Climate change determines the sign of productivity trends in US forests.

Forests are integral to the global land carbon sink, which has sequestered ~30% of anthropogenic carbon emissions over recent decades. The persistence of this sink depends on the balance of positive drivers that increase ecosystem carbon storage-e.g., CO2 fertilization-and negative drivers that decrease it-e.g., intensifying disturbances. The net response of forest productivity to these drivers is uncertain due to the challenge of separating their effects from background disturbance-regrowth dynamics. We fit non-linear models to US forest inventory data (113,806 plot remeasurements in non-plantation forests from ~1999 to 2020) to quantify productivity trends while accounting for stand age, tree mortality, and harvest. Productivity trends were generally positive in the eastern United States, where climate change has been mild, and negative in the western United States, where climate change has been more severe. Productivity declines in the western United States cannot be explained by increased mortality or harvest; these declines likely reflect adverse climate-change impacts on tree growth. In the eastern United States, where data were available to partition biomass change into age-dependent and age-independent components, forest maturation and increasing productivity (likely due, at least in part, to CO2 fertilization) contributed roughly equally to biomass carbon sinks. Thus, adverse effects of climate change appear to overwhelm any positive drivers in the water-limited forests of the western United States, whereas forest maturation and positive responses to age-independent drivers contribute to eastern US carbon sinks. The future land carbon balance of forests will likely depend on the geographic extent of drought and heat stress.

Water addition but not reduction alters plant biomass-diversity relationship.

The relationship between plant aboveground biomass and diversity typically follows a unimodal pattern, showing a positive correlation in resource-poor habitats and a negative correlation in resource-rich environments. Precipitation is a crucial resource for both plant biomass and diversity in terrestrial ecosystems. However, the impact of precipitation changes on the relationship between plant biomass and diversity remains unclear. We conduct a water addition field experiment in a semiarid grassland and identify a unimodal relationship between plant biomass and species richness under ambient conditions. Water addition delays the declining phase of this unimodal curve and shift it upward compared to ambient conditions. Our meta-analysis of water addition experiments conducted across major biomes worldwide (grassland, shrubland, desert, and forest) supports this finding, while water reduction does not alter the biomass-diversity relationship. Water addition increases biomass in all climate but only increases species richness in arid and semiarid climate. Similarly, water reduction decreases biomass in all climate but only reduces species richness in arid and semiarid climate. Species richness in dry subhumid and humid climate does not change significantly. Furthermore, our field experiment shows that water addition increases plant diversity while decreasing soil inorganic nitrogen levels. The increase in one resource, such as water, leads to the scarcity of another, such as nutrient, thus postponing the declining phase of the plant biomass-diversity relationship typically observed in resource-rich habitats. Our research contributes to predicting the plant biomass-diversity relationship under changing precipitation conditions and highlights the complex interplay between water availability, nutrient level, and plant diversity.

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

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.

Estimating high-density aboveground biomass within a complex tropical grassland using Worldview-3 imagery.

A large percentage of native grassland ecosystems have been severely degraded as a result of urbanization and intensive commercial agriculture. Extensive nitrogen-based fertilization regimes are widely used to rehabilitate and boost productivity in these grasslands. As a result, modern management frameworks rely heavily on detailed and accurate information on vegetation condition to monitor the success of these interventions. However, in high-density environments, biomass signal saturation has hampered detailed monitoring of rangeland condition. This issue stems from traditional broad-band vegetation indices (such as NDVI) responding to high levels of photosynthetically active radiation (PAR) absorption by leaf chlorophyll, which affects leaf area index (LAI) sensitivity within densely vegetative regions. Whilst alternate hyperspectral solutions may alleviate the problem to a certain degree, they are often too costly and not readily available within developing regions. To this end, this study evaluated the use of high-resolution Worldview-3 imagery in combination with modified NDVI indices and image manipulation techniques in reducing the effects of biomass signal saturation within a complex tropical grassland. Using the random forest algorithm, several modified NDVI-type indices were developed from all potential dual-band combinations of the Worldview-3 image. Thereafter, linear contrast stretching and histogram equalization were implemented in conjunction with Singular Value Decomposition (SVD) to improve high-density biomass estimation. Results demonstrated that both contrast enhancement techniques, when combined with SVD, improved high-density biomass estimation. However, linear contrast stretching, SVD, and modified NDVI indices developed from the red (630-690 nm), green (510-580 nm), and near-infrared 1 (770-895 nm) bands were found to produce the best biomass predictive model (R2 = 0.71, RMSE = 0.40 kg/m2). The results generated from this research offer a means to alleviate the biomass saturation problem. This framework provides a platform to assist rangeland managers in regionally assessing changes in vegetation condition within high-density grasslands.

