., van Aardt, J., Kunneke, A., and Seifert, T. (2019). Influence of Drone Altitude, Image Overlap, and Optical Sensor Resolution on Multi-View Reconstruction of Forest Images. Remote Sens., 11.","DOI":"10.3390\/rs11101252"},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Lai, F., Bush, A., Yang, X., and Merrick, D. (2021). Opportunities and Challenges of Unmanned Aircraft Systems for Urban Applications. Urban Remote Sens. Monit. Synth. Model. Urban Environ., 47\u201369.","DOI":"10.1002\/9781119625865.ch3"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"107420","DOI":"10.1016\/j.compag.2022.107420","article-title":"Information Fusion Approach for Biomass Estimation in a Plateau Mountainous Forest Using a Synergistic System Comprising UAS-Based Digital Camera and LiDAR","volume":"202","author":"Huang","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Devoto, S., Macovaz, V., Mantovani, M., Soldati, M., and Furlani, S. (2020). Advantages of Using UAV Digital Photogrammetry in the Study of Slow-Moving Coastal Landslides. Remote Sens., 12.","DOI":"10.3390\/rs12213566"},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"731","DOI":"10.1177\/0020294019847688","article-title":"An Overview of Various Kinds of Wind Effects on Unmanned Aerial Vehicle","volume":"52","author":"Wang","year":"2019","journal-title":"Meas. Control"},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"ten Harkel, J., Bartholomeus, H., and Kooistra, L. (2020). Biomass and Crop Height Estimation of Different Crops Using UAV-Based LiDAR. Remote Sens., 12.","DOI":"10.3390\/rs12010017"},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"3578","DOI":"10.1109\/JSTARS.2018.2867945","article-title":"Mapping Three-Dimensional Structures of Forest Canopy Using UAV Stereo Imagery: Evaluating Impacts of Forward Overlaps and Image Resolutions with LiDAR Data as Reference","volume":"11","author":"Ni","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"207","DOI":"10.3832\/ifor2735-012","article-title":"Estimation of Forest Biomass Components Using Airborne Lidar and Multispectral Sensors","volume":"12","author":"Hernando","year":"2019","journal-title":"IForest"},{"key":"ref_119","first-page":"101922","article-title":"Estimating Forest Aboveground Biomass Using Small-Footprint Full-Waveform Airborne LiDAR Data","volume":"83","author":"Luo","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Rueda-Ayala, V.P., Pe\u00f1a, J.M., H\u00f6glind, M., Bengochea-Guevara, J.M., and And\u00fajar, D. (2019). Comparing UAV-Based Technologies and RGB-D Reconstruction Methods for Plant Height and Biomass Monitoring on Grass Ley. Sensors, 19.","DOI":"10.3390\/s19030535"},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"e7593","DOI":"10.7717\/peerj.7593","article-title":"Estimation of Maize Above-Ground Biomass Based on Stem-Leaf Separation Strategy Integrated with LiDAR and Optical Remote Sensing Data","volume":"7","author":"Zhu","year":"2019","journal-title":"PeerJ"},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Michez, A., Lejeune, P., Bauwens, S., Lalaina Herinaina, A.A., Blaise, Y., Mu\u00f1oz, E.C., Lebeau, F., and Bindelle, J. (2019). Mapping and Monitoring of Biomass and Grazing in Pasture with an Unmanned Aerial System. Remote Sens., 11.","DOI":"10.3390\/rs11050473"},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1007\/s10846-019-01001-5","article-title":"High-Throughput Biomass Estimation in Rice Crops Using UAV Multispectral Imagery","volume":"96","author":"Devia","year":"2019","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_124","doi-asserted-by":"crossref","unstructured":"Frey, J., Kovach, K., Stemmler, S., and Koch, B. (2018). UAV Photogrammetry of Forests as a Vulnerable Process. A Sensitivity Analysis for a Structure from Motion RGB-Image Pipeline. Remote Sens., 10.","DOI":"10.3390\/rs10060912"},{"key":"ref_125","first-page":"6735967","article-title":"Canopy Roughness: A New Phenotypic Trait to Estimate Aboveground Biomass from Unmanned Aerial System","volume":"2020","author":"Bucksch","year":"2020","journal-title":"Plant Phenomics"},{"key":"ref_126","doi-asserted-by":"crossref","unstructured":"Wengert, M., Piepho, H.-P., Astor, T., Gra\u00df, R., Wijesingha, J., and 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 Sens., 13.","DOI":"10.3390\/rs13142751"},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"8875606","DOI":"10.1155\/2021\/8875606","article-title":"Unmanned Aircraft System- (UAS-) Based High-Throughput Phenotyping (HTP) for Tomato Yield Estimation","volume":"2021","author":"Chang","year":"2021","journal-title":"J. Sens."