Vertical Vegetation Structure Research Articles

A new method for mapping vegetation structure parameters in forested areas using GEDI data

The spatially continuous mapping of vegetation structure parameters in forested areas serves as a crucial foundation for research in various fields, including ecosystem ecology, climate change, hydrology, and forest management and protection. However, there is a notable scarcity of regional scale data regarding vegetation structure parameters. Therefore, our objective was to fill this data gap by providing a methodology for mapping vegetation structure parameters in forested areas at regional-scale. Global Ecosystem Dynamics Investigation (GEDI) provides precise observational data on vegetation structure parameters across the world. Using GEDI vegetation structure parameter point data, we developed a vegetation state coefficient based on the ratio of plant area index (PAI) and vegetation coverage (COVER). Subsequently, we proposed a method for decomposing vegetation coverage to simulate layered vegetation coverage. Taking Jiangxi Province as a case study, we integrated machine learning models to generate a spatially continuous map of vegetation structure parameters in forested areas, including layered coverage and layered leaf area index, in 2020. The map has a spatial resolution of 30 m and a vertical resolution of 5 m. The model exhibits excellent performance. Among the 140 sets of models, approximately 63 % of them achieved an R2 surpassing 0.6, while 89 % of the models achieved a root mean square error (RMSE) below 0.3. This study can serve as a valuable reference for the decomposition of vegetation structure parameters at the regional scale and provide a more precise depiction of the spatial characteristics of vertical vegetation structure at the regional scale. It contributes by providing data support and methodological guidance for research related to forest structure analysis, resource management, and ecological process simulation at the regional scale.

Amazonian mixed‐species flocks demonstrate flexible preferences for vertical forest structure

AbstractHow species interact with human‐disturbed environments is a central focus of conservation biology. Within disturbed landscapes, regenerating forests have potential to provide habitat for forest species, especially as increasing amounts of primary forest are lost. As secondary forest regenerates beside primary forest, it increases habitat heterogeneity. However, relatively little is known about the influence of habitat heterogeneity on space use. In this study, we analyzed the topography and vertical vegetation structure of regenerating forest, small forest fragments, and undisturbed rainforest in the central Amazon to determine (1) how these structural characteristics influence understory mixed‐species flock space use and (2) how the vegetative preferences of flocks varied across a disturbance gradient. We first used behavioral observations to quantify the vertical foraging niche of flocks and then associated variation in horizontal space use with the three‐dimensional features of forest structure. Surprisingly, we found that flock space use was not consistently associated with any variable, even though available habitat differed both within and across forest types. Overall, the best predictors were elevation and leaf area density within the subcanopy (16–25 m), yet most flock foraging occurred in the midstory (6–15 m). Together, these results indicate that while flocks may have certain habitat preferences, these preferences are flexible or idiosyncratic and do not correspond to a specific vertical profile. For example, flocks spent a disproportionate amount of time in low elevations when available, but not all flocks had access to low‐lying areas within their home ranges. Although other studies show flock size and diversity can be highly sensitive to habitat disturbance, mixed‐species flocks demonstrate remarkable plasticity as a unit, virtually saturating undisturbed and disturbed forest at our site, as long as regeneration has passed a certain threshold.

Evaluation of Tree-Growth Rate in the Laurentides Wildlife Reserve Using GEDI and Airborne-LiDAR Data

Loss of forest cover and derived effects on forest ecosystems services has led to the establishment of land management policies and forest monitoring systems, and consequently to the demand for accurate and multitemporal data on forest extent and structure. In recent years, spaceborne Light Detection and Ranging (LiDAR) missions, such as the Global Ecosystem Dynamics Investigation (GEDI) instrument, have facilitated the repeated acquisition of data on the vertical structure of vegetation. In this study, we designed an approach incorporating GEDI and airborne LiDAR data, in addition to detailed forestry inventory data, for estimating tree-growth dynamics for the Laurentides wildlife reserve in Canada. We estimated an average tree-growth rate of 0.32 ± 0.23 (SD) m/year for the study site and evaluated our results against field data and a time series of NDVI from Landsat images. The results are in agreement with expected patterns in tree-growth rates related to tree species and forest stand age, and the produced dataset is able to track disturbance events resulting in the loss of canopy height. Our study demonstrates the benefits of using spaceborne-LiDAR data for extending the temporal coverage of forestry inventories and highlights the ability of GEDI data for detecting changes in forests’ vertical structure.

