Long-term effects of vegetation restoration and forest management on carbon pools and nutrient storages in northeastern Loess Plateau, China.

It is crucial for understanding the variations of carbon and nutrient pools within the ecosystems during long-term vegetation restoration to accurately assess the effects of different ecological restoration patterns. However, the long-term spatio-temporal variations of carbon and nutrient pools under different vegetation types remain unclear. The sites for long-term natural and planted forests (i.e., Natural secondary forest, Pinus tabulaeformis planted forest, Platycladus orientalis planted forest, and Robinia pseudoacacia planted forest) on the northeastern Loess Plateau, China were selected, to measure and analyze the differences and interannual variations of vegetation attributes at four synusiae and soil properties at 0-100 cm over the period of 12 years (2006-2017). The principal component analysis (PCA) and Mantel test were also conducted to explore the relationships among vegetation attributes, soil properties, and carbon and nutrient pools. The results showed that: compared with the planted forests, the natural secondary forest had lower arborous biomass (84.21 ± 1.53 t hm-2) and higher understory biomass and plant heights. Compared to planted forests, the secondary forest had higher soil carbon and nitrogen contents (13.74 ± 3.50 g kg-1 and 1.16 ± 0.34 g kg-1). The soil carbon pool in the secondary forest was 22.0% higher than planted forests, while the vegetation carbon pool in the P. tabulaeformis was 75.5% higher than other forests. Principal component analysis (PCA) and Mantel test revealed that vegetation attributes and soil properties had significant correlations with carbon and nutrient pools, especially at the arborous synusia (p < 0.01). The findings indicated that in the ecologically fragile Loess Plateau region, the selection of appropriate vegetation restoration types should be guided by varying ecological restoration goals and benefits, aiming to expected ecological outcomes. This insight offers a strategic implication for forest management that is tailored to improve carbon and nutrient pools in areas with similar environmental conditions.

The detection and monitoring of pollution caused by gold mining using a vegetation cover index.

This study explores how a vegetation cover (VC) index can be employed as a pollution warning tool in gold mining areas in the Northwest of Iran. The analysis included the following: (a) the extraction of normalized difference vegetation index (NDVI) maps from Landsat images in three zones, i.e., mining operations, upstream areas without any exploration, and the downstream area of the mining activities, (b) calculation of the zones' VC, (c) investigation of transformation trends in each pixel of VC time series using the Mann-Kendall trend test, (d) determination of the pixels with significant VC reduction and the significant starting points of the trend using the sequential Mann-Kendall test, (e) assessment of the correlation between the zones with significantly reduced VC, and (f) a correlation test between average monthly and annual climate parameters and VC. Our results indicate that although 51 ha of VC has been demolished around the mining activities areas (i.e., zone 1), an overall upward trend in vegetation with no chemical leakage is observed into the downstream area of the basin (i.e., zone 3). This upward trend can be mostly attributed to the increasing precipitation and decreasing temperature in the study period. The fact that the area downstream of the mine shows that the heap leaching method for gold mining in Andaryan mine is currently not damaging the vegetation, this likely means that there is no leakage to the surrounding environment from the mine. Our results further show that using NDVI in a pixel-based scale and statistical methods has a high potential to quantify the effects of human activities on surface biophysical characteristics.

Barriers and opportunities of soil knowledge to address soil challenges: Stakeholders' perspectives across Europe.

Climate-smart sustainable management of agricultural soil is critical to improve soil health, enhance food and water security, contribute to climate change mitigation and adaptation, biodiversity preservation, and improve human health and wellbeing. The European Joint Programme for Soil (EJP SOIL) started in 2020 with the aim to significantly improve soil management knowledge and create a sustainable and integrated European soil research system. EJP SOIL involves more than 350 scientists across 24 Countries and has been addressing multiple aspects associated with soil management across different European agroecosystems. This study summarizes the key findings of stakeholder consultations conducted at the national level across 20 countries with the aim to identify important barriers and challenges currently affecting soil knowledge but also assess opportunities to overcome these obstacles. Our findings demonstrate that there is significant room for improvement in terms of knowledge production, dissemination and adoption. Among the most important barriers identified by consulted stakeholders are technical, political, social and economic obstacles, which strongly limit the development and full exploitation of the outcomes of soil research. The main soil challenge across consulted member states remains to improve soil organic matter and peat soil conservation while soil water storage capacity is a key challenge in Southern Europe. Findings from this study clearly suggest that going forward climate-smart sustainable soil management will benefit from (1) increases in research funding, (2) the maintenance and valorisation of long-term (field) experiments, (3) the creation of knowledge sharing networks and interlinked national and European infrastructures, and (4) the development of regionally-tailored soil management strategies. All the above-mentioned interventions can contribute to the creation of healthy, resilient and sustainable soil ecosystems across Europe.

