{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T21:07:55Z","timestamp":1781989675099,"version":"3.54.5"},"reference-count":85,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2019,4,29]],"date-time":"2019-04-29T00:00:00Z","timestamp":1556496000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>There is ongoing interest in developing remote sensing technology to map and monitor the spatial distribution and carbon stock of mangrove forests. Previous research has demonstrated that the relationship between remote sensing derived parameters and aboveground carbon (AGC) stock varies for different species types. However, the coarse spatial resolution of satellite images has restricted the estimated AGC accuracy, especially at the individual species level. Recently, the availability of unmanned aerial vehicles (UAVs) has provided an operationally efficient approach to map the distribution of species and accurately estimate AGC stock at a fine scale in mangrove areas. In this study, we estimated mangrove AGC in the core area of northern Shenzhen Bay, South China, using four kinds of variables, including species type, canopy height metrics, vegetation indices, and texture features, derived from a low-cost UAV system. Three machine-learning algorithm models, including Random Forest (RF), Support Vector Regression (SVR), and Artificial Neural Network (ANN), were compared in this study, where a 10-fold cross-validation was used to evaluate each model\u2019s effectiveness. The results showed that a model that used all four type of variables, which were based on the RF algorithm, provided better AGC estimates (R2 = 0.81, relative RMSE (rRMSE) = 0.20, relative MAE (rMAE) = 0.14). The average predicted AGC from this model was 93.0 \u00b1 24.3 Mg C ha\u22121, and the total estimated AGC was 7903.2 Mg for the mangrove forests. The species-based model had better performance than the considered canopy-height-based model for AGC estimation, and mangrove species was the most important variable among all the considered input variables; the mean height (Hmean) the second most important variable. Additionally, the RF algorithms showed better performance in terms of mangrove AGC estimation than the SVR and ANN algorithms. Overall, a low-cost UAV system with a digital camera has the potential to enable satisfactory predictions of AGC in areas of homogenous mangrove forests.<\/jats:p>","DOI":"10.3390\/rs11091018","type":"journal-article","created":{"date-parts":[[2019,4,29]],"date-time":"2019-04-29T07:01:22Z","timestamp":1556521282000},"page":"1018","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":49,"title":["Remote Estimation of Mangrove Aboveground Carbon Stock at the Species Level Using a Low-Cost Unmanned Aerial Vehicle System"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5453-468X","authenticated-orcid":false,"given":"Zhen","family":"Li","sequence":"first","affiliation":[{"name":"School of Life Sciences\/Guangzhou Key Laboratory of Urban Landscape Dynamics, Sun Yat-sen University, Guangzhou 510275, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qijie","family":"Zan","sequence":"additional","affiliation":[{"name":"College of Life Sciences and Oceanography, Shenzhen University, Shenzhen 518060, China"},{"name":"Guangdong Neilingding-Futian National Nature Reserve, Shenzhen 518040, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiong","family":"Yang","sequence":"additional","affiliation":[{"name":"Guangdong Neilingding-Futian National Nature Reserve, Shenzhen 518040, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dehuang","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Life Sciences\/Guangzhou Key Laboratory of Urban Landscape Dynamics, Sun Yat-sen University, Guangzhou 510275, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Youjun","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Life Sciences\/Guangzhou Key Laboratory of Urban Landscape Dynamics, Sun Yat-sen University, Guangzhou 510275, China"},{"name":"School of Agronomy and Bioscience, Dali University, Dali 671003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1943-0185","authenticated-orcid":false,"given":"Shixiao","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Life Sciences\/Guangzhou Key Laboratory of Urban Landscape Dynamics, Sun Yat-sen University, Guangzhou 510275, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,4,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1038\/ngeo1123","article-title":"Mangroves among the most carbon-rich forests in the tropics","volume":"4","author":"Donato","year":"2011","journal-title":"Nat. Geosci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"313","DOI":"10.4155\/cmt.12.20","article-title":"Carbon sequestration in mangrove forests","volume":"3","author":"Alongi","year":"2014","journal-title":"Carbon Manag."