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The article thoroughly investigates the relationships between terrain features and tree measurements derived from aerial laser scanning (ALS) data and field surveys in a 1067-hectare forested area. A digital elevation model (DEM) was generated from ALS data, which was then used to derive additional layers such as slope, aspect, topographic position index (TPI), and landforms. The authors developed a mathematical procedure to determine the radii for the topographic position index. The canopy height model was created, and individual trees were segmented using a novel voxel aggregation method, allowing for the calculation of tree height and crown size. Accuracy assessments were conducted between ALS-derived data and field-collected data. Terrain variability within each forest unit was evaluated using characteristics such as standard deviation, entropy, and frequency. The relationships between tree height and the derived topographic features within forest subcompartments, as well as the correlation between the height yield map for the entire area and the TPI layer, were analysed. The authors found strong correlation between the topographic position index and tree heights in both cases. The presented remote-sensing-based methodology and the results can be effectively used in digital forest site mapping, complemented by field sampling and laboratory soil analyses, and, as final goal, in carbon stock assessment. [ABSTRACT FROM AUTHOR]

The role of forests in providing multiple goods and services has been recognized worldwide. In such a context, reliable spatial predictions of forest attributes such as tree volume and current increment are fundamental for conducting forest monitoring, improving restoration programs, and supporting decision-making processes. This article presents the methodology and the results of the wall-to-wall spatialization of the growing stock volume and the current annual increment measured in 273 plots of data of the Italian National Forest Inventory over an area of more than 3260 km2 in the Friuli Venezia Giulia region (Northeast Italy). To this aim, a random forest model was tested using as predictors 4 spectral indices from Sentinel-2, a high-resolution Canopy Height Model derived from LiDAR, and geo-morphological data. According to the Leave One Out cross-validation procedure, the model for the growing stock shows an R2 and an RMSE% of 0.67 and 41%, respectively. Instead, an R2 of 0.47 and an RMSE% of 57% were obtained for the current annual increment. The validation with an independent dataset further improved the models' performances, yielding significantly higher R2 values of 0.84 and 0.83 for volume and for increment, respectively. Our results underline a relatively higher importance of LiDAR-derived metrics compared to other covariates in estimating both attributes, as they were even twice as important as vegetation indices for growing stock. Therefore, these metrics are promising for the development of a national LiDAR-based model. [ABSTRACT FROM AUTHOR]

The Brazilian Atlantic Rainforest presents great diversity of flora and stand structures, making it difficult for traditional forest inventories to collect reliable and recurrent information to classify forest succession stages. In recent years, remote sensing data have been explored to save time and effort in classifying successional forest stages. However, there is a need to understand if any of these sensors stand out for this purpose. Here, we evaluate the use of multispectral satellite data from four different platforms (CBERS-4A, Landsat-8/OLI, PlanetScope, and Sentinel-2) and airborne light detection and ranging (LiDAR) to classify three forest succession stages in a subtropical ombrophilous mixed forest located in southern Brazil. Different features extracted from multispectral and LiDAR data, such as spectral bands, vegetation indices, texture features, and the canopy height model (CHM) and LiDAR intensity, were explored using two conventional machine learning methods such as random trees (RT) and support vector machine (SVM). The statistically based maximum likelihood (MLC) algorithm was also compared. The classification accuracy was evaluated by generating a confusion matrix and calculating the kappa index and standard deviation based on field measurements and unmanned aerial vehicle (UAV) data. Our results show that the kappa index ranged from 0.48 to 0.95, depending on the chosen dataset and method. The best result was obtained using the SVM algorithm associated with spectral bands, CHM, LiDAR intensity, and vegetation indices, regardless of the sensor. Datasets with Landsat-8 or Sentinel-2 information performed better results than other optical sensors, which may be due to the higher intraclass variability and less spectral bands in CBERS-4A and PlanetScope data. We found that the height information derived from airborne LiDAR and its intensity combined with the multispectral data increased the classification accuracy. However, the results were also satisfactory when using only multispectral data. These results highlight the potential of using freely available satellite information and open-source software to optimize forest inventories and monitoring, enabling a better understanding of forest structure and potentially supporting forest management initiatives and environmental licensing programs. [ABSTRACT FROM AUTHOR]

Multispectral and 3D LiDAR remote sensing data sources are valuable tools for characterizing the 3D vegetation structure and thus understanding the relationship between forest structure, biodiversity, and microclimate. This study focuses on mapping riparian forest species in the canopy strata using a fusion of Airborne LiDAR data and multispectral multi-source and multi-resolution satellite imagery: Sentinel-2 and Pleiades at tree level. The idea is to assess the contribution of each data source in the tree species classification at the considered level. The data fusion was processed at the feature level and the decision level. At the feature level, LiDAR 2D attributes were derived and combined with multispectral imagery vegetation indices. At the decision level, LiDAR data were used for 3D tree crown delimitation, providing unique trees or groups of trees. The segmented tree crowns were used as a support for an object-based species classification at tree level. Data augmentation techniques were used to improve the training process, and classification was carried out with a random forest classifier. The workflow was entirely automated using a Python script, which allowed the assessment of four different fusion configurations. The best results were obtained by the fusion of Sentinel-2 time series and LiDAR data with a kappa of 0.66, thanks to red edge-based indices that better discriminate vegetation species and the temporal resolution of Sentinel-2 images that allows monitoring the phenological stages, helping to discriminate the species. [ABSTRACT FROM AUTHOR]

Accurately mapping the forest canopy height is vital for conserving forest ecosystems. Employing the forest height measured by satellite light detection and ranging (LiDAR) systems as ground samples to establish forest canopy height extrapolation (FCHE) models presents promising opportunities for mapping large-scale wall-to-wall forest canopy height. However, despite the potential to provide more samples and alleviate the stripe effect by synergistically using the data from two existing LiDAR datasets, Global Ecosystem Dynamics Investigation (GEDI) and Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), the fundamental differences in their operating principles create measurement biases, and thus, there are few studies combining them for research. Furthermore, previous studies have typically employed existing regression algorithms as FCHE models to predict forest canopy height, without customizing a model that achieves optimal performance based on the current samples. These shortcomings constrain the accuracy of predicting forest canopy height using satellite LiDAR data. To surmount these difficulties, we proposed a genetic programming (GP) guided method for mapping forest canopy height by combining the GEDI and ICESat-2 LiDAR data with Sentinel-1/2, terrain, and climate data. In this method, GP autonomously constructs the fusion model of the GEDI and ICESat-2 datasets (hereafter GIF model) and the optimal FCHE model based on the explanatory variables for the specific study area. The outcomes demonstrate that the fusion of the GEDI and ICESat-2 data shows high consistency ( R 2 = 0.85, RMSE = 2.2 m , pRMSE = 11.24%). The synergistic use of the GEDI and ICESat-2 data, coupled with the optimization of the FCHE model, substantially improves the precision of forest canopy height predictions, and finally achieves R 2 , RMSE, and pRMSE of 0.64, 3.38 m , and 16.08%, respectively. In summary, our research presents a reliable approach to accurately estimate forest canopy height using remote sensing data by addressing measurement biases between the GEDI and ICESat-2 data and overcoming the limitations of traditional FCHE models. [ABSTRACT FROM AUTHOR]

The extrapolation of forest structural attributes from LiDAR has traditionally been restricted to local or regional scales, hindering a thorough assessment of single-year versus time series predictors across expansive spatial scales. We extrapolated the vertical complexity captured by the Land, Vegetation, and Ice Sensor (LVIS) full-wave form LiDAR of boreal forests in the Alaska–Yukon–Northwest Territories region, utilizing predictors from Landsat images from 1989 to 2019. This included both single-year and long-term estimates of vegetation indices, alongside constant factors like terrain slope and location. Random forest regression models comparing the single-year and 15-year and 30-year time series models were applied. Additionally, the potential of estimating horizontal forest complexity from vertical complexity was explored using a moving window approach in the Kluane Valley. While the extended time series marginally enhanced model accuracy, a fine-tuned single-year model proved superior (R2 = 0.84, relative RRMSE = 8.4%). In estimating the horizontal complexity, the variance in a 5 × 5 moving window displayed the most promising results, aligning with traditional horizontal structure measures. Single-year Landsat models could potentially surpass time series models in predicting forest vertical complexity, with the added capability to estimate horizontal complexity using variance in a moving window approach. [ABSTRACT FROM AUTHOR]

The point cloud-based 3D model of forest helps to understand the growth and distribution pattern of trees, to improve the fine management of forestry resources. This paper describes the process of constructing a fine rubber forest growth model map based on 3D point clouds. Firstly, a multi-scale feature extraction module within the point cloud column is used to enhance the PointPillars learning capability. The Swin Transformer module is employed in the backbone to enrich the contextual semantics and acquire global features with the self-attention mechanism. All of the rubber trees are accurately identified and segmented to facilitate single-trunk localisation and feature extraction. Then, the structural parameters of the trunks calculated by RANSAC and IRTLS cylindrical fitting methods are compared separately. A growth model map of rubber trees is constructed. The experimental results show that the precision and recall of the target detection reach 0.9613 and 0.8754, respectively, better than the original network. The constructed rubber forest information map contains detailed and accurate trunk locations and key structural parameters, which are useful to optimise forestry resource management and guide the enhancement of mechanisation of rubber tapping. [ABSTRACT FROM AUTHOR]

