Variation In Aboveground Biomass Research Articles

A Separable Bootstrap Variance Estimation Algorithm for Hierarchical Model‐Based Inference of Forest Aboveground Biomass Using Data From NASA's GEDI and Landsat Missions

ABSTRACTThe hierarchical model‐based (HMB) statistical method is currently applied in connection with NASA's Global Ecosystem Dynamics Investigation (GEDI) mission for assessing forest aboveground biomass (AGB) in areas lacking a sufficiently large number of GEDI footprints for employing hybrid inference. This study focuses on variance estimation using a bootstrap procedure that separates the computations into parts, thus considerably reducing the computational time required and making bootstrapping a viable option in this context. The procedure we propose uses a theoretical decomposition of the HMB variance into two parts. Through this decomposition, each variance component can be estimated separately and simultaneously. For demonstrating the proposed procedure, we applied a square‐root‐transformed ordinary least squares (OLS) model, and parametric bootstrapping, in the first modeling step of HMB. In the second step, we applied a random forest model and pairwise bootstrapping. Monte Carlo simulations showed that the proposed variance estimator is approximately unbiased. The study was performed on an artificial copula‐generated population that mimics forest conditions in Oregon, USA, using a dataset comprising AGB, GEDI, and Landsat variables.

Estimation of above-ground biomass in dry temperate forests using Sentinel-2 data and random forest: a case study of the Swat area of Pakistan

Accurate mapping of above-ground biomass (AGB) is essential for carbon stock quantification and climate change impact assessment, particularly in mountainous areas. This study applies a random forest (RF) regression model to predict the spatial distribution of AGB in Usho (site A) and Utror (site B) forests located in the northern mountainous region of Pakistan. The predicted maps elucidate AGB variations across these sites, with non-forest areas excluded based on an normalized difference vegetation index (NDVI) threshold value of <0.4. Three different combinations of input datasets were used to predict the biomass, including spectral bands (SBs) only, vegetation indexes (VIs) only, and a combination of both spectral bands and vegetation indexes (SBVIs). Utilizing SBs, the biomass ranged between 150 and 286 mg/ha in site A and 99 and 376 mg/ha in site B. Meanwhile, using VIs indicated a biomass range of 163 Mg/ha–337 Mg/ha and 131–392 Mg/ha for sites A and B, respectively. The combination of spectral bands and vegetation indexes yielded AGB values of 145–290 Mg/ha in site A and 116–389 Mg/ha in site B. The northern and western regions of site A, characterized by higher altitudes and lower forest density, notably showed lower biomass values than other regions. Conversely, similar regions in site B, situated at lower latitudes, demonstrated different biomass ranges. The RF model exhibited robust accuracy, with R2 values of 0.74 and 0.83 for spectral bands and vegetation indexes, respectively. However, with a combination of both, an R2 of 0.79 was achieved. Furthermore, altitudinal gradients significantly influence the biomass distribution across both sites, with specific elevation ranges yielding optimal results. The AGB variation along the slope further corroborated these findings. In both sites, the western aspects showed the highest biomass across all combinations of input datasets. The variable importance analysis highlighted that ARVI8a, NDI45, Band12, Band11, TSAVI8, and ARVI8a are significant predictors in sites A and B. This comprehensive analysis enhances our understanding of AGB distribution in the mountainous forests of Pakistan, offering valuable insights for forest management and ecological studies.

Aboveground woody biomass estimation of young bioenergy plantations of Populus and its hybrids using mobile (backpack) LiDAR remote sensing

Woody aboveground biomass (AGB) including short-rotation Populus is used as a feedstock for renewable and carbon-neutral bioenergy. While woody AGB can be estimated with allometric equations requiring labor-intensive field data, remote sensing technologies like mobile terrestrial light detection and ranging (LiDAR) can estimate woody AGB quickly and accurately. Therefore, the goals of this study were to develop a model to predict woody AGB of 2-year-old Populus spp. from three taxa (P. deltoides, P. deltoides × P. maximowiczii and P. deltoides × P. trichocarpa) using allometric (height and diameter at breast height (DBH)) or LiDAR-derived metrics from a mobile terrestrial (backpack) system. Likewise, we sought to compare LiDAR-estimated tree height and DBH with field-measured values. We found that a taxa-specific model containing LiDAR-measured tree height, crown volume, and taxa interactions with the height of the 10th percentile, and the density of the lowest interval (density metric 0) explained 84 % of the variation in woody AGB with a root mean square error (RMSE) of 28.7 % and performed slightly better than the allometric model. The best model excluding taxa had a slightly higher RMSE but lower bias than the allometric model. LiDAR-derived tree heights were highly correlated with field-measured heights, but DBH could not be estimated accurately. Therefore, terrestrial mobile LiDAR systems can accurately estimate woody AGB and tree height of Populus in short rotation systems to aid in the fast and efficient quantification of woody bioenergy production and renewable energy resources.

