Forest Canopy Research Articles

Improving extraction of forest canopy height through reprocessing ICESat-2 ATLAS and GEDI data in sparsely forested plain regions

ABSTRACT Forest canopy height (FCH) is one of the most important variables for carbon stock estimation. While many studies have focused on extracting FCH from spaceborne LiDAR in regions with spatially continuous and large patch sizes of forested lands, limited research has addressed the challenges of FCH extraction in plain regions with sparse and fragmented forest distributions. In this study, we proposed innovative processing approaches to extract FCH from ICESat-2 photons and GEDI footprints in the plain regions of Anhui Province, China. Specifically, we proposed a sectional photon denoising method for processing ICESat-2 data and a geolocation error correction method for processing GEDI data. Airborne LiDAR data were used to validate the extracted FCH products across typical plain regions. The results demonstrated the effectiveness of the proposed methods in improving FCH extraction accuracy. Evaluation indicated that the directly extracted FCH products from ATL08 and GEDI L2A had Pearson’s correlation coefficients (r) of 0.6 and 0.93, respectively. After processing with the proposed methods, the 2019 FCH products from ICESat-2 exhibited r of 0.82 and relative root mean square error (rRMSE) of 31.11% based on 3,217 ICESat-2 segments, and the products from GEDI showed r of 0.96 and rRMSE of 18.35% based on 4,862 GEDI footprints. Further application of these methods to extract FCH products from GEDI and ICESat-2 for the years 2020, 2021, and 2022 indicated their promise for addressing sparse vegetation coverage in plain regions.

Vegetation characteristics of the Javan slow loris habitat in Mount Masigit Kareumbi Protected Area, West Java, Indonesia

Abstract. Allasselcida A, Sumarga E, Sofiatin. 2024. Vegetation characteristics of the Javan slow loris habitat in Mount Masigit Kareumbi Protected Area, West Java, Indonesia. Biodiversitas 25: 2512-2520. Habitat loss and degradation are major threats to the conservation of endemic wildlife in Indonesia, owing to the high rates of deforestation over the past decades. Most of these endemic animals are now at risk of extinction. Javan slow loris (Nycticebus javanicus) is a primate endemic to Java Island and is currently classified as critically endangered by the IUCN. This study analyzes four key aspects of the loris' habitat in Mount Masigit Kareumbi Protected Area (MMKPA), West Java: plant diversity, vegetation community structure and composition, plant species as food sources, and canopy density. A vegetation survey was conducted in three research sites using 41 nested plots measuring 20x20 m for trees, 10x10 m for poles and shrubs, 5x5 m for saplings, and 2x2 m for herbaceous plants. Data on plant species, diameter at breast height (DBH), and abundance (number of individuals) were collected within each plot. Forest Canopy Density (FCD) was mapped using Landsat 8 RAW Imagery and Landsat 8 TOA Reflectance Imagery, involving calculating several indices: Advanced Vegetation Index (AVI), Bare Soil Index (BI), Shadow Index (SI), and Thermal Index (TI). This study identified 104 plant species from 40 families across the three studied sites with dominant tree species were Pinus merkusii, Schima wallichii, Actinodaphne procera, Pinanga coronata, Miconia crenata, and various Setaria species. The diversity index at the three locations ranged from 3.02 to 3.41. This study found 35 species, including the dominant tree species, which served as food sources for slow lorises. The primary food sources available to slow lorises are sap (92.4%), followed by fruit (3.9%) and flowers/nectar (3.7%). The results of FCD analysis indicated that 83% of MMKPA has a "high" category, suggesting the presence of a continuous branch network that facilitates the easy movement of Javan slow loris. This information can be used to develop strategies for improve habitat management in the area, such as enriching fruit-bearing crops to increase food diversity.

