A Review of Research on Forest Ecosystem Quality Assessment and Prediction Methods
Abstract
:1. Introduction
2. Connotation of Forest Ecosystem Quality
3. Methods of Forest Ecosystem Quality Assessment
3.1. Assessment Index System
Dimensions Involved | Indicator Factors | Applications |
---|---|---|
Forest structure | Stand origin, community structure, stand age, canopy structure, stand density, tree species composition, depression | [21,23,26,27,46,52] |
Ecological function | Water conservation, soil conservation, carbon sequestration and oxygen release, air purification, biodiversity conservation, nutrient sequestration, forest recreation, etc. | [53,54] |
Green Vitality | Normalized difference vegetation index (NDVI), stand volume, leaf area index, biomass, forest growth per unit area, litter thickness | [32,55] |
Stability | Net primary productivity (NPP) stability, NDVI stability | [54,56] |
Site conditions | Elevation, slope direction, slope, slope position, soil thickness, soil fertility, soil erosion degree, etc. | [23,27,57,58] |
3.2. Determining the Weight of Indicators
3.3. Assessment Methods
3.3.1. Comprehensive Evaluation Method
3.3.2. Remote Sensing Assessment Method
3.3.3. Process Model Method
3.3.4. Machine Learning Method
4. Forest Ecosystem Quality Prediction
5. Problems and Research Prospects
5.1. Existing Problems
5.1.1. Inadequate Assessment Index System
5.1.2. Inadequate Capacity of Forest Ecological Quality Assessment and Prediction
5.1.3. Dynamic Response of Forest Ecological Quality to Climate Change Is Unknown
5.2. Research Perspectives
- Develop a scientific and standardized evaluation index system. Therefore, in order to effectively promote the pace of ecological civilization construction in the new era and improve the effectiveness of ecosystem quality management, it is necessary to overcome the above-mentioned problems and to improve the existing ecosystem quality assessment system using screening evaluation indicators and clarifying the assessment criteria of each parameter based on the principles of scientificity, operability, comparability, accuracy, and quick sensitivity, so as to reveal the current situation, changes, and restoration potential of its quality in a more realistic way.
- Produce high-quality forest ecological data products and realize the localization of assessment model parameters. High-quality, ground-based, long-term observation data are the basis of scientific research on forest ecosystems, so we should strengthen long-term, ground-based observation of forests, constantly update and improve the basic data, and obtain real-time and effective sample data. Then, use data assimilation and other methods to integrate multi-source heterogeneous data (ground-based, long-term observation data, remote sensing monitoring data, and model simulation data) to produce high-quality forest ecological data products, realize the localization of assessment model parameters, and improve the accuracy of forest ecosystem quality assessments.
- Exploring forest quality–climate change response mechanisms. As one of the most important components of the carbon pool of terrestrial ecosystems, forests play an important role in the carbon balance of terrestrial ecosystems and the carbon cycle of surface systems. It is important to understand the response mechanism of forest ecosystem quality to climate change, simulate and predict forest ecosystem quality under future climate change scenarios, and clarify the heterogeneous response of forest ecosystem quality to climate change in advance so as to formulate forest management measures to cope with global climate change and achieve the goal of “carbon neutrality”.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Costanza, R.; D’Arge, R.; De Groot, R.; Farber, S.; Grasso, M.; Hannon, B.; Limburg, K.; Naeem, S.; O’Neill, R.V.; Paruelo, J.; et al. The value of the world’s ecosystem services and natural capital. Nature 1997, 387, 253–260. [Google Scholar] [CrossRef]
- Brockerhoff, E.G.; Barbaro, L.; Castagneyrol, B.; Forrester, D.I.; Gardiner, B.; González-Olabarria, J.R.; Lyver, P.O.B.; Meurisse, N.; Oxbrough, A.; Taki, H.; et al. Forest biodiversity, ecosystem functioning and the provision of ecosystem services. Biodivers. Conserv. 2017, 26, 3005–3035. [Google Scholar] [CrossRef]
- Chen, S.; Chen, J.; Jiang, C.; Yao, R.T.; Xue, J.; Bai, Y.; Wang, H.; Jiang, C.; Wang, S.; Zhong, Y.; et al. Trends in Research on Forest Ecosystem Services in the Most Recent 20 Years: A Bibliometric Analysis. Forests 2022, 13, 1087. [Google Scholar] [CrossRef]
- Piaggio, M.; Siikamaki, J. The value of forest water purification ecosystem services in Costa Rica. Sci. Total Environ. 2021, 789, 147952. [Google Scholar] [CrossRef]
