Vegetation Structure Parameters 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.

Improving Tree Cover Estimation for Sparse Trees Mixed with Herbaceous Vegetation in Drylands Using Texture Features of High-Resolution Imagery

Tree cover is a crucial vegetation structural parameter for simulating ecological, hydrological, and soil erosion processes on the Chinese Loess Plateau, especially after the implementation of the Grain for Green project in 1999. However, current tree cover products performed poorly across most of the Loess Plateau, which is characterized by grasslands with sparse trees. In this study, we first acquired high-accuracy samples of 0.5 m tree canopy and 30 m tree cover using a combination of unmanned aerial vehicle imagery and WorldView-2 (WV-2) imagery. The spectral and textural features derived from Landsat 8 and WV-2 were then used to estimate tree cover with a random forest model. Finally, the tree cover estimated using WV-2, Landsat 8, and their combination were compared, and the optimal tree cover estimates were also compared with current products and tree cover derived from canopy classification. The results show that (1) the normalized difference moisture index using Landsat 8 shortwave infrared and the standard deviation of correlation metric calculated by means of gray-level co-occurrence matrix using the WV-2 near-infrared band are the optimal spectral feature and textural feature for estimating tree cover, respectively. (2) The accuracy of tree cover estimated using only WV-2 is highest (RMSE = 7.44%), indicating that high-resolution textural features are more sensitive to tree cover than the Landsat spectral features (RMSE = 11.53%) on grasslands with sparse trees. (3) Textural features with a resolution higher than 8 m perform better than the combination of Landsat 8 and textural features, and the optimal resolution is 2 m (RMSE = 7.21%) for estimating tree cover, whereas the opposite is observed when the resolution of textural features is lower than 8 m. (4) The current global product seriously underestimates tree cover on the Loess Plateau, and the tree cover calculation using the canopy classification of high-resolution imagery performs worse than the method of directly using remote sensing features.

Mapping Foliar C, N, and P Concentrations in An Ecological Restoration Area with Mixed Plant Communities Based on LiDAR and Hyperspectral Data

Interactions between carbon (C), nitrogen (N), and phosphorus (P), the vital indicators of ecological restoration, play an important role in signaling the health of ecosystems. Rapidly and accurately mapping foliar C, N, and P is essential for interpreting community structure, nutrient limitation, and primary production during ecosystem recovery. However, research on how to rapidly map C, N, and P in restored areas with mixed plant communities is limited. This study employed laser imaging, detection, and ranging (LiDAR) and hyperspectral data to extract spectral, textural, and height features of vegetation as well as vegetation indices and structural parameters. Causal band, multiple linear regression, and random forest models were developed and tested in a restored area in northern China. Important parameters were identified including (1), for C, red-edge bands, canopy height, and vegetation structure; for N, textural features, height percentile of 40–95%, and vegetation structure; for P, spectral features, height percentile of 80%, and 1 m foliage height diversity. (2) R2 was used to compare the accuracy of the three models as follows: R2 values for C were 0.07, 0.42, and 0.56, for N they were 0.20, 0.48, and 0.53, and for P they were 0.32, 0.39, and 0.44; the random forest model demonstrated the highest accuracy. (3) The accuracy of the concentration estimates could be ranked as C > N > P. (4) The inclusion of LiDAR features significantly improved the accuracy of the C concentration estimation, with increases of 22.20% and 47.30% in the multiple linear regression and random forest models, respectively, although the inclusion of LiDAR features did not notably enhance the accuracy of the N and P concentration estimates. Therefore, LiDAR and hyperspectral data can be used to effectively map C, N, and P concentrations in a mixed plant community in a restored area, revealing their heterogeneity in terms of species and spatial distribution. Future efforts should involve the use of hyperspectral data with additional bands and a more detailed classification of plant communities. The application of this information will be useful for analyzing C, N, and P limitations, and for planning for the maintenance of restored plant communities.

