Changes In Aboveground Biomass Research Articles

Plant functional trait is a strong predictor of ecosystem productivity under altered precipitation in desert steppes

AbstractThe relationship between biodiversity and ecosystem function has always been one of the hot issues in the field of ecology. With the acceleration of global warming, the precipitation pattern has become one of the main drivers of biodiversity loss, which has a profound impact on ecosystem functional services and stability. However, the studies on the effects and mechanisms of plant community diversity and ecosystem productivity under precipitation changes in desert steppe are still unclear. According to the change rate (−41.1% to 39.2%) of precipitation in the study area in recent 50 years, five precipitation gradients (i.e., −40%, −20%, CK, +20% and +40%) were set to simulate the possible future precipitation pattern changes. Aboveground biomass increased with the increase of precipitation. Compared with CK, the aboveground biomass increased by 22.81% with +40% and decreased by 80.71% with −40%, and the negative impact of precipitation decrease on aboveground biomass was more significant. Through multiple stepwise regression analyses, species diversity, functional diversity and phylogenetic diversity were identified as the best models of aboveground biomass. The results showed that the aboveground biomass changes could be explained by 51.3%, 81.6%, 32.6% and 60% respectively. Combined with plant community diversity, the final index model was obtained through multiple stepwise regression analyses, which could explain 88.3% of changes in aboveground biomass. In this model, The average coefficient of specific leaf area and leaf thickness had a very high significance level, and these two functional traits of dominant species had a greater explanatory power for ecosystem system function. There was a nonlinear correlation between precipitation and aboveground biomass, and drought had a more significant negative effect on aboveground biomass. Compared with species diversity and phylogenetic diversity, plant functional traits can better explain ecosystem productivity. Selection effects are the main maintenance mechanism of desert steppe community productivity under the background of precipitation change.

Investigating changes of forest aboveground biomass induced by Moso bamboo expansion with terrestrial laser scanner

As a typical clonal plant, Moso bamboo expands excessively worldwide, causing changes in various aspects of the native forest ecosystem. Among these aspects, aboveground biomass (AGB) is a key indicator characterizing forest productivity and carbon sequestration. However, it is difficult to track AGB changes of a fixed plot in a relatively short period. In this paper, we utilized terrestrial laser scanner (TLS) to investigate AGB changes resulting from the intrusion of Moso bamboo using the space-for-time substitution method. Three sample plots including a China fir stand, a mixed stand and a pure Moso bamboo stand were chosen at an ecotone to represent the different stages of bamboo expansion in Hutou Village, Chongqing, China. Their point clouds were first scanned using TLS, and then segmented into individual plants through refinedly processing the stem intersections. Subsequently, tree and bamboo classification was achieved via combining the structural features, stem texture features, and point distribution features of individual plants. Finally, the compatible biomass models were employed to estimate plant AGBs and analyze the changes. As a result, the overall classification accuracy of trees and bamboos was improved to 92.67 %. The AGB per unit area initially increased and subsequently decreased at three stages of Moso bamboo expansion (5.83 kg/m2, 6.04 kg/m2 and 5.36 kg/m2), and the AGB differences among individual plants showed the similar tendency. Notably, the average AGB of individual China firs in mixed stand (78.97 kg) was higher than that in the pure stand (70.41 kg), so did the average AGB of individual Moso bamboos (21.22 kg vs 18.70 kg). These results indicated that maintaining a certain degree of tree-bamboo mixture was beneficial for improving the gross forest AGB.

Imaging Spectroscopy‐Based Estimation of Aboveground Biomass in Louisiana's Coastal Wetlands: Toward Consistent Spectroscopic Retrievals Across Atmospheric States