Physically based modelling of spectral transmittance through forest canopies

Abstract Physically based models simulating the spectral transmittance of solar radiation through forest canopies are useful tools for examining the connections between the shortwave radiation environment and the productivity and biodiversity of the forest floor. We report a comprehensive evaluation of two approaches simulating forest canopy spectral transmittance. The approaches were (i) three‐dimensional radiative transfer modelling in canopies composed of individual trees filled with turbid media and (ii) photon recollision probability theory (p‐theory), and were implemented using DART‐FT and PARAS models, respectively. The simulations were evaluated against mean and standard deviation of canopy transmittance spectra measured under clear‐sky conditions in forest plots across central and Northern Europe. In general, both models agreed well with the in situ measurements. They performed equally in conifer forests, while PARAS had a slightly lower accuracy than DART‐FT in broadleaved forests. We conclude that both approaches produce realistic simulations of canopy spectral transmittance at the spatial scale tested in this study, and that p‐theory constitutes a computationally efficient and easy‐to‐parameterize alternative to three‐dimensional radiative transfer.

Impacts of forest canopy heterogeneity on plot-scale hydrometeorological variables - Insights from an experiment in the humid boreal forest with the Canadian Land Surface Scheme

High latitude regions, including the circumpolar boreal biome, are experiencing important changes in the availability of usable surface water because of climate change. In this context, an adequate representation of the land-atmosphere interaction is critical to ensure optimal management of current and future water resources, forest management, and climate prediction. However, the task is particularly intricate in high-latitude boreal forest, as land surface model faces several challenges due to the unique environmental conditions and ecological characteristics. The objective of this study is to quantify the impact of forest landscape heterogeneity, specifically stand leaf-area index (LAI), soil texture, and drainage regime, on surface water and energy balance in a small boreal high-latitude sub-catchment. To this end, hydrometeorological conditions at seventeen 20×20 m plots in a 1-km2 boreal forest sub-basin are simulated using the Canadian Land Surface Scheme (CLASS), a land surface model, at the point scale. The subplot-scale soil texture, drainage regime, and vegetation characteristics and type are based as closely as possible on field measurements and observations for the 17 plots. The model-driven experiment comprises two sets of simulations using CLASS, each employing the same model setup and run for the 17 experimental plots. The main set employs meteorological forcing from a local micrometeorological tower within the sub-basin to investigate the plot-to-plot variability of albedo, energy fluxes, and soil state variables. A second set of simulations is conducted using meteorological forcing from the ERA5-Land reanalysis, which spans from 1986 to 2022. This data provides a longer time series, enabling a more accurate representation of the interannual climatic variability in the sub-basin. The results of the main and secondary sets of CLASS simulations are used to assess the plot-to-plot and temporal variability of several key hydrometeorological variables by calculating a monthly spread. In brief, the following conclusions and broader implications can be drawn from the findings: i) The simulated total annual evapotranspiration remains relatively uniform between plots despite notable variation in its partitioning from plot to plot. ii) In the presence of a full snowpack, the albedo exhibits substantial heterogeneity at the subplot scale, linked to the canopy's LAI. iii) Local soil properties, drainage regime, and vegetation structure and type exhibit substantial influence on the plot-to-plot variability in soil water content. iv) When parameterized with localized observations and measurements, CLASS can represent and be responsive to the complex dynamics of energy and water fluxes at the plot scale within the heterogeneous surface of boreal forests.

Tropical Timber Species Grow Faster in Full Sun Than in the Understory of Restoration Plantings

Abstract Tropical timber species are generally shade tolerant, but preliminary studies have indicated that greater light availability can accelerate their growth. In this study, we hypothesized that native timber species can grow faster initially when planted in full sun alongside the pioneer species in high-diversity restoration plantings compared with being planted in the understory of an established forest restoration. We assessed the mortality, relative growth in height (RGH) and stem diameter (RGDGL) of eight native timber species from the Brazilian Atlantic Forest 19 months after planting. Overall seedling mortality was similar across planting conditions, and only Handroanthus heptaphyllus exhibited higher mortality in the shaded planting. The RGH and RGDGL of most species was higher in full sun planting. Therefore, our findings indicate that those tropical timber species grow faster and have higher survival rates when planted in open areas. Study Implications: We evaluated the initial growth and mortality of eight timber species native to the Atlantic Forest, focusing on their performance in forest restoration across two distinct conditions: full sun alongside pioneer species, and natural shade beneath an established canopy. Late successional timber species exhibit accelerated initial growth rates when planted in full sunlight compared with under an established forest canopy due to the greater light intensity. Our findings strongly support the practice of planting native timber species in full sun alongside pioneer species. This approach also provides logistical advantages, potentially reducing costs compared with staggered planting methods.

