Forest Structure Variables Research Articles

Deadwood stocks increase with selective logging and large tree frequency in Gabon.

Deadwood is a major component of aboveground biomass (AGB) in tropical forests and is important as habitat and for nutrient cycling and carbon storage. With deforestation and degradation taking place throughout the tropics, improved understanding of the magnitude and spatial variation in deadwood is vital for the development of regional and global carbon budgets. However, this potentially important carbon pool is poorly quantified in Afrotropical forests and the regional drivers of deadwood stocks are unknown. In the first large-scale study of deadwood in Central Africa, we quantified stocks in 47 forest sites across Gabon and evaluated the effects of disturbance (logging), forest structure variables (live AGB, wood density, abundance of large trees), and abiotic variables (temperature, precipitation, seasonality). Average deadwood stocks (measured as necromass, the biomass of deadwood) were 65Mg ha-1 or 23% of live AGB. Deadwood stocks varied spatially with disturbance and forest structure, but not abiotic variables. Deadwood stocks increased significantly with logging (+38Mg ha-1 ) and the abundance of large trees (+2.4Mg ha-1 for every tree >60cm dbh). Gabon holds 0.74Pg C, or 21% of total aboveground carbon in deadwood, a threefold increase over previous estimates. Importantly, deadwood densities in Gabon are comparable to those in the Neotropics and respond similarly to logging, but represent a lower proportion of live AGB (median of 18% in Gabon compared to 26% in the Neotropics). In forest carbon accounting, necromass is often assumed to be a constant proportion (9%) of biomass, but in humid tropical forests this ratio varies from 2% in undisturbed forest to 300% in logged forest. Because logging significantly increases the deadwood carbon pool, estimates of tropical forest carbon should at a minimum use different ratios for logged (mean of 30%) and unlogged forests (mean of 18%).

Climate affects the structure of mixed rain forest in southern sector of Atlantic domain in Brazil

This study aimed to analyze the influence of environmental factors in determining the variation in forest structure. We obtained the data through sampling units placed regularly in a grid of 10 km × 10 km in the state of Santa Catarina, southern Brazil. The axes of Detendred Correspondence Analysis summarized the vegetation structure and we used these as response variables in ordinary least square models, and environmental variables as predictors. Moran Eigenvector Maps were used as spatial predictors, enabling the variance partitioning. The results revealed influence of climatic factors, especially thermal and rainfall in determining the vegetation structure. The physical geography (high plateaus) and positioning below the Tropic of Capricorn line are the main static elements influencing the climate and therefore the vegetation.

Statistical inference for forest structural diversity indices using airborne laser scanning data and the k-Nearest Neighbors technique

Abstract Forest structural diversity plays a major role for forest management, conservation and restoration and is recognized as a fundamental aspect of forest biodiversity. The assessment, maintenance and restoration of a diversified forest structure have become major foci in the effort to preserve forest ecosystems from loss of biological diversity. However, the assessment of forest biodiversity is difficult because it involves multiple components and is characterized using multiple variables. The objective of the study was to develop a methodological approach for predicting, mapping, and constructing a statistical inference for a multiple-variable index of forest structural diversity. The method included three key components: (i) use of the k-Nearest Neighbors (k-NN) technique, field plot data, and airborne laser scanning metrics to predict multiple forest structural diversity variables simultaneously, (ii) incorporation of multiple diversity variable predictions into a single index, and (iii) construction of a statistically rigorous inference for the population mean of the index. Three structural diversity variables were selected to illustrate the method: growing stock volume and the standard deviations of tree diameter at breast-height and tree height. Optimization of the k-NN technique produced mean relative deviations less in absolute value than 0.04 for predictions for each of the three structural diversity variables, R 2 values between 0.50 and 0.66 which were in the range of values reported in the literature, and a confidence interval for the population mean of the index whose half-width was approximately 5% of the mean. Finally, the spatial pattern depicted in the resulting map of forest structural diversity for the study area contributed to validating the proposed method.

