Remote Mountainous Areas Research Articles

Brickwork in Norway: tradition encounters a new age of industrial construction, 1905–1912

During the first decades of the twentieth century, construction technology experienced rapid developments, which also affected industrial buildings. This paper demonstrates this evolution between 1905 and 1912 through the case of the Rjukan and Notodden UNESCO World Heritage site in central southern Norway. The site was established for the purpose of producing synthetic nitrogen fertiliser by the company Hydro. There, building systems changed from timber frames with brick infill to pure brick bearing walls and finally to more sophisticated wall-braced cage buildings where steel was incorporated. Increasing the structural capacity was the answer to the growing demand for higher and larger industrial buildings. This case study presents the characteristics and establishes an initial account of the types of construction systems. Embedded steel that is concealed within brickwork can be subject to corrosion and compromise structural integrity. Studying these systems and materials in greater detail and identifying conservation issues will support future repair work. The studied buildings reveal also that used systems were not unique to this remote mountain area, but the technology used was in line with developments in leading industrial nations around the world. This places the site also in an international context of architecture and engineering history.

Depopulation in the Central Apennines in the Twentieth Century: An Empirical Investigation

AbstractWe propose an empirical investigation of the population dynamics between 1931 and 2011 in a mountain area in Italy. The main novelty of our work is the usage of sub-municipal data, which makes it possible to disentangle several drivers of the overall depopulation trend. All these factors had been considered previously by separate strands of literature, but never jointly, as we do. One of our most interesting results is that different factors operate in different historical periods. Therefore, we use a flexible strategy by which we divide the sample in four different 20-year spans and adopt a different statistical model for each. Another notable result is that an appropriate quantitative description of the phenomenon must take into account the disappearance of inhabited centres separately from their size in terms on inhabitants. Consequently, the concept of local community is crucial for a systematic understanding of population change and “place-sensitive” policies in remote mountain areas.

Learning lightweight tea detector with reconstructed feature and dual distillation

Currently, image recognition based on deep neural networks has become the mainstream direction of research; therefore, significant progress has been made in its application in the field of tea detection. Many deep models exhibit high recognition rates in tea leaves detection. However, deploying these models directly on tea-picking equipment in natural environments is impractical; the extremely high parameters and computational complexity of these models make it challenging to perform real-time tea leaves detection. Meanwhile, lightweight models struggle to achieve competitive detection accuracy; therefore, this paper addresses the issue of computational resource constraints in remote mountain areas and proposes Reconstructed Feature and Dual Distillation (RFDD) to enhance the detection capability of lightweight models for tea leaves. In our method, the Reconstructed Feature selectively masks the feature of the student model based on the spatial attention map of the teacher model; it utilizes a generation block to force the student model to generate the teacher’s full feature. The Dual Distillation comprises Decoupled Distillation and Global Distillation. Decoupled Distillation divides the reconstructed feature into foreground and background features based on the Ground-Truth. This compels the student model to allocate different attention to foreground and background, focusing on their critical pixels and channels. However, Decoupled Distillation leads to the loss of relation knowledge between foreground and background pixels. Therefore, we further perform Global Distillation to extract this lost knowledge. Since RFDD only requires loss calculation on feature map, it can be easily applied to various detectors. We conducted experiments on detectors with different frameworks, using a tea dataset collected at the Huangshan Houkui Tea Plantation. The experimental results indicate that, under the guidance of RFDD, the student detectors have achieved performance improvements to varying degrees. For instance, a one-stage detector like RetinaNet (ResNet-50) experienced a 3.14% increase in Average Precision (AP) after RFDD guidance. Similarly, a two-stage model like Faster RCNN (ResNet-50) obtained a 3.53% improvement in AP. This offers promising prospects for lightweight models to efficiently perform real-time tea leaves detection tasks.

