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Land scarcity in most cases hampers development and encourages the misuse of land. The suitability of land must be considered before appropriating or allocating land for any use. Land supports the livelihood of every being on the Earth and therefore determines survival, success, and sustainability (sustainable living). This study aimed at integrating remote sensing and GIS-based analysis to determine the rate at which built-up areas have spread across the terrain of Onitsha Metropolis, Anambra State, Nigeria, and the dynamics of other land uses. This research involved both primary and secondary data. The primary data included measurements, direct field observations, and key informant interviews to understand people’s perceptions of the land use in the area. The secondary data included satellite images of the area obtained from USGS and analyzed using ArcGIS 10.2 for variations in the terrain of the Onitsha Metropolis; to determine the land use and land cover change (LULCC) of the Onitsha Metropolis over 40 years, published and unpublished articles and books were also consulted. The geological analysis of the study showed that the area of the Ogwashi/Asaba formation is 318.57 km2; the areas of the Nanka sands and Bende-Ameke are 423.07 km2 and 259.42 km2, respectively. The Nanka sands and Bende-Ameke formations are best suited for engineering construction purposes, while the Ogwashi/Asaba formation is suitable for agriculture and should be designated as a buffer zone or park. However, due to the unavailability of land as a result of the growing population and the proximity of the area to the city center, the area is being encroached upon, and a large area (about 30.40%) has been converted to built-up areas as of 2022. Forecast analysis showed that if the trend continues, 158.28 km2 (49.68%) of the alluvium soils of the Ogwashi/Asaba formation will be covered with buildings by 2072. The geology and the terrain of the Onitsha Metropolis determine the soil characteristics and the land use suitability; mapping the geological formations and overlaying these with the land use and land cover change of the area revealed the extent of the encroachment on the Ogwashi/Asaba formation, which must be discouraged.

Increased availability of QL1/QL2 Lidar terrain data has resulted in large datasets, often including large quantities of redundant points. Because of these large memory requirements, practitioners often use decimation to reduce the number of points used to create models. This paper introduces a novel approach to improve decimation, thereby reducing the total count of ground points in a Lidar dataset while retaining more accuracy than Random Decimation. This reduction improves efficiency of downstream processes while maintaining output quality nearer to the undecimated dataset. Points are selected for retention based on their discrete curvature values computed from the mesh geometry of the TIN model of the points. Points with higher curvature values are preferred for retention in the resulting point cloud. We call this technique Curvature Weighted Decimation (CWD). We implement CWD in a new free, open-source software tool, CogoDN, which is also introduced in this paper. We evaluate the effectiveness of CWD against Random Decimation by comparing the resulting introduced error values for the two kinds of decimation over multiple decimation percentages, multiple statistical types, and multiple terrain types. The results show that CWD reduces introduced error values over Random Decimation when 15 to 50% of the points are retained.

The use of a Machine Learning (ML) classification algorithm to classify airborne urban Light Detection And Ranging (LiDAR) point clouds into main classes such as buildings, terrain, and vegetation has been widely accepted. This paper assesses two strategies to enhance the effectiveness of the Deep Learning (DL) classification algorithm. Two ML classification approaches are developed and compared in this context. These approaches utilize the DL Pipeline Network (DLPN), which is tailored to minimize classification errors and maximize accuracy. The geometric features calculated from a point and its neighborhood are analyzed to select the features that will be used in the input layer of the classification algorithm. To evaluate the contribution of the proposed approach, five point-clouds datasets with different urban typologies and ground topography are employed. These point clouds exhibit variations in point density, accuracy, and the type of aircraft used (drone and plane). This diversity in the tested point clouds enables the assessment of the algorithm’s efficiency. The obtained high classification accuracy between 89% and 98% confirms the efficacy of the developed algorithm. Finally, the results of the adopted algorithm are compared with both rule-based and ML algorithms, providing insights into the positioning of DL classification algorithms among other strategies suggested in the literature.

Satellite imagery-based solar irradiance mapping studies are essential for large-scale solar energy assessments but are limited in spatial resolution and accuracy. Despite efforts to increase map resolution by correcting inaccuracies caused by shadows on the terrain, the computational time of these models and the massive volume of generated data still pose challenges. Particularly, forecasting generates large amounts of time series data, and the data production rate is faster than the computational speed of traditional terrain correction. Moreover, while previous research has been conducted to expedite computations, a novel and innovative technology in terrain correction is still required. Therefore, we propose a new correction method that can bypass complex calculations and process enormous data within seconds. This model extends the lookup table concept, optimizes the results of many shadow operations, and stores them in memory for use. The model enabled 90 m scale computations across Korea within seconds on a local desktop computer. Optimization was performed based on domain knowledge to reduce the required memory to a realistic level. A quantitative analysis of computation time was also conducted, revealing a previously overlooked computational bottleneck. In conclusion, the developed model enables real-time terrain correction and subsequent processing of massive amounts of data.