Effects of Topographical and Edaphic Factors on Tree Community Structure and Diversity of Subtropical Mountain Forests in the Lower Lancang River Basin

We investigated community structure and tree species diversity of six subtropical mountain forests in relation to 11 topographical and edaphic factors in Lower Lancang River Basin, Yunnan Province, China, based on a census of all trees with diameter at breast height ≥5 cm in 45 0.06-ha plots. The forests were as follows: a river valley monsoon forest, semi-humid evergreen broad-leaved forest, monsoon evergreen broad-leaved forest, mid-mountain humid evergreen broad-leaved forest, summit mossy dwarf forest, and warm needle-leaved forest. Owing to the variation in microenvironment, forest structure (tree density, mean height, mean diameter at breast height, mean basal area at breast height) and tree diversity indices (the number of species, Margalef richness, Shannon-Wiener diversity, Simpson’s index, and Pielou’s evenness) differed significantly among forest types but did not differ among sites. We recorded a total of 5155 canopy trees belonging to 204 tree species, 104 genera, and 50 families at three sites, and the co-occurrence of tree species between adjacent communities was higher. A clear forest community distribution along an altitudinal gradient suggested that elevation was important in tree species distribution. Ordination identified elevation, slope degree, slope position, soil pH, organic matter, total nitrogen, and available nitrogen as significant explanatory variables of tree species distribution and showed that elevation was more important than the rest of the environmental variables in affecting local woody plant distribution. Understanding relationships between tree species distribution and environmental factors in subtropical mountain forests of the Lower Lancang River Basin would enable us to apply these findings to forest management and vegetation restoration.

Extending Airborne Lidar-Derived Estimates of Forest Canopy Cover and Height Over Large Areas Using kNN With Landsat Time Series Data

Airborne light detection and ranging (lidar) data provide an accurate and consistent means to obtain reliable forest canopy cover (CC) and height measurements, which are important in determining forest stand structure, volume, and biomass. Extending CC and height measurements over larger areas by integration with satellite imagery increases the value of airborne lidar data. A typical approach has been to use multiple regression, machine-learning, or regression tree methods to determine relationships between the forest structure variables measured in the lidar data and available single-date or multitemporal Landsat sensor reflectance data, and if the relationships are strong enough, to extend those variables over much larger areas than is typically covered by lidar. Such methods can be difficult to apply because of the complexity of the extending models and algorithms, and the long processing times, which may become prohibitive. One machine-learning approach, which uses the k-nearest neighbor (kNN) algorithm, is presented here in a British Columbia, Canada forest environment. Our goal was to simplify the estimation of lidar-derived forest structure variables with available Landsat time series data and compare the results of the kNN model to the traditional multiple regression results, and to those obtained with more complex and computationally demanding random forest (RF) methods. We develop and test the kNN model with airborne lidar-derived estimates of forest CC and height in 1846 relatively young and mature forests. The best kNN model produced estimates of airborne lidar-derived CC in validation sample ( ${\text n} = 1132$ ) of mature forest with three Landsat time series variables with an ${R}^{2} = 0.74$ and RMSE of approximately 10%. This result is comparable to the results obtained in earlier work using more complex machine-learning approaches (in approximately 1/10 the time). Younger (i.e., recently disturbed) forest CC estimation was less successful in the kNN model because of the high degree of structural variability in these forests. Extending airborne lidar estimates of forest height to larger areas was also possible though, as expected, less successful than CC estimation using the kNN approach.

Shifts in community‐level traits and functional diversity in a mixed conifer forest: a legacy of land‐use change

SummaryHistorical reference conditions have long been used to guide the restoration of degraded ecosystems. However, a rapidly changing climate and altered disturbance regimes are calling into question the usefulness of this approach. As a consequence, restoration goals are increasingly focused on creating communities that are resilient to novel environmental stressors and emphasis is being placed on defining functional targets through the use of plant traits. While changes in forest structure and composition have received much attention, long‐term changes in stand‐level functional traits are not well understood.We used dendrochronology to reconstruct historical forest structure and composition in 1880, the year immediately following the disruption of the natural fire regime in a mixed conifer forest in Arizona,USA. We analysed the differences in pre‐settlement and contemporary forest composition, structure and community‐weighted mean (CWM) traits, and functional diversity metrics (Rao's Q and functional richness) for four plant functional traits: leaf nitrogen content (leaf N), specific leaf area (SLA), wood density and bark thickness.We observed significant shifts in forest composition, structure,CWMtraits and functional diversity from 1880 to 2011. These changes reflect a reduction in fire and drought tolerance, driven largely by increases in the relative importanceof Abies concolorandPinus strobiformis. Compositional changes were associated with declines inCWMleaf N,SLA, wood density and bark thickness. We found lower multitrait functional diversity (Rao's Q) in contemporary forests driven primarily by leaf N; however, bark thickness variation was greater in contemporary forests than in 1880.Synthesis and applications. Compositional shifts towards reduced average bark thickness and wood density in contemporary forests driven by land‐use change have likely reduced forest resilience to both fire and drought. Managers can manipulate forest structure and species composition to achieve functional objectives by increasing stand‐level bark thickness to promote fire tolerance and increasing stand‐level wood density to promote drought tolerance. Forecasts for extended fire seasons along with declining precipitation are projected for many ecosystems around the planet. A focus on restoring optimal functional trait combinations may be as important as managing ecosystem structure for restoring resilient ecosystems.

