Topographic Position Research Articles

Snow mechanical property variability at the slope scale – implication for snow mechanical modelling

Abstract. Snow avalanches represent a natural hazard to infrastructure and backcountry recreationists. Risk assessment of avalanche hazard is difficult due to the sparse nature of available observations informing on snowpack mechanical and geophysical properties and overall stability. The spatial variability of these properties also adds complexity to decision-making and route finding in avalanche terrain for mountain users. Snow cover models can simulate snow mechanical properties with good accuracy at fairly good spatial resolution (around 100 m). However, monitoring small-scale variability at the slope scale (5–50 m) remains critical, since slope stability and the possible size of an avalanche are governed by that scale. To better understand and estimate the spatial variability at the slope scale, this work explores links between snow mechanical properties and microtopographic indicators. Six spatial snow surveys were conducted in two study areas across Canada. Snow mechanical properties, such as snow density, elastic modulus and shear strength, were estimated from high-resolution snow penetrometer (SMP) profiles at multiple locations over several studied slopes, in Rogers Pass, British Columbia, and Mt. Albert, Québec. Point snow stability metrics, such as the skier crack length, critical propagation crack length and a skier stability index, were derived using the snow mechanical properties from SMP measurements. Microtopographic indicators, such as the topographic position index (TPI), vegetation height and proximity, wind-exposed slope index, and potential radiation index, were derived from unoccupied aerial vehicle (UAV) surveys with sub-metre resolution. We computed the variogram and the fractal dimension of the snow mechanical properties and stability metrics and compared them. The comparison showed some similarities in the correlation distances and fractal dimensions between the slab thickness and the slab snow density and also between the weak layer strength and the stability metrics. We then spatially modelled snow mechanical properties, including point snow stability, using spatial generalized additive models (GAMs) with microtopographic indicators as covariates. The use of covariates in GAMs suggested that microtopographic indicators can be used to adequately estimate the variation in the snow mechanical properties but not the stability metrics. We observed a difference in the spatial pattern between the slab and the weak layer that should be considered in snow mechanical modelling.

Precipitation, topography, and soil conservation measures determine the spatiotemporal pattern of sediment yield at the regional scale

Exploring the driving effects of control factors on changes in sediment yield from a watershed perspective is of paramount importance for refined watershed management. The objective of this research is to unveil the spatiotemporal variation of sediment yield in the Coarse Sandy Hilly Catchments (CSHC) region from 1956 to 2019 and further investigate the relationship between sediment yield and driving factors. The findings revealed that the mutation years of sediment load (SL) at Toudaoguai and Longmen stations were 1986 and 1996, respectively, with annual SL declining by 66.75 % and 79.04 % after the mutation. Water and sediment discharge at Toudaoguai station exhibited more consistent fluctuations over time, while water and sediment discharge at Longmen station remained relatively consistent before 1967, followed by an entirely inconsistent trend. The spatial pattern of the annual average specific sediment yield (SSY) indicated that the northwest watersheds had a relatively higher erosion rate compared to the southeast. Over time, the SSY of each watershed exhibited a decreasing trend. The SSY showed significant negative correlations with terrace, afforestation, topographic position index (TPI), grass planting (GP), and elevation, while demonstrating significant positive correlations with rainfall indicators and runoff depth. Changes in precipitation, topography, vegetation and engineering measures accounted for 58.5 % of the variance in SSY. The total impact of precipitation on SSY change was positive (standard path coefficient (SPC) = 0.567, P < 0.01), whereas the total impact of topography, vegetation measures, and engineering measures on SSY change was negative (SPC = -0.282, −0.231, and −0.087, respectively). This study quantified the impact of different types of controlling factors on sediment yield at the watershed level. The insights provided by these results can contribute to sustainable watershed management and the optimal allocation of soil and water conservation measures (SWCMs).

