Assessment and monitoring of damage from insects in Australian eucalypt forests and commercial plantations

Abstract   This paper presents a review of recent developments in the assessment and monitoring of health in Australian eucalypt forests and plantations of pine and eucalypt species, with an emphasis on damage caused by herbivorous insects. The diverse range of interests and priorities amongst Australian stakeholders of native forests and plantations influences the scale, resolution and accuracy of results sought, and this in turn influences how the assessment data are collected, analysed and reported. The authors discuss sampling systems that include extensive ground-based surveys, permanent plots and airborne technologies being developed in Australia. In all cases, there is an appreciation that the assessment protocols should be objective, repeatable and cost effective. Significant progress has been made in the application of digital, remotely sensed imagery to detect and classify damaged forest canopies. The success of this approach depends, in part, on a sound understanding of the progression of symptoms at the leaf, tree crown and stand scale, especially those symptoms that influence spectral reflectance behaviour.     

Mapping tropical forest fractional cover from coarse spatial resolution remote sensing imagery

At regional to global scales the only feasible approach to mapping and monitoring forests is through the use of coarse spatial resolution remotely sensed imagery. Significant errors in mapping may arise as such imagery may be dominated by pixels of mixed land cover composition which cannot be accommodated by conventional mapping approaches. This may lead to incorrect assessments of forest extent and thereby processes such as deforestation which may propagate into studies of environmental change. A method to unmix the class composition of image pixels is presented and used to map tropical forest cover in part of the Mato Grosso, Brazil. This method is based on an artificial neural network and has advantages over other techniques used in remote sensing. Fraction images depicting the proportional class coverage in each pixel were produced and shown to correspond closely to the actual land cover. The predicted and actual forest cover were, for instance, strongly correlated (up to r = 0.85, significant at the 99% level of confidence) and the predicted extent of forest over the test site much closer to the actual extent than that derived from a conventional approach to mapping from remotely sensed imagery.     

Applying climate change refugia to forest management and old-growth restoration

Recent studies highlight the potential of climate change refugia (CCR) to support the persistence of biodiversity in regions that may otherwise become unsuitable with climate change. However, a key challenge in using CCR for climate resilient management lies in how CCR may intersect with existing forest management strategies, and subsequently influence how landscapes buffer species from negative impacts of warming climate. We address this challenge in temperate coastal forests of the Pacific Northwestern United States, where declines in the extent of late-successional forests have prompted efforts to restore old-growth forest structure. One common approach for doing so involves selectively thinning forest stands to enhance structural complexity. However, dense canopy is a key forest feature moderating understory microclimate and potentially buffering organisms from climate change impacts, raising the possibility that approaches for managing forests for old-growth structure may reduce the extent and number of CCR. We used remotely sensed vegetation indices to identify CCR in an experimental forest with control and thinned (restoration) treatments, and explored the influence of biophysical variables on buffering capacity. We found that remotely sensed vegetation indices commonly used to identify CCR were associated with understory temperature and plant community composition, and thus captured aspects of landscape buffering that might instill climate resilience and be of interest to management. We then examined the interaction between current restoration strategies and CCR, and found that selective thinning for promoting old-growth structure had only very minor, if any, effects on climatic buffering. In all, our study demonstrates that forest management approaches aimed at restoring old-growth structure through targeted thinning do not greatly decrease buffering capacity, despite a known link between dense canopy and CCR. More broadly, this study illustrates the value of using remote sensing approaches to identify CCR, facilitating the integration of climate change adaptation with other forest management approaches.     

Have we been successful? Monitoring horizontal forest complexity for forest restoration projects

Forest management today often seeks to restore ecological integrity and enhance human well‐being by increasing forest complexity, resilience, and functionality. However, effective and financially expedient monitoring of forest complexity is challenging. In this study, we developed a practical and inexpensive technique to measure horizontal forest complexity. This monitoring method uses intuitively understandable data (imagery) and facilitates stakeholder participation in the adaptive management process within collaborative projects. We used this technique to determine if current restoration projects are successfully achieving their spatial restoration goals. We focused on the Colorado Front Range Landscape Restoration Initiative (CFRLRI) as a representative of the typical collaborative restoration projects underway in formerly fire‐dependent dry conifer forests. The developed monitoring method is practical and cost‐effective by using free aerial imagery to map, quantify, and analyze the distribution of canopy cover pre‐ and post‐treatment. We found the CFRLRI has successfully reduced canopy cover (from 44 to 26% on average) and increased some aspects of horizontal forest complexity. The application of these monitoring techniques has allowed the CFRLRI collaborative group to objectively quantify changes to horizontal forest complexity, and has facilitated stakeholder communication about forest spatial patterns. These methods could be adapted for use by other similar forest restoration projects around the world by utilizing increasingly available satellite or aerial imagery.     

