Flooding is one of the costliest natural disasters in the United States, and a major source of flood damage is to the residential housing stock. Given the value of housing, both as a major source of household wealth and to the residential mortgage market, a long-standing literature has examined how floods impact house prices and evaluated the protective role of flood insurance. In this review, we synthesize the literature examining the impact of flood risk and flood events on housing value. We also examine research themes in the US flood insurance market including the determinants of flood insurance demand, the impact of insurance on household decisions to manage flood risk, and emerging changes to watch in the public and private flood insurance markets. We then survey key data challenges and opportunities and conclude by examining who bears the flood risk in the US housing market. As climate change is expected to change future precipitation patterns and raise sea levels, flood risk and insurance will likely be even more important for housing markets in the future.

Hydrometeorological hazards pose heightened risks to society, whether as standalone events or as multiple hazards that intersect across space and time. A key component of managing these risks is assessing hazard recovery to guide response and mitigate future impacts. Satellite-based Earth Observation (EO) is a valuable data source for mapping physical changes in hazard impacts and recovery, and, in combination with non-EO data, for contextualizing qualitative, functional, resilience, and risk-based components of recovery. We summarize the physical features mapped by EO to monitor recovery from hydrometeorological hazards, and the uses and challenges of integrating non-EO data to achieve contextual measures of recovery. Large-scale measures of land cover and land use classes, and vegetation features, integrate with aggregated non-EO datasets but have limited utility for interrogating fine-scale and temporal changes. Nighttime lights provide high-frequency monitoring of electricity outages and restorations as a proxy for recovery, with a robust empirical linkage to non-EO data, but remain subject to variability in the validity of this relationship across locations. Despite advances in modeling and image resolution for assessing building damage, little attention has been paid to mapping building reconstruction. Inconsistent use of non-EO data to contextualize EO-derived physical changes neglects evaluations of recovery reflecting changes in functional intent, resilience, and risk. We discuss opportunities for achieving more robust assessments while accounting for challenges in monitoring and non-EO integration. Future work is needed to expand integration for monitoring multi-hazard recovery and capture overlapping impacts and asynchronous recovery trajectories in support of reduced disaster risk.

Urban flooding affects lives and infrastructure worldwide. Mapping inundation in complex urban environments from satellite imagery remains challenging due to limited spatial resolution, infrequent acquisitions, and cloud cover. We present Urban Flood Observations (UFO), a global, hand-labeled dataset of post-flood inundation in diverse urban settings. UFO comprises 215 image chips (1024 by 1024 pixels) from 14 flood events between 2017 and 2021, derived from 3 m PlanetScope imagery. Each chip is annotated with two classes: 'inundated' (all visible surface water, including floodwater and pre-existing water bodies (permanent or seasonal)) and 'non-inundated'. To demonstrate the dataset's utility, we trained a segmentation model using leave-one-event-out cross-validation, achieving a mean Intersection over Union (IoU) of 77.3. We also used UFO to evaluate two widely used surface water products, the Sentinel-1-based NASA IMPACT model and Google's 10 m Dynamic World water class, which yielded IoUs of 44.1 and 48.1, respectively. UFO is publicly available to support the development and validation of urban inundation mapping methods.

Property insurance is essential for hurricane recovery, yet timely and fair claims payouts are not guaranteed. Post-disaster litigation over denied or delayed claims is common but understudied in who sues and how this varies sociodemographically. Southwest Louisiana is well situated for such an analysis, where within two years after Hurricane Laura and Hurricane Delta hit the region in 2020, one in five federal civil litigation cases were filed, most regarding insurance. We collate 5,476 court records to geocode implicated properties and assess litigation patterns using property and sociodemographic data. We find litigation is 43 percent more likely in higher percent Black residential areas, with property value amplifying this disparity. Additionally, case duration is longer for residents in these areas, suggesting systemic procedural inequities. Our findings reveal how race, class, and geography relate to insurance litigation, a critical yet overlooked facet of procedural vulnerability that may contribute to inequitable disaster recovery outcomes in the United States.

Purpose - This paper reiterates the intention and importance of positionality statements, encouraging researchers to approach positionality through vignettes to engage with our role and impact as scholars rather than as a formulaic description of a researcher's demographic characteristics and identities. We argue positionality can be a tool to strengthen hazards and disaster research, particularly when approached as an ongoing, iterative process. Design/methodology/approach - As early career researchers conducting fieldwork in the United States Gulf South, we considered the intention, importance and relevancy of positionality statements. We engaged in a collaborative writing process where we discussed our experiences in the field and revisited previously written reflexivity and positionality statements to create vignettes. Through the use of vignettes, we provide a path for researchers to reflect and disclose ethical and moral research considerations and power differentials that shape their research. Findings - Positionality statements can be limited and harmful when applied without critical examination of their intent and are at risk of being detrimental when recycled across multiple projects spanning distinct partnerships and place-based research endeavors. We suggest vignettes can serve a crucial function in developing ethical disaster-related research cognizant of its epistemological engagement with the topic, place of research and the people we engage in the field. Originality/value - Through vignettes, we provide a path to situate one's positionality to further advance ethical disaster research intentional of reducing harm to communities at risk of disasters.

