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Although wall-to-wall, accurate, and up-to-date forest composition maps at the stand level are a fundamental input for many applications, ranging from global environmental issues to local forest management planning, countrywide mapping approaches on the tree type level remain rare. This paper presents and validates an innovative remote sensing based approach for a countrywide mapping of broadleaved and coniferous trees in Switzerland with a spatial resolution of 3 m. The classification approach incorporates a random forest classifier, explanatory variables from multispectral aerial imagery and a Digital Terrain Model (DTM) from Airborne Laser Scanning (ALS) data, digitized training polygons and independent validation data from the National Forest Inventory (NFI). The methodological workflow was optimized for an area of 41,285 km² that is characterized by temperate forests within a complex topography. Whereas high model overall accuracies (0.99) and kappa (0.98) were achieved, the comparison of the tree type map with independent NFI data revealed significant deviations that are related to underestimations of broadleaved trees (median of -3.17%). Constraints of the tree type mapping approach are mostly related to the acquisition date and time of the imagery and the topographic (negative) effects on the prediction. A comparison with the most recent High Resolution Layers (HRL) forest 2012 from the European Environmental Agency revealed that the tree type map is superior regarding spatial resolution, level of detail and accuracy. The high-quality map achieved with the approach presented here is of great value for optimizing forest management and planning activities and is also an important information source for applications outside the forestry sector. [ABSTRACT FROM AUTHOR]

Forest structure reflects the forest disturbance regime and can provide important information about the rate of human impact. A better understanding of the structural variability and large-scale dynamics of natural forests is crucial for 'close to nature' forest management planning. In this study, we developed a partly automated approach to assess the structure of potential primeval and managed beech forests in the Ukrainian Carpathians using WorldView-2 imagery. We analyzed the local (50 m × 50 m scale) canopy closure of these forests by extracting the canopy gaps and determined four forest structure types ranging from very closed to low density. The occurrence and frequencies of these structure types were significantly different in the primeval and managed beech forests. The four forest structure types were predicted and mapped using multinomial logistic regression based on the textural features derived from the original image bands and two vegetation indices. A 10-fold cross-validation resulted in an overall accuracy of 83% and a kappa coefficient of 75%, with the highest agreement for the very closed structure type (87%) and the lowest agreement for the medium density and low density structure types (79%). The forest structure type maps can be helpful for planning management activities in beech forests. [ABSTRACT FROM AUTHOR]

Forest disturbances in central Europe caused by fungal pests may result in widespread tree mortality. To assess the state of health and to detect disturbances of entire forest ecosystems, up-to-date knowledge of the tree species diversity is essential. The German state Mecklenburg-Vorpommern is severely affected by ash (Fraxinus excelsior) dieback caused by the fungal pathogen Hymenoscyphus pseudoalbidus. In this study, species diversity and the magnitude of ash mortality was assessed by classifying seven different tree species and multiple levels of damaged ash. The study is based on a multispectral WorldView-2 (WV-2) scene and uses object-based supervised classification methods based on multinomial logistic regressions. Besides the original multispectral image, a set of remote sensing indices (RSI) was derived, which significantly improved the accuracies of classifying different levels of damaged ash but only slightly improved tree species classification. The large number of features was reduced by three approaches, of which the linear discriminant analysis (LDA) clearly outperformed the more commonly used principal component analysis (PCA) and a stepwise selection method. Promising overall accuracies (83%) for classifying seven tree species and (73%) for classifying four different levels of damaged ash were obtained. Detailed tree damage and tree species maps were visually inspected using aerial images. The results are of high relevance for forest managers to plan appropriate cutting and reforestation measures to decrease ash dieback over entire regions. [ABSTRACT FROM AUTHOR]

