Land cover change and carbon stores in a tropical montane cloud forest in the Sierra Madre Oriental, Mexico
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摘要
Tropical montane cloud forest is one of the ecosystems with the highest biomass worldwide, representing an important carbon store. Globally its deforestation index is –1.1%, but in Mexico it is higher than –3%. Carbon estimates are scarce globally, particularly in Mexico. The objective of this study was to simulate future land-cover scenarios for the Sierra Madre Oriental in Mexico, by analyzing past forest cover changes. Another objective was to estimate stored carbon in the two study areas. These objectives involve the generation of information that could be useful inputs to anti-deforestation public policy such as the REDD+ strategy. Remote sensing was used to measure land cover change and estimate carbon stocks. Satellite images from 2015, 2000 and 1986 were used, and Dinamica EGO freeware generatedmodels of future projections. Between 1986 and 2015, 5171 ha of forest were converted to pasture. The annual deforestation rates were –1.5% for Tlanchinol and –1.3% for the San Bartolo Tutotepec sites. Distance to roads and marginalization were highly correlated with deforestation. By 2030, an estimated 3608 ha of forest in these sites will have been converted to pasture. Stored carbon was estimated at 16.35 Mg C ha-1 for the Tlanchinol site and 12.7 Mg C ha-1 for the San Bartolo site. In the Sierra Madre Oriental deforestation due to land cover change(–1.4%) is higher than levels reported worldwide. Besides having high values of stored carbon(14.5 Mg C ha-1), these forests have high biodiversity. The models' outputs show that the deforestation process will continue if action is not taken to avoid the expansion of livestock pasturing. This can be done by paying incentives for forest conservation to the owners of the land. The results suggest that REDD+ is currently the most viable strategy for reducing deforestation rates in tropical montane cloud forests in Sierra Madre Oriental.
Tropical montane cloud forest is one of the ecosystems with the highest biomass worldwide, representing an important carbon store. Globally its deforestation index is –1.1%, but in Mexico it is higher than –3%. Carbon estimates are scarce globally, particularly in Mexico. The objective of this study was to simulate future land-cover scenarios for the Sierra Madre Oriental in Mexico, by analyzing past forest cover changes. Another objective was to estimate stored carbon in the two study areas. These objectives involve the generation of information that could be useful inputs to anti-deforestation public policy such as the REDD+ strategy. Remote sensing was used to measure land cover change and estimate carbon stocks. Satellite images from 2015, 2000 and 1986 were used, and Dinamica EGO freeware generatedmodels of future projections. Between 1986 and 2015, 5171 ha of forest were converted to pasture. The annual deforestation rates were –1.5% for Tlanchinol and –1.3% for the San Bartolo Tutotepec sites. Distance to roads and marginalization were highly correlated with deforestation. By 2030, an estimated 3608 ha of forest in these sites will have been converted to pasture. Stored carbon was estimated at 16.35 Mg C ha-1 for the Tlanchinol site and 12.7 Mg C ha-1 for the San Bartolo site. In the Sierra Madre Oriental deforestation due to land cover change(–1.4%) is higher than levels reported worldwide. Besides having high values of stored carbon(14.5 Mg C ha-1), these forests have high biodiversity. The models' outputs show that the deforestation process will continue if action is not taken to avoid the expansion of livestock pasturing. This can be done by paying incentives for forest conservation to the owners of the land. The results suggest that REDD+ is currently the most viable strategy for reducing deforestation rates in tropical montane cloud forests in Sierra Madre Oriental.
