Search Results (29)

Terrestrial ecosystems in Hunan Province have undergone marked yet spatially heterogeneous vegetation changes under concurrent climate change and intensifying human activities. The aim of this study is to resolve how vegetation responses vary among land-use types by quantifying kernel Normalized Difference Vegetation Index (kNDVI) dynamics during 2000–2023 using precipitation, temperature, and solar radiation, coupled with trend analysis and a partial-derivative-based attribution. Mean kNDVI increased overall at 0.0016 yr⁻¹; vegetation improved over 76.30% of the area, whereas 5.72% of the area experienced degradation. Built-up land exhibited the largest degraded fraction (35.04%). Human activities and temperature emerged as the dominant drivers of kNDVI change, contributing 62.25% and 27.92%, respectively, while precipitation (3.08%) and solar radiation (6.77%) played comparatively minor roles. Spatially, human activities primarily controlled vegetation dynamics in plains and urban clusters (~78% of the area), whereas temperature constrained vegetation in high-elevation mountain ranges. Analysis along the human footprint (HFP) gradient reveals that driver composition remains steady in resilient ecosystems (farmland and forest), despite increasing anthropogenic pressure, whereas fragile ecosystems (grassland and bareland) exhibited pronounced volatility and heightened sensitivity to environmental constraints. These findings provide a quantitative basis for developing sustainable ecological security strategies, incorporating region-specific measures such as adaptive afforestation, sustainable agricultural management, and strict ecological protection, to enhance ecosystem resilience by prioritizing the climate resilience of mountain forests and the stability of fragile grassland systems. Full article

Vegetation is a fundamental component of terrestrial ecosystems. Understanding how vegetation changes and what drives these evolutions is crucial for developing a high-quality ecological environment and addressing global climate change. Extensive evidence has shown that China has undergone substantial vegetation changes, characterized primarily by greening. To quantify vegetation dynamics in China and assess the contributions of various drivers, we explored the spatiotemporal variations in the kernel Normalized Difference Vegetation Index (kNDVI) from 2001 to 2020, and quantitatively separated the influences of climate and human factors. The kNDVI time series were generated from the MCD19A1 v061 dataset based on the Google Earth Engine (GEE) platform. We employed the Theil-Sen trend analysis, the Mann-Kendall test, and the Hurst index to analyze the historical patterns and future trajectories of kNDVI. Residual analysis was then applied to determine the relative contributions of climate change and human activities to vegetation dynamics across China. The results show that from 2001 to 2020, vegetation in China showed a fluctuating but predominantly increasing trend, with a significant annual kNDVI growth rate of 0.002. The significant greening pattern was observed in over 48% of vegetated areas, exhibiting a clear spatial gradient with lower increases in the northwest and higher amplitudes in the southeast. Moreover, more than 60% of vegetation areas are projected to experience a sustained increase in the future. Residual analysis reveals that climate change contributed 21.89% to vegetation changes, while human activities accounted for 78.11%, being the dominant drivers of vegetation variation. This finding is further supported by partial correlation analysis between kNDVI and temperature, precipitation, and the human footprint. Vegetation dynamics were found to respond more strongly to human influences than to climate drivers, underscoring the leading role of human activities. Further analysis of tree cover fraction and cropping intensity data indicates that the greening in forests and croplands is primarily attributable to large-scale afforestation efforts and improved agricultural management. Full article