},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"22221","DOI":"10.1038\/s41598-021-01763-9","article-title":"Evaluation of the Apple IPhone 12 Pro LiDAR for an Application in Geosciences","volume":"11","author":"Luetzenburg","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_129","doi-asserted-by":"crossref","unstructured":"Almeida, A., Gon\u00e7alves, F., Silva, G., Souza, R., Treuhaft, R., Santos, W., Loureiro, D., and Fernandes, M. (2020). Estimating Structure and Biomass of a Secondary Atlantic Forest in Brazil Using Fourier Transforms of Vertical Profiles Derived from UAV Photogrammetry Point Clouds. Remote Sens., 12.","DOI":"10.3390\/rs12213560"},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"146816","DOI":"10.1016\/j.scitotenv.2021.146816","article-title":"Aboveground Mangrove Biomass Estimation in Beibu Gulf Using Machine Learning and UAV Remote Sensing","volume":"781","author":"Tian","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"127521","DOI":"10.1016\/j.ufug.2022.127521","article-title":"Estimating Aboveground Biomass of Urban Forest Trees with Dual-Source UAV Acquired Point Clouds","volume":"69","author":"Lin","year":"2022","journal-title":"Urban For. Urban Green."},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"784","DOI":"10.3389\/fmars.2019.00784","article-title":"Estimating Mangrove Tree Biomass and Carbon Content: A Comparison of Forest Inventory Techniques and Drone Imagery","volume":"6","author":"Jones","year":"2020","journal-title":"Front. Mar. Sci."},{"key":"ref_133","doi-asserted-by":"crossref","unstructured":"Ni, W., Dong, J., Sun, G., Zhang, Z., Pang, Y., Tian, X., Li, Z., and Chen, E. (2019). Synthesis of Leaf-on and Leaf-off Unmanned Aerial Vehicle (UAV) Stereo Imagery for the Inventory of Aboveground Biomass of Deciduous Forests. Remote Sens., 11.","DOI":"10.3390\/rs11070889"},{"key":"ref_134","doi-asserted-by":"crossref","unstructured":"Srestasathiern, P., Siripon, S., Wasuhiranyrith, R., Kooha, P., and Moukomla, S. (2018, January 10). Estimating above Ground Biomass for Eucalyptus Plantation Using Data from Unmanned Aerial Vehicle Imagery. Proceedings of the Remote Sensing for Agriculture, Ecosystems, and Hydrology XX, Berlin, Germany.","DOI":"10.1117\/12.2323963"},{"key":"ref_135","doi-asserted-by":"crossref","unstructured":"Gennaro, S.F.D., Nati, C., Dainelli, R., Pastonchi, L., Berton, A., Toscano, P., and Matese, A. (2020). An Automatic UAV Based Segmentation Approach for Pruning Biomass Estimation in Irregularly Spaced Chestnut Orchards. Forests, 11.","DOI":"10.3390\/f11030308"},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"313","DOI":"10.3934\/agrfood.2018.3.313","article-title":"Estimating Tree Height and Biomass of a Poplar Plantation with Image-Based UAV Technology","volume":"3","author":"Dorado","year":"2018","journal-title":"AIMS Agric. Food"},{"key":"ref_137","doi-asserted-by":"crossref","unstructured":"Rex, F.E., Silva, C.A., Corte, A.P.D., Klauberg, C., Mohan, M., Cardil, A., da Silva, V.S., de Almeida, D.R.A., Garcia, M., and Broadbent, E.N. (2020). Comparison of Statistical Modelling Approaches for Estimating Tropical Forest Aboveground Biomass Stock and Reporting Their Changes in Low-Intensity Logging Areas Using Multi-Temporal LiDAR Data. Remote Sens., 12.","DOI":"10.3390\/rs12091498"},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"108694","DOI":"10.1016\/j.ecolind.2022.108694","article-title":"Aboveground Biomass of Typical Invasive Mangroves and Its Distribution Patterns Using UAV-LiDAR Data in a Subtropical Estuary: Maoming River Estuary, Guangxi, China","volume":"136","author":"Tian","year":"2022","journal-title":"Ecol. Indic."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.isprsjprs.2018.12.006","article-title":"Estimating Canopy Structure and Biomass in Bamboo Forests Using Airborne LiDAR Data","volume":"148","author":"Cao","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_140","doi-asserted-by":"crossref","unstructured":"Sanaa, F., Imane, S., Mohamed, B., Kenza, A.E.k., Souhail, K., Lfalah, H., and Khadija, M. (2022). Biomass and Carbon Stock Quantification in Cork Oak Forest of Maamora Using a New Approach Based on the Combination of Aerial Laser Scanning Carried by Unmanned Aerial Vehicle and Terrestrial Laser Scanning Data. Forests, 13.","DOI":"10.3390\/f13081211"},{"key":"ref_141","doi-asserted-by":"crossref","unstructured":"Cao, L., Liu, H., Fu, X., Zhang, Z., Shen, X., and Ruan, H. (2019). Comparison of UAV LiDAR and Digital Aerial Photogrammetry Point Clouds for Estimating Forest Structural Attributes in Subtropical Planted Forests. Forests, 10.","DOI":"10.3390\/f10020145"},{"key":"ref_142","doi-asserted-by":"crossref","unstructured":"Tojal, L.T., Bastarrika, A., Barrett, B., Espeso, J.M.S., Lopez-Guede, J.M., and Gra\u00f1a, M. (2019). Prediction of Aboveground Biomass from Low-Density LiDAR Data: Validation over P. Radiata Data from a Region North of Spain. Forests, 10.","DOI":"10.3390\/f10090819"},{"key":"ref_143","first-page":"102014","article-title":"Estimation of Aboveground Biomass of Robinia Pseudoacacia Forest in the Yellow River Delta Based on UAV and Backpack LiDAR Point Clouds","volume":"86","author":"Lu","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_144","doi-asserted-by":"crossref","unstructured":"Esteban, J., McRoberts, R.E., Fern\u00e1ndez-Landa, A., Tom\u00e9, J.L., and N\u00e6sset, E. (2019). Estimating Forest Volume and Biomass and Their Changes Using Random Forests and Remotely Sensed Data. Remote Sens., 11.","DOI":"10.3390\/rs11161944"},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"2143","DOI":"10.1007\/s10531-019-01698-8","article-title":"Spatial Distribution of Mangrove Forest Species and Biomass Assessment Using Field Inventory and Earth Observation Hyperspectral Data","volume":"28","author":"Pandey","year":"2019","journal-title":"Biodivers. Conserv."