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Funder: Agua Salud Project, Funder: U.S. Department of Energy; Id: http://dx.doi.org/10.13039/100000015, Funder: CAPES; Id: http://dx.doi.org/10.13039/501100002322, Data capturing multiple axes of tree size and shape, such as a tree's stem diameter, height and crown size, underpin a wide range of ecological research-from developing and testing theory on forest structure and dynamics, to estimating forest carbon stocks and their uncertainties, and integrating remote sensing imagery into forest monitoring programmes. However, these data can be surprisingly hard to come by, particularly for certain regions of the world and for specific taxonomic groups, posing a real barrier to progress in these fields. To overcome this challenge, we developed the Tallo database, a collection of 498,838 georeferenced and taxonomically standardized records of individual trees for which stem diameter, height and/or crown radius have been measured. These data were collected at 61,856 globally distributed sites, spanning all major forested and non-forested biomes. The majority of trees in the database are identified to species (88%), and collectively Tallo includes data for 5163 species distributed across 1453 genera and 187 plant families. The database is publicly archived under a CC-BY 4.0 licence and can be access from: https://doi.org/10.5281/zenodo.6637599. To demonstrate its value, here we present three case studies that highlight how the Tallo database can be used to address a range of theoretical and applied questions in ecology-from testing the predictions of metabolic scaling theory, to exploring the limits of tree allometric plasticity along environmental gradients and modelling global variation in maximum attainable tree height. In doing so, we provide a key resource for field ecologists, remote sensing researchers and the modelling community working together to better understand the role that trees play in regulating the terrestrial carbon cycle.

Biodiversity monitoring is an almost inconceivable challenge at the scale of the entire Earth. The current (and soon to be flown) generation of spaceborne and airborne optical sensors (i.e., imaging spectrometers) can collect detailed information at unprecedented spatial, temporal, and spectral resolutions. These new data streams are preceded by a revolution in modeling and analytics that can utilize the richness of these datasets to measure a wide range of plant traits, community composition, and ecosystem functions. At the heart of this framework for monitoring plant biodiversity is the idea of remotely identifying species by making use of the ‘spectral species’ concept. In theory, the spectral species concept can be defined as a species characterized by a unique spectral signature and thus remotely detectable within pixel units of a spectral image. In reality, depending on spatial resolution, pixels may contain several species which renders species-specific assignment of spectral information more challenging. The aim of this paper is to review the spectral species concept and relate it to underlying ecological principles, while also discussing the complexities, challenges and opportunities to apply this concept given current and future scientific advances in remote sensing.

© 2021 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). NASA’s Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI’s footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI’s waveform-to- biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g., RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.

Pathogenic wood decay fungi such as species of are some of the most serious forest pathogens in Europe, causing rot of tree boles and loss of growth, with estimated economic losses of eight hundred million euros per year. In conifers with low resinous heartwood such as species of and , these fungi are commonly confined to heartwood and thus external infection signs on the bark or foliage of trees are normally absent. Consequently, determining the extent of disease presence in a forest stand with field surveys is not practical for guiding forest management decisions such as optimal rotation time. Remote sensing technologies such as airborne laser scanning and aerial imagery are already used to reduce the reliance on fieldwork in forest inventories. This study aimed to use remote sensing to detect rot in spruce ( L. Karst.) forests in Norway. An airborne hyperspectral imager provided information for classifying the presence or absence of rot in a single-tree-based framework. Ground reference data showing the presence of rot were collected by harvest machine operators during the harvest of forest stands. Random forest and support vector machine algorithms were used to classify the presence and absence of rot. Results indicate a 64% overall classification accuracy for presence-absence classification of rot, although additional work remains to make the classifications usable for practical forest management.HeterobasidionPiceaAbiesPicea abies

The prediction of forest biophysical parameters is an important task in remote sensing for understanding global carbon cycle. Spectral remote sensing data are available globally at a relatively economical cost making them a viable resource for forest remote sensing. However, the main drawbacks associated with such data is the uncertainty of predictions and cluttered process of selecting band combinations from hyperspectral/multispectral data to produce spectral features for modelling. In this paper, we present an approach that exploits the latest developments in generative variational autoencoders (VAE) that produce disentangled representation from input data to assess the capability of hyperspectral data to model forest aboveground biomass (AGB). The proposed VAE generates a special kind of deep spectral features that are proportional to AGB. A modelling accuracy of R2 = 0.57 (cross-validated) was obtained by the proposed approach, thus pointing out the potential of hyperspectral data to model AGB using disentangled deep spectral features. The proposed approach also enables in bypassing the unreliable process of selecting band combinations to produce spectral features and shows good prospects for mapping global level biomass.

Forest aboveground biomass (AGB) is a prime forest parameter that requires global level estimates to study the global carbon cycle. Light detection and ranging (LiDAR) is the state-of-the-art technology for AGB prediction but it is expensive, and its coverage is restricted to small areas. On the contrary, spaceborne Earth observation data are effective and economical information sources to estimate and monitor AGB at a large scale. In this paper, we present a study on the use of different spaceborne multispectral remote sensing data for the prediction of forest AGB. The objective is to evaluate the effects of temporal, spectral, and spatial capacities of multispectral satellite data for AGB prediction. The study was performed on multispectral data acquired by Sentinel-2, RapidEye, and Dove satellites which are characterized by different spatial resolutions, temporal availability, and number of spectral bands. A systematic process of least absolute shrinkage and selection operator (lasso) variable selection generalized linear modeling, leave-one-out cross-validation, and analysis was accomplished on each satellite dataset for AGB prediction. Results point out that the multitemporal data based AGB models were more effective in prediction than the single-time models. In addition, red-edge and short wave infrared (SWIR) channel dependent variables showed significant improvement in the modeling results and contributed to more than 50% of the selected variables. Results also suggest that high spatial resolution plays a smaller role than spectral and temporal information in the prediction of AGB. The overall analysis emphasizes a good potential of spaceborne multispectral data for developing sophisticated methods for AGB prediction especially with specific spectral channels and temporal information.

