Search

Remote sensing has developed into an omnipresent technology in the scientific field of forestry and is also increasingly used in an operational fashion. However, the pace and level of uptake of remote sensing technologies into operational forest inventory and monitoring programs varies notably by geographic region. Herein, we highlight some key challenges that remote sensing research can address in the near future to further increase the acceptance, suitability and integration of remotely sensed data into operational forest inventory and monitoring programs. We particularly emphasize three recurrent themes: (1) user uptake, (2) technical challenges of remote sensing related to forest inventories and (3) challenges related to map validation. Our key recommendations concerning these three thematic areas include (1) a need to communicate and learn from success stories in those geographic regions where user uptake was successful due to multi-disciplinary collaborations supported by administrative incentives, (2) a shift from regional case studies towards studies addressing ‘real world’ problems focusing on forest attributes that match the spatial scales and thematic information needs of end users and (3) an increased effort to develop, communicate, and apply best-practices for map and model validation including an effort to inform current and future remote sensing scientists regarding the need for and the functionalities of these best practices. Finally, we present information regarding the use of remote sensing for forest inventory and monitoring, combined with recommendations where possible, and highlighting areas of opportunity for additional investigation.

Airborne laser scanning (ALS) data are routinely used to estimate and map structure-related forest inventory variables. The further development, refinement and evaluation of methods to derive forest inventory variables from ALS data require extensive datasets of forest stand information on an individual tree-level and corresponding ALS data. A cost-efficient method to obtain such datasets is the combination of virtual forest stands with a laser scanning simulator. We present an approach to simulate ALS data of forest stands by combining forest inventory information, a tree point cloud database and the laser scanning simulation framework HELIOS++. ALS data of six 1-ha plots were simulated and compared to real ALS data of these plots. The synthetic 3D representations of the forest stands were composed of real laser scanning point clouds of individual trees that were acquired by an uncrewed aerial vehicle (UAV), and, for comparison, simplified tree models with cylindrical stems and spheroidal crowns. The simulated ALS point clouds of the six plots were compared with the real point clouds based on canopy cover, height distribution of returns and several other point cloud metrics. In addition, the performance of biomass models trained using these synthetic data was evaluated. The comparison revealed that, in general, both the real tree models and the simplified tree models can be used to generate synthetic data. The results differed for the different study sites and depending on whether all returns or only first returns were considered. The measure of canopy cover was better represented by the data of the simplified tree models, whereas the height distribution of the returns was – for most of the study sites – better represented by the real tree model data. Training biomass models with metrics derived from the real tree model data led to an overestimation of biomass, while using metrics of the simplified tree model data resulted in an underestimation of biomass. Still, the accuracy of models trained with simulated data was only slightly lower compared to models trained with real ALS data. Our results suggest that the presented approach can be used to generate ALS data that are sufficiently realistic for many applications. The synthetic data may be used to develop new or refine existing ALS-based forest inventory methods, to systematically investigate the relationship between point cloud metrics and forest inventory variables and to analyse how this relationship is affected by laser scanning acquisition settings and field reference data characteristics.

Tree species composition maps derived from hyperspectral data have been found to be accurate but it is still unclear whether an optimal time window exists to acquire the images. Trees in temperate forests are subject to phenological changes that are species-specific and can have an impact on species recognition. Our study examined the performance of a multitemporal hyperspectral dataset to classify tree species in the Polish part of the Białowieża Forest. We classified seven tree species including spruce (Picea abies (L.) H.Karst), pine (Pinus sylvestris L.), alder (Alnus glutinosa Gaertn.), oak (Quercus robur L.), birch (Betula pendula Roth), hornbeam (Carpinus betulus L.) and linden (Tilia cordata Mill.), using Support Vector Machines. We compared the results for three data acquisitions—early and late summer (2–4 July and 24–27 August), and autumn (1–2 October) as well as a classification based on an image stack containing all three acquisitions. Furthermore, the sizes (height and crown diameter) of misclassified and correctly classified trees of the same species were compared. The early summer acquisition reached the highest accuracies with an Overall Accuracy (OA) of 83–94 per cent and Kappa (κ) of 0.80–0.92. The classification based on the stacked multitemporal dataset resulted in slightly higher accuracies (84–94 per cent OA and 0.81–0.92 κ). For some species, e.g. birch and oak, tree size differed notably for correctly and incorrectly classified trees. We conclude that implementing multitemporal hyperspectral data can improve the classification result as compared with a single acquisition. However, the obtained accuracy of the multitemporal image stack was in our case comparable to the best single-date classification and investing more time in identifying regionally optimal acquisition windows may be worthwhile as long hyperspectral acquisitions are still sparse.

Aim: The spectral variability hypothesis (SVH) suggests a link between spectral varia -tion and plant biodiversity. The underlying assumptions are that higher spectral vari-ation in canopy reflectance (depending on scale) is caused by either (1) variation in habitats or linked vegetation types or plant communities with their specific optical community traits or (2) variation in the species themselves and their specific optical traits.Methods: The SVH was examined in several empirical remote-sensing case studies, which often report some correlation between spectral variation and biodiversity- related variables (mostly plant species counts); however, the strength of the observed correlations varies between studies. In contrast, studies focussing on understanding the causal relationship between (plant) species counts and spectral variation remain scarce. Here, we discuss these causal relationships and support our perspectives through simulations and experimental data.Results: We reveal that in many situations the spectral variation caused by species or functional traits is subtle in comparison to other factors such as seasonality and physiological status. Moreover, the degree of contrast in reflectance has little to do with the number but rather with the identity of the species or communities involved. Hence, spectral variability should not be expressed based on contrast but rather based on metrics expressing manifoldness. While we describe cases where a certain link between spectral variation and plant species diversity can be expected, we be -lieve that as a scientific hypothesis (which suggests a general validity of this assumed relationship) the SVH is flawed and requires refinement.Conclusions: To this end we call for more research examining the drivers of spectral variation in vegetation canopies and their link to plant species diversity and biodiver-sity in general. Such research will allow critically assessing under which conditions spectral variation is a useful indicator for biodiversity monitoring and how it could be integrated into monitoring network.

