The spatial resolution of imaging sensors has increased dramatically in recent years, and so too have the challenges associated with extracting meaningful information from their data products. ...Object-based image analysis (OBIA) is gaining rapid popularity in remote sensing science as a means of bridging very high spatial resolution (VHSR) imagery and GIS. Multiscalar image segmentation is a fundamental step in OBIA, yet there is currently no tool available to objectively guide the selection of appropriate scales for segmentation. We present a technique for estimating the scale parameter in image segmentation of remotely sensed data with Definiens Developer®. The degree of heterogeneity within an image-object is controlled by a subjective measure called the 'scale parameter', as implemented in the mentioned software. We propose a tool, called estimation of scale parameter (ESP), that builds on the idea of local variance (LV) of object heterogeneity within a scene. The ESP tool iteratively generates image-objects at multiple scale levels in a bottom-up approach and calculates the LV for each scale. Variation in heterogeneity is explored by evaluating LV plotted against the corresponding scale. The thresholds in rates of change of LV (ROC-LV) indicate the scale levels at which the image can be segmented in the most appropriate manner, relative to the data properties at the scene level. Our tests on different types of imagery indicated fast processing times and accurate results. The simple yet robust ESP tool enables fast and objective parametrization when performing image segmentation and holds great potential for OBIA applications.
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BFBNIB, DOBA, GIS, IJS, IZUM, KILJ, KISLJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Terrestrial laser scanning (TLS) was introduced for basic forest measurements, such as tree height and diameter, in the early 2000s. Recent advances in sensor and algorithm development have allowed ...us to assess in situ 3D forest structure explicitly and revolutionised the way we monitor and quantify ecosystem structure and function. Here, we provide an interdisciplinary focus to explore current developments in TLS to measure and monitor forest structure. We argue that TLS data will play a critical role in understanding fundamental ecological questions about tree size and shape, allometric scaling, metabolic function and plasticity of form. Furthermore, these new developments enable new applications such as radiative transfer modelling with realistic virtual forests, monitoring of urban forests and larger scale ecosystem monitoring through long-range scanning. Finally, we discuss upscaling of TLS data through data fusion with unmanned aerial vehicles, airborne and spaceborne data, as well as the essential role of TLS in validation of spaceborne missions that monitor ecosystem structure.
•Terrestrial laser scanning (TLS) provides explicit in situ 3D forest structure.•We provide a review on current developments in TLS to monitor forest structure.•TLS data opens a realm of untapped ecological questions.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
1. Forest dieback caused by drought-induced tree mortality has been observed world-wide. Forecasting which trees in which locations are vulnerable to drought-induced mortality is important to predict ...the consequences of drought on forest structure, biodiversity and ecosystem function. 2. In this paper, our central aim was to compile a synthesis of tree traits and associated abiotic variables that can be used to predict drought-induced mortality. 3. We reviewed the literature that specifically links drought mortality to functional traits and site conditions (i.e. edaphic variables and biotic conditions), targeting studies that show clear use of tree traits in drought analysis. We separated the review into five climatic zones to determine global vs. regionally restricted relationships between traits and mortality. 4. Our synthesis identifies a number of traits that have clear relationships with droughtinduced mortality (e.g. wood density at the species level and tree size and growth at the individual level). However, the lack of direct relationships between most traits and droughtinduced mortality highlights areas where future research should focus to broaden our understanding. 5. Synthesis and applications. Our synthesis highlights established relationships between traits and drought-induced mortality, presents knowledge gaps for future research focus and suggests monitoring and research avenues for improving our understanding of drought-induced mortality. It is intended to assist ecologists and natural resource managers choose appropriate and measurable parameters for predicting local and regional scale tree mortality risk in different climatic zones within constraints of time and funding availability.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NMLJ, NUK, OILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK
AIM: To infer a forest carbon density map at 0.01° resolution from a radar remote sensing product for the estimation of carbon stocks in Northern Hemisphere boreal and temperate forests. LOCATION: ...The study area extends from 30° N to 80° N, covering three forest biomes – temperate broadleaf and mixed forests (TBMF), temperate conifer forests (TCF) and boreal forests (BFT) – over three continents (North America, Europe and Asia). METHODS: This study is based on a recently available growing stock volume (GSV) product retrieved from synthetic aperture radar data. Forest biomass and spatially explicit uncertainty estimates were derived from the GSV using existing databases of wood density and allometric relationships between biomass compartments (stem, branches, roots, foliage). We tested the resultant map against inventory‐based biomass data from Russia, Europe and the USA prior to making intercontinent and interbiome carbon stock comparisons. RESULTS: Our derived carbon density map agrees well with inventory data at regional scales (r² = 0.70–0.90). While 40.7 ± 15.7 petagram of carbon (Pg C) are stored in BFT, TBMF and TCF contain 24.5 ± 9.4 Pg C and 14.5 ± 4.8 Pg C, respectively. In terms of carbon density, we found 6.21 ± 2.07 kg C m⁻² retained in TCF and 5.80 ± 2.21 kg C m⁻² in TBMF, whereas BFT have a mean carbon density of 4.00 ± 1.54 kg C m⁻². Indications of a higher carbon density in Europe compared with the other continents across each of the three biomes could not be proved to be significant. MAIN CONCLUSIONS: The presented carbon density and corresponding uncertainty map give an insight into the spatial patterns of biomass and stand as a new benchmark to improve carbon cycle models and carbon monitoring systems. In total, we found 79.8 ± 29.9 Pg C stored in northern boreal and temperate forests, with Asian BFT accounting for 22.1 ± 8.3 Pg C.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NMLJ, NUK, OILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK
The reliability of airborne light detection and ranging (LiDAR) for delineating individual trees and estimating aboveground biomass (AGB) has been proven in a diverse range of ecosystems, but can be ...difficult and costly to commission. Point clouds derived from structure from motion (SfM) matching techniques obtained from unmanned aerial systems (UAS) could be a feasible low-cost alternative to airborne LiDAR scanning for canopy parameter retrieval. This study assesses the extent to which SfM three-dimensional (3D) point clouds-obtained from a light-weight mini-UAS quadcopter with an inexpensive consumer action GoPro camera-can efficiently and effectively detect individual trees, measure tree heights, and provide AGB estimates in Australian tropical savannas. Two well-established canopy maxima and watershed segmentation tree detection algorithms were tested on canopy height models (CHM) derived from SfM imagery. The influence of CHM spatial resolution on tree detection accuracy was analysed, and the results were validated against existing high-resolution airborne LiDAR data. We found that the canopy maxima and watershed segmentation routines produced similar tree detection rates (~70%) for dominant and co-dominant trees, but yielded low detection rates (<35%) for suppressed and small trees due to poor representativeness in point clouds and overstory occlusion. Although airborne LiDAR provides higher tree detection rates and more accurate estimates of tree heights, we found SfM image matching to be an adequate low-cost alternative for the detection of dominant and co-dominant tree stands.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Understanding the role that the vast north Australian savannas play in the continental carbon cycle requires reliable quantification of their carbon stock at landscape and regional scales. LiDAR ...remote sensing has proven efficient and accurate for the fine-scale estimation of above-ground tree biomass (AGB) and carbon stocks in many ecosystems, but tropical savanna remain under studied. We utilized a two-phase LiDAR analysis procedure which integrates both individual tree detection (ITC) and area-based approaches (ABA) to better understand how the uncertainty of biomass estimation varies with scale. We used estimations from individual tree LiDAR measurements as training/reference data, and then applied these data to develop allometric equations related to LIDAR metrics. We found that LiDAR individual tree heights were strongly correlated with field-estimated AGB (R2=0.754, RMSE=90kg), and that 63% of individual trees crowns (ITC) could be accurately delineated with a canopy maxima approach. Area-based biomass estimation (ABA), which incorporated errors from the ITC steps, identified the quadratic mean of canopy height (QMCH) as the best single independent variable for different plot sample sizes (e.g. for 4ha plots: R2=0.86, RMSE=3.4Mgha−1; and 1ha plots: R2=0.83, RMSE=4.0Mgha−1). Our results show how ITC and ABA approached can be integrated to understand how biomass uncertainty varies with scale across broad landscapes. Understanding these scaling relationships is critical for operationalizing regional savanna inventories, monitoring and mapping.
