Vegetation Structure Properties Research Articles

Temperature‐driven convergence and divergence of ecohydrological dynamics in the ecosystems of a sky island mountain range

AbstractForest and woodland decline is predicted to be increasingly influenced by meteorological variation and climate change in the future. By determining how meteorological variation leads to similar versus differing ecohydrological dynamics of forest and woodland ecosystems, we can gain insight on how future climate‐driven declines may be realized. We characterized 23 mixed conifer forest (MC), ponderosa pine forest (PP) and piñon pine–juniper woodland (PJ) sites with different canopy covers in southern Nevada, USA. We compared meteorological variation between these sites and employed water balance modelling and information theory to estimate similarity in the density distributions of soil temperature (Ts), soil water potential (SWP) and transpiration partitioning into total evapotranspiration (T/ET) within and across ecosystems in wetter and drier seasons and in cooler and warmer decades. From 1941 to 2020, this location experienced declines in meteorological water deficit due to higher precipitation, although temperatures increased over more recent time periods (1981–2020). From 1981 to 2020, we generally found greater similarity in SWP and T/ET distributions within MC sites and PP sites in the cool season and in the warm season generally found greater similarity in Ts and T/ET distributions within and between PP and PJ sites (excepting T/ET between PJ sites and higher canopy cover PP sites). Recent warm decades promoted convergence in warm and cool season Ts dynamics, such that Ts dynamics generally became more similar between higher elevation MC sites and lower elevation PP–PJ sites. At the same time, warmer decades initiated divergence of SWP and T/ET dynamics within groups of MC–PP and PP–PJ sites that were formerly more similar to each other (excepting SWP in wet seasons). Although their dynamics will remain strongly coupled to precipitation, warming temperatures have the potential to promote divergence in the ecohydrological dynamics of ecosystems at lower and higher elevations in this sky island system and may also promote novel within‐ecosystem divergence associated with variation in vegetation structural attributes.

Leaf properties estimation enhancement over heterogeneous vegetation by correcting for terrestrial laser scanning beam divergence effect

The need for an accurate description of forest structure is increased as it is a key for monitoring carbon storage, energy, and mass exchange, and different ecosystem functions. The leaf area index (LAI), the leaf area density (ul), and the leaf inclination distribution function (LIDF) are regarded as important and critical keys allowing the characterization of the forest structure. The dependency between these properties and the heterogeneity of the forest makes their estimation challenging and delicate. In this context, despite the expanding body of works reporting forest structure properties retrieval using infinitesimal footprint size terrestrial LiDAR system (TLS) data, there seem to be few works evaluating the influence of the TLS beam divergence effect (i.e. its size increase with distance from the sensor), on the estimation accuracy, even though the influence is not negligible. In this study, we aim to jointly estimate LAI/ul and LIDF using non-infinitesimal footprint size TLS data based on forward/inverse radiative transfer (RT) modeling. Our approach assumes a uniform distribution of azimuth angles enabling us to attain valuable insights into inclination angles and leaf area density while simplifying our computational efforts. The forward modeling allows an understanding of the interaction between vegetation structure properties and TLS light beams, while the inversion allows retrieving the properties given the TLS data. The inversion technique is based on the maximum likelihood estimator (MLE). Our problem turns out to be a non-linear and high-dimensional cost function to be optimized. To tackle this complexity, the global optimization technique, the shuffled complex evolution (SCE-UA), is adapted to find the globally optimal solution. Our developed approach is validated with simulated and actual forest TLS point clouds acquired from Estonian Pine, Birch, and Spruce stands. Our findings using simulated data prove that our estimates are in agreement with the actual ul and LIDF values with (MAEul∈0.00120.0043) and (RMSEul∈0.01370.0224). In the actual case, RMSELAI for Pine, Birch and Spruce stands are 0.1066, 0.014, and 0.070, respectively. In all cases, our approach outperforms state-of-the-art techniques.

