Mapping aboveground tree carbon in managed Patula pine forests in Hidalgo, México [Mapeo de carbono arbóreo aéreo en bosques manejados de pino Patula en Hidalgo, México]

This paper presents relations between spectrum data of the SPOT 5 HRG spatial high resolution sensor and aboveground tree carbon Mg ha-1) in a Pinus patula forest in Zacualtipán, Hidalgo, México. First it was necessary to quantify the biomass (Mg ha-1). The multiple linear regression and the non parametric method of the nearest neighbor (k-nn) were used. The analysis of results suggests the presence of a high correlation between forest variables and the spectrum indexes associated with vegetation moisture. During validation, the correlation coefficients between the values observed and estimated for the regression methods and k-nn were highly significant (p = 0.01), and showed their potential for predicting the presence of aboveground tree carbon. The root mean square error (RCME) of the k-nn estimates was 22.24 Mg ha-1 (35.43 %25). The total estimate calculated by using k-nn was the closest to that obtained through traditional stratified sampling. From the results obtained, the contribution of the SPOT 5 images and k-nn method to the development of carbon inventories is confirmed.

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