- Vincent, J.R.; Ahmad, I.; Adnan, N.; Burwell, W.B.; Pattanayak, S.K.; Tan-Soo, J.-S.; Thomas, K. Valuing Water Purification by Forests: An Analysis of Malaysian Panel Data. Environ. Resour. Econ. 2015, 64, 59–80. [Google Scholar] [CrossRef]
- Cook-Patton, S.C.; Leavitt, S.M.; Gibbs, D.; Harris, N.L.; Lister, K.; Anderson-Teixeira, K.J.; Briggs, R.D.; Chazdon, R.L.; Crowther, T.W.; Ellis, P.W.; et al. Mapping carbon accumulation potential from global natural forest regrowth. Nature 2020, 585, 545–550. [Google Scholar] [CrossRef]
- Rhemtulla, J.M.; Mladenoff, D.J.; Clayton, M.K. Historical forest baselines reveal potential for continued carbon sequestration. Proc. Natl. Acad. Sci. USA 2009, 106, 6082–6087. [Google Scholar] [CrossRef]
- Gundersen, P.; Thybring, E.E.; Nord-Larsen, T.; Vesterdal, L.; Nadelhoffer, K.J.; Johannsen, V.K. Old-growth forest carbon sinks overestimated. Nature 2021, 591, E21–E23. [Google Scholar] [CrossRef]
- Han, C.; Zhang, C.; Liu, Y.; Li, Y.; Zhou, T.; Khan, S.; Chen, N.; Zhao, C. The capacity of ion adsorption and purification for coniferous forests is stronger than that of broad-leaved forests. Ecotoxicol. Environ. Saf. 2021, 215, 112137. [Google Scholar] [CrossRef] [PubMed]
- Cachada, A.; Pato, P.; Rocha-Santos, T.; da Silva, E.F.; Duarte, A.C. Levels, sources and potential human health risks of organic pollutants in urban soils. Sci. Total Environ. 2012, 430, 184–192. [Google Scholar] [CrossRef] [PubMed]
- Capuana, M. A review of the performance of woody and herbaceous ornamental plants for phytoremediation in urban areas. iForest—Biogeosciences For. 2020, 13, 139–151. [Google Scholar] [CrossRef]
- Liang, J.; Crowther, T.W.; Picard, N.; Wiser, S.; Zhou, M.; Alberti, G.; Schulze, E.D.; McGuire, A.D.; Bozzato, F.; Pretzsch, H.; et al. Positive biodiversity-productivity relationship predominant in global forests. Science 2016, 354, aaf8957. [Google Scholar] [CrossRef] [PubMed]
- Howard, C.; Flather, C.H.; Stephens, P.A. A global assessment of the drivers of threatened terrestrial species richness. Nat. Commun. 2020, 11, 993. [Google Scholar] [CrossRef] [PubMed]
- Wang, W.; Sun, L.; Luo, Y. Changes in Vegetation Greenness in the Upper and Middle Reaches of the Yellow River Basin over 2000–2015. Sustainability 2019, 11, 2176. [Google Scholar] [CrossRef]
- NASA. Human Activity in China and India Dominates the Greening of Earth, NASA Study Show. Available online: https://www.nasa.gov/feature/ames (accessed on 1 February 2023).
- Chen, C.; Park, T.; Wang, X.; Piao, S.; Xu, B.; Chaturvedi, R.K.; Fuchs, R.; Brovkin, V.; Ciais, P.; Fensholt, R.; et al. China and India lead in greening of the world through land-use management. Nat. Sustain. 2019, 2, 122–129. [Google Scholar] [CrossRef]
- FAO. Global Forest Resources Assessment 2015; FAO (Food and Agriculture Organization of the United Nations): Rome, Italy, 2015. [Google Scholar]
- Riva, F.; Martin, C.J.; Millard, K.; Fahrig, L. Loss of the world’s smallest forests. Glob. Change Biol. 2022, 28, 7164–7166. [Google Scholar] [CrossRef] [PubMed]
- Forzieri, G.; Dakos, V.; McDowell, N.G.; Ramdane, A.; Cescatti, A. Emerging signals of declining forest resilience under climate change. Nature 2022, 608, 534–539. [Google Scholar] [CrossRef]
- Han, Y.; Kang, W.; Thorne, J.; Song, Y. Modeling the effects of landscape patterns of current forests on the habitat quality of historical remnants in a highly urbanized area. Urban For. Urban Green. 2019, 41, 354–363. [Google Scholar] [CrossRef]
- Li, W.; Chen, J.; Zhang, Z. Forest quality-based assessment of the Returning Farmland to Forest Program at the community level in SW China. For. Ecol. Manag. 2020, 461, 117938. [Google Scholar] [CrossRef]
- Sallustio, L.; De Toni, A.; Strollo, A.; Di Febbraro, M.; Gissi, E.; Casella, L.; Geneletti, D.; Munafo, M.; Vizzarri, M.; Marchetti, M. Assessing habitat quality in relation to the spatial distribution of protected areas in Italy. J. Environ. Manag. 2017, 201, 129–137. [Google Scholar] [CrossRef]
- Wang, N.; Bao, Y. Modeling forest quality at stand level: A case study of loess plateau in China. For. Policy Econ. 2011, 13, 488–495. [Google Scholar] [CrossRef]
- Sarker, L.R.; Nichol, J.E. Improved forest biomass estimates using ALOS AVNIR-2 texture indices. Remote Sens. Environ. 2011, 115, 968–977. [Google Scholar] [CrossRef]
- Riedler, B.; Pernkopf, L.; Strasser, T.; Lang, S.; Smith, G. A composite indicator for assessing habitat quality of riparian forests derived from Earth observation data. Int. J. Appl. Earth Obs. Geoinf. 2015, 37, 114–123. [Google Scholar] [CrossRef]
- Zhao, Q.; Yu, S.; Zhao, F.; Tian, L.; Zhao, Z. Comparison of machine learning algorithms for forest parameter estimations and application for forest quality assessments. For. Ecol. Manag. 2019, 434, 224–234. [Google Scholar] [CrossRef]
