Tree species identification within an extensive forest area with diverse management regimes using airborne hyperspectral data

Information on tree species composition is crucial in forest management and can be obtained using remote sensing. While the topic has been addressed frequently over the last years, the remote sensing-based identification of tree species across wide and complex forest areas is still sparse in the literature. Our study presents a tree species classification of a large fraction of the Białowieża Forest in Poland covering 62 000 ha and being subject to diverse management regimes. Key objectives were to obtain an accurate tree species map and to examine if the prevalent management strategy influences the classification results. Tree species classification was conducted based on airborne hyperspectral HySpex data. We applied an iterative Support Vector Machine classification and obtained a thematic map of 7 individual tree species (birch, oak, hornbeam, lime, alder, pine, spruce) and an additional class containing other broadleaves. Generally, the more heterogeneous the area was, the more errors we observed in the classification results. Managed forests were classified more accurately than reserves. Our findings indicate that mapping dominant tree species with airborne hyperspectral data can be accomplished also over large areas and that forest management and its effects on forest structure has an influence on classification accuracies and should be actively considered when progressing towards operational mapping of tree species composition

Detecting semi-arid forest decline using time series of Landsat data

Detecting forest decline is crucial for effective forest management in arid and semi-arid regions. Remote sensing using satellite image time series is useful for identifying reduced photosynthetic activity caused by defoliation. However, current studies face limitations in detecting forest decline in sparse semi-arid forests. In this study, three Landsat time-series-based approaches were used to distinguish non-declining and declining forest patches in the Zagros forests. The random forest was the most accurate approach, followed by anomaly detection and the Sen’s slope approach, with an overall accuracy of 0.75 (kappa = 0.50), 0.65 (kappa = 0.30), and 0.64 (kappa = 0.30), respectively. The classification results were unaffected by the Landsat acquisition times, indicating that rather, environmental variables may have contributed to the separation of declining and non-declining areas and not the remotely sensed spectral signal of the trees. We conclude that identifying declining forest patches in semi-arid regions using Landsat data is challenging. This difficulty arises from weak vegetation signals caused by limited canopy cover before a bright soil background, which makes it challenging to detect modest degradation signals. Additional environmental variables may be necessary to compensate for these limitations

Mapping fractional woody cover in an extensive semi-arid woodland area at different spatial grains with Sentinel-2 and very high-resolution data

Woody canopy cover is an essential variable to characterize and monitor vegetation health, carbon accumulation and land–atmosphere exchange processes. Remote sensing-based global woody and forest cover maps are available, yet with varying qualities. In arid and semi-arid areas, existing global products often underestimate the presence of woody cover due to the sparse woody cover and bright soil background. Case studies on smaller regions have shown that a combination of collected field data and medium-to-high resolution free satellite data (e.g., Landsat / Sentinel-2) can provide woody cover estimates with practically-sufficient accuracies. However, most earlier studies focused on comparably small regions and relied on costly field data. Here, we present a fully remote sensing-based work-flow to derive woody cover estimates over an area covering more than 0.5 million km2. The work-flow is showcased over the Zagros Mountains, a semi-arid mountain range covering western Iran, the northeast of Iraq and some smaller fraction of southeast Turkey. We use the Google Earth Engine to create homogeneous Sentinel-2 mosaics of the region using data from several years. These data are combined with reference woody cover values derived by a semi-automatic procedure from Google® and Bing® very high resolution (VHR) imagery. Several random forest (RF) models at different spatial grains were trained and at each grain validated with iterative splits of the reference data into training and validation sets (100 repetitions). Best results (considering the trade-off between model performance and spatial detail) were obtained for the model with 40 m spatial grain which showed stable relationships between the VHR-derived reference data and the Sentinel-2 based estimates of woody cover density. The model resulted in median values of coefficient of determination (R2) and RMSE of 0.67 and 0.11, respectively. Our work-flow is potentially also applicable to other arid and semi-arid regions and can contribute to improve currently available global woody cover products, which often perform poorly in semi-arid and arid regions. Comparisons between our woody cover products with common global woody or forest-cover products indicate a clear superiority of our approach. In future studies, these results may be further improved by taking into account regional differences in the drivers of woody-cover patterns along the environmental gradient of the Zagros area

