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Background

Natural forests in the Hengduan Mountains Region (HDMR) have pivotal ecological functions and provide diverse ecosystem services. Capturing long-term forest disturbance and drivers at a regional scale is crucial for sustainable forest management and biodiversity conservation.

Methods

We used 30-m resolution Landsat time series images and the LandTrendr algorithm on the Google Earth Engine cloud platform to map forest disturbances at an annual time scale between 1990 and 2020 and attributed causal agents of forest disturbance, including fire, logging, road construction and insects, using disturbance properties and spectral and topographic variables in the random forest model.

Results

The conventional and area-adjusted overall accuracies (OAs) of the forest disturbance map were 92.3% and 97.70% ± 0.06%, respectively, and the OA of mapping disturbance agents was 85.80%. The estimated disturbed forest area totalled 3313.13 km2 (approximately 2.31% of the total forest area in 1990) from 1990 to 2020, with considerable interannual fluctuations and significant regional differences. The predominant disturbance agent was fire, which comprised approximately 83.33% of the forest area disturbance, followed by logging (12.2%), insects (2.4%) and road construction (2.0%). Massive forest disturbances occurred mainly before 2000, and the post-2000 annual disturbance area significantly dropped by 55% compared with the pre-2000 value.

Conclusions

This study provided spatially explicit and retrospective information on annual forest disturbance and associated agents in the HDMR. The findings suggest that China's logging bans in natural forests combined with other forest sustainability programmes have effectively curbed forest disturbances in the HDMR, which has implications for enhancing future forest management and biodiversity conservation.


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Forest disturbances and the attribution derived from yearly Landsat time series over 1990–2020 in the Hengduan Mountains Region of Southwest China

Show Author's information Yating Li1,2Zhenzi Wu1,2Xiao Xu1,2Hui Fan1,2( )Xiaojia Tong1,2Jiang Liu1,2
Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650091, China
Yunnan Key Laboratory of International Rivers and Transboundary Eco-Security, Kunming 650091, China

Abstract

Background

Natural forests in the Hengduan Mountains Region (HDMR) have pivotal ecological functions and provide diverse ecosystem services. Capturing long-term forest disturbance and drivers at a regional scale is crucial for sustainable forest management and biodiversity conservation.

Methods

We used 30-m resolution Landsat time series images and the LandTrendr algorithm on the Google Earth Engine cloud platform to map forest disturbances at an annual time scale between 1990 and 2020 and attributed causal agents of forest disturbance, including fire, logging, road construction and insects, using disturbance properties and spectral and topographic variables in the random forest model.

Results

The conventional and area-adjusted overall accuracies (OAs) of the forest disturbance map were 92.3% and 97.70% ± 0.06%, respectively, and the OA of mapping disturbance agents was 85.80%. The estimated disturbed forest area totalled 3313.13 km2 (approximately 2.31% of the total forest area in 1990) from 1990 to 2020, with considerable interannual fluctuations and significant regional differences. The predominant disturbance agent was fire, which comprised approximately 83.33% of the forest area disturbance, followed by logging (12.2%), insects (2.4%) and road construction (2.0%). Massive forest disturbances occurred mainly before 2000, and the post-2000 annual disturbance area significantly dropped by 55% compared with the pre-2000 value.

Conclusions

This study provided spatially explicit and retrospective information on annual forest disturbance and associated agents in the HDMR. The findings suggest that China's logging bans in natural forests combined with other forest sustainability programmes have effectively curbed forest disturbances in the HDMR, which has implications for enhancing future forest management and biodiversity conservation.