Diversity and production in an Afromontane Forest

This contribution evaluates the effect of forest structure and tree species diversity on plot productivity and individual tree growth in the unique Knysna forests in Southern Africa using mapped tree data from an observational study that has been re-measured over a period of 40 years. The effects of tree species diversity and forest structure on tree growth and forest production are evaluated on three levels of resolution: a) the forest community (canopy, sub-canopy species), b) the subplots (number of trees per ha, skewness of the diameter distribution, diameter coefficient of variation) and c) the immediate neighborhood of selected reference trees (“Mingling”, “Dominance”, Aggregation” and “Size Variation”). An analysis of the community level identified two distinct clusters, one including dominant/canopy species with the highest growth rates and a greater variation of growth, and another cluster which includes the remaining subcanopy species which have a smaller maximum size and lower rates of growth. The area-based structure variables on plot level have a highly significant effect on total basal area growth. However, the effects of forest density and species richness on productivity were not straight forward. Maximum basal area production of about 0.75 m2/ha/year is achieved at medium levels of richness (around 20 species per ha) and medium levels of density (around 30 m2/ha basal area) using percentile regression estimates. The relative “Dominance” of a selected reference tree had a highly significant effect on individual tree growth on all investigated species. Other neighbourhood structure variables were only occasionally significant or not significant at all. This contribution presents a new theoretical framework for analysing natural forests that includes community, plot and neighborhood variables of forest structure and diversity, and a first specific analysis of the structure and dynamics of the Knysna Afromontane Forest, based on a unique set of longterm observations. The species-area (SAR) model developed in this study, represents a new general approach that can be used to derive a common standard of tree species diversity for different plot sizes, the species richness per hectare.

Spatial and temporal mangrove litter production in Barra de Navidad lagoon, Jalisco, México

Barra de Navidad lagoon is a coastal wetland of international importance (Ramsar site) and it is included among the 81 Mexican mangrove priority sites. One of the most valued characteristics of this lagoon is the presence of mangrove forest in a good conservation state. The goal of our research was the measurement of mangrove litter production and environmental factors influencing its dynamics. The mangrove area was divided into seven zones and litterfall was monthly sampled from November 2011 to October 2012 using 0.25 m(2) square collectors made with mosquito mesh (1 mm) and positioned at 1.3 m above the ground. Abiotic variables of the interstitial water were measured simultaneously at each zone in permanent plots using a multi-parameter probe, YSI-556-M. Total mean value of litterfall production, weighted by zone surface, was 19.12 ± 1.23 gPS/m(2).mo (2.29 t/ha.year). This low productivity is a consequence of the region’s dry climate and low tide range. The species Avicennia germinans and Laguncularia racemosa produced more than 80 % of total litterfall, while Rhizophora mangle contributed only 16 % and Conocarpus erectus < 4 %. A significant correlation between litterfall production and abiotic variables was found (e.g. salinity and interstitial water depth). We concluded that there are significant spatial variations in soil abiotic variables which are correlated with differences in mangrove species composition, and produce, together with the life cycles stages of those species, significant variations in the quantity and composition of litterfall. Future research will be focused on quantifying spatial variations in forest structure and their relationship with litterfall production.