Using multiplatform LiDAR to identify relationships between vegetation structure and the abundance and diversity of woodland reptiles and amphibians

AbstractRemotely sensed measures of vegetation structure have been shown to explain patterns in the occurrence and diversity of several animal taxa, including birds, mammals, and invertebrates. However, very little research in this area has focused on reptiles and amphibians (herpetofauna). Moreover, most remote sensing studies on animal–habitat associations have relied on airborne or satellite data that provide coverage over relatively large areas but may not have the resolution or viewing angle necessary to measure vegetation features at scales that are meaningful to herpetofauna. Here, we combined terrestrial laser scanning (TLS), unmanned aerial vehicle laser scanning (ULS), and fused (FLS) data to provide the first test of whether vegetation structural attributes can help explain variation in herpetofauna abundance, species richness, and diversity across a woodland landscape. We identified relationships between the abundance and diversity of herpetofauna and several vegetation metrics, including canopy height, skewedness, vertical complexity, volume of vegetation, and coarse woody debris. These relationships varied across species, groups, and sensors. ULS models tended to perform as well or better than TLS or FLS models based on the methods we used in this study. In open woodland landscapes, ULS data may have some benefits over TLS data for modeling relationships between herpetofauna and vegetation structure, which we discuss. However, for some species, only TLS data identified significant predictor variables among the LiDAR‐derived structural metrics. While the overall predictive power of models was relatively low (i.e., at most R2 = 0.32 for ULS overall abundance and R2 = 0.32 for abundance at the individual species level [three‐toed skink (Chalcides striatus)]), the ability to identify relationships between specific LiDAR structural metrics and the abundance and diversity of herpetofauna could be useful for understanding their habitat associations and managing reptile and amphibian populations.

Characterizing riparian vegetation and classifying riparian extent using airborne laser scanning data

Riparian areas are an integral component of the forest hydrologic system. However, systematic management and conservation of riparian vegetation remains a challenge when the extent and nature of riparian vegetation is not easily characterized. Thus, describing riparian vegetation structural attributes and mapping riparian extent is important for stream protection and restoration. This study discriminated riparian and upland vegetation zones in two watersheds on Vancouver Island, Canada, and characterized vegetation attributes ascribed to streamside forest vegetation using fine-scale airborne laser scanning (ALS) data with field-based measurements for controls. Comparison of ALS metrics for riparian and upland vegetation plots indicated metrics of canopy cover, height, and vertical canopy complexity were significantly different. Machine learning was used to model the probability of riparian and upland vegetation zone extent in each watershed and to determine which ALS metrics were the most important for discriminating between these two zones. Separate models were built for each watershed and results indicated that each watershed's model had different ALS metrics that were important for model development, reflecting the different management histories associated with each watershed. Models achieved overall classification accuracies of 65–77% using a 10-fold cross validation. Probability maps were then developed to characterize riparian extent, indicating that the majority of riparian areas in these watersheds were within 20 m of a stream. Additionally, riparian areas were found up to 200 m away from the stream, which is beyond the current provincial regulation widths set for riparian buffers in forested areas in British Columbia, Canada. However, the majority (roughly 75%) of vegetation within existing management buffers was riparian, although this differed by stream classification. We conclude that ALS data provides a potentially useful source of information for determining the characteristics and extent of riparian vegetation.

Compensatory Structural Growth Responses of Early-Succession Native Warm-Season Grass Stands to Defoliation Management

