Modeling multi-decadal mangrove leaf area index in response to drought along the semi-arid southern coasts of Iran

Mangrove forest health condition from space and the use of in situ data

Remote sensing (RS) is a widely used technology for monitoring mangrove forests, but there are some inconsistencies in their capacity to assess mangrove ecosystem health status. Our review aims to investigate how RS and in situ data are being applied together in assessments of mangrove forest health conditions. Our results showed that commonly the concept of mangrove ecosystem health was not defined and indicators that were not clearly related to it were applied. Furthermore, low to medium spatial resolution satellites were more used to detect changes in the mangrove forests' environmental condition than the high spatial resolution ones, and the use of RS with data collected in situ was present in only 39% of the articles. We concluded that studies consider vegetation indexes the same as vigor, so the mangrove ecosystem health; and vigor as the only indicator needed, not using in situ data to validate the mangrove health status.

Assessment of parent and alkyl -PAHs in surface sediments of Iranian mangroves on the northern coast of the Persian Gulf: Spatial accumulation distribution, influence factors, and ecotoxicological risks

Spatial patterns, potential origins, and ecotoxicological risk of alkylated (APAH) -and parent -(PPAH) polycyclic aromatic hydrocarbons (PAHs) were studied in mangrove surface sediments along the northern coasts of the Persian Gulf, Iran. The mean total concentrations (ngg-1dw) ∑32PAH, ∑PPAHs and ∑APAHs in sediments were 3482 (1689-61228), 2642 (1109-4849), and 840 (478-1273), respectively. The spatial variability was similar among these PAH groups, with the highest levels occurring in Nayband National Marine Park (NNMP). Physicochemical environmental factors, such as sediment grain size, and total organic carbon (TOC) contents, are significant factors of PAH distribution. These findings suggest that PAH pollution level is moderate-to-high, supporting the current view that mangrove ecosystems are under intensive anthropogenic impacts, such as petrochemical, oil and gas loads, port activities, and urbanization. Non-parametric multidimensional scaling (NPMDS) ordination demonstrated that NNMP mangrove is the critical site exhibiting high loading of PAH pollutants. Here, for the first time in this region, Soil quality guidelines (SQGs), Toxic equivalency quotient (TEQ), Mutagenic equivalency quotient (MEQ), and composition indices comprising Mean maximum permissible concentration quotient (m-MPC-Q), and Mean effect range median quotient (m-ERM-Q) methods were used to have a comprehensive risk assessment for PAH compounds and confirmed medium-to-high ecological risks of PAHs in the study area, particularly in the western part of the Gulf, highlighting the industrial impacts on the environment.

Environmental flow assessment for intermittent rivers supporting the most poleward mangroves

The most vulnerable and dynamic ecosystems in terms of response to climate change and fluctuations in hydrological conditions are mangroves, particularly those located on the edge of their latitudinal range limits. The four primary Iranian mangrove forest sites: Nayband, Qeshm, Gabrik, and Govatr, located in the northern part of the Persian Gulf and the Gulf of Oman already exist near the limit of their tolerance to extreme temperature, precipitation, and salinity. Due to extreme climate conditions at these locations, the mangrove trees are usually smaller and less dense as compared with mangroves closer to the equator complicating their monitoring and mapping efforts. Despite the growing attention to the ecological benefits of mangrove forests and their importance in climate change mitigation, there are still a few studies on these marginal mangroves. Therefore, we investigated whether the variation in mangrove ecosystem health is related to the changes in physical parameters and differs between estuarine and sea-side locations. We developed a comprehensive database on NDVI values, associated rainfall, temperature, and river flow based on in-situ and remote sensing measurements. By understanding the normal hydrologic patterns that control the distribution and growth of mangroves in arid and semi-arid regions, we are questioning the need for environmental flow allocation to restore mangrove ecosystem health. This brings us to the second gap in the literature and the need for further studies on Environmental Flow assessment for intermittent and ephemeral rivers. Alike other mangroves studied, forests showed greenness seasonality, positively correlated with rainfall, and negatively correlated with temperature. As there was no clear difference between estuarine and marine sites, freshwater influence in the form of river flow, unlike temperature, cannot be considered a major limiting factor. Nevertheless, during prolonged droughts mangroves could benefit from the recommended allocation of Environmental Flow during the cold period (November-March).

