Topographic Wetness Index calculation guidelines based on measured soil moisture and plant species composition

Dynamics and drivers of mycorrhizal fungi after glacier retreat

The development of terrestrial ecosystems depends greatly on plant mutualists such as mycorrhizal fungi. The global retreat of glaciers exposes nutrient-poor substrates in extreme environments and provides a unique opportunity to study early successions of mycorrhizal fungi by assessing their dynamics and drivers. We combined environmental DNA metabarcoding and measurements of local conditions to assess the succession of mycorrhizal communities during soil development in 46 glacier forelands around the globe, testing whether dynamics and drivers differ between mycorrhizal types. Mycorrhizal fungi colonized deglaciated areas very quickly (< 10 yr), with arbuscular mycorrhizal fungi tending to become more diverse through time compared to ectomycorrhizal fungi. Both alpha- and beta-diversity of arbuscular mycorrhizal fungi were significantly related to time since glacier retreat and plant communities, while microclimate and primary productivity were more important for ectomycorrhizal fungi. The richness and composition of mycorrhizal communities were also significantly explained by soil chemistry, highlighting the importance of microhabitat for community dynamics. The acceleration of ice melt and the modifications of microclimate forecasted by climate change scenarios are expected to impact the diversity of mycorrhizal partners. These changes could alter the interactions underlying biotic colonization and belowground-aboveground linkages, with multifaceted impacts on soil development and associated ecological processes.

Topographic influence on ecohydrology in volcanic watersheds of the western Pacific monsoon area: evidence from water stable isotope composition of meteoric water, thermal water, and plants

In Taiwanese volcanic watersheds, we investigated stable water isotopes in meteoric water, plants, and thermal water. Meteoric water exhibited a seasonal cycle, with heavier isotopes in winter and lighter ones in summer, especially in the southern region. The northern monsoon signal lagged the south by two weeks. In the Tatun mountains, young water fractions indicated prevalent old water sources. In the northern watershed, streamwater mainly came from the winter monsoon, while the southern one was influenced by alternating monsoons. Both indices indicated that winter plants depended on summer rainfall. Streamwater and plants had distinct sources in winter, supporting ecohydrological separation. Thermal spring water's d-excess helped identify water-rock interactions, with low d value signaling such interactions. The topographic wetness index showed a higher summer monsoon contribution to southern streamwater but a lower one to plants. The mean linear channel direction significantly affected the monsoon contribution fraction, with northeast-oriented channels vulnerable to northeastward winter monsoons. Finally, we developed a model illustrating hydrological processes on short and long timescales. Our findings enhance our understanding of hydrological disturbances' impact on water resources and ecosystems.

Assessing wildfire impact on Trigonella elliptica habitat using random forest modeling

Wildfires have a significant impact on ecosystems worldwide, especially on the degradation of arid and semi-arid rangelands. This research focuses on assessing the effects of wildfires on the habitat of Trigonella elliptica, a valuable herb species found in the central rangelands of Iran. To achieve this, the Random Forest (RF) algorithm has been deployed to predict T. elliptica habitat and fire hazard using socio-environmental variables in Yazd province, Iran. 225 fire points and 103 habitat locations were used for model training and testing. The IncNodePurity index and Probability Curves (PC) have been utilized to determine the influence of socio-environmental variables. The combination of the prediction maps of the habitat and wildfires pointed out the possible damage due to fire. The high performance of the RF model is confirmed by the area under the curve (AUC) and the true skill statistic (TSS) values (0.90 and 0.81 for the habitat; 0.92 and 0.82 for the wildfire). The importance assessment of variables revealed that elevation, slope, and precipitation are the most influential variables in the distribution of T. elliptica, while distance to roads, population density, and wind speed are the key factors affecting wildfire occurrence. In the final map, a comparison of different regions of T. elliptica habitat under fire hazard with fire-free habitats using Kruskal-Wallis and Dunn tests indicated that the fire hazard in the T. elliptica habitat is a serious concern. Since the areas with the highest fire hazard and the highest presence of T. elliptica cover approximately 2311.38 km2, neglecting these regions could lead to the gradual reduction of T. elliptica, and create conditions for secondary succession dominated by less valuable annual species. The findings of this study underscore the importance of implementing fire management strategies, protection projects, and continuous monitoring to ensure the safety and conservation of the T. elliptica habitat.

