Efficiency and regional differences of forest restoration across China’s Upper Yangtze River Basin

Zhiwei Lei , Jia Zhou , Yike Li , Yingnan Zhao , Tao Lu

Journal of Forestry Research ›› 2025, Vol. 36 ›› Issue (1) : 114

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Journal of Forestry Research ›› 2025, Vol. 36 ›› Issue (1) :114 DOI: 10.1007/s11676-025-01910-2
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Efficiency and regional differences of forest restoration across China’s Upper Yangtze River Basin

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Abstract

Evaluating the effectiveness of forest restoration projects is crucial for designing adaptive restoration strategies. However, existing studies have primarily focused on ecological outcomes while overlooking cost inputs. This gap can lead to increased uncertainties in restoration planning. Here we investigated forest dynamics in China’s Upper Yangtze River Basin (UYRB) using kernel Normalized Difference Vegetation Index (kNDVI), Leaf Area Index (LAI), Gross Primary Productivity (GPP), Ku-band Vegetation Optical Depth (Ku-VOD) time series and climate data from 1982 to 2020. Subsequently, we employed a residual trend analysis integrating temporal effects to determine the relative contributions of climate change and human activities to forest dynamics before and after the implementation of forest restoration engineering in 1998. Additionally, we developed an Afforestation Efficiency Index (AEI) to quantitatively assess the cost efficiency of afforestation projects. Results indicated that forest in the UYRB showed sustained increases during 1982–2020, with most areas experiencing greater growth after 1998 than before. Temporal effects of climatic factors influenced over 42.7% of the forest, and incorporating time-lag and cumulative effects enhanced climate-based explanations of forest variations by 1.61–24.73%. Human activities emerged as the dominant driver of forest dynamics post 1998, whereas climate variables predominated before this period. The cost-effectiveness of forest restoration projects in the UYRB typically ranges from moderate to high, with higher success predominantly observed in the northeastern and eastern counties, while the central, western, and northwestern counties mainly showed relatively low efficiency. These findings stress the need for assessing forest restoration outcomes from both ecological and cost perspectives, and can offer valuable insights for optimizing the layout of forest restoration initiatives in the UYRB.

Keywords

Forest restoration / Driving force analysis / Temporal effects / Afforestation efficiency

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Zhiwei Lei, Jia Zhou, Yike Li, Yingnan Zhao, Tao Lu. Efficiency and regional differences of forest restoration across China’s Upper Yangtze River Basin. Journal of Forestry Research, 2025, 36(1): 114 DOI:10.1007/s11676-025-01910-2

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References

[1]

Abatzoglou JT, Dobrowski SZ, Parks SA, Hegewisch KC. Terraclimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci Data, 2018, 5 170191

[2]

Anderegg WRL, Schwalm C, Biondi F, Camarero JJ, Koch G, Litvak M, Ogle K, Shaw JD, Shevliakova E, Williams AP, Wolf A, Ziaco E, Pacala S. Pervasive drought legacies in forest ecosystems and their implications for carbon cycle models. Science, 2015, 349(6247): 528-532

[3]

Baker S, Eckerberg K. Ecological restoration success: a policy analysis understanding. Restor Ecol, 2016, 24(3): 284-290

[4]

Busch J, Bukoski JJ, Cook-Patton SC, Griscom B, Kaczan D, Potts MD, Yi YY, Vincent JR. Cost-effectiveness of natural forest regeneration and plantations for climate mitigation. Nat Clim Change, 2024, 14(9): 996-1002

[5]

Busetto L, Colombo R, Migliavacca M, Cremonese E, Meroni M, Galvagno M, Rossini M, Siniscalco C, Morra Di Cella U, Pari E. Remote sensing of larch phenological cycle and analysis of relationships with climate in the Alpine region. Glob Change Biol, 2010, 16(9): 2504-2517

[6]

