Modeling the spatial distribution of above-ground carbon in Mexican coniferous forests using remote sensing and a geostatistical approach
Modeling the spatial distribution of above-ground carbon in Mexican coniferous forests using remote sensing and a geostatistical approach
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DOI：
10.1016/j.jag.2014.02.005
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发表时间：
2014-08
期刊：
影响因子：
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通讯作者：
J. M. Galeana-Pizaña;A. López-Caloca;P. López-Quiroz;J. L. Silván-Cárdenas;S. Couturier
中科院分区：
文献类型：
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作者：
J. M. Galeana-Pizaña;A. López-Caloca;P. López-Quiroz;J. L. Silván-Cárdenas;S. Couturier
Forest conservation is considered an option for mitigating the effect of greenhouse gases on global climate, hence monitoring forest carbon pools at global and local levels is important. The present study explores the capability of remote-sensing variables (vegetation indices and textures derived from SPOT-5; backscattering coefficient and interferometric coherence of ALOS PALSAR images) for modeling the spatial distribution of above-ground biomass in the Environmental Conservation Zone of Mexico City. Correlation and spatial autocorrelation coefficients were used to select significant explanatory variables in fir and pine forests. The correlation for interferometric coherence in HV polarization was negative, with correlations coefficientsr= −0.83 for the fir andr= −0.75 for the pine forests. Regression-kriging showed the least root mean square error among the spatial interpolation methods used, with 37.75 tC/ha for fir forests and 29.15 tC/ha for pine forests. The results showed that a hybrid geospatial method, based on interferometric coherence data and a regression-kriging interpolator, has good potential for estimating above-ground biomass carbon.
