{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T16:37:02Z","timestamp":1778085422017,"version":"3.51.4"},"reference-count":39,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2023,11,21]],"date-time":"2023-11-21T00:00:00Z","timestamp":1700524800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Scientific Research Project of the Department of Education of Liaoning Province, \u201cResearch on Key Issues of Building 3D Reconstruction Based on Open Space Multi-Source Data Fusion\u201d","award":["lnjc202015"],"award-info":[{"award-number":["lnjc202015"]}]},{"name":"School of Transportation and Surveying Engineering, Shenyang Jianzhu University","award":["lnjc202015"],"award-info":[{"award-number":["lnjc202015"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Ecological forests are an important part of terrestrial ecosystems, are an important carbon sink and play a pivotal role in the global carbon cycle. At present, the comprehensive utilization of optical and radar data has broad application prospects in forest parameter extraction and biomass estimation. In this study, tree and topographic data of 354 plots in key nature reserves of Liaoning Province were used for biomass analysis. Remote sensing parameters were extracted from Landsat 8 OLI and Sentinel-1A radar data. Based on the strong correlation factors obtained via Pearson correlation analysis, a linear model, BP neural network model and PSO neural network model were used to simulate the biomass of the study area. The advantages of the three models were compared and analyzed, and the optimal model was selected to invert the biomass of Liaoning province. The results showed that 44 factors were correlated with forest biomass (p &lt; 0.05), and 21 factors were significantly correlated with forest biomass (p &lt; 0.01). The comparison between the prediction results of the three models and the real results shows that the PSO-improved neural network simulation results are the best, and the coefficient of determination is 0.7657. Through analysis, it is found that there is a nonlinear relationship between actual biomass and remote sensing data. Particle swarm optimization (PSO) can effectively solve the problem of low accuracy in traditional BP neural network models while maintaining a good training speed. The improved particle swarm model has good accuracy and speed and has broad application prospects in forest biomass inversion.<\/jats:p>","DOI":"10.3390\/s23239313","type":"journal-article","created":{"date-parts":[[2023,11,21]],"date-time":"2023-11-21T12:12:13Z","timestamp":1700568733000},"page":"9313","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Inversion of Forest Biomass Based on Multi-Source Remote Sensing Images"],"prefix":"10.3390","volume":"23","author":[{"given":"Danhua","family":"Zhang","sequence":"first","affiliation":[{"name":"Traffic and Surveying Engineering College, Shenyang Jianzhu University, Shenyang 110168, China"}]},{"given":"Hui","family":"Ni","sequence":"additional","affiliation":[{"name":"Traffic and Surveying Engineering College, Shenyang Jianzhu University, Shenyang 110168, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"123333","DOI":"10.1016\/j.jclepro.2020.123333","article-title":"Assessment and prediction of carbon sequestration using Markov chain and InVEST model in Sariska Tiger Reserve, India","volume":"278","author":"Babbar","year":"2021","journal-title":"J. Clean. Prod."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3425","DOI":"10.1175\/JCLI-D-13-00177.1","article-title":"Uncertainty of Concentration-Terrestrial Carbon Feedback in Earth System Models","volume":"27","author":"Hajima","year":"2014","journal-title":"J. Clim."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"726","DOI":"10.1890\/12-0279.1","article-title":"Responses of ecosystem carbon cycle to experimental warming: A meta-analysis","volume":"94","author":"Lu","year":"2013","journal-title":"Ecology"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhang, F., Tian, X., Zhang, H., and Jiang, M. (2022). Estimation of Aboveground Carbon Density of Forests Using Deep Learning and Multisource Remote Sensing. Remote Sens., 14.","DOI":"10.3390\/rs14133022"},{"key":"ref_5","first-page":"701","article-title":"Carbon sinks and tropical forest biomass estimation: A review on role of remote sensing in aboveground-biomass modelling","volume":"32","author":"Latif","year":"2016","journal-title":"Geocarto Int."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"111341","DOI":"10.1016\/j.rse.2019.111341","article-title":"Estimating aboveground biomass in subtropical forests of China by integrating multisource remote sensing and ground data","volume":"232","author":"Zhang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_7","first-page":"727","article-title":"Research progress on photosynthesis response models to light and CO2","volume":"34","author":"Ye","year":"2010","journal-title":"Chin. J. Plant Ecol."},{"key":"ref_8","first-page":"9","article-title":"Estimation of forest biomass from GLAS spaceborne lidar and Landsat\/ETM+ data","volume":"43","author":"Chi","year":"2018","journal-title":"Sci. Surv. Mapp."},{"key":"ref_9","first-page":"172","article-title":"Research Progress on Carbon Sequestration Function and Carbon Storage of Forest Ecosystem","volume":"2","author":"Yang","year":"2005","journal-title":"J. Beijing Norm. Univ. (Nat. Sci. Ed.)"},{"key":"ref_10","unstructured":"Zhang, Y. (2016). Estimation of Aboveground Biomass in Forests in Greater Khingan Mountains Based on High-Resolution Remote Sensing and Polarimetric Radar Data. [Master\u2019s Thesis, Beijing Forestry University]."},{"key":"ref_11","first-page":"631","article-title":"Research progress of forest biomass inversion by remote sensing technology","volume":"37","author":"Li","year":"2012","journal-title":"Geomat. Inf. Sci. Wuhan Univ."},{"key":"ref_12","first-page":"1","article-title":"Advances in remote sensing estimation of forest aboveground biomass","volume":"1","author":"Lou","year":"2011","journal-title":"Remote Sens. Land Resour."