Terrain Structure Research Articles

Extraction of Winter Wheat Planting Plots with Complex Structures from Multispectral Remote Sensing Images Based on the Modified Segformer Model

As one of the major global food crops, the monitoring and management of the winter wheat planting area is of great significance for agricultural production and food security worldwide. Today, the development of high-resolution remote sensing imaging technology has provided rich sources of data for extracting the visual planting information of winter wheat. However, the existing research mostly focuses on extracting the planting plots that have a simple terrain structure. In the face of diverse terrain features combining mountainous areas, plains, and saline alkali land, as well as small-scale but complex planting structures, the extraction of planting plots through remote sensing imaging is subjected to great challenges in terms of recognition accuracy and model complexity. In this paper, we propose a modified Segformer model for extracting winter wheat planting plots with complex structures in rural areas based on the 0.8 m high-resolution multispectral data obtained from the Gaofen-2 satellite, which significantly improves the extraction accuracy and efficiency under complex conditions. In the encoder and decoder of this method, new modules were developed for the purpose of optimizing the feature extraction and fusion process. Specifically, the improvement measures of the proposed method include: (1) The MixFFN module in the original Segformer model is replaced with the Multi-Scale Feature Fusion Fully-connected Network (MSF-FFN) module, which enhances the model’s representation ability in handling complex terrain features through multi-scale feature extraction and position embedding convolution; furthermore, the DropPath mechanism is introduced to reduce the possibility of overfitting while improving the model’s generalization ability. (2) In the decoder part, after fusing features at four different scales, a CoordAttention module is added, which can precisely locate important regions with enhanced features in the images by utilizing the coordinate attention mechanism, therefore further improving the model’s extraction accuracy. (3) The model’s input data are strengthened by incorporating multispectral indices, which are also conducive to the improvement of the overall extraction accuracy. The experimental results show that the accuracy rate of the modified Segformer model in extracting winter wheat planting plots is significantly increased compared to traditional segmentation models, with the mean Intersection over Union (mIOU) and mean Pixel Accuracy (mPA) reaching 89.88% and 94.67%, respectively (an increase of 1.93 and 1.23 percentage points, respectively, compared to the baseline model). Meanwhile, the parameter count and computational complexity are significantly reduced compared to other similar models. Furthermore, when multispectral indices are input into the model, the mIOU and mPA reach 90.97% and 95.16%, respectively (an increase of 3.02 and 1.72 percentage points, respectively, compared to the baseline model).

Integrating terrain structure characteristics into generative adversarial nets for hillshade generation

A hillshade is a visualization technique that represents three-dimensional terrain in a two-dimensional plane by illumination mapping. The digital relief shading promotes the visualization of the terrain efficiently using DEM data. However, compared with manual shading, the digital algorithm-based method still has a gap in the visual effect of illumination strategy and terrain generalization. The typical landform characteristics and micro-geomorphic properties usually are destroyed. The reason is that the complexity of illumination rules is hard to summarize for different terrain phenomena. In this study, namely the data-driven artificial intelligent method, an alternative strategy based on generative adversarial networks (GANs) is proposed rather than the rule-based method. The DEM and the terrain skeleton lines are input to the model and part of the manual relief shading of Swisstopo is used for the training samples. Through the GAN training and learning, the manual skill imbedded in the hillshade is discovered in the generation model. The results show that the proposed model performs better than the digital relief shading on various landforms in aesthetic visualization and geo-scientific representation. Compared to other models, including other convolution neural network (CNN) based methods, terrain structure is maintained more significantly through the proposed model.

Foothold selection during locomotion in uneven terrain: Results from the integration of eye tracking, motion capture, and photogrammetry.

Relatively little is known about the way vision is used to guide locomotion in the natural world. What visual features are used to choose paths in natural complex terrain? To answer this question, we measured eye and body movements while participants walked in natural outdoor environments. We incorporated measurements of the 3D terrain structure into our analyses and reconstructed the terrain along the walker's path, applying photogrammetry techniques to the eyetracker's scene camera videos. Combining these reconstructions with the walker's body movements, we demonstrate that walkers take terrain structure into account when selecting paths through an environment. We find that they change direction to avoid taking steeper steps that involve large height changes, instead of choosing more circuitous, relatively flat paths. Our data suggest walkers plan the location of individual footholds and plan ahead to select flatter paths. These results provide evidence that locomotor behavior in natural environments is controlled by decision mechanisms that account for multiple factors, including sensory and motor information, costs, and path planning.