There is a growing recognition of the significance of unique morphological and physiological adaptation of native warm-season grasses (NWSG) of North America as summer forage resources and major grassland ecosystem components. Defoliation management plays a major role in ensuring eco-friendly utilization of grassland natural resources. To assess sward structural responses of big bluestem (BB, Andropogon gerardii Vitman), eastern gamagrass (GG, Tripsacum dactyloides L.), indiangrass (IG, Sorghastrum nutans L. Nash), and switchgrass (SG, Panicum virgatum L.) stands to seasonal changes in harvest regimes, a five-year forage harvesting trial was conducted, in a randomized complete block design, at Virginia State University’s research farm. Vegetation structural response attributes (sward-height, canopy closure, stand density and basal cover) of newly established the NWSG stands to second year changes in harvest regimes were monitored. In 2013, 64 plots of year-old stands of transplanted BB, GG, IG, and SG separated by ≥120-cm alleys were cut once in early-August and mid-November to suppress weeds and promote tillering. Starting June 2014, each plot had three 1.5-m wide side-by-side harvest-strips cut once-, twice-, or thrice year−1 (frequencies) ending mid-Oct for four consecutive years followed by a single mid-summer harvest in 26 June 2018, using a forage plot-harvester. In 2015, harvest frequencies for the three- and single-cut strips, in plots 32–64, were switched/flipped once and never reverted. Data was recorded on four early-summer and late-fall sward heights, from each strip at 60-cm intervals before the first and the last harvest, each year. early-spring basal- and canopy-diameter, for mid-April 2015 and 2016, concurrent early-spring canopy light interception, using the LI-191 Line Quantum Sensor, and season-end visual obstruction heights, for stand density in 2016 and 2017. All regrowth sward-heights showed effects of harvest frequency and exhibited compensatory structural responses to the change in harvest regimes. Basal and canopy diameters tended to be greater for the single-cut strips that were previously cut thrice.

A VOXEL-BASED METHOD FOR THE THREE-DIMENSIONAL MODELLING OF HEATHLAND FROM LIDAR POINT CLOUDS: FIRST RESULTS

Abstract. Bushfires are an intrinsic part of the New South Wales’ (NSW) environment in Australia, especially in the Blue Mountains region (11400km2), that is dominated by fire prone vegetation that includes heathland. Many of the Australian native plants in this region are fire-prone and combustible, and many species even require fire to regenerate. The classification of the lateral and vertical distribution of living vegetation is necessary to manage the complexity of bushfires. Currently, interpretation of aerial and satellite images is the prevalent method for the classification of vegetation in NSW. The result does not represent important vegetation structural attributes, such as vegetation height, subcanopy height, and destiny. This paper presents an automated method for the three-dimensional modelling of heathland and important heathland parameters, such as heath shrub height and continuity, and sparse tree and mallee height and density in support of bushfire behaviour modelling. For this study airborne lidar point clouds with a density of 120 points per square meter are used. For the processing and modelling the study is divided into a point cloud processing phase and a voxel-based modelling phase. The point cloud processing phase consists of the normalisation of the height and extraction of the above ground vegetation, while the voxel phase consists of seeded region growing for segmentation, and K-means clustering for the classification of the vegetation into three different canopy layers: a) heath shrubs, b) sparse trees and mallee, c) tall trees.

New insights of global vegetation structural properties through an analysis of canopy clumping index, fractional vegetation cover, and leaf area index

Canopy clumping index (CI), fractional vegetation cover (FVC), and leaf area index (LAI) are important vegetation structural variables. Characterization of the spatial and temporal variations of these variables is important for understanding the global vegetation properties, canopy radiative transfer processes, and land surface modeling studies. This study explores the global CI, FVC, and LAI and their relationships using currently available global products from 2003−2017: CAS-CI (V1.1), GEOV2 FVC, and MODIS LAI (C6.0). The results show that global FVC and LAI have increased, whereas CI has slightly decreased since 2003. CI shows a negative relationship with both FVC and LAI. The specific vegetation cover (SVC), defined as the ratio of FVC to LAI, shows distinct spatial and temporal patterns for different land cover types and has great potential for vegetation characterization and phenology studies. The base and full CIs, calculated as the CI values during leaf emergence and full ground coverage or maximum LAI, respectively, were proposed to determine the seasonal variations in CI for different vegetation types. A global monthly leaf projection function (G(0)) map was generated for the first time to characterize the leaf angular distribution. Overall, this study enhances our understanding of the global CI, FVC, and LAI and provides new insights on characterizing the biophysical properties of global vegetation. These findings are important for canopy reflectance modeling and parameter retrieval studies.

