Pre- and Post-Urban Wetland Area in Dhaka City,Bangladesh: A Remote Sensing and GIS Analysis

Landscape of Dhaka city - one of the fastest growing mega cities in the world, is undergoing continuous changes and modifications due to progressive urbanization. Pre- and post-urban changes of water bodies in the city were studied using aerial photographs and SPOT images in GIS environment. In 1968, the total area of marshy and peaty inundated low-lying areas was 133 km2, which was depicted to be 67 km2 in the year 2001. The total area of inland lakes as estimated from the aerial photos of 1968 was 5.1 km2 which became 1.8 km2 in the year 2001 as seen in SPOT image. More than 50% of the wetland area reduced over the period 1968 to 2001. Changes of the water body mostly occurred in the regions where majority of the urban expan-sion took place. The urban infrastructures filled and/or compartmentalized the water bodies, causing water loggings problem during wet-season in various part of the city. Development and alteration of the existing water bodies should consider the natural hydrological conditions so that the changes can cope with the artifi-cial intervention.

Development of chemometric model for characterization of non-wood by FT-NIR data

In this study,a model for prediction of lignocellulose components of agricultural residues has been developed with Fourier Transformed Near Infrared(FT-NIR)spectroscopy data.Two calibration techniques(Principal Component Regression(PCR)and Partial Least Square Regression(PLSR))were assessed for prediction of lignin,holocellulose,α-cellulose,pentosan and ash,and found the PLSR better for lignin,holocellulose andα-cellulose.The PCR also produced better results for quantification of pentosan and ash.Spectral range(7000-5000 cm^(-1))showed more informative than other parts of the spectral data.The PLSR showed maximum value of R^(2)(R^(2)=0.91%)for prediction of holocellulose.For the prediction of pentosan,the PCR was better(R^(2)=0.68%).The PCR also showed better results(R^(2)=86%)for quantification of ash.To determine amount of lignin,the PLSR was the best(R^(2)=0.83%)when the spectral data were de-trained and smoothed with Savitzky-Golay(S-G)filtering simultaneously.For prediction ofα-cellulose,the PLSR was the best model(R^(2)=0.94%)when the data were pretreated with mean normalization.Considering the best alternatives inNear Infrared(NIR)data preprocessing and calibration techniques,methods for quantification of lignocellulose components of agricultural residues have been developed which is rapid,cost effective,and less chemical intensive and easily usable in pulp and paper industries and pulp testing laboratories.