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Forest biomass is a critical component of the terrestrial carbon cycle. The highest-biomass forests are those dominated by the tallest species, Sequoia sempervirens. We use ground-based measurements and allometric equations to estimate tree biomass in primary Sequoia forests (40–42° N latitude) recently subjected to spaceborne and airborne laser scanning (GEDI and ALS, respectively), and we develop new biomass allometry using GEDI and ALS predictors. The best GEDI equation for tree (live + dead) aboveground biomass in these forests, which is based on the 88th percentile of relative height by pulse return energy (N = 200 pulses, R 2 = 0.37, RMSE = 48%), predicts average per-hectare values statistically indistinguishable from those predicted by a previously published GEDI equation (916 ± 74 vs. 928 ± 11 Mg ha−1, mean ± 1 S E). The best ALS equation, which is based on the height and crown size of tree approximate objects (dominant trees plus subordinates) segmented from lidar datasets (N = 503 segments, R 2 = 0.64, RMSE = 49%), predicts significantly higher live tree biomass than GEDI across 37465 ha of primary forest surveyed (1384 ± 77 vs. 885 ± 73 Mg ha−1, mean ± 1 S E). Underestimation by GEDI occurs because height alone is a poor predictor of biomass in the tallest forests. The new ALS equation also moderately underestimates biomass, in part because neither height nor crown size can adequately account for giant trunks. Despite these shortcomings, we demonstrate how a hierarchy of allometric equations can be used to map the distribution of biomass across forests with global maximum biomass and carbon density. Among primary forests of seven reserves, average estimated per-hectare biomass exceeds 2000 Mg ha−1 in three, and ultrahigh-biomass (> 3000 Mg ha−1) hectares are sparsely distributed (1%) with the largest concentration occurring on low-elevation alluvial terraces (460 ha) of Humboldt Redwoods State Park. The ALS-predicted biomass map provides realistic and context-specific benchmarks for ongoing restoration management of previously logged forests inside these reserves. • New biomass allometry using terrestrial, spaceborne, and airborne lidar. • GEDI underestimates forests with global maximum biomass. • ALS improves biomass estimates in primary Sequoia forests. • Giant trees underestimated without diameter or volume measurements. [ABSTRACT FROM AUTHOR]

Investigating the quantity of forest aboveground biomass (AGB) is crucial for understanding the role forests play in the global carbon cycle. The canopy height model (CHM) is a critical component in estimating AGB, as it provides a three-dimensional representation of the tree canopy. Traditional CHM estimation methods are time-consuming, labor-intensive, and expensive, particularly at large-scales. Remote sensing is a cost-effective and efficient alternative approach, providing valuable information over large areas in a timely manner. The Global Ecosystem Dynamics Investigation (GEDI) onboard the International Space Station is a space-based light detection and ranging (LiDAR) system designed to collect information on vertical structures of vegetation. One major problem with the collection of GEDI data is that it provides limited information over discrete ground samples, also known as footprints, and thus do not provide wall-to-wall gridded height products. The objective of this study was twofold: a) to integrate the GEDI LiDAR footprint heights with Sentinel-2 multispectral imagery to generate a 10 m wall-to-wall CHM map of New York State (NYS), USA for the year 2019 and b) to improve our previously generated AGB map (both accuracy and resolution) of NYS for the year 2019 by fusing Sentinel-2 multispectral, Sentinel-1 synthetic aperture radar (SAR), and the produced CHM. To generate the 10 m CHM map, the GEDI footprints height measurements were extrapolated using Sentinel-2 imagery and a random forest model. The CHM that was produced was assessed by using GEDI footprints that were not part of the training phase and were therefore independent (extrapolated). Comparing our 10 m CHM with the available global 30 m CHM map provided by Potapov et al. (2021) over NYS shows significant improvement not only in terms of spatial resolution, but also in terms of accuracy. The root mean square error (RMSE) of our 10 m CHM is 4.4 m while this value is 7.49 m for the 30 m CHM over NYS. Similarly, the R2 value for the 10 m CHM map is 0.74, while that of the 30 m CHM is 0.46. Finally, the integration of produced 10 m CHM, Sentinel-1, and Sentinel-2 datasets were utilized to create a 10 m AGB map of NYS with the RMSE of 39.49 Mg/ha, and R2 of 0.65. The results demonstrate the potential of integrating GEDI, Sentinel-1, and Sentinel-2 data for providing a valuable tool for large-scale mapping of forest canopy structure and biomass, which can help to inform forest management and carbon accounting efforts. • GEDI data provides a valuable source for creating a wall-to-wall canopy height map. • Integrated GEDI, Sentinel-1, and Sentinel-2 data show potential for forest mapping. • Sentinel-2 red-edge bands improve 10 m CHM and AGB mapping. • The 10 m CHM and AGB maps capture more details about forest clearcuts than the 30 m maps. [ABSTRACT FROM AUTHOR]

