Surface Vegetation Research Articles

Environmental impacts of cement production in Ewekoro, Ogun State, southwestern Nigeria from 2005 to 2006 on surface water quality, vegetation, and workers' health

ABSTRACT The present study investigated the environmental impacts of cement production on surface water quality and vegetation in the vicinity of the industry. A 2-year study was conducted, covering both wet and dry seasons. Water samples were collected from the Akinbo River, where the cement industry discharges its liquid waste and effluents, and analysed for physical and chemical parameters using standard procedures. The vegetation around the factory was analysed for chlorophyll, plant density, basal areas, and heights of woody species. The study also assessed the health impacts of cement production on the factory employees and the residents living in the vicinity of the factory. The health assessment was based on hospital information obtained from two clinics in the villages near the factory. The water quality results revealed elevated concentrations of Ca and Fe. All the trees sampled around the study area had small basal areas and short heights. There was also a significant reduction in the chlorophyll contents of the vegetation. The health study showed a high incidence of upper-respiratory tract infections, cardiovascular diseases, arthritis, and dermatitis among factory workers. This study suggests a review of the existing dust-suppressing system put in place by the industry, to protect public health.

Warming reality of Kozhikode Urban Area: Uncovering the heat of built-up expansion and vegetation loss

Urban expansion often comes at the expense of natural landscapes, leading to increased temperatures and environmental degradation. This study examines the effects of urbanization on land cover changes and their implications for land surface temperature (LST) by analyzing normalized difference satellite indices in the Kozhikode Urban Area (KUA), a city where substantial urbanization has significantly impacted the natural environment. By investigating changes in vegetation and impervious surfaces from 1993 to 2022, the research highlights a troubling trend: while vegetation cover has sharply declined, impermeable surfaces like roads and buildings have surged, contributing to a dramatic rise in average temperatures. The study reveals that built-up areas significantly elevate LST, whereas vegetation and water bodies offer cooling effects. The results underscore the urgent need for integrating green infrastructure into urban planning to combat rising temperatures and enhance environmental sustainability. This research emphasizes the importance of considering smaller, vegetation-rich cities like Kozhikode in broader urban heat research, as they present unique challenges and opportunities for effective climate adaptation strategies. By highlighting these issues, this study aims to inform policymakers and urban planners about the critical role of urban greening in mitigating the effects of climate change and fostering sustainable urban environments.

Effects of groundwater level changes on soil characteristics and vegetation response in arid and semiarid coal mining areas.

Coal mining in arid and semiarid regions often leads to numerous ecological and environmental problems, such as aquifer depletion, lake shrinkage, vegetation degradation, and surface desertification. The drainage from coal mining activities is a major driving force in the evolution of the groundwater-soil-vegetation system. In order to explore the effect of groundwater level fluctuation on soil properties and the response mechanism of surface vegetation in coal mining areas, this study is based on hydrogeological and ecological vegetation investigations in the Bojianghaizi Basin, and soil and vegetation samples are collected in the areas with different groundwater levels, and soil and vegetation indexes are analyzed with the aid of methods such as numerical statistics, linear regression, and correlation analysis with the aid of the Origin software. The results show that there is a significant negative correlation between groundwater table (GWT) and soil water content (SWC), soil conductivity, soil organic matter (SOM), soil available nitrogen (SAN), and soil available potassium (SAK). Mining activities have led to the destruction of the soil structure, greatly reducing its ability to retain water and fertilizer. The contents of SWC, SOM, and SAN in the mining area are significantly reduced, which are at least 49.73%, 47.56% and 59.90% lower than those around the mining area. On the northern and southern sides of the lake, serious soil salinization exists in the lakeshore zone where the depth to the water table is <0.5m, and the water required for the growth of vegetation here mainly comes from the groundwater, so there are only a few water-loving and saline-resistant plants; when the depth to the water table is 0.5-7m, the growth of surface vegetation is influenced by the double impacts of the water table and atmospheric precipitation with a high degree of species richness; when the depth to the water table is >7m, the surface vegetation is only dependent on the limited atmospheric precipitation for water. When the depth of groundwater is >7m, the surface vegetation only relies on limited atmospheric precipitation for water, and drought-tolerant plants mainly grow in these areas. This study not only provides a scientific basis for the sustainable development and environmental protection of similar mines in the world, but also has important significance in guiding the ecological management and rational utilization of water resources in coal mine areas. What is more, This study provides valuable insights into sustainable water resource management in arid and semi-arid regions, crucial for mitigating the ecological impacts of coal mining activities.