This article develops a robust feature fusion approach to enhance the classification performance of very high resolution (VHR) remote sensing images. Specifically, a novel two-stage multiple feature fusion (TsF) approach is proposed, which includes an intragroup and an intergroup feature fusion stages. In the first fusion stage, multiple features are grouped by clustering, where redundant information between different types of features is eliminated within each group. Then, features are pairwisely fused in an intergroup fusion model based on the guided filtering method. Finally, the fused feature set is imported into a classifier to generate the classification map. In this work, the original VHR spectral bands and their attribute profiles are taken as examples as input spectral and spatial features, respectively, in order to test the performance of the proposed TsF approach. Experimental results obtained on two QuickBird datasets covering complex urban scenarios demonstrate the effectiveness of the proposed approach in terms of generation of more discriminative fusion features and enhancing classification performance. More importantly, the fused feature dimensionality is limited at a certain level; thus, the computational cost will not be significantly increased even if multiple features are considered.

Global mapping of forest aboveground biomass (AGB) is a challenging task and crucial for forest management and planning. Lidar data provides 3D information of forest stands and has been consistently identified as the state-of-the-art technology for monitoring forests. However, lidar data are expensive and their acquisition is spatially restricted to some forest areas. A reasonable solution for overcoming these restrictions is the use of spaceborne data. In recent years, the number of multispectral sensor based satellites have consistently increased, including open data sources like Sentinel-2 and small satellite sources like RapidEye and Dove constellations. In this work, we compared and evaluated different multispectral satellite data like Sentinel-2, RapidEye and Dove on the basis of different available spectral, spatial and temporal information for modelling AGB. We also used airborne lidar data as the state-of-the-art to compare results from multispectral data models. The experiments were performed under a common framework of variable elimination based on autocorrelation analysis and variable selection using stepAIC (Akaike Information Criterion) algorithm. A multiple linear regression with leave-one-out cross validation (LOOCV) was used to perform p-value quartiling for spectral information analysis, generate LOOCV metrics for temporal information analysis and modelling at spectral parity for spatial information analysis. Results demonstrate a clear and extensive influence of spectral information from specific channels like red-edge and SWIR for modelling AGB. Also, the addition of temporal information increases the precision and agreement of multispectral AGB models. Differently, the spatial information may not be relevant unless datasets are at spectral parity.

Change detection from multi-temporal remote sensing images is an effective way to identify the burned areas after forest fires. However, the complex image scenario and the similar spectral signatures in multispectral bands may lead to many false positive errors, which make it difficult to exact the burned areas accurately. In this paper, a novel-burned area change detection approach is proposed. It is designed based on a new Normalized Burn Ratio-SWIR (NBRSWIR) index and an automatic thresholding algorithm. The effectiveness of the proposed approach is validated on three Landsat-8 data sets presenting various fire disaster events worldwide. Compared to eight index-based detection methods that developed in the literature, the proposed approach has the best performance in terms of class separability (2.49, 1.74 and 2.06) and accuracy (98.93%, 98.57% and 99.51%) in detecting the burned areas. Simultaneously, it can also better suppress the complex irrelevant changes in the background.

International audience; Effective strategies for forest characterization and monitoring are important to support sustainable management. Recent advances in remote sensing, like hyperspectral and LiDAR sensors, provide valuable information to describe forests at stand, plot, and tree level. Hyperspectral imaging contains meaningful reflectance attributes of plants or spectral traits, while LiDAR data offer alternatives for analyzing structural properties of canopy. The fusion of these two data sources can improve forest characterization. The method to use for the data fusion should be chosen according to the variables to predict. This work presents a literature review on the integration of hyperspectral imaging and LiDAR data by considering applications related to forest monitoring. Although different authors propose a variety of taxonomies for data fusion, we classified our reviewed methods according to three levels of fusion: low level or observation level, medium level or feature level, and high level or decision level. This review examines the relationship between the three levels of fusion and the methods used in each considered approach.

Forest structure is strongly related to forest ecology, and it is a key parameter to understand ecosystem processes and services. Airborne laser scanning (ALS) is becoming an important tool in environmental mapping. It is increasingly common to collect ALS data at high enough point density to recognize individual tree crowns (ITCs) allowing analyses to move beyond classical stand-level approaches. In this study, an effective and simple method to map ITCs, and their stem diameter and aboveground biomass (AGB) is presented. ALS data were used to delineate ITCs and to extract ITCs' height and crown diameter; then, using newly developed allometries, the ITCs' diameter at breast height (DBH) and AGB were predicted. Gini coefficient of DBHs was also predicted and mapped aggregating ITCs predictions. Two datasets from spruce dominated temperate forests were considered: one was used to develop the allometric models, while the second was used to validate the methodology. The proposed approach provides accurate predictions of individual DBH and AGB (R2 = .85 and .78, respectively) and of tree size distributions. The proposed method had a higher generalization ability compared to a standard area-based method, in particular for the prediction of the Gini coefficient of DBHs. The delineation method used detected more than 50% of the trees with DBH >10 cm. The detection rate was particularly low for trees with DBH below 10 cm, but they represent a small amount of the total biomass. The Gini coefficient of the DBH distribution was predicted at plot level with R2 = .46. The approach described in this work, easy applicable in different forested areas, is an important development of the traditional area-based remote sensing tools and can be applied for more detailed analysis of forest ecology and dynamics.