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.

The Estimation of Grassland Aboveground Biomass and Analysis of Its Response to Climatic Factors Using a Random Forest Algorithm in Xinjiang, China.

Aboveground biomass (AGB) is a key indicator of the physiological status and productivity of grasslands, and its accurate estimation is essential for understanding regional carbon cycles. In this study, we developed a suitable AGB model for grasslands in Xinjiang based on the random forest algorithm, using AGB observation data, remote sensing vegetation indices, and meteorological data. We estimated the grassland AGB from 2000 to 2022, analyzed its spatiotemporal changes, and explored its response to climatic factors. The results showed that (1) the model was reliable (R2 = 0.55, RMSE = 64.33 g·m-2) and accurately estimated the AGB of grassland in Xinjiang; (2) the spatial distribution of grassland AGB in Xinjiang showed high levels in the northwest and low values in the southeast. AGB showed a growing trend in most areas, with a share of 61.19%. Among these areas, lowland meadows showed the fastest growth, with an average annual increment of 0.65 g·m-2·a-1; and (3) Xinjiang's climate exhibited characteristics of warm humidification, and grassland AGB showed a higher correlation with precipitation than temperature. Developing remote sensing models based on random forest algorithms proves an effective approach for estimating AGB, providing fundamental data for maintaining the balance between grass and livestock and for the sustainable use and conservation of grassland resources in Xinjiang, China.

Productivity and Species Diversity of Plant Communities Are Higher inside than outside the West Ordos National Nature Reserve, Northern China.

Nature reserves play an important role in the protection of biological habitats and the maintenance of biodiversity, but the performance and mechanisms of desert steppe nature reserves in improving plant community productivity, biodiversity and soil nutrient content are still largely unknown. To investigate the conservation effects of desert steppe nature reserve management on plant productivity and biodiversity, we compared the plant biomass, diversity and soil nutrient content inside and outside the West Ordos National Nature Reserve through sample survey, biomass determination, diversity index calculation and soil nutrient content determination. We found the following: (1) The aboveground biomass and belowground biomass of plant communities in the nature reserve were significantly larger than those outside the reserve; and the aboveground biomass of plant communities in shrub-steppe was significantly larger than that of herb grassland in both the nature reserve and the outside of the reserve. (2) The Margalef richness index, Shannon-Wiener index and Simpson index were significantly greater in the reserve than outside the nature reserve. In the desert steppe, the establishment of the nature reserve increased the α-diversity of the plant community. (3) The soil organic carbon (SOC) and soil total nitrogen (STN) were greater in the nature reserve than outside the reserve, and for the 10-20 cm and 20-40 cm soil layers, the SOC and STN were significantly greater in the core protected zone of the reserve than outside the reserve. The reserve significantly increased the nutrient content of the deeper soil layers. (4) The aboveground biomass of the plant community had a significant positive linear relationship with the species richness index, the Shannon index, and the Simpson index. There was a positive correlation between the diversity of the plant community and the soil nutrients. In summary, the nature reserve improved local plant productivity, biodiversity and the soil nutrient content, and the soil nutrient content in deeper soil layers may be the driving factor for the increase in productivity and biodiversity, which deepens our understanding of the conservation effectiveness of the nature reserve and its mechanisms.

Climate and soil stressed elevation patterns of plant species to determine the aboveground biomass distributions in a valley-type Savanna.

