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LiDAR (Light Detection and Ranging) systems such as ALS (Airborne Laser Scanning) are increasingly being used in studies that analyse the forest structure and for the characterisation of their ecosystem processes. The main reason is their ability to provide an accurate three-dimensional description of the canopy structure, compared to other existing methods, such as passive sensors or photogrammetry. In addition, the high positional accuracy of ALS and their capacity of penetrating the canopy through small gaps in the forest canopy allow the estimation of parameters such as aboveground biomass, vegetation height, or leaf area index, among others. In forestry applications, the acquisition of these parameters usually requires a pre-processing analysis of the point clouds, which includes ground point filtering, Digital Terrain Model (DTM) and Canopy Height Model (CHM) derivation, tree detection, and segmentation, among other processes.  In the last decades, point cloud processing has benefited by the development of dedicated software packages such as LAStools, FUSION, or Terrascan, focused on obtaining DTM/CHM and LiDAR-derived metrics. However, the recent development of more sophisticated software packages, such as LidR or Pycrown, allow implementing novel and state-of-the-art algorithms as well as specific user-created functions.

The wide variety of licensed and open-source software packages for ALS data processing, together with the increasing diversity of existing algorithms and methodologies, has provoked a multitude of comparative analysis of the most widely used algorithms in the scientific literature.  However, given the recent development of the field, a robust and exhaustive review of the current use of these software and the related algorithms is still missing. In this contribution, we present a synthesis review of 613 papers on the use of software packages and algorithms for ALS processing used between 2016 and 2020. The review focuses in forest environments with a complex structure where the difference in elevation, slope, and the existence of multiple vegetation strata usually requires more complex and specialised algorithms. Therefore, three specific steps of LiDAR processing workflow were considered: ground point filtering, DTM interpolation and crown detection and segmentation. The results showed that ground point filtering (84% of the studies) is the most common step in ALS processing, compared to DTM interpolation (71%) and tree segmentation (36%). For the DTM interpolation step, TIN construction was the most used method (13%) compared to other methods such as ordinary kriging (3%). Conventional software packages that employ algorithms based on progressive TIN densification or hieratical robust interpolation approaches were the most commonly used in ground point filtering for DTM generation. Meanwhile, other user developed advanced algorithms were used more frequently in canopy segmentation processing, especially in those articles using datasets with high point densities (165.93 p/m² on average), compared to datasets processed with more general software solutions as FUSION (13.81 p/m2).

How to cite: Cardenas-Martinez, A., Canero, F. M., Garcia-Perez, M. A., Guisado-Pintado, E., and Rodriguez-Galiano, V.: Use of Airborne LiDAR data processing tools and algorithms in natural forested areas: A systematic review, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6599, https://doi.org/10.5194/egusphere-egu22-6599, 2022.

Terrestrial laser scanning (TLS) is quickly becoming an indispensable tool for quantifying individual tree and whole forest structure. In recent years, developments have been made in several areas, including tree segmentation from whole forest scans, quantitative structural model (QSM) reconstruction and leaf-wood separation (Åkerblom et al., 2017, Burt et al., 2019, Wang et al., 2020). One research area that could benefit greatly from a widespread use of TLS is that of functional-structural plant models (FSPMs), which simulate both the structure and function of plants. In FSPMs, the structural component of these models is usually derived using theoretical methods that reproduce remarkably realistic tree forms (Perttunen and Sievänen, 2005). However, it is often challenging to compare and validate these structural models with real trees, particularly those occurring in ‘natural environments’.  

TLS-derived structural models in FSPMs have the potential to uncover new insights into the role of tree architecture on physiological plant processes, particularly in how tree 3D shape influences resource capture. Here, we will assess the impact of light fluctuations in the crown on individual tree productivity, scaling up to the productivity of whole forest stands. We will outline a new generic method that bridges the gap between TLS data and FSPMs as well as introduce ways of utilising other remote sensing techniques to validate FSPM outputs. Firstly, we will produce individual tree QSMs from a whole forest point cloud. Secondly, we will simulate tree-by-tree productivity in the context of the original forest environment. Lastly, we will use drone and satellite imagery to validate the FSPM productivity outputs.   

Explicit 3D tree structure is an often-overlooked component of vegetation modelling, despite the feedback process between form and productivity.  We aim to highlight the role of light microenvironments within the crown and understand how uneven resource capture might extrapolate to whole forest estimates of productivity. We hope that this new method will encourage overlap of practice between researchers in this growing field and lead to further use of virtual plants in studies of tree evolution and ecology. 