Detection of invasive plants using NAIP imagery and airborne LiDAR in coastal Alabama and Mississippi, USA

Invasive plants have imposed severe threats to native ecosystems worldwide. Triadica sebifera (Tallow tree) and Ligustrum sinense (Chinese privet) are among the most prolific invasive species in the southern United States (US) that needs urgent assessment to protect coastal ecosystems. The lack of spatially explicit assessments of these invasives, coupled with the increasing availability of high-resolution remotely sensed data, represents an opportunity to produce a distribution map for subsequent monitoring. The overall goal of this study was to develop spatially comprehensive maps of Tallow tree and Chinese privet in ecologically sensitive coastal regions, where both invasives have become well established. The study was conducted in three coastal sites within Alabama and Mississippi: (1) Mobile Tensaw River Delta, (2) Bon Secour National Wildlife Refuge, and (3) Mississippi Sandhill Crane National Wildlife Refuge. We implemented three image classification methods, representing unsupervised, supervised, and machine learning techniques, respectively: (1) ISODATA, (2) Maximum Likelihood (ML), and (3) Random Forest (RF). For each classification, a 1 m National Agriculture Imagery Program (NAIP) orthoimage was first examined, then integrated with vegetation structure and topography parameter derived from airborne light detection and ranging (LiDAR). The maximum Overall Accuracy (OA) of 87.5% was obtained using RF model with NAIP stacked image integrated with LiDAR derived variables. Overall, findings highlight the potential for accurately characterizing both Tallow tree and Chinese privet using readily available remote sensing data. Mapped products from this study represent a spatially comprehensive baseline inventory of crucial invasive species and will serve to inform the development of a framework for broader-scale mapping and monitoring efforts.

Response relationship between vegetation structure and runoff-sediment yield in the hilly and gully area of the Loess Plateau, China

The structure of the vegetation has a significant impact on how much runoff and sediment is produced. Choosing the right parameters to evaluate the correlations between vegetation and runoff-sediment yield, however, is fraught with uncertainty. We chose 18 vegetation plots with a range of vegetation types and structural characteristics for this study to investigate the applicability of various vegetation structure parameters in identify in the response relationship between vegetation and runoff-sediment yield through field artificial rainfall experiments. The vegetation coverage, stratified vegetation cover index (Cs), species diversity and functional diversity were among the criteria of vegetation structure. The findings revealed that: (1) There was a significant difference in the runoff start time and sediment yield differed significantly between vegetation types, with shrub communities having a longer start time and the lowest sediment yield; (2) The vegetation coverage of the community had a significant impact on the runoff start time and, when combined with Cs, explained 48% of the runoff start time. (3) Simpson index (D) and functional richness (FRic) significantly influenced the total runoff yield and along with Cs explained 46% of the total runoff yield. Cs, coverage and FRic also positively influenced the runoff end time, with a total explanation of 29%. Functional divergence (FDiv) had a significant negative effect on the total sediment yield. Together with coverage and Cs, FDiv explained 66% of the total sediment yield. These results demonstrated how biodiversity consideration helped to clarify connections between vegetation structure and runoff-sediment yield.

Global Leaf Area Index Research over the Past 75 Years: A Comprehensive Review and Bibliometric Analysis

The leaf area index (LAI) is widely used as an important indicator and ecological parameter of vegetation structure and growth status, but the LAI lacks bibliometric analysis. To further understand the LAI’s research status and frontier dynamics, we used 75 years of data (1947–2021) from the Web of Science for scientific bibliometric analysis. The results showed that 22,276 LAI re-search papers were published from 1947 to 2021. According to the characteristics of the literature growth, LAI research can be divided into five stages: incubation, cultivation, acceleration, evolution, and outbreak periods. The research power at the different stages had different characteristics. The overall research power of the United States is number one globally, followed by China, Canada, and France. The related disciplines were widely varied, involving agriculture (the most studied field of LAI research), environmental science and ecology, remote sensing, and other fields. The development of the Google Earth engine, cloud computing platforms, and unmanned aerial vehicle technology will provide more critical support for LAI research. The results of this paper quantitatively show the development history, research hotspots, and application of LAI research and provide a reference for understanding the current situation and development trends of global LAI research.