Airborne laser scanner (ALS) data and hyperspectral (HS) data have become standard data sources in studies related to forest ecology and environmental mapping. Nowadays it is possible to have combined acquisitions of ALS and HS data, and also to have very high point density ALS data that allows us to study forests at individual tree level. In this paper, a study on the combination of HS and ALS data with models for diameter at breast height (DBH) and aboveground biomass (AGB) prediction at individual tree crown (ITC) level is presented. ALS data were used to delineate ITCs and to estimate height and crown diameter of each ITC, whereas HS data were used to identify the tree species. The proposed approach consists in developing DBH and AGB models using field data of an area and to apply these models to areas with similar characteristics. Two datasets were considered: the first dataset is located in boreal forests and it is composed by three separate study areas (Aurskog-Holand, Hadeland, and Valer); the second dataset is located in temperate forests and it is composed by two separate study areas (Val di Sella, and Pellizzano). Aurskog-Holand and Val di Sella study areas were used only to develop species-specific and non-species specific models for DBH and AGB in the two biomes, while the other study areas were used for validation at plot level with remote sensing data. Local models were also developed using field data and ALS and HS data, and they were compared to the ones developed on Aurskog-Holand and Val di Sella study areas. The results show that the proposed approach provides high accuracies in both biomes, even if problems related to underestimation exists. Predictions with a general non-species specific model developed on one study area did not differ significantly from predictions obtained by using local models

Site productivity is essential information for sustainable forest management and site index (SI) is the most common quantitative measure of it. The SI is usually determined for individual tree species based on tree height and the age of the 100 largest trees per hectare according to stem diameter. The present study aimed to demonstrate and validate a methodology for the determination of SI using remotely sensed data, in particular fused airborne laser scanning (ALS) and airborne hyperspectral data in a forest site in Norway. The applied approach was based on individual tree crown (ITC) delineation: tree species, tree height, diameter at breast height (DBH), and age were modelled and predicted at ITC level using 10-fold cross validation. Four dominant ITCs per 400 m 2 plot were selected as input to predict SI at plot level for Norway spruce ( Picea abies (L.) Karst.) and Scots pine ( Pinus sylvestris L.). We applied an experimental setup with different subsets of dominant ITCs with different combinations of attributes (predicted or field-derived) for SI predictions. The results revealed that the selection of the dominant ITCs based on the largest DBH independent of tree species, predicted the SI with similar accuracy as ITCs matched with field-derived dominant trees (RMSE: 27.6% vs 23.3%). The SI accuracies were at the same level when dominant species were determined from the remotely sensed or field data (RMSE: 27.6% vs 27.8%). However, when the predicted tree age was used the SI accuracy decreased compared to field-derived age (RMSE: 27.6% vs 7.6%). In general, SI was overpredicted for both tree species in the mature forest, while there was an underprediction in the young forest. In conclusion, the proposed approach for SI determination based on ITC delineation and a combination of ALS and hyperspectral data is an efficient and stable procedure, which has the potential to predict SI in forest areas at various spatial scales and additionally to improve existing SI maps in Norway.

This paper proposes a novel feature-level fusion approach for burned area change detection at a fine level. The proposed approach relies on two features. The first feature is a modified normalized burn ratio (MNBR) fire index based on Landsat-8 OLI SWIR data, and the second feature is the Bright temperature (BT) based on Landsat-8 TIR data. Then two features are combined by using the gradient transfer fusion algorithm and a change detection technique to generate a fine burned area change map. A real Landsat-8 data set covering a complex fire disaster scenario is utilized to test the performance of the proposed approach. Experimental results demonstrate the effectiveness of the proposed feature-level fusion approach comparing with the reference methods in term of higher separability value and detection accuracy.

Made available in DSpace on 2019-10-06T16:35:25Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-06-01 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) The use of remote sensing data for tree species classification in tropical forests is still a challenging task, due to their high floristic and spectral diversity. In this sense, novel sensors on board of unmanned aerial vehicle (UAV) platforms are a rapidly evolving technology that provides new possibilities for tropical tree species mapping. Besides the acquisition of high spatial and spectral resolution images, UAV-hyperspectral cameras operating in frame format enable to produce 3D hyperspectral point clouds. This study investigated the use of UAV-acquired hyperspectral images and UAV-photogrammetric point cloud (PPC) for classification of 12 major tree species in a subtropical forest fragment in Southern Brazil. Different datasets containing hyperspectral visible/near-infrared (VNIR) bands, PPC features, canopy height model (CHM), and other features extracted from hyperspectral data (i.e., texture, vegetation indices-VIs, and minimum noise fraction-MNF) were tested using a support vector machine (SVM) classifier. The results showed that the use of VNIR hyperspectral bands alone reached an overall accuracy (OA) of 57% (Kappa index of 0.53). Adding PPC features to the VNIR hyperspectral bands increased the OA by 11%. The best result was achieved combining VNIR bands, PPC features, CHM, and VIs (OA of 72.4% and Kappa index of 0.70). When only the CHM was added to VNIR bands, the OA increased by 4.2%. Among the hyperspectral features, besides all the VNIR bands and the two VIs (NDVI and PSSR), the first four MNF features and the textural mean of 565 and 679 nm spectral bands were pointed out as more important to discriminate the tree species according to Jeffries-Matusita (JM) distance. The SVM method proved to be a good classifier for the tree species recognition task, even in the presence of a high number of classes and a small dataset. Department of Remote Sensing National Institute for Space Research (INPE), Av. dos Astronautas 1758 Department of Sustainable Agro-Ecosystems and Bioresources Research and Innovation Centre Fondazione E. Mach, Via E. Mach 1 Department of Forest Engineering Santa Catarina State University (UDESC), Av. Luiz de Camões 2090 Department of Geography Santa Catarina State University (UDESC), Av. Me. Benvenuta, 2007 Department of Cartography São Paulo State University (UNESP), Roberto Simonsen 305 Department of Cartography São Paulo State University (UNESP), Roberto Simonsen 305 CAPES: 1578589