Introduction: Extreme environments such as prolonged high temperatures and droughts can cause vulnerability of vegetation ecosystems. The dry-hot valleys of Southwestern China, known for their extremely high annual temperature, lack of water, and unique non-zonal "hot island" habitat in the global temperate zone, provide exceptional sites for studying how plant adapts to the prolonged dry and hot environment. However, the specific local biotic-environment relationships in these regions remain incompletely elucidated. The study aims to evaluate how valley-type Savanna vegetation species and their communities adapt to long-term drought and high-temperature stress environments. Methods: The study investigated the changes in species diversity and communities' aboveground biomass of a valley-type Savanna vegetation along an elevation gradient of Yuanmou dry-hot valley in Jinsha River basin, southwest China. Subsequently, a general linear model was utilized to simulate the distribution pattern of species diversities and their constituent biomass along the elevation gradient. Finally, the RDA and VPH mothed were used to evaluate the impacts and contributions of environmental factors or variables on the patterns. Results and discussion: The field survey reveals an altitudinal gradient effect on the valley-type Savanna, with a dominant species of shrubs and herbs plants distribution below an elevation of 1700m, and a significant positive relationship between the SR, Shannon-Wiener, Simpson, and Pielou indices and altitudes. Relatively, the community aboveground biomass did not increase significantly with elevation, which was mainly due to a decreased biomass of herbaceous plants along the elevation. Different regulators of shrub-herbaceous plant species and their functional groups made different elevation patterns of species diversity and aboveground biomass in valley-type Savannas. Herbaceous plants are responsible for maintaining species diversity and ensuring stability in the aboveground biomass of the vegetation. However, the influence of shrubs on aboveground biomass became more pronounced as environmental conditions varied along the altitudinal gradient. Furthermore, species diversity was mainly influenced by soil and climatic environmental factors, whereas community biomass was mainly regulated by plant species or functional groups. The study demonstrates that the spatial pattern of valley-type Savanna was formed as a result of different environmental responses and the productive capacity of retained plant species or functional groups to climate-soil factors, highlighting the value of the Yuanmou dry-hot Valley as a microcosm for exploring the intricate interactions between vegetation evolution and changes in environmental factors.

Long-term species-level observations indicate the critical role of soil moisture in regulating China's grassland productivity relative to phenological and climatic factors.

As a sensitive indicator of climate change and a key variable in ecosystem surface-atmosphere interaction, vegetation phenology, and the growing season length, as well as climatic factors (i.e., temperature, precipitation, and sunshine duration) are widely recognized as key factors influencing vegetation productivity. Recent studies have highlighted the importance of soil moisture in regulating grassland productivity. However, the relative importance of phenology, climatic factors, and soil moisture to plant species-level productivity across China's grasslands remains poorly understood. Here, we use nearly four decades (1981 to 2018) of in situ species-level observations from 17 stations distributed across grasslands in China to examine the key mechanisms that control grassland productivity. The results reveal that soil moisture is the strongest determinant of the interannual variability in grassland productivity. In contrast, the spring/autumn phenology, the length of vegetation growing season, and climate factors have relatively minor impacts. Generally, annual aboveground biomass increases by 3.9 to 25.3 g∙m2 (dry weight) with a 1 % increase in growing season mean soil moisture across the stations. Specifically, the sensitivity of productivity to moisture in wetter and colder environments (e.g., alpine meadows) is significantly higher than that in drier and warmer environments (e.g., temperate desert steppes). In contrast, the sensitivity to the precipitation of the latter is greater than the former. The effect of soil moisture is the most pronounced during summer. Dominant herb productivity is more sensitive to soil moisture than the others. Moreover, multivariate regression analyses show that the primary climatic factors and their attributions to variations in soil moisture differ among the stations, indicating the interaction between climate and soil moisture is very complex. Our study highlights the interspecific difference in the soil moisture dependence of grassland productivity and provides guidance to climate change impact assessments in grassland ecosystems.

Biomass recovery of coastal young mangrove plantations in Central Thailand.

