Airborne Laser Scanning Research Articles

Helicopter-borne RGB orthomosaics and photogrammetric digital elevation models from the MOSAiC Expedition

The Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition took place between October 2019 and September 2020 giving the rare opportunity to monitor sea-ice properties over a full annual cycle. Here we present 24 high-resolution orthomosaics and 14 photogrammetric digital elevation models of the sea-ice surface around the icebreaker RV Polarstern between March and September 2020. The dataset is based on >34.000 images acquired by a helicopter-borne optical camera system with survey flights covering areas between 1.8 and 96.5 km2 around the vessel. Depending on the flight pattern and altitude of the helicopter, ground resolutions of the orthomosaics range between 0.03 and 0.5 m. By combining the photogrammetric products with contemporaneously acquired airborne laser scanner reflectance measurements selected orthomosaics could be corrected for cloud shadows which facilitates their usage for sea-ice and melt pond classification algorithms. The presented dataset is a valuable data source for the interdisciplinary MOSAiC community building a temporal and spatially resolved baseline to accompany various remote sensing and in situ research projects.

Enhancing Forest Canopy Height Retrieval: Insights from Integrated GEDI and Landsat Data Analysis

Canopy height is a crucial indicator for assessing the structure and function of the forest ecosystems. It plays a significant role in carbon sequestration, sink enhancement, and promoting green development. This study aimed to evaluate the accuracy of GEDI L2A version 2 data in estimating ground elevation and canopy height by comparing it with airborne laser scanning (ALS) data. Among the six algorithms provided by the GEDI L2A data, algorithm a2 demonstrated higher accuracy than the others in detecting ground elevation and canopy height. Additionally, a relatively strong correlation (R-squared = 0.35) was observed between rh95 for GEDI L2A and RH90 for ALS. To enhance the accuracy of canopy height estimation, this study proposed three backpropagation (BP) neural network inversion models based on GEDI, Landsat 8 OLI, and Landsat 9 OLI-2 data. Multiple sets of relative heights and vegetation indices were extracted from the GEDI and Landsat datasets. The random forest (RF) algorithm was employed to select feature variables with a cumulative importance score of 90% for training the BP neural network inversion models. Validation against RH90 of ALS revealed that the GEDI model outperformed the OLI or OLI-2 data models in terms of accuracy. Moreover, the quality improvement of OLI-2 data relative to OLI data contributed to enhanced inversion accuracy. Overall, the models based on a single dataset exhibited relatively low accuracy. Hence, this study proposed the GEDI and OLI and GEDI and OLI-2 models, which combine the two types of data. The results demonstrated that the combined model integrating GEDI and OLI-2 data exhibited the highest performance. Compared to the weakest OLI data model, the inversion accuracy R-squared improved from 0.38 to 0.74, and the MAE, RMSE, and rRMSE decreased by 1.21 m, 1.81 m, and 8.09%, respectively. These findings offer valuable insights for the remote sensing monitoring of forest sustainability.

Deep learning for filtering the ground from ALS point clouds: A dataset, evaluations and issues

The capability of partially penetrating vegetation canopy and efficiently collecting high-precision point clouds over large areas makes airborne laser scanning (ALS) a valuable tool for various geospatial applications. However, automated ground filtering (GF), one fundamental and challenging step for most ALS applications, has remained a widely researched yet unsolved problem for decades. The recent breakthroughs in supervised deep learning (DL) techniques, which rely on sufficient and high-quality labeled datasets, provide a new solution to better solve this problem. Unfortunately, public 3D geospatial datasets are scarce, especially for those tailored for the landform-scale GF task. Moreover, whether advanced deep neural networks (DNNs) can be well-scaled to the problem of GF remains an open question. To comprehensively advance the development of effective DL-based GF pipelines, we first publish an ultra-large-scale GF dataset built upon open-access ALS point clouds of four different countries worldwide, which covers over 47 km2 and nine different terrain scenes. Then, multiple attractive advantages of DL techniques in GF are evaluated through extensive experimental comparisons with traditional GF methods on the presented dataset. Furthermore, we reveal several issues faced by generalizing existing advanced 3D DNNs into GF tasks with a series of in-depth experimental analyses. Finally, some promising directions for future research are suggested in response to the identified challenges. Our dataset, named OpenGF, is available at https://github.com/Nathan-UW/OpenGF.

