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The utility of land cover maps for natural resources management relies on knowing the uncertainty associated with each map. The continuous advances typical of remote sensing, including the increasing availability of higher spatial and temporal resolution satellite data and data analysis capabilities, have created both opportunities and challenges for improving the application of accuracy assessment. There are well established accuracy assessment methods, but their underlying assumptions have not changed much in the last couple decades. Consequently, revisiting how map error and accuracy have been performed and reported over the last two decades is timely, to highlight areas where there is scope for better utilization of emerging opportunities. We conducted a quantitative literature review on accuracy assessment practices for mapping via remote sensing classification methods, in both terrestrial and marine environments. We performed a structured search for land and benthic cover mapping, limiting our search to journals within the remote sensing field, and papers published between 1998–2017. After an initial screening process, we assembled a database of 282 papers, and extracted and standardized information on various components of their reported accuracy assessments. We discovered that only 56% of the papers explicitly included an error matrix, and a very limited number (14%) reported overall accuracy with confidence intervals. The use of kappa continues to be standard practice, being reported in 50.4% of the literature published on or after 2012. Reference datasets used for validation were collected using a probability sampling design in 54% of the papers. For approximately 11% of the studies, the sampling design used could not be determined. No association was found between classification complexity (i.e. number of classes) and measured accuracy, independent from the size of the study area. Overall, only 32% of papers included an accuracy assessment that could be considered reproducible; that is, they included a probability-based sampling scheme to collect the reference dataset, a complete error matrix, and provided sufficient characterization of the reference datasets and sampling unit. Our findings indicate that considerable work remains to identify and adopt more statistically rigorous accuracy assessment practices to achieve transparent and comparable land and benthic cover maps.

Increasing human populations around the global coastline have caused extensive loss, degradation and fragmentation of coastal ecosystems, threatening the delivery of important ecosystem services 1 . As a result, alarming losses of mangrove, coral reef, seagrass, kelp forest and coastal marsh ecosystems have occurred 1 – 6 . However, owing to the difficulty of mapping intertidal areas globally, the distribution and status of tidal flats—one of the most extensive coastal ecosystems—remain unknown 7 . Here we present an analysis of over 700,000 satellite images that maps the global extent of and change in tidal flats over the course of 33 years (1984–2016). We find that tidal flats, defined as sand, rock or mud flats that undergo regular tidal inundation 7 , occupy at least 127,921 km 2 (124,286–131,821 km 2 , 95% confidence interval). About 70% of the global extent of tidal flats is found in three continents (Asia (44% of total), North America (15.5% of total) and South America (11% of total)), with 49.2% being concentrated in just eight countries (Indonesia, China, Australia, the United States, Canada, India, Brazil and Myanmar). For regions with sufficient data to develop a consistent multi-decadal time series—which included East Asia, the Middle East and North America—we estimate that 16.02% (15.62–16.47%, 95% confidence interval) of tidal flats were lost between 1984 and 2016. Extensive degradation from coastal development 1 , reduced sediment delivery from major rivers 8 , 9 , sinking of riverine deltas 8 , 10 , increased coastal erosion and sea-level rise 11 signal a continuing negative trajectory for tidal flat ecosystems around the world. Our high-spatial-resolution dataset delivers global maps of tidal flats, which substantially advances our understanding of the distribution, trajectory and status of these poorly known coastal ecosystems. Analyses of over 700,000 satellite images to map the global extent of tidal flats over the past thirty years, and enable assessments of the status and likely future trajectories of these coastal ecosystems.

In the Australian summer season of 2022, exceptional rainfall events occurred in Southeast Queensland and parts of New South Wales, leading to extensive flooding of rural and urban areas. Here, we map the extent of flooding in the city of Brisbane and evaluate the change in electricity usage as a proxy for flood impact using VIIRS nighttime brightness imagery. Scanning a wide range of possible sensors, we used pre-flood and peak-flood PlanetScope imagery to map the inundated areas, using a new spectral index we developed, the Normalized Difference Inundation Index (NDII), which is based on changes in the NIR reflectance due to sediment-laden flood waters. We compared the Capella-Space X-band/HH imaging radar data captured at peak-flood date to the PlanetScope-derived mapping of the inundated areas. We found that in the Capella-Space image, significant flooded areas identified in PlanetScope imagery were omitted. These omission errors may be partly explained by the use of a single-date radar image, by the X-band, which is partly scattered by tree canopy, and by the SAR look angle under which flooded streets may be blocked from the view of the satellite. Using VIIRS nightly imagery, we were able to identify grid cells where electricity usage was impacted due to the floods. These changes in nighttime brightness matched both the inundated areas mapped via PlanetScope data as well as areas corresponding with decreased electricity loads reported by the regional electricity supplier. Altogether we demonstrate that using a variety of optical and radar sensors, as well as nighttime and daytime sensors, enable us to overcome data gaps and better understand the impact of flood events. We also emphasize the importance of high temporal revisit times (at least twice daily) to more accurately monitor flood events.