Differential responses of forest strata species richness to paleoclimate and forest structure

Regional factors, such as historical and contemporary climate conditions, and local factors, such as vegetation structural attributes, can influence current patterns of plant species richness but their relative roles remain unknown, particularly across forest strata. Here, we used a multi-scale survey of temperate forest plots across a large region of Northeast China to explore the relative importance of environmental factors (paleoclimate, contemporary climate, topography and anthropogenic disturbance) and forest structural attributes (stem abundance, stand basal area and tree size variation) on tree, shrub, and herb species richness. Although environmental and forest structural factors all played a role in explaining plant species richness patterns, their relative roles varied in direction and magnitude depending on forest stratum. Tree species richness increased with the magnitude of change in temperature since the Last Glacial Maximum (AnomalyMAT) but declined with increasing magnitude of change in precipitation (AnomalyMAP). By contrast, herb species richness declined with increasing AnomalyMAT but increased with AnomalyMAP, highlighting contrasting processes for tree and herb species richness driven by paleoclimate. Contemporary climate played a lesser role in explaining species richness, but tree species richness increased with diurnal temperature range, shrub species richness increased with the climatic moisture index, and herb species richness increased with both climatic variables. Herb species richness also increased with disturbance intensity, whereas tree and shrub richness declined. Overall, plant species richness increased significantly with all forest structural attributes, except for stem abundance, which had a negative effect on herb species richness, and forest structure mediated the linkages between plant species richness and disturbance or climate. The pronounced influence of paleoclimate on forest plant species richness highlights the potential threat of current climate change for forest diversity. Together, simultaneous consideration of past and current climate as well as forest structural attributes could improve our understanding of the complex mechanisms shaping patterns of plant species richness across forest strata.

Lidar Boosts 3D Ecological Observations and Modelings: A Review and Perspective

The advent of lidar has revolutionized the way we observe and measure vegetation structure from the ground and from above and represents a major advance toward the quantification of 3D ecological observations. Developments in lidar hardware systems and data processing algorithms have greatly improved the accessibility and ease of use of lidar observations in ecological studies. A wide range of studies has been devoted to accurately measuring and modeling vegetation structural and functional attributes from lidar data across a range of spatial scales (from individual organs to global scales) and ecosystem types (e.g., forest, agricultural, grassland, and urban ecosystems).

Tree hollow densities reduced by frequent late dry-season wildfires in threatened Gouldian finch (

Abstract Context. Tree hollows are a key habitat resource for hollow-nesting species, including the northern Australian Gouldian finch (Erythrura gouldiae). Certain fire and disturbance regimes limit tree hollow availability in the northern Australian savannas. Aims. This study investigated the influence of fire regime and vegetation structure on the density of tree hollows at Gouldian finch breeding sites. Methods. Fire scars were mapped across breeding sites by using LANDSAT images. Vegetation plots within sites were spatially stratified according to three fire-regime attributes, namely, fire frequency, late dry-season wildfire frequency and time since the last fire. Tree hollow and vegetation structural attributes were measured at each vegetation plot. We modelled the relationship among hollow density, fire and vegetation attributes by using general linear mixed models with site as the random factor. Key results. We found that the highest tree-hollow density was found at plots with high eucalypt tree density and cover and with the lowest frequency of late dry-season wildfires (<1 wildfire over 5 years). Tree-hollow density declined after >2 years without fire. Hollow density was not directly related to total fire frequency. Conclusions. This study adds to previous work on grass seed resources in highlighting the importance of fire in Gouldian finch ecology. This study particularly highlighted the importance of reducing the impacts of high-intensity late dry-season wildfires because of their negative impacts on tree-hollow density, which is a key resource for breeding Gouldian finches. Implications. We recommend the use of a network of interconnected annual patchy early dry-season prescribed burns for protecting Gouldian breeding habitat from threat of high-intensity wildfires. We do NOT recommend fire exclusion from Gouldian finch breeding habitats. This is because fire risks to hollow-bearing trees, and grass seed resources, increase with the long-term accumulation of savanna litter fuels in the absence of fire.

