{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T05:42:38Z","timestamp":1775022158238,"version":"3.50.1"},"reference-count":76,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2019,5,28]],"date-time":"2019-05-28T00:00:00Z","timestamp":1559001600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the 13th Five -Year Plan for Chinese National Key R&amp;D Project","award":["2017YFC0403203"],"award-info":[{"award-number":["2017YFC0403203"]}]},{"name":"Major Project of Industry -Education - Research Cooperative Innovation in Yangling Demonstration Zone in China","award":["2018CXY-23"],"award-info":[{"award-number":["2018CXY-23"]}]},{"name":"the 111 Project","award":["No.B12007"],"award-info":[{"award-number":["No.B12007"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The rapid, accurate, and economical estimation of crop above-ground biomass at the farm scale is crucial for precision agricultural management. The unmanned aerial vehicle (UAV) remote-sensing system has a great application potential with the ability to obtain remote-sensing imagery with high temporal-spatial resolution. To verify the application potential of consumer-grade UAV RGB imagery in estimating maize above-ground biomass, vegetation indices and plant height derived from UAV RGB imagery were adopted. To obtain a more accurate observation, plant height was directly derived from UAV RGB point clouds. To search the optimal estimation method, the estimation performances of the models based on vegetation indices alone, based on plant height alone, and based on both vegetation indices and plant height were compared. The results showed that plant height directly derived from UAV RGB point clouds had a high correlation with ground-truth data with an R2 value of 0.90 and an RMSE value of 0.12 m. The above-ground biomass exponential regression models based on plant height alone had higher correlations for both fresh and dry above-ground biomass with R2 values of 0.77 and 0.76, respectively, compared to the linear regression model (both R2 values were 0.59). The vegetation indices derived from UAV RGB imagery had great potential to estimate maize above-ground biomass with R2 values ranging from 0.63 to 0.73. When estimating the above-ground biomass of maize by using multivariable linear regression based on vegetation indices, a higher correlation was obtained with an R2 value of 0.82. There was no significant improvement of the estimation performance when plant height derived from UAV RGB imagery was added into the multivariable linear regression model based on vegetation indices. When estimating crop above-ground biomass based on UAV RGB remote-sensing system alone, looking for optimized vegetation indices and establishing estimation models with high performance based on advanced algorithms (e.g., machine learning technology) may be a better way.<\/jats:p>","DOI":"10.3390\/rs11111261","type":"journal-article","created":{"date-parts":[[2019,5,28]],"date-time":"2019-05-28T11:18:09Z","timestamp":1559042289000},"page":"1261","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":158,"title":["Estimating Above-Ground Biomass of Maize Using Features Derived from UAV-Based RGB Imagery"],"prefix":"10.3390","volume":"11","author":[{"given":"Yaxiao","family":"Niu","sequence":"first","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Northwest A&amp;F University, Yangling 712100, Shaanxi, China"},{"name":"Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture, Yangling 712100, Shaanxi, China"}]},{"given":"Liyuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Northwest A&amp;F University, Yangling 712100, Shaanxi, China"},{"name":"Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture, Yangling 712100, Shaanxi, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9781-6086","authenticated-orcid":false,"given":"Huihui","family":"Zhang","sequence":"additional","affiliation":[{"name":"Water Management and Systems Research Unit, USDA-ARS, 2150 Centre Avenue, Bldg. D., Fort Collins, CO 80526, USA"}]},{"given":"Wenting","family":"Han","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Northwest A&amp;F University, Yangling 712100, Shaanxi, China"},{"name":"Institute of Soil and Water Conservation, Northwest A&amp;F University, Yangling 712100, Shaanxi, China"}]},{"given":"Xingshuo","family":"Peng","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Northwest A&amp;F University, Yangling 712100, Shaanxi, China"},{"name":"Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture, Yangling 712100, Shaanxi, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Atkinson, J.A., Jackson, R.J., Bentley, A.R., Ober, E., and Wells, D.M. (2018). Field Phenotyping for the Future. Annu. Plant Rev. Online.","DOI":"10.1002\/9781119312994.apr0651"},{"key":"ref_2","unstructured":"Tubiello, F. (2012). Climate Change Adaption and Mitigation: Challenges and Opportunities in the Food Sector, Natural Resources Management and Environment Department."