Citations: 

Åkerblom, M., Raumonen, P., Mäkipää, R. and Kaasalainen, M., 2017. Automatic tree species recognition with quantitative structure models. Remote Sensing of Environment, 191, pp.1-12 

Perttunen, J. and Sievänen, R., 2005. Incorporating Lindenmayer systems for architectural development in a functional-structural tree model. Ecological modelling, 181(4), pp.479-491. 

Burt, A., Disney, M. and Calders, K., 2019. Extracting individual trees from lidar point clouds using treeseg. Methods in Ecology and Evolution, 10(3), pp.438-445. 

Perttunen, J. and Sievänen, R., 2005. Incorporating Lindenmayer systems for architectural development in a functional-structural tree model. Ecological modelling, 181(4), pp.479-491. 

Wang, D., Momo Takoudjou, S. and Casella, E., 2020. LeWoS: A universal leaf‐wood classification method to facilitate the 3D modelling of large tropical trees using terrestrial LiDAR. Methods in Ecology and Evolution, 11(3), pp.376-389.

How to cite: O'Sullivan, H.: A TLS based model for assessing crown-level light microenvironments on forest stand productivity, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4270, https://doi.org/10.5194/egusphere-egu22-4270, 2022.

The existing forest inventories have difficulties in providing data with sufficient spatial and temporal resolution to quantify dynamic properties of forests, which are mainly caused by short-term events such as drought, storm, snow damages, or pest infestations. 
This study aims to explore in a first step the potential of space-borne LiDAR for forest parameters extraction over Alpine forests in Austria. The space-borne LiDAR data investigated in this study is ICESat2 (Ice, Cloud and Land Elevation Satellite-2) and GEDI (Global Ecosystem Dynamics Investigation). 
GEDI is a full-waveform, multibeam laser altimeter on the International Space Station, with a footprint diameter of about 25m (Dubayah, 2020). As for ICESat-2, it carries a micropulse, multi-beam photon-counting laser altimeter, with a footprint diameter of about 17m (Neuenschwander and Magruder, 2019). Two footprint-level products are used in this study: GEDI L2A and ICESat-2 ATL03. The GEDI L2A product provides footprint-level elevation and height metrics that extract terrain height, canopy height, and relative height metrics from the received waveform. ICESat-2 ATL03 records horizontal coordinates and ellipsoidal heights of all photon data, and the classification labels are extracted from its higher product, ATL08. The DTM with a resolution of 1m and the DSM derived from ALS (Airborne Laser Scanning) point clouds acts as “ground truth” to assess the accuracy of the terrain and canopy height of the two space-borne LiDAR products, respectively. For ICESat-2 ATL03, only photons with a signal confidence flag ranging from medium confidence (sigal_confidence=3) or high confidence (sigal_confidence=4) are included for evaluation. For GEDI L2A, only waveforms with a valid quality flag (quality_flag=1) are included for evaluation. To evaluate the performance of ICESat2 and GEDI for different forest types and topographic conditions, two study sites in Austria are selected: western part of Tyrol and the Vienna Woods.
A preliminary results of terrain and canopy height accuracies shows that the terrain height of the two space-borne LiDAR products fits well with the DTM. Compared to GEDI L2A, ICESat2 ATL03 has a better correlation with DTM values. 
The canopy height accuracy is not as good as the terrain height accuracy. It has been shown that ICESat-2 tend to underestimate the canopy top height as derived from airborne LiDAR. Overall, GEDI has a better canopy height accuracy than ICESat-2.
Furthermore, we have investigated the influence of different beam power, data acquisition time and season. In general, the accuracy of both ICESat-2 and GEDI data acquired in nighttime is higher than that of the daytime data. The statistic also shows that for the ICESat-2 data, the terrain height accuracy of weak beam footprints only slightly worse than that of strong beam footprints. For GEDI, footprints of strong beams always perform better than that of weak beams in terms of terrain and canopy height. Regarding the impact of season, for both ICESat-2 and GEDI, the canopy height is more accurate in summer than the collection in winter. For GEDI, the terrain height can be better measured in winter than in summer. 