In forest management, site index information is essential for planning silvicultural operations and forecasting forest development. Site index is most commonly expressed as the average height of the dominant trees at a certain index age, and can be determined either by photo interpretation, field measurements, or projection of age combined with height estimates from remote sensing. However, recently it has been shown that site index can be accurately predicted from bi-temporal airborne laser scanner (ALS) data. Furthermore, single-time hyperspectral data have also been shown to be correlated to site index. The aim of the current study was to compare the accuracy of modelling site index using (1) data from bi-temporal ALS; (2) single-time hyperspectral data with different types of preprocessing; and (3) combined bi-temporal ALS and single-time hyperspectral data. The period between the ALS acquisitions was 11 years. The preprocessing of the hyperspectral data included an atmospheric correction and/or a normalization of the reflectance. Furthermore, a selection of pixels was carried out based on NDVI and compared to using all pixels. The results showed that bi-temporal ALS data explained about 70% (R2) of the variation in the site index, and the RMSE values from a cross-validation were 3.0 m and 2.2 m for spruce- and pine-dominated plots, respectively. Corresponding values for the different single-time hyperspectral datasets were 54%, 3.9 m, and 2.5 m. With bi-temporal ALS data and hyperspectral data used in combination, the results indicated that the contribution from the hyperspectral data was marginal compared to just using bi-temporal ALS. We also found that models constructed with normalized hyperspectral data produced lower RMSE values compared to those constructed with atmospherically corrected data, and that a selection of pixels based on NDVI did not improve the results compared to using all pixels.

Recent years have seen a rapid surge in the use of remote sensing technologies for characterizing the structure, composition and function of forested landscapes. Yet with few exceptions these studies have only provided snapshots of the ecosystem at one point in time, thereby limiting our ability to understand how forests are responding to global change. New approaches for characterizing forest dynamics using multi-temporal remotely sensed data are therefore urgently needed if we are to integrate these new technologies into conservation and management decision making processes. By combining data from a network of forest plots with repeat airborne lidar, here we develop an approach to (i) map fine-scale variation in aboveground carbon density (ACD) and its change over time across the landscape, and (ii) link these changes in ACD to forest structural attributes, species composition, disturbance regimes and local topography. We tested this framework on a temperate forest in the Alps characterized by the presence of three dominant species: spruce (Picea abies), silver fir (Abies alba) and beech (Fagus sylvatica). We found that between 2007 and 2011 the majority of the landscape (61.0%) increased in ACD, with a much smaller fraction of the study area exhibiting evidence of small-scale natural disturbances (3.7%) or ACD loss as a result of logging activities (13.7%). On average, areas of the landscape that actively sequestered carbon did so at a rate of 3.6% per year. However, rates of ACD accumulation varied considerably across the landscape, being greatest in forest stands characterized by multi-layered heterogeneous canopies, in ones dominated by spruce and at lower elevations. We demonstrated the potential of repeat lidar for characterizing not only the structure, but also the composition and aboveground carbon dynamics of forests. In doing so we open the door to monitor forests across large and inaccessible landscapes in order to better understand how they are responding to rapid global change and refine how we manage and conserve these critical ecosystems.

The insurance industry reports a pronounced intensification, at the global level, of weather-related events such as droughts, windstorms and hailstorms. As an efficient quantification tool, improved capacities can be built adopting innovative remote sensing methods to map vegetation damage spatial distribution, to quantify its intensity and impact. New airborne LiDAR (Light Detection and Ranging) sensors provide high vertical resolution data, which are potentially suitable not only for forest canopies but also for monitoring shorter crop canopies (e.g. corn − Zea mays L.) for crop injury and lodging assessment. To evaluate the potential of LiDAR metrics to map corn canopy height and hail defoliation, a flight campaign was organized in 2014 in Wampersdorf (Austria) in a cropland area affected by a hailstorm. Ground-truth observations were carried out in 16 plots, where defoliation was assessed both visually (observed range from 0% to 70%) and using a biophysical parameter-based method. The performance of both traditional and newly-introduced metrics (i.e. Canopy Metric, Ground Metric) was assessed at different sampling point densities. The results showed the ability of LiDAR data to map both corn canopy height and defoliation (predicted vs. observed regression: R 2 = 0.69 for both canopy height and defoliation; point density 5 and 42 points/m 2 , respectively). The presented approach has distinct advantages compared to previous remote sensing methods and has a clear application potential for farmers and insurance industries. Larger-scale studies are needed to verify its best implementation strategies and to investigate its economic and logistic benefits.

Topography is a key driver of tropical forest structure and composition, as it constrains local nutrient and hydraulic conditions within which trees grow. Yet, we do not fully understand how changes in forest physiognomy driven by topography impact other emergent properties of forests, such as their aboveground carbon density (ACD). Working in Borneo – at a site where 70‐m‐tall forests in alluvial valleys rapidly transition to stunted heath forests on nutrient‐depleted dip slopes – we combined field data with airborne laser scanning and hyperspectral imaging to characterise how topography shapes the vertical structure, wood density, diversity and ACD of nearly 15 km2 of old‐growth forest. We found that subtle differences in elevation – which control soil chemistry and hydrology – profoundly influenced the structure, composition and diversity of the canopy. Capturing these processes was critical to explaining landscape‐scale heterogeneity in ACD, highlighting how emerging remote sensing technologies can provide new insights into long‐standing ecological questions.