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1007\/s12571-011-0140-5","article-title":"Crops that feed the world 6. Past successes and future challenges to the role played by maize in global food security","volume":"3","author":"Shiferaw","year":"2011","journal-title":"Food Secur."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Michez, A., Bauwens, S., Brostaux, Y., Hiel, M.-P., Garr\u00e9, S., Lejeune, P., and Dumont, B. (2018). How Far Can Consumer-Grade UAV RGB Imagery Describe Crop Production? A 3D and Multitemporal Modeling Approach Applied to Zea mays. Remote Sens., 10.","DOI":"10.3390\/rs10111798"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.fcr.2014.05.001","article-title":"Predicting grain yield and protein content in wheat by fusing multi-sensor and multi-temporal remote-sensing images","volume":"164","author":"Wang","year":"2014","journal-title":"Field Crops Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11119-016-9469-2","article-title":"Estimation of water productivity in winter wheat using the AquaCrop model with field hyperspectral data","volume":"19","author":"Jin","year":"2016","journal-title":"Precis. Agric."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"351","DOI":"10.2134\/agronj2012.0421","article-title":"Efficiency of Irrigation Water Use: A Review from the Perspectives of Multiple Disciplines","volume":"105","author":"Nair","year":"2013","journal-title":"Agron. J."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.fcr.2017.11.024","article-title":"Photogrammetry for the estimation of wheat biomass and harvest index","volume":"216","author":"Walter","year":"2018","journal-title":"Field Crops Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.scitotenv.2015.12.104","article-title":"Environmental and economic benefits of variable rate nitrogen fertilization in a nitrate vulnerable zone","volume":"545\u2013546","author":"Basso","year":"2016","journal-title":"Sci. Total Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"e4836","DOI":"10.7717\/peerj.4836","article-title":"Impact of crop residue management on crop production and soil chemistry after seven years of crop rotation in temperate climate, loamy soils","volume":"6","author":"Hiel","year":"2018","journal-title":"PeerJ"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"486","DOI":"10.1016\/j.ecolind.2015.04.016","article-title":"Airborne LiDAR technique for estimating biomass components of maize: A case study in Zhangye City, Northwest China","volume":"57","author":"Li","year":"2015","journal-title":"Ecol. Indic."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1312","DOI":"10.1016\/j.rse.2010.01.010","article-title":"A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data","volume":"114","author":"Vermote","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Lumbierres, M., M\u00e9ndez, P., Bustamante, J., Soriguer, R., and Santamar\u00eda, L. (2017). Modeling Biomass Production in Seasonal Wetlands Using MODIS NDVI Land Surface Phenology. Remote Sens., 9.","DOI":"10.3390\/rs9040392"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Li, M., Wu, J., Song, C., He, Y., Niu, B., Fu, G., Tarolli, P., Tietjen, B., and Zhang, X. (2019). Temporal Variability of Precipitation and Biomass of Alpine Grasslands on the Northern Tibetan Plateau. Remote Sens., 11.","DOI":"10.3390\/rs11030360"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Chen, L., Wang, Y., Ren, C., Zhang, B., and Wang, Z. (2019). Optimal Combination of Predictors and Algorithms for Forest Above-Ground Biomass Mapping from Sentinel and SRTM Data. Remote Sens., 11.","DOI":"10.3390\/rs11040414"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Pandit, S., Tsuyuki, S., and Dube, T. (2018). Landscape-Scale Aboveground Biomass Estimation in Buffer Zone Community Forests of Central Nepal: Coupling In Situ Measurements with Landsat 8 Satellite Data. Remote Sens., 10.","DOI":"10.3390\/rs10111848"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Pham, T.D., and Yoshino, K. (2017). Aboveground biomass estimation of mangrove species using ALOS-2 PALSAR imagery in Hai Phong City, Vietnam. J. Appl. Remote Sens., 11.","DOI":"10.1117\/1.JRS.11.026010"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.isprsjprs.2017.10.016","article-title":"Estimation and mapping of above-ground biomass of mangrove forests and their replacement land uses in the Philippines using Sentinel imagery","volume":"134","author":"Castillo","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"17097","DOI":"10.3390\/rs71215873","article-title":"Radarsat-2 