Article

Estimación de cobertura arbórea mediante imágenes satelitales multiespectrales de alta resolución

Agrociencia, ISSN 1405-3195, Vol. 40, Nº. 3, 2006, pags. 383-394
Source: OAI

ABSTRACT Para evaluar la utilidad de imágenes de alta resolución espacial, se compararon mediciones directas en campo como densitómetro esférico, muestreo de intersección de líneas y estimadores de razón y regresión, con procedimientos que utilizan imágenes satelitales de alta y mediana resolución espacial (Ikonos, QuickBird-2, SPOT-4 y LANDSAT-7) para estimar el porcentaje de cobertura de copa arbórea en un pinar ubicado en el ejido San Rafael Ixtapalucan, Municipio de Tlahuapan, Estado de Puebla, México. Las imágenes Ikonos y QuickBird son adecuadas técnicamente para determinar el porcentaje de cobertura de copa en este tipo de vegetación. Sin embargo, es conveniente considerar los costos de la compra de las imágenes y que se requiere de personal capacitado para su uso.

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Available from: J. Rene Valdez-Lazalde, Aug 12, 2014
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    • "In addition, its evaluation is necessary if the forest is to be promoted as a candidate for the payment of environmental hydrological services in México (Valdez-Lazalde et al., 2006). Given the natural variability and the large size of wooded areas, it is necessary to know in detail the behavior of such geospatial variables of interest -LAI and COB. "
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    ABSTRACT: 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 %). 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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