PurposeThe Disaster Justice Network (DJN) is a volunteer network that emerged during the COVID-19 pandemic to lend support and share critical information that was not easily accessed for the 2020 and 2021 Hurricanes Laura, Delta, Zeta and Ida recovery processes in coastal Louisiana. We discuss lessons learned on navigating challenges, questions and the possibilities for reducing harms and how these insights potentially transfer to other webs of mutual aid and hubs for community-driven actions focused on equitable and just disaster preparedness and recovery.Design/methodology/approachWe employ an ongoing, iterative, non-linear process to nurture visions of a rejuvenated future based on the support of human and environmental rights. DJN draws from a community-based participatory action approach to develop and share strategies for achieving a justice-centered disaster preparedness and recovery process.FindingsThis initiative exposed the racism, violence and erasure of historied communities of color occurring through the official response process. It further brought to the public light the unjust, inequitable and harmful official recovery responses and what is urgently needed to support evacuees and survivors, moving towards a more just recovery process for future disasters. By understanding the historical context and complexities of co-occurring disasters and violence across space and time and what is required to mitigate the effect of these human-caused violations of humanity, we can begin to forcefully address the root causes of harm to more successful ends.Originality/valueIn all, DJN develops strategies to address inequitable disaster response and recovery, confront multiple intersecting and cascading disasters and pave the way for a better future together. This article's authors intend to offer our insights on this process and engage and learn collectively to advance justice-centered disaster planning, preparedness and recovery processes.

Floods impact communities worldwide, resulting in loss of life, damaged infrastructure and natural assets, and threatened livelihoods. Climate change and urban development in flood-prone areas will continue to worsen flood-related losses, increasing the urgency for effective tools to monitor recovery. Many Earth Observation (EO) applications exist for flood-hazard monitoring and provide insights on location, timing, and extent in near real-time and historically to estimate flood risk. Less attention has been paid to flood recovery, even though differing recovery rates and outcomes can have immediate and enduring distributional effects within communities. EO data are uniquely positioned to monitor post-flood recovery and inform policy on hazard mitigation and adaptation but remain underutilized. We encourage the EO and flood research community to refocus on developing flood recovery applications to address growing risk. Translation of EO insights on flood recovery among flood-affected communities and decision-makers is necessary to address underlying social vulnerabilities that exacerbate inequitable recovery outcomes and advocate for redressing injustices where disparate recovery is observed. We identify an unequivocal need for EO to move beyond mapping flood hazard and exposure toward post-flood recovery monitoring to inform recovery across geographic contexts. This commentary proposes a framework for remote sensing scientists to engage community-based partners to integrate EO with non-EO data to advance flood recovery monitoring, characterize inequitable recovery, redistribute resources to mitigate inequities, and support risk reduction of future floods.

To shed light on the politics of remote sensing, a technique often regarded as objective and neutral, the subfield of critical remote sensing has emerged in the social sciences. This perspective translates its key ideas into an actionable framework that offers suggestions for how to transform remote sensing to better engage and empower people and places typically studied at a distance. First, we encourage remote sensing scientists and practitioners to weigh the consequences of exposing inaccessible or off-limits places, incorporate local knowledge and values into research design, methods, and applications, and share skills and data with stakeholders who wish to learn and use remote sensing for their own objectives. Second, we offer suggestions for teaching critical remote sensing and making research accessible and replicable. Third, we stress the importance of acknowledging that despite being conducted from afar, remote sensing can still affect the people and places it observes.

Production bans are a common way for governments to address issues of social concern. However, when consumer demand for banned items is insensitive to price changes, cross-border trade may undermine these efforts. We examine the effects of Kenya’s 2018 moratorium on the extraction of wood products, including logs and charcoal, from public and community forests. The data show an immediate 36% increase in the domestic charcoal price in Kenya, where over 80% of consumers use it as their primary energy source. Subsequently, we document an increase of 133% percent in charcoal imports from Uganda to Kenya during the first 6 months of the ban. Further, we estimate that avoided deforestation in Kenya was likely displaced to Uganda such that net carbon emissions increased. These findings demonstrate the ineffectiveness of the ban as a mechanism to decrease greenhouse gas emissions and biodiversity loss from deforestation.

