Posted on Authorea 12 Mar 2021 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.161554513.39992175/v1 — This a preprint and has not been peer reviewed. Data may be preliminary. as t oetadt hf rmntls ontgi.Hwvr oettasto a loocrunder the occur used We also may regions. transition other forest in However, deforestation but gain. regions net some to in gains loss forest net in from resulting shift policies, to various forestland its cause Abstract: USA *Correspondence 02215, MA Boston, University, Boston Studies, 5 4 3 China 200241, Road, Dongchuan 500, Shanghai, 2 University, Normal China East Sciences, 1 China Province, Xiong Bo Zhejiang from Evidence 2020: and between 2000 deforestation mountainous and urbanization Telecoupling eeopigubnzto n onanu eoetto ewe 00ad22:Evidence 2020: Xiong Bo and 2000 China between Province, deforestation Zhejiang countries. mountainous from transition or regions forest and developing the urbanization in enriching loss decisions Telecoupling to net uses use addition no land land In of future competitive goal for between production. a basis balance and achieve solid good ecology, to a a living, provides seeks strike residents’ study that policy—must as this policy BALS such theory, (BALS) the objectives at land as System different land cross-region policy—such Land have construction this urban use Arable that urban that in land of land, believe increases Whatever We Balance arable witnessed Lishui. the in cropland. Hangzhou as decrease from of as a such slight arise such municipalities causing a may municipalities underdeveloped and 2000, pattern in developed loss change since deforestation that forest large-scale place found this of We taken in with expense has patterns to parallel 169.45%. the such In seeks deforestation by has study continuous behind soared ha. this forces has large-scale that 186,014 2020, land driving by that issue to forestland and show important 2000 of effects, results crucially from loss spillover The total data this regional regions yearbook address some corresponding China. statistical in the to Province, and gains patterns, framework Zhejiang images forest change from telecoupling satellite in shift use resulting series the to land time policies, used forestland understand various Using We its under examined. cause occur regions. been also may rarely may other region transition or in forest country deforestation However, a but gain. in net development to socioeconomic loss that net posits theory transition Forest Abstract 2021 12, March 3 2 1 e aoaoyo egahcIfrainSine iityo dcto,adSho fGeographical of School and Education, of Ministry Science, Information Geographic of Laboratory Key otnUniversity Boston University State Diego San University Normal China East etrfrCmlxHmnEvrnetSses a ig tt nvriy a ig,C 28,USA 92182, CA Diego, San University, State Diego San Systems, Human-Environment Complex for Center rdrc .Pre etrfrteSuyo h ogrRneFtr,FeeikS adeSho fGlobal of School Pardee S. Frederick Future, Longer-Range the of Study the for Center Pardee S. Frederick eateto at n niomn,Bso nvriy otn A025 USA 02215, MA Boston, University, USA Boston Environment, 92182, and CA Earth Diego, of San University, Department State Diego San Geography, of Department 1 ,2 1, usa Chen Ruishan , oettasto hoypst htscocnmcdvlpeti onr rrgo may region or country a in development socioeconomic that posits theory transition Forest usa Chen Ruishan , 1 iAn Li , 1,* iAn Li , 2 iZhang Qi , ,3 2, iZhang Qi , 1 3 n iogXia Zilong and , 4,5 n iogXia Zilong and 1 1 Posted on Authorea 12 Mar 2021 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.161554513.39992175/v1 — This a preprint and has not been peer reviewed. Data may be preliminary. Go un,H,Sn hn,21) n oo.Teln s rniinter ffr hoeia and theoretical Long 2010; offers of Meyfroidt, theory use & sustainable transition Lambin urbanization, use F. accompanying expansion (E. scales land land large land The over and urban & migration resources Li, rural-to-urban on. 2015), natural With Heilig, so Deng, of 2018). Long, and & use Qu, 2015; rational 2016), Song gain & Tu, the W. Zhang, net & for & 2012; Liu, Rudel, support forest (DeFries, Sun, Li, Long, technical to loss of Li, He, & forest related (T. process (Long Huang, includes transition pattern transformation the (Gao, transition land cropland in global use housing 2007), land trajectories the rural of Zhang, change 2010), explain Topologies 2005). Hansen, land to 2011). Lu, & illustrate Meyfroidt, & used Uriarte, to & Xie, be Lambin Li, proposed Tan, F. can been 2011; (Eric 2016), which Li, has al., & development, theory Pykett, et et transition socioeconomic Zou, (Davis (Costello changes use Long, problems security such 2001; environmental land food al., other of The and et thus effects Lambin fertilizer, and cascading F. of conservation overuse E. The cropland scales, 2020; larger to al., 2010). at challenge the affect al., deforestation During greater seriously to et 2007). 2001), even leading al., (Godfray al., an et et systems Liu Lambin pose J. F. consumption may 2012; (E. and Seto, expanding & tremendously production Deng, is to food (Jiang, land challenge base built-up Widespread serious population urbanization, a transition. huge of representing rural-urban a process of scarce, with land. era increasingly China arable critical like resources a country of land into a made loss stepped has has large-scale urbanization China to century, accelerated 21st leading and the municipalities, of around turn the cropland (Dobson Since encroach mankind will beset urbanization and Rapid biodiversity arise a large-scale pandemics—may 2020). expansion, With Tollefson, COVID-19 infrastructure 2020; 2018). the and and al., al., as deforestation, seriously et et development, consequences-such agenda Liu land undesirable (Jianguo SDG unsustainable of easy the to from set reversing taking due far and However, degradation be Na- halting and will United planning. of loss ground the 15 land the challenges, Goal future on global which to it among these implementing pertains (SDGs), with reduction mostly Goals socioeconomic cope and Development degradation effectively degradation and Sustainable To land resources widespread 17 space, natural 2019). in crafted for living al., resulting has demand et energy, 2011), tions the (Pandit al., population, food, services Turner, growing et ecosystem for 2005; rapidly (Foley of al., a worldwide land et With drastically (Foley on 2018). increased services depend al., ecosystem has et of Humans energy Song in provision P. Earth’s the and (X. 2007). the and development degradation surface transformed Reenberg, biodiversity land and greatly & the change has of Lambin, climate nature modification to the human contribute affect which under turn Milo, cycles, change & biogeochemical cover Bar-On, and Grozovski, and balance Ben-Uri, vi- use (Elhacham, of support Land loss they and services degradation 2020). substantial corresponding causing the surface, and land ecosystems Earth’s tal the altered have activities Anthropogenic INTRODUCTION developing 1 in decisions use land future cropland. for of basis loss solid net a enriching Keywords: no to provides addition of In study land goal the countries. production. this competitive or a and from between theory, ecology, regions balance achieve arise living, transition good