Forest Structural Parameters Research Articles

Monthly microclimate models in a managed boreal forest landscape

The majority of microclimate studies have been done in topographically complex landscapes to quantify and predict how near-ground temperatures vary as a function of terrain properties. However, in forests understory temperatures can be strongly influenced also by vegetation. We quantified the relative influence of vegetation features and physiography (topography and moisture-related variables) on understory temperatures in managed boreal forests in central Sweden. We used a multivariate regression approach to relate near-ground temperature of 203 loggers over the snow-free seasons in an area of ∼16,000 km2 to remotely sensed and on-site measured variables of forest structure and physiography. We produced climate grids of monthly minimum and maximum temperatures at 25 m resolution by using only remotely sensed and mapped predictors. The quality and predictions of the models containing only remotely sensed predictors (MAP models) were compared with the models containing also on-site measured predictors (OS models). Our data suggest that during the warm season, where landscape microclimate variability is largest, canopy cover and basal area were the most important microclimatic drivers for both minimum and maximum temperatures, while physiographic drivers (mainly elevation) dominated maximum temperatures during autumn and early winter. The MAP models were able to reproduce findings from the OS models but tended to underestimate high and overestimate low temperatures. Including important microclimatic drivers, particularly soil moisture, that are yet lacking in a mapped form should improve the microclimate maps. Because of the dynamic nature of managed forests, continuous updates of mapped forest structure parameters are needed to accurately predict temperatures. Our results suggest that forest management (e.g. stand size, structure and composition) and conservation may play a key role in amplifying or impeding the effects of climate-forcing factors on near-ground temperature and may locally modify the impact of global warming.

Spaceborne SAR Tomography for Vertical Profile Retrieval of Forest Vegetation

Polarimetric SAR remote sensing has shown its potential for forest structural and biophysical parameter retrieval. Multibaseline polarimetric data in interferometric mode were acquired from space-borne C-band SAR system over Dudhwa National Park, India. Potential of multibaseline SAR tomography data was tested with six scenes of SAR data for forest height retrieval. Capon spectral estimation algorithm was implemented on PolInSAR data for vertical profile generation. Vertical profile generated from capon algorithm had shown different forest height values at different locations in range direction for single azimuth bin. Maximum height value recorded from the vertical profile was 45 m and the minimum height was 5 m. The algorithm was implemented for all the locations for which field data was collected for forest height measurement. Tomography based forest height was compared with field-measured forest height for 190 locations of the study area. Coefficient of determination and root mean square error obtained from the relationship between field and modelled forest heights were 0.91 and 3.91 m respectively. This study concluded that forest height with reliable accuracy can be retrieved with space-borne PolInSAR tomography.

Abundance, richness and composition of lianas in forest communities along an elevation gradient in New Caledonia

Background: Lianas are an abundantand dynamic component of tropical forests, and their abundance ispredicted to increase with global change. A better understandingof factors that explain their distribution is required, especially onoceanic islands for which few data are available.Aims: Identifythe environmental factors that shape liana communities in NewCaledonian forestsMethods: We studied liana abundance,basal area, richness and climbing modes in 27 forest plots (20m × 20 m) along an elevation gradient (sea level to ca. 1000 ma.s.l.) that covers different forest types and precipitation regimes.Results: We inventoried 992 lianas belonging to 71 species. The abundance and basal area decreased logarithmically withincreasing elevation while species richness tended to peak at midelevations.Twining lianas were the most abundant and species-richfunctional group. We found no clear relationship between climbingmodes and forest structure.Conclusions: Our results show thatliana abundance decreases with increasing elevation in responseto lower precipitation in seasonal dry forest. At high elevation (600–1000 m a.s.l.), climbing secondary hemi-epiphytes replace lianasprobably as a result of climate-based niche differentiation. Variationof species richness along the gradient could not be explained byprecipitation or by forest structural parameters.