Estimating the contribution of community landscape construction to urban carbon neutrality: methodology and database construction

Green ecological communities have garnered significant interest due to their role in providing urban ecosystem services, and community greening plays a pivotal role in urban environmental enhancement. In the context of carbon neutrality-oriented goals, it is imperative to acknowledge the significance of various landscape designs in carbon sequestration within community greening initiatives. However, there is currently a lack of consideration for landscape designs that promote high carbon sequestration in community greening projects. Our research with literature research and experimental measurement data as data sources, established a database of carbon sequestration of 138 common vegetation species in Shanghai. Based on the vertical vegetation structure within landscape design, we propose seven modular planting structures that reflect the carbon sequestration potential of high-capacity plants within different community green spaces. Our findings reveal substantial variations in carbon sequestration among different tree species within arbor and shrub categories, whereas the differences in carbon sequestration among various herbaceous plants per unit area are comparatively smaller. Among the different combination patterns, the highest carbon sequestration is achieved by the vegetation configuration of the three-layer structure pattern, and the combination of arbors, shrubs and grasses can maximize the effective use of space. This study holds significant importance in optimizing the utilization of limited green spaces within communities and enhancing the carbon sequestration benefits of community landscapes. Ultimately, these efforts contribute significantly to Shanghai’s journey toward carbon neutrality.

Prioritizing global tall forests toward the 30 × 30 goals.

The Global Deal for Nature sets an ambitious goal to protect 30% of Earth's land and ocean by 2030. The 30 × 30 initiative is a way to allocate conservation resources and extend protection to conserve vulnerable and underprotected ecosystems while reducing carbon emissions to combat climate change. However, most prioritization methods for identifying high-value conservation areas are based on thematic attributes and do not consider vertical habitat structure. Global tall forests represent a rare vertical habitat structure that harbors high species richness in various taxonomic groups and is associated with large amounts of aboveground biomass. Global tall forests should be prioritized when planning global protected areas toward reaching the 30 × 30 goals. We examined the spatial distribution of global tall forests based on the Global Canopy Height 2020 product. We defined global tall forests as areas with the average canopy height above 3 thresholds (20, 25, and 30m). We quantified the spatial distribution and protection level of global tall forests in high-protection zones, where the 30 × 30 goals are being met or are within reach, and low-protection zones, where there is a low chance of reaching 30 × 30 goals. We quantified the protection level by computing the percentage of global tall forest area protected based on the 2017 World Database on Protected Areas. We also determined the global extent and protection level of undisturbed, mature, tall forests based on the 2020 Global Intact Forest Landscapes mask. In most cases, the percentage of protection decreased as forest height reached the top strata. In the low-protection zones, <30% of forests were protected in almost all tall forest strata. In countries such as Brazil, tall forests had a higher percentage of protection (consistently >30%) compared to forests of lower height, presenting a more effective conservation model than in countries such as the United States, where forest protection was almost uniformly <30% across height strata. Our results show an urgent need to target forest conservation in the greatest height strata, particularly in high-protection areas, where most global tall forests are found. Vegetation vertical structure can inform the decision-making process toward the 30 × 30 goals because it can be used to identify areas of high conservation value for biodiversity protection which also contribute to carbon sequestration.

Remote sensing of daily evapotranspiration and gross primary productivity of four forest ecosystems in East Asia using satellite multi-channel passive microwave measurements

Satellite multi-channel passive microwave observations provide valuable information of canopy vertical structure and vegetation water content, while their potential in the simultaneous estimation of forest evapotranspiration (ET) and gross primary productivity (GPP) has not been explored. This study combined satellite multi-channel microwave Emissivity Difference Vegetation Indices (EDVIs) from 6.9 to 36.5 GHz with the Penman-Monteith (PM), Priestley-Taylor (PT) and light use efficiency (LUE) models to estimate daily ET and GPP over four forest ecosystems in East Asia. In-situ measurements of daily ET, GPP and meteorological variables from ChinaFLUX network (2003 to 2010) were used to calculate and validate the method. An exponential relationship was found between EDVIs and canopy conductance across the forests. By inverting EDVIs-based LUE model coupled with PM and PT models when canopy carbon uptake and water content reached a maximum in growing seasons, the maximum LUE (EDVI-LUEmax) was derived and found to agree with other independent studies. Site validations showed that the EDVI-based method performed a good simultaneous estimation of daily ET and GPP at a tropical forest and three sub-tropical and temperate forests (R2 of 0.64 to 0.94 and RMSE of −11.7% to 19.6% for ET, R2 of 0.39 to 0.73 and RMSE of 31.2% to 50.4% for GPP). The modeled relationship between GPP and ET also agreed that from in-situ measurements. The results were also found to correlate with microwave frequencies. In-situ ET and GPP showed a stronger correlation with EDVI at 18.7 and 36.5 GHz than that at 10.7 and 18.7 GHz across the forests. EDVI-LUEmax of a temperate mixed forest increased as EDVI incorporated lower frequency microwave emissions from deeper canopy layers, which reduced the negative bias in GPP estimation. Overall, this study demonstrated the potential of multi-channel microwave measurements in estimating ET and GPP with the potential effects of vegetation vertical structure.

