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Landslides are among the major environmental hazards with large-scale socio-economic and environmental impacts that jeopardize socio-economic wellbeing in mountainous regions. Landslides are due to the interaction of several complex factors such as local or regional geology, geomorphology, topography, and seismic motions. The goal of this study is to review published articles on causes and effects of landslides in Central Asia throughout 2000–2020. In line with this goal, we have collected (using Scopus database), reviewed, and analyzed 79 papers published during 2000–2020. Our results revealed an increasing number of landslide studies in Central Asia during the period under investigation, with authors from Belgium dominating in the published outcomes (28% of total), followed by authors from Central-Asian countries. After then, the paper analyses the mostly applied models and frequently identified driving conditions and triggers of landsliding, such as aspect, altitude, soil types, precipitation, earthquakes and human interventions. Geographic information system (GIS) and remote sensing (RS) had not commonly been used in the papers between 2000 and 2010, and they have progressively been applied in landslide studies in Central Asia in the last decade. According to our analysis, geotechnical, geophysical and statistical methods were preferably used for the landslide studies in Central Asia.

The canopy density of forests is highly influenced by population pressure which cause forest fragmentations, deforestations, forest degradation and also land reclamation for infrastructure and agriculture. This study was envisaged with the objective of mapping the forest canopy density with two different methods by using Landsat 8 OLI dataset of the year 2016 after mapping the vegetation indices. One of the two methods is the semi-expert FCD mapper model, while the other model is newly developed by us and consists of eight vegetation indices. After running the models, the results of both the models were compared. It was found that for the semi-expert FCD model, the three classes viz. high canopy density, moderate canopy and low canopy covered an area of 81,615.51 ha (40%), 84,474.72 ha (41%) and 38,844.18 ha (19%), respectively. And for the modified FCD model, the same classes covered 69,134.670 ha (37%), 84,062.250 ha (45%) and 32,529.150 ha (18%), respectively. It was observed that the difference between semi-expert FCD model and the modified FCD model’s accuracy is about 1.75% and difference in Kappa statistics is 0.0362. Thus, the modified model is more accurate than the semi-expert FCD model and gives us more detailed canopy density map than the semi-expert FCD map.

As satellite data of the Earth surface seems to be of vital importance for many applications, classification of land use and land cover has been found to vary dramatically in different approaches. In this paper, modified classification algorithm of remote sensing data is presented for processing medium and high spatial resolution satellite images like Landsat and Sentinel in Tashkent province of Uzbekistan. The results of NDVI (Normalized difference vegetation index) profile analysis via Spectral Correlation Mapper classification are shown for the period 1994-2017. It is implied, that combination of optical and radar data with application of Spectral Correlation Mapper classification improve the results of classification for a specific dataset by considering such factors as overall classification accuracy and time and labor involved.

We introduce a new fungal species, Ophiobolus hydei, from dead stems of Cirsium alatum (Compositae) and Phlomoides brachystegia (Lamiaceae), based on morphological and phylogenetic evidence. The species was collected from the Mountains of Western Tien Shan and southwestern Hissar in Uzbekistan. Ophiobolus hydei is characterized by globose to subglobose ascomata with short to long papilla, cylindrical to subcylindric-clavate asci, broad pseudoparaphyses, and scolecosporous, yellowish-brown to brown, filiform, multiseptate ascospores that can split into several part-spores at the septa. Multigene phylogenetic analyses using a combined gene analysis of ITS, LSU, SSU, and TEF1-α indicated that the new species has a close affinity to Ophiobolus ponticus, but differs from that species in the micromorphological characteristics of the ascomata, asci, and ascospores, as well as biogeographic distribution. A distribution map, morphological descriptions, and illustrations with colour photographs of the novel species are provided.

Considering the slope units as our reference mapping units, three statistical models [frequency ratio (FR), index of entropy (IOE), and evidential belief function (EBF)] are used in combination by two methods [normalized spatial-correlated scale index (NSCI) and weighted calibrated landslide potential model (WCLPM)]. For this aim, ten conditioning factors correlated with landslide namely, altitude, slope angle, slope aspect, relief amplitude, cutting depth, gully density, surface roughness, distance to roads, rainfall, and lithology are considered. The performance of the models is tested using the area under the receiver operating characteristic (ROC) curve (AUC) and several statistical evaluation measures. The weighted calibrated landslide potential index (WCLPI)-based FR model has the highest predictive capability, followed by the calibrated landslide potential index (CLPI)-based FR, the WCLPI-EBF, the CLPI-EBF, the WCLPI-IOE, the CLPI-IOE, the FR, the EBF, and the IOE models, respectively. Results indicated that hybrid models have improved significantly the performance of single models. This highlights that NSCI and WCLPM hybrid techniques are promising methods for landslide susceptibility assessment.