Short-term effects of clearing restoration on the relationship of plant and moth communities

Insects and plants share a close relationship; plants provide feeding opportunities, ecological niches, and shelters for insects, while insects, in turn, facilitate pollination for plants. Management actions might positively or negatively affect insect species richness and/or abundance, in particular, forest practices can significantly influence insect communities. In this study, we investigated the effects on plant and moth communities following the realization of an ecological corridor (composed of 10 clearings) in forest in NW of Italy. Specifically, through plant surveys and moth light traps, we investigated plant and moth communities and their relationship in newly shaped clearings and their surrounding forest. We found that plants responded quickly to clearing formation, presenting differentiated plant communities between xero-thermophile clearings, mesophile clearings and forest. These differences reflected on moth communities as well, allowing to find indicator species for each ecosystem. Structural variables (i.e. openness) and topography (i.e. elevation) were key factors affecting the differentiation of moth communities. On the other hand, plants explained most of the variance in moth communities, so, through plants, we differentiated moth communities into those typical of xero-thermophile clearings, mesophile clearings and forest. In conclusion, forest management significantly affects plant and moth communities. Newly shaped clearings support higher species richness (including specialist species) and abundance than closed canopy forest. To increase species richness and abundance for both plants and moths, we suggest conservation measures that include creating open habitats within forests at medium elevation (650–950 m a.s.l.).

Evaluating crown scorch predictions from a computational fluid dynamics wildland fire simulator

BackgroundCrown scorch—the heating of live leaves, needles, and buds in the vegetative canopy to lethal temperatures without widespread combustion—is one of the most common fire effects shaping post-fire canopies. Despite the ability of computational fluid dynamic models to finely resolve fire activity and buoyant plume dynamics including heterogenous 3D distributions of forest canopy heating, these models have had only limited use in simulating fire effects and have not been used to evaluate crown scorch. Here, we demonstrate a method of evaluating crown scorch using a computational fluid dynamics model, FIRETEC, and validate this approach by simulating the experiments that were used to develop Van Wagner’s 1973 crown scorch model.ResultsThe average scorch height prediction from FIRETEC compares well with the empirical model derived by Van Wagner, which is the most widely used empirical model for crown scorch. We further find that the 3D buoyant plume dynamics from a steady and homogeneous idealized heat source on the ground results in a spatially heterogenous crown scorch pattern reflecting complex heating dynamics that are best represented by percent scorch rather than height of scorch.ConclusionsThe ability of the computational fluid dynamics model to capture variation in crown scorch due to 3D buoyant plume dynamics provides direct links between forest structure, fire behavior, and fire effects that can be used by forest managers and researchers to better understand how fires result in crown damage under various environmental and management scenarios.

Uncertainty of canopy interception modeling in high-altitude Picea crassifolia forests of Semi-arid regions

The study of physically-based rainfall interception is crucial for comprehending the water balance within forest ecosystems and the contribution of vegetation to the hydrological cycle, particularly in arid/semi-arid ecosystems. Despite its importance, there is a lack of comprehensive sensitivity analysis and parameter optimization, resulting in uncertain or suboptimal predictive accuracy. To mitigate these shortcomings, this research involved the establishment and assessment of three quintessential forest canopy interception models namely, the power Návar model, the reformulated Gash model, and the Liu model, within semi-arid forest environments at two different elevations. A global sensitivity analysis conducted on the three physical models indicated that the canopy saturation point and the mean rainfall intensity required for canopy saturation were the parameters to which the reformulated Gash and Liu models were most sensitive when applied to high-altitude settings. Conversely, for the Návar model, the most sensitive parameters were the interception coefficient of the linear equation, and the parameters of the power equation k and c. The quantification indices of model sensitivity exert a certain influence on the ranking of parameter sensitivities. However, for models with a limited number of parameters, the impact of these results is constrained. Conversely, the identification and utilization of characteristics specific to the parameter tuning process can significantly enhance the efficiency of model calibration. The three models employed by the research institute have all demonstrated commendable performance in modeling the canopy interception process of subalpine P. crassifolia in arid, high-altitude regions, achieving a "good" rating with Nash-Sutcliffe Efficiency values exceeding 0.7. In practical applications, we recommend giving priority to the use of the Liu model. The findings of this study provide a reference for model selection, sensitivity analysis, parameter calibration, and model evaluation in the context of extensive canopy interception modeling in arid areas with significant altitudinal variation. This constitutes an important theoretical support for the refined modeling of hydrological processes in high-altitude forests within arid zones.