Desynchronizing effects of lightning strike disturbances on cyclic forest dynamics in mangrove plantations

Plantations released from management are vulnerable to transient oscillations until cohort dynamics are broken and the vertical and horizontal structures of the plantation are transformed to those of more natural forests. Cohort-desynchronizing factors such as canopy disturbances are expected to accelerate this process. Using well-established mangrove plantations in Can Gio (Viet Nam) as an example, we tested whether lightning gaps can affect transition dynamics of plantations to more natural forests by damping the amplitude or by shortening the period of oscillations in tree densities. This was done by applying point pattern analyses to remotely sensed data, and by further combining statistical and individual-based modelling. The occurrence of lightning gaps was biased by the forest matrix, which presented a challenge for the point pattern analysis. This problem was solved by using the scattered forest area as a binary mask. A Matern cluster process model was found to be suitable for describing the lightning regime. This statistical model was incorporated into the individual-based mangrove model KiWi, and simulation experiments revealed that: (i) the evenly spaced distribution of the tree cohorts in the plantation supports non-linear transition behaviour, i.e. oscillation of tree density, and (ii) the lightning regime in Can Gio damps the oscillation amplitude but is not sufficient to prevent the latter nor to decrease the length of the period of oscillations.     

Crown asymmetry in high latitude forests: disentangling the directional effects of tree competition and solar radiation

Light foraging by trees is a fundamental process shaping forest communities. In heterogeneous light environments this behavior is expressed as plasticity of tree growth and the development of structural asymmetries. We studied the relative influence of neighborhood structure and directional solar radiation on horizontal asymmetry of tree crowns in late‐successional high latitude (67–68°N) forests in northern Fennoscandia. We described crown asymmetries as crown vectors (i.e. horizontal vectors from stem center to crown center), which we obtained from canopy maps based on crown perimeter measurements in the field. To disentangle the influence of the two main determinants, inter‐tree competition and directionality of above‐canopy solar radiation at high latitudes, we applied circular statistical models, utilizing cylindrical distributions, to these data consisting of orientations and intensities of crown asymmetry. At the individual tree level, our model predicted crown asymmetry vectors from the current stand structure, and the predictions became better when the intensity of asymmetry (i.e. crown vector length) was higher. Competition was the main determinant of crown asymmetry for 2/3 of trees, and the model predictions improved when we incorporated the directionality of solar radiation. At the stand‐level, these asymmetries had resulted in a small increment of the projected canopy area and an increased regularity of spatial structure. Our circular statistical modelling approach provided a quantitative evaluation of the relative importance of directionality of solar radiation and neighborhood stand structure, showing how both of these factors play a role in formation of crown asymmetries in high latitude forests. This approach further demonstrated the applicability of circular statistical modeling in ecological studies where the response variable has both orientation and intensity.     

Extended statistical approaches to modelling spatial pattern in biodiversity in northeast New South Wales. II. Community-level modelling

Regional conservation planning can often make more effective use of sparse biological data by linking these data to remotely mapped environmental variables through statistical modelling. While modelling distributions of individual species is the best known and most widely used approach to such modelling, there are many situations in which more information can be extracted from available data by supplementing, or replacing, species-level modelling with modelling of communities or assemblages. This paper provides an overview of approaches to community-level modelling employed in a series of major land-use planning processes in the northeast New South Wales region of Australia, and evaluates how well communities and assemblages derived using these techniques function as surrogates in regional conservation planning. We also outline three new directions that may enhance the effectiveness of community-level modelling by: (1) more closely integrating modelling with traditional ecological mapping (e.g. vegetation mapping); (2) more tightly linking numerical classification and spatial modelling through application of canonical classification techniques; and (3) enhancing the applicability of modelling to data-poor regions through employment of a new technique for modelling spatial pattern in compositional dissimilarity.     