Efficient navigation of off-road vehicles heavily relies on the ability to accurately model the interaction between the vehicle and the terrain. One of the most important parts of this interaction is the deformation of the terrain and the tire. Although high-precision methods like finite element method (FEM) simulation can be used for this purpose, they require significant computational power, which is impractical to install in a vehicle for real-time navigation purposes. Therefore, simplified and less-detailed models are essential for on-board installation in real-time applications. In this study, three two-dimensional static terrain–vehicle models are compared to a detailed FEM reference model, and the results are evaluated both from the perspective of accuracy and computational capacity requirements. The analysis sheds light on the effectiveness of each model in the real-time navigation of off-road vehicles.

The brown marmorated stink bug, Halyomorpha halys Stål (Hemiptera: Pentatomidae), is native to Japan, China, Taiwan, and Korea. Its dispersion from Asia to the United States of America and Europe caused serious damage to fruit, vegetables, and high-value crops. In Greece, damages are reported in kiwi orchards in the regions of Pieria and Imathia, which are the main production areas of kiwifruit. Greek kiwifruit production is expected to increase twofold within the next years. The aim of this research is to study the terrain and canopy properties that may have an impact on the development of H. halys populations. Thus, five kiwi orchards in total were selected in the regions of Pieria and Imathia. Τen traps were installed from early June to late October within each selected kiwi orchard–two types of traps at every side of the orchards and the center. The installed traps were examined weekly and the number of the captured H. halys was recorded. During the same days, sentinel satellite images were analyzed to calculate the vegetation index, NDVI (Normalised Difference Vegetation Index), and NDWI (Normalised Difference Water Index). The results showed population variability within the kiwi orchards since the population of H. halys was higher in areas with high NDVI and NDWI values. Additionally, our research revealed that H. halys prefers to develop populations at higher altitudes at both regional and field scales. The results of this research can be used to reduce damages by H. halys in kiwi orchards using different rates of pesticides depending on the prediction of the population size. There are multiple benefits of the proposed practice, such as a reduction in the production cost of kiwifruits, an increase in farmers’ profit, and environmental protection.

Terrain significantly influences the physical processes and human activities occurring on the Earth’s surface, especially in mountainous areas. The classification and clarification of topographic structures are essential for the quantitative analysis of surface patterns. In this paper, we propose a new method based on the digital elevation model to classify the binary terrain structure. The slope accumulation is constructed to emphasize the accumulated topographic characteristics and is applied to support the segmenting process. The results show that this new method is efficient in increasing the completeness of the segmented results and reducing the classification uncertainty. We verify this method in three areas in South America, North America and Asia to evaluate the method’s robustness. Comparison experiments suggest that this new method outperforms the traditional method in areas with different landforms. In addition, quantitative indices are calculated based on the segmented results. The results indicate that the binary terrain structure benefits the understanding of surface patterns from the perspectives of topographic characteristics, category composition, object morphology and landform spatial distribution. We also assess the transferability of the proposed method, and the results suggest that this method is transferable to different digital elevation models. The proposed method can support the quantitative analysis of land resources, especially in mountainous areas and benefit land management.

Libraries with pre-written codes optimize the workflow in cartography and reduce labour intensive data processing by iteratively applying scripts to implementing mapping tasks. Most existing Geographic Information System (GIS) approaches are based on traditional software with a graphical user’s interface which significantly limits their performance. Although plugins are proposed to improve the functionality of many GIS programs, they are usually ad hoc in finding specific mapping solutions, e.g., cartographic projections and data conversion. We address this limitation by applying the principled approach of Geospatial Data Abstraction Library (GDAL), library for conversions between cartographic projections (PROJ) and Geographic Resources Analysis Support System (GRASS) GIS for geospatial data processing and morphometric analysis. This research presents topographic analysis of the dataset using scripting methods which include several tools: (1) GDAL, a translator library for raster and vector geospatial data formats used for converting Earth Global Relief Model (ETOPO1) GeoTIFF in XY Cartesian coordinates into World Geodetic System 1984 (WGS84) by the ‘gdalwarp’ utility; (2) PROJ projection transformation library used for converting ETOPO1 WGS84 grid to cartographic projections (Cassini–Soldner equirectangular, Equal Area Cylindrical, Two-Point Equidistant Azimuthal, and Oblique Mercator); and (3) GRASS GIS by sequential use of the following modules: r.info, d.mon, d.rast, r.colors, d.rast.leg, d.legend, d.northarrow, d.grid, d.text, g.region, and r.contour. The depth frequency was analysed by the module ‘d.histogram’. The proposed approach provided a systematic way for morphometric measuring of topographic data and combine the advantages of the GDAL, PROJ, and GRASS GIS tools that include the informativeness, effectiveness, and representativeness in spatial data processing. The morphometric analysis included the computed slope, aspect, profile, and tangential curvature of the study area. The data analysis revealed the distribution pattern in topographic data: 24% of data with elevations below 400 m, 13% of data with depths −5000 to −6000 m, 4% of depths have values −3000 to −4000 m, the least frequent data (−6000 to 7000 m)

