Effective estimation of fuel load is critical for mitigating wildfire risks. Here, we evaluate the performance of mobile laser scanning (MLS) and terrestrial laser scanning (TLS) to estimate fuel loads across multiple vegetation layers. Data were collected in two forest regions: the North Kaibab (NK) Plateau in Arizona and Monroe Mountain (MM) in Utah. We used random forest models to predict vegetation attributes, evaluating the performance of full models and transferred models using R², RMSE, and bias. The MLS consistently outperformed the TLS system, particularly for canopy-related attributes and woody biomass components. However, the TLS system showed potential for capturing canopy structure attributes, while offering advantages like operational simplicity, low equipment demands, and ease of deployment in the field, making it a cost-effective alternative for managers without access to more complex and expensive mobile or airborne systems. Our results show that model transferability between NK and MM is highly variable depending on the fuel attributes. Attributes related to canopy biomass showed better transferability, with small losses in predictive accuracy when models were transferred between the two sites. Conversely, surface fuel attributes showed more significant challenges for model transferability, given the difficulty of laser penetration in the lower vegetation layers. In general, models trained in NK and validated in MM consistently outperformed those trained in MM and transferred to NK. This may suggest that the NK plots captured a broader complexity of vegetation structure and environmental conditions from which models learned better and were able to generalize to MM. This study highlights the potential of ground-based LiDAR technologies in providing detailed information and important insights into fire risk and forest structure.

Accurate mapping of aboveground biomass density (AGBD) is vital for ecological research and carbon cycle monitoring. Integrating multi-source remote sensing data offers significant potential to enhance the accuracy and coverage of AGBD estimates. This study evaluated three upscaling frameworks for integrating GEDI LiDAR, SAR, and optical satellite data to create wall-to-wall AGBD maps. The frameworks tested in this paper were: (1) a single-step approach using optical imagery, (2) a two-stage approach with GEDI-derived variables, and (3) a three-stage approach combining imagery and in situ-derived allometries. Internal validation showed that framework 1 achieved the lowest root mean square difference (%RMSD) of 53.3% and highest coefficient of determination (R²) of 0.53. An independent external validation of the AGBD map was performed using in situ observations, also revealing that framework 1 was the most accurate (%RMSD = 39.3% and R² = 0.93), while frameworks 2 and 3 were less accurate (%RMSD = 54.7, 44.7 and R² = 0.95, 0.90, respectively). Herein, we show that upscaling frameworks significantly impacted AGBD map uncertainty and the magnitude of estimate differences. Our findings suggest that upscaling framework 1 based on a single step approach was the most effective for capturing detailed AGBD variations, while careful consideration of model sensitivity and map uncertainties is essential for reliable AGBD estimation. This study provides valuable insights for advancing forest AGBD monitoring and highlights the potential for further enhancements in remote sensing methodologies.

Southern U.S. forests are essential for carbon storage and timber production but are increasingly impacted by natural disturbances, highlighting the need to understand their dynamics and recovery. Canopy cover is a key indicator of forest health and resilience. Advances in remote sensing, such as NASA’s GEDI spaceborne LiDAR, enable more precise mapping of canopy cover. Although GEDI provides accurate data, its limited spatial coverage restricts large-scale assessments. To address this, we combined GEDI with Synthetic Aperture Radar (SAR), and optical imagery (Sentinel-1 GRD and Landsat–Sentinel Harmonized (HLS)) data to create a comprehensive canopy cover map for Florida. Using a random forest algorithm, our model achieved an R² of 0.69, RMSD of 0.17, and MD of 0.001, based on out-of-bag samples for internal validation. Geographic coordinates and the red spectral channel emerged as the most influential predictors. External validation with airborne laser scanning (ALS) data across three sites yielded an R² of 0.70, RMSD of 0.29, and MD of −0.22, confirming the model’s accuracy and robustness in unseen areas. Statewide analysis showed lower canopy cover in southern versus northern Florida, with wetland forests exhibiting higher cover than upland sites. This study demonstrates the potential of integrating multiple remote sensing datasets to produce accurate vegetation maps, supporting forest management and sustainability efforts in Florida.

The extension of NASA’s Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) mission beyond the cryosphere to include the study of vegetation and the land surface has significantly advanced global Earth observation. This review synthesizes findings from 293 peer-reviewed articles and provides a comprehensive assessment of the mission’s contributions to terrestrial ecosystem science. We begin by outlining the mission’s objectives, instrumentation, and core data products—particularly ATL03 (geolocated photons) and ATL08 (terrain and canopy heights)—which have supported a wide array of applications in forestry, terrain analysis, and environmental monitoring. Our analysis shows that most studies focus on temperate and tropical broadleaf forests, with a strong emphasis on estimating canopy height, terrain elevation, and forest structure. Terrain metrics derived from ICESat-2 products typically achieve high accuracy, while vegetation variables, such as canopy height, cover, leaf area index (LAI), and aboveground biomass, demonstrate moderate to strong accuracies across biomes. While parametric models remain the most used approach, machine learning methods are expanding, and more than one third of the reviewed literature incorporates synergistic analyses with other satellite missions. Despite its versatility, ICESat-2 remains underutilized in several key domains—including nonforest vegetation, fire ecology, wildlife habitat assessment, urban monitoring, and disturbance detection—mainly due to the scarcity of standardized algorithms and validated reference datasets. However, the growing availability of open source processing tools presents a significant opportunity for expanding the user base and fostering innovation in these emerging areas. Future advancements, such as the forthcoming ATL18 gridded canopy height and terrain products, alongside synergies with missions like the European Space Agency (ESA)’s BIOMASS and the NASA–Indian Space Research Organisation (ISRO) Synthetic Aperture Radar (NISAR), promise to enhance multisensor integration and ecosystem monitoring. Overall, ICESat-2 continues to evolve as a powerful resource for characterizing vegetation structure, ecosystem dynamics, and land surface processes on a global scale.

Invasive species can fundamentally alter fire regimes by modifying fuel characteristics, yet predicting their impacts on fire behavior remains challenging. Cogongrass ( Imperata cylindrica ), a highly invasive grass spreading across the southeastern US, has been associated with increased fuel loads and fire intensity, but it is unclear how its impacts on fuel load and fire behavior vary across environmental and seasonal gradients. We measured fuel characteristics across 159 cogongrass invaded and non-invaded plots spanning from central Florida to southern Mississippi across multiple seasons. Using these data, we developed custom fuel models in BehavePlus to predict cogongrass effects on surface fire behavior and tree torching across spatial and temporal gradients. Cogongrass invasion increased live fuel loads by up to 1.96 t/ha, with effects strongest in summer and at lower latitudes, and dead fuel loads by up to 1.88 t/ha across all sites. Latitude × invasion interactions indicated that fuel load differences between invaded and non-invaded plots diminished toward northern sites. Cogongrass-driven fuel load alterations elevated surface fire rate of spread (median + 0.75 m/min), and flame length (+ 0.16 m), with the largest increases observed in spring (rate of spread: + 2.60 m/min; flame length: + 0.80 m). Invasion also consistently increased the crown fire transition probability, with magnitudes mirroring those of surface fire effects. Our findings highlight the potential for cogongrass to substantially alter fire behavior across a range of environmental conditions, although the magnitude of these effects varies geographically and tends to be strongest at lower latitudes. These results underscore the importance of targeted management in regional fire planning of invaded landscapes.