Mineralogical compositions of soils under three geological formations in some parts of Ogun state, Nigeria and their agricultural potentials

Investigating the mineralogical compositions of soils under different geological formations becomes imperative for maximizing agricultural productivity and ensuring the long-term viability of agricultural practices. Therefore, studies were carried out on mineral compositions and diversities of soils developed over the Basement complex rock, Coastal plain sands and Ewekoro formations in Ogun state Nigeria. A total of nine profile pits (three per location) of 2 m × 1 m × 2 m size were dug in all the three locations. Soil samples were collected from the pedogenic horizons of each profile pits in replicates into a well labeled polyethylene bag. Using X-ray diffractometry (XRD) and scanning electron microscopy (SEM) the mineral contents and their relative abundance, elemental compositions and morphologies of the fine sand, coarse silt and clay fractions of the soils at different topographic positions were identified, described and compared. Results obtained from XRD and SEM analyses exhibited similarities. The most abundant elements in the basement complex and Ewekoro pedon were oxygen, carbon and silicon whereas in the coastal plain sand pedon, oxygen, carbon and aluminum were the most abundant element. The presence of mixed-layer illite, mica, kaolinite, quartz, hematite, anatase, goethite, and chlorite at varying degrees was observed in the pedons developed on these geological formations, although kaolinite and quartz dominated the soil matrix. The mineralogical complexity of the pedons followed the order of basement complex > coastal plain sand > Ewekoro formation. Profiles developed on the Ewekoro formation exhibited the highest degree of weathering, as evidenced by their chemical properties and mineralogical compositions. The petrographic evaluation of the three geological formations revealed that all pedons were rich in quartz and exhibited varying degrees of mineral complexity and maturation. The overlapping and distinct characteristics among the geologies indicated different stages of weathering. By using the mineral maturity index, profiles developed over the basement complex rock and the coastal plain sand could be regarded as sub-matured and this could have contributed significantly to the native fertility of these soils and profiles from the Ewekoro formation were the most weathered. The use of Ewekoro formation for agriculture would necessitate significant investments in agro-inputs and sound principles of soil management through integrated soil fertility management.

Optimizing flood susceptibility assessment in semi-arid regions using ensemble algorithms: a case study of Moroccan High Atlas

This study explores and compares the predictive capabilities of various ensemble algorithms, including SVM, KNN, RF, XGBoost, ANN, DT, and LR, for assessing flood susceptibility (FS) in the Houz plain of the Moroccan High Atlas. The inventory map of past flooding was prepared using binary data from 2012 events, where “1” indicates a flood-prone area and “0” a non-flood-prone or extremely low area, with 762 indicating flood-prone areas. 15 different categorical factors were determined and selected based on importance and multicollinearity tests, including slope, elevation, Normalized Difference Vegetation Index, Terrain Ruggedness Index, Stream Power Index, Land Use and Land Cover, curvature plane, curvature profile, aspect, flow accumulation, Topographic Position Index, soil type, Hydrologic Soil Group, distance from river and rainfall. Predicted FS maps for the Tensift watershed show that, only 10.75% of the mean surface area was predicted as very high risk, and 19% and 38% were estimated as low and very low risk, respectively. Similarly, the Haouz plain, exhibited an average surface area of 21.76% for very-high-risk zones, and 18.88% and 18.18% for low- and very-low-risk zones respectively. The applied algorithms met validation standards, with an average area under the curve of 0.93 and 0.91 for the learning and validation stages, respectively. Model performance analysis identified the XGBoost model as the best algorithm for flood zone mapping. This study provides effective decision-support tools for land-use planning and flood risk reduction, across globe at semi-arid regions.