Experimental climate change impacts on Baltic coastal wetland plant communities

Coastal wetlands provide a range of important ecosystem services, yet they are under threat from a range of stressors including climate change. This is predominantly as a result of alterations to the hydroregime and associated edaphic factors. We used a three-year mesocosm experiment to assess changes in coastal plant community composition for three plant communities in response to altered water level and salinity scenarios. Species richness and abundance were calculated by year and abundance was plotted using rank abundance curves. The permutational multivariate analysis of variance with Bray-Curtis dissimilarity was used to examine differences among treatments in plant community composition. A Non-metric Multi-dimensional Scaling analysis (NMDS) was used to visualize the responses of communities to treatments by year. Results showed that all three plant communities responded differently to altered water levels and salinity. Species richness and abundance increased significantly in an Open Pioneer plant community while Lower and Upper Shore plant communities showed less change. Species abundances changed in all plant communities with shifts in species composition significantly influenced by temporal effects and treatment. The observed responses to experimentally altered conditions highlight the need for conservation of these important ecosystems in the face of predicted climate change, since these habitats are important for wading birds and livestock grazing.

Monitoring detailed mangrove hurricane damage and early recovery using multisource remote sensing data

Due to their location in tropical latitudes, mangrove forests are susceptible to the impact of hurricanes and can be vastly damaged by their high-speed winds. Given the logistic difficulties regarding field surveys in mangroves, remote sensing approaches have been considered a reliable alternative. We quantified trends in damage and early signs of canopy recovery in a fringe Rhizophora mangle area of Marismas Nacionales, Mexico, following the landfall of Hurricane Willa in October 2018. We monitored (2016-2021) broad canopy defoliation using 21 vegetation indices (VI) from the Google Earth Engine tool (GEE). We also mapped a detailed canopy fragmentation and developed digital surface models (DSM) during five study periods (2018-2021) with a consumer-grade unmanned aerial vehicle (UAV) over an area of 100 ha. Based on optical data from the GEE time series, results indicated an abrupt decline in the overall mangrove canopy. The VARI index was the most reliable VI for the mangrove canopy classification from a standard RGB sensor. The impact of the hurricane caused an overall canopy defoliation of 79%. The series of UAV orthomosaics indicate a gradual recovery in the mangrove canopy, while the linear model predicts at least 8.5 years to reach pre-impact mangrove cover conditions. However, the sequence of DSM estimates that the vertical canopy configuration will require a longer time to achieve its original structure.

Quantifying mangrove leaf area index from Sentinel-2 imagery using hybrid models and active learning

Mangrove forests provide vital ecosystem services. The increasing threats to mangrove forest extent and fragmentation can be monitored from space. Accurate spatially explicit quantification of key vegetation characteristics of mangroves, such as leaf area index (LAI), would further advance our monitoring efforts to assess ecosystem health and functioning. Here, we investigated the potential of radiative transfer models (RTM), combined with active learning (AL), to estimate LAI from Sentinel-2 spectral reflectance in the mangrove-dominated region of Ngoc Hien, Vietnam. We validated the retrieval of LAI estimates against in-situ measurements based on hemispherical photography and compared against red-edge NDVI and the Sentinel Application Platform (SNAP) biophysical processor. Our results highlight the performance of physics-based machine learning using Gaussian processes regression (GPR) in combination with AL for the estimation of mangrove LAI. Our AL-driven hybrid GPR model substantially outperformed SNAP (R2 = 0.77 and 0.44 respectively) as well as the red-edge NDVI approach. Comparing two canopy RTMs, the highest accuracy was achieved by PROSAIL (RMSE = 0.13 m2.m-2, NRMSE = 9.57%, MAE = 0.1 m2.m-2). The successful retrieval of mangrove LAI from Sentinel-2 can overcome extensive reliance on scarce in-situ measurements for training seen in other approaches and present a more scalable applicability by relying on the universal principles of physics in combination with uncertainty estimates. AL-based GPR models using RTM simulations allow us to adapt the genericity of RTMs to the peculiarities of distinct ecosystems such as mangrove forests with limited ancillary data. These findings bode potential for retrieving a wider range of vegetation variables to quantify large-scale mangrove ecosystem dynamics in space and time.