Tropical montane cloud forest is one of the ecosystems with the highest biomass worldwide, representing an important carbon store. Globally its deforestation index is –1.1%, but in Mexico it is higher than –3%. Carbon estimates are scarce globally, particularly in Mexico. The objective of this study was to simulate future land-cover scenarios for the Sierra Madre Oriental in Mexico, by analyzing past forest cover changes. Another objective was to estimate stored carbon in the two study areas. These objectives involve the generation of information that could be useful inputs to anti-deforestation public policy such as the REDD+ strategy. Remote sensing was used to measure land cover change and estimate carbon stocks. Satellite images from 2015, 2000 and 1986 were used, and Dinamica EGO freeware generatedmodels of future projections. Between 1986 and 2015, 5171 ha of forest were converted to pasture. The annual deforestation rates were –1.5% for Tlanchinol and –1.3% for the San Bartolo Tutotepec sites. Distance to roads and marginalization were highly correlated with deforestation. By 2030, an estimated 3608 ha of forest in these sites will have been converted to pasture. Stored carbon was estimated at 16.35 Mg C ha-1 for the Tlanchinol site and 12.7 Mg C ha-1 for the San Bartolo site. In the Sierra Madre Oriental deforestation due to land cover change(–1.4%) is higher than levels reported worldwide. Besides having high values of stored carbon(14.5 Mg C ha-1), these forests have high biodiversity. The models' outputs show that the deforestation process will continue if action is not taken to avoid the expansion of livestock pasturing. This can be done by paying incentives for forest conservation to the owners of the land. The results suggest that REDD+ is currently the most viable strategy for reducing deforestation rates in tropical montane cloud forests in Sierra Madre Oriental.
引文
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Corbera E,Estrada M,Brown K(2010)Reducing greenhouse gas emissions from deforestation in developing countries:revisiting the assumptions.Climatic Change 3-4:355-388.https://doi.org/10.1007/s10584-009-977
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Karafyllidis I,Thanailakis A(1997)A model for predicting forest fire spreading using cellular automata.Ecological Modelling99:87-97.https://doi.org/10.1016/S0304-3800(96)01942-4
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Leija-Loredo EG,Pavón NP(2017)The northernmost tropical rain forest of the Americas:Endangered by agriculture expansion.Tropical Ecology 3:641-652.
Mu?oz-Pi?a C,Alarcón G,Fernández JC,et al.(2003)Pixel patterns of deforestation in Mexico.Mexico:INE-Semarnat(Working paper).Mexico.D.F.p 26.
Mas JF(2005)Change estimates by map comparison:A method to reduce erroneous changes due to positional error.Transactions in GIS 4:619-629.https://doi.org/10.1111/j.1467-9671.2005.00238.x
Mas JF,Flamenco-Sandoval A(2011)Modeling changes in coverage/land use in a tropical region of Mexico.GeoTropico1:1-24.(In Spanish)
Meneses-Tovar CL.(2011).The standardized differential index of vegetation as an indicator of forest degradation.Unasylva,238:38-46.(In Spanish)
Navarrete D,Méndez D,Flamenco A,et al.(2010)Current situation,fragmentation,priority conservation areas and main threats of the mesophilic mountain forest of Chiapas.In:Farrera MáP,Cruz CT,Rivera ES(eds.),The mountain mesophilic forests in Chiapas.University of Sciences and Arts of Chiapas,Tuxtla Gutiérrez,Chiapas,Mexico.pp 295-326.(In Spanish)
Newsham A,Pulido MT,Ulrichs M,et al.(2018)Ecosystemsbased adaptation:Are we being conned?Evidence from Mexico.Global Environmental Change 49:14-26.https://doi.org/10.1016/j.gloenvcha.2018.01.001
Nolte C,Warou YLP,Munger J,et al.(2017).Conditions influencing the adoption of effective anti-deforestation policies in South America’s commodity frontiers.Global Environmental Change 43:1-14.https://doi.org/10.1016/j.gloenvcha.2017.01.001
