NASA's Global Ecosystem Dynamics Investigation (GEDI) is a spaceborne lidar mission which will produce near global (51.6^oS to 51.6^oN) maps of forest structure and above‐ground biomass density (AGBD) during its two year mission. GEDI uses a waveform simulator for calibration of algorithms and assessing mission accuracy. This paper implements a waveform simulator, using the method proposed in Blair and Hofton (1999), and builds upon that work by adding instrument noise and by validating simulated waveforms across a range of forest types, airborne laser scanning (ALS) instruments and survey configurations. The simulator was validated by comparing waveform metrics derived from simulated waveforms against those derived from observed large‐footprint, full‐waveform lidar data from NASA's airborne Land, Vegetation, and Ice Sensor (LVIS). The simulator was found to produce waveform metrics with a mean bias of less than 0.22 m and a root mean square error of less than 5.7 m, as long as the ALS data had sufficient pulse density. The minimum pulse density required depended upon the instrument. Measurement errors due to instrument noise predicted by the simulator were within 1.5 m of those from observed waveforms and 70‐85% of variance in measurement error was explained. Changing the ALS survey configuration had no significant impact on simulated metrics, suggesting that the ALS pulse density is a sufficient metric of simulator accuracy across the range of conditions and instruments tested. These results give confidence in the use of the simulator for the pre‐launch calibration and performance assessment of the GEDI mission.

Exposure to nature provides a wide range of health benefits. A significant proportion of these are delivered close to home, because this offers an immediate and easily accessible opportunity for people to experience nature. However, there is limited information to guide recommendations on its management and appropriate use. We apply a nature dose-response framework to quantify the simultaneous association between exposure to nearby nature and multiple health benefits. We surveyed ca. 1000 respondents in Southern England, UK, to determine relationships between (a) nature dose type, that is the frequency and duration (time spent in private green space) and intensity (quantity of neighbourhood vegetation cover) of nature exposure and (b) health outcomes, including mental, physical and social health, physical behaviour and nature orientation. We then modelled dose-response relationships between dose type and self-reported depression. We demonstrate positive relationships between nature dose and mental and social health, increased physical activity and nature orientation. Dose-response analysis showed that lower levels of depression were associated with minimum thresholds of weekly nature dose. Nearby nature is associated with quantifiable health benefits, with potential for lowering the human and financial costs of ill health. Dose-response analysis has the potential to guide minimum and optimum recommendations on the management and use of nearby nature for preventative healthcare.

Accurate estimation of aboveground forest biomass stocks is required to assess the impacts of land use changes such as deforestation and subsequent regrowth on concentrations of atmospheric CO₂. The Global Ecosystem Dynamics Investigation (GEDI) is a lidar mission launched by NASA to the International Space Station in 2018. GEDI was specifically designed to retrieve vegetation structure within a novel, theoretical sampling design that explicitly quantifies biomass and its uncertainty across a variety of spatial scales. In this paper we provide the estimates of pan-tropical and temperate biomass derived from two years of GEDI observations. We present estimates of mean biomass densities at 1 km resolution, as well as estimates aggregated to the national level for every country GEDI observes, and at the sub-national level for the United States. For all estimates we provide the standard error of the mean biomass. These data serve as a baseline for current biomass stocks and their future changes, and the mission’s integrated use of formal statistical inference points the way towards the possibility of a new generation of powerful monitoring tools from space.

Experiences of nature provide many mental-health benefits, particularly for people living in urban areas. The natural characteristics of city residents’ neighborhoods are likely to be crucial determinants of the daily nature dose that they receive; however, which characteristics are important remains unclear. One possibility is that the greatest benefits are provided by characteristics that are most visible during the day and so most likely to be experienced by people. We demonstrate that of five neighborhood nature characteristics tested, vegetation cover and afternoon bird abundances were positively associated with a lower prevalence of depression, anxiety, and stress. Furthermore, dose–response modeling shows a threshold response at which the population prevalence of mental-health issues is significantly lower beyond minimum limits of neighborhood vegetation cover (depression more than 20% cover, anxiety more than 30% cover, stress more than 20% cover). Our findings demonstrate quantifiable associations of mental health with the characteristics of nearby nature that people actually experience.

Context Urbanisation places increasing stress on ecosystem services; however existing methods and data for testing relationships between service delivery and urban landscapes remain imprecise and uncertain. Unknown impacts of scale are among several factors that complicate research. This study models ecosystem services in the urban area comprising the towns of Milton Keynes, Bedford and Luton which together represent a wide range of the urban forms present in the UK. Objectives The objectives of this study were to test (1) the sensitivity of ecosystem service model outputs to the spatial resolution of input data, and (2) whether any resultant scale dependency is constant across different ecosystem services and model approaches (e.g. stock- versus flow-based). Methods Carbon storage, sediment erosion, and pollination were modelled with the InVEST framework using input data representative of common coarse (25 m) and fine (5 m) spatial resolutions. Results Fine scale analysis generated higher estimates of total carbon storage (9.32 vs. 7.17 kg m⁻²) and much lower potential sediment erosion estimates (6.4 vs. 18.1 Mg km⁻² year⁻¹) than analyses conducted at coarser resolutions; however coarse-scale analysis estimated more abundant pollination service provision. Conclusions Scale sensitivities depend on the type of service being modelled; stock estimates (e.g. carbon storage) are most sensitive to aggregation across scales, dynamic flow models (e.g. sediment erosion) are most sensitive to spatial resolution, and ecological process models involving both stocks and dynamics (e.g. pollination) are sensitive to both. Care must be taken to select model data appropriate to the scale of inquiry.

Plant biodiversity supports life on Earth and provides a range of important ecosystem services, but is under severe pressure by global change. Structural diversity plays a crucial role for carbon, water and energy cycles and animal habitats. However, it is very difficult to map and monitor over large areas, limiting our ability to assess the status of biodiversity and predict change. NASA’s Global Ecosystem Dynamics Investigation (GEDI) provides a new opportunity to measure 3D plant canopy structure of the world’s temperate, Mediterranean and tropical ecosystems, but its potential to map structural diversity is not yet tested. Here, we use wall-to-wall airborne laser scanning (ALS) to simulate GEDI data (GEDI _sim ) over 7380 km² in the southern Sierra Nevada mountains in California and evaluate how well GEDI’s sampling scheme captures patterns of structural diversity. We evaluate functional richness and functional beta diversity in a biodiversity hot spot. GEDI _sim performed well for trait retrievals (r² = 0.68) and functional richness mapping (r² = 0.75) compared to ALS retrievals, despite lower correlations in complex terrain with steep slopes. Functional richness patterns were strongly associated with soil organic carbon stocks and density as well as variables related to water availability and could be appropriately mapped by GEDI _sim with and without cloud cover. Functional beta diversity was more strongly related to local changes in topography and more challenging to map, especially with decreasing sampling density. The reduced number of GEDI _sim shots when simulating cloud cover lead to a strong overestimation of beta diversity and a reduction of r² from 0.64 to 0.40 compared to ALS. The ability to map functional richness has been demonstrated with potential application at continental scales that could be transformative for our understanding of large-scale patterns of plant canopy structure, diversity and potential links to animal diversity, movement and habitats.