Author :
Essono, F.N. ; Coulibaly, L. ; Adegbidi, H.G. ; Fournier, R.
Author_Institution :
Fac. de Foresterie, Univ. de Moncton, Moncton, NB, Canada
Abstract :
This study develops a cartographic index of forest stands vitality (vigour) from Ikonos imagery data. Dendrometric data (dbh, height, age) were collected during field inventories of various forest stands mainly composed of fir, intolerant hardwoods and tolerant hardwoods species typical of the study area of Gounamitz, in the north-west of New Brunswick. These data were used to compute reference estimates of stand vitality for each sample stand, using the Lebel´s vitality equation. Subsequently, corrected reflectance values for inventoried tree species were extracted from an Ikonos satellite image of the study area, after fusion, segmentation and classification were performed. Vegetation (NDVI, MSAVI, TSAVI, etc.) and texture indices were also extracted from the Ikonos image. Regression models (linear, polynomial and logarithmic) were then established between reference estimates of stand vitality computed earlier and extracted remote sensing data. Three of multiple regressions were tested. Obtained results showed that the linear regression model yielded the best estimates of forest stands vitality. Determination coefficients (r2) were 0.771, 0.783 and 0.776 respectively for fir, intolerant hardwoods and tolerant hardwoods species. Further, the regression model used was validated by comparing for 36 reference plots, the values of vitality computed from forest inventory data to values estimated by the model. The overall regression on the studied fir, tolerant and intolerant hardwood stands generated RMSE, of 0.17, 0.29 and 0.10, respectively. In a second step, the results of this study were generalized to map the vitality of forest stands throughout the whole area of the study.
Keywords :
feature extraction; image classification; image fusion; image segmentation; regression analysis; vegetation mapping; Ikonos satellite image; cartographic index; feature extraction; forest stand vitality; image classification; image fusion; image segmentation; linear regression model; remote sensing data; Equations; Image segmentation; Mathematical model; Pixel; Remote sensing; Satellites; Spatial resolution; Forest stands vitality; remote sensing;