Backscattering for the Modeling of Biophysical Parameters of Regenerating Mangrove Forests","volume":"7","author":"Cougo","year":"2015","journal-title":"Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhang, L., Zhang, H., Niu, Y., and Han, W. (2019). Mapping Maize Water Stress Based on UAV Multispectral Remote Sensing. Remote Sens., 11.","DOI":"10.3390\/rs11060605"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.compag.2004.02.006","article-title":"Imaging from an unmanned aerial vehicle: agricultural surveillance and decision support","volume":"44","author":"Herwitz","year":"2004","journal-title":"Comput. Electron. Agric."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Michez, A., Lejeune, P., Bauwens, S., Herinaina, A., Blaise, Y., Castro Mu\u00f1oz, E., 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_23","first-page":"79","article-title":"Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley","volume":"39","author":"Bendig","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.isprsjprs.2017.10.011","article-title":"Unmanned Aerial System (UAS)-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine","volume":"134","author":"Maimaitijiang","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","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_26","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_27","doi-asserted-by":"crossref","unstructured":"Brocks, S., and Bareth, G. (2018). Estimating Barley Biomass with Crop Surface Models from Oblique RGB Imagery. Remote Sens., 10.","DOI":"10.3390\/rs10020268"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhang, H.F., Sun, Y., Chang, L., Qin, Y., Chen, J.J., Qin, Y., Du, J.X., Yi, S.H., and Wang, Y.L. (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_29","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1080\/10106040608542399","article-title":"Vegetation Indices: Advances Made in Biomass Estimation and Vegetation Monitoring in the Last 30 Years","volume":"21","author":"Silleos","year":"2006","journal-title":"Geocarto Int."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"120","DOI":"10.5344\/ajev.2014.14070","article-title":"Characterization of Vitis vinifera L. Canopy Using Unmanned Aerial Vehicle-Based Remote Sensing and Photogrammetry Techniques","volume":"66","author":"Ballesteros","year":"2015","journal-title":"Am. J. Enol. Vitic."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.foreco.2018.10.058","article-title":"Estimating selective logging impacts on aboveground biomass in tropical forests using digital aerial photography obtained before and after a logging event from an unmanned aerial vehicle","volume":"433","author":"Ota","year":"2019","journal-title":"For. Ecol. Manag."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.isprsjprs.2015.08.002","article-title":"Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: From camera calibration to quality assurance","volume":"108","author":"Aasen","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Kachamba, D.J., Orka, H.O., Gobakken, T., Eid, T., and Mwase, W. (2016). Biomass Estimation Using 3D Data from Unmanned Aerial Vehicle Imagery in a Tropical Woodland. Remote Sens., 8.","DOI":"10.3390\/rs8110968"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.compag.2018.05.034","article-title":"High-throughput field phenotyping in dry bean using small unmanned aerial vehicle based multispectral imagery","volume":"151","author":"Sankaran","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_35","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_36","doi-asserted-by":"crossref","unstructured":"Yue, J.B., Feng, H.K., Jin, X.L., Yuan, H.H., Li, Z.H., Zhou, C.Q., Yang, G.J., and Tian, Q.J. (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_37","doi-asserted-by":"crossref","unstructured":"Lu, N., Zhou, J., Han, Z.X., Li, D., Cao, Q., Yao, X., Tian, Y.C., Zhu, Y., Cao, W.X., and Cheng, T. (2019). Improved estimation of aboveground biomass in wheat from RGB imagery and point cloud data acquired with a low-cost unmanned aerial vehicle system. Plant Methods, 15.","DOI":"10.1186\/s13007-019-0402-3"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Han, L., Yang, G.J., Dai, H.Y., Xu, B., Yang, H., Feng, H.K., Li, Z.H., and Yang, X.D. (2019). Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data. Plant Methods, 15.","DOI":"10.1186\/s13007-019-0394-z"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Li, J.T., Shi, Y.Y., Veeranampalayam-Sivakumar, A.N., and Schachtman, D.P. (2018). Elucidating Sorghum Biomass, Nitrogen and Chlorophyll Contents With Spectral and Morphological Traits Derived From Unmanned Aircraft System. Front. Plant Sci., 9.","DOI":"10.3389\/fpls.2018.01406"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.biosystemseng.2015.01.008","article-title":"Multi-temporal imaging using an unmanned aerial vehicle for monitoring a sunflower crop","volume":"132","author":"Vega","year":"2015","journal-title":"Biosyst. Eng."