Campbell JE, Berry JA, Seibt U, Smith SJ, Montzka SA, Launois T, Belviso S, Bopp L, Laine M. Large historical growth in global terrestrial gross primary production. Nature, 2017, 544(7648): 84-87

[7]

Camps-Valls G, Campos-Taberner M, Moreno-Martínez Á, Walther S, Duveiller G, Cescatti A, Mahecha MD, Muñoz-Marí J, García-Haro FJ, Guanter L, Jung M, Gamon JA, Reichstein M, Running SW. A unified vegetation index for quantifying the terrestrial biosphere. Sci Adv, 2021, 7(9 eabc7447

[8]

Cao FF, Li WB, Jiang Y, Gan XL, Zhao CY, Ma JC. Effects of grazing on grassland biomass and biodiversity: a global synthesis. Field Crops Res, 2024, 306 109204

[9]

Chen SS, Wen ZF, Zhang SL, Huang P, Ma MH, Zhou X, Liao T, Wu SJ. Effects of long-term and large-scale ecology projects on forest dynamics in Yangtze River Basin. China for Ecol Manag, 2021, 496 119463

[10]

Cheng YM, Liu L, Cheng L, Fa KY, Liu XC, Huo ZL, Huang GH. A shift in the dominant role of atmospheric vapor pressure deficit and soil moisture on vegetation greening in China. J Hydrol, 2022, 615 128680

[11]

Cheng MM, Wang ZH, Wang SD, Liu XJ, Jiao WZ, Zhang Y. Determining the impacts of climate change and human activities on vegetation change on the Chinese Loess Plateau considering human-induced vegetation type change and time-lag effects of climate on vegetation growth. Int J Digit Earth, 2024, 17(1): 2336075

[12]

Cheng K, Zhang YX, Yang HT, Ren Y, Xiang TY, Chen YL, Yang ZK, Chen MX, Xu JC, Huang GR, Xu GC, Tao SL, Yu Z, Guo QH. China’s naturally regenerated forests currently have greater aboveground carbon accumulation rates than newly planted forests. Commun Earth Environ, 2025, 6: 345

[13]

Crouzeilles R, Curran M, Ferreira MS, Lindenmayer DB, Grelle CEV, Rey Benayas JM. A global meta-analysis on the ecological drivers of forest restoration success. Nat Commun, 2016, 7: 11666

[14]

Deng YJ, Cai WC, Hou MY, Zhang XL, Xu SY, Yao N, Guo YJ, Li H, Yao SB. How eco-efficiency is the forestry ecological restoration program? the case of the sloping land conversion program in the Loess Plateau, China. Land, 2022, 11(5712

[15]

Ding YX, Li Z, Peng SZ. Global analysis of time-lag and-accumulation effects of climate on vegetation growth. Int J Appl Earth Obs Geoinf, 2020, 92 102179

[16]

Ding ZH, Peng J, Qiu SJ, Zhao Y. Nearly half of global vegetated area experienced inconsistent vegetation growth in terms of greenness, cover, and productivity. Earths Future, 2020, 8(10 e2020EF001618

[17]

Ding ZW, Zheng H, Wang J, O’Connor P, Li C, Chen XD, Li RN, Ouyang ZY. Integrating top-down and bottom-up approaches improves practicality and efficiency of large-scale ecological restoration planning: insights from a social–ecological system. Engineering, 2023, 31: 50-58

[18]

Eger AM, Marzinelli EM, Christie H, Fagerli CW, Fujita D, Gonzalez AP, Hong SW, Kim JH, Lee LC, McHugh TA, Nishihara GN, Tatsumi M, Steinberg PD, Vergés A. Global kelp forest restoration: past lessons, present status, and future directions. Biol Rev, 2022, 97(41449-1475

[19]

Fan XL, Qu Y, Zhang J, Bai E. China’s vegetation restoration programs accelerated vegetation greening on the Loess Plateau. Agric for Meteor, 2024, 350 109994

[20]