Identifying barriers for nature-based solutions in flood risk management: An interdisciplinary overview using expert community approach.

The major event that hit Europe in summer 2021 reminds society that floods are recurrent and among the costliest and deadliest natural hazards. The long-term flood risk management (FRM) efforts preferring sole technical measures to prevent and mitigate floods have shown to be not sufficiently effective and sensitive to the environment. Nature-Based Solutions (NBS) mark a recent paradigm shift of FRM towards solutions that use nature-derived features, processes and management options to improve water retention and mitigate floods. Yet, the empirical evidence on the effects of NBS across various settings remains fragmented and their implementation faces a series of institutional barriers. In this paper, we adopt a community expert perspective drawing upon LAND4FLOOD Natural flood retention on private land network (https://www.land4flood.eu) in order to identify a set of barriers and their cascading and compound interactions relevant to individual NBS. The experts identified a comprehensive set of 17 barriers affecting the implementation of 12 groups of NBS in both urban and rural settings in five European regional environmental domains (i.e., Boreal, Atlantic, Continental, Alpine-Carpathian, and Mediterranean). Based on the results, we define avenues for further research, connecting hydrology and soil science, on the one hand, and land use planning, social geography and economics, on the other. Our suggestions ultimately call for a transdisciplinary turn in the research of NBS in FRM.

Examining the status of forest fire emission in 2020 and its connection to COVID-19 incidents in West Coast regions of the United States.

Forest fires impact on soil, water, and biota resources. The current forest fires in the West Coast of the United States (US) profoundly impacted the atmosphere and air quality across the ecosystems and have caused severe environmental and public health burdens. Forest fire led emissions could significantly exacerbate the air pollution level and, therefore, would play a critical role if the same occurs together with any epidemic and pandemic health crisis. Limited research is done so far to examine its impact in connection to the current pandemic. As of October 21, nearly 8.2 million acres of forest area were burned, with more than 25 casualties reported so far. In-situ air pollution data were utilized to examine the effects of the 2020 forest fire on atmosphere and coronavirus (COVID-19) casualties. The spatial-temporal concentrations of particulate matter (PM2.5 and PM10) and Nitrogen Dioxide (NO2) were collected from August 1 to October 30 for 2020 (the fire year) and 2019 (the reference year). Both spatial (Multiscale Geographically Weighted Regression) and non-spatial (Negative Binomial Regression) analyses were performed to assess the adverse effects of fire emission on human health. The in-situ data-led measurements showed that the maximum increases in PM2.5, PM10, and NO2 concentrations (µg/m3) were clustered in the West Coastal fire-prone states during August 1 - October 30, 2020. The average concentration (µg/m3) of particulate matter (PM2.5 and PM10) and NO2 was increased in all the fire states severely affected by forest fires. The average PM2.5 concentrations (µg/m3) over the period were recorded as 7.9, 6.3, 5.5, and 5.2 for California, Colorado, Oregon, and Washington in 2019, increasing up to 24.9, 13.4, 25.0, and 17.0 in 2020. Both spatial and non-spatial regression models exhibited a statistically significant association between fire emission and COVID-19 incidents. Such association has been demonstrated robust and stable by a total of 30 models developed for analyzing the spatial non-stationary and local association. More in-depth research is needed to better understand the complex relationship between forest fire emission and human health.

The role of soils in delivering Nature's Contributions to People.

This theme issue provides an assessment of the contribution of soils to Nature's Contributions to People (NCP). The papers in this issue show that soils can contribute positively to the delivery of all NCP. These contributions can be maximized through careful soil management to provide healthy soils, but poorly managed, degraded or polluted soils may contribute negatively to the delivery of NCP. Soils are also shown to contribute positively to the UN Sustainable Development Goals. Papers in the theme issue emphasize the need for careful soil management. Priorities for soil management must include: (i) for healthy soils in natural ecosystems, protect them from conversion and degradation, (ii) for managed soils, manage in a way to protect and enhance soil biodiversity, health, productivity and sustainability and to prevent degradation, and (iii) for degraded soils, restore to full soil health. Our knowledge of what constitutes sustainable soil management is mature enough to implement best management practices, in order to maintain and improve soil health. The papers in this issue show the vast potential of soils to contribute to NCP. This is not only desirable, but essential to sustain a healthy planet and if we are to deliver sustainable development in the decades to come. This article is part of the theme issue 'The role of soils in delivering Nature's Contributions to People'.