Vegetation diversity, structure, composition and carbon stock of community managed forests of Mid-hills, Nepal

Abstract. Joshi P, Joshi R, Sapkota RP, Panta M, Chand P. 2023. Vegetation diversity, structure, composition and carbon stock of community managed forests of Mid-hills Nepal. Asian J For 7: 29-36. Depending on management practices, forests can serve as both carbon sinks and sources. The goal of reducing carbon emissions and increasing the carbon sink is thought to be feasible if carbon reservoirs in current forests are protected and conserved. This study was objectively conducted to assess the vegetation diversity, structure, and carbon stock of the Mid-hills of Nepal. The study was undertaken in the Lanta Community Forest, Jajarkot District, Nepal, which has an extent of 38.65 hectares. Data for vegetation analysis and carbon stock assessment were collected using systematic random sampling using quadrats of 10×10 m with a total number of 35 quadrats. Within each quadrat, individual trees and bamboo were identified in the sites, and their height (m) and DBH (cm) were measured. Density, frequency, basal area, and Important Value Index (IVI) were calculated as structural parameters of vegetation. The Above-Ground Tree Biomass (AGTB) and Below-Ground Tree Biomass (BGTB) were calculated using an allometric equation based on tree diameter, height, and wood-specific gravity. The species diversity, species richness, and evenness were found to be 2.2, 2.35, and 0.83, respectively. A total of 14 tree species, with 723 individuals and one bamboo species were recorded. Rhododendron arboreum Sm. had the highest tree density with 211 trees/ha, while Tsuga dumosa (D.Don) Eichler had the highest IVI. Total wood volume, biomass, and total carbon stock were estimated at 15.37 m3 ha-1, 31.99 t ha-1, and 15.03 t ha-1, respectively. There was a strong negative correlation (r = -0.59) between R. arboreum and R. campanulatum D. Don and a strong positive correlation (r = +0.65) between Malus sikkimensis and Machilus species. For the preservation and sustainable management of community forests, information regarding the structure, composition, and dominance of tree species is provided by the study. The establishment of community forests is thus demonstrated in this article as a means of promoting the protection and preservation of regional biodiversity.

2020-2050年CO2施肥效应促进全球陆地生态系统碳吸收

PDF HTML阅读 XML下载 导出引用 引用提醒 2020-2050年CO2施肥效应促进全球陆地生态系统碳吸收 DOI: 10.5846/stxb202107051782 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 福建省自然科学基金面上项目(2021J01502);福建省农业科学院自由探索科技创新项目(ZYTS202233);福建省农业高质量发展协同创新工程(XTCXGC2021015);福建省智慧农业科技创新团队 (CXTD2021013-1) CO2 fertilization promoting the carbon uptake of global terrestrial ecosystems in 2020-2050 Author: Affiliation: Fund Project: the Natural Science Foundation of Fujian Province (2021J01502)，Fujian Academy of Agricultural Sciences Free Exploration Science and Technology Innovation Project (ZYTS202233); Collaborative Innovation Project of High-Quality Development of Agriculture in Fujian Province (XTCXGC2021015), and Fujian Intelligent Agricultural Science and Technology Innovation Team (CXTD2021013-1) 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:CO2施肥效应是全球变绿的主要原因,随着大气中CO2浓度的持续增加,预估未来气候变化条件下,CO2施肥效应对陆地生态系统的影响对减缓全球气候变化具有重大意义。基于未来气候情景数据和Farquhar模型,并结合生态过程模型BEPS(Boreal Ecosystem Productivity Simulator),定量化研究2020-2050年CO2施肥效应对全球叶面积指数(LAI)和总初级生产力(GPP)的影响。研究结果显示2020-2050年,在RCP2.6、RCP4.5和RCP8.5气候情景下,CO2施肥效应导致的LAI年际变化趋势分别为0.002、0.003和0.005 m-2m-2a-1;三个气候情景下CO2施肥效应对LAI的影响为CO2每增加0.1%,LAI平均增加约8.1%-9.2%,由此导致GPP对应增加7.9%-14.6%;由CO2施肥效应导致的全球LAI的增加对未来GPP年际变化趋势的贡献分别为66.7%、48.7%和57.1%。表明在未来气候变化情景下,LAI的增加仍然主要受CO2施肥效应的影响,CO2施肥效应导致的LAI的增加将显著促进全球陆地生态系统碳吸收。 Abstract:CO2 fertilization is the main reason for global greening. With the persistent increase in CO2 concentration, it is critically significant to evaluate the effect of CO2 fertilization on the global terrestrial ecosystem. Gross primary productivity (GPP) quantifies the photosynthetic uptake of carbon by terrestrial ecosystems, and it is the basis of the global carbon cycle. Leaf area index (LAI) is a vegetation structural parameter that modulates the interaction between the land surface and the atmosphere and therefore is used in many terrestrial biosphere models. LAI is a critical parameter for leaf-to-canopy upscaling in the terrestrial biosphere models at the regional and global scales. Therefore, a reliable estimation of CO2 fertilization on LAI is crucial for understanding and predicting the terrestrial carbon cycle under future climate change. Due to the complexity and spatiotemporal difference in the global terrestrial ecosystems, there are still large uncertainties in simulating global LAI based on phenology and carbon dynamic allocation in Earth system models. Thus, in this study, we used the future scenario climate data, combined with the Farquhar and Boreal Ecosystem Productivity Simulator (BEPS) model to investigate the effect of CO2 fertilization on the global LAI and GPP during 2020-2050. The results showed that for RCP2.6, RCP4.5, and RCP8.5 scenarios, the CO2 fertilization led to the global LAI interannual trends were 0.002, 0.003, and 0.005 m-2m-2a-1, respectively. The LAI increased 8.1%-9.2%, resulting in a corresponding increase in GPP of 7.9%-14.6% per 0.1% CO2 concentration. The contributions of LAI to the global terrestrial ecosystem GPP were 66.7%, 48.7%, and 57.1%, respectively. It shows that CO2 fertilization is still the primary reason for increasing LAI under future climate scenarios. The increase of LAI caused by CO2 fertilization will significantly promote the carbon uptake of global terrestrial ecosystems. 参考文献 相似文献 引证文献

