Robert Msendo

The aim of the study was to identify the challenges facing, Zambia’s higher institutions of learning in managing manual documents and to determine whether an electronic Document management Systems (EDMS) could be useful. The study in addition had to determine the levels of adoption prevalent in the institutions among the stakeholders to warrant implementing an Electronic Document Management System or otherwise. A quantitative survey was undertaken at three higher learning institutions covering members of staff and learners in the institutions. A questionnaire was designed to help collect the sample data from the three institutions, which was then analyzed using the SPSS application package. The purpose was to use the Technology Acceptance Model (TAM) [3], to model how users come to accept and use technology, the actual system use is the end point where people use the technology. The TAM Model relies on two primary factors influencing an individual’s intention to use new technology ie perceived usefulness, perceived ease of use , to gauge whether they provide positive indicators to show that there are high levels of perception and expectations to influence positively the dependent variables in the TAM model ie. Attitude, behavioral intention and system use. The study revealed high levels of system adoption perceptions overall, in the institutions, to warrant introducing an EDMS technology. The results revealed a very high perceived usefulness and ease of use across the three institutions of higher learning. The study recommended that an Electronic Document Management System be implemented in these higher institutions of learning.

Radar and Optical based satellite sensors were used in the study of the Mumbwa flood of December 2020. The Synthetic Aperture Radar (SAR) based Sentinel-1B was processed in Google Earth Engine (GEE) and utilized to generate an image mosaic from December 2020 to May 2021 to delineate flood extent. A local water histogram threshold change detection approach by image ratio was utilized to determine the flood extent with an intensity value of 1.26 dB as it fitted the study area uniquely as opposed to the global value of 1.25 dB. After extracting the initial flood water extent, it was necessary to filter out regions which inundated during the flood period. This was carried out using the following datasets and parameters: The HydroSHEDS Digital Elevation Model (DEM) was used to filter out regions with a slope value of greater than 7% and the Global Surface Water Layer was used to clip out regions with existing permanent surface water. Once the flooded areas were identified, the Optical based Sentinel-2 was used in the production of a Land Use Land Cover (LULC) Map for August 2020 in order to superimpose the flooded areas with existing land features over the study area. The map also under went pre and post processing in GEE using the Random Forest Classification Algorithm that achieved an Overall Accuracy and Kappa Coefficient of 0.957 and 0.91519 respectively. Thereafter the flood analysis and damage assessment were carried out. The quantitative damages to Landcover were found to be: Wetland 6,338.97 Ha (33.27%), Shrubland 5,117.75 Ha (26.89%), Biochar Soil 3,660.47 Ha (19.21%), Trees 3,466.37 Ha (18.19%), Bare soil 273.47 Ha (1.44%), Crop Fields 190.69 Ha (1%) and Built-Up 4.13 Ha (0.02%). Therefore the use of SAR by local histogram threshold approach with Optical datasets for LULC map production proved successful in the study of flood damage.