Olofsson P,Foody GM,Herold M,et al.(2014)Good practices for estimating area and assessing accuracy of land change.Remote Sensing of Environment 148:42-57.https://doi.org/10.1016/j.rse.2014.02.015
Pijanowski BC,Brown DG,Shellito BA,et al.(2002)Using neural networks and GIS to forecast land use changes:A land transformation model.Computers,Environment and Urban Systems 26:553-576.https://doi.org/10.1016/S0198-9715(01)00015-1
Pérez-Verdín G,Kim YS,Hospodarsky,et al.(2009)Factors driving deforestation in common pool resources in Northern Mexico.Journal of Environmental Management 90:331-340.https://doi.org/10.1016/j.jenvman.2007.10.001
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Rodríguez-Laguna R,Jiménez PJ,Aguirre CO,et al.(2006)Estimation of carbon stored in a cloud forest in Tamaulipas,Mexico.UANL SCIENCE 2:179-187.(In Spanish)
Ramírez-García AG,Castillo-Escalante IC(2009)The socioeconomic environment of the municipalities with the presence of mesophilic mountain forests in the state of Hidalgo.In:Monterroso Rivas AI(eds.),The mountain mesophile forest in the state of Hidalgo:ecological perspective in the face of climate change.1st ed.Chapingo Autonomous University.Mexico.pp 27-38.(In Spanish)
Ruíz YM,Mendoza ME,Huicochea GES,et al.(2016)Spatiotemporal dynamics of the cloud forest and its successional status in the state of Michoacán,Mexico.Geography and Geographic Information Systems(GEOSIG)8:233-247.(In Spanish)
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Sánchez-Ramos G,Dirzo G(2014)The mountain mesophile forest:a threatened priority ecosystem.In.Gual-Díaz M and Rendón-Correa A(eds.),Mesophilic mountain forests of Mexico:diversity,ecology and management.National Commission for the Knowledge and Use of Biodiversity.Mexico.p 352.(In Spanish)
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Buss C,Manh-Pham C,Quang-Nguyen T,et al.(2013)Field Dialogue on REDD+Benefit Sharing,Co-Chairs’Summary Report.The Forests Dialogue.pp 24-27.
Burgheimer J,Wilske B,Maseyk K,et al(2006).Relationships between Normalized Difference Vegetation Index(NDVI)and carbon fluxes of biologic soil crusts assessed by ground measurements.Journal of Arid Environments 64:651-669.https://doi.org/10.1016/j.jaridenv.2005.06.025
Card DH(1982)Using known map category marginal frequencies to improve estimates of thematic map accuracy.Photogrammetric Engineering&Remote Sensing 3:431-439.
Chuvieco E(1998)The temporal factor in remote sensing:phenomenological evolution and analysis of changes.Remote Sensing Magazine 10:1-9.(In Spanish)
CONABIO(2010)The Mountain Mesophilic Forest in Mexico:Threats and Opportunities for its Conservation and Sustainable Management.National Commission for the Knowledge and Use of Biodiversity.Mexico D.F.p 197.Available online at:https://www.biodiversidad.gob.mx/ecosistemas/pdf/BMM_parte%201.pdf,accessed on 2016-08-27(In Spanish)
CONAFOR(2010)National Forestry and Soil Inventory,manual and procedures for field sampling,resampling 2010.Zapopan,Jalisco,Mexico.p 140.Available online at:http://187.218.230.5/media/library/get/004/4164/inventario-nacionalfore.pd,(accessed on 2016-07-14)(In Spanish)
CONAPO(2010)National Population Council.Mexico:Ministry of the Interior.Available online at:http://www.conapo.gob.mx/,accessed on 2016-03-19(In Spanish).
Corbera E,Estrada M,Brown K(2010)Reducing greenhouse gas emissions from deforestation in developing countries:revisiting the assumptions.Climatic Change 3-4:355-388.https://doi.org/10.1007/s10584-009-977
Corbera E,Schroeder H(2011)Governing and implementing REDD+.Environmental Science&Policy 14:89-99.https://doi.org/10.1016/j.envsci.2010.11.002
CONAFOR-UACh(2013)National base line of land degradation and desertification.Final report.National Forestry Commission and Autonomous University Chapingo.Zapopan,Jalisco,Mexico.Available online at:http://www.semarnat.gob.mx/sites/default/files/documentos/fomento/documento s/degradacion-tierras-desertificacion2.pdf,accessed on 2017-02-19(In Spanish).
