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Abstract Increasingly available spaceborne sensors provide unprecedented opportunities for large‐scale, timely and continuous tree species diversity (TSD) monitoring. However, given differences in spectral and spatial resolutions, the choice of sensor is not always straightforward. In this work, we investigated the effects of spatial and spectral resolutions for four spaceborne sensors (RapidEye, Landsat‐8, Sentinel‐2 and PlanetScope) on TSD mapping in an area of approximately 4000 km2 within the Black Forest, Germany. We employed a random forest (RF) regression model to predict Shannon–Wiener diversity based on seven types of spectral heterogeneity metrics (texture, coefficient of variation, Rao's Q, convex hull volume, spectral angle mapper, convex hull area and spectral species diversity) and a full survey dataset from 135 one‐ha sample plots. We compared the RF model's performance across sensors and spatial resolutions. Our results demonstrated that the Sentinel‐2‐based TSD model achieved the highest accuracy (mean R2: 0.477, mean root‐mean‐square error (RMSE): 0.274). The RapidEye‐based TSD model produced lower accuracy (mean R2: 0.346, mean RMSE: 0.303), but it was better than the PlanetScope‐ and Landsat‐based TSD models. The 10 m (for Sentinel‐2 and RapidEye) and 15 m (for PlanetScope) were the best spatial resolutions for predicting TSD. The NIR band was the most favourable spectral band for predicting TSD. Texture metrics and Rao's Q outperformed the other spectral heterogeneity metrics. Our results highlighted that spaceborne optical imagery (especially Sentinel‐2) can be successfully used for large‐scale TSD mapping but that the choice of sensors can significantly affect the resulting mapping accuracy in temperate montane forests.

Exploring the capabilities of remote sensing technologies for identifying stress responses in trees due to environmental pressures is crucial for comprehension, management, and maintenance of forests that are productive, healthy, and resilient. In recent decades, research on forest health monitoring has been predominantly focused on data obtained remotely, either from satellites or crewed aircraft. During the last few years, Uncrewed Aerial Vehicles (UAVs) have gained prominence as invaluable remote sensing platforms, increasingly being employed for forest surveying. As intermediary between traditional remote sensing methods and ground-level observations, UAVs can capture high-resolution imagery from low altitudes, even below cloud cover, in unprecedented detail. This ability allows for the precise detection of stress responses at the individual tree scale. In our study, we have acquired a highly heterogenous, multispectral time-series dataset from the International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests) inventory plots across Bavaria, Germany, focusing on the main tree species. The data was recorded over three consecutive years from a UAV with the objective of monitoring tree physiological stress responses. Concurrently, with the drone flights, the ground-based forest condition surveying (Level-1 monitoring) was conducted, serving as ground-truth validation, and providing detailed information on tree health indicators, such as defoliation and discoloration. Our findings revealed that multispectral imagery obtained from a UAV closely aligns with field data, proving effective detection of physiological stress in trees. Remarkably, in conjunction to the red, red edge, and near-infrared band, the inclusion of the blue band emerged as a critical indicator of tree stress when incorporated into the Blue Normalized Difference Vegetation Index (BNDVI), depending on factors such as tree species, class division, and atmospheric conditions. Furthermore, the averaged values per sample tree over three years, alongside the 5th and 25th percentile of the data distribution, have proven to be of key importance. Based on spectral indices, we achieved good classification accuracies by training species-specific gradient boosting models (macro F1-scores ranging from 0.492 to 0.769). These models can assist in quantifying tree stress responses, thereby supporting the objectives of the ICP Forests program, potentially leading to substantial cost savings or increased coverage in the future.

