Ước tính sự biến động của sinh khối rừng sử dụng công nghệ viễn thám đa thời gian = Extrapolating forest biomass dynamics over large areas using time-series remote sensing

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

i

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

ii

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

iii

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 ha 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 pixel￾based 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

iv

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 ha of forests in Victoria, Australia). Annual predictions of AGB and its change were

v

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.

vi

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 Soto￾Berelov 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, và 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.

vii

Contents

Declaration .........................................................................................................................i

Abstract ......................................................................................................................... ii

Acknowledgement ............................................................................................................vi

Contents ........................................................................................................................vii

List of figures....................................................................................................................ix

List of tables ....................................................................................................................xii

List of publications ........................................................................................................ xiii

Chapter 1. Introduction ......................................................................................................1

1.1. Context...................................................................................................................2

1.2. Methods for estimating forest biomass....................................................................4

1.3. Satellite remote sensing time-series for forest monitoring .......................................9

1.4. Objectives and research questions.........................................................................12

1.5. Study area.............................................................................................................13

1.6. Thesis structure ....................................................................................................14

Chapter 2. 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 3. 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

viii

Chapter 4. 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 5. 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 6. 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

ix

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 4 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

x

Figure 3.11. Examples of disturbances followed by partial or no recovery. (a) Disturbance

and recovery patterns at the local scale (black box in Figure 3.5a) with two

marked examples of HD-NR and HD-PR. Images from Google Earth show the

pre-disturbance and current condition of forests on the ground: (b) a clear-fell

logged area (2013) not recovered yet; (c) an example of partial recovery

following a high intensity fire (2003), the current condition is clearly sparser than

pre-event condition. ........................................................................................76

Figure 4.1. Overall flowchart of steps used for developing and comparing biomass

imputation approaches. ...................................................................................86

Figure 4.2. Study area in Victoria, Australia. (a) Bioregions and VFMP inventory plots with

a local map showing examples of random points selected around an inventory

plot; (b) public land forest extent and Landsat scenes; (c) map of Australia.....87

Figure 4.3. Example of a trajectory of NBR time-series and extracted change metrics. .....91

Figure 4.4. Importance scores of predictor variables (scaled from 0 to 100) to response

variables, reported by the LASSO model. Each box plot associated with a

predictor variable shows the dispersion of importance scores of that predictor

variable to 9 response variables (listed in Table 4.1). ......................................97

Figure 4.5. Generalized root mean squared difference of biomass imputations reported by

kNN models (BM = biomass, BA = basal area, TD = stem density, VL = tree

volume). .........................................................................................................98

Figure 4.6. Relative mean deviation (%) of biomass imputations reported by kNN models

(BM = biomass, BA = basal area, TD = stem density, VL = tree volume). ......99

Figure 4.7. (a) The imputation map of total AGB for 2016 across public land forests in

Victoria. (b–d) Imputation maps of total AGB, AGB of live tree and dead tree,

respectively, at a local scale. (e) A Google Earth image (un-scaled) showing the

same area as the local maps. Predictions of AGB are consistent with forest

conditions displayed on the Google Earth image, with total AGB at a medium

level. Live trees predominate in the top-left corner while dead trees are dominant

in the bottom-right corner as a consequence of a 2007 fire. ...........................100

Figure 4.8. Imputed versus observed biomass values from leave-one-out cross validation (n

= 633), reported by the RF-based BA-TD model...........................................101

Figure 4.9. Boxplots of scaled imputed AGB values by different disturbance severity levels

associated with fire and logging disturbances occurring between 2013 and 2016.

.....................................................................................................................102

xi

Figure 4.10. AGB predictions (scaled values) according to disturbance severity and time

since disturbance (TSD)................................................................................102

Figure 5.1. Study area in Victoria, Australia. (a) The public forest extent, Landsat scenes,

and extent of Lidar capture; (b) IBRA bioregions and forest inventory plots from

the Victorian Forest Monitoring Program (VFMP)........................................115

Figure 5.2. Change metrics extracted from a fitted AGB trajectory.................................121

Figure 5.3. Relationship between Lidar-based and Landsat-based AGB values across the

8210 validation plots, with the 1:1 line in red. Point density is indicated by a

colour gradient from light yellow for high-density to purple for low-density. 123

Figure 5.4. Validation of AGB change according to the history of forest disturbance and

recovery. The 1:1 line is shown in red and the intercept of x and y axes in black.