Pattern and dynamics of biomass stock in old growth forests: The role of habitat and tree size

Forest ecosystems play a fundamental role in the global carbon cycle. However, how stand-level changes in tree age and structure influence biomass stock and dynamics in old-growth forests is a question that remains unclear.In this study, we quantified the aboveground biomass (AGB) standing stock, the coarse woody productivity (CWP), and the change in biomass over ten years (2004–2014) in a 25 ha unmanaged broad-leaved Korean pine mixed forest in northeastern China. In addition, we quantified how AGB stock and change (tree growth, recruitment and mortality) estimations are influenced by the variation in habitat heterogeneity, tree size structure and subplot size.Our analysis indicated that Changbai forest had AGB of 265.4 Mg ha−1 in 2004, and gained1.36 Mg ha−1 y−1 between 2004 and 2014. Despite recruitment having better performance in nutrient rich habitat, we found that there is a directional tree growth trend independent of habitat heterogeneity for available nutrients in this old growth forest. The observed increases in AGB stock (∼70%) are mainly attributed to the growth of intermediate size trees (30–70 cm DBH), indicating that this forest is still reaching its mature stage. Meanwhile, we indicated that biomass loss due to mortality reduces living biomass, not increment, may be the primary factor to affect forest biomass dynamics in this area.Also, spatial variation in forest dynamics is large for small sizes (i.e. coefficient of variation in 20 × 20 m subplots is 53.2%), and more than 90 percent of the inherent variability of these coefficients was predicted by a simple model including plot size. Our result provides a mean by which to estimate within-plot variability at a local scale before inferring any directional change in forest dynamics at a regional scale, and information about the variability of forest structure and dynamics are fundamental to design effective sampling strategies in future study.

Taxonomic and ecological relevance of the chlorophyll a fluorescence signature of tree species in mixed European forests.

The variability of chlorophyll a fluorescence (ChlF) parameters of forest tree species was investigated in 209 stands belonging to six European forests, from Mediterranean to boreal regions. The modifying role of environmental factors, forest structure and tree diversity (species richness and composition) on ChlF signature was analysed. At the European level, conifers showed higher potential performance than broadleaf species. Forests in central Europe performed better than those in Mediterranean and boreal regions. At the site level, homogeneous clusters of tree species were identified by means of a principal component analysis (PCA) of ChlF parameters. The discrimination of the clusters of species was influenced by their taxonomic position and ecological characteristics. The species richness influenced the tree ChlF properties in different ways depending on tree species and site. Tree species and site also affected the relationships between ChlF parameters and other plant functional traits (specific leaf area, leaf nitrogen content, light-saturated photosynthesis, wood density, leaf carbon isotope composition). The assessment of the photosynthetic properties of tree species, by means of ChlF parameters, in relation to their functional traits, is a relevant issue for studies in forest ecology. The connections of data from field surveys with remotely assessed parameters must be carefully explored.

Forests of opportunities and mischief: disentangling the interactions between forests, parasites and immune responses

Habitat characteristics determine the presence of individuals through resource availability, but at the same time, such features also influence the occurrence of parasites. We analyzed how birds respond to changes in interior forest structures, to forest management regimes, and to the risk of haemosporidian infections. We captured and took blood samples from blackcaps (Sylvia atricapilla) and chaffinches (Fringilla coelebs) in three different forest types (beech, mixed deciduous, spruce). We measured birds’ body asymmetries, detected avian haemosporidians, and counted white blood cells as an immune measure of each individual per forest type. We used, to our knowledge for the first time, continuous forest structural parameters to quantify habitat structure, and found significant effects of habitat structure on parasite prevalence that previously have been undetected. We found three times higher prevalence for blackcaps compared with chaffinches. Parasite intensity varied significantly within host species depending on forest type, being lowest in beech forests for both host species. Structurally complex habitats with a high degree of entropy had a positive effect on the likelihood of acquiring an infection, but the effect on prevalence was negative for forest sections with a south facing aspect. For blackcaps, forest gaps also had a positive effect on prevalence, but canopy height had a negative one. Our results suggest that forest types and variations in forest structure influence the likelihood of acquiring an infection, which subsequently has an influence on host health status and body condition; however, responses to some environmental factors are host-specific.

Areas of Agreement and Disagreement Regarding Ponderosa Pine and Mixed Conifer Forest Fire Regimes: A Dialogue with Stevens et al.