How to cite: Li, N., Pfeifer, N., Zotta, R.-M., Dostalova, A., and Hollaus, M.: Exploration of space-borne LiDAR data for forest parameter retrieval for Alpine regions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9341, https://doi.org/10.5194/egusphere-egu22-9341, 2022.

The sweeping transformation of the biosphere by humans over the last millennia leaves only limited windows into its natural state. Much of the forests that dominated temperate and southern boreal regions have been lost and those that remain typically bear a strong imprint of harvest activity. Here, we ask how would the dynamics, structure and function of temperate and boreal forests differ in the absence of harvest? We focused our analysis on the human-induced shift in forest disturbance dynamics and its resultant effects on forest age structure and carbon cycling. We constructed an empirical model of natural disturbance probability as a function of community traits and climate, based on observed disturbance rate and form across 77 protected forest landscapes distributed across three continents. Coupling this to a dynamic vegetation model, we generate estimates of stand-replacing disturbance return intervals and calculate the forest age structure that results. We compare this to best estimates of current age structures based on (a) past land-use change and management and (b) forest inventory observations. Modern forests are typically much younger than those under natural disturbance only, with 43% less old-growth stands. This results in a 33% reduction in vegetation carbon turnover time across temperate forests and a 14% reduction for boreal forests. Understanding the state and dynamics of forests in the absence of harvest provides context for making decisions related to global conservation and climate change mitigation efforts, especially related to nature-based solutions.

How to cite: Pugh, T. A. M., Seidl, R., Liu, D., Lindeskog, M., Chini, L. P., and Senf, C.: The anthropogenic imprint on temperate and boreal forest age structure and carbon turnover, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9422, https://doi.org/10.5194/egusphere-egu22-9422, 2022.

Drylands in Africa, consisting of arid, semi-arid and dry sub-humid areas, are particularly vulnerable to land degradation, due to large climate variability and water scarcity, and land degradation is still largely present in these areas. On top of that, the number of people living in the drylands is expected to increase rapidly, especially in Sub-Saharan Africa, which further increases the number of people subject to land degradation in the future. On the bright side, land degradation in dryland Africa as well as in other parts of the world has not gone unnoticed and several restoration initivatives have emerged to reduce, reverse and prevent further degradation through practices such as reforestation, natural regeneration or agroforestry. Through these practices they aim to improve soil quality, contribute to carbon sequestration, improve the local climate and therefore the overall livelihood of the local people.

In line with this development, the number of land restoration projects has increased rapidly over the past years. However, only a small part of the organisations monitor the trees after planting. On top of that, the organisations that do monitor the projects, often report small survival rates of the plants. In combination with the fact that a complete and open database of land restoration projects does, to our knowledge, not exist, there is a large lack of information on the amount, and effectiveness, of regreening after the implementation of these projects. This negatively affects much needed reflection on the effectiveness of land restoration projects.

Remote sensing can be a practical alternative to detect greening due to land restoration, as vegetation indices like the NDVI are able to detect changes in vegetation greenness over large areas and long time series. Vegetation greenness does, however, not only change through land management, but also through processes such as CO₂ fertilisation, nitrogen deposition, climate change and feedbacks between those, which makes it challenging to directly measure the greening effects of land restoration projects. The aim of this study is to detect regreening trends in semi-arid environments in Africa using remote sensing while correcting for natural climate variability.

To this end, an analysis is performed in Google Earth engine, where MODIS NDVI 16-day time series are pixel-wise compared to a time series created by averaging the neighbourhood of the respective pixel. Because climate induced changes in NDVI are expected to act on a larger scale than changes in land management, subtracting the neighbourhood NDVI from the pixel NDVI corrects the time series for climate induced changes. Next, a BFAST algorithm is applied to the corrected time series to detect breakpoints and trends in NDVI. This method then allows for the detection of small scale greening hotspots across semi-arid Africa. In addition, the method is applied to several case study restoration projects in semi-arid Africa to illustrate the method on smaller scales.

Preliminary results show that small scale regreening hotspots, i.e. increases in NDVI compared to the surrounding area, are more prominent in semi-arid environments than in humid and hyper-arid environments in Africa.

How to cite: Ruijsch, J., Teuling, R., Verbesselt, J., and Hutjes, R.: Detecting regreening effects of land restoration in semi-arid Africa using a spatial-context approach in Google Earth Engine, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9840, https://doi.org/10.5194/egusphere-egu22-9840, 2022.

