Soil Vegetation Research Articles

Effect of shade on simultaneous estimation of non-photosynthetic and photosynthetic vegetation cover using the NDVI-NSSI normalized difference triangular space

ABSTRACT The non-photosynthetic vegetation – soil separation index (NSSI) and normalized difference vegetation index (NDVI) can be used to simultaneously estimate the fractional cover of non-photosynthetic vegetation (fNPV), photosynthetic vegetation (fPV), and bare soil (fBS) in vegetation ecosystems. However, these estimates suffer from problems due to shading by dense vegetation or topography. Based on field-measured data and on hyperspectral data acquired by unmanned aerial vehicles, we analyse how shading affects the morphology of NDVI-NSSI and enhanced vegetation index – NSSI (EVI-NSSI). We also investigate the simultaneous estimation of fNPV, fPV, and fBS. The results show that the NDVI-NSSI normalized difference feature space mitigates the impact of shade. Shade causes the NDVI-NSSI and EVI-NSSI to shift rightward parallel to the BS axis and leftward parallel to the PV axis, respectively. Although such shifts may hinder the determination of NPV, PV, and BS endmembers, they do not affect the spectral separation of NPV. Overall, with the NDVI-NSSI and EVI-NSSI methods, the estimation error for fNPV under shaded conditions is approximately 5% greater than that under illuminated conditions. Shaded green vegetation more strongly affects the estimation error of fPV and fBS in EVI-NSSI than in NDVI-NSSI, whereas fBS and fPV depend more strongly on shade than fNPV.

Diminishing legacy effects from forest fertilization on stand structure, vegetation community, and soil function

While there is consensus that fertilization with nitrogen (N) is a cost-effective way of increasing both forest biomass yield and timber harvest profitability, the strength and longevity of legacy effects are debated. To quantify legacy effects of past fertilization, we analysed 21 mixed Pinus sylvesteris and Picea abies stands. The stands, on average 23 years old at the time of this study, were either unfertilized (n=7), fertilized with 150 kg N ha−1 once 36 years ago (n=7), or twice, 45 and 36 years ago, respectively (n=7), during the previous stand rotation. We performed measurements on soil N mineralisation and N availability, forest growth, ground vegetation community composition, soil and vegetation C/N ratios and soil C and N stocks, many of which responded to legacy N fertilization earlier in stand development. Our results show that the legacy effects of fertilization during the previous stand rotation have diminished through time, indicating an eventual convergence of stand properties. Specifically, all significant effects present in the previous measurement period (over a decade ago), were weaker or completely absent in the current study (i.e. 36 years after fertilization and 23 years after initiation of the new stands). None-the-less, this indicates a longer legacy effect of N fertilization than what is normally considered and suggests that care should be taken to mitigate unwanted, long-term effects when utilizing N addition to promote tree growth in boreal forests.

Modeling the carbon dynamics of ecosystem in a typical permafrost area

Climate change poses mounting threats to fragile alpine ecosystem worldwide. Quantifying changes in carbon stocks in response to the shifting climate was important for developing climate change mitigation and adaptation strategies. This study utilized a process-based land model (Community Land Model 5.0) to analyze spatiotemporal variations in vegetation carbon stock (VCS) and soil organic carbon stock (SOCS) across a typical permafrost area - Qinghai Province, China, from 2000 to 2018. Multiple potential factors influencing carbon stocks dynamics were analyzed, including climate, vegetation, soil hydrothermal status, and soil properties. The results indicated that provincial vegetation carbon storage was 0.22 PgC (0.32 kg/m2) and soil organic carbon pool was 9.12 PgC (13.03 kg/m2). VCS showed a mild increase while SOCS exhibited fluctuating uptrends during this period. Higher carbon stocks were observed in forest (21.74 kg/m2) and alpine meadow (18.08 kg/m2) compared to alpine steppes (9.63 kg/m2). Over 90 % of the carbon was stored in the 0-30 cm topsoil layer. The contribution rates of soil carbon in the 30-60 cm and 60-100 cm soil layers were significantly small, despite increasing stocks across all depths. Solar radiation, temperature, and NDVI emerged as primary influential factors for overall carbon stocks, exhibiting noticeable spatial variability. For SOCS at different depths, the normalized differential vegetation index (NDVI) was the foremost predictor of landscape-level carbon distributions, which explained 52.8 % of SOCS variability in shallow layers (0-30 cm) but dropped to just 12.97 % at the depth of 30-60 cm. However, the dominance of NDVI diminished along the soil depth gradients, superseded by radiation and precipitation. Additionally, with an increase in soil depth, the influence of inherent soil properties also increased. This simulation provided crucial insights for landscape-scale carbon responses to climate change, and offered valuable reference for other climate change-sensitive areas in terms of ecosystem carbon management.