Canopy closure estimation in a temperate forest using airborne LiDAR and LANDSAT ETM+ data

Aims　Forest canopy closure is one of the essential factors in forest survey, and plays an important role in forest ecosystem management. It is of great significance to study how to apply LiDAR (light detection and ranging) data efficiently in remote sensing estimation of forest canopy closure. LiDAR can be used to obtain data fast and accurately and therefore be used as training and validation data to estimate forest canopy closure in large spatial scale. It can compensate for the insufficiency (e.g. labor-intensive, time-consuming) of conventional ground survey, and provide foundations to forest inventory.Methods　In this study, we estimated canopy closure of a temperate forest in Genhe forest of Da Hinggan Ling area, Nei Mongol, China, using LiDAR and LANDSAT ETM+ data. Firstly, we calculated the canopy closure from ALS (Airborne Laser Scanning) high density point cloud data. Then, the estimated canopy closure from ALS data was used as training and validation data to modeling and inversion from eight vegetation indices computed from LANDSAT ETM+ data. Three approaches, multi-variable stepwise regression (MSR), random forest (RF) and Cubist, were developed and tested to estimate canopy closure from these vegetation indices, respectively.Important findings The validation results showed that the Cubist model yielded the highest accuracy compared to the other two models (determination coefficient (R²) = 0.722, root mean square error (RMSE) = 0.126, relative root mean square error (rRMSE) = 0.209, estimation accuracy (EA) = 79.883%). The combination of LiDAR data and LANDSAT ETM+ showed great potential to accurately estimate the canopy closure of the temperate forest. However, the model prediction capability needs to be further improved in order to be applied in larger spatial scale. More independent variables from other remotely sensed datasets, e.g. topographic data, texture information from high-resolution imagery, should be added into the model. These variables can help to reduce the influence of optical image, vegetation indices, terrain and shadow and so on. Moreover, the accuracy of the LiDAR-derived canopy closure needs to be further validated in future studies.     

Ant mosaics in a tropical rainforest in Australia and elsewhere: A critical review

Abstract The concept of ‘ant mosaics’ has been established to describe the structure of arboreal ant communities in plantations and other relatively simple forest systems. It is essentially built upon the existence of negative and positive associations between ant species plus the concept of dominance hierarchies. Whether this concept can be applied to ant communities in more complex mature tropical rain forests has been questioned by some authors. Here we demonstrate that some previous attempts to prove or disprove the existence of such ant mosaics sampled by knockdown insecticide canopy fogging in near pristine tropical forests may have been thwarted by poor statistical power and too coarse spatial resolution, and the conclusions may be highly dependent on ant species and forest stratum selected for the study. Moreover, the presence or absence of ant mosaics may be driven by the density of suitable resources. We use an intensively studied ant community in the lowland rainforests of North‐East Queensland, Australia to outline processes that may lead to ant mosaic patterns, reasoning that competition for highly predictable resources in space and time such as honeydew and nectar is a fundamental process to maintain the mosaic structure. Honeydew and nectar sources, particularly their amino acids, are of crucial importance for nourishment of arboreal ant species. We use canopy fogging data from the same site in Australia and from two mature rainforests in South‐East Asia to compare spatial avoidance and co‐occurrence patterns implied by ant mosaics. Significant negative and positive associations were found among the three most abundant ant species in each dataset. Several problems with such spatial analyses are discussed, and we suggest that studies of ant mosaics in complex rainforest communities would benefit from a more focused approach on patterns of resource distribution and their differential utilisation by ants.     