Russian Arctic Vegetation Archive—A new database of plant community composition and environmental conditions

AbstractMotivationThe goal of the Russian Arctic Vegetation Archive (AVA‐RU) is to unite and harmonize data of plot‐based plant species and their abundance, vegetation structure and environmental variables from the Russian Arctic. This database can be used to assess the status of the Russian Arctic vegetation and as a baseline to document biodiversity changes in the future. The archive can be used for scientific studies as well as to inform nature protection and restoration efforts.Main types of variables containedThe archive contains 2873 open‐access geobotanical plots. The data include the full species. Most plots include information on the horizontal (cover per species and morphological group) and vertical (average height per morphological group) structure of vegetation, site and soil descriptions and data quality estimations. In addition to the open‐access data, the AVA‐RU website contains 1912 restricted‐access plots.Spatial location and grainThe plots of 1–100 m2 size were sampled in Arctic Russia and Scandinavia. Plots in Russia covered areas from the West to the East, including the European Russian Arctic (Kola Peninsula, Nenets Autonomous district), Western Siberia (Northern Urals, Yamal, Taza and Gydan peninsulas), Central Siberia (Taymyr peninsula, Bolshevik island), Eastern Siberia (Indigirka basin) and the Far East (Wrangel island). About 72% of the samples are georeferenced.Time period and grainThe data were collected once at each location between 1927 and 2022.Major taxa and level of measurementPlots include observations of &gt;1770 vascular plant and cryptogam species and subspecies.Software formatCSV files (1 file with species list and abundance, 1 file with environmental variables and vegetation structure) are stored at the AVA‐RU website (https://avarus.space/), and are continuously updated with new datasets. The open‐access data are available on Dryad and all the datasets have a backup on the server of the University of Zurich. The data processing R script is available on Dryad.

A more accurate approach to estimate the C-factor of RUSLE by coupling stratified vegetation cover index in southern China

Timely and accurate monitoring and forecasting of the cover and management factor (C-factor) is critical for soil erosion assessment at the national or regional scale. The stratified vegetation cover index (Cs) considers both the horizontal and vertical structure of vegetation. It is an ideal indicator for estimating the C-factor, but its applicability is limited in southern China where evergreen species dominate. To address this issue, this study proposed to reconstruct the structured vegetation index (S-Cs) in Nanjing, China, and sift out the optimal vegetation index for assessing the C-factor by establishing regression equations between the measured C-factor and S-Cs, fractional vegetation cover (FVC), Cs, and green coverage (VG). The results indicated that S-Cs can fully reflect the soil and water conservation benefits of different vegetation types and structures in southern China. Also, mixed coniferous and broadleaf forests had stronger soil and water conservation benefits than monocultures, and shrub-grass mixtures reduced soil erosion more than tree-grass mixtures. Besides, S-Cs was proved to be the best index to estimate the C-factor compared with Cs, FVC, and VG. Additionally, the C-factor fitting equation based on S-Cs was established as C = 0.213exp(−5.995 × S-Cs), which had good performance (R2 = 0.743, NSE = 0.507). This study broadened the application area of structured vegetation index and provided a new perspective for national or regional soil erosion assessment by coupling the S-Cs and the C-factor.

Spatial heterogeneity of global forest aboveground carbon stocks and fluxes constrained by spaceborne lidar data and mechanistic modeling.