The recent innovations in remote sensing technologies have given rise to the efficient mapping and monitoring of forests. The developments in the sensor implementation have mainly focused on optimizing the payload of the UAV system and allowed the users to acquire the data simultaneously with a range of active and passive sensors like high-resolution RGB cameras and multispectral cameras LiDAR (Laser Imaging Detection and Ranging). The main objective of this research contribution is to combine the Digital Elevation Model (DEMs) from quadcopter Unmanned Aerial Vehicles (UAVs), Fixed Wing UAV-based cameras, and iPhone datasets for the forest plots. The datasets from two vegetation seasons, namely leaf-off and leaf-on, were used to combine the Digital Elevation Models from different data acquisition platforms. This internship research work aims to create and experiment with new methods, techniques, and technologies for the applications of UAV photogrammetry and iPhone LiDAR in forest napping and inventory management. CHMs are also generated in this work which helps assess the conditions of the forests in the recreational areas, and the possibility of solutions like iPhone LiDAR and UAV photogrammetry would be highly efficient and economical. The leaf-off and leaf-on datasets were processed in Agisoft Metashape Professional software to generate dense point clouds for the forest plots. The point cloud from the leaf-on dataset was rasterized to generate a DSM whereas the leaf-off point cloud generated a DSM of the forest plots after ground filtering with Cloth Simulation Filter (CSF) plugin. The iPhone LiDAR point was also rasterized to a DTM product after pre-processing steps and noise removal. The Canopy Height Models (CHMs) were generated by subtracting UAV and iPhone LiDAR based DTMs from the UAV leaf on DSM. Finally, the accuracy assessment of CHMs from UAB datasets and their integration with iPhone LiDAR has been assessed using the accurate tree heights measured during the forest field visits. The proposed methodology can be used for forest mapping purposes where a moderate accuracy is requested. [ABSTRACT FROM AUTHOR]

Forest canopy cover is an essential biophysical parameter of ecological significance, especially for characterizing woodlands and forests. This research focused on using data from the ICESat-2/ATLAS spaceborne lidar sensor, a photon-counting altimetry system, to map the forest canopy cover over a large country extent. The study proposed a novel approach to compute categorized canopy cover using photon-counting data and available ancillary Landsat images to build the canopy cover model. In addition, this research tested a cloud-mapping platform, the Google Earth Engine (GEE), as an example of a large-scale study. The canopy cover map of the Republic of Türkiye produced from this study has an average accuracy of over 70%. Even though the results were promising, it has been determined that the issues caused by the auxiliary data negatively affect the overall success. Moreover, while GEE offered many benefits, such as user-friendliness and convenience, it had processing limits that posed challenges for large-scale studies. Using weak or strong beams' segments separately did not show a significant difference in estimating canopy cover. Briefly, this study demonstrates the potential of using photon-counting data and GEE for mapping forest canopy cover at a large scale. [ABSTRACT FROM AUTHOR]

Panoptic segmentation is the combination of semantic and instance segmentation: assign the points in a 3D point cloud to semantic categories and partition them into distinct object instances. It has many obvious applications for outdoor scene understanding, from city mapping to forest management. Existing methods struggle to segment nearby instances of the same semantic category, like adjacent pieces of street furniture or neighbouring trees, which limits their usability for inventory- or management-type applications that rely on object instances. This study explores the steps of the panoptic segmentation pipeline concerned with clustering points into object instances, with the goal to alleviate that bottleneck. We find that a carefully designed clustering strategy, which leverages multiple types of learned point embeddings, significantly improves instance segmentation. Experiments on the NPM3D urban mobile mapping dataset and the FOR-instance forest dataset demonstrate the effectiveness and versatility of the proposed strategy. [ABSTRACT FROM AUTHOR]

The aim of this study was to analyze the accuracy of predictions of dominant height, mean height, basal area, and volume from the nationwide forest attribute map (SR16). The analysis took advantage of field observations from 33 different forest inventory projects across Norway used for validation. Forest attributes for more than 5000 plots were predicted using non-stratified and stratified models of SR16 and the predictions were compared against corresponding ground reference values. Finally, the effect of different factors that might have influenced the prediction errors were analyzed using partial least squared regression (PLSR) to determine under which conditions the SR16 is less apt. The overall results across all plots were adequate (RMSE of 10%, MD of 2% for dominant and mean height; RMSE of 28%, MD of 4% for basal area; RMSE of 31%, MD of 5% for volume). However, when the accuracy was assessed locally for each inventory project, large differences in accuracy were observed. The MD% values for some inventory projects were substantial (>30% for basal area and volume). The results showed that stratification did not necessarily improve the results and that factors related to the forest structure had the greatest impact on the PLSR analysis. [ABSTRACT FROM AUTHOR]

The automated mapping of forest burn severity using remote sensing imagery has been popular over the last decade. However, there is a lack of studies examining the performance of a range of classifiers for forest burn severity mapping for different burn severity classes. In this study, the performance of three supervised classifiers, maximum likelihood (ML), spectral angle mapper (SAM), and deep learning (U-Net), was evaluated for mapping forest burn severity under different burn severity class settings (two-level burn severity classes: burned and unburned; four-level burn severity classes: crown fire, heat-damaged, ground fire, and unburned). Multispectral unmanned aerial vehicle (UAV) images and light detection and ranging (LiDAR) points obtained from forest fire areas of Andong in South Korea were used to evaluate burn severity. The results show that all classifiers were capable of mapping the two-level burn severity with high overall accuracy (OA) (SAM: OA = 92.05%, kappa coefficient (K) = 0.84; U-Net: OA = 91.83%, K = 0.83; ML: OA = 90.92%, K = 0.82). For four-level burn severity mapping, U-Net (OA = 79.23%, K = 0.64) outperformed the conventional classifiers of SAM (OA = 50.61%, K = 0.38) and ML (OA = 46.85%, K = 0.34). Regarding class separability, SAM and U-Net showed good performance in detecting the severe burn severity class (crown fire areas), whereas a high rate of misclassification occurred in identifying the moderate burn severity classes (heat-damaged, ground fire) for all classifiers. In particular, ML and SAM showed a low capability in identifying unburned areas, while U-Net showed the lowest capability in mapping heat-damaged and ground fire areas. Overall, our study demonstrated that the reliable mapping of burn severity for Korea's forest fires largely depends on the number of levels of burn severity classes as well as the classifier's capability in discriminating moderate burn severity classes. [ABSTRACT FROM AUTHOR]

Forest ecosystems can be regarded as huge carbon sinks. In order to effectively assess carbon balance in such ecosystems, rapid and accurate estimation of the aboveground biomass of a forest is critically needed. However, the current methods for biomass estimation and mapping are of limited spatial resolution and mostly depend on large numbers of measurements. In order to obtain better biomass estimation outcomes with higher spatial resolution, a rapid method is introduced for region-scale biomass estimation in alpine and canyon areas using space-borne light detection and ranging (LiDAR) data and optical remote-sensing images. Specifically, we explored alpine and canyon areas in Shangri-La City in China using space-borne LiDAR data from ICESAT-2 and optical remote-sensing images from Landsat8 OLI, Sentinel-2, and Microwave remote sensing Sentinel-1. An extrapolation model of the forest canopy heights in these areas was constructed with a 30-m resolution of continuous canopy height outputs. For continuously estimating the diameter at breast height (DBH) in Shangri-La City, a tree height-DBH growth model was constructed based on the LiDAR and remote-sensing measurements. Finally, based on the average DBH of the explored forests, a model was constructed for estimating and mapping the aboveground biomass and carbon storage in Shangri-La with a spatial resolution of 30 m. The results show that the forest canopy height in Shangri-La City is mainly in the range of 2.82–30.96 m, and that the estimation accuracy is verified by the LiDAR-based canopy height model (CHM) with a coefficient of determination of R2 = 0.7143. The inversion results were still largely affected by geospatial location factors (longitude, latitude), terrain factors (slope, elevation), and vegetation indices (NBR, NDGI, NDVI). Based on the relationship between the tree height and the DBH, the DBH of trees in Shangri-La City was estimated to be mainly in the range of 20 cm to 30 cm, and this estimate was verified by actual measurements with R2 greater than 0.7 all. Finally, the established model estimated the aboveground forest biomass and carbon storage of the study area of Shangri-La City in 2020 to be 1.28 × 108 t and 6.41 × 107 t, respectively. These estimates correspond to total accuracies of 92.28%, respectively. [ABSTRACT FROM AUTHOR]

The availability of canopy height information in the Ice, Cloud, and Land Elevation Satellite-2's (ICESat-2's) land and vegetation product, or ATL08, presents opportunities for developing full-coverage products over broad spatial scales. The primary goal of this study was to develop a 30-meter canopy height map over the southeastern US, for the Southeastern Plains ecoregion and the Middle Atlantic Coastal Plains ecoregion. More specifically, this work served to compare well-known modeling approaches for upscaling canopy information from ATL08 to develop a wall-to-wall product. Focusing on only strong beams from nighttime acquisitions, the h_canopy parameter was extracted from ATL08 data. Landsat-8 bands and derived vegetation indices (normalized difference vegetation index, enhanced vegetation index, and modified soil-adjusted vegetation index) along with National Land Cover Database's canopy cover and digital elevation models were used to extrapolate ICESat-2 canopy height from tracks to the regional level. Two different modeling techniques, random forest (RF) and regression kriging (RK), were applied for estimating canopy height. The RF model estimated canopy height with a coefficient of determination (R2) value of 0.48, root-mean-square error (RMSE) of 4.58 m, mean absolute error (MAE) of 3.47 and bias of 0.23 for independent validation, and an R2 value of 0.38, RMSE of 6.39 m, MAE of 5.04 and bias of −1.39 when compared with airborne lidar-derived canopy heights. The RK model estimated canopy heights with an R2 value of 0.69, RMSE of 3.49 m, MAE of 2.61 and bias of 0.03 for independent validation, and an R value of 0.68, R2 value of 0.47, RMSE of 5.96m, MAE of 4.52 and bias of −1.81 when compared with airborne lidar-derived canopy heights. The results suggest feasibility for the implementation of the RK method over a larger spatial extent and potential for combining other remote sensing and satellite data for future monitoring of canopy height dynamics. [ABSTRACT FROM AUTHOR]