AbstractDeveloping accurate landscape‐scale aboveground biomass (AGB) maps is critical to understanding coastal deltaic wetland resilience, as AGB influences stability and elevation dynamics in herbaceous wetlands. Here we used AVIRIS‐NG imaging spectrometer (or “hyperspectral”) data from NASA's 2021 Delta‐X mission in coastal Louisiana to map seasonal changes in herbaceous AGB across two deltaic basins with contrasting sediment delivery and hydrologic regimes: the Atchafalaya (active) and Terrebonne (inactive). We assessed the impact of atmospheric effects on our retrievals, as high water vapor content in August 2021 caused significant noise in the 880–1,000 and 1,080–1,200 nm near‐infrared (NIR) wavelengths. We hypothesized that correcting these wavelengths with our conditional Gaussian interpolation algorithm would improve AGB estimates due to their association with plant canopy water content. We empirically assessed the performance of the corrected spectra on AGB estimates using Partial Least Squares Regression (PLSR), finding that the corrected NIR bands attained high variable importance and reduced estimation errors. Our Random Forest regression approach based on the corrected spectra attained equivalent error metrics via leave‐one‐out‐cross‐validation as the PLSR models (R2 = 0.43, mean absolute error = 257.3 g/m2) while greatly improving the AGB maps' visual quality, having better captured variability while reducing noise and discontinuities in AGB estimates across flightlines. The maps show differing seasonal growth, with the Atchafalaya and Terrebonne Basins' AGB increasing from means of 4.3–9.4 and 4.6–8.9 Mg/ha, respectively. We demonstrated that imaging spectroscopy can be applied to assess herbaceous biomass stocks, growth patterns, and resilience in coastal ecosystems.

Impact of functional groups on aboveground biomass in alpine grassland communities

Alpine grasslands on the Tibetan Plateau (TP) are highly vulnerable to various treatments and face significant degradation risks due to global environmental changes. However, the response of these grasslands to different external treatments remains uncertain, and the patterns behind functional group responses are unclear, impeding our ability to restore alpine grasslands under changing climate. To address this gap, we compiled a comprehensive database of 797 experimental observations of alpine grasslands in the TP, classified these communities into four common functional groups (e.g., grass and sedge), and conducted a meta-analysis to evaluate the response of aboveground biomass (AGB) to nine different treatments (e.g., grazing and nitrogen addition). Meta-regression was used to analyze the changes in AGB with treatment intensity and duration. We also used functional group asynchrony and unevenness to describe the complementary effects (CEs) and selection effects (SEs) of communities under these treatments. We found that among all the nine treatments, grazing had the biggest negative impact on the alpine grassland community by reducing AGB by 40.3%, mainly through decreasing the biomass of grass and sedge. Conversely, nitrogen and phosphorus addition had the largest positive effect, increasing AGB by 39.4% mainly by promoting the growth of grass. However, the increase in AGB was not significant in the interactive experiments involving grazing and nitrogen addition. The unevenness of the community decreased as grazing time prolongs, simplifying the structure of the community, with severe nutrient losses and weakened CE, while nitrogen addition could rapidly increase the AGB of grass and enhance the SE. CE or SE of communities can be enhanced through the restoration of specific functional groups, favoring the recovery of vegetation. Our study also revealed the potential of nitrogen compensation for repairing the damage caused by overgrazing on alpine grasslands.

Aboveground biomass in a post-mining forest succession in the Colombian Pacific

Introduction: Mining is one of the main drivers of deforestation of tropical forests. This activity affects the storage of aboveground biomass of these ecosystems; therefore, their ability to contribute to the mitigation of global climate change. Objective: To assess the influence of soil properties on the aboveground biomass storage of post-mining forests in the Colombian Pacific. Methods: Plots were established in areas post-mining and with different successional ages (12-15 years, 30-35 years, and mature forest). The aboveground biomass and physicochemical parameters of the soil were measured. Results: An aboveground biomass of 15.58 t ha-1, 35.17 t ha-1, and 178.32 t ha-1 was recorded at 12-15 years, 30-35 years, and mature forests, respectively. The species with the highest biomass content in post-mining forests were Cespedesia spathulata and Clidemia septuplinervia. The aboveground biomass was positively correlated with organic matter (OM), calcium (Ca), magnesium (Mg), CICE, total nitrogen (N), and silt. In contrast, the relationship was negative with sand, aluminum (Al), and potassium (K) content. It was evidenced that the relationship between aboveground biomass and soils differed in each successional age. When evaluating the changes of aboveground biomass and soils in the succession, it was observed that the aboveground biomass and total N increased with the recovery time. At the same time, the P and K decreased with succession. On the other hand, the contents of OM, Mg, Al, Ca, and CICE showed curvilinear tendencies since they increased in the first stages and then decreased in the advanced successional stages. Conclusions: Aboveground biomass increases with forest recovery time in the study area. This increase is influenced by the presence of two dominant species shared among the investigated ecosystems and by the soil's N, P, and K content.