- Wu, G. The Study on Indicator System and Evaluation Method of Forest Resources Quality at County-Level. Ph.D. Thesis, Beijing Forestry University, Beijing, China, 2010. [Google Scholar]
- Zhao, T.; Ouyang, Z.; Zheng, H.; Wang, X.; Miao, H. Forest ecosystem services and their valuation in China. J. Nat. Resour. 2004, 4, 480–491. [Google Scholar]
- Feng, J.; Ding, L.; Wang, J.; Yao, P.; Yao, S.; Wang, Z. Case-based evaluation of forest ecosystem service function in China. Chin. J. Appl. Ecol. 2016, 27, 1375–1382. [Google Scholar]
- Han, H.; Wan, R. A review on the methods of ecological evaluation of forest quality. Ecol. Sci. 2021, 40, 212–222. [Google Scholar]
- Dudley, N.; Schlaepfer, R.; Jackson, W.; Jackson, W. Forest Quality: Assessing Forests at a Landscape Scale; Routledge: London, UK, 2012. [Google Scholar]
- Ding, Z.; Li, R.; O’Connor, P.; Zheng, H.; Huang, B.; Kong, L.; Xiao, Y.; Xu, W.; Ouyang, Z. An improved quality assessment framework to better inform large-scale forest restoration management. Ecol. Indic. 2021, 123, 107370. [Google Scholar] [CrossRef]
- Haase, P.; Tonkin, J.D.; Stoll, S.; Burkhard, B.; Frenzel, M.; Geijzendorffer, I.R.; Häuse, C.; Klotz, S.; Kühn, I.; McDowell, W.H.; et al. The next generation of site-based long-term ecological monitoring: Linking essential biodiversity variables and ecosystem integrity. Sci. Total Environ. 2018, 613, 1376–1384. [Google Scholar] [CrossRef] [PubMed]
- Costanza, R.; Norton, B.; Haskell, B. Ecosystem Health: New Goals for Environmental Management. Bull. Sci. Technol. Soc. 1992, 14, 230–231. [Google Scholar]
- Bennett, D.D.; Tkacz, B.M. Forest health monitoring in the United States: A program overview. Aust. For. 2008, 71, 223–228. [Google Scholar] [CrossRef]
- Wardlaw, T. A review of the outcomes of a decade of forest health surveillance of state forests in Tasmania. Aust. For. 2008, 71, 254–260. [Google Scholar] [CrossRef]
- Bussotti, F.; Feducci, M.; Iacopetti, G.; Maggino, F.; Pollastrini, M.; Selvi, F. Linking forest diversity and tree health: Preliminary insights from a large-scale survey in Italy. For. Ecosyst. 2018, 5, 12. [Google Scholar] [CrossRef]
- Sampson, R.N.; Adams, D.L.; Hamilton, S.S.; Mealey, S.P.; Steele, R.; Van De Graaff, D. Assessing Forest Ecosystem Health in the Inland West. J. Sustain. For. 2008, 2, 3–10. [Google Scholar] [CrossRef]
- Xing, S.; Ji, W.; Guo, N.; Cui, G. Forest ecosystem health: Its research progress. Chin. J. Ecol. 2009, 28, 2102–2106. [Google Scholar]
- Ma, L.; Han, H.; Ma, Q.; Liu, H. Research progress of forest ecosystem health. For. Inventory Plan. 2007, 32, 103–110. [Google Scholar]
- Shi, C.; Wang, L. Connotation of forest resources quality. Probl. For. Econ. 2007, 3, 221–224. [Google Scholar]
- He, N.; Xu, L.; He, H. The methods of evaluation ecosystem quality: Ideal reference and key parameters. Acta Eco-Log. Sin. 2020, 40, 1877–1886. [Google Scholar]
- Pan, J.; Dong, L. Comprehensive evaluation of ecosystem quality in the Shule River basin, Northwest China from 2001 to 2010. Chin. J. Appl. Ecol. 2016, 27, 2907–2915. [Google Scholar]
- Chen, Q.; Chen, Y.H.; Wang, M.J.; Jiang, W.G.; Hou, P.; Li, Y. Ecosystem quality comprehensive evaluation and change analysis of Dongting Lake in 2001–2010 based on remote sensing. Acta Ecol. Sin. 2015, 35, 4347–4356. [Google Scholar]
- Feng, J.; Wang, J.; Yao, S.; Ding, L. Dynamic assessment of forest resources quality at the provincial level using AHP and cluster analysis. Comput. Electron. Agric. 2016, 124, 184–193. [Google Scholar] [CrossRef]
- Norris, W.R.; Farrar, D.R. A method for the natural evaluation of Central Hardwood forests in the Upper Midwest, USA. Nat. Areas J. 2001, 21, 313–323. [Google Scholar]
- Wang, B.; Niu, X.; Wei, W. National Forest Ecosystem Inventory System of China: Methodology and applications. Forests 2020, 11, 732. [Google Scholar] [CrossRef]
- Chave, J.; Davies, S.J.; Phillips, O.L.; Lewis, S.L.; Sist, P.; Schepaschenko, D.; Armston, J.; Baker, T.R.; Coomes, D.; Disney, M.; et al. Ground Data are Essential for Biomass Remote Sensing Missions. Surv. Geophys. 2019, 40, 863–880. [Google Scholar] [CrossRef]
- Zhao, M.; Zhou, G.-S. Estimation of biomass and net primary productivity of major planted forests in China based on forest inventory data. For. Ecol. Manag. 2005, 207, 295–313. [Google Scholar] [CrossRef]
- Du, H.; Cui, R.; Zhou, G.; Shi, Y.; Xu, X.; Fan, W.; Lü, Y. The responses of Moso bamboo (Phyllostachys heterocycla var. pubescens) forest aboveground biomass to Landsat TM spectral reflectance and NDVI. Acta Ecol. Sin. 2010, 30, 257–263. [Google Scholar] [CrossRef]