Fire Impacts on Soil Properties and Implications for Sustainability in Rotational Shifting Cultivation: A Review

Fire, a prevalent land management tool in rotational shifting cultivation (RSC), has long been debated for its immediate disruption of surface soil, vegetation, and microbial communities. While low-intensity and short-duration slash-and-burn techniques are considered beneficial for overall soil function, the dual nature of fire’s impact warrants a comprehensive exploration. This review examines both the beneficial and detrimental effects of fire on soil properties within the context of RSC. We highlight that research on soil microbial composition, carbon, and nitrogen dynamics following fire events in RSC is gaining momentum. After fires, soil typically shows decreases in porosity, clay content, aggregation, and cation exchange capacity, while sand content, pH, available phosphorus, and organic nitrogen tend to increase. There remains ongoing debate regarding the effects on bulk density, silt content, electrical conductivity, organic carbon, total nitrogen, and exchangeable ions (K+, Ca2+, Mg2+). Certain bacterial diversity often increases, while fungal communities tend to decline during post-fire recovery, influenced by the soil chemical properties. Soil erosion is a major concern because fire-altered soil structures heighten erosion risks, underscoring the need for sustainable post-fire soil management strategies. Future research directions are proposed, including the use of advanced technologies like remote sensing, UAVs, and soil sensors to monitor fire impacts, as well as socio-economic studies to balance traditional practices with modern sustainability goals. This review aims to inform sustainable land management practices that balance agricultural productivity with ecological health in RSC systems.

Changes in Vegetation Cover and the Relationship with Surface Temperature in the Cananéia–Iguape Coastal System, São Paulo, Brazil

The objective of this article is to investigate the possible correlations between vegetation indices and surface temperature in the Cananéia–Iguape Coastal System (CICS), in São Paulo (Brazil). Vegetation index data from MODIS orbital products were used to carry out this work. The Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) were acquired from the MODIS/Aqua sensor (MYD13Q1) and the leaf area index (LAI) from the MODIS/Terra (MOD15A2H). Surface temperature data were acquired from MODIS/Aqua (MYD11A2). The data were processed using Google Earth Engine and Google Colab. The data were collected, and spatial and temporal correlations were applied. Correlations were applied in the annual and seasonal period. The annual temporal correlation between vegetation indices and surface temperature was positive, but statistically significant for the LAI, with r = 0.43 (90% significance). In the seasonal period, positive correlations occurred in JFM for all indices (95% significance). Spatially, the results of this research indicate that the largest area showed a positive correlation between VI and LST. The hottest and rainiest periods (OND and JFM) had clearer and more significant correlations. In some regions, significant and clear correlations were observed, such as in some areas in the north, south and close to the city of Iguape. This highlights the complexity of the interactions between vegetation indices and climatic attributes, and highlights the importance of considering other environmental variables and processes when interpreting changes in vegetation. However, this research has significantly progressed the field, by establishing new correlations and demonstrating the importance of considering climate variability, for a more accurate understanding of the impacts on vegetation indices.

