Muhammad Kamal

Bio: Muhammad Kamal is an academic researcher from Gadjah Mada University. The author has contributed to research in topics: Government & Mangrove. The author has an hindex of 27, co-authored 150 publications receiving 3377 citations. Previous affiliations of Muhammad Kamal include University of Queensland & Universiti Malaysia Pahang.

IT innovation adoption in the government sector: identifying the critical success factors

Abstract: Purpose – This paper aims to acquire underlying knowledge of how IT is adopted in private sector organisations and further explore what factors impact its adoption (optimistically and pessimistically).Design/methodology/approach – An interpretive and qualitative multiple case study approach was selected to test and validate the conceptual model empirically. The selection of the interpretivism viewpoint in the context of this research is to understand how government organisations adopt new technologies and support their decisions and actions. The interpretive research methodology is related to data gathering and generating solid descriptions and interpretations and further allows theory building. Through a multiple case study strategy, factors influencing EAI adoption in the government sector are investigated. In doing so, various data collection methods such as interviews, documentation, and observation are adopted.Findings – The author identified 42 critical success factors (CSF) for IT innovation adopti...

Hyperspectral Data for Mangrove Species Mapping: A Comparison of Pixel-Based and Object-Based Approach

Abstract: Visual image interpretation and digital image classification have been used to map and monitor mangrove extent and composition for decades. The presence of a high-spatial resolution hyperspectral sensor can potentially improve our ability to differentiate mangrove species. However, little research has explored the use of pixel-based and object-based approaches on high-spatial hyperspectral datasets for this purpose. This study assessed the ability of CASI-2 data for mangrove species mapping using pixel-based and object-based approaches at the mouth of the Brisbane River area, southeast Queensland, Australia. Three mapping techniques used in this study: spectral angle mapper (SAM) and linear spectral unmixing (LSU) for the pixel-based approaches, and multi-scale segmentation for the object-based image analysis (OBIA). The endmembers for the pixel-based approach were collected based on existing vegetation community map. Nine targeted classes were mapped in the study area from each approach, including three mangrove species: Avicennia marina, Rhizophora stylosa, and Ceriops australis. The mapping results showed that SAM produced accurate class polygons with only few unclassified pixels (overall accuracy 69%, Kappa 0.57), the LSU resulted in a patchy polygon pattern with many unclassified pixels (overall accuracy 56%, Kappa 0.41), and the object-based mapping produced the most accurate results (overall accuracy 76%, Kappa 0.67). Our results demonstrated that the object-based approach, which combined a rule-based and nearest-neighbor classification method, was the best classifier to map mangrove species and its adjacent environments.

Factors influencing e‐government adoption in Pakistan

Abstract: Purpose – The purpose of this paper is to identify critical factors that enable citizens to adopt e‐Government services in a society which is at a rudimentary stage of e‐Government adoption. To achieve the objective of the research study, an integrated conceptual model has been proposed and validated which is based on sound theoretical conditions, keeping in view the local context of Pakistani society.Design/methodology/approach – An integrated conceptual model is proposed highlighting a comprehensive set of potential factors influencing the adoption of e‐Government services. The conceptual model was developed using related literature review and also by conducting expert reviews with government officials, academic researchers and software engineers in Pakistan.Findings – From expert reviews, the results indicate that awareness, trust in the internet and trust in the government are the significant variables specific to the context of Pakistani society. The results of this study prove that awareness is a si...

Object-Based Approach for Multi-Scale Mangrove Composition Mapping Using Multi-Resolution Image Datasets

Abstract: Providing accurate maps of mangroves, where the spatial scales of the mapped features correspond to the ecological structures and processes, as opposed to pixel sizes and mapping approaches, is a major challenge for remote sensing. This study developed and evaluated an object-based approach to understand what types of mangrove information can be mapped using different image datasets (Landsat TM, ALOS AVNIR-2, WorldView-2, and LiDAR). We compared and contrasted the ability of these images to map five levels of mangrove features, including vegetation boundary, mangrove stands, mangrove zonations, individual tree crowns, and species communities. We used the Moreton Bay site in Australia as the primary site to develop the classification rule sets and Karimunjawa Island in Indonesia to test the applicability of the rule sets. The results demonstrated the effectiveness of a conceptual hierarchical model for mapping specific mangrove features at discrete spatial scales. However, the rule sets developed in this study require modification to map similar mangrove features at different locations or when using image data acquired by different sensors. Across the hierarchical levels, smaller object sizes (i.e., tree crowns) required more complex classification rule sets. Incorporation of contextual information (e.g., distance and elevation) increased the overall mapping accuracy at the mangrove stand level (from 85% to 94%) and mangrove zonation level (from 53% to 59%). We found that higher image spatial resolution, larger object size, and fewer land-cover classes result in higher mapping accuracies. This study highlights the potential of selected images and mapping techniques to map mangrove features, and provides guidance for how to do this effectively through multi-scale mangrove composition mapping.

Remote Sensing And Image Interpretation

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Competing for the Future

Abstract: Everybody knows that digital technology has revolutionized our economy and our lifestyles. But how many of us really understand the drivers behind the technology – the significance of going digital; the miniaturization of electronic devices; the role of venture capital in financing the revolution; the importance of research and development? How many of us understand what it takes to make money from innovative technologies? Should we worry about manufacturing going offshore? What is the role of India and China in the digital economy? Drawing on a lifetime’s experience in the industry, as an engineer, a senior manager, and as a partner in a global venture capital firm, Henry Kressel offers an expert personalized answer to all these questions. He explains how the technology works, why it matters, how it is financed, and what the key lessons are for public policy.