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 and wood industry, Springer, pp 1-24 Houghton, RA 2005, 'Tropical deforestation as a source of greenhouse gas emissions', Tropical deforestation and climate change, vol., p 13 Houghton, RA, Hall, F & Goetz, SJ 2009, 'Importance of biomass in the global carbon cycle', Journal of Geophysical Research: Biogeosciences, vol 114, no G2, pp n/a-n/a Huang, C, Goward, SN, Masek, JG, Thomas, N, Zhu, Z & Vogelmann, JE 2010, 'An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks', Remote Sensing of Environment, vol 114, no 1, pp 183-198 Huang, C, Wylie, B, Yang, L, Homer, C & Zylstra, G 2002, 'Derivation of a tasselled cap transformation based on Landsat at-satellite reflectance', International Journal of Remote Sensing, vol 23, no 8, pp 1741-1748 Hudak, AT, Crookston, NL, Evans, JS, Hall, DE & Falkowski, MJ 2008, 'Nearest neighbor imputation of species-level, plot-scale forest structure attributes from LiDAR data', Remote Sensing of Environment, vol 112, no 5, pp 2232-2245 Hudak, AT, Strand, EK, Vierling, LA, Byrne, JC, Eitel, JUH, Martinuzzi, S & Falkowski, MJ 2012, 'Quantifying aboveground forest carbon pools and fluxes from repeat LiDAR surveys', Remote Sensing of Environment, vol 123, pp 25-40 Huete, AR 1988, 'A soil-adjusted vegetation index (SAVI)', Remote Sensing of Environment, vol 25, no 3, pp 295-309 Huete, AR 2012, 'Vegetation indices, remote sensing and forest monitoring', Geography Compass, vol 6, no 9, pp 513-532 Hunt, ER, Daughtry, C, Eitel, JU & Long, DS 2011, 'Remote sensing leaf chlorophyll content using a visible band index', Agronomy Journal, vol 103, no 4, pp 1090-1099 Hussin, YA, Reich, RM & Hoffer, RM 1991, 'Estimating splash pine biomass using radar backscatter', IEEE Transactions on Geoscience and Remote Sensing, vol 29, no 3, pp 427-431 Ioki, K, Tsuyuki, S, Hirata, Y, Phua, M-H, Wong, WVC, Ling, Z-Y, Saito, H & Takao, G 2014, 'Estimating above-ground biomass of tropical rainforest of different degradation levels in Northern Borneo using airborne LiDAR', Forest Ecology and Management, vol 328, pp 335-341 IPCC 2006, Guidelines for National Greenhouse Gas Inventories – Volume – Agriculture, Forestry and other Land Use, Institute for Global Environmental Strategies, Japan 160 Isenburg, M 2015, LAStools-efficient tools for LiDAR processing, Version 150304 Jiménez, E, Vega, JA, Fernández-Alonso, JM, Vega-Nieva, D, Ortiz, L, López-Serrano, PM & López-Sánchez, CA 2017, 'Estimation of aboveground forest biomass in Galicia (NW Spain) by the combined use of LiDAR, LANDSAT ETM+ and National Forest Inventory data', iForest Biogeosciences and Forestry, vol 10, no 3, pp 590-596 Kauth, RJ & Thomas, G 'The tasselled cap a graphic description of the spectral-temporal development of agricultural crops as seen by Landsat', West Lafayette, Indiana, USA, The Institute of Electrical and Electronics Engineers, p 159 Kelsey, K & Neff, J 2014, 'Estimates of Aboveground Biomass from Texture Analysis of Landsat Imagery', Remote Sensing, vol 6, no 7, pp 6407-6422 Kennedy, R, Yang, Z, Gorelick, N, Braaten, J, Cavalcante, L, Cohen, W & Healey, S 2018, 'Implementation of the LandTrendr Algorithm on Google Earth Engine', Remote Sensing, vol 10, no 5, p 691 Kennedy, RE, Ohmann, J, Gregory, M, Roberts, H, Yang, Z, Bell, DM, Kane, V, Hughes, MJ, Cohen, WB, Powell, S, Neeti, N, Larrue, T, Hooper, S, Kane, J, Miller, DL, Perkins, J, Braaten, J & Seidl, R 2018, 'An empirical, integrated forest biomass monitoring system', Environmental Research Letters, vol 13, no 2, p 025004 Kennedy, RE, Yang, Z, Braaten, J, Copass, C, Antonova, N, Jordan, C & Nelson, P 2015, 'Attribution of disturbance change agent from Landsat time-series in support of habitat monitoring in the Puget Sound region, USA', Remote Sensing of Environment, vol 166, pp 271-285 Kennedy, RE, Yang, Z & Cohen, WB 2010, 'Detecting trends in forest disturbance and recover y using yearly Landsat time series: LandTrendr — Temporal segmentation algorithms', Remote Sensing of Environment, vol 114, no 12, pp 2897-2910 Kennedy, RE, Yang, Z, Cohen, WB, Pfaff, E, Braaten, J & Nelson, P 2012, 'Spatial and temporal patterns of forest disturbance and regrowth within the area of the Northwest Forest Plan', Remote Sensing of Environment, vol 122, pp 117-133 Key, C & Benson, N 2005, 'Landscape assessment: remote sensing of severity, the normalized burn ratio and ground measure of severity, the composite burn index', FIREMON: Fire effects monitoring and inventory system Ogden, Utah: USDA Forest Service, Rocky Mountain Res Station, vol Kieth, H, Barrett, D & Keenan, R 2000, Review of allometric relationships for estimating woody biomass for New South Wales, the Australian Capital Territory, Victoria, Tasmania and South Australia, 5B, Technical Report, Canberra, 161 Knudby, A, LeDrew, E & Brenning, A 2010, 'Predictive mapping of reef fish species richness, diversity and biomass in Zanzibar using IKONOS imagery and machine-learning techniques', Remote Sensing of Environment, vol 114, no 6, pp 1230-1241 Kruskal, WH & Wallis, WA 1952, 'Use of ranks in one-criterion variance analysis', Journal of the American statistical Association, vol 47, no 260, pp 583-621 Kuenzer, C, Ottinger, M, Wegmann, M, Guo, H, Wang, C, Zhang, J, Dech, S & Wikelski, M 2014, 'Earth observation satellite sensors for biodiversity monitoring: potentials and bottlenecks', International Journal of Remote Sensing, vol 35, no 18, pp 6599-6647 Labrecque, S, Fournier, R, Luther, J & Piercey, D 2006, 'A comparison of four methods to map biomass from Landsat-TM and inventory data in western Newfoundland', Forest Ecology and Management, vol 226, no 1-3, pp 129-144 Lasaponara, R & Lanorte, A 2012, 'Satellite time-series analysis', International Journal of Remote Sensing, vol 33, no 15, pp 4649-4652 Le Toan, T, Beaudoin, A, Riom, J & Guyon, D 1992, 'Relating forest biomass to SAR data', IEEE Transactions on Geoscience and Remote Sensing, vol 30, no 2, pp 403-411 Le Toan, T, Quegan, S, Davidson, MWJ, Balzter, H, Paillou, P, Papathanassiou, K, Plummer, S, Rocca, F, Saatchi, S, Shugart, H & Ulander, L 2011, 'The BIOMASS mission: Mapping global forest biomass to better understand the terrestrial carbon cycle', Remote Sensing of Environment, vol 115, no 11, pp 2850-2860 Lefsky, MA 2009, 'Biomass accumulation rates of Amazonian secondary forest and biomass of oldgrowth forests from Landsat time series and the Geoscience Laser Altimeter System', Journal of Applied Remote Sensing, vol 3, no 1, p 033505 Lehmann, EA, Caccetta, P, Lowell, K, Mitchell, A, Zhou, Z-S, Held, A, Milne, T & Tapley, IJRSoE 2015, 'SAR and optical remote sensing: Assessment of complementarity and interoperability in the context of a large-scale operational forest monitoring system', vol 156, pp 335-348 Lehmann, EA, Wallace, JF, Caccetta, PA, Furby, SL & Zdunic, K 2013, 'Forest cover trends from time series Landsat data for the Australian continent', International Journal of Applied Earth Observation and Geoinformation, vol 21, pp 453-462 Lewis, A, Oliver, S, Lymburner, L, Evans, B, Wyborn, L, Mueller, N, Raevksi, G, Hooke, J, Woodcock, R, Sixsmith, J, Wu, W, Tan, P, Li, F, Killough, B, Minchin, S, Roberts, D, Ayers, D, Bala, B, Dwyer, J, Dekker, A, Dhu, T, Hicks, A, Ip, A, Purss, M, Richards, C, Sagar, S, Trenham, C, Wang, P & Wang, L-W 2017, 'The Australian Geoscience Data Cube — Foundations and lessons learned', Remote Sensing of Environment, vol 202, pp 276-292 162 Li, J & Roy, DP 2017, 'A Global Analysis of Sentinel-2A, Sentinel-2B and Landsat-8 Data Revisit Intervals and Implications for Terrestrial Monitoring', Remote Sensing, vol 9, no Liaw, A & Wiener, M 2002, 'Classification and regression by randomForest', R news, vol 2, pp 1822 Libiseller, C & Grimvall, A 2002, 'Performance of partial Mann–Kendall tests for trend detection in the presence of covariates', Environmetrics: The official journal of the