Pollution of Heavy Metals in Topsoil of Remote Mountainous Areas in Western Yunnan-Guizhou Plateau

Heavy metals from anthropogenic emissions have had a negative impact on the ecological environment in remote regions. A total of 69 topsoil samples were collected from 13 remote mountainous areas in the western Yunnan-Guizhou Plateau at altitudes of 2 563-4 037 m, and the concentrations of ten heavy metals in the samples were determined. Enrichment characteristics and pollution sources of heavy metals in topsoil were discussed by referencing the enrichment factor （EF）, positive matrix factorization （PMF）, and Pb isotopes. The results showed that the average concentrations of Al, Fe, Cu, V, and Zn in the topsoil were lower than the soil background values in Yunnan Province； the average concentrations of Ni and Pb were similar to the background values； and the average concentrations of Cd, Cr, and Hg were 1.8-3.6 times higher than the background values. The average EF values of Pb, Cr, and Ni were 3.8, 3.4, and 2.3, respectively, showing moderate enrichment according to the EF classification criteria； the average EF values of Cd and Hg were 15.2 and 10.0, reflecting significant enrichment； and the average EF values of the other metals ranged from 1.1 to 1.9, displaying none-weak enrichment. Combining the comparisons of heavy metal concentrations and ratios in topsoil and bedrock and the EF and PMF results, Al, Fe, V, Cr, Cu, Ni, and Zn in topsoil were considered primarily from detrital sources, and the spatial concentration variations of the metals should have been mainly regulated by the parent material. Cadmium, Hg, and Pb were obviously polluted by anthropogenic emissions, and the main sources were non-ferrous metal smelting and coal combustion. The areas with relatively high Cd, Hg, and Pb pollution were mainly distributed in the Jiaozi snow mountain, Bitahai watershed, Luoji Mountain, and Laojun Mountain areas. Anthropogenic emissions contributed 23.8% of the accumulation of heavy metals in the topsoil.

Dental caries prevalence and caries-associated risk factors of students aged 12–15 in Xide County of Liangshan Prefecture, China: a cross-sectional study

ObjectiveAbout 2 billion people have caries in permanent teeth, and 514 million children have caries in primary teeth worldwide. Regional disparities in caries prevalence are evident in various areas of...

An Ultra-Compact and Low-Cost LAMP-Based Virus Detection Device.

Timely and accurate detection of viruses is crucial for infection diagnosis and treatment. However, it remains a challenge to develop a portable device that meets the requirement of being portable, powerless, user-friendly, reusable, and low-cost. This work reports a compact ∅30 × 48 mm portable powerless isothermal amplification detection device (material cost ∼$1 USD) relying on LAMP (Loop-Mediated Isothermal Amplification). We have proposed chromatographic-strip-based microporous permeation technology which can precisely control the water flow rate to regulate the exothermic reaction. This powerless heating combined with phase-change materials can maintain a constant temperature between 50 and 70 °C for a duration of up to 49.8 min. Compared with the conventional methods, it avoids the use of an additional insulation layer for heat preservation, greatly reducing the size and cost. We have also deployed a color card and a corresponding algorithm to facilitate color recognition, data analysis, and storage using a mobile phone. The experimental results demonstrate that our device exhibits the same limit of detection (LOD) as the ProFlex PCR for SARS-CoV-2 pseudovirus samples, with that for both being 103 copies/μL, verifying its effectiveness and reliability. This work offers a timely, low-cost, and easy way for respiratory infectious disease detection, which could provide support in curbing virus transmission and protecting the health of humans and animals, especially in remote mountainous areas without access to electricity or trained professionals.

Remote Mountainous Area Inevitably Becomes Temporal Sink for Microplastics Driven by Atmospheric Transport.