UAVIMALS: THE "OPEN" REMOTE SENSING SYSTEM FOR SURFACE ARCHAEOLOGICAL INVESTIGATIONS

Abstract. Today, there is an increasing use of airborne sensors in archaeology, especially to investigate the surface of more or less vast territories quickly and accurately. In particular, airborne laser scanning technologies from small remotely piloted aircraft are rapidly developing towards increasingly high-performance solutions for the investigation of archaeological evidence hidden by vegetation or more or less substantial soil deposits. The proposed contribution intends to insert itself within this field of archaeological research by presenting "UAVIMALS" (Unmanned Aerial Vehicle Integrated with Micro Airborne Laser Scanner), an aerial remote sensing system of "soil marks", designed for surface archaeological investigations and the result of an Early Career Grant from the National Geographic Society. The system, consisting of a customised drone based on an open architecture and software for vehicle control and data processing, integrates a solid-state laser sensor, commonly engineered for obstacle avoidance, but here exploited to process accurate DTM (Digital Terrain Model) of small land surfaces with a significant reduction in cost and acquisition time. The system, whose engineering was contributed by the BioRobotics Institute of the S. Anna University of Pisa, was tested within the archaeological context of Leopoli - Cencelle (Tarquinia, Italy). A mediaeval city that has been researched for about 25 years by the Chair of Christian and Medieval Archaeology at the 'Sapienza' University of Rome. Experimentation missions carried out on the site, which is still only partially investigated, have been successful in bringing to light some urban areas that had not yet been investigated.

Modeling windthrow effects on water runoff and hillslope stability in a mountain catchment affected by the VAIA storm

Windthrows seriously affect forest landscapes, causing several issues in hydrological and geomorphological terms. In this regard, Airborne Laser Scanning (ALS) topographic data recently increased the opportunity to investigate in detail physical processes at the catchment scale. Moreover, topographically based hydrological and geomorphological models allow quantifying runoff alteration due to windthrows-driven land cover changes and detect the occurrence of land degradative processes at the sub-catchment scale. In this connection, accurate investigations about windthrows role in varying local runoff regimes over time are still obscure, as well as the possibility of predicting terrain instabilities due to windstorm occurrence. This research aims to investigate the interaction between windthrows, runoff alterations and hillslope failures affecting a landslide-prone mountain catchment (northern Italy). Hydrological HEC-HMS and geomorphological RESS models were applied. Windthrows' role in altering runoff regimes and hillslope stability was investigated starting from the elaboration of ALS-derived points clouds acquired before and after the occurrence of the Vaia storm. Digital Terrain Models (DTMs) were elaborated for the two scenarios to compare daily runoff variations and predict the activation of terrain instabilities by looking at land cover changes driven by the blowdown event at the sub-catchment detail. Results attested the key role of windstorms in altering local runoff, with a maximum relative runoff increment equal to 2.56 % and a maximum runoff difference equal to 3.12 mmh−1, as well as in encouraging the activation of the observed shallow landslide. The correlation between windthrows occurrence and runoff alterations was validated by performing regression analysis (R2 = 0.76), while the accuracy of instabilities predictions was tested through the Distance to Perfect Classification (D2PC) index and True Skill Statistic (TSS) score, respectively resulted equal to 0.076 and 0.898. This research represents a valid tool for investigating similar issues at a wider scale, also providing suggestions for promoting interventions in wind-disturbed forest areas.

Flying Laboratory of Imaging Systems: Fusion of Airborne Hyperspectral and Laser Scanning for Ecosystem Research

Synergies of optical, thermal and laser scanning remotely sensed data provide valuable information to study the structure and functioning of terrestrial ecosystems. One of the few fully operational airborne multi-sensor platforms for ecosystem research in Europe is the Flying Laboratory of Imaging Systems (FLIS), operated by the Global Change Research Institute of the Czech Academy of Sciences. The system consists of three commercial imaging spectroradiometers. One spectroradiometer covers the visible and near-infrared, and the other covers the shortwave infrared part of the electromagnetic spectrum. These two provide full spectral data between 380–2450 nm, mainly for the assessment of biochemical properties of vegetation, soil and water. The third spectroradiometer covers the thermal long-wave infrared part of the electromagnetic spectrum and allows for mapping of surface emissivity and temperature properties. The fourth instrument onboard is the full waveform laser scanning system, which provides data on landscape orography and 3D structure. Here, we describe the FLIS design, data acquisition plan and primary data pre-processing. The synchronous acquisition of multiple data sources provides a complex analytical and data framework for the assessment of vegetation ecosystems (such as plant species composition, plant functional traits, biomass and carbon stocks), as well as for studying the role of greenery or blue-green infrastructure on the thermal behaviour of urban systems. In addition, the FLIS airborne infrastructure supports calibration and validation activities for existing and upcoming satellite missions (e.g., FLEX, PRISMA).