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1111\/j.1466-8238.2010.00584.x","article-title":"Status and distribution of mangrove forests of the world using earth observation satellite data","volume":"20","author":"Giri","year":"2011","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1146\/annurev-marine-010213-135020","article-title":"Carbon cycling and storage in mangrove forests","volume":"6","author":"Alongi","year":"2014","journal-title":"Annu. Rev. Mar. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1126\/science.317.5834.41b","article-title":"A world without mangroves?","volume":"317","author":"Duke","year":"2007","journal-title":"Science"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1111\/geb.12449","article-title":"Creation of a high spatio-temporal resolution global database of continuous mangrove forest cover for the 21st century (CGMFC-21)","volume":"25","author":"Hamilton","year":"2016","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1089","DOI":"10.1038\/nclimate2734","article-title":"The potential of Indonesian mangrove forests for global climate change mitigation","volume":"5","author":"Murdiyarso","year":"2015","journal-title":"Nat. Clim. Chang."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1038\/s41558-018-0090-4","article-title":"Global carbon stocks and potential emissions due to mangrove deforestation from 2000 to 2012","volume":"8","author":"Hamilton","year":"2018","journal-title":"Nat. Clim. Chang."},{"key":"ref_9","unstructured":"Solomon, S., Intergovernmental Panel on Climate Change, and Working Group I (2007). Climate Change 2007: The Physical Science Basis: Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.jenvman.2013.11.037","article-title":"Carbon stocks and potential carbon storage in the mangrove forests of China","volume":"133","author":"Liu","year":"2014","journal-title":"J. Environ. Manag."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/S0378-1127(97)00054-6","article-title":"Mangroves of China: A brief review","volume":"96","author":"Li","year":"1997","journal-title":"For. Ecol. Manag."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Jia, M., Liu, M., Wang, Z., Mao, D., Ren, C., and Cui, H. (2016). Evaluating the effectiveness of conservation on mangroves: A remote sensing-based comparison for two adjacent protected areas in Shenzhen and Hong Kong, China. Remote Sens., 8.","DOI":"10.3390\/rs8080627"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1007\/s11355-010-0126-z","article-title":"Wetland changes and mangrove restoration planning in Shenzhen Bay, Southern China","volume":"7","author":"Ren","year":"2010","journal-title":"Landsc. Ecol. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"878","DOI":"10.3390\/rs3050878","article-title":"Remote sensing of mangrove ecosystems: A review","volume":"3","author":"Kuenzer","year":"2011","journal-title":"Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.ecss.2017.11.004","article-title":"Spatial complexities in aboveground carbon stocks of a semi-arid mangrove community: A remote sensing height-biomass-carbon approach","volume":"200","author":"Hickey","year":"2018","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.isprsjprs.2017.03.013","article-title":"Monitoring mangrove biomass change in Vietnam using SPOT images and an object-based approach combined with machine learning algorithms","volume":"128","author":"Pham","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"6765","DOI":"10.1080\/01431161.2010.512944","article-title":"Integrated LiDAR and IKONOS multispectral imagery for mapping mangrove distribution and physical properties","volume":"32","author":"Chadwick","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"7878","DOI":"10.3390\/rs6097878","article-title":"Estimating forest aboveground biomass by combining ALOS PALSAR and WorldView-2 Data: A case study at Purple Mountain National Park, Nanjing, China","volume":"6","author":"Deng","year":"2014","journal-title":"Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1551","DOI":"10.1080\/01431161.2017.1283072","article-title":"Mangrove above-ground carbon stock mapping of multi-resolution passive remote-sensing systems","volume":"38","author":"Wicaksono","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.rse.2014.04.029","article-title":"L-band ALOS PALSAR for biomass estimation of Matang Mangroves, Malaysia","volume":"155","author":"Hamdan","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"12192","DOI":"10.3390\/rs70912192","article-title":"Retrieval of mangrove aboveground biomass at the individual species level with WorldView-2 Images","volume":"7","author":"Zhu","year":"2015","journal-title":"Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.rse.2012.01.021","article-title":"Integration of airborne lidar and vegetation types derived from aerial photography for mapping aboveground live biomass","volume":"121","author":"Chen","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.biocon.2015.03.031","article-title":"Using lightweight unmanned aerial vehicles to monitor tropical forest recovery","volume":"186","author":"Zahawi","year":"2015","journal-title":"Biol. Conserv."