},{"key":"ref_146","doi-asserted-by":"crossref","unstructured":"Hu, T., Zhang, Y.Y., Su, Y., Zheng, Y., Lin, G., and Guo, Q. (2020). Mapping the Global Mangrove Forest Aboveground Biomass Using Multisource Remote Sensing Data. Remote Sens., 12.","DOI":"10.3390\/rs12101690"},{"key":"ref_147","doi-asserted-by":"crossref","unstructured":"Fu, Y., Yang, G., Song, X., Li, Z., Xu, X., Feng, H., and Zhao, C. (2021). Improved Estimation of Winter Wheat Aboveground Biomass Using Multiscale Textures Extracted from UAV-Based Digital Images and Hyperspectral Feature Analysis. Remote Sens., 13.","DOI":"10.3390\/rs13040581"},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1186\/s13007-019-0402-3","article-title":"Improved Estimation of Aboveground Biomass in Wheat from RGB Imagery and Point Cloud Data Acquired with a Low-Cost Unmanned Aerial Vehicle System","volume":"15","author":"Lu","year":"2019","journal-title":"Plant Methods"},{"key":"ref_149","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1007\/s11119-017-9501-1","article-title":"Predicting Cover Crop Biomass by Lightweight UAS-Based RGB and NIR Photography: An Applied Photogrammetric Approach","volume":"19","author":"Roth","year":"2018","journal-title":"Precis. Agric."},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.isprsjprs.2019.02.022","article-title":"Estimate of Winter-Wheat above-Ground Biomass Based on UAV Ultrahigh-Ground-Resolution Image Textures and Vegetation Indices","volume":"150","author":"Yue","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_151","doi-asserted-by":"crossref","unstructured":"Song, Y., Wang, J., Shang, J., and Liao, C. (2020). Using UAV-Based SOPC Derived LAI and SAFY Model for Biomass and Yield Estimation of Winter Wheat. Remote Sens., 12.","DOI":"10.3390\/rs12152378"},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1186\/s13007-019-0418-8","article-title":"Dynamic Monitoring of Biomass of Rice under Different Nitrogen Treatments Using a Lightweight UAV with Dual Image-Frame Snapshot Cameras","volume":"15","author":"Cen","year":"2019","journal-title":"Plant Methods"},{"key":"ref_153","doi-asserted-by":"crossref","unstructured":"Colorado, J.D., Calderon, F., Mendez, D., Petro, E., Rojas, J.P., Correa, E.S., Mondragon, I.F., Rebolledo, M.C., and Jaramillo-Botero, A. (2020). A Novel NIR-Image Segmentation Method for the Precise Estimation of above-Ground Biomass in Rice Crops. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0239591"},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1016\/j.ecolind.2019.03.011","article-title":"Combining Hyperspectral Imagery and LiDAR Pseudo-Waveform for Predicting Crop LAI, Canopy Height and above-Ground Biomass","volume":"102","author":"Luo","year":"2019","journal-title":"Ecol. Indic."},{"key":"ref_155","doi-asserted-by":"crossref","unstructured":"Masjedi, A., Zhao, J., Thompson, A.M., Yang, K.W., Flatt, J.E., Crawford, M.M., Ebert, D.S., Tuinstra, M.R., Hammer, G., and Chapman, S. (2018, January 22\u201327). Sorghum Biomass Prediction Using Uav-Based Remote Sensing Data and Crop Model Simulation. Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8519034"},{"key":"ref_156","doi-asserted-by":"crossref","unstructured":"Banerjee, B.P., Spangenberg, G., and Kant, S. (2020). Fusion of Spectral and Structural Information from Aerial Images for Improved Biomass Estimation. Remote Sens., 12.","DOI":"10.3390\/rs12193164"},{"key":"ref_157","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1007\/s11119-018-9600-7","article-title":"Improved Estimation of Rice Aboveground Biomass Combining Textural and Spectral Analysis of UAV Imagery","volume":"20","author":"Zheng","year":"2019","journal-title":"Precis. Agric."},{"key":"ref_158","doi-asserted-by":"crossref","unstructured":"Han, S., Zhao, Y., Cheng, J., Zhao, F., Yang, H., Feng, H., Li, Z., Ma, X., Zhao, C., and Yang, G. (2022). Monitoring Key Wheat Growth Variables by Integrating Phenology and UAV Multispectral Imagery Data into Random Forest Model. Remote Sens., 14.","DOI":"10.3390\/rs14153723"},{"key":"ref_159","doi-asserted-by":"crossref","first-page":"105026","DOI":"10.1016\/j.compag.2019.105026","article-title":"Estimating Biomass of Winter Oilseed Rape Using Vegetation Indices and Texture Metrics Derived from UAV Multispectral Images","volume":"166","author":"Liu","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_160","doi-asserted-by":"crossref","first-page":"1363","DOI":"10.1016\/j.cj.2022.04.005","article-title":"Evaluation of UAV-Derived Multimodal Remote Sensing Data for Biomass Prediction and Drought Tolerance Assessment in Bioenergy Sorghum","volume":"10","author":"Li","year":"2022","journal-title":"Crop J."