Historical charcoal-burning mounds in the Engure area, Latvia, and their significance for the history of forest exploitation

The application of airborne laser scanning (LiDAR) has led to the discovery of numerous man-made mounds encircled by pits in the forests near Engure, north-western Latvia. Field survey revealed these to be historical features remaining from charcoal-burning in mound kilns. Archaeological excavation was undertaken at one of the features, along with analysis of recovered wood charcoal, dendrochronological and radiocarbon (14C) dating and archival research into documents relating to historical charcoal production in the area. All of the analysed charcoal samples are from Scots pine (Pinus sylvestris L.), the dominant tree species in this area. It is concluded that the large-scale charcoal production served the needs of the ironworks operating at Engure in the 17th–18th century. The study of the charcoal-burning mounds has permitted estimation of the source area and volume of the wood supplied for charcoal production, and the period during which it was undertaken. Wood harvesting on such a scale had a significant impact on the surrounding forests and also had some effect on subsequent forest growth in the area. These findings demonstrate that features of this kind deserve to be regarded as archaeological remains of considerable research potential. Keywords: Duchy of Courland and Semigallia, forest history, Scots pine, charcoal-burning mounds, charcoal kilns, wood charcoal

Testing Textural Information Base on LiDAR and Hyperspectral Data for Mapping Wetland Vegetation: A Case Study of Warta River Mouth National Park (Poland)

One of the key issues in wetland monitoring is mapping vegetation. Remote sensing and machine learning are used to acquire vegetation maps, which, due to the development of sensors and data processing and analysis methods, have increasingly high accuracy. The objectives of this study were to test: (i) which of the textural information (TI) features have the highest information potential for identifying wetland communities; and (ii) whether the use of TI improves the accuracy of wetland communities mapping using hyperspectral (HS) and Airborne Laser Scanning (ALS) data. The analysis indicated that the mean and entropy features of the Gray Level Co-occurrence Matrix had the highest potential to differentiate between various wetland communities. Adding these features to the dataset resulted in a small increase (0.005) in average F1 accuracy based on HS data and 0.011 for HS and ALS scenarios in wetland communities classification, and adding TI improved the delineation of patch boundaries. A higher increase was noted for forest and scrub vegetation (by 0.019 for the HS scenario and 0.022 for the HS and ALS scenario) and rushes (only for the HS and ALS scenario 0.017). It can be concluded that it is reasonable to use textural information for mapping wetland communities, especially for areas with a high proportion of scrub and forest and rushes vegetation included in the analysis.

DGCN-ED: dynamic graph convolutional networks with encoder–decoder structure and its application for airborne LiDAR point classification

ABSTRACT Achieving high-precision automatic classification in real-world applications for airborne laser scanning point clouds is a challenging task duo to their unstructured nature, uneven density distribution, high redundancy, incompleteness and scene complexity. Graph Convolutional Neural Networks can process scattered point clouds directly without regularization, which avoids the loss of depth information，and has recently become a topic of increased interest. Therefore, this study proposed an extension of the Graph-Unet network architecture named DGCN-ED for airborne LiDAR point classification, which uses a Graph Convolutional Neural Network as a representation to describe complex object relationships and an encoder-decoder architecture to capture the multi-scaled point features and describe objects in the high-level feature space. The two-layer dynamic update Graph Convolutional Neural Network is designed to expand the effective range of nodes and enhance the representation ability of learned pointwise features. The effectiveness of the proposed method is evaluated by an experiment on the ISPRS Vaihingen 3D semantic labelling benchmark dataset. Moreover, experiments on the IEEE 2019 Data Fusion Contest Dataset were conducted to demonstrate the generalization abilities of the proposed method. The results show that our method achieved on average 2.8% higher overall accuracy than existing methods, with an overall accuracy of 98.0% and an average F1 score of 0.797.

