Abstract
A large part of arid areas in tropical and sub-tropical regions are dominated by sparse xerophytic vegetation, which are essential for providing products and services for local populations. While a large number of researches already exist for the derivation of wall-to-wall estimations of above ground biomass (AGB) with remotely sensed data, only a few of them are based on the direct use of non-photogrammetric aerial photography. In this contribution we present an experiment carried out in a study area located in the Santiago Island in the Cape Verde archipelago where a National Forest Inventory (NFI) was recently carried out together with a new acquisition of a visible high-resolution aerial orthophotography. We contrasted two approaches: single-tree, based on the automatic delineation of tree canopies; and area-based, on the basis of an automatic image classification. Using 184 field plots collected for the NFI we created parametric models to predict AGB on the basis of the crown projection area (CPA) estimated from the two approaches. Both the methods produced similar root mean square errors (RMSE) at pixel level 45% for the single-tree and 42% for the area-based. However, the latest was able to better predict the AGB along all the variable range, limiting the saturation problem which is evident when the CPA tends to reach the full coverage of the field plots. These findings demonstrate that in regions dominated by sparse vegetation, a simple aerial orthophoto can be used to successfully create AGB wall-to-wall predictions. The level of these estimations’ uncertainty permits the derivation of small area estimations useful for supporting a more correct implementation of sustainable management practices of wood resources.
Highlights
Tree biomass is useful in assessing forest structure and condition, to estimate forest productivity and carbon fluxes; in providing a means of assessing sequestration of carbon in wood, leaves, and roots; and as an indicator of both the biological and economic value of forest ecosystems.the estimation of forest biomass at different geographical scales becomes significant in reducing the uncertainty in the estimation of carbon sequestration, for measuring land degradation, and in understanding the roles that forests play in environmental processes [1]
NXobs where Xobs,i is the above ground biomass (AGB) measured in the i-th sampling unit and Xmodel,i is the AGB estimated for the root mean square error (RMSE) Xobs is the average of the AGB measured in same i-th unit from the single-tree or area-based approach
Results where X obs,i is the AGB measured in the i-th sampling unit and X mo del,i is the AGB estimated for
Summary
Tree biomass is useful in assessing forest structure and condition, to estimate forest productivity and carbon fluxes; in providing a means of assessing sequestration of carbon in wood, leaves, and roots; and as an indicator of both the biological and economic value of forest ecosystems.the estimation of forest biomass at different geographical scales (from local to global) becomes significant in reducing the uncertainty in the estimation of carbon sequestration, for measuring land degradation, and in understanding the roles that forests play in environmental processes [1]. In arid and sub-arid areas, rural populations depend greatly on fuelwood produced by sparse scrublands and woodlands In such contexts, quick and cost-effective estimation and mapping of biomass availability is crucial for implementing proper management practices [2]. The direct measure of biomass with destructive methods is possible, but it is extremely time consuming [3]. For this reason, species-specific allometric models are used to estimate tree biomass on the basis of variables more measured in the field, such as tree diameter at breast height (DBH), tree height, crown projected area and/or wood density [4,5,6,7]
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