Trade-offs and synergies in the supply of forest ecosystem services are common but the drivers of these relationships are poorly understood. To guide management that seeks to promote multiple ...services, we investigated the relationships between 12 stand-level forest attributes, including structure, composition, heterogeneity and plant diversity, plus 4 environmental factors, and proxies for 14 ecosystem services in 150 temperate forest plots. Our results show that forest attributes are the best predictors of most ecosystem services and are also good predictors of several synergies and trade-offs between services. Environmental factors also play an important role, mostly in combination with forest attributes. Our study suggests that managing forests to increase structural heterogeneity, maintain large trees, and canopy gaps would promote the supply of multiple ecosystem services. These results highlight the potential for forest management to encourage multifunctional forests and suggest that a coordinated landscape-scale strategy could help to mitigate trade-offs in human-dominated landscapes.
In forest ecosystems, many functional processes are governed by local canopy gap dynamics, caused by either natural or anthropogenic factors. Quantifying the size and spatial distribution of canopy ...gaps enables an improved understanding and predictive modelling of multiple environmental phenomena. For instance knowledge of canopy gap dynamics can help us elucidate time‐integrated effects of tree mortality, regrowth and succession rates, carbon flux patterns, species heterogeneity and three‐dimensional spacing within structurally complex forest ecosystems.
Airborne Laser Scanning (ALS) has emerged as a technology that is well‐suited for mapping forest canopy gaps in a wide variety of forest ecosystems and across spatial scales. New technological and algorithmic advances, including ALS remote‐sensing, coupled with optimized frameworks for data processing and detection of forest canopy gaps, are allowing an enhanced understanding of forest structure and functional processes.
This paper introduces ForestGapR, a cutting‐edge open source r package for forest gap analysis from canopy height models derived from ALS and other remote sensing sources. The ForestGapR package offers tools to (a) automate forest canopy gap detection, (b) compute a series of gap statistics, including gap‐size frequency distributions and spatial distribution, (c) map gap dynamics (when multitemporal ALS data are available) and (d) convert forest canopy gaps detected into raster or vector layers as per user requirements.
As case studies, we run ForestGapR on ALS data collected over four different tropical forest regions worldwide. We hope this new package will enable further research towards understanding the distribution, dynamics and role of canopy gaps not only in tropical forests, but in other forest types elsewhere.
Spatially explicit information on tree species composition of managed and natural forests, plantations and urban vegetation provides valuable information for nature conservationists as well as for ...forest and urban managers and is frequently required over large spatial extents. Over the last four decades, advances in remote sensing technology have enabled the classification of tree species from several sensor types.
While studies using remote sensing data to classify and map tree species reach back several decades, a recent review on the status, potentials, challenges and outlooks in this realm is missing. Here, we search for major trends in remote sensing techniques for tree species classification and discuss the effectiveness of different sensors and algorithms based on a literature review.
This review demonstrates that the number of studies focusing on tree species classification has increased constantly over the last four decades and promising local scale approaches have been presented for several sensor types. However, there are few examples for tree species classifications over large geographic extents, and bridging the gap between current approaches and tree species inventories over large geographic extents is still one of the biggest challenges of this research field. Furthermore, we found only few studies which systematically described and examined the traits that drive the observed variance in the remote sensing signal and thereby enable or hamper species classifications. Most studies followed data-driven approaches and pursued an optimization of classification accuracy, while a concrete hypothesis or a targeted application was missing in all but a few exceptional studies.
We recommend that future research efforts focus stronger on the causal understanding of why tree species classification approaches work under certain conditions or – maybe even more important - why they do not work in other cases. This might require more complex field acquisitions than those typically used in the reviewed studies. At the same time, we recommend reducing the number of purely data-driven studies and algorithm-benchmarking studies as these studies are of limited value, especially if the experimental design is limited, e.g. the tree population is not representative and only a few sensors or acquisition settings are simultaneously investigated.
•A review of remote-sensing based tree species classification is provided.•Descriptive statistics on publication year, biomes, species numbers & sensor types•Species related traits, classification methods and research gaps are discussed.•We identified a lack of studies working with hypothesis driven questions.•We identified a lack of studies focusing on larger geographic extents.
•Experimental riparian canopy gaps mimicked natural gaps in old-growth forests.•Relative increase in maximum summer stream temperature ranged from 0.01 to 0.36 °C.•Stream temperature increases were ...strongly negatively correlated with stream size.•Larger local responses persisted further downstream.
