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Mapping Forest Dynamics in Bangladesh from Satellite Images: Implications for the Global REDD+ Program

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Mapping Forest Dynamics in Bangladesh from Satellite Images: Implications for the Global REDD+ Program Mapping forest dynamics in Bangladesh from satellite images: Implications for the global REDD+ program Mohammad Redowan B.Sc. (Hons) and M.Sc. in Forestry M.Sc. in Geoinformation Science and Earth Observation for Environmental Modelling and Management (GEM) A thesis document for the degree of Doctor of Philosophy at The University of Queensland in 2018 School of Earth and Environmental Sciences Abstract Reducing emissions from deforestation and forest degradation (REDD+) is a global climate change mitigation initiative under negotiation by the United Nations Framework Convention on Climate Change (UNFCCC). This initiative provides financial incentives to developing countries for enhancing carbon stocks in their forests by abstaining from deforestation and forest degradation, which would lead to emission of CO2 into the atmosphere. To implement a REDD+ project, developing countries need to measure and monitor anthropogenic changes in their forests and as well as forest carbon to account for CO2 emissions and removals from such changes. It is also important to predict future scenarios of forest/landuse changes and to identify the areas at risk of changes so that appropriate conservation initiatives can be taken. Bangladesh is steadily progressing through its REDD+ roadmap. However, several key research issues still to address include: using remote sensing technology to detect deforestation, forest degradation and associated changes in forest carbon stock, and predict future forest/land-use pattern at local environmental setting. This study developed and evaluated approaches to detect the extent of historic (1995-2015) deforestation and forest degradation (Objective 1), to estimate the emissions of forest aboveground carbon due to deforestation and degradation activities (Objective 2), and to assess the future scenarios, driving forces and risk of deforestation and forest degradation (Objective 3) at Raghunandan Hill Reserve in north-eastern Bangladesh using a combination of satellite and field-level data employing various geospatial, statistical and modeling tools. The thesis comprises seven chapters. The significance and background of the research along with aim, objectives, research questions and available literature on the topic were described in chapter 1, 2 and 3. Chapter 4 dealt with the first objective, where changes of forest areas to non-forest areas and one forest strata to another during 1995-2015 were detected with high accuracy (>90%) by applying Monte-Carlo spectral unmixing algorithm to Landsat images, followed by knowledge-based classification approach. The classification was verified using independent randomly drawn reference sample points using high-resolution Google Earth images. A post-classification comparison method was applied to quantify the spatial extent, location and rate of changes of forest classes by generating transition matrices. -1 In chapter 5, emissions of carbon (t CO2e yr ) from deforestation and forest degradation activities during 1995-2005 and 2005-2015 periods at Raghunandan forest were estimated using Landsat satellite and field-level biomass-carbon data applying regression analysis and geospatial techniques with acceptable accuracy. The accuracy of the estimate was verified using root mean square error ii (RMSE) of estimated carbon with field reference carbon. Results indicated that, during 1995-2005, -1 -1 the total estimated emission was 4589 tonnes (t) CO2e yr from deforestation and 704 t CO2e yr -1 -1 from degradation, and during 2005-2015, 2981 t CO2e yr from deforestation and 886 t CO2e yr from degradation. Findings suggest that medium-resolution Landsat data has the application potential to quantify the levels of forest biomass-carbon emission at tropical forest conditions like Raghunandan in Bangladesh. Chapter 6 predicted scenarios and risks of forest changes by 2025 and 2035, with respect to the spatial biophysical driver variables, using a multilayer perceptron neural network (MLPNN) with the Markov Chain machine learning algorithm. Some of the driver variables were the derivatives of 30m-resolution Digital Elevation Model of the study area, while others were created employing geospatial technique on the Landsat image other spatial data. Results revealed that the forest area at Raghunandan is likely to increase slightly by 2025 and 2035, covering nearly 40% of the reserve, which was 30-35% in 2015. Landsat images, along with other spatial variables, detected the transitions of forest/land-use classes and the associated risk of transition for each 30m pixel. The findings can be useful in identifying areas vulnerable and at risk of future