Doumenge C,Gilmour D,Pérez MR,et al.(1995)Tropical montane cloud forests:conservation status and management issues.In Hamilton LS,Juvik JO,Scatena FN(eds)(1995)Tropical montane cloud forests.Springer,New York,NY.pp24-37.https://doi.org/10.1007/978-1-4612-2500-3_2
FAO(1996)Forest resources assessment 1990.Survey of tropical forest cover and study of change processes,Rome.https://www.jstor.org/stable/42607210
FAO(2010)Food and Agriculture Organization.FAO Forestry Paper 159:Impact of the global forest industry on atmospheric greenhouse gases,Rome.p 86.Available online at:http://www.fao.org/docrep/012/i1580e/i1580e00.pdf,accessed on 2017-02-20.
FRA(2015)Evaluación de los Recursos Forestales Mundiales.Food and Agriculture Organization of the United Nations.Data compendium.Rome.p 253.Available online at:http://www.fao.org/3/a-i4808s.pdf,accessed on 2016-05-28
Garbulsky MF,Pe?uelas J,Ourcival JM,et al.(2008)Estimation of the efficiency of the use of radiation in Mediterranean forests from MODIS data.Use of the Photochemical Reflectance Index(PRI).Ecosystems 3:89-97.(In Spanish).
Geist HJ,Lambin EF(2001)What drives tropical deforestation.LUCC Report series 4:116.Available online at:https://www.pik-potsdam.de/members/cramer/teaching/0607/Geist_2001_LUCC_Report.pdf,accessed on 2017-04-21.
González-Espinosa M,Meave JA,Ramírez-Marcial N,et al.(2012)The cloud forests of Mexico:conservation and restoration of its arboreal component.Ecosystems Magazine1-2:36-52.(In Spanish)
Hamilton LS(1996)A Campaign for Cloud Forests:Unique and Valuable Ecosystems at Risk.The George Wright Forum 13:29-39.
Hamilton LS,Juvik JO,Scatena FN(Eds.)(2012)Tropical montane cloud forests(Vol.110).Springer Science&Business Media,New York.p 401.https://dx.doi.org/10.1007/978-1-4612-2500-3.
Harris NL,Brown S,Hagen SC,et al.(2012)Baseline map of carbon emissions from deforestation in tropical regions.Science 336(6088):1573-1576.https://dx.doi.org/10.1126/science.1217962.
INEGI(2005)National Institute of Statistics,Geography and Informatics,Letter of Current Use of Soil and Vegetation,Series III(2002),scale 1:250,000.INEGI,Mexico.(In Spanish)
INEGI(2013)National Land and Vegetation Use Set:Scale 1:250 000(vector).Series V.DGG-INEGI Mexico.(In Spanish).
Karafyllidis I,Thanailakis A(1997)A model for predicting forest fire spreading using cellular automata.Ecological Modelling99:87-97.https://doi.org/10.1016/S0304-3800(96)01942-4
Le QuéréC,Andrew RM,Friedlingstein P,et al.(2016)Global Carbon Budget 2016.Earth System Science Data 8:605-649.https://doi.org/10.5194/essd-8-605-2016
Leija-Loredo EG,Reyes-Hernández H,Fortanelli J,et al.(2011)Current situation of the cloud forest in the state of San Luis Potosí.Research and Science 53:3-11.(In Spanish)
Leija-Loredo EG,Pavón NP(2017)The northernmost tropical rain forest of the Americas:Endangered by agriculture expansion.Tropical Ecology 3:641-652.
Mu?oz-Pi?a C,Alarcón G,Fernández JC,et al.(2003)Pixel patterns of deforestation in Mexico.Mexico:INE-Semarnat(Working paper).Mexico.D.F.p 26.