Interspecific, conspecific, and ontogenetic responses of tree rings to climate: A case study utilizing Carya glabra, Carya ovata, Carya tomentosa, and Quercus montana from an Oak-Hickory Forest in Southeastern Ohio

Climate change in eastern North America is likely to impact the abundance and distribution of the region’s tree species. However, determining the degree to which species will be impacted by altered climates is challenging. Dendrochronology research aimed at understanding relationships between climate and annual ring-width is one way of understanding how climate change may impact forest communities. Oak (Quercus spp.) and hickory (Carya spp.) are two foundational groups of trees likely to undergo changes in abundance and distribution due to climate change. The goal of this study was to compare the radial growth climate sensitivity of three common and co-occurring hickory species (Carya glabra, Carya ovata, and Carya tomentosa) to ecologically similar Quercus montana in southeast Ohio. Also, this study compared conspecific radial-growth climate responses between canopy and subcanopy trees to assess the impacts of climate and drought on subcanopy forest layers. All four species in the forest canopy demonstrated significant positive relationships to growing season precipitation, significant negative relationships with growing season temperature, and significant positive relationships with growing season site water balance. Subcanopy chronologies for all four species demonstrated weaker growth responses to climate, with only Carya glabra demonstrating significant growth relationships with May precipitation and site water balance. Additionally, the increased drought resistance of subcanopy trees provided some evidence of the forest overstory buffering the impacts of climate variability on understory trees. Overall, ontogenetic differences in tree sensitivity to climate variability and drought show that climate change likely has the potential to influence the forest understory, but the degree to which systems are impacted may be highly species-specific.

Evaluating effects of silvicultural treatments on forest canopy structure outcomes

Traditional forest management can homogenize forests, and management strategies that can restore complexity and promote adaptive capacity to global change are needed. However, effects of different silvicultural practices on complexity of forest structural components, such as the canopy, are not well understood mechanistically. We conducted a coupled field and simulation-modeling study to evaluate: 1) how near-term effects of silviculture on canopy structure and complexity differ across treatment types, and 2) how outcomes of common silvicultural treatments compare to a spectrum of randomly implemented removals. Thirteen different silvicultural treatments replicated across 12 study plots were simulated within 3D models derived from terrestrial lidar data. Treatment types often differed in their multi-dimensional structural outcomes, including vertical heterogeneity and canopy structural complexity. Moderate intensity thinning treatments that preferentially removed smaller trees increased near-term canopy structural complexity, while diameter-limit cutting often reduced complexity. Silvicultural treatments collectively produced a wide range of residual canopy conditions; however, variability among structural outcomes within individual treatment types or categories was limited relative to the range of possible outcomes from random removals. Most treatments induced shifts in canopy structure outside the spectrum of random removal, suggesting ample space for adapting management to promote forest heterogeneity.

Spatio‐temporal analysis of snow depth and snow water equivalent in a mountainous catchment: Insights from in‐situ observations and statistical modelling