Impact of vegetation composition and seasonality on sensitivity of modelled CO2 exchange in temperate raised bogs

Encroachment of vascular plants (VP) in temperate raised bogs, as a consequence of altered hydrological conditions and nutrient input, is widely observed. Effects of such vegetation shift on water and carbon cycles are, however, largely unknown and identification of responsible plant physiological traits is challenging. Process-based modelling offers the opportunity of gaining insights into ecosystem functioning beyond observations, and to infer decisive trait shifts of plant functional groups. We adapted the Soil–Vegetation–Atmosphere Transfer model pyAPES to a temperate raised bog site by calibration against measured peat temperature, water table and surface CO2 fluxes. We identified the most important traits determining CO2 fluxes by conducting Morris sensitivity analysis (MSA) under changing conditions throughout the year and simulated VP encroachment. We further investigated transferability of results to other sites by extending MSA to parameter ranges derived from literature review. We found highly variable intra-annual plant traits importance determining ecosystem CO2 fluxes, but only a partial shift of importance of photosynthetic processes from moss to VP during encroachment. Ecosystem respiration was dominated by peat respiration. Overall, carboxylation rate, base respiration rate and temperature sensitivity (Q10) were most important for determining bog CO2 balance and parameter ranking was robust even under the extended MSA.

Vegetation degradation alters soil physicochemical properties and potentially affects ecosystem services in green spaces of a tropical megacity (Lubumbashi, DR Congo)

Urban soils are degraded by various human pressures, including vegetation degradation, leading to changes in physical and chemical characteristics and affecting important ecosystem services. Soil physical properties are an important fertility control parameter, providing the basis for sustainable soil use in urban conditions; however, they do not receive sufficient attention in work on tropical cities. We assessed the impact of vegetation degradation in six urban green spaces (bare soils versus vegetated soils) on the physical (texture, soil bulk density and structure) and chemical (pH, cation exchange capacity (CEC), organic carbon (OC), nitrogen (N), phosphorus (P), potassium (K), copper (Cu) and manganese (Mn)) qualities of soils in a tropical megacity (Lubumbashi, DR Congo). Vegetated soils presented better physical and chemical qualities than bare soils. Vegetated soils were characterized by a high clay and silt content and a good consistency (soil structure), while bare soils were characterized by a high sand content and high bulk density. Vegetated soils were characterized by higher pH, OC, N, C/N ratio, CEC, P, and K. There was no significant difference in Mn or Cu between bare and vegetated soils. Cu was highly variable between sites (from 99 ± 61 mg.kg−1 in VS to 8559 ± 151 mg.kg−1 in BS). Our results demonstrate that the destruction of vegetation, leading to bare soil, negatively affects soil properties and may interfere with ecosystem services provided by urban soils in tropical climates. The physical properties observed in bare soils in this study, including silt, clay, and sand content, soil structure, and soil bulk density, along with chemical properties such as soil pH, cation exchange capacity, and soil organic carbon, can influence the ecosystem services provided by urban soils. These services include regulating water flow and nutrient cycling, enhancing nutrient availability, and supporting ecosystem functions through the cycling of water and nutrients.