Leguminous tree species create islands of fertility and influence the understory vegetation on nickel-mine tailings of different ages

Understanding the patterns of vegetation establishment on rehabilitated mine tailings is critical in evaluating the effectiveness of rehabilitation programs, but is still largely unknown. The concept of formation of fertility islands and their effect on vegetation establishment on two rehabilitated nickel mine tailings of different ages (20 and 40 years) since last depositions in tropical Zimbabwe was investigated to compare: (1) the tailings physicochemical properties: nitrogen (N), phosphorus (P), potassium (K), organic matter (OM), calcium (Ca), pH, salinity, texture and bulk density between perceived fertility islands (tree-canopy sites) and off- canopy sites, (2) vegetation structural and compositional attributes of canopy and the adjacent off-canopy sites, on the two tailings dumps, and, (3) to identify substrate correlates to observed vegetation attributes. We hypothesized that: (1) total N, Mineral N, OM and available P were significantly higher on canopy than adjacent off- canopy sites, (2) vegetation cover and species richness were higher on canopy than on off-canopy sites, and, (3) dump age and tree species had significant effect on substrate and vegetation attributes. We sampled substrate and vegetation in 1 m2 quadrats laid under planted tree canopies and the adjacent off-canopy sites on tailings dumps that were rehabilitated by planting leguminous trees. The Factorial ANOVA test showed that dump age and tree species had no significant effect on substrate properties and vegetation attributes. Mineral N, total N, available P, OM, and clay content were significantly higher on canopy than on the adjacent off-canopy sites while bulk density was significantly lower on canopy than on off-canopy sites. All vegetation structural attributes (overall plant aerial cover, grass cover, forb cover, woody seedlings cover and tuft density), species richness were significantly higher on canopy than on off-canopy sites. Plant communities on canopy sites were distinct from those on off-canoppy sites, regardless of dump age. Results demonstrate the ecological engineering potential of the leguminous trees through creating fertility islands that influence understory vegetation establishment on mine tailings.

Moving from plot-based to hillslope-scale assessments of savanna vegetation structure with long-range terrestrial laser scanning (LR-TLS)

Reliable quantification of savanna vegetation structure is critical for accurate carbon accounting and biodiversity assessment under changing climate and land-use conditions. Inventories of fine-scale vegetation structural attributes are typically conducted from field-based plots or transects, while large-area monitoring relies on a combination of airborne and satellite remote sensing. Both of these approaches have their strengths and limitations, but terrestrial laser scanning (TLS) has emerged as the benchmark for vegetation structural parameterization – recording and quantifying 3D structural detail that is not possible from manual field-based or airborne/spaceborne methods. However, traditional TLS approaches suffer from similar spatial constraints as field-based inventories. Given their small areal coverage, standard TLS plots may fail to capture the heterogeneity of landscapes in which they are embedded. Here we test the potential of long-range (>2000 m) terrestrial laser scanning (LR-TLS) to provide rapid and robust assessment of savanna vegetation 3D structure at hillslope scales. We used LR-TLS to sample entire savanna hillslopes from topographic vantage points and collected coincident plot-scale (1 ha) TLS scans at increasing distances from the LR-TLS station. We merged multiple TLS scans at the plot scale to provide the reference structure, and evaluated how 3D metrics derived from LR-TLS deviated from this baseline with increasing distance. Our results show that despite diluted point density and increased beam divergence with distance, LR-TLS can reliably characterize tree height (RMSE = 0.25–1.45 m) and canopy cover (RMSE = 5.67–15.91%) at distances of up to 500 m in open savanna woodlands. When aggregated to the same sampling grain as leading spaceborne vegetation products (10–30 m), our findings show potential for LR-TLS to play a key role in constraining satellite-based structural estimates in savannas over larger areas than traditional TLS sampling can provide.

