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Abstract Forest biomass, accounting for over 80% of global vegetation biomass, is considered a key factor in terrestrial ecology, atmospheric processes and the water and carbon cycles. Forest biomass has been recently recognised as a Global Climate Observing System (GCOS) Essential Climate Variable (ECV), which is an important input to the United Nations’ Reducing Emissions from Deforestation and forest Degradationplus (REDD+) program and Earth system models. Reducing carbon emissions from forest changes is one of the core requirements to mitigate the impacts of climate change on Earth. Consequently, monitoring forest biomass dynamics is an international concern which has attracted attention from government (at local, regional, national and international levels), academics and the general public. According to the Global Forest Resources Assessment 2015, deforestation and forest degradation have been persisting in tropical developing countries where demand for exploiting natural resources are high and significantly increasing. Thus, these countries urgently need a robust and costeffective national forest biomass monitoring system that can support their policymaking processes that aim to protect ecosystem integrity in forests and reduce greenhouse gas emissions while simultaneously maintaining their socialeconomic development needs. While improving the quality of carbon reporting is needed, it is challenging for most developing countries due to their low capacities to perform national forest inventory on a regular basis. Forest inventory data may be available in these countries, but they are often outofdate. Using remote sensing data, such as Landsat satellite imagery, is one of the most practical and costeffective alternatives to enable developing countries to overcome this current challenge. Landsat satellites are unique as they have been creating the longest continuouslyacquired, spacebased and moderateresolution data collection since 1972. The free access and use data policy of the Landsat archive since 2008 has revolutionized the use of Landsat data for worldwide forest research and monitoring activities, especially forest biomass monitoring. This research first comprehensively reviewed the state and improvements of current approaches using Landsat timeseries (LTS) for characterising forest biomass dynamics. This literature review indicated that the use of LTS not only enables production of spatially and temporally explicit estimates of biomass but also can improve the quality and accuracy of biomass models. Many innovative approaches for estimating forest biomass across space and time from LTS have been recently demonstrated. However, most of these methods have
Extrapolating forest biomass dynamics over large areas using time-series remote sensing A thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy Huy Trung Nguyen Bsc (Hons), Thai Nguyen University, Vietnam Msc Environmental Science, Thai Nguyen University, Vietnam School of Science College of Science, Engineering and Health RMIT University February 2020 Declaration I certify that except where due acknowledgement has been made, the work is that of the author alone; the work has not been submitted previously, in whole or in part, to qualify for any other academic award; the content of the thesis is the result of work which has been carried out since the official commencement date of the approved research program; any editorial work, paid or unpaid, carried out by a third party is acknowledged; and, ethics procedures and guidelines have been followed I acknowledge the support I have received for my research through the provision of an Australian Government Research Training Program Scholarship Trung Nguyen 26 February 2020 i Abstract Forest biomass, accounting for over 80% of global vegetation biomass, is considered a key factor in terrestrial ecology, atmospheric processes and the water and carbon cycles Forest biomass has been recently recognised as a Global Climate Observing System (GCOS) Essential Climate Variable (ECV), which is an important input to the United Nations’ Reducing Emissions from Deforestation and forest Degradation-plus (REDD+) program and Earth system models Reducing carbon emissions from forest changes is one of the core requirements to mitigate the impacts of climate change on Earth Consequently, monitoring forest biomass dynamics is an international concern which has attracted attention from government (at local, regional, national and international levels), academics and the general public According to the Global Forest Resources Assessment 2015, deforestation and forest degradation have been persisting in tropical developing countries where demand for exploiting natural resources are high and significantly increasing Thus, these countries urgently need a robust and cost-effective national forest biomass monitoring system that can support their policy-making processes that aim to protect ecosystem integrity in forests and reduce greenhouse gas emissions while simultaneously maintaining their social-economic development needs While improving the quality of carbon reporting is needed, it is challenging for most developing countries due to their low capacities to perform national forest inventory on a regular basis Forest inventory data may be available in these countries, but they are often out-of-date Using remote sensing data, such as Landsat satellite imagery, is one of the most practical and cost-effective alternatives to enable developing countries to overcome this current challenge