Publications

We developed a tropical forest vulnerability index (TFVI) to detect and evaluate the vulnerability of global tropical forests to threats across space and time. Four decades of satellite data show widespread vulnerability across the tropics, while the response of rainforests to heat and drying varies across the continents. The early warning from the index can identify regions for conservation and restoration.

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.

This study assesses the impact of achieving cereal self‐sufficiency by the year 2050 for 10 sub-Saharan countries on GHG emissions related to different scenarios of increasing cereal production, ranging from intensifying production to agricultural area expansion.

Countries with limited forest monitoring capabilities in the tropics and subtropics rely on IPCC 2006 default aboveground net biomass change (∆AGB) rates. As part of the 2019 Refinement to these guidelines, we provide a rigorous and traceable updates of the IPCC 2006 default rates in tropical and subtropical ecological zones. This study is an important step towards quantifying the role of tropical and subtropical forests as carbon sinks with higher accuracy and our new rates can be used for large‐scale GHG accounting by governmental bodies, nongovernmental organizations and in scientific research.

We present an overview of the location, goals and activities, and an estimated climate change mitigation potential of 154 recent, ongoing and planned restoration projects in Latin America and the Carribean.

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.

Greenhouse gas emissions reduction from the land use sector requires that accurate, consistent and comparable datasets are available for transparent reference and progress monitoring. Through an online survey, we investigated stakeholders’ data needs for estimating forest area and change, forest biomass and emission factors, and AFOLU GHG emissions. Our results show that current open and freely available datasets and portals are only able to fulfil stakeholder needs to a certain degree. We also identify key elements for increasing overall transparency of data sources, definitions and methodologies.

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.

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.

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.

In order to tackle deforestation and forest degradation (DD), REDD+ policy interventions should adress the drivers of DD. In this article we compare drivers of DD with REDD+ interventions reported by 43 REDD+ countries in 98 readiness documents. In addition, we discuss the implications for monitoring the effectiveness of proposed interventions.

REDD+ countries are required to establish a national monitoring system. Community-based monitoring (CBM) can be useful for tracking locally driven forest change activities and their impacts. In this paper, we review some of the key issues regarding CBM and options to link it to national forest monitoring systems.

Countries are encouraged to identify drivers of deforestation and forest degradation (DD) in the development of national strategies and action plans for REDD+. In this letter we provide an assessment of proximate drivers of DD by synthesizing empirical data reported by countries as part of their REDD+ readiness activities and scientific literature.

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.