An error in the unit conversion from pixels to hectares lead to all the areal quantities in the text being smaller than they should have been. Only the number of hectares were changed; none of the text nor tables were changed. The changes do not affect the overall results or conclusions. AbstractShifting cultivation has traditionally been practiced in the Democratic Republic of Congo (DRC) by carving agricultural fields out of primary and secondary forest, resulting in the rural complex: a characteristic land cover mosaic of roads, villages, active and fallow fields and secondary forest. Forest clearing has varying impacts depending on where it occurs relative to this area: whether inside it, along its primary forest interface, or in more isolated primary forest areas. The spatial contextualization of forest cover loss is therefore necessary to understand its impacts and plan its management. We characterized forest clearing using spatial
Shifting cultivation has been shown to be the primary cause of land use change in the Democratic Republic of Congo (DRC). Traditionally, forested and fallow land are rotated in a slash and burn cycle that has created an agricultural mosaic, including secondary forest, known as the rural complex. This study investigates the land use context of new forest clearing (during 2000–2015) in primary forest areas outside of the established rural complex. These new forest clearings occur as either rural complex expansion (RCE) or isolated forest perforations (IFP), with consequent implications on the forest ecosystem and biodiversity habitat. During 2000–2015, subsistence agriculture was the dominant driver of forest clearing for both extension of settled areas and pioneer clearings removed from settled areas. Less than 1% of clearing was directly attributable to land uses such as mining, plantations, and logging, showing that the impact of commercial operations in the DRC is currently dwarfed by a reliance on small-holder shifting cultivation. However, analyzing the landscape context showed that large-scale agroindustry and resource extraction activities lead to increased forest loss and degradation beyond their previously-understood footprints. The worker populations drawn to these areas create communities that rely on shifting cultivation and non-timber forest products (NTFP) for food, energy, and building materials. An estimated 12% of forest loss within the RCE and 9% of the area of IFP was found to be within 5 km of mines, logging, or plantations. Given increasing demographic and commercial pressures on DRC’s forests, it will be crucial to factor in this landscape-level land use change dynamic in land use planning and sustainability-focused governance.
The rural complex is the inhabited agricultural land cover mosaic found along the network of rivers and roads in the forest of the Democratic Republic of Congo. It is a product of traditional small-holder shifting cultivation. To date, thanks to its distinction from primary forest, this area has been mapped as relatively homogenous, leaving the proportions of land cover heterogeneity within it unknown. However, the success of strategies for sustainable development, including land use planning and payment for ecosystem services, such as Reduced Emissions from Deforestation and Degradation, depends on the accurate characterization of the impacts of land use on natural resources, including within the rural complex. We photo-interpreted a simple random sample of 1000 points in the established rural complex, using 3106 high resolution satellite images obtained from the National Geospatial-Intelligence Agency, together with 406 images from Google Earth, spanning the period 2008-2016. Results indicate that nationally the established rural complex includes 5% clearings, 10% active fields, 26% fallows, 34% secondary forest, 2% wetland forest, 11% primary forest, 6% grasslands, 3% roads and settlements and 2% commercial plantations. Only a small proportion of sample points were plantations, while other commercial dynamics, such as logging and mining, were not detected in the sample. The area of current shifting cultivation accounts for 76% of the established rural complex. Added to primary forest (11%), this means that 87% of the rural complex is available for shifting cultivation. At the current clearing rate, it would take ∼18 years for a complete rotation of the rural complex to occur. Additional pressure on land results in either the cultivation of non-preferred land types within the rural complex (such as wetland forest), or expansion of agriculture into nearby primary forests, with attendant impacts on emissions, habitat loss and other ecosystems services.
Abstract. Increasing amounts of data, together with more computing power and better machine learning algorithms to analyse the data, are causing changes in almost every aspect of our lives. This trend is expected to continue as more data keep becoming available, computing power keeps improving and machine learning algorithms keep improving as well. Flood risk and impact assessments are also being influenced by this trend, particularly in areas such as the development of mitigation measures, emergency response preparation and flood recovery planning. Machine learning methods have the potential to improve accuracy as well as reduce calculating time and model development cost. It is expected that in the future more applications will become feasible and many process models and traditional observation methods will be replaced by machine learning. Examples of this include the use of machine learning on remote sensing data to estimate exposure and on social media data to improve flood response. Some improvements may require new data collection efforts, such as for the modelling of flood damages or defence failures. In other components, machine learning may not always be suitable or should be applied complementary to process models, for example in hydrodynamic applications. Overall, machine learning is likely to drastically improve future flood risk and impact assessments, but issues such as applicability, bias and ethics must be considered carefully to avoid misuse. This paper presents some of the current developments on the application of machine learning in this field and highlights some key needs and challenges.
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