#PAGE_PARAMS# #ADS_HEAD_SCRIPTS# #MICRODATA#

A geographically weighted random forest approach for evaluate forest change drivers in the Northern Ecuadorian Amazon


Autoři: Fabián Santos aff001;  Valerie Graw aff002;  Santiago Bonilla aff001
Působiště autorů: Research Center for the Territory and Sustainable Habitat, Universidad Tecnológica Indoamérica, Quito, Ecuador aff001;  Center of Remote Sensing of Land Surfaces (ZFL), University of Bonn, Bonn, Germany aff002;  Departament of Forest Engineering. E.T.S.I.A.M., Campus de Excelencia Internacional Agroalimentario (ceiA3), Universidad de Córdoba, Córdoba, Spain aff003
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0226224

Souhrn

The Tropical Andes region includes biodiversity hotspots of high conservation priority whose management strategies depend on the analysis of forest dynamics drivers (FDDs). These depend on complex social and ecological interactions that manifest on different space–time scales and are commonly evaluated through regression analysis of multivariate datasets. However, processing such datasets is challenging, especially when time series are used and inconsistencies in data collection complicate their integration. Moreover, regression analysis in FDD characterization has been criticized for failing to capture spatial variability; therefore, alternatives such as geographically weighted regression (GWR) have been proposed, but their sensitivity to multicollinearity has not yet been solved. In this scenario, we present an innovative methodology that combines techniques to: 1) derive remote sensing time series products; 2) improve census processing with dasymetric mapping; 3) combine GWR and random forest (RF) to derive local variables importance; and 4) report results based in a clustering and hypothesis testing. We applied this methodology in the northwestern Ecuadorian Amazon, a highly heterogeneous region characterized by different active fronts of deforestation and reforestation, within the time period 2000–2010. Our objective was to identify linkages between these processes and validate the potential of the proposed methodology. Our findings indicate that land-use intensity proxies can be extracted from remote sensing time series, while intercensal analysis can be facilitated by calculating population density maps. Moreover, our implementation of GWR with RF achieved accurate predictions above the 74% using the out-of-bag samples, demonstrating that derived RF features can be used to construct hypothesis and discuss forest change drivers with more detailed information. In the other hand, our analysis revealed contrasting effects between deforestation and reforestation for variables related to suitability to agriculture and accessibility to its facilities, which is also reflected according patch size, land cover and population dynamics patterns. This approach demonstrates the benefits of integrating remote sensing–derived products and socioeconomic data to understand coupled socioecological systems more from a local than a global scale.

Klíčová slova:

Agriculture – Algorithms – Census – Deforestation – Ecuador – Forests – Population density – Remote sensing


Zdroje

1. Olson DM, Dinerstein E, Wikramanayake ED, Burgess ND, Powell GVN, Underwood EC, et al. Terrestrial Ecoregions of the World: A New Map of Life on Earth. 2001;51: 933–938.

2. Armenteras D, Rodríguez N, Retana J, Morales M. Understanding deforestation in montane and lowland forests of the Colombian Andes. Reg Environ Chang. 2011;11: 693–705. doi: 10.1007/s10113-010-0200-y

3. Brooks TM, Mittermeier AR, da Fonseca GBA, Gerlach J, Hoffmann M, Lamoreux JF, et al. Global Biodiversity Conservation Priorities. 2006;313: 58–62.

4. Myers N, Mittermeier R, Mittermeier C, Fonseca G da, Kent J. Biodiversity hotspots for conservation priorities. Nature. 2000;403: 853–858. doi: 10.1038/35002501 10706275

5. Cincotta RP, Wisnewski J, Engelman R. Human population in the biodiversity hotspots. Nat. 2000;404: 990–992. doi: 10.1038/35010105 10801126

6. Armenteras D, María J, Rodríguez N, Retana J. Deforestation dynamics and drivers in different forest types in Latin America: Three decades of studies (1980–2010). Glob Environ Chang. 2017;46: 139–147. doi: 10.1016/j.gloenvcha.2017.09.002

7. Buytaert W, Cuesta-Camacho F, Tobón C. Potential impacts of climate change on the environmental services of humid tropical alpine regions. Glob Ecol Biogeogr. 2011;20: 19–33. doi: 10.1111/j.1466-8238.2010.00585.x

8. Grau HR, Aide M. Globalization and land-use transitions in Latin America. Ecol Soc. 2008;13. doi: 10.1057/9780230603554

9. Rudel TK, Bates D, Machinguiashi R. A tropical forest transition? Agricultural change, out-migration, and secondary forests in the Ecuadorian Amazon. Ann Assoc Am Geogr. 2002;92: 87–102. doi: 10.1111/1467-8306.00281