residents’ to may forest as a such seeks pattern strike the objectives policy—must change that different BALS have land policy the that cross-region as uses (BALS) policy—such this System use underdeveloped that land in such Land Whatever deforestation believe municipalities in Arable large-scale We decrease developed of slight of that expense a Lishui. found Balance the and We at loss as land forest 169.45%. such this urban by municipalities with in since soared parallel increases place In witnessed has taken Hangzhou land ha. as has 186,014 construction in by deforestation urban patterns land forestland continuous such land, understand of large-scale behind arable loss to that forces total seeks show a driving study results causing and this 2000, The effects, 2020, time spillover China. to Using regional Province, 2000 examined. corresponding Zhejiang from been the data rarely patterns, yearbook has change statistical that use and issue important images crucially satellite this series address to framework telecoupling eoetto;raiaintlculn;adueplc;hjagProvince policy;Zhejiang use deforestation;urbanization;telecoupling;land 2 Posted on Authorea 12 Mar 2021 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.161554513.39992175/v1 — This a preprint and has not been peer reviewed. Data may be preliminary. ak h orhadtefit nCias3 rvne,rsetvl.Tegorpia iest n rapid 2019, and in diversity USD geographical 16,474 The of respectively. capita provinces, per 31 GDP China’s and in Statistical USD 70% Zhejiang fifth billion which from of 954 the (citation million, of area and 58.5 land GDP fourth was total a population the the with total mountainous of ranks the province, 61.15% and 2019, was Zhejiang northeast coverage of the book). forest end in Yr the the mainly where of areas cities, As (˜70%), in flat mountains 1). lived With by (Figure with annually). mostly southwest (˜10%), average characterized the body province on in mountainous water mm areas a and the 2,000 is (˜20%), to at our Zhejiang (1,100 land located ha, be rainfall agricultural is 10,550,600 abundant to of Zhejiang with area 27 China, chosen climate between land of is Lying monsoon a coast subtropical 1). China, southeastern a (Figure the in has Delta River at it provinces Yangtze Located the developed of reasons. fringe socioeconomically following southern the most for the area of study one Province, Zhejiang area Study 2.1 sustainable MATERIALS of goal AND the METHOD towards measures 2 effective promote to aim related to challenges we attempts the large, urbanization, of study at of recognition This policy context land. In of the loss” how forces. use places. in net driving to Province distant “no corresponding relation Zhejiang the the deforestation; other in in and to driven in patterns effects, mechanism have deforestation spillover change the that regional use in factors (d) their land resulted policy and understanding has (b) to linkage urbanization; region contribute its Province; and and one 2020 Zhejiang deforestation process to in in deforestation data between 2000 change urbanization the al., with relationships from (a) use et conjunction Province the reveal: land (Long in to Zhejiang of (c) aim datasets policies in types We to dynamic sensing use other sources. contribute use remote other land to only and land Globeland30 ineffective not yearbooks the and reform may statistical analyze datasets from change help we deforestation use also Wan, research, Hanson’s land Zhou, but present the using Su, of the use, 2010; and dimension In land Long, land institutional & sustainable cultivated 2018). Wang, the for Liu, of Exploring S. solutions shrinkage (Y. 2016). finding the areas Kong, fringe by use & rural-urban & represented accelerated Land at Li, Glendinning, China’s mainly land Lin, Under 2020). construction is (Wang, of Leffel, 2019). transformation China expansion Kalantari, & in use & Kim, development Keesstra, land Frazier, socioeconomic (Halbac-Cotoara-Zamfir, urbanization, Turner, and elsewhere (Stuhlmacher, and policy change 2018) both Xu, land by of influenced widespread driver is caused key change policies a these is how and Institution protection understand Province ecological to areas Zhejiang of needed related strategy in is national in deforestation 2016). degradation. work proposed systems widespread the land Su, more after ecological shown Therefore, & even have social Zhang, 2020) civilization. Anker, studies on Zhao, ecological 2018), & Previous Feng, Ye, impacts Li, Xia, Liu, & their explored. Chen, (Xin (Y. and (Xiong, fully places latter polices amount been rural the the remote these not maintain in to have of to 2012; environment areas is effectiveness Zou, the urban policies of the downgrade adjacent & these However, increase of from or Woods, goal land destroy the Liu, common arable may couple A Li, relocating which to 2014). by System (Long, Pijanowski, land Land policy Land & arable Arable the Song Construction of of Wei and Balance Rural 2017; al., the program, Reducing to et including System with production, Shen loss, Protection government Land from food central land Cropland Construction from China’s (Chaplin- arable Basic security, Urban ranging hold production food multifunction, the to ensure food has policy, policies To of land securing (BALS) 2020). series Arable for (Long, a services prerequisite executed 2011). ecological has a al., to et is and Foley security, land social 2015; research arable of al., topic of et hot amount Kramer a been adequate has an accelerated which The China, Maintaining in 2018). use 2014). al., land (Long, et in years (Long shift many stakeholders dramatic for other a and triggered policy-makers has of process urbanizing concern central a becomes land 3 ° 12 ´ -31 ° 30 ´ n 119 and N ° 42 ´ -122 ° 06 ´ E, Posted on Authorea 12 Mar 2021 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.161554513.39992175/v1 — This a preprint and has not been peer reviewed. Data may be preliminary. eut undt efil od ehv civda vrl cuayo 0 ae nHnnsdt in data Hanen’s products on Hansen based of 80% generation next of the accuracy that overall believe an we and addition, achieved error In have data 2020). we any al., minimize validation good: et to The (Xiong fairly order surveys. other Province field In be Zhejiang and with 2013). datasets to al., surveys sensing et accuracy, LiDAR turned remote overall (Hansen as both combined results The 99% previous have reality. over using loss we in Hansen’s be accuracy, statistics forest growth 2013). to classification (FAO) forest al., and shown improve from Organization et been height” biased Agriculture (Hansen has be in pixel” data, and may quality 5m satellite 30m Food thus by above high and the a 30m 2012 vegetation in by a in has “all images assessed was in product gain covers as ETM+ forest tree The trees 7 of all loss. defines Landsat update of forest removal dataset 654,178 cover or of canopy The processing mortality year tree 2013). by “the the 2000 and synthesized al., of website regarding is et layers (GEE) information and (Hansen Update) the Engine the resolution contains 1.7 Earth providing (Version spatial dataset Google loss, version 30m updated the latest forest most on the The 2001-2019 online used website. available and Watch we is Forest which loss, all forestland. Global and dataset, forest typology, as