Landsat satellites are unique as they have been creating the longest continuously-acquired, space-based and moderate-resolution data collection since 1972 The free access and use data policy of the Landsat archive since 2008 has revolutionized the use of Landsat data for worldwide forest research and monitoring activities, especially forest biomass monitoring This research first comprehensively reviewed the state and improvements of current approaches using Landsat time-series (LTS) for characterising forest biomass dynamics This literature review indicated that the use of LTS not only enables production of spatially and temporally explicit estimates of biomass but also can improve the quality and accuracy of biomass models Many innovative approaches for estimating forest biomass across space and time from LTS have been recently demonstrated However, most of these methods have ii been developed for areas that are supported by comprehensive forest inventories and/or Lidar datasets Therefore, it is important to demonstrate an approach that is more possible for applications in developing countries where forest inventory data are measured for a single-time step which is often out-of-date This research develops a robust and consistent Landsat-based framework that can support developing countries improve their capacities in monitoring and reporting forest biomass and carbon stocks and changes across large areas The framework is developed by utilising a 30-year annual time-series of Landsat images (1988-2017) and one-off inventory data, which are commonly available in developing countries The study area comprised over 7.1 million of public forests in Victoria, south-eastern Australia Although Victoria is not a country, its size / forest inventory scenario is similar to many developing countries, making it a good case study LTS data were processed through several steps to produce a stack of cloud-free, annual mosaic composites This dataset was then used as a foundation input in further analyses for characterising forest disturbance and recovery and estimating forest biomass dynamics across space and time In the first stage, LTS data were utilised for developing a robust approach for mapping forest disturbance and recovery at a landscape scale Forest changes were detected through pixelbased change detection process using the LandTrendr temporal segmentation algorithm A two-phase classification process was then developed using the Random Forest (RF) algorithm to predictively map disturbance and recovery levels (high, medium and low) and disturbance causal agents (including wildfire, planned burns, clear-fell logging, selective logging) for multiple detected disturbance events (both primary and secondary events) Model explanatory data included a range of trajectory-based change metrics derived from the LandTrendr analysis, while model training and validation data were derived from a human-interpreted reference dataset In addition, a space-time data cube concept was introduced to simultaneously report on both newly detected disturbance events (detected disturbances) as well as events that have previously occurred but are ongoing (ongoing disturbances), which has been often under-reported RF classification models obtained high overall accuracies (73-81%) The data cube analysis revealed that although annual disturbance area was dominated by newly detected disturbances, ongoing disturbances accounted for a considerable area (over 50% of newly detected disturbances) These results iii indicate the utility of LTS in accurately capturing and mapping forest disturbance and recovery, facilitating further analyses on biomass estimates The second stage of this research tested and compared different modelling approaches for estimating forest biomass using Landsat time-series and inventory data This analysis used the outputs from the first stage (i.e., spectral change metrics, predicted disturbance and recovery levels and causal agents) in combination with data extracted from forest inventory field plots In particular, 18 k-nearest neighbour (kNN) imputation models were tested to predict three aboveground biomass (AGB) variables (total AGB, AGB of live trees and AGB of dead trees) These models were developed using different distance techniques (RF, Gradient Nearest Neighbour (GNN), and Most Similar Neighbour (MSN)) and different combinations of response variables (model scenarios) Direct biomass imputation models were trained according to the biomass variables while indirect biomass imputation models were trained according to combinations of forest structure variables (e.g., basal area, stem density and stem volume of live and dead-standing trees) The results show that RF consistently outperformed MSN and GNN distance techniques across different model scenarios and biomass variables The indirect imputation method generally achieved better biomass predictions than the direct imputation method In particular, the RF-based kNN model trained with the combination of basal area and stem density variables was the most robust for estimating forest biomass As the kNN imputation method is increasingly being used by land managers and researchers to map forest biomass, this analysis helps those using these methods to ensure their modelling and mapping practices are optimized The last stage presented a consistent approach for estimating forest AGB dynamics across space and time using LTS and single-date inventory data This approach consisted of three components: (1) a modelling method for creating annual forest AGB maps from Landsat time-series and one-off inventory