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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.

Accurate inundation maps for flooded wetlands and rivers are a critical resource for their management and conservation. In this paper, we automate a method (thresholding of the short-wave infrared band) for classifying peak inundation in the Okavango Delta, northern Botswana, using Landsat imagery and Google Earth Engine. Inundation classification in the Okavango Delta is complex owing to the spectral overlap between inundated areas covered with aquatic vegetation and dryland vegetation classes on satellite imagery, and classifications have predominately been implemented on broad spatial resolution imagery. We present the longest time series to date (1990–2019) of inundation maps for the peak flood season at a high spatial resolution (30 m) for the Okavango Delta. We validated the maps using image-based and in situ data accuracy assessments, with overall accuracy ranging from 91.5% to 98.1%. Use of Landsat imagery resulted in consistently lower (on average, 692 km2) estimates of inundation extent than previous studies that used Moderate Resolution Imaging Spectroradiometer (MODIS) and National Oceanic and Atmospheric Administration Advanced Very-High-Resolution Radiometer (NOAA AVHRR) imagery, likely owing to the increased number of mixed pixels that occur when using broad spatial resolution imagery, which can lead to overestimations of the size of inundated areas. We provide the inundation maps and Google Earth Engine code for public use. This classification method can likely be adapted for inundation mapping in other regions.

Wildfires in 2019—2020 broke global records for extent and severity, affirming the arrival of the megafire era. Frequent megafires reflect changes to fire regimes that can negatively impact species and ecosystems. Here, we offer what we believe to be the first comprehensive analysis of megafire impacts on southeastern Australian vegetation communities, combining remote-sensing data, fire-history records, and plant trait-derived fire interval thresholds. In our study area, fires burned over 5.5 million ha. We found that one-third of all native vegetation in this region has burned too frequently following the megafires, particularly impacting fire-sensitive vegetation (for example, rainforests). This represents a single-year increase of 36% in the vegetation at risk of interval squeeze (vegetation transitions driven by altered fire regimes) compared to the previous 59 years combined. We demonstrate that megafires can overrun recently burned vegetation and infiltrate refugia, reducing fire intervals beyond the persistence thresholds of plant species and increasing the risk of ecosystem collapse. Averting this will require innovative approaches to fire management. However, if climate change is not addressed, ecosystem collapse may be unavoidable especially for ecosystems adapted to infrequent, high-severity fire.

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.

When characterizing the processes that shape ecosystems, ecologists increasingly use the unique perspective offered by repeat observations of remotely sensed imagery. However, the concept of change embodied in much of the traditional remote-sensing literature was primarily limited to capturing large or extreme changes occurring in natural systems, omitting many more subtle processes of interest to ecologists. Recent technical advances have led to a fundamental shift toward an ecological view of change. Although this conceptual shift began with coarser-scale global imagery, it has now reached users of Landsat imagery, since these datasets have temporal and spatial characteristics appropriate to many ecological questions. We argue that this ecologically relevant perspective of change allows the novel characterization of important dynamic processes, including disturbances, long-term trends, cyclical functions, and feedbacks, and that these improvements are already facilitating our understanding of critical driving forces, such as climate change, ecological interactions, and economic pressures.

Using drones to count wildlife saves time and resources and allows access to difficult or dangerous areas. We collected drone imagery of breeding waterbirds at colonies in the Okavango Delta (Botswana) and Lowbidgee floodplain (Australia). We developed a semi-automated counting method, using machine learning, and compared effectiveness of freeware and payware in identifying and counting waterbird species (targets) in the Okavango Delta. We tested transferability to the Australian breeding colony. Our detection accuracy (targets), between the training and test data, was 91% for the Okavango Delta colony and 98% for the Lowbidgee floodplain colony. These estimates were within 1–5%, whether using freeware or payware for the different colonies. Our semi-automated method was 26% quicker, including development, and 500% quicker without development, than manual counting. Drone data of waterbird colonies can be collected quickly, allowing later counting with minimal disturbance. Our semi-automated methods efficiently provided accurate estimates of nesting species of waterbirds, even with complex backgrounds. This could be used to track breeding waterbird populations around the world, indicators of river and wetland health, with general applicability for monitoring other taxa.