Carbon pools in forest systems and new estimation based on an investigation of carbon sequestration

Forests, the ancient wooden giants, are both symbols of natural beauty and reservoirs of carbon stocks. The current climate crisis has created an urgent need for an in-depth study of forest ecosystems and carbon stocks. Based on forest inventory data from field surveys and four bioclimatic zones [Zone 1 (Z1, humid forest), Zone 2 (Z2, semi-humid forest), Zone 3 (Z3, semi-humid to semi-arid forest-grassland), and Zone 4 (Z4, semi-arid typical grassland)], two methods [Method 1 (M1) and Method 2 (M2)] were used to estimate carbon stocks in forest ecosystems in Shaanxi Province, China, and explored the spatial patterns of carbon pools and potential influences. The total forest ecosystem carbon pool amounted to 520.80 Tg C, of which 53.60% was stored aboveground, 17.16% belowground, and 29.24% in soil (depth of 0–10 cm). Spatially, there were marked north-south gradients in both biomass (Z2 > Z3 > Z1 > Z4) and soil organic carbon densities (Z1 > Z2 > Z3 > Z4). The differences between aboveground and belowground biomass carbon density across broadleaf, needle-leaf, and broadleaf and needle-leaf mixed forest were not pronounced, while soil organic carbon density had the order of broadleaf (18.38 Mg C/ha) > needle-leaf (11.29 Mg C/ha) > broadleaf and needle-leaf mixed forest (10.33 Mg C/ha). Under an ideal scenario that excludes external factors, mainly forest growth, the sequestration potential of forest biomass by 2032 was estimated by M1 as 85.43 Tg, and by M2 to be substantially higher at 176.21 Tg. As of 2062, M1 estimated 155.97 Tg of sequestration potential for forest biomass. The spatial patterns of forest biomass and soil carbon density were closely related to climatic factors, and these relationships allowed the spatial division into two distinct climatic regions. Moreover, biomass carbon density was significantly correlated with the normalized difference vegetation index, soil silt, and elevation. This study provides key information for promoting the strategic shift from light-green to deep-green forest systems in Shannxi Province and updates the estimation methods of forest ecosystems’ carbon pools based on field surveys.

Recent Developments to the SimSphere Land Surface Modelling Tool for the Study of Land–Atmosphere Interactions

Soil–Vegetation–Atmosphere Transfer (SVAT) models are a promising avenue towards gaining a better insight into land surface interactions and Earth’s system dynamics. One such model developed for the academic and research community is the SimSphere SVAT model, a popular software toolkit employed for simulating interactions among the layers of vegetation, soil, and atmosphere on the land surface. The aim of the present review is two-fold: (1) to deliver a critical assessment of the model’s usage by the scientific and wider community over the last 15 years, and (2) to provide information on current software developments implemented in the model. From the review conducted herein, it is clearly evident that from the models’ inception to current day, SimSphere has received notable interest worldwide, and the dissemination of the model has continuously grown over the years. SimSphere has been used so far in several applications to study land surface interactions. The validation of the model performed worldwide has shown that it is able to produce realistic estimates of land surface parameters that have been validated, whereas detailed sensitivity analysis experiments conducted with the model have further confirmed its structure and architectural coherence. Furthermore, the recent inclusion of novel functionalities in the model, as outlined in the present review, has clearly resulted in improving its capabilities and in opening up new opportunities for its use by the wider community. SimSphere developments are also ongoing in different aspects, and its use as a toolkit towards advancing our understanding of land surface interactions from both educational and research points of view is anticipated to grow in the coming years.

Remote sensing of vegetation and soil moisture content in Atlantic humid mountains with Sentinel-1 and 2 satellite sensor data