A 30-m annual corn residue coverage dataset from 2013 to 2021 in Northeast China

Crop residue cover plays a key role in the protection of black soil by covering the soil in the non-growing season against wind erosion and chopping for returning to the soil to increase organic matter in the future. Although there are some studies that have mapped the crop residue coverage by remote sensing technique, the results are mainly on a small scale, limiting the generalizability of the results. In this study, we present a novel corn residue coverage (CRC) dataset for Northeast China spanning the years 2013-2021. The aim of our dataset is to provide a basis to describe and monitor CRC for black soil protection. The accuracy of our estimation results was validated against previous studies and measured data, demonstrating high accuracy with a coefficient of determination (R2) of 0.7304 and root mean square error (RMSE) of 0.1247 between estimated and measured CRC in field campaigns. In addition, it is the first of its kind to offer the longest time series, enhancing its significance in long-term monitoring and analysis.

The possibility of spatial mapping of SOC content in olive groves under integrated production using easy-to-obtain ancillary data in a Mediterranean area

Background

Unlike most of Europe, Andalucía in southern Spain as a Mediterranean area still lacks digital maps of SOC content provided by machine learning algorithms. The wide diversity of climate, geology, hydrology, landscape, topography, vegetation, and micro-relief data as easy-to-obtain covariates facilitated the development of digital soil mapping (DSM). The purpose of this research is to model and map the spatial distribution of SOC at three depths, in an area of approximately 10000 km 2 located in Seville and Cordoba Provinces, and to use R programming to compare two machine learning techniques (cubist and random forest) for developing SOC maps at multiple depths.

Methods

Environmental covariates used in this research include nine derivatives from digital elevation models (DEM), three climatic variables and finally eighteen remotely-sensed spectral data (band ratios calculated by the acquired Landsat-8 OLI and Sentinel-2A MSI in July 2019). In total, 300 soil samples from 100 points were taken (0-25 cm). The purpose of this research is to model and map the spatial distribution of SOC, in an area with approximately 10000 km2 located in Seville and Cordoba Provinces, and to compare two machine learning techniques (cubist and random forest) by R programming.

Results

The findings showed that the novel approach for integrating the indices using Landsat-8 OLI and Sentinel-2A MSI satellite data had a better result.

Conclusions

Finally, we obtained evidence that the resolution of satellite images is more important in modelling and digital mapping.

Topography modulates climate sensitivity of multidecadal trends of holm oak decline

Forest decline events have increased worldwide over the last decades being holm oak (Quercus ilex L.) one of the tree species with the most worrying trends across Europe. Since this is one of the tree species with the southernmost distribution within the European continent, its vulnerability to climate change is a phenomenon of enormous ecological importance. Previous research identified drought and soil pathogens as the main causes behind holm oak decline. However, despite tree health loss is a multifactorial phenomenon where abiotic and biotic factors interact in time and space, there are some abiotic factors whose influence has been commonly overlooked. Here, we evaluate how land use (forests versus savannas), topography, and climate extremes jointly determine the spatiotemporal patterns of holm oak defoliation trends over almost three decades (1987-2014) in Spain, where holm oak represents the 25% of the national forested area. We found an increasing defoliation trend in 119 out of the total 134 holm oak plots evaluated, being this defoliation trend significantly higher in forests compared with savannas. Moreover, we have detected that the interaction between topography (which covariates with the land use) and summer precipitation anomalies explains trends of holm oak decline across the Mediterranean region. While a higher occurrence of dry summers increases defoliation trends in steeper terrains where forests dominate, an inverse relationship was found in flatter terrains where savannas are mainly located. These opposite relationships suggest different causal mechanisms behind decline. Whereas hydric stress is likely to occur in steeper terrains where soil water holding capacity is limited, soil waterlogging usually occurs in flatter terrains what increases tree vulnerability to soil pathogens. Our results contribute to the growing evidence of the influence of local topography on forest resilience and could assist in the identification of potential tree decline hotspots and its main causes over the Mediterranean region.