Evaluating Data Inter-Operability of Multiple UAV–LiDAR Systems for Measuring the 3D Structure of Savanna Woodland

For vegetation monitoring, it is crucial to understand which changes are caused by the measurement setup and which changes are true representations of vegetation dynamics. UAV–LiDAR offers great possibilities to measure vegetation structural parameters; however, UAV–LiDAR sensors are undergoing rapid developments, and the characteristics are expected to keep changing over the years, which will introduce data inter-operability issues. Therefore, it is important to determine whether datasets acquired by different UAV–LiDAR sensors can be interchanged and if changes through time can accurately be derived from UAV–LiDAR time series. With this study, we present insights into the magnitude of differences in derived forest metrics in savanna woodland when three different UAV–LiDAR systems are being used for data acquisition. Our findings show that all three systems can be used to derive plot characteristics such as canopy height, canopy cover, and gap fractions. However, there are clear differences between the metrics derived with different sensors, which are most apparent in the lower parts of the canopy. On an individual tree level, all UAV–LiDAR systems are able to accurately capture the tree height in a savanna woodland system, but significant differences occur when crown parameters are measured with different systems. Less precise systems result in underestimations of crown areas and crown volumes. When comparing UAV–LiDAR data of forest areas through time, it is important to be aware of these differences and ensure that data inter-operability issues do not influence the change analysis. In this paper, we want to stress that it is of utmost importance to realise this and take it into consideration when combining datasets obtained with different sensors.

An Improved LAI Estimation Method Incorporating with Growth Characteristics of Field-Grown Wheat

Leaf area index (LAI), which is an important vegetation structure parameter, plays a crucial role in evaluating crop growth and yield. Generally, it is difficult to accurately estimate LAI only using vegetation index in remote sensing (RS), especially for unmanned aerial vehicle (UAV) based RS, as its high-resolution advantage has not been fully utilized. This study aims to propose an improved LAI estimation method that comprehensively considers spectral information and structural information provided by the UAV-based RS to improve the LAI estimation accuracy of field-grown wheat. Specifically, this method introduces the canopy height model (CHM) to compensate for the lack of structural information in LAI estimation, and then takes canopy coverage (CC) as a correction parameter to alleviate the LAI overestimation. Finally, the performance of this method is verified on RGB and multispectral images, respectively. The results show that canopy structure, namely CHM and CC, can significantly improve the accuracy of LAI estimation. Compared with the traditional method, the proposed method improves the accuracy by 22.6% on multispectral images (R2 = 0.72, RMSE = 0.556) and by 43.6% on RGB images (R2 = 0.742, RMSE = 0.534). This study provides a simple and practical method for UAV-based LAI estimation, especially for the application of low-cost RGB sensors in precision agriculture and other fields.