The satellite monitoring of vegetation moisture content (VMC) and soil moisture content (SMC) in Southern European Atlantic mountains remains poorly understood but is a fundamental tool to better manage landscape moisture dynamics under climate change. In the Atlantic humid mountains of Portugal, we investigated an empirical model incorporating satellite (Sentinel-1 radar, S1; Sentinel-2 optical, S2) and ancillary predictors (topography and vegetation cover type) to monitor VMC (%) and SMC (%). Predictors derived from the S1 (VV, HH and VV/HH) and S2 (NDVI and NDMI) are compared to field measurements of VMC (n = 48) and SMC (n = 48) obtained during the early, mid and end of summer. Linear regression modelling was applied to uncover the feasibility of a landscape model for VMC and SMC, the role of vegetation type models (i.e. native forest, grasslands and shrubland) to enhance predictive capacity and the seasonal variation in the relationships between satellite predictors and VMC and SMC. Results revealed a significant but weak relationship between VMC and predictors at landscape level (R2 = 0.30, RMSEcv = 69.9 %) with S2_NDMI and vegetation cover type being the only significant predictors. The relationship improves in vegetation type models for grasslands (R2 = 0.35, RMSEcv = 95.0 % with S2_NDVI) and shrublands conditions (R2 = 0.52, RMSEcv = 45.3 %). A model incorporating S2_NDVI and S1_VV explained 52 % of the variation in VMC in shrublands. The relationship between SMC and satellite predictors at the landscape level was also weak, with only the S2_NDMI and vegetation cover type exhibiting a significant relationship (R2 = 0.28, RMSEcv = 18.9 %). Vegetation type models found significant associations with SMC only in shrublands (R2 = 0.31, RMSEcv = 9.03 %) based on the S2_NDMI and S1_VV/VH ratio. The seasonal analysis revealed however that predictors associated to VMC and SMC may vary over the summer. The relationships with VMC were stronger in the early summer (R2 = 0.31, RMSEcv = 90.1 %; based on S2_NDMI) and mid (R2 = 0.37, RMSEcv = 70.8 %; based on S2_NDVI), butnon-significant in the end of summer. Similar pattern was found for SMC, where the link with predictors decreases from the early summer (R2 = 0.33, RMSEcv = 16.0 %; based on S1_VH) and mid summer (R2 = 0.30, RMSEcv = 17.8 %; based on S2_NDMI) to the end of summer (non-significant). Overall, the hypothesis of a universal landscape model for VMC and SMC was not fully supported. Vegetation type models showed promise, particularly for VMC in shrubland conditions. Sentinel optical and radar data were the most significant predictors in all models, despite the inclusion of ancillary predictors. S2_NDVI, S2_NDMI, S1_VV and S1_VV/VH ratio were the most relevant predictors for VMC and, to a lesser extent, SMC. Future research should quantify misregistration effects using plot vs. moving window values for the satellite predictors, consider meteorological control factors, and enhance sampling to overcome a main limitation of our study, small sample size.

Seasonality and vegetation shape the taxonomic and functional diversity of microorganisms in islands of fertility of a semi-arid region in the Colombian tropics

The islands of fertility or resource islands play a crucial role in combating desertification, as they harbor diverse microbial communities essential for their establishment. The objective of this study was to determine the taxonomic and functional diversity of soil microorganisms in a semi-arid region of the Colombian tropics using a metagenomic approach. To achieve this, we sampled three species of nurse trees as well as bare soil during both the dry and wet seasons. Total DNA was extracted and sequenced extensively. Taxonomic diversity was established by comparing against the SILVA database, while functional diversity was determined using the DIAMOND program and the KEGG database. The analysis unveiled seasonal microbial dynamics influenced by vegetation cover. Islands of fertility exhibited higher diversity and nutritional value compared to bare soil. Wet season conditions boosted microbial diversity, narrowing the gap between vegetated and unvegetated soils. Under drought conditions, Proteobacteria predominated in the resource islands, while Actinobacteria prevailed in the bare soil. Both islands of fertility and the rainy season are significant drivers of microbial communities in these semi-arid environments. The presence of unique genera indicated specialized adaptations, revealing an under-explored microbial diversity. Although complete methanogenesis was absent, methane oxidation pathways were detected. Bacteria demonstrated notable adaptations for nitrate reduction, even under organic substrate limitation. Organic matter and anaerobic microenvironments could play a crucial role in nitrate reduction and denitrification. Sulfate reduction prevailed during the dry season, whereas thiosulfate oxidation was more prominent during the wet season. These findings underscore the remarkable adaptability of microbial communities in challenging environments and provide key insights for addressing desertification in semi-arid zones.