SEPARATED GEOTECHNICAL ENVIRONMENTS DURING THE RESEARCH OF THE TERRAIN AT THE LOCATION PLANNED FOR THE CONSTRUCTION OF THE SILOS IN KOZLUK NEAR ZVORNIK

<p>The construction of high-rise objects in solid rocks characterized by frequent changes in its vertical profile requires a more detailed study of lithological layers, tectonic characteristics and analysis of physical and mechanical parameters. Layers of different strength and characteristics in all elements make it possible to separate it by geological environments, in order to choose the most favorable one for the foundation of objects. As part of the construction of additional silos at the existing "Molaris" Mill complex in Kozluk near Zvornik, research was conducted at the central points of the future facilities and the immediate surroundings.</p> <p>Investigation works up to a depth of 12.0 m defined all the complexity of the terrain structure in a small area. Conducted research on rock samples from exploration works and from the open terrain profile showed differences in the physical and mechanical characteristics of softer rocks susceptible to solubility in water. Separated environments show that the foundation of objects cannot be done only in one environment, given the folds of the layers in a very small area.</p>

Modeling computer sight based on DEM data to detect terrain breaks caused by gully erosion on the loess Plateau

As a type of terrain break, the gully shoulder line is of great significance to locate the scope of surface failure and soil erosion. Based on the knowledge of the terrain structure of loess gullies, this paper modeled sight lines to calculate the three-dimensional maximum elevation angle (3D MEA). Then, a critical MEA value of 35° was determined via human vision to extract negative terrain and shoulder lines considering unmanned aerial vehicle (UAV)- and light detection and ranging (lidar)-based digital elevation models (DEMs). In addition to the DEM resolution, the uncertainties caused by the direction number, look distance and skip radius of computer sight were further examined. It was suggested that the process should involve observing at least 12 directions to detect complete information on terrain breaks. The simulation process required a relatively large look distance to analyze global terrain changes because a small distance could limit sight within the local window. The skip radius could result in the process ignoring nearby abrupt changes in terrain, thus causing negative terrain to be misclassified as positive terrain near shoulder lines. The concept of wall morphology was introduced to attribute the critical value to the repose angle of loess gullies. Eventually, the MEA was compared with many other parameters. It could be concluded that the slope is limited to the local scale, while the MEA could combine local and global scales using a large look distance. The hillshade is limited to one viewing direction, but the 3D MEA is a comprehensive layered index from multiple directions. This study is a novel exploration of interdisciplinary hydraulic engineering and information technology. This approach reduces terrain breaks from three-dimensional space into a one-dimensional index and simplifies shoulder lines as a critical 3D MEA value related to loess material properties. This research could be adopted for soil and water conservation.

A continuous digital elevation representation model for DEM super-resolution

The surface of the Earth is continuous, and obtaining precise elevation data at arbitrary query positions is essential for many applications and analyses. However, existing digital elevation models suffer from a precision gap caused by discretization. Therefore, we develop a new continuous representation model (CDEM) that allows height values to be obtained at any arbitrary query position. Inspired by recent research on the implicit neural representation model, we train an encoder–decoder network to learn CDEM from discrete elevation data for DEM super-resolution tasks. The encoder targets generating latent codes from discrete elevation data, while the decoder composes these latent codes with query positions to predict corresponding elevation values. Such a learning pipeline is well-suited for DEM super-resolution tasks. To improve model accuracy, we also propose predicting the bias of elevation values between the query position and its closest known position. Real-world terrain surfaces exhibit inherent roughness with numerous small variations in localized regions, resulting in high-frequency targets that are difficult for neural networks to fit. In order to facilitate the network’s ability to model data with high-frequency variations, we introduce positional encoding to map query positions into a higher-dimensional space. Compared to the Bicubic interpolation method and state-of-the-art TfaSR model, our method is demonstrated to obtain more accurate elevation values and preserve more details of terrain structure on the TFASR30, Pyrenees, and Tyrol datasets. Specifically, our EBCF-CDEM model demonstrates performance improvements over the TfaSR model, with reductions in the RMSE of elevation, slope, and aspect by 7.03%, 4.81%, and 3.07% respectively at ×4 super-resolution scale on the TFASR30 dataset, by 9.92%, 7.06%, and 6.15% at ×8 super-resolution scale on the Pyrenees dataset. Extensive experiments further validate the generalizability of our EBCF-CDEM, compared to the TfaSR model, our results in terms of RMSE of elevation, slope, and aspect are reduced by 14.78%, 12.12%, and 8.99% respectively at ×8 super-resolution scale on the Tyrol dataset, and 26.92%, 7.14% and 5.36% on the TIFASR30to10 dataset. We release our source code (including the datasets) at https://github.com/AlcibiadesTophetScipio/EBCF-CDEM to reproduce our results and encourage future research.

Directed Positive Negative Terrain Structure Graph Attention Network for Genetic Landform Recognition

Directed Positive Negative Terrain Structure Graph Attention Network for Genetic Landform Recognition

Interactive Authoring of Terrain using Diffusion Models

AbstractGenerating heightfield terrains is a necessary precursor to the depiction of computer‐generated natural scenes in a variety of applications. Authoring such terrains is made challenging by the need for interactive feedback, effective user control, and perceptually realistic output encompassing a range of landforms. We address these challenges by developing a terrain‐authoring framework underpinned by an adaptation of diffusion models for conditional image synthesis, trained on real‐world elevation data. This framework supports automated cleaning of the training set; authoring control through style selection and feature sketches; the ability to import and freely edit pre‐existing terrains, and resolution amplification up to the limits of the source data. Our framework improves on previous machine‐learning approaches by: expanding landform variety beyond mountainous terrain to encompass cliffs, canyons, and plains; providing a better balance between terseness and specificity in user control, and improving the fidelity of global terrain structure and perceptual realism. This is demonstrated through drainage simulations and a user study testing the perceived realism for different classes of terrain. The full source code, blender add‐on, and pre‐trained models are available.