Using the ERA5 reanalysis dataset during the period 1979–2019, the diurnal variation in summer water vapor budget (Bt) over the Tibetan Plateau (TP) is investigated in this study. It is found that the TP Bt shows a distinct diurnal cycle. It tends to increase in the morning, reaches a peak in the afternoon, and falls to a minimum in the early morning. The diurnal variations in four boundary water vapor budgets of the TP contribute to the growth in the TP Bt from the early morning to the afternoon, of which the western and eastern boundaries are more important. To understand the reasons for the diurnal variations in boundary water vapor budgets, the temporal evolutions of water vapor transports and relevant circulations at the four boundaries are examined. The results show that the temporal evolutions of water vapor transports and budgets at the four boundaries are essentially regulated by the changes in the orographic thermodynamic effect. Specifically, rapid and strong warming (cooling) on the TP slopes generates anomalous water vapor inputs (outputs) by anomalous upslope (downslope) flows during the daytime (nighttime). At the southern and western boundaries, apart from the terrain effects, the diurnal variation in the Indian southerly monsoon also has an effect on the changes in water vapor budgets by modulating the water vapor input towards the TP below 700 hPa. At the northern and eastern boundaries, under the orographic thermodynamic effects, low-level water vapor transports towards the TP accompanying by plateau-scale vertical circulations, exist significant diurnal variations and thereby adjust the boundary water vapor budgets. In this study, it is also found that the deviated water vapor flux vectors over the TP present a daily clockwise rotation, which mainly results from the diurnal variation in wind below 450 hPa. In addition, the largest amount of precipitation over the TP occurs 2–3 h after the Bt peak.

It is widely recognized that regions with complex heterogeneous topography and land-use properties produce a variety of diurnal mesoscale and microscale flows, which can be modified or even masked by significant large-scale synoptic forcing. These flows can be produced through both dynamic and thermal-forcing processes. Recent field programs such as the Terrain-induced Rotor Experiment (T-REX), Mountain Terrain Atmospheric Modeling and Observations Program (MATERHORN), and Perdigao have been used to observe and model flow behaviors under different topographical and large-scale meteorological conditions. Using the Advanced research version of the Weather Research and Forecast (WRF-ARW) model, we applied multi-nesting using an interactive one-way nesting approach to resolve to a sub-kilometer inner-grid spacing (0.452 km). Our interest was in the intensive observation period 6 (IOP6) of the Fall 2012 MATERHORN campaign conducted over Dugway Proving Ground (DPG) in Utah. An initial review of the IOP6 suggested that a range of diurnal flows were present, and that a relatively small subset of model setup configurations would be able to capture the general flows of this period. The review also led us to believe that this same subset would be able to capture differences due to variations in choice of model boundary-layer physics, land surface physics, land use/soil type specifications, and larger-scale meteorological conditions. A high model vertical resolution was used, with 90 vertical sigma levels applied. The IOP6 spanned the period of 2012 0800 UTC 14 October–0800 UTC 15 October. Based upon a lack of deep convection and moist microphysics throughout IOP6, we included comparison of planetary boundary layer (PBL) turbulence parameterization schemes even at the sub-kilometer grid spacing. We focused upon the gross model performance over our inner nest; therefore, a detailed comparison of the effects of model horizontal resolution are excluded. For surface parameters of wind and temperature, we compare mean absolute error and bias scores throughout the period at a number of surface meteorological observing sites. We found that despite attention given to the boundary layer turbulence physics, radiation physics and model vertical resolution, the results seemed to indicate more impact from the choices of thermal soil conductivity parameterization, land surface/soil texture category classification (and associated static property-parameter values), and large-scale forcing model. This finding lends support to what other researchers have found related to how these same forcings can exert a strong influence upon mesoscale flows around DPG. Our findings suggest that the two nights of IOP6 offer a pair of excellent consecutive nights to explore many of the forcing features important to local complex terrain flow. The flows of interest in this case included valley, anabatic/katabatic, and playa breeze systems. Subjective evidence was also found to support an influence provided by the modest synoptic northwesterly flow present within the lower troposphere (mainly on the night of 14 October). Follow-on research using the WRF-ARW capability to nest directly from mesoscale-to-LES can leverage IOP6 further. For example, to uncover more detailed and focused aspects of the dynamic and thermodynamic forcings contributing to the DPG diurnal flows.