Relationship between Above-ground Biomass and Different Vegetation Indices of Tea Plantation of Alipurduar District, West Bengal, India

This study investigates the relationship between above-ground biomass (AGB) and various vegetation indices in the tea plantations of Alipurduar District, West Bengal, India. The research was conducted in three major tea estates: Kumargram, Sankos and Newlands, using stratified random sampling across 36 plots. Field measurements of trees, shrubs and herbs were taken and AGB was estimated using allometric equations. Sentinel-2 satellite data was utilized to derive vegetation indices such as NDVI, GNDVI, SAVI, MSAVI, EVI-1, EVI-2, NDVIRE, RDVI, DVI, OSAVI and ARVI. The study found significant variation in AGB, ranging from 31.40 Mg ha-1 to 68.84 Mg ha-1, with an average of 47.22 Mg ha-1. Strong positive correlations were observed between AGB and indices like GNDVI (r=0.96) and EVI-2 (r=0.96), indicating their effectiveness in biomass prediction. The integration of remote sensing technologies enhances the scalability and precision of biomass estimation, providing valuable insights into the carbon storage potential and ecological health of tea plantations. These findings have implications for sustainable management and climate change mitigation in agroforestry systems.

Integration of machine learning and remote sensing for above ground biomass estimation through Landsat-9 and field data in temperate forests of the Himalayan region

Accurately estimating aboveground biomass (AGB) in forest ecosystems facilitates efficient resource management, carbon accounting, and conservation efforts. This study examines the relationship between predictors from Landsat-9 remote sensing data and several topographical features. While Landsat-9 provides reliable data crucial for long-term monitoring, it is part of a broader suite of available remote sensing technologies. We employ machine learning algorithms such as Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), and Random Forest (RF), alongside linear regression techniques like Multiple Linear Regression (MLR). The primary objectives of this study encompass two key aspects. Firstly, the research methodically selects optimal predictor combinations from four distinct variable groups: Landsat-9 (L1) data, a fusion of Landsat-9 data and Vegetation-based indices (L2), and the integration of Landsat-9 data with the Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM) (L3) and the combination of best predictors (L4) derived from L1, L2, and L3. Secondly, the research systematically assesses the effectiveness of different algorithms to identify the most precise method for establishing any potential relationship between field-measured AGB and predictor variables. Our study revealed that the Random Forest (RF) model was the most efficient method utilizing Landsat-9 OLI and SRTM DEM (L3) predictors, achieving remarkable accuracy. This conclusion was reached by assessing its outstanding performance when compared to an independent validation dataset. The RF model exhibited remarkable accuracy, presenting relative mean absolute error (RMAE), relative root mean square error (RRMSE), and R2 values of 14.33%, 22.23%, and 0.81, respectively. The XGBoost model is the subsequent choice with RMAE, RRMSE, and R2 values of 15.54%, 23.85%, and 0.77, respectively. The study further highlights the significance of specific spectral bands, notably B4 and B5 from Landsat 9 OLI data, in capturing spatial AGB distribution patterns. Integration of vegetation-based indices, including TNDVI, NDVI, RVI, and GNDVI, further refines AGB mapping precision. Elevation, slope, and the Topographic Wetness Index (TWI) are crucial proxies for representing biophysical and biological mechanisms impacting AGB. Through the utilization of openly accessible fine-resolution data and employing the RF algorithm, the research demonstrated promising outcomes in the identification of optimal predictor-algorithm combinations for forest AGB mapping. This comprehensive approach offers a valuable avenue for informed decision-making in forest management, carbon assessment, and ecological monitoring initiatives.