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.biocon.2016.03.027","article-title":"Seeing the forest from drones: Testing the potential of lightweight drones as a tool for long-term forest monitoring","volume":"198","author":"Zhang","year":"2016","journal-title":"Biol. Conserv."},{"key":"ref_25","first-page":"2427","article-title":"Forestry applications of UAVs in Europe: A review","volume":"38","author":"Chiara","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Cao, J., Leng, W., Liu, K., Liu, L., He, Z., and Zhu, Y. (2018). Object-based bangrove species classification using unmanned aerial vehicle hyperspectral images and digital surface models. Remote Sens., 10.","DOI":"10.3390\/rs10010089"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Pham, T., Yokoya, N., Bui, D., Yoshino, K., and Friess, D. (2019). Remote sensing approaches for monitoring mangrove species, structure, and biomass: Opportunities and challenges. Remote Sens., 11.","DOI":"10.3390\/rs11030230"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wang, D., Wan, B., Qiu, P., Su, Y., Guo, Q., and Wu, X. (2018). Artificial mangrove species mapping using pl\u00e9iades-1: An evaluation of pixel-based and object-based classifications with selected machine learning algorithms. Remote Sens., 10.","DOI":"10.3390\/rs10020294"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.geomorph.2012.08.021","article-title":"\u2018Structure-from-Motion\u2019 photogrammetry: A low-cost, effective tool for geoscience applications","volume":"179","author":"Westoby","year":"2012","journal-title":"Geomorphology"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.foreco.2017.05.013","article-title":"Applicability of different non-invasive methods for tree mass estimation: A review","volume":"398","author":"Dittmann","year":"2017","journal-title":"For. Ecol. Manag."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Alonzo, M., Andersen, H.-E., Morton, D., and Cook, B. (2018). Quantifying boreal forest structure and composition using UAV structure from motion. Forests, 9.","DOI":"10.3390\/f9030119"},{"key":"ref_32","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_33","doi-asserted-by":"crossref","unstructured":"Jensen, J., and Mathews, A. (2016). Assessment of image-based point cloud products to generate a bare earth surface and estimate canopy heights in a woodland ecosystem. Remote Sens., 8.","DOI":"10.3390\/rs8010050"},{"key":"ref_34","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_35","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.isprsjprs.2017.11.002","article-title":"Above-bottom biomass retrieval of aquatic plants with regression models and SfM data acquired by a UAV platform\u2014A case study in Wild Duck Lake Wetland, Beijing, China","volume":"134","author":"Jing","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.foreco.2017.12.049","article-title":"Managing mangrove forests from the sky: Forest inventory using field data and Unmanned Aerial Vehicle (UAV) imagery in the Matang Mangrove Forest Reserve, peninsular Malaysia","volume":"411","author":"Otero","year":"2018","journal-title":"For. Ecol. Manag."},{"key":"ref_37","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_38","doi-asserted-by":"crossref","unstructured":"Navarro, J.A., Algeet, N., Fern\u00e1ndez-Landa, A., Esteban, J., Rodr\u00edguez-Noriega, P., and Guill\u00e9n-Climent, M.L. (2019). Integration of UAV, Sentinel-1, and Sentinel-2 data for mangrove plantation aboveground biomass monitoring in Senegal. Remote Sens., 11.","DOI":"10.3390\/rs11010077"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zheng, H., Cheng, T., Zhou, M., Li, D., Yao, X., Tian, Y., Cao, W., and Zhu, Y. (2018). Improved estimation of rice aboveground biomass combining textural and spectral analysis of UAV imagery. Precis. Agric.","DOI":"10.1007\/s11119-018-9600-7"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1080\/17538947.2014.990526","article-title":"A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems","volume":"9","author":"Lu","year":"2014","journal-title":"Int. J. Digit. Earth"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Gao, Y., Lu, D., Li, G., Wang, G., Chen, Q., Liu, L., and Li, D. (2018). Comparative analysis of modeling algorithms for forest aboveground biomass estimation in