Forest carbon is a large and uncertain component of the global carbon cycle. An important source of complexity is the spatial heterogeneity of vegetation vertical structure and extent, which results from variations in climate, soils, and disturbances and influences both contemporary carbon stocks and fluxes. Recent advances in remote sensing and ecosystem modeling have the potential to significantly improve the characterization of vegetation structure and its resulting influence on carbon. Here, we used novel remote sensing observations of tree canopy height collected by two NASA spaceborne lidar missions, Global Ecosystem Dynamics Investigation and ICE, Cloud, and Land Elevation Satellite 2, together with a newly developed global Ecosystem Demography model (v3.0) to characterize the spatial heterogeneity of global forest structure and quantify the corresponding implications for forest carbon stocks and fluxes. Multiple-scale evaluations suggested favorable results relative to other estimates including field inventory, remote sensing-based products, and national statistics. However, this approach utilized several orders of magnitude more data (3.77 billion lidar samples) on vegetation structure than used previously and enabled a qualitative increase in the spatial resolution of model estimates achievable (0.25° to 0.01°). At this resolution, process-based models are now able to capture detailed spatial patterns of forest structure previously unattainable, including patterns of natural and anthropogenic disturbance and recovery. Through the novel integration of new remote sensing data and ecosystem modeling, this study bridges the gap between existing empirically based remote sensing approaches and process-based modeling approaches. This study more generally demonstrates the promising value of spaceborne lidar observations for advancing carbon modeling at a global scale.

Effect of complete abolition of Eucalyptus species on under canopy species diversity in Gullele Botanic Garden, Ethiopia

Fast-growing, stress-tolerant tree species belonging to the genus Eucalyptus have historically been introduced in the central highlands of Ethiopia to support various economic and environment-regulating functions. While these non-native tree species are often highly useful, many are simultaneously invasive, generating negative environmental impacts and becoming the greatest global threat to biodiversity. This study is aimed at determining the impacts of the complete removal of the eucalyptus and its replacement by an indigenous species on the undergrowth vegetation of the Gulele Botanic Garden (GBG), Ethiopia. Vegetation data were collected from a total of 60 plots with a dimension of 20 m x 20 m that were laid out and equally distributed among invaded and indigenous species that dominate the landscape. Beside, diversity indices and population structural change parameters were employed for the comparison of changes. Results show that a total of 118 species (103 genera and 57 families) were recorded, and out of those, Eucalyptus camaldulensis was the most dominant species. On the other hand, the richest landscape (Juniperus procera plantation) contained 105 species and 51 families, whereas the poorest landscape (Eucalyptus plantation) was comprised of 66 species and 36 families. Furthermore, the alpha diversity index and vegetation vertical structure of the dominant Eucalyptus plantation differed significantly from those of other landscapes. Regeneration potential was not observed in Eucalyptus-dominant plantations since the inherent physiology of Eucalyptus species repels the regeneration of others. The population structure of the wood species showed an inverse-J curve. The total basal area (BA) of woody species decreased from 64.14 ± 1.54 m2 ha−1, 21.46 ± 0.54 m2 ha−1, and 17.18 m2 ha−1 ± 1.04 for Juniperus procera plantation, mixed plantation, and eucalyptus plantation, respectively. It was concluded that E. camaldulensis was one of the most dominant invasive species that reduced species diversity in the understory. While landscapes replaced after abolition by indigenous plantations demonstrated "fair" to "good" regeneration potential and an alpha diversity index.

Severe, large-scale bushfire threatens metapopulation function of quokka (Setonix brachyurus) in south-western Australia

Background The risk of large, severe bushfires is increasing in Australia, and little is known about their impact on threatened species. Aims This study aimed to investigated the abundance, occupancy and metapopulation structure of the quokka (Setonix brachyurus) for 5 years following a large, severe bushfire in Western Australia. Methods Data collected across spatiotemporally replicated transects were used to estimate probabilities of occupancy and abundance, and to evaluate the influence of covariates. Key results Quokka abundance was reduced to 16% of pre-fire levels, with distances up to 74 km separating subpopulations. Recolonising quokkas occupied habitat in parts of the landscape that had retained some vertical vegetation structure and contained a sedge-dominated understorey, and where damage by feral pigs was low. This coincided with topographic refugia that consisted of deep, mesic riparian systems or perched wetland systems. After 5 years, abundance had increased to 46% of pre-fire levels, with distances of 5–30 km separating subpopulations. Conclusion Quokka recovery rate is slower than expected, and the metapopulation remains vulnerable to further fragmentation arising from disturbances that affect habitat patch suitability and movement corridors. Implications Proactive protection of refugia and spatiotemporal habitat connectivity within and surrounding the fire scar is critical for metapopulation recovery.

