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NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.

Geospatial data has proven useful for monitoring urbanization and guiding sustainable development in rapidly urbanizing regions. The United Nations' (UN) Sustainable Development Goal (SDG) Indicator 11.3.1 leverages geospatial data to estimate rates of urban land and population change, providing insight on urban land use expansion patterns and thereby informing sustainable urbanization initiatives (i.e., SDG 11). Our work enhances a UN proposed delineation method by integrating various open-source datasets and tools (e.g., OpenStreetMap and openrouteservice) and advanced geospatial analysis techniques to automate the delineation of individual functional urban agglomerations across a country and, subsequently, calculate SDG Indicator 11.3.1 and related metrics for each. We applied our automated geospatial approach to three rapidly urbanizing countries in Africa: Ethiopia, Nigeria, and South Africa, to conduct multi-level examinations of urban land use expansion, including identifying hotspots of SDG Indicator 11.3.1 where the percentage growth of urban land was greater than that of the urban population. The urban agglomerations of Ethiopia, Nigeria, and South Africa displayed a 73%, 14%, and 5% increase in developed land area from 2016 to 2020, respectively, with new urban development being of an outward type in Ethiopia and an infill type in Nigeria and South Africa. On average, Ethiopia's urban agglomerations displayed the highest SDG Indicator 11.3.1 values across urban agglomerations, followed by those of South Africa and Nigeria, and secondary cities of interest coinciding as SDG Indicator 11.3.1 hotspots included Mekelle, Ethiopia; Benin City, Nigeria; and Polokwane, South Africa. The work presented in this study contributes to knowledge of urban land use expansion patterns in Ethiopia, Nigeria, and South Africa, and our approach demonstrates effectiveness for multi-level evaluations of urban land expansion according to SDG Indicator 11.3.1 across urbanizing countries. [ABSTRACT FROM AUTHOR]

Continuous characterizations of forest structure are critical for modeling wildlife habitat as well as for assessing trade-offs with additional ecosystem services. To overcome the spatial and temporal limitations of airborne lidar data for studying wide-ranging animals and for monitoring wildlife habitat through time, novel sampling data sources, including the space-borne Global Ecosystem Dynamics Investigation (GEDI) lidar instrument, may be incorporated within data fusion frameworks to scale up satellite-based estimates of forest structure across continuous spatial extents. The objectives of this study were to: 1) investigate the value and limitations of satellite data sources for generating GEDI-fusion models and 30m resolution predictive maps of eight forest structure measures across six western U.S. states (Colorado, Wyoming, Idaho, Oregon, Washington, and Montana); 2) evaluate the suitability of GEDI as a reference data source and assess any spatiotemporal biases of GEDI-fusion maps using samples of airborne lidar data; and 3) examine differences in GEDI-fusion products for inclusion within wildlife habitat models for three keystone woodpecker species with varying forest structure needs. We focused on two fusion models, one that combined Landsat, Sentinel-1 Synthetic Aperture Radar, disturbance, topographic, and bioclimatic predictor information (combined model), and one that was restricted to Landsat, topographic, and bioclimatic predictors (Landsat/topo/bio model). Model performance varied across the eight GEDI structure measures although all representing moderate to high predictive performance (model testing R2 values ranging from 0.36 to 0.76). Results were similar between fusion models, as well as for map validations for years of model creation (2019-2020) and hindcasted years (2016-2018). Within our wildlife case studies, modeling encounter rates of the three woodpecker species using GEDI-fusion inputs yielded AUC values ranging from 0.76-0.87 with observed relationships that followed our ecological understanding of the species. While our results show promise for the use of remote sensing data fusions for scaling up GEDI structure metrics of value for habitat modeling and other applications across broad continuous extents, further assessments are needed to test their performance within habitat modeling for additional species of conservation interest as well as biodiversity assessments. [ABSTRACT FROM AUTHOR]

Forest inventory and planning decisions are frequently informed by LiDAR data. Repeated LiDAR acquisitions offer an opportunity to update forest inventories and potentially improve forest inventory estimates through time. We leveraged repeated LiDAR and ground measures for a study area in northern Idaho, U.S.A., to predict (via imputation)--across both space and time--four forest inventory attributes: aboveground carbon (AGC), basal area (BA), stand density index (SDI), and total stem volume (Vol). Models were independently developed from 2003 and 2009 LiDAR datasets to spatially predict response variables at both times. Annual rates of change were calculated by comparing response variables between the two collections. Additionally, a pooled model was built by combining reference observations from both years to test if imputation can be performed across measurement dates. The [R.sup.2] values for the pooled model were 0.87, 0.90, 0.89, and 0.87 for AGC, BA, SDI, and Vol, respectively. Mapping response variables at the landscape level demonstrates that the relationship between field data and LiDAR metrics holds true even though the data were collected in different years. Pooling data across time increases the number of reference observations available to resource managers and may ultimately improve inventory predictions. Key words: repeated LiDAR acquisitions, imputation, forest inventory, aboveground carbon, change detection. Les decisions concernant la planification et les inventaires forestiers s'appuient souvent sur des donnees lidar. Les acquisitions lidar recurrentes offrent l'opportunite de mettre a jour les inventaires forestiers et possiblement d'ameliorer avec le temps les estimations faites a partir de l'inventaire forestier. Nous avons tire parti de mesures effectuees sur le terrain et au moyen du lidar dans une aire d'etude du nord de l'Idaho, aux E.-U., pour predire par imputation, dans l'espace et dans le temps, quatre attributs de l'inventaire forestier) : le carbone aerien (CA), la surface terriere (ST), l'indice de densite du peuplement (IDP) et le volume total de la tige (Vol). Des modeles ont ete developpes de facon independante a partir de donnees lidar de 2003 et 2009 pour predire les variables reponse dans l'espace pour chacune des deux annees. Les taux annuels de changement ont ete calcules en comparant les variables reponse entre les deux ensembles. De plus, un modele regroupe a ete elabore en combinant les observations de reference des deux annees pour verifier si une imputation pouvait etre realisee a partir des donnees regroupees des deux annees. Les valeurs de [R.sup.2] du modele regroupe etaient, respectivement, 0,87, 0,90, 0,89 et 0,87 pour CA, ST, IDP et Vol. La cartographie des variables reponse a l'echelle du paysage demontre que la relation entre les donnees terrain et les mesures obtenues grace au lidar ne change pas meme si les donnees n'ont pas ete recueillies durant la meme annee. Le fait de regrouper les donnees de plusieurs annees augmente le nombre d'observations de reference disponibles pour les gestionnaires des ressources et peut ultimement ameliorer les previsions fondees sur l'inventaire. [Traduit par la Redaction] Mots-cles: acquisitions lidar recurrentes, imputation, inventaire forestier, carbone aerien, detection du changement., Introduction A detailed forest inventory allows resource managers to make timely and appropriate decisions concerning various management objectives for a forest. Traditionally, continuous forest inventory occurs by measuring permanent sample [...]