Search Results (14)

Despite growing importance, agricultural landscapes face threats, like fragmentation, shrinkage, and degradation, due to climate change. Although remote sensing and GIS are widely used in monitoring croplands, integrating machine learning, remote sensing, GIS, and landscape metrics for the holistic management of this landscape remains underexplored. Thus, this study monitored crop patterns using random forest (94% accuracy), the role of geographical factors (such as elevation, aspect, slope, maximum and minimum temperature, rainfall, cation exchange capacity, NPK, soil pH, soil organic carbon, soil type, soil water content, proximity to drainage, proximity to market, proximity to road, population density, and profit per hectare production), diversity, combinations, and fragmentation using landscape metrics and a fragmentation index. Findings revealed that slope, rainfall, temperature, and profit per hectare production emerged as significant drivers in shaping crop patterns. However, anthropogenic drivers became deciding factors during spatial overlaps between crop suitability zones. Rice belts were the least fragmented and highly productive with a risk of monoculture. Croplands with a combination of soybean, black grams, and maize were highly fragmented, despite having high diversity with comparatively less production per field. These diverse fields were providing higher profits and low risks of crop failure due to the crop combinations. Equally, intercropping balanced the nutrient uptakes, making the practice sustainable. Thus, it can be suggested that productivity and diversity should be prioritized equally to achieve sustainable land use. The development of the PCA-weighted fragmentation index offers an efficient tool to measure fragmentation across similar agricultural regions, and the integrated approach provides a scalable framework for holistic management, sustainable land use planning, and precision agriculture. Full article

Recently, the development of autonomous vehicles (AVs) and intelligent driver assistance systems has drawn significant attention from the public. Despite these advancements, AVs may encounter critical situations in real-world scenarios that can lead to severe traffic accidents. This review paper investigated these critical scenarios, categorizing them under weather conditions, environmental factors, and infrastructure challenges. Factors such as attenuation and scattering severely influence the performance of sensors and AVs, which can be affected by rain, snow, fog, and sandstorms. GPS and sensor signals can be disturbed in urban canyons and forested regions, which pose vehicle localization and navigation problems. Both roadway infrastructure issues, like inadequate signage and poor road conditions, are major challenges to AV sensors and navigation systems. This paper presented a survey of existing technologies and methods that can be used to overcome these challenges, evaluating their effectiveness, and reviewing current research to improve AVs’ robustness and dependability under such critical situations. This systematic review compares the current state of sensor technologies, fusion techniques, and adaptive algorithms to highlight advances and identify continuing challenges for the field. The method involved categorizing sensor robustness, infrastructure adaptation, and algorithmic improvement progress. The results show promise for advancements in dynamic infrastructure and V2I systems but pose challenges to overcoming sensor failures in extreme weather and on non-maintained roads. Such results highlight the need for interdisciplinary collaboration and real-world validation. Moreover, the review presents future research lines to improve how AVs overcome environmental and infrastructural adversities. This review concludes with actionable recommendations for upgrading physical and digital infrastructures, adaptive sensors, and algorithmic upgrades. Such research is important for AV technology to remain in the zone of advancement and stability. Full article

Employing a small baseline subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) and hotspot analysis, this study identified 81 potential landslides in a 768.7 km² area of Xiaojin county, eastern Tibetan Plateau. Subsequent time-series deformation analysis revealed that these potential landslides are in the secondary creep stage. The newly identified landslides were compared to a landslide inventory (LI), established through field surveying, in terms of causative factors, including altitude, slope, relief amplitude, distance to river, distance to road, and slope curvature. From the comparison, the InSAR technique showed the following advantages: (1) it identified 25 potential landslides at high altitudes (>3415 m) in addition to the low-altitude landslides identified through the field survey. (2) It obtained approximately 37.5% and 70% increases in the number of potential landslides in the slope angle ranges of 20°–30° and 30°–40°, respectively. (3) It revealed significant increases in potential landslides in every relief amplitude bin, especially in the range from 58 m to 92 m. (4) It can highlight key geological factors controlling landslides, i.e., the stratigraphic occurrence and key joints as the InSAR technique is a powerful tool for identifying landslides in all dip directions. (5) It reveals the dominant failure modes, such as sliding along the soil–rock interface and/or interfaces formed by complicated combinations of discontinuities. This work presents the significant potential of InSAR techniques in gaining deeper knowledge on landslide development in alpine forest regions. Full article