Relationship between some wild mammals and forest structural diversity parameters

Relationship between some wild mammals and forest structural diversity parameters

Estimating Forest Structural Parameters Using Canopy Metrics Derived from Airborne LiDAR Data in Subtropical Forests

Accurate and timely estimation of forest structural parameters plays a key role in the management of forest resources, as well as studies on the carbon cycle and biodiversity. Light Detection and Ranging (LiDAR) is a promising active remote sensing technology capable of providing highly accurate three dimensional and wall-to-wall forest structural characteristics. In this study, we evaluated the utility of standard metrics and canopy metrics derived from airborne LiDAR data for estimating plot-level forest structural parameters individually and in combination, over a subtropical forest in Yushan forest farm, southeastern China. Standard metrics, i.e., height-based and density-based metrics, and canopy metrics extracted from canopy vertical profiles, i.e., canopy volume profile (CVP), canopy height distribution (CHD), and foliage profile (FP), were extracted from LiDAR point clouds. Then the standard metrics and canopy metrics were used for estimating forest structural parameters individually and in combination by multiple regression models, including forest type-specific (coniferous forest, broad-leaved forest, mixed forest) models and general models. Additionally, the synergy of standard metrics and canopy metrics for estimating structural parameters was evaluated using field measured data. Finally, the sensitivity of vertical and horizontal resolution of voxel size for estimating forest structural parameters was assessed. The results showed that, in general, the accuracies of forest type-specific models (Adj-R2 = 0.44–0.88) were relatively higher than general models (Adj-R2 = 0.39–0.77). For forest structural parameters, the estimation accuracies of Lorey’s mean height (Adj-R2 = 0.61–0.88) and aboveground biomass (Adj-R2 = 0.54–0.81) models were the highest, followed by volume (Adj-R2 = 0.42–0.78), DBH (Adj-R2 = 0.48–0.74), basal area (Adj-R2 = 0.41–0.69), whereas stem density (Adj-R2 = 0.39–0.64) models were relatively lower. The combination models (Adj-R2 = 0.45–0.88) had higher performance compared with models developed using standard metrics (only) (Adj-R2 = 0.42–0.84) and canopy metrics (only) (Adj-R2 = 0.39–0.83). The results also demonstrated that the optimal voxel size was 5 × 5 × 0.5 m3 for estimating most of the parameters. This study demonstrated that canopy metrics based on canopy vertical profiles can be effectively used to enhance the estimation accuracies of forest structural parameters in subtropical forests.

Comparison of Canopy Cover Estimations From Airborne LiDAR, Aerial Imagery, and Satellite Imagery

Canopy cover is an important forest structure parameter for many applications in ecology, hydrology, and forest management. Light detection and ranging (LiDAR) is a promising tool for estimating canopy cover because it can penetrate forest canopy. Various algorithms have been developed to calculate canopy cover from LiDAR data. However, little attention was paid to evaluating how different factors, such as estimation algorithm, LiDAR point density and scan angle, influence canopy cover estimates; and how LiDAR-derived canopy cover differs from estimates using traditional methods, such as field measurements, aerial and satellite imagery. In this study, we systematically compared canopy cover estimations from LiDAR data, quick field measurements, aerial imagery, and satellite imagery using different algorithms. The results show that LiDAR-derived canopy cover estimates are marginally influenced by the estimation algorithms. LiDAR data with a point density of 1 point/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> can generate comparable canopy cover estimates to data with a higher density. The uncertainty of canopy cover estimates from LiDAR data increased drastically as scan angles exceed 12°. Plot-level canopy cover estimates derived from quick field measurements do not have strong correlation with LiDAR-derived estimations. Both the aerial imagery-derived and satellite imagery-derived canopy cover estimates are comparable to LiDAR-derived canopy cover estimates at the forest stand scale, but tend to be overestimated in sparse forests and be underestimated in dense forests, particularly for the aerial imagery-derived estimates. The results from this study can provide practical guidance for the selection of data sources, sampling schemes, and estimation methods in regional canopy cover mapping.