Relationship analysis of vegetation structural properties and the aboveground carbon stock of mangrove forest

Mangrove forests have important ecological functions as a controller of environmental quality in coastal areas and absorber of atmospheric carbon. The mangrove ecosystem has a unique vegetation structure which form vegetation zonation that consists of different forest stands characteristics and tree species. This study aims to analyze the relationship between vegetation structural properties and the estimated aboveground carbon (AGC) stock of mangrove forest in Bedul mangrove, Banyuwangi, East Java, Indonesia. The study was conducted by field observation at some purposively selected sample locations. Field measurement was aimed to collect data about mangrove tree diameter at breast height (DBH), species, tree height, and fractional canopy cover. Mangrove biomass was indirectly calculated by allometric method based on mangrove species to estimate AGC in mangrove stands. The highest total AGC found in this study was 114.09 tons/ha at Rhizophora mucronata dominated forest, while the lowest total AGC was 12.86 tons/ha with Ceriops tagal as the dominant species. The AGC estimation in mangrove stands correlated positively with DBH and tree height. However, the biomass content and AGC are affected by the number of mangrove stands. The difference in the number of stands affect the amount of carbon content at each sample point.

Flooding regime drives tree community structure in Neotropical dry forests

AbstractQuestionsRiverine and associated vegetation communities are periodically affected by floods. However, there have been few quantitative studies on the structural responses of Neotropical tree communities in semi‐arid regions to this perturbance. Here, we ask whether flooding regimes and soil attributes affect tree species diversity, composition, community structure, and soil attributes in Neotropical dry forests.LocationsSoutheast Brazil.MethodsWe sampled 120 forest plots of 400 m² totaling 4.8 ha across four tributaries of the São Francisco River with plots situated in areas representing three flooding regimes: (a) annually flooded; (b) occasionally flooded; and (c) never subjected to flooding. Using linear mixed‐effects models, we modelled how flooding regime and soil properties (fertility and texture) affect vegetation diversity (species richness), species composition and structural attributes (abundance of individuals, above‐ground biomass [AGB], and a community‐weighted trait average of multistemness [CWMms]) of these forest plots.ResultsSpecies richness in the annually flooded forests was significantly lower than in occasionally flooded or never flooded forests, in agreement with our models, which showed that flooding regime was a significant predictor for this variable. Flooding regime was a significant predictor of CWMms, with plots in annually flooded forest sites having the highest CWMms values. Soil chemical attributes were a significant predictor of AGB across all plots but not between flooding regime categories.ConclusionsIn Neotropical dry forests, environmental filters established by flooding can lead to differences among distinct biomes in vegetation structure, diversity patterns as well as system productivity. Conservation strategies for dry forests should therefore take these factors into consideration. Our study also highlights annually flooded dry forest as a significant component of the regional diversity of dry forests deserving of management attention.

La densidad, riqueza y composición arbóreas definen parches detectados remotamente en una selva subperennifolia

A proposal for characterizing habitat of forests, obtained from an object-oriented classification of a RapidEye multiespectral imagery, based on dissimilarity matrices of vegetation structure, species diversity and composition is presented. The study area is a forested landscape mosaic after slash and burn agriculture (Ac: 8-23 years ago), selective logging (Fs: 43-53 years ago), and selective logging and forest fire (Fc: 21-28 years ago). The site is located in the central part of Quintana Roo, México, where three vegetation patches were delineated according to remotely sensed multiespectral imagery. Mean differences between vegetation structure properties of each vegetation patch were obtained through a permutational multivariate analysis of variance (p <0.001). Species richness, stem density per hectare, and the axis-1 scores of the non-metric multidimensional scaling ordination of specific composition were identified as the vegetation attributes more relevant to differentiate the vegetation patches by a multinomial logistic model. Fc vegetation patch is characterized by the greatest mean values on Shannon-Wiener index, species richness, and stem density. The Fs has the greatest mean values of canopy height, basal area and biomass at 80 percentile, and the Ac vegetation patch has the lowest values of all mentioned metrics. The species with the greatest relative importance value were: Ac: Bursera simaruba and Psidia psipula, Fs: Gymnanthes lucida andManilkara zapota, Fc: G. lucida and B. simaruba. The uncertainty associated with the metrics assessed by vegetation patch was smaller than the uncertainty of the whole area, because of the efficient variability aggregation of the field data. We conclude that multiespectral information is a reliable tool for distinguishing vegetation patches with specific features, as stem density, specific composition, and species richness.