Multispectral LiDAR is a recent sensor in the remote sensing domain. In this study the potential of multispectral LiDAR data to model and predict some forest attributes (aboveground biomass per hectare, Gini coefficient of the diameters at breast height, and Shannon diversity index of the tree species) are explored. In particular, Optech Titan LiDAR data characterized by three spectral channels were considered. The results showed that all the three attributes can be accurately predicted with multispectral LiDAR data with high accuracy. Differences emerged on the contribution of the three respective channels of the multispectral LiDAR to the various models.

An international data science challenge, called NEON NIST data science evaluation, was set up in autumn 2017 with the goal to improve the use of remote sensing data in ecological applications. The competition was divided into three tasks: 1) segmentation of tree crowns; 2) data alignment; and 3) tree species classification. In this paper the methods and results of team FEM in the NEON NIST data science evaluation challenge are presented. The individual tree crown (ITC) segmentation (Task 1 of the challenge) was done using a region growing method applied to a near-infrared band of the hyperspectral images. The optimization of the parameters of the segmentation algorithm was done in a supervised way on the basis of the Jaccard score using the training set provided by the organizers. The alignment (Task 2) between the segmented ITCs and the ground measured trees was done using an Euclidean distance among the position, the height, and the crown radius of the ITCs and the ground trees. The classification (Task 3) was performed using a Support Vector Machine classifier applied to a selection of the hyperspectral bands. The selection of the bands was done using a Sequential Forward Floating Selection method and the Jeffries Matusita distance. The results in the three tasks were very promising: team FEM ranked first in Task 1 and 2, and second in Task 3. The segmentation results showed that the proposed approach segmented both small and large crowns. The alignment was correctly done for all the test samples. The classification results were good, even if the accuracy was biased towards the most represented species.

Background Forests are a key component of the global carbon cycle, and research is needed into the effects of human-driven and natural processes on their carbon pools. Airborne laser scanning (ALS) produces detailed 3D maps of forest canopy structure from which aboveground carbon density can be estimated. Working with a ALS dataset collected over the 8049-km2 Wellington Region of New Zealand we create maps of indigenous forest carbon and evaluate the influence of wind by examining how carbon storage varies with aspect. Storms flowing from the west are a common cause of disturbance in this region, and we hypothesised that west-facing forests exposed to these winds would be shorter than those in sheltered east-facing sites. Methods The aboveground carbon density of 31 forest inventory plots located within the ALS survey region were used to develop estimation models relating carbon density to ALS information. Power-law models using rasters of top-of-the-canopy height were compared with models using tree-level information extracted from the ALS dataset. A forest carbon map with spatial resolution of 25 m was generated from ALS maps of forest height and the estimation models. The map was used to evaluate the influences of wind on forests. Results Power-law models were slightly less accurate than tree-centric models (RMSE 35% vs 32%) but were selected for map generation for computational efficiency. The carbon map comprised 4.5 million natural forest pixels within which canopy height had been measured by ALS, providing an unprecedented dataset with which to examine drivers of carbon density. Forests facing in the direction of westerly storms stored less carbon, as hypothesised. They had much greater above-ground carbon density for a given height than any of 14 tropical forests previously analysed by the same approach, and had exceptionally high basal areas for their height. We speculate that strong winds have kept forests short without impeding basal area growth. Conclusion Simple estimation models based on top-of-the canopy height are almost as accurate as state-of-the-art tree-centric approaches, which require more computing power. High-resolution carbon maps produced by ALS provide powerful datasets for evaluating the environmental drivers of forest structure, such as wind.

In this study, the potential of multispectral airborne laser scanner (ALS) data to model and predict some forest characteristics was explored. Four complementary characteristics were considered, namely, aboveground biomass per hectare, Gini coefficient of the diameters at breast height, Shannon diversity index of the tree species, and the number of trees per hectare. Multispectral ALS data were acquired with an Optech Titan sensor, which consists of three scanners, called channels, working in three wavelengths (532 nm, 1064 nm, and 1550 nm). Standard ALS data acquired with a Leica ALS70 system were used as a reference. The study area is located in Southern Norway, in a forest composed of Scots pine, Norway spruce, and broadleaf species. ALS metrics were extracted for each plot from both elevation and intensity values of the ALS points acquired with both sensors, and for all three channels of the ALS multispectral sensor. Regression models were constructed using different combinations of metrics. The results showed that all four characteristics can be accurately predicted with both sensors (the best R2 being greater than 0.8), but the models based on the multispectral ALS data provide more accurate results. There were differences regarding the contribution of the three channels of the multispectral ALS. The models based on the data of the 532 nm channel seemed to be the least accurate.

Fusion of ALS and hyperspectral data can offer a powerful basis for the discrimination of tree species and enables an accurate prediction of species-specific attributes. In this study, the fused airborne laser scanning (ALS) data and hyperspectral images were used to model and predict the total and species-specific volumes based on three forest inventory approaches, namely the individual tree crown (ITC) approach, the semi-ITC approach, and the area-based approach (ABA). The performances of these inventory approaches were analyzed and compared at the plot level in a complex Alpine forest in Italy. For the ITC and semi-ITC approaches, an ITC delineation algorithm was applied. With the ITC approach, the species-specific volumes were predicted with allometric models for each crown segment and aggregated to the total volume. For the semi-ITC and ABA, a multivariate k-most similar neighbor method was applied to simultaneously predict the total and species-specific volumes using leave-one-out cross-validation at the plot level. In both methods, the ALS and hyperspectral variables were important for volume modeling. The total volume of the ITC, semi-ITC, and ABA resulted in relative root mean square errors (RMSEs) of 25.31%, 17.41%, 30.95% of the mean and systematic errors (mean differences) of 21.59%, −0.27%, and −2.69% of the mean, respectively. The ITC approach achieved high accuracies but large systematic errors for minority species. For majority species, the semi-ITC performed slightly better compared to the ABA, resulting in higher accuracies and smaller systematic errors. The results indicated that the semi-ITC outperformed the two other inventory approaches. To conclude, we suggest that the semi-ITC method is further tested and assessed with attention to its potential in operational forestry applications, especially in cases for which accurate species-specific forest biophysical attributes are needed.