10. Nagendra H. Drivers of reforestation in human-dominated forests. Proc Natl Acad Sci. 2007;104: 15218–15223. doi: 10.1073/pnas.0702319104 17881576

11. Rudel TK, Coomes OT, Moran E, Achard F, Angelsen A, Xu J, et al. Forest transitions: Towards a global understanding of land use change. Glob Environ Chang. 2005;15: 23–31. doi: 10.1016/j.gloenvcha.2004.11.001

12. Mosandl R, Günter S, Stimm B, Weber M. Ecuador Suffers the Highest Deforestation Rate in South America. In: Beck E, Bendix J, Kottke I, Makeschin F, Mosandl R, editors. Gradients in a Tropical Mountain Ecosystem of Ecuador. Berlin, Heidelberg: Springer Berlin Heidelberg; 2008. pp. 37–40. doi: 10.1007/978-3-540-73526-7_4

13. FAO. State of the World’s Forests. 2007.

14. Geist HJ, Lambin EF. Proximate Causes and Underlying Driving Forces of Tropical Deforestation. Bioscience. 2002;52: 143. doi: 10.1641/0006-3568(2002)052[0143:PCAUDF]2.0.CO;2

15. Salvini G, Herold M, De Sy V, Kissinger G, Brockhaus M, Skutsch M. How countries link REDD+ interventions to drivers in their readiness plans: implications for monitoring systems. Environ Res Lett. 2014;9: 074004. doi: 10.1088/1748-9326/9/7/074004

16. Da Ponte E, Fleckenstein M, Leinenkugel P, Parker A, Oppelt N, Kuenzer C. Tropical forest cover dynamics for Latin America using Earth observation data: a review covering the continental, regional, and local scale. Int J Remote Sens. 2015;36: 3196–3242. doi: 10.1080/01431161.2015.1058539

17. Grainger A. Difficulties in tracking the long-term global trend in tropical forest area. Proc Natl Acad Sci. 2008;105: 818–823. doi: 10.1073/pnas.0703015105 18184819

18. Meyfroidt P, Lambin EF, Erb KH, Hertel TW. Globalization of land use: Distant drivers of land change and geographic displacement of land use. Curr Opin Environ Sustain. 2013;5: 438–444. doi: 10.1016/j.cosust.2013.04.003

19. Mertens B, Sunderlin WD, Ndoye O, Lambin EF. Impact of macroeconomic change on deforestation in South Cameroon: Integration of household survey and remotely-sensed data. World Dev. 2000;28: 983–999. doi: 10.1016/S0305-750X(00)00007-3

20. Southgate D, Sierra R, Brown L. The causes of tropical deforestation in Ecuador: A statistical analysis. World Dev. 1991;19: 1145–1151. doi: 10.1016/0305-750X(91)90063-N

21. Mena CF, Bilsborrow RE, McClain ME. Socioeconomic drivers of deforestation in the Northern Ecuadorian Amazon. Environ Manage. 2006;37: 802–815. doi: 10.1007/s00267-003-0230-z 16555027

22. Stephen J. Walsh, Yang Shao, Carlos F. Mena, McCleary and AL. Integration of Hyperion Satellite Data and Household Social Survey to Characterize the Causes and Consequences of Reforestation Patterns in the Northern Ecuadorian Amazon. Photogramm Eng Remote Sens. 2008;74: 725–735.

23. Bonilla-Bedoya S, Estrella-Bastidas A, Molina JR, Herrera MÁ. Socioecological system and potential deforestation in Western Amazon forest landscapes. Sci Total Environ. 2018;644: 1044–1055. doi: 10.1016/j.scitotenv.2018.07.028 30743818

24. Wulder MA, Hilker T, White JC, Coops NC, Masek JG, Pflugmacher D, et al. Virtual constellations for global terrestrial monitoring. Remote Sens Environ. 2015;170: 62–76. doi: 10.1016/j.rse.2015.09.001

25. Hansen MC, Potapov P, Moore R, Hancher M, Turubanova S, Tyukavina A, et al. High-Resolution Global Maps of 21st-Century Forest Cover Change. Science (80-). 2013;342: 850–853. doi: 10.1126/science.1244693 24233722

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

27. Hernández A, Miranda MD, Arellano EC, Dobbs C. Landscape trajectories and their effect on fragmentation for a Mediterranean semi-arid ecosystem in Central Chile. 2016;127: 74–81. doi: 10.1016/j.jaridenv.2015.10.004

28. Logan JR, Xu Z, Stults B. 1970 to 2010: A Longtitudinal Tract Database. Prof Geogr. 2014;66: 412–420. doi: 10.1080/00330124.2014.905156 25140068