Keywords: Landsat, Change detection, Disturbance attribution, LandTrendr, Hengduan Mountains region

References(84)

Attiwill PM (1994) The disturbance of forest ecosystems - the ecological basis for conservative management. Forest Ecol Manag 63(2-3): 247-300. https://doi.org/10.1016/0378-1127(94)90114-7

Banskota A, Kayastha N, Falkowski MJ, Wulder MA, Froese RE, White JC (2014) Forest monitoring using Landsat time series data: a review. Can J Remote Sens 40(5): 362-384. https://doi.org/10.1080/07038992.2014.987376

Barlow J, Lennox GD, Ferreira J, Berenguer E, Lees AC, Mac Nally R, Thomson JR, de Barros Ferraz SF, Louzada J, Fonseca Oliveira VH, Parry L, de Castro Solar RR, Vieira ICG, Aragao LEOC, Begotti RA, Braga RF, Cardoso TM, de Oliveira RC Jr, Souza CM Jr, Moura NG, Nunes SS, Siqueira JV, Pardini R, Silveira JM, Vaz-de-Mello FZ, Stulpen Veiga RC, Venturieri A, Gardner TA (2016) Anthropogenic disturbance in tropical forests can double biodiversity loss from deforestation. Nature 535(7610): 144-147. https://doi.org/10.1038/nature18326

Betts MG, Wolf C, Ripple WJ, Phalan B, Millers KA, Duarte A, Butchart SHM, Levi T (2017) Global forest loss disproportionately erodes biodiversity in intact landscapes. Nature 547(7664): 441-444. https://doi.org/10.1038/nature23285

Bonan GB (2008) Forests and climate change: Forcings, feedbacks, and the climate benefits of forests. Science 320(5882): 1444-1449. https://doi.org/10.1126/science.1155121

Breiman L (2001) Random forests. Machine Learn 45(1): 5-32. https://doi.org/10.1023/A:1010933404324

Bryan BA, Gao L, Ye Y, Sun X, Connor JD, Crossman ND, Stafford-Smith M, Wu J, He C, Yu D, Liu Z, Li A, Huang Q, Ren H, Deng X, Zheng H, Niu J, Han G, Hou X (2018) China's response to a national land-system sustainability emergency. Nature 559(7713): 193-204. https://doi.org/10.1038/s41586-018-0280-2

Cohen WB, Healey SP, Yang Z, Stehman SV, Brewer CK, Brooks EB, Gorelick N, Huang C, Hughes MJ, Kennedy RE, Loveland TR, Moisen GG, Schroeder TA, Vogelmann JE, Woodcock CE, Yang L, Zhu Z (2017) How similar are forest disturbance maps derived from different Landsat time series algorithms? Forests 8(4): 98-116. https://doi.org/10.3390/f8040098

Cohen WB, Yang Z, Kennedy R (2010) Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync — tools for calibration and validation. Remote Sens Environ 114(12): 2911-2924. https://doi.org/10.1016/j.rse.2010.07.010

Cohen WB, Yang Z, Stehman SV, Schroeder TA, Bell DM, Masek JG, Huang C, Meigs GW (2016) Forest disturbance across the conterminous United States from 1985-2012: the emerging dominance of forest decline. Forest Ecol Manag 360: 242-252. https://doi.org/10.1016/j.foreco.2015.10.042

Congalton RG (1991) A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens Environ 37(1): 35-46. https://doi.org/10.1016/0034-4257(91)90048-B

Czerwinski CJ, King DJ, Mitchell SW (2014) Mapping forest growth and decline in a temperate mixed forest using temporal trend analysis of Landsat imagery, 1987-2010. Remote Sens Environ 141: 188-200. https://doi.org/10.1016/j.rse.2013.11.006

DeVries B, Decuyper M, Verbesselt J, Zeileis A, Herold M, Joseph S (2015) Tracking disturbance-regrowth dynamics in tropical forests using structural change detection and Landsat time series. Remote Sens Environ 169: 320-334. https://doi.org/10.1016/j.rse.2015.08.020

FAO (2020) Global Forest Resources Assessment 2020: Main report. Rome, Italy
FAO, UNEP (2020) The state of the World's forests 2020: forests, biodiversity and people. Rome, Italy

Grogan K, Pflugmacher D, Hostert P, Kennedy R, Fensholt R (2015) Cross-border forest disturbance and the role of natural rubber in mainland Southeast Asia using annual Landsat time series. Remote Sens Environ 169: 438-453. https://doi.org/10.1016/j.rse.2015.03.001