},{"key":"ref_161","doi-asserted-by":"crossref","unstructured":"Astor, T., Dayananda, S., Nautiyal, S., and Wachendorf, M. (2020). Vegetable Crop Biomass Estimation Using Hyperspectral and RGB 3D UAV Data. Agronomy, 10.","DOI":"10.3390\/agronomy10101600"},{"key":"ref_162","doi-asserted-by":"crossref","unstructured":"Moeckel, T., Dayananda, S., Nidamanuri, R.R., Nautiyal, S., Hanumaiah, N., Buerkert, A., and Wachendorf, M. (2018). Estimation of Vegetable Crop Parameter by Multi-Temporal UAV-Borne Images. Remote Sens., 10.","DOI":"10.3390\/rs10050805"},{"key":"ref_163","doi-asserted-by":"crossref","first-page":"28","DOI":"10.3389\/frai.2020.00028","article-title":"Predicting Biomass and Yield in a Tomato Phenotyping Experiment Using UAV Imagery and Random Forest","volume":"3","author":"Johansen","year":"2020","journal-title":"Front. Artif. Intell."},{"key":"ref_164","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.isprsjprs.2019.03.003","article-title":"Vegetation Index Weighted Canopy Volume Model (CVMVI) for Soybean Biomass Estimation from Unmanned Aerial System-Based RGB Imagery","volume":"151","author":"Maimaitijiang","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_165","doi-asserted-by":"crossref","first-page":"3336","DOI":"10.1038\/s41598-021-82797-x","article-title":"Validation of UAV-Based Alfalfa Biomass Predictability Using Photogrammetry with Fully Automatic Plot Segmentation","volume":"11","author":"Tang","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_166","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2135\/tppj2019.02.0003","article-title":"Lidar and RGB Image Analysis to Predict Hairy Vetch Biomass in Breeding Nurseries","volume":"2","author":"Wiering","year":"2019","journal-title":"Plant Phenome J."},{"key":"ref_167","doi-asserted-by":"crossref","unstructured":"Trepekli, K., Westergaard-Nielsen, A., and Friborg, T. (2023, May 29). Application of Drone Borne LiDAR Technology for Monitoring Agricultural Biomass and Plant Growth. In EGU General Assembly Conference Abstracts. Available online: https:\/\/ui.adsabs.harvard.edu\/link_gateway\/2020EGUGA..22.9802T\/doi:10.5194\/egusphere-egu2020-9802.","DOI":"10.5194\/egusphere-egu2020-9802"},{"key":"ref_168","doi-asserted-by":"crossref","first-page":"603921","DOI":"10.3389\/fpls.2020.603921","article-title":"Prediction of Biomass and N Fixation of Legume\u2013Grass Mixtures Using Sensor Fusion","volume":"11","author":"Astor","year":"2021","journal-title":"Front. Plant Sci."},{"key":"ref_169","doi-asserted-by":"crossref","first-page":"105331","DOI":"10.1016\/j.compag.2020.105331","article-title":"Monitoring of Sugar Beet Growth Indicators Using Wide-Dynamic-Range Vegetation Index (WDRVI) Derived from UAV Multispectral Images","volume":"171","author":"Cao","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_170","doi-asserted-by":"crossref","first-page":"850486","DOI":"10.34133\/2022\/9850486","article-title":"Deep Learning for Strawberry Canopy Delineation and Biomass Prediction from High-Resolution Images","volume":"2022","author":"Zheng","year":"2022","journal-title":"Plant Phenomics"},{"key":"ref_171","doi-asserted-by":"crossref","first-page":"034525","DOI":"10.1117\/1.JRS.13.034525","article-title":"Estimating Biomass in Temperate Grassland with High Resolution Canopy Surface Models from UAV-Based RGB Images and Vegetation Indices","volume":"13","author":"Lussem","year":"2019","journal-title":"J. Appl. Remote Sens."},{"key":"ref_172","doi-asserted-by":"crossref","first-page":"350","DOI":"10.2489\/jswc.74.4.350","article-title":"Unmanned Aerial Vehicle\u2013Based Assessment of Cover Crop Biomass and Nitrogen Uptake Variability","volume":"74","author":"Yuan","year":"2019","journal-title":"J. Soil Water Conserv."},{"key":"ref_173","doi-asserted-by":"crossref","unstructured":"Castro, W., Junior, J.M., Polidoro, C., Osco, L.P., Gon\u00e7alves, W., Rodrigues, L., Santos, M., Jank, L., Barrios, S., and Valle, C. (2020). Deep Learning Applied to Phenotyping of Biomass in Forages with Uav-Based Rgb Imagery. Sensors, 20.","DOI":"10.3390\/s20174802"},{"key":"ref_174","doi-asserted-by":"crossref","unstructured":"Nguyen, P., Badenhorst, P.E., Shi, F., Spangenberg, G.C., Smith, K.F., and Daetwyler, H.D. (2021). Design of an Unmanned Ground Vehicle and Lidar Pipeline for the High-Throughput Phenotyping of Biomass in Perennial Ryegrass. Remote Sens., 13.","DOI":"10.3390\/rs13010020"},{"key":"ref_175","doi-asserted-by":"crossref","unstructured":"Th\u00e9au, J., Lauzier-Hudon, \u00c9., Aub\u00e9, L., and Devillers, N. (2021). Estimation of Forage Biomass and Vegetation Cover in Grasslands Using UAV Imagery. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0245784"},{"key":"ref_176","doi-asserted-by":"crossref","first-page":"1437","DOI":"10.1007\/s11119-021-09790-2","article-title":"Forage Yield and Quality Estimation by Means of UAV and Hyperspectral Imaging","volume":"22","author":"Geipel","year":"2021","journal-title":"Precis. Agric."