Analysis and Evaluation of Heavy Metal Pollution in Farmland Soil in China：A Meta-analysis

In this study, a Meta-analysis was used to investigate the pollution status of eight farmland soil heavy metal elements (As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn) in China. Meanwhile, their spatiotemporal changes and differences between different types of cultivated land were explored. The research data were chosen from 449 relevant literature data collected by CNKI and Web of Science from 2005 to 2021, and the Meta-analysis used a weighted method based on "sampling numbers", "study area", and "standard deviation". The results showed that the national average values of the eight heavy metal elements in Chinese farmland soil were ω(As)11.00 mg·kg-1, ω(Cd)0.350 2 mg·kg-1, ω(Cr)62.91 mg·kg-1, ω(Cu)28.87 mg·kg-1, ω(Hg)0.135 1 mg·kg-1, ω(Ni)28.91 mg·kg-1, ω(Pb)34.67 mg·kg-1,and ω(Zn)90.24 mg·kg-1. Compared with their background values, all elements except As accumulated to some extent, and Cd and Hg accumulated the most, exceeding their background values by 177.9% and 340.3%, respectively. The research results indicated that Cd and Hg were the main pollution elements in farmland soil in China, and their accumulation was mainly influenced by human activities. In terms of their temporal and spatial changes, the Yunnan-Guizhou Plateau and the eastern coast were the most concentrated areas of pollution cases, and the pollution center shifted from the middle reaches of the Yangtze River to the southwest over time. The accumulation of heavy metals in farmland soil was affected by crop planting types, and the accumulation of heavy metals in vegetable and paddy soil was significantly greater than that in other cultivated land types.

Consistent land use and land cover classification across 20 years of various high-resolution images for detecting soil sealing in Murcia, Spain

Artificial human-induced soil sealing has numerous negative consequences. The extent of impervious surfaces is a key indicator of the location and intensity of human activity; however, it is also proof of damage to the natural environment as a result of the sealing and modification of ecosystems. Remote sensing techniques can help detect and monitor changes in land use and cover over an extended period. However, the limited availability of consistent satellite images with high spatiotemporal resolutions covering several decades poses major challenges for achieving high overall classification accuracy. An accurate methodology for the multitemporal detection of artificial land cover classes was developed and applied to a case study of the metropolitan area of Murcia (Spain) with its challenging landscape conditions due to the frequent presence of bare soil. For this purpose, a variety of high-resolution satellite images from SPOT 5, Rapid Eye, and PlanetScope covering a period of 20 years were used. To improve the automated detection of built-up areas, the reflectance values of the images, normalised difference vegetation index (NDVI) and soil adjusted vegetation index (SAVI), and a building surface digital model were used as inputs for the supervised classification model. We applied a random forest algorithm to non-public, high-resolution images in the Google Earth Engine (GEE) as a processing environment to identify eight target land cover classes. The results show that the proposed methodology leads to a substantial improvement, after including the indices and the digital building model, in the overall accuracy (from 93.16 to 95.97%) and in all classes. This improvement was significant for the artificial classes and was particularly noticeable for the built-up areas (from 91.1 to 95.64%) because their confusion with bare soil was considerably reduced. This work demonstrates the effectiveness of the building-surface digital model as a tool for training the classification model, as it reduces uncertainty in confusion with other spectrally similar classes and its applicability to multisource imagery.

Response of Streamflow to Future Land Use and Cover Change and Climate Change in the Source Region of the Yellow River

This study utilizes meteorological and leaf area index (LAI) data for three shared socioeconomic pathways (SSP1–2.6, SSP2–4.5, and SSP5–8.5) from four general circulation models (GCMs) of the sixth climate model intercomparison project (CMIP6) spanning from 2015 to 2099. Employing calibrated data and incorporating future land use data under three SSPs, the distributed hydrology soil vegetation model (DHSVM) is employed to simulate streamflow in the source region of the Yellow River (SRYR). The research aims to elucidate variations in streamflow across different future scenarios and to estimate extreme streamflow events and temporal distribution changes under future land use and cover change (LUCC) and climate change scenarios. The main conclusions are as follows: The grassland status in the SRYR will significantly improve from 2020 to 2099, with noticeable increases in temperature, precipitation, and longwave radiation, alongside a pronounced decrease in wind speed. The probability of flooding events increases in the future, although the magnitude of the increase diminishes over time. Both LUCC and climate change contribute to an increase in the multi-year average streamflow in the region, with respective increments of 48.8%, 24.5%, and 18.9% under SSP1–2.6, SSP2–4.5, and SSP5–8.5. Notably, the fluctuation in streamflow is most pronounced under SSP5–8.5. In SSP1–2.6, the increase in streamflow during the near future (2020–2059) exceeds that of the distant future (2059–2099). Seasonal variations in streamflow intensify across most scenarios, leading to a more uneven distribution of streamflow throughout the year and an extension of the flood season.