Evaluation of the Consistency of the Vegetation Clumping Index Retrieved from Updated MODIS BRDF Data

The clumping index (CI) quantifies the foliage grouping within distinct canopies relative to randomly distributed canopies, which plays an important role in the vegetation radiative regime. Among the vegetation structure parameters, the global CI map can be retrieved by using multiangle remote sensing observations. The bidirectional reflectance distribution function (BRDF)/Albedo product (MCD43) of the Moderate-Resolution Imaging Spectroradiometer (MODIS) is the crucial input data of the global CI product, which provides validated spatiotemporal continuous directional reflectance data. To determine the impacts of updating the MCD43 product on the consistency and performance of global CI products, CIs retrieved from different MCD43 versions (Collection V005/V006, C5/6) were compared on a global scale and validated with field-measured CI data. The results showed that the global and seasonal comparisons of C5 and C6 CI data are generally consistent. Compared to C5 CI data, C6 CI data have improved quality with more main algorithm retrievals and fewer case of missing data. The comparisons over the field measurements indicate that both versions of CI data agree with field-measured CI data in terms of values and seasonal variations, while C6 CI data (R2 = 0.89, RMSE = 0.05, bias = 0.02) are closer to field CIs than C5 CI data (R2 = 0.80, RMSE = 0.07, bias = 0.03), indicating a higher accuracy for C6 CI data. The monthly CI is recommended for characterizing the overall seasonal patterns of surface CIs.

Revealing the Structure and Composition of the Restored Vegetation Cover in Semi-Arid Mine Dumps Based on LiDAR and Hyperspectral Images

Remotely sensed images with low resolution can be effectively used for the large-area monitoring of vegetation restoration, but are unsuitable for accurate small-area monitoring. This limits researchers’ ability to study the composition of vegetation species and the biodiversity and ecosystem functions after ecological restoration. Therefore, this study uses LiDAR and hyperspectral data, develops a hierarchical classification method for classifying vegetation based on LiDAR technology, decision tree and a random forest classifier, and applies it to the eastern waste dump of the Heidaigou mining area in Inner Mongolia, China, which has been restored for around 15 years, to verify the effectiveness of the method. The results were as follows. (1) The intensity, height, and echo characteristics of LiDAR point cloud data and the spectral, vegetation indices, and texture features of hyperspectral image data effectively reflected the differences in vegetation species composition. (2) Vegetation indices had the highest contribution rate to the classification of vegetation species composition types, followed by height, while spectral data alone had a lower contribution rate. Therefore, it was necessary to screen the features of LiDAR and hyperspectral data before classifying vegetation. (3) The hierarchical classification method effectively distinguished the differences between trees (Populus spp., Pinus tabuliformis, Hippophae sp. (arbor), and Robinia pseudoacacia), shrubs (Amorpha fruticosa, Caragana microphylla + Hippophae sp. (shrub)), and grass species, with classification accuracy of 87.45% and a Kappa coefficient of 0.79, which was nearly 43% higher than an unsupervised classification and 10.7–22.7% higher than other supervised classification methods. In conclusion, the fusion of LiDAR and hyperspectral data can accurately and reliably estimate and classify vegetation structural parameters, and reveal the type, quantity, and diversity of vegetation, thus providing a sufficient basis for the assessment and improvement of vegetation after restoration.