Differences of soil carbon pools and crop growth across different typical agricultural fields in China: The role of geochemistry and climate change

Carbon storage and the aboveground biomass of farmland provide practical significance for understanding global changes and ensuring food production and quality. Based on soil carbon storage, aboveground biomass, climate, geochemistry, and other data from 19 farmland ecological stations in China, we analysed the distribution characteristics of farmland carbon storage in topsoil and aboveground biomass. We notably revealed the response direction and degree of climate and geochemical factors to farmland carbon storage in topsoil and aboveground biomass. The results indicated that the average carbon stocks of farmland in different regions ranged from 0.28 to 7.91 kg m−2, the average fresh weight of the aboveground biomass (FAB) ranged from 1370.64 to 5997.28 g m−2, and the average dry weight of the aboveground biomass (DAB) ranged from 119.95 to 852.35 g m−2. The least angle regression (LARS) and the best subsection selection regression (BSS) showed that evapotranspiration and extreme low temperatures were significant climatic factors affecting carbon sequestration and aboveground biomass on long-time scales. The linear mixed-effects model (LMM) further showed that AN and AP had significant long-term effects on carbon sequestration and aboveground biomass (p < 0.05), with AN having the highest contribution to SOC%, FAB, and DAB. The structural equation model (SEM) showed that carbon sequestration and aboveground biomass in agricultural fields were significantly positively correlated (p < 0.05). Moreover, the climate had a less direct contribution to carbon sequestration and above-ground biomass compared to geochemistry (PCc < 0.1<PCG), and its effect was more indirect. When the geochemical variables were removed, the correlation between climate and carbon and aboveground biomass variables decreased significantly. We conclude that it is possible that extremes climate and geochemistry control carbon sequestration and above-ground biomass in long-term cropland through interactions in the context of global change, which provides new insights into the evolution of soil organic carbon in long-term cropland and the scientific formulation of policies to increase food production and preserve quality.

Temporal Variations in Aboveground Biomass, Nutrient Content, and Ecological Stoichiometry in Young and Middle-Aged Stands of Chinese Fir Forests.

Understanding the ecological dynamics of forest ecosystems, particularly the influence of forest age structure on soil carbon (C), nitrogen (N), and phosphorus (P) content, is crucial for effective forest management and conservation. This study aimed to investigate the nutrient storage and ecological stoichiometry across different-aged stands of Chinese fir forests. Soil samples were collected from various depths (0-15 cm, 15-30 cm, and 30-45 cm) across four age groups of Chinese fir forests (8-year-old, 12-year-old, 20-year-old, and 25-year-old) in the Forest Farm, Pingjiang County, China. Soil organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP) were measured, and their stoichiometries were calculated. The results showed that both individual tree biomass and stand biomass, along with SOC, TN, and TP content, increased with stand age, highlighting the significant importance of stand age on biomass production and nutrient accumulation in forests. Specifically, soil C and P contents significantly increased as the forest aged, while variation in N content was relatively minor. Soil C/N and C/P ratios exhibited variation corresponding to forest age, suggesting alterations in the ecological stoichiometry characteristics of the forests over time. These findings are crucial for understanding the dynamics of ecosystem functioning and nutrient cycling within Chinese fir forests and provide a solid scientific basis for the effective management and conservation of these vital forest ecosystems.

Regional differences in the impact paths of climate on aboveground biomass in alpine grasslands across the Qinghai-Tibet Plateau