Around one-third of the world's most carbon-rich ecosystems, mangrove forests, have already been destroyed in Thailand owing to coastal development and aquaculture. Improving these degraded areas through mangrove plantations can restore various coastal ecosystem services, including CO2 absorption and protection against wave action. This study examines the biomass of three coastal mangrove plantations (Avicennia alba) of different ages in Samut Prakarn province, Central Thailand. Our aim was to understand the forest biomass recovery during the early stages of development, particularly fine root biomass expansion. In the chronosequence of the mangrove plantations, woody biomass increased by 40% over four years from 79.7 ± 11.2 Mg C ha-1 to 111.7 ± 12.3 Mg C ha-1. Fine root biomass up to a depth of 100 cm was 4.47 ± 0.33 Mg C ha-1, 4.24 ± 0.63 Mg C ha-1, and 6.92 ± 0.32 Mg C ha-1 at 10, 12, and 14 year-old sites, respectively. Remarkably, the fine root biomass of 14-year-old site was significantly higher than those of the younger sites due to increase of the biomass at 15-30 cm and 30-50 cm depths. Our findings reveal that the biomass recovery in developing mangrove plantations exhibit rapid expansion of fine roots in deeper soil layers.

Effects of nitrogen addition and Bothriochloa ischaemum and Lespedeza davurica mixture on plant chlorophyll fluorescence and community production in semi-arid grassland.

Background: Grass-legume mixture can effectively improve productivity and stimulate overyielding in artificial grasslands, but may be N-limited in semi-arid regions. This study investigated the effects of N addition on chlorophyll fluorescence and production in the grass-legume mixtures community. Methods: An N addition experiment was conducted in the Bothriochloa ischaemum and Lespedeza davurica mixture community, with seven mixture ratios (B0L10, B2L8, B4L6, B5L5, B6L4, B8L2, and B10L0) according to the sowing abundance of B.ischaemum and L.davurica and four N addition levels, N0, N25, N50, and N75 (0,25,50,75kgNhm-2 a-1), respectively. We analyzed the response of chlorophyll fluorescence parameters of the two species, the rapid light-response curves of chlorophyll fluorescence, as well as aboveground biomass (AGB) and overyielding. Results: Our results showed that the two species showed different photosynthetic strategies, with L.davurica having significantly higher initial fluorescence (Fo), effective photochemical quantum yield of PSII (ΦPSII), and coefficient of photochemical fluorescence quenching (qP) than B. ischaemum, consisting with results of rapid light-response curves. N addition and mixture ratio both had significant effects on chlorophyll fluorescence and AGB (p<0.001). The ΦPSII and qP of L.davurica were significantly lowest in B5L5 and B6L4 under N addition, and the effect of N varied with mixture ratio. The photosynthetic efficiency of B. ischaemum was higher in mixture than in monoculture (B10L0), and ΦPSII was significantly higher in N50 than in N25 and N50 at mixture communities except at B5L5. The community AGB was significantly higher in mixture communities than in two monocultures and highest at B6L4. In the same mixture ratio, the AGB was highest under the N50. The overyielding effects were significantly highest under the N75 and B6L4 treatments, mainly attributed to L.davurica. The partial least squares path models demonstrated that adding N increased soil nutrient content, and complementary utilization by B.ischaemum and L.davurica increased the photosynthetic efficiency. However, as the different photosynthetic strategies of these two species, the effect on AGB was offset, and the mixture ratio's effects were larger than N. Our results proposed the B6L4 and N50 treatments were the optimal combination, with the highest AGB and overyielding, moderate grass-legume ratio, optimal community structure, and forage values.

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 â†’ POS â†’ 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.

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.

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.

Monitoring and influencing factors of grassland livestock overload in Xinjiang from 1982 to 2020.