Laser scanning-based tree detection has been used for many years to complement sample data of forest inventories. Local Maxima (LM) detection methods are suitable for individual tree detection in the forest canopy and allow for detection over large areas due to their computational efficiency. However, the performance of LM methods depends on factors such as the resolution of the input data (point density of aerial laser scanning (ALS) and spatial resolution of the derived rasters), the pre-processing of the input data as well as the structure and species of the detected forest. The main objective of our study was to evaluate to what extent LM tree detection can be improved by considering prior knowledge about forest structure using statistical modelling. To achieve this goal, we developed a statistical model for selecting between 10 different crown height model (CHM) pre-processing methods based on forest structure variables derived from remote sensing data. We fitted linear regression models predicting the error between the number of detected trees and the field inventoried number of the trees reaching the canopy in the sample plot. The model used dominant canopy height, the degree of coverage overall and for different forest layers derived from the CHM, the dominant leaf type derived from Sentinel-2 data, and terrain characteristics as explanatory variables. The model performance was evaluated by assessing tree detection errors using all national forest inventory plots in Switzerland using 10-fold cross-validation. The results showed a reduction of the RMSE to 91 stems per ha (respectively 1.3 when normalized by the inventoried stem number) using the model-based pre-processed CHM for detection compared to 205 stems per ha (normalized = 4) when detecting trees using an unprocessed CHM (number of used inventory plots n=5254). Excluding inventory plots with an ALS point density of less than 15 points per square meter (n=3797) improved the RMSE to 89 stems per ha (normalized = 1.25).The RMSE further improves to 85 stems per ha (normalized = 1.2) by additionally excluding plots with more than 6 years between ALS acquisition and inventory (n=2676). Although the results show a clear reduction of the detection error by our model, they also indicate potential for further refinements. Especially the integration of high-quality ALS data (becoming available for the entire area of Switzerland until 2024), detailed tree species data, and additional, more recent inventory data are recommended. In the future, a combination of our method with point cloud-based approaches will probably be able to further reduce detection errors at national scale.

How to cite: Schaller, C., Ginzler, C., van Loon, E., Moos, C., and Dorren, L.: Improving Local Maxima-based Individual Tree Detection using statistically modelled Forest Structure Information, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11723, https://doi.org/10.5194/egusphere-egu22-11723, 2022.

In Europe, more than half of all the damages to forest by volume results from windstorms. In Norway, forests cover more than a third of the country’s land surface, and are important economically, culturally, and socially. Storm damage can have a range of consequences for the forestry industry and society including dangerous forest operations, reduced wood quality, reduced timber prices, electric outages, and increased risk for bark beetle outbreaks. It is crucial for all the actors in the forestry sector to understand wind damage. The recent storm of November 19^th, 2021 highlighted this need and provided a unique opportunity to assess the research tools at our disposal to model wind damage risk in Norwegian forests.

One of these tools is the model ForestGales developed by the UK Forest commission to predict critical wind speeds for damage in a forest stand. The critical wind speed is a common measure of a tree's susceptibility to wind damage, defined as the wind speed that would cause tree failure due to wind, either by uprooting or breakage of the trunk at 1.3 m high. Used together with models describing the extreme wind speed distribution over a region, probabilities of wind damage can be drawn at the individual tree or forest stand level. The ForestGales model was modified to suit Norwegian conditions using the current available data and applied to two different situations:

• Trees along powerlines. Tree failure can lead to powerline failure with potentially severe economic and social consequences. In this context, the ForestGales model could provide a tool to identify the risk trees and adapt management accordingly. We used the model on several sites along powerlines in the southern Norway and assessed its efficiency in predicting tree falls between summer 2020 and summer 2021, without any major storm events.
• the Norwegian forest resource map SR16. The 16 x 16 m map product contains information relative to tree species, height, volume and biomass and is useful in large-scale analyses of the forest resources in the country. Using ForestGales on the SR16 map product would enable us to assess the fine-scale risk of wind damage over the entire country and inspect the impacts of changed forest structure following climate change and/or changes in forest management on the forest vulnerability to wind damage. The mapped damage from the storm of November 19^th, 2021 will provide a unique opportunity to apply and test the validity and accuracy of the ForestGales model in Norway after a storm.

How to cite: Merlin, M., Zubkov, P., Sunde, K., Cattaneo, N., Solberg, S., and Astrup, R.: Assessing the vulnerability of Norwegian forested landscapes to extreme wind speeds using the ForestGales model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11158, https://doi.org/10.5194/egusphere-egu22-11158, 2022.