A Bi-Temporal Airborne Lidar Shrub-to-Tree Aboveground Biomass Model for the Taiga of Western Canada

Monitoring aboveground biomass (AGB) is critical for carbon reporting and quantifying ecosystem change. AGB from field data can be scaled to the region using airborne lidar. However, lidar-based AGB products emphasize upland forests, which may not represent the conditions in rapidly changing peatland complexes in the southern Taiga of western Canada. In addition, to ensure that modeled AGB changes do not incorporate systematic error due to differences between older and newer lidar technologies, model transfer tests are required. The aim of this study was to develop one bi-temporal lidar-based AGB model applicable to (1) vegetation structures at varying vertical and horizontal continuity in this region and to (2) data collected with an earlier generation lidar system for which Canada-wide aerial coverage is available. Goodness-of-fit metrics show that AGB can be modeled with moderate (R2 = 48%–58% Taiga Shield, peatlands) to high accuracies (R2 = 83%–89% Taiga Plains, upland/permafrost plateau forests including ecotones) by using the point clouds average height and 90th height percentile within a weighted approach as function of modeled AGB and calibrating the earlier lidar data. These results are important for quantifying climate change effects on forest to peatland ecotones.

Remote Sensing for Restoration Change Monitoring in Tropical Peat Swamp Forests in Malaysia

Effective planning and management strategies for restoring and conserving tropical peat swamp ecosystems require accurate and timely estimates of aboveground biomass (AGB), especially when monitoring the impacts of restoration interventions. The aim of this research is to assess changes in AGB and evaluate the effectiveness of restoration efforts in the North Selangor Peat Swamp Forest (NSPSF), one of the largest remaining peat swamp forests in Peninsular Malaysia, using advanced remote sensing techniques. A Random Forest machine learning method was employed to upscale AGB estimates, derived from a ‘LiDAR AGB model’, to larger landscape-scale areas with Sentinel-2 spectral and textural variables. The time period under investigation (2015–2018) marked a concentrated phase of restoration and regeneration efforts in NSPSF. The results demonstrate an overall increase in tropical peat swamp AGB during these years, where the total amount of estimated AGB stored in NSPSF increased from 19.3 Tg in 2015 to an estimated 19.8 Tg in 2018. The research found that a tailored variable selection approach improved predictions of AGB, with optimised input variables (n = 62) and parameter adjustments producing a good plausible result (R2 = 0.80; RMSE = 55.2 Mg/ha). This paper concludes by emphasizing the importance of long-term studies (>5 years) for analyzing the success of tropical peat swamp restoration methods, with a potential for integrating remote sensing technology.

Measuring leaf and root functional traits uncovers multidimensionality of plant responses to arbuscular mycorrhizal fungi.

While many studies have measured the aboveground responses of plants to mycorrhizal fungi at a single time point, little is known about how plants respond belowground or across time to mycorrhizal symbiosis. By measuring belowground responses and growth over time in many plant species, we create a more complete picture of how mycorrhizal fungi benefit their hosts. We grew 26 prairie plant species with and without mycorrhizal fungi and measured 14 functional traits to assess above- and belowground tissue quality and quantity responses and changes in resource allocation. We used function-valued trait (FVT) modeling to characterize changes in species growth rate when colonized. While aboveground biomass responses were positive, the response of traits belowground were much more variable. Changes in aboveground biomass accounted for 60.8% of the variation in mycorrhizal responses, supporting the use of aboveground biomass response as the primary response trait. Responses belowground were not associated with aboveground responses and accounted for 18.3% of the variation. Growth responses over time were highly variable across species. Interestingly, none of the measured responses were phylogenetically conserved. Mycorrhizal fungi increase plant growth in most scenarios, but the effects of these fungi belowground and across time are more complicated. This study highlights how differences in plant allocation priorities might affect how they utilize the benefits from mycorrhizal fungi. Identifying and characterizing these differences is a key step to understanding the effects of mycorrhizal mutualisms on whole plant physiology.