Anthropogenic revegetation allows effective and timely soil development in mine restoration areas. The evaluation of soil quality is one of the most important criteria for measuring reclamation effectiveness, providing scientific reference for the subsequent management of ecological restoration projects. The aim of this research was to further investigate the influence of revegetation on mine-reclaimed soils in a semi-arid region. Thus, a coal-gangue dump within the afforestation chronosequence of 1 and 19 years in Shanxi Province, China, was selected as the study area. We assessed the physicochemical properties and nutrient stock of topsoils under four revegetation species, i.e., Pinus tabuliformis (PT), Medicago sativa (MS), Styphnolobium japonicum (SJ), and Robinia pseudoacacia ‘Idaho’ (RP). A two-way ANOVA revealed that reclamation age significantly affected SOC, TN, EC, moisture, and BD (p < 0.05), while the interaction effects of revegetation type and age were also significant for TN and moisture. In addition, SOC and TN stocks at 0–30 cm topsoil at the RP site performed the best among 19-year reclaimed sites, with an accumulation of 62.09 t ha⁻¹ and 4.23 t ha⁻¹, respectively. After one year of restoration, the MS site showed the highest level of SOC and TN accumulation, which increased by 186.8% and 88.5%, respectively, compared to bare soil in the 0–30 cm interval, but exhibited declining stocks during the 19-year restoration, possibly due to species invasion and water stress. In addition, an integrated soil quality index (ISQI) and the partial least squares structural equation model (PLS-SEM) were used to estimate comprehensive soil quality along with the interrelationship among influencing factors. The reclaimed sites with an ISQI value > 0 were 19-RP (3.906) and 19-SJ (0.165). In conclusion, the restoration effect of the PR site after 19 years of remediation was the most pronounced, with soil quality approaching that of the undisturbed site, especially in terms of soil carbon and nitrogen accumulation. These findings clearly revealed the soil dynamics after afforestation, further providing a scientific basis for choosing mining reclamation species in the semi-arid regions. Full article

Understanding the spatiotemporal dynamics of vegetation net primary productivity (NPP) and its drivers is critical to sustainable land -carbon management, carbon-neutral development, and ecological restoration in fragile karst landscapes. This study proposes a Pearson Correlation—Deep Transformer (PCADT) model that integrates attention mechanisms and geospatial covariates to enhance NPP estimation accuracy in Guangxi, China—a global karst hotspot. Leveraging multisource remote sensing data (2015–2020), PCADT achieves 10.7% higher predictive accuracy (R² = 0.83 vs. conventional models) at 500 m resolution, thereby capturing the fine-scale heterogeneity essential for sustainability planning. The results reveal a significant annual NPP increase (4.14 gC·m⁻²·a⁻¹, p < 0.05), with eastern areas exhibiting higher baseline productivity (1129 gC·m⁻² in Wuzhou) but western regions showing steeper growth rates (5.2% vs. 2.1%). Vegetation carbon sequestration capacity, validated against MOD17A3HGF data (R² = 0.998), demonstrates spatial consistency (east > west), with forest-dominated Wuzhou contributing 6.5 TgC annually. Mechanistic analyses identify precipitation as the dominant climatic driver (partial r = 0.62, p < 0.01), surpassing temperature sensitivity, while bimodal NPP-altitude peaks (300 m and 900 m) and land -use transitions (e.g., forest-to-cropland conversions) explain 18.5% of NPP variability and reduce regional carbon stocks by 4593 tC. The PCADT framework offers a scalable solution for precision carbon management by emphasizing the role of anthropogenic interventions—such as afforestation—in alleviating climatic constraints. It advocates for spatially adaptive strategies to optimize water resource utilization, enhance forest conservation, and promote sustainable land -use transitions. By identifying areas where water -scarcity relief and targeted afforestation would yield the highest carbon returns, the PCADT framework directly supports SDG 13 (Climate Action) and SDG 15 (Life on Land), providing a strategic blueprint for sustainable development in water-limited karst regions globally. Full article