In a recent PLOS ONE paper, we conducted an evidence-based analysis of current versus historical fire regimes and concluded that traditionally defined reference conditions of low-severity fire regimes for ponderosa pine (Pinus ponderosa) and mixed-conifer forests were incomplete, missing considerable variability in forest structure and fire regimes. Stevens et al. (this issue) agree that high-severity fire was a component of these forests, but disagree that one of the several sources of evidence, stand age from a large number of forest inventory and analysis (FIA) plots across the western USA, support our findings that severe fire played more than a minor role ecologically in these forests. Here we highlight areas of agreement and disagreement about past fire, and analyze the methods Stevens et al. used to assess the FIA stand-age data. We found a major problem with a calculation they used to conclude that the FIA data were not useful for evaluating fire regimes. Their calculation, as well as a narrowing of the definition of high-severity fire from the one we used, leads to a large underestimate of conditions consistent with historical high-severity fire. The FIA stand age data do have limitations but they are consistent with other landscape-inference data sources in supporting a broader paradigm about historical variability of fire in ponderosa and mixed-conifer forests than had been traditionally recognized, as described in our previous PLOS paper.

Epiphyte communities in Mediterranean fragmented forests: importance of the fragment size and the surrounding matrix

Mediterranean forests have been fragmented intensively over time, thereby yielding small and isolated forest remnants. They host a rich variety of epiphytes, which may be affected by landscape structure. Previous studies have analyzed the influence of habitat quality on these epiphytic communities, but there is little knowledge of the effects of other fragment features. We evaluated the impacts of forest loss and fragmentation on epiphytic communities (lichens and bryophytes) at plot and fragment scales after controlling the variation in forest structure and management. We considered 40 fragments of dense oak forests in a human-modified landscape. We quantified their spatial attributes (size and shape), the quality of the surrounding matrix and the forest stand structure. We modeled community traits, and the presence and abundance of species at fragment and plot scales. Fragment size, shape, and the quality of the surrounding matrix were key factors that affected epiphytic richness and diversity. Larger and more regularly shaped fragments hosted the richest and most diverse communities, possibly offering a larger core area and thus favoring the entry of typical forest species. A high-contrast matrix was only favorable in small fragments, probably allowing the arrival of propagules. The species-level response was highly variable. Landscape structure provides powerful explanations of the richness and diversity losses among epiphytes. Forest management should ensure the retention of the largest possible continuous forests. The management strategy of the matrix will depend on the conservation goal, since we observed different effects related with quality and fragment size.

Landsat-8 OLI uydu görüntüsünden çıkarılan arazi çeşitliliği ile kuş türü zenginliği arasındaki ilişkiler

Preparation of the base maps in conserving biodiversity gain importance gradually in forest inventory. Such maps are needed to either in arrangement of forest management maps, or in managing other semi-natural ecosystems. Biodiversity maps can be prepared particularly based on the variables to be easily measured such as forest structural diversity and bird species richness. Preparation of these maps for large geographic areas using field measurements and observations is difficult, costly, and time consuming. The potential of satellite data has been intensively investigated on this subject. The aim of this study is to determine the relations between bird species richness and the landscape diversity calculated based on Landsat-8 OLI data and image segmentation. The results showed that there is a statistically important relation between bird species richness and landscape diversity (r=0,58 and p<0,01). This study suggests that Landsat-8 OLI data has a great potential in modelling and mapping bird species richness. However, using satellite data alone for an accurate mapping may be insufficient. Estimation accuracy can be improved by integrating other environmental variables and features extracted from satellite data. For this purpose, to repeat this study is considered very important in larger geographic regions using more bird observation plots and sophisticated modelling methods. Keywords: Bird species richness, Satellite image, Biological diversity

Mangrove plantation over a limestone reef – Good for the ecology?