Continuity, Resilience, and Change in Rural Settlement Patterns from the Roman to Islamic Period in the Sicani Mountains (Central-Western Sicily)

This study aims to analyze the dynamics of change in settlement models from the Roman, late antique, and Byzantine periods, focusing on how these transformations influenced the formation of Islamic societies in the rural landscapes of western Sicily. The study is centered around the territory of Corleone in the Sicani Mountains (central-western Sicily). This region, strategically located between the significant cities of Palermo on the Tyrrhenian Sea and Agrigento on the Strait of Sicily, has been pivotal in the communication network spanning from the Roman era to the Middle Ages and beyond. The area has been subject to extensive surveys and excavations, revealing diverse dynamics of continuity, resilience, and innovation in settlement patterns from the Roman to the Islamic periods. Beyond presenting the results of archaeological fieldwork, this study employs GIS-based spatial and statistical analyses and utilizes a range of topographic (elevation, slope, aspect, topographic position index (TPI), and distance to water sources) and ecological factors (vegetation series). These analyses aim to assess the evolving relationships and site positioning within the territory over time. Combining archaeological data with topographic and ecological landscape analysis, this integrated approach elucidates the complex transition dynamics from the Roman settlement system to the Islamic age’s landscape formation in western Sicily’s rural areas. The study thereby contributes to a deeper understanding of the intricate interplay between historical developments and environmental factors in shaping rural settlement patterns.

Mapping coastal groundwater potential zones using remote sensing based AHP model in Al Qunfudhah region along Red Sea, Saudi Arabia

Due to the increases in agriculture and industry sector as well as high population, lack of water is becoming a major problem in the Middle East especially in arid regions. Saudi Arabia needs more groundwater research and explorations because of its higher water use and no source of freshwater. Assessing groundwater zonation in semi-arid locations is essential due to the significant degree of variation in groundwater depth, aquifer features, topographical characteristics, and insufficient precipitation. Mapping prospective groundwater zones in Al Qunfudhah region of southwestern Saudi Arabia has utilized the capability of the multi-criteria decision approaches (MCDA), and the Geographic information system (GIS). We have used the analytical hierarchy process (AHP) as one of the MCDA that is applied to achieve the objective of the current study by integrating twelve controlling factors. These factors are represented by the thematic layers; slope, precipitation, soil type, land use/cover (LULC), drainage density (DD), normalized difference vegetation index (NDVI), curvature, topographic position index (TPI), Terrain Ruggedness Index (TRI), drainage density (DD), and Lineament Density (LD). These thematic layers are combined with GIS to delineate the zones of groundwater potentialities. All factors were classified and weighted according to their importance and its effect on groundwater zones. Their normalized weights were evaluated using a pairwise comparison matrix. The present study shows that the groundwater potential zones (GWPZs) map is represented by five groups ranging between a very high zone with an area of 23781.06 Km2 that represents 4.04 % of the studied area, and a very poor GWPZ with an area of 182944.4 Km2 that represents 31.09 % of the studied area. The AHP model suggests that lineament density, slope, and drainage density are more important for determining the groundwater potentiality than other physiographic factors. The study's findings will be helpful in developing practical strategies for the region's groundwater supply. This analysis shows how the methodology may be used to study a broad coastal groundwater basin. The current study will give the decision makers to select suitable sites with a high groundwater potential.

DIGITAL ELEVATION MODEL CORRECTION IN URBAN AREAS USING EXTREME GRADIENT BOOSTING, LAND COVER AND TERRAIN PARAMETERS

Abstract. The accuracy of digital elevation models (DEMs) in urban areas is influenced by numerous factors including land cover and terrain irregularities. Moreover, building artefacts in global DEMs cause artificial blocking of surface flow pathways. This compromises their quality and adequacy for hydrological and environmental modelling in urban landscapes where precise and accurate terrain information is needed. In this study, the extreme gradient boosting (XGBoost) ensemble algorithm is adopted for enhancing the accuracy of two medium-resolution 30-metre DEMs over Cape Town, South Africa: Copernicus GLO-30 and ALOS World 3D (AW3D). XGBoost is a scalable, portable and versatile gradient boosting library that can solve many environmental modelling problems. The training datasets are comprised of eleven predictor variables including elevation, urban footprints, slope, aspect, surface roughness, topographic position index, terrain ruggedness index, terrain surface texture, vector roughness measure, forest cover and bare ground cover. The target variable (elevation error) was calculated with respect to highly accurate airborne LiDAR. After training and testing, the model was applied for correcting the DEMs at two implementation sites. The corrections achieved significant accuracy gains which are competitive with other proposed methods. There was a 46–53% reduction in the root mean square error (RMSE) of Copernicus DEM, and a 72–73% reduction in the RMSE of AW3D DEM. These results showcase the potential of gradient-boosted decision trees for enhancing the quality of global DEMs, especially in urban areas.