Regular biomass estimations for natural and plantation forests are important to support sustainable forestry and to calculate carbon-related statistics. The application of remote sensing data to estimate biomass of forests has been amply demonstrated but there is still space for increasing the efficiency of current approaches. Here, we investigated the influence of field plot and sample sizes on the accuracy of random forest models trained with information derived from Pléiades very high resolution (VHR) stereo images applied to plantation forests in an arid environment. We collected field data at 311 locations with three different plot area sizes (100, 300 and 500 m2). In two experiments, we demonstrate how plot and sample sizes influence the accuracy of biomass estimation models. In the first experiment, we compared model accuracies obtained with varying plot sizes but constant number of samples. In the second experiment, we fixed the total area to be sampled to account for the additional effort to collect large field plots. Our results for the first experiment show that model performance metrics Spearman’s r, RMSErel and RMSEnor improve from 0.61, 0.70 and 0.36 at a sample size of 24–0.79, 0.51 and 0.15 at a sample size of 192, respectively. In the second experiment, highest accuracies were obtained with a plot size of 100 m2 (most samples) with Spearman’s r = 0.77, RMSErel = 0.59 and RMSEnor = 0.15. Results from an analysis of variance type-II suggest that the overall most important factors to explain model performance metrics for our biomass models is sample size. Our results suggest no clear advantage for any plot size to reach accurate biomass estimates using VHR stereo imagery in plantations. This is an important finding, which partly contradicts the suggestions of earlier studies but requires validation for other forest types and remote sensing data types (e.g. LiDAR).

Disturbances caused by biotic and abiotic agents are inherent processes of forest ecosystems and often play a key role in forming the ecosystems themselves. However, they also may negatively affect wood production and alter ecosystem services. Thus, precise and up-to-date spatial information on invasion presence and dynamic is essential when planning protective and sanitary actions. Gathering such data is still challenging if large areas, with limited access, are affected. This study addresses this issue by suggesting a GIS-workflow based on high-resolution multi-temporal remote sensing data to describe the spread of a bark beetle outbreak by mapping dead trees on a single tree level and estimating timber losses associated with the infestations in the Polish part of the Bialowieza Forest (BF) in 2015. Along with field inventories, airborne hyperspectral data were collected in 2015 at three acquisition dates: 2–4 July, between August 24th and 27th and between 1st and 2nd October with a HySpex VNIR-1800 camera. Airborne laser scanning data were collected during 2–5 July 2015 using a Riegl LMS-Q680i scanner. The analysis consisted of three steps: (i) hyperspectral images were used to stratify the area into living vegetation and all other classes (including dead trees); (ii) a single tree delineation was performed and individual tree volumes were calculated based on a volume ∼ height allometry; (iii) the delineated trees were assigned as either living trees or dead trees (snags). Based on these data bark beetle dynamics between the three time steps were analysed. The results show that the Ips typographus outbreak in BF in 2015 was very dynamic. The volume of dead standing spruce detected in subsequent dates was as follows: July – 503 410 m3; August – 640 692 m3; October – 737 978 m3 New groups of dead trees in the flat landscape of Bialowieza forest appeared preferably but not only in the immediate vicinity of trees classified as dead in the previous period. New infestations were also identified over 100 m away from earlier infested trees. On the other hand, we could not identify any tendencies in terms of the spread direction which makes the prediction of the spread-dynamics challenging in our study area.

Invasive plant species can pose major threats to biodiversity, ecosystem functioning and services. Satellite based remote sensing has evolved as an important technology to spatially map the occurrence of invasive species in space and time. With the new era of the Sentinel missions, Synthetic Aperture Radar (SAR) and multispectral data are now freely available and repeatedly acquired on a high spatial and temporal resolution for the entire globe. However, the high potential of such sensors for automatic mapping procedures cannot be fully harnessed without sufficient and appropriate reference data for model calibration. Reference data are commonly acquired in field surveys, which however, are often relatively expensive and affected by sampling and observer bias. Moreover, a direct transferability to the remote sensing perspective and scale is difficult. Accordingly, we firstly assess the potential of Unmanned Aerial Vehicles (UAV) for semi-automatic reference data acquisition on species cover of three woody invasive species Pinus radiata, Ulex europaeus and Acacia dealbata occurring in Chile. Secondly, we test the upscaling of the estimated species cover to the spatial scale of Sentinel-1 and Sentinel-2. The proposed workflow includes the visual sampling of respective canopies in UAV orthomosaics and the subsequent spatial extrapolations using MaxEnt with spectral (RGB, Hyperspectral), textural (2D) and canopy structural (3D) predictors derived from UAV-based photogrammetry. These UAV-based maps are then used to train random forest models with multitemporal Sentinel-1 and Sentinel-2 data to map the invasive species cover on large spatial scales. Our results show that the semi-automatic UAV-based mapping of the three invasive species results in accurate predictions. Depending on the predictor combination, the correlation was 0.70, 0.77 and 0.90 for Pinus radiatia, Ulex europaeus, Acacia dealbata, respectively. Among the three species, we observed clear differences in the model performance between the tested photogrammetric predictors and their combinations (spectral, 2D texture or 3D structure). For scaling up the UAV-based estimates to the satellite-scale, the Sentinel-2 data (multispectral) were more important than Sentinel-1 data (SAR). An independent validation revealed that the R2 of the upscaling accounted for 0.78 or higher for all species and RMSE lower than 12%. Our results hence demonstrate that UAV-based reference data acquisitions are a promising alternative to traditional field surveys if the target species are directly identifiable in the UAV data.