},{"key":"ref_177","first-page":"352","article-title":"Evaluation of 3D Point Cloud-Based Models for the Prediction of Grassland Biomass","volume":"78","author":"Wijesingha","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_178","doi-asserted-by":"crossref","unstructured":"Doughty, C.L., and Cavanaugh, K.C. (2019). Mapping Coastal Wetland Biomass from High Resolution Unmanned Aerial Vehicle (UAV) Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11050540"},{"key":"ref_179","doi-asserted-by":"crossref","unstructured":"Farris, A.S., Defne, Z., and Ganju, N.K. (2019). Identifying Salt Marsh Shorelines from Remotely Sensed Elevation Data and Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11151795"},{"key":"ref_180","doi-asserted-by":"crossref","unstructured":"D\u00f6pper, V., Gr\u00e4nzig, T., Kleinschmit, B., and F\u00f6rster, M. (2020). Challenges in UAS-Based TIR Imagery Processing: Image Alignment and Uncertainty Quantification. Remote Sens., 12.","DOI":"10.3390\/rs12101552"},{"key":"ref_181","doi-asserted-by":"crossref","unstructured":"Chen, J., Li, X., Wang, K., Zhang, S., and Li, J. (2022). Estimation of Seaweed Biomass Based on Multispectral UAV in the Intertidal Zone of Gouqi Island. Remote Sens., 14.","DOI":"10.3390\/rs14092143"},{"key":"ref_182","doi-asserted-by":"crossref","unstructured":"Ban, S., Liu, W., Tian, M., Wang, Q., Yuan, T., Chang, Q., and Li, L. (2022). Rice Leaf Chlorophyll Content Estimation Using UAV-Based Spectral Images in Different Regions. Agronomy, 12.","DOI":"10.3390\/agronomy12112832"},{"key":"ref_183","doi-asserted-by":"crossref","unstructured":"Hassanzadeh, A., Zhang, F., van Aardt, J., Murphy, S.P., and Pethybridge, S.J. (2021). Broadacre Crop Yield Estimation Using Imaging Spectroscopy from Unmanned Aerial Systems (UAS): A Field-Based Case Study with Snap Bean. Remote Sens., 13.","DOI":"10.3390\/rs13163241"},{"key":"ref_184","doi-asserted-by":"crossref","unstructured":"Guimar\u00e3es-Steinicke, C., Weigelt, A., Ebeling, A., Eisenhauer, N., and Wirth, C. (2022). Diversity Effects on Canopy Structure Change throughout a Growing Season in Experimental Grassland Communities. Remote Sens., 14.","DOI":"10.3390\/rs14071557"},{"key":"ref_185","doi-asserted-by":"crossref","unstructured":"Hu, Y., Shen, J., and Qi, Y. (2021). Estimation of Rice Biomass at Different Growth Stages by Using Fractal Dimension in Image Processing. Appl. Sci., 11.","DOI":"10.3390\/app11157151"},{"key":"ref_186","doi-asserted-by":"crossref","first-page":"107494","DOI":"10.1016\/j.ecolind.2021.107494","article-title":"An Improved Approach to Estimate Above-Ground Volume and Biomass of Desert Shrub Communities Based on UAV RGB Images","volume":"125","author":"Mao","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_187","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.biosystemseng.2020.10.001","article-title":"Field Identification of Weed Species and Glyphosate-Resistant Weeds Using High Resolution Imagery in Early Growing Season","volume":"200","author":"Shirzadifar","year":"2020","journal-title":"Biosyst. Eng."},{"key":"ref_188","doi-asserted-by":"crossref","first-page":"109214","DOI":"10.1016\/j.ejrad.2020.109214","article-title":"Mid-Term Response Assessment in Multiple Myeloma Using a Texture Analysis Approach on Dual Energy-CT-Derived Bone Marrow Images\u2014A Proof of Principle Study","volume":"131","author":"Reinert","year":"2020","journal-title":"Eur. J. Radiol."},{"key":"ref_189","doi-asserted-by":"crossref","unstructured":"Avila-Reyes, S.V., M\u00e1rquez-Morales, C.E., Moreno-Le\u00f3n, G.R., Jim\u00e9nez-Aparicio, A.R., Arenas-Ocampo, M.L., Solorza-Feria, J., Garc\u00eda-Armenta, E., and Villalobos-Espinosa, J.C. (2022). Comparative Analysis of Fermentation Conditions on the Increase of Biomass and Morphology of Milk Kefir Grains. Appl. Sci., 12.","DOI":"10.3390\/app12052459"},{"key":"ref_190","first-page":"472","article-title":"Lettuce Canopy Area Measurement Using Static Supervised Neural Networks Based on Numerical Image Textural Feature Analysis of Haralick and Gray Level Co-Occurrence Matrix","volume":"42","author":"Lauguico","year":"2020","journal-title":"AGRIVITA J. Agric. Sci."},{"key":"ref_191","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1049\/iet-ipr.2019.1024","article-title":"New Design of Adaptive Gabor Wavelet Filter Bank for Medical Image Retrieval","volume":"14","author":"Samantaray","year":"2020","journal-title":"IET Image Process."},{"key":"ref_192","doi-asserted-by":"crossref","first-page":"30335","DOI":"10.1007\/s11042-019-07863-z","article-title":"Human Vision Inspired Feature Extraction for Facial Expression Recognition","volume":"78","author":"Sadeghi","year":"2019","journal-title":"Multimed. Tools Appl."},{"key":"ref_193","first-page":"116","article-title":"Effective Plant Discrimination Based on the Combination of Local Binary Pattern Operators and Multiclass Support Vector Machine Methods","volume":"6","author":"Le","year":"2019","journal-title":"Inf. Process. Agric."},{"key":"ref_194","doi-asserted-by":"crossref","unstructured":"Farooq, A., Jia, X., Hu, J., and Zhou, J. (2019). Multi-Resolution Weed Classification via Convolutional Neural Network and Superpixel Based Local Binary Pattern Using Remote Sensing Images. Remote Sens., 11.","DOI":"10.3390\/rs11141692"},{"key":"ref_195","doi-asserted-by":"crossref","first-page":"5528","DOI":"10.1007\/s11227-020-03474-w","article-title":"Classification of Hyperspectral Remote Sensing Image via Rotation-Invariant Local Binary Pattern-Based Weighted Generalized Closest Neighbor","volume":"77","author":"Sharma","year":"2021","journal-title":"J. Supercomput."