Modeling Forest Stand Volume and Live Aboveground Woody Biomass Using Remote Sensing and Gis: a Case Study in Chancho Eucalyptus Globulus Plantation Forest, Oromia Regional State, Ethiopia

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Addis Ababa University


This study presents the utility of Landsat 5 TM satellite imagery spectral and textural features for the estimation of stem volume and aboveground biomass (AGB) in Chancho Eucalyptus globulus plantation forest, Oromia Regional State, Ethiopia. Stem volume and AGB are arguably the most important variables among forest attributes since they play an important role in understanding the function of forests in the environmental and ecosystem services. Both of them can be estimated using different data and approaches, like using field observation data (classical approach), remote sensing data, and GIS data (modern approach). Even though, field-based forest surveying provides highly accurate measurements, it has limitations with regard to incurring high cost, being time consuming and having low spatial coverage and frequency. In addition to this, in some cases, destructive sampling is laborious and negatively affect environment. This makes sustaining the socio-economic and ecological benefits of forests under challenge. On the other hand, although Remote Sensing and GIS approaches overcome these limitations, they are site and species specific and are highly uncertain. In general in Ethiopian and in particular in the present study site both stem volume and AGB are estimated based on the classical approach. Thus, the present study is conducted to improve accuracy and decrease uncertainties in the modern approach in general, and replace the classical approach in the study site in particular by developing a function that estimate both attributes (dependent variables) as a function spectral and textural features (independent variables) of Landsat 5 TM image acquisition date of January 10, 2011. Based on Pearson correlation statistics test result among dependents and independents variables , Tasseled Cap brightness, GLCM Dissimilarity and GLC Variance were found as best explanatory variables for stem volume estimation. Whereas, Landsat 5 TM Band 5, GLCM Dissimilarity and GLCM Variance found to be as best explanatory variables for AGB estimation. The modeling of the stem volume and AGB equations as a function of spectral and textural independent variables were developed using Ordinary Least Square Regression method. The modern approach estimated almost similar mean stem volume and aboveground biomass abundance with field measurement data. The overall findings presented in this study are encouraging and show that Landsat 5 TM imagery was successful in predicting both attributes with reasonable accuracy (Adjusted R2 is 0.50 and 0.51 for stem volume and AGB, respectively; mean residual is 0 for both stem volume and AGB). Further research is recommended to document the performance of the Landsat 5 TM satellite data under different environmental conditions and topographical changes, as well as for other species.



Aboveground Biomass, GIS, OLS, Remote Sensing, Stem Volume