Mapping leaf area index and canopy cover using hemispherical photography and spot 5 HRG data: Regression and k-nn [Mapeo del índice de área foliar y cobertura arbórea mediante fotografía hemisférica y datos spot 5 HRG: Regresión y k-nn]

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 (%25) using multispectral data from SPOT 5 satellite in stands of different ages in a managed forest of Pinus patula in Zacualtipán, Hidalgo, México. 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 LAIcalibrated (r2=0.93, RMSE=0.50; coefficient of determination and root mean squared error) and the COB (r2=0.96, RMSE=4.57 %25), with the bands and spectral indices constructed from them. The average estimates for forested stands were: LAI = 6.5; COB=80 %25. 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.

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