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.