Quantifying Sahel Runoff Sensitivity to Climate Variability, Soil Moisture and Vegetation Changes Using Analytical Methods

AbstractWhilst substantial efforts have been deployed to understand the “Sahel hydrological paradox”, most of the studies focused on small experimental watersheds around the central and western Sahel. To our knowledge, there is no study on this issue covering all the watersheds located within the Sahelian belt. The absence of relevant studies may be attributed to a sparsity of in situ data leading to a dearth of knowledge on the Sahel hydrology. To fill this knowledge gap, the present study leverages analytical methods and freely available geospatial datasets to understand the effects of climatic factors, soil moisture and vegetation cover changes on surface runoff in 45 watersheds located within the Sahelian belt over two decades (2000–2021). Analyses show increasing trends in annual precipitation and potential evapotranspiration (PET) in more than 80% of the watersheds. Surface runoff, soil moisture (SM), and vegetation cover measured using the normalised difference vegetation index (NDVI) also show increasing trends in all the watersheds. Multivariable linear regression (MLR) analyses reveal that precipitation, PET, SM, and NDVI contribute about 62% of surface runoff variance. Further analyses using MLR, and the partial least squares regression (PLSR) show that precipitation and NDVI are the main factors influencing surface runoff in the Sahel. Elasticity coefficients reveal that a 10% increase in precipitation, SM and NDVI may lead to about 22%, 26% and 45% increase in surface runoff respectively. In contrast, a 10% increase in PET may lead to a 61% decline in surface runoff in the Sahel. This is the first hydrological study covering all the watersheds located within the Sahelian belt with results showing that surface runoff is influenced by climate, SM and NDVI to varying degrees. Given the unique hydrological characteristics of the Sahel, a better understanding of the different factors influencing surface runoff may be crucial for enhancing climate adaptation and ecological restoration efforts in the region such as the Great Green Wall Initiative.

Global assessment of climatic responses to ozone–vegetation interactions

Abstract. The coupling between surface ozone (O3) and vegetation significantly influences the regional to global climate. O3 uptake by plant stomata inhibits the photosynthetic rate and stomatal conductance, impacting evapotranspiration through land surface ecosystems. Using a climate–vegetation–chemistry coupled model (the NASA GISS ModelE2 coupled with the Yale Interactive terrestrial Biosphere, or ModelE2-YIBs), we assess the global climatic responses to O3–vegetation interactions during the boreal summer of the present day (2005–2014). High O3 pollution reduces stomatal conductance, resulting in warmer and drier conditions worldwide. The most significant responses are found in the eastern US and eastern China, where the surface air temperature increases by +0.33 ± 0.87 and +0.56 ± 0.38 °C, respectively. These temperature increases are accompanied by decreased latent heat and increased sensible heat in both regions. The O3–vegetation interaction also affects atmospheric pollutants. The surface maximum daily 8 h average O3 concentrations increase by +1.46 ± 3.02 ppbv in eastern China and +1.15 ± 1.77 ppbv in the eastern US due to the O3-induced inhibition of stomatal uptake. With reduced atmospheric stability following a warmer climate, increased cloud cover but decreased relative humidity jointly reduce aerosol optical depth by −0.06 ± 0.01 (−14.67 ± 12.15 %) over eastern China. This study suggests that vegetation feedback should be considered for a more accurate assessment of climatic perturbations caused by tropospheric O3.

Greenhouse Gas Emissions from a 20-Year-Old Restored Peatland

Peatlands are ecosystems with unique hydrological and ecological properties that serve as critical carbon reservoirs, helping facilitate the reduction of greenhouse gases (GHG). In Canada, the peat industry uses vacuum harvesting to extract peat for horticultural use. This disturbs the natural ecosystem, increasing CO2 and decreasing CH4 emissions due to complete removal of surface vegetation and lowering of the water table. Following years of extraction, if these peatlands are not properly restored, GHG emissions will increase, negatively impacting the climate. The moss layer transfer technique (MLTT) is the primary method for restoring peatlands in Canada. The MLTT allows peatlands to regain their natural ecosystem function and to reaccumulate peat. This is done by revegetation of key species including sphagnum moss, and rewetting of the peatland through the blockage of drainage ditches. My research project investigates a peatland in Pointe-Lebel, Quebec restored in 2004 using MLTT following vacuum extraction. I used the closed chamber method to measure the CO2 and CH4 exchanges at the plant community scale and three repetitions of each of moss, shrubs, and cotton grass communities were followed throughout the summer. Wells at each collar provided the water table depth; a controlling variable for GHG fluxes. I conducted a vegetation survey at peak plant productivity to characterize the proportions of the plant communities and to allow chamber measurements of GHG to be scaled by the contribution of each plant community. If successful, a restored peatland will be a net sink of carbon. Preliminary data analysis at this 20-year-old restored site indicates a net CO2 sink during the summer of 2024. My data also suggests that the site is a small source of CH4. The results from my project will aid in advancing research on peatlands, stressing the importance of taking the proper steps towards restoration.