Frappart F, Wigneron JP, Li XJ, Liu XZ, Al-Yaari A, Fan L, Wang MJ, Moisy C, Le Masson E, Aoulad Lafkih Z, Vallé C, Ygorra B, Baghdadi N. Global monitoring of the vegetation dynamics from the vegetation optical depth (VOD): a review. Remote Sens, 2020, 12(18): 2915

[21]

Fu BJ, Liu YX, Meadows ME (2023) Ecological restoration for sustainable development in China. Natl Sci Rev 10: nwad033. https://doi.org/10.1093/nsr/nwad033

[22]

Gao FF, Zhou JS, Jiang HD, Yang W, Wang GY. Assessing the true value of ecological restoration in mining areas: an input-output approach based on ecosystem service valuation. Ecol Indic, 2024, 166 112591

[23]

He W, Di BF, Zeng YJ, Duan YN, Li JH, Qiu LK, Balikuddembe JK, Peng QQ, Zeng W, Stamatopoulos CA, Luo B, Pan J. Reconsidering the eco-economic benefits of grain for green program in Sichuan Province, China. Ecol Indic, 2023, 157 111225

[24]

Holl KD, Brancalion PHS. Tree planting is not a simple solution. Science, 2020, 368(6491580-581

[25]

Kimball S, Lulow M, Sorenson Q, Balazs K, Fang YC, Davis SJ, O’Connell M, Huxman TE. Cost-effective ecological restoration. Restor Ecol, 2015, 23(6): 800-810

[26]

Li XY, Wang K, Huntingford C, Zhu ZC, Peñuelas J, Myneni RB, Piao SL. Vegetation greenness in 2023. Nat Rev Earth Environ, 2024, 5(4): 241-243

[27]

Li H, Yan XB, Su PY, Su YM, Li JF, Xu ZX, Gao CR, Zhao Y, Feng MC, Shafiq F, Xiao LJ, Yang WD, Qiao XX, Wang C. Estimation of winter wheat LAI based on color indices and texture features of RGB images taken by UAV. J Sci Food Agric, 2025, 105(1): 189-200

[28]

Liang HX, Zhang D, Wang WS, Yu SY, Nimai S. Evaluating future water security in the Upper Yangtze River Basin under a changing environment. Sci Total Environ, 2023, 889 164101

[29]

Liu SR, Shi ZM, Ma JM, Zhao CM, Zhang YD, Liu XL. Ecological strategies for restoration and reconstruction of degraded natural forests on the upper reaches of the Yangtze River. Sci Silvae Sin, 2009, 45(2): 120-124(in Chinese)

[30]

Liu R, Liu Y, Chen J. Globmap global leaf area index since 1981 dataset2021Zenodo

[31]

Liu Y, Liu HH, Chen Y, Gang CC, Shen YF. Quantifying the contributions of climate change and human activities to vegetation dynamic in China based on multiple indices. Sci Total Environ, 2022, 838 156553

[32]

Liu CX, Shi S, Wang T, Gong W, Xu L, Shi ZX, Du J, Qu FF. Analysis of net primary productivity variation and quantitative assessment of driving forces—a case study of the Yangtze River Basin. Plants, 2023, 12(19): 3412

[33]

Liu HK, Shi H, Zhou Q, Hu M, Shu X, Zhang KR, Zhang QF, Dang HS. Habitat heterogeneity and biotic interactions mediate climate influences on seedling survival in a temperate forest. For Ecosyst, 2023, 10 100138

[34]

Liu YH, Zhong YF, Ma AL, Zhao J, Zhang LP. Cross-resolution national-scale land-cover mapping based on noisy label learning: a case study of China. Int J Appl Earth Obs Geoinf, 2023, 118 103265

[35]

Ma ZH, Guo JH, Li WM, Cai ZY, Cao SX. Regional differences in the factors that affect vegetation cover in China. Land Degrad Dev, 2021, 32(51961-1969

[36]

Ma BX, He CX, Jing JL, Wang YF, Liu B, He HC. Attribution of vegetation dynamics in southwest China from 1982 to 2019. Acta Geogr Sin, 2023