Estimating Mangrove Canopy Height and Above-Ground Biomass in the Everglades National Park with Airborne LiDAR and TanDEM-X Data

Mangrove forests are important natural ecosystems due to their ability to capture and store large amounts of carbon. Forest structural parameters, such as canopy height and above-ground biomass (AGB), provide a good measure for monitoring temporal changes in carbon content. The protected coastal mangrove forest of the Everglades National Park (ENP) provides an ideal location for studying these processes, as harmful human activities are minimal. We estimated mangrove canopy height and AGB in the ENP using Airborne LiDAR/Laser (ALS) and TanDEM-X (TDX) datasets acquired between 2011 and 2013. Analysis of both datasets revealed that mangrove canopy height can reach up to ~25 m and AGB can reach up to ~250 Mg•ha−1. In general, mangroves ranging from 9 m to 12 m in stature dominate the forest canopy. The comparison of ALS and TDX canopy height observations yielded an R2 = 0.85 and Root Mean Square Error (RMSE) = 1.96 m. Compared to a previous study based on data acquired during 2000–2004, our analysis shows an increase in mangrove stature and AGB, suggesting that ENP mangrove forests are continuing to accumulate biomass. Our results suggest that ENP mangrove forests have managed to recover from natural disturbances, such as Hurricane Wilma.

Atmospheric and Forest Decoupling of Passive Microwave Brightness Temperature Observations Over Snow-Covered Terrain in North America

This study addresses two significant sources of uncertainty prevalent in snow water equivalent (SWE) retrievals derived from advanced microwave scanning radiometer (AMSR-E) passive microwave (PMW) brightness temperature (Tb) observations at 18.7 and 36.5 GHz. Namely, atmospheric and overlying forest effects are decoupled from the original AMSR-E PMW Tb observations using relatively simple, physically based radiative transfer models. Comparisons against independent Tb measurements collected during airborne PMW Tb surveys highlight the effectiveness of the proposed AMSR-E atmospheric decoupling procedure. The atmospheric contribution to Tb ranges from 1 to 3 K depending on the frequency and polarization measured as well as meteorologic conditions at the time of AMSR-E overpass. It is further shown that forest decoupling should be conducted as a function of both land cover type and snow cover class. The exponential decay relationship between the forest structure parameter, namely, MODIS-derived leaf area index (LAI) and forest transmissivity, is fitted across snow-covered terrain in North America. The fitted exponential function can be utilized during forest decoupling activities for evergreen needle-leaved forest and woody savanna regions, but remains uncertain in other forest types due to a sparsity of snow-covered areas. By removing forest-related Tb contributions from the original AMSR-E observations, the results suggest that Tb spectral difference between 18.7 and 36.5 GHz, in general, increases across thinly vegetated to heavily vegetated regions, which can be beneficial when applied to traditional SWE retrieval algorithms.

Quantifying individual tree growth and tree competition using bi-temporal airborne laser scanning data: a case study in the Sierra Nevada Mountains, California

ABSTRACTImproved monitoring and understanding of tree growth and its responses to controlling factors are important for tree growth modeling. Airborne Laser Scanning (ALS) can be used to enhance the efficiency and accuracy of large-scale forest surveys in delineating three-dimensional forest structures and under-canopy terrains. This study proposed an ALS-based framework to quantify tree growth and competition. Bi-temporal ALS data were used to quantify tree growth in height (ΔH), crown area (ΔA), crown volume (ΔV), and tree competition for 114,000 individual trees in two conifer-dominant Sierra Nevada forests. We analyzed the correlations between tree growth attributes and controlling factors (i.e. tree sizes, competition, forest structure, and topographic parameters) at multiple levels. At the individual tree level, ΔH had no consistent correlations with controlling factors, ΔA and ΔV were positively related to original tree sizes (R > 0.3) and negatively related to competition indices (R < −0.3). At the forest-stand level, ΔH and ΔA were highly correlated to topographic wetness index (|R| > 0.7), ΔV was positively related to original tree sizes (|R| > 0.8). Multivariate regression models were simulated at individual tree level for ΔH, ΔA, and ΔV with the R2 ranged from 0.1 to 0.43. The ALS-based tree height estimation and growth analysis results were consistent with field measurements.