},{"key":"ref_196","doi-asserted-by":"crossref","first-page":"9913581","DOI":"10.1155\/2021\/9913581","article-title":"An Improved Tool Wear Monitoring Method Using Local Image and Fractal Dimension of Workpiece","volume":"2021","author":"Yu","year":"2021","journal-title":"Math. Probl. Eng."},{"key":"ref_197","doi-asserted-by":"crossref","first-page":"819","DOI":"10.1007\/s10044-019-00839-7","article-title":"Fractal Dimension of Synthesized and Natural Color Images in Lab Space","volume":"23","author":"Panigrahy","year":"2020","journal-title":"Pattern Anal. Appl."},{"key":"ref_198","doi-asserted-by":"crossref","first-page":"101517","DOI":"10.1016\/j.jocs.2021.101517","article-title":"Above-Ground Biomass Estimation from LiDAR Data Using Random Forest Algorithms","volume":"58","author":"Bastarrika","year":"2022","journal-title":"J. Comput. Sci."},{"key":"ref_199","doi-asserted-by":"crossref","unstructured":"Gao, L., and Zhang, X. (2021). Above-Ground Biomass Estimation of Plantation with Complex Forest Stand Structure Using Multiple Features from Airborne Laser Scanning Point Cloud Data. Forests, 12.","DOI":"10.3390\/f12121713"},{"key":"ref_200","doi-asserted-by":"crossref","unstructured":"Novotn\u00fd, J., Navr\u00e1tilov\u00e1, B., Janoutov\u00e1, R., Oulehle, F., and Homolov\u00e1, L. (2020). Influence of Site-Specific Conditions on Estimation of Forest above Ground Biomass from Airborne Laser Scanning. Forests, 11.","DOI":"10.3390\/f11030268"},{"key":"ref_201","first-page":"101899","article-title":"Tree Height in Tropical Forest as Measured by Different Ground, Proximal, and Remote Sensing Instruments, and Impacts on above Ground Biomass Estimates","volume":"82","author":"Ding","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_202","doi-asserted-by":"crossref","first-page":"108419","DOI":"10.1016\/j.fcr.2021.108419","article-title":"A VI-Based Phenology Adaptation Approach for Rice Crop Monitoring Using UAV Multispectral Images","volume":"277","author":"Yang","year":"2022","journal-title":"Field Crops Res."},{"key":"ref_203","doi-asserted-by":"crossref","unstructured":"Lin, J., Wang, M., Ma, M., and Lin, Y. (2018). Aboveground Tree Biomass Estimation of Sparse Subalpine Coniferous Forest with UAV Oblique Photography. Remote Sens., 10.","DOI":"10.3390\/rs10111849"},{"key":"ref_204","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1016\/j.ecolind.2016.03.036","article-title":"Remote Estimation of Canopy Height and Aboveground Biomass of Maize Using High-Resolution Stereo Images from a Low-Cost Unmanned Aerial Vehicle System","volume":"67","author":"Li","year":"2016","journal-title":"Ecol. Indic."},{"key":"ref_205","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1111\/1365-2745.12847","article-title":"Above-Ground Biomass Is Driven by Mass-Ratio Effects and Stand Structural Attributes in a Temperate Deciduous Forest","volume":"106","author":"Fotis","year":"2018","journal-title":"J. Ecol."},{"key":"ref_206","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1111\/1365-2745.13036","article-title":"Annual Ring Growth of a Widespread High Arctic Shrub Reflects Past Fluctuations in Community-Level Plant Biomass","volume":"107","author":"Buchwal","year":"2019","journal-title":"J. Ecol."},{"key":"ref_207","doi-asserted-by":"crossref","unstructured":"Luo, M., Wang, Y., Xie, Y., Zhou, L., Qiao, J., Qiu, S., and Sun, Y. (2021). Combination of Feature Selection and Catboost for Prediction: The First Application to the Estimation of Aboveground Biomass. Forests, 12.","DOI":"10.3390\/f12020216"},{"key":"ref_208","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1080\/15481603.2017.1408892","article-title":"Less Is More: Optimizing Classification Performance through Feature Selection in a Very-High-Resolution Remote Sensing Object-Based Urban Application","volume":"55","author":"Georganos","year":"2018","journal-title":"GIScience Remote Sens."},{"key":"ref_209","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.rse.2018.11.017","article-title":"Integration of Multi-Resource Remotely Sensed Data and Allometric Models for Forest Aboveground Biomass Estimation in China","volume":"221","author":"Huang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_210","doi-asserted-by":"crossref","unstructured":"Li, Y., Li, C., Li, M., and Liu, Z. (2019). Influence of Variable Selection and Forest Type on Forest Aboveground Biomass Estimation Using Machine Learning Algorithms. Forests, 10.","DOI":"10.3390\/f10121073"},{"key":"ref_211","doi-asserted-by":"crossref","first-page":"106839","DOI":"10.1016\/j.csda.2019.106839","article-title":"Benchmark for Filter Methods for Feature Selection in High-Dimensional Classification Data","volume":"143","author":"Bommert","year":"2020","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_212","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1016\/j.rse.2018.07.022","article-title":"Quantification of Uncertainty in Aboveground Biomass Estimates Derived from Small-Footprint Airborne LiDAR","volume":"216","author":"Xu","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_213","doi-asserted-by":"crossref","first-page":"119648","DOI":"10.1016\/j.foreco.2021.119648","article-title":"Impacts of Selective Logging on Amazon Forest Canopy Structure and Biomass with a LiDAR and Photogrammetric Survey Sequence","volume":"500","author":"Figueiredo","year":"2021","journal-title":"For. Ecol. Manag."