Forests are the largest terrestrial ecosystem carbon pool and provide the most important nature-based climate mitigation pathway. Compared with belowground biomass (BGB) and soil carbon, aboveground biomass (AGB) is more sensitive to human disturbance and climate change. Therefore, accurate forest AGB mapping will help us better assess the mitigation potential of forests against climate change. Here, we developed six models to estimate national forest AGB using six machine learning algorithms based on 52,415 spaceborne Light Detection and Ranging (LiDAR) footprints and 22 environmental features for China in 2007. The results showed that the ensemble model generated by the stacking algorithm performed best with a determination coefficient (R2) of 0.76 and a root mean square error (RMSE) of 22.40 Mg/ha. The verifications at pixel level (R2 = 0.78, RMSE = 16.08 Mg/ha) and provincial level (R2 = 0.53, RMSE = 14.05 Mg/ha) indicated the accuracy of the estimated forest AGB map is satisfactory. The forest AGB density of China was estimated to be 53.16 ± 1.63 Mg/ha, with a total of 11.00 ± 0.34 Pg. Net primary productivity (NPP), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), average annual rainfall, and annual temperature anomaly are the five most important environmental factors for forest AGB estimation. The forest AGB map we produced is expected to reduce the uncertainty of forest carbon source and sink estimations. [ABSTRACT FROM AUTHOR]

Accurately assessing forest structures at a national level is essential for promoting sustainable development, managing forests effectively, and fulfilling commitments under the Paris Agreement to combat climate change. In Japan, the traditional method of estimating forest carbon stock through National Forest Inventories (NFI) has certain limitations. The limited scope of forest survey data samples, coupled with the extended duration of surveys, typically spanning five years, induces significant biases in the carbon stock estimation. Achieving comprehensive forest mapping through the integration of remote sensing data and ground-referenced data for validation remains challenging due to the limited number and representativeness of referenced data, particularly at large scales. To address these issues, we collected LiDAR data from 17 sites across Japan between 2016 and 2022, covering an area of 4185.50 km2. This effort resulted in 212,067 squared plots, each with a standard size of 40 m × 40 m, which were used to develop regression models for forest structures. We utilized time-series analysis of Sentinel-2 data from 2015 to 2023 and Sentinel-2 data from different seasons in 2020. These datasets were integrated with 10 m resolution ALOS PALSAR-2 Synthetic Aperture Radar (SAR) data from JAXA and topographic data from the Shuttle Radar Topography Mission (SRTM), combined with machine learning algorithms to map forest structures throughout Japan. We initially produced a forest height map with a spatial resolution of 10 m and achieved a Relative Root Mean Square Error (rRMSE) of 21.58%. Subsequently, the estimated forest height was used as an additional predictor in machine learning models to estimate forest volume and aboveground biomass (AGB), achieving rRMSE values of 43.67% and 44.63%, respectively. Based on the predicted AGB, we calculated that the total aboveground carbon stock in Japanese forests is 1440.26 Teragrams (Tg) with an uncertainty of ±564.58 Tg. Our carbon stock estimation results outperformed those from ESA CCI, GEDI L4B, and the Ecosystem Demography Model-LiDAR (ED-LiDAR), representing the lowest rRMSE (18.07%) compared with the airborne LiDAR data. Overall, using time-series data from Sentinel-2 improved the estimation of forest structures. This study presents the first 10 m resolution, national-scale estimation of forest structures (height, volume, AGB), and forest carbon stock for the entirety of Japan. It introduces a new framework for estimating forest structures and carbon stock that supports forest management and sustainable development goals. • Mapping of multiple forest structures and carbon stock at 10-m resolution throughout Japan • Generating uncertainty maps of forest structures and carbon stock at 10-m resolution • Forest carbon stocks predictions outperformed those from other global products. [ABSTRACT FROM AUTHOR]

With the upcoming spaceborne synthetic aperture radar (SAR) missions (BIOMASS, LuTan-1, NISAR, and TanDEM-L), it will become possible to extract vegetation height at a global scale by utilizing spaceborne low-frequency (L- and P-band) polarimetric synthetic aperture radar interferometry (PolInSAR) data. However, in the context of single-baseline parameter retrieval by the random volume over ground (RVoG) model, three main error sources that affect the inversion accuracy should be carefully considered, i.e., the ground scattering contribution, the spatial baseline configuration, and the temporal decorrelation (the main part of non-volume decorrelation). To make the estimation more reliable, several kinds of multibaseline PolInSAR inversion methods have been proposed over the past few years and have achieved improved inversion performances. Dual-baseline inversion effectively avoids the ambiguity of the ground contribution, whereas the performance is highly dependent on the appropriate combination of two spatial baselines as well as the mitigation of the non-volume decorrelation. In this study, we conducted in-depth research into the effect of these influencing factors on dual-baseline inversion, aiming to provide effective guidance on dual-baseline combination selection among multibaseline data. Accordingly, a novel multibaseline inversion scheme (MBLFPI) suitable for low-frequency PolInSAR data is proposed in this paper. The significant advantage of the new method is that the three aforementioned error sources can be taken into account simultaneously, without relying on external data. The proposed scheme was validated using L- and P-band SAR data acquired by the DLR's E-SAR/F-SAR and ONERA's SETHI systems, as well as corresponding light detection and ranging (LiDAR) data collected during the BioSAR-2008, AfriSAR-2016, and TropiSAR-2009 campaigns. A series of experiments was performed to evaluate the applicability and generalizability of the proposed method. The results showed that this innovative scheme produced forest height maps with a root-mean-square error (RMSE) of 2.37 m (R2 = 0.88) and 3.13–4.43 m (R2 = 0.35–0.94) in the L-band and P-band scenarios (boreal and tropical forest), respectively, indicating a significant improvement over the three conventional multibaseline methods and pure dual-baseline inversion. The comprehensive analysis provided in this paper should assist with and provide strong support for SAR system and mission design, and the proposed scheme could be considered a promising way for future spaceborne missions to invert vegetation parameters at a global scale. • A generalizable MB method is proposed for global forest mapping by BIOMASS and LT-1. • Three general issues in low-frequency PolInSAR inversion are simultaneously solved. • B1 is more sensitive than B2 in dual-baseline PolInSAR geometric inversion method. • An error model of dual-baseline forest height inversion is constructed. • Findings help for system design of spaceborne low-frequency PolInSAR missions. [ABSTRACT FROM AUTHOR]

Continuous large-scale mapping of forest canopy height is crucial for estimating and reporting forest carbon content, analyzing forest degradation and restoration, or to model ecosystem variables such as aboveground biomass. Over the last years, the spaceborne Light Detection and Ranging (LiDAR) sensor specifically designed to acquire forest structure information, Global Ecosystem Dynamics Investigation (GEDI), has been used to extract forest canopy height information over large areas. Yet, GEDI has no spatial coverage for most forested areas in Canada and other high latitude regions. On the other hand, the spaceborne LiDAR called Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) provides a global coverage but was not specially developed to study forested ecosystems. Nonetheless, both spaceborne LiDAR sensors obtain point-based information, making spatially continuous forest canopy height estimation very challenging. This study compared the performance of both spaceborne LiDAR, GEDI and ICESat-2, combined with ALOS-2/PALSAR-2 and Sentinel-1 and -2 data to produce continuous canopy height maps in Canada for the year 2020. A set-aside dataset and airborne LiDAR (ALS) from a national LiDAR campaign were used for accuracy assessment. Both maps overestimated canopy height in relation to ALS data, but GEDI had a better performance than ICESat-2 with a mean difference (MD) of 0.9 m and 2.9 m, and a root mean square error (RMSE) of 4.2 m and 5.2 m, respectively. However, as both GEDI and ALS have no coverage in most of the hemi-boreal forests, ICESat-2 captures the tall canopy heights expected for these forests better than GEDI. PALSAR-2 HV polarization was the most important covariate to predict canopy height, showing the great potential of L-band in comparison to C-band from Sentinel-1 or optical data from Sentinel-2. The approach proposed here can be used operationally to produce annual canopy height maps for areas that lack GEDI and ICESat-2 coverage. [ABSTRACT FROM AUTHOR]

Spatial data on forest structure, composition, regeneration and deadwood are required for informed assessment of forest condition and subsequent management decisions. Here, we estimate 27 forest metrics from small-footprint full-waveform airborne laser scanning (ALS) data using a random forest (RF) and automated variable selection (Boruta) approach. Modelling was conducted using leaf-off (April) and leaf-on (July) ALS data, both separately and combined. Field data from semi-natural deciduous and managed conifer plantation forests were used to generate the RF models. Based on NRMSE and NBias, overall model accuracies were good, with only two of the best 27 models having an NRMSE > 30% and/or NBias > 15% (Standing deadwood decay class and Number of sapling species). With the exception of the Simpson index of diversity for native trees, both NRMSE and NBias varied by less than ±4.5% points between leaf-on only, leaf-off only and combined leaf-on/leaf-off models per forest metric. However, whilst model performance was similar between ALS datasets, model composition was often very dissimilar in terms of input variables. RF models using leaf-on data showed a dominance of height variables, whilst leaf-off models had a dominance of width variables, reiterating that leaf-on and leaf-off ALS datasets capture different aspects of the forest and that structure and composition across the full vertical profile are highly inter-connected and therefore can be predicted equally well in different ways. A subset of 17 forest metrics was subsequently used to assess favourable conservation status (FCS), as a measure of forest condition. The most accurate RF models relevant to the 17 FCS indicator metrics were used to predict each forest metric across the field site and thresholds defining favourable conditions were applied. Binomial logistic regression was implemented to evaluate predicative accuracy probability relative to the thresholds, which varied from 0.73–0.98 area under the curve (AUC), where 11 of 17 metrics were >0.8. This enabled an index of forest condition (FCS) based on structure, composition, regeneration and deadwood to be mapped across the field site with reasonable certainty. The FCS map closely and consistently corresponded to forest types and stand boundaries, indicating that ALS data offer a feasible approach for forest condition mapping and monitoring to advance forest ecological understanding and improve conservation efforts. [ABSTRACT FROM AUTHOR]