Streamside (riparian) areas in the western United States and across much of North America are dominated by young, regenerating forests with closed canopies, which shade the understory and reduce light to streams. The addition of canopy gaps has been suggested as a management tool to accelerate development of riparian forest complexity, create stream light conditions that mimic those in late successional forests, and enhance in-stream productivity. Although gaps form naturally in late-successional forests, explicitly adding gaps is a concern because the increases in light that accompany a canopy gap may have the potential to increases stream temperature, which is an important ecological driver and regulatory metric in streams. The goal of this study was to determine whether and to what degree riparian forest canopy gaps that reflect localized disturbance events (mortality of one to a few dominant canopy trees) affect stream temperatures. We created experimental gaps in young regenerating riparian forests along six replicate headwater streams in western Oregon. Gaps increased light along 90 m study reaches by 3.91 (±1.63) moles of photons m−2 day−1, similar to that of a naturally occurring gap in a late-successional forest. Using a Before-After-Control-Impact study design, we assessed stream temperature by tracking multiple responses in each reach including: maximum seven day moving average of daily maximums (T7DayMax), maximum seven day moving average of daily means (T7DayMean), daily maximum, and mean summer temperatures, as well as within-reach (every 30 m) responses, and downstream recovery. Over a 40-day period in summer (July 22nd - August 30th), the mean response in T7DayMax across the six replicate streams was 0.21 °C ± 0.12, and the mean response in T7DayMean was 0.15 °C ± 0.14. Although the mean response in T7DayMax (a key regulatory metric) was small, changes varied across individual study streams, and the magnitude of the relative increase in stream T7DayMax as a result of the canopy gap was strongly negatively correlated with stream size. T7DayMax was not correlated with the size of the canopy opening or change in reach-scale light availability (within the range of gap sizes from 514 m2 to 1,374 m2 (0.05 to 0.14 ha)). Overall, riparian forest canopy gaps have the potential to increase stream temperatures, but in the western Cascade Mountain headwaters studied here, gap effects were small (all < 0.5 °C), and temperature responses declined as stream size increased.
Plants use photoreceptor proteins to detect the proximity of other plants and to activate adaptive responses. Of these photoreceptors, phytochrome B (phyB), which is sensitive to changes in the red ...(R) to far‐red (FR) ratio of sunlight, is the one that has been studied in greatest detail. The molecular connections between the proximity signal (low R:FR) and a model physiological response (increased elongation growth) have now been mapped in considerable detail in Arabidopsis seedlings. We briefly review our current understanding of these connections and discuss recent progress in establishing the roles of other photoreceptors in regulating growth‐related pathways in response to competition cues. We also consider processes other than elongation that are controlled by photoreceptors and contribute to plant fitness under variable light conditions, including photoresponses that optimize the utilization of soil resources. In examining recent advances in the field, we highlight emerging roles of phyB as a major modulator of hormones related to plant immunity, in particular salicylic acid and jasmonic acid (JA). Recent attempts to manipulate connections between light signals and defence in Arabidopsis suggest that it might be possible to improve crop health at high planting densities by targeting links between phyB and JA signalling.
During the last four decades, there has been an enormous increase in our understanding of how plants sense shading and the proximity of neighbours and how they activate adaptive morphological and physiological responses. Important elements of the signal transduction pathways that connect informational photoreceptors with functional responses have been elucidated, and shade avoidance has become a textbook example of adaptive plasticity. It is now becoming clear that proximity perception leads to a complete reconfiguration of plant function. This reconfiguration allows the plant to optimize the deployment of leaves into light gaps, balance resource allocation between shoots and roots, optimize leaf gas exchange and nutrient uptake as a function of the degree of shading and adaptively regulate interactions with herbivores, pathogens and beneficial microorganisms. In this review, we describe the progress in understanding shade‐avoidance mechanisms and highlight the diversity of plant processes and functions that are controlled by canopy light signals.
Indicators of vegetation cover and structure are widely available for monitoring and managing rangeland wind erosion. Identifying which indicators are most appropriate for managers could improve wind ...erosion mitigation and restoration efforts. Vegetation cover directly protects the soil surface from erosive winds and reduces wind erosivity by extracting momentum from the air. The portion of the soil surface that is directly protected by vegetation is adequately described by fractional ground cover indicators. However, the aerodynamic sheltering effects of vegetation, which are more important for wind erosion than for water erosion, are not captured by these indicators. As wind erosion is a lateral process, the vertical structure and spatial distribution of vegetation are most important for controlling where, when, and how much wind erosion occurs on rangelands. These controlling factors can be described by indicators of the vegetation canopy gap size distribution and vegetation height, for which data are collected widely in the United States by standardized rangeland monitoring and assessment programs. In this paper we address why canopy gap size distribution and vegetation height are critical indicators of rangeland wind erosion and health. We review wind erosion processes to explain the physical role of these vegetation attributes. We then address the management implications including availability of data on the indicators on rangelands and needs to make the indicators and model estimates of wind erosion more accessible to the range management community.