EFFECT OF HUMAN DISTURBANCES ON ANT COMMUNITY AND AMAZONIAN LANDSCAPE

The present work aimed to evaluate the effect of anthropogenic disturbances on the ant community and on the Amazonian landscape in areas of tropical rain forest. The study was conducted in two areas: Cabo Rosa Forest and Tauari Forest with different levels of human disturbances in the municipality of Marabá, Pará, Brazil. A characterization of the descriptors of anthropogenic activities was carried out for each study area in order to generate an index of anthropogenic disturbance. The vegetation structure was classified into: exposed soil, low, medium and high vegetation cover through NDVI. Ant sampling was carried out using pitfall traps. The vegetation structure, diversity and abundance parameters were related to the areas. Similarities between the species composition of the ant communities were verified by a similarity analysis. We found that human disturbances are modifying the vegetation structure by reducing the forest cover of the tree stratum and leaving the landscape with a higher occurrence of open areas. Regarding the ant community, we corroborate the hypothesis that anthropic disturbances are reducing the local biodiversity. Thus, this study shows that anthropogenic disturbances have negative effects on the Amazonian landscape and on the ant community through the reduction of forest cover and decrease in ant biodiversity. These changes can cause the conversion of climax communities in early successional stages and reduce the ecological services provided by ants (e.g. seed dispersal).

Revisit the Performance of MODIS and VIIRS Leaf Area Index Products from the Perspective of Time-Series Stability

As an essential vegetation structural parameter, Leaf Area Index (LAI) is involved in many critical biochemical processes such as photosynthesis, respiration, and precipitation interception. The MODerate resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imager Radiometer Suite (VIIRS) LAI sequence products have long supported various global climate, biogeochemistry, and energy flux research. These applications all rely on the accuracy of the product's long time series. However, uncontrolled interferences (e.g., adverse observation conditions and sensor uncertainties) potentially introduce substantial uncertainties to time series in product applications. As one of the most sensitive areas in response to global climate change, the Tibet Plateau (TP) has been treated as a crucial testing ground for thousands of studies on vegetation. To ensure the credibility of the studies arising from MODIS/VIIRS LAI products, the temporal quality uncertainties of data need to be clarified. This paper proposed a method to revisit the temporal stability of the MODIS (MOD and MYD) and VIIRS (VNP) LAI in the TP, expecting to provide useful information for better accounting for the uncertainties in this area. Results show that the MODIS and VIIRS LAI were relatively stable in time series and available to be used continuously, among which the temporal quality of the MODIS LAI was the most stable. Moreover, the MODIS and VIIRS LAI products performed similarly in both time-series stability and time-series anomaly distribution, magnitudes and fluctuations. The time-series stability evaluation strategy applied to the MODIS and VIIRS LAI can also be employed to other remote sensing products.

Poplar trees do not always act as a water pump: Evidence from modeling deep drainage in a low-coverage-mode shelterbelt in China

Large-scale afforestation can combat desertification, provide ecosystem services, and mitigate climate change. However, it can also have negative effects on the hydrological cycle and water budget, and thus on water security in dryland regions depending on the afforestation strategy used. Currently, the effects of different afforestation strategies on soil moisture dynamics, evapotranspiration (ET), and water budget at the stand scale are unclear. In this study, we investigated the water budget in two poplar stands, one planted using a low-coverage-mode shelterbelt (LCMS) strategy and the other planted using a conventional strategy (control). Deep drainage (DP), as an indicator of water budget, was estimated using a Hydrus-1D model calibrated with the observed soil moisture at different soil depths (across 0–200 cm) during the growing seasons in 2016, 2017, and 2019. The average DP in the LCMS stand was 107.0–149.6 mm, accounting for 36.5–42.4% of the rainfall occurring during the growing seasons, which was slightly higher than that in the control stand containing younger trees at a higher density. However, DP was more spatially heterogenous in the LCMS stand compared with in the control stand. Specifically, in the LCMS stand, the inter-canopy DP was 195.3–237.5 mm, accounting for 66.6–69.7% of the rainfall, whereas the under-canopy DP was only 18.7–65.0 mm, accounting for 6.4–18.4% of the rainfall during the corresponding period. Structural equation modeling indicated that, on a daily scale, soil moisture had the greatest effect on DP, followed by precipitation and soil saturated hydraulic conductivity. Other climate factors (global radiation, air temperature, vapor pressure deficit and wind speed) and vegetation structure parameters (leaf area index and rooting depth) affected ET, which had a significantly negative relationship with DP. These results help explain the effects of plantation strategy on the water budget, with important implications for artificial forest management, ecological restoration, and protection of local water resources in water-limited environments.

