Assessment and prediction of above-ground biomass in selectively logged forest concessions using field measurements and remote sensing data : case study in South East Cameroon

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http://urn.fi/URN:NBN:fi:hulib-201507212030
Title: Assessment and prediction of above-ground biomass in selectively logged forest concessions using field measurements and remote sensing data : case study in South East Cameroon
Author: Gideon Neba, Shu
Other contributor: Helsingin yliopisto, Maatalous-metsätieteellinen tiedekunta, Metsätieteiden laitos
University of Helsinki, Faculty of Agriculture and Forestry, Department of Forest Sciences
Helsingfors universitet, Agrikultur- och forstvetenskapliga fakulteten, Institutionen för skogsvetenskaper
Publisher: Helsingfors universitet
Date: 2013
Language: eng
URI: http://urn.fi/URN:NBN:fi:hulib-201507212030
http://hdl.handle.net/10138/39106
Thesis level: master's thesis
Discipline: Skoglig ekologi och resurshushållning
Forest Ecology and Management
Metsien ekologia ja käyttö
Abstract: This study quantified above-ground biomass affected by selective logging in the tropical rainforest of South East Cameroon and also investigated the suitability of the density of logging roads, the density of log yards as well as variables from MODIS 250 m data (Red, NIR, MIR, NDVI, EVI) in explaining above-ground biomass logged. Above-ground biomass logged was quantified using allometric equations. The surface area of logging roads and log yards were quantified and used in the determination of above-ground biomass affected by these infrastructures based on a national reference baseline value for the forest zone of Cameroon. A comparative analysis revealed that 50% of potentially exploitable commercial tree species were effectively harvested with a harvesting intensity of 0.78 trees ha-1 representing an average above-ground biomass of 3.51 Mg ha-1. The results also indicated that 5.65 Mg ha-1 of above-ground biomass was affected by logging infrastructure .i.e. 62% as compared to 38% of above-ground biomass that was logged. Correlation and regression analysis showed that the density of the logging roads explained 66% of the variation in above-ground biomass logged and 73% of the variation in above-ground biomass logged was explained by the density of the logging roads and NDVI from MODIS data. The density of log yards and the variables from MODIS data were generally weak in explaining the variation in above-ground biomass logged.
Subject: AGB
selective logging
tropical rainforest
MODIS data
correlation
linear regression


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