Big data reveals new, stark pictures of the state of our environments. It also reveals ‘bright spots’ amongst the broad pattern of decline and—crucially—the key conditions for these cases. Big data analyses could benefit the planet if tightly coupled with ongoing sustainability efforts.

Reef islands are small, low-lying landforms composed of unconsolidated bioclastic materials and are highly vulnerable to coastal hazards exacerbated by climate change. This vulnerability has driven extensive research interest in shoreline changes across temporal scales ranging from short-term (seasonal) to long-term (decadal) dynamics. In this review, we first conducted an exploratory search of publicly available databases to assess the global distribution of reef islands and their potential for providing baseline data. Based on the PRISMA 2020 framework, we then examined 74 studies to identify data sources and methods commonly used to analyse reef island shoreline changes. Our findings indicate that no global dataset currently exists that specifically identifies reef islands, despite the potential value of such a dataset. Shoreline changes have been assessed for over 91 atolls and 119 non-atoll reef islands (excluding a global study) spanning the Pacific, Indian, and Atlantic Oceans. However, inconsistencies in time spans, reporting practices, and error assessments make cross-study comparisons challenging. Analysis of data sources revealed that 40% of studies were purely desktop-based, while only 11% relied solely on field data. Most used a combination of remote sensing and field-based approaches. Emerging technologies such as drones and LiDAR remain underutilised in reef island research, although they provide promising opportunities for high-resolution mapping and monitoring. This review provides a methodological framework to guide future research on reef island shoreline changes.

In the Yellow Sea region of East Asia, tidal wetlands are the frontline ecosystem protecting a coastal population of more than 60 million people from storms and sea-level rise. However, unprecedented coastal development has led to growing concern about the status of these ecosystems. We developed a remote-sensing method to assess change over ~4000 km of the Yellow Sea coastline and discovered extensive losses of the region's principal coastal ecosystem - tidal flats - associated with urban, industrial, and agricultural land reclamations. Our analysis revealed that 28% of tidal flats existing in the 1980s had disappeared by the late 2000s (1.2% annually). Moreover, reference to historical maps suggests that up to 65% of tidal flats were lost over the past five decades. With the region forecast to be a global hotspot of urban expansion, development of the Yellow Sea coastline should pursue a course that minimizes the loss of remaining coastal ecosystems.

Coastal managers require reliable spatial data on the extent and timing of potential coastal inundation, particularly in a changing climate. Most sea level rise (SLR) vulnerability assessments are undertaken using the easily implemented bathtub approach, where areas adjacent to the sea and below a given elevation are mapped using a deterministic line dividing potentially inundated from dry areas. This method only requires elevation data usually in the form of a digital elevation model (DEM). However, inherent errors in the DEM and spatial analysis of the bathtub model propagate into the inundation mapping. The aim of this study was to assess the impacts of spatially variable and spatially correlated elevation errors in high-spatial resolution DEMs for mapping coastal inundation. Elevation errors were best modelled using regression-kriging. This geostatistical model takes the spatial correlation in elevation errors into account, which has a significant impact on analyses that include spatial interactions, such as inundation modelling. The spatial variability of elevation errors was partially explained by land cover and terrain variables. Elevation errors were simulated using sequential Gaussian simulation, a Monte Carlo probabilistic approach. 1,000 error simulations were added to the original DEM and reclassified using a hydrologically correct bathtub method. The probability of inundation to a scenario combining a 1 in 100 year storm event over a 1 m SLR was calculated by counting the proportion of times from the 1,000 simulations that a location was inundated. This probabilistic approach can be used in a risk-aversive decision making process by planning for scenarios with different probabilities of occurrence. For example, results showed that when considering a 1% probability exceedance, the inundated area was approximately 11% larger than mapped using the deterministic bathtub approach. The probabilistic approach provides visually intuitive maps that convey uncertainties inherent to spatial data and analysis.