Examining the Ecosystem-Scale GHG Exchange Following the Acrotelm Harvesting Method (ACM) in Eastern Quebec

Traditional horticultural peat production involves the removal of vegetation and drainage of peatlands, resulting in significant ecological disturbances and greenhouse gas (GHG) emissions. The Acrotelm Harvesting Method (ACM) was developed as a more sustainable alternative, designed to reduce these impacts by allowing peat extraction without large-scale ecosystem alterations. However, the ACM still necessitates the passage of machinery over the peat, potentially disturbing surface vegetation and altering the carbon dynamics. My project, based at a field site near Baie-Comeau, QC, represents the first comprehensive assessment of the ACM’s impact on net ecosystem exchanges of main GHGs. I focused on evaluating the ecosystem-scale effects by analyzing data collected from paired eddy covariance towers installed at both the control (unharvested) and harvested sites. These towers provide temporally continuous, spatially integrated flux measurements. Preliminary findings indicate that the site subjected to ACM exhibits altered surface topography, leading to overall wetter conditions, compaction of hummocks, and vegetation damage. These changes have resulted in reduced carbon dioxide uptake and increased methane emissions at the harvested site. Through the analysis and interpretation of this data, my research contributes to a better understanding of the ACM’s environmental impact and its potential as a sustainable peatland management strategy. This research was conducted under the supervision of Dr. Ian Strachan in the Atmospheric Environmental Research (AER) lab.

Development of a Simple Observation System to Monitor Regional Wind Erosion

Dryland occupies about 46% of the global land surface area (except Antarctica) and is the most vulnerable area to climate change. From the conditions of vegetation and land surface wetness and blown sand phenomena, a simple observation system was developed to monitor regional wind erosion and applied to Khuld of Mongolia, which is sensitive to drought and desertification. The system was composed of instruments that observed blown sand, vegetation amount, land surface wetness, and landscape features related to regional wind erosion. Sixteen blown sand and eight sandstorm events were evaluated from 5 March to 5 June 2023 (i.e., during the Asian dust season in northeast Asia). The normalized difference vegetation index and visible images showed that the vegetation amount was considerably less, and the developed moisture index related to land surface wetness indicated dry conditions. Combining the results of blown sand, these indices, and visible images, land surface conditions during the analysis period were likely to occur with blown sand events.

Utilization of Google Earth to Identify Building Density

Land-use change is now common due to human activities, including economic, social, and cultural activities. Areas transforming for economic, social, and cultural purposes experience changes in land use, leading to a transition from green open spaces to buildings and resulting in hot daily surface temperatures due to a lack of vegetation. The purpose of this study is to examine the changes in built and non-built land in the Kartasura District. This study examines the relationship between surface temperature, vegetation, and building levels by using NDVI, NDBI, and ESG analysis, which is processed using the Google Earth Engine programming language. This study uses a qualitative descriptive method with a spatial approach, one of the approaches in the field of geography. The spatial approach in this study is more emphasized on spatial process analysis. The results indicate that there is a correlation between high surface temperature and low vegetation density, as well as high building density.