Forests influence climate through a myriad of chemical, physical and biological processes and are an essential lever in the efforts to counter climate change. The majority of studies investigating potential climate benefits from forests have focused on forest area changes, while changes to forest management, in particular those affecting species composition, have received much less attention. Using a statistical model based on remote sensing observations over Europe, we show that broad-leaved tree species locally reduce land surface temperatures in summer compared to needle-leaved species. The summer mean cooling effect related to an increase in broad-leaved tree fraction of 80% is relatively modest (~ 0.3–0.75 K), but is amplified during exceptionally warm periods. The reduction of daily maximum temperatures during the hottest days reaches up to 1.8 K in the Atlantic region and up to 1.5 K in Continental and Mediterranean regions. Hot temperature extremes adversely affect humans and ecosystems and are expected to become more frequent in a future climate. Thus, forest management strategies aiming to increase the fraction of broad-leaved species could help to reduce some of the adverse local impacts caused by hot temperature extremes. However, the overall benefits and trade-offs related to an increase in the broad-leaved tree fraction in European forests needs to be further investigated and assessed carefully when adapting forest management strategies. [ABSTRACT FROM AUTHOR]

• Information on forest type proportions improved the accuracy of volume predictions. • Forest type proportions weighted by canopy height outperformed area proportions. • The best model performance was obtained using forest type maps from leaf-off LiDAR. Managing forests for ecosystem services and biodiversity requires accurate and spatially explicit forest inventory data. A major objective of forest management inventories is to estimate the standing timber volume for certain forest areas. In order to improve the efficiency of an inventory, field based sample-plots can be statistically combined with remote sensing data. Such models usually incorporate auxiliary variables derived from canopy height models. The inclusion of forest type variables, which quantify broadleaf and conifer volume proportions, has been shown to further improve model performance. Currently, the most common way of quantifying broadleaf and conifer forest types is by calculating the proportions of the corresponding areas of the canopy cover. This practice works well for single-layer forests with only a few species, but we hypothesized that this is not best practice for heterogeneously structured and mixed forests, where the area proportion does not accurately reflect the timber volume proportion. To better represent the broadleaf and conifer volume proportions, we introduced two new auxiliary variables in which the area proportion is weighted by height information from a canopy height model. The main objectives of this study were: (1) to demonstrate the advantage of including forest type (broadleaf/conifer distinction) information in ordinary least squares regression models for timber volume prediction using widely available data sources, and (2) to investigate the hypothesis that including the broadleaf and conifer proportions, weighted by canopy height information, as additional auxiliary variables is favourable over including simple area proportions. The study was conducted in three areas in Switzerland, all of which have heterogeneously structured and mixed forests. Our main findings were that the best model performance can generally be achieved: (1) by deriving conifer and broadleaf proportions from a high-resolution broadleaf/conifer map derived from leaf-off airborne laser scanning data, and (2) by using broadleaf/conifer proportions weighted by height information from a canopy height model. Incorporating the so-derived conifer and broadleaf proportions increased the model accuracy by up to 9 percentage points in root mean square error (RMSE) compared with models not using any forest type information, and by up to 2 percentage points in RMSE compared with models using conifer and broadleaf proportions based solely on the corresponding area proportions, as done in current practice. Our findings are particularly relevant for mixed and heterogeneously structured forests, such as those managed to achieve multiple functions or to adapt effectively to climate change. [ABSTRACT FROM AUTHOR]

• Automatic quantification and detection of forest edge changes for a large area. • Using kernel density estimation (KDE) to describe ALS data. • 3 indicators describe the KDE curves and reflected the complexity. • Promising tool for using ALS data to analyze the change of forest edges. Due to the strong impact of the composition and structure of the forest edge on forest ecosystems, the value of investigating changes at the forest edge has been recognized for a few decades. Automatic quantification and detection of forest edge changes has advantages over fieldwork with respect to the time and cost required. Based on the structural features extracted from Airborne Laser Scanning (ALS) data, in this study an automatic method using Kernel Density Estimation (KDE) was developed to describe ALS point data related to the forest edge in terms of a finite number of interpretable indicators. The method was applied to identify patterns in data sets at the forest edge between two given dates (2001 and 2014) in European temperate mixed forests. The study area has an extent of 1403 km2 and the total length of forest boundary is approx. 5000 km. The calculated structural features explained the vegetation's height distribution and the structural variance at the forest edge. The kernel density curves represented the general structural diversity of the study area. Three extracted indicators described the shape of the kernel density curves and reflected the dynamics of the studied forest edge area. Overall, results from this study show that the proposed approach is a promising tool for using remote sensing data to analyze whether or not the vertical structure of the forest edge for the entire study area has changed and thus to assess automatically the progress of forest management at forest edge. [ABSTRACT FROM AUTHOR]