Using a landscape ecological perspective to analyze regime shifts in social–ecological systems: a case study on grassland degradation of the Tibetan Plateau

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

Mapping the fractional coverage of the invasive shrub Ulex europaeus with multi-temporal Sentinel-2 imagery utilizing UAV orthoimages and a new spatial optimization approach

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

Explaining Sentinel 2-based dNBR and RdNBR variability with reference data from the bird’s eye (UAS) perspective

Characterizing the spatial variability of the severity of wildfires is important to assess ecological and economic consequences and to coordinate mitigation strategies. Vegetation indices such as the differenced Normalized Burn Ratio (dNBR) have become a standard tool to assess burn or fire severity across larger areas and are being used operationally. Despite the frequent application of dNBR-like vegetation indices, it is not yet fully understood which variables exactly drive the variability in dNBR observed by multispectral satellites. One reason for this is the lack of high quality prefire information about vegetation structure and composition. Consequently, the influence of prefire vegetation composition and other potentially influential variables such as cast shadows has hardly been examined. Here, we use very high resolution Unmanned Aerial System (UAS) orthoimages collected briefly before and after the large wildfires in Central Chile in the fire season 2016/2017 to derive variables related to the pre- and postfire landscape composition and structure. The variables are used as predictors in Generalized Additive Models (GAM) to explain the spatial variability in dNBR and RdNBR pixel values as observed by Sentinel-2. Our models explain more than 80% and 75% of the variability in dNBR and RdNBR values, respectively, using a sparse set of five predictors. The results suggest that in our study area the largest fraction of variability in Sentinel-2 based dNBR and RdNBR values can be explained by variables related to the fraction of consumed canopy cover while the vegetation composition before the fire does not have a large influence on dNBR and RdNBR. Our results further suggest that cast-shadows of snags and standing dead trees with remaining crown structure have a notable influence on the dNBR signal which may have been underestimated so far. We conclude that spatially continuous, very high spatial resolution data from UAS can be a valuable data source for an improved understanding of the exact meaning of common vegetation index products, operationally used for monitoring the environment

About the link between biodiversity and spectral variation

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

Differentiating plant functional types using reflectance: which traits make the difference?

Abiotic ecosystem properties together with plant species interaction create differences in structural and physiological traits among plant species. Certain plant traits cause a spatial and temporal variation in canopy reflectance that enables the differentiation of plant functional types, using earth observation data. However, it often remains unclear which traits drive the differences in reflectance between plant functional types, since the spectral regions in which electromagnetic radiation is influenced by certain plant traits are often overlapping. The present study aims to assess the relative (statistical) contributions of plant traits to the separability of plant functional groups using their reflectance. We apply the radiative transfer model PROSAIL to simulate optical canopy reflectance of 38 herbaceous plant species based on field‐measured traits such as leaf area index, leaf inclination distribution, chlorophyll content, carotenoid content, water and dry matter content. These traits of the selected grassland species were measured in an outdoor plant experiment. The 38 species differed in growth form and strategy types according to Grime′s CSR model and hence represented a broad range of plant functioning. We determined the relative (statistical) contribution of each plant trait for separating plant functional groups via reflectance. Therein we used a separation into growth forms, that is graminoids and herbs, and into CSR strategy types. Our results show that the relative contribution of plant traits to differentiate between the examined plant functional types (PFT) groups using canopy reflectance depends on the PFT scheme applied. Plant traits describing the canopy structure were more important in this regard than leaf traits. Accordingly, leaf area index (LAI) and leaf inclination showed consistently high importance for separating the examined PFT groups. This indicates that the role of canopy structure for spectrally differentiating PFT might have been underestimated

Opaque voxel-based tree models for virtual laser scanning in forestry applications

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