Trends in Emissions from Road Traffic in Rapidly Urbanizing Areas

The process of urbanization has facilitated the exponential growth in demand for road traffic, consequently leading to substantial emissions of CO2 and pollutants. However, with the development of urbanization and the expansion of the road network, the distribution and emission characteristics of CO2 and pollutant emissions are still unclear. In this study, a bottom-up approach was initially employed to develop high-resolution emission inventories for CO2 and pollutant emissions (NOx, CO, and HC) from primary, secondary, trunk, and tertiary roads in rapidly urbanizing regions of China based on localized emission factor data. Subsequently, the standard road length method was utilized to analyze the spatiotemporal distribution of CO2 emissions and pollutant emissions across different road networks while exploring their spatiotemporal heterogeneity. Finally, the influence of elevation and surface vegetation cover on traffic-related CO2 and pollutant emissions was taken into consideration. The results indicated that CO2, CO, HC, and NOx emissions increased significantly in 2020 compared to those in 2017 on trunk roads, and the distribution of CO2 and pollutant emissions in Fuzhou was uneven; in 2017, areas of high emissions were predominantly concentrated in the central regions with low vegetation coverage levels and low topography but expanded significantly in 2020. This study enhances our comprehension of the spatiotemporal variations in carbon and pollutant emissions resulting from regional road network expansion, offering valuable insights and case studies for regions worldwide undergoing similar infrastructure development.

Value assessment of carbon sequestration in generalized black soil distribution area of Amur River Basin, China

ABSTRACT In recent years, the understanding and resolution of imbalances in ecosystem carbon sequestration services (ECSS) have increasingly relied on robust data quantification and value-accounting mechanisms. Based on the interpretation of primary data, including surface vegetation, 30 cm soil cover, and hydrological information of the Amur River Basin, a carbon sequestration evaluation framework centered on the black soil belt of the river basin (BSBB) was constructed. The results showed that: (1) The annual average carbon sequestration of the BSBB from 2000 to 2019 is estimated to be 12.41 Gt C, with an expected economic value of approximately 607.61 billion yuan (China Yuan, CNY); (2) Daxinganling (DXAL, 3.26 Gt), Heihe (HH, 2.72 Gt) and Mudanjiang (MDJ, 1.81 Gt) were the three sub-regions with the highest annual carbon sequestration service, respectively, and the overall carbon density center has shifted from the south to the north within a decade; (3) The results from the geographic detector and partial correlation analysis showed that the forest area (q = 0.97, r = 0.4) and non-cultivated black soil area (q = 0.96, r = 0.97) were both clear driving factors and strong driving relationships. An increase in the forest area of the BSBB is conducive to upgrading the ECSS. This study helps promote the implementation of a carbon sink balance strategy on a global scale, provides theoretical support for future black soil resource management based on trade-offs and synergies, and provides a dual perspective for balancing ecological environmental protection with social and economic development.

Automatic high-detailed building reconstruction workflow for urban microscale simulations

Reconstructing urban scenarios for computational fluid dynamics simulations typically requires significant manual effort, especially when higher geometrical details are required. To address this issue, we present a workflow to automatically reconstruct buildings in three levels of detail (LoDs): LoD1.2, LoD1.3, and LoD2.2, tailored to urban microscale simulations. The workflow uses a combination of building footprints and a point cloud to segment roof planes, create partitions, optimise planes, and finally assemble roof planes into 3D building models. Reconstructed buildings are seamlessly integrated into the terrain together with different surface layers such as water, low vegetation, and paved surfaces. Apart from three general LoDs, building footprints can be simplified as a part of the 2D generalisation; additionally, smaller surfaces such as chimneys and ventilation shafts can be removed using a graph-cut optimisation. The integrated geometry validator can report on validity of building models, such as watertightness, manifoldness, or occurrences of self-intersections. In the case of invalid geometries, we can generate an approximation: geometry repair the with alpha wrapping algorithm, or reconstruction in lower LoD. We tested our implementation on two different real-world datasets — one in The Netherlands, and another one in the USA. The results showed that 95% (Dutch dataset) and 90% (US dataset) buildings were valid according to the ISO 19107 standard. Generated grids showed satisfactory quality as we observed monotonous convergence in simulations with grid convergence indices up to 3.8% for pressure and velocity variables. These results indicate that the workflow is suitable for typical urban microscale simulations.

