Airborne and Terrestrial Laser Scanning Data for the Assessment of Standing and Lying Deadwood: Current Situation and New Perspectives

LiDAR technology is finding uses in the forest sector, not only for surveys in producing forests but also as a tool to gain a deeper understanding of the importance of the three-dimensional component of forest environments. Developments of platforms and sensors in the last decades have highlighted the capacity of this technology to catch relevant details, even at finer scales. This drives its usage towards more ecological topics and applications for forest management. In recent years, nature protection policies have been focusing on deadwood as a key element for the health of forest ecosystems and wide-scale assessments are necessary for the planning process on a landscape scale. Initial studies showed promising results in the identification of bigger deadwood components (e.g., snags, logs, stumps), employing data not specifically collected for the purpose. Nevertheless, many efforts should still be made to transfer the available methodologies to an operational level. Newly available platforms (e.g., Mobile Laser Scanner) and sensors (e.g., Multispectral Laser Scanner) might provide new opportunities for this field of study in the near future

Monitoring post-fire forest recovery using multi-temporal Digital Surface Models generated from different platforms

Wildfires can greatly affect forest dynamics. Given the alteration of fire regimes foreseen globally due to climate and land use changes, greater attention should be devoted to prevention and restoration activities. Concerning in particular post-fire restoration actions, it is fundamental, together with a better understanding of ecological processes resulting from the disturbance, to define techniques and protocols for long-term monitoring of burned areas. This paper presents the results of a study conducted within an area affected by a stand-replacing crown fire (Verrayes, Aosta (AO), Italy) in 2005, which is part of a long-term monitoring research on post-fire restoration dynamics. We performed a change detection analysis through a time sequence (2008-2015) of DSMs (Digital Surface Models) obtained from LiDAR (ALS - Airborne Laser Scanner) and digital images (UAV - Unmanned Aerial Vehicle flight) to test the ability of the systems (platform + sensor) to identify the ongoing processes. New technologies providing high-resolution information and new devices (i.e. UAV) able to acquire geographic data “on demand” demonstrated great potential for monitoring post disturbance recovery dynamics of vegetation

Assessing Forest Type and Tree Species Classification Using Sentinel-1 C-Band SAR Data in Southern Sweden

The multitemporal acquisition of images from the Sentinel-1 satellites allows continuous monitoring of a forest. This study focuses on the use of multitemporal C-band synthetic aperture radar (SAR) data to assess the results for forest type (FTY), between coniferous and deciduous forest, and tree species (SPP) classification. We also investigated the temporal stability through the use of backscatter from multiple seasons and years of acquisition. SAR acquisitions were pre-processed, histogram-matched, smoothed, and temperature-corrected. The normalized average backscatter was extracted for interpreted plots and used to train Random Forest models. The classification results were then validated with field plots. A principal component analysis was tested to reduce the dimensionality of the explanatory variables, which generally improved the results. Overall, the FTY classifications were promising, with higher accuracies (OA of 0.94 and K = 0.86) than the SPP classification (OA of 0.66 and K = 0.54). The use of merely winter images (OA = 0.89) reached, on average, results that were almost as good as those using of images from the entire year. The use of images from a single winter season reached a similar result (OA = 0.87). We conclude that multiple Sentinel-1 images acquired in winter conditions are feasible to classify forest types in a hemi-boreal Swedish forest

Potential of ALOS2 and NDVI to estimate forest above-ground biomass, and comparison with lidar-derived estimates