International Environmetrics Society, vol 13, no 1, pp 71-84 Liu, HQ & Huete, A 1995, 'A feedback based modification of the NDVI to minimize canopy background and atmospheric noise', IEEE Transactions on Geoscience and Remote Sensing, vol 33, no 2, pp 457-465 Liu, L, Peng, D, Wang, Z & Hu, Y 2014, 'Improving artificial forest biomass estimates using afforestation age information from time series Landsat stacks', Environmental monitoring and assessment, vol 186, no 11, pp 7293-7306 Liu, S, Wei, X, Li, D & Lu, D 2017, 'Examining Forest Disturbance and Recovery in the Subtropical Forest Region of Zhejiang Province Using Landsat Time-Series Data', Remote Sensing, vol 9, no 5, p 479 Lu, D 2006, 'The potential and challenge of remote sensing‐based biomass estimation', International Journal of Remote Sensing, vol 27, no 7, pp 1297-1328 Lu, DJIjors 2006, 'The potential and challenge of remote sensing‐based biomass estimation', vol 27, no 7, pp 1297-1328 Ma, W, Domke, GM, D’Amato, AW, Woodall, CW, Walters, BF & Deo, RK 2018, 'Using matrix models to estimate aboveground forest biomass dynamics in the eastern USA through various combinations of LiDAR, Landsat, and forest inventory data', Environmental Research Letters, vol 13, no 12 Main-Knorn, M, Cohen, WB, Kennedy, RE, Grodzki, W, Pflugmacher, D, Griffiths, P & Hostert, P 2013, 'Monitoring coniferous forest biomass change using a Landsat trajectory-based approach', Remote Sensing of Environment, vol 139, pp 277-290 Main-Knorn, M, Moisen, GG, Healey, SP, Keeton, WS, Freeman, EA & Hostert, P 2011, 'Evaluating the Remote Sensing and Inventory-Based Estimation of Biomass in the Western Carpathians', Remote Sensing, vol 3, no 12, pp 1427-1446 Mann, HB 1945, 'Nonparametric tests against trend', Econometrica: Journal of the Econometric Society, vol., pp 245-259 163 Masek, JG, Vermote, EF, Saleous, NE, Wolfe, R, Hall, FG, Huemmrich, KF, Gao, F, Kutler, J & Lim, T-K 2006, 'A Landsat surface reflectance dataset for North America, 1990-2000', IEEE Geoscience and Remote Sensing Letters, vol 3, no 1, pp 68-72 Matasci, G, Hermosilla, T, Wulder, MA, White, JC, Coops, NC, Hobart, GW, Bolton, DK, Tompalski, P & Bater, CW 2018, 'Three decades of forest structural dynamics over Canada's forested ecosystems using Landsat time-series and lidar plots', Remote Sensing of Environment, vol 216, pp 697-714 Matasci, G, Hermosilla, T, Wulder, MA, White, JC, Coops, NC, Hobart, GW & Zald, HSJ 2018, 'Large-area mapping of Canadian boreal forest cover, height, biomass and other structural attributes using Landsat composites and lidar plots', Remote Sensing of Environment, vol 209, pp 90-106 Matasci, G, Hermosilla, T, Wulder, MA, White, JC, Hobart, GW, Zald, HSJ & Coops, NC 'A spacetime data cube: Multi-temporal forest structure maps from landsat and lidar', 23-28 July 2017, pp 2581-2584 Meigs, GW, Kennedy, RE & Cohen, WB 2011, 'A Landsat time series approach to characterize bark beetle and defoliator impacts on tree mortality and surface fuels in conifer forests', Remote Sensing of Environment, vol 115, no 12, pp 3707-3718 Meyer, V, Saatchi, SS, Chave, J, Dalling, JW, Bohlman, S, Fricker, GA, Robinson, C, Neumann, M & Hubbell, S 2013, 'Detecting tropical forest biomass dynamics from repeated airborne lidar measurements', Biogeosciences, vol 10, no 8, pp 5421-5438 Moeur, M & Stage, AR 1995, 'Most similar neighbor: an improved sampling inference procedure for natural resource planning', Forest science, vol 41, no 2, pp 337-359 Mora, B, Herold, M, de Sy, V, Wijaya, A, Verchot, LV, Penman, J & eds 2012, Capacity development in national forest monitoring: Experiences and progress for REDD+, Center for International Forestry Research (CIFOR), Bogor, Indonesia, Morel, AC, Fisher, JB & Malhi, Y 2012, 'Evaluating the potential to monitor aboveground biomass in forest and oil palm in Sabah, Malaysia, for 2000–2008 with Landsat ETM+ and ALOS-PALSAR', International Journal of Remote Sensing, vol 33, no 11, pp 