- Zhang, C.; Denka, S.; Cooper, H.; Mishra, D.R. Quantification of sawgrass marsh aboveground biomass in the coastal Everglades using object-based ensemble analysis and Landsat data. Remote Sens. Environ. 2018, 204, 366–379. [Google Scholar] [CrossRef]
- Sun, X. Research On Forest Quality Evaluation and Improvement Countermeasure of Wunuer Forestry Bureau. Master’s Thesis, Inner Mongolia Agricultural University, Huhehaote, Mongolia, 2020. [Google Scholar]
- Dan, H. The Evaluation of Forest Ecological Quality in Liling of Hunan Province. Master’s Thesis, Central South University of Forestry & Technology, Changsha, China, 2017. [Google Scholar]
- Zhang, M.; Zhang, L.; He, H.; Ren, X.; Niu, Z.; Lü, Y.; Xu, Q.; Chang, Q.; Liu, W.; Li, P. Quality changes of China′s ter-restrial ecosystem based on reference system. Acta Ecol. Sin. 2021, 41, 7100–7113. [Google Scholar]
- Li, Y.; Wu, Y.; Liu, X. Regional ecosystem health assessment using the GA-BPANN model: A case study of Yunnan Province, China. Ecosyst. Health Sustain. 2022, 8, 2084458. [Google Scholar] [CrossRef]
- De Keersmaecker, W.; Lhermitte, S.; Honnay, O.; Farifteh, J.; Somers, B.; Coppin, P. How to measure ecosystem stability? An evaluation of the reliability of stability metrics based on remote sensing time series across the major global ecosystems. Glob. Chang. Biol. 2014, 20, 2149–2161. [Google Scholar] [CrossRef]
- Meng, Y.; Cao, B.; Dong, C.; Dong, X. Mount Taishan Forest Ecosystem Health Assessment Based on Forest Inventory Data. Forests 2019, 10, 657. [Google Scholar] [CrossRef]
- Burger, J.A.; Kelting, D.L. Using soil quality indicators to assess forest stand management. For. Ecol. Manag. 1999, 122, 155–166. [Google Scholar] [CrossRef]
- Zhang, F.; Zhang, L.; Hu, W.; Liang, C. Research on forest quality evaluation in county based on the subcompartment scale. For. Environ. Sci. 2020, 36, 21–29. [Google Scholar]
- Zhang, B.; Wang, Z.; Lei, F. Quality evaluation of forest resources based on analytic hierarchy process and matter element analysis—A case study of Yanchuan County. J. Northwest For. Univ. 2022, 37, 208–215. [Google Scholar]
- Guo, X. Muti-Scale Assessments on Ecological Quality of Urban Forest in Shanghai. Ph.D. Thesis, East China Normal University, Shanghai, China, 2017. [Google Scholar]
- Huang, L.; Huang, J.; Liao, N.; Liu, F.; Jiang, Y.; Luo, D.; Peng, Z.; Liang, C.; Chen, S. forest quality evaluation based on principal component analysis and cluster analysis—A case study of Guangxi State-owned Bobai forest farm. Guangxi For. Sci. 2022, 51, 543–548. [Google Scholar]
- Salvati, L.; Carlucci, M. A composite index of sustainable development at the local scale: Italy as a case study. Ecol. Indic. 2014, 43, 162–171. [Google Scholar] [CrossRef]
- Guan, X.; Qin, H.; Meng, Y.; Wu, Z. Comprehensive evaluation of water-use efficiency in China’s Huai river basin using a cloud—Compound fuzzy matter element—Entropy combined model. J. Earth Syst. Sci. 2019, 128, 179. [Google Scholar] [CrossRef]
- Li, X.; Zhang, G.; Li, J. Spatial structure evaluation of natural secondary forest around Dongting Lake based on entropy weight—Cloud model. J. Coast. Res. 2020, 103, 484–489. [Google Scholar] [CrossRef]
- Meng, X.; Zhang, J.; Ren, G. An evaluation model of agricultural entrepreneurial ecological environment quality oriented to public health topic using the optimized neural network algorithm. J. Environ. Public Health 2022, 2022, 8735069. [Google Scholar] [CrossRef]
- Buckland, S.T.; Magurran, A.E.; Green, R.E.; Fewster, R.M. Monitoring change in biodiversity through composite indices. Philos. Trans. R. Soc. B 2005, 360, 243–254. [Google Scholar] [CrossRef]
- Li, J.; Min, Q.; Li, W.; Bai, Y.; Yang, L.; Dhruba Bijaya, G.C. Evaluation of water resources conserved by forests in the Hani rice terraces system of Honghe County, Yunnan, China: An application of the fuzzy comprehensive evaluation model. J. Mt. Sci. 2016, 13, 744–753. [Google Scholar] [CrossRef]
- Yu, J.; Fang, L.; Cang, D.; Zhu, L.; Bian, Z. Evaluation of land eco-security in Wanjiang district base on entropy weight and matter element model. Trans. Chin. Soc. Agric. Eng. 2012, 28, 260–266. [Google Scholar]
- Tan, F.; Zhang, M.; Li, H.; Lu, Z. Assessment on coordinative ability of sustainable development of Bei-jing-Tianjin-Hebei Region based on set pair analysis. Acta Ecol. Sin. 2014, 34, 3090–3098. [Google Scholar]
- Feng, X.; Yan, J.; Xue, Z.; Li, H. Assessment of Eco-environment Quality of Shanxi Province. J. Anhui Agric. Sci. 2012, 40, 14448–14452. [Google Scholar]
- Ouyang, Z.; Wang, Q.; Zheng, H.; Zhang, F.; Peng, H. National ecosystem survey and assessment of China (2000–2010). Bull. Chin. Acad. Sci. 2014, 29, 462–466. [Google Scholar]
- Lu, H.; Huang, Q.; Zhu, J.; Zheng, T.; Yan, Y.; Wu, G. Ecosystem type and quality changes in Lasa river basin and their effects on ecosystem services. Acta Ecol. Sin. 2018, 38, 8911–8918. [Google Scholar]