Mas JF(2005)Change estimates by map comparison:A method to reduce erroneous changes due to positional error.Transactions in GIS 4:619-629.https://doi.org/10.1111/j.1467-9671.2005.00238.x
Mas JF,Flamenco-Sandoval A(2011)Modeling changes in coverage/land use in a tropical region of Mexico.GeoTropico1:1-24.(In Spanish)
Meneses-Tovar CL.(2011).The standardized differential index of vegetation as an indicator of forest degradation.Unasylva,238:38-46.(In Spanish)
Navarrete D,Méndez D,Flamenco A,et al.(2010)Current situation,fragmentation,priority conservation areas and main threats of the mesophilic mountain forest of Chiapas.In:Farrera MáP,Cruz CT,Rivera ES(eds.),The mountain mesophilic forests in Chiapas.University of Sciences and Arts of Chiapas,Tuxtla Gutiérrez,Chiapas,Mexico.pp 295-326.(In Spanish)
Newsham A,Pulido MT,Ulrichs M,et al.(2018)Ecosystemsbased adaptation:Are we being conned?Evidence from Mexico.Global Environmental Change 49:14-26.https://doi.org/10.1016/j.gloenvcha.2018.01.001
Nolte C,Warou YLP,Munger J,et al.(2017).Conditions influencing the adoption of effective anti-deforestation policies in South America’s commodity frontiers.Global Environmental Change 43:1-14.https://doi.org/10.1016/j.gloenvcha.2017.01.001
Olofsson P,Foody GM,Herold M,et al.(2014)Good practices for estimating area and assessing accuracy of land change.Remote Sensing of Environment 148:42-57.https://doi.org/10.1016/j.rse.2014.02.015
Pijanowski BC,Brown DG,Shellito BA,et al.(2002)Using neural networks and GIS to forecast land use changes:A land transformation model.Computers,Environment and Urban Systems 26:553-576.https://doi.org/10.1016/S0198-9715(01)00015-1
Pérez-Verdín G,Kim YS,Hospodarsky,et al.(2009)Factors driving deforestation in common pool resources in Northern Mexico.Journal of Environmental Management 90:331-340.https://doi.org/10.1016/j.jenvman.2007.10.001
Ponce-Reyes R,Reynoso-Rosales VH,Watson JE,et al.(2012)Vulnerability of cloud forest reserves in Mexico to climate change.Nature Climate Change 6:448-452.https://doi.org/10.1038/nclimate1453
Rodríguez-Laguna R,Jiménez PJ,Aguirre CO,et al.(2006)Estimation of carbon stored in a cloud forest in Tamaulipas,Mexico.UANL SCIENCE 2:179-187.(In Spanish)
Ramírez-García AG,Castillo-Escalante IC(2009)The socioeconomic environment of the municipalities with the presence of mesophilic mountain forests in the state of Hidalgo.In:Monterroso Rivas AI(eds.),The mountain mesophile forest in the state of Hidalgo:ecological perspective in the face of climate change.1st ed.Chapingo Autonomous University.Mexico.pp 27-38.(In Spanish)
Ruíz YM,Mendoza ME,Huicochea GES,et al.(2016)Spatiotemporal dynamics of the cloud forest and its successional status in the state of Michoacán,Mexico.Geography and Geographic Information Systems(GEOSIG)8:233-247.(In Spanish)
Ramírez-Bautista A,Sánchez-González A,Sánchez-Rojas G,et al.(eds.)(2017)Biodiversity of the state of Hidalgo.Volume II.Autonomous University of the State of Hidalgo/National Council of Science and Technology.Pachuca,Hidalgo,Mexico.pp 368.(In Spanish)
Ramírez BH,Teuling AJ,Ganzeveld L,et al.(2017)Tropical Montane Cloud Forests:Hydrometeorological variability in three neighbouring catchments with different forest cover.Journal of Hydrology 552:151-167.https://doi.org/10.1016/j.jhydrol.2017.06.023
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