•Automatic organ segmentation in 3D medical scans is an important yet challenging problem for medical image analysis, especially the pancreas.•As a solution, we present an automated system based on a ...two-stage cascaded approach: pancreas localization and pancreas segmentation.•We design a complete deep-learning approach based on efficient holistically-nested convolutional networks applied to three orthogonal views.•Quantitative evaluation on a public CT dataset of 82 patients shows state-of-the art performance with 81.27 ± 6.27% Dice score in validation.
Display omitted
Accurate and automatic organ segmentation from 3D radiological scans is an important yet challenging problem for medical image analysis. Specifically, as a small, soft, and flexible abdominal organ, the pancreas demonstrates very high inter-patient anatomical variability in both its shape and volume. This inhibits traditional automated segmentation methods from achieving high accuracies, especially compared to the performance obtained for other organs, such as the liver, heart or kidneys. To fill this gap, we present an automated system from 3D computed tomography (CT) volumes that is based on a two-stage cascaded approach—pancreas localization and pancreas segmentation. For the first step, we localize the pancreas from the entire 3D CT scan, providing a reliable bounding box for the more refined segmentation step. We introduce a fully deep-learning approach, based on an efficient application of holistically-nested convolutional networks (HNNs) on the three orthogonal axial, sagittal, and coronal views. The resulting HNN per-pixel probability maps are then fused using pooling to reliably produce a 3D bounding box of the pancreas that maximizes the recall. We show that our introduced localizer compares favorably to both a conventional non-deep-learning method and a recent hybrid approach based on spatial aggregation of superpixels using random forest classification. The second, segmentation, phase operates within the computed bounding box and integrates semantic mid-level cues of deeply-learned organ interior and boundary maps, obtained by two additional and separate realizations of HNNs. By integrating these two mid-level cues, our method is capable of generating boundary-preserving pixel-wise class label maps that result in the final pancreas segmentation. Quantitative evaluation is performed on a publicly available dataset of 82 patient CT scans using 4-fold cross-validation (CV). We achieve a (mean ± std. dev.) Dice similarity coefficient (DSC) of 81.27 ± 6.27% in validation, which significantly outperforms both a previous state-of-the art method and a preliminary version of this work that report DSCs of 71.80 ± 10.70% and 78.01 ± 8.20%, respectively, using the same dataset.
Forest canopy gaps are important to ecosystem dynamics. Depending on tree species, small canopy openings may be associated with intra-crown porosity and with space among crowns. Yet, literature on ...the relationships between very fine-scaled patterns of canopy openings and biodiversity features is limited. This research explores the possibility of: (1) mapping forest canopy gaps from a very high spatial resolution orthomosaic (10 cm), processed from a versatile unmanned aerial vehicle (UAV) imaging platform, and (2) deriving patch metrics that can be tested as covariates of variables of interest for forest biodiversity monitoring. The orthomosaic was imaged from a test area of 240 ha of temperate deciduous forest types in Central Italy, containing 50 forest inventory plots each of 529 m2 in size. Correlation and linear regression techniques were used to explore relationships between patch metrics and understory (density, development, and species diversity) or forest habitat biodiversity variables (density of micro-habitat bearing trees, vertical species profile, and tree species diversity). The results revealed that small openings in the canopy cover (75% smaller than 7 m2) can be faithfully extracted from UAV red, green, and blue bands (RGB) imagery, using the red band and contrast split segmentation. The strongest correlations were observed in the mixed forests (beech and turkey oak) followed by intermediate correlations in turkey oak forests, followed by the weakest correlations in beech forests. Moderate to strong linear relationships were found between gap metrics and understory variables in mixed forest types, with adjusted R2 from linear regression ranging from 0.52 to 0.87. Equally strong correlations in the same forest types were observed for forest habitat biodiversity variables (with adjusted R2 ranging from 0.52 to 0.79), with highest values found for density of trees with microhabitats and vertical species profile. In conclusion, this research highlights that UAV remote sensing can potentially provide covariate surfaces of variables of interest for forest biodiversity monitoring, conventionally collected in forest inventory plots. By integrating the two sources of data, these variables can be mapped over small forest areas with satisfactory levels of accuracy, at a much higher spatial resolution than would be possible by field-based forest inventory solely.
PDF