Modelling permafrost distribution using the temperature at top of permafrost model in the boreal forest environment of Whatì, NT

Current permafrost models in Canadian boreal forests are generally of low spatial resolution as they cover regional or continental scales. This study aims to understand the viability of creating a temperature at the top of permafrost (TTOP) model on a local scale in the boreal wetland environment of Whatì, Northwest Territories from short-term field-collected temperature data. The model utilizes independent variables of vegetation, topographic position index, and elevation, with the dependent variables being ground surface temperature collected from 60 ground temperature nodes and 1.5 m air temperature collected from 10 temperature stations. In doing this, the study investigates the relationship vegetation and disturbance have on ground temperature and permafrost distribution. The model predicts that 31% of the ground is underlain by permafrost, based on a mean annual temperature at TTOP of &lt;0 °C. This model shows an accuracy of 62.5% when compared to cryotic assessment sites (CAS). Most inaccuracies, showing the limitations of the TTOP model, came from peat plateaus that had been burned in the most recent forest fire in 2014. These resulted in out-of-equilibrium permafrost and climatic conditions that TTOP cannot handle well. Commonly, permafrost mapping places Whatì in the extensive discontinuous zone, estimating that between 50% and 90% of the ground is underlain by permafrost. The study shows that a climatically driven TTOP model calibrated with CAS can be used to illustrate ground temperature heterogeneity from short-term data in boreal forest wetland environments. However, this approach likely underestimates permafrost extent and is perhaps not the best-suited modelling choice for near-surface permafrost, which is currently out of equilibrium with the current climate.

Comparison of tree-based ensemble learning algorithms for landslide susceptibility mapping in Murgul (Artvin), Turkey

Turkey’s Artvin province is prone to landslides due to its geological structure, rugged topography, and climatic characteristics with intense rainfall. In this study, landslide susceptibility maps (LSMs) of Murgul district in Artvin province were produced. The study employed tree-based ensemble learning algorithms, namely Random Forest (RF), Light Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), and eXtreme Gradient Boosting (XGBoost). LSM was performed using 13 factors, including altitude, aspect, distance to drainage, distance to faults, distance to roads, land cover, lithology, plan curvature, profile curvature, slope, slope length, topographic position index (TPI), and topographic wetness index (TWI). The study utilized a landslide inventory consisting of 54 landslide polygons. Landslide inventory dataset contained 92,446 pixels with a spatial resolution of 10 m. Consistent with the literature, the majority of landslide pixels (70% – 64,712 pixels) were used for model training, and the remaining portion (30% – 27,734 pixels) was used for model validation. Overall accuracy, precision, recall, F1-score, root mean square error (RMSE), and area under the receiver operating characteristic curve (AUC-ROC) were considered as validation metrics. LightGBM and XGBoost were found to have better performance in all validation metrics compared to other algorithms. Additionally, SHapley Additive exPlanations (SHAP) were utilized to explain and interpret the model outputs. As per the LightGBM algorithm, the most influential factors in the occurrence of landslide in the study area were determined to be altitude, lithology, distance to faults, and aspect, whereas TWI, plan and profile curvature were identified as the least influential factors. Finally, it was concluded that the produced LSMs would provide significant contributions to decision makers in reducing the damages caused by landslides in the study area.

Feasibility of Immediate Implant Placement in Maxillary First Premolars: Prediction of Implant Locations Using Restorations- A Radiographic Study.