Microbial diversity and ecological interactions of microorganisms in the mangrove ecosystem: Threats, vulnerability, and adaptations

Mangroves are among the world's most productive ecosystems and a part of the "blue carbon" sink. They act as a connection between the terrestrial and marine ecosystems, providing habitat to countless organisms. Among these, microorganisms (e.g., bacteria, archaea, fungi, phytoplankton, and protozoa) play a crucial role in this ecosystem. Microbial cycling of major nutrients (carbon, nitrogen, phosphorus, and sulfur) helps maintain the high productivity of this ecosystem. However, mangrove ecosystems are being disturbed by the increasing concentration of greenhouse gases within the atmosphere. Both the anthropogenic and natural factors contribute to the upsurge of greenhouse gas concentration, resulting in global warming. Changing climate due to global warming and the increasing rate of human interferences such as pollution and deforestation are significant concerns for the mangrove ecosystem. Mangroves are susceptible to such environmental perturbations. Global warming, human interventions, and its consequences are destroying the ecosystem, and the dreadful impacts are experienced worldwide. Therefore, the conservation of mangrove ecosystems is necessary for protecting them from the changing environment-a step toward preserving the globe for better living. This review highlights the importance of mangroves and their microbial components on a global scale and the degree of vulnerability of the ecosystems toward anthropic and climate change factors. The future scenario of the mangrove ecosystem and the resilience of plants and microbes have also been discussed.

Incorporating Vegetation Type Transformation with NDVI Time-Series to Study the Vegetation Dynamics in Xinjiang

Time-series normalized difference vegetation index (NDVI) is commonly used to conduct vegetation dynamics, which is an important research topic. However, few studies have focused on the relationship between vegetation type and NDVI changes. We investigated changes in vegetation in Xinjiang using linear regression of time-series MOD13Q1 NDVI data from 2001 to 2020. MCD12Q1 vegetation type data from 2001 to 2019 were used to analyze transformations among different vegetation types, and the relationship between the transformation of vegetation type and NDVI was analyzed. Approximately 63.29% of the vegetation showed no significant changes. In the vegetation-changed area, approximately 93.88% and 6.12% of the vegetation showed a significant increase and decrease in NDVI, respectively. Approximately 43,382.82 km2 of sparse vegetation and 25,915.44 km2 of grassland were transformed into grassland and cropland, respectively. Moreover, 17.4% of the area with transformed vegetation showed a significant increase in NDVI, whereas 14.61% showed a decrease in NDVI. Furthermore, in areas with NDVI increased, the mean NDVI slopes of pixels in which sparse vegetation transferred to cropland, sparse vegetation transferred to grassland, and grassland transferred to cropland were 9.8 and 3.2 times that of sparse vegetation, and 1.97 times that of grassland, respectively. In areas with decreased NDVI, the mean NDVI slopes of pixels in which cropland transferred to sparse vegetation, grassland transferred to sparse vegetation were 1.75 and 1.36 times that of sparse vegetation, respectively. The combination of vegetation type transformation NDVI time-series can assist in comprehensively understanding the vegetation change characteristics.

Brazilian Semi-Arid Mangroves-Associated Microbiome as Pools of Richness and Complexity in a Changing World

Mangrove microbiomes play an essential role in the fate of mangroves in our changing planet, but the factors regulating the biogeographical distribution of mangrove microbial communities remain essentially vague. This paper contributes to our understanding of mangrove microbiomes distributed along three biogeographical provinces and ecoregions, covering the exuberant mangroves of Amazonia ecoregion (North Brazil Shelf) as well as mangroves located in the southern limit of distribution (Southeastern ecoregion, Warm Temperate Southwestern Atlantic) and mangroves localized on the drier semi-arid coast (Northeastern ecoregion, Tropical Southwestern Atlantic), two important ecotones where poleward and landward shifts, respectively, are expected to occur related to climate change. This study compared the microbiomes associated with the conspicuous red mangrove (Rhizophora mangle) root soils encompassing soil properties, latitudinal factors, and amplicon sequence variants of 105 samples. We demonstrated that, although the northern and southern sites are over 4,000 km apart, and despite R. mangle genetic divergences between north and south populations, their microbiomes resemble each other more than the northern and northeastern neighbors. In addition, the northeastern semi-arid microbiomes were more diverse and displayed a higher level of complexity than the northern and southern ones. This finding may reflect the endurance of the northeast microbial communities tailored to deal with the stressful conditions of semi-aridity and may play a role in the resistance and growing landward expansion observed in such mangroves. Minimum temperature, precipitation, organic carbon, and potential evapotranspiration were the main microbiota variation drivers and should be considered in mangrove conservation and recovery strategies in the Anthropocene. In the face of changes in climate, land cover, biodiversity, and chemical composition, the richness and complexity harbored by semi-arid mangrove microbiomes may hold the key to mangrove adaptability in our changing planet.