Atmospheric transport drives the widespread distribution of microplastic (MP) in various ecosystems, posing a growing potential threat to environmental safety and human health. Understanding the source and fate of atmospheric MPs is thus crucial to constrain MP's widespread exposure. However, the source-sink dynamics of atmospheric MPs, especially in remote areas, are uncertain, and their transport routes have yet to be identified. Here, we conducted a 13-month monitoring of the atmospheric MPs in the uninhabited area of Mount Taibai, estimated the potential risk of MP exposure to the environment, and modeled the MP trajectory to analyze their transportation. We first found that as many as 15 polymer types of MPs, whose shapes mainly include fiber, fragments, films, and granules, maintained abundance (0.7 and 0.3 particle/m3 for PM10 and PM2.5, respectively) in the mountain atmosphere at respirable sizes. It is worth noting that the risk assessment results that comprehensively consider the influences of abundance and morphological characteristics suggest that the exposure level of MPs exhibits a risk even in this remote mountainous area that is not disturbed by frequent human activities. Backward trajectories revealed the likely source of MPs in the sparsely populated Liupan Mountains and Qinling Mountains of short-range transport. Further, polymer characteristics of MPs and airflow-based source analysis indicated the emission source of MPs in southern Xianyang in a longer-range transport. MPs were directionally transported to Mount Taibai through atmospheric transport under the premise of stable climate and geographical conditions. These suggest that MPs inevitably occur in remote mountainous areas driven by atmospheric transport, and the mountainous areas are persistently bearing the environmental impact of MP exposure. This study reveals the risk impacts of MP exposure and the transport dynamics of atmospheric MPs in a mountain ecosystem.

Dynamics in construction land patterns and its impact on water-related ecosystem services in Chengdu-Chongqing urban agglomeration, China: A multi-scale study

Water-related ecosystem services (WES) are paramount for the development of sustainable urban agglomerations. The impact of construction land pattern (CLP) changes on WES varies by spatial scale; however, a comprehensive multi-scale analysis of CLPs and their impact on WES in urban agglomerations is still lacking. Focusing on the Chengdu-Chongqing urban agglomeration (CCUA) within the Yangtze River Economic Belt, China, this study investigated the CLPs using multidimensional measures (intensity, complexity, and aggregation) at various scales: watershed, county, township, and grid. Using geographically weighted regression (GWR), the multi-scale impacts of the multidimensional CLP on WES (including water yield, soil retention, water purification, and habitat quality) were assessed and the scale effects and spatial heterogeneity were explored. The key findings include the following: (1) Significant changes in the CLPs within the main stream of the Qingyi and Minjiang watersheds have been observed over the past two decades. (2) The CCUA forms a network of construction land connecting all cities, with Chengdu and Chongqing as the main nodes. (3) Construction land complexity demonstrates a concentric distribution, with increasing complexity from the center outward and simplification toward unurbanized areas. (4) Substantial local and regional impacts of the CLPs on WES were found across multiple scales within the CCUA. (5) Increasing the complexity and aggregation of construction land facilitates the protection of WES in rapidly urbanizing areas (e.g., megacities), whereas the opposite is true in small cities and remote mountainous areas. This research enhances our understanding of construction land dynamics and their implications for WES, contributing vital insights into water-related ecological safeguarding and fostering sustainable regional development in urban agglomerations.

Assessing the informativeness of a coupled surface–subsurface watershed model for understanding debris flow: a hydrological perspective

ABSTRACT Characterization of debris flow is critical to both risk assessment and hazard mitigation. Recent technologies enable onsite environmental monitoring sensors for geological disaster monitoring. However, the spatiotemporal understanding of debris flow in remote mountainous areas is still limited due to difficulties in observation networks and its complex driving conditions. Here we apply a coupled surface–subsurface hydrological model to examine the characteristics of the water movements near debris flow sites in southwest China. Our approach captured the temporal dynamics of infiltration, redistribution of soil moisture, groundwater storage, and lateral groundwater fluxes. The lateral groundwater flux and groundwater storage were informative indicators in identifying debris flow location. Such informativeness was only effective when hourly dynamics were analysed. Our findings provide new insight into quantifying debris flow susceptibility. This study suggests that the coupled surface–subsurface watershed modelling approach can be informative for preliminary monitoring, planning and management of debris flow.