the cover criteria regarding classification reclassified forest change classification were land Hansen’s shrubs annual IPCC our of and the with the trees assess alignment to mosaic further for and according To trees here products with (Defourny analysis; the types 71.5% further cover be reclassified for machine- land to we ESA-CCI class a reported study, is forest (using shrub, map our the pixels tree, the In of extracted classified of vegetation 2017). accuracy which natural The al., mosaic 2018, 2013). instance, et to al., (for et 2000 categories (300m) (Bontemps cover from vegetation) medium-resolution land coverage herbaceous a 22 With over global into source. a algorithm) data Space has learning another European the as dataset selected (CCI) the also Land Initiative since we Zhejiang Change Province resolution, Province, from Zhejiang Climate Zhejiang data in - in change used (ESA) loss use also forest Agency we land capture al., Province, comprehensively on et more Zhejiang statistics To in official Liang 2000. change analyzed 2014; use we Li, Specifically, land 230,000 Yearbook. & of over Statistics results Ban, on the (Jun, based (for analyze model results better GlobeLand30 To validation sampling on our index based to classification landscape according of using 0.82 accuracy datasets in overall is 2015). points whole the coefficient 150,000 the over hand, Kappa from on other the based points is the and effort 2010) On validation 85.72% (for total. from is GlobeLand30 is in 2020) result on 2015). 853 This al., based of body, 0.78. et classification tiles is water Chen of 80 coefficient (J. wetland, accuracy Kappa pixels overall land, the mosaic the and image shrub no evaluation, 83.50% with of grassland, third-party ice, timing a land, and on the snow forest includes Based perennial data, scheme land, and acquire classification land, agricultural The to bare coverage. are surface, difficult overall which artificial the are tundra, types, of that integrity cover areas the land season For ensure growing to 10 cloud. vegetation adjusted least the be with can of acquisition or images multispectral cloud-free select are 2020 The to images of images. is multispectral dataset dataset Satellite) the the Resolution in within meters particular, High selection 30 In (China image models a GF-1 procedures. of machine-learning resolution with principle processing using 16-meter cover object-based the imageries land Landsat incorporates and 20,000 Satellite) on over also pixel-level Reduction from worldwide derived with Disaster product is combination dataset and only GlobeLand30 in Environment the The sensing (China 2020. remote is and HJ-1 on dataset 2010 2000 based 2000, and This from database of years 2015). cover Province the land al., Zhejiang for global in resolution et fine-scale changes Chen open-source, use (J. first land the models analyze is and GlobeLand30 detect 2020. to to GlobeLand30 primarily utilized We processing and sources Data 2.2 China Province, Zhejiang case of ideal Elevation an and Province deforestation. Location Zhejiang related 1. deforestation renders policy Recently, Figure which on use. problem, research land conducting serious and for a cover area become land study has in urbanization changes fundamental with to associated led have development economic ± er ftebsln eri hc h aawr eeae n pae,poie htthe that provided updated, and generated were data the which in year baseline the of years 2 4 Posted on Authorea 12 Mar 2021 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.161554513.39992175/v1 — This a preprint and has not been peer reviewed. Data may be preliminary. uiiaiisi hjagPoic,tegets osi oetcvrwsosre nLsu municipality the Lishui all northeastern in Among the observed was in 2c). cover small (Figure forest relatively level in was municipality loss area the greatest lost at the mainly the patterns mainly Province, loss happened while heterogenous Zhejiang forest loss in exhibited Zhejiang, in but forest landscape loss general, municipalities of Overall, The Forest in parts 2c). interconnected province. southern (Figure rarely whole part. province and and the using the scattered across western Province within be parts in Zhejiang regions to many forest in mountainous shown in Province, the gain was Zhejiang phenomenon dominated and loss prevalent of space loss forest a the of forest be Over terms of to d). distribution found and was spatial 2c (Figure biased. loss the images less analyzed government satellite from be we on high-resolution to Zhejiang rely GlobeLand30 time, likely in not more same ha do thus the 80,400 results are change of At resolutions, land Zhe- magnitude cover spatial based but the forest sensing higher 2000-2018, at have remote that during forest that and shows https://gadm.org/download ha believe in statistics We 186,014 show dataset increase ESA-CCI also 2b). Globeland30 an and (Figure show ESA-CCI The 2000-2018 2000-2020 data and during 2b). yearbook Globeland30 Zhejiang (Figure statistical in including jiang’s 2020 increased ha products after monotonically 22,823 data Province by loss other during decreased Zhejiang forest stable dataset, in cumulative be Hansen’s loss The to the forest appeared year. from to http://www.esa-landcover-cci.org/ loss each increase forest addition ha six-fold the In an 2008, 16,282 Zhejiang period. After representing of in study 2001-2008, 2008. rate entire cover in during high the ha forest year a during Annually, 26,993 per of at nearly baseline. ha loss 2000 to but 12,549 the 2001 net 2009-2019, from about in forest total ha is in the 4,245 https://data.cnki.net/ decline loss net 4.7% about that forest http://earthenginepartners.appspot.com/science-2013-global-forest a of of shows 2a), distribution (Figure rate / datasets Spatial ha the http://www.globeland30.org/ 279,501 Hansen’s (d) is the 2001-2019 2020. 2020 from on Province to to based 2000 2000 from estimation from The classifications classifications Our land change. land other forest distribution data other from of Spatial yearbook derives to Scale (c) statistical converted gain (b) forest and loss 2000-2018). products 2019. 2020. forest yearbook, change to to net / Statistical land-cover 2000 2001 of 2000–2018; satellite-based from from ESA-CCI, on Province (bar) 2000–2020; Annually based Zhejiang loss (GlobeLand30, estimates Annually in annual are deforestation and charts of (line) bar patterns loss spatial forest Cumulative and changes (a) Temporal / 2. Figure / Province Zhejiang in Deforestation date 300m to 3.1 Up 30m RESULTS / 2000-2018 2001–2019 m 30 2000-2018 GADM 2000/2010/2020 yearbook Statistical ESA-CCI v1.7 Hansen GlobeLand30 benefi- is Data analyses land transformation. data of sources use Areas these data patterns land of Administrative land of results temporal Different characteristics the Global and 1. temporal of Table spatial and from use spatial the combined derived the the showing exploring and in is in 1), (Table strengths cial the provincial Province own defining Zhejiang Zhejiang their of in have change threshold of datasets the data These as (GADM). 