3614-3639 Nelson, BW, Mesquita, R, Pereira, JL, De Souza, SGA, Batista, GT & Couto, LB 1999, 'Allometric regressions for improved estimate of secondary forest biomass in the central Amazon', Forest Ecology and Management, vol 117, no 1, pp 149-167 Nguyen, H-T, Soto-Berelov, M, Jones, SD, Haywood, A & Hislop, S 2017, Mapping forest disturbance and recovery for forest dynamics over large areas using Landsat time-series remote 164 sensing, SPIE 10421, Remote Sensing for Agriculture, Ecosystems, and Hydrology XIX, Warsaw, Poland Nguyen, HC, Jung, J, Lee, J, Choi, SU, Hong, SY & Heo, J 2015, 'Optimal Atmospheric Correction for Above-Ground Forest Biomass Estimation with the ETM+ Remote Sensor', Sensors (Basel), vol 15, no 8, pp 18865-18886 Nguyen, T, Jones, S, Soto-Berelov, M, Haywood, A & Hislop, S 2018, 'A Comparison of Imputation Approaches for Estimating Forest Biomass Using Landsat Time-Series and Inventory Data', Remote Sensing, vol 10, no 11, p 1825 Nguyen, TH, Jones, S, Soto-Berelov, M, Skidmore, A, Haywood, A & Hislop, S 2019, 'Estimate forest biomass dynamics using multi-temporal lidar and single-date inventory data', in IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Yokohama, Japan Nguyen, TH, Jones, SD, Soto-Berelov, M, Haywood, A & Hislop, S 2018, 'A spatial and temporal analysis of forest dynamics using Landsat time-series', Remote Sensing of Environment, vol 217, pp 461-475 Nguyen, TH, Jones, SD, Soto-Berelov, M, Haywood, A & Hislop, S 2020, 'Monitoring aboveground forest biomass dynamics over three decades using Landsat time-series and single-date inventory data', International Journal of Applied Earth Observation and Geoinformation, vol 84, p 101952 Norovsuren, B, Tseveen, B, Batomunkuev, V & Renchin, T 'Estimation for forest biomass and coverage using Satellite data in small scale area, Mongolia', IOP Publishing, p 012019 Ohmann, JL & Gregory, MJ 2002, 'Predictive mapping of forest composition and structure with direct gradient analysis and nearest- neighbor imputation in coastal Oregon, U.S.A', Canadian Journal of Forest Research, vol 32, no 4, pp 725-741 Ohmann, JL, Gregory, MJ & Roberts, HM 2014, 'Scale considerations for integrating forest inventory plot data and satellite image data for regional forest mapping', Remote Sensing of Environment, vol 151, pp 3-15 Ohmann, JL, Gregory, MJ, Roberts, HM, Cohen, WB, Kennedy, RE & Yang, Z 2012, 'Mapping change of older forest with nearest-neighbor imputation and Landsat time-series', Forest Ecology and Management, vol 272, pp 13-25 Overman, JPM, Witte, HJL & Saldarriaga, JG 1994, 'Evaluation of regression models for aboveground biomass determination in Amazon rainforest', Journal of tropical Ecology, vol 10, no 02, pp 207-218 165 Palace, M, Keller, M, Asner, GP, Hagen, S & Braswell, B 2008, 'Amazon forest structure from IKONOS satellite data and the automated characterization of forest canopy properties', Biotropica, vol 40, no 2, pp 141-150 Pandit, S, Tsuyuki, S & Dube, T 2018, 'Estimating above-ground biomass in sub-tropical buffer zone community forests, Nepal, using Sentinel data', Remote Sensing, vol 10, no 4, p 601 Pardini, M, Armston, J, Qi, W, Lee, SK, Tello, M, Cazcarra Bes, V, Choi, C, Papathanassiou, KP, Dubayah, RO & Fatoyinbo, LE 2019, 'Early Lessons on Combining Lidar and Multi-baseline SAR Measurements for Forest Structure Characterization', Surveys in Geophysics, vol 40, no 4, pp 803837 Pflugmacher, D, Cohen, WB & E Kennedy, R 2012, 'Using Landsat-derived disturbance history (1972–2010) to predict current forest structure', Remote Sensing of Environment, vol 122, pp 146165 Pflugmacher, D, Cohen, WB, Kennedy, RE & Yang, Z 2014, 'Using Landsat-derived disturbance and recovery history and lidar to map forest biomass dynamics', Remote Sensing of Environment, vol 151, pp 124-137 Phua, M-H & Saito, H 2003, 'Estimation of biomass of a mountainous tropical forest using Landsat TM data', Canadian Journal of Remote Sensing, vol 