Monitoring forest areas from continental to territorial levels using a sample of medium spatial resolution satellite imagery |
| |
| Authors: | Hugh Eva Silvia Carboni Frédéric Achard Nicolas Stach Laurent Durieux Jean-François Faure Danilo Mollicone |
| |
| Institution: | 1. SIRS, Parc de la Cimaise, 27 rue du Carrousel, 59650 Villeneuve d''Ascq, France;2. Northern Research Station, U.S. Forest Service, Saint Paul, MN, USA;3. AGEOS, BP 546 Libreville, Gabon;1. Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA;2. NASA Goddard Space Flight Center Code 619, 8800 Greenbelt Road, Greenbelt, MD 20771, USA;3. Space Research Institute NAS Ukraine & SSA Ukraine, Kyiv 03680, Ukraine;1. University of Natural Resources and Life Sciences, Vienna (BOKU), Institute of Surveying, Remote Sensing and Land Information, Peter-Jordan-Straße 82,1190 Vienna, Austria;2. German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), Department of Land Surface, Münchner Straße 20, 82234 Weßling, Germany;3. Bavarian State Institute of Forestry (LWF), Department of Information Technology, Research Group: Remote Sensing, Hans-Carl-von-Carlowitz-Platz 1, 85354 Freising, Germany |
| |
| Abstract: | A global systematic sampling scheme has been developed by the UN FAO and the EC TREES project to estimate rates of deforestation at global or continental levels at intervals of 5 to 10 years. This global scheme can be intensified to produce results at the national level. In this paper, using surrogate observations, we compare the deforestation estimates derived from these two levels of sampling intensities (one, the global, for the Brazilian Amazon the other, national, for French Guiana) to estimates derived from the official inventories. We also report the precisions that are achieved due to sampling errors and, in the case of French Guiana, compare such precision with the official inventory precision.We extract nine sample data sets from the official wall-to-wall deforestation map derived from satellite interpretations produced for the Brazilian Amazon for the year 2002 to 2003. This global sampling scheme estimate gives 2.81 million ha of deforestation (mean from nine simulated replicates) with a standard error of 0.10 million ha. This compares with the full population estimate from the wall-to-wall interpretations of 2.73 million ha deforested, which is within one standard error of our sampling test estimate. The relative difference between the mean estimate from sampling approach and the full population estimate is 3.1%, and the standard error represents 4.0% of the full population estimate.This global sampling is then intensified to a territorial level with a case study over French Guiana to estimate deforestation between the years 1990 and 2006. For the historical reference period, 1990, Landsat-5 Thematic Mapper data were used. A coverage of SPOT-HRV imagery at 20 m × 20 m resolution acquired at the Cayenne receiving station in French Guiana was used for year 2006.Our estimates from the intensified global sampling scheme over French Guiana are compared with those produced by the national authority to report on deforestation rates under the Kyoto protocol rules for its overseas department. The latter estimates come from a sample of nearly 17,000 plots analyzed from same spatial imagery acquired between year 1990 and year 2006. This sampling scheme is derived from the traditional forest inventory methods carried out by IFN (Inventaire Forestier National). Our intensified global sampling scheme leads to an estimate of 96,650 ha deforested between 1990 and 2006, which is within the 95% confidence interval of the IFN sampling scheme, which gives an estimate of 91,722 ha, representing a relative difference from the IFN of 5.4%.These results demonstrate that the intensification of the global sampling scheme can provide forest area change estimates close to those achieved by official forest inventories (<6%), with precisions of between 4% and 7%, although we only estimate errors from sampling, not from the use of surrogate data.Such methods could be used by developing countries to demonstrate that they are fulfilling requirements for reducing emissions from deforestation in the framework of an REDD (Reducing Emissions from Deforestation in Developing Countries) mechanism under discussion within the United Nations Framework Convention on Climate Change (UNFCCC). Monitoring systems at national levels in tropical countries can also benefit from pan-tropical and regional observations, to ensure consistency between different national monitoring systems. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