Soil-derived Nature's Contributions to People and their contribution to the UN Sustainable Development Goals.

This special issue provides an assessment of the contribution of soils to Nature's Contributions to People (NCP). Here, we combine this assessment and previously published relationships between NCP and delivery on the UN Sustainable Development Goals (SDGs) to infer contributions of soils to the SDGs. We show that in addition to contributing positively to the delivery of all NCP, soils also have a role in underpinning all SDGs. While highlighting the great potential of soils to contribute to sustainable development, it is recognized that poorly managed, degraded or polluted soils may contribute negatively to both NCP and SDGs. The positive contribution, however, cannot be taken for granted, and soils must be managed carefully to keep them healthy and capable of playing this vital role. A priority for soil management must include: (i) for healthy soils in natural ecosystems, protect them from conversion and degradation; (ii) for managed soils, manage in a way to protect and enhance soil biodiversity, health and sustainability and to prevent degradation; and (iii) for degraded soils, restore to full soil health. We have enough knowledge now to move forward with the implementation of best management practices to maintain and improve soil health. This analysis shows that this is not just desirable, it is essential if we are to meet the SDG targets by 2030 and achieve sustainable development more broadly in the decades to come. This article is part of the theme issue 'The role of soils in delivering Nature's Contributions to People'.

The role of soils in regulation and provision of blue and green water.

The United Nations Sustainable Development Goal 6 aims for clean water and sanitation for all by 2030, through eight subgoals dealing with four themes: (i) water quantity and availability, (ii) water quality, (iii) finding sustainable solutions and (iv) policy and governance. In this opinion paper, we assess how soils and associated land and water management can help achieve this goal, considering soils at two scales: local soil health and healthy landscapes. The merging of these two viewpoints shows the interlinked importance of the two scales. Soil health reflects the capacity of a soil to provide ecosystem services at a specific location, taking into account local climate and soil conditions. Soil is also an important component of a healthy and sustainable landscape, and they are connected by the water that flows through the soil and the transported sediments. Soils are linked to water in two ways: through plant-available water in the soil (green water) and through water in surface bodies or available as groundwater (blue water). In addition, water connects the soil scale and the landscape scale by flowing through both. Nature-based solutions at both soil health and landscape-scale can help achieve sustainable future development but need to be embedded in good governance, social acceptance and economic viability. This article is part of the theme issue 'The role of soils in delivering Nature's Contributions to People'.

Integration of hard and soft supervised machine learning for flood susceptibility mapping.

Flooding is a destructive natural phenomenon that causes many casualties and property losses in different parts of the world every year. Efficient flood susceptibility mapping (FSM) can reduce the risk of this hazard, and has become the main approach in flood risk management. In this study, we evaluated the prediction ability of artificial neural network (ANN) algorithms for hard and soft supervised machine learning classification in FSM by using three ANN algorithms (multi-layer perceptron (MLP), fuzzy adaptive resonance theory (FART), self-organizing map (SOM)) with different activation functions (sigmoidal (-S), linear (-L), commitment (-C), typicality (-T)). We used integration of these models for predicting the spatial expansion probability of flood events in the Ajichay river basin, northwest Iran. Inputs to the ANN were spatial data on 10 flood influencing factors (elevation, slope, aspect, curvature, stream power index, topographic wetness index, lithology, land use, rainfall, and distance to the river). The FSMs obtained as model outputs were trained and tested using flood inventory datasets earned based on previous records of flood damage in the region for the Ajichay river basin. Sensitivity analysis using one factor-at-a-time (OFAT) and all factors-at-a-time (AFAT) demonstrated that all influencing factors had a positive impact on modeling to generate FSM, with altitude having the greatest impact and curvature the least. Model validation was carried out using total operating characteristic (TOC) with an area under the curve (AUC). The highest success rate was found for MLP-S (92.1%) and the lowest for FART-T (75.8%). The projection rate in the validation of FSMs produced by MLP-S, MLP-L, FART-C, FART-T, SOM-C, and SOM-T was found to be 90.1%, 89.6%, 71.7%, 70.8%, 83.8%, and 81.1%, respectively. While integration of hard and soft supervised machine learning classification with activation functions of MLP-S and MLP-L showed a strong flood prediction capability for proper planning and management of flood hazards, MLP-S is a promising method for predicting the spatial expansion probability of flood events.