29, no 4, pp 429-440 Pickell, PD, Hermosilla, T, J Frazier, R, Coops, NC & Wulder, MA 2015, 'Forest recovery trends derived from Landsat time series for North American boreal forests', International Journal of Remote Sensing, vol 37, no 1, pp 138-149 Potapov, P, Turubanova, S & Hansen, MC 2011, 'Regional-scale boreal forest cover and change mapping using Landsat data composites for European Russia', Remote Sensing of Environment, vol 115, no 2, pp 548-561 Powell, SL, Cohen, WB, Healey, SP, Kennedy, RE, Moisen, GG, Pierce, KB & Ohmann, JL 2010, 'Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: A comparison of empirical modeling approaches', Remote Sensing of Environment, vol 114, no 5, pp 1053-1068 Powell, SL, Cohen, WB, Kennedy, RE, Healey, SP & Huang, C 2013, 'Observation of Trends in Biomass Loss as a Result of Disturbance in the Conterminous U.S.: 1986–2004', Ecosystems, vol 17, no 1, pp 142-157 Qi, W, Saarela, S, Armston, J, Ståhl, G & Dubayah, R 2019, 'Forest biomass estimation over three distinct forest types using TanDEM-X InSAR data and simulated GEDI lidar data', Remote Sensing of Environment, vol 232, p 111283 166 Qiu, S, Lin, Y, Shang, R, Zhang, J, Ma, L & Zhu, Z 2018, 'Making Landsat Time Series Consistent: Evaluating and Improving Landsat Analysis Ready Data', Remote Sensing, vol 11, no 1, p 51 Reiche, J, Lucas, R, Mitchell, AL, Verbesselt, J, Hoekman, DH, Haarpaintner, J, Kellndorfer, JM, Rosenqvist, A, Lehmann, EA & Woodcock, CE 2016, 'Combining satellite data for better tropical forest monitoring', Nature Climate Change, vol 6, no 2, p 120 Reiche, J, Verbesselt, J, Hoekman, D & Herold, MJRSoE 2015, 'Fusing Landsat and SAR time series to detect deforestation in the tropics', vol 156, pp 276-293 Rew, R & Davis, G 1990, 'NetCDF: an interface for scientific data access', IEEE computer graphics and applications, vol 10, no 4, pp 76-82 Ripple, WJ 1986, 'Spectral reflectance relationships to leaf water stress', Photogrammetric Engineering and Remote Sensing, vol 52, no 10, pp 1669-1675 Romijn, E, Lantican, CB, Herold, M, Lindquist, E, Ochieng, R, Wijaya, A, Murdiyarso, D & Verchot, L 2015, 'Assessing change in national forest monitoring capacities of 99 tropical countries', Forest Ecology and Management, vol 352, pp 109-123 Roy, DP, Ju, J, Kline, K, Scaramuzza, PL, Kovalskyy, V, Hansen, M, Loveland, TR, Vermote, E & Zhang, C 2010, 'Web-enabled Landsat Data (WELD): Landsat ETM+ composited mosaics of the conterminous United States', Remote Sensing of Environment, vol 114, no 1, pp 35-49 Saarela, S, Holm, S, Healey, S, Andersen, H-E, Petersson, H, Prentius, W, Patterson, P, Næsset, E, Gregoire, T & Ståhl, G 2018, 'Generalized Hierarchical Model-Based Estimation for Aboveground Biomass Assessment Using GEDI and Landsat Data', Remote Sensing, vol 10, no 11 Sarker, LR & Nichol, JE 2011, 'Improved forest biomass estimates using ALOS AVNIR-2 texture indices', Remote Sensing of Environment, vol 115, no 4, pp 968-977 Schepaschenko, D, Chave, J, Phillips, OL, Lewis, SL, Davies, SJ, Rejou-Mechain, M, Sist, P, Scipal, K, Perger, C, Herault, B, Labriere, N, Hofhansl, F, Affum-Baffoe, K, Aleinikov, A, Alonso, A, Amani, C, Araujo-Murakami, A, Armston, J, Arroyo, L, Ascarrunz, N, Azevedo, C, Baker, T, Balazy, R, Bedeau, C, Berry, N, Bilous, AM, Bilous, SY, Bissiengou, P, Blanc, L, Bobkova, KS, Braslavskaya, T, Brienen, R, Burslem, D, Condit, R, Cuni-Sanchez, A, Danilina, D, Del Castillo Torres, D, Derroire, G, Descroix, L, Sotta, ED, d'Oliveira, MVN, Dresel, C, Erwin, T, Evdokimenko, MD, Falck, J, Feldpausch, TR, Foli, EG, Foster, R, Fritz, S, Garcia-Abril, AD, Gornov, A, Gornova, M, Gothard-Bassebe, E, Gourlet-Fleury, S, Guedes, M, Hamer, KC, Susanty, FH, Higuchi, N, Coronado, ENH, Hubau, W, Hubbell, S, Ilstedt, U, Ivanov, VV, Kanashiro, M, Karlsson, A, Karminov, VN, Killeen, T, Koffi, JK, Konovalova, M, Kraxner, F, Krejza, J, Krisnawati, H, Krivobokov, LV, Kuznetsov, MA, Lakyda, I, Lakyda, PI, Licona, JC, Lucas, RM, Lukina, N, Lussetti, D, Malhi, Y, Manzanera, JA, Marimon, B, Junior, BHM, Martinez, RV, Martynenko, OV, Matsala, M, Matyashuk, RK, Mazzei, L, Memiaghe, H, Mendoza, C, Mendoza, AM, Moroziuk, OV, Mukhortova, L, Musa, S, Nazimova, DI, Okuda, T, Oliveira, LC, Ontikov, PV, Osipov, AF, Pietsch, 