It is crucial to estimate the theoretical carrying capacity of grasslands in Xinjiang to attain a harmonious balance between grassland and livestock, thereby fostering sustainable development in the livestock industry. However, there has been a lack of quantitative assessments that consider long-term, multi-scale grass-livestock balance and its impacts in the region. This study utilized remote sensing and empirical models to assess the theoretical livestock carrying capacity of grasslands. The multi-scale spatiotemporal variations of the theoretical carrying capacity in Xinjiang from 1982 to 2020 were analyzed using the Sen and Mann-Kendall tests, as well as the Hurst index. The study also examined the county-level grass-livestock balance and inter-annual trends. Additionally, the study employed the geographic detector method to explore the influencing factors. The results showed that: (1) The overall theoretical livestock carrying capacity showed an upward trend from 1982 to 2020; The spatial distribution gradually decreased from north to south and from east to west. In seasonal scale from large to small is: growing season > summer > spring > autumn > winter; at the monthly scale, the strongest livestock carrying capacity is in July. The different grassland types from largest to smallest are: meadow > alpine subalpine meadow > plain steppe > desert steppe > alpine subalpine steppe. In the future, the theoretical livestock carrying capacity of grassland will decrease. (2) From 1988 to 2020, the average grass-livestock balance index in Xinjiang was 2.61%, showing an overall increase. At the county level, the number of overloaded counties showed an overall increasing trend, rising from 46 in 1988 to 58 in 2020. (3) Both single and interaction factors of geographic detectors showed that annual precipitation, altitude and soil organic matter were the main drivers of spatiotemporal dynamics of grassland load in Xinjiang. The results of this study can provide scientific guidance and decision-making basis for achieving coordinated and sustainable development of grassland resources and animal husbandry in the region.

[Climate change affects plant aboveground biomass by regulating the growth periods in alpine grasslands of the Tibetan Plateau, China]. / 气候变化通过调控青藏高原高寒草原植物生育期影响地上生物量积累.

Climate change significantly affects plant biomass and phenological occurrence time in alpine grasslands of Tibetan Plateau. The changes in phenological periods are closely related to the length of vegetative and reproductive growth periods, which may further affect aboveground biomass accumulation. In this study, based on fixed-point observations of plant biomass and phenology as well as the corresponding climatic data from 1997 to 2020 in the alpine grasslands of Tibetan Plateau, we used statistical methods such as ordinary linear regression and piecewise structural equation model to explore the characteristics of interannual climate change in the study area, the variation trends of plant biomass and phenological periods, and the correlations between biomass and phenological and climatic factors. The results showed that mean annual temperature and annual precipitation in the study area increased significantly from 1997 to 2020, suggesting a clear "warm-wet" trend. Aboveground biomass and relative biomass of Stipa sareptana var. krylovii (the dominant species) decreased significantly. However, absolute and relative biomass of subdominant species (Kobresia humilis) increased significantly, indicating that the dominance of K. humilis increased. The warm-wet climates enhanced aboveground biomass accumulation of K. humilis by extending the period of reproductive growth. Mean annual temperature and annual precipitation decreased aboveground biomass of S. sareptana by shortening the length of vegetative growth period. In a word, the warmer and wetter climate significantly affected aboveground biomass accumulation by regulating the changes in the phenological period, and the interspecific difference in their response resulted in a larger change in community composition. This study area may show a trend from alpine grassland to alpine meadow, and thus further works are urgently needed.

Extraction of vegetation disturbance range using aboveground biomass estimated from Sentinel-2 imagery in coal mining areas with high groundwater table.

Coal mining in regions characterized by high groundwater table markedly predisposes to surface subsidence and water accumulation, thereby engendering substantial harm to surface vegetation, soil, and hydrological resources. Developing effective methods to extract surface disturbance information aids in quantitatively assessing the comprehensive impacts of coal mining on land, ecology, and society. Due to the shortcomings of traditional indicators in reflecting mining disturbance, vegetation aboveground biomass (AGB) is introduced as the primary indicator for extracting the mining disturbance range. Taking the Huaibei Coal Base as an example, Sentinel-2 MSI imagery is firstly used to calculate spectral factors and vegetation indices. Multiple machine learning algorithms are coupled to perform remote sensing estimation and spatial inversion of vegetation AGB based on measured samples of vegetation AGB. Secondly, an Orientation Distance-AGB (OD-AGB) curve is constructed outward from the center of subsidence water areas (SWA), with the Boltzmann function used for curve fitting. According to the location of the inflection point of the curve, the boundary points of vegetation disturbance are identified, and then the disturbance range is divided. The results show that (1) the TV-SVM model, utilizing total variables and support vector machine, achieves the highest estimation accuracy, with σMAE and σRMSE values of 208.47 g/m2 and 290.19 g/m2, respectively, for the validation set. (2) Thirty-six effective disturbance areas, totaling 29.89 km2, are identified; the Boltzmann function provides a good fit for the OD-AGB curve, with an R2 exceeding 0.8 for typical disturbance areas. (3) Analysis of general statistical laws indicates that disturbance distance conforms to the general characteristics of normal distribution, exhibiting boundedness and directional heterogeneity. The research is expected to provide scientific guidance for hierarchical zoning management, land reclamation, and ecological restoration in coal mining areas with high groundwater table.