Remote sensing supports carbon estimation, allowing the upscaling of field measurements to large extents. Lidar is considered the premier instrument to estimate above ground biomass, but data are expensive and collected on-demand, with limited spatial and temporal coverage. The previous JERS and ALOS SAR satellites data were extensively employed to model forest biomass, with literature suggesting signal saturation at low-moderate biomass values, and an influence of plot size on estimates accuracy. The ALOS2 continuity mission since May 2014 produces data with improved features with respect to the former ALOS, such as increased spatial resolution and reduced revisit time. We used ALOS2 backscatter data, testing also the integration with additional features (SAR textures and NDVI from Landsat 8 data) together with ground truth, to model and map above ground biomass in two mixed forest sites: Tahoe (California) and Asiago (Alps). While texture was useful to improve the model performance, the best model was obtained using joined SAR and NDVI (R2 equal to 0.66). In this model, only a slight saturation was observed, at higher levels than what usually reported in literature for SAR; the trend requires further investigation but the model confirmed the complementarity of optical and SAR datatypes. For comparison purposes, we also generated a biomass map for Asiago using lidar data, and considered a previous lidar-based study for Tahoe; in these areas, the observed R2 were 0.92 for Tahoe and 0.75 for Asiago, respectively. The quantitative comparison of the carbon stocks obtained with the two methods allows discussion of sensor suitability. The range of local variation captured by lidar is higher than those by SAR and NDVI, with the latter showing overestimation. However, this overestimation is very limited for one of the study areas, suggesting that when the purpose is the overall quantification of the stored carbon, especially in areas with high carbon density, satellite data with lower cost and broad coverage can be as effective as lidar

Responding to Large-Scale Forest Damage in an Alpine Environment with Remote Sensing, Machine Learning, and Web-GIS

This paper reports a semi-automated workflow for detection and quantification of forest damage from windthrow in an Alpine region, in particular from the Vaia storm in October 2018. A web-GIS platform allows to select the damaged area by drawing polygons; several vegetation indices (VIs) are automatically calculated using remote sensing data (Sentinel-2A) and tested to identify the more suitable ones for quantifying forest damage using cross-validation with ground-truth data. Results show that the mean value of NDVI and NDMI decreased in the damaged areas, and have a strong negative correlation with severity. RGI has an opposite behavior in contrast with NDVI and NDMI, as it highlights the red component of the land surface. In all cases, variance of the VI increases after the event between 0.03 and 0.15. Understorey not damaged from the windthrow, if consisting of 40% or more of the total cover in the area, undermines significantly the sensibility of the VIs to detecting and predicting severity. Using aggregational statistics (average and standard deviation) of VIs over polygons as input to a machine learning algorithm, i.e., Random Forest, results in severity prediction with regression reaching a root mean square error (RMSE) of 9.96, on a severity scale of 0–100, using an ensemble of area averages and standard deviations of NDVI, NDMI, and RGI indices. The results show that combining more than one VI can significantly improve the estimation of severity, and web-GIS tools can support decisions with selected VIs. The reported results prove that Sentinel-2 imagery can be deployed and analysed via web-tools to estimate forest damage severity and that VIs can be used via machine learning for predicting severity of damage, with careful evaluation of the effect of understorey in each situation

Interface fires in built-up areas. A real-case study on the risk assessment of fires interacting with urban domains.

Fire scenarios may pose serious risks and induce severe damages to anthropic structures, activities and business. These can be represented by typical fires in industrial facilities or also atypical scenarios involving differentiated targets as in the case of interface fires. The occasion of the collaboration between our two departments arouse from the EU Interreg Progect CROSSIT SAFER, intended to enhance collaboration between actors and institutions of the Civil Protection for the management of natural disasters in the Italia-Slovenija cross border area. Risk assessment of atypical scenarios requires improved approaches since a multi-risk framework can arise including the interactions between the fire and surrounding domains. An effective hazard investigation and management should therefore include estimations of consequences based on the results of models\u2019 simulation. The present study deals with a preliminary risk assessment methodology applied to fires interacting with an existing urban area. The fire spread is approached through a dedicated tool and a GIS-based system is used to spatially map expected consequences. Starting from these data, a preliminary risk estimation is proposed with the aim of mapping hazardous areas. In this sense, a combined approach based on fire simulation tools and exposure functions is employed. Major risk areas are identified and expected results can be used to support land planning and emergency-related operations

Logging Residue Assessment in Salvage Logging Areas: a Case Study in the North-Eastern Italian Alps

