Mapeo del 赤ndice de 芍rea foliar y cobertura arb車rea mendiante fotograf赤a hemisf谷rica y datos SPOT 5 HRG: regresi車n y k-nn

作者：Aguirre Salado; Carlos A; Valdez Lazalde; Jose R; angeles Perez; Gregorio; de los Santos Posadas; Hector M; Aguirre Salado; Alejandro I

来源：Agrociencia, 2011.

• pinus patula
• applied geomatics
• satellite image
• vegetation index
• forest inventory
• hidalgo
• mexico.

leaf area index (lai) is a useful variable for characterizing the dynamics and productivity of forest ecosystems. canopy cover (cob), on the other hand, regulates the amount of penetrating light that controls certain light-dependent processes, and promotes the infiltration of rainfall as an environment hydrological service. this paper addresses the estimation of lai and cob (%) using multispectral data from spot 5 satellite in stands of different ages in a managed forest of pinus patula in zacualtip芍n, hidalgo, m谷xico. the lai was obtained by the allometric calibration of optical measurements taken with hemispherical photographs (pseudo r2=0.79). geospatial estimates were made using two methods: the multiple linear regression analysis and the nonparametric estimator of the nearest neighbor (k-nn). the analysis of the results showed a high ratio between lai calibrated (r2=0.93, rmse=0.50; coefficient of determination and root mean squared error) and the cob (r2=0.96, rmse=4.57 %), with the bands and spectral indices constructed from them. the average estimates for forested stands were: lai = 6.5; cob=80 %. the estimates per hectare of both methods (regression and k-nn) were comparable between them; however, k-nn required a considerable computational effort in calculating the spectral distances between the target pixel and the pixels in the sample.