29. Holt DT, Steel DG, Tranmer M, Wrigley N. Aggregation and Ecological Effects in Geographically Based Data. Geogr Anal. 1996;28: 244–261. doi: 10.1111/j.1538-4632.1996.tb00933.x

30. Krivoruchko K, Gribov A, Krause E. Multivariate areal interpolation for continuous and count data. Procedia Environ Sci. 2011;3: 14–19. doi: 10.1016/j.proenv.2011.02.004

31. Semenov Tian-Shansky. Metody dazimetrii (Methods of Dasymetric Mapping). Dazimetrichskaya Karta Evropeiskoi. Petrograd: Scientific Chemistry and Technology Publishing; 1923. pp. 18–26.

32. Tobler WR. Smooth pycnopylactic interpolation for geographical regions. J Am Stat Assoc. 1979;74: 519–530. doi: 10.1080/01621459.1979.10481647 12310706

33. Kraus SP, Senger LW, Ryerson JM. Estimating population from photographically determined residential land use types. Remote Sens Environ. 1974;3: 35–42. https://doi.org/10.1016/0034-4257(74)90036-4

34. Stevens FR, Gaughan AE, Linard C, Tatem AJ. Disaggregating census data for population mapping using Random forests with remotely-sensed and ancillary data. PLoS One. 2015;10: 1–22. doi: 10.1371/journal.pone.0107042 25689585

35. Petrov A. One Hundred Years of Dasymetric Mapping: Back to the Origin. Cartogr J. 2012;49: 256–264. doi: 10.1179/1743277412Y.0000000001

36. Zandbergen PA, Ignizio DA. Comparison of Dasymetric Mapping Techniques for Small-Area Population Estimates. Cartogr Geogr Inf Sci. 2010;37: 199–214. doi: 10.1559/152304010792194985

37. Brunsdon C, Fotheringham A, Charlton ME. Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity. Geogr Anal. 1996;28: 281–298. doi: 10.1111/j.1538-4632.1996.tb00936.x

38. Pineda Jaimes NB, Bosque Sendra J, Gómez Delgado M, Franco Plata R. Exploring the driving forces behind deforestation in the state of Mexico (Mexico) using geographically weighted regression. Appl Geogr. 2010;30: 576–591. doi: 10.1016/j.apgeog.2010.05.004

39. de Freitas MWD, Santos JR dos, Alves DS. Land-use and land-cover change processes in the Upper Uruguay Basin: Linking environmental and socioeconomic variables. Landsc Ecol. 2013;28: 311–327. doi: 10.1007/s10980-012-9838-9

40. Wheeler D, Tiefelsdorf M. Multicollinearity and correlation among local regression coefficients in geographically weighted regression. J Geogr Syst. 2005;7: 161–187. doi: 10.1007/s10109-005-0155-6

41. Breiman L. Random forests. Mach Learn. 2001;45: 5–32. doi: 10.1023/A:1010933404324

42. Wright MN, Ziegler A. ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R. 2015;77: 1–17. doi: 10.18637/jss.v077.i01

43. Georganos S, Grippa T, Niang Gadiaga A, Linard C, Lennert M, Vanhuysse S, et al. Geographical random forests: a spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling. Geocarto Int. 2019;0: 1–16. doi: 10.1080/10106049.2019.1595177

44. Anderson A. Dollarization: A Case Study of Ecuador. Imp J Interdiscip Res. 2016;2: 2454–1362. Available: http://www.onlinejournal.in

45. Sierra R, Campos F, Chamberlin J. Assessing biodiversity conservation priorities: Ecosystem risk and representativeness in continental Ecuador. Landsc Urban Plan. 2002;59: 95–110. doi: 10.1016/S0169-2046(02)00006-3

46. MAE. Metodología para la representación Cartográfica de los Ecosistemas del Ecuador Continental. Subsecretaría de Patrimonio Natural, editor. Quito—Ecuador: Ministerio del Ambiente del Ecuador (MAE); 2013.

47. Huttel C, Zebrowski C, Gondard P. Paisajes Agrarios del Ecuador. IGM; 1999.

48. Eberhart N. Transformaciones agrarias en el frente de colonización de la Amazonia ecuatoriana. 1998; 186 pages.

49. Coq-Huelva D, Torres-Navarrete B, Bueno-Suárez C. Indigenous worldviews and Western conventions: Sumak Kawsay and cocoa production in Ecuadorian Amazonia. Agric Human Values. 2017;0: 1–17. doi: 10.1007/s10460-017-9812-x

50. Pierre G, Juan L, Paola S, Chiriboa M, Cuvi M, Emmanuel F, et al. Transformaciones Agrarias En El Ecuador. Juan L, Pierre P, editors. Centro Ecuatoriano de Investigación Geográfica. Quito—Ecuador: IGM; 1988.