Forest map products are widely used and have taken benefit from progresses in the multisource forest inventory approaches, which are meant to improve the precision of forest inventory estimates at high spatial resolution. However, estimating errors of pixel-wise predictions remains difficult, and reconciling statistical outcomes with map products is still an open and important question. We address this problem using an original approach relying on a model-based inference framework and k -nearest neighbours (k -NN) models to produce pixel-wise estimations and related quality assessment. Our approach takes advantage of the resampling properties of a model-based estimator and combines it with geometrical convex-hull models to measure respectively the precision and accuracy of pixel predictions. A measure of pixel reliability was obtained by combining precision and accuracy. The study was carried out over a 7,694 km2 area dominated by structurally complex broadleaved forests in centre of France. The targeted forest attributes were growing stock volume, basal area and growing stock volume increment. A total of 819 national forest inventory plots were combined with auxiliary data extracted from a forest map, Landsat 8 images, and 3D point clouds from both airborne laser scanning and digital aerial photogrammetry. k -NN models were built independently for both 3D data sources. Both selected models included 5 auxiliary variables, and were generated using 5 neighbours, and most similar neighbours distance measure. The models showed relative root mean square error ranging from 35.7% (basal area, digital aerial photogrammetry) in calibration to 63.4% (growing stock volume increment, airborne laser scanning) in the validation set. At pixel level, we found that a minimum of 86.4% of the predictions were of high precision as their bootstrapped coefficient of variation fall below calibration's relative root mean square error. The amount of extrapolation varied from 4.3% (digital aerial photogrammetry) to 6.3% (airborne laser scanning). A relationship was found between extrapolation and k -NN distance, opening new opportunities to correct extrapolation errors. At the population level, airborne laser scanning and digital aerial photogrammetry performed similarly, offering the possibility to use digital aerial photogrammetry for monitoring purposes. The proposed method provided consistent estimates of forest attributes and maps, and also provided spatially explicit information about pixel predictions in terms of precision, accuracy and reliability. The method therefore produced high resolution outputs, significant for either decision making or forest management purposes. [ABSTRACT FROM AUTHOR]

The Amazon region in Brazil contains c. 5% of the palm species of the world. However, palm cover at macroecological scales has not yet been quantified in this biome. Here, we used high spatial resolution LiDAR data, acquired from 610 flightlines over the Brazilian Amazon, to map canopy palm cover for the first time using a deep learning approach. The image segmentation model from U‐Net deep learning was selected for mapping palm segments using the LiDAR canopy height model (CHM) at 0.5‐m spatial resolution. To train and validate the model, we manually delineated 6971 canopy palm segments over 931.43 ha of forests on four training sites by inspecting their unique star‐shaped crown architecture in the CHM. The results indicated an accuracy of 80% to automatically map canopy palm area. The approach detected >1.1 million palm segments over the 480 000 ha sampled by LiDAR and roughly estimated 1.05 billion palm segments for the Brazilian Amazon. Palm cover was not evenly distributed over the Amazon, revealing undocumented hotspots of high cover (>5%) in eastern Amazon (Pará state), and confirming documented hotspots in southwest (Acre state) and north of the region (Roraima state). Palm segment height was strongly and positively correlated with forest height, where palm segments showed overall lower height. A higher canopy palm cover was observed over shorter forests, while the opposite was found over taller forests, where palms may not be visible from the canopy. Palm segments occurred more frequently at valleys but they were also observed in other landscapes, depending on site location and forest height. Our findings highlight the disproportional occurrence of palm cover in some Amazonian canopies. This fact should be taken into account to improve regional carbon cycle representation and promote initiatives of biodiversity conservation and bioeconomic use of these forests. [ABSTRACT FROM AUTHOR]

So far, large-scale projects aiming to map forest attributes using aerial LiDAR data have been developed using ground sample plots acquired synchronously with LiDAR. No large projects have been developed using aerial LiDAR acquired independent of ground sample plot datasets. The goal of this study was to develop and validate large-scale parametric merchantable wood volume estimation models using existing historical ground sample plots. The models can be applied to large LiDAR datasets to map merchantable wood volume as a 10 × 10 m raster. The study demonstrated that a relative density index (RDI) based on a self-thinning equation and dominant height were suitable variables that can be calculated both for ground sample plots and LiDAR datasets. The resulting volume raster showed sound accuracy rates when compared to validation zones: R², 82.25%; RMSE, 13.7 m3/ha; and bias, −4.09 m3/ha. The results show that ground sample plot datasets acquired synchronously with LiDAR can be used to calculate the RDI and dominant height. These variables can consequently be used to map forest attributes over a large area with a high level of accuracy, thus not requiring the implementation of new costly sample plots. [ABSTRACT FROM AUTHOR]

Accurate and effective mapping of forest aboveground biomass (AGB) in heterogeneous mountainous regions is a huge challenge but an urgent demand for resource managements and carbon storage monitoring. Conventional studies have related the plot-measured or LiDAR-based biomass to remote sensing data using pixel-based approaches. The object-based relationship between AGB and multi-source data from LiDAR, multi-frequency radar, and optical sensors were insufficiently studied. It deserves the further exploration that maps forest AGB using the object-based approach and combines LiDAR data with multi-sensor images, which has the smaller uncertainty of positional discrepancy and local heterogeneity, in heterogeneous mountainous regions. To address the improvement of mapping accuracy, satellite LiDAR data from GEDI and ICEsat-2, and images of ALOS-2 yearly mosaic L band SAR (Synthetic Aperture Radar), Sentinel-1 C band SAR, Sentinel-2 MSI, and ALOS-1 DSM were combined for pixel- and object-based forest AGB mapping in a vital heterogeneous mountainous forest. For the object-based approach, optimized objects during a multiresolution segmentation were acquired by the ESP (Estimation of the Scale Parameter) tool, and suitable predictors were selected using an algorithm named VSURF (Variable Selection Using Random Forests). The LiDAR variables at the footprint-level were extracted to connect field plots to the multi-sensor objects as a linear bridge. It was shown that forests' AGB values varied by elevations with a mean value of 142.58 Mg/ha, ranging from 12.61 to 514.28 Mg/ha. The north slope with the lowest elevation (<1100 m) had the largest mean AGB, while the smallest mean AGB was located in the south slope with the altitude above 2000 m. Using independent validation samples, it was indicated by the accuracy comparison that the object-based approach performed better on the precision with relative improvement based on root-mean-square errors (RIRMSE) of 4.46%. The object-based approach also selected more optimized predictors and markedly decreased the prediction time than the pixel-based analysis. Canopy cover and height explained forest AGB with their effects on biomass varying according to the elevation. The elevation from DSM and variables involved in red-edge bands from MSI were the most contributive predictors in heterogeneous temperate forests. This study is a pioneering exploration of object-based AGB mapping by combining satellite data from LiDAR, MSI, and SAR, which offers an improved methodology for regional carbon mapping in the heterogeneous mountainous forests. [ABSTRACT FROM AUTHOR]

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Karst landscapes have an abundance of enclosed depressions. Many studies have detected depressions and have calculated geomorphometric characteristics with computer techniques. These outcomes are somewhat determined by the methods and data used. We aim to highlight the applicability of high-resolution relief laser scanning data in geomorphological studies of karst depressions. We set two goals: geomorphometrically to characterize depressions in different karst plateaus and to examine the influence of data preprocessing and detection methods on the results. The study was performed in three areas of the Slovene Dinaric Karst using the following steps: preprocessing digital elevation models (DEMs), enclosed depression detection, calculating geomorphometric characteristics, and comparing the characteristics of selected areas. We discovered that different combinations of methods influenced the number and geomorphometric characteristics of depressions. The range of detected depressions in the three areas were 442–491, 364–403, and 366–504, and the share of the depressions' area confirmed with all the approaches was 23%, 29%, and 47%, which resulted in different geomorphometric properties. Comparisons between the study areas were also influenced by the methods, which was confirmed by the Mann–Whitney test. We concluded that preprocessing of high-resolution relief data and the detection methods in karst environments significantly impact analyses and must be taken into account when interpreting geomorphometric results. [ABSTRACT FROM AUTHOR]

Accurate tree positioning and measurement of structural parameters are the basis of forest inventory and mapping, which are important for forest biomass calculation and community dynamics analyses. Portable backpack lidar that integrates the simultaneous localization and mapping (SLAM) technique with a global navigation satellite system receiver has greater flexibility for tree inventory than terrestrial laser scanning, but it has never been used to measure and map forest structure in a large area (>101 hectares) with high tree density. In the present study, we used the LiBackpack DG50 backpack lidar system to obtain the point cloud data of a 10 ha plot of subtropical evergreen broadleaved forest, and applied these data to quantify errors and related factors in the diameter at breast height (DBH) measurements and positioning for more than 1900 individual trees. We found an average error of 4.19 cm in the DBH measurements obtained by lidar, compared with manual field measurements. The incompleteness of the tree stem point clouds was the main factor that caused the DBH measurement errors, and the field DBH measurements and density of the point clouds also had significant impacts. The average tree positioning error was 4.64 m, and it was significantly affected by the distance and route length from the measured trees to the data acquisition start position, whereas it was affected little by the habitat complexity and characteristics of tree stems. The tree positioning measurement error led to increases in the mean value and variability of paired-tree distance error as the sample plot scale increased. We corrected the errors based on the estimates of predictive models. After correction, the DBH measurement error decreased by 31.3%, the tree positioning error decreased by 44.3%, and the paired-tree distance error decreased by 56.3%. As the sample plot scale increased, the accumulated paired-tree distance error stabilized gradually. [ABSTRACT FROM AUTHOR]

Background: The Norwegian forest resource map (SR16) maps forest attributes by combining national forest inventory (NFI), airborne laser scanning (ALS) and other remotely sensed data. While the ALS data were acquired over a time interval of 10 years using various sensors and settings, the NFI data are continuously collected. Aims of this study were to analyze the effects of stratification on models linking remotely sensed and field data, and assess the accuracy overall and at the ALS project level. Materials and methods: The model dataset consisted of 9203 NFI field plots and data from 367 ALS projects, covering 17 Mha and 2/3 of the productive forest in Norway. Mixed-effects regression models were used to account for differences among ALS projects. Two types of stratification were used to fit models: 1) stratification by the three main tree species groups spruce, pine and deciduous resulted in species-specific models that can utilize a satellite-based species map for improving predictions, and 2) stratification by species and maturity class resulted in stratum-specific models that can be used in forest management inventories where each stand regularly is visually stratified accordingly. Stratified models were compared to general models that were fit without stratifying the data. Results: The species-specific models had relative root-mean-squared errors (RMSEs) of 35%, 34%, 31%, and 12% for volume, aboveground biomass, basal area, and Lorey's height, respectively. These RMSEs were 2–7 percentage points (pp) smaller than those of general models. When validating using predicted species, RMSEs were 0–4 pp. smaller than those of general models. Models stratified by main species and maturity class further improved RMSEs compared to species-specific models by up to 1.8 pp. Using mixed-effects models over ordinary least squares models resulted in a decrease of RMSE for timber volume of 1.0–3.9 pp., depending on the main tree species. RMSEs for timber volume ranged between 19%–59% among individual ALS projects. Conclusions: The stratification by tree species considerably improved models of forest structural variables. A further stratification by maturity class improved these models only moderately. The accuracy of the models utilized in SR16 were within the range reported from other ALS-based forest inventories, but local variations are apparent. [ABSTRACT FROM AUTHOR]