In the Maipo watershed, situated in central Chile, mining activities are impacting high altitude Andean wetlands through the consumption and exploitation of water and land. As wetlands are vulnerable and particularly susceptible to changes of water supply, alterations and modifications in the hydrological regime have direct effects on their ecophysiological condition and vegetation cover. The aim of this study was to evaluate the potential of Worldview-2 and Sentinel-2 sensors to identify and map Andean wetlands through the use of the one-class classifier Bias support vector machines (BSVM), and then to estimate soil moisture content of the identified wetlands during snow-free summer using partial least square regression. The results obtained in this research showed that the combination of remote sensing data and a small sample of ground reference measurements enables to map Andean high altitude wetlands with high accuracies. BSVM was capable to classify the meadow areas with an overall accuracy of over ∼78% for both sensors. Our results also indicate that it is feasible to map surface soil moisture with optical remote sensing data and simple regression approaches in the examined environment. Surface soil moisture estimates reached r2 values of up to 0.58, and normalized mean square errors of 19% using Sentinel-2 data, while Worldview-2 estimates resulted in non-satisfying results. The presented approach is particularly valuable for monitoring high-mountain wetland areas with limited accessibility such as in the Andes.

Rain throughfall under vegetation is determined by characteristics of the vertical structure and the associated plant traits. It goes both ways: A protective layer of ground covering vegetation or leaf litter can decrease throughfall kinetic energy (TKE), whereas the formation of large drips in the canopy layers has been found to increase TKE. Abstracting the three-dimensional vegetation structure into usable quantitative metrics is challenging, and therefore these processes have not yet been sufficiently integrated into spatial erosion models. The vegetation splash factor (VSF) was designed to close this gap (Senn et al. 2020, DOI: 10.1002/esp.4820). The VSF quantifies the influence of vegetation on TKE and can be calculated from aerial lidar point clouds. In the first step, we derive the vegetation cover in a voxel space, which then allows modelling the proportional contribution of drips per layer to reach the ground. Hence, the approach is strictly based on the 3D structure rather than conventional forestry parameters, e.g. crown diameter or leaf sizes. Here, we present the result of the first application of the VSF in a small scale field study using splash cup measurements to validate and refine the concept.We implemented the experiment in a mixed-broadleaf forest near Bretten, Germany with a beech and an oak-dominated plot to cover a variety of vertical forest structure configurations and a diverse composition of species. Each plot comprised two transects of ten splash cups to measure sand loss - as a proxy for TKE - during six individual rainfall events. In addition, we used micro-scale runoff plots to determine the effect of soil covering layers such as leaf litter or biological soil crusts in comparison to bare soil. The VSF was calculated in R with a voxel resolution of 0.5 x 0.5 x 0.25 m using a UAV lidar dataset. Initial results from the splash cup measurements showed that young oak induced about 70 % higher TKE than adult beech trees. Among the individual cup positions, the lowest energy values were measured without canopy influence as freefall kinetic energy (FKE), TKE at positions with an intermediate young growth and shrub layer showed medium values. In near-trunk and mid-positions without intermediate layers, we measured TKE values more than twice as high as FKE. This resulted in significant sediment removal beneath the tree layer when the ground covering vegetation layer was removed, which is in accordance with studies from other ecosystems. Grouped according to these conventional vegetation structural criteria, we found that the calculated VSF values clustered around similar values and correlated with sand loss from splash cups. From these initial results, we assume general suitability of the VSF to reflect the influence of vegetation structure on TKE. Further, more detailed analysis will now be done to adjust and calibrate the VSF model to produce more indicative results. The preliminary findings presented here will be further expanded to be presented at vEGU21.

Virtual laser scanning (VLS) is a valuable method to complement expensive laser scanning data acquisition in the field. VLS refers to the simulation of LiDAR to create 3D point clouds from models of scenes, platforms and sensors mimicking real world acquisitions. In forestry, this can be used to generate training and testing data with complete ground truth for algorithms performing essential tasks such as tree detection or tree species classification. Furthermore, VLS allows for the in-depth investigation of the influence of different acquisition parameters on the point clouds and thus also the behaviour of algorithms, which is important when relating point cloud metrics to forest inventory variables. Finally, VLS can be used for acquisition planning and optimisation, as different configurations can be tested regarding their ability to create data of the required quality with minimal effort. For these purposes, we developed the open source Heidelberg LiDAR Operations Simulator HELIOS++ (written in C++) which is available on GitHub (https://github.com/3dgeo-heidelberg/helios), as a precompiled command line tool, and as Python package (pyhelios). HELIOS++ provides a high-fidelity framework for full 3D laser scanning simulations with multiple platforms and a flexible system to represent the scene. HELIOS++ models the beam divergence and supports the recording of the full waveform.One important premise for the usefulness of VLS data is the use of an adequate 3D scene in the simulation. In this context, we conducted a study investigating point clouds simulated based on opaque voxel-based forest models computed from terrestrial laser scanning data using different voxel sizes. Coupling the LiDAR simulation with a database containing point clouds of single trees from terrestrial, UAV-borne and airborne acquisitions, allowed us to compare metrics derived from real and simulated data. Furthermore, by including the tree neighbourhood in the scene, we were able to consider occlusion effects between the trees.We found that the voxel size is an important parameter, where values of e.g. 0.25 m lead to unrealistic occlusion effects of the mid- and understory, as only few gaps remain in the forest models through which the laser beam can pass. This results in fewer multiple returns, the vertical point distribution is shifted upwards, and tree metrics such as crown projection area and crown base height are estimated poorly. Smaller voxel sizes are therefore preferable, though the appropriate voxel size depends on the resolution of the input point cloud. With very small voxels, the voxel model may become too transparent. To achieve realistic simulations without the need for a high number of voxels we suggest variable downscaling of voxel cubes based on appropriate local metrics such as the plant area density. This approach decreases the computational requirements for the simulation, as fewer primitives are present in the scene. In our study, the use of such scaled voxels derived for a grid size of 0.25 m achieves equally and partly more reliable estimates of point cloud and tree metrics than regular voxels at fixed side lengths of 0.05 and 0.02 m.