51. Brown L, Smith R. Frameworks of Urban System Evolution in Frontier Settings and the Ecuador Amazon. 1994; 72–96.

52. Natural Earth. Free vector and rater map data at 1:10m, 1:50m, and 1:110m scales. 2019 [cited 24 Jan 2019]. Available: https://www.naturalearthdata.com/

53. IGM. Base escala 1:50.000 y 250.000. Quito—Ecuador: IGM; 2011.

54. MAE-MAGAP. Protocolo metodológico para la elaboración del Mapa de cobertura y uso de la tierra del Ecuador continental 2013–2014, escala 1:100.000. Ministerio del Ambiente (MAE), Ministerio de Agricultura, Ganadería, Acuacultura y Pesca (MAGAP); 2015. doi: 10.1017/CBO9781107415324.004

55. Perreault T. Developing Identities: Indigenous Mobilization, Rural Livelihoods, and Resource Access in Ecuadorian Amazonia. Ecumene. 2001;8.

56. MAE. Documentation of the information used for the establishment of Ecuador’s Forest Reference Emission Level. 2017 [cited 16 Aug 2017]. Available: http://suia.ambiente.gob.ec/web/suia/anexos-nivel-referencia

57. Sierra R. Dynamics and patterns of deforestation in the western Amazon: The Napo deforestation front, 1986–1996. Appl Geogr. 2000;20: 1–16. doi: 10.1016/S0143-6228(99)00014-4

58. Wasserstrom R, Southgate D. Deforestation, Agrarian Reform and Oil Development in Ecuador, 1964–1994. Nat Resour. 2013;04: 31–44. doi: 10.4236/nr.2013.41004

59. Barrera D. Gestión del territorio y manejo de bienes comunes en contextos extractivos: una aproximación al caso de las comunidades Kichwas del Cantón Arajuno en la Provincia de Pastaza, Ecuador. Facultad Latinoamericana de Ciencias Sociales (FLACSO). 2014.

60. FAO. Global Forest Resources Assessment 2015: Desk Reference. 2015. doi: 10.1002/2014GB005021

61. Bertoli S, Moraga JFH, Ortega F. Immigration policies and the ecuadorian exodus. World Bank Econ Rev. 2011;25: 57–76. doi: 10.1093/wber/lhr004

62. Gray CL, Bilsborrow RE. Consequences of out-migration for land use in rural Ecuador. Land use policy. 2014;36: 182–191. doi: 10.1016/j.landusepol.2013.07.006 24187416

63. R Development Core Team. The R Project for Statistical Computing, Version 3.4.3. GNU project; 2017. Available: http://www.r-project.org/

64. Pebesma E, Bivand R, Rowlingson B, Gomez-Rubio V, Hijmans R, Sumner M, et al. sp: Classes and Methods for Spatial Data, Version 1.2–5. 2017. Available: https://github.com/edzer/sp/ https://edzer.github.io/sp/

65. Hijmans R, Etten J van, Cheng J, Mattiuzzi M, Sumner M, Greenberg JA, et al. raster: Geographic Data Analysis and Modeling, Version 2.6–7. 2017. Available: http://www.rspatial.org/

66. Dowle M, Srinivasen A, Gorecki J, Short T, Lianoglou S, Antonyan E. data.table: Extension of “data frame”, Version 1.10.4–3. 2017. Available: http://r-datatable.com

67. Revolution Analytics, Weston S. foreach: Provides Foreach Looping Construct for R, Version 1.4.3. 2015. Available: https://cran.r-project.org/package=foreach

68. Wickham H, Chang W. Package ‘ggplot2’, version 2.2.1. 2016. doi: 10.1093/bioinformatics/btr406

69. QGIS Development Team. QGIS Geographic Information System. Open Source Geospatial Foundation Project; 2019. Available: https://www.qgis.org/en/site/index.html

70. Santos F, Meneses P, Hostert P. Monitoring long-term forest dynamics with scarce data: a multi-date classification implementation in the Ecuadorian Amazon. Manuscr Submitt Publ. 2018.

71. USGS. Earth Resources Observation and Science (EROS) Center Science Processing Architecture (ESPA) On Demand Interface. 2014 [cited 14 Jan 2017]. Available: https://espa.cr.usgs.gov

72. Puyravaud JP. Standardizing the calculation of the annual rate of deforestation. For Ecol Manage. 2003;177: 593–596. doi: 10.1016/S0378-1127(02)00335-3

73. Lambin EF, Turner BL, Geist HJ, Agbola SB, Angelsen A, Folke C, et al. The causes of land-use and land-cover change: moving beyond the myths. 2001;11: 261–269.