The expansion of mountainous urban areas and road networks can influence the terrain, vegetation, and material characteristics, thereby altering the susceptibility of landslides. Understanding the relationship between human engineering activities and landslide occurrence is of great significance for both landslide prevention and land resource management. In this study, an analysis was conducted on the landslide caused by Typhoon Megi in 2016. A representative mountainous area along the eastern coast of China—characterized by urban development, deforestation, and severe road expansion—was used to analyze the spatial distribution of landslides. For this purpose, high-precision Planet optical remote sensing images were used to obtain the landslide inventory related to the Typhoon Megi event. The main innovative features are as follows: (i) the newly developed patch generating land-use simulation (PLUS) model simulated and analyzed the driving factors of land-use land-cover (LULC) from 2010 to 2060; (ii) the innovative stacking strategy combined three strong ensemble models—Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM)—to calculate the distribution of landslide susceptibility; and (iii) distance from road and LULC maps were used as short-term and long-term dynamic factors to examine the impact of human engineering activities on landslide susceptibility. The results show that the maximum expansion area of built-up land from 2010 to 2020 was 13.433 km², mainly expanding forest land and cropland land, with areas of 8.28 km² and 5.99 km², respectively. The predicted LULC map for 2060 shows a growth of 45.88 km² in the built-up land, mainly distributed around government residences in areas with relatively flat terrain and frequent socio-economic activities. The factor contribution shows that distance from road has a higher impact than LULC. The Stacking RF-XGB-LGBM model obtained the optimal AUC value of 0.915 in the landslide susceptibility analysis in 2016. Furthermore, future road network and urban expansion have intensified the probability of landslides occurring in urban areas in 2015. To our knowledge, this is the first application of the PLUS and Stacking RF-XGB-LGBM models in landslide susceptibility analysis in international literature. The research results can serve as a foundation for developing land management guidelines to reduce the risk of landslide failures. Full article

Although the causes of and impacts against forest road failure differ according to the type of damage that occurs, the statistical understanding of the trends in the type of failure is insufficient. In this study, we collected data on 526 forest road failures due to heavy rainfall during 2006–2010 in the mountainous regions of Japan and statistically analyzed the characteristics. The forest roads covered in this study include those used primarily for timber extraction as well as those used for public purposes. Forest road segments were classified into four categories: streamside, stream crossings, zero-order basin, and others, and comparisons were made regarding the length of damage, the relative probability of occurrence, repair costs, and induced rainfall intensity in each category. Streamside segments accounted for only 15% of the total length of routes analyzed but 42% of all damaged segments. Furthermore, the relative risk of the streamside segments was about 6.0 times higher than that of the other categories of segments, indicating that they were the most likely to be damaged in this analysis. It is clear that the most important issue in the target area is to prevent damage to streamside segments. Full article

This paper focuses on the investigation of the material properties of FC500 foam concrete. Innovation is very important for the solution of cast-in-place concrete forms in practice today. Part of its innovative construction application is the possibility of using foam concrete in a composite structure and the use of its mechanical properties in the load-bearing parts of civil engineering structures. The method of detecting the mechanical properties of foam concrete by using non-standard cantilever test is also innovative. Here, an advanced approach of modelling specimens using powerful computational systems based on the finite element method is used. This modern material is researched especially for its use in transportation structures. For its application, it is necessary to define its resistance to mechanical loads. The main content of the research consists of correlations between experimental measurements and analytical and numerical results. This is the principle of quasi-linear identification of the non-linear behavior of polymeric cementitious porous material during tests on specimens. The focus of the research is an extensive experiment: measurements of the deformation of the specimens until failure. The following methods were chosen to investigate the material properties: small cantilever test, standard tensile test and compression test. The cantilever test was performed for the individual components of the FC500 composite and cement concrete, but also as a compact composite. Numerical simulation models were developed to correlate the individual results in order to validate the uniaxial test results. The conclusions of the research led to the definition of standardized stress–strain diagrams of the FC500 material for compression and especially tension. This is a definition of the behavior of this polymer composite, usable for the development of numerical models of full-scale structures. The results of the research will be applied in the development of national standards for the use of advanced materials in transportation structures (cycle paths, parking lots, traffic playgrounds, lightly trafficked forest roads and trails, etc.). Full article