In the semiarid climate of Central Asia the rivers and their associated floodplain ecosystems have a high relevance as regional hotspots of biodiversity and for the provision of ecosystem services. One of these rivers is the Naryn River in Kyrgyzstan. Upstream of the Toktogul Reservoir, which is the first barrier in the river course, the Naryn is still in a nearly natural state. The floodplain forests in its corridor depend directly on the disturbance regime of this river. Despite their ecological relevance they have not been investigated yet in detail. In particular the role of natural disturbance and anthropogenic effects for succession trajectories are not yet understood. This is a crucial issue for biodiversity conservation as ongoing plans for dam construction will lead to heavy modification of the natural disturbance regime. 
In this study, we contribute to fill this knowledge gap and use remote sensing to derive detailed ecological information for the entire central Naryn basin. We use multispectral satellite data of Sentinel-2 and digital elevation data from TanDEM-X to derive the floodplain forests in a supervised classification approach. The floodplain forests include among others pioneer vegetation, several classes of herbaceous vegetation and different forest types. 500 ground control points were collected in the field in 2019 and were complemented with additional points created based on high resolution rgb imagery. These points have been split into a training and validation data set to create a random forest classification model. As predictors, different multispectral indices like the NDVI and temporal metrics of them were used along with different terrain attributes like the distance to the river channel.
The results show that the random forest model with the combination of Sentinel-2 and TanDEM-X data can represent the complex structure of the floodplain forests along the Naryn river with high accuracies ranging from 62.4% for pioneer vegetation and 99.8% for open broad-leaved shrub. The forest structure shows a very heterogenous distribution along the longitudinal and lateral profile. The ecosystem response on the potential modification of the disturbance regime due to dam constructions is expected to be spatially heterogenous as well. Detailed forest habitat maps derived by remote sensing help to better understand natural processes and the potential effects of anthropogenic activities. Sentinel-2 data have high potentials for a efficient monitoring of forest habitats and their disturbance. Thus they are a very interesting data source for supporting forest conservation. Our forest habitat mapping for the Naryn floodplain provides a basis for further research, conservation planning and efficient monitoring.

How to cite: Lauermann, M., Betz, F., and Cyffka, B.: Mapping disturbance-dependent floodplain forests in the Naryn basin, Kyrgyzstan, using optical satellite imagery, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10134, https://doi.org/10.5194/egusphere-egu22-10134, 2022.

Climate-extreme induced disturbances such as summer droughts and late spring frosts (LF), can affect productivity and tree growth in temperate forests. In this study we investigated how LFs affect canopy cover and radial growth in European beech (Fagus sylvatica) forests along an elevation gradient at four sites in the Italian Apennines. We combined tree-ring and remote-sensing data to analyse the vulnerability and recovery capacity of beech populations to LFs. We computed population and individual climate-growth relationships to test their responses at different elevation. Using climatic records, we reconstructed LF events and assessed their immediate and carry-over effects on growth. We also checked the role played by spatial and structural variables as drivers of LF rings occurrence at population and individual scales. We computed Normalized Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and LAI (Leaf Area Index) using satellite images to evaluate the post-LF canopy recovery. The growth reduction in LF-affected trees ranged from 36% to 84%. We detected a negative impact of LF on growth only during the year of LF occurrence, with growth recovery in 1-2 years after the event. Water deficit during the previous and current summers and cold spring temperatures are the main factors limiting beech growth. LFs affected stands feature low NDVI, EVI and LAI values until late June. Frost rings formation is enhanced at mid rather than low and high elevations, induced by spring leaf phenology. An increasing frequency of LF events could alter the resilience of mountain beech forests, but nowadays they show a high recovery capacity and no legacy effects. A broader geographic area, especially in marginal sites, and the use of other tree-ring variables (anatomy, isotopes), could improve the assessment of post-LF resilience in beech forests. Such improvement would help managers in preserving forest ecosystem services.

How to cite: Vitali, A., Tonelli, E., Malandra, F., Camarero, J. J., Colangelo, M., Nolè, A., Ripullone, F., Carrer, M., and Urbinati, C.: Combining dendroecology and remote sensing to assess how late spring frosts affect European beech forests, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5514, https://doi.org/10.5194/egusphere-egu22-5514, 2022.