In recent years, forest fires have become more frequent in central Europe. As the frequency and magnitude of future extreme weather events such as droughts are projected to increase, also the trend of increasing fire frequency in temperate forests is expected to continue. However, knowledge about fire behavior and spread dynamics in these forests is scarce. One of the key drivers of fire behavior is the availability of flammable vegetation, i.e. fuels. In the project ErWiN, we aim to describe the amount and distribution of fuels in different forest types in Southwestern Germany. Detailed field inventories of fuels in all vertical strata of the stands allow a first classification into different fuel types, which can be used in fire behavior simulations to obtain estimates of fire spread and intensity. In a further step, deep learning algorithms will be trained on recognizing these fuel types on GNSS located photos of forest stand situations to provide an efficient solution for mapping fuels in the field. By coupling field data with detailed remotely sensed information on forest structure obtained from airborne laserscanning, continuous fuel maps will be derived. Such fuel maps in turn allow landscape-scale analysis of fire behavior and can be useful in forest management decisions as well as in developing firefighting strategies. We thus hope to make a contribution to a better understanding of fuel-driven fire risk in central European forests and to facilitate the operational use of fire behavior models. In this contribution we present the concept developed in the ErWiN project and present first results obtained from the field survey of fuel types in Southwestern Germany.

Mapping the occurrence patterns of invasive plant species and understanding their invasion dynamics is a crucial requirement for preventing further spread to so far unaffected regions. An established approach to map invasive species across large areas is based on the combination of satellite or aerial remote sensing data with ground truth data from fieldwork. Unmanned aerial vehicles (UAV, also referred to as unmanned aerial systems (UAS)) may represent an interesting and low-cost alternative to labor-intensive fieldwork. Despite the increasing use of UAVs in the field of remote sensing in the last years, operational methods to combine UAV and satellite data are still sparse. Here, we present a new methodological framework to estimate the fractional coverage (FC%) of the invasive shrub species Ulex europaeus (common gorse) on Chilo´e Island (south-central Chile), based on ultrahigh- resolution UAV images and a medium resolution intra-annual time-series of Sentinel-2. Our framework is based on three steps: 1) Land cover classification of the UAV orthoimages, 2) reduce the spatial shift between UAV-based land cover classification maps and Sentinel-2 imagery and 3) identify optimal satellite acquisition dates for estimating the actual distribution of Ulex europaeus. In Step 2 we translate the challenging co-registration task between two datasets with very different spatial resolutions into an (machine learning) optimization problem where the UAV-based land cover classification maps obtained in Step 1 are systematically shifted against the satellite images. Based on several Random Forest (RF) models, an optimal fit between varying land cover fractions and the spectral information of Sentinel-2 is identified to correct the spatial offset between both datasets. Considering the spatial shifts of the UAV orthoimages and using optimally timed Sentinel-2 acquisitions led to a significant improvement for the estimation of the current distribution of Ulex europaeus. Furthermore, we found that the Sentinel-2 acquisition from November (flowering time of Ulex europaeus) was particularly important in distinguishing Ulex europaeus from other plant species. Our mapping results could support local efforts in controlling Ulex europaeus. Furthermore, the proposed workflow should be transferable to other use cases where individual target species that are visually detectable in UAV imagery are considered. These findings confirm and underline the great potential of UAV-based groundtruth data for detecting invasive species.

Context Landscape ecology thinking and social–ecological system (SES) thinking investigate human–environment relationships from the perspective of ‘space’ and ‘system’, respectively. To date, empirical landscape ecology studies attempting to understand SES complexities are rare. Objectives Using the Tibetan pastoral landscape as an empirical example, we conceptualize the black-soil formation as SES regime shifts. We seek to illustrate the spatial patterns of black-soil formation in the Tibetan SES, and to reveal their underlying ecological processes. Methods We conducted interdisciplinary research in a Tibetan pastoral village. We obtained quantitative data on historical land-use intensity (LUI) and the associated management narratives. Landsat-based NDVI time series were used to derive a grassland productivity proxy and to reconstruct the process leading to the up-scaling of the regime shift of degradation. Results Important SES features, such as LUI, productivity and degradation risk are heterogeneously distributed in space. Land-use intensification at farm-scales in the 1990s increased landscape-scale degradation risks. Eventually the regime shift of degradation scaled up from the plot level to the landscape level in the 2010s. The time lag was related to the gradual invasion of a native burrowing animal, the plateau pika, which inhabits low-vegetation height pastures. Conclusions Our study shows that landscape ecology thinking provides an important spatial perspective to understanding SES complexities. The finding that unfavorable SES regime shifts are strongly linked across spatial scales implies that an ‘entry point’ into an adaptive management circle should be initiated when local-scale regime shifts are perceived and interpreted as early warning signals.