3D Forest: An application for descriptions of three-dimensional forest structures using terrestrial LiDAR.

Terrestrial laser scanning is a powerful technology for capturing the three-dimensional structure of forests with a high level of detail and accuracy. Over the last decade, many algorithms have been developed to extract various tree parameters from terrestrial laser scanning data. Here we present 3D Forest, an open-source non-platform-specific software application with an easy-to-use graphical user interface with the compilation of algorithms focused on the forest environment and extraction of tree parameters. The current version (0.42) extracts important parameters of forest structure from the terrestrial laser scanning data, such as stem positions (X, Y, Z), tree heights, diameters at breast height (DBH), as well as more advanced parameters such as tree planar projections, stem profiles or detailed crown parameters including convex and concave crown surface and volume. Moreover, 3D Forest provides quantitative measures of between-crown interactions and their real arrangement in 3D space. 3D Forest also includes an original algorithm of automatic tree segmentation and crown segmentation. Comparison with field data measurements showed no significant difference in measuring DBH or tree height using 3D Forest, although for DBH only the Randomized Hough Transform algorithm proved to be sufficiently resistant to noise and provided results comparable to traditional field measurements.

Der Schweizer Wald und seine Biodiversität: LiDAR ermöglicht neue Waldstrukturanalysen

The Swiss forest and its biodiversity: LiDAR allows for novel forest structure assessments Availability of comprehensive biodiversity maps and standlevel habitat management recommendations to forest services is often limited, despite the need for such information for multifunctional forestry. Here, we combined forest structural parameters derived from nation-wide available Light Detection and Ranging (LiDAR) data with parameters describing climate, topography and soil pH, to analyse and spatially predict the species richness of different taxa on two different scales. Vascular plants, bryophytes and land snails were analysed on the stand scale (900 m2), where we also identified target variables for habitat management by using data from the National Forest Inventory. On the landscape scale (1 km2), we analysed vascular plants, butterflies and breeding birds with a tight association to forests. LiDAR-derived forest structure parameters were consistently important predictors of species richness across taxa. Species richness patterns tended to be taxon-specific with low spatial congruence across taxa. The habitat quality for many forest land snail species, for example, increased with increasing proportions of trees from the genera Fraxinus, Tilia, Ulmus and Acer, or with increasing overstory cover and deadwood volume. Many butterfly species, on the other hand, responded positively to a heterogeneous understory. This study shows that important structural parameters for forest biodiversity can be derived area-wide and across large regions by using LiDAR. The growing availability of LiDAR data thus provides very useful information for conserving and promoting biodiversity in multifunctional forestry.

Comparing image-based point clouds and airborne laser scanning data for estimating forest heights

Accurate and updated knowledge of forest tree heights is fundamental in the context of forest management. However, measuring canopy height over large forest areas using traditional inventory techniques is laborious, time-consuming and excessively expensive. In this study, image-based point clouds produced from stereo aerial photographs (AP) were used to estimate forest height, and compared to Airborne Laser Scanning (ALS) data. We generated image-based Canopy Height Models (CHM) using different image-matching algorithms (SGM: Semi-Global Matching; eATE: enhanced Automatic Terrain Extraction), which were compared with a pure ALS-derived CHM. Additionally, plot-level height and density metrics were extracted from CHMs and used as explanatory variables for predicting the Lorey’s mean height (LMH), which was measured at 296 reference points on the ground. CHMSGM and CHMALS showed similar results in predicting LMH at sample plot locations (RMSE% = 8.54 vs. 7.92, respectively), while CHMeATE had lower accuracy (RMSE% = 13.23). Similarly, CHMSGM showed a lower normalized median absolute deviation (NMAD) from CHMALS (0.68 m) compared to CHMeATE (1.1 m). Our study revealed that image-based point clouds using SGM in the presence of high-resolution ALS-derived digital terrain model (DTM) provide comparable results with ALS data, while the performance of image-based point clouds using eATE is poorer than ALS for forest height estimation. The findings of this study provide a viable and cost-effective option for assessing height-related forest structural parameters. The proposed methodology can be usefully applied in all those countries where AP are updated on a regular basis and pre-existing historical ALS-derived DTMs are available.