AbstractThis research, conducted in the mountainous catchment near Abant Lake in the Western Black Sea region of Türkiye, aimed to investigate the spatiotemporal variations of snow depth (SD) and snow water equivalent (SWE) throughout the snow season from December 2019 to March 2020, encompassing both accumulation and melting periods. In total, 14 snow surveys were conducted, covering 58 permanent snow measurement points (PSMP) marked with snow poles. The classification and regression tree (CART) method was employed to statistically analyse their relationships with eight variables: snow period, forest canopy, aspect, slope, elevation, slope position, plan and profile curvature. The root mean square error (RMSE) for SD and SWE was determined to be 0.15 m and 46 mm, respectively. The study findings revealed that mean SD and SWE values were higher in forest gaps compared with under‐forest and open areas. Although the snow cover disappeared earliest in under‐forest areas, the melting rate was observed to be 43% and 17% slower compared with forest gaps and open areas, respectively. Wind redistribution resulted in minimum snow accumulation on western aspects, upper slope positions and ridges, while maximum accumulation was observed on southern aspects, valleys and lower slope positions. Higher elevations (>1580 meters) experienced faster snow melting rates, leading to earlier disappearance of snow cover. PSMPs located on slopes with lower degrees (<15°) exhibited lesser accumulation and earlier snow disappearance. The CART model identified the snow period as the most significant factor in predicting SD and SWE, based on variations in snowfall and air temperature. Other significant variables included forest canopy, aspect and elevation. The study suggests that the CART method is well‐suited for modelling complex snow dynamics, providing valuable insights into spatiotemporal variations in SD and SWE in mountainous regions.

Species assemblages and their drivers differ between trees and lianas in a seasonal evergreen forest in Thailand

AbstractDespite a long tradition in ecology of studying tree species assembly and its potential drivers in tropical forest communities, little information exists with respect to lianas (woody climbers), the second most abundant life form of woody plants in tropical forests. Lianas influence forest diversity and stability and provide critical resources for forest fauna. Using a unique dataset of a 30‐ha plot in Thailand, where tree and liana individuals were fully mapped, we investigated the degree to which local species assemblages of trees and lianas of different size classes (i.e., seedlings, established individuals, and large individuals) are related to local environmental conditions. We asked (1) What are the spatial patterns and environmental drivers of local tree and liana species assemblages? (2) How do such patterns and drivers differ among size classes? (3) Which species associate with these assemblages? Local assemblages of established trees showed substantial structuring by environmental variables, whereas we found only weakly structured assemblages of tree seedlings, large trees, and lianas of all size classes. Our results indicated that the biotic and abiotic drivers of local species assemblages differed strongly between tree and liana communities and across size classes. Species assemblages of trees were mainly driven by soil nutrients, leading to patchy assemblages associated with high base saturation (Alfisols) and assemblages associated with lower levels of base saturation and higher aluminum (Ultisols), whereas tree seedling assemblages were only weakly structured by riparian zones. In contrast, species assemblages of established and large lianas were primarily associated with forest canopy structure, separating low‐canopy forests from high‐canopy forests, whereas soil nutrients were the only factors associated with liana seedling assemblages. The weak environmental structuring of tree seedlings and large trees suggests that other mechanisms, such as stochastic disturbances, competition for space, or animal seed dispersal, may play an important role in structuring tree communities in this seasonal tropical forest. The weak patterns observed in liana communities across all life stages raise questions about the underlying mechanisms of liana community assembly, and further research should focus on liana niches, their dispersal mechanisms, and host tree relations.

Forest defoliation by an invasive outbreak insect: Catastrophic consequences for a charismatic mega moth.

Earth is now experiencing declines in insect abundance and diversity unparalleled in human history. The drivers underlying those declines are many, complex, and incompletely known. Here, using a natural experiment, we report the first test of the hypothesis that forest defoliation by an invasive outbreak insect compromises the fitness of a native insect via damage-induced increases in toxicity of the forest canopy. We demonstrate that defoliation by the invasive spongy moth (Lymantria dispar) elicits an average 8.4-fold increase in foliar defense expression among aspen (Populus tremuloides) genotypes. In turn, elevated defense dramatically reduces survivorship, feeding, and growth of a charismatic mega moth (Anthereae polyphemus). This work suggests that changes to the phytochemical landscape of forests, mediated by invasive outbreak insects, are likely to negatively impact native insects, with potential repercussions for community diversity and ecosystem function across expansive scales.