Light availability and distribution play an important role in every ecosystem as these affect a variety of ecosystem processes and functions. To estimate light availability and distribution, light simulations can be used. Many previous models were based on highly simplified tree models and geometrical assumptions about tree form, or were sophisticated and computationally demanding models based on 3D data which had to be acquired in every season to be simulated. The aim of this study was to model the shadow cast by individual trees at high spatial and temporal resolution without the need for repeated data collection during multiple seasons. For our approach, we captured trees under leaf-off conditions using terrestrial laser scanning and simulated leaf-on conditions for individual trees over the remainder of the year. The model was validated against light measurements (n=20,436) collected using 60 quantum sensors underneath an apple tree (Malus domestica Borkh.) on a sunny and cloudless summer day. On this day, the leaves and the shadow were simulated with a high spatial (1 cm) and temporal resolution (1 min). The simulated values were highly correlated with the measured radiation at r=0.84. Additionally, we simulated the radiation for a whole year for the sample apple tree (tree height: 6.6 m, crown width: 7.6 m) with a resolution of 10 cm and a temporal resolution of 10 min. Below the tree, an area of 49.55 m² is exposed to a radiation reduction of at least 10%, 17.74 m² to at least 20% and only 0.12 m² to at least 30%. The model could be further improved by incorporating branch growth, curved leaf surfaces, and gravity to take the weight of the foliage into account. The presented approach offers a high potential for modelling the light availability in the surroundings of trees with an unprecedented spatial and temporal resolution.

Trees are able to adapt to changing environmental conditions through modifications in their crown morphology and branch architecture. In light-limited environments, tree structures are mainly optimised to increase light interception. This might have side effects on other properties such as the hydraulic system that determines water conduction and tree reactions to drought events. Given the increasing exposure of forest ecosystems to drought stress it is thus crucial to investigate possible correlations between the crown morphology and branch architecture and drought responses. Our study aimed to compare different crown archetypes and branch characteristics of European beech (Fagus sylvatica L.) in their reaction to drought, which was assessed by measuring tree-ring widths of increment cores sampled at breast height. Using Terrestrial Laser Scanning and Quantitative Structure Models, we explored various crown morphological and branch architectural characteristics of 67 beech trees and identified three species-specific crown archetypes. The crown archetypes and branch variables were contrasted with growth responses using linear mixed models. While productivity levels differed, the negative impact of drought stress on radial growth was consistent across all crown archetypes. Nevertheless, certain branch architectural variables were important predictors for radial growth in drought situations. Specifically, long water conduction paths and many branching nodes along those paths were positively influencing growth. Our results indicate that trees showing these characteristics might have a competitive edge regarding drought-affected radial growth compared to others. They could be promoted through thinning measures, which would allow improving the adaption of existing beech forests in situ to climate change and drought stress.

Increasing tree mortality due to climate change has been observed globally. Remote sensing is a suitable means for detecting tree mortality and has been proven effective for the assessment of abrupt and large-scale stand-replacing disturbances, such as those caused by windthrow, clear-cut harvesting, or wildfire. Non-stand replacing tree mortality events (e.g., due to drought) are more difficult to detect with satellite data – especially across regions and forest types. A common limitation for this is the availability of spatially explicit reference data. To address this issue, we propose an automated generation of reference data using uncrewed aerial vehicles (UAV) and deep learning-based pattern recognition. In this study, we used convolutional neural networks (CNN) to semantically segment crowns of standing dead trees from 176 UAV-based very high-resolution (

Unoccupied aerial vehicles (UAV) with RGB-cameras are affordable and versatile devices for the generation of a series of remote sensing products that can be used for forest inventory tasks, such as creating high-resolution orthomosaics and canopy height models. The latter may serve purposes including tree species identification, forest damage assessments, canopy height or timber stock assessments. Besides flight and image acquisition parameters such as image overlap, flight height, and weather conditions, the focal length, which determines the opening angle of the camera lens, is a parameter that influences the reconstruction quality. Despite its importance, the effect of focal length on the quality of 3D reconstructions of forests has received little attention in the literature. Shorter focal lengths result in more accurate distance estimates in the nadir direction since small angular errors lead to large positional errors in narrow opening angles. In this study, 3D reconstructions of four UAV-acquisitions with different focal lengths (21, 35, 50, and 85 mm) on a 1 ha mature mixed forest plot were compared to reference point clouds derived from high quality Terrestrial Laser Scans. Shorter focal lengths (21 and 35 mm) led to a higher agreement with the TLS scans and thus better reconstruction quality, while at 50 mm, quality losses were observed, and at 85 mm, the quality was considerably worse. F1-scores calculated from a voxel representation of the point clouds amounted to 0.254 with 35 mm and 0.201 with 85 mm. The precision with 21 mm focal length was 0.466 and 0.302 with 85 mm. We thus recommend a focal length no longer than 35 mm during UAV Structure from Motion (SfM) data acquisition for forest management practices.