Examining the effects of green revolution led agricultural expansion on net ecosystem service values in India using multiple valuation approaches.

Ecosystem Services (ESs) are bundles of natural processes and functions that are essential for human well-being, subsistence, and livelihoods. The 'Green Revolution' (GR) has substantial impact on the agricultural landscape and ESs in India. However, the effects of GR on ESs have not been adequately documented and analyzed. This leads to the main hypothesis of this work - 'the incremental trend of ESs in India is mainly prompted by GR led agricultural innovations that took place during 1960 - 1970'. The analysis was carried out through five successive steps. First, the spatiotemporal Ecosystem Service Values (ESVs) in Billion US$ for 1985, 1995, and 2005 were estimated using several value transfer approaches. Second, the sensitivity and elasticity of different ESs to land conversion were carried out using coefficient of sensitivity and coefficient of elasticity. Third, the Geographically Weighted Regression model was performed using five explanatory factors, i.e., total crop area, crop production, crop yield, net irrigated area, and cropping intensity, to explore the cumulative and individual effects of these driving factors on ESVs. Fourth, Multi-Layer Perceptron based Artificial Neural Network was employed to estimate the normalized importance of these explanatory factors. Fifth, simple and multiple linear regression modeling was done to assess the linear associations between the driving factors and the ESs. During the observation periods, cropland, forestland and water bodies contributed to 80%-90% of ESVs, followed by grassland, mangrove, wetland and urban built-up. In all three evaluation years, the highest estimated ESVs among the nine ES categories was provided by water regulation, followed by soil formation and soil-water retention, biodiversity maintenance, waste treatment, climate regulation, and greenhouse gas regulation. Among the five explanatory factors, total crop area, crop production, and net irrigated area showed strong positive associations with ESVs, while cropping intensity exhibited a negative association. Therefore, the study reveals a strong association between GR led agricultural expansion and ESVs in India. This study suggests that there should be an urgent need for formulation of rigorous ecosystem management strategies and policies to preserve ecological integrity and flow of uninterrupted ESs and to sustain human well-being.

Effectiveness of soil erosion barriers to reduce sediment connectivity at small basin scale in a fire-affected forest.

Forest fires and post-fire management practices (PFMP) cause changes in the hydrological response of a hillslope. This study evaluates the effect of log erosion barriers (LB) and Easy-Barriers® (EB) on the spatial patterns and values of structural sediment connectivity (SC) in a Mediterranean mountainous pine forest affected by an arson fire in August 2017. A drone flight was done in July 2019 (23 months after the fire and 11 months after the PFMP) to obtain a high-resolution orthomosaic and DEM (at 0.05 m). Two contrasted areas, with and without PFMP, were selected along the same hillslope and 26 small basins were identified: 16 in the treated area (mean area, slope and vegetation recovery of 916 m2, 60% and 25%; with 94 LB and 39 EB) and 10 in the untreated area (1952 m2, 75% and 20%). The aggregated index of sediment connectivity (AIC) was chosen to compute SC in three temporal scenarios: Before and just after the fire and when all PFMP were implemented including the incipient vegetation recovery. Output normalization allowed the comparison of the non-nested basins among them. After accounting the intrinsic differences among the basins and areas, and the temporal changes of SC between the three scenarios, the contribution of the barriers was estimated in 27% from the total decrease of SC in the treated area (-8.5%). The remaining 73% was explained by the vegetation recovery. The effectiveness of the LB (11.3% on average) and EB (13.4%) did not diminish with increasing slope gradients. These percentages become relevant considering the small area affected by the LB (2.8%) and EB (1.3%). Independent metrics (convergence index, flow width, flat areas and LS factor) also reported clear differences between the two areas -higher soil erosive intensity in the untreated area- and in accordance with the AIC results.

Identification of Conservation Priority Zones Using Spatially Explicit Valued Ecosystem Services: A Case from the Indian Sundarbans.