Taxodium ‘Zhongshanshan’ serves as a primary afforestation species in coastal saline–alkali soils, yet its healthy growth is significantly constrained by excessive soil salinity and nutrient deficiencies. This study investigated the synergistic effects of arbuscular mycorrhizal fungi (AMF) with organic amendments (biochar/straw) on ameliorating soil amelioration and plant adaptation. Six treatments were implemented: Control (CK), Biochar (B), Straw (S), AMF (M), AMF+Biochar (M+B), and AMF+Straw (M+S), with physiological and edaphic parameters monitored over two growth cycles. The results revealed that the M+B treatment demonstrated superior performance, achieving the lowest soil pH (8.06) and electrical conductivity (0.25 mS/cm) alongside reduced Na⁺ accumulation in plant tissues (0.28–0.88 mg/g). Synergistic effects were evident in enhanced chlorophyll synthesis, soluble protein production, and antioxidant enzyme activation. Partial Least Squares Path Modeling (PLS-PM) analysis revealed that soil nitrogen availability indirectly stimulated growth through upregulation of soluble proteins (path coefficient: 0.54) and antioxidant activity (0.22), with cumulative indirect effects (0.88) outweighing direct inhibition (−0.36). These finding provide actionable insights for coastal afforestation strategies using microbial–organic material co-application. Full article

The semi-arid region of North China has undergone extensive afforestation to prevent land degradation. Although afforestation was considered an effective way to improve soil water retention, the mechanism by which it affects soil hydraulic properties remained uncertain. In this study, soil water retention curve (SWRC), soil water-stable aggregates, and other soil physicochemical properties were determined in short-term abandoned cropland (AC), shrubland (SL), and woodland (WL) that had been converted from cropland for 1, 8, and 24 years, respectively. Pearson correlation analysis and partial least-squares structural equation modeling methods were used to identify the main factors affecting soil hydraulic properties. Results showed that the SWRCs of all three land uses were well-fitted by a double-exponential model. The WL and SL land uses exhibited higher soil field capacity (0.33–0.37 cm³ cm⁻³), wilting point (0.20–0.23 cm³ cm⁻³), and available water content (0.13–0.15 cm³ cm⁻³). Surface soil exhibits a more pronounced trend in water retention capacity changes compared to subsoil under vegetation restoration. The WL and SL land uses showed more soil macroaggregates and intra-aggregate pores at surface layers, which mainly explained the variations in hydraulic properties. The main factors influencing soil hydraulic properties were soil aggregates, matrix and structural porosity, soil organic carbon (SOC), and soil bulk density (BD). Overall, afforestation can improve soil hydraulic properties and could be an effective practice for soil and water conservation in the semi-arid region of North China. Full article

Afforestation is regarded as a crucial approach to enhancing terrestrial carbon sinks. Nevertheless, in ecologically fragile regions, the impacts of afforestation on carbon in biomass and soil remain highly uncertain. This study employed field investigations to explore the effects of forestry ecological projects on carbon stocks in biomass and soil within the Qinghai–Tibet Plateau, and to deeply analyze its key influencing factors. The key findings are summarized as follows: (1) The total vegetation carbon stocks of arbor forests and shrub forests (ranging from 7.7 to 24.0 Mg/ha) are 1.3–6.8 times that of grasslands (ranging from 3.5 to 6.1 Mg/ha). Afforestation-induced changes in biomass carbon are primarily attributed to the increase in carbon storage within the arbor-shrub layer, while exhibiting negligible effects on herbaceous layer carbon. (2) The soil organic carbon (SOC) stocks (0–100 cm depth) of forestland, shrubland, and grassland are 39.6–64.5 Mg/ha, 40.7–100.2 Mg/ha, and 43.1–121.9 Mg/ha, respectively. There are no significant differences in SOC stocks among shrubland, forestland, and grassland at either the 10- or 25-year development stage. The SOC stocks of 40-year-old shrubland and forestland are 1.5 and 2.3 times that of grassland, respectively. (3) For 10-year-old and 25-year-old arbor and shrub afforestation, biomass carbon increased while SOC decreased, showing a trade-off. In the case of 40- year-old afforestation, both biomass carbon and SOC increased synergistically. (4) Results from the random forest analysis indicate that the understory herbaceous diversity in this region has a significant impact on biomass carbon sequestration, and that soil total nitrogen, ammonium nitrogen, and nitrate nitrogen determine SOC sequestration. (5) Partial least squares analysis further demonstrates that afforestation promotes the retention of SOC stocks by increasing soil nutrients (especially nitrogen and nitrogen availability). Afforestation in alpine and arid regions, especially 40-year shrub afforestation, holds great carbon sequestration potential. The supplementation of soil nitrogen and phosphorus can enhance the carbon sequestration of this system. Full article