- Xiao, Y.; Ouyang, Z.; Wang, L.; Rao, E.; Jiang, L.; Zhang, L. Ecosystem type and quality changes in Lhasa River Ba-sin and their effects on ecosystem services. Acta Ecol. Sin. 2016, 36, 6019–6030. [Google Scholar]
- Wang, Z.; Hu, Y.; Zhang, Y.; Dian, Y.; Wang, Y.; Yuan, L. Evaluation of forest stand quality based on remote sensing data. Hubei For. Sci. Technol. 2022, 51, 33–38. [Google Scholar]
- Zhao, Y.C. New Vegetation Models for Hyperspectral Remote Sensing Progress of Agricultural Information Technology; International Academic Publishers: Beijing, China, 2000. [Google Scholar]
- Borja, A.; Bricker, S.B.; Dauer, D.M.; Demetriades, N.T.; Ferreira, J.G.; Forbes, A.T.; Hutchings, P.; Jia, X.; Kenchington, R.; Carlos Marques, J.; et al. Overview of integrative tools and methods in assessing ecological integrity in estuarine and coastal systems worldwide. Mar. Pollut. Bull. 2008, 56, 1519–1537. [Google Scholar] [CrossRef]
- Osborne, C.P.; Woodward, F.I. Biological mechanisms underlying recent increases in the NDVI of Mediterranean shrublands. Int. J. Remote Sens. 2010, 22, 1895–1907. [Google Scholar] [CrossRef]
- Clark, R.N.; King, T.V.V.; Klejwa, M.; Swayze, G.A.; Vergo, N. High spectral resolution reflectance spectroscopy of minerals. J. Geophys. Res. 1990, 95, 12653–12680. [Google Scholar] [CrossRef]
- Sun, Z.; Qian, W.; Huang, Q.; Lv, H.; Yu, D.; Ou, Q.; Lu, H.; Tang, X. Use Remote Sensing and Machine Learning to Study the Changes of Broad-Leaved Forest Biomass and Their Climate Driving Forces in Nature Reserves of Northern Subtropics. Remote Sens. 2022, 14, 1066. [Google Scholar] [CrossRef]
- Li, T.; Li, M.; Ren, F.; Tian, L. Estimation and Spatio-Temporal Change Analysis of NPP in Subtropical Forests: A Case Study of Shaoguan, Guangdong, China. Remote Sens. 2022, 14, 2541. [Google Scholar] [CrossRef]
- Lavorel, S.; Hutchings, M. Plant functional effects on ecosystem services. J. Ecol. 2013, 101, 4–8. [Google Scholar] [CrossRef]
- Qiu, J.; Zipper, S.C.; Motew, M.; Booth, E.G.; Kucharik, C.J.; Loheide, S.P. Nonlinear groundwater influence on biophysical indicators of ecosystem services. Nat. Sustain. 2019, 2, 475–483. [Google Scholar] [CrossRef]
- Oleson, K.W.; Lawrence, D.M.; Bonan, G.B.; Flanner, M.G.; Kluzek, E.; Lawrence, P.J.; Zeng, X. Technical Description of Version 4.0 of the Community Land Model (CLM) (No. NCAR/TN-478+STR); University Corporation for Atmospheric Research: Boulder, CO, USA, 2010. [Google Scholar]
- Sitch, S.; Smith, B.; Prentice, I.C.; Arneth, A.; Bondeau, A.; Cramer, W.; Kaplan, J.O.; Levis, S.; Lucht, W.; Sykes, M.T.; et al. Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Glob. Change Biol. 2003, 9, 161–185. [Google Scholar] [CrossRef]
- Parton, W.J. The CENTURY model. In Evaluation of Soil Organic Matter Models; Powlson, D.S., Smith, P., Smith, J.U., Eds.; Springer: Berlin, Germany, 1996; pp. 283–291. [Google Scholar]
- Thornton, P.E.; Law, B.E.; Gholz, H.L.; Clark, K.L.; Falge, E.; Ellsworth, D.S.; Goldstein, A.H.; Monson, R.K.; Hollinger, D.; Falk, M.; et al. Modeling and measuring the effects of disturbance history and climate on carbon and water budgets in evergreen needleleaf forests. Agric. For. Meteorol. 2002; in press. [Google Scholar]
- Cao, M.; Woodward, F.I. Dynamic responsesof terrestrial ecosystem carboncycling toglobal climate change. Nature 1998, 393, 249–252. [Google Scholar] [CrossRef]
- Niu, Z.; He, H.; Peng, S.; Ren, X.; Zhang, L.; Gu, F.; Zhu, G.; Peng, C.; Li, P.; Wang, J.; et al. A Process-Based Model Integrating Remote Sensing Data for Evaluating Ecosystem Services. J. Adv. Model. Earth Syst. 2021, 13, e2020MS002451. [Google Scholar] [CrossRef]
- Redhead, J.W.; Stratford, C.; Sharps, K.; Jones, L.; Ziv, G.; Clake, D.; Oliver, T.H.; Bullock, J.M. Empirical validation of the InVEST water yieldecosystem service model at a national scale. Sci. Total Environ. 2016, 1, 1418–1426. [Google Scholar] [CrossRef]
- Hamel, P.; Guswa, A.J. Uncertainty analysis of a spatially explicit annual water-balance model: Case study of the Cape Fear basin, North Carolina. Hydrol. Earth Syst. Sci. 2015, 19, 839–853. [Google Scholar] [CrossRef]
- Bastola, S.; Seong, Y.J.; Lee, S.H.; Jung, Y. Water vield estimation of the Bagmati basin of Nepal using ClS based lnVEST modlel. J. Korea Water Resour. Assoc. 2019, 52, 637–645. [Google Scholar]
- Jafaradleh, A.A.; MaHhdlavi, A.; Shamsi, S.F.; Youselpour, R. Anmual water yield estimation for dffrent lanmd uses by CIS-Basel InVEST model (CaseStudy: Mish-khas Catchment, llam Province, Iran). J. Rangel. 2019, 9, 1–12. [Google Scholar]
- Yu, X.; Zhou, B.; Lv, X.; Yang, Z. Evaluation of water conservation function in mountain forest areas of Beijing based on InVEST model. Sci. Silvae Sin. 2012, 48, 1–5. [Google Scholar]