Using a Logistic Regression Model to Examine the Variables Influencing Changes in Northern Thailand’s Forest Cover and Comparing Machine Learning Algorithms

Protecting biodiversity and keeping the Earth’s temperature stable are both very important jobs performed by tropical forests. In the last few decades, remote sensing has given us new tools and ways to track changes in land cover. To understand what causes changes in forest cover, it is important to look at the things that affect those changes. However, there is not enough research that uses a logistic regression model (LRM) and compares the results with machine learning (ML) techniques to investigate the specific factors that cause forest cover change in remote mountainous areas like Thailand’s Mae Hong Son and Chiang Mai Provinces. Following a comparison of an LRM, a random forest, and an SVM, this study of the causes of changes in forest cover in Mae Hong Son found six important factors: soil series, rock types, slope, the NDVI, the NDWI, and the distances to city areas. Compared to the LRM, both the RF and SVM machine learning algorithms had higher values for the kappa coefficient, sensitivity, specificity, accuracy, positive and negative predictions, and sensitivity, especially the RF. Following what was found in Mae Hong Son, when the important factors were examined in Chiang Mai, the RF came out on top. It is believed that these results can be used in more situations to help make plans for restoring ecosystems and to promote long-lasting methods of managing land use.

Ultrasonography screening of hepatic cystic echinococcosis in sheep flocks used for evaluating control progress in a remote mountain area of Hejing County, Xinjiang

BackgroundAlthough ultrasonography (US) has been widely used in the diagnosis of human diseases to monitor the progress of cystic echinococcosis (CE) control, the screening method for hepatic CE in sheep flocks requires adjustment. In this study, we used a US scanner to screen sheep flocks and evaluated the efficacy of dosing dogs once a year with praziquantel for 7 years from 2014 to 2021.MethodsAll sheep in the three flocks were screened using an ultrasound scanner in 2014 and compared with the prevalence of infection in 2021 in Bayinbuluke, Xinjiang, China. Sheep age was determined using incisor teeth. Cyst activity and calcification were determined using US images. The dogs were dewormed with praziquantel once a year to control echinococcosis in the community.ResultsThree flocks had 968 sheep in 2014, with 13.22%, 22.62%, 18.7%, 27.27%, 11.88%, and 6.3% of sheep aged 1, 2, 3, 4, 5, and ≥ 6 years old, respectively. US scanning revealed that the overall CE prevalence was 38.43% (372/968), with active cysts and calcified cysts present in 9.40% (91/968) and 29.02% (281/968) of the sheep, respectively. For the young sheep aged 1 and 2 years, the prevalence of active and calcified cysts was: 1.56% and 0.91%, and 10.94% and 18.72%, respectively. Approximately 15.15% and 16.52% of the 4- and 5-year-old sheep, respectively, harbored active cysts. There was no significant difference in the infection rates of sheep between 2014 and 2021 (P > 0.05).ConclusionsUS is a practical tool for the field screening of CE in sheep flocks. One-third of the sheep population in the flocks was 1–2 years old, and these sheep played a very limited role in CE transmission, as most of the cysts were calcified. Old sheep, especially culled aged sheep, play a key role in the transmission of CE. Dosing dogs once a year did not affect echinococcosis control.

Establishment of a reverse transcription-recombinase polymerase amplification-lateral flow dipstick method for the dual detection of Israeli acute paralysis virus and chronic bee paralysis virus.