Maxillary premolars have a unique anatomical location. This is an CBCT based study where the suitability of maxillary premolars for immediate implant placement (IIP) is evaluated. Based on prosthetically driven treatment treatment planning a simple classification system is put forth. 150 CBCTs of maxillary first premolars were analysed in BlueskyBio software. The topographic position of the tooth was determined by analysing the dimensions of the buccal and lingual cortical plates, the distance between the bucco-lingual plates and the residual bone height from the root apex to the floor of the sinus. Virtual placement of an implant was carried out such that the implant would be positioned 1 mm apical to the buccal bone crest, would engage 3 mm of bone apical to the root apex, and would have a trajectory so that the abutment access was from the central fossa. Four categories were identified and the classification was proposed. It was observed that 74% of cases had buccal bone<1mm,26% had buccal bone >1mm. 79% cases had an average distance >3mm between root apex and maxillary sinus, 21% had an average distance of root apex and maxillary sinus <3mm. The categorizations of implant placement were as follows -Type 1- 24%, Type 2- 56.6%, Type 3-43.3%, Type 4- 0%. In majority of maxillary 1st premolars an IIP is possible with the implants to be placed in the palatal sockets or the furcation area. In cases were the buccal plate thickness is inadequate, simultaneous grafting should be considered between the implant position and buccal plate.

Optimizing Tourism Development Through Landslide Hazard Mapping in Raung Volcano

The series of volcanic activities of Mount Raung triggered primary and secondary hazards in the area around the volcano. Kalibaru watershed is one of the areas prone to landslides due to secondary hazards of eruption activity in the direction of west and northwest part of this region. This study aims to optimize tourism by mapping landslide hazard using Spatial Multi-Criteria Evaluation (SMCE) around Mount Raung. This research used 46 points of landslide data through remote sensing, field observation. Ten landslide triggering factors, namely TPI (Topographic Position Index), TWI (Topographic Wetness Index), SPI (Stream Power Index), slope, distance to river, rainfall, geology, land use, distance to road, and soil type was used to map the landslide hazard. This study used ROC (Receiver Operating Characteristic) analysis to validate the landslide susceptibility mapping with an AUC (Area Under Curve) value of 0.93, which indicates that the mapping has a high accuracy value. The results showed that the landslide susceptibility is divided into three classes: high susceptible, moderate susceptible, and low susceptible. The high susceptible area covers 151.62 km2 (21%), the moderate susceptible area covers 407.99 km2 (56%), and the low susceptible area covers 166.79 km2 (23%). Based on the results of the mapping, tourism development in the area of Mount Raung is recommended in areas that are classified as medium and low landslide susceptibility.Keywords: Mount Raung, Landslides, SMCE, Tourism Development

Spatiotemporal evolution characteristics and the driving force of habitat quality in the Qinghai–Tibet Plateau in topographic view (2000–2020)

IntroductionAs a key indicator to evaluate biodiversity and eco-function, habitat quality shows the value of ecosystem services. The Qinghai–Tibet Plateau occupies the highest elevation in the global dimension, where the regional environment is greatly affected by multiple factors including the terrain comprehensively.MethodsIn the present study, the InVEST model was employed to assess the habitat quality across varying terrain gradients. The study observed the displacement of the center of gravity of habitat quality under different topographical factors over the past 20 years to learn the trend of changes. Additionally, the Geo-detector model was enhanced to explore the contribution rate of driving factors including biodiversity in different topographic positions and overall.ResultsThe results found the following: (1) The habitat quality of the Qinghai–Tibet Plateau was positively correlated with the terrain gradient, and the mean values of habitat quality increased with the gradual increase in terrain gradient. In the period between 2000 and 2020, mean habitat quality values within the study area exhibited a slight fluctuating trend with the spatial distribution characterized by higher values in the southeast and lower values in the northwest. (2) Among different terrain gradients, trends in the mean center’s shift of habitat quality were diverse. The mean center of gradient I went north but gradient V went west. The mean center of the habitat quality in gradients II–IV showed an uncertain trend. (3) The NDVI exerted the most vital influence on the quality of habitats irrespective of different terrain gradients. Annual mean temperature, soil type, and slope occupied two to four places in each terrain.DiscussionThe study indicates that in the last two decades, the habitat quality on the Qinghai–Tibet Plateau has gradually increased across different terrain locations. In terms of policies to protect habitats, terrain should be classified and targeted accordingly.