Carbon stock increase during post-agricultural succession in central France: no change of the superficial soil stock and high variability within forest stages

Forest development following agricultural abandonment concerns extensive areas including the Massif Central region of France where this study was undertaken. This land-use and land-cover change is expected to have effects on biodiversity and ecosystem services, including an increase of carbon sequestration—a major concern in the face of climate change. Nevertheless, uncertainties about carbon stock changes during successions are remaining, especially as to the total stock and the contribution of the different carbon pools. Our work contributes to this field by studying carbon stocks in multiple plots of different successional stages. We measured and estimated carbon stocks in aboveground and belowground vegetation, deadwood, litter and superficial soil, and surveyed plant communities and plot conditions (slope, aspect, soil characteristics). The average total carbon stock increased along the succession from 70.60 at stage 0 to 314.19 tC ha−1 at stage 5. However, the total carbon stocks at the young forest stage (abandoned for 74 years maximum) and the older forest stage (forested for at least 74 years) were not significantly different, and probably reflected strong local heterogeneity in the older forest stage. An increase of the carbon stock was found in all pools, except the soil pool that did not vary significantly between the successional stages. The aboveground carbon stock was found strongly related to the woody species cover, especially the macrophanerophyte cover. This case study supports the view that the succession dynamics of former agricultural plots participates in carbon sequestration, sometimes with great local variations.

Spatial variation characteristics and influencing factors of sediment connectivity in the black soil region of northeast China

Connectivity has become an important indicator of the sediment transfer potential through sediment sources to catchment sinks. Connectivity plays a vital role in investigating the rate of soil erosion caused by runoff and sediment output across the watershed landscape. However, there have been few quantitative studies on the spatial variations in sediment connectivity in the black soil region of Northeast China. Seven classic watersheds in the northeast black soil region were selected to discover spatial variations in sediment connectivity and their influencing factors. The connectivity index (IC) was used to study the spatial variation of sediment connectivity, and the weight of IC was calculated using the vegetation cover management factor (C), which reflected the influence of human activities on the soil erosion process caused by cultivated land expansion and encroachment of forest grassland. The redundancy analysis method (RDA) was used to determine the main factors affecting the spatial variation of IC. The results showed that IC decreased from north (0.45) to south (0.12). The spatial variations of watersheds in sediment connectivity showed significant spatial heterogeneity. IC was correlated with topographic, climatic, and artificial management characteristics at 0.01 or 0.05 levels except soil structural index; RDA results showed that topography and human activities were the main factors affecting sediment connectivity. Topographic and climatic factors explained more sediment connectivity changes in the southern watersheds than in the northern watersheds, while the human activities factor explanation was the opposite of topographic factors. Human activities refer to changes in vegetation cover and soil structure and properties resulting from human logging and farming that affect sediment connectivity.

Assessing gully erosion susceptibility dynamics using information value and hazard index methods: A case study of Agulu-Nanka watershed, Southeast Nigeria