Tree species classification accuracy at the individual tree crown (ITC) level depends on many factors, among which in this paper we analyzed: i) the remote sensing data used for the ITC delineation process carried out prior to the classification, and ii) the pixels considered inside each ITC during the classification process. These two factors were analyzed on the ITC level classification accuracy of boreal tree species (Pine, Spruce and Broadleaves), considering two remote sensing data types: hyperspectral and airborne laser scanning (ALS). ITCs were delineated automatically on ALS and on hyperspectral data. A manual ITC delineation was used as reference in the analysis. The pixel level classification was performed on the hyperspectral bands using a non-linear support vector machine. The classification at ITC level was obtained by applying a majority voting rule to the classified pixels confined by each ITC. The results showed that ITCs automatically delineated from hyperspectral data were usually smaller than those from ALS, and the tree detection rate for hyperspectral data was much lower compared to ALS data (28.4 versus 48.5%). Regarding the classification results, using only manually delineated ITCs a kappa accuracy of 0.89 was obtained, while using only automatically delineated ITCs from hyperspectral or ALS data reduced the kappa values to 0.79 and 0.76, respectively. Slightly different results were achieved using semi-automatic approaches based on both manual and automatically delineated ITC (0.81 and 0.74, respectively). A selection of only certain pixels inside each ITC improved the classification accuracy from 1 to 7 percentage points. A selection based on the spectral values of the pixels was found more influential than the one based on the ALS-derived canopy height model. The best results were obtained after a selection based on the spectral values in the bands in the blue region of the spectrum using either the Otsu method or an ad-hoc percentile-based thresholding method.

Competitive interactions are important predictors of tree growth. Spatial and temporal changes in resource availability, and variation in species and spatial patterning of trees alter competitive interactions, thus affecting tree growth and, hence, biomass. Competition indices are used to quantify the level of competition among trees. As these indices are normally computed only over small areas, where field measurements are done, it would be useful to have a tool to predict them over large areas. On this regard, remote sensing, and in particular light detection and ranging (lidar) data, could be the perfect tool. The objective of this study was to use lidar metrics to predict competition (on the basis of distance-dependent competition indices) of individual trees and to relate them with tree aboveground biomass (AGB). The selected study area was a mountain forest area located in the Italian Alps. The analyses focused on the two dominant species of the area: Silver fir (Abies alba Mill.) and Norway spruce (Picea abies (L.) H. Karst). The results showed that lidar metrics could be used to predict competition indices of individual trees (R2 above 0.66). Moreover, AGB decreased as competition increased, suggesting that variations in the availability of resources in the soil, and the ability of plants to withstand competition for light may influence the partitioning of biomass.

The Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) is capable of observing reflected lunar radiances at night with its high gain stage (HGS), and the radiometric calibration is traceable to the sun through gain transfer from the low gain stage (LGS) calibrated near the terminator with the solar diffuser. Meanwhile, deep convective clouds (DCC) are known to have a stable reflectance in the visible spectral range. Therefore, the reflected lunar radiance at night from the DCC provides a unique dataset for the inter-calibration of VIIRS DNB on different satellites such as Suomi National Polar-orbiting Partnership (NPP) and NOAA-20, as well as quantifying the lunar radiance as a function of lunar phase angle. This study demonstrates a methodology for comparing nighttime Suomi NPP and NOAA-20 VIIRS DNB measured DCC reflected lunar radiance at various phase angles using data from July 2018 to March 2019 with an 86 second sampling interval and comparing Suomi NPP VIIRS DNB measured lunar radiances with those from lunar model predictions. The result shows good consistency between these two instruments on the two satellites, although a low bias in the NOAA-20 VIIRS DNB of ~5% is found. Also, observed lunar radiance from VIIRS DNB on Suomi NPP is found to be consistent with model predictions within 3% ± 5% (1σ) for a large range of lunar phase angles. However, discrepancies are significant near full moon, due to lunar opposition effects, and limitations of the lunar models. This study is useful not only for monitoring the DNB calibration stability and consistency across satellites, but also may help validate lunar models independently.

An international data science challenge, called National Ecological Observatory Network—National Institute of Standards and Technology data science evaluation, was set up in autumn 2017 with the goal to improve the use of remote sensing data in ecological applications. The competition was divided into three tasks: (1) individual tree crown (ITC) delineation, for identifying the location and size of individual trees; (2) alignment between field surveyed trees and ITCs delineated on remote sensing data; and (3) tree species classification. In this paper, the methods and results of team Fondazione Edmund Mach (FEM) are presented. The ITC delineation (Task 1 of the challenge) was done using a region growing method applied to a near-infrared band of the hyperspectral images. The optimization of the parameters of the delineation algorithm was done in a supervised way on the basis of the Jaccard score using the training set provided by the organizers. The alignment (Task 2) between the delineated ITCs and the field surveyed trees was done using the Euclidean distance among the position, the height, and the crown radius of the ITCs and the field surveyed trees. The classification (Task 3) was performed using a support vector machine classifier applied to a selection of the hyperspectral bands and the canopy height model. The selection of the bands was done using the sequential forward floating selection method and the Jeffries Matusita distance. The results of the three tasks were very promising: team FEM ranked first in the data science competition in Task 1 and 2, and second in Task 3. The Jaccard score of the delineated crowns was 0.3402, and the results showed that the proposed approach delineated both small and large crowns. The alignment was correctly done for all the test samples. The classification results were good (overall accuracy of 88.1%, kappa accuracy of 75.7%, and mean class accuracy of 61.5%), although the accuracy was biased toward the most represented species.