Modelling height growth of temperate mixedwood forests using an age-independent approach and multi-temporal airborne laser scanning data

Forest inventories provide information regarding the status of a range of attributes as well as enabling predictive applications. Growth and yield models are essential tools for sustainable forest management, importantly enabling projections of future forest conditions (such as height growth). To select the most appropriate growth trajectory, site index models are commonly used to quantify the productivity of a given site. However, applying these methods to more complex, multi-species, and multi-age forests can be challenging due to deviations from the assumptions made for even-aged stands. In this study, we provide a comprehensive indicator of site quality for more complex and irregular stand structures by developing age-independent height growth models for various forest types. We used multi-temporal airborne laser scanning (ALS) data from 2005, 2012, and 2018 in the Great Lakes–St. Lawrence forest region in southern Ontario, Canada. The stochastic differential equations approach was used to develop age-independent height models and a height growth rate index as a proxy of site quality from ALS-derived height metrics. We evaluated the sensitivity of the models using two different modelling approaches and found that the model that incorporated data from both periods (i.e., 2005–2012 and 2012–2018) generally provided the lower root mean square error (RMSE) value for most forest types. Overall, our results showed good agreement between the model predictions of top height and observed top height in 2018 from field plots for all forest types. We demonstrated the use of these models by creating a system of height growth curves for each forest type and producing a map of site quality for a mixedwood forest (∼10,000 ha) at a spatial resolution of 25 m. The approach developed herein leverages the accurate, spatially detailed characterization of canopy heights afforded by ALS data and is independent of stand age, which is challenging to measure accurately and is typically not available at a spatial resolution that is commensurate with the ALS data. Additionally, the demonstrated approach can be adapted to other data sources that accurately capture canopy heights (i.e., digital aerial photogrammetric or DAP), thereby increasing the possible geographic extent of height growth estimates.

Landscape structure model based estimation of the wind erosion risk in Brandenburg, Germany

The paper presents the development, adaptive improvement and use of the method to estimate the wind erosion risk in Germany for Cross Compliance (CC) regulations, based on the German standard DIN19706. It is illustrated by the example of the Federal State of Brandenburg. A landscape structure model was developed which calculates the sheltering effects of landscape elements. Basic inputs are the heights of all landscape elements and the frequencies and directions of erosive winds. In combination with the soil map of erodibility the wind erosion risk is derived in a high spatial resolution according to the CC requirements. In addition to improving the input data in terms of its spatial resolution by using air-borne laser scanning data, an innovative approach is presented which derives the sheltered areas behind landscape elements from the transport capacities of wind speeds above a threshold. Thus, our analysis represents one of the most comprehensive wind erosion assessment of cropland that can be used for landscape structure assessment well beyond CC use. The derivation of effective protection zones from the frequencies of erosive winds when critical thresholds are adjusted represents an innovative approach that provides an objective and transferable assessment of wind protection of landscape features in different wind regimes.

Remote Sensing‐Based Forest Modeling Reveals Positive Effects of Functional Diversity on Productivity at Local Spatial Scale

AbstractForest biodiversity is critical for many ecosystem functions and services. Yet, it remains uncertain how plant functional diversity influences ecosystem functioning across environmental gradients and contiguous larger areas. We integrated remote sensing and terrestrial biosphere modeling to explore functional diversity–productivity relationships at multiple spatial scales for a heterogeneous forest ecosystem in Switzerland. We initialized forest structure and composition in the ecosystem demography model (ED2) through a combination of ground‐based surveys, airborne laser scanning and imaging spectroscopy for forest patches at 10 × 10‐m spatial grain. We derived morphological and physiological forest traits and productivity from model simulations at patch‐level to relate morphological and physiological aspects of functional diversity to the average productivity from 2006 to 2015 at 20 × 20 to 100 × 100‐m spatial extent. We did this for model simulations under observed and experimental conditions (mono‐soils, mono‐cultures and mono‐structures). Functional diversity increased productivity significantly (p < 0.001) across all simulations at 20 × 20 to 30 × 30 m scale, but at 100 × 100‐m scale positive relationships disappeared under homogeneous soil conditions potentially due to the low beta diversity of this forest and the saturation of functional richness represented in the model. Although local functional diversity was an important driver of productivity, environmental context underpinned the variation of productivity (and functional diversity) at larger spatial scales. In this study, we could show that the integration of remotely sensed information on forest composition and structure into terrestrial biosphere models is important to fill knowledge gaps about how plant biodiversity affects carbon cycling and biosphere feedbacks onto climate over large contiguous areas.