GEDI waveform metrics in vegetation mapping—a case study from a heterogeneous tropical forest landscape

The distribution of different vegetation types is important information for landscape management, especially in the context of tackling global environmental change. Vegetation types can be mapped using satellite and airborne passive remote sensing. However, spectrally similar yet structurally different vegetation types, like different tree-dominated land covers, are often challenging to map using spectral information alone. We examined the potential of vertical vegetation structure acquired in the global ecosystem dynamics investigation (GEDI) mission that harnesses a space-borne waveform lidar sensor in vegetation mapping across a heterogeneous tropical landscape in Cambodia. We extracted 121 waveform metrics from Level-1B and Level-2A data products at 1062 locations across five key vegetation types. After reducing the relative height variables’ dimensionality through simple linear regressions, we developed a Random Forest classifier to predict vegetation classes based on 23 GEDI metrics. We then used this model to classify the vegetation types across more than 77 000 GEDI footprints in the study area. GEDI metrics alone were useful in identifying vegetation types with 81% accuracy. Cropland/grassland class had the highest prediction accuracy (user’s accuracy [UA] = 89%; producer’s accuracy [PA] = 91%), while dry deciduous forest had the lowest accuracy (UA = 73%; PA = 69%). By comparing the GEDI-only classification with an optical-radar map, we found that structural and topographic information from GEDI Level-1B and Level-2A can complement the spectral information in assessing natural habitats that neighbor other vegetation types in a heterogeneous landscape. The highest classification accuracy at the footprint scale was obtained from the combination of GEDI, Sentinel-1, and Sentinel-2 (88.3%). We also demonstrated how wall-to-wall vegetation mapping is possible by combining the three data sources. These findings expand the potential use of GEDI waveform lidar data in supporting the development of policy-relevant maps that depict the distribution of forests together with other vegetation types.

First validation of GEDI canopy heights in African savannas

Savannas have complex, discontinuous woody vegetation structures that vary greatly in vertical and spatial arrangement and change due to climatic, ecological and management impacts. While airborne laser scanning (ALS) data have provided detailed information on vertical vegetation structure and is widely used in ecological studies, it is lacking in availability and repeat frequency. Although the Global Ecosystem Dynamics Investigation (GEDI) waveform Light Detection and Ranging (LiDAR) sensor and algorithms were optimized for measuring dense forests, it was anticipated that GEDI metrics could provide useful characterization of lower stature, sparse savannas structures. This study provided the first baseline validation of Version 2 GEDI (L2A) relative height 98 (RH98) by comparing the on-orbit GEDI-RH98orb to the simulated GEDI-RH98sim derived from ALS data across diverse savanna vegetation. It furthermore determined the influence of various factors on error, e.g. algorithm setting group (SGs), beam type, day vs. night, beam sensitivity, and vegetation phenology. After applying quality flags, 22,813 GEDI footprints were analyzed across 11 sites. SGs 4–6 that are aimed at dense forests had much larger errors than SGs 1–3. The phenological conditions at the time of GEDI data acquisition had a very large influence on the error of RH98orb. During leaf-on conditions for savanna vegetation with RH98sim < 15 m, RH98orb was very accurate with R2 = 0.61, mean bias = −0.55 m, %bias = −11.1%, RMSE = 1.64 m and %RMSE = 29.8%. In leaf-off conditions where RH98sim < 15 m, RH98orb was less accurate with R2 = 0.43, mean bias = −1.47 m, %bias = −26.5%, RMSE = 2.03 m and %RMSE = 40.9%. During leaf-off conditions, the GEDI LiDAR signal at the start of the waveform may be weaker as it interacts with denuded branches and may be truncated as noise, leading to a large negative height bias. Therefore, assessments of deciduous vegetation structures should be conducted during leaf-on periods. In leaf-on conditions, GEDI's RH98orb was very accurate between canopy heights of 3 and 7 m, with a mean bias of −0.79 m (−10%). The bias of RH98orb was not influenced by canopy cover. Due to the GEDI LiDAR pulse width of 15.6 ns, the GEDI-RH98 data product cannot reliably estimate canopy heights of shrubs below 2.34 m and will require more complex deconvolution of the waveform. GEDI's RH98 accurately estimates the canopy height of trees between 3 and 15 m allowing assessment of canopy heights over vast savanna areas.