[37]

Ma MY, Wang QM, Liu R, Zhao Y, Zhang DQ. Effects of climate change and human activities on vegetation coverage change in northern China considering extreme climate and time-lag and-accumulation effects. Sci Total Environ, 2023, 860 160527

[38]

Ma Q, Su YJ, Niu CY, Ma Q, Hu TY, Luo XZ, Tai XN, Qiu T, Zhang Y, Bales RC, Liu LL, Kelly M, Guo QH. Tree mortality during long-term droughts is lower in structurally complex forest stands. Nat Commun, 2023, 14: 7467

[39]

Ma Y, He Q, Zhang Y, Shi Y, Li J, Yuan F. Influences of climate factors and human acintes on vegeaton leaf area index dynamics m the Songliao River Basin considering time-lag and cumulative effects. Acta Ecol Sin, 2024

[40]

Meesters AGCA, De Jeu RAM, Owe M. Analytical derivation of the vegetation optical depth from the microwave polarization difference index. IEEE Geosci Remote Sens Lett, 2005, 2(2): 121-123

[41]

Mirón IJ, Linares C, Díaz J. The influence of climate change on food production and food safety. Environ Res, 2023, 216 114674

[42]

Moesinger L, Dorigo W, de Jeu R, van der Schalie R, Scanlon T, Teubner I, Forkel M. The global long-term microwave vegetation optical depth climate archive (VODCA). Earth Syst Sci Data, 2020, 12(1177-196

[43]

Newton AC, Hodder K, Cantarello E, Perrella L, Birch JC, Robins J, Douglas S, Moody C, Cordingley J. Cost–benefit analysis of ecological networks assessed through spatial analysis of ecosystem services. J Appl Ecol, 2012, 49(3): 571-580

[44]

Novick KA, Ficklin DL, Grossiord C, Konings AG, Martínez-Vilalta J, Sadok W, Trugman AT, Williams AP, Wright AJ, Abatzoglou JT, Dannenberg MP, Gentine P, Guan KY, Johnston MR, Lowman LEL, Moore DJP, McDowell NG. The impacts of rising vapour pressure deficit in natural and managed ecosystems. Plant Cell Environ, 2024, 47(9): 3561-3589

[45]

Parr CL, te Beest M, Stevens N. Conflation of reforestation with restoration is widespread. Science, 2024, 383(6684): 698-701

[46]

Peng SZ, Ding YX, Liu WZ, Li Z. 1 km monthly temperature and precipitation dataset for China from 1901 to 2017. Earth Syst Sci Data, 2019, 11(4): 1931-1946

[47]

Pettitt AN. A non-parametric approach to the change-point problem. J R Stat Soc Ser C Appl Stat, 1979, 28(2): 126-135

[48]

Pinzon JE, Pak EW, Tucker CJ, Bhatt US, Frost GV, Macander MJ (2023) Global Vegetation Greenness (NDVI) from AVHRR GIMMS-3G+, 1981–2022, ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/2187

[49]

Prach K, Durigan G, Fennessy S, Overbeck GE, Torezan JM, Murphy SD. A primer on choosing goals and indicators to evaluate ecological restoration success. Restor Ecol, 2019, 27(5): 917-923

[50]

Qu S, Wang LC, Lin AW, Zhu HJ, Yuan MX. What drives the vegetation restoration in Yangtze River basin, China: climate change or anthropogenic factors?. Ecol Indic, 2018, 90: 438-450

[51]

Qu S, Wang LC, Lin AW, Yu DQ, Yuan MX, Li CA. Distinguishing the impacts of climate change and anthropogenic factors on vegetation dynamics in the Yangtze River Basin, China. Ecol Indic, 2020, 108 105724

[52]

Sarmah S, Singha M, Wang JS, Dong JW, Deb Burman PK, Goswami S, Ge Y, Ilyas S, Niu SL. Mismatches between vegetation greening and primary productivity trends in South Asia–a satellite evidence. Int J Appl Earth Obs Geoinf, 2021, 104 102561