Soil erosion and salinity are long-standing afflictions of Uzbekistan. Regional climate change, already evident, is likely to exacerbate droughts and high summer temperatures; the future rainfall regime is unknown. All these hazards will increase the risk of land degradation. The Aral Sea has been declared a zone of environmental innovation and technology but, beyond sowing halophytes in its dry bed, we are a long way from restoration. New and different ways have to be found to combat these challenges including science-based crop rotation taking into account soil characteristics; sustainable farming systems adapted to the harsh landscape; widespread adoption of agro-ecotechnology, biotechnology and information technology in soil conservation and land use planning; and effective ways to combat salinization, erosion, depletion of soil organic matter, and compaction. All need a sound theoretical base. Innovations under trial include soil improvement with a range of vegetable crops and legumes, application of various composts including worm compost from household waste and biogas production residue, microbiological preparations, and systematic reclamation of gypsum soils.

Sagar Island is a very popular pilgrimage destination located in the western part of Indian Sundarbans. This study employs Digital Shoreline Analysis System (DSAS) extension tool in ArcGIS platform to study and analyse the shoreline dynamics of Sagar Island by utilizing satellite images extending 40 years (1975–2015). 44 transects with 100 m spacing were laid and divided into six littoral cells (LCs). End Point Rate (EPR) and Linear Regression (LR) models were utilized to analyze the shoreline change patterns and also for predicting the future shoreline positions. It was observed that almost the entire southern portion of Sagar Island is susceptible to high rate of shoreline erosion. Most of the erosion occurred in Dhablat (LC 4 (a)) in the south eastern part at a rate of 11.695 ± 2.1 m/year. The mean shoreline change rate was also high in LC5 (±23.525 m/year). However, the overall shoreline change rate for the island was 4.94 m/year and uncertainty of total shoreline change rate was ±4.4 m/year. The study shows the usefulness of DSAS as a scientific tool for shoreline change studies and highlights state of erosion in the study area.

The present study shows the groundwater potential assessment and an effective management of the groundwater resources in Bostanlik district, Uzbekistan based on the integrated utilization of remote sensing and GIS techniques. For the current study, various thematic spatial layers such as soil map, geology map, land use/land cover (LULC), elevation, slope, stream proximity, stream density, curvature, topographic wetness index (TWI) and precipitation are used to delineate groundwater potential zones. GIS based multi-criteria decision analysis (MCDA) was applied. The percentage of groundwater potential zones identified using given factors is shown as 6%, 45%, 26%, 13%, and 10% respectively from very high to very low classes. High and very high probability of groundwater existence occurs within or across the Charvak reservoir and along the Chirchik river. Territories, covered by mountains have low and very low potential of groundwater resources. The rest of the territory has moderate potential for groundwater resources. For the validation of obtained results we have used 31 observed spring locations. Results of overlaying the final map with spring locations shows relatively high accuracy rate. The results of the research can be helpful for determining potential groundwater reserves of the Bostanlik district.

The Bostanlik district, Uzbekistan, is characterized by mountainous terrain susceptible to landslides. The present study aims at creating a statistically derived landslide susceptibility map - the first of its type for Uzbekistan - for part of the area in order to inform risk management. Statistical index (SI), frequency ratio (FR) and certainty factor (CF) are employed and compared for this purpose. Ten predictor layers are used for the analysis, including geology, soil, land use and land cover, slope, aspect, elevation, distance to lineaments, distance to faults, distance to roads, and distance to streams. 170 landslide polygons are mapped based on GeoEye-1 and Google Earth imagery. 119 (70%) out of them are randomly selected and used for the training of the methods, whereas 51 (30%) are retained for the evaluation of the results. The three landslide susceptibility maps are split into five classes, i.e. very low, low, moderate, high, and very high. The evaluation of the results obtained builds on the area under the success rate and prediction rate curves (AUC). The training accuracies are 82.1%, 74.3% and 74%, while the prediction accuracies are 80%, 70% and 71%, for the SI, FR and CF methods, respectively. The spatial relationships between the landslides and the predictor layers confirmed the results of previous studies conducted in other areas, whereas model performance was slightly higher than in some earlier studies - possibly a benefit of the polygon-based landslide inventory.