Output characteristics and driving factors of non-point source nitrogen (N) and phosphorus (P) in the Three Gorges reservoir area (TGRA) based on migration process: 1995-2020

Although physical models at present have made important achievements in the assessment of non-point source pollution (NPSP), the requirement for large volumes of data and their accuracy limit their application. Therefore, constructing a scientific evaluation model of NPS nitrogen (N) and phosphorus (P) output is of great significance for the identification of N and P sources as well as pollution prevention and control in the basin. We considered runoff, leaching and landscape interception conditions, and constructed an input-migration-output (IMO) model based on the classic export coefficient model (ECM), and identified the main driving factors of NPSP using geographical detector (GD) in Three Gorges Reservoir area (TGRA). The results showed that, compared with the traditional export coefficient model, the prediction accuracy of the improved model for total nitrogen (TN) and total phosphorus (TP) increased by 15.46 % and 20.17 % respectively, and the error rates with the measured data were 9.43 % and 10.62 %. It was found that the total input volume of TN in the TGRA had declined from 58.16 × 10⁴ t to 48.37 × 10⁴ t, while the TP input volume increased from 2.76 × 10⁴ t to 4.11 × 10⁴ t, and then decreased to 4.01 × 10⁴ t. In addition Pengxi River, Huangjin River and the northern part of Qi River were high value areas of NPSP input and output, but the range of high value areas of migration factors has narrowed. Pig breeding, rural population and dry land area were the main driving factors of N and P export. The IMO model can effectively improve prediction accuracy, and has significant implications for the prevention and control of NPSP.

Fine-scale mapping of urban malaria exposure under data scarcity: an approach centred on vector ecology

BACKGROUND

Although malaria transmission has experienced an overall decline in sub-Saharan Africa, urban malaria is now considered an emerging health issue due to rapid and uncontrolled urbanization and the adaptation of vectors to urban environments. Fine-scale hazard and exposure maps are required to support evidence-based policies and targeted interventions, but data-driven predictive spatial modelling is hindered by gaps in epidemiological and entomological data. A knowledge-based geospatial framework is proposed for mapping the heterogeneity of urban malaria hazard and exposure under data scarcity. It builds on proven geospatial methods, implements open-source algorithms, and relies heavily on vector ecology knowledge and the involvement of local experts.

METHODS

A workflow for producing fine-scale maps was systematized, and most processing steps were automated. The method was evaluated through its application to the metropolitan area of Dakar, Senegal, where urban transmission has long been confirmed. Urban malaria exposure was defined as the contact risk between adult Anopheles vectors (the hazard) and urban population and accounted for socioeconomic vulnerability by including the dimension of urban deprivation that is reflected in the morphology of the built-up fabric. Larval habitat suitability was mapped through a deductive geospatial approach involving the participation of experts with a strong background in vector ecology and validated with existing geolocated entomological data. Adult vector habitat suitability was derived through a similar process, based on dispersal from suitable breeding site locations. The resulting hazard map was combined with a population density map to generate a gridded urban malaria exposure map at a spatial resolution of 100 m.

RESULTS

The identification of key criteria influencing vector habitat suitability, their translation into geospatial layers, and the assessment of their relative importance are major outcomes of the study that can serve as a basis for replication in other sub-Saharan African cities. Quantitative validation of the larval habitat suitability map demonstrates the reliable performance of the deductive approach, and the added value of including local vector ecology experts in the process. The patterns displayed in the hazard and exposure maps reflect the high degree of heterogeneity that exists throughout the city of Dakar and its suburbs, due not only to the influence of environmental factors, but also to urban deprivation.

CONCLUSIONS

This study is an effort to bring geospatial research output closer to effective support tools for local stakeholders and decision makers. Its major contributions are the identification of a broad set of criteria related to vector ecology and the systematization of the workflow for producing fine-scale maps. In a context of epidemiological and entomological data scarcity, vector ecology knowledge is key for mapping urban malaria exposure. An application of the framework to Dakar showed its potential in this regard. Fine-grained heterogeneity was revealed by the output maps, and besides the influence of environmental factors, the strong links between urban malaria and deprivation were also highlighted.

Improved export coefficient model for identification of watershed environmental risk areas

As a complex system under the joint action of man and nature, land use/cover directly or indirectly affects the environmental quality of the freshwater ecosystem. Studying the response of water environment quality to land use/cover change was significant to accurately simulate lake water quality and effectively enhance the management level. As an empirical model, the classical export coefficient model has been widely used and developed in agricultural non-point source pollution research because of its simple structure and convenient application. However, it assumes that the export coefficient of a particular type of land use/cover was constant, ignoring the influence of surface runoff and interception on the output intensity of pollutants in pollutant migration. This study improved the classical export coefficient model by adding factors such as precipitation, surface cover, and topography, evaluated the contribution of land use/cover to total nitrogen load into the lake in Dianchi Lake Basin, and applied the pollution assessment results to the identification of watershed environmental risk areas. The results showed that the improved export coefficient model could better simulate the relationship between land use/cover and total nitrogen load into Dianchi Lake from the basin. At the same time, spatial characteristics of the total nitrogen load contribution of the terrestrial could be represented. The high-risk areas in the basin were mainly cultivated land and construction areas with low vegetation coverage around lakes or downstream. The contribution per unit area to the TN load into the lake from areas with a high risk was 14.28 t/km², which was 3.47 times that of medium-high-risk areas and 52.28 times that of the medium-risk area. Land use control measures in high-risk areas in the basin should be further strengthened, especially in the lakeside zone.

