Next Article in Journal
Iterative Models for Early Detection of Invasive Species across Spread Pathways
Previous Article in Journal
Atypical Pattern of Soil Carbon Stocks along the Slope Position in a Seasonally Dry Tropical Forest in Thailand
Previous Article in Special Issue
Validation and Application of European Beech Phenological Metrics Derived from MODIS Data along an Altitudinal Gradient
Open AccessArticle

The Potential of High Resolution (5 m) RapidEye Optical Data to Estimate Above Ground Biomass at the National Level over Tanzania

Facultat de Física, Universitat de València, Burjassot, 46100 València, Spain
Sustainable Resources Directorate, Joint Research Centre (JRC), European Commission, Via E. Fermi, 2749, TP 261, VA 21027 Ispra, Italy
Author to whom correspondence should be addressed.
Forests 2019, 10(2), 107;
Received: 3 December 2018 / Revised: 17 January 2019 / Accepted: 23 January 2019 / Published: 29 January 2019
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. View Full-Text
Keywords: forests biomass; remote sensing; REDD+; random forest; Tanzania; RapidEye forests biomass; remote sensing; REDD+; random forest; Tanzania; RapidEye
Show Figures

Figure 1

MDPI and ACS Style

Hojas Gascón, L.; Ceccherini, G.; García Haro, F.J.; Avitabile, V.; Eva, H. The Potential of High Resolution (5 m) RapidEye Optical Data to Estimate Above Ground Biomass at the National Level over Tanzania. Forests 2019, 10, 107.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop