Publications

In this paper, we assess the potential of spatial, temporal and spatio-temporal deep learning methods for large-scale classification of land-use following tropical deforestation using dense satellite time series on the pan-tropical scale. Continental models performed better than the pan-tropical model, while overall spatio-temporal models performed better than spatial or temporal ones.

In this paper, we assess the use and quality of forest monitoring data sources for national reporting to the FRA in 236 countries and territories. More specifically, we analyze the use of remote sensing and for forest monitoring in FRA 2005–2020, assess data quality in FRA 2020 using FAO tier-based indicators, and zoom in to investigate changes in tropical forest monitoring capacities in FRA 2010–2020.

We utilized dense optical and radar satelite time series and active fire alerts to individually map forest disturbances in the province of Riau (Indonesia) and classified their temporal relationship (predating, coinciding, postdating) into seven so-called archetypes of fire-related forest disturbances. These archetypes can be related to different magnitudes of fire-related forest disturbances and burn severities and can be associated with specific land management practices, such as slash-and-burn agriculture and salvage logging.

Disturbed African tropical forests and woodlands have the potential to contribute to climate change mitigation. Therefore, there is a need to understand how carbon stocks of disturbed and recovering tropical forests are determined by environmental conditions and human use. In this case study, we explore how gradients in environmental conditions and human use determine aboveground biomass in national forest inventory plots located in forests and woodlands in mainland Tanzania.

Our interdisciplinary approach revealed the complexities of local direct and indirect DD drivers, and the complementarity of remotely sensed, spatially modelled and locally reported methods for driver identification. Overall, REDD+ interventions were found to be aligned with deforestation drivers.

This study quantified post-deforestation land use across the tropics for the period 1990–2000. This dataset was then combined with a pan-tropical AGB map at 30 m resolution to refine emission factor from forest conversion by matching deforestation areas with their carbon stock before and after clearing and to assess spatial dynamics by follow-up land use.

Assessing the performance of REDD+ efforts requires data on forest cover change. Innovations in remote sensing and forest monitoring provide ever-increasing levels of coverage, spatial and temporal detail, and accuracy. In this paper we analyse (1) differences in accuracy between datasets of forest cover change; (2) if and how combinations of datasets can increase accuracy; and we demonstrate (3) the effect of (not) doing accuracy assessments for REDD+ performance measurements.

Unprecedented opportunities to provide forest biomass data are created by a series of upcoming space-based missions. We assess various policy needs for biomass data and recommend a long-term collaborative effort among forest biomass data producers and users to meet these needs.

Limited data exists on emissions from agriculture-driven deforestation, and available data are typically uncertain. In this paper, we provide comparable estimates of emissions from all deforestation and agriculture-driven deforestation, with uncertainties for 91 countries across the tropics between 1990 and 2015.

Subnational REDD+ initiatives present an opportunity to compare different approaches to quantifying impacts on carbon emissions. This study (1) develops a Before-After-Control-Intervention (BACI) method to assess the effectiveness of 23 subnational REDD+ initiatives in Brazil, Peru, Cameroon, Tanzania, Indonesia and Vietnam; (2) compares the results at different scales; and (3) compares BACI with the simpler Before-After (BA) results.

The need for data on drivers and activities causing forest carbon change have been highlighted as central components in REDD+ readiness efforts. Assessment of direct and indirect drivers on the national level is often lacking or incomplete. This thesis explores the role of remote sensing for monitoring tropical forests for REDD+ in general, and for assessing land use and related carbon emissions linked to drivers of tropical deforestation in particular.

Land use change in South America, mainly deforestation, is a large source of anthropogenic CO2 emissions. This study quantifies proximate drivers of deforestation and related carbon losses in South America based on remote sensing time series in a systematic, spatially explicit manner.

Remote sensing technologies can provide objective, practical and cost-effective solutions for developing and maintaining REDD+ monitoring systems. This paper reviews the potential and status of available remote sensing data sources with a focus on synergies among various approaches and evolving technologies.