Han J, Shen Z, Li Y, Luo C, Xu Q, Yang K, Zhang Z (2018) Beta diversity patterns of post-fire forests in Central Yunnan plateau, Southwest China: disturbances intensify the priority effect in the community assembly. Front Plant Sci 9: 1000. https://doi.org/10.3389/fpls.2018.01000

Hansen MC, Potapov PV, Goetz SJ, Turubanova S, Tyukavina A, Krylov A, Kommareddy A, Egorov A (2016) Mapping tree height distributions in sub-Saharan Africa using Landsat 7 and 8 data. Remote Sens Environ 185: 221-232. https://doi.org/10.1016/j.rse.2016.02.023

Hansen MC, Potapov PV, Moore R, Hancher M, Turubanova SA, Tyukavina A, Thau D, Stehman SV, Goetz SJ, Loveland TR, Kommareddy A, Egorov A, Chini L, Justice CO, Townshend JR (2013) High-resolution global maps of 21st-century forest cover change. Science 342(6160): 850-853. https://doi.org/10.1126/science.1244693

Hansen MJ, Franklin SE, Woudsma C, Peterson M (2001) Forest structure classification in the North Columbia mountains using the Landsat TM tasseled cap wetness component. Can J Remote Sens 27(1): 20-32. https://doi.org/10.1080/07038992.2001.10854916

Hermosilla T, Wulder MA, White JC, Coops NC (2019) Prevalence of multiple forest disturbances and impact on vegetation regrowth from interannual Landsat time series (1985-2015). Remote Sens Environ 233: 111403. https://doi.org/10.1016/j.rse.2019.111403

Hermosilla T, Wulder MA, White JC, Coops NC, Hobart GW (2015) Regional detection, characterization, and attribution of annual forest change from 1984 to 2012 using Landsat-derived time-series metrics. Remote Sens Environ 170: 121-132. https://doi.org/10.1016/j.rse.2015.09.004

Hirschmugl M, Deutscher J, Sobe C, Bouvet A, Mermoz S, Schardt M (2020) Use of SAR and optical time series for tropical forest disturbance mapping. Remote Sens 12(4): 727-55. https://doi.org/10.3390/rs12040727

Huang C, Goward SN, Masek JG, Gao F, Vermote EF, Thomas N, Schleeweis K, Kennedy RE, Zhu Z, Eidenshink JC, Townshend JRG (2009) Development of time series stacks of Landsat images for reconstructing forest disturbance history. Int J Digital Earth 2(3): 195-218. https://doi.org/10.1080/17538940902801614

Huffman MR (2014) Making a world of difference in fire and climate change. Fire Ecol 10(3): 90-101. https://doi.org/10.4996/fireecology.1003090

Kelly LT, Giljohann KM, Duane A, Aquilue N, Archibald S, Batllori E, Bennett AF, Buckland ST, Canelles Q, Clarke MF, Fortin M-J, Hermoso V, Herrando S, Keane RE, Lake FK, McCarthy MA, Moran-Ordonez A, Parr CL, Pausas JG, Penman TD, Regos A, Rumpff L, Santos JL, Smith AL, Syphard AD, Tingley MW, Brotons L (2020) Fire and biodiversity in the Anthropocene. Science 370(6519): 929-941. https://doi.org/10.1126/science.abb0355

Kennedy RE, Cohen WB, Schroeder TA (2007) Trajectory-based change detection for automated characterization of forest disturbance dynamics. Remote Sens Environ 110(3): 370-386. https://doi.org/10.1016/j.rse.2007.03.010

Kennedy RE, Yang Z, Braaten J, Copass C, Antonova N, Jordan C, Nelson P (2015) Attribution of disturbance change agent from Landsat time-series in support of habitat monitoring in the Puget Sound region, USA. Remote Sens Environ 166: 271-285. https://doi.org/10.1016/j.rse.2015.05.005

Kennedy RE, Yang Z, Cohen WB (2010) Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr — temporal segmentation algorithms. Remote Sens Environ 114(12): 2897-2910. https://doi.org/10.1016/j.rse.2010.07.008