As an important social insect, honey bees play crucial roles in agricultural production, sustainable development of agricultural production, and the balance of the natural environment. However, in recent years, Israeli acute paralysis virus (IAPV) and chronic bee paralysis virus (CBPV), the main pathogens of bee paralysis, have continuously harmed bee colonies and caused certain losses to the beekeeping industry. Some beekeeping farms are located in wild or remote mountainous areas, and samples from these farms cannot be sent to the laboratory for testing in a timely manner, thereby limiting the accurate and rapid diagnosis of the disease. In this study, we used a reverse transcription-recombinase polymerase amplification-lateral flow dipstick (RT-RPA-LFD) method for the dual detection of IAPV and CBPV. RPA primers and LFD detection probes were designed separately for their conserved genes. Primers and probes were screened, and the forward and reverse primer ratios, reaction times, and temperatures were optimized. According to the results of the optimization tests, the optimal reaction temperature for RT-RPA is 37°C, and when combined with LFD, detection with the naked eye requires <20 min. The developed RPA-LFD method specifically targets IAPV and CBPV and has no cross-reactivity with other common bee viruses. In addition, the minimum detection limit of the RT-RPA-LFD method is 101 copies/μL. Based this study, this method is suitable for the detection of clinical samples and can be used for field detection of IAPV and CBPV.

Intelligent Logistics Vehicle Path Planning Using Fused Optimization Ant Colony Algorithm With Grid

Aiming at the problem that the environmental planning path of intelligent logistics vehicles on urban roads and remote mountainous areas cannot fit the actual driving scene well. This study creates the algorithm model that combines an ant colony algorithm with a dynamic window algorithm and a Bessel smoothing strategy. Compared to the traditional colony algorithm with the same parameters, this fusion algorithm makes the path smoother by 72.2% when used on an urban highway. It also follows the right-hand rule for right-turn intersections. When the vehicle's height is determined in a mountain environment, this fusion algorithm reduces the driving's mean square deviation of height by 81.5% and shortens the path distance by 38.7%. The fusion algorithm can plan the target path of intelligent logistics vehicles and has the characteristics of scenarios available, multiple factors coordinated, and driving safety. It has provided certain research value and ideas for the digital transformation of the logistics industry.

Current Edge-Control Strategy for Multiple Energy Routers Based on Cyber-Energy Dual Modulations

Recently, energy routers have been widely applied in the dc microgrid to achieve the power balance among buses, and the relative control strategies with communication network assumption have been proposed. However, the communication network assumption cannot be satisfied in remote mountainous area and island area. To this end, this paper proposes one communication-free edge-control strategy for energy routers based on cyber-energy dual modulation to achieve power balance without communication network assumption. First, the network structure and system model are introduced, which is an fundamental pretreatment for the subsequent controller design. Second, the cyber-energy dual modulation approach without additional communication line is designed to exchange the information among distributed generations (DGs), which is an indispensable communication environment design for the subsequent controller design. Considering the system model feature and communication environment feature, the periodic dynamic event-triggered-based edge-control strategy is proposed in this paper. Finally, the proposed strategy effectiveness of the energy router is well verified through DC-DC converter experiment set and OPAL-RT test system.

Dendroarchaeology in Greece – From humble beginnings to promising future

It was back in the early 1960s when Bryant Bannister recognized the potential of dendrochronological research on wooden cultural heritage in Greece. More than two decades later, in the late 1980s, P.I. Kuniholm and C.L. Striker started collecting and analyzing tree-ring series from several historical buildings, forests, and archeological sites in Greece and the surrounding Aegean region. Despite highly promising results, especially from the northern and western parts of the country, dendroarchaeology did not attract much attention in the subsequent decades. It was only near the end of the 2000s that dendroarchaeology was reintroduced: first in Crete through the Cretan Dendrochronology Project, and then by another independent project concerning the restoration of a historical building on the island of Euboea. These isolated case studies inspired a five-year systematic research program called the “Balkan-Aegean Dendrochronology Project: Tree-Ring Research for the Study of Southeast-European and East Mediterranean Civilizations” (BAD Project). Dendroarchaeological surveys of historical buildings and archaeological sites were conducted throughout Greece with an emphasis on regions that had been previously ignored, such as the southern part of the country and the Aegean islands. Priority was given to buildings under restoration since in such cases original timbers were usually accessible and the architects, structural engineers, and archaeologists in charge were willing to collaborate. Our goals were not only to date timbers or provide information about the species used or the possible origin of the wood, but also to document the date, evolution, interventions and even the construction phases of historical buildings, as well as to help all those interested parties (academics, non-academics, researchers, professionals, local communities, etc.,) see the value of such information and how dendroarchaeology can contribute to the reconstruction of local history and the protection of cultural heritage. Tree-ring analysis led to the development of 18 chronologies from historical timbers and forests for six different species and different parts of the country from remote mountainous areas to small islands across the Aegean. The current dataset covers more than a thousand (1000) years and demonstrates the further potential of dendroarchaeology in the region.