167 S, Playfair, M, Poulsen, J, Radchenko, VG, Rodney, K, Rozak, AH, Ruschel, A, Rutishauser, E, See, L, Shchepashchenko, M, Shevchenko, N, Shvidenko, A, Silveira, M, Singh, J, Sonke, B, Souza, C, Sterenczak, K, Stonozhenko, L, Sullivan, MJP, Szatniewska, J, Taedoumg, H, Ter Steege, H, Tikhonova, E, Toledo, M, Trefilova, OV, Valbuena, R, Gamarra, LV, Vasiliev, S, Vedrova, EF, Verhovets, SV, Vidal, E, Vladimirova, NA, Vleminckx, J, Vos, VA, Vozmitel, FK, Wanek, W, West, TAP, Woell, H, Woods, JT, Wortel, V, Yamada, T, Nur Hajar, ZS & Zo-Bi, IC 2019, 'The Forest Observation System, building a global reference dataset for remote sensing of forest biomass', Sci Data, vol 6, no 1, p 198 Schroeder, TA, Schleeweis, KG, Moisen, GG, Toney, C, Cohen, WB, Freeman, EA, Yang, Z & Huang, C 2017, 'Testing a Landsat-based approach for mapping disturbance causality in U.S forests', Remote Sensing of Environment, vol 195, pp 230-243 Senf, C, Pflugmacher, D, Wulder, MA & Hostert, P 2015, 'Characterizing spectral–temporal patterns of defoliator and bark beetle disturbances using Landsat time series', Remote Sensing of Environment, vol 170, pp 166-177 Sessa, R & Dolman, H 2008, Terrestrial Essential Climate Variables For climate change assessment, mitigation and adaptation (GTOS 52), FAO, Rome Shafran-Natan, R & Svoray, T 'Solving spatio-temporal non-stationarity in raster database with fuzzy logic', Springer, pp 603-609 Shi, L & Liu, S 2017, 'Methods of Estimating Forest Biomass: A Review', in Biomass Volume Estimation and Valorization for Energy Shimizu, K, Ahmed, OS, Ponce-Hernandez, R, Ota, T, Win, ZC, Mizoue, N & Yoshida, S 2017, 'Attribution of Disturbance Agents to Forest Change Using a Landsat Time Series in Tropical Seasonal Forests in the Bago Mountains, Myanmar', Forests, vol 8, no 6, p 218 Sloan, S & Sayer, JA 2015, 'Forest Resources Assessment of 2015 shows positive global trends but forest loss and degradation persist in poor tropical countries', Forest Ecology and Management, vol 352, pp 134-145 Soenen, SA, Peddle, DR, Hall, RJ, Coburn, CA & Hall, FG 2010, 'Estimating aboveground forest biomass from canopy reflectance model inversion in mountainous terrain', Remote Sensing of Environment, vol 114, no 7, pp 1325-1337 Song, C & Woodcock, CE 2003, 'Monitoring forest succession with multitemporal Landsat images: Factors of uncertainty', IEEE Transactions on Geoscience and Remote Sensing, vol 41, no 11, pp 2557-2567 Song, C, Woodcock, CE, Seto, KC, Lenney, MP & Macomber, SA 2001, 'Classification and change detection using Landsat TM data: when and how to correct atmospheric effects?', Remote Sensing of Environment, vol 75, no 2, pp 230-244 168 Soto-Berelov, M, Haywood, A, Jones, SD, Hislop, S & Nguyen, HT 2018, Creating robust reference (training) datasets for large area time series disturbance attribution, Taylor and Francis, 978-1-13805459-2 Sun, G, Ranson, KJ, Guo, Z, Zhang, Z, Montesano, P & Kimes, D 2011, 'Forest biomass mapping from lidar and radar synergies', Remote Sensing of Environment, vol 115, no 11, pp 2906-2916 Thenkabail, PS, Stucky, N, Griscom, BW, Ashton, MS, Diels, J, Van der Meer, B & Enclona, E 2004, 'Biomass estimations and carbon stock calculations in the oil palm plantations of African derived savannas using IKONOS data', International Journal of Remote Sensing, vol 25, no 23, pp 5447-5472 Thiel, CJ, Thiel, C & Schmullius, CC 2009, 'Operational large-area forest monitoring in Siberia using ALOS PALSAR summer intensities and winter coherence', IEEE Transactions on Geoscience and Remote Sensing, vol 47, no 12, pp 3993-4000 Tomppo, E, Gschwantner, T, Lawrence, M & McRoberts, RE (eds) 2009, National