{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T18:55:51Z","timestamp":1774724151645,"version":"3.50.1"},"reference-count":67,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,5,26]],"date-time":"2022-05-26T00:00:00Z","timestamp":1653523200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31770679"],"award-info":[{"award-number":["31770679"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Exploring the spatial and temporal dynamic characteristics of regional forest net primary productivity (NPP) in the context of global climate change can not only provide a theoretical basis for terrestrial carbon cycle studies, but also provide data support for medium- and long-term sustainable management planning of regional forests. In this study, we took Shaoguan City, Guangdong Province, China as the study area, and used Landsat images and National Forest Continuous Inventory (NFCI) data in the corresponding years as the main data sources. Random forest (RF), multiple linear regression (MLR), and BP neural network were the three models applied to estimate forest NPP in the study area. Theil\u2013Sen estimation, Mann\u2013Kendall trend analysis and the standard deviation ellipse (SDE) were chosen to analyze the spatial and temporal dynamic characteristics of NPP, whereas structural equation modeling (SEM) was used to analyze the driving factors of NPP changes. The results show that the performance of the RF model is better than the MLR and BP neural network models. The NPP in the study area showed an increasing trend, as the NPP was 5.66 t\u00b7hm\u22122\u00b7a\u22121, 7.68 t\u00b7hm\u22122\u00b7a\u22121, 8.17 t\u00b7hm\u22122\u00b7a\u22121, 8.25 t\u00b7hm\u22122\u00b7a\u22121, and 10.52 t\u00b7hm\u22122\u00b7a\u22121 in 1997, 2002, 2007, 2012, and 2017, respectively. Spatial aggregation of NPP was increased in the period of 1997\u20132017, and the center shifted from the mid-west to the southwest. In addition, the forest stand factors had the greatest effect on NPP in the study area. The forest stand factors and environmental factors had a positive effect on NPP, and understory factors had a negative effect. Overall, although forest NPP has fluctuated due to the changes of forestry policies and human activities, forest NPP in Shaoguan has been increasing. In the future, the growth potential of NPP in Shaoguan City can be further increased by continuously expanding the area proportion of mixed forests and rationalizing the forest age group structure.<\/jats:p>","DOI":"10.3390\/rs14112541","type":"journal-article","created":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T00:25:12Z","timestamp":1653956712000},"page":"2541","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Estimation and Spatio-Temporal Change Analysis of NPP in Subtropical Forests: A Case Study of Shaoguan, Guangdong, China"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5258-7849","authenticated-orcid":false,"given":"Tao","family":"Li","sequence":"first","affiliation":[{"name":"College of Forestry, Nanjing Forestry University, Nanjing 210037, China"}]},{"given":"Mingyang","family":"Li","sequence":"additional","affiliation":[{"name":"College of Forestry, Nanjing Forestry University, Nanjing 210037, China"}]},{"given":"Fang","family":"Ren","sequence":"additional","affiliation":[{"name":"College of Forestry, Nanjing Forestry University, Nanjing 210037, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6740-1608","authenticated-orcid":false,"given":"Lei","family":"Tian","sequence":"additional","affiliation":[{"name":"College of Forestry, Nanjing Forestry University, Nanjing 210037, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,26]]},"reference":[{"key":"ref_1","unstructured":"Stocker, T. (2014). Climate Change 2013, Cambridge University Press."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ravindranath, N.H., and Sathaye, J.A. (2002). Climate Change and Developing Countries, Kluwer Academic Publishers.","DOI":"10.1007\/0-306-47980-X"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"108589","DOI":"10.1016\/j.ecolind.2022.108589","article-title":"Dynamics of the alpine timberline and its response to climate change in the Hengduan mountains over the period 1985\u20132015","volume":"135","author":"Tian","year":"2022","journal-title":"Ecol. Indic."},{"key":"ref_4","first-page":"29","article-title":"Carbon dioxide concentration, photosynthesis, and dry matter production","volume":"31","author":"Kramer","year":"1981","journal-title":"Bio Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"284","DOI":"10.2307\/2260557","article-title":"Forest ecosystems: Concepts and management","volume":"75","author":"Barkham","year":"1987","journal-title":"J. Ecol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1126\/science.263.5144.185","article-title":"Carbon pools and flux of global forest ecosystems","volume":"263","author":"Dixon","year":"1994","journal-title":"Science"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Lieth, H., and Whittaker, R.H. (1975). Primary Productivity of the Biosphere, Springer. Ecological Studies.","DOI":"10.1007\/978-3-642-80913-2"},{"key":"ref_8","first-page":"146","article-title":"Estimation of net primary productivity of vegetation in Jiangsu Province based on open datasets","volume":"25","author":"Wu","year":"2010","journal-title":"J. Northwest For. Univ."