Demarcation of conservation priority zones (CPZs) using spatially explicit models is the new challenge in ecosystem services (ESs) research. This study identifies the CPZs of the Indian Sundarbans by integrating 2 different approaches, that is, ESs and ecosystem health (EH). Five successive steps were followed to conduct the analysis: First, the ESs were estimated using biophysical and economic methods and a hybrid method (that combines biophysical and economic methods); second, the vigor-organization-resilience (VOR) model was used for estimating EH; third, the risk characterization value (RCV) of ESs was measured using the function of EH and ESs; fourth, Pearson correlation test was performed to analyze the interaction between ESs and EH components; and fifth, the CPZs were defined by considering 7 relevant components: ecosystem vigor, ecosystem organization, ecosystem resilience, RCV, EH, ESs, and the correlation between EH and ESs. Among the major ecoregions of the Sundarbans, the highest ESs value in economic terms is provided by the mangrove ecosystem (US$19 144.9 million per year). The highest conservation priority score was projected for the Gosaba block, which is dominated by dense mangrove forests. The estimated CPZs were found to be highly consistent with the existing biodiversity zonations. The outcome of this study could be a reference for environmentalists, land administrators, researchers, and decision makers to design relevant policies to protect the high values of the Sundarbans ecosystem. Integr Environ Assess Manag 2020;16:773-787. © 2020 SETAC.

Examining the effects of forest fire on terrestrial carbon emission and ecosystem production in India using remote sensing approaches.

Remote sensing techniques are effectively used for measuring the overall loss of terrestrial ecosystem productivity and biodiversity due to forest fires. The current research focuses on assessing the impacts of forest fires on terrestrial ecosystem productivity in India during 2003-2017. Spatiotemporal changes of satellite remote sensing derived burn indices were estimated for both fire and normal years to analyze the association between forest fires and ecosystem productivity. Two Light Use Efficiency (LUE) models were used to quantify the terrestrial Net Primary Productivity (NPP) of the forest ecosystem using the open-source and freely available remotely sensed data. A novel approach (delta NPP/delta burn indices) is developed to quantify the effects of forest fires on terrestrial carbon emission and ecosystem production. During 2003-2017, the forest fire intensity was found to be very high (>2000) across the eastern Himalayan hilly region, which is mostly covered by dense forest and thereby highly susceptible to wildfires. Scattered patches of intense forest fires were also detected in the lower Himalayan and central Indian states. The spatial correlation between the burn indices and NPP were mainly negative (-0.01 to -0.89) for the fire-prone states as compared to the other neighbouring regions. Additionally, the linear approximation between the burn indices and NPP showed a positive relation (0.01 to 0.63), suggesting a moderate to high impact of the forest fires on the ecosystem production and terrestrial carbon emission. The present approach has the potential to quantify the loss of ecosystem productivity due to forest fires.

Responses of ecosystem services to natural and anthropogenic forcings: A spatial regression based assessment in the world's largest mangrove ecosystem.

Most of the Earth's Ecosystem Services (ESs) have experienced a decreasing trend in the last few decades, primarily due to increasing human dominance in the natural environment. Identification and categorization of factors that affect the provision of ESs from global to local scales are challenging. This study makes an effort to identify the key driving factors and examine their effects on different ESs in the Sundarbans region, India. We carry out the analysis following five successive steps: (1) quantifying biophysical and economic values of ESs using three valuation approaches; (2) identifying six major driving forces on ESs; (3) categorizing principal data components with dimensionality reduction; (4) constructing multivariate regression models with variance partitioning; (5) implementing six spatial regression models to examine the causal effects of natural and anthropogenic forcings on ESs. Results show that climatic factors, biophysical factors, and environmental stressors significantly affect the ESs. Among the six driving factors, climate factors are highly associated with the ESs variation and explain the maximum model variances (R2 = 0.75-0.81). Socioeconomic (R2 = 0.44-0.66) and development (R2 = 27-0.44) factors have weak to moderate effects on the ESs. Furthermore, the joint effects of the driving factors are much higher than their individual effects. Among the six spatial regression models, Geographical Weighted Regression (GWR) performs the most accurately and explains the maximum model variances. The proposed hybrid valuation method aggregates biophysical and economic estimates of ESs and addresses methodological biases existing in the valuation process. The presented framework can be generalized and applied to other ecosystems at different scales. The outcome of this study could be a reference for decision-makers, planners, land administrators in formulating a suitable action plan and adopting relevant management practices to improve the overall socio-ecological status of the region.

TERRAenVISION: Science for Society. Environmental issues today.