74. NOAA. Version 4 DMSP-OLS Nighttime Lights Time Series. 2019 [cited 5 Mar 2019]. Available: https://ngdc.noaa.gov/eog/dmsp/downloadV4composites.html

75. Ghosh T, Anderson SJ, Elvidge CD, Sutton PC. Using nighttime satellite imagery as a proxy measure of human well-being. Sustain. 2013;5: 4988–5019. doi: 10.3390/su5124988

76. Oda T, Maksyutov S. A very high-resolution (1km×1 km) global fossil fuel CO 2 emission inventory derived using a point source database and satellite observations of nighttime lights. Atmos Chem Phys. 2011;11: 543–556. doi: 10.5194/acp-11-543-2011

77. Proville J, Zavala-Araiza D, Wagner G. Night-time lights: A global, long term look at links to socio-economic trends. PLoS One. 2017;12: 1–12. doi: 10.1371/journal.pone.0174610 28346500

78. Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens Environ. 2017;202: 18–27. doi: 10.1016/j.rse.2017.06.031

79. INEC. Censo de Población y Vivienda 2010. Instituto Nacional de Estadísticas y Censos; 2010. Available: http://www.inec.gob.ec/estadisticas/

80. INEC. Censo de Población y Vivienda 2001. Instituto Nacional de Estadísticas y Censos; 2001. Available: http://www.inec.gob.ec/estadisticas/

81. Carr D. Population and deforestation: Why rural migration matters. Prog Hum Geogr. 2009;33: 355–378. doi: 10.1177/0309132508096031 20485541

82. Mennis J. Generating Surface Models of Population Using Dasymetric Mapping. Prof Geogr. 2003;55: 31–42.

83. SIGTIERRAS U-M-P. Metodología Accesibilidad. Proyecto: “Levantamiento de cartografía temática escala 1:25000, lote 2”. Temáticas Nacionales. 2015. Available: http://metadatos.sigtierras.gob.ec:8080/geonetwork/srv/spa/catalog.search#/search?resultType = details&any = accesibilidad&from = 1&to = 20&sortBy = relevance

84. Pan W, Walsh SJ, Bilsborrow RE, Frizzelle BG, Erlien CM, Baquero F. Farm-level models of spatial patterns of land use and land cover dynamics in the Ecuadorian Amazon. Agric Ecosyst Environ. 2004;101: 117–134. doi: 10.1016/j.agee.2003.09.022

85. Langford M, Unwin DJ. Generating and mapping population density surfaces within a geographical information system. Cartogr J. 1994;31: 21–26. doi: 10.1179/000870494787073718 12288211

86. Gray CL, Bilsborrow RE, Bremner JL, Lu F. Indigenous Land Use in the Ecuadorian Amazon: A Cross-cultural and Multilevel Analysis. Hum Ecol. 2008;36: 97–109. doi: 10.1007/s10745-007-9141-6

87. Jian W. The relationship between culture and language. ELT J. 2000;54/4: 328–334.

88. SNI. Archivos de Información Geográfica—Sistema Nacional de Información (SNI). 2017 [cited 20 Dec 2017]. Available: http://sni.gob.ec/coberturas

89. NOAA. Version 4 DMSP-OLS Nighttime Lights Time Series. 2019.

90. Gollini I, Lu B, Charlton M, Brunsdon C, Harris P. GWmodel: an R Package for Exploring Spatial Heterogeneity using Geographically Weighted Models. 2013. doi: 10.1080/10095020.2014.917453

91. Gao J, Li S. Detecting spatially non-stationary and scale-dependent relationships between urban landscape fragmentation and related factors using Geographically Weighted Regression. Appl Geogr. 2011;31: 292–302. doi: 10.1016/j.apgeog.2010.06.003

92. Guo L, Ma Z, Zhang L. Comparison of bandwidth selection in application of geographically weighted regression: a case study. Can J For Res. 2008;38: 2526–2534. doi: 10.1139/X08-091

93. Farber S, Páez A. A systematic investigation of cross-validation in GWR model estimation: Empirical analysis and Monte Carlo simulations. J Geogr Syst. 2007;9: 371–396. doi: 10.1007/s10109-007-0051-3

94. Chiu D. Machine Learning with R Cookbook. Birmingham, UK: Packt Publishing Ltd.; 2015.

95. Belgiu M, Drăgu L. Random forest in remote sensing: A review of applications and future directions. ISPRS J Photogramm Remote Sens. 2016;114: 24–31. doi: 10.1016/j.isprsjprs.2016.01.011

96. Strobl C, Malley J, Gerhard T. An Introduction to Recursive Partitioning: Rationale, Application Psychol Methods. Psychol Methods. 2009;14: 323–348. doi: 10.1037/a0016973 19968396

97. Binbin L, Harris P, Charlton M, Bruns-don C, Nakaya T, Gollini I. GWmodel: Geographically-Weighted Models, Version 2.0–5. 2017. Available: http://gwr.nuim.ie/

98. Genuer R, Poggi J, Tuleau-malot C. Variable selection using Random Forests. Pattern Recognit Lett. 2012;31: 2225–2236.