There have been efforts to restore degraded tropical and subtropical mangrove forests. While there have been many failures, there have been some successes but these were seldom evaluated to test to what level the created mangrove wetlands reproduce the characteristics of the natural ecosystem and thus what ecosystem services they can deliver. We provide such a detailed assessment for the case of Olango and Banacon Islands in the Philippines where the forest was created over a limestone reef where mangroves did not exist in one island but they covered most of the other island before deforestation in the 1940s and 1950s. The created forest appears to have reached a steady state after 60 years. As is typical of mangrove rehabilitation efforts worldwide, planting was limited to a single Rhizophora species. While a forest has been created, it does not mimic a natural forest. There is a large difference between the natural and planted forests in terms of forest structure and species diversity, and tree density. The high density of planted trees excludes importing other species from nearby natural forests; therefore the planted forest remains mono-specific even after several decades and shows no sign of mimicking the characteristics of a natural forest. The planted forests provided mangrove propagules that invaded nearby natural forests. The planted forest has also changed the substratum from sandy to muddy. The outline of the crown of the planted forest has become smooth and horizontal, contrary to that of a natural forest, and this changes the local landscape. Thus we recommend that future mangrove restoration schemes should modify their methodology in order to plant several species, maintain sufficient space between trees for growth, include the naturally dominant species, and create tidal creeks, in order to reproduce in the rehabilitated areas some of the key ecosystem characteristics of natural mangrove forests.

Factors driving structure of natural and anthropogenic forest edges from temperate to boreal ecosystems

AbstractQuestionsWhat factors control broad‐scale variation in edge length and three‐dimensional boundary structure for a large region extending across two biomes? What is the difference in structure between natural and anthropogenic edges?LocationTemperate and boreal forests across all of Sweden, spanning latitudes 55–69° N.MethodsWe sampled more than 2000 forest edges using line intersect sampling in a monitoring programme (National Inventory of Landscapes in Sweden). We compared edge length, ecosystem attributes (width of adjacent ecosystem, canopy cover, canopy height, patch contrast in canopy height, forest type) and boundary attributes (profile, abruptness, shape) of natural edges (lakeshore, wetland) with anthropogenic edges (clear‐cut, agricultural, linear disturbance) in five regions.ResultsAnthropogenic edges were nearly twice as abundant as natural edges. Length of anthropogenic edges was largest in southern regions, while the abundance of natural edges increased towards the north. Edge types displayed unique spectrums of boundary structures, but abrupt edges dominated, constituting 72% of edge length. Anthropogenic edges were more abrupt than natural edges; wetland edges had the most gradual and sinuous boundaries. Canopy cover, canopy height, patch contrast and forest type depended on region, whereas overall boundary abruptness and shape showed no regional pattern. Patch contrast was related to temperature sum (degree days ≥ 5 °C), suggesting that regional variability can be predicted from climate‐controlled forest productivity. Boundary abruptness was coupled with the underlying environmental gradient, land use and forest type, with higher variability in deciduous than in conifer forest.ConclusionsEdge origin, land use, climate and tree species are main drivers of broad‐scale variability in forest edge structure. Our findings have important implications for developing ecological theory that can explain and predict how different factors affect forest edge structure, and help to understand how land use and climate change affect biodiversity at forest edges.

Estimation of Forest Structural Diversity Using the Spectral and Textural Information Derived from SPOT-5 Satellite Images

Uneven-aged forest management has received increasing attention in the past few years. Compared with even-aged plantations, the complex structure of uneven-aged forests complicates the formulation of management strategies. Forest structural diversity is expected to provide considerable significant information for uneven-aged forest management planning. In the present study, we investigated the potential of using SPOT-5 satellite images for extracting forest structural diversity. Forest stand variables were calculated from the field plots, whereas spectral and textural measures were derived from the corresponding satellite images. We firstly employed Pearson’s correlation analysis to examine the relationship between the forest stand variables and the image-derived measures. Secondly, we performed all possible subsets multiple linear regression to produce models by including the image-derived measures, which showed significant correlations with the forest stand variables, used as independent variables. The produced models were evaluated with the adjusted coefficient of determination ( R a d j 2 ) and the root mean square error (RMSE). Furthermore, a ten-fold cross-validation approach was used to validate the best-fitting models ( R a d j 2 &gt; 0.5). The results indicated that basal area, stand volume, the Shannon index, Simpson index, Pielou index, standard deviation of DBHs, diameter differentiation index and species intermingling index could be reliably predicted using the spectral or textural measures extracted from SPOT-5 satellite images.