Vegetation structure derived from airborne laser scanning to assess species distribution and habitat suitability: The way forward

AbstractEcosystem structure, especially vertical vegetation structure, is one of the six essential biodiversity variable classes and is an important aspect of habitat heterogeneity, affecting species distributions and diversity by providing shelter, foraging, and nesting sites. Point clouds from airborne laser scanning (ALS) can be used to derive such detailed information on vegetation structure. However, public agencies usually only provide digital elevation models, which do not provide information on vertical vegetation structure. Calculating vertical structure variables from ALS point clouds requires extensive data processing and remote sensing skills that most ecologists do not have. However, such information on vegetation structure is extremely valuable for many analyses of habitat use and species distribution. We here propose 10 variables that should be easily accessible to researchers and stakeholders through national data portals. In addition, we argue for a consistent selection of variables and their systematic testing, which would allow for continuous improvement of such a list to keep it up‐to‐date with the latest evidence. This initiative is particularly needed not only to advance ecological and biodiversity research by providing valuable open datasets but also to guide potential users in the face of increasing availability of global vegetation structure products.

Urban forest fragments vs residential neighborhoods: Urban habitat preference of migratory birds

Both residential areas and urban forest fragments can provide stopover habitat for migrating birds. However, it is not clear which migrant species use urban forest fragments versus residential areas. Objectives for this study were to determine: (1) which species primarily use urban forest fragments versus adjacent residential areas during migration; (2) whether birds in different vertical strata guilds (canopy and under canopy) are associated with urban forest fragments versus residential areas, and which vegetation characteristics are important for certain species that preferentially utilize one habitat over the other. Conducted in Gainesville, Florida, 39 random point counts were situated in urban forest fragments, adjacent residential areas, and forest-residential edges during spring and fall migration. We recorded 44 migrant species across all point counts. Ten (38%) under-canopy birds significantly preferred urban forest fragments compared to residential neighborhoods. Canopy birds typically used forest fragments and residential areas more equally, with only one species showing significant preference for the residential habitat. Residential neighborhoods that retain vertical vegetation structure were more likely to have more species of birds. These results show the importance of conserving and restoring certain vegetative characteristics in urban ecosystems: preserving large trees in residential neighborhoods, retaining small forest remnant patches throughout the urban matrix, and increasing vertical vegetation structure in yards. These practices will help provide avian habitat during migration, increasing avian diversity in urban areas.

Radar and multispectral remote sensing data accurately estimate vegetation vertical structure diversity as a fire resilience indicator

AbstractThe structural complexity of plant communities contributes to maintaining the ecosystem functioning in fire‐prone landscapes and plays a crucial role in driving ecological resilience to fire. The objective of this study was to evaluate the resilience to fire off several plant communities with reference to the temporal evolution of their vertical structural diversity (VSD) estimated from the data fusion of C‐band synthetic aperture radar (SAR) backscatter (Sentinel‐1) and multispectral remote sensing reflectance (Sentinel‐2) in a burned landscape of the western Mediterranean Basin. We estimated VSD in the field 1 and 2 years after fire using Shannon's index as a measure of vertical heterogeneity in vegetation structure from the vegetation cover in several strata, both in burned and unburned control plots. Random forest (RF) was used to model VSD in the control (analogous to prefire scenario) and burned plots (1 year after fire) using as predictors (i) Sentinel‐1 VV and VH backscatter coefficients and (ii) surface reflectance of Sentinel‐2 bands. The transferability of the RF model from 1 to 2 years after wildfire was also evaluated. We generated VSD prediction maps across the study site for the prefire scenario and 1 to 4 years postfire. RF models accurately explained VSD in unburned control plots (R2 = 87.68; RMSE = 0.16) and burned plots 1 year after fire (R2 = 80.48; RMSE = 0.13). RF model transferability only involved a reduction in the VSD predictive capacity from 0.13 to 0.20 in terms of RMSE. The VSD of each plant community 4 years after the fire disturbance was significantly lower than in the prefire scenario. Plant communities dominated by resprouter species featured significantly higher VSD recovery values than communities dominated by facultative or obligate seeders. Our results support the applicability of SAR and multispectral data fusion for monitoring VSD as a generalizable resilience indicator in fire‐prone landscapes.

Study on the Characteristics of Soil Erosion in the Black Soil Area of Northeast China under Natural Rainfall Conditions: The Case of Sunjiagou Small Watershed