The montane subtropical broad-leaved humid forests of Meghalaya (Northeast India) are highly diverse and situated at the transition zone between the Eastern Himalayas and Indo-Burma biodiversity hotspots. In this study, we have used inventory data from seedlings to canopy level to assess the impact of both biotic and abiotic disturbances on structure, composition, and regeneration potential of the Fagaceae trees of these forests. Fagaceae trees are considered as the keystone species in these forests due to their regional dominance and their importance as a fuel wood source, and also because they form an important component of climax community in these forests. Unfortunately, these forests are highly degraded and fragmented due to anthropogenic disturbances. We have assessed, for the first time, the restoration potential (i.e., capacity to naturally regenerate and sustain desired forest structure) of Fagaceae species in the genera Lithocarpus Blume, Castanopsis (D. Don) Spach, and Quercus Linn. We also evaluated how biotic and abiotic factors, as well as anthropogenic disturbances, influence the restoration potential of these species in six fragmented forest patches located along an elevational gradient on south-facing slopes in the Khasi Hills, Meghalaya. Fagaceae was the most dominant family at all sites except one site (Laitkynsew), where it was co-dominant with Lauraceae. Fagaceae forests have shown high diversity and community assemblages. Fagaceae species had high levels of natural regeneration (i.e., seedlings and saplings) but low recruitment to large trees (diameter at breast height or DBH &ge, 10 cm) at all sites. The ability to sprout was higher in Fagaceae tree species than non-Fagaceae tree species. We have shown that human disturbance and structural diversity were positively related to regeneration of Fagaceae tree species due to high sprouting. However, with increasing human disturbance, recruitment of saplings and pole-sized trees to mature trees hampered the resulting proportion of mature Fagaceae tree species. This study provides a means for assessing regeneration and a basis for forest management strategies in degraded and fragmented forests of Meghalaya.

Soil loss caused by erosion has enormous economic and social impacts. Splash effects of rainfall are an important driver of erosion processes; however, effects of vegetation on splash erosion are still not fully understood. Splash erosion processes under vegetation are investigated by means of throughfall kinetic energy (TKE). Previous studies on TKE utilized a heterogeneous set of plant and canopy parameters to assess vegetation's influence on erosion by rain splash but remained on individual plant‐ or plot‐levels. In the present study we developed a method for the area‐wide estimation of the influence of vegetation on TKE using remote sensing methods. In a literature review we identified key vegetation variables influencing splash erosion and developed a conceptual model to describe the interaction of vegetation and raindrops. Our model considers both amplifying and protecting effect of vegetation layers according to their height above the ground and aggregates them into a new indicator: the Vegetation Splash Factor (VSF). It is based on the proportional contribution of drips per layer, which can be calculated via the vegetation cover profile from airborne LiDAR datasets. In a case study, we calculated the VSF using a LiDAR dataset for La Campana National Park in central Chile. The studied catchment comprises a heterogeneous mosaic of vegetation layer combinations and types and is hence well suited to test the approach. We calculated a VSF map showing the relation between vegetation structure and its expected influence on TKE. Mean VSF was 1.42, indicating amplifying overall effect of vegetation on TKE that was present in 81% of the area. Values below 1 indicating a protective effect were calculated for 19% of the area. For future work, we recommend refining the weighting factor by calibration to local conditions using field‐reference data and comparing the VSF with TKE field measurements. © 2020 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd, The Vegetation Splash Factor maps the amplification or reduction of rain‐fall kinetic energy based on the three‐dimensional vegetation structure. The presented approach allows the area‐wide application based on aerial lidar point clouds and can improve the representation of vegetation in erosion models. This study features a literature review and a case study documenting the development of the new approach., Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659

Long-term analyses of vegetation succession after catastrophic events are of high interest for an improved understanding of succession dynamics. However, in many studies such analyses were restricted to plot-based measurements. Contrarily, spatially continuous observations of succession dynamics over extended areas and time-periods are sparse. Here, we applied a change vector analysis (CVA) to investigate vegetation succession dynamics at Mount St. Helens after the great volcanic eruption in 1980 using Landsat. We additionally applied a supervised random forest classification using Sentinel-2 data to map the currently prevailing vegetation types. Change vector analysis was performed with the normalized difference vegetation index (NDVI) and the urban index (UI) for three subsequent decades after the eruption as well as for the whole observation time between 1984 and 2016. The influence of topography on the current vegetation distribution was examined by comparing altitude, slope angles and aspect values of vegetation classes derived by the random forest classification. Wilcox- Rank-Sum test was applied to test for significant differences between topographic properties of the vegetation classes inside and outside of the areas affected by the eruption. For the full time period, a total area of 516 km2 was identified as re-vegetated, whereas the area and magnitude of re-growing vegetation decreased during the three decades and migrated closer to the volcanic crater. Vegetation losses were mainly observed in regions unaffected by the eruption and related mostly to timber harvesting. The vegetation type classification reached a high overall accuracy of approximately 90%. 36 years after the eruption, coniferous and deciduous trees have established at formerly devastated areas dominating with a proportion of 66%, whereas shrubs are more abundant in riparian zones. Sparse vegetation dominates at regions very close to the crater. Elevation was found to have a great influence on the reestablishment and distribution of the vegetation classes within the devastated areas showing in almost all cases significant differences in altitude distribution. Slope was less important for the different classes - only representing significantly higher values for meadows, whereas aspect seems to have no notable influence on the reestablishment of vegetation at Mount St. Helens. We conclude that major vegetation succession dynamics after catastrophic events can be assessed and characterized over large areas from freely available remote sensing data and hence contribute to an improved understanding of succession dynamics.