Modelling LiDAR derived tree canopy height from Landsat TM, ETM+ and OLI satellite imagery—A machine learning approach

Understanding ecological changes in native vegetation communities often requires information over long time periods (multiple decades). Tropical cyclones can have a major impact on woody vegetation structure across northern Australia, however understanding the impacts on woody vegetation structure is limited. Woody vegetation structural attributes such as height are used in ecological studies to identify long term changes and trends. LiDAR has been used to measure woody vegetation structure, however LiDAR datasets cover relatively small areas and historical coverage is restricted, limiting the use of this technology for monitoring long-term change. The Landsat archive spans multiple decades and is suitable for regional/continental assessment. Advances in predictive modelling using machine learning algorithms have enabled complex relationships between dependent and independent variables to be identified. The aim of this study is to develop a predictive model to estimate woody vegetation height from Landsat imagery to assist in understanding change through space and time. A LiDAR canopy height model was produced covering a range of vegetation communities in northern Australia (Darwin region) for use as the dependent variable. A random forest regression model was developed to predict mean LiDAR canopy height (30 m spatial resolution) from Landsat-5 Thematic Mapper (TM). Validation of the random forest model was undertaken on independent data (n = 30,500) resulting in an overall R2 = 0.53, RMSE of 2.8 m. Assessment of the RMSE within four broad vegetation communities ranged from 2.5 to 3.7 m with the two dominant communities in the study area Mangrove forests and Eucalyptus communities recording an RMSE value of 2.9 m and 2.5 m respectively. The model was also applied to Landsat-7 Enhanced Thematic Mapper Plus (ETM+) resulting in an R2 of 0.49, RMSE of 2.8 m. The model was then applied to all cloud free Landsat-5 TM, Landsat-7 ETM+ and Landsat-8 Operational Land Imager (OLI) imagery (106/69 path/row) available between the months April, May and June for 1987 to 2016 to produce annual estimates (29 years) of canopy height. A number of time traces were produced to illustrate tree canopy height through time in the Darwin region which was severely impacted by cyclone (hurricane) Tracy on the 25th December 1974.

Integrating airborne lidar and satellite imagery to model habitat connectivity dynamics for spatial conservation prioritization

The application of regional-level airborne lidar (light detection and ranging) data to characterize habitat patches and model habitat connectivity over large landscapes has not been well explored. Maintaining a connected network of habitat in the presence of anthropogenic disturbances is essential for regional-level conservation planning and the maintenance of biodiversity values. We quantified variation in connectivity following simulated changes in land cover and contrasted outcomes when different conservation priorities were emphasized. First, we defined habitat patches using vegetation structural attributes identified via lidar. Second, habitat networks were constructed for different forest types and assessed using network connectivity metrics. And finally, land cover change scenarios were simulated using a series of habitat patch removals, representing the impact of implementing different spatial prioritization schemes. Networks for different forest structure types produced very different patch distributions. Conservation scenarios based on different schemes led to contrasting changes during land cover change simulations: the scheme prioritizing only habitat area resulted in immediate near-term losses in connectivity, whereas the scheme considering both habitat area and their spatial configurations maintained the overall connectivity most effectively. Adding climate constraints did not diminish or improve overall connectivity. Both habitat area and habitat configuration should be considered in dynamic modeling of habitat connectivity under changing landscapes. This research provides a framework for integrating forest structure and cover attributes obtained from remote sensing data into network connectivity modeling, and may serve as a prototype for multi-criteria forest management and conservation planning.