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Li, T., Li, M.Y., and Tian, L. (2021). Dynamics of carbon storage and its drivers in Guangdong Province from 1979 to 2012. Forests, 12.","DOI":"10.3390\/f12111482"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1046\/j.1365-2486.1999.00003.x","article-title":"Comparing global models of terrestrial net primary productivity (NPP): Global pattern and differentiation by major biomes","volume":"5","author":"Kicklighter","year":"1999","journal-title":"Glob. Chang. Biol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"27735","DOI":"10.1029\/1999JD900768","article-title":"Net primary productivity distribution in the BOREAS region from a process model using satellite and surface data","volume":"104","author":"Liu","year":"1999","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_12","first-page":"49","article-title":"Response of net primary productivity to climate warming in Northeast China","volume":"4","author":"Wu","year":"1997","journal-title":"Econ. Geogr."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"769","DOI":"10.2307\/1313568","article-title":"Terrestrial Biomass and the effects of deforestation on the global carbon cycle","volume":"49","author":"Potter","year":"1999","journal-title":"BioScience"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1175\/EI-D-17-0032.1","article-title":"Nonuniform time-lag effects of asymmetric warming on net primary productivity across global terrestrial biomes","volume":"22","author":"Wen","year":"2018","journal-title":"Earth Interact."},{"key":"ref_15","first-page":"7","article-title":"Remote sensing estimation and application analysis of forest biomass","volume":"8","author":"Xu","year":"2006","journal-title":"Geo-Inf. Sci."},{"key":"ref_16","first-page":"153","article-title":"Combining crown density to estimate forest net primary productivity by using remote sensing data","volume":"45","author":"Li","year":"2021","journal-title":"J. Nanjing For. Univ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"102870","DOI":"10.1016\/j.pce.2020.102870","article-title":"Rainfall and runoff trend analysis in the Limpopo river basin using the Mann Kendall statistic","volume":"117","author":"Nyikadzino","year":"2020","journal-title":"Phys. Chem. Earth Parts A\/B\/C"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.9734\/JSRR\/2018\/42029","article-title":"Trend analysis of temperature in gombe state using Mann Kendall trend Test","volume":"20","author":"Alhaji","year":"2018","journal-title":"J. Sci. Res. Rep."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.rse.2015.12.008","article-title":"Spatiotemporal patterns of remotely sensed PM 2.5 concentration in China from 1999 to 2011","volume":"174","author":"Peng","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"119939","DOI":"10.1016\/j.techfore.2020.119939","article-title":"Analysis on net primary productivity change of forests and its multilevel driving mechanism\u2014A case study in Changbai mountains in northeast China","volume":"153","author":"Wang","year":"2020","journal-title":"Technol. Forecast. Soc. Chang."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"nwab032","DOI":"10.1093\/nsr\/nwab032","article-title":"The global significance of biodiversity science in China: An overview","volume":"8","author":"Mi","year":"2021","journal-title":"Natl. Sci. Rev."},{"key":"ref_22","first-page":"980","article-title":"Dynamic evaluation of forest eco-system services in Guangdong Province from 1987 to 2004","volume":"31","author":"Lin","year":"2009","journal-title":"Resour. Sci."},{"key":"ref_23","first-page":"123","article-title":"The Evaluation of Forest Resources and development counter measures in Guangdong Province","volume":"2","author":"Ma","year":"2007","journal-title":"J. Taiyuan Norm. Univ."},{"key":"ref_24","unstructured":"(2022, April 20). Shaoguan Municipal People\u2019s Government, Available online: https:\/\/www.sg.gov.cn\/."},{"key":"ref_25","unstructured":"(2020). Technical Regulations for Continuous Forest Inventory (Standard No. GB\/T 38590-2020)."},{"key":"ref_26","first-page":"542","article-title":"Dynamic change of net production and mean net primary productivity of China\u2019s forests","volume":"27","author":"Yu","year":"2014","journal-title":"For. Res."},{"key":"ref_27","unstructured":"Zhang, J.B. (2009). Principles and Applications of Remote Sensing, Wuhan University Press."