Comprehensively clarifying the influencing factors of carbon emissions is crucial to realizing carbon emission reduction targets in China. To address this issue, this paper develops a four-level carbon emission influencing factor system from six perspectives: population, economy, energy, water resources, main pollutants, and afforestation. To analyze how these factors affect carbon emissions, we propose an improved partial least squares structural equation model (PLS-SEM) based on a random forest (RF), named RF-PLS-SEM. In addition, the entropy weight method (EWM) is employed to evaluate the low-carbon development level according to the results of the RF-PLS-SEM. This paper takes Shandong Province as an example for empirical analysis. The results demonstrate that the improved model significantly improves accuracy from 0.8141 to 0.9220. Moreover, water resources and afforestation have relatively small impacts on carbon emissions. Primary and tertiary industries are negative influencing factors that inhibit the growth of carbon emissions, whereas total energy consumption, the volume of wastewater discharged and of common industrial solid waste are positive and direct influencing factors, and population density is indirect. In particular, this paper explores the important role of fisheries in reducing carbon emissions and discusses the relationship between population aging and carbon emissions. In terms of the level of low-carbon development, the assessment system of carbon emission is constructed from four dimensions, namely, population, economy, energy, and main pollutants, showing weak, basic, and sustainable stages of low-carbon development during the 1997–2012, 2013–2020, and 2021–2022 periods, respectively. Full article

With the advancement of urbanization, land resources are becoming increasingly strained, particularly for urban greening purposes. In this context, a large number of newly cultivated lands dominated by construction waste and backfill soil are emerging in cities. Assessing the soil quality of these newly cultivated lands and achieving their rational utilization accurately and quantitatively has become an urgent issue. In this study, soil samples of five land use types, namely newly cultivated land (NCL, control), adjacent cropland (CL), arbor–shrub mixed forest (ASF), arbor forest (AF), and shrubland (SL) were selected around Beijing, China. ASF, AF, and SL are also newly cultivated lands composed of construction waste and backfill before greening. Based on principal component analysis (PCA), a total data set (TDS) and a minimum data set (MDS) were used to construct the soil quality index (SQI) model. Soil quality indicators covering the physical and chemical characteristics of the soil and their relationships with land use types were studied with the Partial Least Squares Path Model (PLS-PM). The results were summarized as follows: (1) The soil quality index under different land use types in the Beijing plain area were in the order of arbor–shrub mixed forest (ASF) > arbor forest (AF) > shrubland (SL) > cropland (CL) > newly cultivated land (NCL). (2) Soil organic carbon (SOC), soil water content (SWC), maximum water-holding capacity (MWHC), capillary water-holding capacity (CWHC), Pb, and Cd were identified as the MDS. The MDS of the soil quality assessment model showed a linear relationship with the TDS (y = 0.946x + 0.050, R² = 0.51). (3) Land use types have an indirect impact on soil quality by changing the content of Pb. The chemical indicators’ coefficient (0.602) contributed more to the SQI than did the physical indicators’ (0.259) and heavy metal elements’ (−0.234). In general, afforestation and agricultural production could improve the newly cultivated lands’ soil quality, but afforestation is much better than agricultural production. These results will help to evaluate the SQI in the Beijing plain area objectively and accurately, and they have significant implications for soil restoration and management. Full article