This review applies an environmental justice perspective to synthesize knowledge of flood-related health disparities across demographic groups in the USA. The primary aim is to examine differential impacts on physical and mental health outcomes while also assessing methodological considerations such as flood exposure metrics, baseline health metrics, and community engagement. In our review (n = 27), 65

The Global Human Settlement Layer (GHSL) project fosters an enhanced, public understanding of the human presence on Earth. A decade after its inception in the Digital Earth 2020 vision, GHSL is an established project of the European Commission’s Joint Research Centre and an integral part of the Copernicus Emergency Management Service. The 2023 GHSL edition, a result of rigorous research on Earth Observation data and population censuses, contributes significantly to understanding worldwide human settlements. It introduces new elements like 10-m-resolution, sub-pixel estimation of built-up surfaces, global building height and volume estimates, and a classification of residential and non-residential areas, improving population density grids. This paper evaluates GHSL’s key components, including the Symbolic Machine Learning approach, using novel reference data. These data enable a comparative assessment of GHSL model predictions on the evolution of built-up surface, building heights, and resident population. Empirical evidence suggests that GHSL estimates are the most accurate in the public domain today (e.g. IoU 0.98 water class, 0.92 built-up class, 0.8 non-residential class, 6% MAE for the 100 m built-up surface or 2.27 m MAE for the building height, 83% TAA for resident population). The paper consolidates GHSL’s theoretical foundation and highlights its innovative features for transparent Artificial Intelligence, facilitating international decision-making processes.

Floods impact communities worldwide, resulting in an estimated $651 billion (USD) in damages, countless fatalities, and threatened livelihoods over the last two decades alone. Climate change and urban development in flood-prone areas will continue to worsen flood-related losses increasing the urgency for effective tools to monitor recovery. Many Earth Observation (EO) applications exist for flood-hazard monitoring and provide insights on location, timing, and extent in near real-time and historically to estimate flood risk. Less attention has been paid to flood recovery, even though differing recovery rates and outcomes can have immediate and enduring effects within communities. Here, we define post-flood recovery as a change in land cover types, conditions, or land surface features in the days, weeks, months, or years following a flood event. EO data are uniquely positioned to monitor post-flood recovery and inform policy on hazard mitigation and adaptation but remain underutilized. We urge the EO and flood research community to renew focus on developing flood recovery applications to address growing flood risk. Both methodological innovations and translation of EO insights on flood recovery among flood-affected communities and decision-makers are necessary to address underlying vulnerabilities in social systems that exacerbate flooding. We identify an unequivocal need for EO to move beyond hazard mapping to post-flood recovery monitoring to inform recovery across geographic contexts. This commentary proposes a framework to use EO to advance flood recovery monitoring, characterize inequitable recovery, redistribute resources to mitigate inequities, and support risk reduction of future floods.

The majority of the world's land is held in customary tenure systems, often with overlapping claims. Designing effective policy to reduce emissions from deforestation and degradation requires understanding land management choices within these systems. Using a nation-wide random sample of over 300,000 hectares of forested land in Uganda from 2000 to 2019, we examine how deforestation trends across a system of overlapping rights, known as mailo land tenure, change in response to legal amendments intended to increase land tenure security. Graphical analysis reveals that mailo land has always had higher deforestation rates, compared to private and customary land, which increased relative to other tenure systems beginning in 2010 when a law was passed to protect tenants on mailo land. Statistical analysis controlling for spatial and time effects shows that prior to 2010, trends across tenure systems were similar. After 2010, deforestation increased significantly on land with overlapping rights and then began to decrease after 2017 relative to rates on customary or fully privatized land. We hypothesize that the uptick in deforestation resulted from unintended, increased uncertainty generated by the 2010 law, which changed owner/tenant relations on land with overlapping rights. The decrease in deforestation rates after 2017 was consistent with increased tenure security from an acceleration in the uptake of permanent certificates of occupancy. These findings demonstrate that outcomes under systems of overlapping rights can be destabilized by well-intentioned reform, and that securing tenant rights can reduce deforestation.

AbstractDespite the global refugee population's continued growth and the long-term habitation of many refugee settlements, there has been little overall attention from the Earth observation community toward environmental conditions and change in these settlements and their surrounding landscapes. However, considering the persistent concerns regarding the sustainability of environmental resource usage by refugees, potential environmental conflicts with nearby communities, and the impact of environmental hazards on refugee populations, it is crucial to gain a better understanding of the diverse and evolving refugee environment contexts using the unique information provided by Earth observation. In this chapter, we aim to demonstrate the value of using satellite imagery and satellite-derived data to map a refugee settlement in Uganda, estimate its population, and assess the land cover changes within and around the settlement.