- Elkin, C.; Gutierrez, A.G.; Leuzinger, S.; Manusch, C.; Temperli, C.; Rasche, L.; Bugmann, H. A 2 degrees C warmer world is not safe for ecosystem services in the European Alps. Glob. Chang. Biol. 2013, 19, 1827–1840. [Google Scholar] [CrossRef] [PubMed]
- Stürck, J.; Poortinga, A.; Verburg, P.H. Mapping ecosystem services: The supply and demand of flood regulation services in Europe. Ecol. Indic. 2014, 38, 198–211. [Google Scholar] [CrossRef]
- Gutsch, M.; Lasch-Born, P.; Kollas, C.; Suckow, F.; Reyer, C.P.O. Balancing trade-offs between ecosystem services in Germany’s forests under climate change. Environ. Res. Lett. 2018, 13, 045012. [Google Scholar] [CrossRef]
- Ren, X.; He, H.; Liu, M.; Zhang, L.; Zhou, L.; Yu, G.; Wang, H. Modeling of carbon and water fluxes of Qianyanzhou subtropical coniferous plantation using model-data fusion approach. Acta Ecol. Sin. 2012, 32, 7313–7326. [Google Scholar]
- Luo, Y.Q.; Weng, E.S.; Wu, X.W.; Gao, C.; Zhou, X.H.; Zhang, L. Parameter identifiability, constraint, and equifinality in data assimilation with ecosystem models. Ecol. Appl. A Publ. Ecol. Soc. Am. 2009, 19, 571–574. [Google Scholar] [CrossRef]
- Gu, F.; Zhang, Y.; Huang, M.; Tao, B.; Liu, Z.; Hao, M.; Guo, R. Climate-driven uncertainties in modeling terrestrial ecosystem net primary productivity in China. Agric. For. Meteorol. 2017, 246, 123–132. [Google Scholar] [CrossRef]
- Kun, Z. Parameter Sensitivity Analysis and Optimization for Remote Sensing Based Evapotranspiration Model College of Earth and Environmental Sciences. Ph.D. Thesis, Lanzhou University, Lanzhou, China, 2018. [Google Scholar]
- Zhang, X.; Wang, W.; Wang, L.; Wang, S.; Li, C.B. Drought variations and their influential climate factors in the Shi-yang River Basin. J. Lanzhou Univ. 2017, 53, 598–603, 608. [Google Scholar]
- Maxwell, R.M.; Condon, L.E. Connections between groundwater flow and transpiration partitioning. Science 2016, 353, 377–380. [Google Scholar] [CrossRef]
- McDonnell, J.J.; Sivapalan, M.; Vaché, K.; Dunn, S.; Grant, G.; Haggerty, R.; Hinz, C.; Hooper, R.; Kirchner, J.; Roderick, M.L.; et al. Moving beyond heterogeneity and process complexity: A new vision for watershed hydrology. Water Resour. Res. 2007, 43, W07301. [Google Scholar] [CrossRef]
- Li, Y.; Zhang, T.; Liu, Q.; Li, Y. Study on space-time heterogeneity of ecological process model’s sensitive parame-ters—The BIOME-BGC model as an example. Chin. J. Appl. Ecol. 2018, 29, 84–92. [Google Scholar]
- Raupach, M.R.; Gloor, M.; Sarmiento, J.L.; Canadell, J.G.; Frölicher, T.L.; Gasser, T.; Houghton, R.A.; Le Quéré, C.; Trudinger, C.M. The declining uptake rate of atmospheric CO2 by land and ocean sinks. Biogeosciences 2014, 11, 3453–3475. [Google Scholar] [CrossRef]
- Ren, X.; He, H.; Moore, D.J.P.; Zhang, L.; Liu, M.; Li, F.; Yu, G.; Wang, H. Uncertainty analysis of modeled carbon and water fluxes in a subtropical coniferous plantation. J. Geophys. Res. Biogeosciences 2013, 118, 1674–1688. [Google Scholar] [CrossRef]
- Wang, Y.; Zhang, N.; Yu, G. Simulation of carbon cycle in Qianyanzhou artificial masson pine forest ecosystem and sensitivity analysis of model parameters. Chin. J. Appl. Ecol. 2010, 21, 1656–1666. [Google Scholar]
- He, L.; Wang, H.; Lei, X. Parameter sensitivity of simulating net primary productivity of Larix olgensis forest based on BIOME-BGC model. Chin. J. Appl. Ecol. 2016, 27, 412–420. [Google Scholar]
- Trotsiuk, V.; Hartig, F.; Cailleret, M.; Babst, F.; Forrester, D.I.; Baltensweiler, A.; Buchmann, N.; Bugmann, H.; Gessler, A.; Gharun, M.; et al. Assessing the response of forest productivity to climate extremes in Switzerland using model-data fusion. Glob. Change Biol. 2020, 26, 2463–2476. [Google Scholar] [CrossRef] [PubMed]
- Lavorel, S.; Bayer, A.; Bondeau, A.; Lautenbach, S.; Ruiz-Frau, A.; Schulp, N.; Seppelt, R.; Verburg, P.; Teeffelen, A.v.; Vannier, C.; et al. Pathways to bridge the biophysical realism gap in ecosystem services mapping approaches. Ecol. Indic. 2017, 74, 241–260. [Google Scholar] [CrossRef]
- Bultan, S.; Nabel, J.; Hartung, K.; Ganzenmuller, R.; Xu, L.; Saatchi, S.; Pongratz, J. Tracking 21(st) century anthropogenic and natural carbon fluxes through model-data integration. Nat. Commun. 2022, 13, 5516. [Google Scholar] [CrossRef]
- Russell, S.; Norvig, P. Artificial Intelligence: A Modern Approach, 3rd ed.; Prentice Hall: Upper Saddle River, NJ, USA, 2010. [Google Scholar]
- Zhou, L.; Pan, S.; Wang, J.; Vasilakos, A.V. Machine learning on big data: Opportunities and challenges. Neurocomputing 2017, 237, 350–361. [Google Scholar] [CrossRef]
- Zhang, L.; Luo, Y.; Yu, G.; Zhang, L. Estimated carbon residence times in three forest ecosystems of eastern China: Applications of probabilistic inversion. J. Geophys. Res. 2010, 115, G01010. [Google Scholar] [CrossRef]