Snowdrift‐Permitting Simulations of Seasonal Snowpack Processes Over Large Mountain Extents

AbstractThe melt of seasonal snowpack in mountain regions provides downstream river basins with a critical supply of freshwater. Snowdrift‐permitting models have been proposed as a way to accurately simulate snowpack heterogeneity that stems from differences in energy inputs, over winter redistribution, sublimation, melt, and variations in precipitation. However, these spatial scales can be computationally intractable for large extents. In this work, the multiscale Canadian Hydrological Model (CHM) was applied to simulate snowpacks at snowdrift‐permitting scales (≈50 m) across the Canadian Cordillera and adjacent regions (1.37 million km2) forced by downscaled atmospheric data. The use of a multiscale land surface representation resulted in a reduction of computational elements of 98% while preserving land‐surface heterogeneity. CHM includes complex terrain windflow and radiative transfer calculations, lapses temperature, humidity, and precipitation with elevation, redistributes snow by avalanching, wind transport and forest canopy interception and calculates the energetics of canopy and surface snowpacks. Model outputs were compared to a set of multiscale observations including snow‐covered area (SCA) from Sentinel and Landsat imagery, snow depth from uncrewed aerial system lidar, and point surface observations of depth and density. Including snow redistribution and sublimation processes improved the summer SCA r2 from 0.7 to 0.9. At larger scales, inclusion of snow redistribution processes delayed full snowpack ablation by an average of 33 days, demonstrating process emergence with scale. These simulations show how multiscale modeling can improve snowpack predictions to support prediction of water supply, droughts, and floods.

Continuous mapping of forest canopy height using ICESat-2 data and a weighted kernel integration of multi-temporal multi-source remote sensing data aided by Google Earth Engine.

Forest Canopy Height (FCH) is a crucial parameter that offers valuable insights into forest structure. Spaceborne LiDAR missions provide accurate FCH measurements, but a significant challenge is their point-based measurements lacking spatial continuity. This study integrated ICESat-2's ATL08-derived FCH values with multi-temporal and multi-source remote sensing (RS) datasets to generate continuous FCH maps for northern forests in Iran. Sentinel-1/2, ALOS-2 PALSAR-2, and FABDEM datasets were prepared in Google Earth Engine (GEE) for FCH mapping, each possessing unique spatial and geometrical characteristics that differ from those of the ATL08 product. Given the importance of accurately representing the geometrical characteristics of the ATL08 segments in modeling FCH, a novel Weighted Kernel (WK) approach was proposed in this paper. The WK approach could better represent the RS datasets within the ATL08 ground segments compared to other commonly used resampling approaches. The correlation between all RS data features improved by approximately 6% compared to previously employed approaches, indicating that the RS data features derived after convolving the WK approach are more predictive of FCH values. Furthermore, the WK approach demonstrated superior performance among machine learning models, with random forests outperforming other models, achieving a coefficient of determination (R2) of 0.71, root mean square error (RMSE) of 4.92m, and mean absolute percentage error (MAPE) of 29.95%. Furthermore, in contrast to previous studies using only summer datasets, this study included spring and autumn data from Sentinel-1/2, resulting in a 6% increase in R2 and a 0.5-m decrease in RMSE. The proposed methodology filled the research gaps and improved the accuracy of FCH estimations.

Forest Clearance and Fragmentation for Charcoal Production: The Case of Bimbia Bonadikombo Forest, South West Region, Cameroon

The exploitation activities of man on natural resources have hardly been sustainable. This study examined the implication of charcoal production on the Bimbia Bonadikombo forest and it’s environ. The study employed both direct field observation and questionnaire administration for data gathering. Questionnaires were administered to charcoal producers and persons involved in charcoal related activities. Persons were selected using snowball technique. A total of 42 copies of questionnaires were administered to charcoal producers and related activities. Tree species used for charcoal production were noted and identified. The questionnaire was divided into five sections:(i), socio-demographic characteristics of charcoal producers, (ii), methods of charcoal production and species selected (iii), aspects influencing charcoal production and the implication on the ecosystem, (iv), seasonal trends and challenges faced (v) mitigation strategies used). Data collected were organized in Microsoft Excel and Statistical Package for Social Sciences (SPSS) was used for the analyses. Pearson (ρ) correlation association was used for the relationship between variables of production and the demand of charcoal. The result revealed that, majority of the respondents, 78.57%, were males while females were least 21.43%. Majority of the respondents (54.76%) were between the ages of 31-46 while the least with ages > 47. Lophira alata 14 (35.71%) was the tree species ranked 1st while Irvingia gabonensis1 (2.38%) was the least and ranked 8th. The main aspect that influenced production was revenue generated 14 (33.33%) while weak institutions 2(4.76%) and crises situations 2(2(4.76%) were the least. Deforestation 12(28.57%) and habitats destruction 8(10.04%) were reported as the main effects caused by charcoal production. Production and the demand of charcoal variables showed a positive association (0.153±.361). The management strategies employed were: only matured trees were used for charcoal production, permanent charcoal pit kiln are dug out of forest canopy and only few tree species are used for charcoal production. For mitigation purposes used are: domestication, protecting wild charcoal trees in their farms and nurseries establishment. This study recommends that charcoal is a major source of energy and alternative source of energy when others failed or insufficient. Therefore, this source of energy can be enhanced to be more eco friendly and environmentally sustainable.