Gully erosion is one of the major causes of land degradation leading to the loss of productive lands globally. The Benue Trough in Nigeria is plagued with the problem of gully erosion, with a myriad of natural and anthropogenic factors considered as contributory factors. In this study, quantitative and qualitative methods were combined to evaluate gully susceptibility in the Agulu-Nanka watershed located in the lower part of Benue Trough between 2000 and 2018. Landsat 7 and 8 images, Digital Elevation Model (DEM), geological, soil, and rainfall erosivity maps were used to generate the gully erosion controlling factors including vegetation cover, lithology, soil, land use/cover, slope, curvature, aspect and rainfall erosivity. Additionally, high resolution image was used to develop gully inventory map. The information value method was used evaluate the influence of various classes of gully erosion controlling factors on gully distribution in the watershed. This enabled objective derivation of weights for the various classes of these factors. Information value analysis reveal that low percentage vegetation cover (<10 %), Ameki Group lithological unit, high slope gradient (17 − 43°) and Dystric Fluvisols soil type have the highest influence on gully erosion in the watershed. Since the information value method does not consider interrelationship between factors, the rank sum technique was used to derive factor weights based on the relationship between the factors. To develop the gully susceptibility map for the Agulu-Nanka watershed for the years 2000 and 2018, the erosion controlling factors were integrated using factor class weights derived through information value and factor weights derived through rank sum method. The analysis of gully erosion hazard maps show that approximately 48 % of the watershed was under low erosion risk over the period of 18 years. Areas under moderate, high and severe erosion risk decreased from 27.45 %, 9.45 % and 0.27 % of the watershed in year 2000, to 21.30 %, 8.41 %, and 0.20 % of the watershed respectively in 2018. On the other hand, the areas under negligible erosion risk increased from 14.69 % in year 2000, to 21.26 % of the watershed in 2018. The validation of the gully erosion susceptibility map for years 2000 and 2018 using the Receiver Operating Curve (ROC) showed ROC-AUC values of 0.81 % and 0.88 % respectively suggesting good model results. While the result of study indicates that restoration efforts through afforestation is yielding some result, the minimal reduction in the size of areas under severe and high gully erosion susceptibilities indicate that additional mitigation measures need to be combined with intensified afforestation efforts to effectively combat gully erosion in the watershed.

Use of Google Earth Engine for the estimation and variation of the vegetable cover of the amazon forest generated by anthropic activity in Huepetuhe period 2016 to 2022

The estimation and variation of the vegetation cover of the Amazon forest generated by anthropic activity in Huepetuhe had the purpose of estimating the loss of vegetation and the deforestation rates for an Amazon forest, the study was carried out using Landsat 8 satellite images, the treatment of the raster images was made using the ERDAS software, the images were subjected to geometric and radiometric corrections, for this purpose control points were taken on the ground, for the analysis four categories of forest degradation were established: Forest with null vegetation with presence of bare soil, forest with sparse vegetation, forest with moderate vegetation and total area of deterioration. The results from the analysis of the multispectral images and the NDVI show that compared to 2016, 30.1 ha increased in the forest category with no vegetation, 28.8 ha in forests with sparse vegetation, 236.9 ha for forests with moderate vegetation and the area of global deterioration increased by 295.8 ha. The annual deforestation rate was 5% (6.02 ha/year) for forests with no vegetation and presence of bare soil and 9% (59.16 ha/year) for global degradation, the coefficient of determination for forests with no vegetation and area naked was R2=0.9704, and for global deterioration of the forest R2=0.957.

Spatial-temporal variation and correlation between vegetation coverage and soil conservation of Ten Kongdui watershed in the Yellow River basin

The Ten Kongdui watershed is an essential part of the upper Yellow River baisn of China. The ecosystem in this region has low stability and hazards of sandstorm and soil erosion are serious. By studying the spatio-temporal changes and spatial correlation between vegetation cover and soil conservation, the inhibition effect of vegetation on soil erosion in time and space can be clarified, and the driving factors of soil erosion in a certain area can be further explained. The research results provide data support for artificial vegetation construction and soil erosion control in the Yellow River basin in the future. In this study, the invest model was used to calculate the soil retention of the watershed, and then the spatial autocorrelation analysis was used to analyze the vegetation coverage and soil retention in space. The relationship between changes of the vegetation coverage and soil conservation during 2000–2020 in the Ten Kongdui watershed is analyzed in this paper. The research shows that: (1) During the research period, the vegetation coverage maintained an increasing trend in Ten Kongdui watershed; (2) From 2000 to 2020, with the increased vegetation coverage, soil retention increased, indicating that increasing vegetation coverage can effectively prevent soil erosion; and (3) Vegetation coverage and soil conservation presented a local spatial auto-correlation, Moran’s I index was less than 0, but the years from 2000 to 2020 witnessed gradual growth of the correlation coefficient. There are significant differences in spatial distribution. ‘High-high’ and ‘low-low’ are sporadically distributed in the sandstorm area on the middle reaches of Kubuqi and sporadically in the hilly and gully areas on the upper reaches. Moreover, ‘high-low’ and ‘low-high’ correlation coefficients were distributed in gully areas on the upper reaches and alluvial plains on the lower reaches. The alluvial plains on the lower reaches are mainly farmland ecosystems, with a high NDVI in the growing season but poor soil erosion resistance and weak soil conservation ability. The results emphasized the importance of vegetation coverage change in space for soil conservation of different type of underlying surfaces. The vegetation coverage change effects of soil conservation in plain terrain will be muted. This work provided a certain theory and criterion for us to further research the predictive model of soil erosion .