Alpine grasslands on the Qinghai-Tibet Plateau (QTP) play an essential role in water conservation, biodiversity protection and climate feedback, with aboveground biomass (AGB) serving as a crucial indicator of grassland health and functionality. While previous studies have independently explored the phenological differences, cumulative effects, and spatial variability of climatic impacts on biomass/productivity in alpine grasslands, the cascading effects regarding climate and phenology on AGB still present knowledge gaps. Here, using peak AGB measurements, remote sensing and gridded climate data in the QTP alpine grasslands during 2002–2018, we systematically analyzed the impact paths of climatic variables (i.e., cumulative precipitation, CP; growing degree-days, GDD) and phenology-mediated paths (start and peak date of the growing season, SOS and POS) on AGB and their regional differences. During the preseason (pre60) or the growing season (sos-pos), climate primarily directly impacted variations in AGB across different climatic regions, although a phenology-mediated path by which climate indirectly affected AGB existed (i.e., GDDsos-pos → POS → AGB). Three general patterns were revealed: In the plateau temperate arid regions, an increase in CPpre60 significantly promoted AGB (path coefficients w = 0.61–0.71), whereas an increase in GDDpre60 inhibited AGB (w = −0.42 ~ −0.49); In the plateau sub-cold regions, increases in both CPsos-pos and GDDsos-pos significantly promoted AGB, respectively (w = 0.46–0.81 and w = 0.37–0.70); Similarly, in the plateau temperate arid or semi-arid regions, increases in CPsos-pos also significantly promoted the AGB (w = 0.56–0.73). This study highlights that the water and heat accumulation mainly exert direct impacts on alpine grassland AGB across various climatic regions and phenological stages, providing insights into the mechanism driving AGB by climate and phenology during spring and summer.

Capturing woody aboveground biomass historical change and potential under climate change using Landsat time-series for afforestation in dryland of China

Capturing long-term dynamics and the potential under climate change of woody aboveground biomass (AGB) is imperative for calculating and raising carbon sequestration of afforestation in dryland. It is always been a great challenge to accurately capture AGB dynamics of sparse woody vegetation mixed with grassland using only Landsat time-series, resulting in changing trajectory of woody AGB estimates cannot accurately reflect woody vegetation growth regularity in dryland. In this study, surface reflectance (SR) sensitive to woody AGB was firstly selected and interannual time-series of composited SR was smoothed using S-G filter for each pixel, and then optimal machine learning algorithm was selected to estimate woody AGB time-series. Pixels that have reached AGB potential were detected based on the AGB changing trajectory, and the potential was spatial-temporal extended using random forest model combining environmental variables under current climate condition and CMIP6 climate models. Results show that: 1) minimum value composite based on NIRv during Jul.-Sep. is more capable of explaining woody AGB variation in dryland (R = 0.87, p < 0.01), and Random Forest (RF) model has the best performance in estimating woody AGB (R2 = 0.75, RMSE = 4.74 t·ha−1) among sis commonly used machine learning models. 2) Annual woody AGB estimates can be perfectly fitted with a logistic growth curve (R2 = 0.97, p < 0.001) indicating explicit growth regularity of woody vegetation, which provides physiological foundation for determining woody AGB potential. 3) Woody AGB potential can be accurately simulated by RF combining environmental variables (R2 = 0.95, RMSE = 2.89 t·ha−1), and current woody AGB still has a potential of small increase, whereas the overall losses of woody AGB potential were observed in 2030, 2040 and 2050 under CMIP6 SSP-RCP scenarios.

How do short-term and long-term factors impact the aboveground biomass of grassland in Northern China?

The aboveground biomass (AGB) of grassland, a crucial indicator of productivity, is anticipated to widespread changes in key ecosystem attributes, functions and dynamics. Variations in grassland AGB have been extensively documented across various spatial and temporal scales. However, a precise method to disentangle long-term effects from short-term effects on grassland AGB and assess the attribution of explanatory factors for AGB change remains elusive. This study aimed to quantify the impact of key climatic factors, soil properties, and grazing intensity on grassland AGB changes, utilizing data spanning the 1980s and the 2000s in Northern China. The Co-regression model was explored to separate the long-term effects and short-term effects of grassland AGB, while the Generalized Linear Model (GLM) was utilized to analyze the contributions of key variables to AGB. This approach effectively avoids issues related to regression to the mean and mathematical coupling. The results revealed that the influence of climatic variables, soil texture and grazing intensity on grassland AGB changes could be decomposed into long-term, short-term and random effects. Long-term effects explained 73.6% of AGB variation, whereas short-term effect only accounted for 5.9% of AGB change. Additionally, the short-term effect was divided into direct and indirect effects, with the direct effect explaining 1.3% of AGB variation, and the indirect effect explained 4.6% of AGB dynamics. The relative importance of key variables in grassland AGB was assessed, identifying soil parameters and precipitation as the main driving factors in the study area. This study introduces a robust methodology to enhance model performance in distinguishing long-term and short-term effects on grassland AGB, contributing to the sustainable development of grassland ecology in similar regions.