Significance of AI-assisted techniques for epiphyte plant monitoring and identification from drone images

Monitoring forest canopies is vital for ecological studies, particularly for assessing epiphytes in rain forest ecosystems. Traditional methods for studying epiphytes, such as climbing trees and building observation structures, are labor, cost intensive and risky. Unmanned Aerial Vehicles (UAVs) have emerged as a valuable tool in this domain, offering botanists a safer and more cost-effective means to collect data. This study leverages AI-assisted techniques to enhance the identification and mapping of epiphytes using UAV imagery. The primary objective of this research is to evaluate the effectiveness of AI-assisted methods compared to traditional approaches in segmenting/identifying epiphytes from UAV images collected in a reserve forest in Costa Rica. Specifically, the study investigates whether Deep Learning (DL) models can accurately identify epiphytes during complex backgrounds, even with a limited dataset of varying image quality. Systematically, this study compares three traditional image segmentation methods Auto Cluster, Watershed, and Level Set with two DL-based segmentation networks: the UNet and the Vision Transformer-based TransUNet. Results obtained from this study indicate that traditional methods struggle with the complexity of vegetation backgrounds and variability in target characteristics. Epiphyte identification results were quantitatively evaluated using the Jaccard score. Among traditional methods, Watershed scored 0.10, Auto Cluster 0.13, and Level Set failed to identify the target. In contrast, AI-assisted models performed better, with UNet scoring 0.60 and TransUNet 0.65. These results highlight the potential of DL approaches to improve the accuracy and efficiency of epiphyte identification and mapping, advancing ecological research and conservation.

Unoccupied aerial system (UAS) Structure-from-Motion canopy fuel parameters: Multisite area-based modelling across forests in California, USA

There is a pressing need for well-informed management to reduce wildfire hazard and restore fire's beneficial ecological role in the Mediterranean- and temperate-climate forests of California, USA. These efforts rely upon the accessibility of high spatial and temporal resolution data on biomass and canopy fuel parameters such as canopy base height (CBH), mean canopy height, canopy bulk density (CBD), canopy cover, and leaf area index (LAI). Remote sensing using unoccupied aerial system Structure-from-Motion (UAS-SfM) presents a promising technology for this application due to its accessibility, relatively low cost, and possibility for high temporal cadence. However, to date, this method has not been studied in the complex mosaic of forest types found across California. In this study we examined the capacity of structural and multispectral information obtained from UAS-SfM, in conjunction with machine learning methods, to model aboveground biomass and forest canopy fuel structural parameters using an area-based approach across multiple sites representing a diversity of forest types in California.Based on correlations with field measurements, fuel parameters separated into vertical (biomass, CBH, and mean height) and horizontal (LAI, CBD, canopy cover) groups. UAS-SfM random forest models performed well for modelling the vertical structure canopy fuels parameters (R2 0.69–0.75). These models exhibited strong performance in comparison to ALS, as well as when transferred to a novel site. Vertical structure predictors were prominent in these models, and did not improve with the addition of spectral predictors. UAS-SfM random forest models of horizontal structure parameters mainly used raster-based spectral indices (primarily NDVI) and had relatively low performance (R2 0.49–0.59). In addition, these models underperformed ALS and had poor performance when applied to a novel site. When applied to a region with widespread UAS-SfM coverage, models from both groups successfully produced contiguous maps that could be used for modelling fire behavior or in management decision making and monitoring.These findings indicate that UAS-SfM, without the need for multispectral sensors, is well suited for mapping area-based vertical-structure canopy parameters across diverse landscapes supporting a wide range of forest types. In contrast, the identification of spectral mean variables for modelling horizontal structure canopy fuels suggests the potential of multi- or hyperspectral sensors or high-resolution satellite imagery for meeting management information needs.