With the maturation of methods for estimating aboveground forest biomass by remote sensing, researchers increasingly need test data, particularly ground reference data, that are large enough to fine-tune existing approaches and test their robustness under diverse conditions. In this context, realistic synthetic datasets present an interesting alternative to costly and limited field data. Here, we present a new approach to simulate realistic canopy height and cover type data by combining an individual-tree forest simulator with real LiDAR point clouds of individual trees. We demonstrate the utility of our approach by re-examining the influence of field plot size on the predictive power of remote-sensing models for biomass estimation. Our approach with a complete (wall-to-wall) field reference dataset and matching synthetic remote sensing data allowed us to not only perform internal cross-validations with field plots that were used to fit the model (as in studies with real data), but to also consider the quality of model predictions to a standardized spatial grid or to the entire region. Our results confirm earlier reports of smaller predictive errors with increased field plot sizes under internal model validation (RMSE of 125 t/ha at 10 m field plot size and RMSE of 40 t/ha for 50 m field plots). However, we show that this is mainly an artifact of comparing the models with the same data they were fit, thus with validation data of different scales. When validating on a grid with standardized scale, smaller field plots performed almost equally well as larger field plots (small RMSE decrease between 4 t/ha and 7 t/ha when going from 10 m to 50 m plots), and even outperformed them if we assumed that increasing the plot size means that fewer field plots can be obtained (small RMSE increase between 9 t/ha and 12 t/ha when going from 10 m to 50 m plots). We conclude that synthetic remote sensing datasets are a useful tool for method testing. The suggested approach may be used to reexamine our current methodological understanding, which is often based on tests with real data of very limited sizes, as well as to optimize workflows, model choices and data collection. A wider use of synthetic data could be instrumental in improving remote sensing methodology.

W dniach 12–14 marca 2019 r. w Instytucie Badawczym Leśnictwa odbyła się XI Sesja Zimowej Szkoły Leśnej (ZSL), której tematem było „Zastosowanie geoinformatyki w leśnictwie”. Współorganizatorem Sesji była Dyrekcja Generalna Lasów Państwowych. Merytoryczne aspekty XI Sesji zostały przygotowane i opracowane przez Radę Programową Zimowej Szkoły Leśnej w składzie: prof. dr hab. Andrzej Grzywacz, prof. dr hab. Jacek Hilszczański, dr inż. Krzysztof Janeczko, prof. dr hab. Andrzej Klocek, mgr inż. Jacek Przypaśniak, mgr inż. Marek Roman, dr inż. Jacek Sagan, dr hab. Krzysztof Stereńczak, prof. IBL (przewodniczący), mgr inż. Adam Wasiak, prof. dr hab. Tomasz Zawiła-Niedźwiecki. Rada Programowa określiła nie tylko zakres problematyki XI Sesji, ale także propozycje tematyczne referatów zamawianych oraz wytypowania ich autorów. W trakcie XI Sesji referaty wygłosili zaproszeni goście zagraniczni reprezentujący Remote Sensing Research and Consulting Ltd. (Szwajcaria), Wyższą Szkołę Techniczną w Zurychu (Szwajcaria), Norweski Uniwersytet Przyrodniczy – NMBU, Federalny Instytut Badawczy Lasu, Śniegu i Krajobrazu (Szwajcaria), Uniwersytet we Fryburgu (Niemcy), Uniwersytet Florencki (Włochy), Instytut Geografii i Geoekologii i Instytut Technologii w Karlsruhe (Niemcy), Instytut Lasu im. Sukaczewa Syberyjskiej Filii Rosyjskiej Akademii Nauk, Federalne Centrum Badawcze «Krasnoyarsk Science Center SB RAS» i Syberyjski Uniwersytet Federalny (Rosja) oraz Uniwersytet w Cambridge (Wielka Brytania). Poza gośćmi z zagranicy na Zimowej Szkole Leśnej wystąpiło szerokie grono autorów z Polski, prezentujących wiele przykładów wykorzystania geoinformatyki w leśnictwie. W tym miejscu składam serdeczne podziękowanie autorom za przygotowanie i wygłoszenie interesujących referatów, a także za opracowanie tekstów wystąpień, które znajdą Państwo w niniejszych materiałach posesyjnych. W XI Sesji ZSL wzięło udział prawie 400 osób reprezentujących m.in.: kierownictwo Lasów Państwowych oraz jednostki organizacyjne PGL LP i Ministerstwo Środowiska. Najliczniejszą grupę słuchaczy Szkoły stanowili zarówno geomatycyreprezentujący szeroko rozumiany świat nauki, jak i firmy zajmujące się wdrożeniami i praktyką, pracownicy nadleśnictw oraz biur urządzania lasu i geodezji leśnej, na co dzień korzystających z narzędzi geoinformatycznych. Liczne grono stanowili przedstawiciele ośrodków naukowych, a także studentów polskich wydziałów leśnych.

Imaging spectroscopy is a powerful tool for mapping chemical leaf traits at the canopy level. However, covariance with structural canopy properties is hampering the ability to predict leaf biochemical traits in structurally heterogeneous forests. Here, we used imaging spectroscopy data to map canopy level leaf nitrogen (Nₘₐₛₛ) and phosphorus concentrations (Pₘₐₛₛ) of a temperate mixed forest. By integrating predictor variables derived from airborne laser scanning (LiDAR), capturing the biophysical complexity of the canopy, we aimed at improving predictions of Nₘₐₛₛ and Pₘₐₛₛ. We used partial least squares regression (PLSR) models to link community weighted means of both leaf constituents with 245 hyperspectral bands (426–2425 nm) and 38 LiDAR-derived variables. LiDAR-derived variables improved the model's explained variances for Nₘₐₛₛ (R²cᵥ 0.31 vs. 0.41, % RSMEcᵥ 3.3 vs. 3.0) and Pₘₐₛₛ (R²cᵥ 0.45 vs. 0.63, % RSMEcᵥ 15.3 vs. 12.5). The predictive performances of Nₘₐₛₛ models using hyperspectral bands only, decreased with increasing structural heterogeneity included in the calibration dataset. To test the independent contribution of canopy structure we additionally fit the models using only LiDAR-derived variables as predictors. Resulting R²cᵥ values ranged from 0.26 for Nₘₐₛₛ to 0.54 for Pₘₐₛₛ indicating considerable covariation between biochemical traits and forest structural properties. Nₘₐₛₛ was negatively related to the spatial heterogeneity of canopy density, whereas Pₘₐₛₛ was negatively related to stand height and to the total cover of tree canopies. In the specific setting of this study, the importance of structural variables can be attributed to the presence of two tree species, featuring structural and biochemical properties different from co-occurring species. Still, existing functional linkages between structure and biochemistry at the leaf and canopy level suggest that canopy structure, used as proxy, can in general support the mapping of leaf biochemistry over broad spatial extents. ispartof: Remote Sensing of Environment vol:211 pages:13-25 status: published