30% division loss. this forest set In administrative of 2018). we al., analyses The website, et following Watch (Zeng the Forest loss all Global and for growth the cover forest canopy to actual referring on information by more assessment, provide may 2.0) Version (e.g., Period pta eouinTmoa eouinDt Sources Data Resolution Temporal Resolution Spatial 5 country v3.html Posted on Authorea 12 Mar 2021 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.161554513.39992175/v1 — This a preprint and has not been peer reviewed. Data may be preliminary. iue6 oieooi hnei aghuadLsu uiiaiyo hjagPoic rm20 to 2000 from Province Zhejiang of municipality 5). Lishui (Figure and forest municipality land Hangzhou and Hangzhou cultivated in cropland of from cultivated between that change is of conversion 2019 than Socioeconomic Hangzhou ha to larger due in 16,910 is 6. mainly change loss only Figure forestland is land and municipality of biggest cropland, Lishui area the to in its that land change and show forest 37,198 The land, was results of conversion land. These ha maximum urban the 36,787 to to area. Lishui, conversion land, land In urban cultivated largest forest from land. to the to cultivated ha land that to 37,036 land by land found cultivated followed forest we of ha, from Lishui, 77,288 ha ha was and 26,551 Hangzhou and Hangzhou in land of area forest urban 2020 patterns to to change land 2000 use cultivated from from land Lishui the and comparing Hangzhou By in change use Land Hangzhou 5. Figure and Lishui urbanization: and as deforestation increases It of land arable Linkages small. relatively while 3.3 are (260,322 land, uses arable land land of forest other losses to in from changes land deforestation. comes The arable of primarily ha). from result expansion (210,474 conversions land a land land arable by urban arable from followed urban to that conversion ha), between land The seems conversions (562,399 forest use 4). largest from land (Figure and the of Province ha) form is Zhejiang the area in in 2020 forest urban place to gains, and took to 2020 2000 land, area to arable from urban 2000 Province land, largest from construction Zhejiang changes the in use had Transformation land use largest Hangzhou d). The Land and and of Ningbo 3c net Map these, (Figure Sankey of Of gains amount 4. smallest the Figure but gain. although the Zhejiang province, net areas had the in the gained Zhoushan over the of all and changes to almost Lishui 6% land compared in while around smaller urban occurred only much gain of is and and loss distribution dispersed loss that area were spatial found land patches urban Spatially, the lost arable we the and d). analyze data, and forest to 3c Globeland30 of (Figure area GlobeLand30 the Province the Using used while 3b). we 169.45% (Figure 3a). by Meanwhile, respectively (Figure increased 19.20%, use decrease and 5.26%. land by and gradual 0.40% urban decreased by area a of area decreased land area land had urban arable the types of the 2000-2020, proportions while land during respectively, the of 1.45%, period, and rest same 70.29% The the by Province increased During Zhejiang span area in (d) 2018. time area land urban and same forest 2000-2020. of 2001 the proportion from between over the classifications 3a), steadily classifications temporal (Figure land increased land (2001-2018) of yearbook other other statistical Data from to a to derives converted According 2000- Province. gain loss between forest Zhejiang forest GlobeLand30 net (b) net in of and of land 2000-2018 distribution distribution between Spatial urban Yearbook Spatial of Statistical (c) (a) patterns from 2020. spatial are use and land changes of change Temporal 3. Figure Province 2d). Zhejiang largest (Figure cultivated in Hangzhou the converting expansion and has through Lishui Urban Hangzhou is by most municipalities, gain 3.2 followed in Wenzhou, forest the forestland in of all in most source Among observed increase main is the The forests. which But of forest, increase. to loss smallest Province. land the the Zhejiang than Jiaxing of and less areas increase was (the all regions conversion almost Shanghai to those in to due of occurred proximity mainly Lishui. was also in in loss gain Forest municipality occurring economically Forest amount forest. Jiaxing most of largest (the loss the province). least Zhejiang with the the West cropland, possessed of to in China) city eastern municipality in capital Hangzhou center the economic by followed and is city which developed Zhejiang, South in 6 Posted on Authorea 12 Mar 2021 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.161554513.39992175/v1 — This a preprint and has not been peer reviewed. Data may be preliminary. a eutdi ovrinfo giutrlln oubnln Fgr ) eie,w loosre large a observed also 2020 we to Besides, (Vina, 2000 4). rises from (Figure capita Province land per urban Zhejiang GDP to in China’s land as urbanization agricultural lost accelerated from be The conversion to in then 2016). tends resulted soybean), forestland Liu, has been (DeFries America gain that & exports South show also net Xu, agricultural (e.g., also a Yang, and has flows studies to McConnell, population trade it Some urban loss cross-region However, of 2010). net in growth al., a the engaged et 2010). from with is correlate shift Lambin, region positively will with or may & country region deforestation patterns Rudel, a a objective if or (Meyfroidt, that country show development shown a to socio-economic in likely with forest have that along more assumes also are theory may transition statistics, remotely 2018). thus Forest that government al., believe and et may on we (Zeng 2006), categories area, relying reliability loss mosaic Woodcock, high forest not mosaic These relatively & in data, use changes (Ozdogan types. inconsistent to land the cover land scheme despite sensed land agricultural Yet, classification other of of loss. a with forest underestimation requires area underestimated cropland its product the mixing for (300m) algorithm, underestimate in account ESA-CCI This also result may the might The fields, of algorithm. m) which crop resolution learning dataset. (30 classes, mountain deforestation The machine Hansen’s GlobeLand30 shaped extensive the trained the loss. irregularly that to globally However, forest detecting show compared a for 2020). dataset deforestation with suitable al., forest less produced less et showed Hansen’s was (Xiong resolution, The products dataset 2000s higher cover GlobaLand30 land early a 2018). various the has al., among forest in which loss et of imagery, Province forest definition Zeng Zhejiang inconsistent of 2020; uniform, rates in not different al., occurred are the et decline explaining forest Chen for Zhejiang on reason (H. in area data plausible forest While area in a decrease forest 2b). be substantial (Figure a may in detected 2000s we decrease early products, cover a the land showed in and use data land satellite-based sensing to remote According period, 2020. study and 2000 the change between use During land Province significant a region. representing the 2000, since in deforestation massive Province experienced has Zhejiang Province Zhejiang in deforestation of Causes 4.1 DISCUSSION 4 and 7). degradation (Figure land result land each from a what of suffer as Lishui get also region need whereas to may the urbanization, in flows Lishui in for areas of Meanwhile, Lishui ecosystems other form from of compensation. the of resources loss in corresponding land agent, need regions back-up a the an such buy receive development, two meet of may could