[53]

Song WQ, Feng YH, Wang ZH. Ecological restoration programs dominate vegetation greening in China. Sci Total Environ, 2022, 848 157729

[54]

Song XY, Xie PJ, Sun WY, Mu XM, Gao P. The greening of vegetation on the Loess Plateau has resulted in a northward shift of the vegetation greenness line. Glob Planet Change, 2024, 237 104440

[55]

Stavi I, Islam KR, Rahman MA, Gusarov Y, Laham J, Comay O, Basson U, Xu C, Xu ZW, Argaman E. Unexpected consequences of afforestation in degraded drylands: divergent impacts on soil and vegetation. J Environ Manage, 2023, 345 118703

[56]

Stuble KL, Bennion LD, Kuebbing SE. Plant phenological responses to experimental warming—a synthesis. Glob Change Biol, 2021, 27(17): 4110-4124

[57]

Thomas CC, Huber C, Skrabis KE, Hoelzle TB. A framework for estimating economic impacts of ecological restoration. Environ Manage, 2024, 74(6): 1239-1259

[58]

van Leeuwen WJD, Huete AR, Laing TW. MODIS vegetation index compositing approach a prototype with AVHRR data. Remote Sens Environ, 1999, 69(3): 264-280

[59]

Wainaina P, Minang PA, Gituku E, Duguma L. Cost-benefit analysis of landscape restoration: a stocktake. Land, 2020, 9(11): 465

[60]

Walther S, Guanter L, Heim B, Jung M, Duveiller G, Wolanin A, Sachs T. Assessing the dynamics of vegetation productivity in circumpolar regions with different satellite indicators of greenness and photosynthesis. Biogeosciences, 2018, 15(206221-6256

[61]

Wang SH, Zhang YG, Ju WM, Qiu B, Zhang ZY. Tracking the seasonal and inter-annual variations of global gross primary production during last four decades using satellite near-infrared reflectance data. Sci Total Environ, 2021, 755 142569

[62]

Wang TH, Shi RJ, Yang DW, Yang SY, Fang BJ. Future changes in annual runoff and hydroclimatic extremes in the Upper Yangtze River Basin. J Hydrol, 2022, 615 128738

[63]

Wang YM, Zhang ZX, Chen X. The dominant driving force of forest change in the Yangtze River Basin, China: climate variation or anthropogenic activities?. Forests, 2022, 13(1): 82

[64]

Wang L, Cao W, Huang L. Integrated analysis of ecological effectiveness of major ecological projects in China over the past 40 years. Shengtai Xuebao, 2024

[65]

Wang XP, Wu BQ, Zhou GL, Wang T, Meng FW, Zhou L, Cao H, Tang ZY. How a vast digital twin of the Yangtze River could prevent flooding in China. Nature, 2025, 639(8054303-305

[66]

Wei W, Wang N, Yin L, Guo SY, Bo LM. Spatio-temporal evolution characteristics and driving mechanisms of Urban–Agricultural–Ecological space in ecologically fragile areas: a case study of the upper reaches of the Yangtze River Economic Belt, China. Land Use Policy, 2024, 145 107282

[67]

Wei XX, Liu RG, Liu Y, He JY, Chen JL, Qi L, Zhou YL, Qin YW, Wu CY, Dong JW, Xiao XM, Chen JM, Ge QS. Forest areas in China are recovering since the 21st century. Geophys Res Lett, 2024, 51(22 e2024GL110312

[68]

Wen YY, Liu XP, Xin QC, Wu J, Xu XC, Pei FS, Li X, Du GM, Cai YL, Lin K, Yang J, Wang YP. Cumulative effects of climatic factors on terrestrial vegetation growth. J Geophys Res Biogeosci, 2019, 124(4): 789-806

[69]

