Search Results (83)

Leaf chlorophyll content is a major indicator of plant health and productivity. Optical remote sensing estimation of chlorophyll limits its retrievals to two-dimensional (2D) estimates, not allowing examination of its distribution within the canopy, although it exhibits large variation across the vertical profile. Multispectral and hyperspectral Terrestrial Laser Scanning (TLS) instruments can produce three-dimensional (3D) chlorophyll estimates but are not widely available. Thus, in this study, 14 chlorophyll vegetation indices were developed using six wavelengths employed in commercial TLS instruments (532 nm, 670 nm, 808 nm, 785 nm, 1064 nm, and 1550 nm). For this, 200 simulations were carried out using the novel bidirectional mode in the Discrete Anisotropic Radiative Transfer (DART) model and a realistic forest stand. The results showed that the Green Normalized Difference Vegetation Index (GNDVI) of the 532 nm and either the 808 nm or the 785 nm wavelengths were highly correlated to the chlorophyll content (R² = 0.74). The Chlorophyll Index (CI) and Green Simple Ratio (GSR) of the same wavelengths also displayed good correlation (R² = 0.73). This study was a step towards canopy 3D chlorophyll retrieval using commercial TLS instruments, but methods to couple the data from the different instruments still need to be developed. Full article

Effective forest management is essential for mitigating climate change effects. This is why understanding forest growth dynamics is critical for its sustainable management. Thus, characterizing forest plot deadwood levels is vital for understanding forest dynamics, and for assessments of biomass, carbon stock, and biodiversity. For the first time, this study used the leaf area index (LAI) and L-moments to characterize and model forest plot deadwood levels in the Bavarian Forest National Park from airborne laser scanning (ALS) data. This study proposes methods that can be tested for forests, especially those in temperate climates with frequent cloud coverage and limited access. The proposed method is practically significant for effective planning and management of forest resources. First, plot decay levels were characterized based on their canopy leaf area density (LAD). Then, the deadwood levels were modeled to assess the relationships between the vegetation area index (VAI), gap fraction (GF), and the third L-moment ratio (T3). Finally, we tested the rule-based methods for classifying plot decay levels based on their biophysical structures. Our results per the LAD vertical profiles clearly showed the declining levels of decay from Level 1 to 5. Our findings from the models indicate that at a 95% confidence interval, 96% of the variation in GF was explained by the VAI with a significant negative association (VAIslope = −0.047; R² = 0.96; (p < 0.001)), while the VAI explained 92% of the variation in T3 with a significant negative association (VAIslope = −0.50; R² = 0.92; (p < 0.001)). Testing the rule-based methods, we found that the first rule (Lcv = 0.5) classified Levels 1 and 2 at (Lcv < 0.5) against Levels 3 to 5 at (Lcv > 0.5). However, the second rule (Lskew = 0) classified Level 1 (healthy plots) as closed canopy areas (Lskew < 0) against Levels 2 to 5 (deadwood) as open canopy areas (Lskew > 0). This approach is simple and more convenient for forest managers to exploit for mapping large forest gap areas for planning and managing forest resources for improved and effective forest management. Full article

Drone lidar has the potential to provide detailed measurements of vertical forest structure throughout large areas, but a systematic evaluation of unsampled forest structure in comparison to independent reference data has not been performed. Here, we used ray tracing on a high-resolution voxel grid to quantify sampling variation in a temperate mountain forest in the southwest Czech Republic. We decoupled the impact of pulse density and scan-angle range on the likelihood of generating a return using spatially and temporally coincident TLS data. We show three ways that a return can fail to be generated in the presence of vegetation: first, voxels could be searched without producing a return, even when vegetation is present; second, voxels could be shadowed (occluded) by other material in the beam path, preventing a pulse from searching a given voxel; and third, some voxels were unsearched because no pulse was fired in that direction. We found that all three types existed, and that the proportion of each of them varied with pulse density and scan-angle range throughout the canopy height profile. Across the entire data set, 98.1% of voxels known to contain vegetation from a combination of coincident drone lidar and TLS data were searched by high-density drone lidar, and 81.8% of voxels that were occupied by vegetation generated at least one return. By decoupling the impacts of pulse density and scan angle range, we found that sampling completeness was more sensitive to pulse density than to scan-angle range. There are important differences in the causes of sampling variation that change with pulse density, scan-angle range, and canopy height. Our findings demonstrate the value of ray tracing to quantifying sampling completeness in drone lidar. Full article

