摘要
Masting is a well-marked variation in yields of oak forests. In Japan, this phenomenon is also related to wildlife management and oak regeneration practices. This study demonstrates the capability of integrating remote sensing techniques into map- ping spatial variation of acorn production. The hyperspectral images in 72 wavelengths （407-898 nm） were acquired over the study area ten times over a period of three years （2003-2005） during the early growing season of Quercus serrata using the Airborne Im- aging Spectrometer Application （AISA） Eagle System. With the canopy spectral reflectance values of 22 sample trees extracted from the images, yield estimation models were developed via multiple linear regression （MLR） analyses. Using the object-oriented classi- fication approach in eCognition, canopies representative of individual oak trees （Q. serrata） were identified from the corresponding hyperspectral imagery and combined with the fitted estimation models developed, acorn yield over the entire forest were estimated and visualized into maps. Three estimation models, obtained for June 27 in 2003, July 13 in 2004 and June 21 in 2005, showed good performance in acorn yield estimation both for the training and validation datasets, all with R2 〉 0.4, p 〈 0.05 and RRMSE 〈 1 （the relative root mean square of error）. The present study shows the potential of airborne hyperspectral imagery not only in estimating acorn yields during early growing seasons, but also in identifying Q. serrata from other image objects, based on which of the spatial distribution patterns of acorn production over large areas could be mapped. The yield map can provide within-stand abundance and valuable information for the size and spatial synchrony of acorn production.
Masting is a well-marked variation in yields of oak forests. In Japan, this phenomenon is also related to wildlife management and oak regeneration practices. This study demonstrates the capability of integrating remote sensing techniques into map- ping spatial variation of acorn production. The hyperspectral images in 72 wavelengths （407-898 nm） were acquired over the study area ten times over a period of three years （2003-2005） during the early growing season of Quercus serrata using the Airborne Im- aging Spectrometer Application （AISA） Eagle System. With the canopy spectral reflectance values of 22 sample trees extracted from the images, yield estimation models were developed via multiple linear regression （MLR） analyses. Using the object-oriented classi- fication approach in eCognition, canopies representative of individual oak trees （Q. serrata） were identified from the corresponding hyperspectral imagery and combined with the fitted estimation models developed, acorn yield over the entire forest were estimated and visualized into maps. Three estimation models, obtained for June 27 in 2003, July 13 in 2004 and June 21 in 2005, showed good performance in acorn yield estimation both for the training and validation datasets, all with R2 〉 0.4, p 〈 0.05 and RRMSE 〈 1 （the relative root mean square of error）. The present study shows the potential of airborne hyperspectral imagery not only in estimating acorn yields during early growing seasons, but also in identifying Q. serrata from other image objects, based on which of the spatial distribution patterns of acorn production over large areas could be mapped. The yield map can provide within-stand abundance and valuable information for the size and spatial synchrony of acorn production.
基金
supported by the Japan Society for the Promotion of Science (JSPS) through its grant-in-aid for scientific research projects (No. 14360148)