Storm events are capable of causing windthrow to large forest areas. A rapid detection of the spatial distribution of the windthrown areas is crucial for forest managers to help them direct their limited resources. Since synthetic aperture radar (SAR) data is acquired largely independent of daylight or weather conditions, SAR sensors can produce temporally consistent and reliable data with a high revisit rate. In the present study, a straightforward approach was developed that uses Sentinel-1 (S-1) C-band VV and VH polarisation data for a rapid windthrow detection in mixed temperate forests for two study areas in Switzerland and northern Germany. First, several S-1 acquisitions of approximately 10 before and 30 days after the storm event were radiometrically terrain corrected. Second, based on these S-1 acquisitions, a SAR composite image of before and after the storm was generated. Subsequently, after analysing the differences in backscatter between before and after the storm within windthrown and intact forest areas, a change detection method was developed to suggest potential locations of windthrown areas of a minimum extent of 0.5 ha—as is required by the forest management. The detection is based on two user-defined parameters. While the results from the independent study area in Germany indicated that the method is very promising for detecting areal windthrow with a producer's accuracy of 0.88, its performance was less satisfactory at detecting scattered windthrown trees. Moreover, the rate of false positives was low, with a user's accuracy of 0.85 for (combined) areal and scattered windthrown areas. These results underscore that C-band backscatter data have great potential to rapidly detect the locations of windthrow in mixed temperate forests within a short time (approx. two weeks) after a storm event. Furthermore, the two adjustable parameters allow a flexible application of the method tailored to the user's needs. [ABSTRACT FROM AUTHOR]

In recent years, extreme storm events have regularly hit central Europe and their frequency is expected to increase in future due to a changing climate. A rapid detection of the spatial distribution of the windthrown areas is crucial for forest managers to help them direct their limited resources.In contrast to optical remote sensing data, Synthetic Aperture Radar (SAR) data is acquired largely independent of daylight or weather conditions. Thus, SAR sensors can produce temporally consistent and reliable data with a high revisit rate. In the present study [1], a straightforward approach was developed that uses Sentinel-1 (S-1) C-band VV and VH polarisation data for a rapid windthrow detection in mixed temperate forests for two study areas in Switzerland and Germany. First, several S-1 acquisitions of approximately 10 before and 30 days after the storm were radiometrically terrain corrected [2]. Second, based on these S-1 acquisitions, a SAR composite image of before and after the storm was generated. Then, after analysing the differences in backscatter between before and after the storm for windthrown and intact forest areas, a change detection method, based on two user-defined parameters, was developed to suggest potential locations of windthrown areas of an extent of 0.5 ha – as required by the forest management. The results confirmed a successful detection of most windthrows, however always slightly underestimated in area. While results from the independent study area in Germany indicated that the method is very promising for detecting areal windthrow with a producer's accuracy of 0.88, its performance was less satisfactory in detecting single scattered windthrown trees. Moreover, windthrow in dense forest is expected to have a larger difference in backscatter than one in open forest. Nevertheless, C-band backscatter data have great potential to rapidly detect the locations of windthrow in mixed temperate forests within approx. 2 weeks after a storm. We are currently applying our method to the entire region of north-east Italy, which was strongly hit by the last year's October storm Vaia. Preliminary results are also very promising for regions in alpine terrain.Moreover, with the launch of the RADARSAT Constellation Mission (RCM) planned in 2019, an increased number of SAR C-band acquisitions will be potentially available soon. Combined with S-1 data, the time span could be substantially shorter to receive the number of required acquisitions for a windthrow detection of similar quality. This would make the approach using reliable SAR data worthy of consideration, even if its performance is worse than methods using more expensive ALS or optical data with longer latencies due to meteorological conditions. Forest managers would greatly benefit from an immediate first overview map of the windthrow locations, as it allows them to begin their planning of the clearing and reforestation of the windthrown areas as soon as possible.[1] Rüetschi, M., Small, D., Waser, L.T., 2019. "Rapid Detection of Windthrows Using Sentinel-1 C-band SAR Data", accepted for Remote Sensing.[2] Small, D., "Flattening Gamma: Radiometric Terrain Correction for SAR Imagery," IEEE Trans. Geosci. Remote Sens., 49(8), 3081–3093, Aug. 2011. [ABSTRACT FROM AUTHOR]