Forest Inventories, Springer, Dordrecht Tomppo, E, Olsson, H, Ståhl, G, Nilsson, M, Hagner, O & Katila, M 2008, 'Combining national forest inventory field plots and remote sensing data for forest databases', Remote Sensing of Environment, vol 112, no 5, pp 1982-1999 Tsui, OW, Coops, NC, Wulder, MA, Marshall, PL & McCardle, A 2012, 'Using multi-frequency radar and discrete-return LiDAR measurements to estimate above-ground biomass and biomass components in a coastal temperate forest', ISPRS Journal of Photogrammetry and Remote Sensing, vol 69, pp 121-133 Tucker, CJ 1979, 'Red and photographic infrared linear combinations for monitoring vegetation', Remote Sensing of Environment, vol 8, no 2, pp 127-150 U S Geological Survey 2015, Landsat—Earth observation satellites, 2015-3081, Report, Reston, VA, USGS Publications Warehouse database, U S Geological Survey 2019, Landsat 9, 2019-3008, Report, Reston, VA, USGS Publications Warehouse database, UN-REDD Programme Secretariat 2013, National Forest Monitoring Systems: Monitoring and Measurement, Reporting and Verification (M & MRV) in the context of REDD+ Activities, FAO, the 7th Meeting of the UN-REDD Programme Policy Board, Berlin, 169 USGS 2019, Landsat Collection1 Level Product Definition, LSDS-1656,LSDS-1656, United State Geological Survey, Vermote, E, Justice, C, Claverie, M & Franch, B 2016, 'Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product', Remote Sensing of Environment, vol 185, pp 46-56 Vermote, EF, Tanre, D, Deuze, JL, Herman, M & Morcette, J 1997, 'Second Simulation of the Satellite Signal in the Solar Spectrum, 6S: an overview', IEEE Transactions on Geoscience and Remote Sensing, vol 35, no 3, pp 675-686 Viridans 2016, Victorian Ecosystems and Vegetation,, http://www.viridans.com/ECOVEG/, viewed 27 August 2018, Vogeler, JC, Braaten, JD, Slesak, RA & Falkowski, MJ 2018, 'Extracting the full value of the Landsat archive: Inter-sensor harmonization for the mapping of Minnesota forest canopy cover (1973–2015)', Remote Sensing of Environment, vol 209, pp 363-374 Waser, L, Ginzler, C & Rehush, N 2017, 'Wall-to-Wall Tree Type Mapping from Countrywide Airborne Remote Sensing Surveys', Remote Sensing, vol 9, no White, J, Wulder, M, Hobart, G, Luther, J, Hermosilla, T, Griffiths, P, Coops, N, Hall, R, Hostert, P & Dyk, A 2014, 'Pixel-based image compositing for large-area dense time series applications and science', Canadian Journal of Remote Sensing, vol 40, no 3, pp 192-212 White, JC, Coops, NC, Wulder, MA, Vastaranta, M, Hilker, T & Tompalski, P 2016, 'Remote Sensing Technologies for Enhancing Forest Inventories: A Review', Canadian Journal of Remote Sensing, vol 42, no 5, pp 619-641 White, JC, Wulder, MA, Hermosilla, T, Coops, NC & Hobart, GW 2017, 'A nationwide annual characterization of 25 years of forest disturbance and recovery for Canada using Landsat time series', Remote Sensing of Environment, vol 194, pp 303-321 Wilcoxon, F 1945, 'Individual comparisons by ranking methods', Biometrics bulletin, vol 1, no 6, pp 80-83 Wilson, BT, Knight, JF & McRoberts, RE 2018, 'Harmonic regression of Landsat time series for modeling attributes from national forest inventory data', ISPRS Journal of Photogrammetry and Remote Sensing, vol 137, pp 29-46 Woodcock, CE, Allen, R, Anderson, M, Belward, A, Bindschadler, R, Cohen, W, Gao, F, Goward, SN, Helder, D & Helmer, E 2008, 'Free access to Landsat imagery', Science, vol 320, no 5879, pp 1011-1011 170 Woodwell, GM & Whittaker, RH 1968, 'Primary production in terrestrial ecosystems', American Zoologist, vol 8, no 1, pp 19-30 Wulder, M, Skakun, R, Kurz, W & White, J 2004, 'Estimating time since forest harvest using segmented Landsat ETM+ imagery', Remote Sensing of Environment, vol 93, no 1, pp 179-187 Wulder, M, White, J, Fournier, R, Luther, J & Magnussen, S 2008, 'Spatially Explicit Large Area Biomass Estimation: Three Approaches Using Forest Inventory and Remotely Sensed Imagery in a GIS', Sensors, vol 8, no 