economic between Hangzhou to role exchanged instance, of resources the For be level land Through may need. lower reserved money development. and its its urban Land municipality supply to slower supply. The can due and government government). land resources local population, higher-level back-up land a a rural the find back-up to as of of policies sufficient (such out-migration needs enact agent has massive could intermediate the surrounding however, governments an The from meet Lishui, of resources area. of longer role land land As the no arable arable through in can took urbanized. reduction resources resources inevitably a less to expansion land comparatively leading urban Zhejiang is limited areas, In Therefore, Lishui its 2020). an while urbanization. al., increased, municipality illustrate region’s et population urbanized Jing they Sun urban most municipalities, 2017; Lishui the Hangzhou’s Liu, (Jianguo and is concept Hangzhou Hangzhou telecoupling in Province, the demonstrating change for use Lishui case and ideal land Hangzhou representative in the urbanization 6). Considering and (Figure deforestation million of 2.21 2000-2019 Telecoupling during to 7. yuan 2.16 10.36 billion Figure 147.661 to growthfrom to 7.02 population 13.676 from resident from 148% increased slow by only a hand, increased municipality other with representing the Hangzhou 2000-2019, on of GDP, during Lishui’s population yuan resident million. billion The 1537.305 to increase. 138.256 11-fold from an increase to continued GDP Hangzhou’s 7 Posted on Authorea 12 Mar 2021 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.161554513.39992175/v1 — This a preprint and has not been peer reviewed. Data may be preliminary. eore n upr ainlfo euiyadeooia aey nZein,freape h conversion the example, for cropland temporal Zhejiang, high-quality In and protect safety. spatial effectively ecological over to and future greatly the security the varies assess food in cropland to adjusted conse- national As difficult further support its and be evaluate and 2017). balance to resources to quantitative al., needs the needs et policy assessing China, the (Lin on in dimensions, balance focus policy to ecological clear BALS easy and a is the qualitative of It implement provides functions effectiveness. to ecological This and province the quences first on the provinces. more 2018). Province, different al., focusing Zhejiang for et resources, in scheme Long land areas management 2020; arable (Long, a of urban use with management and land unified management along rural the coordinated land national for between arable a direction of transfer issued replenishment Resources land inter-provincial In Natural management construction the of and proposed. Ministry integration establish was land unified the to remediation maintain the 2018, strengthen decree and In and to land improvement land Resources resources. paddy quality Natural natural and arable of of land of of Ministry combination replenished high-quality the management a of formed of the 2016, occupation China quality 2018, the in strengthen the that compensated; to proposed ensure be 2012 government To Chinese The should in the issued 2014, provinces. policy. was In between ecological quality. decree policy compensation and a a and qualitative, emphasizing land, by occupation quantitative, arable approach land of management “Trinity” their balancing improved a of (Long, further by have ensured authorities characterized relatively government was was Chinese land stage arable third of quantity rules The productivity the operational the stage, subsequent this and At 2009, and protected, In land. was occupation, arable improved. land 2020). land replenished be arable of would before of management quality and replenished quality its quality and fully and in cropland the reduced strengthen basic be increases to be permanent to to issued not designate were required linked would to land was proposed be arable China land land of 2008, arable quantity construction In the rural 2012). that in ensure Li, which to & decreases introduced, (Long that were to land mechanisms focus demanding construction assessment of urban projects shift institutional government pilot a executive specific the saw several and chief several 2005 2004, quantity and issued 2006, of assessment In the year In target The that accountability system. policies. assuring standards. an responsibility of conversions, national implement meet to land series should starting during a accountability, land land administrative issuing arable of created land, newly quality arable of the quality created control newly to been has of measures government Chinese introduced quality onwards, the 2004 From period evolution on improvement. this policy policy focusing (BALS) systematic during featured System consolidation stage Land second land The Arable food of of ensure Balance focus industrialization, 2014). The and Pijanowski, The urbanization & 8. for Song Figure decline. space (Wei in provide income land, not farmers’ arable increase was of and land amount ensuring security, all land land, the arable land, 2001, arable arable increase In of arable of to new 1999. amount area protect was a in the To policy in total with ha” balance land the one a 1998. arable achieved replenish that and basically in of ha regions) amount law one autonomous the “occupy (including Management the provinces maintain to Land The to rise new Law giving 1997. mechanism, Management in the replenishment Land system systematic into for compensation the was written and amended stage acquisition formally China initial land The was times. arable 8). several the policy (Figure changed proposed 2012; BALS stages was first three al., China and into et divided debate construction. roughly (Long great policy be guaranteeing attracted goal can of also policy this BALS role policy achieved China’s dual this have However, a might play 2018). environment, to Li, the & established policy, (BALS) Xin protecting BALS and System the development Land of economic Arable implementation and of establishment Such Balance The the 2020). community. on al., fragmentation scientific evolution et the forest Policy and (Xiong continued governments 4.2 land and to agricultural concern 4) of of (Figure be reclamation land) should from arable deforestation arise rapid to might and converted which large-scale land Province, (i.e., Zhejiang loss in forest of amount 8 Posted on Authorea 12 Mar 2021 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.161554513.39992175/v1 — This a preprint and has not been peer reviewed. Data may be preliminary. yrligo h eeopigfaeok eaeal obte xli adueadln oe change through cover “compensated” land been and illustration, has an use Hangzhou as land in municipalities development explain (south) urban better Lishui by and to land in (west) cultivated able Hangzhou but of are the encroachment areas we Using the adjacent framework, mechanisms. in scale, telecoupling and not sub-provincial the patterns occurred the on BALS At relying by By land. induced which cultivated period, types of study different shrinkage the among and during places. conversions area distant land forest use urban of land of decrease in large such expansion change (BALS) for a by System forest was guidelines Land on accompanied there study Arable emphasis practical that is of