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2490","DOI":"10.1109\/36.964986","article-title":"Atmospheric correction of Landsat ETM+ land surface imagery. I. Methods","volume":"39","author":"Liang","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"89","DOI":"10.2113\/gseegeosci.13.1.89","article-title":"Introductory digital image processing: A remote sensing perspective, third edition","volume":"13","year":"2007","journal-title":"Environ. Eng. Geosci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1080\/07038992.1982.10855028","article-title":"On the slope-aspect correction of multispectral scanner data","volume":"8","author":"Teillet","year":"1982","journal-title":"Can. J. Remote Sens."},{"key":"ref_31","unstructured":"Xiao, X.W. (2005). Study on Forest Biomass and Productivity in China. [Ph.D. Thesis, Northeast Forestry University]."},{"key":"ref_32","first-page":"10","article-title":"Progress of vegetation index research","volume":"4","author":"Tian","year":"1998","journal-title":"Adv. Earth Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1093\/oxfordjournals.aob.a083148","article-title":"Comparative physiological studies on the growth of field crops: I. variation in net assimilation rate and leaf area between species and varieties, and within and between years","volume":"11","author":"Watson","year":"1947","journal-title":"Ann. Bot."},{"key":"ref_34","unstructured":"Kauth, R.J., and Thomas, G.S. (July, January 29). The Tasselled-cap\u2014A graphic description of the spectral-temporal development of agricultural crops as seen by Landsat. Proceedings of the Symposium on Machine Processing of Remotely Sensed Data, West Lafayette, IN, USA."},{"key":"ref_35","first-page":"84","article-title":"Remotely sensed estimation of forest canopy density: A comparison of the performance of four methods","volume":"8","author":"Joshi","year":"2006","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_36","unstructured":"Rikimaru, A. (August, January 30). Landsat TM data processing guide for forest canopy density mapping and monitoring model. Proceedings of the ITTO Workshop on Utilization of Remote Sensing in Site Assessment and Planning for Rehabilitation of Logged-Over Forests, Bangkok, Thailand."},{"key":"ref_37","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_38","doi-asserted-by":"crossref","unstructured":"Cutler, A., Cutler, D.R., and Stevens, J.R. (2012). Random forests. Ensemble Machine Learning, Springer.","DOI":"10.1007\/978-1-4419-9326-7_5"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1137\/1128044","article-title":"Limit Distributions of the height of a random forest","volume":"28","author":"Pavlov","year":"1984","journal-title":"Theory Probab. Its Appl."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1007\/BF02291100","article-title":"Multiple linear regression analysis of RF values of chlorinated catechols and guaiacols","volume":"14","author":"Kolehmainen","year":"1981","journal-title":"Chromatographia"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"012030","DOI":"10.1088\/1742-6596\/974\/1\/012030","article-title":"Forecasting the mortality rates of indonesian population by using neural network","volume":"974","author":"Safitri","year":"2018","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_42","unstructured":"Breiman, L., and Cutler, A. (2008). randomForest: Breiman and Cutler\u2019s Random Forests for Classification and Regression, The R Foundation for Statistical Computing. R Package Version 4.6-7."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Li, Y.C., Li, C., Li, M.Y., and Liu, Z.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_44","first-page":"31","article-title":"Stepwise regression analysis method and its application","volume":"14","author":"You","year":"2017","journal-title":"Stat. Decis."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1007\/s11135-006-9018-6","article-title":"A caution regarding rules of thumb for variance inflation factors","volume":"41","author":"Robert","year":"2007","journal-title":"Qual. Quant."},{"key":"ref_46","first-page":"392","article-title":"Regression diagnostics: Identifying influential data and sources of collinearity","volume":"18","author":"Bollinger","year":"1981","journal-title":"J. Mark. Res."},{"key":"ref_47","first-page":"443","article-title":"Daily sediment yield modeling with Artificial Neural Network using 10-fold cross validation method: A small agricultural watershed, Kapgari, India","volume":"4","author":"Singh","year":"2011","journal-title":"Int. J. Earth Sci. Eng."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1771","DOI":"10.1002\/1097-0258(20000715)19:13<1771::AID-SIM485>3.0.CO;2-P","article-title":"Summarizing the predictive power of a generalized linear model","volume":"19","author":"Zheng","year":"2000","journal-title":"Stat. Med."