In the current context of carbon neutrality, afforestation is an effective means of absorbing carbon dioxide. Stock can be used not only as an economic value index of forest wood resources but also as an important index of biomass and carbon storage estimation in forest emission reduction project evaluation. In this paper, we propose a data-driven machine learning framework and method for predicting plantation stock based on airborne LiDAR + satellite remote sensing, and carried out experimental verification at the site of the National Forest emission reduction project in Southern China. We used step-up regression and random forest (RF) to screen LiDAR and Landsat 8 OLI multispectral indicators suitable for the prediction of plantation stock, and constructed a plantation stock model based on machine learning (support vector machine regression, RF regression). Our method is compared with traditional statistical methods (stepwise regression and partial least squares regression). Through the verification of 57 plantation field survey data, the accuracy of the stand estimation model constructed using the RF method is generally better (ΔR² = 0.01~0.27, ΔRMSE = 1.88~13.77 m³·hm⁻², ΔMAE = 1.17~13.57 m³·hm⁻²). The model evaluation accuracy based on machine learning is higher than that of the traditional statistical method, and the fitting R² is greater than 0.91, while the fitting R² of the traditional statistical method is 0.85. The best fitting models were all support vector regression models. The combination of UAV point clouds and satellite multi-spectral images has the best modeling effect, followed by LiDAR point clouds and Landsat 8. At present, this method is only applicable to artificial forests; further verification is needed for natural forests. In the future, the density and quality of higher clouds could be increased. The validity and accuracy of the method were further verified. This paper provides a method for predicting the accumulation of typical Chinese plantations at the forest farm scale based on the “airborne LiDAR + satellite remote sensing” data-driven machine learning modeling, which has potential application value for the current carbon neutrality goal of the southern plantation forest emission reduction project. Full article

Poplar (Populus spp.) is a valuable tree species with multiple applications in afforestation. However, its growth in saline areas, including coastal regions, is limited. This study aimed to investigate the physiological mechanisms of arbuscular mycorrhizal fungi (AMF) symbiosis with 84K (P. alba × P. tremula var. glandulosa) poplar under salt stress. We conducted pot experiments using NaCl solutions of 0 mM (control), 100 mM (moderate stress), and 200 mM (severe stress) and evaluated the colonization of AMF and various physiological parameters of plants, including photosynthesis, biomass, antioxidant enzyme activity, nutrients, and ion concentration. Partial least squares path modeling (PLS-PM) was employed to elucidate how AMF can improve salt tolerance in poplar. The results demonstrated that AMF successfully colonized the roots of plants under salt stress, effectively alleviated water loss by increasing the transpiration rate, and significantly enhanced the biomass of poplar seedlings. Mycorrhiza reduced proline and malondialdehyde accumulation while enhancing the activity of antioxidant enzymes, thus improving plasma membrane stability. Additionally, AMF mitigated Na⁺ accumulation in plants, contributing to the maintenance of a favorable ion balance. These findings highlight the effectiveness of using suitable AMF to improve conditions for economically significant tree species in salt-affected areas, thereby promoting their utilization. Full article

The growth and survival of trees in the Siberian Mountains are experiencing a strong influence on climate warming. We analyzed Siberian pine (SP, Pinus sibirica) growth within the treeline ecotone in high (>1000 m) and low (<900 m) lands. We used ground surveys, dendrochronology, and climate variable data analysis. We found a contrasting response of SP growth with increasing air temperature and moisture parameters along the elevation gradient. In the treeline ecotone and highlands, the tree’s growth has been increasing since warming onset in the 1970s, whereas in the lowlands, the initial growth increase switched to a growth drop since the beginning of the 2000s, with a consequent partial mortality of the Siberian pine forest caused by warming-driven water stress in combination with bark borers’ attacks. This mortality suggests the retraction of the Siberian pine range in the lowlands of the Siberian Mountains. The projected drought increase will likely lead to the substitution of Siberian pine with drought-tolerant species. The tree’s growth index (GI) dependence on air temperature and moisture variables includes two phases. In the first phase (since the warming onset in the 1970s), the trees’ GI was positively correlated with elevated temperature, whereas correlations with precipitation and soil moisture were negative. During the second phase (since the increase in warming in the 2000s), negative correlations between the GI and moisture variables switched to positive ones. The correlations of the GI with air temperature switched from positive to mostly insignificant. The wind’s influence on the trees’ growth changed from negative to insignificant since the 2000s within all elevation belts. Afforestation within the areas of Siberian pine mortality should not be based on the planting of Siberian pine but on drought-tolerant species such as larch (Larix sibirica) and Scots pine (Pinus sylvestris). Full article