Winter Wheat Aboveground-Biomass Estimation and Its Dynamic Variation during Coal Mining—Assessing by Unmanned Aerial Vehicle-Based Remote Sensing

Underground coal mining in coal-grain overlapped areas leads to land subsidence and deformation above the goaf, damaging cultivated land. Understanding the influencing process of coal mining on cultivated land and crops is important for carrying out timely land reclamation and stabilizing crop yield. Research has been carried out by using crop growth parameters to evaluate the damaging degree of cultivated land when the mining subsidence is stable, but few studies focus on the influence of land damage on crop growth when the subsidence is unstable during coal mining. Therefore, this study tracked the three growth stages of winter wheat by using UAV multispectral imagery to explore the dynamic influence of underground mining on winter wheat aboveground biomass (AGB). Firstly, a winter-wheat-AGB estimation model (R2 = 0.89, RMSE = 2.18 t/ha) was developed by using vegetation indexes (VIs), textures, and terrain data extracted from UAV imagery. Secondly, based on the winter-wheat-AGB estimation model, the winter wheat AGB was successfully estimated and mapped at different growth stages. The AGB of winter wheat in the coal mining-affected area was approximately 5.59 t/ha at the reviving stage, 8.2 t/ha at the jointing stage, and 15.6 t/ha at the flowering stage. Finally, combined with the progress of coal mining, the dynamic changing process of crops during underground mining can be inferred by analyzing the spatiotemporal variation in winter wheat AGB. Results showed that, in the dip direction, winter wheat AGB at the flowering stage was the highest at the compression zone, followed by the inner stretch zone, outer stretch zone, and neutral zone. The distance from the waterlogged area and the existence of cracks were found to be the important moderating variables affecting the crop growth status in the mining subsidence area. In the strike direction, there were significant differences in the wheat AGB-affected area as the mining proceeded. Even areas where AGB had previously significantly increased gradually transitioned to significant decreases with the end of mining. The research explores the dynamic changes in winter wheat AGB and land damage status during coal mining. It provides a rapid and non-destructive land-damage-monitoring method to protect cultivated land in mining areas.

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.

Quantifying Mangrove aboveground biomass changes: Analysis of conservation impact in blue forests projects using sentinel-2 satellite imagery

Achieving a precise assessment of the aboveground biomass (AGB) in mangroves is a critical foundation for sustainable management and carbon sequestration assessment within climate change mitigation strategies. Conserving existing mangrove forests can maintain and enhance AGB over time. This study used a support vector regression (SVR) model, and vegetation indices derived from Sentinel-2 data for a comparative analysis of mangrove AGB within conservation and restoration projects in Madagascar and Abu Dhabi Emirate. The results confirmed that the SVR model based on vegetation indices, once calibrated against the field inventory data, was effective for estimating mangrove AGB at project scale. For the Madagascar project, the model achieved an R2 of 0.81 and RMSE of 27.99 Mg ha−1 for the 2015 timepoint, and an R2 of 0.79 and RMSE of 32.82 Mg ha−1 for 2019. In Abu Dhabi, an R2 of 0.76 and RMSE of 20.51 Mg ha−1 in 2015 and R2 of 0.75 and RMSE of 22.29 Mg ha−1 in 2020 were obtained. The SVR model estimated a decline in AGB from 116.78 Mg ha−1 to 109.63 Mg ha−1 during 2015–2019 in Madagascar, representing a decrease of 7.15 Mg ha−1 (6.13%). While in Abu Dhabi, the average AGB was estimated at 37.51 Mg ha−1 in 2015 and 42.69 Mg ha−1 in 2020, indicating an increase of 5.18 Mg ha−1 (13.76%) which can be attributed to conservation efforts in this region. This study suggested that Sentinel-2 can facilitate the comparison of AGB changes before and after conservation efforts, enabling the quantification of long-term shifts in mangrove AGB.