Modeling of solar radiation and sub-canopy light regime on forest inventory plots of mixed conifer and deciduous temperate forests using point clouds from personal laser scanning

Solar radiation is a major driver of forest ecosystems and affects the hydrological cycle and energy balance. The vitality of trees strongly depends on the amount of light input to individual trees, and successful forest regeneration demands a certain level of solar radiation. Therefore, characterization of the light regime inside a plant canopy (i.e., quantification and timing of light penetration) is of particular importance. Because these parameters greatly depend on the 3D structure of the forest canopy, Light Detection and Ranging (LiDAR) systems provide suitable technology for solving this task. In this study, an algorithm was developed to estimate the amount of potential solar radiation reaching the forest floor from a point cloud collected in a temperate mixed forest located in Lanzenkirchen (Lower Austria) using terrestrial LiDAR. The path length through the canopy was calculated for 40 forest inventory plots as the distance between the first and last canopy hit along a sun ray, that is, for each day during the vegetation period at an hourly resolution. This distance was easily derived from the z-coordinates of the point cloud after transforming the latter into a coordinate system in which the z-axis ran parallel to the sun rays. Applying Beer’s law, the amount of solar radiation on the forest floor was expressed as a function of the solar radiation above the canopy and the path length through the canopy. To validate the results, we used two sets of reference data: (1) hemispherical photographs taken at the same plots, using a camera on a self-levelling mount, which were processed with the HemiView software, and (2) data from a Solariscope survey. Both the Solariscope and HemiView software calculate below-canopy radiation as a percentage of above-canopy radiation, joining direct and diffuse radiation (Total Site Factor, TSF). The TSF values obtained from our modeling procedure were consistent with the Solariscope (R²=0.61) and HemiView (R²=0.71) results for the entire growing season. Because modern LiDAR-based forest inventories provide all the necessary data for our light modeling approach (the point cloud, the geographical position, and a record of the occurring tree species) without additional requirements, together with the easy applicability of the algorithm, this tool is a promising asset for ecological and silvicultural activities.

Insectivorous birds and bats outperform ants in the top-down regulation of arthropods across strata of a Japanese temperate forest.

Birds, bats and ants are recognised as significant arthropod predators. However, empirical studies reveal inconsistent trends in their relative roles in top-down control across strata. Here, we describe the differences between forest strata in the separate effects of birds, bats and ants on arthropod densities and their cascading effects on plant damage. We implemented a factorial design to exclude vertebrates and ants in both the canopy and understorey. Additionally, we separately excluded birds and bats from the understorey using diurnal and nocturnal exclosures. At the end of the experiments, we collected all arthropods and assessed herbivory damage. Arthropods responded similarly to predator exclusion across forest strata, with a density increase of 81% on trees without vertebrates and 53% without both vertebrates and ants. Additionally, bird exclusion alone led to an 89% increase in arthropod density, while bat exclusion resulted in a 63% increase. Herbivory increased by 42% when vertebrates were excluded and by 35% when both vertebrates and ants were excluded. Bird exclusion alone increased herbivory damage by 28%, while the exclusion of bats showed a detectable but non-significant increase (by 22%). In contrast, ant exclusion had no significant effect on arthropod density or herbivory damage across strata. Our results reveal that the effects of birds and bats on arthropod density and herbivory damage are similar between the forest canopy and understorey in this temperate forest. In addition, ants were not found to be significant predators in our system. Furthermore, birds, bats and ants appeared to exhibit antagonistic relationships in influencing arthropod density. These findings highlight, unprecedentedly, the equal importance of birds and bats in maintaining ecological balance across different strata of a temperate forest.