This article reviews the recent literature concerning airborne laser scanning for forestry purposes in Italy, and presents the current methodologies used to extract forest characteristics from discrete return ALS (Airborne Laser Scanning) data. Increasing interest in ALS data is currently being shown, especially for remote sensingbased forest inventories in Italy; the driving force for this interest is the possibility of reducing costs and providing more accurate and efficient estimation of forest characteristics. This review covers a period of approximately ten years, from the first application of laser scanning for forestry purposes in 2003 to the present day, and shows that there are numerous ongoing research activities which use these technologies for the assessment of forest attributes (e.g., number of trees, mean tree height, stem volume) and ecological issues (e.g., gap identification, fuel model detection). The basic approaches – such as single tree detection and areabased modeling – have been widely examined and commented in order to explore the trend of methods in these technologies, including their applicability and performance. Finally this paper outlines and comments some of the common problems encountered in operational use of laser scanning in Italy, offering potentially useful guidelines and solutions for other countries with similar conditions, under a rather variable environmental framework comprising Alpine, temperate and Mediterranean forest ecosystems L'articolo è disponibile sul sito dell'editore http://www.journals.elsevier.com/international-journal-of-applied-earth-observation-and-geoinformation/

This paper presents a 3D delineation method for airborne laser scanning point cloud. The method is based on an unsupervised clustering technique applied on horizontal slices followed by vertical me...

The identification of tree species is an important issue in forest management. In recent years, many studies have explored this topic using hyperspectral, multispectral, and LiDAR data. In this study we analyzed two multi-sensor set-ups: 1) airborne high spatial resolution hyperspectral images combined with LiDAR data; and 2) high spatial resolution satellite multispectral images combined with LiDAR data. Two LiDAR acquisitions were considered: low point density (approx. 0.48 points per m2) and high point density (approx. 8.6 points per m2). The aims of this work were: i) to understand what level of classification accuracy can be achieved using a high spectral and spatial resolution multi-sensor data set-up (very high spatial and spectral resolution airborne hyperspectral images integrated with high point density LiDAR data), over a mountain area characterized by many species, both broadleaf and coniferous; ii) to understand the implications of a downgrading of the data characteristics (in terms of spectral resolution of spectral data and point density of LiDAR data), on species separability, with respect to the previous set-up; and iii) to understand the differences between high- and low-point density LiDAR acquisitions on tree species classification. The study region was a mountain area in the Southern Alps characterized by many tree species (7 species and a “non-forest” class), either coniferous or broadleaf. For each set-up a specific processing chain was adopted, from the pre-processing of the raw data to the classification (two classifiers were used: support vector machine and random forest). Different class definitions were tested, including general macro-classes, forest types, and finally single tree species. Experimental results showed that the set-up based on hyperspectral data was effective with general macro-classes, forest types, and single species, reaching high kappa accuracies (93.2%, 82.1% and 76.5%, respectively). The use of multispectral data produced a reduction in the classification accuracy, which was sharp for single tree species, and still high for forest types. Considering general macro-classes, the multispectral set-up was still very accurate (85.8%). Regarding LiDAR data, the experimental analysis showed that high density LiDAR data provided more information for tree species classification with respect to low density data, when combined with either hyperspectral or multispectral data.

Forest inventories are important tools for the management of forests. In this context, the estimation of the tree stem volume is a key issue. In this paper, we present a system for the estimation of forest stem diameter and volume at individual tree level from multireturn light detection and ranging (LIDAR) data. The proposed system is made up of a preprocessing module, a LIDAR segmentation algorithm (aimed at retrieving tree crowns), a variable extraction and selection procedure, and an estimation module based on support vector regression (SVR) (which is compared with a multiple linear regression technique). The variables derived from LIDAR data are computed from both the intensity and elevation channels of all available returns. Three different methods of variable selection are analyzed, and the sets of variables selected are used in the estimation phase. The stem volume is estimated with two methods: 1) direct estimation from the LIDAR variables and 2) combination of diameters and heights estimated from LIDAR variables with the species information derived from a classification map according to standard height/diameter relationships. Experimental results show that the system proposed is effective and provides high accuracies in both the stem volume and diameter estimations. Moreover, this paper provides useful indications on the effectiveness of SVR with LIDAR in forestry problems.

Ground reference data collection represents an important element in the prediction of stem volume with LiDAR-derived variables, and at present it is the most expensive part of such analyses. In this paper two aspects of ground reference data collection were analyzed: (1) the positioning error of the ground plots; and (2) the optimal number of training plots. A system for the prediction of stem volume at area-based level was adopted. LiDAR data were preprocessed and 13 variables describing both height and coverage were extracted. Models were defined using a stepwise ordinary least square (OLS) regression. Three experiments were conducted: (i) the role of the plots positioning error on prediction accuracy; (ii) the influence of random downsampling of plot numbers on prediction accuracy; and (iii) the influence of a stratified downsampling of plot numbers on prediction accuracy based on LiDAR-derived variables. A dataset comprising 799 ground plots was used. They were distributed throughout a mountainous area in the Southern Alps, where the presence of a complex landscape increases the uncertainty of the Global Positioning System (GPS) accuracy, and where a large variety of tree forest species and climatic environments make it necessary to have a large number of sample plots for accurate characterization of the study area. All the experiments provided important indications for LiDAR based forest inventories: the GPS error did not significantly influence the prediction accuracy and it was possible to reduce the number of training samples without compromising the generalization ability of the prediction model. Leading on from these findings, a new ground sampling protocol based on genetic algorithms was proposed. The new protocol allowed us to obtain promising results for the considered dataset: using only 53 training plots, instead of 534 in the original dataset, we obtained the same results for the validation set. These results, obtained in a complex mountainous area, are representative of Alpine environments and allow us to infer that similar (or better) results could also be obtained within non mountainous areas.