Fall risk assessment needs contemporary approaches based on habitual data. Currently, inertial measurement unit (IMU)-based wearables are used to inform free-living spatio-temporal gait characteristics to inform mobility assessment. Typically, a fluctuation of those characteristics will infer an increased fall risk. However, current approaches with IMUs alone remain limited, as there are no contextual data to comprehensively determine if underlying mechanistic (intrinsic) or environmental (extrinsic) factors impact mobility and, therefore, fall risk. Here, a case study is used to explore and discuss how contemporary video-based wearables could be used to supplement arising mobility-based IMU gait data to better inform habitual fall risk assessment. A single stroke survivor was recruited, and he conducted a series of mobility tasks in a lab and beyond while wearing video-based glasses and a single IMU. The latter generated topical gait characteristics that were discussed according to current research practices. Although current IMU-based approaches are beginning to provide habitual data, they remain limited. Given the plethora of extrinsic factors that may influence mobility-based gait, there is a need to corroborate IMUs with video data to comprehensively inform fall risk assessment. Use of artificial intelligence (AI)-based computer vision approaches could drastically aid the processing of video data in a timely and ethical manner. Many off-the-shelf AI tools exist to aid this current need and provide a means to automate contextual analysis to better inform mobility from IMU gait data for an individualized and contemporary approach to habitual fall risk assessment.

Long-term and high-resolution reanalysis precipitation datasets provide important support for research on climate change, hydrological forecasting, etc. The comprehensive evaluation of the error performances of the newly released ERA5-Land and CRA40-Land reanalysis precipitation datasets over the Yongding River Basin in North China was based on the two error decomposition schemes, namely, decomposition of the total mean square error into systematic and random errors and decomposition of the total precipitation bias into hit bias, missed precipitation, and false precipitation. Then, the error features of the two datasets and precipitation intensity and terrain effects against error features were analyzed in this study. The results indicated the following: (1) Based on the decomposition approach of systematic and random errors, the total error of ERA5-Land is generally greater than that of CRA40-Land. Additionally, the proportion of random errors was higher in summer and over mountainous areas, specifically, the ERA5-Land accounts for more than 75%, while the other was less than 70%; (2) Considering the decomposition method of hit, missed, and false bias, the total precipitation bias of ERA5-Land and CRA40-Land was consistent with the hit bias. The magnitude of missed precipitation and false precipitation was less than the hit bias. (3) When the precipitation intensity is less than 38 mm/d, the random errors of ERA5-Land and CRA40-Land are larger than the systematic error. The relationship between precipitation intensity and hit, missed, and false precipitation is complicated, for the hit bias of ERA5-L is always smaller than that of CRA40-L, and the missed precipitation and false precipitation are larger than those ofCRA40-L when the precipitation is small. The error of ERA5-Land and CRA40-Land was significantly correlated with elevation. A comprehensive understanding of the error features of the two reanalysis precipitation datasets is valuable for error correction and the construction of a multi-source fusion model with gauge-based and satellite-based precipitation datasets.

As the most basic physical geographic elements, basin terrain and river networks have high spatial complexity and are closely related. However, there is little research on the correlation between terrain and river networks. In this paper, the Yellow River Basin was selected as the study area. Topographic factors of multiple dimensions were calculated. The influence of different topographic factors on the river network structure at different scales and their correlation from a multifractal perspective based on geographical detectors and a geographically weighted regression model were determined. The explanatory power of topography on the river network structure at different scales was: multifractal spectrum width > multifractal spectrum difference > slope > average elevation > elevation maximum > elevation minimum, which generally indicated that the topographic factor that has the greatest influence on the river network structure is the complexity and singularity of the terrain. The second-order clustering of regression coefficients from the results of the geographically weighted regression model revealed that the Yellow River basin was divided into three types of high-aggregation areas, which are dominated by the Qinghai-Tibet Plateau, the Loess Plateau, and the Huang-Huaihai Plain, respectively. The clustering results also revealed that the river network structure was affected by different key topographic factors in the different types of areas. This research studies and quantifies the relationship between basin topography and river network structure from a new perspective and provides a theoretical basis for unraveling the development of topography and river networks.