Mapping canopy nitrogen‐scapes to assess foraging habitat for a vulnerable arboreal folivore in mixed‐species Eucalyptus forests

Herbivore foraging decisions are closely related to plant nutritional quality. For arboreal folivores with specialized diets, such as the vulnerable greater glider (Petauroides volans), the abundance of suitable forage trees can influence habitat suitability and species occurrence. The ability to model and map foliar nitrogen would therefore enhance our understanding of folivore habitat use at finer scales. We tested whether high‐resolution multispectral imagery, collected by a lightweight and low‐cost commercial unoccupied aerial vehicle (UAV), could be used to predict total and digestible foliar nitrogen (N and digN) at the tree canopy level and forest stand‐scale from leaf‐scale chemistry measurements across a gradient of mixed‐species Eucalyptus forests in southeastern Australia. We surveyed temperate Eucalyptus forests across an elevational and topographic gradient from sea level to high elevation (50–1200 m a.s.l.) for forest structure, leaf chemistry, and greater glider occurrence. Using measures of multispectral leaf reflectance and spectral indices, we estimated N and digN and mapped N and favorable feeding habitat using machine learning algorithms. Our surveys covered 17 Eucalyptus species ranging in foliar N from 0.63% to 1.92% dry matter (DM) and digN from 0.45% to 1.73% DM. Both multispectral leaf reflectance and spectral indices were strong predictors for N and digN in model cross‐validation. At the tree level, 79% of variability between observed and predicted measures of nitrogen was explained. A spatial supervised classification model correctly identified 80% of canopy pixels associated with high N concentrations (≥1% DM). We developed a successful method for estimating foliar nitrogen of a range of temperate Eucalyptus species using UAV multispectral imagery at the tree canopy level and stand scale. The ability to spatially quantify feeding habitat using UAV imagery allows remote assessments of greater glider habitat at a scale relevant to support ground surveys, management, and conservation for the vulnerable greater glider across southeastern Australia.     

Extended statistical approaches to modelling spatial pattern in biodiversity in northeast New South Wales. I. Species-level modelling

Statistical modelling of biological survey data in relation to remotely mapped environmental variables is a powerful technique for making more effective use of sparse data in regional conservation planning. Application of such modelling to planning in the northeast New South Wales (NSW) region of Australia represents one of the most extensive and longest running case studies of this approach anywhere in the world. Since the early 1980s, statistical modelling has been used to extrapolate distributions of over 2300 species of plants and animals, and a wide variety of higher-level communities and assemblages. These modelled distributions have played a pivotal role in a series of major land-use planning processes, culminating in extensive additions to the region's protected area system. This paper provides an overview of the analytical methodology used to model distributions of individual species in northeast NSW, including approaches to: (1) developing a basic integrated statistical and geographical information system (GIS) framework to facilitate automated fitting and extrapolation of species models; (2) extending this basic approach to incorporate consideration of spatial autocorrelation, land-cover mapping and expert knowledge; and (3) evaluating the performance of species modelling, both in terms of predictive accuracy and in terms of the effectiveness with which such models function as general surrogates for biodiversity.     

Quantifying Tropical Dry Forest Type and Succession: Substantial Improvement with LiDAR

Improved technologies are needed to advance our knowledge of the biophysical and human factors influencing tropical dry forests, one of the world's most threatened ecosystems. We evaluated the use of light detection and ranging (LiDAR) data to address two major needs in remote sensing of tropical dry forests, i.e., classification of forest types and delineation of forest successional status. We evaluated LiDAR‐derived measures of three‐dimensional canopy structure and subcanopy topography using classification‐tree techniques to separate different dry forest types and successional stages in the Guánica Biosphere Reserve in Puerto Rico. We compared the LiDAR‐based results with classifications made from commonly used remote sensing data, including Landsat satellite imagery and radar‐based topographic data. The accuracy of the LiDAR‐based forest type classification (including native‐ and exotic‐dominated forest classes) was substantially higher than those from previously available data (kappa = 0.90 and 0.63, respectively). The best result was obtained when combining LiDAR‐derived metrics of canopy structure and topography, and adding Landsat spectral data did not improve the classification. For the second objective, we observed that LiDAR‐derived variables of vegetation structure were better predictors of forest successional status (i.e., mid‐secondary, late‐secondary, and primary forests) than was spectral information from Landsat. Importantly, the key LiDAR predictors identified within each classification‐tree model agreed with previous ecological knowledge of these forests. Our study highlights the value of LiDAR remote sensing for assessing tropical dry forests, reinforcing the potential for this novel technology to advance research and management of tropical forests in general.     