A universal triangle method for evapotranspiration estimation with MODIS products and routine meteorological observations: Algorithm development and global validation

The surface temperature (Ts) and vegetation index (VI) triangle method is one of the most famous and widely applied methods for regional evapotranspiration (ET) estimation. However, no robust validation has been performed yet to evaluate its accuracy on a global scale, making its accuracy unclear when compared with other global ET models. The main reason behind it is that most traditional triangle models depend on the Ts-VI feature space within a certain spatial domain to parameterize their boundary conditions. The domain dependency makes it impossible to derive universal boundaries for continental or global application. Under this background, this study presented a universal triangle method for ET estimation with Moderate Resolution Imaging Spectroradiometer (MODIS) products and routine meteorological observations. The solution for boundary conditions was performed pixel by pixel without the consideration of spatial domain based configuration. Specifically, the dry boundary was solved theoretically based on the surface energy balance equation, and the wet boundary was substituted by wet-bulb temperature estimated from dew point temperature and air temperature. The validation against ground observations collected from 33 flux towers worldwide shows that this universal triangle method achieved evaporative fraction (EF) estimation with the correlation coefficient of 0.65, the mean absolute error of 0.17, the root-mean-square-error of 0.20, and the bias of 0.13. These four metrics of daily ET estimation were 0.83, 0.71 mm/d, 0.90 mm/d, and 0.41 mm/d, respectively. This analysis is the first comprehensive validation of the triangle method on a global scale. The comparison with early work suggests that our universal triangle method on a global scale has reached a comparable accuracy to traditional triangle models at regional scale. However, the universal triangle method has the capacity to monitor ET continuously pixel by pixel without domain dependency. This is just what most traditional triangle methods do not possess.

The leaf-scale mass-based photosynthetic optimization model better predicts photosynthetic acclimation than the area-based.

Leaf-scale photosynthetic optimization models can quantitatively predict photosynthetic acclimation and have become an important means of improving vegetation and land surface models. Previous models have generally been based on the optimality assumption of maximizing the net photosynthetic assimilation per unit leaf area (i.e. the area-based optimality) while overlooking other optimality assumptions such as maximizing the net photosynthetic assimilation per unit leaf dry mass (i.e. the mass-based optimality). This paper compares the predicted results of photosynthetic acclimation to different environmental conditions between the area-based optimality and the mass-based optimality models. The predictions are then verified using the observational data from the literatures. The mass-based optimality model better predicted photosynthetic acclimation to growth light intensity, air temperature and CO2 concentration, and captured more variability in photosynthetic traits than the area-based optimality models. The findings suggest that the mass-based optimality approach may be a promising strategy for improving the predictive power and accuracy of optimization models, which have been widely used in various studies related to plant carbon issues.

Hyperspectral characterization of vegetation in hemiboreal, boreal and Arctic peatlands using a geographically extensive field dataset

Northern peatlands store up to 25% of global soil organic carbon and function as important hotspots for biodiversity. However, they are facing degradation from climate change driven by human activities as well as anthropogenic land use changes, up to the point of endangering the ecosystems' functioning and the storage of soil organic carbon entailed within them. The surface vegetation of northern peatlands is an important indicator of the ecosystem's functioning and ecohydrology, highlighting the importance of its large-scale, continuous monitoring. Approaches utilizing hyperspectral data for monitoring vegetation health and species composition can also be applied to peatland vegetation. To support the development of methods for interpreting hyperspectral satellite data from peatlands, we conducted a comprehensive in situ study of hemiboreal, boreal, sub-Arctic and Arctic peatland vegetation. We measured the reflectance spectra (350–2500 nm), soil moisture, and various vegetation-related attributes from a total of 446 vegetation plots in Estonia and Finland, from a 1500 km south-north interval. We then investigated (i) the spectral variation in surface vegetation of hemiboreal, boreal, sub-Arctic and Arctic peatlands and (ii) explored its connection to plant functional types (PFTs) and soil moisture, as well as evaluated the potential of hyperspectral data in estimating PFT cover using simple vegetation indices and partial least square (PLS) regression. Our results indicate that (i) the best spectral regions to retrieve information regarding the PFT vary greatly especially between vascular plants and bryophytes, (ii) the reflectance at an individual wavelength as well as simple vegetational index can, to some extent, predict the PFT, and that (iii) the PLS regression can predict the PFT with good accuracy. Overall, our findings demonstrate the potential of using hyperspectral data in monitoring PFTs in northern peatlands. The spectral library and the ancillary data from the peatland sites collected for this study are available as open data.