Tree-based algorithms for spatial modeling of soil particle distribution in arid and semi-arid region.

Accurate estimation of particle size distribution across a large area is crucial for proper soil management and conservation, ensuring compatibility with capabilities and enabling better selection and adaptation of precision agricultural techniques. The study investigated the performance of tree-based models, ranging from simpler options like CART to sophisticated ones like XGBoost, in predicting soil texture over a wide geographic region. Models were constructed using remotely sensed plant and soil indexes as covariates. Variable selection employed the Boruta approach. Training and testing data for machine learning models consisted of particle size distribution results from 622 surface soil samples collected in southeastern Turkey. The XGBoostClay model emerged as the most accurate predictor, with an R2 value of 0.74. Its superiority was further underlined by a 21.36% relative improvement in XGBoostClay RMSE compared to RFClay and 44.5% compared to CARTClay. Similarly, the R2 values for XGBoostSilt and XGBoostSand models reached 0.71 and 0.75 in predicting sand and silt content, respectively. Among the considered covariates, the normalized ratio vegetation index and slope angle had the highest impact on clay content (21%), followed by topographic position index and simple ratio clay index (20%), while terrain ruggedness index had the least impact (18%). These results highlight the effectiveness of Boruta approach in selecting an adequate number of variables for digital mapping, suggesting its potential as a viable option in this field. Furthermore, the findings of this study suggest that remote sensing data can effectively contribute to digital soil mapping, with tree-based model development leading to improved prediction performance.

Fast building detection using new feature sets derived from a very high-resolution image, digital elevation and surface model

ABSTRACT Detecting building rooftops with very high-resolution (VHR) images is an important issue in many fields, including disaster management, urban planning, and climate change research. Buildings with varying geometrical features are challenging to detect accurately from VHR image due to complicated image scenes containing spectrally similar objects, illumination, occlusions, viewing angles, and shadows. This study aims to detect building rooftops with high accuracy using a new framework that includes VHR image, visible band difference vegetation index, digital surface and elevation models, the terrain ruggedness and the topographic position index. Five distinct feature sets were generated in order of importance by exposing the ten related stacking features to a feature selection procedure using the maximum relevance minimum redundancy method. Then, Auto-Encoder, k-NN, decision tree, RUSBoost, and random forest machine learning algorithms were utilized for binary classification. Random forest yielded the highest accuracy (97.2% F-score, 98.72% accuracy) when all features (F10) were used, while decision tree was the least successful (59.16% F-score, 83.56% accuracy) for RGB feature set (FRGB). It was revealed that classification of F10 with random forest increased F-score by about 23% compared to classification with FRGB. Additionally, McNemar’s tests showed no statistically significant difference between random forest vs k-NN and decision tree vs RUSBoost.

Variability of micronutrients status along topographic positions and toxicity potential of soils under rice cultivation in Niger State, Nigeria

Micronutrients are important for good crop performance. There is limited quantitative data on the content and distribution of micronutrients in soils under rice cultivation in Niger state, particularly, on the soils of Baddeggi and Makusidi. To achieve this, soil samples were purposively collected along topographic positions (upper-slope, mid-slope, lower-slope and bottomlands) in the two areas. To assess the distribution of iron (Fe), manganese (Mn), zinc (Zn) and copper (Cu), a total of sixty-four composite samples were collected at 0-15 and 15-30 cm depth and were determined by Atomic Absorption Spectrophotometry. The results revealed that the mean values of available Fe, Mn, Zn and Cu were 182.40, 41.90, 7.90 and 6.50 mg/kg for Baddeggi and 254.30, 275.13, 16.25 and 7.88 mg/kg for Makusidi respectively. The very high Fe and Mn in the study sites may lead to toxicity of these elements. The Cu and Zn were also relatively high, and their deficiencies are unlikely to occur in the study area.