a subtropical region. Remote Sens., 10.","DOI":"10.3390\/rs10040627"},{"key":"ref_42","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_43","doi-asserted-by":"crossref","unstructured":"Zhu, J., Huang, Z., Sun, H., and Wang, G. (2017). Mapping forest ecosystem biomass density for Xiangjiang river basin by combining plot and remote sensing data and comparing spatial extrapolation methods. Remote Sens., 9.","DOI":"10.3390\/rs9030241"},{"key":"ref_44","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_45","doi-asserted-by":"crossref","unstructured":"Yue, J., Feng, H., Yang, G., and Li, Z. (2018). A comparison of regression techniques for estimation of above-ground winter wheat biomass using near-surface spectroscopy. Remote Sens., 10.","DOI":"10.3390\/rs10010066"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Chen, L., Ren, C., Zhang, B., Wang, Z., and Xi, Y. (2018). Estimation of forest above-ground biomass by geographically weighted regression and machine learning with Sentinel Imagery. Forests, 9.","DOI":"10.3390\/f9100582"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Vafaei, S., Soosani, J., Adeli, K., Fadaei, H., Naghavi, H., Pham, T., and Tien Bui, D. (2018). Improving accuracy estimation of forest aboveground biomass based on incorporation of ALOS-2 PALSAR-2 and Sentinel-2A imagery and machine learning: A case study of the Hyrcanian Forest Area (Iran). Remote Sens., 10.","DOI":"10.3390\/rs10020172"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1093\/jpe\/rtp009","article-title":"Recent progresses in mangrove conservation, restoration and research in China","volume":"2","author":"Chen","year":"2009","journal-title":"J. Plant Ecol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11355-006-0018-4","article-title":"Degraded ecosystems in China: Status, causes, and restoration efforts","volume":"3","author":"Ren","year":"2007","journal-title":"Landsc. Ecol. Eng."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2914","DOI":"10.1007\/s11356-016-7885-5","article-title":"Influence of introduced Sonneratia apetala on nutrients and heavy metals in intertidal sediments, South China","volume":"24","author":"Li","year":"2017","journal-title":"Environ. Sci. Pollut. Res. Int."},{"key":"ref_51","first-page":"12","article-title":"A comparison of mangrove community distribution and landscape pattern between Futian and Maipo Nature Reserve at Shenzhen Bay","volume":"56","author":"Li","year":"2017","journal-title":"Zhongshan Daxue Xuebao"},{"key":"ref_52","first-page":"544","article-title":"Ecological assessment on the introduced Sonneratia caseolaris and S. apetala at the Mangrove Forest of Shenzhen Bay, China","volume":"45","author":"Zan","year":"2003","journal-title":"Acta Bot. Sin."},{"key":"ref_53","unstructured":"Kauffman, J.B., and Donato, D.C. (2012). Protocols for the Measurement, Monitoring and Reporting of Structure, Center for International Forestry Research."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/BF00029126","article-title":"Community structure and standing crop biomass of a mangrove forest in Futian Nature Reserve, Shenzhen, China","volume":"295","author":"Tam","year":"1995","journal-title":"Hydrobiologia"},{"key":"ref_55","first-page":"2059","article-title":"Vegetation carbon stocks and net primary productivity of the mangrove forests in Shenzhen, China","volume":"27","author":"Peng","year":"2016","journal-title":"Chin. J. Appl. Ecol."},{"key":"ref_56","first-page":"47","article-title":"Studies on the biomass of Sonneratia caseolaris stand","volume":"3","author":"Liao","year":"1990","journal-title":"For. Res."},{"key":"ref_57","first-page":"391","article-title":"Biomass and net productivity of Sonneratia apetala, S. caseolaris mangrove manmade forest","volume":"19","author":"Zan","year":"2001","journal-title":"J. Wuhan Bot. Res."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"20170038","DOI":"10.1098\/rsfs.2017.0038","article-title":"Comparing terrestrial laser scanning and unmanned aerial vehicle structure from motion to assess top of canopy structure in tropical forests","volume":"8","author":"Rosca","year":"2018","journal-title":"Interface Focus"},{"key":"ref_59","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_60","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_61","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural features for image classification","volume":"SMC-3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"2341","DOI":"10.1016\/j.rse.2007.11.001","article-title":"Angular sensitivity analysis of vegetation indices derived from CHRIS\/PROBA data","volume":"112","author":"Verrelst","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1104\/pp.110.160820","article-title":"Cryptochrome as a sensor of the blue\/green ratio of natural radiation in Arabidopsis","volume":"154","author":"Sellaro","year":"2010","journal-title":"Plant Physiol"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"2369","DOI":"10.3390\/rs2102369","article-title":"Applicability of green-red vegetation index for remote sensing of vegetation phenology","volume":"2","author":"Motohka","year":"2010","journal-title":"Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1080\/10106040108542184","article-title":"Spatially located platform and aerial photography for documentation of grazing impacts on wheat","volume":"16","author":"Louhaichi","year":"2008","journal-title":"Geocarto Int."