- Richardson, A.D.; Williams, M.; Hollinger, D.Y.; Moore, D.J.P.; Dail, D.B.; Davidson, E.A.; Scott, N.A.; Evans, R.S.; Hughes, H.; Lee, J.T.; et al. Estimating parameters of a forest ecosystem C model with measurements of stocks and fluxes as joint constraints. Oecologia 2010, 164, 25–40. [Google Scholar] [CrossRef]
- Ge, R.; He, H.; Ren, X.; Zhang, L.; Yu, G.; Smallman, T.L.; Zhou, T.; Yu, S.Y.; Luo, Y.; Xie, Z.; et al. Underestimated ecosystem carbon turnover time and sequestration under the steady state assumption: A perspective from long-term data assimilation. Glob. Chang. Biol. 2019, 25, 938–953. [Google Scholar] [CrossRef]
- Niu, Z.; He, H.; Zhu, G.; Ren, X.; Zhang, L.; Zhang, K.; Yu, G.; Ge, R.; Li, P.; Zeng, N.; et al. An increasing trend in the ratio of transpiration to total terrestrial evapotranspiration in China from 1982 to 2015 caused by greening and warming. Agric. For. Meteorol. 2019, 279, 107701. [Google Scholar] [CrossRef]
- Gao, X.; Lin, S.; Wong, T.Y. Automatic feature learning to grade nuclear cataracts based on deep learning. IEEE Trans. Biomed. Eng. 2015, 62, 2693–2701. [Google Scholar] [CrossRef]
- Mohamed, A.A.; Berg, W.A.; Peng, H.; Luo, Y.; Jankowitz, R.C.; Wu, S. A deep learning method for classifying mammographic breast density categories. Med. Phys. 2018, 45, 314–321. [Google Scholar] [CrossRef]
- Wang, Y.; Zhang, W.; Gao, R.; Jin, Z.; Wang, X. Recent advances in the application of deep learning methods to forestry. Wood Sci. Technol. 2021, 55, 1171–1202. [Google Scholar] [CrossRef]
- Rodríguez-Veiga, P.; Quegan, S.; Carreiras, J.; Persson, H.J.; Fransson, J.E.S.; Hoscilo, A.; Ziółkowski, D.; Stereńczak, K.; Lohberger, S.; Stängel, M.; et al. Forest biomass retrieval approaches from earth observation in different biomes. Int. J. Appl. Earth Obs. Geoinf. 2019, 77, 53–68. [Google Scholar] [CrossRef]
- Schratz, P.; Muenchow, J.; Iturritxa, E.; Richter, J.; Brenning, A. Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data. Ecol. Model. 2019, 406, 109–120. [Google Scholar] [CrossRef] [Green Version]
- Li, S.; Zhang, R.; Xie, L.; Zhan, J.; Song, Y.; Zhan, R.; Shama, A.; Wang, T. A Factor Analysis Backpropagation Neural Network Model for Vegetation Net Primary Productivity Time Series Estimation in Western Sichuan. Remote Sens. 2022, 14, 3961. [Google Scholar] [CrossRef]
- Nandy, S.; Singh, R.; Ghosh, S.; Watham, T.; Kushwaha, S.P.S.; Kumar, A.S.; Dadhwal, V.K. Neural network-based modelling for forest biomass assessment. Carbon Manag. 2017, 8, 305–317. [Google Scholar] [CrossRef]
- Srinet, R.; Nandy, S.; Patel, N.R. Estimating leaf area index and light extinction coefficient using Random Forest regression algorithm in a tropical moist deciduous forest, India. Ecol. Inform. 2019, 52, 94–102. [Google Scholar] [CrossRef]
- Wang, Z.; Zhang, X.; Zhang, J.; Chhin, S. Effects of stand factors on tree growth of Chinese fir in the subtropics of China depends on climate conditions from predictions of a deep learning algorithm: A long-term spacing trial. For. Ecol. Manag. 2022, 520, 120363. [Google Scholar] [CrossRef]
- Görgens, E.B.; Montaghi, A.; Rodriguez, L.C.E. A performance comparison of machine learning methods to estimate the fast-growing forest plantation yield based on laser scanning metrics. Comput. Electron. Agric. 2015, 116, 221–227. [Google Scholar] [CrossRef]
- Ercanlı, İ. Innovative deep learning artificial intelligence applications for predicting relationships between individual tree height and diameter at breast height. For. Ecosyst. 2020, 7, 3–20. [Google Scholar] [CrossRef]
- Li, H.; Niu, X.; Wang, B. Prediction of Ecosystem Service Function of Grain for Green Project Based on Ensemble Learning. Forests 2021, 12, 537. [Google Scholar] [CrossRef]
- Weiskittel, A.R.; Crookston, N.L.; Radtke, P.J. Linking climate, gross primary productivity, and site index across forests of the western United States. Can. J. For. Res. 2011, 41, 1710–1721. [Google Scholar] [CrossRef]
- Shi, X. Study on Evaluation and Predictive of Forest Ecosystem ServiceValues for Jilin Forest Industry Group. Ph.D. Thesis, Beijing Forestry University, Beijing, China, 2015. [Google Scholar]
- Liu, J.; Liu, X.; Zheng, P.; Zhu, X. The study of ecological water footprint based on the comprehensive value of regional forest’s ecological function in Yunnan Province. Ecol. Econ. 2019, 35, 191–199. [Google Scholar]
- Moore, J.W.; Schindler, D.E. Getting ahead of climate change for ecological adaptation and resilience. Science 2022, 376, 1421–1426. [Google Scholar] [CrossRef]