Kennedy RE, Yang Z, Gorelick N, Braaten J, Cavalcante L, Cohen WB, Healey S (2018) Implementation of the LandTrendr algorithm on Google earth engine. Remote Sens 10(5): 691-700. https://doi.org/10.3390/rs10050691

Kim D, Sexton JO, Noojipady P, Huang C, Anand A, Channan S, Feng M, Townshend JR (2014) Global, Landsat-based forest-cover change from 1990 to 2000. Remote Sens Environ 155: 178-193. https://doi.org/10.1016/j.rse.2014.08.017

Li B (1987) On the boundaries of the Hengduan Mountains. J Mountain Res 5(2): 74-82

Li S, Hughes AC, Su T, Anberree JL, Oskolski AA, Sun M, Ferguson DK, Zhou Z (2017) Fire dynamics under monsoonal climate in Yunnan, SW China: past, present and future. Palaeogeogr Palaeoclimatol Palaeoecol 465: 168-176. https://doi.org/10.1016/j.palaeo.2016.10.028

Liaw A, Wiener M (2002)Classification and regression by randomForest. R News 2(3): 18-22

Liu C, Frazier P, Kumar L (2007) Comparative assessment of the measures of thematic classification accuracy. Remote Sens Environ 107(4): 606-616. https://doi.org/10.1016/j.rse.2006.10.010

Liu J, Coomes DA, Gibson L, Hu G, Liu J, Luo Y, Wu C, Yu M (2019) Forest fragmentation in China and its effect on biodiversity. Biol Rev 94(5): 1636-1657. https://doi.org/10.1111/brv.12519

Liu J, Liu M, Zhuang D, Zhang Z, Deng X (2003a) Study on spatial pattern of land-use change in China during 1995-2000. Sci China Ser D - Earth Sci 46(4): 373-384. https://doi.org/10.1360/03yd9033

Liu J, Zhang Z, Zhuang D, Wang Y, Zhou W, Zhang S, Li R, Jiang N, Wu S (2003b) A study on the spatial-temporal dynamic changes of land-useand driving forces analyses of China in the 1990s. Geogr Res 22(1): 1-12. https://doi.org/10.11821/yj2003010001

Margono BA, Turubanova S, Zhuravleva I, Potapov P, Tyukavina A, Baccini A, Goetz S, Hansen MC (2012) Mapping and monitoring deforestation and forest degradation in Sumatra (Indonesia) using Landsat time series data sets from 1990 to 2010. Environ Res Lett 7(3): 034010. https://doi.org/10.1088/1748-9326/7/3/034010

Masek JG, Huang C, Wolfe R, Cohen W, Hall F, Kutler J, Nelson P (2008) North American forest disturbance mapped from a decadal Landsat record. Remote Sens Environ 112(6): 2914-2926. https://doi.org/10.1016/j.rse.2008.02.010

Meigs GW, Campbell JL, Zald HSJ, Bailey JD, Shaw DC, Kennedy RE (2015) Does wildfire likelihood increase following insect outbreaks in conifer forests? Ecosphere 6(7): art118. https://doi.org/10.1890/ES15-00037.1

Moisen GG, Meyer MC, Schroeder TA, Liao X, Schleeweis KG, Freeman EA, Toney C (2016) Shape selection in Landsat time series: a tool for monitoring forest dynamics. Glob Chang Biol 22(10): 3518-3528. https://doi.org/10.1111/gcb.13358

Neigh CSR, Bolton DK, Williams JJ, Diabate M (2014) Evaluating an automated approach for monitoring forest disturbances in the Pacific northwest from logging, fire and insect outbreaks with Landsat time series data. Forests 5(12): 3169-3198. https://doi.org/10.3390/f5123169

Nguyen TH, Jones SD, Soto-Berelov M, Haywood A, Hislop S (2018) A spatial and temporal analysis of forest dynamics using Landsat time-series. Remote Sens Environ 217: 461-475. https://doi.org/10.1016/j.rse.2018.08.028