Wu DH, Zhao X, Liang SL, Zhou T, Huang KC, Tang BJ, Zhao WQ. Time-lag effects of global vegetation responses to climate change. Glob Change Biol, 2015, 21(9): 3520-3531

[70]

Wu XT, Wang S, Fu BJ, Liu JG. Spatial variation and influencing factors of the effectiveness of afforestation in China’s Loess Plateau. Sci Total Environ, 2021, 771 144904

[71]

Wu K, Hu ZM, Wang XH, Chen JH, Yang H, Yuan WP. Widespread increase in sensitivity of vegetation growth to climate variability on the Tibetan Plateau. Agric for Meteorol, 2024, 358 110260

[72]

Wu TT, Xu L, Lv Y, Cai RN, Pan ZW, Zhang XH, Zhang X, Chen NC. Integrating causal inference with ConvLSTM networks for spatiotemporal forecasting of root zone soil moisture. J Hydrol, 2025, 659 133246

[73]

Xiao X, Guan QY, Zhang ZP, Liu HQ, Du QQ, Yuan TW. Investigating the underlying drivers of vegetation dynamics in cold-arid mountainous. CATENA, 2024, 237 107831

[74]

Xu H, Yue C, Zhang Y, Liu D, Piao S. Forestation at the right time with the right species can generate persistent carbon benefits in China. Proc Natl Acad Sci U S A, 2023, 120 e2304988120

[75]

Yan W, Wang HS, Jiang C, Sun OJ, Chu JM, Zhang AZ. Afforestation boosted gross primary productivity of China: evidence from remote sensing. J for Res, 2025, 36(1): 40

[76]

Yeganeh KH. A typology of sources, manifestations, and implications of environmental degradation. Manag Environ Qual, 2020, 31(3765-783

[77]

Yuan M, Ouyang JY, Zheng SN, Tian Y, Sun R, Bao R, Li T, Yu TS, Li S, Wu D, Liu YJ, Xu CY, Zhu Y. Research on ecological effect assessment method of ecological restoration of open-pit coal mines in alpine regions. Int J Environ Res Public Health, 2022, 19(13): 7682

[78]

Zhan C, Liang C, Zhao L, Jiang SZ, Niu KJ, Zhang YL, Cheng L. Detection and attribution of vegetation dynamics in the National Barrier Zone of China by considering climate temporal effects. Int J Appl Earth Obs Geoinf, 2023, 116 103140

[79]

Zhang MY, Liu HY, Wang KL, Chen Y, Ren YJ, Yue YM, Deng ZH, Zhang CH. Nonlinear trends of vegetation changes in different geomorphologic zones and land use types of the Yangtze River basin, China. Land Degrad Dev, 2023, 34(92548-2559

[80]

Zhang J, Guan QY, Zhang ZP, Shao WY, Zhang EY, Kang TT, Xiao X, Liu HQ, Luo HP. Characteristics of spatial and temporal dynamics of vegetation and its response to climate extremes in ecologically fragile and climate change sensitive areas–a case study of Hexi region. CATENA, 2024, 239 107910

[81]

Zhao QY, Xu CX, An WL, Liu YC, Xiao GQ, Huang CJ. Increasing tree growth in subalpine forests of Central China due to earlier onset of the thermal growing season. Agric for Meteorol, 2023, 333 109391

[82]

Zhou X, Yamaguchi Y, Arjasakusuma S. Distinguishing the vegetation dynamics induced by anthropogenic factors using vegetation optical depth and AVHRR NDVI: a cross-border study on the Mongolian Plateau. Sci Total Environ, 2018, 616–617: 730-743

[83]

Zhou DC, Zhang LX, Hao L, Sun G, Xiao JF, Li X. Large discrepancies among remote sensing indices for characterizing vegetation growth dynamics in Nepal. Agric for Meteorol, 2023, 339 109546

[84]

Zong XZ, Liu Y, Yin YH. Identifying the dominant compound events and their impacts on vegetation growth in China. Weather Clim Extrem, 2024, 45 100715

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