Contrasting trajectories of biodiversity loss and urban expansion make it imperative to understand biodiversity persistence in cities. Size‐, local‐, and landscape‐level habitat factors of greenspaces in cities may be critical for future design and management of urban greenspaces in conserving bird biodiversity. Most current understanding of bird communities in cities has come from disparate analyses of single cities, over relatively short time periods, producing limited understanding of processes and characteristics of bird patterns for improved biodiversity management of the world's cities. We analyzed bird biodiversity in 112 urban greenspaces from 51 cities across eight countries, using eBird, a broadscale citizen science project. Species richness and Shannon diversity were used as response variables, while percent tree cover, percent water cover, and vegetation index were used as habitat predictor variables at both a landscape (5 and 25 km radius) and local‐scale level (specific to an individual greenspace) in the modeling process, retrieved using Google Earth Engine. Area of a greenspace was the most important predictor of bird biodiversity, underlining the critical importance of habitat area as the most important factor for increasing bird biodiversity and mitigating loss from urbanization. Surprisingly, distance from the city center and distance from the coast were not significantly related to bird biodiversity. Landscape‐scale habitat predictors were less related to bird biodiversity than local‐scale habitat predictors. Ultimately, bird biodiversity loss could be mitigated by protecting and developing large greenspaces with varied habitat in the world's cities.

Comparisons of recent global forest products at higher resolutions that are only available annually have shown major disagreements among forested areas in highly fragmented landscapes. A holistic reductionist framework and colourimetry were applied to create a chorologic typology of environmental indicators to map forest extent with an emphasis on large-scale performance, interpretability/communication, and spatial–temporal scalability. Interpretation keys were created to identify forest and non-forest features, and a set of candidate tree cover indices were developed and compared with a decision matrix of prescribed criteria. The candidate indices were intentionally limited to those applying only the visible and NIR bands to obtain the highest possible resolution and be compatible with commonly available multispectral satellites and higher resolution sensors, including aerial and potentially UAV/drone sensors. A new High-Resolution Tree Cover Index (HRTCI) in combination with the Green band was selected as the best index based on scores from the decision matrix. To further improve the performance of the indices, the chorologic typology included two insolation indices, a water index and a NIR surface saturation index, to exclude any remaining spectrally similar but unrelated land cover features such as agriculture, water, and built-up features using a process of elimination. The approach was applied to the four seasons across a wide range of ecosystems in south-eastern Australia, with and without regionalisation, to identify which season produces the most accurate results for each ecoregion and to assess the potential for mitigating the spatial–temporal scaling effects of the Modifiable Spatio-Temporal Unit Problem. Autumn was found to be the most effective season, yielding overall accuracies of 94.19% for the full extent, 95.79% for the temperate zone, and 95.71% for the arid zone. It produced the greatest spatial agreement between two recognised global products, the GEDI forest heights extent and the ESA WorldCover Tree cover class. The performance, transparency, and scalability of the approach should provide the basis for a framework for globally relatable forest monitoring.

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

Assessments of the status of tidal flats, one of the most extensive coastal ecosystems, have been hampered by a lack of data on their global distribution and change. Here we present globally consistent, spatially-explicit data of the occurrence of tidal flats, defined as sand, rock or mud flats that undergo regular tidal inundation. More than 1.3 million Landsat images were processed to 54 composite metrics for twelve 3-year periods, spanning four decades (1984–1986 to 2017–2019). The composite metrics were used as predictor variables in a machine-learning classification trained with more than 10,000 globally distributed training samples. We assessed accuracy of the classification with 1,348 stratified random samples across the mapped area, which indicated overall map accuracies of 82.2% (80.0–84.3%, 95% confidence interval) and 86.1% (84.2–86.8%, 95% CI) for version 1.1 and 1.2 of the data, respectively. We expect these maps will provide a means to measure and monitor a range of processes that are affecting coastal ecosystems, including the impacts of human population growth and sea level rise. Measurement(s) ecosystem occurrence Technology Type(s) earth observation Sample Characteristic - Environment tidal flats • coastal wetlands Sample Characteristic - Location global