Farmers' decision to use drought early warning system in developing countries

Drought is a persistent, sluggish natural disaster in developing countries that has generated a financial burden and an unstable climate. Farmers should adopt early warning systems (EWS) in their strategies for monitoring drought to reduce its serious consequences. However, farmers in developing countries are reluctant to use EWS as their management strategies. Hence, the aim of this study was to investigate the decision of farmers to use climate knowledge through the model of farming activity in Kermanshah Township, Iran. A surveyor questionnaire was used to gather data from 370 wheat farmers using random sampling methods in multi-stage clusters. Results revealed that the decision to use climate information is affected by personal factors, attitude towards climate information, objectives of using climate information, and external/physical farming factors. The result of this study has implications for drought management practitioners. To be specific, the results can aid policymakers to design early alert programs to minimize the risk of drought and thus move from conventional to climate smart agriculture.

Extrapolating canopy phenology information using Sentinel-2 data and the Google Earth Engine platform to identify the optimal dates for remotely sensed image acquisition of semiarid mangroves

Continuum monitoring of mangrove ecosystems is required to maintain and improve upon national mangrove conservation strategies. In particular, mangrove canopy assessments using remote sensing methods can be undertaken rapidly and, if freely available, optimize costs. Although such spaceborne data have been used for such purposes, their application to map mangroves at the species level has been limited by the capacity to provide continuous data. The objective of this study was to assess mangrove seasonal patterns using seven multispectral vegetation indices based on a Sentinel-2 (S2) time series (July 2018 to October 2019) to assess phenological trajectories of various semiarid mangrove classes in the Google Earth Engine platform using Fourier analysis for an area located in Western Mexico. The results indicate that the months from November through December and from May through July were critical in mangrove species discrimination using the EVI2, NDVI, and VARI series. The Random Forest classification accuracy for the S2 image was calculated at 79% during the optimal acquisition period (June 25, 2019), whereas only 55% accuracy was calculated for the non-optimal image acquired date (March 2, 2019). Although mangroves are considered evergreen forests, the phenological pattern of various mangrove canopies, based on these indices, were shown to be very similar to the surrounding land-based semiarid deciduous forest. Consequently, it is believed that the rainfall pattern is likely to be the key environmental factor driving mangrove phenology in this semiarid coastal system and thus the degree of success in mangrove remote sensing classification endeavors. Identifying the optimal dates when canopy spectral conditions are ideal in achieving mangrove species discrimination could be of utmost importance when purchasing more expensive very-high spatial resolution satellite images or collecting spatial data from UAVs.

Carbon sequestration and storage in Norwegian Arctic coastal wetlands: Impacts of climate change

Coastal wetlands contain some of the largest stores of pedologic and biotic carbon pools, and climate change is likely to influence the ability of these ecosystems to sequester carbon. Recent studies have attempted to provide data on carbon sequestration in both temperate and tropical coastal wetlands. Alteration of Arctic wetland carbon sequestration rates is also likely where coastal forcing mechanisms interact directly with these coastal systems. At present there are no data available to provide a detailed understanding of present day and historical carbon sequestration rates within Arctic coastal wetlands. In order to address this knowledge gap, rates of carbon sequestration were assessed within five Arctic coastal wetland sites in Norway. This was undertaken using radiometric dating techniques (²¹⁰Pb and ¹³⁷Cs) to establish a geochronology for recent wetland development, and soil carbon stocks were estimated from cores. Average carbon sequestration rates were varied, both between sites and over time, ranging between 19 and 603 g C m² y^-1, and these were correlated with increases in the length of the growing season. Stocks ranged between 3.67 and 13.79 Mg C ha^-1, which is very low compared with global average estimations for similar coastal systems, e.g. 250 Mg C ha^-1 for temperate salt marshes, 280 Mg C ha^-1 for mangroves, and 140 Mg C ha^-1 for seagrasses. This is most likely due to isostatic uplift and sediment accretion historically outpacing sea level rise, which results in wetland progradation and thus a continuous formation of new marsh with thin organic soil horizons. However, with increasing rates of sea level rise it is uncertain whether this trend is set to continue or be reversed.