Oeser J, Pflugmacher D, Senf C, Heurich M, Hostert P (2017) Using intra-annual Landsat time series for attributing forest disturbance agents in Central Europe. Forests 8(7): 251-274. https://doi.org/10.3390/f8070251

Olofsson P, Foody GM, Herold M, Stehman SV, Woodcock CE, Wulder MA (2014) Good practices for estimating area and assessing accuracy of land change. Remote Sens Environ 148: 42-57. https://doi.org/10.1016/j.rse.2014.02.015

Olofsson P, Foody GM, Stehman SV, Woodcock CE (2013) Making better use of accuracy data in land change studies: estimating accuracy and area and quantifying uncertainty using stratified estimation. Remote Sens Environ 129: 122-131. https://doi.org/10.1016/j.rse.2012.10.031

Pan Y, Birdsey RA, Fang J, Houghton R, Kauppi PE, Kurz WA, Phillips OL, Shvidenko A, Lewis SL, Canadell JG, Ciais P, Jackson RB, Pacala SW, McGuire AD, Piao S, Rautiainen A, Sitch S, Hayes D (2011) A large and persistent carbon sink in the world's forests. Science 333(6045): 988-993. https://doi.org/10.1126/science.1201609

Pausas JG, Su W, Luo C, Shen Z (2021) A shrubby resprouting pine with serotinous cones endemic to Southwest China. Ecology 102(5): e03282. https://doi.org/10.1002/ecy.3282

Pettorelli N, Wegmann M, Skidmore A, Mucher S, Dawson TP, Fernandez M, Lucas R, Schaepman ME, Wang T, O'Connor B, Jongman RHG, Kempeneers P, Sonnenschein R, Leidner AK, Bohm M, He KS, Nagendra H, Dubois G, Fatoyinbo T, Hansen MC, Paganini M, de Klerk HM, Asner GP, Kerr JT, Estes AB, Schmeller DS, Heiden U, Rocchini D, Pereira HM, Turak E, Fernandez N, Lausch A, Cho MA, Alcaraz-Segura D, McGeoch MA, Turner W, Mueller A, St-Louis V, Penner J, Vihervaara P, Belward A, Reyers B, Geller GN (2016) Framing the concept of satellite remote sensing essential biodiversity variables: challenges and future directions. Remote Sens Ecol Conserv 2(3): 122-131. https://doi.org/10.1002/rse2.15

Pickell PD, Hermosilla T, Coops NC, Masek JG, Franks S, Huang C (2014) Monitoring anthropogenic disturbance trends in an industrialized boreal forest with Landsat time series. Remote Sens Lett 5(9): 783-792. https://doi.org/10.1080/2150704X.2014.967881

Potapov PV, Turubanova SA, Hansen MC, Adusei B, Broich M, Altstatt A, Mane L, Justice CO (2012) Quantifying forest cover loss in Democratic Republic of the Congo, 2000-2010, with Landsat ETM plus data. Remote Sens Environ 122: 106-116. https://doi.org/10.1016/j.rse.2011.08.027

Powell SL, Cohen WB, Healey SP, Kennedy RE, Moisen GG, Pierce KB, Ohmann JL (2010) Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: a comparison of empirical modeling approaches. Remote Sens Environ 114(5): 1053-1068. https://doi.org/10.1016/j.rse.2009.12.018

Qu S, Wang L, Lin A, Zhu H, Yuan M (2018) What drives the vegetation restoration in Yangtze River basin, China: climate change or anthropogenic factors? Ecol Indic 90: 438-450. https://doi.org/10.1016/j.ecolind.2018.03.029

Ren G, Young SS, Wang L, Wang W, Long Y, Wu R, Li J, Zhu J, Yu DW (2015) Effectiveness of China's National Forest Protection Program and nature reserves. Conserv Biol 29(5): 1368-1377. https://doi.org/10.1111/cobi.12561

Roy DP, Kovalskyy V, Zhang HK, Vermote EF, Yan L, Kumar SS, Egorov A (2016) Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity. Remote Sens Environ 185(Iss 1): 57-70. https://doi.org/10.1016/j.rse.2015.12.024