Modelling hydrological factors from DEM using GIS

Hydrological modelling is a precondition for many scientific researches such as species distribution models, ecological models, agricultural suitability models, climatological models, hydrological models, flood and flash flood models, landslide models etc. Even the topographic control over many hydrological factors has also been studied. Over time different hydrological models have been developed and extensively used. Recently, these models have been used to prepare different types of conditional factors that are widely used in hazard modelling such as floods, flash floods, landslides etc. Quantitative analysis of the Digital Elevation Model (DEM) according to different models by engaging Geographic Information Systems (GIS) supports users to extract various types of information about landscapes where hydrological and topographic information are most important. Methods to prepare hydrological factors namely TWI, TRI, SPI, STI, TPI, stream density and distance to stream by processing DEM in GIS are discussed in this paper. These common hydrological factors are extensively used in many scientific research papers either for modelling or to measure their relationship with other environmental factors.•Hydrological factors have great importance in understanding the landscape and are widely used in scientific research, especially geo-environmental hazard mapping.•Physically based hydrological methods are engaged in ArcMap 10.5 software.•Commonly used hydrological factors are processed using freely available DEM and ArcMap 10.5 software.

Environmental Filtering Drives Fungal Phyllosphere Community in Regional Agricultural Landscapes

To adapt to climate change, several agricultural strategies are currently being explored, including a shift in land use areas. Regional differences in microbiome composition and associated phytopathogens need to be considered. However, most empirical studies on differences in the crop microbiome focused on soil communities, with insufficient attention to the phyllosphere. In this study, we focused on wheat ears in three regions in northeastern Germany (Magdeburger Börde (MBB), Müncheberger Sander (MSA), Uckermärkisches Hügelland (UKH)) with different yield potentials, soil, and climatic conditions. To gain insight into the fungal community at different sites, we used a metabarcoding approach (ITS-NGS). Further, we examined the diversity and abundance of Fusarium and Alternaria using culture-dependent and culture-independent techniques. For each region, the prevalence of different orders rich in phytopathogenic fungi was determined: Sporidiobolales in MBB, Capnodiales and Pleosporales in MSA, and Hypocreales in UKH were identified as taxonomic biomarkers. Additionally, F. graminearum was found predominantly in UKH, whereas F. poae was more abundant in the other two regions. Environmental filters seem to be strong drivers of these differences, but we also discuss the possible effects of dispersal and interaction filters. Our results can guide shifting cultivation regions to be selected in the future concerning their phytopathogenic infection potential.

A low-cost approach for soil moisture prediction using multi-sensor data and machine learning algorithm

A high-resolution soil moisture prediction method has recently gained its importance in various fields such as forestry, agricultural and land management. However, accurate, robust and non- cost prohibitive spatially monitoring of soil moisture is challenging. In this research, a new approach involving the use of advance machine learning (ML) models, and multi-sensor data fusion including Sentinel-1(S1) C-band dual polarimetric synthetic aperture radar (SAR), Sentinel-2 (S2) multispectral data, and ALOS Global Digital Surface Model (ALOS DSM) to predict precisely soil moisture at 10 m spatial resolution across research areas in Australia. The total of 52 predictor variables generated from S1, S2 and ALOS DSM data fusion, including vegetation indices, soil indices, water index, SAR transformation indices, ALOS DSM derived indices like digital model elevation (DEM), slope, and topographic wetness index (TWI). The field soil data from Western Australia was employed. The performance capability of extreme gradient boosting regression (XGBR) together with the genetic algorithm (GA) optimizer for features selection and optimization for soil moisture prediction in bare lands was examined and compared with various scenarios and ML models. The proposed model (the XGBR-GA model) with 21 optimal features obtained from GA was yielded the highest performance (R² = 0. 891; RMSE = 0.875%) compared to random forest regression (RFR), support vector machine (SVM), and CatBoost gradient boosting regression (CBR). Conclusively, the new approach using the XGBR-GA with features from combination of reliable free-of-charge remotely sensed data from Sentinel and ALOS imagery can effectively estimate the spatial variability of soil moisture. The described framework can further support precision agriculture and drought resilience programs via water use efficiency and smart irrigation management for crop production.