Forest spatial structure describes the relationships among different species in the same forest community. Automation in the monitoring of the structural forest changes and forest mapping is one of the main utilities of applications of modern geoinformatics methods. The obtaining objective information requires the use of spatial data derived from photogrammetry and remote sensing. This paper investigates the possibility of applying light detection and ranging (LiDAR) point clouds and geographic information system (GIS) analyses for automated mapping and detection changes in vegetation structure during a year of study. The research was conducted in an area of the Ourense Province (NWSpain). The airborne laser scanning (ALS) data, acquired in August 2019 and June of 2020, reveal detailed changes in forest structure. Based on ALS data the vegetation parameters will be analysed.To study the structural behaviour of the tree vegetation, the following parameters are used in each one of the sampling areas: (1) Relationship between the tree species present and their stratification; (2) Vegetation classification in fuel types; (3) Biomass (Gi); (4) Number of individuals per area; and (5) Canopy cover fraction (CCF). Besides, the results were compared with the ground truth data recollected in the study area.The development of a quantitative structural model based on Aerial Laser Scanning (ALS) point clouds was proposed to accurately estimate tree attributes automatically and to detect changes in forest structure. Results of statistical analysis of point cloud show the possibility to use UAV LiDAR data to characterize changes in the structure of vegetation. [ABSTRACT FROM AUTHOR]

Spatially explicit data on forest canopy fuel parameters provide critical information for wildfire propagation modelling, emission estimations and risk assessment. LiDAR observations enable accurate retrieval of the vertical structure of vegetation, which makes them an excellent alternative for characterising forest fuel structures. In most cases, fuel parameterisation has been based on Airborne Laser Scanning (ALS) observations, which are costly and best suited for local research. Spaceborne LiDAR acquisitions overcome the limited spatiotemporal coverage of airborne systems, as they can cover much wider geographical areas. However, they do not provide continuous geographical data, requiring spatial interpolation methods to obtain wall-to-wall information. We developed a two-step, easily replicable methodology to estimate forest canopy fuel parameters for the entire European territory, based on data from the Global Ecosystem Dynamics Investigation (GEDI) sensor, onboard the International Space Station (ISS). First, we simulated GEDI pseudo-waveforms from discrete ALS data about forest plots. We then used metrics derived from the GEDI pseudo-waveforms to estimate mean canopy height (Hm), canopy cover (CC) and canopy base height (CBH), for which we used national forest inventory data as reference. The RH80 metric had the strongest correlation with Hm for all fuel types (r = 0.96–0.97, Bias = −0.16-0.30 m, RMSE = 1.53–2.52 m, rRMSE = 13.23–19.75%). A strong correlation was also observed between ALS-CC and GEDI-CC (r = 0.94, Bias = −0.02, RMSE = 0.09, rRMSE = 16.26%), whereas weaker correlations were obtained for CBH estimations based on forest inventory data (r = 0.46, Bias = 0 m, RMSE = 0.89 m, rRMSE = 39.80%). The second stage was to generate wall-to-wall maps for the continent of Europe of canopy fuel parameters at a resolution of 1 km using a spatial interpolation of GEDI-based estimates for within-fuel polygons covered by GEDI footprints. GEDI observations were not available for some of the polygons (mainly Northern latitudes, above 51.6°N). In these cases, the parameters were estimated using random forest regression models based on multispectral and SAR imagery and biophysical variables. Errors were higher than from direct GEDI retrievals, but still within the range of previous results (r = 0.72–0.82, Bias = −0.18-0.29 m, RMSE = 3.63–4.18 m and rRMSE = 28.43–30.66% for Hm; r = 0.82–0.91, Bias = 0, RMSE = 0.07–0.09 and rRMSE = 10.65–14.42% for CC; r = 0.62–0.75, Bias = 0.01–0.02 m, RMSE = 0.60–0.74 m and rRMSE = 19.16–22.93% for CBH). Uncertainty maps for the estimated parameters were provided at the grid level, for which purpose we considered the propagation of individual errors for each step in the methodology. The final outputs, which are publicly available (https://doi.org/10.21950/KTALA8), provide a wall-to-wall estimation for the continent of Europe of three critical parameters for modelling crown fire propagation potential and demonstrate the capacity of GEDI observations to improve the characterisation of fuel models. [Display omitted] • Forest canopy parameters for Europe were derived from GEDI at 1 km resolution. • Canopy height, canopy cover and canopy base height were estimated from GEDI data. • Parameters outside GEDI cover were estimated by satellite data in a RF framework. [ABSTRACT FROM AUTHOR]

• Old-growth definition criteria greatly impacts amount estimates and maps. • Physically-based old-growth definitions are detectable via lidar remote sensing. • Age-based old-growth definitions are difficult to detect using lidar remote sensing. • Age-based old-growth forests are extremely diverse in physical structure. Old-growth forests have been widely studied for decades. The extreme diversity of old forest characteristics has inspired an equally diverse set of old-growth definitions, and makes mapping old-growth difficult across large areas and different forest types. While the use of remote sensing in old-growth research is not new, there is a growing need for large scale mapping to improve understanding of old forest processes and to support old-growth conservation. Old-growth mapping requires definitions that are ecologically relevant to old forests while also transferable to remote sensing data. In this paper we develop a conceptual framework to evaluate three dimensions of old-growth—a temporal dimension related to tree ages, a physical dimension related to tree sizes, and a functional dimension related to forest processes. In the first part of our analysis, we classify forests throughout the eastern US as old or not with respect to each old-growth dimension using existing old-growth definitions and data from the US Forest Inventory and Analysis (FIA) program. We estimate the proportion of forest classified as old within a hexagon grid, resulting in a unique map of old forest proportion (OFP) for each dimension. Subsequently, we use spaceborne lidar data from NASA's Global Ecosystem Dynamics Investigation (GEDI) to reproduce each OFP map in a modeling framework designed to 1) assess the extent to which each dimension of forest oldness can be mapped at large spatial scales, and 2) identify biophysical GEDI variables related to each dimension of forest oldness. We estimate that only 2% of forest classified as old in any dimension satisfied the old criteria in all three dimensions. We found substantial spatial variation in the mapped OFP estimates across the three dimensions, highlighting how definition criteria impacts old-growth maps. We also found that physically old forests were more effectively mapped using GEDI data than functionally or temporally old forests, and that physically old forests were more structurally similar to one another than temporally or functionally old forests. Our modeling results indicate that while remote sensing may be best suited to mapping physical old-growth characteristics, definitions that rely solely on physical characteristics do not adequately represent old forests throughout the eastern US. We propose that future efforts to map old-growth with spaceborne remote sensing data may maximize utility through collaboration between western and indigenous old-growth experts to determine broad yet nuanced approaches that are appropriately tailored to the target variable of old forests. These efforts should balance explicit and ecologically relevant old-growth definitions specifically for mapping that can be linked to remotely sensed data, 2) appropriate spatial resolutions, and 3) flexible quantitative frameworks that encompass the complexities and heterogeneity of old forests. [ABSTRACT FROM AUTHOR]

Forest planning, forest management, and forest policy require updated, reliable, and harmonized spatial datasets. In Italy a national geographic Forest Information System (FIS) designed to store and facilitate the access and analysis of spatial datasets is still missing. Among the different information layers which are useful to start populating a FIS, two are essential for their multiple use in the assessment of forest resources: (i) forest mapping, and (ii) data from Airborne Laser Scanning (ALS). Both layers are not available wall-to-wall for Italy, though different local sources of information potentially useful for their implementation already exist. The objectives of this work were to: (i) review forest maps and ALS data availability in Italy; (ii) develop for the first time a high resolution forest mask of Italy which was validated against the official statistics of the Italian National Forest Inventory; (iii) develop the first mosaic of all the main ALS data available in Italy producing a consistent Canopy Height Model (CHM). An on-line geographic FIS with free access to both layers from (ii) and (iii) was developed for demonstration purposes. The total area of forest and other wooded lands computed from the forest mask was 102,608.82 km2 (34% of the Italian territory), i.e., 1.9% less than the NFI benchmark estimate. This map is currently the best wall-to-wall forest mask available for Italy. We showed that only the 63% of the Italian territory (the 60% of the forest area) is covered by ALS data. These results highlight the urgent need for a national strategy to complete the availability of forest data in Italy. [ABSTRACT FROM AUTHOR]

Terrestrial lidar data are known to be useful for estimating the three‐dimensional (3D) distribution of leaf area in forests. This type of product holds great potential for modelling canopy reflectance and light interception to study the links between structure and function. However, little is currently known about its potential and limits in dense forests. Higher leaf area density implies that more laser pulses emitted by the ground‐based instrument are intercepted in lower canopy levels, and the implications of such occlusion effects on radiative transfer simulations are unknown. Occlusion effects can be minimized by increasing the number of locations lidar data is acquired from; how many locations are required for a forest with a given structure? This paper aims to address these knowledge gaps.We acquired terrestrial lidar data using a very high density of scanning positions (5 m between positions) over four dense forest 60 m × 60 m plots along a structural gradient. Occlusion effects were quantified, and the 3D distribution of leaf area density was mapped using voxels (cubic volumes) for four different scan densities (one original and three downsampled). The voxel arrays were then input into a radiative transfer model to simulate bidirectional reflectance factors and vertical fraction of absorbed radiation.We found that the summation of leaf area estimates for all voxels within the plot provided leaf area index (LAI) values close to LAI values estimated using traditional methods at each site. Occluded areas occurred mostly at the top of bottom heavy canopies. Radiative transfer simulations suggest that modelling small scale (<1 m) bidirectional reflectance factors (BRF) and light interception requires the highest scan position density used (5 m between scan positions), particularly at bottom heavy sites, and that 10 m between scan positions can be used for plot scale BRF simulations in forests with foliage density and vertical profiles similar to those tested here.This work establishes some initial guidelines for establishing terrestrial lidar survey protocols for mapping leaf area density in forests. The leaf area density voxel arrays derived are among the most accurate plot‐level 3D characterizations of foliage arrangement produced to date. [ABSTRACT FROM AUTHOR]