Through the analyses of the observed concentrations of ozone (O3) in Tai’an from 2016 to 2021, the results show that O3 pollution was relatively serious, with the mean concentrations of the 90th percentile of daily maximum 8-h O3 (O3-8h-90per) above 180 μg/m3. O3 pollution in Tai’an mainly occurred from May to September, accounting for 69%~100% of the total O3 pollution days, of which the most serious pollution occurred in June. Combined with the analyses of temperature, humidity, and wind speeds, the probability of O3 exceedances in Tai’an increased significantly under the conditions with the temperatures higher than 30 °C, the relative humidity of 20%~40%, and the wind speeds of 1~3 m/s. The dominant wind directions on O3 pollution days in Tai’an were southerly winds, based on the analyses of wind directions and their clustering trajectories. Based on the results at three monitoring stations at different distances from Mount Tai from May to September in 2021, the average O3 concentrations at the Renkou School station near the mountain was about 13~15 μg/m3 higher than those at the other two stations (Dianli College and Shandong First Medical University) which are far away from the mountain, indicating that the Renkou School site was more affected by the obstruction of the mountain. In addition, the WRF-CMAQ model was used to simulate ten O3 pollution events in 2021, showing that the average O3 concentrations in Tai’an were reduced by 1.7~7.5 μg/m3 after changing the topographic height of Mount Tai.

Snow is involved in and influences water–energy processes at multiple scales. Studies on land surface snow phenology are an important part of cryosphere science and are a hot spot in the hydrological community. In this study, we improved a statistical downscaling method by introducing a spatial probability distribution function to obtain regional snow depth data with higher spatial resolution. Based on this, the southern Gansu Plateau (SGP), an important water source region in the upper reaches of the Yellow River, was taken as a study area to quantify regional land surface snow phenology variation, together with a discussion of their responses to land surface terrain and local climate, during the period from 2003 to 2018. The results revealed that the improved downscaling method was satisfactory for snow depth data reprocessing according to comparisons with gauge-based data. The downscaled snow depth data were used to conduct spatial analysis and it was found that snow depth was on average larger and maintained longer in areas with higher altitudes, varying and decreasing with a shortened persistence time. Snow was also found more on steeper terrain, although it was indistinguishable among various aspects. The former is mostly located at high altitudes in the SGP, where lower temperatures and higher precipitation provide favorable conditions for snow accumulation. Climatically, factors such as precipitation, solar radiation, and air temperature had significantly singular effectiveness on land surface snow phenology. Precipitation was positively correlated with snow accumulation and maintenance, while solar radiation and air temperature functioned negatively. Comparatively, the quantity of snow was more sensitive to solar radiation, while its persistence was more sensitive to air temperature, especially extremely low temperatures. This study presents an example of data and methods to analyze regional land surface snow phenology dynamics, and the results may provide references for better understanding water formation, distribution, and evolution in alpine water source areas.

The characteristics and causes of the persistent precipitation of an extreme-rainfall tropical cyclone (TC), In-Fa, in 2021 are studied. It is shown that the extremity of In-Fa’s precipitation was mainly due to two aspects: massively accumulated quantity and an extremely long impact time. The heavy precipitation in Zhejiang resulted from the accumulation of very long but moderate precipitation, while that in Jiangsu resulted from the coaction of both long duration and strong intensity. The weak steering flow brought about by the large scale environment and the long continuation of the TC’s circulation were the two most important background conditions for the extremely long duration of heavy rainfall in Zhejiang and Jiangsu. Continuous energy input through the transportation of warmer and wetter air resulted in the persistence of In-Fa’s circulation. The terrain effect under the continuous northeasterly/northerly airflow caused by the slow movement of the TC was the major influencing factor for the extreme precipitation in Zhejiang. The convergence brought about by the TC’s vortex during landfall played another role in Zhejiang’s heavy rainfall. On the one hand, the terrain led to the development of low-level vertical circulation, resulting in convergence and updraft in the windward side of Siming Mountain; on the other hand, the terrain also provided for the vertical transport of water vapor. The main factors for the extreme precipitation in Jiangsu were the long continuation of low-level jets that caused persistent low-level convergence and the development of a mesoscale rainband. The convergence zone was located in the western front and evolved with the changing of the jets. The high-energy/high-humidity conditions and their consistency with the location of the convergence provided favorable conditions for the triggering and development of convection.