Use of Airborne Laser Scanning to assess effects of understorey vegetation structure on nest‐site selection and breeding performance in an Australian passerine bird

AbstractIn wild bird populations, the structure of vegetation around nest‐sites can influence the risk of predation of dependent offspring, generating selection for nest‐sites with vegetation characteristics associated with lower predation rates. However, vegetation structure can be difficult to quantify objectively in the field, which might explain why there remains a general lack of understanding of which characteristics are most important in determining predation rates. Airborne laser scanning (ALS) offers a powerful means of measuring vegetation structure at unprecedented resolution. Here, we combined ALS with 11 years of breeding data from a wild population of superb fairy‐wrens Malurus cyaneus in southeastern Australia, a species which nests relatively close to the ground and has high rates of nest and fledgling predation. We derived structural measurements of understorey (0–8 m) vegetation from a contiguous grid of 30 × 30 m resolution cells across our c. 65 hectares study area. We found that cells with nests (nest‐cells) differed in their understorey vegetation structure characteristics compared to unused cells, primarily in having denser vegetation in the lowest layer of the understorey (0–2 m; the ‘groundstorey’ layer). The average height of understorey vegetation was also lower in cells with nests than in those without nests. However, relationships between understorey vegetation structure characteristics and breeding performance were mixed. Nest success rates decreased with higher volumes of groundstorey vegetation, as did fledgling survival rates, though only in nest‐cells with lower height vegetation. Our results indicate that ALS can identify vegetation characteristics relevant for superb fairy‐wren nest‐site selection, but that nesting preferences are not beneficial under current predation pressures. The study illustrates the potential for using ALS to investigate how ecological conditions affect behaviour and life‐histories in wild animal populations.

Differences in Canopy Cover Estimations from ALS Data and Their Effect on Fire Prediction

Canopy cover is a primary attribute used in empirical wildfire models for certain fuel types. Accurate estimation of canopy cover is a key to ensuring accurate prediction of fire spread and behaviour in these fuels. Airborne Laser Scanning (ALS) is a promising active remote sensing technology for estimating canopy cover in natural ecosystems since it can penetrate and measure the vegetation canopy. Various methods have been developed to estimate canopy cover from ALS data. However, little attention has been given to the evaluation of algorithms used to calculate canopy cover and the subsequent influence these algorithms can have on wildfire behaviour models. In this study we evaluate the effect of using different algorithms to calculate canopy cover on the performance of the Australian Mallee-heath fire spread model. ALS data was used to derive five canopy cover models. Fire spread metrics including burned area, unburned area within the fire extent, and extent of fire were compared for different model run times. The results show that these metrics are strongly influenced by choice of algorithm used to calculate canopy cover. The results from this study may provide practical guidance for the optimal selection of estimation methods in canopy cover mapping.

POTENTIAL OF MOBILE MAPPING TO CREATE DIGITAL TWINS OF FORESTS

Abstract. Forests are irreplaceable and are being studied extensively. Better forest inventory and understanding necessitate effective mapping, modeling, and automatic analysis. As a result, considerable research effort is being devoted to digitizing forest environments. Recently, digital twins have come to the attention of the geospatial community as a virtual representation of the Earth’s surface linked to its corresponding physical asset. This concept is applicable to forests and has been studied in the literature. This requires initial input data obtained through reality capture. Among mapping techniques, laser scanning has emerged as a state-of-the-art technology for vegetation modeling. In this paper, we look into the potential of mobile laser scanning for forest digital twinning. While most studies concentrate on single tree detection, modeling, and estimation of dendrometric parameters, we also include lower vegetation in our investigations. To accomplish this, we first detect single trees and then investigate different vegetation densities and levels using geometric metrics. We also demonstrate how to model the underlying layers of vegetation in a digital twin. We perform the tests on data from mobile laser scanning (MLS) and compare the results to those from airborne laser scanning (ALS).We show that single tree detection based on crown separation using MLS data works similarly to or slightly better than ALS data. Furthermore, we demonstrate that MLS data allows for more detailed analysis of understory vegetation taking into account different height levels and a multi-level representation, whereas ALS data only allows for rough analysis of the lower parts of forest vegetation.