The impact of anthropogenic actions on the environment and climate has recently increased the need to map the afforested areas. In this context, the three-dimensional (3D) measurement of vegetation structures plays an important role in having an efficient forest inventory and management. Nowadays, the airborne LiDAR (Light Detection And Ranging) system offers high horizontal resolution as well as vertical dimension information, making it possible to estimate both three-dimensional characteristics of individual trees and to identify the distribution of forest resources in the region. This study aims to present a processing approach for the determination of each tree's position (X and Y location, as well as tree height) and its dimensions (crown diameter, area and volume) using geometrically accurate 3D point clouds (data sets were collected in a forested area in Arge? County, Romania). To a better understanding of the forest features and to explore the potential of remote sensing for such analysis, it was further exploited Digital Terrain Model (DTM), Digital Surface Model (DSM), and Canopy Height Model (CHM) derivation. [ABSTRACT FROM AUTHOR]

Since 1999, Estonia has conducted the National Forest Inventory (NFI) on the basis of sample plots. This paper presents a new module, incorporating remote-sensing feature variables from airborne laser scanning (ALS) and from multispectral satellite images, for the construction of maps of forest height, standing-wood volume, and tree species composition for the entire country. The models for sparse ALS point clouds yield coefficients of determination of 89.5–94.8% for stand height and 84.2–91.7% for wood volume. For the tree species prediction, the models yield Cohen's kappa values (taking 95% confidence intervals) of 0.69–0.72 upon comparing model results against a previous map, and values of 0.51–0.54 upon comparing model results against NFI sample plots. This paper additionally examines the influence of foliage phenology on the predictions and discusses options for further enhancement of the system. [ABSTRACT FROM AUTHOR]

Forestry is a major industry in many parts of the world, yet this potential domain of application area has been overlooked by the robotics community. For instance, forest inventory, a cornerstone of efficient and sustainable forestry, is still traditionally performed manually by qualified professionals. The lack of automation in this particular task, consisting chiefly of measuring tree attributes, limits its speed, and, therefore, the area that can be economically covered. To this effect, we propose to use recent advancements in three‐dimensional mapping approaches in forests to automatically measure tree diameters from mobile robot observations. While previous studies showed the potential for such technology, they lacked a rigorous analysis of diameter estimation methods in challenging and large‐scale forest environments. Here, we validated multiple diameter estimation methods, including two novel ones, in a new publicly‐available dataset which includes four different forest sites, 11 trajectories, totaling 1458 tree observations, and 14,000 m2. From our extensive validation, we concluded that our mapping method is usable in the context of automated forest inventory, with our best diameter estimation method yielding a root mean square error of 3.45 cm for our whole dataset and 2.04 cm in ideal conditions consisting of mature forest with well‐spaced trees. Furthermore, we release this dataset to the public (https://norlab.ulaval.ca/research/montmorencydataset), to spur further research in robotic forest inventories. Finally, stemming from this large‐scale experiment, we provide recommendations for future deployments of mobile robots in a forestry context. [ABSTRACT FROM AUTHOR]

• LiDAR and WorldView-3 data were used for the automatic generation of stand map. • LiDAR/WorldView-3 data set provides a better image segmentation quality. • The combination of LiDAR and multispectral image improves classification accuracy. • The stand map derived is not sufficient for tactical forest management plans. Although stand delineation approach based on aerial photographs and field survey produces high accuracy maps, it is labour-intensive and time consuming. Furthermore, conventional forest stand maps may have some uncertainties that can hardly be verified due to the experiments and skills of photo-interpreters. Therefore, researchers have been seeking more objective and cost-effective methods for forest mapping. LiDAR (Light Detection and Ranging) data have a high potential to automatically delineate forest stands. Unlike optical sensors, LiDAR height data provides information about both the horizontal and vertical structural characteristics of forest stands. However, it deprives of spectral data that may be successfully used in separating tree species. In this study, we investigate the potential of LiDAR – WorldView-3 data synergy for the automatic generation of a detailed forest stand map which can be used for a tactical forest management plan. Firstly, image segmentation was applied to LiDAR data alone and LiDAR/WorldView-3 data set in order to obtain the most suitable image objects representing forest stands. Visual inspection of the segmentation results showed that image objects based on the LiDAR/WorldView-3 data set were more compatible with the reference forest stand boundaries. After the segmentation process, the LiDAR and LiDAR/WorldView-3 data sets were independently classified using object-based classification method. We tested two levels of classification. The first was a detailed classification with 14 classes considering reference stand types. The second was the rough classification with 9 classes where some stand types were combined. The mean, standard deviation and texture features of LiDAR metrics and spectral information were used in the classification. The accuracy assessment results of LiDAR data showed that the Overall Accuracy (OA) was calculated as 0.31 and 0.43, and the Kappa Index (KIA) was calculated as 0.26 and 0.32 for the detailed and rough classifications, respectively. For the LiDAR/WorldView-3 data set, the OA values were calculated as 0.50 and 0.61, and the KIA were calculated as 0.46 and 0.55 for the detailed and rough classifications, respectively. These results showed that the forest stand map derived from the LiDAR/WorldView-3 data synergy is more compatible with the reference forest stand map. In conclusion, it can be said that the forest stand maps produced in this study may provide strategic forest planning needs, but it is not sufficient for tactical forest management plans. [ABSTRACT FROM AUTHOR]

Precise structural information collected from plots is significant in the management of and decision-making regarding forest resources. Currently, laser scanning is widely used in forestry inventories to acquire three-dimensional (3D) structural information. There are three main data-acquisition modes in ground-based forest measurements: single-scan terrestrial laser scanning (TLS), multi-scan TLS and multi-single-scan TLS. Nevertheless, each of these modes causes specific difficulties for forest measurements. Due to occlusion effects, the single-scan TLS mode provides scans for only one side of the tree. The multi-scan TLS mode overcomes occlusion problems, however, at the cost of longer acquisition times, more human labor and more effort in data preprocessing. The multi-single-scan TLS mode decreases the workload and occlusion effects but lacks the complete 3D reconstruction of forests. These problems in TLS methods are largely avoided with mobile laser scanning (MLS); however, the geometrical peculiarity of forests (e.g., similarity between tree shapes, placements, and occlusion) complicates the motion estimation and reduces mapping accuracy. Therefore, this paper proposes a novel method combining single-scan TLS and MLS for forest 3D data acquisition. We use single-scan TLS data as a reference, onto which we register MLS point clouds, so they fill in the omission of the single-scan TLS data. To register MLS point clouds on the reference, we extract virtual feature points that are sampling the centerlines of tree stems and propose a new optimization-based registration framework. In contrast to previous MLS-based studies, the proposed method sufficiently exploits the natural geometric characteristics of trees. We demonstrate the effectiveness, robustness, and accuracy of the proposed method on three datasets, from which we extract structural information. The experimental results show that the omission of tree stem data caused by one scan can be compensated for by the MLS data, and the time of the field measurement is much less than that of the multi-scan TLS mode. In addition, single-scan TLS data provide strong global constraints for MLS-based forest mapping, which allows low mapping errors to be achieved, e.g., less than 2.0 cm mean errors in both the horizontal and vertical directions. [ABSTRACT FROM AUTHOR]

Forest canopy cover (CC) directly and indirectly influences various processes of forest ecosystems. Airborne light detection and ranging (LiDAR) can be used to characterize forest spatial structures and further obtain estimates of forest CC. However, nonreturn laser pulses from targets of interest impact the estimation accuracy of forest CC. The objective of this article was to develop a novel method of estimating the nonreturn laser pulses to improve the estimation accuracy of forest CC using LiDAR data. The improved models for estimating forest CC were developed by introducing the loss ratio of laser pulses and a CC coefficient into the original models. The forest CC reference data were collected and used to validate the forest CC estimates. The results show that the loss ratio for forested areas was much higher than that for open ground areas. The range between the sensor and a target was a crucial factor that caused the loss of returns. The relationship between the range and the loss ratio was nonlinear in both open ground and forested areas. Compared with the original models, the improved models combining the loss ratio and the CC coefficient statistically significantly increased the estimation accuracy of the forest CC. Moreover, the forest CC estimates from the canopy height model (CHM) were more accurate than those from the height normalized point cloud (NPC) data. In addition, the simplified models were more generalized than the other models. This article is novel and has great potential to improve mapping of forest CC. [ABSTRACT FROM AUTHOR]

Under the Paris Agreement, signatories are obliged to reduce carbon emissions and enhance carbon sinks. Multiple global aboveground biomass products are now available; however, they still require additional calibration and validation datasets at the local scale. I use South Africa's Afrotemperate forests as a case study to investigate best practice for remote sensing aboveground carbon for improving the accuracy of reporting under the Paris Agreement. I collected both field and LiDAR data for three forests in KwaZulu-Natal, South Africa, and tested three different allometric equations for calculating AGC. I used linear models to predict AGC from LiDAR data for both local and multi-site models and then compared the results with two global biomass products. The locally derived allometric produced intermediate AGC values to the temperate and pan-tropical equations and is recommended for use in South African forests. Local LiDAR models for each forest varied in performance with R2 values ranging from 0.16 to 0.75, and RMSE% of 7.3–50%. Overall, the multi-site LiDAR model performed better than two of the three local models with a conditional pseudo-R2 of 0.82 and RMSE on 24% (30 MgC.ha−1) and is recommended in the absence of field data. However, since the forest physiognomy is diverse, it is important to take local context into account when interpreting AGC results. Although global biomass products tend to overestimate the carbon in these forests, they can still be used for the UNFCCC's 2023 global carbon stocktake until further calibration and validation data have been included. While generalized mapping techniques may produce some errors in AGC estimates, it's important to use the data that's currently available and report the uncertainty in the estimates as part of mitigating anthropogenic changes. • The locally-derived biomass allometric equation is best suited for Afrotemperate forests. • A multi-site LiDAR model performs well across primary and secondary forests. • Afrotemperate forests can contribute a potential 21.9 – 34.01 MtC.ha−1 MtC.ha−1 to South Africa's aboveground carbon. • Aboveground carbon estimates from remote sensing must include error estimates [ABSTRACT FROM AUTHOR]