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/S0034-4257(01)00289-9","article-title":"Novel algorithms for remote estimation of vegetation fraction","volume":"80","author":"Gitelson","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.compag.2014.02.009","article-title":"Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV","volume":"103","author":"Pena","year":"2014","journal-title":"Comput. Electron. Agric."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v036.i11","article-title":"Feature selection with the Boruta Package","volume":"36","author":"Kursa","year":"2010","journal-title":"J. Stat. Softw."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Song, R., Lin, H., Wang, G., Yan, E., and Ye, Z. (2017). Improving selection of spectral variables for vegetation classification of East Dongting Lake, China, using a Gaofen-1 image. Remote Sens., 10.","DOI":"10.3390\/rs10010050"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1080\/01431160701352154","article-title":"The application of artificial neural networks to the analysis of remotely sensed data","volume":"29","author":"Mas","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.isprsjprs.2010.11.001","article-title":"Support vector machines in remote sensing: A review","volume":"66","author":"Mountrakis","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.rse.2014.07.028","article-title":"Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass","volume":"154","author":"Fassnacht","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/s00442-005-0100-x","article-title":"Tree allometry and improved estimation of carbon stocks and balance in tropical forests","volume":"145","author":"Chave","year":"2005","journal-title":"Oecologia"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"3177","DOI":"10.1111\/gcb.12629","article-title":"Improved allometric models to estimate the aboveground biomass of tropical trees","volume":"20","author":"Chave","year":"2014","journal-title":"Glob. Chang. Biol."},{"key":"ref_77","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_78","first-page":"399","article-title":"High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm","volume":"18","author":"Mutanga","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"1170","DOI":"10.1109\/36.469481","article-title":"Evaluation of textural and multipolarization radar features for crop classification","volume":"33","author":"Anys","year":"1995","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"15467","DOI":"10.3390\/rs71115467","article-title":"Using UAV-based photogrammetry and hyperspectral imaging for mapping bark beetle damage at tree-level","volume":"7","author":"Honkavaara","year":"2015","journal-title":"Remote Sens."},{"key":"ref_81","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_82","first-page":"107","article-title":"Retrieving aboveground biomass of wetland Phragmites australis (common reed) using a combination of airborne discrete-return LiDAR and hyperspectral data","volume":"58","author":"Luo","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.ecoleng.2016.12.004","article-title":"Mapping forest aboveground biomass using airborne hyperspectral and LiDAR data in the mountainous conditions of Central Europe","volume":"100","author":"Brovkina","year":"2017","journal-title":"Ecol. Eng."},{"key":"ref_84","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_85","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1111\/conl.12060","article-title":"Predicting global patterns in mangrove forest biomass","volume":"7","author":"Hutchison","year":"2014","journal-title":"Conserv. Lett."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/9\/1018\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:48:02Z","timestamp":1760186882000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/9\/1018"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,4,29]]},"references-count":85,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2019,5]]}},"alternative-id":["rs11091018"],"URL":"https:\/\/doi.org\/10.3390\/rs11091018","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,4,29]]}}}