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Remote sensing data are most often used in water bodies' extraction studies and the type of remote sensing data used also play a crucial role on the accuracy of the extracted water features. The performance of the proposed water indexes among the various satellite images is not well documented in literature. The proposed water indexes were initially developed with a particular type of data and with advancement and introduction of new satellite images especially Landsat 8 and Sentinel, therefore the need to test the level of performance of these water indexes as new image datasets emerged. Landsat 8 and Sentinel 2A image of part Volta River was used. The water indexes were performed and then ISODATA unsupervised classification was done. The overall accuracy and kappa coefficient values range from 98.0% to 99.8% and 0.94 to 0.98 respectively. Most of water bodies enhancement indexes work better on Sentinel 2A than on Landsat 8. Among the Landsat based water bodies enhancement ISODATA unsupervised classification, the modified normalized water difference index (MNDWI) and normalized water difference index (NDWI) were the best classifier while for Sentinel 2A, the MNDWI and the automatic water extraction index (AWEI_nsh) were the optimal classifier. The least performed classifier for both Landsat 8 and Sentinel 2A was the automatic water extraction index (AWEI_sh). The modified normalized water difference index (MNDWI) has proved to be the universal water bodies enhancement index because of its performance on both the Landsat 8 and Sentinel 2A image.

Biomass estimation has become a critical element in global environmental studies, because the change in biomass is deemed as an important component of climate change. The aim of this research is to estimate and map carbon stocks in Bosomkese forest reserve using remote sensing, GIS applications and field measurement method. Out of the six carbon pools of terrestrial ecosystem involving biomass (aboveground biomass, belowground biomass, deadwood, non tree, litter and soil organic matter), carbon sequestration of three (aboveground, belowground and deadwood) were assessed. Advanced Land Observing Satellite (ALOS) image acquired in 2010 was classified using Erdas Imagine. Total of five land use/cover classes were identified; Closed canopy natural forest, open canopy natural forest, plantation, farmland and fallow land. Diameter at breast height and total height of standing trees as well as the end diameters and the length of downed deadwood were measured in fifty sample plots in the five land use classes. These measurements were converted into aboveground carbon (AGC), belowground carbon (BGC) and deadwood carbon (DWC) using allometric equations developed in 2012 by Forest Research Institute of Ghana (FORIG). Total carbon for each plot was the summation of AGC, BGC and DWC. This research showed that closed canopy natural forest (1748.37 ton/Ha) contained more carbon than the rest of the land use/cover classes. This was followed by open canopy natural forest (1164.12 ton/Ha), plantation (775 ton/Ha), fallow land (110.69 ton/Ha) and farmland (45.13 ton/Ha) in descending order of total carbon stocks. The carbon/carbon dioxide equivalent values together with the plots coordinates were used to generate carbon stock and carbon dioxide equivalent map using Geostatistics tool of ArcGIS 10.0. The total carbon stock for the whole Bosomkese forest is in the range of 2,236,938.90 -2,865,148.33 tons and carbon dioxide equivalent in the range of 8,534,225.45 -10,507,952.05 tons.

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