Monitoring tropical forest dynamics using Landsat time series and community-based data

Tropical forests cover a significant portion of the earth's surface and provide a range of

ecosystem services, but are under increasing threat due to human activities. Deforestation

and forest degradation in the tropics are responsible for a large share of global CO2

emissions. As a result, there has been increased attention and effort invested in the

reduction of emission from deforestation and degradation and the protection of remaining

tropical forests in recent years. Methods for tropical forest monitoring are therefore vital

to track progress on these goals. Two data streams in particular have the potential to

play an important role in forest monitoring systems. First, satellite remote sensing is

recognized as a vital technology in supporting the monitoring of tropical forests, of which

the Landsat family of satellite sensors has emerged as one of the most important. Owing

to its open data policy, a large range of methods using dense Landsat time series have

been developed recently which have the potential to greatly enhance forest monitoring

in the tropics. Second, community-based monitoring is supported in many developing

countries as a way to engage forest communities and lower costs of monitoring activities.

The development of operational monitoring systems will need to consider how these data

streams can be integrated for the effective monitoring of forest dynamics.

This thesis is concerned with the monitoring of tropical forest dynamics using a combi-

nation of dense Landsat time series and community-based monitoring data. The added

value conferred by these data streams in monitoring deforestation, degradation and re-

growth in tropical forests is assessed. This goal is approached from two directions. First,

the application of econometric structural change monitoring methods to Landsat time

series is explored and the efficacy and accuracy of these methods over several tropical

forest sites is tested. Second, the integration of community-based monitoring data with

Landsat time series is explored in an operational setting. Using local expert monitoring

data, the reliability and consistency of these data against very high resolution optical

imagery are assessed. A bottom-up approach to characterize forest change in high the-

matic detail using a priori community-based observations is then developed based on

these findings.

Chapter 2 presents a robust data-driven approach to detect small-scale forest disturbances

driven by small-holder agriculture in a montane forest in southwestern Ethiopia. The

Breaks For Additive Season and Trend Monitoring (BFAST Monitor) method is applied

to Landsat NDVI time series using sequentially defined one-year monitoring periods. In

addition to time series breakpoints, the median magnitude of residuals (expected versus

observed observations) is used to characterize change. Overall disturbances are mapped

with producer's and user's accuracies of 73%. Using ordinal logistic regression (OLR)

models, the extent to which degradation and deforestation can be separately mapped is

explored. The OLR models fail to distinguish between deforestation and degradation,

however, owing to the subtle and diffuse nature of forest degradation processes.

Chapter 3 expands upon the approach presented in Chapter 2 by tracking post-disturbance

forest regrowth in a lowland tropical forest in southeastern Peru using Landsat Normalized

Difference Moisture Index (NDMI) time series. Disturbance between 1999 and 2013 are

mapped using the same sequential monitoring method as in Chapter 2. Pixels where

disturbances are detected are then monitored for follow-up regrowth using the reverse of

the method employed in Chapter 2. The time of regrowth onset is recorded based on a

comparison to defined stable history period. Disturbances are mapped with 91% accuracy,

while post-disturbance regrowth is mapped with a total accuracy of 61% for disturbances

before 2006.

Chapter 4 and 5 explore the integration of community-based forest monitoring data and

remote sensing data streams. Major advantages conferred by community-based forest dis-

turbance observations include the ability to report on drivers and other thematic details

of forest change and the ability to detect low-level forest degradation before these changes

are visible above the forest canopy. Chapter 5 builds on these findings and presents a

novel bottom-up approach to characterize forest changes using local expert disturbance

reports to calibrate and validate forest change models based on Landsat time series. Using

random forests and a selection of Landsat spectral and temporal metrics, models describ-

ing forest state variables (deforested, degraded or stable) at a given time are produced.

As local expert data are continually acquired, the ability of these models to predict forest

degradation are shown to improve.

Chapter 6 summarizes the main findings of the thesis and provides a future outlook, given

the prospect of increasing availability of satellite and in situ data for tropical forest mon-

itoring. This chapter argues that forest change methods should strive to utilize satellite

time series and ground data to their maximum potential. As “big data" emerges in the

field of earth observation, new data streams need to be accommodated in monitoring

methods. Operational forest monitoring systems that are able to integrate such diverse

data streams can support broader forest monitoring goals such as quantitative monitoring

of forest dynamics. Even with a wealth of time series based forest disturbance methods

developed recently, forest monitoring systems require locally calibrated forest change esti-

mates with higher spatial, temporal and thematic resolution to support a variety of forest

monitoring objectives.