Accompanying climate change and sea level rise, tidal marsh mortality in coastal wetlands has been globally observed that urges the documentation of high-resolution, 3D marsh inventory to assist resilience planning. Drone Lidar has proven useful in extracting the fine-scale bare earth terrain and canopy height. Beyond that, this study performed marsh biomass mapping from drone Lidar point cloud in a S. alterniflora-dominated estuary on the Southeast U.S. coast. Three point classes (ground, low-veg, and high-veg) were classified via point cloud deep learning. Considering only vegetation points in the vertical profile, a profile area-weighted height (H_PA) was extracted at a grid size of 50 cm × 50 cm. Vegetation point densities were also extracted at each grid. Adopting the plant-level allometric equations of stem biomass from long-term S. alterniflora surveys, a Lidar biomass index (Lidar_BI) was built to represent the relative quantity of marsh biomass in a range of [0, 1] across the estuary. Compared with the clipped dry biomass samples, it achieved a comparable and slightly better performance (R² = 0.5) than the commonly applied spectral index approaches (R² = 0.4) in the same marsh field. This study indicates the feasibility of the drone Lidar point cloud for marsh biomass mapping. More advantageously, the drone Lidar approach yields information on plant community architecture, such as canopy height and plant density distributions, which are key factors in evaluating marsh habitat and its ecological services. Full article

Wetlands are amongst Earth’s most dynamic and complex ecological resources, serving productive and biodiverse ecosystems. Enhancing the quality of wetland mapping through Earth observation (EO) data is essential for improving effective management and conservation practices. However, the achievement of reliable and accurate wetland mapping faces challenges due to the heterogeneous and fragmented landscape of wetlands, along with spectral similarities among different wetland classes. The present study aims to produce advanced 10 m spatial resolution wetland classification maps for four pilot sites on the Island of Newfoundland in Canada. Employing a comprehensive and multidisciplinary approach, this research leverages the synergistic use of optical, synthetic aperture radar (SAR), and light detection and ranging (LiDAR) data. It focuses on ecological and hydrological interpretation using multi-source and multi-sensor EO data to evaluate their effectiveness in identifying wetland classes. The diverse data sources include Sentinel-1 and -2 satellite imagery, Global Ecosystem Dynamics Investigation (GEDI) LiDAR footprints, the Multi-Error-Removed Improved-Terrain (MERIT) Hydro dataset, and the European ReAnalysis (ERA5) dataset. Elevation data and topographical derivatives, such as slope and aspect, were also included in the analysis. The study evaluates the added value of incorporating these new data sources into wetland mapping. Using the Google Earth Engine (GEE) platform and the Random Forest (RF) model, two main objectives are pursued: (1) integrating the GEDI LiDAR footprint heights with multi-source datasets to generate a 10 m vegetation canopy height (VCH) map and (2) seeking to enhance wetland mapping by utilizing the VCH map as an input predictor. Results highlight the significant role of the VCH variable derived from GEDI samples in enhancing wetland classification accuracy, as it provides a vertical profile of vegetation. Accordingly, VCH reached the highest accuracy with a coefficient of determination (R2) of 0.69, a root-mean-square error (RMSE) of 1.51 m, and a mean absolute error (MAE) of 1.26 m. Leveraging VCH in the classification procedure improved the accuracy, with a maximum overall accuracy of 93.45%, a kappa coefficient of 0.92, and an F1 score of 0.88. This study underscores the importance of multi-source and multi-sensor approaches incorporating diverse EO data to address various factors for effective wetland mapping. The results are expected to benefit future wetland mapping studies. Full article

Accurate identification and segmentation of individual tree points are crucial for assessing forest spatial distribution, understanding tree growth and structure, and managing forest resources. Traditional methods based on Canopy Height Models (CHM) are simple yet prone to over- and/or under-segmentation. To deal with this problem, this paper introduces a novel approach that combines marker-controlled watershed segmentation with a spectral clustering algorithm. Initially, we determined the local maxima within a series of variable windows according to the lower bound of the prediction interval of the regression equation between tree crown radius and tree height to preliminarily segment individual trees. Subsequently, using this geometric shape analysis method, the under-segmented trees were identified. For these trees, vertical tree crown profile analysis was performed in multiple directions to detect potential treetops which were then considered as inputs for spectral clustering optimization. Our experiments across six plots showed that our method markedly surpasses traditional approaches, achieving an average Recall of 0.854, a Precision of 0.937, and an F1-score of 0.892. Full article