Virtual laser scanning (VLS), the simulation of laser scanning in a computer environment, is a useful tool for field campaign planning, acquisition optimisation, and development and sensitivity analyses of algorithms in various disciplines including forestry research. One key to meaningful VLS is a suitable 3D representation of the objects of interest. For VLS of forests, the way trees are constructed influences both the performance and the realism of the simulations. In this contribution, we analyse how well VLS can reproduce scans of individual trees in a forest. Specifically, we examine how different voxel sizes used to create a virtual forest affect point cloud metrics (e.g., height percentiles) and tree metrics (e.g., tree height and crown base height) derived from simulated point clouds. The level of detail in the voxelisation is dependent on the voxel size, which influences the number of voxel cells of the model. A smaller voxel size (i.e., more voxels) increases the computational cost of laser scanning simulations but allows for more detail in the object representation. We present a method that decouples voxel grid resolution from final voxel cube size by scaling voxels to smaller cubes, whose surface area is proportional to estimated normalised local plant area density. Voxel models are created from terrestrial laser scanning point clouds and then virtually scanned in one airborne and one UAV-borne simulation scenario. Using a comprehensive dataset of spatially overlapping terrestrial, UAV-borne and airborne laser scanning field data, we compare metrics derived from simulated point clouds and from real reference point clouds. Compared to voxel cubes of fixed size with the same base grid size, using scaled voxels greatly improves the agreement of simulated and real point cloud metrics and tree metrics. This can be largely attributed to reduced artificial occlusion effects. The scaled voxels better represent gaps in the canopy, allowing for higher and more realistic crown penetration. Similarly high accuracy in the derived metrics can be achieved using regular fixed-sized voxel models with notably finer resolution, e.g., 0.02 m. But this can pose a computational limitation for running simulations over large forest plots due to the ca. 50 times higher number of filled voxels. We conclude that opaque scaled voxel models enable realistic laser scanning simulations in forests and avoid the high computational cost of small fixed-sized voxels.

Grasslands are one of the ecosystems that have been strongly affected by anthropogenic impacts. The state-of-the-art in monitoring changes in grassland species composition is to conduct repeated plot-based vegetation surveys that assess the occurrence and cover of plants. These plot-based surveys are typically limited to comparably small areas and the quality of the cover estimates depends strongly on the experience and performance of the surveyors. Here, we investigate the possibility of a semi-automated, image-based method for cover estimates, by analyzing the applicability of very high spatial resolution hyperspectral data to classify grassland species at the level of individuals. This individual-oriented approach is seen as an alternative to community-oriented remote sensing depicting canopy reflectance as the total of mixed species reflectance. An AISA + imaging spectrometer mounted on a scaffold was used to scan 1 m 2 grassland plots and assess the impact of four sources of variation on the predicted species cover: (1) the spatial resolution of the scans, (2) complexity, i.e. species number and structural diversity, (3) the species cover and (4) the share of functional types (graminoids and forbs). Classifications were conducted using a support vector machine classification with a linear kernel, obtaining a median Kappa of ~ 0.8. Species cover estimations reached median r 2 and root mean square errors (RMSE) of ~ 0.6 and ~ 6.2% respectively. We found that the spatial resolution and diversity level (mainly structural diversity) were the most important sources of variation affecting the performance of the proposed approach. A spatial resolution below 1 cm produced relatively good models for estimating species-specific coverages (r 2 = ~ 0.6; RMSE = ~ 7.5%) while predictions using pixel sizes over that threshold failed in this individual-oriented approach (r 2 = ~ 0.17; RMSE = ~ 20.7%). Areas with low inter-species overlap were better suited than areas with frequent inter-species overlap. We conclude that the application of very high resolution hyperspectral remote sensing in environments with low structural heterogeneity is suited for individual-oriented mapping of grassland plant species.

Remote sensing can be a valuable tool for supporting nature conservation monitoring systems. However, for many areas of conservation interest, there is still a considerable gap between field-based operational monitoring guidelines and the current remote sensing-based approaches. This hampers application in practice of the latter. Here, we propose a remote sensing approach for mapping the conservation status of Calluna-dominated Natura 2000 dwarf shrub habitats that is closely related to field mapping schemes. We transferred the evaluation criteria of the field guidelines to three related variables that can be captured by remote sensing: (1) coverage of the key species, (2) stand structural diversity, and (3) co-occurring species. Continuous information on these variables was obtained by regressing ground reference data from field surveys and UAV flights against airborne hyperspectral imagery. Merging the three resulting quality layers in an RGB representation allowed for illustrating the habitat quality in a continuous way. User-defined thresholds can be applied to this stack of quality layers to derive an overall assessment of habitat quality in terms of nature conservation, i.e. the conservation status. In our study, we found good accordance of the remotely sensed data with field-based information for the three variables key species, stand structural diversity and co-occurring vegetation (R2 of 0.79, 0.69, and 0.71, respectively) and it was possible to derive meaningful habitat quality maps. The conservation status could be derived with an accuracy of 65%. In interpreting these results it should be considered that the remote sensing based layers are independent estimates of habitat quality in their own right and not a mere replacement of the criteria used in the field guidelines. The approach is thought to be transferable to similar regions with minor adaptions. Our results refer to Calluna heathland which we consider a comparably easy target for remote sensing. Hence, the transfer of field guidelines to remote sensing indicators was rather successful in this case but needs further evaluation for other habitats.