In the black soil area, where soil erosion is severe and the soil is in urgent need of ecological restoration, providing reasonable and practical measures to prevent soil erosion and restore the soil is an urgent issue at present. In this study, nine runoff plots were deployed in Bin County, the core area of soil erosion control, to monitor runoff and soil loss long-term, simulated by the erosion potential method (EPM) for comparison. Studies have shown that soil erosion is strongly influenced by soil conservation measures, land use, and vegetation cover. In contrast, slope, pre-soil moisture content, and soil crusting due to rainfall can affect erosion in a single rainfall event. The most severe soil erosion was in bare land, up to 1093.58 t km−2 a−1, followed by longitudinal ridge tillage land (751.88 t km−2 a−1) and cross ridge tillage land (31.58 t km−2 a−1). The shrublands and mixed forests planted for ecological restoration experienced almost no erosion. The soil loss rate of the cross ridge tillage and ecological restoration plots was much lower than the allowable soil loss rate (200 t km−2 a−1). Under erosive storms, longitudinal ridge tillage can produce soil loss rates that far exceed those of bare ground. The change from longitudinal ridge tillage to cross ridge tillage on gentle slopes can effectively prevent soil erosion in the study area. The vegetation restoration measures of planting shrubs such as Amorpha fruticosa Linn. are incredibly effective in the ecological restoration of wastelands in black soil areas. For the black soil area after the retreat, the vertical structure of vegetation can be improved by planting shrubs at the bottom, thus achieving a good restoration effect.

Analyzing Canopy Height Patterns and Environmental Landscape Drivers in Tropical Forests Using NASA’s GEDI Spaceborne LiDAR

Canopy height is a fundamental parameter for determining forest ecosystem functions such as biodiversity and above-ground biomass. Previous studies examining the underlying patterns of the complex relationship between canopy height and its environmental and climatic determinants suffered from the scarcity of accurate canopy height measurements at large scales. NASA’s mission, the Global Ecosystem Dynamic Investigation (GEDI), has provided sampled observations of the forest vertical structure at near global scale since late 2018. The availability of such unprecedented measurements allows for examining the vertical structure of vegetation spatially and temporally. Herein, we explore the most influential climatic and environmental drivers of the canopy height in tropical forests. We examined different resampling resolutions of GEDI-based canopy height to approximate maximum canopy height over tropical forests across all of Malaysia. Moreover, we attempted to interpret the dynamics underlining the bivariate and multivariate relationships between canopy height and its climatic and topographic predictors including world climate data and topographic data. The approaches to analyzing these interactions included machine learning algorithms, namely, generalized linear regression, random forest and extreme gradient boosting with tree and Dart implementations. Water availability, represented as the difference between precipitation and potential evapotranspiration, annual mean temperature and elevation gradients were found to be the most influential determinants of canopy height in Malaysia’s tropical forest landscape. The patterns observed are in line with the reported global patterns and support the hydraulic limitation hypothesis and the previously reported negative trend for excessive water supply. Nevertheless, different breaking points for excessive water supply and elevation were identified in this study, and the canopy height relationship with water availability observed to be less significant for the mountainous forest on altitudes higher than 1000 m. This study provides insights into the influential factors of tree height and helps with better comprehending the variation in canopy height in tropical forests based on GEDI measurements, thereby supporting the development and interpretation of ecosystem modeling, forest management practices and monitoring forest response to climatic changes in montane forests.

Factors affecting relative height and ground elevation estimations of GEDI among forest types across the conterminous USA

ABSTRACT The Global Ecosystem Dynamics Investigation (GEDI), a new spaceborne LiDAR system of the National Aeronautics and Space Administration (NASA), has the potential to revolutionize global measurements of vertical vegetation structure. However, GEDI performance among different forest types and factors influencing GEDI performance needs to be evaluated against similar measurements from existing airborne LiDAR platforms. Ideally, comparisons across diverse forest types will inform future work quantifying biomass or mapping species habitats. Thus, we compared the second version of GEDI L2A product (GEDI V2) with Airborne Observation Platform (AOP) leaf-on LiDAR data across 33 National Ecological Observation Network (NEON) sites. Comparisons were made for ground elevation and relative height (RH) of GEDI with simulated airborne laser scanning (ALS) waveforms from discrete point cloud LiDAR. Results indicated that GEDI V2 obtained high accuracy on ground elevation and RH100 estimations (3σ) with RMSEs of 1.38 m and 2.62 m, respectively. GEDI produced forest height estimations (RH100) for all 12 forest types with a %RMSE below 25%. GEDI RHs were sensitive to ground finding accuracy, and GEDI performance of RH estimation varied from forest profiles of different forest types. For factors influencing GEDI performance, greater than 21% of GEDI RH95 and 33% of ground elevation variations can be explained by land surface attributes, observing sensor system characteristics, and the collection time differences between GEDI and NEON LiDAR. Furthermore, geolocation error remains an essential factor affecting GEDI performance, which varies among forest and land cover types, especially for canopy height estimation. The findings reported here can provide insights to guide and enhance future GEDI-based global forest structure mapping and applications.