present an Balance found provide with the investigation The change of also The patterns implementation use policy. the but temporal land under development. investigate theory and 2000-2020 to during socioeconomic spatial transition Province sources Zhejiang multiple the sustainable forest from Understanding promoting the datasets on advance and world. draws only the degradation of not land areas may preventing many loss in forest place of taken has loss Forest 2018). al., et CONCLUSIONS Li 5 Chen, (S. & BALS Song, of (Zhang, efficiency areas rural and in effectiveness land 2018). the arable Li, challenge of emergence & further amounts the each to large (Xin will lead of ecological development which with inevitably abandonment 2018), will in socioeconomic well the cities and resulted with to coordinate villages population line or hollow not rural in of land, of 2018). do migration be arable al., continuing they must et the of (Bryan urbanization, as policies productivity development With use sustainable aspects the for land some ensure strategy among appropriate implemented China’s in to Moreover, balance contradict policies irrational failed clearly the use which either are reconcile land degradation, have years many land to Xing, policies However, 20 needs Cai, These than Ye, 9). system Chen, (Figure more other. management (R. land for and resources ecological China related planning and other in land land, and revitalization rational living land rural land, A for and productive planning conservation, 2014). and ecological policy Chen, development, China’s & urban for for challenge a land land is a of ecological As demand and urbanization. simultaneous living, of The productive, level between lower loss. relationships the its ecosystem Mutual becomes of and municipality 9. because degradation Lishui Figure towns land context, and experience this allows cities likely urbanization In policy other will BALS for distant stable province. Lishui the resources the A result, because contrast, less-urbanized land within In largely supplies The 2020). resources places. that pressure al., land surrounding place 5). of et in deforestation deployment (Figure land (Xiong higher the arable Hangzhou Province in for a in Zhejiang increases face in that demand and would may area than land processes deforested arable larger landscape arable most between of loss depopulated conversions conversion the forestland mutual the or has feature of is also changes of Hangzhou area municipality loss major in the Lishui massive the change with the Lishui, land for in land largest balance land blamed while The forest the arable often land; policy, 2020). to urban are BALS al., land to Hangzhou the et forest like land converting of (Hu municipalities to context land large leads the arable China, source inevitably high-quality In In largest which the 4). guaranteed, Province. and be (Figure Zhejiang shows land, to in land study arable to needs forest is Our quantity lead converting Province cropland may Zhejiang through 2018). of in policies, is Ibisch, land land with & construction arable conform The Verendel, urban of and of Ostwald, Province. source demands Zhejiang (Henders, largest market Sun in deforestation the satisfy phenomenon that 2017; as to deforestation such Liu, able particular problems (Jianguo this although ecological mechanisms to land, change apply arable may land of which long-range expansion 2020), the policy al., explain et cropland Jing can of framework loss telecoupling net The no the and inevitably Telecoupling implementation, policy 4.3 2020). BALS al., the et with (Xiong parallel forestland in of occurred loss has to land leading agricultural to forestland of 9 Posted on Authorea 12 Mar 2021 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.161554513.39992175/v1 — This a preprint and has not been peer reviewed. Data may be preliminary. eory . ice,G,Bokan . otce,M,Ptr,M,Bnep,S,...Snoo .(2017). M. Santoro, . . . S., Bontemps, Meet- M., Peters, (2016). CCI. M., P. cover Boettcher, D’Odorico, Land C., Brockmann, & G., N., Kirches, P., J. Defourny, Galloway, out-migration resources. M., agricultural rural A. current Leach, J. with of A., Lubchenco, demand https://doi.org/10.1016/j.gloenvcha.2016.05.004 K. Emery, impact . food A., future . J. The ing Gephart, . F., E., K. H. trend. Froehlich, Davis, (2014). sea. M., and the C. Q. Free, from present A., food Chen, M. of 2616-y Cisneros-Mata, Past, future & S., The Gelcich, (2020). X., L., China: Cao, Xing, C., in Costello, Y., transition mapping Cai, cover use land https://doi.org/10.1016/j.landusepol.2013.10.003 C., Global land (2015). Ye, J. on Mills, R., . . approach. Chen, . operational X., POK-based Chen, A L., 103 Chen, resolution: Sensing, X., m Cao, Forest A., 30 on Liao, Uncertainty at Datasets. J., Large Cover Chen, Land (2020). J., Global J. Chen, M. Used Liu, P. Widely & among H., Kareiva, (2018). Yang, Century V., 21st X. 112 . Lakshmi, Early 12 America, Hou, L., the Peng, in of . J., Change Wu, States Area . storage. Z., . United Zeng, carbon I., the H., and . Chen, of Butnar, biodiversity Sciences J., on of . impacts Johnson, Academy determine D., S., https://doi.org/10.1073/pnas.1406485112 National expansion Sim, N. agricultural the L., of Crossman, of Proceedings Mandle, patterns D., P., Spatial J. R. emergency. (2015). Sharp, Connor, sustainability R., X., land-system Chaplin-Kramer, Sun, national Y., a Ye, to ESA’s https://doi.org/10.1038/s41586-018-0280-2 L., the response Gao, of China’s achievements A., (2013). current G. B. Kirches, communities: Bryan, . modelling . climate . for CCI. F., Achard, maps cover C., cover land Lamarche, land E., Bogaert, global Van Consistent J., Radoux, P., Defourny, S., Bontemps, (grant State China Diego Reference San of thank Foundation support. We Science logistic 201906140195). Natural the number National (grant for the Council University Scholarship from China support and with 41771119), conducted number been has study This land future for ACKNOWLEDGMENTS basis a providing transition, the At forest reach of degradation. to theory land executed decisions. the preventing appropriately or enriches and policies and further production, use study designed can food this sophistically interconnections), securing scales, telecoupled be development, broader the policy promoting types (e.g., use land among relationships multiple land harmony their of comprehensive and and patterns losses more land) societal the such involving a considering ecological behind those fully cropland, mechanisms By especially urban, and planning, protection. (e.g., losses and agricultural and forest policies and balancing development quality, land-based of quantity, economic of implementing of framework, mindful “Trinity” and principle this the be designing on Under the should focusing while theory. Policymakers to to telecoupling protection according quantity protection. the on Lishui, supports ecological focusing empirically in from evolved finding deforestation has This BALS of BALS. in sacrifice land the cultivated at expansion agricultural 2) https://doi.org/10.3390/rs12213502 (21). -7 https://doi.org/10.1016/j.isprsjprs.2014.09.002 7-27. , ae rsne ttePoednso h S iigPae ypsu,Edimburgh. Symposium, Planet Living ESA the of Proceedings the at presented Paper rdc srGieVrin20 2 2.0, Version Guide User Product aue 588 Nature, 10 . 