Improving Mountain Snowpack Estimation Using Machine Learning With Sentinel‐1, the Airborne Snow Observatory, and University of Arizona Snowpack Data

AbstractAccurate mapping of snow amount in the mountains is critical as mountain snowpacks are water supply for millions of people. Satellite remote sensing has been largely unable to reliably detect the amount of snowpack in these areas. Recently, C‐band Synthetic Aperture Radar (SAR) data from the Sentinel‐1 (S1) satellites have shown potential for measuring snow depth in the mountains. However, their spatiotemporal coverage is incomplete, and their evaluation with robust, aerial snow depth data is limited. Here, we evaluate two S1 snowpack datasets with some of the best available gridded snowpack data over the Colorado Rockies and Sierra Nevada mountains in the western US: the Airborne Snow Observatory (ASO) and the University of Arizona (UA) snowpack datasets. Compared to ASO and UA data, the S1 data are biased high when snow is shallow, and biased low when snow is deep (particularly later in spring when there is wet snow), though these biases are reduced for deep snow areas when wet snow pixels are removed. We then apply corrections based on machine learning that account for physiographic characteristics to improve the accuracy of the S1 data. Furthermore, we fill gaps in the S1 data by using snow persistence, but also account for potential snow accumulation and ablation, to generate temporally complete snow depth maps over mountainous areas. Corrected and gap‐filled S1 snow depth mapping could be especially important for snow monitoring in remote mountain areas where other techniques for snow mapping do not work or are logistically infeasible or cost‐prohibitive.

Land use land cover and land surface temperature changes and their relationship with human modification in Islamabad Capital Territory, Pakistan.

Human activities are altering the existing patterns of Land Use Land Cover (LULC) and Land Surface Temperature (LST) on a global scale. However, long-term trends of LULC and LST are largely unknown in many remote mountain areas such as the Karakorum. . The objective of our study therefore was to evaluate the historical changes in land use and land cover (LULC) in an alpine environment located in Islamabad Capital Territory, Pakistan. We used Landsat satellite pictures (namely Landsat 5 TM and Landsat 8 OLI) from the years 1988, 2002, and 2016 and applied the Maximum Likelihood Classification (MLC) approach to categorize land use classes. Land Surface Temperatures (LST) were calculated using the thermal bands (6, 10, and 11) of Landsat series data. The correlation between the Human Modification Index (HMI) and LULC as well as LST was evaluated by utilizing data from Google Earth Engine (GEE). Over the study period, the urbanized area increased by 9.94%, whilst the agricultural and bare soil areas decreased by 3.81% and 3.94%, respectively. The findings revealed a significant change in the LULC with a decrease of 1.99% in vegetation. The highest LST class exhibited a progressive trend, with an increase from 12.27% to 48.48%. Based on the LST analysis, the built-up area shows the highest temperature, followed by the barren, agricultural, and vegetation categories. Similarly, the HMI for different LST categories indicates that higher LST categories have higher levels of human alteration compared to lower LST categories, with a strong correlation (R-value = 0.61) between HMI and LST. The findings can be utilized to promote sustainable urban management and for biodiversity conservation efforts. The work also has the potential of utilizing it to protect delicate ecosystems from human interference and to formulate strategies and regulations for sustainable urban growth, including aspects of land utilization and zoning, reduction of urban heat stress, and urban infrastructure.