data; (2) evaluation of the robustness and transferability of applying a single model through time to estimate AGB dynamics; (3) a spatial and temporal analysis of AGB dynamics according to forest disturbance and recovery histories, from which to inform jurisdictions as to how these ecological changes impact AGB dynamics These analyses were based on the findings of the first two stages A RF-based kNN imputation model, which was defined as the most accurate method in the second stage, was developed to produce annual maps of AGB for 30 years (from 1988 to 2017 over 7.2 million of forests in Victoria, Australia) Annual predictions of AGB and its change were iv independently evaluated using multi-temporal Lidar data These obtained relatively high accuracies, indicating the robustness and transferability over time of the developed modelling method Temporal trends of AGB were analysed according to forest disturbance and recovery levels and causal agents (derived in the first stage) in order to understand how AGB responds to both natural and anthropogenic processes Specifically, change metrics (e.g., AGB loss and gain, Years to Recovery - Y2R) were calculated at the pixel level to characterise the patterns of AGB dynamics resulting from forest changes AGB change metrics showed that changes in AGB values associated with forest disturbance and recovery (decrease and increase, respectively) were captured by predicted maps Results also indicated that AGB loss and Y2R varied across the states’ biogeographic regions and were highly dependent on the level of disturbance severity (i.e., a greater loss and longer recovery time were associated with a higher severity disturbance) The framework presented in this research has potential for application in different forest areas to support forest managers and policy makers to measure and report on forest biomass changes This research focuses on providing a solution for developing countries, where only single-date (often out-of-date) and sparse inventory data are available, to improve their capacities in monitoring and reporting forest carbon stocks and changes The findings from this research also demonstrate the utility of Earth Observation satellite data in monitoring forests across large areas (a difficult task when only reliant on field-based methods) Furthermore, regular and consistent observations acquired through LTS can provide us with a better understanding of the complexity and dynamic nature of forested systems and help us meet forest related sustainable management and development goals v Acknowledgement I would like to take the opportunity to specifically thank those who have contributed to this research and support me throughout my PhD Without your help, it could not be completed My first gratitude goes to my panel of supervisors Prof Simon Jones and Dr Mariela SotoBerelov from RMIT, and Dr Andrew Haywood from the European Forest Institute For all of you, I would like to thank for your patience and understanding my strengths and weakness Your supports through the last four years are unwavering and invaluable Also, I would like to thank you for adding me in the LandFor project team that allowed me to conduct me PhD research in a collaborative approach and to achieve high quality outputs Simon, thank you for accepting me onto this PhD from a very early date (nearly five years ago) and for your on-going support and encouragement since then Mariela, thank you for being not only my supervisor but also one of my best friends in Australia Your advice has been always invaluable Andrew, your industry perspective and high-level strategic advice have been of great benefits to my PhD research I also thank to my PhD companion, Samuel Hislop, for his support and contribution throughout our shared PhD journeys I would like to extend my gratitude to my RMIT fellow PhD and postdocs: Sam (Hislop), Chithra, Ahmad, Nenad, Luke, Sam (Hillman), Bryant, Daisy, Chats, Shirley, Eloise, Jenna, Fiona and Jing I appreciate your friendship and support for the last four years I was not alone on my PhD journey as we were always together I would like to acknowledge the Victorian Forest Monitoring Program team (Salahuddin Ahmad and Liam Costello) at the Department of Environment, Land, Water and Planning, who provided forest inventory data and support for this research I would like to acknowledge the Australian Award Scholarship (AAS) for providing the funding that made my PhD in Australia possible My thank goes also to Jamie Low, AAS coordinator at RMIT, for her assistance in various matters I also thank FrontierSI (formally CRCSI) for providing me a top-up scholarship to improve the quality of this research I greatly appreciate the constant support of my friends (in both Vietnam and Australia) during the last four years Finally, to my family (bố Quang, mẹ Lan, bố Mẫn, mẹ Xuân, chị Hiền, Trang, Hiếu Hạnh), without you I was not able to achieve this PhD Mom and Dad, I know you will never read and understand what I am writing here (and I will also never tell you) but you are always my greatest motivation Most importantly I would like to thank my wife, Hòa, and my two daughters, Chi and little Cherry; the reasons I get out of bed in the morning and come back home in the evening! Thank you for always with me, for your unwavering love and patience Chi, you had obtained your first master’s with your mom, and now your first PhD with me We are so proud of you! Thank you, everyone vi Contents Declaration i Abstract ii Acknowledgement vi Contents vii List of figures ix List of tables xii List of publications xiii Chapter