Question Do spatial gradients of plant strategies correspond to patterns of plant traits obtained from a physically based model and hyperspectral imagery? It has been shown before that reflectance can be used to map plant strategies according to the established CSR scheme. So far, these approaches were based on empirical links and lacked transferability. Therefore, we test if physically-based derivations of plant traits may help in finding gradients in traits that are linked to strategies. Location A raised bog and minerotrophic fen complex, Murnauer Moos, Germany. Methods Spatial distributions of plant traits were modelled by adopting an inversion of the PROSAIL radiative transfer model on airborne hyperspectral imagery. The traits are derived from reflectance without making use of field data but only of known links between reflectance and traits. We tested whether previously found patterns in CSR plant strategies were related to the modelled traits. Results The results confirm close relationships between modelled plant traits and C, S and R strategies that were previously found in the field. The modelled plant traits explained different dimensions of the CSR-space. Leaf Area Index (LAI) and the reciprocal of Specific Leaf Area appeared to be good candidates for reproducing CSR scores as community traits using remote sensing. LAI has not been used in previous studies to allocate plant strategies. Conclusions Combining RTMs and the CSR model is a promising approach for establishing a robust link between airborne or spaceborne imagery and plant functioning. The demonstrated potential to map traits with close relation to CSR gradients using only our understanding of the relation between traits and reflectance is a step forward towards an operational use of the CSR model in remote sensing. This article is protected by copyright. All rights reserved.

The eastern Qinghai-Tibetan Plateau is a cultural landscape where traditional pastoralism substantially shaped the present mosaic structure of the alpine grasslands. During the past two decades, however, severe grassland degradations of this region has been considered as the major ecological concern. In this study we took an interdisciplinary approach to investigate the impact of the historical land-use regimes to the observed degradation, by conducting an in-depth case study in a local pastoral village in the Nyanpo Yutse region. Firstly, we mapped historical land-use intensities (LUIs) of the study area at land-use transition time points of 1970s, 1984, 1994 and 2015 with oral history and participatory GIS (PGIS) approaches. Secondly, we conducted Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) time series analysis to detect the temporal and spatial patterns of the degradation. Thirdly, we discussed the causal relations between the land-use and land-cover changes. Livestock and pasture privatization from 1984 to 1994 created the land-use regime shift which resulted in a marked increase in LUIs and decreased pastoral mobility. The LUI increase in this transition period fostered the establishment of short-grass vegetation which facilitated the spreading of plateau pikas. The installment of iron fences as private pasture borders from 2004 to 2007 eventually started the onset of degradation. Our case study illustrates that the past land-use regime shift triggered the recent ecological regime shift in Nyanpo Yutse. Severe grassland degradation occurred with a time lag during which cumulative LUIs surpassed the vulnerability threshold of the biophysical system.

The estimation of various forest inventory attributes from high spatial resolution airborne remote sensing data has been widely examined and proved to be successful at the experimental level. Nevertheless, the operational use of these data in automated procedures to support forest inventories and forest management is still limited to a small number of cases. The reasons for this are high data costs, limited availability of remote sensing data over large areas and resistance from practitioners. In this review the main aim is to stimulate debate about spaceborne very high resolution stereo-imagery (VHRSI) as an alternative to airborne remote sensing data by presenting: (1) a case study on the retrieval of stand density, aboveground biomass and tree species using a set of easy-to-calculate variables obtained from VHRSI data combined with image processing and nonparametric classification and modelling approaches; and (2) the results of an expert opinion survey on the potential of VHRSI as compared with Light Detection and Ranging (LiDAR), hyperspectral and airborne digital imagery to derive a range of forest inventory attributes. In the case study, stand density was estimated with r² = 0.71 and RMSE = 156 trees (rel./norm. RMSE = 24.9 per cent/12.4 per cent), biomass with r² = 0.64 and RMSE of 36.7 t/ha (rel./norm. RMSE = 20.0 per cent/12.8 per cent) while tree species classifications with five species reached overall accuracies of 84.2 per cent (kappa = 0.81). These results were comparable to earlier studies in the same test site, obtained with more expensive airborne acquisitions. Expert opinions were more diverse for VHRSI and aerial photographs (Shannon index values of 0.94 and 0.97) than for LiDAR and hyperspectral data (Shannon index values 0.69 and 0.88). In our opinion, this reflects the current state-of-the-art in the application of VHRSI for automatically retrieving forest inventory attributes. The number of studies using these data is still limited, and the full potential of these datasets is not yet completely explored. Compared with LiDAR and hyperspectral data, which both mostly received high scores for forest inventory products matching the sensor systems’ strengths, VHRSI and aerial photographs received more homogeneous scores indicating their potential as multi-purpose instruments to collect forest inventory information. In summary, considering the simpler acquisition, reasonable price and the comparably easy data format and handling of VHRSI compared with other sensor types, we recommend further research on the application of these data for supporting operational forest inventories.

This study explores the use of TerraSAR-X (X-Band) and Sentinel-1 (C-Band) SAR data for detecting degraded forests and deforestation in the Sumaco Biosphere Reserve, Ecuador. The study area shows a complex topography and permanent cloud cover, which complicates forest monitoring from optical remote sensing. Thresholds of canopy cover for degraded forest were identified by field data. Then a straightforward object-based approach was applied. Statistics on SAR backscatter were estimated from the objects and used as input to a random forest classification to map forest and non-forest areas. Finally, forest degradation was assessed using a moving window procedure applying the earlier identified thresholds on the forest / non-forest map. The SAR data were found adequate to map deforestation (overall accuracy of 81% for forest/non forest maps) but less accurate to fully assess forest degradation. However, multi-temporal SAR data can be useful to construct baseline data of the forest extent. Polarization and interferometric information could enhance the presented results for classifying forest / non forest areas and mapping forest degradation.