The Use of Unmanned Aerial Vehicles to Determine Differences in Vegetation Cover: A Tool for Monitoring Coastal Wetland Restoration Schemes

Managed realignment (MR) sites are being implemented to compensate for the loss of natural saltmarsh habitat due to sea level rise and anthropogenic pressures. However, MR sites have been recognised to have lower morphological variability and coverage of saltmarsh vegetation than natural saltmarsh sites, which have been linked with the legacy of the historic (terrestrial) land use. This study assesses the relationship between the morphology and vegetation coverage in three separate zones, associated with the legacy of historic reclamation, of a non-engineered MR site. The site was selected due to the phased historical reclamation, and because no pre-breaching landscaping or engineering works were carried out prior to the more recent and contemporary breaching of the site. Four vegetation indices (Excess Green Index, Green Chromatic Coordinate, Green-Red Vegetation Index, and Visible Atmospherically Resistant Index) were calculated from unmanned aerial vehicle imagery; elevation, slope, and curvature surface models were calculated from a digital surface model (DSM) generated from the same imagery captured at the MR site. The imagery and DSM summarised the three zones present within the MR site and the adjacent external natural marsh, and were used to examine the site for areas of differing vegetation cover. Results indicated statistically significant differences between the vegetation indices across the three zones. Statistically significant differences in the vegetation indices were also found between the three zones and the external natural saltmarsh. However, it was only in the zone nearest the breach, and for three of the four indices, that a moderate to strong correlation was found between elevation and the vegetation indices (r = 0.53 to 0.70). This zone was also the lowest in elevation and exhibited the lowest average value for all indices. No relationship was found between the vegetation indices and either the slope or curvature in any of the zones. The approach outlined in this paper provides coastal managers with a relatively low-cost, low-field time method of assessing the areas of vegetation development in MR sites. Moreover, the findings indicate the potential importance of considering the historic morphological and sedimentological changes in the MR sites. By combining data on the areas of saltmarsh colonisation with a consideration of the site’s morphological and reclamation history, the areas likely to support saltmarsh vegetation can be remotely identified in the design of larger engineered MR sites maximising the compensation for the loss of saltmarsh habitat elsewhere.

Vulnerability of coastal communities to climate change: Thirty-year trend analysis and prospective prediction for the coastal regions of the Persian Gulf and Gulf of Oman

This study relates changes in social vulnerability of 20 counties on the northern coasts of the Persian Gulf (PG) and the Gulf of Oman (GO) over a 30-year period (1988-2017) to changing socio-economic conditions and environmental (climate) hazard. Social vulnerability in 2030, 2040 and 2050 is predicted based on the RCP8.5 climate change scenario that projects drought intensities and rising sea levels. Social vulnerability was based on the three dimensions of sensitivity, exposure, and adaptive capacity using 18 socio-economic and five climate indicators identified by experts. All but one indicator related very strongly to the dimension it sought to represent. Despite improvements in adaptive capacity over time, social vulnerability increased between 1988 and 2017 and rates of change accelerated after change point years that occurred between 1998 and 2002 in most counties. Extrapolating past changes of each indicator over time enabled forecasts of social vulnerability in the future. While social variability decreased between 2017 and 2030, it increased again between 2030 and 2050. The lowest future social vulnerability is expected along the eastern PG coast, the greatest along the western PG and the GO. The worsening of socio-economic indicators contributed to increased sensitivity, and increased drought intensities plus the expected rise in sea levels will lead to social vulnerabilities in 2050 comparable to present levels. Between 1.4 and 1.7 M people will live in areas that are likely submerged by water in the future. About 80% of these people live in six counties with variable social vulnerabilities. While counties with lower social variabilities might be better able to cope with the challenges posed by climate change, adaptation programs to enhance the resilience of the residents in these and the remaining counties along the PG and the GO need to be implemented soon to avoid uncontrolled mass migration of millions of people from the region.

Spatial modeling of exposure of mangrove ecosystems to multiple environmental hazards

Determining the level of ecosystems exposure to multiple environmental hazards or risk factors is of paramount importance for developing, adopting, and planning management strategies to minimize the harmful effects of these hazards. We quantified the level of exposure of mangroves on the northern coasts of the Persian Gulf (PG) and the Gulf of Oman (GO) between 1986 and 2019 to eight environmental hazards, i.e., drought, maximum temperatures, rising sea levels, change of freshwater inflows to coasts, extreme storm surges, significant wave height (SWH), seaward edge retreat in the mangroves, and fishery intensity. Based on expert opinion, fuzzy weights were used to integrate these exposures into a single index (EI) for the region. Experts gave the greatest weight/importance to the risks posed by sea-level rise and seaward retreat of mangroves and the lowest risk to significant wave height and fishery intensity in coastal waters. The overall EI and six of eight individual variables (except fishery intensity and maximum temperatures) pointed to exposure levels of mangroves that increased from the coasts of the PG (EI 0.69) to the GO (EI 6.69). Since these hazards are expected to continue in the future, local/regional management responses should focus on minimizing regional anthropogenic threats and halt conversion of natural areas to agricultural and open areas to maintain freshwater inputs to coastal areas, particularly on the GO. Further, uplands that may serve as future refugia into which mangroves may expand over time as sea levels continue to rise should be protected from development. This was the first study that used an analytic framework to compute a mangrove exposure index to a suite of physical and socio-economic hazards across a region. This framework may provide insights into cost-effective resilience-based design and management of socio-ecologically coupled ecosystems in an era of increasing types and intensities of environmental hazards.

