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|Title:||The potential of high resolution (5 m) RapidEye optical data to estimate above ground biomass at the national level over Tanzania|
|Authors:||HOJAS GASCÓN LORENA; CECCHERINI GUIDO; GARCIA-HARO JAVIER; AVITABILE VALERIO; EVA HUGH|
|Citation:||FORESTS vol. 10 no. 2 p. 107|
|Type:||Articles in periodicals and books|
|Abstract:||In this paper we review the potential of high resolution optical satellite data to reduce the significant investment in resources required for a national field survey for producing estimates of above ground biomass (AGB). We use 5 m resolution RapidEye optical data to support a country wide biomass inventory with the objective of bringing to the attention of the traditional forestry sector the advantages of integrating remote sensing data in the planning and execution of field data acquisition. We analysed the relationship between AGB estimates from a subset of the national survey field plot data collected by the Tanzania Forest Service, with a set of remote sensing biophysical parameters extracted from a sample of fine spatial (5 m) resolution RapidEye images using a regression estimator. We processed RapidEye data using image segmentation for 76 sample sites each of 20 km by 20 km (covering 2.3% of the land area of the country) to image objects of 1 ha. We extracted reflectance and texture information from those objects which overlapped with the field plot data and tested correlations between the two using four different models: two models from inferential statistics and two models from machine learning. The best results were found using the Random Forests algorithm (R2= 0.69). The most important explicative factor extracted from the remote sensing data was the shadow index, measuring the absorption of light in the visible bands. The model was then applied to all image objects on the RapidEye images so as to obtain AGB for each of the 76 sample sites which were then interpolated to estimate the AGB stock at national scale. Using the relative efficiency measure we assessed the improvement that the introduction of remote sensing data brings to obtain an AGB estimate at the national level, with the same precision as the full survey. The improvement in the precision of the estimate (by reducing its variance) results in a relative efficiency of 3.2. This demonstrates that the introduction of remote sensing data at this fine resolution can substantially reduce the number of field plots required, in this case threefold.|
|JRC Directorate:||Sustainable Resources|
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