In this study, we explored the use of long time series of Sentinel-1 SAR images for the detection of bark beetle outbreaks in temperate forests. Bark beetle attacks induce a gradual deterioration in the health of trees, ultimately leading to their death. Accordingly, time series of remote sensing data are crucial to detecting them. In this study, time series of Sentinel-1 data were collected in reference areas attacked by bark beetle in the period 2019-2022 and then the backscatter values were extracted and analyzed. A method for the automatic detection of bark beetle attacks has been developed and tested on the same polygons. The preliminary results showed that the distribution of the Sentinel-1 backscatter values in VV and VH polarizations inside the attacked polygons before and after that attack is significantly different. The detection method achieved a detection accuracy above 65% for all the dates considered.

Biomass estimation is a crucial component in the areas of carbon storage and forest management. Biomass can be analyzed rather at forest or at Single Tree Level (STL) and studies in both directions can be found in literature. Nevertheless, working at STL provides more accurate estimations. Such a level of information can be obtained by means of LiDAR data, but this type of data is usually expensive and not available for all Countries. Thus, few studies at STL can be found in developing countries and tropical forests. This study presents one of the first works for biomass estimation in tropical forest located in Gorgona Island, Colombia. Adaptations have been made to state-of-the-art methods for them to properly work in a complex forest, where trees sizes vary a lot from one area to another. Preliminary results are shown for some areas, allowing to see the capabilities of the adaptation.

Wind disturbances represent a great source of damage in forests, and an assessment of such damage is very important for adequate forest management. Remote sensing is an effective tool for this purpose and can be used by considering different data sources: active vs passive sensors. While passive sensors can provide a direct view of windthrows, they are often affected by clouds. Active sensors have the significant advantage of not being affected by the presence of clouds which can be prevalent in certain seasons in mountain areas. The objective of this study is to compare the capability of active (Cosmo SkyMed SAR sensor) and passive (Sentinel-2 and Planet sensors) data in detecting windthrows in different seasons of image acquisition. A study site was analysed, located in the Trentino-South Tyrol region (Italy), which was affected by the Vaia storm on 27-30 October 2018, which caused significant forest damage.

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.

Light detection and ranging (LiDAR) provides high-resolution geometric information for monitoring forests at individual tree crown (ITC) level. An important task for ITC delineation is segmentation, and previous studies showed that the adaptive 3-D mean shift (AMS3D) algorithm provides effective results. AMS3D for ITC segmentation has three components for the kernel profile: shape, weight, and size. In this letter, we present an AMS3D approach based on the adaptation of the kernel profile size through an ellipsoid crown shape model. The algorithm parameters are estimated based on allometry equations derived from 22 forest plots in two study sites. After computing the mean shift (MS) vector, we initialize the parameters of the ellipsoid crown shape model to derive the kernel profile size, and further tested two crown shape models for adapting the size of the superellipsoid (SE) kernel profile. These schemes are compared with two other MS algorithms with and without kernel profile size adaptation. We select the best algorithm output per plot based on the maximum F1-score. The ellipsoid crown shape model with a SE kernel profile of$n = 1.5$presents the highest recall and the best Jaccard index, especially for conifers.

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.

Tree species classification at individual tree crowns (ITCs) level using remote sensing data requires the availability of a sufficient number of reliable reference samples. Two main issues that affect the classification performance are: a) an imbalanced distribution of the tree species classes; and b) the presence of unreliable samples due to field collection errors, coordinates misalignments, etc. In this study, we present a weighted Support Vector Machine (WSVM) classifier that addresses these problems by considering: 1) different weights for different classes of tree species to mitigate the effects of the class imbalance distribution; and 2) different weights for different training samples according to their importance for the considered classification problem. Experimental results obtained on a study area located in the Italian Alps showed that the proposed method increased the overall and kappa accuracies of about 2%, and the mean class accuracy of about 10% with respect to a standard SVM.

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.

In this paper a study on the use of multi-temporal LiDAR data to monitor forest changes is presented. LiDAR data acquired at two different times (2007 and 2011) were chosen in order to analyze in details the changes associated to different forest conditions in the temporal domain such as tree growth, tree cuts, and changes in forest volume. The preliminary experimental results show that LiDAR data allow an effective and accurate monitoring of the forest changes.