Spatial variation in grassland vegetation and herbage mass in the Ejina oasis along the Heihe River in the southern Gobi Desert, China

AbstractOur study area, Ejina, is located in the southern Gobi Desert, Inner Mongolia, China, where the annual precipitation is &lt;50 mm and the annual potential evaporation is &gt;3,600 mm. The Heihe River flows from the Qilian Range through the study area, and the desert landscape along the river includes oases formed by forests and grasslands. We established a survey area of approximately 1.5 ha in grassland facing a seasonal swamp, a branch of the Heihe River. We posed the following questions in this desert grassland: (1) how do soil water content (SWC) and salinity change with distance from the riverbank? (2) How do vegetation, aboveground biomass, and species richness change with distance from the riverbank? (3) How much can be supplied to grazing animals, and how many animals can be grazed? Our results indicated that (1) the SWC is high within 20 m of the riverbank and low beyond 20 m, the standing biomass is positively correlated with SWC, but there was no evidence of a correlation between salinity and distance to water; the electrical conductivity was high both inland and adjacent to the riparian swamp. (2) Vegetation could be classified to six classes, following a gradient from the riverbank (swamp margin) to the dry inland area beyond 20 m from the riverbank. Species richness and aboveground biomass were highly correlated. Classification result led us to recommend that generalist species tolerant of the distinct moisture gradient in the site be used in restoration efforts. (3) Per‐hectare aboveground biomass was 478.93 dry weight (dw) kg, 321.2 ha of equivalent grassland would be required to graze 100 goats annually.

UAV and Satellite Synergies for Mapping Grassland Aboveground Biomass in Hulunbuir Meadow Steppe.

Aboveground biomass (AGB) is an important indicator of the grassland ecosystem. It can be used to evaluate the grassland productivity and carbon stock. Satellite remote sensing technology is useful for monitoring the dynamic changes in AGB across a wide range of grasslands. However, due to the scale mismatch between satellite observations and ground surveys, significant uncertainties and biases exist in mapping grassland AGB from satellite data. This is also a common problem in low- and medium-resolution satellite remote sensing modeling that has not been effectively solved. The rapid development of uncrewed aerial vehicle (UAV) technology offers a way to solve this problem. In this study, we developed a method with UAV and satellite synergies for estimating grassland AGB that filled the gap between satellite observation and ground surveys and successfully mapped the grassland AGB in the Hulunbuir meadow steppe in the northeast of Inner Mongolia, China. First, based on the UAV hyperspectral data and ground survey data, the UAV-based AGB was estimated using a combination of typical vegetation indices (VIs) and the leaf area index (LAI), a structural parameter. Then, the UAV-based AGB was aggregated as a satellite-scale sample set and used to model satellite-based AGB estimation. At the same time, spatial information was incorporated into the LAI inversion process to minimize the scale bias between UAV and satellite data. Finally, the grassland AGB of the entire experimental area was mapped and analyzed. The results show the following: (1) random forest (RF) had the best performance compared with simple regression (SR), partial least squares regression (PLSR) and back-propagation neural network (BPNN) for UAV-based AGB estimation, with an R2 of 0.80 and an RMSE of 76.03 g/m2. (2) Grassland AGB estimation through introducing LAI achieved higher accuracy. For UAV-based AGB estimation, the R2 was improved by an average of 10% and the RMSE was reduced by an average of 9%. For satellite-based AGB estimation, the R2 was increased from 0.70 to 0.75 and the RMSE was decreased from 78.24 g/m2 to 72.36 g/m2. (3) Based on sample aggregated UAV-based AGB and an LAI map, the accuracy of satellite-based AGB estimation was significantly improved. The R2 was increased from 0.57 to 0.75, and the RMSE was decreased from 99.38 g/m2 to 72.36 g/m2. This suggests that UAVs can bridge the gap between satellite observations and field measurements by providing a sufficient training dataset for model development and AGB estimation from satellite data.