GIS-Based Spatial Modeling of Snow Avalanches Using Analytic Hierarchy Process. A Case Study of the Šar Mountains, Serbia

Snow avalanches are one of the most devastating natural hazards in the highlands that often cause human casualties and economic losses. The complex process of modeling terrain susceptibility requires the application of modern methods and software. The prediction of avalanches in this study is based on the use of geographic information systems (GIS), remote sensing, and multicriteria analysis—analytic hierarchy process (AHP) on the territory of the Šar Mountains (Serbia). Five indicators (lithological, geomorphological, hydrological, vegetation, and climatic) were processed, where 14 criteria were analyzed. The results showed that approximately 20% of the investigated area is highly susceptible to avalanches and that 24% of the area has a medium susceptibility. Based on the results, settlements where avalanche protection measures should be applied have been singled out. The obtained data can will help local self-governments, emergency management services, and mountaineering services to mitigate human and material losses from the snow avalanches. This is the first research in the Republic of Serbia that deals with GIS-AHP spatial modeling of snow avalanches, and methodology and criteria used in this study can be tested in other high mountainous regions.

Unmanned Aircraft System (UAS) Structure-From-Motion (SfM) for Monitoring the Changed Flow Paths and Wetness in Minerotrophic Peatland Restoration

Peatland restoration aims to achieve pristine water pathway conditions to recover dispersed wetness, water quality, biodiversity and carbon sequestration. Restoration monitoring needs new methods for understanding the spatial effects of restoration in peatlands. We introduce an approach using high-resolution data produced with an unmanned aircraft system (UAS) and supported by the available light detection and ranging (LiDAR) data to reveal the hydrological impacts of elevation changes in peatlands due to restoration. The impacts were assessed by analyzing flow accumulation and the SAGA Wetness Index (SWI). UAS campaigns were implemented at two boreal minerotrophic peatland sites in degraded and restored states. Simultaneously, the control campaigns mapped pristine sites to reveal the method sensitivity of external factors. The results revealed that the data accuracy is sufficient for describing the primary elevation changes caused by excavation. The cell-wise root mean square error in elevation was on average 48 mm when two pristine UAS campaigns were compared with each other, and 98 mm when each UAS campaign was compared with the LiDAR data. Furthermore, spatial patterns of more subtle peat swelling and subsidence were found. The restorations were assessed as successful, as dispersing the flows increased the mean wetness by 2.9–6.9%, while the absolute changes at the pristine sites were 0.4–2.4%. The wetness also became more evenly distributed as the standard deviation decreased by 13–15% (a 3.1–3.6% change for pristine). The total length of the main flow routes increased by 25–37% (a 3.1–8.1% change for pristine), representing the increased dispersion and convolution of flow. The validity of the method was supported by the field-determined soil water content (SWC), which showed a statistically significant correlation (R2 = 0.26–0.42) for the restoration sites but not for the control sites, possibly due to their upslope catchment areas being too small. Despite the uncertainties related to the heterogenic soil properties and complex groundwater interactions, we conclude the method to have potential for estimating changed flow paths and wetness following peatland restoration.

Mapping Gully Erosion Variability and Susceptibility Using Remote Sensing, Multivariate Statistical Analysis, and Machine Learning in South Mato Grosso, Brazil

In Brazil, the development of gullies constitutes widespread land degradation, especially in the state of South Mato Grosso, where fighting against this degradation has become a priority for policy makers. However, the environmental and anthropogenic factors that promote gully development are multiple, interact, and present a complexity that can vary by locality, making their prediction difficult. In this framework, a database was constructed for the Rio Ivinhema basin in the southern part of the state, including 400 georeferenced gullies and 13 geo-environmental descriptors. Multivariate statistical analysis was performed using principal component analysis (PCA) to identify the processes controlling the variability in gully development. Susceptibility maps were created through four machine learning models: multivariate discriminant analysis (MDA), logistic regression (LR), classification and regression tree (CART), and random forest (RF). The predictive performance of the models was analyzed by five evaluation indices: accuracy (ACC), sensitivity (SST), specificity (SPF), precision (PRC), and Receiver Operating Characteristic curve (ROC curve). The results show the existence of two major processes controlling gully erosion. The first is the surface runoff process, which is related to conditions of slightly higher relief and higher rainfall. The second also reflects high surface runoff conditions, but rather related to high drainage density and downslope, close to the river network. Human activity represented by peri-urban areas, construction of small earthen dams, and extensive rotational farming contribute significantly to gully formation. The four machine learning models yielded fairly similar results and validated susceptibility maps (ROC curve > 0.8). However, we noted a better performance of the random forest (RF) model (86% and 89.8% for training and test, respectively, with an ROC curve value of 0.931). The evaluation of the contribution of the parameters shows that susceptibility to gully erosion is not governed primarily by a single factor, but rather by the interconnection between different factors, mainly elevation, geology, precipitation, and land use.

