Abstract
Maps of forest biomass are important tools for managing natural resources and reporting terrestrial carbon stocks. Using the San Juan National Forest in Southwest Colorado as a case study, we evaluate regional biomass maps created using physical variables, spectral vegetation indices, and image textural analysis on Landsat TM imagery. We investigate eight gray level co-occurrence matrix based texture measures (mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment and correlation) on four window sizes (3 × 3, 5 × 5, 7 × 7, 9 × 9) at four offsets ([1,0], [1,1], [0,1], [1,−1]) on four Landsat TM bands (2, 3, 4, and 5). The map with the highest prediction quality was created using three texture metrics calculated from Landsat Band 2 on a 3 × 3 window and an offset of [0,1]: entropy, mean and correlation; and one physical variable: slope. The correlation of predicted versus observed biomass values for our texture-based biomass map is r = 0.86, the Root Mean Square Error is 45.6 Mg∙ha−1, and the Coefficient of Variation of the Root Mean Square Error is 0.31. We find that models including image texture variables are more strongly correlated with biomass than models using only physical and spectral variables. Additionally, we suggest that the use of texture appears to better capture the magnitude and direction of biomass change following disturbance compared to spectral approaches. The biomass mapping methods we present here are widely applicable throughout the US, as they are based on publically available datasets and utilize relatively simple analytical routines.
Highlights
Accurate spatial maps of forest biomass are necessary for managing forest resources, informing climate change modeling studies, and meeting national and international reporting requirements for greenhouse gas inventories [1,2]
Our final biomass map was constructed using the best performing neural network model constructed from the texture metrics of entropy, mean and correlation calculated from Landsat
In this study we use a combination of physical variables, spectral information and image texture metrics calculated from Landsat TM imagery to create a local forest biomass map within San Juan National Forest in Southwest Colorado, USA
Summary
Accurate spatial maps of forest biomass are necessary for managing forest resources, informing climate change modeling studies, and meeting national and international reporting requirements for greenhouse gas inventories [1,2]. There are few spatially explicit regional and local biomass maps available, and as a consequence, relatively few resources available to determine how local biomass changes with disturbance. The first is an approach that assigns a biomass value, or a range of biomass values, to areas of land distinguished by characteristics such as vegetation type or land use. This approach, frequently referred to as “stratify and multiply”, uses ground-based measurements to determine biomass values, and spatial datasets to delineate mapping units. The stratify and multiply approach is relatively simple to implement, there are some limitations to this technique, namely the ambiguities present in land area classification, and the wide range of variability in aboveground biomass within a given land cover type [4]
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