Retrieval of Leaf Area Index by Linking the PROSAIL and Ross-Li BRDF Models Using MODIS BRDF Data

Canopy structure parameters (e.g., leaf area index (LAI)) are key variables of most climate and ecology models. Currently, satellite-observed reflectances at a few viewing angles are often directly used for vegetation structure parameter retrieval; therefore, the information content of multi-angular observations that are sensitive to canopy structure in theory cannot be sufficiently considered. In this study, we proposed a novel method to retrieve LAI based on modelled multi-angular reflectances at sufficient sun-viewing geometries, by linking the PROSAIL model with a kernel-driven Ross-Li bi-directional reflectance function (BRDF) model using the MODIS BRDF parameter product. First, BRDF sensitivity to the PROSAIL input parameters was investigated to reduce the insensitive parameters. Then, MODIS BRDF parameters were used to model sufficient multi-angular reflectances. By comparing these reference MODIS reflectances with simulated PROSAIL reflectances within the range of the sensitive input parameters in the same geometries, the optimal vegetation parameters were determined by searching the minimum discrepancies between them. In addition, a significantly linear relationship between the average leaf angle (ALA) and the coefficient of the volumetric scattering kernel of the Ross-Li model in the near-infrared band was built, which can narrow the search scope of the ALA and accelerate the retrieval. In the validation, the proposed method attains a higher consistency (root mean square error (RMSE) = 1.13, bias = −0.19, and relative RMSE (RRMSE) = 36.8%) with field-measured LAIs and 30-m LAI maps for crops than that obtained with the MODIS LAI product. The results indicate the vegetation inversion potential of sufficient multi-angular data and the ALA relationship, and this method presents promise for large-scale LAI estimation.

The possibility of reclamation criteria success in Indonesia: soil condition, vegetation structure and species composition

There are two regulations for mine reclamation success in the forestry area in Indonesia, namely Minister of Forestry Regulation No. P.60/Menhut-II/2009 and Minister of Energy and Mineral Resources Decree No. 1827.K/30/MEM/2018. Both regulations rule vegetation and soil success. This study aims to analyse criteria parameters from both regulations in the mine reclamation and compare them to the surrounding secondary natural forest (SNF). This study was conducted in 6 six types of mine reclamation stand structures: 1, 4, 6, 9, 11-year-old plantation and SNF using 1 hectare of the circular plot each (total 6 ha). Soil samples were collected from 40 cm depth to analyse physical, biological and chemical conditions. Mine reclamation areas had almost similar physical, biological and chemical soil conditions with SNF. Nevertheless, due to the potential acid-forming (PAF) material from overburden, the 1-year-old plantation had pH = 3.23-3.27. The highest diversity index and the number of species and families in all reclamation areas were H’ = 1.82 (11-year-old); 14 species (9-year-old); and 11 families (9-year-old), comparing with SNF were H’ = 3.48; 67 species, and 31 families. Conversely, vegetation structure parameters in mine reclamation areas were higher than SNF (diameter at height breast (DBH; 1.3 m) = 28.42 cm; tree density = 469/ha; basal area = 35.04 m2/ha; and total height = 16.85 m). Compared to the SNF, vegetation structure and soil conditions are mostly possible for mine reclamation success. Still, species composition needs to be considered further as a standard interval to meet the criteria.

Small-scale environmental variations drive vegetation structure and diversity in Amazon riverine forests

Riverine forests in the Brazilian Amazon provide a series of ecosystem services and harbor one of the world's most diverse floras, but the main drivers of variation in small spatial scales in such tropical forests are not well understood. Our aim was to evaluate tree community structure and diversity patterns in the Teles Pires River to identify the main drivers of small-scale variations in the area. We sampled 4,180 individuals from 49 botanical families, 164 genera and 347 species by using six transects of five pairs of 50 m x 40 m permanent plots totalizing 60 permanent plots in a 12-hectare area. We utilized generalized linear mixed models to assess whether the effects of the environmental variables in the vegetation structural parameters. Altitude and groundwater depth were the main drivers influencing species diversity patterns. The trees had similar total biomass in areas within the same distance from the river margins. There was a lower species richness and tree density closer to the river margins. Otherwise, we found larger trees (higher individual biomass) closer to the river margins when compared to areas distant from the margins. Our results indicate a high species turnover from the river margins to the forest interior, which highlights the importance of assessing local effects on biological communities in tropical forests, especially in the Brazilian Amazon where several high-impact dams are planned.