UAV-based modelling of vegetation recovery under extreme habitat stresses in the water level fluctuation zone of the Three Gorges Reservoir, China

Impoundment of the Three Gorges Reservoir on the upper Yangtze River has remarkably altered hydrological regime within the dammed reaches, triggering structural and functional changes of the riparian ecosystem. Up to date, how vegetation recovers in response to compound habitat stresses in the water level fluctuation zone remains inexplicitly understood. In this study, plant above-ground biomass (AGB) in a selected water level fluctuation zone was quantified to depict its spatial and temporal pattern using unmanned aerial vehicle (UAV)-derived multispectral images and screened empirical models. The contributions of multiple habitat stressors in governing vegetation recovery dynamics along the environmental gradient were further explored. Screened random forest models indicated relatively higher accuracy in AGB estimation, with R2 being 0.68, 0.79 and 0.62 during the sprouting, growth, and mature periods, respectively. AGB displayed a significant linear increasing trend along the elevational gradient during the sprouting and early growth period, while it showed an inverted U-shaped pattern during late growth and mature period. Flooding duration, magnitude and timing were found to exert greater negative effects on plant sprouting and biomass accumulation and acted as decisive factors in governing the elevation-dependent pattern of AGB. Localized spatial variations in AGB were modulated by other stressors such as sediment burial, soil erosion, soil moisture and nutrient content. Occurrence of episodic summer floods and vegetation distribution were responsible for an inverted U-shaped pattern of AGB during the late growth and mature period. Generally, AGB reached its peak in August, thereafter an obvious decline by an unprecedent dry-hot climatic event. The water level fluctuations with cumulative flooding effects exerted substantial control on AGB temporal dynamics, while climatic condition played a secondary role. Herein, further restorative efforts need to be directed to screening suitable species, maintaining favorable soil condition, and improving vegetation pattern to balance the many trade-offs.

Global carbon balance of the forest: satellite-based L-VOD results over the last decade

Monitoring forest carbon (C) stocks is essential to better assess their role in the global carbon balance, and to better model and predict long-term trends and inter-annual variability in atmospheric CO2 concentrations. On a national scale, national forest inventories (NFIs) can provide estimates of forest carbon stocks, but these estimates are only available in certain countries, are limited by time lags due to periodic revisits, and cannot provide spatially continuous mapping of forests. In this context, remote sensing offers many advantages for monitoring above-ground biomass (AGB) on a global scale with good spatial (50–100 m) and temporal (annual) resolutions. Remote sensing has been used for several decades to monitor vegetation. However, traditional methods of monitoring AGB using optical or microwave sensors are affected by saturation effects for moderately or densely vegetated canopies, limiting their performance. Low-frequency passive microwave remote sensing is less affected by these saturation effects: saturation only occurs at AGB levels of around 400 t/ha at L-band (frequency of around 1.4 GHz). Despite its coarse spatial resolution of the order of 25 km × 25 km, this method based on the L-VOD (vegetation optical depth at L-band) index has recently established itself as an essential approach for monitoring annual variations in forest AGB on a continental scale. Thus, L-VOD has been applied to forest monitoring in many continents and biomes: in the tropics (especially in the Amazon and Congo basins), in boreal regions (Siberia, Canada), in Europe, China, Australia, etc. However, no reference study has yet been published to analyze L-VOD in detail in terms of capabilities, validation and results. This paper fills this gap by presenting the physical principles of L-VOD calculation, analyzing the performance of L-VOD for monitoring AGB and reviewing the main applications of L-VOD for tracking the carbon balance of global vegetation over the last decade (2010–2019).