Measuring diversity of tree distribution patterns in natural forests

Patterns of tree distribution are an important part of forest structure and directly affect the health and stability of forest ecosystems. Maintaining and preserving forest structural diversity has often been considered the best way to protect biodiversity. One method measuring the diversity of tree distribution patterns was discussed in this paper in order to provide a theoretical basis for the expression of forest structure diversity. The key to the study of distribution pattern diversity is to select the appropriate biodiversity measuring method and index which has a distributed attribute. In this paper, we put forward a method to express tree distribution pattern diversity which counted the distributions frequency of uniform angle index and Voronoi polygon side, and calculate uniform angle index diversity and Voronoi polygon side distribution diversity by the Simpson index, respectively. The distribution pattern diversity of three long-term monitoring Pinus koreansis broad-leaved forest plots (area = 100 m × 100 m) in northeastern China was analyzed by this method. The results showed that both of the distribution of uniform angle index and the Voronoi polygon side were close to the normal distribution. The frequency of randomly distributed trees was the maximum and more than 55% in the uniform angle index distribution; the type of Voronoi polygon side was great than 10 and over 50% of the trees had 5–6 closest neighboring trees. The result of using the Simpson index to analyze tree distribution pattern diversity showed that the tree distribution pattern diversity was higher in the cluster ·研究报告· 第 3期 惠刚盈等: 林木分布格局多样性测度方法: 以阔叶红松林为例 281 distribution stand than the random distribution stand. We also found that Simpson index values were different when different distribution pattern diversity methods were used, and the uniform angle distribution diversity was significantly lower than that of the Voronoi polygon side distribution diversity, which mainly due to the different quantity grade of each index. Therefore, study on the diversity of forest distribution pattern should choose distribution pattern indices with distribution attribute. The uniform angle index distribution and Voronoi polygon side distribution used in this paper had this attribute, however, different indices reflected different aspects of distribution pattern, so the same pattern analysis method should be used in the analysis and comparison of distribution pattern diversity of different forest stands.

Estimating Logged-Over Lowland Rainforest Aboveground Biomass in Sabah, Malaysia Using Airborne LiDAR Data

Unprecedented deforestation and forest degradation in recent decades have severely depleted the carbon storage in Borneo. Estimating aboveground biomass (AGB) with high accuracy is crucial to quantifying carbon stocks for Reducing Emissions from Deforestation and Forest Degradation-plus implementation (REDD+). Airborne Light Detection and Ranging (LiDAR) is a promising remote sensing technology that provides fine-scale forest structure variability data. This paper highlights the use of airborne LiDAR data for estimating the AGB of a logged-over tropical forest in Sabah, Malaysia. The LiDAR data was acquired using an Optech Orion C200 sensor onboard a fixed wing aircraft. The canopy height of each LiDAR point was calculated from the height difference between the first returns and the Digital Terrain Model (DTM) constructed from the ground points. Among the obtained LiDAR height metrics, the mean canopy height produced the strongest relationship with the observed AGB. This single-variable model had a root mean squared error (RMSE) of 80.02 t ha-1 or 22.31% of the mean AGB, which performed exceptionally when compared with recent tropical rainforest studies. Overall, airborne LiDAR did provide fine-scale canopy height measurements for accurately and reliably estimating the AGB in a logged-over forest in Sabah, thus supporting the state's effort in realizing the REDD+ mechanism.

Estimation of Alpine Forest Structural Variables from Imaging Spectrometer Data

Spatial information of forest structural variables is crucial for sustainable forest management planning, forest monitoring, and the assessment of forest ecosystem productivity. We investigate a complex alpine forest ecosystem located in the Swiss National Park (SNP) and apply empirical models to retrieve the structural variables canopy closure, basal area, and timber volume at plot scale. We used imaging spectrometer (IS) data from the Airborne Prism EXperiment (APEX) in combination with in-situ measurements of forest structural variables to develop empirical models. These models are based on simple and stepwise multiple regressions, while all potential two narrow-band combinations of the Simple Ratio (SR), the Normalized Difference Vegetation Index (NDVI), the perpendicular vegetation index (PVI), the second soil-adjusted vegetation index (SAVI2), and band depth indices were tested. The accuracy of the estimated structural attributes was evaluated using a leave-one-out cross-validation technique. Using stepwise multiple regression models, we obtained a moderate to good accuracy when estimating canopy closure (R2 = 0.81, rRMSE = 10%), basal area (R2 = 0.68, rRMSE = 20%), and timber volume (R2 = 0.73, rRMSE = 22%). We discuss the reliability of empirical approaches for estimates of canopy structural parameters considering the causality of light interaction and surface information.