Attaining the canopy in dry and moist tropical forests: strong differences in tree growth trajectories reflect variation in growing conditions

Availability of light and water differs between tropical moist and dry forests, with typically higher understorey light levels and lower water availability in the latter. Therefore, growth trajectories of juvenile trees—those that have not attained the canopy—are likely governed by temporal fluctuations in light availability in moist forests (suppressions and releases), and by spatial heterogeneity in water availability in dry forests. In this study, we compared juvenile growth trajectories of Cedrela odorata in a dry (Mexico) and a moist forest (Bolivia) using tree rings. We tested the following specific hypotheses: (1) moist forest juveniles show more and longer suppressions, and more and stronger releases; (2) moist forest juveniles exhibit wider variation in canopy accession pattern, i.e. the typical growth trajectory to the canopy; (3) growth variation among dry forest juveniles persists over longer time due to spatial heterogeneity in water availability. As expected, the proportion of suppressed juveniles was higher in moist than in dry forest (72 vs. 17%). Moist forest suppressions also lasted longer (9 vs. 5 years). The proportion of juveniles that experienced releases in moist forest (76%) was higher than in dry forest (41%), and releases in moist forests were much stronger. Trees in the moist forest also had a wider variation in canopy accession patterns compared to the dry forest. Our results also showed that growth variation among juvenile trees persisted over substantially longer periods of time in dry forest (>64 years) compared to moist forest (12 years), most probably because of larger persistent spatial variation in water availability. Our results suggest that periodic increases in light availability are more important for attaining the canopy in moist forests, and that spatial heterogeneity in water availability governs long-term tree growth in dry forests.     

Allometric equations for integrating remote sensing imagery into forest monitoring programmes

Remote sensing is revolutionizing the way we study forests, and recent technological advances mean we are now able – for the first time – to identify and measure the crown dimensions of individual trees from airborne imagery. Yet to make full use of these data for quantifying forest carbon stocks and dynamics, a new generation of allometric tools which have tree height and crown size at their centre are needed. Here, we compile a global database of 108753 trees for which stem diameter, height and crown diameter have all been measured, including 2395 trees harvested to measure aboveground biomass. Using this database, we develop general allometric models for estimating both the diameter and aboveground biomass of trees from attributes which can be remotely sensed – specifically height and crown diameter. We show that tree height and crown diameter jointly quantify the aboveground biomass of individual trees and find that a single equation predicts stem diameter from these two variables across the world's forests. These new allometric models provide an intuitive way of integrating remote sensing imagery into large‐scale forest monitoring programmes and will be of key importance for parameterizing the next generation of dynamic vegetation models.     

Landscape‐scale lidar analysis of aboveground biomass distribution in secondary Brazilian Atlantic Forest

Secondary forests account for more than half of tropical forests and represent a growing carbon sink, but rates of biomass accumulation vary by a factor of two or more even among plots in the same landscape. To better understand the drivers of this variability, we used airborne lidar to measure forest canopy height and estimate biomass over 4529 ha at Serra do Conduru Park in Southern Bahia, Brazil. We measured trees in 30 georeferenced field plots (0.25‐ha each) to estimate biomass using allometry. Then we estimated aboveground biomass density (ABD) across the lidar study area using a statistical model developed from our field plots. This model related the 95th percentile of the distribution of lidar return heights to ABD. We overlaid this map of ABD on a Landsat‐derived forest age map to determine rates of biomass accumulation. We found rapid initial biomass regeneration (~6 Mg/ha yr), which slowed as forests aged. We also observed high variability in both height and biomass across the landscape within forests of similar age. Nevertheless, a regression model that accounted for spatial autocorrelation and included forest age, slope, and distance to roads or open areas explained 62 and 77 percent of the landscape variation in ABD and canopy height, respectively. Thus, while there is high spatial heterogeneity in forest recovery, and the drivers of this heterogeneity warrant further investigation, we suggest that a relatively simple set of predictor variables is sufficient to explain the majority of variance in both height and ABD in this landscape.     