Forest canopy mean height (CMH) and aboveground biomass (AGB) are key indicators for evaluating forest ecosystem productivity. In this study, we proposed a new approach to integrate field measurement data, GEDI LiDAR, sentinel, and terrain data to construct multi-source data-driven forest CMH and AGB models at a 30-m resolution. First, we employed the RFE-SVM (Recursive Feature Elimination- Support Vector Machine) method to determine the features sensitive to forest height and AGB. Second, we used three regression models to construct the CMH model to extend the GEDI point data to wall-to-wall CMH maps thereby providing sensitive features for AGB estimation. Third, we jointly selected the features and field measurement data to build a model to estimate AGB. The CMH and AGB models, evaluated within the study area, achieved R2 values of 0.64 and 0.89, respectively. Fourth, we performed transferability tests for the AGB model. The AGB model built based on data from study area was applied to three other test areas, resulting in R2 values of 0.66, 0.76, and 0.91, respectively. Overall, this study presented a method that utilizes extensive open data with great potential for mapping forest CMH and AGB over large areas. [Display omitted] • Using GEDI and Sentinel to estimate forest canopy mean height and aboveground biomass. • Feature filtering by RFE-SVM to improve the estimation accuracy. • Discussing the transferability of the AGB model. [ABSTRACT FROM AUTHOR]

To better understand forest carbon budgets and design forest-based climate change mitigation solutions, reliable biomass estimation is critical. Integrating multi-sensor remote sensing data can improve forest monitoring and evaluation. This study adopted a one-hectare plot size sampling design to improve the integration of GEDI footprints with in-situ, optical, and SAR data for the estimation of forest above-ground biomass (AGB). The study was carried out for a managed tropical forest in the Himalayan foothills of India. The space-borne GEDI retrieved the canopy height of the study area with an RMSE of 3.36 m and Adj. R2 of 0.70. Extrapolating GEDI footprint heights (RH95) with Landsat 8 indices using Random Forest (RF) yielded a spatial canopy height of the study area with an R2 of 0.97 and RMSE of 2.32 m. Using the GEDI canopy height, foliage density, and plant area index, the AGB at GEDI footprints level was estimated using RF, with an R2 of 0.91 and an RMSE of 20.10 Mg ha−1. The spatial AGB model that only considered ALOS-2 SAR variables had an R2 of 0.61 and an RMSE of 18.27 Mg ha−1, whereas the RF model that considered both SAR variables and canopy height had a superior R2 of 0.77 and a lower RMSE of 13.86 Mg ha−1. The addition of canopy height data reduced the RMSE of the AGB model by 4.41 Mg ha−1 and also predicted a higher range of AGB. The study demonstrates the effectiveness of combining GEDI data with other sensors (optical and SAR data) to provide precise AGB of multistage managed forests using a one-hectare plot size sampling design. • One-hectare plot sampling design adopted to integrate GEDI, Landsat-8, and ALOS-2 SAR. • Random Forest algorithm used to predict canopy height and AGB. • Integration of canopy height with ALOS-2 SAR-derived variables improved AGB prediction and reduced AGB uncertainty. [ABSTRACT FROM AUTHOR]

[Display omitted] • Monitoring forest biodiversity is crucial to prevent its decline. • The Height Variation Hypothesis (HVH) is a key tool for biodiversity monitoring. • Tree Height Heterogeneity assessed by GEDI data is a good proxy of species diversity. • CHM spatial resolution, heterogeneity indices and forest density affect the HVH. • Space-borne LiDAR GEDI derived CHMs can be used to assess forest biodiversity. The "Height Variation Hypothesis" is an indirect approach used to estimate forest biodiversity through remote sensing data, stating that greater tree height heterogeneity (HH) measured by CHM LiDAR data indicates higher forest structure complexity and tree species diversity. This approach has traditionally been analyzed using only airborne LiDAR data, which limits its application to the availability of the dedicated flight campaigns. In this study we analyzed the relationship between tree species diversity and HH, calculated with four different heterogeneity indices using two freely available CHMs derived from the new space-borne GEDI LiDAR data. The first, with a spatial resolution of 30 m, was produced through a regression tree machine learning algorithm integrating GEDI LiDAR data and Landsat optical information. The second, with a spatial resolution of 10 m, was created using Sentinel-2 images and a deep learning convolutional neural network. We tested this approach separately in 30 forest plots situated in the northern Italian Alps, in 100 plots in the forested area of Traunstein (Germany) and successively in all the 130 plots through a cross-validation analysis. Forest density information was also included as influencing factor in a multiple regression analysis. Our results show that the GEDI CHMs can be used to assess biodiversity patterns in forest ecosystems through the estimation of the HH that is correlated to the tree species diversity. However, the results also indicate that this method is influenced by different factors including the GEDI CHMs dataset of choice and their related spatial resolution, the heterogeneity indices used to calculate the HH and the forest density. Our finding suggest that GEDI LIDAR data can be a valuable tool in the estimation of forest tree heterogeneity and related tree species diversity in forest ecosystems, which can aid in global biodiversity estimation. [ABSTRACT FROM AUTHOR]