Mapping the microclimate effect of forest canopies on understory temperature requires spatially explicit predictors at very fine spatial resolutions. Light Detection And Ranging (LiDAR) offers promising prospects in that regard, as it allows capturing the vertical dimension of vegetation structure at a very high resolution over large areas. To explore the potential of airborne LiDAR-derived metrics to predict understory temperature, we focused on the forest of Blois (France), a 2740-ha lowland managed forest dominated by oak (Quercus petraea). We installed HOBO sensors measuring microclimate air temperature at one-metre height in 53 stands of contrasting vegetation structure, from open to very dense and from young regeneration to mature stages. Using a nearby weather station as the macroclimate temperature reference, we calculated the slope (log scale) coefficient of the linear regression between microclimate and macroclimate, as a simple parameter describing the microclimatic buffering (log(slope) < 0) or amplification (log(slope) > 0) capacity of the habitat. An airborne LiDAR flight was conducted during summer 2021, matching the timing of our temperature measurements. From the resulting 3D point cloud, three complementary metrics of forest structure were derived: the maximum height, the Plant Area Index and the Vertical Complexity Index. They were calculated for circular buffers of different radii (1 m to 100 m) centred on each HOBO sensor. We found that the 5-m radius combining the three metrics into a single multivariate model explained the greatest proportion of variance in the microclimate effect of each stand (R2 = 0.91). We mapped the buffering or amplification effect of vegetation structure on understory temperatures over the entire forest of Blois at a 10-m resolution. 91.4% of the surface of the forest was significantly buffered relative to macroclimate temperature, while 2.7% was amplified, especially in road verges, clear-cut and regeneration areas. Based on our simple linear model, we were able to derive understory air temperature maps for any temporal resolution (i.e. hourly, daily, or seasonal). The results highlight the great capacity of airborne LiDAR to retrieve forest structure parameters and generate high-resolution maps of the thermal environment. Applications for mapping the buffering or amplification of microclimate temperature are plentiful, especially in the context of climate change. They include improving the understanding of physiological processes such as thermoregulation or phenology, modelling the thermal connectivity of the landscape, improving species redistribution models, spotting microrefugia for conservation, informing forest management for tree regeneration or wildfire control, or even prioritising cooling recreational areas for humans to escape heatwaves in urban forests. [Display omitted] • Airborne LiDAR is ideal to predict forest thermal environment at a high resolution. • We used Maximum height, Plant Area & Vertical Complexity Index as structure metrics. • Forest structure explained 91% of microclimate variability at a 10-m resolution. • We mapped the buffering or amplification effect of forest on microclimate temperature. • Ecologists, conservationists and forest managers would highly benefit from such maps. [ABSTRACT FROM AUTHOR]

Gathering information about forest variables is an expensive and arduous activity. Therefore, directly collecting the data required to produce high-resolution maps over large spatial domains is infeasible. Next-generation collection initiatives for remotely sensed light detection and ranging (LiDAR) data are specifically aimed at producing complete-coverage maps over large spatial domains. Given that Li-DAR data and forest characteristics are often strongly correlated, it is possible to use the former to model, predict, and map forest variables over regions of inter- est. This entails dealing with high-dimensional (~10²) spatially dependent LiDAR outcomes over a large number of locations (~105 - 106). With this in mind, we develop the spatial factor nearest neighbor Gaussian process (SF-NNGP) model, which we embed in a two-stage approach that connects the spatial structure found in LiDAR signals with forest variables. We provide a simulation experiment that demonstrates the inferential and predictive performance of the SF-NNGP, and use the two-stage modeling strategy to generate complete-coverage maps of the for- est variables, with associated uncertainty, over a large region of boreal forests in interior Alaska. [ABSTRACT FROM AUTHOR]

There is currently much interest in developing general approaches for mapping forest aboveground carbon density using structural information contained in airborne LiDAR data. The most widely utilized model in tropical forests assumes that aboveground carbon density is a compound power function of top of canopy height (a metric easily derived from LiDAR), basal area and wood density. Here we derive the model in terms of the geometry of individual tree crowns within forest stands, showing how scaling exponents in the aboveground carbon density model arise from the height−diameter (H−D) and projected crown area−diameter (C−D) allometries of individual trees. We show that a power function relationship emerges when the C−D scaling exponent is close to 2, or when tree diameters follow a Weibull distribution (or other specific distributions) and are invariant across the landscape. In addition, basal area must be closely correlated with canopy height for the approach to work. The efficacy of the model was explored for a managed uneven−aged temperate forest in Ontario, Canada within which stands dominated by sugar maple (Acer saccharum Marsh.) and mixed stands were identified. A much poorer goodness−of−fit was obtained than previously reported for tropical forests (R2 = 0.29 vs. about 0.83). Explanations for the poor predictive power on the model include: (1) basal area was only weakly correlated with top canopy height; (2) tree size distributions varied considerably across the landscape; (3) the allometry exponents are affected by variation in species composition arising from timber management and soil conditions; and (4) the C-D allometric power function was far from 2 (1.28). We conclude that landscape heterogeneity in forest structure and tree allometry reduces the accuracy of general power-function models for predicting aboveground carbon density in managed forests. More studies in different forest types are needed to understand the situations in which power functions of LiDAR height are appropriate for modelling forest carbon stocks. [ABSTRACT FROM AUTHOR]

Highlights • Both definitional difference and estimation error affect tree cover mapping. • Three specified tree cover definitions linked to different remote sensing systems. • High accuracy of lidar-based and low consistency of Landsat-based tree cover. • Recommend integrated use of lidar and Landsat for large-area tree cover mapping. Abstract The accuracy of mapping forest extent from satellite imagery largely depends on both semantic definition and estimation quality of tree cover at the pixel level. In this study, we conducted a comparative analysis of different tree cover data sets, derived from field sample, terrestrial and airborne lidar scans, and Landsat imagery, to investigate factors affecting tree cover estimation in a western mountainous conifer forest in the United States. We found that satellite-based tree cover maps, even those derived from the same sensor, tended to have lower agreements over sparsely vegetated areas, due to both definitional discrepancies and estimation errors in establishing tree cover. When compared with tree cover measurements from ground-based hemispherical photography and terrestrial laser scanner (TLS), both airborne waveform lidar and discrete return lidar (DRL) provided consistently more accurate tree cover estimates (r 2 ranging from 0.61 to 0.91, RMSE ≈ 15% and bias < 5%) than three of the existing Landsat-based tree cover products (r 2 ranging from 0.64 to 0.78, RMSE ≈ 20% and bias varying from 3% to 10%). The agreement between the two lidar-based tree cover data sets was high (r 2 ≈ 0.8, RMSE ≈ 10% and bias < 5%), and their differences were mainly due to different definitions of tree cover (in particular, as to whether within-crown gaps were included or not). In contrast, there were only moderate agreements among the Landsat products (r 2 = 0.66 ˜ 0.76, RMSE = 16% ˜ 30%, bias = 10% ˜ 24%), caused by both definitional differences and estimation errors. Because of the capability to simultaneously derive tree cover and tree height at a high accuracy, and the increasing availability of lidar data, we recommend incorporating lidar data as a complement or an alternative to ultra-fine resolution images in training Landsat-class images (e.g. Sentinel-2AB) for more precise mapping of forest extent. [ABSTRACT FROM AUTHOR]

Abstract The effect of terrain slope on large footprint lidar waveforms is still a great challenge to the accurate mapping of forest aboveground biomass (AGB), although previous studies have made significant progress for slopes lower than 15°. A new method was proposed to calculate slope-adaptive waveform metrics for forest AGB mapping over mountainous areas. First, a theoretical model was proposed to calculate lidar waveforms of bare ground with known terrain slopes. The forest waveform and its corresponding calculated bare ground waveform were then aligned by the signal ending points. Their energy quartiles were extracted separately by taking the signal ending points as references. The height differences between the corresponding energy quartiles were defined as slope-adaptive waveform metrics, which were then used as independent variables for the estimation of the forest AGB. The proposed method was examined with both simulated lidar waveforms of forest stands and real waveforms acquired by the Geoscience Laser Altimeter System (GLAS). Two typical footprint sizes (i.e. , 25 m and 70 m in diameter) and 21 terrain slopes (i.e. , from 0° to 40° at an interval of 2°) were used in the simulation of the lidar waveforms. The terrain slopes of the GLAS waveforms were between 5.54° and 34.08°. The results showed that the terrain stretching on the forest waveform could be compensated for by the bare ground waveform. Compared with the Gaussian decomposition and waveform parameter methods, the proposed method gave the best estimation of the forest AGB with R2 = 0.92 and RMSE = 15.95 Mg/ha using simulated waveforms with a footprint size of 70 m and R2 = 0.84 and RMSE = 34.99 Mg/ha using the GLAS waveforms. The results demonstrated that the proposed method had great potential for the estimation of forest AGB using lidar waveforms over mountainous areas. Highlights • Terrain effect on LiDAR waveform is still a great challenge for mapping forest AGB. • A model is proposed to calculate bare ground waveforms with known terrain slope. • Slope-adaptive waveforms metrics are defined by proposed PIPE method. • Height metrics are defined by taking signal ending point of waveform as references. • Newly defined metrics works well on mapping forest AGB within slopes of 0°–40°. [ABSTRACT FROM AUTHOR]