Environmental risk of trace elements in mangrove ecosystems: An assessment of natural vs oil and urban inputs

The petrochemical industry and urban activities are widely recognized worldwide as a source of pollution to mangrove environments. They can supply pollutants such as trace elements that can modify the ecosystem structure and associated services, as well as human populations. Through geochemical data, multivariate statistical analysis and pollution indices such as the enrichment factor (EF), geo-accumulation index (Igeo), adverse effect index (AEI) and the pollution load index (PLI), we evaluated the factors that control trace element distribution, punctual sources and determined the pollution level of sediments and their potential biological impact in the mangrove ecosystem of Isla del Carmen, Mexico. The factor and cluster analysis highlighted that the distribution of trace elements is influenced by the mineralogy, texture as well as urban derived sources. The pollution indices showed values in the punctual sources from the urban area of EF > 10, Igeo > 3, AEI > 3, PLI > 1 by Cu, Zn and Pb. Finally, the results revealed that mangroves from Isla del Carmen has a major influence from urban activities and natural sources rather than oil industry and also indicate a degraded environment as a result of anthropogenic activities that could have knock-on effect for human health if polluted marine organisms derived from the urban mangroves are consumed. CAPSULE ABSTRACT: Surface sediments show the influence of point sources on selected trace element concentrations correlated with human activities within the mangroves of Isla del Carmen, Mexico.

Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment

We used AdaBoost (AB), alternating decision tree (ADTree), and their combination as an ensemble model (AB-ADTree) to spatially predict landslides in the Cameron Highlands, Malaysia. The models were trained with a database of 152 landslides compiled using Synthetic Aperture Radar Interferometry, Google Earth images, and field surveys, and 17 conditioning factors (slope, aspect, elevation, distance to road, distance to river, proximity to fault, road density, river density, normalized difference vegetation index, rainfall, land cover, lithology, soil types, curvature, profile curvature, stream power index, and topographic wetness index). We carried out the validation process using the area under the receiver operating characteristic curve (AUC) and several parametric and non-parametric performance metrics, including positive predictive value, negative predictive value, sensitivity, specificity, accuracy, root mean square error, and the Friedman and Wilcoxon sign rank tests. The AB model (AUC = 0.96) performed better than the ensemble AB-ADTree model (AUC = 0.94) and successfully outperformed the ADTree model (AUC = 0.59) in predicting landslide susceptibility. Our findings provide insights into the development of more efficient and accurate landslide predictive models that can be used by decision makers and land-use managers to mitigate landslide hazards.

Performance Evaluation of Machine Learning Methods for Forest Fire Modeling and Prediction

Predicting and mapping fire susceptibility is a top research priority in fire-prone forests worldwide. This study evaluates the abilities of the Bayes Network (BN), Naïve Bayes (NB), Decision Tree (DT), and Multivariate Logistic Regression (MLP) machine learning methods for the prediction and mapping fire susceptibility across the Pu Mat National Park, Nghe An Province, Vietnam. The modeling methodology was formulated based on processing the information from the 57 historical fires and a set of nine spatially explicit explanatory variables, namely elevation, slope degree, aspect, average annual temperate, drought index, river density, land cover, and distance from roads and residential areas. Using the area under the receiver operating characteristic curve (AUC) and seven other performance metrics, the models were validated in terms of their abilities to elucidate the general fire behaviors in the Pu Mat National Park and to predict future fires. Despite a few differences between the AUC values, the BN model with an AUC value of 0.96 was dominant over the other models in predicting future fires. The second best was the DT model (AUC = 0.94), followed by the NB (AUC = 0.939), and MLR (AUC = 0.937) models. Our robust analysis demonstrated that these models are sufficiently robust in response to the training and validation datasets change. Further, the results revealed that moderate to high levels of fire susceptibilities are associated with ~19% of the Pu Mat National Park where human activities are numerous. This study and the resultant susceptibility maps provide a basis for developing more efficient fire-fighting strategies and reorganizing policies in favor of sustainable management of forest resources.