- Popkin, G. How much can forests fight climate change? Nature 2019, 565, 280–282. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Lawrence, D.; Coe, M.; Walker, W.; Verchot, L.; Vandecar, K. The Unseen Effects of Deforestation: Biophysical Effects on Climate. Front. For. Glob. Change 2022, 5, 756115. [Google Scholar] [CrossRef]
- Feeley, K.J.; Zuleta, D. Changing forests under climate change. Nat. Plants 2021, 8, 984–985. [Google Scholar] [CrossRef] [PubMed]
- IPCC. Climate Change 2022: Impacts, Adaptation and Vulnerability; Cambridge University Press: Cambridge, UK, 2022. [Google Scholar]
- Zellweger, F.; De Frenne, P.; Lenoir, J.; Vangansbeke, P.; Verheyen, K. Forest microclimate dynamics drive plant responses to warming. Science 2020, 368, 772–775. [Google Scholar] [CrossRef] [PubMed]
- Esquivel-Muelbert, A.; Baker, T.R.; Dexter, K.G.; Lewis, S.L.; Brienen, R.J.W.; Feldpausch, T.R.; Lloyd, J.; Monteagudo-Mendoza, A.; Arroyo, L.; Álvarez-Dávila, E.; et al. Compositional response of Amazon forests to climate change. Glob. Chang. Biol. 2019, 25, 39–56. [Google Scholar] [CrossRef] [Green Version]
Methods | Main Features | Input Data | Advantages | Disadvantages |
---|---|---|---|---|
Comprehensive evaluation method | Combination of qualitative and quantitative | Ground monitoring data | Simple method; intuitive evaluation results with high accuracy; high information utilization | The evaluation results may be biased by obscuring some factors that have a greater impact |
Remote sensing assessment method | High assessment efficiency; suitable for large-scale forest quality assessment | Ground monitoring data; multi-source remote sensing data | Saves human and material resources; fast evaluation; high evaluation efficiency | Remote sensing images are often affected by satellite type, weather, cloudiness, etc. Remote sensing inversion of forest quality-related indicators needs to be verified by ground monitoring data |
Process modeling method | Lateral reflection of forest quality through assessment of forest ecological functions | Ground monitoring data; multi-source remote sensing data | Expression formulas are clear, can capture the intrinsic linkages of ecosystem services, and are highly interpretable | Limitations in input data, model structure, and model parameters make simulation results subject to large uncertainties |
Machine learning method | Adept at handling high-dimensional data and non-linear ecological relationships | Ground monitoring data; model simulation data; multi-source remote sensing data | It is self-learning and self-adaptive, greatly reducing the influence of subjective weights on evaluation results; it can couple ecological big data, process models, and use artificial intelligence to invert key parameters or optimize model parameters, thus improving evaluation accuracy | Its data demand is large, over-fitting or under-fitting problems may occur, and the interpretability of simulation results needs to be improved. |
Machine Learning Algorithms | Characteristic | Applications |
---|---|---|
K-nearest neighbor (KNN) | No parameter estimation; simple and easy to implement; increases the workload and overfitting problem when the sample size is large. | [122,123] |
Artificial neural networks (ANN) | Suitable for dealing with multi-factor influence and ambiguous information, no assumptions are required about the data, which can effectively deal with non-linearity, non-normality, and covariance in the data; overfitting can occur. | [26,55,124,125] |
Random forest (RF) | It can handle complex, nonlinear ecological relationships and has the advantages of efficient processing of massive data, less human interference, strong resistance to noise, and less likely to produce overfitting; however, it is sensitive to the interrelationship between input variables and will produce bias in the prediction tree, so the importance of variables needs to be measured. | [26,123,126,127,128] |
Support vector machine (SVM) | It can be used in classification and regression analysis to produce higher classification or more accurate estimates in solving small, non-linear, and high-dimensional pattern recognition problems. | [26,123,128] |
Deep learning (DL) | Ideal for classifying audio, text, and image data but requires large amounts of data for training. | [127,129] |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Guo, K.; Wang, B.; Niu, X. A Review of Research on Forest Ecosystem Quality Assessment and Prediction Methods. Forests 2023, 14, 317. https://doi.org/10.3390/f14020317
Guo K, Wang B, Niu X. A Review of Research on Forest Ecosystem Quality Assessment and Prediction Methods. Forests. 2023; 14(2):317. https://doi.org/10.3390/f14020317
Chicago/Turabian StyleGuo, Ke, Bing Wang, and Xiang Niu. 2023. "A Review of Research on Forest Ecosystem Quality Assessment and Prediction Methods" Forests 14, no. 2: 317. https://doi.org/10.3390/f14020317