},{"key":"ref_214","doi-asserted-by":"crossref","first-page":"1935","DOI":"10.1007\/s11277-021-08712-9","article-title":"Mutual Information Feature Selection (MIFS) Based Crop Yield Prediction on Corn and Soybean Crops Using Multilayer Stacked Ensemble Regression (MSER)","volume":"126","author":"Iniyan","year":"2022","journal-title":"Wirel. Pers. Commun."},{"key":"ref_215","doi-asserted-by":"crossref","first-page":"118805","DOI":"10.1016\/j.foreco.2020.118805","article-title":"Determinants of Aboveground Biomass in Forests across Three Climatic Zones in China","volume":"482","author":"Ding","year":"2021","journal-title":"For. Ecol. Manag."},{"key":"ref_216","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1111\/nph.16666","article-title":"Increased Above-Ground Resource Allocation Is a Likely Precursor for Independent Evolutionary Origins of Annuality in the Pooideae Grass Subfamily","volume":"228","author":"Lindberg","year":"2020","journal-title":"New Phytol."},{"key":"ref_217","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Guan, F., Fan, S., Yan, X., and Huang, L. (2021). Biomass Estimation, Nutrient Content, and Decomposition Rate of Shoot Sheath in Moso Bamboo Forest of Yixing Forest Farm, China. Forests, 12.","DOI":"10.3390\/f12111555"},{"key":"ref_218","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1080\/22797254.2021.1901063","article-title":"Biomass Retrieval Based on Genetic Algorithm Feature Selection and Support Vector Regression in Alpine Grassland Using Ground-Based Hyperspectral and Sentinel-1 SAR Data","volume":"54","author":"Chiarito","year":"2021","journal-title":"Eur. J. Remote Sens."},{"key":"ref_219","doi-asserted-by":"crossref","first-page":"101754","DOI":"10.1016\/j.ecoinf.2022.101754","article-title":"Influences of Vegetation, Model, and Data Parameters on Forest Aboveground Biomass Assessment Using an Area-Based Approach","volume":"70","author":"Brovkina","year":"2022","journal-title":"Ecol. Inform."},{"key":"ref_220","doi-asserted-by":"crossref","unstructured":"Mauro, F., Monleon, V.J., Gray, A.N., Kuegler, O., Temesgen, H., Hudak, A.T., Fekety, P.A., and Yang, Z. (2022). Comparison of Model-Assisted Endogenous Poststratification Methods for Estimation of Above-Ground Biomass Change in Oregon, USA. Remote Sens., 14.","DOI":"10.3390\/rs14236024"},{"key":"ref_221","doi-asserted-by":"crossref","first-page":"12763","DOI":"10.1080\/10106049.2022.2071475","article-title":"Decision Tree-Based Machine Learning Models for above-Ground Biomass Estimation Using Multi-Source Remote Sensing Data and Object-Based Image Analysis","volume":"37","author":"Tamiminia","year":"2022","journal-title":"Geocarto Int."},{"key":"ref_222","doi-asserted-by":"crossref","unstructured":"L\u00f3pez-Serrano, P.M., Dom\u00ednguez, J.L.C., Corral-Rivas, J.J., Jim\u00e9nez, E., L\u00f3pez-S\u00e1nchez, C.A., and Vega-Nieva, D.J. (2020). Modeling of Aboveground Biomass with Landsat 8 Oli and Machine Learning in Temperate Forests. Forests, 11.","DOI":"10.3390\/f11010011"},{"key":"ref_223","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1080\/2150704X.2018.1452058","article-title":"Estimation of Biophysical Parameters in Wheat Crops in Golestan Province Using Ultra-High Resolution Images","volume":"9","author":"Sharifi","year":"2018","journal-title":"Remote Sens. Lett."},{"key":"ref_224","doi-asserted-by":"crossref","first-page":"105385","DOI":"10.1016\/j.compag.2020.105385","article-title":"An Automatic Method for Weed Mapping in Oat Fields Based on UAV Imagery","volume":"173","author":"Zrinjski","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_225","doi-asserted-by":"crossref","unstructured":"Dente, L., Guerriero, L., Carvalhais, N., Silva, P.F., Soares, P., Ferrazzoli, P., and Pierdicca, N. (2018, January 9). Potential of UAV GNSS-R for Forest Biomass Mapping. Proceedings of the Active and Passive Microwave Remote Sensing for Environmental Monitoring II, Berlin, Germany.","DOI":"10.1117\/12.2327130"},{"key":"ref_226","doi-asserted-by":"crossref","first-page":"1075","DOI":"10.1080\/10106049.2019.1624988","article-title":"Application of Feature Selection Methods and Machine Learning Algorithms for Saltmarsh Biomass Estimation Using Worldview-2 Imagery","volume":"36","author":"Rasel","year":"2021","journal-title":"Geocarto Int."},{"key":"ref_227","doi-asserted-by":"crossref","first-page":"943","DOI":"10.1080\/15481603.2020.1829377","article-title":"Peatland Leaf-Area Index and Biomass Estimation with Ultra-High Resolution Remote Sensing","volume":"57","author":"Juutinen","year":"2020","journal-title":"GIScience Remote Sens."