Comparison of three hybrid models to simulate land use changes: a case study in Qeshm Island, Iran

Land use change simulation is an important issue for its role in predicting future trends and providing implications for sustainable land management. Hybrid models have become a recognized strategy to inform decision-makers, but further attempts are needed to warrant the reliability of their projected results. In view of this, three hybrid models, including the cellular automata-Markov chain-artificial neural network, cellular automata-Markov chain-logistic regression, and Markov chain-artificial neural network, were applied to simulate land use change on the largest island in Iran, Qeshm Island. The Figure of Merit (FOM) was used to measure the modeling accuracy of the simulations, with the FOMs for the three models 6.7, 5.1, and 4.5, respectively. Consequently, the cellular automata-Markov chain-artificial neural network most precisely simulates land use change on Qeshm Island and is, thus, used to simulate land use change until 2026. The simulation shows that the incremental trend of the built-up class will continue in the coming years. Meanwhile, the areas of valuable ecosystems, such as mangroves, tend to decrease. Despite the protection plans for mangroves, these areas require more attention and conservation planning. This study demonstrates a referential example to select the proper land use models for informing planning and management in similar coastal zones.

Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam

The main aim of this study is to assess groundwater potential of the DakNong province, Vietnam, using an advanced ensemble machine learning model (RABANN) that integrates Artificial Neural Networks (ANN) with RealAdaBoost (RAB) ensemble technique. For this study, twelve conditioning factors and wells yield data was used to create the training and testing datasets for the development and validation of the ensemble RABANN model. Area Under the Receiver Operating Characteristic (ROC) curve (AUC) and several statistical performance measures were used to validate and compare performance of the ensemble RABANN model with the single ANN model. Results of the model studies showed that both models performed well in the training phase of assessing groundwater potential (AUC ≥ 0.7), whereas the ensemble model (AUC = 0.776) outperformed the single ANN model (AUC = 0.699) in the validation phase. This demonstrated that the RAB ensemble technique was successful in improving the performance of the single ANN model. By making minor adjustment in the input data, the ensemble developed model can be adapted for groundwater potential mapping of other regions and countries toward more efficient water resource management. The present study would be helpful in improving the groundwater condition of the area thus in solving water borne disease related health problem of the population.

Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping

Groundwater potential maps are one of the most important tools for the management of groundwater storage resources. In this study, we proposed four ensemble soft computing models based on logistic regression (LR) combined with the dagging (DLR), bagging (BLR), random subspace (RSSLR), and cascade generalization (CGLR) ensemble techniques for groundwater potential mapping in Dak Lak Province, Vietnam. A suite of well yield data and twelve geo-environmental factors (aspect, elevation, slope, curvature, Sediment Transport Index, Topographic Wetness Index, flow direction, rainfall, river density, soil, land use, and geology) were used for generating the training and validation datasets required for the building and validation of the models. Based on the area under the receiver operating characteristic curve (AUC) and several other validation methods (negative predictive value, positive predictive value, root mean square error, accuracy, sensitivity, specificity, and Kappa), it was revealed that all four ensemble learning techniques were successful in enhancing the validation performance of the base LR model. The ensemble DLR model (AUC = 0.77) was the most successful model in identifying the groundwater potential zones in the study area, followed by the RSSLR (AUC = 0.744), BLR (AUC = 0.735), CGLR (AUC = 0.715), and single LR model (AUC = 0.71), respectively. The models developed in this study and the resulting potential maps can assist decision-makers in the development of effective adaptive groundwater management plans.

Climate Change Affected Vegetation Dynamics in the Northern Xinjiang of China: Evaluation by SPEI and NDVI

Drought and vegetation dynamics in the northern Xinjiang Uygur Autonomous Region of China (NXC), the centre of Asia with arid climate, were assessed using the standardized precipitation evapotranspiration index (SPEI) and the normalized difference vegetation index (NDVI). Analyses were performed through the use of Sen’s method and Spearman’s correlation to investigate variations in the NDVI and the impacts of drought on vegetation from 1998 to 2015. The severity of droughts in the NXC was assessed by the SPEI, which was revealed to increase over the last 60 years at a rate of 0.017 per decade. This indicates that an alleviating tendency of drought intensity occurred in the NXC. Specifically, the spatial pattern of drought intensity increased gradually from the north-western to south-eastern regions. The average yearly NDVI was 0.28 and increased slightly by 0.001 yr−1 (r = 0.94, p = 3.64) between 1998 and 2015. Additionally, the NDVI showed an obviously spatial heterogeneity, with greater values in the west and small values in the east. Significantly, positive correlations between SPEI and NDVI were observed, while drought exerted a five-year lag effect on vegetation.