Our Planet suffers from human activities. As scientists, we know more and more about our environment, about processes, rates of change, new threats, and risks. However, the challenges we face seem to grow quicker than the solutions we can create. To achieve sustainability, the key is to make solutions not only functional from an environmental point of view, but also socially acceptable and economically viable. In this context, the TERRAenVISION conference series gathers diverse groups of scientists to discuss sustainability. The first TERRAenVISION meeting in January 2018 was framed around 7 themes: (1) Climate Change: Mitigation and Adaptation, (2) Water Resources: Quality and Quantity, (3) Land Degradation and Restoration, (4) Nature-based Solutions, (5) Fire in the Earth System, Effects, and Prevention, (6) Ecosystem Services and Health, and (7) Science Interface with Policy and Public. Among the works presented in the conference, this Special Issue collates 22 papers that illustrate the best, problems and solutions the scientific community is currently working on to achieve sustainability. Similar to the concept of the SDGs, paper subjects often intertwine and bridge the conference themes. The papers are grouped in two main chapters dealing with Water and Land, with two additional cross cutting chapters of Scientific Tools and Science-Policy Interface. Drawing from the conclusions of these works as well as those of the TERRAenVISION 2018 conference, we make recommendations regarding raising awareness, connecting scientific fields, and supporting robust economic and policy transitions.

Post-fire management treatment effects on soil properties and burned area restoration in a wildland-urban interface, Haifa Fire case study.

In November 2016, the urban dry streams (wadis) of the city of Haifa in Northern Israel were on fire. However, it was not just the fire that was threatening urban areas. Post-fire precipitation could turn into urban floods, further aggravating the fire damages. Several months after the fire a considerable restoration effort was initiated to restore the burned areas and mitigate future events. For urban forests the rehabilitation strategy was planned and implemented according to the topographic structure of the burned site and anticipated soil erosion. Accordingly, various post-fire management techniques were used: salvage-logging, afforestation, log erosion barriers and coconut fibre-webs. This study aimed to look at the effects of these methods on soil properties, namely, gravimetrical soil moisture, soil organic matter content, pH, electrical conductivity, hydraulic conductivity and soil water repellency. Results indicate that the control (burned, non-managed) site was the highest in soil moisture, organic matter and electrical conductivity compared to all other sites, however, the existence of ash cover made the response to precipitation unpredictable. The hydraulic conductivity (K) of the black ash (24.1 ± 8.6 mm/h), the white ash (19.0 ± 10.7 mm/h) and the disturbed (mixed) ash (11.7 ± 3.7 mm/h) were significantly higher than the underlying soil (3.3 ± 0.7 mm/h). As a result of these differences in K value, precipitation only infiltrates through the ash layers and then flows along the interface of the ash and the soil, triggering soil erosion. Most of the sites that were salvage logged showed signs of erosion. The log barriers were only effective for downstream areas. The afforestation could help to homogenise the soil, but the vegetation cover would be less dense and stable than after natural reforestation. Furthermore, the coconut fibre webs helped to improve the soil water retention and decreased the direct impact of rainfall.

Impact of desertification on soil and plant nutrient stoichiometry in a desert grassland.

Grassland degradation resulting from desertification often alters the carbon (C), nitrogen (N) and phosphorus (P) cycles within grassland ecosystems. To estimate the effects of desertification on the C, N, and P concentrations and C:N:P stoichiometry of plants and soil, we examined C, N, and P concentrations in plant tissues (leaves, roots and litter) and soil across five degrees of desertification in the desert grassland of Ningxia, China (control, light, moderate, severe and very severe desertification stages). The C, N, and P concentrations and C:N:P stoichiometry of the leaves, roots and litter differed among the different desertification stages. Desertification resulted in opposing trends between the leaf N concentration and leaf C:N ratio. With the exception of the very severe desertification stage, the leaf N:P ratio decreased over the process of grassland desertification. The soil C, N, and P concentrations and soil N:P and C:P ratios decreased significantly along the grassland desertification gradient. In contrast, the soil C:N ratio remained relatively stable during desertification (10.85 to 11.48). The results indicate that desertification is unfavourable to C and N fixation and has a negative effect on the ecosystem structure and function of desert grassland.

Ecosystem service value assessment of a natural reserve region for strengthening protection and conservation.