Improving potato AGB estimation to mitigate phenological stage impacts through depth features from hyperspectral data

The accurate estimation of above ground biomass (AGB) is valuable in grasping the status of potato growth and assessing yield. Spectral analysis techniques play an important role in AGB estimation by virtue of its non-destructive and rapid advantages. However, the models constructed based on spectral features at the entire growth stages due to differences in phenological stages, such as vegetation indices (VIs) and locations at fixed wavelengths, can be not applicable to single growth stages. Therefore, the main objective of this study is to improve the applicability of the AGB estimation model to mitigate the impacts of phenological stages by mining depth features from hyperspectral data. The canopy hyperspectral and AGB data of potato tuber formation, tuber growth and starch accumulation stages were measured in 2017–2019. The acquired canopy hyperspectral reflectance was smoothed by Savitzky-Golay (SG) filter, and the green edge (GE), the red edge (RE), red edge chlorophyll index (CIred-edge), optimized soil adjusted vegetation index (OSAVI), green normalized difference vegetation index (GNDVI), and normalized difference vegetation index (NDVI) were extracted. The spectral changes were analyzed at different growth stages of each year. To obtain features in response to AGB changes, a new approach is proposed to analyze the hidden depth features between sensitive wavelengths by combining a successive projections algorithm (SPA) and a long-short-term memory network (LSTM). The AGB estimation models were constructed by partial least squares regression (PLSR) based on traditional VIs + GE + RE, full spectrum, sensitive wavelengths obtained by SPA, and depth features obtained by SPA-LSTM. The results showed that GE, RE and VIs alone explained only 24 %-36 % of the variation in AGB. The accuracy of estimating AGB at sensitive wavelengths obtained after SPA was higher (R2 = 0.70, RMSE = 406 kg/hm2, and NRMSE = 27.14 %) than that of conventional VIs + GE + RE (R2 = 0.51, RMSE = 536 kg/hm2, and NRMSE = 35.81 %) and full spectrum (R2 = 0.63, RMSE = 452 kg/hm2, and NRMSE = 30.19 %) in the three-year dataset. The depth features extracted by the SPA-LSTM method proposed in this study achieved the best AGB estimation (R2 = 0.82, RMSE = 318 kg/hm2, and NRMSE = 21.25 %). The applicability of the model was validated in different years and growth stages. The R2 of each year was in the range of 0.48–0.77, the RMSE was in the range of 290–367 kg/hm2, and the NRMSE was in the range of 16.69 %-25.81 %. The R2 of each growth stage within three year was in the range of 0.48–0.78, the RMSE was in the range of 184–386 kg/hm2, and the NRMSE was in the range of 14.91 %-29.45 %. The method proposed in this study mitigates the impacts of phenological stage and improves the robustness and accuracy of the AGB estimation model, which provides remote sensing technical support for realizing potato growth monitoring and yield assessment in the field.

Natural vegetation biomass and the dimension of forest quality in tropical agricultural landscapes.

Forest cover has been a pivotal indicator of biological conservation and carrying capacity for wildlife in forest ecoregions. Such a relationship underpins policies focused on the extension of protected lands. Here, we estimate aboveground biomass (AGB) as a proxy for habitat quality in seminatural rural patches and provide a comparison with approaches that only consider forest cover. We hypothesize that recommendations for biological conservation in agricultural landscapes are substantially improved if habitat quality is also taken into account, and thus consider the possibility of forest quality being modulated by land-use amount, type, and age. We assessed AGB in a densely farmed Brazilian region using a straightforward approach designed to be affordable at large scales, focusing on two expanding and contrasting land uses: sugarcane, and eucalyptus plantations. At a detailed scale, we confirmed through field surveys and AGB estimation using 3D-multispectral imagery (i.e., AGB = 0.842 × vegetation heightNDVI+1) that AGB variation could be predicted with forest degradation classes that are visually distinguishable with high-resolution images: 9.33 t ha-1 (90% predictive intervals [PI] = [3.23, 26.97]) in regenerating fields (RF), 31.12 t ha-1 (90% PI = [10.77, 89.90]) in pioneer woods (PW), and 149.04 t ha-1 (90% PI = [51.59, 430.58]) in dense forests (DF). Applying these values to land units sampled across the study region, we found an average land use of 88.5%, together with 11.5% of land set aside for conservation, which reduced AGB to less than 4.2% of its potential (averages of 5.85 t ha-1 in sugarcane-dominated areas and 6.56 t ha-1 in eucalyptus-dominated areas, with secondary forests averaging 149.04 t ha-1). This imbalance between forest cover and AGB resulted from forest quality decay, which was similarly severe among land-use types, ages, and extensions. Therefore, the shortage of trophic resources is likely more critical to wildlife than spatial limitations in vastly deforested tropical ecoregions, where AGB and carbon sinks can be more than doubled just by restoring forests in lands currently spared by agriculture.