Many biochemical processes and dynamics are strongly controlled by terrain topography, making digital elevation models (DEM) a fundamental dataset for a range of applications. This study investigates the quality of four pan-Arctic DEMs (Arctic DEM, ASTER DEM, ALOS DEM and Copernicus DEM) within the Kalix River watershed in northern Sweden, with the aim of informing users about the quality when comparing these DEMs. The quality assessment focuses on both the vertical accuracy of the DEMs and their abilities to model two fundamental elevation derivatives, including topographic wetness index (TWI) and landform classification. Our results show that the vertical accuracy is relatively high for Arctic DEM, ALOS and Copernicus and in our study area was slightly better than those reported in official validation results. Vertical errors are mainly caused by tree cover characteristics and terrain slope. On the other hand, the high vertical accuracy does not translate directly into high quality elevation derivatives, such as TWI and landform classes, as shown by the large errors in TWI and landform classification for all four candidate DEMs. Copernicus produced elevation derivatives with results most similar to those from the reference DEM, but the errors are still relatively high, with large underestimation of TWI in land cover classes with a high likelihood of being wet. Overall, the Copernicus DEM produced the most accurate elevation derivatives, followed by slightly lower accuracies from Arctic DEM and ALOS, and the least accurate being ASTER.

When driving microscale large-eddy simulations with mesoscale model solutions, turbulence will take space to develop, known as fetch, on the microscale domain. To reduce fetch, it is common to add perturbations near the boundaries to speed up turbulence development. However, when simulating domains over complex terrain, it is possible that the terrain itself can quickly generate turbulence within the boundary layer. It is shown here that rugged terrain is able to generate turbulence without the assistance of a perturbation strategy; however, the levels of turbulence generated are improved when adding perturbations at the inlet. Flow over smoothed, but not flat, terrain fails to generate adequate turbulence throughout the boundary layer in all tests conducted herein. Sensitivities to the strength of the mean wind speed and boundary layer height are investigated and show that higher wind speeds produce turbulence over terrain features that slower wind speeds do not. Further, by increasing the height of the capping inversion, the effectiveness of topography alone to generate turbulence throughout the depth of the boundary is diminished. In all cases, the inclusion of a perturbation strategy improved simulation performance with respect to turbulence development.

The history of modern maps in Japan began with Inoh’s map that was made by surveying the whole of Japan on foot 200 years ago. Inoh’s team investigated coastlines, major roads, and geographical features such as rivers, lakes, temples, forts, village names, etc. The survey was successively conducted ten times from 1800 to 1816. Inoh’s map is known as the first scientific map in Japan using a systematic method. However, the actual survey was conducted only for 75% of the coastlines in Japan and the remaining 25% was drawn by Inoh’s estimation (observation). This study investigated how the non-surveyed (estimated) coastlines were distributed in the map and why the actual survey was not conducted in these non-surveyed coastlines. Using GIS, we overlaid the geometrically corrected Inoh’s map (Digital Inoh’s Map Professional Edition) with the current map published by the Geospatial Information Authority (GSI) of Japan for examining the spatial difference. We found that the non-surveyed coastlines were in places where the practice of actual surveying was topographically difficult because of the limited surveying technology of those days. The analytical result shows that 38.6% of the non-surveyed coastlines were cliffs, 25.7% were rocky beaches, and 6.2% were wetlands and tidal lands (including rice fields and tidal flats).

Wind-break walls along Lanxin High-Speed Railway II were studied and approved as effective measures to reduce strong wind damage to the high-speed trains. The results show that sand sedimentation on the leeward sides of wind-break walls along the railway within Gobi Desert could significantly threaten the operation safety of running trains. Different from the current sand sedimentation prevention measures without adequate consideration of the deposition process of airborne sand particles, this study revealed the mechanism of sand sedimentation on the leeward sides of three wind-break walls within different terrains. A series of wind-tunnel experiments were carried out to measure the horizontal velocity, number density, transport flux, and deposition rate of sand particles, and it was found that the horizontal speed of sand particles was first increased and then decreased on the railway track, and the peak speed over the concave subgrade was much smaller than those over convex and flat subgrades. The number density and horizontal sand flux were largest over the concave subgrade, and were the smallest over the convex subgrade. The sand particle deposition rate and distribution were also the largest within the concave subgrade, and some measures were also proposed to prevent sand sedimentation on the leeward sides of wind-break walls.

