A study was conducted to estimate the forest cover change, quantify and map tree above-ground carbon stock using Remote sensing and GIS techniques together with forest inventory. Landsat images of 1980, 1995 and 2010 acquired during dry season were used in the estimation of cover changes. Supervised image classification using Maximum Likeli-hood Classifier was performed in ERDAS Imagine software to analyze the images and further analysis was performed in Arc GIS 9.3 software. Stratified sampling procedure was used to select concentric inventory plots in Pugu Forest Reserve (PFR) and Kazimzumbwi Forest Reserve (KFR). Plots were laid according to NAFORMA, and the tree parameters in each sampling plot were collected. A Microsoft Excel spreadsheet was used to compute the above-ground bio- mass for each plot using an empirical equation relating wood basic density and tree height. The above-ground carbon was calculated using a conversion factor of 0.49. Geostatistical method in ArcGIS was used to analyze and map carbon. Results revealed that for the periods 1980-1995 and 1995-2010, Closed Forest in PFR decreased by 4.5% and 25.3% respectively, while for KFR, Closed Forest decreased by 11.9% and 31.3% respectively. The mean carbon density for PFR and KFR were respectively 5.72 tC/ha and 0.98 tC/ha while carbon stocks were 14 730.41 tC and 7 206.46 tC re- spectively. The revealed low carbon densities were attributable to decline in area under Closed Forest in the two Forest Reserves. The study recommends concerted efforts to enhance proper management of the forests so that the two forest reserves may contribute to REDD initiatives.
Global warming which is largely contributed by increased anthropogenic Green House Gases (GHGs) represents significant development challenges for this 21st Century. It is well known that the main cause of global warming is the increase of anthropogenic Carbon dioxide (CO2) emission, which accounts to 77% of all GHGs [
Reducing carbon emissions is of great important in this era of climate change. Several mechanisms have been developed by United Nations Framework Convention on Climate Change (UNFCCC), these includes cutting down CO2 emissions from Annex 1 countries, and reducing deforestation and forest degradation in developing countries. Although UNFCCC requires cutting down of CO2 emissions, tropical deforestation contributes to 20% - 25% of annual global emissions of CO2 [
Reducing Emissions from Deforestation and forest Degradation (REDD) concept requires developing countries to be involved in the management of forests including reforestation and afforestation. The REDD concept is proposing to finance all activities that contributes to the improvement of forests condition, whereby developing countries that contributes to the reduction of carbon emissions may be able to sell carbon credits to the international carbon markets. In order to finance for carbon emission reduction, quantification of forest carbon is of great important.
This paper presents an attempt to integrate satellite imageries and ground based inventories in the estimation of tree above ground biomass and carbon for the Pugu and Kazimzumbwi Forest Reserves (PKFR) in the coastal areas of Tanzania. PKFR are among of the important biodiversity hotspots in Tropical Africa [
Pugu and Kazimzumbwi Forest Reserves (PKFR) are located in Kisarawe District in the Coast region of Tanzania (
TM = Thematic Mapper; MSS = Multi Spectral Scanner.
effects.
The acquired image scenes of the years 1980 and 2010 had already been geo-rectified by the supplier. To ensure accurate identification of temporal changes and geometric compatibility with other sources of information, image to image geo-correction was conducted to rectify the 1995 imagery based on 2010 image. Images enhancement was performed using a 4,5,3 color composite band combination and its contrast was stretched using the Gaussian distribution function followed by high pass filter 3 × 3 to increase the visibility of the ground control points in both images. The first order polynomial transformation and nearest neighborhood interpolation [
Base maps were prepared based on the image acquired on 7th July 2010 and used in ground truthing exercise. The essence of conducting ground truthing was to verify different covers types as described on the base maps and for collection of ground points for the classification accuracy assessment. Supervised classification, using Maximum Likelihood Classifier [7-9] was performed applying ERDAS IMAGINE software. Training fields were identified by inspecting an enhanced colour composite imagery. Areas with similar spectral characteristics were trained and classified. The error matrices [
To analyse the changes between different time epochs, change detection analysis was performed. Many change detection methods have been developed and used for various applications. However, they can broadly be divided into: post-classification approaches and spectral change detection approaches [
Stratified sampling technique [
Concentric plot [
where N = number of sample plots, = Total area of the forest, = Plot size and = Sampling intensity, while the distance between plots was determined by the formula:
where D = inter plots distance (m), Af = Area of the forest (ha) and N = number of plots.
The adopted sampling intensity was atleast 0.1%. Therefore for KFR, the total area of Bushland, Closed Forest, Grassland and Open Forest, based on 2010 landsat image classification was 4820.8 ha, making a total of 68 plots while in PFR, the total area for Bushland, Closed Forest, Grassland and Open Forest based on 2010 landsat image was 2230.1 ha, making a total of 33 plots. Transects were created, where in each transect, concentric plots of radius 15 m (0.07 ha) were systematically located at 842 m and 822 m intervals from each other in the North-South direction in KFR and PFR respectively, (
Tree above ground biomass (AGB) was computed as a product of total tree volume and wood basic density. The average wood density of 0.58g cm−3 for natural forest was used [
where = Volume of the ith tree (m3)
g = the tree basal area (m2)
0.5 = tree form factor.
The value recommended in natural forests of Tanzania without distinction of the vegetation type involved [
The value of tree biomass was converted to carbon using a biomass-carbon ratio of 0.49 [12,18]. The carbon density for the whole forest was obtained by averaging carbon density from each individual forest stratum. Carbon stock was obtained by summing the products of stratum’s carbon density and their corresponding cover area. The carbon stocks for 1980 and 1995 were obtained by assuming that individual cover class’s carbon densities didn’t change [
According to [
The results from classification accuracy assessment revealed that the overall accuracy of classification for PFR was 84.85% and that of KFR was 82.35%. According to [
The land cover maps for the period 1980, 1995 and 2010 are presented in
Results (
During the period 1980-1995, the result (