The increase in the global atmospheric CO₂ concentration is expected to increase the productivity of forests, but the dynamic processes of such increased productivity in the forest canopy remain unclear. In this study, diurnal and seasonal variations and vertical changes in photosynthetic rates were investigated in Camphor tree (Cinnamomum camphora) forests in subtropical China. The effect of photosynthetically active radiation (PAR) and CO₂ concentrations on photosynthetic rates were also examined in the studied forests. Results showed the diurnal patterns of photosynthesis exhibited two peaks on sunny days, but only one peak on cloudy days. The daily average photosynthetic rate on cloudy days was approximately 74% of that on sunny days. The photosynthetic rate decreased along the vertical forest canopy profile. If the photosynthetic rate in the upper canopy layer was 100%, the corresponding rates were 83% and 25% in the middle and lower canopy layers, respectively. The rates of dark respiration derived from the PAR response curve were 1.73, 1.25, and 1.0 µmol m⁻² s⁻¹ for the upper, middle, and lower canopy layers, respectively. The apparent quantum yield of photosynthesis was 0.0183, 0.0186, and 0.0327 µmol CO₂ µmol⁻¹ PAR for the upper, middle, and lower canopy, respectively. The initial slope of the photosynthetic response curve to CO₂ was highest in the upper canopy and lowest in the lower canopy. The seasonal variation in photosynthetic rates exhibited a two-peaked pattern at all canopy positions, with the two peaks occurring in June and September. The stand biomass and biomass carbon storage were 144.7 t ha⁻¹ and 71.6 t C ha⁻¹ in the examined forests, respectively. The study provides a scientific reference for future research on accessing carbon sequestration and designing forest management practices, specifically in regulating canopy structure in subtropical regions. Full article

The wind characteristics of the atmospheric boundary layer in forested regions exhibit a significant complexity due to rugged terrain, seasonal climate variability, and seasonal growth of vegetation, which play a key role not only in designing optimal blades to gain better performance but also in assessing the structural response, and there is a paucity of research on such wind fields. Therefore, this paper investigates wind characteristics via on-site wind field measurement. The mean and fluctuating wind characteristics of the forested region in different seasons were analyzed based on the field measurement data. The results show that for the mean wind characteristics, the seasonally fitted exponents play a decisive role in characterizing the mean wind profile, while the season and temperature are the key factors affecting the mean wind direction in forested regions. For fluctuating wind characteristics, the seasonal power-law function can accurately characterize the turbulence intensity profile. Moreover, the ratio of the three turbulence intensity components is significantly affected by temperature and season, and the Von Kármán spectrum has better applicability in the cold and less canopy-disturbed winter than in the other three seasons. The proposed seasonally fitted parameters show better applicability in terms of vertical coherence. Full article

The current study determined the floristic composition, structure, and aboveground biomass (AGB) of the individuals of the Moraceae family. This occurred in order to value them as a source of biomass carbon, which itself is dependent on the altitudinal gradient (601–1000 m.a.s.l.) in the evergreen foothill forest of the Ecuadorian Amazon. The study encountered 117 individuals belonging to the Moraceae family, which was grouped into 32 species. Hereby, the most abundant were the genus Ficus sp., with 9.40% relative abundance, Brosimun alicastrum with 6.84%, and Aucleopsis sp. with 5.98%. Forest structural characteristics, such as the horizontal and vertical structure, diameter at breast height (DBH), and the diameter of the tree crown, were considered for the analysis. The horizontal profile determined that the crowns of the species of the Moraceae family cover approximately 16.43% of the upper canopy within the sampling unit area. The trees of the Moraceae family have a carbon capture capacity in the projected AGB per hectare of 35.09 (Mg ha⁻¹), with the Ficus cuatracasana Dugand species being the species with the highest projected capture per hectare, with 15.737 (Mg ha⁻¹). These results highlight the relevance of similar studies assessing the carbon accumulation capacity of species from other families, emphasizing high commercial value species due to their timber resource. Full article

Soil moisture is a key factor controlling vegetation construction and ecological restoration in arid and semiarid areas. Understanding its spatiotemporal patterns and influencing factors is essential for effective vegetation water management. In this study, we analyzed the spatiotemporal characteristics of black locust plants using field investigations and statistical analyses and determined the effects of the rainfall and plant characteristics on the soil moisture content (SMC) in a typical watershed in the Loess Plateau, China. The results show that the SMC increases with increasing distance from the tree trunk in the horizontal direction. The vertical profile of the SMC includes layers characterized by rapid decrease, decreased fluctuation, and slow increase. Temporal SMC changes exhibit higher variabilities in the surface layer than in deeper soil layers. Rainfall characteristics notably affect soil moisture. The influence of the rainfall amount is stronger than that of the rainfall duration and intensity. The diameter at breast height, tree height, and canopy width positively affects the soil moisture, whereas the leaf area index and canopy openness negatively affect it. The results of this study provide insights into soil moisture change mechanisms and theoretical references for sustainable plant water use management in arid and semiarid areas. Full article