Coral reef management and conservation stand to benefit from improved high-resolution global mapping. Yet classifications underpinning large-scale reef mapping to date are typically poorly defined, not shared or region-specific, limiting end-users’ ability to interpret outputs. Here we present Reef Cover , a coral reef geomorphic zone classification, developed to support both producers and end-users of global-scale coral reef habitat maps, in a transparent and version-based framework. Scalable classes were created by focusing on attributes that can be observed remotely, but whose membership rules also reflect deep knowledge of reef form and functioning. Bridging the divide between earth observation data and geo-ecological knowledge of reefs, Reef Cover maximises the trade-off between applicability at global scales, and relevance and accuracy at local scales. Two case studies demonstrate application of the Reef Cover classification scheme and its scientific and conservation benefits: 1) detailed mapping of the Cairns Management Region of the Great Barrier Reef to support management and 2) mapping of the Caroline and Mariana Island chains in the Pacific for conservation purposes. Measurement(s) habitat Technology Type(s) satellite imaging • digital curation Sample Characteristic - Organism Anthozoa Sample Characteristic - Environment marine biome • coral reef Sample Characteristic - Location global Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.14397182

Providing accurate maps of mangroves, where the spatial scales of the mapped features correspond to the ecological structures and processes, as opposed to pixel sizes and mapping approaches, is a major challenge for remote sensing. This study developed and evaluated an object-based approach to understand what types of mangrove information can be mapped using different image datasets (Landsat TM, ALOS AVNIR-2, WorldView-2, and LiDAR). We compared and contrasted the ability of these images to map five levels of mangrove features, including vegetation boundary, mangrove stands, mangrove zonations, individual tree crowns, and species communities. We used the Moreton Bay site in Australia as the primary site to develop the classification rule sets and Karimunjawa Island in Indonesia to test the applicability of the rule sets. The results demonstrated the effectiveness of a conceptual hierarchical model for mapping specific mangrove features at discrete spatial scales. However, the rule sets developed in this study require modification to map similar mangrove features at different locations or when using image data acquired by different sensors. Across the hierarchical levels, smaller object sizes (i.e., tree crowns) required more complex classification rule sets. Incorporation of contextual information (e.g., distance and elevation) increased the overall mapping accuracy at the mangrove stand level (from 85% to 94%) and mangrove zonation level (from 53% to 59%). We found that higher image spatial resolution, larger object size, and fewer land-cover classes result in higher mapping accuracies. This study highlights the potential of selected images and mapping techniques to map mangrove features, and provides guidance for how to do this effectively through multi-scale mangrove composition mapping.

Visual image interpretation and digital image classification have been used to map and monitor mangrove extent and composition for decades. The presence of a high-spatial resolution hyperspectral sensor can potentially improve our ability to differentiate mangrove species. However, little research has explored the use of pixel-based and object-based approaches on high-spatial hyperspectral datasets for this purpose. This study assessed the ability of CASI-2 data for mangrove species mapping using pixel-based and object-based approaches at the mouth of the Brisbane River area, southeast Queensland, Australia. Three mapping techniques used in this study: spectral angle mapper (SAM) and linear spectral unmixing (LSU) for the pixel-based approaches, and multi-scale segmentation for the object-based image analysis (OBIA). The endmembers for the pixel-based approach were collected based on existing vegetation community map. Nine targeted classes were mapped in the study area from each approach, including three mangrove species: Avicennia marina, Rhizophora stylosa, and Ceriops australis. The mapping results showed that SAM produced accurate class polygons with only few unclassified pixels (overall accuracy 69%, Kappa 0.57), the LSU resulted in a patchy polygon pattern with many unclassified pixels (overall accuracy 56%, Kappa 0.41), and the object-based mapping produced the most accurate results (overall accuracy 76%, Kappa 0.67). Our results demonstrated that the object-based approach, which combined a rule-based and nearest-neighbor classification method, was the best classifier to map mangrove species and its adjacent environments.