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1392","DOI":"10.3390\/rs4051392","article-title":"An Automated Technique for Generating Georectified Mosaics from Ultra-High Resolution Unmanned Aerial Vehicle (UAV) Imagery, Based on Structure from Motion (SfM) Point Clouds","volume":"4","author":"Turner","year":"2012","journal-title":"Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Duan, T., Zheng, B., Guo, W., Ninomiya, S., Guo, Y., and Chapman, S.C. (2017). Comparison of ground cover estimates from experiment plots in cotton, sorghum and sugarcane based on images and ortho-mosaics captured by UAV. Funct. Plant Biol., 44.","DOI":"10.1071\/FP16123"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1016\/j.biosystemseng.2014.11.007","article-title":"Monitoring of crop biomass using true colour aerial photographs taken from a remote controlled hexacopter","volume":"129","author":"Jannoura","year":"2015","journal-title":"Biosyst. Eng."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1007\/s11119-005-2324-5","article-title":"Evaluation of Digital Photography from Model Aircraft for Remote Sensing of Crop Biomass and Nitrogen Status","volume":"6","author":"Hunt","year":"2005","journal-title":"Precis. Agric."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Zhang, M., Zhang, X., Zeng, H., and Wu, B. (2016). Mapping Winter Wheat Biomass and Yield Using Time Series Data Blended from PROBA-V 100- and 300-m S1 Products. Remote Sens., 8.","DOI":"10.3390\/rs8100824"},{"key":"ref_46","first-page":"235","article-title":"Assessment of RapidEye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops","volume":"34","author":"Kross","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_47","first-page":"13","article-title":"Estimating biomass of winter wheat using narrowband vegetation","volume":"3","author":"Ambrus","year":"2015","journal-title":"J. Cent. Eur. Green Innov."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Schirrmann, M., Giebel, A., Gleiniger, F., Pflanz, M., Lentschke, J., and Dammer, K.-H. (2016). Monitoring Agronomic Parameters of Winter Wheat Crops with Low-Cost UAV Imagery. Remote Sens., 8.","DOI":"10.3390\/rs8090706"},{"key":"ref_49","unstructured":"Kataoka, T., Kaneko, T., Okamoto, H., and Hata, S. (2003, January 20\u201324). Crop growth estimation system using machine vision. Proceedings of the IEEE\/ASME International Conference on Advanced Intelligent Mechatronics, Kobe, Japan."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s11119-005-6787-1","article-title":"Automated Crop and Weed Monitoring in Widely Spaced Cereals","volume":"7","author":"Hague","year":"2006","journal-title":"Precis. Agric."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.compag.2010.09.013","article-title":"Automatic segmentation of relevant textures in agricultural images","volume":"75","author":"Guijarro","year":"2011","journal-title":"Comput. Electron. Agric."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.compag.2016.04.024","article-title":"A survey of image processing techniques for plant extraction and segmentation in the field","volume":"125","author":"Hamuda","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2009","DOI":"10.1080\/01431160902929271","article-title":"It\u2019s all about the format\u2014Unleashing the power of RAW aerial photography","volume":"31","author":"Verhoeven","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1834","DOI":"10.3389\/fpls.2018.01834","article-title":"Potential of UAV-Based Active Sensing for Monitoring Rice Leaf Nitrogen Status","volume":"9","author":"Li","year":"2018","journal-title":"Front. Plant Sci."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1109\/TAC.1974.1100705","article-title":"A new look at the statistical model identification","volume":"19","author":"Hirotugu","year":"1974","journal-title":"Autom. Control Comput. Sci."},{"key":"ref_56","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_57","doi-asserted-by":"crossref","first-page":"2150","DOI":"10.1080\/01431161.2016.1226002","article-title":"Assessment of a canopy height model (CHM) in a vineyard using UAV-based multispectral imaging","volume":"38","author":"Matese","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1016\/j.ecolind.2016.10.001","article-title":"Fusion of airborne LiDAR data and hyperspectral imagery for aboveground and belowground forest biomass estimation","volume":"73","author":"Luo","year":"2017","journal-title":"Ecol. Indic."