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Hall, R. J., Case, B. B., Arsenault, E., Price, D. T., Luther, J. E., Piercey, D. E., Fournier, R. A., & Guindon, L. (2002). Modeling and Mapping Forest Biomass Using Forest Inventory and Landsat TM Data: Results from the Foothills Model Forest, Alberta. In Proceedings of the International Geoscience and Remote Sensing Symposium and 24th Canadian Symposium on Remote Sensing (CD-ROM). Toronto: IEEE.

has been cited by the following article:

• TITLE: Linking Ground Forest Inventory and NDVI in Mapping above Ground Carbon Stock in Kasane Forest Reserve, Botswana

  AUTHORS: Lesika Basalumi, Lesika Basalumi, Charles Joseph Kilawe, Ernest William Mauya

  KEYWORDS: Carbon, Miombo Woodlands, NDVI

  JOURNAL NAME: Open Journal of Forestry, Vol.8 No.3, July 31, 2018

  ABSTRACT: Quantification of the above ground carbon stock (AGC) is important in sustainable forest management and policy advice on climate change mitigation. Traditional ground vegetation survey methods have been used to provide data for estimation of AGC stock but constrained by inadequate time and often too costly. Remote sensing when combined with few ground collected data has the potential of improving forest resource assessment even though this opportunity has not well been utilised. In this study, we mapped AGC through combination of ground survey data collected from 51 permanent sapling plots with Normalized Difference Vegetation Index (NDVI) derived from Landsat 5 Thematic Mapper image. Linkage of the two data sources was made during a training stage of supervised classification. The overall classification accuracy was 98%, suggesting that reliable estimate of AGC for a large area can be made through combination of medium resolution satellite imagery and few samples from the ground.