Do linear clearings in boreal peatlands recover? Comparing taxonomic, phylogenetic, and functional plant diversity

Land-use changes and anthropogenic disturbances are major threats to biodiversity, affecting ecosystem function and recovery. In boreal Alberta, Canada, petroleum development has led to extensive landscape fragmentation, notably through linear clearings created for seismic exploration that remove surface vegetation and microtopography. Despite partial recovery on forested seismic lines, peatland recovery is often arrested, impacting wildlife and carbon dynamics. Restoration efforts employ silviculture techniques to create microtopography and foster tree growth, but the efficacy of restoration treatments in restoring peatland plant diversity remains uncertain. We compared taxonomic, phylogenetic, and functional diversity of understory plant communities across treated and untreated seismic lines, alongside reference sites in treed bogs and fens. Treated lines generally had a twofold increase in plant height, leaf dry matter content, and foliar nitrogen and phosphorus contents compared to reference sites. In bogs, treated lines differed from reference conditions driven by increases in herbaceous taxa, while fens displayed disparities mainly in taxonomic and phylogenetic diversity. Differential responses of bogs and fens underline the necessity for tailored management strategies. Changes in plant diversity away from restoration targets have implications for ecosystem recovery, emphasizing the importance of long-term monitoring to evaluate the effectiveness of silviculture techniques in restoring boreal peatland plant communities.

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 208.47g/m2 and 290.19g/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.

Uncovering the impacts of LUCC on ecological connectivity in suburban open-pit mining concentration areas: A pattern collection of changing relationships between ecological resistance and ecological network elements

The suburban open-pit mining concentration area couples two human activities, urban sprawl and open-pit mining, while destruction of surface vegetation and ecological restoration coexist. This has also resulted in complex land use shifts that significantly affect regional ecological connectivity. However, the impact of such complex Land use/land cover changes (LUCC) on ecological connectivity in this region is not well understood Therefore, this study takes the suburban open-pit mining concentration area of Jinan City as a case study and uses Land use/land cover (LULC) data from the years 2000, 2010, and 2020 to simulate the spatio-temporal evolution of ecological networks (ENs). Subsequently, a pattern collection of changing relationships was proposed that relate changes in ecological resistance to changes in EN elements, addressing the dynamic transmission process from "spatial field" to "spatial structure entity". Based on this approach, GeoDetector is employed to reveal the response extent of EN elements to LUCC. The results indicate: (1) In the past 20 years, the area of construction land has increased by 100 %, while mining land has increased by 69 %. This significant increase in LUCC has led to a notable enhancement in ecological resistance in the study area, driving the generation of 20 % more ecological pinch points and barriers. Additionally, it has resulted in a twofold increase in both the number and length of level 1 ecological corridors. (2) The dominant status and structure of grassland, agriculture land, and woodland remain relatively stable; however, the interflow among them has caused approximately 25 % of the ecological corridors to undergo changes such as disappearance and path alteration. (3) The implementation of ecological protection and restoration has led to a continuous increase in the area of ecological sources, particularly with the mining rehabilitation increasing by 32.7 times compared to 2000, thereby driving gradual improvement in 25 % of the ecological network elements. (4) Although the quantity and length of ecological corridors in the study area remain relatively stable, evaluation results indicate that its spatial structure is gradually deteriorating, with ecological connectivity decreasing by 7.7 %. This study highlights the importance of restoration of ecological processes in sub-urban open-pit mining concentration areas. The study results can provide a scientific basis for ecological restoration and sustainable development in resource-based cities.