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Fernandez-Alvarez, M., Armesto, J., and Picos, J. (2019). LiDAR-Based Wildfire Prevention in WUI: The Automatic Detection, Measurement and Evaluation of Forest Fuels. Forests, 10.","DOI":"10.3390\/f10020148"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"2954","DOI":"10.1080\/01431161.2017.1285083","article-title":"An integrated UAV-borne lidar system for 3D habitat mapping in three forest ecosystems across China","volume":"38","author":"Guo","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Madec, S., Baret, F., de Solan, B., Thomas, S., Dutartre, D., Jezequel, S., Hemmerle, M., Colombeau, G., and Comar, A. (2017). High-Throughput Phenotyping of Plant Height: Comparing Unmanned Aerial Vehicles and Ground LiDAR Estimates. Front. Plant Sci., 8.","DOI":"10.3389\/fpls.2017.02002"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Wang, D.L., Xin, X.P., Shao, Q.Q., Brolly, M., Zhu, Z.L., and Chen, J. (2017). Modeling Aboveground Biomass in Hulunber Grassland Ecosystem by Using Unmanned Aerial Vehicle Discrete Lidar. Sensors, 17.","DOI":"10.3390\/s17010180"},{"key":"ref_63","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_64","unstructured":"Iizuka, K., Yonehara, T., Itoh, M., and Kosugi, Y. (2018). Estimating Tree Height and Diameter at Breast Height (DBH) from Digital Surface Models and Orthophotos Obtained with an Unmanned Aerial System for a Japanese Cypress (Chamaecyparis obtusa) Forest. Remote Sens., 10."},{"key":"ref_65","first-page":"157","article-title":"Replacing Manual Rising Plate Meter Measurements with Low-cost UAV-Derived Sward Height Data in Grasslands for Spatial Monitoring","volume":"86","author":"Bareth","year":"2018","journal-title":"PFG-J. Photogramm. Remote Sens. Geoinf. Sci."},{"key":"ref_66","unstructured":"Wang, X.Q., Zhang, R.Y., Song, W., Han, L., Liu, X.L., Sun, X., Luo, M.J., Chen, K., Zhang, Y.X., and Yang, H. (2019). Dynamic plant height QTL revealed in maize through remote sensing phenotyping using a high-throughput unmanned aerial vehicle (UAV). Sci. Rep., 9."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Schirrmann, M., Hamdorf, A., Giebel, A., Gleiniger, F., Pflanz, M., and Dammer, K.H. (2017). Regression Kriging for Improving Crop Height Models Fusing Ultra-Sonic Sensing with UAV Imagery. Remote Sens., 9.","DOI":"10.3390\/rs9070665"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Ziliani, M.G., Parkes, S.D., Hoteit, I., and McCabe, M.F. (2018). Intra-Season Crop Height Variability at Commercial Farm Scales Using a Fixed-Wing UAV. Remote Sens., 10.","DOI":"10.3390\/rs10122007"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Gruner, 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_70","doi-asserted-by":"crossref","first-page":"10395","DOI":"10.3390\/rs61110395","article-title":"Estimating Biomass of Barley Using Crop Surface Models (CSMs) Derived from UAV-Based RGB Imaging","volume":"6","author":"Bendig","year":"2014","journal-title":"Remote Sens."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Wang, C., Nie, S., Xi, X., Luo, S., and Sun, X. (2016). Estimating the Biomass of Maize with Hyperspectral and LiDAR Data. Remote Sens., 9.","DOI":"10.3390\/rs9010011"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"11449","DOI":"10.3390\/rs70911449","article-title":"Fusion of Plant Height and Vegetation Indices for the Estimation of Barley Biomass","volume":"7","author":"Tilly","year":"2015","journal-title":"Remote Sens."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"538","DOI":"10.3390\/agriculture5030538","article-title":"Transferability of Models for Estimating Paddy Rice Biomass from Spatial Plant Height Data","volume":"5","author":"Tilly","year":"2015","journal-title":"Agriculture"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Yue, J.B., Yang, G.J., Li, C.C., Li, Z.H., Wang, Y.J., Feng, H.K., and Xu, B. (2017). Estimation of Winter Wheat Above-Ground Biomass Using Unmanned Aerial Vehicle-Based Snapshot Hyperspectral Sensor and Crop Height Improved Models. Remote Sens., 9.","DOI":"10.3390\/rs9070708"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"733","DOI":"10.5194\/bg-14-733-2017","article-title":"Remote sensing of plant trait responses to field-based plant-soil feedback using UAV-based optical sensors","volume":"14","author":"Kooistra","year":"2017","journal-title":"Biogeosciences"},{"key":"ref_76","first-page":"66","article-title":"Combined use of agro-climatic and very high-resolution remote sensing information for crop monitoring","volume":"72","author":"Ballesteros","year":"2018","journal-title":"Int. J. Appl. Earth Obs. 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