Airborne microplastics in urban, rural and wildland environments on the Tibetan Plateau

The concentration of airborne microplastics is largely unknown in the remote high mountain area of the Tibetan Plateau. Here we report airborne microplastic concentrations of 2.5–58.8 n/m3 in urban, rural and wildland areas across the Tibetan Plateau, with smaller (∼89% <100 µm) fragments (>80%) dominating. Polyethylene terephthalate, polyethylene, polyamide and polystyrene were the dominant polymers of airborne microplastics on the Tibetan Plateau. Distribution of airborne microplastics was positively correlated with anthropogenic activity indices, such as population density and nighttime light intensity. Although the contribution of long-range atmospheric transport is valid, dispersed villages also appear to be a source of airborne microplastics for wildland areas across the Tibetan Plateau.

Assessing the Vulnerability of Nomadic Pastoralists’ Livelihoods to Climate Change in the Zhetysu Region of Kazakhstan

Kazakhstan is historically a livestock-producing country. For the first time in this study, we attempted to assess the vulnerability of nomadic pastoralists in Kazakhstan to climate change using the Livelihood Vulnerability Index (LVI). To collect data, a survey of 100 household heads was conducted on fourteen main components and fifty-six sub-components. The study was conducted in the period from May to July 2022 in the Panfilov (PD) and Kerbulak (KD) districts of the Zhetysu region, where the Altyn-Emel State National Nature Park is located. The results of the study were combined using a composite index method and comparing different vulnerability indicators. Natural disasters, which manifest as the effects of drought, temperature fluctuations, and precipitation, contribute most to the vulnerability of nomads living in remote mountain areas with a complex infrastructure. According to the results of the study, nomads of both regions have high vulnerability in such components as natural resources, human–wildlife conflict, housing type, agriculture and food security, and social networks. High vulnerability in the “Finances and incomes” component was found only in the pastoralists of the PD. Identifying the levels of vulnerability of nomadic households to climate change, as well as understanding their adaptation strategies, will enable pastoralists to gain access to new ways of reducing the vulnerability of their livelihoods. Currently, the country practices a strategy to reduce the vulnerability of pastoral nomads’ livelihoods by insuring livestock against natural or natural hazards and other risks; involving the population in environmental-protection activities and helping them to obtain sustainable financial resources when they refuse to hunt endangered animals; non-agricultural diversification of high-altitude ecotourism in rural areas in their area of residence; and improving financial literacy by providing training and providing information on low-interest loans under state projects and livestock subsidy mechanisms, as well as training in organizing cooperatives within the framework of legal status, which will ensure them stable sales of products and income growth. The results of software research serve as a basis for taking measures within the framework of the development and implementation of state programs for climate change adaptation of the Environmental Code of the Republic of Kazakhstan, where agriculture is one of the priority areas of management.

Digitally mapping soil carbon of the uThukela headwater catchment in the Maloti-Drakensberg, a remote Afromontane mountain region

ABSTRACT Although regional soil mapping is commended, site-specific studies are required for mapping and quantifying soil organic carbon (SOC) stocks at a landscape scale for management and rehabilitation purposes. Site-specific studies are especially important in remote mountainous areas where soil data are largely absent. The aim of this study was to quantify and map the SOCs of an alpine region in the Maloti-Drakensberg. The samples collected in-field along with digital soil mapping techniques were used to map the SOCs. The models were SoLIM’s rule based (RB) and sample based (SB), random forest (RF), least absolute shrinkage and selection operator (LASSO), regression kriging with cubist (RK-CB) and universal kriging. From the results, the mean SOC for the validation dataset of the study area was 12.44 kg organic carbon (OC) m−2. The best model was RK-CB (mean = 12.43 kg OC m−2), with R 2 and root mean square error (RMSE) of 0.61 and 4.01 kg OC m−2, respectively. The underperforming model was SoLIM-RB (mean = 13.27 kg OC m−2), with R 2 = 0.27 and RMSE = 5.75 kg OC m−2. The RK-CB model from this study significantly outperformed a region-scaled model, proving that site-specific studies in small catchments should be preferred to, especially if there are no soil data available for that area.