•A two-phase LiDAR procedure of estimation of AGB is explored in tropical savanna.•63% of individual trees were delineated by using a canopy maxima approach.•The quadratic mean of canopy height is the best variable for biomass estimation.•RMSE increased from 3.4 to 9.12Mgha−1 from 4ha to 0.0625ha plot size.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
The ability to map burn severity and to understand how it varies as a function of time of year and return frequency is an important tool for landscape management and carbon accounting in tropical ...savannas. Different indices based on optical satellite imagery are typically used for mapping fire scars and for estimating burn severity. However, cloud cover is a major limitation for analyses using optical data over tropical landscapes. To address this pitfall, we explored the suitability of C-band Synthetic Aperture Radar (SAR) data for detecting vegetation response to fire, using experimental fires in northern Australia. Pre- and post-fire results from Sentinel-1 C-band backscatter intensity data were compared to those of optical satellite imagery and were corroborated against structural changes on the ground that we documented through terrestrial laser scanning (TLS). Sentinel-1 C-band backscatter (VH) proved sensitive to the structural changes imparted by fire and was correlated with the Normalised Burn Ratio (NBR) derived from Sentinel-2 optical data. Our results suggest that C-band SAR holds potential to inform the mapping of burn severity in savannas, but further research is required over larger spatial scales and across a broader spectrum of fire regime conditions before automated products can be developed. Combining both Sentinel-1 SAR and Sentinel-2 multi-spectral data will likely yield the best results for mapping burn severity under a range of weather conditions.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Recent progress in remote sensing provides much-needed, large-scale spatio-temporal information on habitat structures important for biodiversity conservation. Here we examine the potential of a newly ...launched satellite-borne radar system (Sentinel-1) to map the biodiversity of twelve taxa across five temperate forest regions in central Europe. We show that the sensitivity of radar to habitat structure is similar to that of airborne laser scanning (ALS), the current gold standard in the measurement of forest structure. Our models of different facets of biodiversity reveal that radar performs as well as ALS; median R² over twelve taxa by ALS and radar are 0.51 and 0.57 respectively for the first non-metric multidimensional scaling axes representing assemblage composition. We further demonstrate the promising predictive ability of radar-derived data with external validation based on the species composition of birds and saproxylic beetles. Establishing new area-wide biodiversity monitoring by remote sensing will require the coupling of radar data to stratified and standardized collected local species data.
Vegetation structure influences landscape use and habitat quality for many bird species. Owing to the difficulties associated with collecting structural data from traditional field measurements, ...numerous studies have investigated the utility of Light detection and ranging (LiDAR) for providing landscape-scale structural information that may be useful for exploring animal-habitat associations. Notably, almost all of these studies have involved the use of LiDAR from airborne rather than terrestrial platforms. However, vegetation metrics that might be important for explaining bird species occurrence and diversity, such as understory vegetation complexity and overall vegetation volume, may be partially obscured from airborne sensors by tree canopy cover. These challenges might be overcome by terrestrial and UAV LiDAR sensors that can provide detailed information of understory forest strata. For the first time, we collected terrestrial LiDAR (TLS) and unoccupied aerial vehicle LiDAR (ULS) data in a woodland landscape to compare the ability of both sensors to identify relationships among vegetation structural metrics and bird species richness and abundance. Overall, TLS and ULS models provided similar results based on the sampling methodology we used for LiDAR data collection in an open woodland landscape. Canopy roughness, ground vegetation vertical complexity, total vegetation volume and canopy height derived from these sensors were among the most common significant variables in explaining avian diversity and individual species abundance. Individual species abundance models provided better prediction power (up to R2 = 0.82 (TLS) and R2 = 0.83 (ULS)) than bird community abundance by functional guilds (up to R2 = 0.40 (TLS), R2 = 0.41 (ULS)) and overall bird abundance (R2 = 0.10 (TLS), R2 = 0.16 (ULS)), species richness (R2 = 0.14 (TLS), R2 = 0.14 (ULS)) and diversity (R2 = 0.17 (TLS), R2 = 0.16 (ULS)). Additionally, we found that several vulnerable bird species are strongly associated with LiDAR structural variables, which may assist with habitat assessment and conservation management.