Retrieval and validation of vertical LAI profile derived from airborne and spaceborne LiDAR data at a deciduous needleleaf forest site

ABSTRACT Leaf area index (LAI) is defined as one half of the total green leaf area per unit ground surface area. Its vertical profile is critical for understanding the remote sensing radiative transfer processes. LAI profile has been derived from airborne and spaceborne LiDAR data, such as the Global Ecosystem Dynamics Investigation (GEDI) installed on the International Space Station. However, the capability of various algorithms for the LAI profile estimation with airborne LiDAR is not clearly evaluated, and the estimated LAI profiles, including the GEDI LAI products, are not been fully validated. This study conducted a quantitative retrieval and validation of the LAI profiles using terrestrial and airborne laser scanning (TLS and ALS) and spaceborne GEDI data over a deciduous needleleaf forest site in northern China. The vertical LAI profile was estimated in the field using an upward digital hemispherical photography (DHP) attached to a portable measurement system in 2020 and 2021. A suite of new LiDAR indices combining both LiDAR return number and return intensity was explored for the LAI profile estimation. All LAI profiles obtained from the DHP, TLS, ALS, and GEDI during the leaf-on season and leaf-off season were compared. The DHP shows a good agreement with the TLS LAI profiles (R2 = 0.97). The LAI profile derived from the ALS data using the combined light penetration index (LPIRI) agrees well (R2 ≥0.86) with the DHP, TLS, and GEDI estimates. In general, the LPIRI is advantageous for regional LAI profile mapping from ALS. The GEDI cumulative LAI corresponds well with the DHP during the leaf-on season (R2 = 0.90, RMSE = 0.23), but underestimates during the leaf-off season (R2 = 0.70, RMSE = 0.14, bias=−0.13). The underestimation is attributed to the higher canopy and ground reflectance ratio (ρv/ρg) assigned in the algorithm and the height discrepancy between the GEDI and field measurements. For the GEDI LAI profile product, further validation and improvement are necessary for other biome types and landscape conditions, especially during the leaf-off season.

Characterizing riparian vegetation and classifying riparian extent using airborne laser scanning data

Riparian areas are an integral component of the forest hydrologic system. However, systematic management and conservation of riparian vegetation remains a challenge when the extent and nature of riparian vegetation is not easily characterized. Thus, describing riparian vegetation structural attributes and mapping riparian extent is important for stream protection and restoration. This study discriminated riparian and upland vegetation zones in two watersheds on Vancouver Island, Canada, and characterized vegetation attributes ascribed to streamside forest vegetation using fine-scale airborne laser scanning (ALS) data with field-based measurements for controls. Comparison of ALS metrics for riparian and upland vegetation plots indicated metrics of canopy cover, height, and vertical canopy complexity were significantly different. Machine learning was used to model the probability of riparian and upland vegetation zone extent in each watershed and to determine which ALS metrics were the most important for discriminating between these two zones. Separate models were built for each watershed and results indicated that each watershed's model had different ALS metrics that were important for model development, reflecting the different management histories associated with each watershed. Models achieved overall classification accuracies of 65–77% using a 10-fold cross validation. Probability maps were then developed to characterize riparian extent, indicating that the majority of riparian areas in these watersheds were within 20 m of a stream. Additionally, riparian areas were found up to 200 m away from the stream, which is beyond the current provincial regulation widths set for riparian buffers in forested areas in British Columbia, Canada. However, the majority (roughly 75%) of vegetation within existing management buffers was riparian, although this differed by stream classification. We conclude that ALS data provides a potentially useful source of information for determining the characteristics and extent of riparian vegetation.

