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 to obtain AGB for each of the 76 sample sites, which were then interpolated to estimate the AGB stock at the 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) resulted 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.
Envisat's Medium Resolution Imaging Spectrometer (MERIS) acquired multi-spectral imagery of the Earth in the optical domain over terrestrial surfaces for a decade at global scale. For the last ten years, scientists have used multi-spectral data or terrestrial geophysical products for characterizing the state of the global system and its variability. Our paper shows highlights of several achievements of the use of MERIS data over terrestrial surfaces but specifically focuses on regional to global scale applications. We first summarize daily operational biophysical parameters and present examples of their uses for the monitoring of land surface states and changes, especially related to ECVs. In addition, specific projects for deriving a series of land cover maps will be presented and we conclude on the MERIS data exploitation and highlights future applications.Index Terms-MERIS, FAPAR, MTCI, Land Surface Albedo, Land Cover Map.
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