Deep learning and particularly Convolutional Neural Networks (CNN) in concert with remote sensing are becoming standard analytical tools in the geosciences. A series of studies has presented the seemingly outstanding performance of CNN for predictive modelling. However, the predictive performance of such models is commonly estimated using random cross-validation, which does not account for spatial autocorrelation between training and validation data. Independent of the analytical method, such spatial dependence will inevitably inflate the estimated model performance. This problem is ignored in most CNN-related studies and suggests a flaw in their validation procedure. Here, we demonstrate how neglecting spatial autocorrelation during cross-validation leads to an optimistic model performance assessment, using the example of a tree species segmentation problem in multiple, spatially distributed drone image acquisitions. We evaluated CNN-based predictions with test data sampled from 1) randomly sampled hold-outs and 2) spatially blocked hold-outs. Assuming that a block cross-validation provides a realistic model performance, a validation with randomly sampled holdouts overestimated the model performance by up to 28%. Smaller training sample size increased this optimism. Spatial autocorrelation among observations was significantly higher within than between different remote sensing acquisitions. Thus, model performance should be tested with spatial cross-validation strategies and multiple independent remote sensing acquisitions. Otherwise, the estimated performance of any geospatial deep learning method is likely to be overestimated.

Abstract Retention forestry, which retains a portion of the original stand at the time of harvesting to maintain continuity of structural and compositional diversity, has been originally developed to mitigate the impacts of clear‐cutting. Retention of habitat trees and deadwood has since become common practice also in continuous‐cover forests of Central Europe. While the use of retention in these forests is plausible, the evidence base for its application is lacking, trade‐offs have not been quantified, it is not clear what support it receives from forest owners and other stakeholders and how it is best integrated into forest management practices. The Research Training Group ConFoBi (Conservation of Forest Biodiversity in Multiple‐use Landscapes of Central Europe) focusses on the effectiveness of retention forestry, combining ecological studies on forest biodiversity with social and economic studies of biodiversity conservation across multiple spatial scales. The aim of ConFoBi is to assess whether and how structural retention measures are appropriate for the conservation of forest biodiversity in uneven‐aged and selectively harvested continuous‐cover forests of temperate Europe. The study design is based on a pool of 135 plots (1 ha) distributed along gradients of forest connectivity and structure. The main objectives are (a) to investigate the effects of structural elements and landscape context on multiple taxa, including different trophic and functional groups, to evaluate the effectiveness of retention practices for biodiversity conservation; (b) to analyze how forest biodiversity conservation is perceived and practiced, and what costs and benefits it creates; and (c) to identify how biodiversity conservation can be effectively integrated in multi‐functional forest management. ConFoBi will quantify retention levels required across the landscape, as well as the socio‐economic prerequisites for their implementation by forest owners and managers. ConFoBi's research results will provide an evidence base for integrating biodiversity conservation into forest management in temperate forests.

Abstract One of the most important drivers for the coexistence of plant species is the resource heterogeneity of a certain environment, and several studies in different ecosystems have supported this resource heterogeneity–diversity hypothesis. However, to date, only a few studies have measured heterogeneity of light and soil resources below forest canopies to investigate their influence on understory plant species richness. Here, we aim to determine (1) the influence of forest stand structural complexity on the heterogeneity of light and soil resources below the forest canopy and (2) whether heterogeneity of resources increases understory plant species richness. Measures of stand structural complexity were obtained through inventories and remote sensing techniques in 135 1‐ha study plots of temperate forests, established along a gradient of forest structural complexity. We measured light intensity and soil chemical properties on six 25 m² subplots on each of these 135 plots and surveyed understory vegetation. We calculated the coefficient of variation of light and soil parameters to obtain measures of resource heterogeneity and determined understory plant species richness at plot level. Spatial heterogeneity of light and of soil pH increased with higher stand structural complexity, although heterogeneity of soil pH did not increase in conditions of generally high levels of light availability. Increasing light heterogeneity was also associated with increasing understory plant species richness. However, light heterogeneity had no such effects in conditions where soil resource heterogeneity (variation in soil C:N ratios) was low. Our results support the resource heterogeneity–diversity hypothesis for temperate forest understory at the stand scale. Our results also highlight the importance of interaction effects between the heterogeneity of both light and soil resources in determining plant species richness.