.....................................................................................................................125

Figure 5.5. Predicted AGB maps of 2003 (a) and 2007 (b) in eastern Victoria, Australia;

AGB change over 15-year periods: 2003-1988 (c) and 2017-2003 (b)...........126

Figure 5.6. AGB change metrics across Victoria’s public forests during 1988-2017. (a) and

(b) show AGB loss and gain (∆AGBloss and ∆AGBgain) as a result of the greatest

disturbance and subsequent recovery, respectively. (c) and (d) show the RI and

Y2R at a local scale (black box in (a))...........................................................127

Figure 5.7. Temporal patterns of AGB loss caused by (a) fire and (b) logging................129

Figure 5.8. Distribution of rAGBloss and Y2R by disturbance levels with results from variance

tests, (**** is noted for the significance level of p < 0.0001). .........................129

Figure 5.9. Means of AGB loss and Y2R associated with fire disturbance across bioregions,

with 95% confidence intervals. Notes: H = High, M = Medium, and L = Low

disturbance level)..........................................................................................131

Figure 5.10. Means of AGB loss and number of Y2R associated with logging disturbance

across bioregions. Notes: H = High, M = Medium, and L = Low disturbance

level) ............................................................................................................132

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

xii

List of tables

Table 2.1. Common publicly available and automate change detection algorithms using

LTS. See Table A.1 for a description of spectral indices. ................................22

Table 2.2. Benefits of using LTS to improve the estimation of forest AGB (both single-date

and over time).................................................................................................24

Table 2.3. A summary of studies that used LTS-based approaches for estimating forest

AGB dynamics. See Table A.1 for a description of spectral indices. ...............30

Table 3.1. Predictor variables used for modelling phases (1+2). For each phase, these

variables were used to derive both primary and secondary disturbance events.57

Table 3.2. Definition of disturbance and recovery levels used in this study.......................61

Table 3.3. Categories describing disturbance and recovery patterns..................................62

Table 3.4. The OOB accuracy assessments of modelling disturbance and recovery patterns

(phase one). The number in each cell corresponds to the number of reference

pixels in that category (HD = High disturbance, MD = Medium disturbance, LD

= Low disturbance, FR = Full recovery, PR = Partial recovery, NR = No

recovery; PA = producer’s accuracy, UA = user’s accuracy). ..........................68

Table 3.5. Out-of-bag accuracy assessments for modelling disturbance agents (phase two).

The number in each cell corresponds to the number of patches in that category.

.......................................................................................................................69

Table 3.6. The most important variables for predicting disturbance causal agents (phase two).

.......................................................................................................................70

Table 4.1. Forest biomass and structure variables extracted from inventory data. .............89

Table 4.2. Predictor variables derived from LTS and topographic and climatic data. ........90

Table 4.3. Model scenarios developed for each distance technique (BM = biomass, BA =

basal area, TD = stem density, VL = tree volume, X = denotes response variable

group in each model). .....................................................................................94

Table 5.1. Internal assessment of the RF-based kNN model via bootstrapping (n = 633) 123

Table 5.2. Time-series validation of AGB predictions using un-changed Lidar pixels. The

colour ramp dark to light grey corresponds with higher to lower accuracies,

respectively. .................................................................................................124

Table 5.3. Spatial summary of AGB dynamics by bioregion and at the state level during

1988-2017 ....................................................................................................128

Table A.1. Landsat spectral indices commonly used for forest AGB estimates. ..............173

xiii

List of publications

Published peer-reviewed journal articles

Nguyen, T.H., Jones, S.D., 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, 84. p.101952. https://doi.org/10.1016/j.jag.2019.101952.

Nguyen, T.H., Jones, S.D., Soto-Berelov, M., Haywood, A., & Hislop, S. (2020). Landsat

Time-Series for Estimating Forest Aboveground Biomass and Its Dynamics across Space

and Time: A Review. Remote Sensing, 12(1), p.98. https://doi.org/10.3390/rs12010098.

Nguyen, T.H., 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, 10(11), p.1825.

https://doi.org/10.3390/rs10111825.

Nguyen, T.H., Jones, S.D., Soto-Berelov, M., Haywood, A., & Hislop, S. (2018). A

spatial and temporal analysis of forest dynamics using Landsat time-series. Remote

Sensing of Environment, 217, pp.461-475. https://doi.org/10.1016/j.rse.2018.08.028.

Peer-reviewed conference proceedings

Nguyen, T.H., Jones, S.D., Soto-Berelov, M., Haywood, A., & Hislop, S., (2019).

“Estimate forest biomass dynamics using multi-temporal lidar and single-date inventory

data”. In IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing

Symposium (pp. 7338-7341). IEEE. Yokohama, Japan.

https://doi.org/10.1109/IGARSS.2019.8897905.

Nguyen, T.H., Jones, S.D., Soto-Berelov, M., Haywood, A., & Hislop, S., (2018).

“Extrapolating single-date forest inventory attributes through space and time using

Landsat time series”. ForestSAT 2018 conference. Maryland, USA.

Nguyen, T.H., Jones, S.D., Soto-Berelov, M., Haywood, A., & Hislop, S., (2017).

"Mapping forest disturbance and recovery for forest dynamics over large areas using

Landsat time-series remote sensing", In: SPIE 10421, Remote Sensing for Agriculture,

Ecosystems, and Hydrology XIX (pp. 104210W-110421-104211). Warsaw, Poland.

http://dx.doi.org/10.1117/12.2276913.

1

Chapter 1. Introduction

This study aims to develop a robust and consistent remote sensing-based framework that can

support developing countries to improve their capacities in monitoring and reporting forest

biomass and carbon stocks and changes across large areas. This chapter introduces the

research context, provides an overview of biomass estimation methods as well asthe concept

of satellite remote sensing time-series for forest monitoring. Finally, an outline of the

research questions and the structure of the thesis is given.