利用遥感技术建立墨西哥热带地上生物量模型(英文)

编号 zgly0001585834

文献类型 期刊论文

文献题名 利用遥感技术建立墨西哥热带地上生物量模型(英文)

作者 AGUIRRE-SALADOCarlosArturo  TREVIO-GARZAEduardoJavier  AGUIRRE-CALDERóNOscarAlberto  JIMéNEZ-PéREZJavier  GONZáLEZ-TAGLEMarcoAurelio  VALDEZ-LAZALDEJoséRené  MIRANDA-ARAGóNLiliana  AGUIRRE-SALADOAlejandroIván 

作者单位 AutonomousUniversityofNuevoLeon  LinaresNL67700  Mexico  AutonomousUniversityofSanLuisPotosi  SLP78290  Mexico  TheCollegeofPostgraduates  TexcocoMEX56230  Mexico 

母体文献 Journal of Geographical Sciences 

年卷期 2012年04期

年份 2012 

分类号 Q948 

关键词 MODIS  MCD43A4  MOD44B  forestinventory  regression 

文摘内容 Spatially-explicit estimation of aboveground biomass(AGB) plays an important role to generate action policies focused in climate change mitigation,since carbon(C) retained in the biomass is vital for regulating Earth’s temperature.This work estimates AGB using both chlorophyll(red,near infrared) and moisture(middle infrared) based normalized vegetation indices constructed with MCD43A4 MODerate-resolution Imaging Spectroradiometer(MODIS) and MOD44B vegetation continuous fields(VCF) data.The study area is located in San Luis Potosí,Mexico,a region that comprises a part of the upper limit of the intertropical zone.AGB estimations were made using both individual tree data from the National Forest Inventory of Mexico and allometric equations reported in scientific literature.Linear and nonlinear(expo-nential) models were fitted to find their predictive potential when using satellite spectral data as explanatory variables.Highly-significant correlations(p = 0.01) were found between all the explaining variables tested.NDVI62,linked to chlorophyll content and moisture stress,showed the highest correlation.The best model(nonlinear) showed an index of fit(Pseudo-r2) equal to 0.77 and a root mean square error equal to 26.00 Mg/ha using NDVI62 and VCF as explanatory variables.Validation correlation coefficients were similar for both models:lin-ear(r = 0.87**) and nonlinear(r = 0.86**).