Multi-hazards vulnerability assessment of southern coasts of Iran

Coastal vulnerability assessment has become one of the most important tools for decision making and providing effective managerial solutions to reduce adverse socio-economic impacts of multiple environmental hazards on coupled social-ecological systems of coastal areas. The aim of this study was to assess the vulnerability of the northern coasts of the Persian Gulf (PG) and the Gulf of Oman (GO) in the Hormozgan province of Iran. Nine variables of vulnerability that included the rate of coastline change, relative sea level rise, coastal slope, mean tidal range, coastal geomorphology, significant wave height (SWH), extreme storm surge, population density, and fishing intensity were weighted, mapped, and combined into the Coastal vulnerability index (CVI). Experts viewed sea level rise, shoreline change and extreme storm surge as most important for imparting vulnerabilities on the northern coasts of PG and GO. Socio-economic variables (i.e., population density and fishery intensity) were considered least important. Of the total length of the provincial shoreline, 27% were classified into the very low vulnerability class, 31% into the low, 17.4% into the moderate, 15.4% into the high, and 9.2% into the very high vulnerability class. About 1295 km (58%) of shorelines were classified into the low and very low vulnerability classes (CVI value ≤ 8.32) and mainly consisted of shorelines on the western coast along the PG. In contrast, 553 km (24.6%) of shorelines were classified into the high and very high vulnerability classes (CVI values > 13.39) and were located along the central coasts (especially in the Qeshm Island and Strait of Hormuz) and on the east coasts of the GO. At least a quarter of all shorelines in the province have high and very high vulnerability to environmental hazards that are the harbingers of climate change.

Prediction Success of Machine Learning Methods for Flash Flood Susceptibility Mapping in the Tafresh Watershed, Iran

Floods are some of the most destructive and catastrophic disasters worldwide. Development of management plans needs a deep understanding of the likelihood and magnitude of future flood events. The purpose of this research was to estimate flash flood susceptibility in the Tafresh watershed, Iran, using five machine learning methods, i.e., alternating decision tree (ADT), functional tree (FT), kernel logistic regression (KLR), multilayer perceptron (MLP), and quadratic discriminant analysis (QDA). A geospatial database including 320 historical flood events was constructed and eight geo-environmental variables—elevation, slope, slope aspect, distance from rivers, average annual rainfall, land use, soil type, and lithology—were used as flood influencing factors. Based on a variety of performance metrics, it is revealed that the ADT method was dominant over the other methods. The FT method was ranked as the second-best method, followed by the KLR, MLP, and QDA. Given a few differences between the goodness-of-fit and prediction success of the methods, we concluded that all these five machine-learning-based models are applicable for flood susceptibility mapping in other areas to protect societies from devastating floods.

Use of Intensity Analysis to Characterize Land Use/Cover Change in the Biggest Island of Persian Gulf, Qeshm Island, Iran

In this study, land use/cover change was systematically investigated in the Qeshm Island to understand how human and nature interact in the largest island of Persian Gulf. Land-use maps were prepared for 1996, 2002, 2008, and 2014 using Landsat satellite imagery in six classes including agriculture, bare-land, built-up, dense-vegetation, mangrove, and water-body, and then dynamic of changes in the classes was evaluated using intensity analysis at three levels: interval, category, and transition. Results illustrated that, while the land changes were fast over the first and third time intervals (1996–2002 and 2008–2014), the trend of changes was slow in the second period (2002–2008). Driven by high demand for construction and population growth, the built-up class was identified as an active gainer in all the three time intervals. The class of bare-land was the main supplier of the land for other classes especially for built-up area, while built-up did not act as the active supplier of the land for other classes. The dense-vegetation class was active in all three time intervals. As for the mangrove class, drought and cutting by residents had negative effects, while setting up protected areas can effectively maintain this valuable ecosystem. High demands were observed for land change in relation to built-up and agriculture classes among other classes. The findings of this study can advance our understanding of the relationship and behavior of land use/cover classes among each other over 18 years in a coastal island with arid climate.