Accurately estimating forest biomass based on spaceborne lidar on a county scale is challenging due to the incomplete coverage of spaceborne lidar data. Therefore, this research aims to interpolate GEDI spots and explore the feasibility of approaches to improving Quercus forest biomass estimation accuracy in the alpine mountains of Yunnan Province, China. This paper uses GEDI data as the main information source and a typical mountainous area in Shangri-La, northwestern Yunnan Province, China, as the study area. Based on the pre-processing of light spots. A total of 38 parameters were extracted from the canopy and vertical profiles of 1307 light spots in the study area, and the polygon data of the whole study area were obtained from the light spot data through Kriging interpolation. Multiple linear regression, support vector regression, and random forest were used to establish biomass models. The results showed that the optimal model is selected using the semi-variance function for the Kriging interpolation of each parameter of GEDI spot, the optimal model of modis_nonvegetated is a linear model, and the optimal model for rv, sensitivity, and modis_treecover is the exponential model. Analysis of the correlation between 39 parameters extracted from GEDI L2B and three topographic factors with oak biomass showed that sensitivity had a highly significant positive correlation (p < 0.01) with Quercus biomass, followed by a significant negative correlation (p < 0.05) with aspect and modis_nonvegation. After variable selection, the estimation model of Quercus biomass established using random forest had R² = 0.91, RMSE = 19.76 t/hm², and the estimation accuracy was better than that of multiple linear regression and support vector regression. The estimated total biomass of Quercus in the study area was mainly distributed between 26.48 and 257.63 t/hm², with an average value of 114.33 t/hm² and a total biomass of about 1.26 × 10⁷ t/hm². This study obtained spatial consecutive information using Kriging interpolation. It provided a new research direction for estimating other forest structural parameters using GEDI data. Full article

We present a modeling tool capable of computing carbon dioxide (CO₂) fluxes over a non-uniform boreal peatland. The three-dimensional (3D) hydrodynamic model is based on the “one-and-a-half” closure scheme of the system of the Reynolds-Averaged Navier–Stokes and continuity equations. Despite simplifications used in the turbulence description, the model allowed obtaining the spatial steady-state distribution of the averaged wind velocities and coefficients of turbulent exchange within the atmospheric surface layer, taking into account the surface heterogeneity. The spatial pattern of CO₂ fluxes within and above a plant canopy is derived using the “diffusion–reaction–advection” equation. The model was applied to estimate the spatial heterogeneity of CO₂ fluxes over a non-uniform boreal ombrotrophic peatland, Staroselsky Moch, in the Tver region of European Russia. The modeling results showed a significant effect of vegetation heterogeneity on the spatial pattern of vertical and horizontal wind components and on vertical and horizontal CO₂ flux distributions. Maximal airflow disturbances were detected in the near-surface layer at the windward and leeward forest edges. The forest edges were also characterized by maximum rates of horizontal CO₂ fluxes. Modeled turbulent CO₂ fluxes were compared with the mid-day eddy covariance flux measurements in the southern part of the peatland. A very good agreement of modeled and measured fluxes (R² = 0.86, p < 0.05) was found. Comparisons of the vertical profiles of CO₂ fluxes over the entire peatland area and at the flux tower location showed significant differences between these fluxes, depending on the prevailing wind direction and the height above the ground. Full article