Estimating weakening on hillslopes caused by strong earthquakes

The weakening of hillslopes during strong earthquakes increases landsliding rates in post-seismic periods. However, very few studies have addressed the amount of coseismic reduction in shear strength of hillslope materials. This makes estimation of post-seismic landslide susceptibility challenging. Here we propose a method to quantify the maximum shear-strength reduction expected on seismically disturbed hillslopes. We focus on a subset of the area affected by the 2008 Mw 7.9 Wenchuan, China earthquake. We combine physical and data-driven modeling approaches. First, we back-analyze shear-strength reduction at locations where post-seismic landslides occurred. Second, we regress the estimated shear-strength reduction against peak ground acceleration, local relief, and topographic position index to extrapolate the shear-strength reduction over the entire study area. Our results show a maximum of 60%–75% reduction in near-surface shear strength over a peak ground acceleration range of 0.5–0.9 g. Reduction percentages can be generalized using a data-driven model.

Spatial and Temporal Variation in Primary Forest Growth in the Northern Daxing’an Mountains Based on Tree-Ring and NDVI Data

We used tree-ring width data of Larix gmelinii and Pinus sylvestris var. mongolica from the northern region of the Daxing’an Mountains, China; normalized difference vegetation index (NDVI) data; and microtopographic information (elevation, slope direction, slope gradient, and topographic location index) to assess spatiotemporal dynamics in the growth of the boreal forest and topographic patterns of forest decline under the background of climate warming. Forest growth trends were determined based on tree growth decline indicators and NDVI time series trends, and topographic patterns of forest decline were analyzed using the C5.0 decision tree model. More climatic information was present in the radial growth of the trees at higher elevations, and P. sylvestris var. mongolica was influenced strongly by climatic factors of the previous year. Since 1759, tree radial growth trends in the study area have experienced two recessions during 1878–1893 and 1935–1943, which were characterized by persistent narrow whorls of tree rings of below-average growth. Changes in NDVI and tree-ring information were similar, and they together indicate a high risk of declining forest growth in the northern Daxing’an Mountains after 2010, especially at higher elevations. The NDVI time series showed that the high temperatures in 2003 negatively affected forest growth in the study area, which was confirmed by the tree-ring data. The decision tree terrain model results had an accuracy of 0.861, and elevation was the most important terrain factor affecting forest decline. The relative importance of elevation, topographic position index, aspect, and slope was 58.41%, 17.70%, 16.81%, and 7.08%, respectively. Classification rule-based decision tree models can be used to quantify the effects of terrain factors on tree growth. This research methodology can aid the management of regional forestry resources and the conservation of forest resources under the background of climate change, which increases the risk of forest decline.

Floristic diversity of patches generated by protected Ficus ‘trees outside forests’ in agricultural landscapes in Madagascar

AbstractFicus trees play an important role as key species in both ecological and sociocultural networks in Madagascar. This study focused on species of isolated Ficus growing in agricultural areas adjoining a forest corridor linking the Ranomafana and Andringitra National Parks in east‐central Madagascar, with the aim of understanding the structural and floristic characteristics of the vegetation regenerating under these Ficus ‘trees outside forests’. Two main aspects were studied: (1) the characteristics of the habitats under the crown of isolated Ficus species (Ficus tiliifolia, Ficus reflexa and Ficus lutea), and (2) the specific structure and composition of the vegetation under these Ficus trees: minimum area, species richness, floristic regularity, diversity and similarity, biological type, vegetation type, proportion of annual and herbaceous species, and seed dispersal methods. The results indicate that each of these three Ficus species has its own community composition. The vegetation under F. reflexa is a shrubby formation dominated by autochorous and zoochorous species located on high slopes with a minimum area of 6–12 m2. The vegetation under F. lutea is a highly anthropized herbaceous formation dominated by autochorous species located on high slopes at an altitude of 1150–1200 m, with a southwest exposure and a minimum area that ranges from 6 to 9 m2. Under F. tiliifolia, different types of plant formations are found in different topographic positions: herbaceous, shrubby or tree‐like, with different modes of seed dispersal.