deforestation and forest degradation so that conservation measures can be prioritized. The approaches presented in this thesis were useful to derive accurate and detailed information regarding deforestation and forest degradation, forest carbon and its emissions, and simulating the future scenarios and risk of forest conversion at Raghunandan Hill Reserve at the local scale. The approaches, methods and findings of the present investigation could be beneficial for the forestry and environmental professionals, decision-makers, the scientific community, planners and after all forest and natural resources managers. This thesis addressed three main practical research issues for implementing any REDD+ project, showing the approaches may be successfully applied to other forests of Bangladesh that are under consideration for REDD+ including Raghunandan. Linking field-measured biomass-carbon with the medium resolution Landsat data to estimate emission factors and emissions was a challenge. Further studies can investigate if a better relationship can be obtained using high resolution images to ensure higher accuracies in the estimates. Besides, due to time constraints, this research could not substantiate the uncertainties in the estimates of emissions factors and emissions of carbon. Further investigations can provide the level of uncertainties in the estimates in the form of 95% confidence interval about the mean. iii Declaration by author This thesis is composed of my original work, and contains no material previously published or written by another person except where due reference has been made in the text. I have clearly stated the contribution of others to jointly-authored works that I have included in my thesis. I have clearly stated the contribution of others to my thesis as a whole, including statistical assistance, survey design, data analysis, significant technical procedures, professional editorial advice, financial support and any other original research work used or reported in my thesis. The content of my thesis is the result of work I have carried out since the commencement of my higher degree by research candidature and does not include a substantial part of work that has been submitted to qualify for the award of any other degree or diploma in any university or other tertiary institution. I have clearly stated which parts of my thesis, if any, have been submitted to qualify for another award. I acknowledge that an electronic copy of my thesis must be lodged with the University Library and, subject to the policy and procedures of The University of Queensland, the thesis be made available for research and study in accordance with the Copyright Act 1968 unless a period of embargo has been approved by the Dean of the Graduate School. I acknowledge that the copyright of all material contained in my thesis resides with the copyright holder(s) of that material. Where appropriate I have obtained copyright permission from the copyright holder to reproduce material in this thesis and have sought permission from co-authors for any jointly authored works included in the thesis. Mohammad Redowan St Lucia, Brisbane, Australia June 2018 iv Publications included in this thesis No publications included. Articles are under review process. Submitted manuscripts included in this thesis 1. Redowan, M, Phinn, S, Roelfsema, C & Aziz, AA, 2018, A REDD+ project design study for quantifying activity data for historic deforestation and forest degradation in a Bangladesh forest using Landsat data. International Journal of Remote Sensing, Taylor & Francis (incorporated as Chapter 4) Contributor Statement of contribution Conception and design (90%) Mohammad Redowan Analysis and interpretation (100%) Drafting and production (90%) Conception and design (7%) Stuart Phinn Analysis and interpretation (0%) Drafting and production (6%) Conception and design (2%) Chris Roelfsema Analysis and interpretation (0%) Drafting and production (2%) Conception and design (1%) Ammar Abdul Aziz Analysis and interpretation (0%) Drafting and production (2%) v 2. Redowan, M, Phinn, S, Roelfsema, C & Aziz, AA, 2018, Satellite estimation of atmospheric emissions of carbon due to deforestation and forest degradation in Bangladesh: Implications for the local REDD+ programme. GIScience & Remote Sensing, Taylor & Francis (incorporated as Chapter 5) Contributor Statement of contribution Conception and design (90%) Mohammad Redowan Analysis and interpretation (100%) Drafting and production (90%) Conception and design (7%) Stuart Phinn Analysis and interpretation (0%) Drafting and production (6%) Conception and design (2%) Chris Roelfsema Analysis and interpretation (0%) Drafting and production (2%) Conception and design (1%) Ammar Abdul Aziz Analysis and interpretation (0%) Drafting and production (2%) 3. Redowan, M, Phinn, S, Roelfsema, C & Aziz, AA, 2018, Predictive modelling of spatiotemporal
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