Hybrid inversion of radiative transfer models based on topographically corrected Landsat surface reflectance improves leaf area index and aboveground biomass retrievals of grassland on the hilly Loess Plateau

ABSTRACT Accurate monitoring of the leaf area index (LAI) and aboveground biomass (AGB) using remote sensing at a fine scale is crucial for understanding the spatial heterogeneity of vegetation structure in mountainous ecosystems. Understanding discrepancies in various retrieval strategies considering topographic effects or not is necessary to improve LAI and AGB estimations over mountainous areas. In this study, the performances of the look-up table method (LUT) using radiative transfer model (RTM), machine learning algorithms (MLAs), and hybrid RTM integrating RTM and MLAs based on Landsat surface reflectance (SR) before and after topographic correction were compared and analyzed. The results show that topographic correction improves the accuracies of retrieval methods involving RTM more significantly than the MLAs, meanwhile, it reduces the performance variability of different MLAs. Based on the topographically corrected Landsat SR, the random forest (RF) combined with RTM improves the retrieval accuracy of RTM-based LUT by 7.7% for LAI and 13.8% for AGB, and reduces the simulation error of MLA by 15.1% for LAI and 20.1% for AGB. Compared with available remote sensing products, the hybrid RTM based on Landsat SR with topographic correction has better feasibility to capture LAI and AGB variation at 30 m scale over mountainous areas.

Forestry Applications of Space-Borne LiDAR Sensors: A Worldwide Bibliometric Analysis.

The 21st century has seen the launch of new space-borne sensors based on LiDAR (light detection and ranging) technology developed in the second half of the 20th century. Nowadays, these sensors offer novel opportunities for mapping terrain and canopy heights and estimating aboveground biomass (AGB) across local to regional scales. This study aims to analyze the scientific impact of these sensors on large-scale forest mapping to retrieve 3D canopy information, monitor forest degradation, estimate AGB, and model key ecosystem variables such as primary productivity and biodiversity. A worldwide bibliometric analysis of this topic was carried out based on up to 412 publications indexed in the Scopus database during the period 2004-2022. The results showed that the number of published documents increased exponentially in the last five years, coinciding with the commissioning of two new LiDAR space missions: Ice, Cloud, and Land Elevation Satellite (ICESat-2) and Global Ecosystem Dynamics Investigation (GEDI). These missions have been providing data since 2018 and 2019, respectively. The journal that demonstrated the highest productivity in this field was "Remote Sensing" and among the leading contributors, the top five countries in terms of publications were the USA, China, the UK, France, and Germany. The upward trajectory in the number of publications categorizes this subject as a highly trending research topic, particularly in the context of improving forest resource management and participating in global climate treaty frameworks that require monitoring and reporting on forest carbon stocks. In this context, the integration of space-borne data, including imagery, SAR, and LiDAR, is anticipated to steer the trajectory of this research in the upcoming years.

Woody plant species richness and productivity relationship in a subtropical forest: The predominant role of common species.

A long-standing key issue for examining the relationships between biodiversity and ecosystem functioning (BEF), such as forest productivity, is whether ecosystem functions are influenced by the total number of species or the properties of a few key species. Compared with controlled ecosystem experiments, the BEF relationships in secondary forest remain unclear, as do the effects of common species richness and rare species richness on the variation in ecosystem functions. To address this issue, we conducted field surveys at five sampling sites (1 ha each) with subtropical secondary evergreen broad-leaved forest vegetation. We found (1) a positive correlation between species richness and standing aboveground biomass (AGB); (2) that common species were primarily responsible for the distribution patterns of species abundance and dominance; although they accounted for approximately 25% of the total species richness on average, they represented 86-91% of species abundance and 88-97% of species dominance; and (3) that common species richness could explain much more of the variation in AGB than total species richness (common species plus rare species) at both the site and plot scales. Because rare species and common species were not equivalent in their ability to predict productivity in the biodiversity-ecosystem productivity model, redundant information should be eliminated to obtain more accurate results. Our study suggested that woody plant species richness and productivity relationship in subtropical forest ecosystem can be explained and predicted by a few common species.