Retrieving Directional Gap Fraction, Extinction Coefficient, and Effective Leaf Area Index by Incorporating Scan Angle Information From Discrete Aerial Lidar Data

The scan angle information implicitly contained within the 3-D point cloud data (PCD) generated from light detection and ranging strongly affects the retrieval accuracy of the forest canopy structural parameters. Using information generated from overlapping aerial laser scanning (ALS) flight paths with multiple scan angles over a forest canopy can help to remove the occlusion effects between foliage elements, ultimately creating a relative comprehensive PCD. In this paper, we develop a novel physically based scan angle correction algorithm to retrieve the effective leaf area index (LAIe) of a forest canopy using ALS. Furthermore, we investigate the effects of scan angle and the number of ALS overpass lines from adjacent flight paths over a forest canopy on directional gap fraction (DGF) estimates. Our results suggest that ALS-based LAIe estimates capture 71.35% of the variations in LAIe derived from digital hemispherical photography. A forest canopy point cloud created using PCD from multiple overlapping ALS flight paths was sufficient to quantitatively reveal the anisotropy characteristics of DGF variations. These results suggest that scan angle information should not be neglected in retrieving forest canopy structural parameters, especially when using ALS data collected with a wide scan angle (i.e., −30° to 30° in this paper). Finally, this paper provides a solid foundation to characterize the 3-D spatial distribution of a forest radiation regime using ALS-based forest PCD.

The Potential of Forest Biomass Inversion Based on Vegetation Indices Using Multi-Angle CHRIS/PROBA Data

Multi-angle remote sensing can either be regarded as an added source of uncertainty for variable retrieval, or as a source of additional information, which enhances variable retrieval compared to traditional single-angle observation. However, the magnitude of these angular and band effects for forest structure parameters is difficult to quantify. We used the Discrete Anisotropic Radiative Transfer (DART) model and the Zelig model to simulate the forest canopy Bidirectional Reflectance Distribution Factor (BRDF) in order to build a look-up table, and eight vegetation indices were used to assess the relationship between BRDF and forest biomass in order to find the sensitive angles and bands. Further, the European Space Agency (ESA) mission, Compact High Resolution Imaging Spectrometer onboard the Project for On-board Autonomy (CHRIS-PROBA) and field sample measurements, were selected to test the angular and band effects on forest biomass retrieval. The results showed that the off-nadir vegetation indices could predict the forest biomass more accurately than the nadir. Additionally, we found that the viewing angle effect is more important, but the band effect could not be ignored, and the sensitive angles for extracting forest biomass are greater viewing angles, especially around the hot and dark spot directions. This work highlighted the combination of angles and bands, and found a new index based on the traditional vegetation index, Atmospherically Resistant Vegetation Index (ARVI), which is calculated by combining sensitive angles and sensitive bands, such as blue band 490 nm/−55°, green band 530 nm/55°, and the red band 697 nm/55°, and the new index was tested to improve the accuracy of forest biomass retrieval. This is a step forward in multi-angle remote sensing applications for mining the hidden relationship between BRDF and forest structure information, in order to increase the utilization efficiency of remote sensing data.

