Urbanisation trends and global environmental change are two of the most critical modern-day stressors threatening the resilience of cities around the world. This paper focuses on Honiara, the capital city of the Solomon Islands, which is experiencing rapid urbanisation and a resultant spread of informal settlements. Similar to other primary cities in the South Pacific, the rate of urbanisation is severely testing the local government’s ability to respond to growing levels of informality; and increasing the climate vulnerability of residents. Based on recent urban climate resilience and land tenure research conducted for the United Nations Human Settlements Programme (UN-Habitat) and the Global Land Tool Network (GLTN) respectively, the paper analyses land issues in two informal settlements in Honiara to highlight the inter-linkages between security of land tenure and climate vulnerability, and how insecure land tenure adversely impacts local adaptive capacity and adaptation planning. This analysis is embedded in the context of the South Pacific region, where duality tensions exist between Western-influenced land tenure arrangements within cities - a legacy of colonial times - and customary arrangements that operate in the surrounding peri-urban and provincial areas. Given the identification of strong links between security of land tenure and climate vulnerability, and the complexity of property rights in the region, the paper argues that principles of good land governance are an essential component of climate resilience thinking and actions.

Mapping informal settlements is crucial for resource and utility management and planning. In 2003, the UN-Habitat developed a process for mapping and monitoring urban inequality to support reporting against the sustainable development goals (SDGs). Informal settlement indicators are used as a framework to carry out image analysis, and include vegetation extent, lacunarity of housing structures / vacant land, road segment type and materials, texture measures of built-up areas, roofing extent of built-up areas and dwelling size. Object-based image analysis (OBIA) methods are recommended to identify informal settlements. This paper documents the application of OBIA to map informal settlements, drawing on the ontology of Kohli et al. (2012) and the indicators of Owen and Wong (2013) for a Middle Eastern city. Three informal settlements with different land use histories were selected to represent old and new informal settlements in the city of Jeddah, Saudi Arabia. Vegetation extent was the most successful indicator detected, with 100% producer accuracy and over 84% user accuracy, followed by the road network, with 84% producer and user accuracies in older informal settlements and 73% producer accuracy and 96% user accuracy across all case studies. Lacunarity of housing structures / vacant land was detected well in informal settlements. The texture measure indicator was detected using with low producer accuracy across all case studies. The roofing extent of the built-up area is detected with better producer and user accuracies than texture measures. The dwellings size indicator generally failed to distinguish formal from informal settlements. Informal and formal were distinguished with an overall accuracy of 83%. This research concludes that OBIA is a useful method to map informal settlement indicators in Middle Eastern cities. However, a generic ruleset for mapping informal settlements remains elusive, and each indicator requires significant localised ‘tuning’.

The identification of informal settlements in urban areas is an important step in developing and implementing pro-poor urban policies. Understanding when, where and who lives inside informal settlements is critical in efforts to improve their resilience. This study aims to analyse the capability of machine-learning (ML) methods to map informal settlement areas in Jeddah, Saudi Arabia, using very-high-resolution (VHR) imagery and terrain data. Fourteen indicators of settlement characteristics were derived and mapped using an object-based ML approach and VHR imagery. These indicators were categorized according to three different spatial levels: environ, settlement and object. The most useful indicators for prediction were found to be density and texture measures, (with random forest (RF) relative importance measures of over 25% and 23% respectively). The success of this approach was evaluated using a small, fully independent validation dataset. Informal areas were mapped with an overall accuracy of 91%. Object-based ML as a processing chain approach performed better (8%) than object-based image analysis alone due to its ability to encompass all available geospatial levels.