The hazardousness of Mediterranean landscapes has increased since the second half of the 20th century, and fuel loads of highly flammable vegetation types have increased throughout the region. Moreover, under the context of more severe fire weather, large fires of high intensity may cause losses in ecosystem services. Accordingly, fire prevention tools to monitor when and where a fire will have the most negative effects through increases in fire severity are required. Fuel characterization is key to wildfire prevention as fuel is one of the primary factors affecting wildfire risk and behavior. Here, we characterized the valuable natural vegetation in Cabañeros National Park (Central Spain) (part of the Natura-2000 network), composed of typical Mediterranean ecosystems, by using LiDAR and other auxiliary data. LiDAR data were obtained from the first Spanish National LiDAR flight, carried out over the study area in 2009–2010. LiDAR data were pre-processed and ground returns were classified using the progressive TIN filter algorithm, carrying out a sensitivity analysis using different settings. Later, the height of the points above the ground were normalized and the Canopy Height Model (CHM) was calculated. Fuel models were derived using the Prometheus fuel classification framework, and they were determined using several LiDAR height metrics and some compositional metrics (i.e., percentage covered by different height ranges) [<0.6 m, 0.6–1 m, 1–2 m, 2–4 m and ≥4 m] at 30 m. All these metrics allowed for the estimation of fractional canopy cover, fuel height, and vertical continuity. Moreover, tree-tops and crowns were delimited and standard height metrics, as well as vertical profiles, were obtained. All these data, combined with information about the flammability of dominant species and the vulnerability to fire based on functional traits, allowed for the identification of which resource values were most severely exposed to wildfires. Full article

Exploring improved tropical forages is considered to be an important approach in delivering quality and consistent feed options for dairy cattle in tropical and subtropical regions. The present study aimed to study the suitability of three improved tropical grasses, Chloris gayana ‘Rhodes grass cv. Reclaimer’ (RR), Megathyrsus maximus ‘Gatton Panic’ (GP), and Brachiaria ruziziensis x B. decumbens x B. brizantha ‘Brachiaria Mulato II’ (BM) evaluating their carbon assimilation, canopy structure, herbage plant–part accumulation and quality parameters under irrigated conditions. An experiment was conducted at Gatton Research Dairy (27°54′ S, 152°33′ E, 89 m asl) Queensland, Australia, which has a predominantly subtropical climate. Photosynthesis biochemistry, canopy structure, herbage accumulation, plant part composition, and nutritive value were evaluated. Photosynthesis biochemistry differed between pasture species. Efficiency of CO₂ assimilation was highest for GP and quantum efficiency was highest for BM. Pasture canopy structure was significantly affected by an interaction between pasture species and harvest. Forage biomass accumulation was highest in GP, while BM produced more leaf and less stem compared to both GP and RR. A greater leafy stratum and lower stemmy stratum depth were observed in the vertical sward structure of BM. Brachiaria Mulato II showed greater carbon partitioning to leaves, leaf: stem ratio, canopy, and leaf bulk density. It also demonstrated greater nutritive value (Total digestible nutrients (TDN), acid detergent fibre (ADF), neutral detergent fibre (NDF), neutral detergent insoluble protein (NDICP), Starch, nonfibre carbohydrates (NFC), metabolisable energy (ME), mineral profile (Mg, P, K, Fe, Zn) and dietary cation–anion difference (DCAD) for leaf, stem, and the whole plant. Greater quantum efficiency, leaf accumulation, and nutritive value of BM observed in the present study suggest BM as an attractive forage option for dairying that warrants further research in pasture-based systems in tropical and subtropical climates. Full article

Monitoring forest structural properties is critical for a range of applications because structure is key to understanding and quantifying forest biophysical functioning, including stand dynamics, evapotranspiration, habitat, and recovery from disturbances. Monitoring of forest structural properties at desirable frequencies and cost globally is enabled by space-borne LiDAR missions such as the global ecosystem dynamics investigation (GEDI) mission. This study assessed the accuracy of GEDI estimates for canopy height, total plant area index (PAI), and vertical profile of plant area volume density (PAVD) and elevation over a gradient of canopy height and terrain slope, compared to estimates derived from airborne laser scanning (ALS) across two forest age-classes in the Central Highlands region of south-eastern Australia. ALS was used as a reference dataset for validation of GEDI (Version 2) dataset. Canopy height and total PAI analyses were carried out at the landscape level to understand the influence of beam-type, height of the canopy, and terrain slope. An assessment of GEDI’s terrain elevation accuracy was also carried out at the landscape level. The PAVD profile evaluation was carried out using footprints grouped into two forest age-classes, based on the areas of mountain ash (Eucalyptus regnans) forest burnt in the Central Highlands during the 1939 and 2009 wildfires. The results indicate that although GEDI is found to significantly under-estimate the total PAI and slightly over-estimate the canopy height, the GEDI estimates of canopy height and the vertical PAVD profile (above 25 m) show a good level of accuracy. Both beam-types had comparable accuracies, with increasing slope having a slightly detrimental effect on accuracy. The elevation accuracy of GEDI found the RMSE to be 10.58 m and bias to be 1.28 m, with an R² of 1.00. The results showed GEDI is suitable for canopy densities and height in complex forests of south-eastern Australia. Full article