Tree-related microhabitats (TreMs) play an important role in maintaining forest biodiversity and have recently received more attention in ecosystem conservation, forest management and research. However, TreMs have until now only been assessed by experts during field surveys, which are time-consuming and difficult to reproduce. In this study, we evaluate the potential of close-range terrestrial laser scanning (TLS) for semi-automated identification of different TreMs (bark, bark pockets, cavities, fungi, ivy and mosses) in dense TLS point clouds using machine learning algorithms, including deep learning. To classify the TreMs, we applied: (1) the Random Forest (RF) classifier, incorporating frequently used local geometric features and two additional self-developed orientation features, and (2) a deep Convolutional Neural Network (CNN) trained using rasterized multiview orthographic projections (MVOPs) containing top view, front view and side view of the point's local 3D neighborhood. The results confirmed that using local geometric features is beneficial for identifying the six groups of TreMs in dense tree-stem point clouds, but the rasterized MVOPs are even more suitable. Whereas the overall accuracy of the RF was 70%, that of the deep CNN was substantially higher (83%). This study reveals that close-range TLS is promising for the semi-automated identification of TreMs for forest monitoring purposes, in particular when applying deep learning techniques. [ABSTRACT FROM AUTHOR]

Spatially explicit information on tree species composition of managed and natural forests, plantations and urban vegetation provides valuable information for nature conservationists as well as for forest and urban managers and is frequently required over large spatial extents. Over the last four decades, advances in remote sensing technology have enabled the classification of tree species from several sensor types. While studies using remote sensing data to classify and map tree species reach back several decades, a recent review on the status, potentials, challenges and outlooks in this realm is missing. Here, we search for major trends in remote sensing techniques for tree species classification and discuss the effectiveness of different sensors and algorithms based on a literature review. This review demonstrates that the number of studies focusing on tree species classification has increased constantly over the last four decades and promising local scale approaches have been presented for several sensor types. However, there are few examples for tree species classifications over large geographic extents, and bridging the gap between current approaches and tree species inventories over large geographic extents is still one of the biggest challenges of this research field. Furthermore, we found only few studies which systematically described and examined the traits that drive the observed variance in the remote sensing signal and thereby enable or hamper species classifications. Most studies followed data-driven approaches and pursued an optimization of classification accuracy, while a concrete hypothesis or a targeted application was missing in all but a few exceptional studies. We recommend that future research efforts focus stronger on the causal understanding of why tree species classification approaches work under certain conditions or – maybe even more important - why they do not work in other cases. This might require more complex field acquisitions than those typically used in the reviewed studies. At the same time, we recommend reducing the number of purely data-driven studies and algorithm-benchmarking studies as these studies are of limited value, especially if the experimental design is limited, e.g. the tree population is not representative and only a few sensors or acquisition settings are simultaneously investigated. [ABSTRACT FROM AUTHOR]