Satellite-based broad-scale (i.e., global and continental) human settlement data are essential for diverse applications spanning climate hazard mitigation, sustainable development monitoring, spatial epidemiology and demographic modeling. Many human settlement products report exceptional detection accuracies above 85%, but there is a substantial blind spot in that product validation typically focuses on large urban areas and excludes rural, small-scale settlements that are home to 3.4 billion people around the world. In this study, we make use of a data-rich sample of 30 refugee settlements in Uganda to assess the small-scale settlement detection by four human settlement products, namely, Geo-Referenced Infrastructure and Demographic Data for Development settlement extent data (GRID3-SE), Global Human Settlements Built-Up Sentinel-2 (GHS-BUILT-S2), High Resolution Settlement Layer (HRSL) and World Settlement Footprint (WSF). We measured each product's areal coverage within refugee settlement boundaries, assessed detection of 317,416 building footprints and examined spatial agreement among products. For settlements established before 2016, products had low median probability of detection and F1-score of 0.26 and 0.24, respectively, a high median false alarm rate of 0.59 and tended to only agree in regions with the highest building density. Individually, GRID3-SE offered more than five-fold the coverage of other products, GHS-BUILT-S2 underestimated the building footprint area by a median 50% and HRSL slightly underestimated the footprint area by a median 7%, while WSF entirely overlooked 8 of the 30 study refugee settlements. The variable rates of coverage and detection partly result from GRID3-SE and HRSL being based on much higher resolution imagery, compared to GHS-BUILT-S2 and WSF. Earlier established settlements were generally better detected than recently established settlements, showing that the timing of satellite image acquisition with respect to refugee settlement establishment also influenced detection results. Nonetheless, settlements established in the 1960s and 1980s were inconsistently detected by settlement products. These findings show that human settlement products have far to go in capturing small-scale refugee settlements and would benefit from incorporating refugee settlements in training and validating human settlement detection approaches.

In 2015, 193 countries declared their commitment to “leave no one behind” in pursuit of 17 Sustainable Development Goals (SDGs). However, the world’s refugees have been routinely excluded from national censuses and representative surveys, and, as a result, have broadly been overlooked in SDG evaluations. In this study, we examine the potential of OpenStreetMap (OSM) data for monitoring SDG progress in refugee settlements. We collected all available OSM data in 28 refugee and 26 nearby non-refugee settlements in the major refugee-hosting country of Uganda. We created a novel SDG-OSM data model, measured the spatial and temporal coverages of SDG-relevant OSM data across refugee settlements, and compared these results to non-refugee settlements. We found 11 different SDGs represented across 92% (21,950) of OSM data in refugee settlements, compared to 78% (1919 nodes) in non-refugee settlements. However, most data were created three years after refugee arrival, and 81% of OSM data in refugee settlements were never edited, both of which limit the potential for long-term monitoring of SDG progress. In light of our findings, we offer suggestions for improving OSM-driven SDG monitoring in refugee settlements that have relevance for development and humanitarian practitioners and research communities alike.

Since 2015, Uganda has welcomed over 700,000 refugees from South Sudan, Democratic Republic of the Congo, Burundi, and other East African nations, and currently hosts over 1.4 million refugees with 92% of that population living in UNHCR-managed settlements. Despite refugee settlements being essential spaces for physical protection and humanitarian aid distribution and reception, the sheer rate of refugee influx and settlement growth has introduced uncertainties around site planning, aid delivery, food security, and landscape change. For example, there is little publicly available information on settlement establishment, growth, or changes in land use/land cover for the vast majority of UNHCR-managed settlements in Uganda and around the world. To address this shortcoming, this research characterizes the spatial and temporal patterns of refugee settlement landscape dynamics using the case study of the Pagirinya Refugee Settlement in Northern Uganda, Landsat and Sentinel-2 satellite image time series, and BFAST, an automated land cover disturbance detection algorithm. To delineate the extent of the settlement and surrounding disturbance, a refugee settlement boundary was generated using a 2018 Landsat NDVI composite, which included land disturbed by settlement establishment and subsequent growth. Landsat time series data were sampled within this boundary to parametrize a BFAST model to detect settlement disturbance, which was deployed over 351 Landsat images from 2005 to 2018. This approach yielded sub-monthly land cover disturbances from 2016 to 2017 with an accuracy of 87.5% resulting from the rapid (within one month) settlement establishment, road construction, the spread of dwellings and other built-up infrastructure throughout the settlement, as well as the conversion of natural grassland to small-scale agriculture within the first six months after refugee settlement began. These results were generated using open-access data and open-source algorithms to pave the way for developing a near real-time satellite image-based settlement monitoring framework, which would aid refugee response and evaluation efforts that are central to Uganda's refugee hosting and settlement plans, as well as implementation of the Global Compact on Refugees.