基于街景测量的南昌市行道树结构特征与健康状况研究

行道树作为城市绿地系统的网络和骨架, 在城市居民生活中发挥着无可替代的作用。快速准确的掌握城市行道树的结构特征和健康状况, 有利于城市森林建设与管理, 进而提升其生态服务功能。基于百度街景平台, 通过街景测树法对南昌市的963个样方, 共计26727株行道树展开调查。测量内容包括行道树大小指标(树高、胸径、冠下高和冠幅)、健康状况(支架比例、枯稍比例、叶色不健康比例和死亡比例)和植被垂直结构配置情况; 并且探究了不同行政区和不同城市化强度(环城道路发展下的城乡梯度)下行道树的空间分布特征。结果表明: (1)南昌市行道树小树比例较高: 胸径在10-20 cm的占65%, 冠下高在1.5-2.5 m间的占76%, 树高在4-8 m间的占71%, 冠幅在2-6 m间的占76%;行道树生长整体表现为健康, 各指标所占比例较小, 但是叶片健康较差; 35%的样地无叶色不健康, 77%的样地无支架, 76%的样地无枯梢, 93%的样地无死亡; 植被垂直结构配置状况良好, 以乔-灌-草复合结构为主(比重为75%); (2)在空间分布上, 不同行政区行道树大小指标和垂直结构配置上差异显著、健康状况(支架比例和叶色不健康比例)均表现显著性差异(P < 0.05);不同环路行道树大小指标存在显著差异, 表现为1环>2环>3环, 垂直结构从内环到外环更为复杂, 配置等级为1环 < 2环 < 3环, 不同环路行道树健康状况(支架比例和叶色不健康比例)表现显著性差异(P < 0.05)。综上所述, 南昌市行道树整体健康状况良好, 行道树个体较小, 而且以复层结构为主, 本研究基于百度街景对南昌市行道树结构特征和健康状况进行比较全面的评价, 为南昌市今后城市行道树的规划和管理提供了基础数据支持。;As the network and skeleton of urban green space system, roadside trees play an irreplaceable role in urban residents' life. Rapid and accurate grasp of the structural characteristics and health status of urban roadside trees is conducive to the construction and management of urban forest, thus improving its ecological service function. Based on the Baidu Street View platform, this study investigated 963 quadrats, a total of 26, 727 roadside trees, in Nanchang by street view tree measurement method. Measures included indicators of street tree sizes (diameter at breast height (DBH), underbranch height (UBH), tree height (TH) and canopy size (CS)), health status (the proportion of Tripod-supporting, the proportion of Dieback, the proportion of Non-greenness leaf color and the proportion of Death trees), and vertical structure configuration of vegetation. Moreover, the spatial distribution characteristics of road trees under different administrative regions and different urbanization intensities (urban and rural gradient under the development of ring roads) were explored. The results showed that: (1) there was a high proportion of small trees in Nanchang: DBH of 10─20 cm accounted for 65%, UBH of 1.5─2.5 m accounted for 76%, TH of 4─8 m accounted for 71%, CS of 2─6 m accounted for 76%. The overall performance of the growth of street trees was healthy, the proportion of each index was small, but the leaf health was poor. In 35% of the plots, no leaf color was unhealthy, 77% of the plots had no Tripod-supporting, 76% of the plots had no Dieback and 93% of the plots had no death. The vertical structure of vegetation was in good condition, mainly composed of arbor-shrub-herb (75% of the sample land). (2) In terms of spatial distribution, there are significant differences in the sizes index and vertical structure configuration of road trees in different administrative areas, and significant differences in health status (proportion of Tripod-supporting and proportion of Non-greenness leaf color) (P < 0.05). There were urban-rural differences in the size index of road trees in different ring-road areas, which are manifested as 1^st ring > 2^nd ring > 3^rd ring, the vertical structure of the outer ring is more complex, configuration grade is 1^st ring < 2^nd ring < 3^rd ring. There were significant differences in the health status of different ring-road (proportion of Tripod-supporting and proportion of Non-greenness leaf color) (P < 0.05). To sum up, the overall health status of the roadside trees in Nanchang is good, the individual of the roadside trees is small, and they are mainly composed of multiple layers. Based on the Baidu Street View, this study makes a relatively comprehensive evaluation of the structural characteristics and health status of the roadside trees in Nanchang, which provides basic data support for the planning and management of the urban roadside trees in Nanchang in the future.