In this paper we present a study on the estimation of the aboveground biomass in tropical forests at single tree level using airborne laser scanning (ALS) data. Individual tree crowns (ITCs) are firstly detected using a method based on an adaptive window that change its size according to tree height. The diameter at breast height (DBH) and the aboveground biomass (AGB) of each ITC then are predicted using standard allometric models. Lastly, the AGB values are aggregated at plot level, and compared with field measured values. The results show that it is possible to accurately predict the aboveground biomass of tropical forests at single tree level using ALS data.

In this paper a system for the fusion of hyperspectral and airborne laser scanning (ALS) data for the estimation of forest attributes is presented. In particular we focused on the classification of tree species, the estimation of stem diameter at breast height (DBH) and the estimation of the stem volume. The results showed that the fusion of hyperspectral and ALS data improve the estimation results respect to the use of only one data source.

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.

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.

In this study we introduced a novel unsupervised selection method for collecting training samples for tree species classification at individual tree crown (ITC) level using hyperspectral data. The selection process is based on a search strategy and a distance metric defined among the percentiles derived from the spectral distributions of the pixels inside the ITCs. The method was developed using two kinds of samples: i) plots, and ii) ITCs. The experimental results indicated that the method allows reducing the amount of training samples needed for the classification process, without significantly decreasing the classification accuracy.

This study presents a cost-sensitive active learning method for optimizing the field surveys by a human expert in the classification of single tree species using hyperspectral images. The goal of the proposed method is to guide the human expert in the collection of labeled samples in order to maximize the ratio between the classification accuracy with respect to the travelling costs. Experiments carried out in the context of a real study on forest inventory show the effectiveness of the proposed method.

Tree species mapping in forest areas is an important topic in forest inventory. In recent years, several studies have been carried out using different types of hyperspectral sensors under various forest conditions. The aim of this work was to evaluate the potential of two high spectral and spatial resolution hyperspectral sensors (HySpex-VNIR 1600 and HySpex-SWIR 320i), operating at different wavelengths, for tree species classification of boreal forests. To address this objective, many experiments were carried out, taking into consideration: 1) three classifiers (support vector machines (SVM), random forest (RF), and Gaussian maximum likelihood); 2) two spatial resolutions (1.5 m and 0.4 m pixel sizes); 3) two subsets of spectral bands (all and a selection); and 4) two spatial levels (pixel and tree levels). The study area is characterized by the presence of four classes 1) Norway spruce, 2) Scots pine, together with 3) scattered Birch and 4) other broadleaves. Our results showed that: 1) the HySpex VNIR 1600 sensor is effective in boreal tree species classification with kappa accuracies over 0.8 (with Pine and Spruce reaching producer's accuracies higher than 95%); 2) the role of the HySpex-SWIR 320i is limited, and its bands alone are able to properly separate only Pine and Spruce species; 3) the spatial resolution has a strong effect on the classification accuracy (an overall decrease of more than 20% between 0.4 m and 1.5 m spatial resolution); and 4) there is no significant difference between SVM or RF classifiers.

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.

The estimation of stem diameters can be very useful in the study of forests, as together with height and tree specie, it is one of the most important parameters used in forest inventories. In this paper a system for the estimation of stem diameters with LIDAR and hyperspectral data (both separately and combined in a data fusion framework) is presented. An analysis on the effectiveness of these data in the estimation process and on the accuracy and robustness of different estimation algorithms is presented. Experimental results point out the effectiveness and the properties of the proposed system.

In this paper, we propose an analysis on the joint effect of hyperspectral and light detection and ranging (LIDAR) data for the classification of complex forest areas. In greater detail, we present: 1) an advanced system for the joint use of hyperspectral and LIDAR data in complex classification problems; 2) an investigation on the effectiveness of the very promising support vector machines (SVMs) and Gaussian maximum likelihood with leave-one-out-covariance algorithm classifiers for the analysis of complex forest scenarios characterized from a high number of species in a multisource framework; and 3) an analysis on the effectiveness of different LIDAR returns and channels (elevation and intensity) for increasing the classification accuracy obtained with hyperspectral images, particularly in relation to the discrimination of very similar classes. Several experiments carried out on a complex forest area in Italy provide interesting conclusions on the effectiveness and potentialities of the joint use of hyperspectral and LIDAR data and on the accuracy of the different classification techniques analyzed in the proposed system. In particular, the elevation channel of the first LIDAR return was very effective for the separation of species with similar spectral signatures but different mean heights, and the SVM classifier proved to be very robust and accurate in the exploitation of the considered multisource data.