Estimating biomass and soil carbon change at the level of forest stands using repeated forest surveys assisted by airborne laser scanner data

BackgroundUnder the growing pressure to implement mitigation actions, the focus of forest management is shifting from a traditional resource centric view to incorporate more forest ecosystem services objectives such as carbon sequestration. Estimating the above-ground biomass in forests using airborne laser scanning (ALS) is now an operational practice in Northern Europe and is being adopted in many parts of the world. In the boreal forests, however, most of the carbon (85%) is stored in the soil organic (SO) matter. While this very important carbon pool is “invisible” to ALS, it is closely connected and feeds from the growing forest stocks. We propose an integrated methodology to estimate the changes in forest carbon pools at the level of forest stands by combining field measurements and ALS data.ResultsALS-based models of dominant height, mean diameter, and biomass were fitted using the field observations and were used to predict mean tree biophysical properties across the entire study area (50 km2) which was in turn used to estimate the biomass carbon stocks and the litter production that feeds into the soil. For the soil carbon pool estimation, we used the Yasso15 model. The methodology was based on (1) approximating the initial soil carbon stocks using simulations; (2) predicting the annual litter input based on the predicted growing stocks in each cell; (3) predicting the soil carbon dynamics of the annual litter using the Yasso15 soil carbon model. The estimated total carbon change (standard errors in parenthesis) for the entire area was 0.741 (0.14) Mg ha−1 yr−1. The biomass carbon change was 0.405 (0.13) Mg ha−1 yr−1, the litter carbon change (e.g., deadwood and leaves) was 0.346 (0.027) Mg ha−1 yr−1, and the change in SO carbon was − 0.01 (0.003) Mg ha−1 yr−1.ConclusionsOur results show that ALS data can be used indirectly through a chain of models to estimate soil carbon changes in addition to changes in biomass at the primary level of forest management, namely the forest stands. Having control of the errors contributed by each model, the stand-level uncertainty can be estimated under a model-based inferential approach.

Review on the Possibilities of Mapping Old-Growth Temperate Forests by Remote Sensing in Europe

AbstractOld-growth forests (OGF) provide valuable ecosystem services such as habitat provision, carbon sequestration or recreation maintaining biodiversity, carbon storage, or human well-being. Long-term human pressure caused OGFs in Europe to be rare and scattered. Their detailed extent and current status are largely unknown. This review aims to identify potential methods to map temperate old-growth forests (tOGF) by remote sensing (RS) technology, highlights the potentials and benefits, and identifies main knowledge gaps requesting further research. RS offers a wide range of data and methods to map forests and their properties, applicable from local to continental scale. We structured existing mapping approaches in three main groups. First, parameter-based approaches, which are based on forest parameters and usually applied on local to regional scale using detailed data, often from airborne laser scanning (ALS). Second, direct approaches, usually employing machine learning algorithms to generate information from RS data, with high potential for large-area mapping but so far lacking operational applications and related sound accuracy assessment. Finally, indirect approaches integrating various existing data sets to predict OGF existence. These approaches have also been used for large area mapping with a main drawback of missing physical evidence of the identified areas to really hold OGFs as compared to the likelihood of OGF existence. In conclusion, studies dealing with the mapping of OGF using remote sensing are quite limited, but there is a huge amount of knowledge from other forestry-related applications that is yet to be leveraged for OGF identification. We discuss two scenarios, where different data and approaches are suitable, recognizing that one single system cannot serve all potential needs. These may be hot spot identification, detailed area delineation, or status assessment. Further, we pledge for a combined method to overcome the identified limitations of the individual approaches.

Detecting the presence of standing dead trees using airborne laser scanning and optical data

ABSTRACT Deadwood is an important indicator of biodiversity in forest ecosystems. Identifying areas with large density of standing dead trees through field inventory is challenging, and remotely sensed data can provide a more systematic approach. In this study, we used metrics derived from airborne laser scanning (ALS) data (7.1 points m−2) and vegetation indices from optical images (HySpex sensor VNIR-1800: 0.3 m, SWIR-384: 0.7 m) to predict the presence of standing dead trees over a 15.9 km2 managed forest in Southern Norway. The dead basal area (DBA) of 40 sample plots was computed and used to classify the plots into presence/absence of standing dead trees. An area-based approach (ABA) using logistic regression was initially tested, but due to limited ground reference information, no statistically significant models could be formulated. A tree-based approach (TBA) was used to overcome this limitation. It identified trees on the ALS point cloud with a local maxima function and used a vegetation index to determine if the trees were dead. Between 18% and 42% of the predicted area with standing dead trees intersected a field recorded validation dataset. The TBA provided a good alternative to area-based regression models in the context of few standing dead trees.