Accuracy assessment of a large-scale forest cover map of central Siberia from synthetic aperture radar
- 1 December 2002
- journal article
- Published by Taylor & Francis in Canadian Journal of Remote Sensing
- Vol. 28 (6) , 719-737
- https://doi.org/10.5589/m02-067
Abstract
Russia's boreal forests host 11% of the world's live forest biomass. They play a critical role in Russia's economy and in stabilizing the global climate. The boreal forests of central and western Siberia represent the largest unbroken tracts of forest in the world. The European Commission funded SIBERIA project aimed at producing a forest map covering an area of 1.2 million square kilometres. Three synthetic aperture radars (SAR) on board the European remote sensing satellites ERS-1 and ERS-2 and the Japanese Earth resources satellite JERS-1 were used to collect remote sensing data. Radar is the only sensor capable of penetrating cloud cover and imaging at night. An adaptive, model-based, contextual classification to derive ranked total growing stock volume classes suitable for large-scale mapping is described. The accuracy assessment of the Siberian forest cover map is presented. The weighted coefficient of agreement κw is calculated to quantify the agreement between the classified map and the reference ... Les forêts boréales de Russie contiennent 11% de la biomasse vivante mondiale. Elles jouent un rôle primordial dans l'économie Russe et, en outre, aident à stabiliser le climat mondial. Les forêts boréales de Sibérie Centrale et Occidentale représentent la plus large étendue de forêt continue dans le monde. Le projet SIBERIA financé par la communauté Européenne a pour but de produire une carte de ces forêts sur une surface de 1.2 millions de km2. Trois systèmes de radar à synthèse d'ouverture (SAR) montés sur les satellites ERS-1, ERS-2 and JERS-1 ont fourni les données de télédétection requises pour cette étude. Le radar est le seul capteur capable de pénétrer à travers les nuages et d'imager pendant la nuit. Une procédure contextuelle de classification est développée afin d'extraire les classes de la volume de tronc requises. L'évaluation de l'exactitude de la carte de couverture forestière de Sibérie est présentée. Le coefficient pesé κw est calculé afin de quantifier la conformité entre la carte de cl...Keywords
This publication has 21 references indexed in Scilit:
- Regression Modelling of Weighted κ by Using Generalized Estimating EquationsJournal of the Royal Statistical Society Series C: Applied Statistics, 2000
- Estimation of stem volume in boreal forests using ERS-1 C- and JERS-1 L-band SAR dataInternational Journal of Remote Sensing, 1999
- Synthetic aperture radar interferometryInverse Problems, 1998
- A three-component scattering model for polarimetric SAR dataIEEE Transactions on Geoscience and Remote Sensing, 1998
- C-band repeat-pass interferometric SAR observations of the forestIEEE Transactions on Geoscience and Remote Sensing, 1997
- Review Article SAR interferometry—issues, techniques, applicationsInternational Journal of Remote Sensing, 1996
- Identifying terrestrial carbon sinks: Classification of successional stages in regenerating tropical forest from Landsat TM dataRemote Sensing of Environment, 1996
- The geometry of SAR images for geocoding and stereo applicationsInternational Journal of Remote Sensing, 1992
- Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit.Psychological Bulletin, 1968
- A Coefficient of Agreement for Nominal ScalesEducational and Psychological Measurement, 1960