Can high-resolution topography and forest canopy structure substitute microclimate measurements? Bryophytes say no

Increasingly available high-resolution digital elevation models (DEMs) facilitate the use of fine-scale topographic variables as proxies for microclimatic effects not captured by the coarse-grained macroclimate datasets. Species distributions and community assembly rules are, however directly shaped by microclimate and not by topography. DEM-derived topography, sometimes combined with vegetation structure, is thus widely used as a proxy for microclimatic effects in ecological research and conservation applications. However, the suitability of such a strategy has not been evaluated against in situ measured microclimate and species composition. Because bryophytes are highly sensitive to microclimate, they are ideal model organisms for such evaluation. To provide this much needed evaluation, we simultaneously recorded bryophyte species composition, microclimate, and forest vegetation structure at 218 sampling sites distributed across topographically complex sandstone landscape. Using a LiDAR-based DEM with a 1 m resolution, we calculated eleven topographic variables serving as a topographic proxy for microclimate. To characterize vegetation structure, we used hemispherical photographs and LiDAR canopy height models. Finally, we calculated eleven microclimatic variables from a continuous two-year time- series of air and soil temperature and soil moisture. To evaluate topography and vegetation structure as substitutes for the ecological effect of measured microclimate, we partitioned the variation in bryophyte species composition and richness explained by microclimate, topography, and vegetation structure. In situ measured microclimate was clearly the most important driver of bryophyte assemblages in temperate coniferous forests. The most bryophyte-relevant variables were growing degree days, maximum air temperature, and mean soil moisture. Our results thus showed that topographic variables, even when derived from high-resolution LiDAR data and combined with in situ sampled vegetation structure, cannot fully substitute effects of in situ measured microclimate on forest bryophytes.

Fine-scale topographic influence on the spatial distribution of tree species diameter in old-growth beech (Fagus orientalis Lipsky.) forests, northern Iran

The Hyrcanian forest in northern Iran is threatened by human use and encroachment and has suffered degradation in some areas. The forest has been declared a World Heritage Site and management in the region is shifting from timber production to conservation. There is considerable interest in developing a greater understanding of these diverse forest communities to inform forest management and multiple use plans to maintain the diversity and resilience of these forests. The Hyrcanian forest is characterized by a complex topography of catenas ranging up mountain slopes. Topographic gradients greatly influence microhabitat conditions which in turn impact tree distribution. To date there has been limited research on the impacts of this diverse topography on the spatial distribution of tree species and tree diameters in Hyrcanian forests. Such information is necessary to better understand the regional traits of tree diameters in these natural mixed temperate forests before forest management occurs. We examined the influence of the area’s catena topography on the spatial pattern of tree species and on species stand structure in terms of tree diameter distribution. To quantify these dynamics, we conducted a complete enumeration inventory of all trees with dbh >12 cm within a 7.947 ha study area that included three C-shaped (concave) and three V-shaped (convex) catenas. Geostatistical variogram analysis and Clark and Evans aggregation index were utilized to study the spatial distribution of tree diameters. Beech, alder, hornbeam, linden and Persian maple exhibited clustered patterns, and sour cherry, ash, and oak exhibited random patterns. Geostatistical analysis clearly revealed the substantial influence of catena topography on the diameter distributions of alder and linden, more subtle influence on the diameter distributions of beech, and a possible influence on Persian maple, providing valuable insight into stand structure over neighborhood-based indices alone. Alder and linden both exhibited strong spatial structure in their diameter distributions (56% and 86%, respectively) where their diameter was strongly correlated with trees within 108 m and 83 m, respectively, sharing more similar diameters to each other than trees beyond that distance. Beech, maple, and hornbeam exhibited very weak if any spatial structure over short distances. These findings can be used to support the alignment of forest management practices in managed Hyrcanian forests with goals of protecting and maintaining biodiversity and sustainable forest ecosystems, and to inform geospatial modeling of species diameter distributions in areas where a complete stem-map is not feasible.