73) 510 https://doi.org/10.1038/s41586-020- 95-100. (7836), sr ora fPoormer n Remote And Photogrammetry Of Journal Isprs lblEvrnetlCag,39 Change, Environmental Global adUePlc,40 Policy, Use Land aue 559 Nature, 71) 193-204. (7713), eoeSensing, Remote 2) 7402-7407. (24), 101-110. , 125-132. , Posted on Authorea 12 Mar 2021 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.161554513.39992175/v1 — This a preprint and has not been peer reviewed. Data may be preliminary. abn .F,Tre,B . es,H . goa .B,Agle,A,Bue .W,...X,J (2001). J. Xu, . . . W., J. Bruce, myths. A., the Angelsen, beyond B., Moving S. change: Agbola, land-cover J., and H. 11 land-use Geist, of looming L., causes the B. The Turner, and F., globalization, E. economic Lambin, change, use land Global https://doi.org/10.1073/pnas.1100480108 3465-3472. (2011). P. Meyfroidt, scarcity. land & F., socio-economic map. E. versus land-cover feedback Lambin, Socio-ecological Earth transitions: to use Land access (2010). P. Open change. Meyfroidt, & F., (2014). E. Lambin, S. land cultivated Li, and & expansion Y., urban https://doi.org/10.1038/514434c Ban, of C., modeling China. Jun, Multi-level impacts in the (2012). counties minimize C. to cities? hotspot K. How https://doi.org/10.1016/j.landurbplan.2012.08.008 small urban Seto, (2020). many X. & for or Liu, X., conversion large . Deng, few . L., a Jiang, . developing Y., Liu, loss: X., farmland Meng, on Q., https://doi.org/10.1007/s10980-020-01073-x Ma, expansion B.-B., urban Zhou, of biodiver- X., UN Li, the G., under driver? Hu, strategies deforestation national as Do consumption commodity (2018). 70 agricultural P. Policy, address Ibisch, conventions & climate V., and Verendel, sity M., Townshend, Change. Ostwald, Cover . S., Forest . Henders, 21st-Century of . A., Maps Tyukavina, Global A., manage- https://doi.org/10.1126/science.1244693 S. High-Resolution Turubanova, 850-853. M., (2013). land Hancher, G. R., R. Moore, sustainable V., J. P. for Potapov, C., M. techniques Romania. Toul- Hansen, socio-economic in friendly . political, mitigation environment of . change impact implementing https://doi.org/10.1016/j.scitotenv.2018.11.160 The climate . on and (2019). F., factors Z. J. ment people. Kalantari, Muir, cultural billion & D., 9 S., and Lawrence, feeding Keesstra, L., of R., Haddad, challenge Halbac-Cotoara-Zamfir, R., The I. security: Crute, Food A R., https://doi.org/10.1126/science.1185383 China? J. (2010). in Beddington, C. cities across J., min, differ C. sprawl H. does Godfray, How (2016). D. data. Zhang, census & and M. https://doi.org/10.1016/j.landurbplan.2015.12.006 Z., P. light D. Sun, nighttime Zaks, C., using investigation . He, multi-scale Q., . (2005). Huang, . K. M., B., P. Johnston, Gao, Snyder, S., planet. J. . cultivated Gerber, a S., . for E. Cassidy, Solutions . A., R., K. (2011). Brauman, S. N., mass Carpenter, Ramankutty, A., human-made G., J. Foley, Bonan, Global C., use. Barford, land (2020). P., of R. G. consequences Asner, Milo, J. Global R., & Engelmann, DeFries, A., M., . J. Y. Foley, . Bar-On, . biomass. J., W., living A. Grozovski, popu- all Ando, L., exceeds urban A., Ben-Uri, J. by E., Ahumada, prevention. driven L., Elhacham, pandemic Kaufman, for Deforestation L., economics Hannah, and L., Ecology (2010). century. S. (2020). M. Pimm, twenty-first P., Hansen, A. the Dobson, & in M., trade agricultural Uriarte, https://doi.org/10.1038/ngeo756 and T., growth Rudel, lation S., R. DeFries, 4,2129 https://doi.org/10.1016/S0959-3780(01)00007-3 261-269. (4), adUePlc,27 Policy, Use Land 8-9.https://doi.org/10.1016/j.landusepol.2017.10.043 580-590. , rceig fteNtoa cdm fSine fteUie ttsO mrc,108 America, Of States United the Of Sciences Of Academy National the Of Proceedings Nature 2,1818 https://doi.org/10.1016/j.landusepol.2009.09.003 108-118. (2), cec,309 Science, https://doi.org/10.1038/s41586-020-3010-5 . aue 478 Nature, 53) 7-7.https://doi.org/10.1126/science.1111772 570-574. (5734), 11 adcp n ra lnig 108 Planning, Urban and Landscape 76) 3-4.https://doi.org/10.1038/nature10452 337-342. (7369), cec fteTtlEvrnet 654 Environment, Total the of Science cec,369 Science, adcp n ra lnig 148 Planning, Urban and Landscape aueGocec,3 Geoscience, Nature 60) 379-381. (6502), lblEvrnetlChange, Environmental Global cec,327 Science, aue 514 Nature, cec,342 Science, adcp Ecology Landscape 72) 434-434. (7523), 56) 812-818. (5967), 24,131-139. (2-4), 3,178-181. (3), 418-429. , adUse Land 89-98. , (6160), (9), . Posted on Authorea 12 Mar 2021 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.161554513.39992175/v1 — This a preprint and has not been peer reviewed. Data may be preliminary. og . i . i,Y,Wos . o,J 21) ceeae etutrn nrrlCiafee by fueled China rural in villages. hollowed restructuring with Accelerated dealing (2012). for J. policy https://doi.org/10.1016/j.landusepol.2011.04.003 Zou, land-use & 11-22. balance’ M., (1), Woods, decreasing Y., vs. Liu, land ’increasing Y., housing Li, rural H., and Long, farmland of mechanism and characteristics change: coupling 0946-x land-use The and China. (2012). in development T. China. transition Li, Socio-economic River, in & Yangtse H., the interrelations (2007). Long, of M. man-land Transect the Zhang, Changing in https://doi.org/10.1016/j.landusepol.2005.11.003 & transition 141-153. X., (2018). land (1), Li, housing L. rural K., Ma, of G. Analysis & Heilig, security. Y., H., food Long, Qu, for implications S., its Tu, and 209 urbanization Y., Introduction. Management, under (Ed.), Zhang, area Long D., farming H. China’s China: Ge, In H., in rural China. Long, on in policy Transition impact Use China its Farmland use in and Restructuring (2020). loss Land H. land Long, arable of Analysis (2014). https://doi.org/10.1016/j.landusepol.2014.03.006 H. (2010). L. Long, H. Long, China. & https://doi.org/10.1016/j.jenvman.2009.09.028 of Province Y., 653. W. Jiangsu J. Southern Taylor, fragmenta- Wang, in and sustainability S., loss implications. forest Y. policy of . and drivers Liu, schemes Socioeconomic planning (2016). use S. land . to Su, different approaches & 54 between Nexus Q., comparison Zhang, (2018). A Z., S. . Li, tion: Zhao, Y., . E., Feng, . Y., Liu, . Moran, H., Hoff, C., P., Gleick, systems. D., Folke, 0135-8 Tilman, natural J., development. M., C. sustainable and H. Godfray, global Alberti, V., world. human Hull, R., J., coupled Liu, metacoupled S. of a Complexity Qual- Carpenter, https://doi.org/10.1126/science.1144004 T., across (2017). (2007). K. Dietz, Integration Wang, W. J., . Liu, . (2017). China. . Wenzhou, consis- J., https://doi.org/10.5751/es-09830-220429 in J. the Deng, Land Cultivated M., of Liu, Weston, of Evaluation Balance A., Dynamic Shahtahmassebi, (2015). the M., https://doi.org/10.3390/su9010095 case F. on Gan, A Perspective Yang, Z., transition datasets: ity Ye, & GlobeLand30 land L., m L., housing Lin, 30 Teng, Chinese rural on J., China. of based Province, Zhao, (MCD12Q1) analysis https://doi.org/10.3390/ijgi4042519 Anhui product L., Multi-scale in cover Rim. Huang, land study MODIS Bohai Y., (2015). of of Zuo, tency S. case D., of Tu, extent The Liang, & the of Y., urbanization: estimation Liu, An rapid https://doi.org/10.1016/j.habitatint.2015.04.002 H., China’s (2018). Long, S. under Tramberend, T., & G., Li, Fischer, G., China. of