Salvage logging operations often occur after large disturbances and usually leave behind a substantial quantity of residues, which is fundamental for maintaining soil fertility and facilitating ecosystem dynamics. This study aims to estimate the amount of logging residues following salvage operations categorized by two wood harvesting systems: Cut-To-Length (CTL) and Full-Tree System (FT). Logging residues in the harvested areas were sampled using linear transects and the data collected were divided into classes based on diameter. The quantity of residues was estimated using the Brown method for Fine Wood Debris (FWD) and the Van Wagner method for Coarse Wood Debris (CWD). Furthermore, the carbon and nutrient content associated with logging residues were also determined, considering their interaction with the soil organic layer. Overall, a higher quantity of FWD was detected in the sites cleared with the FT system and a higher quantity of CWD in the sites logged with the CTL system. Differences could be observed for all three years and systems considered, but only the third year reported statistically significant results (p<0.01). The soil and residue chemical analysis for carbon and nutrient contents revealed a high amount of carbon stored in a potential layer of 10 cm of soil (up to 85 Mg·C·ha-1), while only up to 15 Mg·C·ha-1 for the woody material

Detection of Wet Riparian Areas using Very High Resolution Multispectral UAS Imagery Based on a Feature-based Machine Learning Algorithm

Unmanned Aerial System (UAS) imagery has enabled very high-resolution multispectral image acquisition. Detection of wet areas and classification of land cover based on these images using the Machine Learning (ML) algorithm named Random Forest (RF) is our main purpose in this paper. Very high-resolution UAS images have been used as inputs for a machine learner to access the capability of different spectral bands and spectral vegetation indices, elevation, and texture features in the classification of land cover and detection of the wet riparian area in the case study in two different epochs. There are many existing methods for the classification of land cover based on UAS images, but very high-resolution centimeter-level data are of main importance in this analysis. Outstanding results have been produced in both epochs considering three extremely accurate performance analysers. Additionally, in this research, the most decisive and effective features have been discovered to compromise accuracy and the number of effectual features

Post-Fire Management Impact on Natural Forest Regeneration through Altered Microsite Conditions

High severity stand-replacing wildfires can deeply affect forest ecosystems whose composition includes plant species lacking fire-related traits and specific adaptations. Land managers and policymakers need to be aware of the importance of properly managing these ecosystems, adopting post-disturbance interventions designed to reach management goals, and restoring the required ecosystem services. Recent research frequently found that post-fire salvage logging negatively affects natural regeneration dynamics, thereby altering successional pathways due to a detrimental interaction with the preceding disturbance. In this study, we compared the effects of salvage logging and other post-disturbance interventions (adopting different deadwood management strategies) to test their impact on microclimatic conditions, which potentially affect tree regeneration establishment and survival. After one of the largest and most severe wildfires in the Western Alps that affected stand-replacing behavior (100% tree mortality), a mountain forest dominated by Pinus sylvestris L., three post-fire interventions were adopted (SL-Salvage Logging, logging of all snags; CR-Cut and Release, cutting snags and releasing all deadwood on the ground; NI-No Intervention, all snags left standing). The differences among interventions concerning microclimatic conditions (albedo, surface roughness, solar radiation, soil moisture, soil temperature) were analyzed at different spatial scales (site, microsite). The management interventions influenced the presence and density of safe sites for regeneration. Salvage logging contributed to the harsh post-fire microsite environment by increasing soil temperature and reducing soil moisture. The presence of deadwood, instead, played a facilitative role in ameliorating microclimatic conditions for seedlings. The CR intervention had the highest soil moisture and the lowest soil temperature, which could be crucial for seedling survival in the first post-fire years. Due to its negative impact on microclimatic conditions affecting the availability of preferential microsites for regeneration recruitment, salvage logging should not be considered as the only intervention to be applied in post-fire environments. In the absence of threats or hazards requiring specific management actions (e.g., public safety, physical hazards for facilities), in the investigated ecosystems, no intervention, leaving all deadwood on site, could result in better microclimatic conditions for seedling establishment. A preferred strategy to speed-up natural processes and further increase safe sites for regeneration could be felling standing dead trees whilst releasing deadwood (at least partially) on the ground