•Woodland vegetation structural metrics were derived from terrestrial and UAV LiDAR.•We assessed the ability of this data to model bird-habitat associations.•TLS and ULS models provided similar results based on this study's methodologies.•Individual species and guilds were modelled better than species richness/abundance.•We discuss effects of incident angle, returns, coverage, scale, and landscape-type.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
•Non-destructive individual tree AGB estimation based on TLS and UAV-LS data.•Analysis at 4 sites across 3 biomes totalling 871 trees.•Applying field-calibrated models on LiDAR data possibly leads to ...strong biases.•Novel LiDAR graph-based tree metric linearly correlated to tree AGB.•UAV-LS and ML required≤55 training samples to achieve population bias <5 %.
Calibration and validation of aboveground biomass (AGB) (AGB) products retrieved from satellite-borne sensors require accurate AGB estimates across hectare scales (1 to 100ha). Recent studies recommend making use of non-destructive terrestrial laser scanning (TLS) based techniques for individual tree AGB estimation that provide unbiased AGB predictors. However, applying these techniques across large sites and landscapes remains logistically challenging. Unoccupied aerial vehicle laser scanning (UAV-LS) has the potential to address this through the collection of high density point clouds across many hectares, but estimation of individual tree AGB based on these data has been challenging so far, especially in dense tropical canopies. In this study, we investigated how TLS and UAV-LS can be used for this purpose by testing different modelling strategies with data availability and modelling framework requirements. The study included data from four forested sites across three biomes: temperate, wet tropical, and tropical savanna. At each site, coincident TLS and UAV-LS campaigns were conducted. Diameter at breast height (DBH) and tree height were estimated from TLS point clouds. Individual tree AGB was estimated for ≥170 trees per site based on TLS tree point clouds and quantitative structure modelling (QSM), and treated as the best available, non-destructive estimate of AGB in the absence of direct, destructive measurements. Individual trees were automatically segmented from the UAV-LS point clouds using a shortest-path algorithm on the full 3D point cloud. Predictions were evaluated in terms of individual tree root mean square error (RMSE) and population bias, the latter being the absolute difference between total tree sample population TLS QSM estimated AGB and predicted AGB. The application of global allometric scaling models (ASM) at local scale and across data modalities, i.e., field-inventory and light detection and ranging LiDAR metrics, resulted in individual tree prediction errors in the range of reported studies, but relatively high population bias. The use of adjustment factors should be considered to translate between data modalities. When calibrating local models, DBH was confirmed as a strong predictor of AGB, and useful when scaling AGB estimates with field inventories. The combination of UAV-LS derived tree metrics with non-parametric modelling generally produced high individual tree RMSE, but very low population bias of ≤5% across sites starting from 55 training samples. UAV-LS has the potential to scale AGB estimates across hectares with reduced fieldwork time. Overall, this study contributes to the exploitation of TLS and UAV-LS for hectare scale, non-destructive AGB estimation relevant for the calibration and validation of space-borne missions targeting AGB estimation.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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