Introduction 1.1 Context 1.2 Methods for estimating forest biomass 1.3 Satellite remote sensing time-series for forest monitoring 1.4 Objectives and research questions 12 1.5 Study area 13 1.6 Thesis structure 14 Chapter Landsat time-series for large area estimating of forest aboveground biomass dynamics: A review 15 2.1 Introduction 17 2.2 Advanced preprocessing and change detection methods for LTS 18 2.3 How has LTS been utilised to improve the estimation of AGB? 24 2.4 What LTS-based approaches have been demonstrated for estimating AGB and its dynamics across space and time? 29 2.5 Conclusions and future opportunities 45 Chapter A spatial and temporal analysis of forest dynamics over large areas using Landsat time-series 47 3.1 Introduction 49 3.2 Study area 52 3.3 Methods 54 3.4 Results 66 3.5 Discussion 74 3.6 Conclusion 79 vii Chapter A comparison of imputation approaches for estimating forest biomass using Landsat time-series and inventory data 80 4.1 Introduction 82 4.2 Materials and methods 85 4.3 Results 97 4.4 Discussion 103 4.5 Conclusions 108 Chapter Monitoring aboveground forest biomass dynamics over three decades using Landsat time-series and single-date inventory data 109 5.1 Introduction 111 5.2 Study area 114 5.3 Materials and methods 115 5.4 Results 122 5.5 Discussion 132 5.6 Conclusion 137 Chapter Synthesis 138 6.1 Research questions 139 6.2 Application in developing countries 146 6.3 Future directions and opportunities 148 Bibliography 152 Appendices 173 viii List of figures Figure 1.1 Timelines of major Earth observation satellites with optical/multispectral sensors (Modified and adapted from Kuenzer et al (2014)) 10 Figure 2.1 A common concept for estimating AGB dynamics using LTS data 35 Figure 3.1 Study area in Eastern Victoria, Australia, covered by four Landsat WRS-2 scenes 52 Figure 3.2 Australian forest structural definitions 53 Figure 3.3 Overall research methodology flowchart for characterising forest dynamics using Landsat time-series 54 Figure 3.4 LandTrendr-derived fitted trajectory of NBR and extracted disturbance and recovery metrics 56 Figure 3.5 Disturbance and recovery maps of public forests in Eastern Victoria (a) and (b) onset years (grouped in year intervals) of primary and secondary disturbances, respectively (the black box is the insert shown in Figure 3.10 and Figure 3.11) (c) and (d) the primary disturbance and recovery levels (see Table 3.3 for description of categories) and the associated causal agents, respectively 67 Figure 3.6 Rankings of variable importance as reported by the RF models of disturbance and recovery levels (phase one) Importance is defined by the mean decrease accuracy 69 Figure 3.7 a) Forest disturbance and recovery in 2003 (at the local scale) extracted from the FDDC b) Annual disturbance rates combining yearly detected and ongoing disturbance 71 Figure 3.8 Average annual disturbance rates by different (a) causal agents and (b) disturbance levels 72 Figure 3.9 Annual disturbance rates by (a) wildfire and (b) clear-fell disturbances 72 Figure 3.10 Tracking 30-year history of pixels of interest using the FDDC (a) Prediction maps of disturbance and recovery ingested into the FDDC (at the local scale, insert box in Figure 3.5a) (b) A Hovmoller graph displays the time-series arrays (Mxy) of pixels along a 12 km transect (the black line in the maps) The vertical axis is the distance along the transect, horizontal axis is time It is important to note that a “Full/Partial Recovery” status should be interpreted with its associated time period For example, a “Full Recovery” labelled for a 10-year period following a fire means that it took 10 years for fully recovering after the fire 73 ix Houghton, R 1998, 'Historic role of forests in the global carbon cycle', in Carbon dioxide mitigation in forestry 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commonly used for forest AGB estimates Landsat spectral index Normalized Difference Vegetation Index (NDVI) Normalized Burn Ratio (NBR) Normalized Difference Moisture Index (NDMI) Enhanced Vegetation Index (EVI) Soil Adjusted Vegetation Index (SAVI) Chlorophyll Vegetation Index (CVI) Difference Vegetation Index (DVI) Linear transform of multiple bands Integrated Forest Z-score (IFZ) Tasseled Cap (TC) transformations: TC brightness (TCB); TC greenness (TCG); TC wetness (TCW) TC angle (TCA) TC distance (TCD) TC Disturbance Index (DI) Calculation Reference NDVI = (NIR - R) / (NIR + R) (Tucker 1979) NBR = (NIR - SWIR) / (NIR + SWIR) (Key & Benson 2005) NDMI = (NIR - SWIR) / (NIR + SWIR) EVI = G * ((NIR - R) / (NIR + C1 * R – C2 * B + L)) L = value to adjust for canopy background, C = coefficients for atmospheric resistance, B = the blue band SAVI = ((NIR - R) / (NIR + R + L)) * (1 + L) CVI = (NIR x R) / G G = the green band (Huete 1988) (Hunt et al 2011) DVI = NIR - R (Phua & Saito 2003) VIS123 = B + G + R MID57 = TM band + TM band (SWIR) z-score measure of a pixel likelihood of being forested, using TM bands 3, and (Zhang, J et al 2018) TCW, TCB, and TCG are calculated by multiplying Landsat band pixel values with TC coefficients See the coefficients in references (Baig et al 2014; Crist 1985; Huang et al 2002; Kauth & Thomas 1976) TCA = arctan(TCG/TCB) (Powell et al 2010) TCD = √TCB + TCG DI =TCBr – (TCGr+TCWr) r = denotes rescaled TC indices based upon the mean and standard deviation of the scene's forest values (Duane et al 2010) 173 (Huang et al 2010) (Healey et al 2005) Figure A.1 AGB dynamics in un-disturbed forests across bioregions from 1988 to 2017 p and z statistics are reported by Mann-Kendall trend tests 174