Global oil palm suitability assessment

The palm oil boom of recent years has brought about both positive - economic development - and negative impacts - deforestation, habitat losses and increased GHG emissions - in the main producer countries in South-East Asia. As global demand for palm oil is still increasing, governments of developing and emerging countries increasingly promote oil palm cultivation as a major contributor to economic development, but there are concerns about the potential negative impacts of oil palm expansion on the natural environment in these countries. Against this backdrop, we present a global oil palm suitability map on the spatial resolution of 0.5 arc minutes (approximately 1 km) in order to i) help land use planning for production and conservation in tropical and sub-tropical areas, and ii) provide insights about the sustainability of further oil palm production expansion in the future iii) help identify potential trade-offs between further oil palm plantation expansion, forest conservation, and use planning at the regional level. By combining climate, soil and topography data, we find that global suitable areas are concentrated in nine tropical countries, with Brazil harboring the largest tracts of suitable land, followed by the Democratic Republic of the Congo and Indonesia. We conclude that this map will most useful to achieve the goals stated above when combined with a number of socio-economic factors that also drive of oil palm expansion dynamics

Simple proof of confidentiality for private quantum channels in noisy environments

Complete security proofs for quantum communication protocols can be notoriously involved, which convolutes their verification, and obfuscates the key physical insights the security finally relies on. In such cases, for the majority of the community, the utility of such proofs may be restricted. Here we provide a simple proof of confidentiality for parallel quantum channels established via entanglement distillation based on hashing, in the presence of noise, and a malicious eavesdropper who is restricted only by the laws of quantum mechanics. The direct contribution lies in improving the linear confidentiality levels of recurrence-type entanglement distillation protocols to exponential levels for hashing protocols. The proof directly exploits the security relevant physical properties: measurement-based quantum computation with resource states and the separation of Bell-pairs from an eavesdropper. The proof also holds for situations where Eve has full control over the input states, and obtains all information about the operations and noise applied by the parties. The resulting state after hashing is private, i.e., disentangled from the eavesdropper. Moreover, the noise regimes for entanglement distillation and confidentiality do not coincide: Confidentiality can be guaranteed even in situation where entanglement distillation fails. We extend our results to multiparty situations which are of special interest for secure quantum networks.Comment: 5 + 11 pages, 0 + 4 figures, A. Pirker and M. Zwerger contributed equally to this work, replaced with accepted versio

Long-range big quantum-data transmission

We introduce an alternative type of quantum repeater for long-range quantum communication with improved scaling with the distance. We show that by employing hashing, a deterministic entanglement distillation protocol with one-way communication, one obtains a scalable scheme that allows one to reach arbitrary distances, with constant overhead in resources per repeater station, and ultrahigh rates. In practical terms, we show that also with moderate resources of a few hundred qubits at each repeater station, one can reach intercontinental distances. At the same time, a measurement-based implementation allows one to tolerate high loss, but also operational and memory errors of the order of several percent per qubit. This opens the way for long-distance communication of big quantum data.Comment: revised manuscript including new result

Shifting patterns of oil palm driven deforestation in Indonesia and implications for zero-deforestation commitments

Oil palm plantations in Indonesia have been linked to substantial deforestation in the 1990s and 2000s, though recent studies suggest that new plantations are increasingly developed on non-forest land. Without nationwide data to establish recent baseline trends, the impact of commitments to eliminate deforestation from palm oil supply chains could therefore be overestimated. We examine the area and proportion of plantations replacing forests across Sumatra, Kalimantan, and Papua up to 2015, and map biophysically suitable areas for future deforestation-free expansion. We created new maps of oil palm plantations for the years 1995, 2000, 2005, 2010 and 2015, and examined land cover replaced in each period. Nationwide, oil palm plantation expansion occurred at an average rate of 450,000 ha yr−1, and resulted in an average of 117,000 ha yr−1 of deforestation, during 1995–2015. Our analysis of the most recent five-year period (2010–2015) shows that the rate of deforestation due to new plantations has remained relatively stable since 2005, despite large increases in the extent of plantations. As a result, the proportion of plantations replacing forests decreased from 54% during 1995–2000, to 18% during 2010–2015. In addition, we estimate there are 30.2 million hectares of non-forest land nationwide which meet biophysical suitability criteria for oil palm cultivation. Our findings suggest that recent zero-deforestation commitments may not have a large impact on deforestation in Sumatra, where plantations have increasingly expanded onto non-forest land over the past twenty years, and which hosts large potentially suitable areas for future deforestation-free expansion. On the other hand, these pledges could have more influence in Kalimantan, where oil palm driven deforestation increased over our study period, and in Papua, a new frontier of expansion with substantial remaining forest cover

Modeling Burned Areas in Indonesia: The FLAM Approach

Large-scale wildfires affect millions of hectares of land in Indonesia annually and produce severe smoke haze pollution and carbon emissions, with negative impacts on climate change, health, the economy and biodiversity. In this study, we apply a mechanistic fire model to estimate burned area in Indonesia for the first time. We use the Wildfire Climate Impacts and Adaptation Model (FLAM) that operates with a daily time step on the grid cell of 0.25 arc degrees, the same spatio-temporal resolution as in the Global Fire Emissions Database v4 (GFED). GFED data accumulated from 2000–2009 are used for calibrating spatially-explicit suppression efficiency in FLAM. Very low suppression levels are found in peatland of Kalimantan and Sumatra, where individual fires can burn for very long periods of time despite extensive rains and fire-fighting attempts. For 2010–2016, we validate FLAM estimated burned area temporally and spatially using annual GFED observations. From the validation for burned areas aggregated over Indonesia, we obtain Pearson’s correlation coefficient separately for wildfires and peat fires, which equals 0.988 in both cases. Spatial correlation analysis shows that in areas where around 70% is burned, the correlation coefficients are above 0.6, and in those where 30% is burned, above 0.9