Postdispersal survivorship of north Queensland rainforest seeds and fruits: Effects of forest,habitat and species

Abstract Diaspore (seed and/or fruit) survivorship of 12 species of North Queensland rainforest trees was monitored over a 4 week period in two rainforests on the Atherton Tableland, north Queensland, Australia, with replicated sites in forest interiors, canopy gaps, forest edges and adjacent pastures. General linear modelling was used to estimate the relative importance of forest site, habitat, species and diaspore type on survival. The probability of survival varied significantly among species, between habitat, forest and diaspore types. Survival averaged 41.2% at day 28, was greater in one forest (Curtain Fig, 56.1%) than the other (Lamins Hill, 25.3%) and was apparently inversely related to the abundance of rodents. In both forests, survival declined in the order: pasture ≥ forest edge ≥ forest interior = canopy gap. A lack of significant difference between the forest interior and canopy gap is consistent with findings in other rainforest sites within Australia, but contrasts with most results from other continents. The study also indicated that diaspore weight was an important intrinsic variable affecting survival, the predators (mostly rodents) taking small to moderately large diaspores rather than the very large ones.     

Semivariograms from a forest transect gap model compared with remotely sensed data

Abstract. A spatially linked version of a forest gap model, ZELIG, parameterized for the H. J. Andrews Experimental Forest, Oregon, was used to generate structural properties (i.e. biomass, leaf area, and maximum tree height) of young (80 yr), mature (140 yr), and old-growth (450 yr) Pseudotsuga menziesii (Douglas fir) forests. Semivariograms were produced at 10 and 30 m resolution to describe the spatio-temporal patterns of variation of the simulated structural features along a 5 km transect of contiguous 10 m x 10 m grid cells. These semivariograms from the simulations were compared with semivariograms from matrices of pixel digital values obtained from aerial videography of similarly aged stands. Although autocorrelative spatial patterning was absent from both the remotely sensed imagery (except at < 20 m for the 450 yr stand) and the model output, the pixel-to-pixel and plot-to-plot variances exhibited similar patterns across the chronosequence at both resolutions. This suggests that gap models are able to capture temporal aspects of landscape dynamics associated with canopy texture of Pacific Northwest forests.     

中国亚热带4个森林动态监测样地无人机可见光遥感影像数据集

常绿阔叶林是我国亚热带地区的地带性植被类型。由于亚热带森林植物群落垂直结构复杂、林冠郁闭度高, 对常绿阔叶林冠层的研究尚缺乏高质量的监测数据。本数据集包含浙江天童山、浙江百山祖、广东车八岭、广东鼎湖山4个大于20 ha的森林动态监测样地2014年8月或2016年9月采集的无人机可见光遥感影像。本数据集是通过将无人机影像、地面控制点和地面调查数据相结合而获得的。每个样地的数据集包括4个文件: ~5 cm空间分辨率的正射影像图和数字表面模型、1 m空间分辨率的森林冠层高度数据和正射影像质量报告。本数据集可为常绿阔叶林的林冠生态学、生物多样性、生态系统功能等研究提供数据支撑。     

山东蒙山森林冠层绿度与树干径向生长的关系

森林冠层绿度和树木年轮宽度是描述森林生长过程的重要指标,它们之间存在怎样的关系以及这种关系的稳定性如何目前还没有清晰的回答。森林冠层绿度通过遥感影像计算,在空间上连续,而树木年轮宽度是树木健康的综合指标,样点上具有代表性。森林冠层绿度和树木年轮宽度的关系的研究能增进对森林生长的多角度理解和森林生长状况的尺度转换。在山东蒙山地区采集了4个赤松(Pinus densiflora)林样点的树木年轮样本,获得了树木年轮宽度数据,分析了增强型植被指数(Enhanced Vegetation Index,EVI)与树木年轮宽度的关系。结果显示:1)对于健康森林,4月和6月的冠层绿度与树木年轮宽度存在因果关系;森林不健康时,两者关系较为复杂;2)其他月份冠层绿度与树干径向生长不存在因果关系,而是共同受其他环境因子,如气候因子的驱动;3)弱冠层绿度降低后5年内有显著的径向生长恢复,但是恢复年份少;强冠层绿度降低之前,树干径向生长已经开始降低,之后的5年内有着持续的径向生长降低。这些结果表明森林冠层绿度的降低并不能反映树干径向生长降低的开始,只有健康的森林冠层绿度和年轮宽度有相关关系。冠层绿度的降低对森林健康有强烈的影响,冠层绿度降低导致的径向生长的降低很难恢复。