Evaluating The Performance Of CMIR For Estimating The Spatial Distribution Of Mangroves At The Keta Lagoon Complex In The Volta Region Of Ghana

Abstract

Data obtained from remote sensing is useful for evaluating and mapping infrastructure and natural resources including vegetation. Over the past years, a number of vegetation indices have been developed to detect vegetation with the use of satellite imageries to monitor the distribution and phenology of mangroves. Forest managers and environmental scientists have developed a wide range of indices for delineating and assessing the health of different vegetation and forest cover. This study will evaluate the performance of Combined Mangrove Recognition Index (CMRI) for estimating and distinguishing mangroves in the Keta Lagoon Complex. The CMRI was compared to the Normalized Vegetation Index (NDVI), a widely used vegetation index and supervised classification (maximum likelihood) which were selected based on their classification accuracies of about 80% in the estimation of vegetation. Sentinel 2 imagery was used to generate vegetation maps for the NDVI and CMRI indices and a land cover map generated using the supervised classification (maximum likelihood) technique. The threshold value method was used to extract the values of mangrove areas for each index and used to delineate areas of mangrove and non-mangrove using binary data with the use of UAV imagery for validation. Random points with their coordinates were generated as reference points on the UAV imagery and overlaid on the other maps. Areas of mangroves were denoted “1” and areas with non-mangroves were denoted “0”. The Cochran’s Q test, used for statistical analysis of binary data was used to derive the p-value after which the area coverage of mangroves in the study area was estimated. From the study, the threshold values used to mask out mangroves were observed to be between 0.27 and 0.37, and between 0.51 and 0.70 for NDVI and CMRI respectively. UAV imagery was used to validate the area coverage due to its high resolution. The imagery covered an area of 1.8 km2 and was used as a subset for the mangrove area coverage comparison. Mangrove area coverage was estimated to be 0.32km2 , University of Ghana http://ugspace.ug.edu.gh iv 0.30km2 , and 0.25km2 for NDVI, supervised classification and CMRI respectively. All techniques used in classification showed no statistical significance (>0.05) when compared to ground truth data. The CMRI was observed to have performed better and hence confirmed its sensitivity in estimating mangroves and that other satellite missions with optical sensors and multiple bands can be used to generate the index with high accuracy.