Information about the distribution of standing dead trees (SDT) is essential for forest biodiversity estimation, forest disturbances monitoring, and forest management strategy planning. Although remote sensing techniques offer unique capabilities to map SDT over large areas, three major hurdles exist: (1) the sporadic distribution of SDT in the study area; (2) often poor spectral separability between SDT and bare ground in forests; (3) the prominent spectral variability within SDT due to variations in background effect and canopy illumination. To address these problems, we proposed a pixel- and object-based image fusion (POBIF) approach using very high-resolution (VHR) stereo WorldView-3 (WV-3) data. The stereo WV-3 derived spectral bands, canopy height model (CHM), vegetation indices (VIs), and texture features were used as inputs in six classification scenarios with different variable combinations. A deep learning algorithm, deep neural network (DNN), and two machine learning algorithms, support vector machine (SVM) and random forest (RF), were utilized to process the pixel-based (PB) and object-based (OB) information. All PB and OB classifiers were then combined using a stacked generalization strategy to develop the POBIF model. Comparing the six scenarios we assessed the importance of the CHM, VIs, and textures for accurate SDT mapping. As a result, we found (1) the POBIF outperformed both PB and OB methods for SDT mapping, generating notably higher F1-score (p

In recent years, technological advances have led to the increasing use of unmanned aerial vehicles (UAVs) for forestry applications. One emerging field for drone application is forest health monitoring (FHM). Common approaches for FHM involve small-scale resource-extensive fieldwork combined with traditional remote sensing platforms. However, the highly dynamic nature of forests requires timely and repetitive data acquisition, often at very high spatial resolution, where conventional remote sensing techniques reach the limits of feasibility. UAVs have shown that they can meet the demands of flexible operation and high spatial resolution. This is also reflected in a rapidly growing number of publications using drones to study forest health. Only a few reviews exist which do not cover the whole research history of UAV-based FHM. Since a comprehensive review is becoming critical to identify research gaps, trends, and drawbacks, we offer a systematic analysis of 99 papers covering the last ten years of research related to UAV-based monitoring of forests threatened by biotic and abiotic stressors. Advances in drone technology are being rapidly adopted and put into practice, further improving the economical use of UAVs. Despite the many advantages of UAVs, such as their flexibility, relatively low costs, and the possibility to fly below cloud cover, we also identified some shortcomings: (1) multitemporal and long-term monitoring of forests is clearly underrepresented; (2) the rare use of hyperspectral and LiDAR sensors must drastically increase; (3) complementary data from other RS sources are not sufficiently being exploited; (4) a lack of standardized workflows poses a problem to ensure data uniformity; (5) complex machine learning algorithms and workflows obscure interpretability and hinders widespread adoption; (6) the data pipeline from acquisition to final analysis often relies on commercial software at the expense of open-source tools.

Spartina alterniflora (S. alterniflora) is one of the worst plant invaders in the coastal wetlands of China. Accurate and repeatable mapping of S. alterniflora invasion is essential to develop cost-effective management strategies for conserving native biodiversity. Traditional remote-sensing-based mapping methods require a lot of fieldwork for sample collection. Moreover, our ability to detect this invasive species is still limited because of poor spectral separability between S. alterniflora and its co-dominant native plants. Therefore, we proposed a novel scheme that uses an ensemble one-class classifier (EOCC) in combination with phenological Normalized Difference Vegetation Index (NDVI) time-series analysis (TSA) to detect S. alterniflora. We evaluated the performance of the EOCC algorithm in two scenarios, i.e., single-scene analysis (SSA) and NDVI-TSA in the core zones of Yancheng National Natural Reserve (YNNR). Meanwhile, a fully supervised classifier support vector machine (SVM) was tested in the two scenarios for comparison. With these scenarios, the crucial phenological stages and the advantage of phenological NDVI-TSA in S. alterniflora recognition were also investigated. Results indicated the EOCC using only positive training data performed similarly well with the SVM trained on complete training data in the YNNR. Moreover, the EOCC algorithm presented a more robust transferability with notably higher classification accuracy than the SVM when being transferred to a second site, without a second training. Furthermore, when combined with the phenological NDVI-TSA, the EOCC algorithm presented more balanced sensitivity–specificity result, showing slightly better transferability than it performed in the best phenological stage (i.e., senescence stage of November). The achieved results (overall accuracy (OA), Kappa, and true skill statistic (TSS) were 92.92%, 0.843, and 0.834 for the YNNR, and OA, Kappa, and TSS were 90.94%, 0.815, and 0.825 for transferability to the non-training site) suggest that our detection scheme has a high potential for the mapping of S. alterniflora across different areas, and the EOCC algorithm can be a viable alternative to traditional supervised classification method for invasive plant detection.