Determination of Potential Aquifer Recharge Zones Using Geospatial Techniques for Proxy Data of Gilgel Gibe Catchment, Ethiopia

The lack of valuable baseline information about groundwater availability hinders the robust decision-making process of water management in humid, arid, and semi-arid climate regions of the world. In sustainable groundwater management, identifying the spatiotemporal and extrapolative monitoring of potential zone is crucial. Thus, the present study focused on determining potential aquifer recharge zones using geospatial techniques for proxy data of the Gilgel Gibe catchment, Ethiopia. Proxy data are site information derived from satellite imageries or conventional sources that are operated as a layer attribute in the geographical information system (GIS) to identify groundwater occurrence. First, GIS and analytical hierarchy process (AHP) were applied to analyze ten groundwater recharge controlling factors: slope, lithology, topographic position index lineament density, rainfall, soil, elevation, land use/cover, topographic wetness index, and drainage density. Each layer was given relative rank priority depending on the predictive implication of groundwater potentiality. Next, the normalized weight of thematic layers was evaluated using a multi-criteria decision analysis AHP algorithm with a pairwise comparison matrix based on aquifer infiltration relative significance. Lithology, rainfall, and land use/cover were dominant factors covering a weight of 50%. The computed consistency ratio (CR = 0.092, less than 10%) and consistency index (CI = 0.1371) revealed the reliability of input proxy layers’ in the analysis. Then, a GIS-based weighted overlay analysis was performed to delineate very high, high, moderate, low, and very low potential aquifer zones. The delineated map ensures very high (29%), high (25%), moderate (28%), low (13%), and very low (5%) of the total area. According to validation, most of the inventory wells are located in very high (57%), high (32), and moderate (12%) zones. The validation results realized that the method affords substantial results supportive of sustainable development and groundwater exploitation. Therefore, this study could be a vigorous input to enhance development programs to alleviate water scarcity in the study area.

Fire Risk Probability Mapping Using Machine Learning Tools and Multi-Criteria Decision Analysis in the GIS Environment: A Case Study in the National Park Forest Dadia-Lefkimi-Soufli, Greece

Fire risk will increase in the upcoming years due to climate change. In this context, GIS analysis for fire risk mapping is an important tool to identify high risk areas and allocate resources. In the present study, we aimed to create a fire risk estimation model that incorporates recent land cover changes, along with other important risk factors. As a study area, we selected Dadia-Lefkimi-Soufli National Forest Park and the surrounding area since it is one of the most important protected areas in Greece. The area selected for the case study is a typical Mediterranean landscape. As a result, the outcome model is generic and can be applied to other areas. In order to incorporate land cover changes in our model, we used a support vector machine (SVM) algorithm to classify a satellite image captured in September 2021 and an image of the same period two years ago to obtain comparable results. Next, two fire risk maps were created with a combination of land cover and six other factors, using the analytic hierarchy process (AHP) on a GIS platform. The results of our model revealed noticeable clusters of extreme high risk areas, while the overall fire risk in the National Park Forest of Dadia-Lefkimi-Soufli was classified as high. The wildfires of 1st October 2020 and 9th July 2021 confirmed our model and contributed to quantification of their impact on fire risk due to land cover change.

Local fresh- and sea-water effects on fog occurrence

Fog is an important atmospheric phenomenon highly relevant to ecosystems and/or the environment. Two essential prerequisites of fog formation are the presence of fog condensation nuclei and water in the atmosphere. The aim of our study was to examine in detail how fog occurrence is influenced by water areas in the immediate vicinity of the fog observation site. We have used as input data long-term observations on fog occurrence measured at 56 professional meteorological stations in Romania in 1981-2017 and GIS-derived information on water areas and on two topographical indices, TWI and TPI, in the neighbourhood of these stations. We formulated three alternative models of different complexity based on a semiparametric generalised additive logistic model for the probability of fog occurrence with potentially nonlinear, smooth effects modelled via penalised splines. A radius of 9 km appeared to be the most influential when considering the water area in a circle around the fog observation station. Based on our results, we concluded that (i) the water area in the vicinity of the station is a factor influencing fog occurrence, (ii) the water's effect differs according to water type (freshwater or seawater proximity), and (iii) GIS-derived topographical indices are informative for the explanation of fog occurrence and their inclusion enhanced the fit of the models substantially. Our findings, based on a reliable long-term data set of fog occurrence and recent GIS-derived data, explored by a relevant statistical approach will enhance further considerations related to fog formation and its environmental consequences.