99. Saary MJ. Radar plots: a useful way for presenting multivariate health care data. J Clin Epidemiol. 2008;61: 311–317. doi: 10.1016/j.jclinepi.2007.04.021 18313553

100. Scrucca L, Fop M, Murphy TB, Raftery AE. mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models. R J. 2016;8: 289–317. Available: https://www.ncbi.nlm.nih.gov/pubmed/27818791 27818791

101. Charrad M, Ghazzali N, Boiteau V, Niknafs A. NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set. J Stat Softw. 2014;61. doi: 10.18637/jss.v061.i06

102. Wilcoxon F. Individual Comparisons by Ranking Methods. Biometrics Bull. 1945;1: 80–83. doi: 10.2307/3001968

103. Cliff N. Ordinal Methods for Behavioral Data Analysis. Taylor & Francis Group; 2016. Available: https://books.google.com.ec/books?id = yPvbjwEACAAJ

104. Romano J, Kromrey JD, Coraggio J, Skowronek J, Devine L. Exploring methods for evaluating group differences on the NSSE and other surveys: Are the t-test and Cohen’s d indices the most appropriate choices? Annu Meet South Assoc Institutional Res. 2006; 14–17. doi: 10.1017/CBO9781107415324.004

105. Hermosilla T, Wulder MA, White JC, Coops NC, Hobart GW. An integrated Landsat time series protocol for change detection and generation of annual gap-free surface reflectance composites. Remote Sens Environ. 2015;158: 220–234. doi: 10.1016/j.rse.2014.11.005

106. Oeser J, Pflugmacher D, Senf C, Heurich M, Hostert P. Using intra-annual Landsat time series for attributing forest disturbance agents in Central Europe. Forests. 2017;8. doi: 10.3390/f8070251

107. Rufin P, Müller H, Pflugmacher D, Hostert P. Land use intensity trajectories on Amazonian pastures derived from Landsat time series. Int J Appl Earth Obs Geoinf. 2015;41: 1–10. doi: 10.1016/j.jag.2015.04.010

108. Griffiths P, Nendel C, Pickert J, Hostert P. Towards national-scale characterization of grassland use intensity from integrated Sentinel-2 and Landsat time series. Remote Sens Environ. 2019; 1–12. doi: 10.1016/j.rse.2019.03.017

109. Sirro L, Häme T, Rauste Y, Kilpi J, Hämäläinen J, Gunia K, et al. Potential of different optical and SAR data in forest and land cover classification to support REDD+ MRV. Remote Sens. 2018;10. doi: 10.3390/rs10060942

110. Dostálová A, Wagner W, Milenković M, Hollaus M. Annual seasonality in Sentinel-1 signal for forest mapping and forest type classification. Int J Remote Sens. 2018;39: 7738–7760. doi: 10.1080/01431161.2018.1479788

111. Hu Q, Sulla-Menashe D, Xu B, Yin H, Tang H, Yang P, et al. A phenology-based spectral and temporal feature selection method for crop mapping from satellite time series. Int J Appl Earth Obs Geoinf. 2019;80: 218–229. doi: 10.1016/j.jag.2019.04.014

112. Ravanelli R, Nascetti A, Cirigliano RV, Di Rico C, Leuzzi G, Monti P, et al. Monitoring the impact of land cover change on surface urban heat island through Google Earth Engine: Proposal of a global methodology, first applications and problems. Remote Sens. 2018;10: 1–21. doi: 10.3390/rs10091488

113. Myers A. Camp Delta, Google Earth and the ethics of remote sensing in archaeology. World Archaeol. 2010;42: 455–467. doi: 10.1080/00438243.2010.498640

114. Lo CP, Yang X. Drivers of Land-Use / Land-Cover Changes and Dynamic Modeling for the Atlanta, Georgia Metropolitan Area. October. 2002;68: 1073–1082. Cited By (since 1996) 5\rExport Date 23 November 2011