Stand age-driven tree size variation and stand type regulate aboveground biomass in alpine-subalpine forests, South Korea.

Alpine and subalpine forests in mountains worldwide are ecologically significant because of their unique biodiversity and increased vulnerability to climate change. This study was conducted to explore the possibilities and ways to preserve the ecological diversity of alpine-subalpine forests and their function as important carbon sinks. In this study, data from 664 plots (400 m2) were collected in the alpine-subalpine zones above 1000 m elevation in South Korea, we divided 664 plots into four stand types: conifer, conifer-dominant mixed, broadleaved-dominant mixed, and broadleaved stands. Abiotic drivers and forest successional stage-related factor including topographic, climatic drivers and stand age class were used. Biotic drivers including taxonomic, phylogenetic, functional, stand structural diversity, and community-weighted mean of functional traits were used to find independent variables controlling aboveground biomass (AGB) for each stand type. We employed multi-model averaging approach as well as piecewise structural equation modeling (pSEM) for the identification of the most influential variables affecting AGB in each stand type of alpine-subalpine forests and to quantify their interrelationships and strengths. The main results showed that tree size variation (i.e., DBH STD) induced by stand age had direct effects on AGB, with varying degrees of significance (ß) ranging from 0.146 to 0.241 across all stand types in alpine-subalpine forests. Following these results, as forest succession progresses, tree species adapted to the specific environmental conditions, such as topography and climate, become dominant by creating their own niche, which increases AGB in each stand type. Additionally, climatic and topographic conditions played an important role in controlling biotic drivers depending on the stand type. In this study, we suggest that AGB should be managed and conserved depending on forest stand types according to forest succession. Furthermore, increasing size variation among tree individuals through proper forest treatments is important for increasing AGB in alpine-subalpine forests.

Mapping and analyzing the spatiotemporal dynamics of forest aboveground biomass in the ChangZhuTan urban agglomeration using a time series of Landsat images and meteorological data from 2010 to 2020.

In the context of global warming, there is a substantial demand for accurate and cost-effective assessment and comprehensive understanding of forest above-ground biomass (AGB) dynamics. The timeliness and low cost of optical remote sensing data enable the mapping of large-scale forest AGB dynamics. However, mapping forest AGB with optical remote sensing data presents challenges primarily due to data uncertainty and the complex nature of the forest environment. Previous studies have demonstrated the potential of meteorological data in enhancing forest AGB mapping. To accurately capture the dynamics of forest AGB, we initially acquired Landsat datasets, digital elevation model (DEM), and meteorological datasets (temperature, humidity, and precipitation) from 2010 to 2020 in Changsha-Zhuzhou-Xiangtan urban agglomeration (CZT) located in Hunan Province, China. Spectral variables (SVs), including spectral bands and vegetation indices, were extracted from Landsat images, while meteorological variables (MVs) were derived from the monthly meteorological data using the Savitzky-Golay (S-G) filtering algorithm. Additionally, terrain variables (TVs) were also extracted from the DEM data. Three modelling models, multiple linear regression (MLR), K nearest neighbor (KNN) and random forest (RF), were developed for mapping the dynamics of forest AGB in CZT. The result revealed that MVs have the potential to improve forest AGB mapping. Integration of MVs into the models resulted in a significant reduction in root mean square error (RMSE) ranging from 32.85 % to 19.25 % compared to utilizing only SVs. However, minimal improvement was observed with the inclusion of TVs due to negligible topographic relief within the study area. An upward trend of forest AGB in CZT was observed during this period, which can be attributed to the effective implementation of government environmental protection policies. It is confirmed that the meteorological data has significant contribution to forest AGB mapping, thereby endorsing advancements in forest resource monitoring and management programs.