TouchTerrain is a simple-to-use web application that makes creating 3D printable terrain models from anywhere on the globe accessible to a wide range of users, from people with no GIS expertise to power users. For coders, a Python-based standalone version is available from the open-source project’s GitHub repository. Analyzing 18 months of web analytics gave us a preliminary look at who is using the TouchTerrain web application and what their models are used for; and to map out what terrains on the globe they chose to 3D print. From July 2019 to January 2021, more than 20,000 terrain models were downloaded. Models were created for many different use cases, including education, research, outdoor activities and crafting mementos. Most models were realized with 3D printers, but a sizable minority used CNC machines. Our own experiences with using 3D printed terrain in a university setting have been very positive so far. Anecdotal evidence points to the strong potential for 3D printed terrain models to provide significant help with specific map-related tasks. For the introductory geology laboratory, 3D printed models were used as a form of “training wheels” to aid beginning students in learning to read contour maps, which are still an important tool for geology.

The status, changes, and disturbances in geomorphological regimes can be regarded as controlling and regulating factors for biodiversity. Therefore, monitoring geomorphology at local, regional, and global scales is not only necessary to conserve geodiversity, but also to preserve biodiversity, as well as to improve biodiversity conservation and ecosystem management. Numerous remote sensing (RS) approaches and platforms have been used in the past to enable a cost-effective, increasingly freely available, comprehensive, repetitive, standardized, and objective monitoring of geomorphological characteristics and their traits. This contribution provides a state-of-the-art review for the RS-based monitoring of these characteristics and traits, by presenting examples of aeolian, fluvial, and coastal landforms. Different examples for monitoring geomorphology as a crucial discipline of geodiversity using RS are provided, discussing the implementation of RS technologies such as LiDAR, RADAR, as well as multi-spectral and hyperspectral sensor technologies. Furthermore, data products and RS technologies that could be used in the future for monitoring geomorphology are introduced. The use of spectral traits (ST) and spectral trait variation (STV) approaches with RS enable the status, changes, and disturbances of geomorphic diversity to be monitored. We focus on the requirements for future geomorphology monitoring specifically aimed at overcoming some key limitations of ecological modeling, namely: the implementation and linking of in-situ, close-range, air- and spaceborne RS technologies, geomorphic traits, and data science approaches as crucial components for a better understanding of the geomorphic impacts on complex ecosystems. This paper aims to impart multidimensional geomorphic information obtained by RS for improved utilization in biodiversity monitoring.

The relative importance of topography and soil moisture on the initiation of an afternoon deep convection under weak synoptic-scale forcing was investigated using the weather research and forecasting (WRF) model with high resolution (1.33 km). The convection occurred on 29 June 2017, over the Liupan Mountains, west of the Loess Plateau. The timing and location of the convective initiation (CI) simulated by the WRF model compared well with the radar observations. It showed that the warm and humid southerly airflow under 700 hPa was divided into east and west flows due to the blockage of the Liupan Mountains. The warm and humid air on the west side was forced to climb along the slope and enhanced the humidity near the ridge. The accumulation of unstable energy in the middle and north of the ridge led to a strong vertical convergence and triggered the convection. Sensitivity experiments showed that terrain played a dominant role in triggering the convection, while the spatial heterogeneity of soil moisture played an indirect role by affecting the local circulation and the partition of surface energy.

As digital terrain models are indispensable for visualizing and modeling geographic processes, terrain information content is useful for terrain generalization and representation. For terrain generalization, if the terrain information is considered, the generalized terrain may be of higher fidelity. In other words, the richer the terrain information at the terrain surface, the smaller the degree of terrain simplification. Terrain information content is also important for evaluating the quality of the rendered terrain, e.g., the rendered web terrain tile service in Google Maps (Google Inc., Mountain View, CA, USA). However, a unified definition and measures for terrain information content have not been established. Therefore, in this paper, a definition and measures for terrain information content from Digital Elevation Model (DEM, i.e., a digital model or 3D representation of a terrain’s surface) data are proposed and are based on the theory of map information content, remote sensing image information content and other geospatial information content. The information entropy was taken as the information measuring method for the terrain information content. Two experiments were carried out to verify the measurement methods of the terrain information content. One is the analysis of terrain information content in different geomorphic types, and the results showed that the more complex the geomorphic type, the richer the terrain information content. The other is the analysis of terrain information content with different resolutions, and the results showed that the finer the resolution, the richer the terrain information. Both experiments verified the reliability of the measurements of the terrain information content proposed in this paper.

