Sarah Carter

Performance of vegetation indices from Landsat time series in deforestation monitoring

Abstract The performance of Landsat time series (LTS) of eight vegetation indices (VIs) was assessed for monitoring deforestation across the tropics. Three sites were selected based on differing remote sensing observation frequencies, deforestation drivers and environmental factors. The LTS of each VI was analysed using the Breaks For Additive Season and Trend (BFAST) Monitor method to identify deforestation. A robust reference database was used to evaluate the performance regarding spatial accuracy, sensitivity to observation frequency and combined use of multiple VIs. The canopy cover sensitive Normalized Difference Fraction Index (NDFI) was the most accurate. Among those tested, wetness related VIs (Normalized Difference Moisture Index (NDMI) and the Tasselled Cap wetness (TCw)) were spatially more accurate than greenness related VIs (Normalized Difference Vegetation Index (NDVI) and Tasselled Cap greenness (TCg)). When VIs were fused on feature level, spatial accuracy was improved and overestimation of change reduced. NDVI and NDFI produced the most robust results when observation frequency varies.

Expert Perceptions of the Role of Biochar as a Carbon Abatement Option with Ancillary Agronomic and Soil-Related Benefits

Biochar is the solid remains of organic material that has been heated to > 350°C in an oxygen-limited environment, frequently intended to be mixed with soils. Biochar usually contains 80 – 90% stable aromatic carbon that is resistant to decomposition and mineralization (possibly for hundreds to thousands of years): it is, therefore, a potential way of removing carbon from the atmosphere for storage and has received considerable attention in the specialist media. Because the field is new there is no authoritative scientific assessment of the state-of-knowledge and its certainty. We therefore undertook an internet-based survey (n=145) to elicit expert opinion on the state-of-knowledge on biochar science and engineering. While expert opinion broadly supports the proposed benefits of biochar, the survey also identified a high degree of uncertainty attached to most of the knowledge-claims: more basic underpinning R&D will be necessary before policymakers will have confidence in implementing biochar projects.

Open land cover from OpenStreetMap and remote sensing

OpenStreetMap (OSM) tags were used to produce a global Open Land Cover (OLC) product with fractional data gaps available at osmlanduse.org. Data gaps in the global OLC map were filled for a case study in Heidelberg, Germany using free remote sensing data, which resulted in a land cover (LC) prototype with complete coverage in this area. Sixty tags in the OSM were used to allocate a Corine Land Cover (CLC) level 2 land use classification to 91.8% of the study area, and the remaining gaps were filled with remote sensing data. For this case study, complete are coverage OLC overall accuracy was estimated 87%, which performed better than the CLC product (81% overall accuracy) of 2012. Spatial thematic overlap for the two products was 84%. OLC was in large parts found to be more detailed than CLC, particularly when LC patterns were heterogeneous, and outperformed CLC in the classification of 12 of the 14 classes. Our OLC product represented data created in different periods; 53% of the area was 2011–2016, and 46% of the area was representative of 2016–2017.

Climate-smart land use requires local solutions, transdisciplinary research, policy coherence and transparency

ABSTRACT Successfully meeting the mitigation and adaptation targets of the Paris Climate Agreement (PA) will depend on strengthening the ties between forests and agriculture. Climate-smart land use can be achieved by integrating climate-smart agriculture (CSA) and REDD+. The focus on agriculture for food security within a changing climate, and on forests for climate change mitigation and adaptation, can be achieved simultaneously with a transformational change in the land-use sector. Striving for both independently will lead to competition for land, inefficiencies in monitoring and conflicting agendas. Practical solutions exist for specific contexts that can lead to increased agricultural output and forest protection. Landscape-level emissions accounting can be used to identify these practices. Transdisciplinary research agendas can identify and prioritize solutions and targets for integrated mitigation and adaptation interventions. Policy coherence must be achieved at a number of levels, from international to local, to avoid conflicting incentives. Transparency must lastly be integrated, through collaborative design of projects, and open data and methods. Climate-smart land use requires all these elements, and will increase the likelihood of successful REDD+ and CSA interventions. This will support the PA as well as other initiatives as part of the Sustainable Development Goals.