THE ASSESSMENT OF MANGROVE AREAS USING HIGH-RESOLUTION MULTISPECTRAL AIRBORNE IMAGERY

Airborne multispectral sensors combine many of the advantages inherent in both satellite systems and aerial photography. However, they have not been used in remote sensing studies of mangrove areas which have t raditionally utilised the latter two approaches. High resolution (1 m) multispectral imagery of mangroves in the Turks and Caicos Islands wa s collected using a Compact Airborne Spectrographic Imager (CASI). Hie rarchical agglomerative clustering with group-average sorting identifi ed six mangrove classes which were used to direct a supervised classif ication (overall accuracy 78.2%). Normalised difference vegetation ind ex (NDVI) was calculated from CASI data: linear regression models were used to predict leaf area index and percent canopy closure from NDVI. LAI and canopy closure data, estimated from field measurements for a set of sites different to those used to derive the regression models, were used to test the accuracy of LAI and canopy closure prediction. A ccuracy was defined as the proportion of accuracy sites at which the L AI or percent canopy closure value (as estimated from field measuremen ts) lay within the 95% confidence interval for the predicted value. Ac curacy was high: 94% for LAI and 80% for canopy closure. The superior spatial and spectral resolution of CASI allows mangrove areas to be as sessed to a greater level of detail and accuracy than with satellite s ensors. Some logistics for planning CASI campaigns are discussed.