Impacts of stocking densities on soil biochemical and microbial properties in a mixed-grass prairie ecosystem at two landscape positions

Grazing management is a critical land-use requirement that facilitates the preservation of plant community composition, soil properties and environmental quality. Grazing density of livestock has a significant impact on soil health, and there is a need to study the interactions of grazing densities and topographical positions influencing soil biochemical and microbial properties. This study was conducted at Cottonwood Field Station in Philip, South Dakota to assess the influence of more than 7 years of low, medium, and high grazing stocking densities (0.33, 0.41, 0.72 animal units/ac, respectively) at summit and footslope landscape positions on soil carbon (C) and nitrogen (N) fractions, microbial community composition, and enzymatic activities in a mixed-grass prairie ecosystem. Medium grazing density showed a 16% increase in soil N at the footslope compared with summit. Low grazing density significantly reduced microbial biomass C (~269 μg g−1 soil) and N (~26 μg g−1 soil) at summit compared with other grazing densities and landscape positions, except, the summit at high grazing density. Medium grazing density significantly enhanced hot-water extractable N by 21–23% at footslope compared with low grazing density at the footslope and high grazing density at the summit. Low grazing density increased urease (3.64 μg NH4+ g−1 soil h−1) at footslope than all other grazing densities and landscape positions. Low grazing density enhanced β-glucosidase by 75% than high grazing density; alkaline phosphatase was significantly greater by 60% at footslope than summit. High grazing density at the summit decreased total PLFA (mean 56.53 nmol g−1 soil) due to lower AM fungi, G (+), G (−) and actinomycetes biomass. Microbial stress indicators such as G (+)/G (−), saturated/unsaturated, monosaturated/polysaturated, GNeg stress revealed that high grazing density especially at summit position posed elevated physiological stressed conditions to the microbial community. Overall, long-term medium grazing density of 0.41 animal units/ac may enhance soil N, microbial composition, microbial biomass C and N, hot-water extractable C and N fractions, and reduce stress conditions for microbial community at both footslope as well as summit landscape positions. Moreover, long-term overgrazing of pastures, particularly at summit slopes, appears to inhibit microbial populations and degrade overall soil health.

Energy potential assessment and techno–economic analysis of micro hydro–photovoltaic hybrid system in Goda Warke village, Ethiopia

Abstract Rural Ethiopia has significant untapped potential for hydro and solar energy generation systems. However, challenges arise from seasonal variations and unfavourable topographic positions of flowing rivers, hindering the efficient exploitation of these resources. Despite the country’s abundance in hydro and solar energy resources, &amp;gt;75% of the population still lack access to electricity from the national grid. This work deals with energy resource potential assessment and techno–economic analysis of micro hydro–photovoltaic (PV) hybrid systems, considered in the case study of Goda Warke village, located in the Yaya Gulele district. A novel framework is proposed that utilizes the Natural Resource Soil Conservation Service curve number method to assess the energy potential of micro-hydro energy in ungauged basins, specifically at the exit point of the Girar River basin catchment. The average monthly flow rate in the basin is 0.975 m3/s, while the area exhibits a solar radiation potential of 5.39 kWh/m2/day. Energy policy promotes expanding access to modern energy sources and utilization of indigenous energy resources. Simulation results indicate that the hydro/PV/diesel generator (DG)/battery and hydro/PV/battery systems are the most optimal choices based on net present cost, with the inclusion of a DG for economic comparison. Micro-hydro energy covers most of the electric load in the area, achieving a capacity factor of 47.5%. The cost of energy and net present cost were found to be sensitive to variables such as the price of diesel fuel, pipe head loss, and the growth of the village load. The optimized system demonstrated a hydro energy potential of 1405.37 MWh/year and a PV energy output of 274.04 MWh/year, resulting in a levelized cost of energy of 0.0057 and 0.049 $/kWh for the hydro and PV components, respectively.