Cao, https://doi.org/10.1002/ldr.2924 regions 1342. L., mountainous Sun, in abandonment X., cropland Li, S., Li, 86.https://doi.org/10.1016/j.landusepol.2016.01.016 58-68. , 4-5.https://doi.org/10.1016/j.jenvman.2017.12.047 440-451. , ora fGorpia cecs 22 Sciences, Geographical of Journal p.3-6) igpr:Srne Singapore. Springer Singapore: 31-160). (pp. aueSsanblt,1 Sustainability, Nature SR nentoa ora fGoIfrain 4 Geo-Information, of Journal International ISPRS 12 ora fEvrnetlMngmn,91 Management, Environmental of Journal 9,4646 https://doi.org/10.1038/s41893-018- 466-476. (9), 3,5852 https://doi.org/10.1007/s11442-012- 548-562. (3), adDgaain&Dvlpet 29 Development, & Degradation Land aia nentoa,48 International, Habitat cec,317 Science, clg n oit,22 Society, and Ecology adUeTastosadRural and Transitions Use Land adUePlc,40 Policy, Use Land ora fEnvironmental Of Journal utiaiiy 9 Sustainability, adUePlc,29 Policy, Use Land adUePlc,24 Policy, Use Land 54) 1513-1516. (5844), adUePolicy, Use Land 4,2519-2541. (4), 227-238. , 5,1327- (5), 3,646- (3), 1-5. , (4). (1). Posted on Authorea 12 Mar 2021 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.161554513.39992175/v1 — This a preprint and has not been peer reviewed. Data may be preliminary. ia . conl,W . ag .B,X,Z . i,J .(06.Eet fcnevto oiyon policy conservation of Effects (2016). G. J. Liu, & C., Z. Xu, in global B., for H. recovery. loss Yang, science forest J., change China’s W. land land McConnell, of A., emergence arable Vina, The (2007). and 104 A. America, Reenberg, Of expansion & States sustainability. F., and land E. change Lambin, environmental Urban II, likely. L., more B. (2005). pandemics Turner, make region. C. extinctions and Beijing-Tianjin-Hebei Lu, deforestation 175-176. Why of (2020). & J. study Tollefson, H., case Xie, (2020). plantati- a Z. X., ps://doi.org/10.1016/j.landusepol.2004.03.003 crop Jinsong, - . cash Li, . China to . M., Yingying, changes Yao Tan, use Yuchu, applicationss. Qin Land its Fuqiang, and (2016). framework Zhao Telecoupling 75 Xinjun, Anthropocene: W. Yang the in Kong, Jianguo, Sustainability Liu & implications. Jing, Y., policy Sun Li, and determinants C., multilevel Wan, ps://doi.org/10.1016/j.landusepol.2015.10.003 types, X., Crop balance Zhou, ons: S., land landscape Su, and cultivated shifts architecture. Institutional system land (2020). China’s Cuba’s J. on Per´ıodo Leffel, 15 & Especial the Y., of of Kim, impact the E., A. Townshend,change: Frazier, effects & L., B. F., Turner, E. M., Vermote, The Stuhlmacher, scale. A., 2016. Tyukavina, to V., 1982 P. national from Potapov, change V., land 018-0411-9 a S. Global (2014). Stehman, (2018). C., R. at M. J. C. Hansen, P., productivity X. B. Song, land Pijanowski, potential https://doi.org/10.1016/j.apgeog.2013.11.009 in & on services ecosystem on program W., loss land cultivated Song, urbanization-induced of Effects plain. China targets? (2015). centralized north X. or the Deng, interests & Local Balance. W., (2017). Requisition-Compensation Song, G. of Li, policy & farmland W., 67 Ntshotsho, Luo, the Policy, Z., . implements Use Lu, government . T., and local Lv, . planning China’s C., improved Y., How Wu, Anker, for L., Wang, M., responses X., L. restoration Shen, D. and Vieira, degradation L., land D. evaluate Karlen, to decision-making. framework K., A A. develop- Chaudhary, areas. (2019). A., cultivated P. of J. transformation sensing Parrotta, remote R., rural in Pandit, errors 103 dependent of Environment, Resolution of (2006). Analysis Sensing E. Remote C. displacement Woodcock, global & M., the Ozdogan, and (2011). trade, millennium. transitions, Forest Y. new https://doi.org/10.1073/pnas.1014773107 (2010). F. 20917-20922. E. the Li, use. Lambin, land & of & K., of T. turn Rudel, J., P., Meyfroidt, the Pykett, since perspective. feedback J., China mutual https://doi.org/10.1016/j.apgeog.2011.02.006 A in Zou, management: land ment H., and transitions use Long, Land 74 (2018). Policy, Y. Use Qu, Land & H., Long, 1) 4821.https://doi.org/10.11821/dlxb202011010 2408-2416. (11), https://doi.org/10.1080/1747423x.2020.1829119 690-706. (5), rceig fteNtoa cdm fSine fteUie ttso mrc,107 America, of States United the of Sciences of Academy National the of Proceedings 1-2.https://doi.org/10.1016/j.landusepol.2017.06.012 716-724. , cssesadPol,16 People, and Ecosystems 1-2.https://doi.org/10.1016/j.landusepol.2017.03.021 111-120. , nris 8 Energies, CEC DACS 2 ADVANCES, SCIENCE 5) 06-07.https://doi.org/10.1073/pnas.0704119104 20666-20671. (52), 6,57-63 https://doi.org/10.3390/en8065678 5678-5693. (6), 2,2327 https://doi.org/10.1016/j.rse.2006.04.004 203-217. (2), rceig fteNtoa cdm fSine fteUnited the Of Sciences Of Academy National the Of Proceedings 1,11.https://doi.org/10.1080/26395916.2019.1697756 1-18. (1), 3,7 https://doi.org/10.1126/sciadv.1500965 7. (3), aue 560 Nature, 13 adUePlc,22 Policy, Use Land 3-4.https://doi.org/10.1038/s41586- 639-643. , ple egah,31 Geography, Applied ple egah,46 Geography, Applied adUePlc,50 Policy, Use Land ora fLn s Science, Use Land of Journal caGorpiaSinica, Geographica Acta aue 584 Nature, 3,1716 htt- 187-196. (3), 3,1094-1105. (3), 7-8.htt- 379-389. , 158-170. , (7820), Land (49), Posted on Authorea 12 Mar 2021 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.161554513.39992175/v1 — This a preprint and has not been peer reviewed. Data may be preliminary. hn,Q,Sn,C,&Ce,X 21) ffcso hn’ amn o csse evcsporm on programs services ecosystem China. for Anhui, payment Township, China’s Tiantangzhai of in Effects Highland study (2018). (2018). https://doi.org/10.1016/j.landusepol.2018.01.001 case E. X. 239-248. Wood, A Chen, F. & abandonment: . C., cropland . Song, . Q., century. twenty-first F., Zhang, the Hua, in T., Asia Searchinger, Southeast A., https://doi.org/10.1038/s41561-018-0166-9 in Chen, loss 556-562. D., forest areas and A. mountainous expansion Ziegler, of cropland L., deforestation Estes, Large-scale Z., (2020). Zeng, Province. Y. Zhejiang Anker, & in C., Century Ye, https://doi.org/10.1002/ldr.3563 21st Z., the Xia, farmland. R., during new Chen, reclaim massively B., not Xiong, should China (2018). X. China’s https://doi.org/10.1016/j.landusepol.2017.12.023 in Li, evolution & policies land L., and Xin, changes Land-use (2018). Y. Xu, processes. & A., urbanization Glendinning, Y., Lin, J., Wang, adUePlc,75 Policy, Use Land 7-8.https://doi.org/10.1016/j.landusepol.2018.04.011 375-387. , adDgaain&Dvlpet 31 Development, & Degradation Land 14 adUePlc,72 Policy, Use Land aueGocec,11 Geoscience, Nature adUePlc,73 Policy, Use Land 1) 1761-1774. (14), 12-15. , (8), , Posted on Authorea 12 Mar 2021 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.161554513.39992175/v1 — This a preprint and has not been peer reviewed. Data may be preliminary. 15 Posted on Authorea 12 Mar 2021 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.161554513.39992175/v1 — This a preprint and has not been peer reviewed. Data may be preliminary. 16 Posted on Authorea 12 Mar 2021 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.161554513.39992175/v1 — This a preprint and has not been peer reviewed. Data may be preliminary. 17 Posted on Authorea 12 Mar 2021 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.161554513.39992175/v1 — This a preprint and has not been peer reviewed. Data may be preliminary. 18