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1379","DOI":"10.1080\/01621459.1968.10480934","article-title":"Estimates of the regression coefficient based on Kendall\u2019s Tau","volume":"63","author":"Sen","year":"1968","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_50","first-page":"2373","article-title":"Spatiotemporal variation of vegetation net primary productivity and its driving factors from 2000 to 2015 in Qinling-Daba Mountains","volume":"29","author":"Wang","year":"2018","journal-title":"Chin. J. Appl. Ecol."},{"key":"ref_51","first-page":"1034","article-title":"Spatio-temporal pattern evolution of eco-efficiency and the forecast in mainland of China","volume":"37","author":"Zheng","year":"2018","journal-title":"Geogr. Res."},{"key":"ref_52","first-page":"157","article-title":"Research on the influence of land use classification on landscape metrics","volume":"61","author":"Peng","year":"2006","journal-title":"Acta Geogr. Sin."},{"key":"ref_53","first-page":"1161","article-title":"Research on NDVI variation characteristics and precipitation sensitivity of the Yuanjiang River Basin in Guizhou Province","volume":"40","author":"Cheng","year":"2020","journal-title":"Acta Ecol. Sin."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1080\/10705511.2021.1910038","article-title":"Review of composite-based Structural Equation Modeling: Analyzing latent and emergent variables","volume":"28","year":"2021","journal-title":"Struct. Equ. Model. A Multidiscip. J."},{"key":"ref_55","unstructured":"Brandmaier, A.M. (2012). Permutation Distribution Clustering and Structural Equation Model Trees. [Ph.D. Thesis, Saarland University]."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"78","DOI":"10.52041\/serj.v6i2.486","article-title":"A structural equation model analyzing the relationship of students\u2019 attitudes toward statistics, prior reasoning abilities and course performance","volume":"6","author":"Tempelaar","year":"2007","journal-title":"Stat. Educ. Res. J."},{"key":"ref_57","first-page":"5","article-title":"Structural equation modeling in practice: A review and recommended two-step approach","volume":"27","author":"Anderson","year":"1988","journal-title":"Psychol. Bull."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1007\/BF02294210","article-title":"A general structural equation model with dichotomous, ordered categorical, and continuous latent variable indicators","volume":"49","year":"1984","journal-title":"Psychometrika"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1037\/0022-006X.62.3.427","article-title":"Introduction to the special section: Structural equation modeling in clinical research","volume":"62","author":"Hoyle","year":"1994","journal-title":"J. Consult. Clin. Psychol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1023\/A:1015670628990","article-title":"Evaluation of the life satisfaction questionnaire (LSQ) using structural equation modelling (SEM)","volume":"11","author":"Carlsson","year":"2002","journal-title":"Qual. Life Res."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1177\/0011000099274002","article-title":"Implications of recent developments in structural equation modeling for counseling psychology","volume":"27","author":"Quintana","year":"1999","journal-title":"Couns. Psychol."},{"key":"ref_62","first-page":"186","article-title":"Structural equation model: Cutoff criteria for goodness of fit indices and chi-square test","volume":"36","author":"Wen","year":"2004","journal-title":"Acta Psychol. Sin."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.gloplacha.2006.12.007","article-title":"Spatiotemporal dynamics of forest net primary production in China over the past two decades","volume":"61","author":"Wang","year":"2008","journal-title":"Glob. Planet. Chang."},{"key":"ref_64","first-page":"4936","article-title":"Analysis of spatial and temporal variation of net primary productivity and climate controls in China from 2001 to 2014","volume":"37","author":"Liu","year":"2017","journal-title":"Acta Ecol. Sin."},{"key":"ref_65","first-page":"699","article-title":"Spatial patterns of net primary productivity of global forest ecosystems and their regional characteristics","volume":"34","author":"Jiao","year":"2014","journal-title":"Quat. Sci."},{"key":"ref_66","unstructured":"Li, H. (2021). Spatiotemporal Evolution of Fractional Vegetation Cover and Net Primary Productivity in the Subtropical Region and Climate Driving. [Master\u2019s Thesis, Zhejiang A&F University]."},{"key":"ref_67","first-page":"3","article-title":"Study on the Cooperation Networks Evolution of Forestry Policy-making Authorities in China","volume":"42","author":"Yu","year":"2020","journal-title":"For. Econ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/11\/2541\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:19:02Z","timestamp":1760138342000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/11\/2541"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,26]]},"references-count":67,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["rs14112541"],"URL":"https:\/\/doi.org\/10.3390\/rs14112541","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,26]]}}}