Abstract Lidar is an established tool for mapping forest structure, but its sparse spatial and temporal coverage often preclude its use in studying forest disturbance. In contrast, aerial imagery has been and continues to be regularly collected in many regions, and advances in stereo image matching have automated the creation of dense photogrammetric point clouds, which can also be used to map forest structure when paired with an accurate digital terrain model. As part of a study of the physical and ecological impacts of the 2012 High Park fire in Colorado, we generated a photogrammetric point cloud from pre-fire aerial imagery collected in 2008 and combined it with a digital terrain model generated from a 2013 post-fire lidar collection to produce canopy metrics commonly used in modelling of forest structure. We explore the correlation structure between the lidar and photogrammetry-derived canopy metrics, and the relationships between those metrics and forest structure attributes measured at unburned plots in the vicinity of the burn scar. Most corresponding lidar- and photogrammetry- derived canopy metrics had strong linear relationships between them (median r = 0.82), and metrics from both datasets yielded similar root mean square errors in multiple regression models of aboveground biomass (29.3% and 31.0%), basal area (29.8% and 27.7%), and several other forest structure attributes. We found the source of the canopy metrics (lidar or photogrammetry) to be a non-significant factor in some models of forest structure, suggesting that these datasets may be interchangeable in particular cases. Models derived from pre-fire aerial imagery were combined with burn severity information to quantify loss of biomass (2.31 ± 0.001 Tg) and examine possible relationships between forest structure and burn severity. These applications illustrate how the broad spatial and temporal coverage of aerial imagery and growing coverage of lidar can be utilized to improve understanding of changes in forest structure, including assessments of forest carbon flux. Highlights • Photogrammetric canopy indices show predictable relationships to lidar indices. • Structure estimates from photogrammetry are nearly equivalent to those from lidar. • Lidar- & photo-based models may be interchangeable for mapping past forest states. • Biomass loss and causes of burn severity are assessed from forest structure maps. [ABSTRACT FROM AUTHOR]

The assessment of forest aboveground biomass (AGB) can contribute to reducing uncertainties associated with the amount and distribution of terrestrial carbon. The Ice, Cloud and land Elevation Satellite-2 (ICESat-2) was launched on September 15th, 2018 and will provide data which will offer the possibility of assessing AGB and forest carbon at multiple spatial scales. The primary goal of this study was to develop an approach for utilizing data similar to ICESat-2's land-vegetation along track product (ATL08) to generate wall-to-wall AGB maps. Utilizing simulated daytime and nighttime ICESat-2 data from planned ICESat-2 tracks over vegetation conditions in south-east Texas, we investigated the integration of Landsat data and derived products for AGB model and map production. Linear regression models were first used to relate simulated photon-counting lidar (PCL) metrics for 100 m segments along ICESat-2 tracks to reference airborne lidar-estimated AGB over Sam Houston National Forest (SHNF) in south-east Texas. Random Forest (RF) was then used to create AGB maps from predicted AGB estimates and explanatory data consisting of spectral metrics derived from Landsat TM imagery and land cover and canopy cover data from the National Land Cover Database (NLCD). Using RF, AGB and AGB uncertainty maps produced at 30 m spatial resolution represented three data scenarios; (1) simulated ICESat-2 PCL vegetation product without the impact of noise (no noise scenario), (2) simulated ICESat-2 PCL vegetation product from data with noise levels associated with daytime operation of ICESat-2 (daytime scenario), and (3) simulated ICESat-2 PCL vegetation product from data with noise levels associated with nighttime operation of ICESat-2 (nighttime scenario). The RF models exhibited moderate accuracies (0.42 to 0.51) with RMSE values between 19 Mg/ha to 20 Mg/ha with a separate test set. The adoption of a combinatory approach of simulated ICESat-2 and Landsat data could be implemented at larger spatial scales and in doing so, ancillary data such as climatic and topographic variables may be examined for improving AGB predictions. [ABSTRACT FROM AUTHOR]

Understanding historical forest dynamics, specifically changes in forest biomass and carbon stocks, has become critical for assessing current forest climate benefits and projecting future benefits under various policy, regulatory, and stewardship scenarios. Carbon accounting frameworks based exclusively on national forest inventories are limited to broad-scale estimates, but model-based approaches that combine these inventories with remotely sensed data can yield contiguous fine-resolution maps of forest biomass and carbon stocks across landscapes over time. Here we describe a fundamental step in building a map-based stock-change framework: mapping historical forest biomass at fine temporal and spatial resolution (annual, 30 m) across all of New York State (USA) from 1990 to 2019, using freely available data and open-source tools. Using Landsat imagery, US Forest Service Forest Inventory and Analysis (FIA) data, and off-the-shelf LiDAR collections we developed three modeling approaches for mapping historical forest aboveground biomass (AGB): training on FIA plot-level AGB estimates (direct), training on LiDAR-derived AGB maps (indirect), and an ensemble averaging predictions from the direct and indirect models. Model prediction surfaces (maps) were tested against FIA estimates at multiple scales. All three approaches produced viable outputs, yet tradeoffs were evident in terms of model complexity, map accuracy, saturation, and fine-scale pattern representation. The resulting map products can help identify where, when, and how forest carbon stocks are changing as a result of both anthropogenic and natural drivers alike. These products can thus serve as inputs to a wide range of applications including stock-change assessments, monitoring reporting and verification frameworks, and prioritizing parcels for protection or enrollment in improved management programs. • Used Landtrendr to segment, smooth, and gap-fill Landsat timeseries imagery. • Leveraged stacked ensembles of machine learning algorithms. • Direct modeling with field inventory, and indirect modeling with LiDAR predictions. • Developed a novel ensemble of direct and indirect modeling approaches. • Annual (1990-2019) forest biomass maps at 30 m resolution across New York State. [ABSTRACT FROM AUTHOR]

[Display omitted] • A set of vertical structure parameters of the forest stand is defined based on the continuous vertical canopy profile. • A novel approach for classifying vertical forest structures is developed based on the vertical structure parameters. • The approach exhibits high accuracy, excellent generalizations, and meaningful outputs in ecology and forestry. Vertical forest structure (VFS) refers to the vertical stratification or layering of forest communities in space, which is a fundamental characteristic of a plant community. It plays a vital role in forest vitality and facilitates various ecological activities and processes. The mapping of VFS is of significant value for both ecological and forestry purposes. In this paper, we presented a novel approach for the automated mapping of VFS in a large subtropical region based on discrete airborne LiDAR data. Firstly, the LiDAR point clouds of a stand (plot or grid cell) were segmented into 100 height bins from the top to the ground, and a height-frequency histogram was obtained by calculating the proportion of the number of returns in the bins to the total number of returns, which objectively represented the vertical distribution of canopy material. Secondly, a univariate ten-order polynomial was used to fit the height-frequency histogram, enabling the generation of a continuous vertical canopy profile (pseudo-waveform) of the stand. Thirdly, a comprehensive set of vertical structure parameters was defined and extracted based on the pseudo-waveforms, which effectively characterized the vertical profile layer and the canopy layer. Fourthly, to construct a comprehensive framework, 43 model profiles were summarized from the field plots, taking into account the number of effective peaks in the pseudo-waveforms and other vertical structure parameters. Finally, 43 classification rules were developed and 18 judgment criteria were established using the vertical structure parameters of the mode profiles. They classified vertical forest structures into 24 classes with explicit spatial definitions. The classification of 1147 field plots resulted in an overall accuracy of 94.7% and a kappa coefficient of 0.937. The VFS mapping over a large area demonstrated an effective execution rate of 99.8% for both rules and criteria. The proposed approach exhibits high accuracy and excellent generalization ability across different forest types, species, and study sites, highlighting its ecological and forestry significance. It represents a significant advance in the automated classification and mapping of VFS in large subtropical regions using airborne LiDAR data. However, the proposed approach needs to be validated in other vegetation zones to assess its generalizability and extend its applicability. [ABSTRACT FROM AUTHOR]

• Innovative approach using mobile laser scanning for rapid mapping of forest stands. • Forest management, by itself, had a strong effect on tree mortality probability. • Tree death is more likely in uneven-aged stands than in even-aged or unmanaged stands. • Silviculture can be used to cope with the effects of climate change on mortality. With global change, forest trees will be exposed to increasing stress in the coming decades with various studies demonstrating that stress-related mortality will increase in forests. While tree death can be triggered by a single factor, it is often caused by the accumulation and the complex interaction of various stressors. Several silvicultural strategies have been developed to cope with global change but very few studies have addressed the ways in which silvicultural regimes interact with various stressors to influence tree mortality. This lack of research on the effects of forest management regimes on tree mortality may be due to the challenge of acquiring large (or long-term) datasets to assess tree mortality in forests. Within this context and using a mobile terrestrial LiDAR approach for rapid 3D-mapping of forest stands, we aimed to (i) compare recent tree mortality patterns in temperate forests among contrasting forest management regimes (even-aged silviculture, uneven-aged silviculture and unmanaged forests), and (ii) evaluate the relative influence of regeneration harvest severity on tree mortality compared to other spatially explicit factors (i.e., localized competition and slope position) and non-spatially explicit factors (i.e. tree DBH -diameter at breast height- and tree species group). In a permanent sample plot network, we mapped 15 508 dead and living trees (>9.1 cm DBH) within 37 sugar maple-dominated stands: 14 even-aged, 16 uneven-aged and 7 unmanaged stands. We separated the relative role of forest management on individual tree mortality from other factors such as size, species, slope position, and localized competition by modeling the probability of mortality for each tree. Localized competition or slope position were not significant factors describing tree mortality. Results showed that tree mortality was influenced by tree species, DBH and forest management regime. Models indicated that forest management regime, by itself, had a strong effect on tree mortality proportion. Results also indicated that trees in uneven-aged stands had a higher probability of dying than the those in even-aged or unmanaged stands. However, we do not advocate for the replacement of uneven-aged regimes in favour of even-aged ones. Instead, we believe that, in the context of global change, adjustments should be made to uneven-aged regimes to reduce risk of mortality. Moreover, our study reinforces the idea that silviculture can be applied in such a way as to enhance stand resistance and resilience to cope with global change; for example, by influencing size structure and species composition, which greatly influence tree mortality. [ABSTRACT FROM AUTHOR]