Monitoring the Amazon’s Secondary Forests Through Earth Observations

The Amazon contains approximately half of the remaining tropical forest area on Earth, but deforestation has already resulted in an estimated loss of ~20% of its original forest cover. In order to address deforestation in Amazonia, Brazil has developed state-of-the art programs for monitoring annual forest loss¹ and raising daily deforestation alerts². However, these efforts focus exclusively on primary forests and do not include re-growing secondary forests on previously deforested landscapes. Secondary forests (SFs) in some stage of re-growth now occupy approximately 20% of the deforested land area in the Brazilian Amazon and provide a number of important but poorly quantified ecosystem services.  The supervisory team recently showed that secondary forest loss rates have increased markedly over time, accounting for approximately three-quarters of Brazilian Amazon forest loss in 2014³. While increased secondary forest cutting has prevented the accumulation of substantial carbon stocks, it has also buffered the loss of primary forests whose biodiversity value is irreplaceable.

This PhD project aims to provide a methodological and scientific basis that will allow for better management of secondary forests by environmental agencies, resource managers and land use planners in Amazonian countries. More specifically, the project will harness the computational power of the Google Earth Engine (GEE) platform to achieve three objectives: 1) produce annual, high-resolution spatial products of secondary forest extent for the entire Amazon, 2) contribute to the development of a prototype secondary deforestation alerts system for the Brazilian Amazon state of Pará and 3) conduct scenario modelling to explore how secondary forest management could most effectively contribute to Brazil’s climate mitigation targets. In achieving these objectives, the PhD student will gain expertise in a variety of high-level computational skills including development of multivariate machine learning algorithms to discriminate secondary forests from other post-deforestation land cover types using historical time series of Landsat data⁴ and advanced change detection techniques applied to both radar (e.g. Sentinel-1 C-band SAR) and optical data (e.g. Landsat, Sentinel-2 MSI) to detect cutting of secondary forests (Objective 2). The large volumes of Landsat imagery available on GEE will allow for annual secondary forest loss to be mapped from the 1980’s until the present day. The fusion of optical and radar satellite data envisaged for the real-time alerts system is particularly powerful as it allows for detection of forest loss even during the wet season, when high cloud cover limits data availability from optical sensors. The The successful student will have the opportunity to visit Dr. Adami’s group in the INPE Amazon Regional Centre (INPE-CRA) in Belém to work with geoscientists responsible for operationalising Brazil’s Amazon deforestation alert system (DETER) and to conduct fieldwork in Amazonia to validate the secondary forest maps and alert products developed in the PhD.

The diverse supervisory team has a sustained track record in studying land use change and its impacts in Amazonia and is highly multidisciplinary in nature, with expertise spanning all project components.  The proposed industrial placement with Earth Blox should provide the student with an opportunity towards the end of the PhD to translate the major products of the PhD into a visualisation tool that can be easily used by resource managers. The student will be based at the School of Geography, University of Leeds within the Ecology and Global Change cluster, a world-leading research group with a strong focus on tropical forests.

This PhD is part of the NERC and UK Space Agency funded Centre for Doctoral Training "SENSE": the Centre for Satellite Data in Environmental Science. SENSE will train 50 PhD students to tackle cross-disciplinary environmental problems by applying the latest data science techniques to satellite data. All our students will receive extensive training on satellite data and AI/Machine Learning, as well as attending a field course on drones, and residential courses hosted by the Satellite Applications Catapult (Harwell), and ESA (Rome). All students will experience extensive training on professional skills, including spending 3 months on an industry placement. See http://www.eo-cdt.org