Environmental and Household-Based Spatial Risks for Tungiasis in an Endemic Area of Coastal Kenya

Tungiasis is a cutaneous parasitosis caused by an embedded female sand flea. The distribution of cases can be spatially heterogeneous even in areas with similar risk profiles. This study assesses household and remotely sensed environmental factors that contribute to the geographic distribution of tungiasis cases in a rural area along the Southern Kenyan Coast. Data on household tungiasis case status, demographic and socioeconomic information, and geographic locations were recorded during regular survey activities of the Health and Demographic Surveillance System, mainly during 2011. Data were joined with other spatial data sources using latitude/longitude coordinates. Generalized additive models were used to predict and visualize spatial risks for tungiasis. The household-level prevalence of tungiasis was 3.4% (272/7925). There was a 1.1% (461/41,135) prevalence of infection among all participants. A significant spatial variability was observed in the unadjusted model (p-value < 0.001). The number of children per household, earthen floor, organic roof, elevation, aluminum content in the soil, and distance to the nearest animal reserve attenuated the odds ratios and partially explained the spatial variation of tungiasis. Spatial heterogeneity in tungiasis risk remained even after a factor adjustment. This suggests that there are possible unmeasured factors associated with the complex ecology of sand fleas that may contribute to the disease's uneven distribution.

ForestTemp - Sub-canopy microclimate temperatures of European forests

Ecological research heavily relies on coarse-gridded climate data based on standardized temperature measurements recorded at 2 m height in open landscapes. However, many organisms experience environmental conditions that differ substantially from those captured by these macroclimatic (i.e. free air) temperature grids. In forests, the tree canopy functions as a thermal insulator and buffers sub-canopy microclimatic conditions, thereby affecting biological and ecological processes. To improve the assessment of climatic conditions and climate-change-related impacts on forest-floor biodiversity and functioning, high-resolution temperature grids reflecting forest microclimates are thus urgently needed. Combining more than 1200 time series of in situ near-surface forest temperature with topographical, biological and macroclimatic variables in a machine learning model, we predicted the mean monthly offset between sub-canopy temperature at 15 cm above the surface and free-air temperature over the period 2000-2020 at a spatial resolution of 25 m across Europe. This offset was used to evaluate the difference between microclimate and macroclimate across space and seasons and finally enabled us to calculate mean annual and monthly temperatures for European forest understories. We found that sub-canopy air temperatures differ substantially from free-air temperatures, being on average 2.1°C (standard deviation ± 1.6°C) lower in summer and 2.0°C higher (±0.7°C) in winter across Europe. Additionally, our high-resolution maps expose considerable microclimatic variation within landscapes, not captured by the gridded macroclimatic products. The provided forest sub-canopy temperature maps will enable future research to model below-canopy biological processes and patterns, as well as species distributions more accurately.

Effects of topography and soil properties on the distribution and fractionation of REEs in topsoil: A case study in Sichuan Basin, China

In order to investigate how topographic factors and soil physicochemical properties influenced the distribution and fractionation of rare earth elements (REEs) in soil, Jiangjin district of Sichuan Basin, an area with mountainous topography, was selected as a study area. The concentration of REEs, pH and organic matter (OM) and major elements in 156 topsoil samples were measured and analyzed. The topographic factors considered were elevation, slope, and topographic wetness index (TWI), which were extracted by using the digital elevation model (DEM). The median concentration of total REEs in topsoil of the study area was 147 mg/kg, lower than the Chinese soil background value (164 mg/kg). The concentration of LREEs and HREEs, and the ratio of LREEs/HREEs and La_N/Yb_N indicated that the distribution and fractionation patterns of REEs in topsoil were LREEs-enriched. Significant Eu negative anomalies and weak Ce negative anomalies were observed in topsoil according to the median values of δEu (0.57) and δCe (0.89). The coefficient of weathering and eluviation (BA), an important factor affecting the distribution and fractionation of REEs, was substantially correlated with δEu (r = 0.344, p < 0.01), δCe (r = -0.252, p < 0.01), ∑REEs (r = 0.135, p < 0.01), and LREEs/HREEs (r = -0.281, p < 0.01) in topsoil. Soil pH and OM had some influence on the distribution and fractionation of REEs. Under the geographical environment of the study area, Ce was positive anomaly with the elevation and slope increasing. The enrichment of LREEs was more significant than HREEs as elevation increased. The findings revealed that topographical attributes and soil physicochemical properties integratedly influenced the distribution and fractionation of REEs in topsoil.