Polarimetric Measures in Biomass Change Prediction Using ALOS-2 PALSAR-2 Data

The use of multiple synthetic aperture radar polarizations can improve biomass estimations compared to using a single polarization. In this study, we compared predictions of aboveground biomass change from ALOS-2 PALSAR-2 backscatter using linear regression based on (1) the cross-polarization channels, (2) the co- and cross-polarizations from fully polarimetric SAR, (3) Freeman–Durden polarimetric decomposition, and (4) the polarimetric radar vegetation index (RVI). Additionally, the impact of forest structure on the sensitivity of the polarimetric backscatter to AGB and AGB change was assessed. The biomass consisted of mainly coniferous trees at the hemi-boreal test site Remningstorp, located in southern Sweden. We found some improvements in the predictions when quad-polarized data (RMSE = 79.4 tons/ha) were used instead of solely cross-polarized data (RMSE = 84.9 tons/ha). However, when using Freeman–Durden decomposition, the prediction accuracy improved further (RMSE = 69.7 tons/ha), and the highest accuracy was obtained with the radar vegetation index (RMSE = 50.4 tons/ha). The corresponding R2 values ranged from 0.45 to 0.82. The bias was less than 1 t/ha for all models. An analysis of forest variables showed that the sensitivity to AGB was reduced for high values of basal-area-weighted mean height, basal area, and stem density when predicting absolute AGB, but the best change prediction model was sensitive to changes larger than the apparent saturation point for AGB state estimates. We conclude that by using fully polarimetric SAR images, forest biomass changes can be estimated more accurately compared to using single- or dual-polarization images. The results were improved the most (in terms of RMSE and R2) by using the Freeman–Durden decomposition model or the RVI, which captured especially the large changes better.

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.

Aboveground Biomass Mapping and Analysis of Spatial Drivers in the Qinghai-Xizang Plateau Permafrost Zone: A Case Study of the Beilu River Basin.

Aboveground biomass (AGB) serves as a crucial measure of ecosystem productivity and carbon storage in alpine grasslands, playing a pivotal role in understanding the dynamics of the carbon cycle and the impacts of climate change on the Qinghai-Xizang Plateau. This study utilized Google Earth Engine to amalgamate Landsat 8 and Sentinel-2 satellite imagery and applied the Random Forest algorithm to estimate the spatial distribution of AGB in the alpine grasslands of the Beiliu River Basin in the Qinghai-Xizang Plateau permafrost zone during the 2022 growing season. Additionally, the geodetector technique was employed to identify the primary drivers of AGB distribution. The results indicated that the random forest model, which incorporated the normalized vegetation index (NDVI), the enhanced vegetation index (EVI), the soil-adjusted vegetation index (SAVI), and the normalized burn ratio index (NBR2), demonstrated robust performance in regards to AGB estimation, achieving an average coefficient of determination (R2) of 0.76 and a root mean square error (RMSE) of 70 g/m2. The average AGB for alpine meadows was determined to be 285 g/m2, while for alpine steppes, it was 204 g/m2, both surpassing the regional averages in the Qinghai-Xizang Plateau. The spatial pattern of AGB was primarily driven by grassland type and soil moisture, with q-values of 0.63 and 0.52, and the active layer thickness (ALT) also played a important role in AGB change, with a q-value of 0.38, demonstrating that the influences of ALT should not be neglected in regards to grassland change.

Dynamic monitoring of aboveground biomass in inner Mongolia grasslands over the past 23 Years using GEE and analysis of its driving forces

Aboveground biomass (AGB) in grasslands directly reflects the net primary productivity, making it a sensitive indicator of grassland resource quality and ecological degradation. Accurately estimating AGB over large regions to reveal long-term AGB evolution trends remains a formidable challenge. In this study, we divided Inner Mongolia Autonomous Region (IMAR) grasslands into three study regions based on their spatial distribution of grassland types. We combined remote sensing data with ground-based sample data collected over the past 19 years from 6114 field plots using the Google Earth Engine platform. We constructed random forest (RF) and traditional regression AGB inversion models for each region and selected the best-performing model through accuracy assessment to estimate IMAR grassland AGB for the period 2000–2022. We also examined the trends in AGB changes and identified the driving forces affecting IMAR grasslands through the application of Theil-Sen estimation, Mann-Kendall trend analysis, and the Geodetector model. The main findings are as follows: (1) Compared with the univariate parametric traditional regression model, the AGB monitoring accuracy of the multivariate non-parametric RF model in the three study regions increased by 5.94%, 5.08% and 19.14%, respectively. (2) The average AGB per unit area of IMAR grasslands from 2000 to 2022 was 731.41 kg/hm2, with alpine meadow having the highest average AGB (1271.70 kg/hm2) and temperate grassland desertification having the lowest (469.06 kg/hm2). IMAR grasslands exhibited an overall increasing trend in AGB over the past 23 years (6.01 kg/hm2•yr), with the increasing trend covering 83.52% of the grassland area and the decreasing trend covering 16.48%. (3) Spatially, IMAR grassland AGB showed a gradual decline from northeast to southwest and exhibited an increasing trend with increasing longitude (45.423 kg/hm2 per degree) and latitude (71.9 kg/hm2 per degree). (4) Meteorological factors were the most significant factors affecting IMAR grassland AGB, with precipitation (five-year average q value of 0.61) being the most prominent. In the western part of IMAR, where precipitation is consistently limited throughout the year, the primary drivers of influence were human activities, with particular emphasis on the number of livestock (with a five-year average q value of 0.44). It is evident that reducing human activity disturbance and pressure in fragile grassland areas or implementing near-natural restoration measures will be beneficial for the sustainable development of grassland ecosystems. The results of this research hold substantial reference importance for the protection and restoration of grasslands, the supervision and administration of grassland resources, as well as the development of policies related to grassland management.