Lead Isotope Signatures and Source Identification of Heavy Metals in Vegetable Soils Irrigated with Swine Wastewater of Jiangxi Province, China.

The rapid development of livestock and poultry industry in China has caused serious environment pollution problems. To understand the heavy metals accumulation and identify their sources, 7 heavy metals contents and lead isotope ratios were determined in 24 soil samples from vegetable fields irrigated with swine wastewater in Dongxiang County, Jiangxi Province, China. The results showed that the concentration of Cr, Ni, Cu, Zn, As, Cd and Pb in the swine wastewater irrigated vegetable soils varied from 38.5 to 86.4, 7.57 to 30.6, 20.0 to 57.1, 37.5 to 174, 9.18 to 53.1, 0.043 to 0.274 and 12.8 to 37.1mg/kg, respectively. The soils were moderately to heavily polluted by As, moderately polluted by Cr, Ni, Cu, Zn and Cd, and unpolluted to moderately polluted by Pb. Sampling soils were classified as moderately polluted according to the Nemerow comprehensive pollution index. Lead isotope and Principal Component Analysis (PCA) analysis indicated that swine wastewater irrigation and atmospheric deposition were the primary sources of the heavy metals.

Evaluating carbon stocks in soils of fragmented Brazilian Atlantic Forests (BAF) based on soil features and different methodologies.

Brazil's Atlantic Forest (BAF) is a highly fragmented, strategic environmental and socio-economic region that represents the fourth biodiversity hotspot while also producing many commodities that are exported globally. Human disturbance plays a pivotal role as a driver of BAF's soil dynamics and behaviors. The soils under Late Primary and Secondary Semideciduous Seasonal Forests (LPSF and LSSF) were characterized by high to moderate resilience, with improved chemical properties as human disturbance decreased. The Transitional Forest to Cerrado (TFC) had the worst soil conditions. Disturbed Primary and Secondary Semideciduous Seasonal Forests (DPSF and DSSF) represent a transitional stage between LPSF/LSSF and TFC. Accordingly, SOCs stocks increased from TFC << DPSF, DSSF < LPSF, LSSF. In BAF soils, to avoid unreliable data, SOCs measurements should be (i) conducted to at least 1m soil depth and (ii) quantified with a CHN analyzer. Human disturbance strongly affected the positive feedback between vegetation succession, SOCs, and soil nutrition. Soil development decreased as human disturbance increased, thus negatively affecting SOCs. Soils in the BAF require a long time to recover after the end of human disturbance, thus suggesting that preservation strategies should be prioritized in remnant BAF fragments.

Relation between the soil erosion cover management factor and vegetation index in semi-arid basins

The cover management factor is one of the main five factors that is used within the universal soil loss equation to reflect the effect of cropping and management practices on soil erosion rates. It is determined through tables and equations derived in tropical and European conditions, which are not suitable for semi-arid regions with different climate, topography, and soil characteristics. Therefore, this study considers al-Arish basin in Sinai Peninsula, Egypt as a semi-arid study area to generate a cover management factor’s equation in terms of the Normalized Difference Vegetation Index using hydrological modeling and satellite images processing. To verify the proposed equation, it was applied to the Derna catchment in Libya and compared with European and tropical ones with respect to the hydrological outputs. Statistical analysis indicates that the proposed equation determines the cover management factor more accurately than those developed for tropical or European regions, as the correlation coefficient between the cover management factor and the hydrological results was 0.71, while it was about 0.20 for the European and tropical, equations, also the degree of agreement between the proposed equation’s results and the hydrological simulation was 0.768 while it was 0.001 and 0.02 for the tropical and European equations respectively. By utilizing the newly developed equation, the soil erosion can be estimated more accurately for the semi-arid regions, and a better understanding of the relation between the vegetation cover and soil erosion can be drawn.