Ecosystem Services (ESs) refer to the direct and indirect contributions of ecosystems to human well-being and subsistence. Ecosystem valuation is an approach to assign monetary values to an ecosystem and its key ecosystem goods and services, generally referred to as Ecosystem Service Value (ESV). We have measured spatiotemporal ESV of 17 key ESs of Sundarbans Biosphere Reserve (SBR) in India using temporal remote sensing (RS) data (for years 1973, 1988, 2003, 2013, and 2018). These mangrove ecosystems are crucial for providing valuable supporting, regulatory, provisioning, and cultural ecosystem services. We have adopted supervised machine learning algorithms for classifying the region into different ecosystem units. Among the used machine learning models, Support Vector Machine (SVM) and Random Forest (RF) algorithms performed the most accurate and produced the best classification estimates with maximum kappa and an overall accuracy value. The maximum ESV (derived from both adjusted and non-adjusted units, million US$ year-1) is produced by mangrove forest, followed by the coastal estuary, cropland, inland wetland, mixed vegetation, and finally urban land. Out of all the ESs, the waste treatment (WT) service is the dominant ecosystem service of SBR. Additionally, the mangrove ecosystem was found to be the most sensitive to land use and land cover changes. The synergy and trade-offs between the ESs are closely associated with the spatial extent. Therefore, accurate estimates of ES valuation and mapping can be a robust tool for assessing the effects of poor decision making and overexploitation of natural resources on ESs.

Gully erosion susceptibility assessment and management of hazard-prone areas in India using different machine learning algorithms.

Gully erosion is one of the most effective drivers of sediment removal and runoff from highland areas to valley floors and stable channels, where continued off-site effects of water erosion occur. Gully initiation and development is a natural process that greatly impacts natural resources, agricultural activities, and environmental quality as it promotes land and water degradation, ecosystem disruption, and intensification of hazards. In this research, an attempt is made to produce gully erosion susceptibility maps for the management of hazard-prone areas in the Pathro River Basin of India using four well-known machine learning models, namely, multivariate additive regression splines (MARS), flexible discriminant analysis (FDA), random forest (RF), and support vector machine (SVM). To support this effort, observations from 174 gully erosion sites were made using field surveys. Of the 174 observations, 70% were randomly split into a training data set to build susceptibility models and the remaining 30% were used to validate the newly built models. Twelve gully erosion conditioning factors were assessed to find the areas most susceptible to gully erosion. The predisposing factors were slope gradient, altitude, plan curvature, slope aspect, land use, slope length (LS), topographical wetness index (TWI), drainage density, soil type, distance from the river, distance from the lineament, and distance from the road. Finally, the results from the four applied models were validated with the help of ROC (Receiver Operating Characteristics) curves. The AUC value for the RF model was calculated to be 96.2%, whereas for those for the FDA, MARS, and SVM models were 84.2%, 91.4%, and 88.3%, respectively. The AUC results indicated that the random forest model had the highest prediction accuracy, followed by the MARS, SVM, and FDA models. However, it could be concluded that all the machine learning models performed well according to their prediction accuracy. The produced GESMs can be very useful for land managers and policy makers as they can be used to initiate remedial measures and erosion hazard mitigation in prioritized areas.

Land subsidence hazard modeling: Machine learning to identify predictors and the role of human activities.

Land subsidence caused by land use change and overexploitation of groundwater is an example of mismanagement of natural resources, yet subsidence remains difficult to predict. In this study, the relationship between land subsidence features and geo-environmental factors is investigated by comparing two machine learning algorithms (MLA): maximum entropy (MaxEnt) and genetic algorithm rule-set production (GARP) algorithms in the Kashmar Region, Iran. Land subsidence features (N = 79) were mapped using field surveys. Land use, lithology, the distance from traditional groundwater abstraction systems (Qanats), from afforestation projects, from neighboring faults, and the drawdown of groundwater level (DGL) (1991-2016) were used as predictive variables. Jackknife resampling showed that DGL, distance from afforestation projects, and distance from Qanat systems are major factors influencing land subsidence, with geology and faults being less important. The GARP algorithm outperformed the MaxEnt algorithm for all performance metrics. The performance of both models, as measured by the area under the receiver-operator characteristic curve (AUROC), decreased from 88.9-94.4% to 82.5-90.3% when DGL was excluded as a predictor, though the performance of GARP was still good to excellent even without DGL. MLAs produced maps of subsidence risk with acceptable accuracy, both with and without data on groundwater drawdown, suggesting that MLAs can usefully inform efforts to manage subsidence in data-scarce regions, though the highest accuracy requires data on changes in groundwater level.