Estimation of aboveground biomass of senescence grassland in China's arid region using multi-source data

Aboveground Biomass (AGB) in the grassland senescence period is a key indicator for assessing grassland fire risk and autumnal pasture carrying capacity. Despite the advancement of remote sensing in rapid monitoring of AGB on a regional scale, accurately monitoring AGB during the senescence period in vast arid areas remains a major challenge. Using remote sensing, environmental data, and 356 samples of grassland senescence period AGB data, this study utilizes the Gram-Schmidt Pan Sharpening (GS) method, multivariate selection methods, and machine learning algorithms (RF, SVM, and BP_ANN) to construct a model for AGB during senescence grassland, and applies the optimal model to analyze spatio-temporal pattern changes in AGB from 2000 to 2021 in arid regions. The results indicate that the GS method effectively enhances the correlation between measured AGB and vegetation indices, reducing model error to some extent; The accuracy of grassland AGB inversion models based on a single vegetation index is low (0.03 ≤ |R| ≤ 0.63), while the RF model constructed with multiple variables selected by the Boruta algorithm is the optimal model for estimating AGB in arid regions during the senescence period (R2 = 0.71, RMSE = 519.74 kg/ha); In the span of 22 years, the annual average AGB in the senescence period of arid regions was 1413.85 kg/ha, with regions of higher AGB primarily located in the northeast and southwest of the study area. The area experiencing an increase in AGB during the senescence period (79.97 %) was significantly larger than that with decreased AGB (20.03 %).

Assessing the impact of climate warming on tree species composition and distribution in the forest region of Northeast China.

Global climate change has markedly influenced the structure and distribution of mid-high-latitude forests. In the forest region of Northeast China, the magnitude of climate warming surpasses the global average, which presents immense challenges to the survival and habitat sustainability of dominant tree species. We predicted the potential changes in aboveground biomass, dominant tree species composition, and distribution in the forest region of Northeast China over the next century under different climatic conditions encompassing the current scenario and future scenarios (RCP2.6, RCP4.5, and RCP8.5). Forest ecosystem process model LINKAGES 3.0 was used to simulate dynamic changes in species-level aboveground biomass under four climate scenarios at the homogeneous land-type unit level. The potential spatial distribution of tree species was investigated based on three indicators: extinction, colonization, and persistence. The results showed that LINKAGES 3.0 model effectively simulated the aboveground biomass of 17 dominant tree species in the forest region of Northeast China, achieving a high accuracy with R² = 0.88. Under the current, RCP2.6, and RCP4.5 climate scenarios, the dominant tree species presented gradual increases in aboveground biomass, whereas under RCP8.5, an initial increase and subsequent decline were observed. With increasing warming magnitude, cold-temperate coniferous tree species will gradually be replaced by other temperate broad-leaved tree species. Furthermore, a large temperature increase under RCP8.5 will likely produce a significant contraction in the potential distribution range of tree species like Larch, Scotch pine, Ribbed birch, Spruce and Fir, while most temperate broad-leaved tree species and Korean pine are expected to demonstrate a northward migration. These findings provide guidance for enhancing the adaptability and resilience of forest ecosystems in middle and high latitudes and addressing the threats posed by climate warming.