Forest Vertical Parameter Estimation Using PolInSAR Imagery Based on Radiometric Correction

This work introduces an innovative radiometric terrain correction algorithm using PolInSAR imagery for improving forest vertical structure parameter estimation. The variance of radar backscattering caused by terrain undulation has been considered in this research by exploiting an iteration optimization procedure to improve the backscattering estimation for a Synthetic Aperture Radar (SAR) image. To eliminate the variance of backscatter coefficients caused by the local incident angle, a radiometric normalization algorithm has been investigated to compensate the influence of terrain on backscattering values, which hinders forest vertical parameter estimation. In vertical parameter estimation, species diversity and the spatial distribution of different vegetation have been modeled. Then, a combination of Fisher’s Alpha-Diversity model parameter estimation and the three-stage inversion method was designed for the vertical structure parameter. To demonstrate the efficiency of the proposed method in forest height estimation, the classical phase difference and three-stage inversion approach have been performed for the purpose of comparison. The proposed algorithm is tested on ALOS PALSAR (Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar) and RADARSAT-2 (Radio Direction and Range Satellite 2) data sets for the Great Xing’an Mountain area and BioSAR (Biomass Synthetic Aperture Radar) 2007 data sets for the Remningstorp area. Height estimation results have also been validated using in-situ measurements. Experiments indicate the proposed method has the ability to compensate the influence of terrain undulation and improving the accuracy of forest vertical structure parameter estimation.

ESTIMATION OF AERODYNAMIC ROUGHNESS AND ZERO PLANE DISPLACEMENT USING MEDIUM DENSITY OF AIRBORNE LIDAR DATA

Abstract. This paper presents a framework to estimate aerodynamic roughness over specific height (zo/H) and zero plane displacement (d/H) over various landscapes in Kelantan State using airborne LiDAR data. The study begins with the filtering of airborne LiDAR, which produced ground and non-ground points. The ground points were used to generate digital terrain model (DTM) while the non-ground points were used for digital surface model (DSM) generation. Canopy height model (CHM) was generated by subtracting DTM from DSM. Individual trees in the study area were delineated by applying the Inverse Watershed segmentation method on the CHM. Forest structural parameters including tree height, height to crown base (HCB) and diameter at breast height (DBH) were estimated using existing allometric equations. The airborne LiDAR data was divided into smaller areas, which correspond to the size of the zo/H and d/H maps i.e. 50 m and 100 m. For each area individual tree were reconstructed based on the tree properties, which accounts overlapping between crowns and trunks. The individual tree models were used to estimate individual tree frontal area and the total frontal area over a specific ground surface. Finally, three roughness models were used to estimate zo/H and d/H for different wind directions, which were assumed from North/South and East/West directions. The results were shows good agreements with previous studies that based on the wind tunnel experiments.

Fine-resolution forest tree height estimation across the Sierra Nevada through the integration of spaceborne LiDAR, airborne LiDAR, and optical imagery

ABSTRACTForests of the Sierra Nevada (SN) mountain range are valuable natural heritages for the region and the country, and tree height is an important forest structure parameter for understanding the SN forest ecosystem. There is still a need in the accurate estimation of wall-to-wall SN tree height distribution at fine spatial resolution. In this study, we presented a method to map wall-to-wall forest tree height (defined as Lorey’s height) across the SN at 70-m resolution by fusing multi-source datasets, including over 1600 in situ tree height measurements and over 1600 km2 airborne light detection and ranging (LiDAR) data. Accurate tree height estimates within these airborne LiDAR boundaries were first computed based on in situ measurements, and then these airborne LiDAR-derived tree heights were used as reference data to estimate tree heights at Geoscience Laser Altimeter System (GLAS) footprints. Finally, the random forest algorithm was used to model the SN tree height from these GLAS tree heights, optical imagery, topographic data, and climate data. The results show that our fine-resolution SN tree height product has a good correspondence with field measurements. The coefficient of determination between them is 0.60, and the root-mean-squared error is 5.45 m.