Spatiotemporal analyses of urban vegetation structural attributes using multitemporal Landsat TM data and field measurements

We conducted spatiotemporal analyses of urban vegetation structural attributes using multitemporal Landsat TM data and field measurements. We showed that multitemporal TM data has the potential of rapidly estimating urban vegetation structural attributes including LAI, CC , and BA at an urban landscape level. Urban vegetation structural properties/attributes are closely linked to their ecological functions and thus directly affect urban ecosystem process such as energy, water, and gas exchange. Understanding spatiotemporal dynamics of urban vegetation structures is important for sustaining urban ecosystem service and improving the urban environment. The purposes of this study were to evaluate the potential of estimating urban vegetation structural attributes from multitemporal Landsat TM imagery and to analyze spatiotemporal changes of the urban structural attributes. We first collected three scenes of TM images acquired in 1997, 2004, and 2010 and conducted a field survey to collect urban vegetation structural data (including crown closure (CC), tree height (H), leaf area index (LAI), basal area (BA), stem density (SD), diameter at breast height (DBH), etc.). We then calculated and normalized NDVI maps from the multitemporal TM images. Finally, spatiotemporal urban vegetation structural maps were created using NDVI-based urban vegetation structure predictive models. The results show that NDVI can be used as a predictor for some selected urban vegetation structural attributes (i.e., CC, LAI, and BA), but not for the other attributes (i.e., H, SD, and DBH) that are well predicted by NDVI in natural vegetation. The results also indicate that urban vegetation structural attributes (i.e., CC, LAI, and BA) in the study area decreased sharply from 1997 to 2004 but increased slightly from 2004 to 2010. The CC, LAI, and BA class distributions were all skewed toward low values in 1997 and 2004. Moreover, LAI, CC, and BA of urban vegetation all present a decreasing trend from suburban areas to urban central areas. The experimental results demonstrate that Landsat TM imagery could provide a fast and cost-effective method to obtain a spatiotemporal 30-m resolution urban vegetation structural dataset (including CC, LAI, and BA).

The importance of travelling stock reserves for maintaining high-quality threatened temperate woodlands

Travelling stock reserves (TSRs) are thought to represent some of the highest-quality and least degraded remnants of threatened temperate woodland in south-eastern Australia. These public reserves have not had the same high levels of grazing pressure and other disturbances as woodland remnants on private land. Thus, TSRs are expected to be important for the protection of biodiversity in heavily cleared and modified landscapes. We tested the hypothesis that land tenure had significant effects on the quality of woodlands by comparing vegetation structural attributes between TSRs and remnant vegetation used for primary production purposes. Vegetation attributes were monitored in 155 permanent plots over 5 years in remnant temperate woodland sites in the Riverina bioregion of New South Wales. Overall, TSRs supported higher native plant species richness and were characterised by higher ground cover of native shrubs and less cover of exotic plant species than agricultural production areas. We found land tenure had significant effects on some vegetation attributes demonstrated to be important for threatened fauna. We attribute these results to TSRs having a history of lower grazing pressure compared with remnants managed for agricultural production. Our study provides empirical evidence to support the high conservation value of TSRs in formerly woodland-dominated, but now extensively cleared agricultural landscapes.

Conserving long unburnt vegetation is important for bird species, guilds and diversity

Landscape-level wildfires have a major role in structuring faunal assemblages, particularly in fire-prone landscapes. These effects are mediated by changes to vegetation structure and composition that directly influence the availability of shelter, feeding and breeding resources. We investigated the response of a semi-arid shrubland bird community in Western Australia to the prevailing fire regime by examining the abundance, diversity and guild structure in relation to time since fire. We also examined vegetation structural attributes in relation to time since fire. We surveyed 32 sites ranging in age from 12 to 84 years since last fire. A total of 845 birds from 40 species were recorded. Vegetation structure varied with fire history with old and very old sites characterised by less bare ground, more leaf litter cover and greater canopy cover. Bird community composition varied with time since fire, driven by increased bird species richness and abundance of insectivores, granivores/frugivores, golden whistlers, grey shrike-thrush and red-capped robins with time since fire. Frequent, intense landscape-scale fires transform the landscape into homogeneous young shrublands, which may render vegetation unsuitable for several species and guilds.