1, p 529 Wulder, MA, Coops, NC, Roy, DP, White, JC & Hermosilla, T 2018, 'Land cover 2.0', International Journal of Remote Sensing, vol 39, no 12, pp 4254-4284 Wulder, MA, Hilker, T, White, JC, Coops, NC, Masek, JG, Pflugmacher, D & Crevier, Y 2015, 'Virtual constellations for global terrestrial monitoring', Remote Sensing of Environment, vol 170, pp 62-76 Wulder, MA, Loveland, TR, Roy, DP, Crawford, CJ, Masek, JG, Woodcock, CE, Allen, RG, Anderson, MC, Belward, AS, Cohen, WB, Dwyer, J, Erb, A, Gao, F, Griffiths, P, Helder, D, Hermosilla, T, Hipple, JD, Hostert, P, Hughes, MJ, Huntington, J, Johnson, DM, Kennedy, R, Kilic, A, Li, Z, Lymburner, L, McCorkel, J, Pahlevan, N, Scambos, TA, Schaaf, C, Schott, JR, Sheng, Y, Storey, J, Vermote, E, Vogelmann, J, White, JC, Wynne, RH & Zhu, Z 2019, 'Current status of Landsat program, science, and applications', Remote Sensing of Environment, vol 225, pp 127-147 Wulder, MA, Masek, JG, Cohen, WB, Loveland, TR & Woodcock, CE 2012, 'Opening the archive: How free data has enabled the science and monitoring promise of Landsat', Remote Sensing of Environment, vol 122, pp 2-10 Wulder, MA, White, JC, Bater, CW, Coops, NC, Hopkinson, C & Chen, G 2014, 'Lidar plots — a new large-area data collection option: context, concepts, and case study', Canadian Journal of Remote Sensing, vol 38, no 5, pp 600-618 Zald, HSJ, Ohmann, JL, Roberts, HM, Gregory, MJ, Henderson, EB, McGaughey, RJ & Braaten, J 2014, 'Influence of lidar, Landsat imagery, disturbance history, plot location accuracy, and plot size on accuracy of imputation maps of forest composition and structure', Remote Sensing of Environment, vol 143, pp 26-38 Zald, HSJ, Wulder, MA, White, JC, Hilker, T, Hermosilla, T, Hobart, GW & Coops, NC 2016, 'Integrating Landsat pixel composites and change metrics with lidar plots to predictively map forest structure and aboveground biomass in Saskatchewan, Canada', Remote Sensing of Environment, vol 176, pp 188-201 171 Zhang, J, Lu, C, Xu, H & Wang, G 2018, 'Estimating aboveground biomass of Pinus densatadominated forests using Landsat time series and permanent sample plot data', Journal of Forestry Research, vol 30, no 05, pp 1689-1706 Zhang, X & Kondragunta, S 2006, 'Estimating forest biomass in the USA using generalized allometric models and MODIS land products', Geophysical research letters, vol 33, no Zheng, D, Rademacher, J, Chen, J, Crow, T, Bresee, M, Le Moine, J & Ryu, S-R 2004, 'Estimating aboveground biomass using Landsat ETM+ data across a managed landscape in northern Wisconsin, USA', Remote Sensing of Environment, vol 93, no 3, pp 402-411 Zheng, G, Chen, JM, Tian, QJ, Ju, WM & Xia, XQ 2007, 'Combining remote sensing imagery and forest age inventory for biomass mapping', Journal of Environmental Management, vol 85, no 3, pp 616-623 Zhu, X & Liu, D 2015, 'Improving forest aboveground biomass estimation using seasonal Landsat NDVI time-series', ISPRS Journal of Photogrammetry and Remote Sensing, vol 102, pp 222-231 Zhu, Z 2017, 'Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications', ISPRS Journal of Photogrammetry and Remote Sensing, vol 130, pp 370-384 Zhu, Z & Woodcock, CE 2012, 'Object-based cloud and cloud shadow detection in Landsat imagery', Remote Sensing of Environment, vol 118, pp 83-94 Zhu, Z & Woodcock, CE 2014, 'Continuous change detection and classification of land cover using all available Landsat data', Remote Sensing of Environment, vol 144, pp 152-171 Zhu, Z, Woodcock, CE, Holden, C & Yang, Z 2015, 'Generating synthetic Landsat images based on all available Landsat data: Predicting Landsat surface reflectance at any given time', Remote Sensing of Environment, vol 162, pp 67-83 Zhu, Z, Woodcock, CE & Olofsson, P 2012, 'Continuous monitoring of forest disturbance using all available Landsat imagery', Remote Sensing of Environment, vol 122, pp 75-91 172 Appendices Table A.1 Landsat spectral indices 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