Segmentation and focus-point location based on boundary analysis in forest canopy hemispherical photography

Analysis of forest canopy hemisphere images is one of the most important methods for measuring forest canopy structure parameters. In this study, our main focus was on using circular image region segmentation, which is the basis of forest canopy hemispherical photography. The boundary of a forest canopy hemisphere image was analyzed via histogram, rectangle, and Fourier descriptors. The image boundary characteristics were defined and obtained based on the following: (1) an edge model that contains three parts, i.e., step, ramp, and roof; (2) boundary points of discontinuity; (3) an edge that has a linear distribution of scattering points. On this basis, we proposed a segmentation method for the circular region in a forest canopy hemisphere image, fitting the circular boundary and computing the center and radius by the least squares method. The method was unrelated to the parameters of the image acquisition device. Hence, this study lays a foundation for automatically adjusting the parameters of high-performance image acquisition devices used in forest canopy hemispherical photography.

Effects of forest structure on litter production, soil chemical composition and litter-soil interactions

Litter production in forest ecosystems is a major indicator of primary productivity because litter helps incorporate carbon and nutrients from plants into the soil and is directly involved in plant-soil interactions. To our knowledge, few studies have investigated the relationship between species diversity and ecosystem processes in subtropical forest fragments. In this work, we determined forest structural parameters and assessed seasonal leaf litter input, leaf decomposition rate, litter quality and soil characteristics in two subtropical Atlantic Forest fragments. Litter production was greater in the native fragment with the higher species diversity (FN1). The two native fragments (FN1 and FN2) differed in basal area, volume and dominance in the upper stratum, which were positively correlated with litter production in FN1 but negatively correlated in FN2. Soil in FN1 exhibited higher contents of organic C, available phosphorus and exchangeable calcium, and the leaf litter had a higher C:N ratio. Although these results are consistent with a plant-soil feedback, which suggests the presence of a complementary effect, the dominance of certain families in subtropical forest fragments results in a selection effect on litter productivity and decomposition.

MANGROVE PLANTATION FOREST ASSESSMENT USING STRUCTURAL ATTRIBUTES DERIVED FROM LIGHT DETECTION AND RANGING (LiDAR) DATA

Estimating the structural and functional attributes of forests is integral in performing management strategies and for understanding forest ecosystem functions. Field sampling methods through plot level is one of the known strategies in forest studies; however, these methods have its limitations and are prone to subjected biases. Remote Sensing data, particularly that of Light Detection and Ranging (LiDAR) can be utilized to alleviate the limitations of extracting forest structure parameters. The study aims to characterize a Rhizophoraceae-dominated mangrove forest plantation. Point cloud distribution within a 1-hectare plot was processed by utilizing thirty (30) samples of 5x5 meter plots, which were analysed for the characterization and forest structure assessment. Point densities were grouped at intervals of 10% of the plot’s maximum height (&amp;lt;i&amp;gt;Height at Bincentile or HB&amp;lt;sub&amp;gt;n&amp;lt;/sub&amp;gt;&amp;lt;/i&amp;gt;) to determine where the clustering of points occur per plot. The result shows that most of the points are clustered at &amp;lt;i&amp;gt;HB&amp;lt;sub&amp;gt;n&amp;lt;/sub&amp;gt;&amp;lt;/i&amp;gt; with height values ranging from 2.98 to 4.15 meters for plots located at the middle part of the forest, with a standard deviation of 1.78 to 3.69, respectively. On the other hand, sample plots that are located at the periphery part of the forest shows that the point clustering occurs at different heights ranging from 1.71 meters to 4.43 meters, with standard deviation values ranging from 1.69 to 3.81.Plots that are located along the fringes of the forest reflect a stunted clustering of points, while plots that explicitly show mangrove trimmings and cuts reflect even distribution in terms of point density within each &amp;lt;i&amp;gt;HB&amp;lt;sub&amp;gt;n&amp;lt;/sub&amp;gt;&amp;lt;/i&amp;gt;. Both species present in the area (&amp;lt;i&amp;gt;R. mucronata and R. apiculata&amp;lt;/i&amp;gt;) exhibits similar clustering, which could represent detection of Rhizophoraceae mangroves.