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The ability to measure the ecological effects of understory fire in the Amazon on a landscape scale remains a frontier in remote sensing. The Global Ecosystem Dynamics Investigation's (GEDI) LiDAR data have been widely suggested as a critical new tool in this field. In this paper, we use the GEDI Simulator to quantify the nuanced effects of understory fire in the Amazon, and assess the ability of on-orbit GEDI data to do the same. While numerous ecological studies have used simulated GEDI data, on-orbit constraint may limit ecological inference. This is the first study that we are aware of that directly compares methods using simulated and on-orbit GEDI data. Simulated GEDI data showed that fire effects varied nonlinearly through the canopy and then moved upward with time since burn. Given that fire effects peaked in the mid-canopy and were often on the scale of 2 to 3 m in height difference, it is unlikely that on-orbit GEDI data will have the sensitivity to detect these same changes. [ABSTRACT FROM AUTHOR]

For vegetation monitoring, it is crucial to understand which changes are caused by the measurement setup and which changes are true representations of vegetation dynamics. UAV–LiDAR offers great possibilities to measure vegetation structural parameters; however, UAV–LiDAR sensors are undergoing rapid developments, and the characteristics are expected to keep changing over the years, which will introduce data inter-operability issues. Therefore, it is important to determine whether datasets acquired by different UAV–LiDAR sensors can be interchanged and if changes through time can accurately be derived from UAV–LiDAR time series. With this study, we present insights into the magnitude of differences in derived forest metrics in savanna woodland when three different UAV–LiDAR systems are being used for data acquisition. Our findings show that all three systems can be used to derive plot characteristics such as canopy height, canopy cover, and gap fractions. However, there are clear differences between the metrics derived with different sensors, which are most apparent in the lower parts of the canopy. On an individual tree level, all UAV–LiDAR systems are able to accurately capture the tree height in a savanna woodland system, but significant differences occur when crown parameters are measured with different systems. Less precise systems result in underestimations of crown areas and crown volumes. When comparing UAV–LiDAR data of forest areas through time, it is important to be aware of these differences and ensure that data inter-operability issues do not influence the change analysis. In this paper, we want to stress that it is of utmost importance to realise this and take it into consideration when combining datasets obtained with different sensors. [ABSTRACT FROM AUTHOR]

Forest structure is a key driver of forest functional processes. The characterization of forest structure across spatiotemporal scales is essential for forest monitoring and management. LiDAR data have proven particularly useful for cost-effectively estimating forest structural attributes. This paper evaluates the ability of combined forest inventory data and low-density discrete return airborne LiDAR data to discriminate main forest structural types in the Mediterranean-temperate transition ecotone. Firstly, we used six structural variables from the Spanish National Forest Inventory (SNFI) and an aridity index in a k-medoids algorithm to define the forest structural types. These variables were calculated for 2770 SNFI plots. We identified the main species for each structural type using the SNFI. Secondly, we developed a Random Forest model to predict the spatial distribution of structural types and create wall-to-wall maps from LiDAR data. The k-medoids clustering algorithm enabled the identification of four clusters of forest structures. A total of six out of forty-one potential LiDAR metrics were utilized in our Random Forest, after evaluating their importance in the Random Forest model. Selected metrics were, in decreasing order of importance, the percentage of all returns above 2 m, mean height of the canopy profile, the difference between the 90th and 50th height percentiles, the area under the canopy curve, and the 5th and the 95th percentile of the return heights. The model yielded an overall accuracy of 64.18%. The producer's accuracy ranged between 36.11% and 88.93%. Our results confirm the potential of this approximation for the continuous monitoring of forest structures, which is key to guiding forest management in this region. [ABSTRACT FROM AUTHOR]