The deep-seated landslide in the Lantai area, Taiwan, has a long history of landslide activity and often damages the sole access road to the Tai-Ping Mountain National Forest Recreation Area. This study adopted the high-resolution digital terrain model (DTMH) derived from UAV mounted LiDAR point cloud data for mapping geological structures and verified through field investigation. A slope model was proposed with mapped geological structures and shear zone, and numerical analysis was conducted using finite difference analysis. The failure mechanism was found to be significantly affected by the shear zone bounded by geological structures, which would not have been uncovered without the high-resolution DTM (DTMH). The resulting landslide behavior consisted well with mapped scarp, borehole data, and conformed with the event records. These results provided vital information supporting hazard mitigation strategy. Full article

The disruptions to wildland fires, such as firebreaks, roads and rivers, can limit the spread of wildfire propagating through surface or crown fire. A large forest can be separated into different zones by carefully constructing firebreaks through modification of vegetation in firebreak regions. However, the wildland fire behaviour can be unpredictable due to the presence of either wind- or buoyancy-driven flow in the fire. In this study, we aim to test the efficacy of an idealised firebreak constructed by unburned vegetation. The physics-based large eddy simulation (LES) simulation is conducted using Wildland–urban interface Fire Dynamic Simulator (WFDS). We have carefully chosen different wind velocities with low to high values, 2.5~12.5 m/s, so the different fire behaviours can be studied. The behaviour of surface fire is studied by Australian grassland vegetation, while the crown fire is represented by placing cone-shaped trees with grass underneath. With varying velocity and vegetation, four values of firebreak widths (Lc), ranging from 5~20 m, is tested for successful break distance needed for the firebreak. For each failure or successful firebreak width, we have assessed the characteristics of fire intensity, mechanism of heat transfer, heat flux, and surface temperature. It was found that with the inclusion of forest trees, the heat release rate (HRR) increased substantially due to greater amount of fuel involved. The non-dimensional Byram’s convective number (N_C) was calculated, which justifies simulated heat flux and fire characteristics. For each case, HRR, total heat fluxes, total preheat flux, total preheat radiation and convective heat flux, surface temperature and fire propagation mode are presented in the details. Some threshold heat flux was observed on the far side of the firebreak and further studies are needed to identify them conclusively. Full article

Ground surface changes caused by freeze-thaw action affect agriculture and forestry, as well as artificial structures such as roads. In this study, an area is examined in which reforestation is urgently needed but the growth of naturally restored seedlings and planted trees is impaired by freeze-thaw action. Thus, a method of measuring freeze-thaw induced ground surface changes and mitigating their negative impacts is needed. Real-time kinematic unmanned aerial vehicle and structure-from-motion multiview stereophotogrammetry are used on slope-failure sites in forest areas to observe the ground surface changes caused by freeze-thaw action over a wide area, in a nondestructive manner. The slope characteristics influencing the ground-surface changes were examined, and it was confirmed that it is possible to observe minute topographical changes of less than ±5 cm resulting from freeze-thaw action. Statistical models show that the amount of freeze-thaw action is mostly linked to the cumulative solar radiation, daily ground-surface temperature range, and topographic-wetness index, which influence the microscale dynamics of the ground surface. The proposed method will be useful for future quantitative assessments of ground-surface conditions. Further, efficient reforestation could be implemented by considering the effects of the factors identified on the amount of freeze-thaw action. Full article

Forest roads are often constructed in environments with low bearing capacity of the subgrade. The subgrade then has an effect on their service life and damage. According to the methodology of the American Association of State Higway and Transportation Officiales AASHTO, the design of pavement is divided into three levels according to the intensity of the traffic load. For pavements with the highest load intensity, preparing the resilient modulus from a cyclic triaxial test is required. For other traffic load classes, including forest roads, the methodology allows the use of the estimate of resilient modulus value determined from other tests. In the laboratory at the Faculty of Forestry, Mendel University of Brno, the method from the Delft University 2009 was tested and subsequently modified, using a standard CBR machine for repeated loading. A total of 276 samples from various types of forest road subgrade from the Czech Republic were tested by the method of repeated loading on the CBR machine, from which the values of the Resilient Modulus were newly labelled M_r,CBR. The results of the statistical analysis showed a large variability of M_r,CBR values and wide intervals of its occurrence for individual types of subgrade. The variability was subjected to analysis and the influence of basic geotechnical parameters on the values of M_r,CBR was analyzed. A fundamental correlation was found between the value of M_r,CBR and the value of the plunger stress, which reached values exceeding the bearing capacity of the soil types using the Delft University method. It is necessary to limit the plunger stress during cyclic loading up to the failure limit or even better to the expected traffic load. The modified procedure results show a more consistent behavior of the modulus. Full article