Schleeweis KG, Moisen GG, Schroeder TA, Toney C, Freeman EA, Goward SN, Huang C, Dungan JL (2020) US national maps attributing forest change: 1986-2010. Forests 11(6): 653-72. https://doi.org/10.3390/f11060653

Schroeder TA, Cohen WB, Yang Z (2007) Patterns of forest regrowth following clearcutting in western Oregon as determined from a Landsat time-series. Forest Ecol Manag 243(2-3): 259-273. https://doi.org/10.1016/j.foreco.2007.03.019

Schroeder TA, Schleeweis KG, Moisen GG, Toney C, Cohen WB, Freeman EA, Yang Z, Huang C (2017) Testing a Landsat-based approach for mapping disturbance causality in U.S. forests. Remote Sens Environ 195: 230-243. https://doi.org/10.1016/j.rse.2017.03.033

Schroeder TA, Wulder MA, Healey SP, Moisen GG (2011) Mapping wildfire and clearcut harvest disturbances in boreal forests with Landsat time series data. Remote Sens Environ 115(6): 1421-1433. https://doi.org/10.1016/j.rse.2011.01.022

Senf C, Seidl R (2020) Mapping the forest disturbance regimes of Europe. Nat Sustain 4(1): 63-70. https://doi.org/10.1038/s41893-020-00609-y

Senf C, Seidl R, Hostert P (2017) Remote sensing of forest insect disturbances: current state and future directions. Int J Appl Earth Observ Geoinform 60: 49-60. https://doi.org/10.1016/j.jag.2017.04.004

Shimizu K, Ahmed OS, Ponce-Hernandez R, Ota T, Win ZC, Mizoue N, Yoshida S (2017) Attribution of disturbance agents to forest change using a landsat time series in tropical seasonal forests in the Bago Mountains, Myanmar. Forests 8(6): 218-233. https://doi.org/10.3390/f8060218

Shimizu K, Ota T, Mizoue N, Yoshida S (2019) A comprehensive evaluation of disturbance agent classification approaches: strengths of ensemble classification, multiple indices, spatio-temporal variables, and direct prediction. ISPRS J Photogramm Remote Sens 158: 99-112. https://doi.org/10.1016/j.isprsjprs.2019.10.004

Su W, Shi Z, Zhou R, Zhao Y, Zhang G (2015) The role of fire in the Central Yunnan plateau ecosystem, southwestern China. Forest Ecol Manag 356: 22-30. https://doi.org/10.1016/j.foreco.2015.05.015

Sun H, Zhang J, Deng T, Boufford DE (2017) Origins and evolution of plant diversity in the Hengduan Mountains, China. Plant Divers 39(4): 161-166. https://doi.org/10.1016/j.pld.2017.09.004

Sun W, Zhang E, Shen J, Chen R, Liu E (2016) Black carbon record of the wildfire history of western Sichuan Province in China over the last 12.8 ka. Front Earth Sci 10(4): 634-643. https://doi.org/10.1007/s11707-015-0546-z

Tang D, Fan H, Yang K, Zhang Y (2019) Mapping forest disturbance across the China-Laos border using annual Landsat time series. Int J Remote Sens 40(8): 2895-2915. https://doi.org/10.1080/01431161.2018.1533662

Vogelmann JE, Xian G, Homer C, Tolk B (2012) Monitoring gradual ecosystem change using Landsat time series analyses: case studies in selected forest and rangeland ecosystems. Remote Sens Environ 122: 92-105. https://doi.org/10.1016/j.rse.2011.06.027

White JC, Wulder MA, Hermosilla T, Coops NC, Hobart GW (2017) A nationwide annual characterization of 25 years of forest disturbance and recovery for Canada using Landsat time series. Remote Sens Environ 194: 303-321. https://doi.org/10.1016/j.rse.2017.03.035