In this paper, we present a study on the efficiency of multi-return LIDAR (Light Detection Ranging) data in the estimation of forest stem volume over a multi-layered forest area in the Italian Alps. The goals of this paper are (1) to verify the usefulness of multi-return LIDAR data compared to single-return data in forest volume estimation and (2) to define the optimal resolution of a stem volume distribution raster map over the investigated area. To achieve these goals, raw data were segmented into a net, and different cell dimensions were investigated to maximize the relationship between the LIDAR data and the ground-truth information. Twenty predicting variables (e.g., mean height, coefficient of variation) have been extracted from multi-return LIDAR data, and a multiple linear regression analysis has been used for predicting tree stem volume. Experimental results found that the optimal resolutions of the net square cells were 40 m. The analysis indicated that in a mixed multi-layered forest, characterized by a complex vertical structure, the correct selection of the map spatial resolution and the inclusion of the secondary-return data were important factors for improving the effectiveness of the laser scanning approach in forest inventories. The experimental tests showed that the chosen model is effective for the estimation of stem volume over the analyzed area, providing good results on all the three considered validation methods.

Small footprint Light Detection and Ranging (LiDAR) data have been shown to be a very accurate technology to predict stem volume. In particular, most recent sensors are able to acquire multiple return (more than 2) data at very high hit density, allowing one to have detailed characterization of the canopy. In this paper, we utilize very high density ( >8 hits per m2) LiDAR data acquired over a forest stand in Italy. Our approach was as follows: Individual trees were first extracted from the LiDAR data and a series of attributes from both the first, and non-first (multiple), hits associated with each crown were then extracted. These variables were then correlated with ground truth individual estimates of stem volume. Our results indicate that: (i) non-first returns are informative for the estimation of stem volume (in particular the second return); (ii) some attributes (e.g., maximum at the power of n) better emphasize the information content of returns different from the first respect to other metrics (e.g., minimum, mean); and (iii) the combined use of variables belonging to different returns slightly increases the overall model accuracy. Moreover, we found that the best model for stem volume estimation (adj - R2 = 0.77, P < 0.0001, SE = 0.06) comprised four variables belonging to three returns (first, second, and third). The results of this analysis are important as they underline the effectiveness of the use of multiple return LiDAR data, underling the connection between LiDAR hits different from the first and tree structure and characteristics.

International audience; In this study, eight airborne laser scanning (ALS)-based single tree detection methods are benchmarked and investigated. The methods were applied to a unique dataset originating from different regions of the Alpine Space covering different study areas, forest types, and structures. This is the first benchmark ever performed for different forests within the Alps. The evaluation of the detection results was carried out in a reproducible way by automatically matching them to precise in situ forest inventory data using a restricted nearest neighbor detection approach. Quantitative statistical parameters such as percentages of correctly matched trees and omission and commission errors are presented. The proposed automated matching procedure presented herein shows an overall accuracy of 97%. Method based analysis, investigations per forest type, and an overall benchmark performance are presented. The best matching rate was obtained for single-layered coniferous forests. Dominated trees were challenging for all methods. The overall performance shows a matching rate of 47%, which is comparable to results of other benchmarks performed in the past. The study provides new insight regarding the potential and limits of tree detection with ALS and underlines some key aspects regarding the choice of method when performing single tree detection for the various forest types encountered in alpine regions.

Agricultural and forest monitoring is a valued instrument needed by public authorities (PA) for determining land uses, planning natural resources management and collecting taxes. Remote Sensing (RS...

In this paper four different delineation methods based on airborne laser scanning (ALS) and hyperspectral data are compared over a forest area in the Italian Alps. The comparison was carried out in...

Species-specific information is important for many tasks related to forest management. We review the use of airborne laser scanning (ALS) and complementary data for providing this information. The main ALS-based information is related to structural features, intensity of the echoes, and waveform parameters, whereas spectral information may be provided by fusing data from different sensors. Various types of classifiers are applied, the current emphasis being in non-linear or otherwise complex techniques. The results are successful with respect to the main species, whereas the overall accuracy depends on the desired level of detail in the classification. We expect fusion approaches combining ALS and especially hyperspectral data to become more common and further improvements by the development of advanced sensor technology.

Active learning typically aims at minimizing the number of labeled samples to be included in the training set to reach a certain level of classification accuracy. Standard methods do not usually take into account the real annotation procedures and implicitly assume that all samples require the same effort to be labeled. Here, we consider the case where the cost associated with the annotation of a given sample depends on the previously labeled samples. In general, this is the case when annotating a queried sample is an action that changes the state of a dynamic system, and the cost is a function of the state of the system. In order to minimize the total annotation cost, the active sample selection problem is addressed in the framework of a Markov decision process, which allows one to plan the next labeling action on the basis of an expected long-term cumulative reward. This framework allows us to address the problem of optimizing the collection of labeled samples by field surveys for the classification of remote sensing data. The proposed method is applied to the ground sample collection for tree species classification using airborne hyperspectral images. Experiments carried out in the context of a real case study on forest inventory show the effectiveness of the proposed method.

Airborne laser scanning (ALS) data are an important source of information for forest inventory purposes. In particular they allow us to delineate individual tree crowns (ITC) that are at the basis of the individual tree-based inventories. In multi-layered forests various tree species are mixed together and trees usually grow in a different vertical layers, leading to a relevant problem in detecting subdominant and suppressed trees. Thus, the purpose of this study is to present an approach for ITC delineation using clustering techniques at both 2D and 3D level based on raw ALS point cloud. The preliminary results showed that forest structure strongly affect the performance of the proposed algorithm. Thus, different criteria were chosen with a priori knowledge from ground truth data. The proposed algorithm achieved comparable or superior results as compared to conventional methods.