The current methods that aim at monitoring the structural health status (SHS) of road pavements allow detecting surface defects and failures. This notwithstanding, there is a lack of methods and systems that are able to identify concealed cracks (particularly, bottom-up cracks) and monitor their growth over time. For this reason, the objective of this study is to set up a supervised machine learning (ML)-based method for the identification and classification of the SHS of a differently cracked road pavement based on its vibro-acoustic signature. The method aims at collecting these signatures (using acoustic-sensors, located at the roadside) and classifying the pavement’s SHS through ML models. Different ML classifiers (i.e., multilayer perceptron, MLP, convolutional neural network, CNN, random forest classifier, RFC, and support vector classifier, SVC) were used and compared. Results show the possibility of associating with great accuracy (i.e., MLP = 91.8%, CNN = 95.6%, RFC = 91.0%, and SVC = 99.1%) a specific vibro-acoustic signature to a differently cracked road pavement. These results are encouraging and represent the bases for the application of the proposed method in real contexts, such as monitoring roads and bridges using wireless sensor networks, which is the target of future studies. Full article

The probabilistic mapping of landslide occurrence at a high spatial resolution and over a large geographic extent is explored using random forests (RF) machine learning; light detection and ranging (LiDAR)-derived terrain variables; additional variables relating to lithology, soils, distance to roads and streams and cost distance to roads and streams; and training data interpreted from high spatial resolution LiDAR-derivatives. Using a large training set and all predictor variables, an area under the receiver operating characteristic (ROC) curve (AUC) of 0.946 is obtained. Our findings highlight the value of a large training dataset, the incorporation of a variety of terrain variables and the use of variable window sizes to characterize the landscape at different spatial scales. We also document important variables for mapping slope failures. Our results suggest that feature selection is not required to improve the RF modeling results and that incorporating multiple models using different pseudo absence samples is not necessary. From our findings and based on a review of prior studies, we make recommendations for high spatial resolution, large-area slope failure probabilistic mapping. Full article

Driver perception, decision, and control behaviors are easily affected by traffic conditions and driving style, showing the tendency of randomness and personalization. Brake intention and intensity are integrated and control-oriented parameters that are crucial to the development of an intelligent braking system. In this paper, a composite machine learning approach was proposed to predict driver brake intention and intensity with a proper prediction horizon. Various driving data were collected from Controller Area Network (CAN) bus under a real driving condition, which mainly contained urban and rural road types. ReliefF and RReliefF (they don’t have abbreviations) algorithms were employed as feature subset selection methods and applied in a prepossessing step before the training. The rank importance of selected predictors exhibited different trends or even negative trends when predicting brake intention and intensity. A soft clustering algorithm, Fuzzy C-means, was adopted to label the brake intention into categories, namely slight, medium, intensive, and emergency braking. Data sets with misplaced labels were used for training of an ensemble machine learning method, random forest. It was validated that brake intention could be accurately predicted 0.5 s ahead. An open-loop nonlinear autoregressive with external input (NARX) network was capable of learning the long-term dependencies in comparison to the static neural network and was suggested for online recognition and prediction of brake intensity 1 s in advance. As system redundancy and fault tolerance, a close-loop NARX network could be adopted for brake intensity prediction in the case of possible sensor failure and loss of CAN message. Full article

Community forestry (CF) is increasingly used in developing countries to achieve both the socioeconomic outcome of poverty reduction and an ecological outcome. There have been many single case studies in a specific region to identify the factors affecting the success or failure of CF. Other studies have used large-N data collected from multiple countries. However, there is a dearth of large-N studies within a single country. In this study, we used a country scale dataset of 197 CF projects, established between 1994 and 2005 across Cambodia, to identify the biophysical factors that affected forest cover changes from 2005 to 2016. A mixed-effects logistic regression model was used for a total of 71,252 randomly sampled data pixels nested in the 197 CF. Results showed that deforestation in CF was likely to increase with increasing size of CF area at lower elevations and on gentler slopes. Deforestation also increased if CF was located close to villages, markets and CF boundaries, but further away from main roads. These findings on biophysical factors can help the government to decide on priority locations for further conservation interventions or for the establishment of new CF projects. Full article