White JC, Wulder MA, Hobart GW, Luther JE, Hermosilla T, Griffiths P, Coops NC, Hall RJ, Hostert P, Dyk A, Guindon L (2014) Pixel-based image compositing for large-area dense time series applications and science. Can J Remote Sens 40(3): 192-212. https://doi.org/10.1080/07038992.2014.945827

Wulder MA, Loveland TR, Roy DP, Crawford CJ, Masek JG, Woodcock CE, Allen RG, Anderson MC, Belward AS, Cohen WB, Dwyer J, Erb A, Gao F, Griffiths P, Helder D, Hermosillo T, Hipple JD, Hostert P, Hughes MJ, Huntington J, Johnson DM, Kennedy R, Kilic A, Li Z, Lymburner L, McCorkel J, Pahlevan N, Scambos TA, Schaaf C, Schott JR, Sheng Y, Storey J, Vermote E, Vogelmann J, White JC, Wynne RH, Zhu Z (2019) Current status of Landsat program, science, and applications. Remote Sens Environ 225: 127-147. https://doi.org/10.1016/j.rse.2019.02.015

Wulder MA, Masek JG, Cohen WB, Loveland TR, Woodcock CE (2012) Opening the archive: how free data has enabled the science and monitoring promise of Landsat. Remote Sens Environ 122: 2-10. https://doi.org/10.1016/j.rse.2012.01.010

Xiao X, Haberle SG, Shen J, Xue B, Burrows M, Wang S (2017) Postglacial fire history and interactions with vegetation and climate in southwestern Yunnan Province of China. Clim Past 13(6): 613-627. https://doi.org/10.5194/cp-13-613-2017

Xing Y, Ree RH (2017) Uplift-driven diversification in the Hengduan Mountains, a temperate biodiversity hotspot. PNAS 114(17): E3444-E3451. https://doi.org/10.1073/pnas.1616063114

Yang H (2017) China's natural forest protection program: progress and impacts. Forest Chron 93(2): 113-117. https://doi.org/10.5558/tfc2017-017

Yin L, Dai E, Zheng D, Wang Y, Ma L, Tong M (2020) Spatio-temporal analysis of the human footprint in the Hengduan Mountain region: assessing the effectiveness of nature reserves in reducing human impacts. J Geograph Sci 30(7): 1140-1154. https://doi.org/10.1007/s11442-020-1774-z

Zhang P, Shao G, Zhao G, Le Master DC, Parker George R, Dunning John B, Li Q (2000) China's forest policy for the 21st century. Science 288(5474): 2135-2136. https://doi.org/10.1126/science.288.5474.2135

Zhang R, Zheng D, Yang Q (1997) Physical geography of Hengduan Mountains (in Chinese). Science Press, Beijing

Zhu Z (2017) Change detection using landsat time series: a review of frequencies, preprocessing, algorithms, and applications. ISPRS J Photogramm Remote Sens 130:370-384. https://doi.org/10.1016/j.isprsjprs.2017.06.013

Zhu Z, Wang S, Woodcock CE (2015) Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4-7, 8, and sentinel 2 images. Remote Sens Environ 159:269-277. https://doi.org/10.1016/j.rse.2014.12.014

Zhu Z, Woodcock CE, Olofsson P (2012) Continuous monitoring of forest disturbance using all available Landsat imagery. Remote Sens Environ 122:75-91. https://doi.org/10.1016/j.rse.2011.10.030

Zhu Z, Wulder MA, Roy DP, Woodcock CE, Hansen MC, Radeloff VC, Healey SP, Schaaf C, Hostert P, Strobl P, Pekel J-F, Lymburner L, Pahlevan N, Scambos TA (2019) Benefits of the free and open Landsat data policy. Remote Sens Environ 224:382-385. https://doi.org/10.1016/j.rse.2019.02.016

Zhu Z, Zhang J, Yang Z, Aljaddani AH, Cohen WB, Qiu S, Zhou C (2020) Continuous monitoring of land disturbance based on Landsat time series. Remote Sens Environ 238:111116. https://doi.org/10.1016/j.rse.2019.03.009

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Received: 18 June 2021
Accepted: 24 October 2021
Published: 22 November 2021
Issue date: December 2021

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