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.rse.2005.03.005","article-title":"Mapping forest structure for wildlife habitat analysis using waveform lidar: Validation of montane ecosystems","volume":"96","author":"Hyde","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1839","DOI":"10.3390\/f6061839","article-title":"Sparse Density, Leaf-Off Airborne Laser Scanning Data in Aboveground Biomass Component Prediction","volume":"6","author":"Kankare","year":"2015","journal-title":"Forests"},{"key":"ref_15","first-page":"359","article-title":"Mapping forest biomass from space-Fusion of hyperspectral EO1-hyperion data and Tandem-X and WorldView-2 canopy height models","volume":"35","author":"Kattenborn","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.isprsjprs.2019.03.016","article-title":"Estimation of the forest stand mean height and aboveground biomass in Northeast China using SAR Sentinel-1B, multispectral Sentinel-2A, and DEM imagery","volume":"151","author":"Liu","year":"2019","journal-title":"Isprs J. Photogramm. Remote Sens."},{"key":"ref_17","first-page":"202","article-title":"SRTM and TanDEM-X mapping boreal forest biomass based on canopy height model and Landsat spectral index","volume":"68","author":"Sadeghi","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geogr. Inf."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.compag.2019.105089","article-title":"High performance prediction of eucalyptus biomass based on multispectral and SAR data by artificial neural network","volume":"168","author":"Domingues","year":"2020","journal-title":"Agric. Comput. Electron."},{"key":"ref_19","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_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2021.3090410","article-title":"Local similarity-based spatial\u2013spectral fusion hyperspectral image classification with deep CNN and Gabor filtering","volume":"60","author":"Bhatti","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2046","DOI":"10.3390\/rs70202046","article-title":"Classification of herbaceous vegetation using airborne hyperspectral imagery","volume":"7","author":"Burai","year":"2015","journal-title":"Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Kwak, G., and Park, N. (2019). Impact of texture information on crop classification with machine learning and UAV images. Appl. Sci., 9.","DOI":"10.3390\/app9040643"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1080\/22797254.2017.1299557","article-title":"Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images","volume":"50","author":"Raczko","year":"2017","journal-title":"Eur. J. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"8342104","DOI":"10.1155\/2023\/8342104","article-title":"Deep learning with graph convolutional networks: An overview and latest applications in computational intelligence","volume":"2023","author":"Bhatti","year":"2023","journal-title":"Int. J. Intell. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1109\/LGRS.2017.2681128","article-title":"Deep learning classification of land cover and crop types using remote sensing data","volume":"14","author":"Kussul","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ji, S., Zhang, C., Xu, A., Shi, Y., and Duan, Y. (2018). 3D convolutional neural networks for crop classification with multi-temporal remote sensing images. Remote Sens., 10.","DOI":"10.3390\/rs10010075"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"121282","DOI":"10.1016\/j.eswa.2023.121282","article-title":"Interactive medical image annotation using improved Attention U-net with compound geodesic distance","volume":"237","author":"Zhang","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"120496","DOI":"10.1016\/j.eswa.2023.120496","article-title":"MFFCG\u2013Multi feature fusion for hyperspectral image classification using graph attention network","volume":"229","author":"Bhatti","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"458","DOI":"10.18517\/ijaseit.1.4.93","article-title":"Tuning of PID Controller Using Particle Swarm Optimization (PSO)","volume":"1","author":"Solihin","year":"2011","journal-title":"Int. J. Adv. Sci. Eng. Inf. Technol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2445","DOI":"10.1007\/s00500-017-2940-9","article-title":"A novel intelligent diagnosis method using optimal LS-SVM with improved PSO algorithm","volume":"23","author":"Deng","year":"2019","journal-title":"Soft Comput."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3137","DOI":"10.1016\/j.solener.2012.08.005","article-title":"Modeling global solar radiation using Particle Swarm Optimization (PSO)","volume":"86","author":"Mohandes","year":"2012","journal-title":"Sol. Energy"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wang, J., Gao, Y., Liu, W., Sangaiah, A.K., and Kim, H.-J. (2019). An Improved Routing Schema with Special Clustering Using PSO Algorithm for Heterogeneous Wireless Sensor Network. Sensors, 19.","DOI":"10.3390\/s19030671"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"13485","DOI":"10.3390\/rs71013485","article-title":"Comparison of the Continuity of Vegetation Indices Derived from Landsat 8 OLI and Landsat 7 ETM+ Data among Different Vegetation Types","volume":"7","author":"She","year":"2015","journal-title":"Remote Sens."},{"key":"ref_34","unstructured":"Dong, L. (2015). Research on the Biomass Model of Main Tree Species and Stand Types in the Northeast Forest Region. [Ph.D. Thesis, Northeast Forestry University]."},{"key":"ref_35","unstructured":"Liu, S. (2020). Estimation of Forest Biomass in Nanchuan District, Chongqing City Based on Sentinel-1\/2. [Master\u2019s Thesis, Chengdu University of Technology]."},{"key":"ref_36","first-page":"650","article-title":"Completion of the Native American National Land Cover Dataset for the 1990s from LANDSAT Thematic Mapper Data and Auxiliary Data Sources","volume":"67","author":"Vogelmann","year":"2001","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biophysical performance of the MODIS vegetation indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1139\/x02-011","article-title":"Predictive Mapping of Forest Composition and Structure by Direct Gradient Analysis and Nearest Neighbor Imputation in Coastal Oregon, USA","volume":"32","author":"Ohmann","year":"2002","journal-title":"Can. J. For. Res."},{"key":"ref_39","unstructured":"Guo, Z., Peng, S., and Wang, B. (2002). Using TM data to extract forest biomass in western Guangdong. Ecol. J., 22."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/23\/9313\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:26:35Z","timestamp":1760131595000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/23\/9313"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,21]]},"references-count":39,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["s23239313"],"URL":"https:\/\/doi.org\/10.3390\/s23239313","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,21]]}}}