土地利用、覆被变化的过程、趋势及其与变化的沿海生态系统的联系-以伊朗克什姆岛为例(外语版).pdf
《土地利用、覆被变化的过程、趋势及其与变化的沿海生态系统的联系-以伊朗克什姆岛为例(外语版).pdf》由会员分享,可在线阅读,更多相关《土地利用、覆被变化的过程、趋势及其与变化的沿海生态系统的联系-以伊朗克什姆岛为例(外语版).pdf(135页珍藏版)》请在咨信网上搜索。
1、Table of Cont ent sTable of ContentsTable of Contents.iList of Tables.ivList of Figures.vAbbreviations.vii摘要.viiiAbstract.xiChapter 1 Introduction.11.1 Research background.11.2 Research objectives.31.3 Analytical framework.4Chapter 2 Literature review.62.1 Land-use/cover change detection.62.2 Land-u
2、se/cover modeling.72.3 Response of coastal ecosystems to LUCC.112.3.1 Impact of LUCC on coast al ar eas.112.3.2 Measur ing t he st at e of coast al ecosyst ems under t he changingenvir onment.122.4 Summary.15Chapter 3 Materials,and methods.163.1 Study areas.163.1.1 Qeshm Island.163.1.2 Gabr ik r egi
3、on.183.2 Remotely sensed data.193.2.1 Landsat dat a.193.2.2 MODIS dat a.203.3 Methods to measure LUCC.203.3.1 Pr epar ing Land-Use/Cover dat a.203.3.2 Int ensit y analysis t o measur e LUCC.213.4 Methods to simulate LUCC.24Table of Cont ent s3.4.1 Cellular aut omat a.253.4.2 Mar kov chain model.253.
4、4.3 Mult i-Layer per cept r on(MLP)neur al net wor k.;.263.4.4 Logist ic r egr ession(LR).283.4.5 CA-Mar kov.293.4.6 Sensit ivit y analysis t hr ough adding/delet ing appr oach.293.4.7 Validat ion.303.5 Methods to link LUCC with EWS.323.5.1 Google ear t h engine-GEE.333.5.2 R language.333.5.3 Remot
5、ely sensed indices.343.5.4 Used st at ist ical analyses for ear ly war ning signal.36Chapter 4 Measuring LUCC in Qeshm Island.384.1 Land use/cover maps.384.2 Measuring land change at three levels:intervals,category,and transition.424.3 Driving forces of LUCC in Qeshm Island.464.4 Patterns to process
6、es in LUCC.474.5 Summary.50Chapter 5 Simulating LUCC in Qeshm Island.525.1 An adding/deleting approach to improve LUCC modeling.525.1.1 Model configur at ion.525.1.2 Analyzing t r ansit ion pot ent ial maps(TPMs).545.1.3 An adding/delet ing appr oach t o impr ove t he model per for mance 57-5.2 Comp
7、arison of four hybrid models to simulate the LUCC.615.3 Prediction of land use/cover change(LUCC)in 2026.655.4 Discussion.665.5 Summary.71Chapter 6 Linking LUCC with shifting regime in coastal ecosystems.736.1 Detecting LUCC.736.2 Time series of three indices derived from MODIS from 2000 to 2018 756
8、.3 Identification of early warning signals(EWS).77iiTable of Cont ent s6.4 Discussion.796.4.1 Effect iveness of t hr ee indices for ident ifying EWS.796.4.2 Linking LUCC wit h shift ing r egime in coast al ecosyst ems.816.5 Summary.82Chapter 7 Conclusion,and recommendation.847.1 Conclusion.847.2 Inn
9、ovative of this study.867.3 Limitation of this study.867.4 Recommendation.87References 88Acknowledgments.108Projects and publications.109Appendices 1 Google Earth Engine used code to extract RS indices.IllAppendices 2 R used code for statistical analysis related to Early Warning.114111List ofTablesL
10、ist ofTablesTable 3-1 Mean annual t emper at ur e,and aver age annual pr ecipit at ion.17Table 3-2 List of Landsat sat ellit e images used in t he r esear ch.19Table 3-3 Specificat ions of MODISs bands 1 t o 7(USGS).20Table 3-4 List of LULC classes,and descr ipt ion for each class.21Table 3-5 Mat he
11、mat ical not at ion following Aldwaik,and Pont ius(2012).22Table 4-1 Over all accur acy fbr t he land-use maps.40Table 4-2 Var iat ion mat r ix fbr each land-use class based on t he number of pixels.41Table 5-1 A summar y of t he Mar kov pr obabilit y mat r ix fbr simulat ing t he t r ansit ionbet w
12、een t he pr imar y,and final cellular st at es.54Table 5-2 Result s of Adding/Delet ing appr oach fbr each var iable.59Table 5-3 Validat ion of t he model befor e,and aft er adding/delet ing appr oach.60Table 5-4 Validat ion of t he used modeling appr oach in compar ison wit h ot her modeling met ho