115. Reibel M, Agrawal A. Areal interpolation of population counts using pre-classified land cover data. Popul Res Policy Rev. 2007;26: 619–633. doi: 10.1007/s11113-007-9050-9

116. Pappalardo SE, De Marchi M, Ferrarese F. Uncontacted Waorani in the Yasuní Biosphere Reserve: Geographical Validation of the Zona Intangible Tagaeri Taromenane (ZITT). PLoS One. 2013;8: 21–25. doi: 10.1371/journal.pone.0066293 23840436

117. Schillinger K, Lycett SJ. The Flow of Culture: Assessing the Role of Rivers in the Inter-community Transmission of Material Traditions in the Upper Amazon. J Archaeol Method Theory. 2019;26: 135–154. doi: 10.1007/s10816-018-9369-z

118. Fotheringham A, Charlton ME, Brunsdon C. Geographically weighted regression: a natural evolution of the expansion method for spatial data analysis. Environ Plan A. 1998;30: 1905–1927. doi: 10.1068/a301905

119. Tu J. Spatially varying relationships between land use and water quality across an urbanization gradient explored by geographically weighted regression. Appl Geogr. 2011;31: 376–392. doi: 10.1016/j.apgeog.2010.08.001

120. Wheeler D. Diagnostic tools and a remedial method for collinearity in geographically weighted regression. Environ Plan A. 2007;39: 2464–2481. doi: 10.1068/a38325

121. Harris P, Fotheringham AS, Juggins S. Robust Geographically Weighted Regression: A Technique for Quantifying Spatial Relationships Between Freshwater Acidification Critical Loads and Catchment Attributes. Ann Assoc Am Geogr. 2010;100: 286–306. doi: 10.1080/00045600903550378

122. Ellenberg JH. Selection bias in observational and experimental studies. Stat Med. 1994;13: 557–567. doi: 10.1002/sim.4780130518 8023035

123. J C. The Earth is Round (p < .05). Am Psychol. 2004;49: 997–1003. Available: http://d.wanfangdata.com.cn/NSTLQK_10.1037-0003-066X.49.12.997.aspx%0Apapers2://publication/livfe/id/199

124. Kim J, Bang H. Three common misuses of P values. Dent Hypotheses. 2016;7: 73. doi: 10.4103/2155-8213.190481 27695640

125. Cho S, Lambert DM, Kim SG, Jung S. Extreme Coefficients in Geographically Weighted Regression and Their Effects on Mapping. GIScience Remote Sens. 2009;46: 273–288. doi: 10.2747/1548-1603.46.3.273

126. Tantithamthavorn C, Hassan AE, Matsumoto K. The Impact of Class Rebalancing Techniques on the Performance and Interpretation of Defect Prediction Models. IEEE Trans Softw Eng. 2018; 1–20. doi: 10.1109/TSE.2018.2876537

127. Li L. Geographically weighted machine learning and downscaling for high-resolution spatiotemporal estimations of wind speed. Remote Sens. 2019;11. doi: 10.3390/rs11111378

128. Páez A, Farber S, Wheeler D. A simulation-based study of geographically weighted regression as a method for investigating spatially varying relationships. Environ Plan A. 2011;43: 2992–3010. doi: 10.1068/a44111

129. Wheeler DC. Geographically weighted regression. Handb Reg Sci. 2019; 1–27.

130. Harris P, Brunsdon C, Fotheringham AS. Links, comparisons and extensions of the geographically weighted regression model when used as a spatial predictor. Stoch Environ Res Risk Assess. 2011;25: 123–138. doi: 10.1007/s00477-010-0444-6

131. Aide TM, Clark LM, Grau R, López-Carr D, Levy AM, Redo D, et al. Deforestation and Reforestation of Latin America and the Caribbean (2001–2010). Biotropica. 2012;1: 1–10.

132. Fagua JC, Baggio JA, Ramsey RD. Drivers of forest cover changes in the Chocó‐Darien Global Ecoregion of South America. Ecosphere. 2019;10: e02648. doi: 10.1002/ecs2.2648

133. Pirker J, Mosnier A, Kraxner F, Havlík P, Obersteiner M. What are the limits to oil palm expansion? Glob Environ Chang. 2016;40: 73–81. doi: 10.1016/j.gloenvcha.2016.06.007

134. Castro M, Sierra R, Calva O, Camacho J, Lopez F. Zonas de Procesos Homogéneos de Deforestación del Ecuador. Factores promotores y tendencias al 2020. Quito—Ecuador: Programa GESOREN-GIZ y Ministerio de Ambiente del Ecuador.; 2013. doi: 10.13140/2.1.3210.2081