The increased frequency of climate change-induced droughts poses a survival challenge for forest trees, particularly for the common beech (Fagus sylvatica L.). Drought conditions adversely affect water supply and nutrient uptake, yet there is limited understanding of the intricate interplay between nutrient availability and drought stress on the physiology, growth, and biomass accumulation in young trees. We aimed to address this knowledge gap by examining the effects of irrigation and fertilisation and their interaction with various parameters in common beech saplings, including foliar and root N, P, and K concentrations; height and diameter increments; and aboveground and belowground biomass production. Our findings revealed that a higher fertilisation dose increased nutrient availability, also partially mitigating immediate drought impacts on foliar N concentrations. Also, higher fertilisation supported the post-drought recovery of foliar phosphorus levels in saplings. Prolonged drought affected nitrogen and potassium foliar concentrations, illustrating the lasting physiological impact of drought on beech trees. While drought-stressed beech saplings exhibited reduced height increment and biomass production, increased nutrient availability positively impacted root collar diameters. These insights have potential implications for forest management practices, afforestation strategies, and our broader understanding of the ecological consequences of climate change on forests. Full article

The growth quality of Pinus massoniana (Lamb.) seedlings is closely related to the survival rate of afforestation. Moisture content detection is an important indicator in the cultivation of forest seedlings because it can directly reflect the adaptability and growth potential of the seedlings to the soil environment. To improve the accuracy of quantitative analysis of moisture content in P. massoniana seedlings using near-infrared spectroscopy, a total of 100 P. massoniana seedlings were collected, and their near-infrared diffuse reflectance spectra were measured in the range of 2500 to 800 nm (12,000 to 4000 cm⁻¹). An integrated learning framework was introduced, and a quantitative detection model for moisture content in P. massoniana seedlings was established by combining preprocessing and feature wavelength selection methods in chemometrics. Our results showed that the information carried by the spectra after multiple scattering correction (MSC) preprocessing had a good response to the target attribute. The stacking learning model based on the full-band spectrum had a prediction coefficient of determination R² of 0.8819, and the prediction accuracy of moisture content in P. massoniana seedlings could be significantly improved compared to the single model. After variable selection, the spectrum processed by MSC and feature selection with uninformative variable elimination (UVE) showed good prediction effects in all models. Additionally, the prediction coefficient of determination R² of the support vector regression (SVR)—adaptive boosting (AdaBoost)—partial least squares regression (PLSR) + AdaBoost model reached 0.9430. This indicates that the quantitative analysis model of moisture content in P. massoniana seedlings established through preprocessing, feature selection, and stacking learning models can achieve high accuracy in predicting moisture content in P. massoniana seedlings. This model can provide a feasible technical reference for the precision cultivation of P. massoniana seedlings. Full article

Historical maps, especially those at a small scale and rich in detail (e.g., the old “Cadastres”), represent an exceptionally important tool for understanding the recent historical evolution of landscapes. The note describes the example of the territory of Gubbio, in Umbria (Central Italy), where a map from the end of the 16th century shows a drawing of the hydrographic network partially different from the current one. A multidisciplinary study based on field surveys, observations of satellite images, archaeological discoveries, and archival research proved useful to confirm what was reported by the cartographer at the time. The possible causes that led to this variation of the surface hydrography up to its current configuration are then discussed in the light of other documentary finds from the archives, taken from the chronicles of the time, which have made it possible to identify, with sufficient approximation, the period where this change occurred. All this leads to a highlighting of a profound evolution of fluvial and slope morphogenetic processes that have affected the study area in recent centuries, in which the regulation of surface waters and afforestation, conducted during the 20th century, have played a decisive role. Full article