13、ds include CA-MC-ANN(Cellular Aut omat a-Mar kov Chain-Ar t ificial Neur al Net wor k),MC-ANN(Mar kov Chain-Ar t ificial Neur al Net wor k),and CA-MC-LR(Cellular Aut omat a-Mar kov chain-logist ic r egr ession).62Table 5-5 Kappa st andar d coefficient fbr each class.63Table 5-6 Compar ison of t he a
14、r ea of each land-use class bet ween 2014,and 2026.65Table 6-1 Ar ea of changes r elat ed t o human-made usages include Built-up,and agr icult ur e(Number of pixels).75Table 6-2 Compar at ive t able which shows t he value of t he indices in t he t wo r egions.77Table 6-3 Kendalr s t t r end and it s
15、 associat ed significance level.What ever Kendall5s t be closer t o 1,it means mor e upwar d t r end and indicat es an Ear ly War ning at t he ecosyst em.79ivList of Figur esList of FiguresFig.1-1 Analyt ical fr amewor k of t his st udy.5Fig.2-1 Descr ipt ion of shift ing st able st at es,st abilit
16、y,and r esilience t heor y(Clement s&Ozgul 2018).12Fig.3-1 Locat ion of main st udy ar ea(Qeshm Island),and cont r ol ar ea(Gabr ik).16Fig.3-2 Aver age mont hly t emper at ur e,and pr ecipit at ion in t he Qeshm Island.17Fig.3-3 Flowchar t of using int ensit y analysis t o measur e LUCC.24Fig.3-4 Fl
17、owchar t of LUCC modeling in t his st udy.31Fig.3-5 Pr oposed appr oach t o link LUCC wit h EWS.32Fig.3-6 The flowchar t of linking LUCC wit h EWS.37Fig.4-1 Land-use/cover maps of Qeshm Island in 6 classes at four t ime int er vals.39Fig.4-2 Rat e of change at t ime level,and int ensit y of changes.
18、42Fig.43 Rat e of change at cat egor y level,and int ensit y of changes associat ed wit h land-use classes.43Fig.4-4 Tr ansit ion int ensit y given cat egor y gains dur ing t hr ee-t ime int er vals.45Fig.5-1 TPMs MLP-ANN a)Agr icult ur e b)Bar e-land c)Built-up d)Dense-veget at ion e)Mangr ove f)Wa
19、t er-body.55Fig.5-2 TPMs Logist ic-Regr ession a)Agr icult ur e b)Bar e-land c)Built-up d)Dense-veget at ion e)Mangr ove f)Wat er-body.56Fig.5-3 Value pf ar ea under t he cur ve(AUC).57Fig.5-4 Simulat ed fbr Land-use simulat ed for 2014.This map was used t o compar e wit h t he act ual map of t he s
20、ame year t o measur e t he accur acy of four modeling met hods t o pr ove t hat t he adding/delet ing appr oach has ult imat ely helped t o incr ease t he accur acy of t he modeling.57Fig.5-5 Gr een bar s show t he cor r ect pr edict ed changes aft er adding/delet ing,and t he r ed bar s show r ight
21、 pr edict ed changes befor e Adding/Delet ing(A-left,unit:km2)-The number of var iables used t o model in ever y class(B-r ight).61vList of Figur esFig.5-6 Agr eement,and disagr eement component s in 1)CA-MC-LR 2)MC-ANN3)CA-MC-ANN 4)CA-MC-ANN-SA.62Fig.5-7 Compar ison of t he simulat ed map aft er ad
22、ding/delet ing(t op)wit h t hesimulat ed map befor e adding/delet ing(down).64Fig.5-8 Pr edict ed map of 2026-Using int egr at ion of CA-Mar kov-ANN-AD.66Fig.5-9 Gains,and Losses bet ween 2014,and 2026(hect ar es)for each land-use class.66Fig.6-1 Land-use maps of Qeshm Island(up),and Gabr ik(down)ex
23、t r act ed fr om Landsat image for 1996,and 2014.74Fig.6-2 The t ime ser ies of t hr ee r emot ely sensed indices,Nor malized Differ ence Veget at ion Index(NDVI),Modified Nonnalized Wat er Index(MNDWI),and Modified Veget at ion Wat er Rat io(MVWR)t hat have been used as t he var iables in Qeshm Isl
24、and(r ight),and Gabr ik(left).The r ed line illust r at es t he t r end obt ained using a moving aver age wit h a window size of 20-t ime st eps.76Fig.6-3 Met r ic-based leading indicat or s based on NDVI,MNDWI,and MVWR for Qeshm Island(3 t op r ows),and Gabr ic(3 bot t om r ows).The fir st,second,a
25、nd t hir d r ows show aut ocor r elat ion at lag 1(ACF),st andar d deviat ion(SD),and skewness of each spect r al indices,r espect ively.78VIAbbr eviat ionsAbbreviationsANNAr t ificial Neur al Net wor kATFAut o-Cor r elat ion Funct ionAUCAr ea Under t he Cur veCACellular Aut omat aEWSsEar ly War nin
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