The retention of structural elements such as habitat trees in forests managed for timber production is essential for fulfilling the objectives of biodiversity conservation. This paper seeks to predict tree-related microhabitats (TreMs) by close-range remote sensing parameters. TreMs, such as cavities or crown deadwood, are an established tool to quantify the suitability of habitat trees for biodiversity conservation. The aim to predict TreMs based on remote sensing (RS) parameters is supposed to assist a more objective and efficient selection of retention elements. The RS parameters were collected by the use of terrestrial laser scanning as well as unmanned aerial vehicles structure from motion point cloud generation to provide a 3D distribution of plant tissue. Data was recorded on 135 1-ha plots in Germany. Statistical models were used to test the influence of 28 RS predictors, which described TreM richness (R2: 0.31) and abundance (R2: 0.31) in moderate precision and described a deviance of 44% for the abundance and 38% for richness of TreMs. Our results indicate that multiple RS techniques can achieve moderate predictions of TreM occurrence. This method allows a more efficient and objective selection of retention elements such as habitat trees that are keystone features for biodiversity conservation, even if it cannot be considered a full replacement of TreM inventories due to the moderate statistical relationship at this stage.

Forests are one of the most important terrestrial ecosystems for the protection of biodiversity, but at the same time they are under heavy production pressures. In many cases, management optimized for timber production leads to a simplification of forest structures, which is associated with species loss. In recent decades, the concept of retention forestry has been implemented in many parts of the world to mitigate this loss, by increasing structure in managed stands. Although this concept is widely adapted, our understanding what forest structure is and how to reliably measure and quantify it is still lacking. Thus, more insights into the assessment of biodiversity-relevant structures are needed, when aiming to implement retention practices in forest management to reach ambitious conservation goals. In this study we compare expert ratings on forest structural richness with a modern light detection and ranging (LiDAR) -based index, based on 52 research sites, where terrestrial laser scanning (TLS) data and 360° photos have been taken. Using an online survey (n = 444) with interactive 360° panoramic image viewers, we sought to investigate expert opinions on forest structure and learn to what degree measures of structure from terrestrial laser scans mirror experts’ estimates. We found that the experts’ ratings have large standard deviance and therefore little agreement. Nevertheless, when averaging the large number of participants, they distinguish stands according to their structural richness significantly. The stand structural complexity index (SSCI) was computed for each site from the LiDAR scan data, and this was shown to reflect some of the variation of expert ratings (p = 0.02). Together with covariates describing participants’ personal background, image properties and terrain variables, we reached a conditional R2 of 0.44 using a linear mixed effect model. The education of the participants had no influence on their ratings, but practical experience showed a clear effect. Because the SSCI and expert opinion align to a significant degree, we conclude that the SSCI is a valuable tool to support forest managers in the selection of retention patches.

Structural analysis of forests by UAV is currently growing in popularity. Given the reduction in platform costs, and the number of algorithms available to analyze data output, the number of applications has grown rapidly. Forest structures are not only linked to economic value in forestry, but also to biodiversity and vulnerability issues. LiDAR remains the most promising technique for forest structural assessment, but small LiDAR sensors suitable for UAV applications are expensive and are limited to a few manufactures. The estimation of 3D-structures from two-dimensional image sequences called ‘Structure from motion’ (SfM) overcomes this limitation by photogrammetrically reconstructing point clouds similar to those rendered from LiDAR sensors. The result of these techniques in highly structured terrain strongly depends on the methods employed during image acquisition, therefore structural indices might be vulnerable to misspecifications in flight campaigns. In this paper, we outline how image overlap and ground sampling distances affect image reconstruction completeness in 2D and 3D. Higher image overlaps and coarser GSDs have a clearly positive influence on reconstruction quality. Therefore, higher accuracy requirements in the GSD must be compensated by a higher image overlap. The best results are achieved with an image overlap of > 95% and a resolution of > 5 cm. The most important environmental factors have been found to be wind and terrain elevation, which could be an indicator of vegetation density.