We present a novel technique to infer ground slope angle from waveform LiDAR, known as the independent slope method (ISM). The technique is applied to large footprint waveforms (\(\sim\) mean diameter) from the Ice, Cloud and Land Elevation Satellite (ICESat) Geoscience Laser Altimeter System (GLAS) to produce a slope dataset of near-global coverage at \(0.5^{\circ} \times 0.5^{\circ}\) resolution. ISM slope estimates are compared against high resolution airborne LiDAR slope measurements for nine sites across three continents. ISM slope estimates compare better with the aircraft data (R\(^{2}=0.87\) and RMSE\(=5.16^{\circ}\)) than the Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM) inferred slopes (R\(^{2}=0.71\) and RMSE\(=8.69^{\circ}\)) ISM slope estimates are concurrent with GLAS waveforms and can be used to correct biophysical parameters, such as tree height and biomass. They can also be fused with other DEMs, such as SRTM, to improve slope estimates.

Farmers experience whole-body vibrations when they drive tractors. Among the various factors that influence the vibrations to which the driver is exposed are terrain roughness, tractor speed, tire type and pressure, rear axle width, and tractor seat height above the ground. In this paper the influence of tractor seat height above the ground on the lateral vibrations to which the tractor driver is exposed is studied by means of a geometrical and an experimental analysis. Both analyses show that: (i) lateral vibrations experienced by a tractor driver increase linearly with tractor-seat height above the ground; (ii) lateral vibrations to which the tractor driver is exposed can equal or exceed vertical vibrations; (iii) in medium-size tractors, a feasible 30 cm reduction in the height of the tractor seat, which represents only 15% of its current height, will reduce the lateral vibrations by around 20%; and (iv) vertical vibrations are scarcely influenced by tractor-seat height above the ground. The results suggest that manufacturers could increase the comfort of tractors by lowering tractor-seat height above the ground, which will reduce lateral vibrations.

Home range estimation is the basis of ecology and animal behavior research. Some popular estimators have been presented; however, they have not fully considered the impacts of terrain and obstacles. To address this defect, a novel estimator named the density-based fuzzy home range estimator (DFHRE) is proposed in this study, based on the active learning method (ALM). The Euclidean distance is replaced by the cost distance-induced geodesic distance transformation to account for the effects of terrain and obstacles. Three datasets are used to verify the proposed method, and comparisons with the kernel density-based estimator (KDE) and the local convex hulls (LoCoH) estimators and the cross validation test indicate that the proposed estimator outperforms the KDE and the LoCoH estimators.

This paper reviews LiDAR ground filtering algorithms used in the process of creating Digital Elevation Models. We discuss critical issues for the development and application of LiDAR ground filtering algorithms, including filtering procedures for different feature types, and criteria for study site selection, accuracy assessment, and algorithm classification. This review highlights three feature types for which current ground filtering algorithms are suboptimal, and which can be improved upon in future studies: surfaces with rough terrain or discontinuous slope, dense forest areas that laser beams cannot penetrate, and regions with low vegetation that is often ignored by ground filters.

The brown marmorated stink bug, Halyomorpha halys Stål (Hemiptera: Pentatomidae), is native to Japan, China, Taiwan, and Korea. Its dispersion from Asia to the United States of America and Europe caused serious damage to fruit, vegetables, and high-value crops. In Greece, damages are reported in kiwi orchards in the regions of Pieria and Imathia, which are the main production areas of kiwifruit. Greek kiwifruit production is expected to increase twofold within the next years. The aim of this research is to study the terrain and canopy properties that may have an impact on the development of H. halys populations. Thus, five kiwi orchards in total were selected in the regions of Pieria and Imathia. Τen traps were installed from early June to late October within each selected kiwi orchard-two types of traps at every side of the orchards and the center. The installed traps were examined weekly and the number of the captured H. halys was recorded. During the same days, sentinel satellite images were analyzed to calculate the vegetation index, NDVI (Normalised Difference Vegetation Index), and NDWI (Normalised Difference Water Index). The results showed population variability within the kiwi orchards since the population of H. halys was higher in areas with high NDVI and NDWI values. Additionally, our research revealed that H. halys prefers to develop populations at higher altitudes at both regional and field scales. The results of this research can be used to reduce damages by H. halys in kiwi orchards using different rates of pesticides depending on the prediction of the population size. There are multiple benefits of the proposed practice, such as a reduction in the production cost of kiwifruits, an increase in farmers' profit, and environmental protection.