},{"key":"ref_228","doi-asserted-by":"crossref","first-page":"107450","DOI":"10.1016\/j.ecolind.2021.107450","article-title":"A Method to Avoid Spatial Overfitting in Estimation of Grassland Above-Ground Biomass on the Tibetan Plateau","volume":"125","author":"Yu","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_229","doi-asserted-by":"crossref","unstructured":"Liu, Y., Feng, H., Yue, J., Fan, Y., Jin, X., Zhao, Y., Song, X., Long, H., and Yang, G. (2022). Estimation of Potato Above-Ground Biomass Using UAV-Based Hyperspectral Images and Machine-Learning Regression. Remote Sens., 14.","DOI":"10.3390\/rs14215449"},{"key":"ref_230","doi-asserted-by":"crossref","first-page":"107581","DOI":"10.1016\/j.compag.2022.107581","article-title":"UAS-Based Imaging for Prediction of Chickpea Crop Biophysical Parameters and Yield","volume":"205","author":"Avneri","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_231","doi-asserted-by":"crossref","first-page":"154226","DOI":"10.1016\/j.scitotenv.2022.154226","article-title":"Spatiotemporal Dynamics of Grassland Aboveground Biomass and Its Driving Factors in North China over the Past 20 Years","volume":"826","author":"Ge","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_232","doi-asserted-by":"crossref","first-page":"1751","DOI":"10.1038\/s41598-021-81267-8","article-title":"Estimating Above-Ground Biomass of Subtropical Forest Using Airborne LiDAR in Hong Kong","volume":"11","author":"Chan","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_233","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1186\/s13021-020-00151-6","article-title":"Improving Aboveground Biomass Maps of Tropical Dry Forests by Integrating LiDAR, ALOS PALSAR, Climate and Field Data","volume":"15","author":"Mas","year":"2020","journal-title":"Carbon Balance Manag."},{"key":"ref_234","first-page":"102239","article-title":"Biomass and Vegetation Coverage Survey in the Mu Us Sandy Land\u2014Based on Unmanned Aerial Vehicle RGB Images","volume":"94","author":"Guo","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_235","doi-asserted-by":"crossref","first-page":"642906","DOI":"10.3389\/fenvs.2021.642906","article-title":"UAV to Inform Restoration: A Case Study From a California Tidal Marsh","volume":"9","author":"Haskins","year":"2021","journal-title":"Front. Environ. Sci."},{"key":"ref_236","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1007\/s10712-019-09519-x","article-title":"The Relevance of Forest Structure for Biomass and Productivity in Temperate Forests: New Perspectives for Remote Sensing","volume":"40","author":"Fischer","year":"2019","journal-title":"Surv. Geophys."},{"key":"ref_237","doi-asserted-by":"crossref","first-page":"964","DOI":"10.1080\/01431161.2020.1820618","article-title":"Assessing of Urban Vegetation Biomass in Combination with LiDAR and High-Resolution Remote Sensing Images","volume":"42","author":"Zhang","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_238","doi-asserted-by":"crossref","first-page":"2206","DOI":"10.1016\/j.renene.2019.10.020","article-title":"Lignin Plays a Key Role in Determining Biomass Recalcitrance in Forage Grasses","volume":"147","author":"Oliveira","year":"2020","journal-title":"Renew. Energy"},{"key":"ref_239","doi-asserted-by":"crossref","unstructured":"Kudela, R.M., Hooker, S.B., Houskeeper, H.F., and McPherson, M. (2019). The Influence of Signal to Noise Ratio of Legacy Airborne and Satellite Sensors for Simulating Next-Generation Coastal and Inland Water Products. Remote Sens., 11.","DOI":"10.3390\/rs11182071"},{"key":"ref_240","first-page":"100771","article-title":"Development of a Methodological Approach to Estimate Vegetation Biomass Using Remote Sensing in the Brazilian Semiarid NE Region","volume":"27","author":"Sales","year":"2022","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_241","doi-asserted-by":"crossref","first-page":"1058","DOI":"10.1007\/s12237-020-00828-8","article-title":"Quantifying Vegetation and Landscape Metrics with Hyperspatial Unmanned Aircraft System Imagery in a Coastal Oligohaline Marsh","volume":"45","author":"Broussard","year":"2020","journal-title":"Estuaries Coasts"},{"key":"ref_242","doi-asserted-by":"crossref","unstructured":"Moradi, F., Darvishsefat, A.A., Pourrahmati, M.R., Deljouei, A., and Borz, S.A. (2022). Estimating Aboveground Biomass in Dense Hyrcanian Forests by the Use of Sentinel-2 Data. Forests, 13.","DOI":"10.3390\/f13010104"},{"key":"ref_243","doi-asserted-by":"crossref","unstructured":"Han, Y., Tang, R., Liao, Z., Zhai, B., and Fan, J. (2022). A Novel Hybrid GOA-XGB Model for Estimating Wheat Aboveground Biomass Using UAV-Based Multispectral Vegetation Indices. Remote Sens., 14.","DOI":"10.3390\/rs14143506"},{"key":"ref_244","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1080\/07038992.2021.1968811","article-title":"Multi-Sensor Aboveground Biomass Estimation in the Broadleaved Hyrcanian Forest of Iran","volume":"47","author":"Ronoud","year":"2021","journal-title":"Can. J. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/14\/3543\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:11:59Z","timestamp":1760127119000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/14\/3543"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,14]]},"references-count":244,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["rs15143543"],"URL":"https:\/\/doi.org\/10.3390\/rs15143543","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,14]]}}}