135. Finer M, Jenkins CN. Proliferation of hydroelectric dams in the andean amazon and implications for andes-amazon connectivity. PLoS One. 2012;7: 1–9. doi: 10.1371/journal.pone.0035126 22529979

136. Torres B, Maza OJ, Aguirre P, Hinojosa L, Günter S. The Contribution of Traditional Agroforestry to Climate Change Adaptation in the Ecuadorian Amazon: The Chakra System. In: Leal Filho W, editor. Handbook of Climate Change Adaptation. Berlin, Heidelberg: Springer Berlin Heidelberg; 2015. pp. 1973–1994. doi: 10.1007/978-3-642-38670-1_102

137. Custode E, Sourdat M. Paisajes y suelos de la Amazonía ecuatoriana: entre la conservación y la explotación. Cult del Banco Cent Ecuador. 1986;8: 325–338.

138. Bonilla-Bedoya S, López-Ulloa M, Vanwalleghem T, Herrera-Machuca MÁ. Effects of Land Use Change on Soil Quality Indicators in Forest Landscapes of the Western Amazon. Soil Sci. 2017;182. Available: https://journals.lww.com/soilsci/Fulltext/2017/04000/Effects_of_Land_Use_Change_on_Soil_Quality.2.aspx

139. Kirk D. Demographic Transition Theory. Popul Stud (NY). 1996;50: 361–387. doi: 10.1080/0032472031000149536 11618374

140. Godoy R, Groff S, O’Neill K. The Role of Education in Neotropical Deforestation: Household Evidence from Amerin dians in Honduras. Hum Ecol. 1998;26. doi: 0300-7839/98/1200-0649

141. Dolisca F, McDaniel JM, Teeter LD, Jolly CM. Land tenure, population pressure, and deforestation in Haiti: The case of Forêt des Pins Reserve. J For Econ. 2007;13: 277–289. doi: 10.1016/j.jfe.2007.02.006

142. Moran EF, Siqueira A, Brondizio E. Household Demographic Structure and Its Relationship to Deforestation in the Amazon Basin. In: Fox J, Rindfuss RR, Walsh SJ, Mishra V, editors. People and the Environment: Approaches for Linking Household and Community Surveys to Remote Sensing and GIS. Boston, MA: Springer US; 2003. pp. 61–89. doi: 10.1007/0-306-48130-8_3

143. Barbieri AF, Carr DL. Gender-specific out-migration, deforestation and urbanization in the Ecuadorian Amazon. Glob Planet Change. 2005;47: 99–110. doi: 10.1016/j.gloplacha.2004.10.005 19657469

144. Sellers S. HHS Public Access. 2018;38: 424–447. doi: 10.1007/s11111-017-0275-1.Family

145. Sierra R. Patrones y factores de deforestación en el Ecuador continental, 1990–2010. Y un acercamiento a los próximos 10 años. Conservación Internacional Ecuador, Forest Trends and Ministry of Environment of Ecuador; 2013. p. 51. Available: http://www.forest-trends.org/documents/files/doc_3396.pdf

146. Villamor GB, Desrianti F, Akiefnawati R, Amaruzaman S, van Noordwijk M. Gender influences decisions to change land use practices in the tropical forest margins of Jambi, Indonesia. Mitig Adapt Strateg Glob Chang. 2014;19: 733–755. doi: 10.1007/s11027-013-9478-7

147. Hutchison HC, Vallejo I. La deforestación y la participación de mujeres en el manejo de recursos naturales: una comparación de casos de estudio de comunidades indígenas y colonas en la provincia de Napo, Ecuador. 2016.


Článek vyšel v časopise

PLOS One


2019 Číslo 12
Nejčtenější tento týden
Nejčtenější v tomto čísle
Kurzy

Zvyšte si kvalifikaci online z pohodlí domova

KOST
Koncepce osteologické péče pro gynekology a praktické lékaře
nový kurz
Autoři: MUDr. František Šenk

Sekvenční léčba schizofrenie
Autoři: MUDr. Jana Hořínková

Hypertenze a hypercholesterolémie – synergický efekt léčby
Autoři: prof. MUDr. Hana Rosolová, DrSc.

Svět praktické medicíny 5/2023 (znalostní test z časopisu)

Imunopatologie? … a co my s tím???
Autoři: doc. MUDr. Helena Lahoda Brodská, Ph.D.

Všechny kurzy
Kurzy Podcasty Doporučená témata Časopisy
Přihlášení
Zapomenuté heslo

Zadejte e-mailovou adresu, se kterou jste vytvářel(a) účet, budou Vám na ni zaslány informace k nastavení nového hesla.

Přihlášení

Nemáte účet?  Registrujte se

#ADS_BOTTOM_SCRIPTS#