Functional trait diversity and aboveground biomass of herbaceous vegetation in temperate forests of Kashmir Himalaya.

Studying functional trait diversity can provide crucial clues about the adaptive survival strategies of regional plant species pool. Despite large-scale trait datasets available worldwide, the plant trait data from many biodiversity hotpot regions, like the Himalaya is still scarce. In this study, we aimed to investigate the plant functional traits and aboveground biomass of understory herbaceous vegetation in temperate forests of Overa-Aru wildlife sanctuary of Kashmir Himalaya. We also investigate how these functional traits correlate and what is the magnitude of trait-biomass relationship across the herbaceous species pool. For this, we conducted field sampling and measured leaf functional traits and aboveground biomass of 38 plant species in the study region during peak growing season (July-August) in the year 2021. The results revealed a significant interspecific trait variability among the species studied. We observed a high variability in leaf size and type spectra of the species, with nanophyll and simple leaf lamina, respectively, the most common types among the species studied. The correlation analysis revealed that plant height was positively correlated with aboveground biomass. The variation partitioning analysis revealed that the plant height explained the maximum fraction of variation in aboveground biomass, while least by specific leaf area. Overall, the findings from the present study provide useful insights in understanding trait-trait relationship and trait-environment interaction at the regional scale and can also help in recognizing adaptive functional traits of plant species that determine plant survival under the changing climate in this Himalayan region.

Spatial Distribution and Determinants of Aboveground Biomass in a Subalpine Coniferous Forest in Southwestern China

Aboveground biomass (AGB) is the most dynamic carbon pool in forest ecosystems and is sensitive to biotic and abiotic factors. Previous studies on AGB have mostly focused on tropical and temperate forests, while studies on AGB and its determinants in subalpine coniferous forests are lacking and the mechanisms are not yet clear. Here, we systematically investigated all woody plants in 630 subplots (20 m × 20 m) in the Wanglang Plot (25.2 ha) to explore the spatial distribution of AGB and the effects of topography, soil, and stand structure on AGB. The results showed that AGB varied remarkably among different subplots with an average of 184.42 Mg/ha. AGB increased significantly with aspect, soil organic matter, maximum DBH, and important value of spruce–fir, while it decreased significantly with slope, total phosphorus, and stem density. Stand structure exerted greater influences than topography and soil factors, and especially maximum DBH determines the variation of AGB. Our results are of great significance to accurately estimate and predict the productivity of this forest type, and can provide insights into the diversity maintenance of subalpine coniferous forests as well as the conservation and management of forest ecosystems.

Nitrogen availability affects the responses of marsh grass and sedge plants (Phragmites australis and Bolboschoenus planiculmis) to flooding time

Flooding time and external nitrogen (N) input have been projected to be the main threats to marsh ecosystems in the scenario of more intense flooding events and N deposition. How flooding and N addition experienced at different growth stages interact in determining phenotypic change remains scarce. We established a controlled experiment (3 flooding time treatments x 5 N addition levels) using two herbaceous marsh species (Phragmites australis and Bolboschoenus planiculmis) to assess the responses of six key traits to environmental changes and the indication of plant performance. Early flooding reduced plant height and aboveground biomass of P. australis and below/aboveground biomass ratio of B. planiculmis and increased below/aboveground biomass ratio of P. australis and root biomass of B. planiculmis, whereas late flooding reduced root biomass of P. australis and ramet number and aboveground biomass of B. planiculmis. The combination of flooding and high N (16 and 32 g N m−2) exerted negative effects on ramet number of both plant species. The interaction of early flooding and low-medium N (8 and 16 g N m−2) inhibited clonal/belowground biomass ratio of both plant species. The combination of early flooding and low N (0, 4 and 8 g N m−2) promoted root biomass and below/aboveground biomass ratio of P. australis. Ramet number, plant height, and root biomass explained 80–90 % of aboveground biomass variation of both plant species, and the contribution of ramet number was greater than that of the other two traits. These results highlight that the influence of flooding time and external N input on the performance of marsh plants depends on species identity. Meanwhile, the ramet number-plant height-root biomass (RHR) strategy is supposed to be the adaptation strategy of wetland clonal plants to environmental changes, and clonal reproductive traits should be incorporated into vegetation dynamics models for marsh plants.