• Wildlife conservation requires an understanding of animal-environment relationships. • We upscaled GEDI LiDAR metrics leveraging continuous Landsat spectral data. • GEDI forest canopy metrics improved models for coyotes, red squirrels, and snowshoe hares. • Carnivores and their prey were influenced by other variables, including snow depth. • We provide an example of integrating spaceborne LiDAR to advance animal ecology. Animal conservation requires understanding animal-habitat relationships. The integration of novel remote sensing platforms such as Light Detection and Ranging (LiDAR) technology has dramatically improved the resolution of insight when evaluating animal-habitat relationships by characterizing forest structure. However, conventional LiDAR collection (e.g., airborne or terrestrial laser scanning) may be limited by small spatial extents and logistical constraints (e.g., budget) associated with sampling. NASA's Global Ecosystem Dynamics Investigation (GEDI) mission provides an alternative and complement to conventional LiDAR sampling with globally available waveform LiDAR, which is being collected to characterize vertical and horizontal structure of Earth's forests. Forest carnivores are wide-ranging species occupying forested ecosystems, and are generally associated with vertical and horizontal forest structure for their survival and reproduction. We evaluated patterns in occurrence and habitat use of forest carnivores, which included Pacific martens (Martes caurina), Rocky Mountain red foxes (Vulpes vulpes macroura), and coyotes (Canis latrans) and patterns in occurrence of their prey; American red squirrels (Tamiasciurus hudsonicus) and snowshoe hares (Lepus americanus). Camera trap data were collected during the 2014–2017 winters in the Greater Yellowstone Ecosystem in Wyoming, USA. Our objectives were to (1) combine GEDI samples with multispectral satellite imagery from Landsat 8 to upscale vertical forest structure metrics; (2) assess the relative importance of environmental characteristics influencing occurrence and habitat use of forest-associated predators and prey; and (3) determine if GEDI-derived variables aided our efforts in characterizing animal-environment relationships. We used Random Forest regression models to upscale GEDI samples across our study area and implemented a multi-tiered approach using generalized linear mixed effect models to simultaneously evaluate animal-environment relationships and how GEDI-derived metrics improved the animal-habitat models. GEDI-derived metrics of relative height and foliage height diversity improved our animal-environment models and were among the strongest covariates (effect sizes were 1.3–1.8 times larger than the next closest) in the coyote, red squirrel, and snowshoe hare models. All five species were influenced to some degree by the frequency of rebaiting a camera trap and varying conditions of snow depth. Collectively, our work indicates forest canopy height and complexity variables significantly improved our ability to assess the importance of forest characteristics on forest carnivores and their prey. Indeed, there is an untapped opportunity to enhance animal ecology and conservation planning with continued integration of GEDI information with freely available satellite data to characterize attributes of forest structure across expansive areas. [ABSTRACT FROM AUTHOR]

In Romania, a wide-spectrum of registered entrepreneurial activities can be observed, even though they survive in unfavourable conditions with a very low impact on the economic growth (Szabo and Herman, 2012). This paper presents an analysis of Romania's entrepreneurial performance in the European context based on the Global Entrepreneurship and Development Index (GEDI) proposed by Acs&Szerb (Ács et al., 2013a). GEDI permits a multidimensional analysis of entrepreneurship in comparison with GEM and WB data. Moreover, in order to identify possible policies that foster productive entrepreneurship, a statistical analysis of the Global Competitiveness Index (WEF, 2013) and the Summary Innovation Index (EU, 2013) was made. [ABSTRACT FROM AUTHOR]

The article presents information on a study which investigated time-warped comparison of genetic expression in adaptive and maladaptive cardiac hypertrophy. It identifies the genetic hypertrophy-associated genes from stimulus-dependent transcripts. It explores the methodology of selecting hypertrophy model datasets. It also reveals the findings of exercise and banding stimuli that elicit a similar magnitude of myocardial growth.