Meta-analysis of leaf area index, canopy height and root depth of three bioenergy crops and their effects on land surface modeling

The expected large-scale expansion of biofuel production in climate change mitigation scenarios calls for improvements in the representation of bioenergy crops in land surface models. Leaf area index (LAI), canopy height (CH) and root depth (RD) are key parameters that regulate exchanges of heat and moisture between land and atmosphere. This study performs a meta-analysis combining unique data from 34 observational studies and 14 countries to estimate monthly variability in LAI, CH, and RD of three main bioenergy crops: miscanthus (MSC), switchgrass (SWG), and reed canary grass (RCG). Using the Community Land Model v.5.0 and the results from the meta-analysis, we also tested the effects that variability in parameterization of LAI and CH have on key components of the surface energy budget, relative to prescribed values. Results from the meta-analysis show a strong seasonality of LAI and CH, with mean (± one standard error) LAI values at the peak summer month of 6.05 ± 0.84, 5.56 ± 0.75, and 5.39 ± 1.15 m2/m2, and maximum CH of 246 ± 23, 147 ± 16, and 156 ± 10 cm, for MSC, SWG, and RCG, respectively. These values are typically larger than the default parameterizations in CLM. Information on RD was limited and average values are 172 ± 56, 165 ±46, and 193 ± 11 cm, for MSC, SWG, and RCG, respectively. The seasonal cycles of latent heat, sensible heat and surface albedo are primarily affected by the range of LAI values, and less sensitive to variability in CH. Relative to the default values, higher LAI values increase latent heat and decrease sensible heat, with the highest absolute changes in summer. They also decrease surface albedo in winter months due to a larger snow masking effect. Our results offer a basis to compare experimental work and modelling studies, improve parameterization in land surface models, and identify the importance of vegetation structure parameters to evaluate key climate processes in response to bioenergy crops.

Vegetation structure parameters determine high burn severity likelihood in different ecosystem types: A case study in a burned Mediterranean landscape

The design and implementation of pre-fire management strategies in heterogeneous landscapes requires the identification of the ecological conditions contributing to the most adverse effects of wildfires. This study evaluates which features of pre-fire vegetation structure, estimated through broadband land surface albedo and Light Detection and Ranging (LiDAR) data fusion, promote high wildfire damage across several fire-prone ecosystems dominated by either shrub (gorse, heath and broom) or tree species (Pyrenean oak and Scots pine). Topography features were also considered since they can assist in the identification of priority areas where vegetation structure needs to be managed. The case study was conducted within the scar of a mixed-severity wildfire that occurred in the Western Mediterranean Basin. Burn severity was estimated using the differenced Normalized Burn Ratio index computed from Sentinel-2 multispectral instrument (MSI) Level 2 A at 10 m of spatial resolution and validated in the field using the Composite Burn Index (CBI). Ordinal regression models were implemented to evaluate high burn severity outcome based on three groups of predictors: topography, pre-fire broadband land surface albedo computed from Sentinel-2 and pre-fire LiDAR metrics. Models were validated both by 10-fold cross-validation and external validation. High burn severity was largely ecosystem-dependent. In oak and pine forest ecosystems, severe damage was promoted by a high canopy volume (model accuracy = 79%) and a low canopy base height (accuracy = 82%), respectively. Land surface albedo, which is directly related to aboveground biomass and vegetation cover, outperformed LiDAR metrics to predict high burn severity in ecosystems with sparse vegetation. This is the case of gorse and broom shrub ecosystems (accuracy of 80% and 77%, respectively). The effect of topography was overwhelmed by that of the vegetation structure portion of the fire triangle behavior, except for heathlands, in which warm and steep slopes played a key role in high burn severity outcome together with horizontal and vertical fuel continuity (accuracy = 71%). The findings of this study support the fusion of LiDAR and satellite albedo data to assist forest managers in the development of ecosystem-specific management actions aimed at reducing wildfire damage and promote ecosystem resilience.