A photoelastic method for stress analysis of granular terrain beneath a wheel

This paper presents a photoelastic method for the stress analyses of granular terrains beneath a traveling wheel. Recently, wheel terramechanics have been studied using various advanced techniques such as particle velocimetry and machine learning. However, the dynamic profiles of the stress distributions in soil have not been directly observed. In this paper, we propose a photoelastic method to experimentally demonstrate the two-dimensional stress analysis of a granular terrain. This method uses photoelastic disks as terrain particles; thus, the internal stress and propagation of the granular terrain can be visualized using the interference fringe of light. This study aimed to observe the dynamic behavior of grains, typically simulating circular or spherical objects of the granular terrain. To demonstrate the photoelastic method for simulated ground, we developed a single-wheel testbed using photoelastic materials. Moreover, the effects of mixing with non-photoelastic materials were investigated to simulate steady states with small and large wheel-slip conditions. This paper presents a force chain analysis of the photoelastic particles beneath a single traveling wheel. The force chain structure was geometrically compared with the propagation angle and length. In addition, the orientational orders of the force chains were quantitatively evaluated under both small and large wheel slip conditions. The experimental results confirmed that the photoelastic method can reveal dynamic changes in the internal stress structures of granular terrain beneath the wheel under various slip conditions.

Multiple wind turbines shielding model of lightning attractiveness for mountain wind farms

In this study, a lightning attractiveness assessment method was proposed for mountain wind farms (WFs). A full-size wind turbine (WT) model accounting for terrain topography was established. Then, the lightning collection area (LCA) of WTs was calculated using the self-consistent leader inception and propagation model (SLIM). Finally, a quantitative relationship between terrain structure and the LCA of a WT was obtained, and a shielding model was incorporated to realize differentiated lightning attractiveness assessment of WFs. Results showed that higher terrain height and steeper ground slope angle both increase the size and the height of the LCA, meaning that lightning attractiveness can vary because of terrain differences, even for WTs with the same elevation. The influence of ground slope angle on the LCA showed a saturation trend. The variation of the LCA was analyzed based on the distortion effect of the terrain structure on the electric field. By comparison, the simulation results of lightning attractiveness for mountain WFs in this paper are in good agreement with the actual observation results in flat and mountainous areas. The proposed model could be used to identify weaknesses in the lightning protection of mountain WFs.

Correlations between the Topography-Induced Gravity, Terrain Structure and the Seismicity in the Gulf of Panama

This study presents new maps of the topographic and geophysical setting and seismicity in the region of the Gulf of Panama. The spatial analysis is based on the comparative analysis of the datasets on geoid, free-air gravity anomaly, topography and earthquakes. The cartographic framework is developed using the Generic Mapping Tools (GMT) scripting toolset. Seismic activity in the Central America is high due to the complex geologic setting, tectonic activity and lithosphere plate subduction. The data include the Earth Gravitational Model (EGM2008), the General Bathymetric Chart of the Oceans (GEBCO) and gravity grids. The seismicity data were collected from the Incorporated Research Institutions for Seismology (IRIS) catalogue on 1970–2021. The variations in data were compared to analyse correlations between the geophysical, seismic and topographic parameters. Free-air gravity, geoid and topographic data derived from the high-resolution datasets were used to investigate their effects on the main seismic sources in the region. The comparison of the maps showed that the distribution of the shallow earthquakes in the Pacific segment of Panama coincides with negative free-air anomalies and lower geoid values. The results revealed high values of geoid in the high mountainous regions of Panama (Cordilliera de Talamanca, southern coast of Peninsula de Azuero and eastern Panama, 77.5–78.5°W), which correspond to the topographic roughness in the highlands. Negative values of geoid are found over the Caribbean Sea basin (−4 to 0 m). The analyses of seismicity showed 1740 earthquake events varying by magnitudes from 2.9 to 7.8 at the depths up to 225 m (near the west coast of Colombia). A high concentration of the earthquakes is in the western region of the Panama’s shelf waters (~82–83.5°W), and on the border with Colombia (~77–78.5°W). High gravity anomalies (over 220 mGal) are found in the mountainous regions which match the geodynamic processes associated with the Earth structure and geodetic and geophysical effects. The regions of the high seismicity were defined in the Gulf of Chiriqui and eastern part of the Gulf of Panama.

Assessment of Using UAV Photogrammetry Based DEM and Ground-Measurement Based DEM in Computer-Assisted Forest Road Design

Computer-assisted forest road design mainly relies on a high-resolution digital elevation model (DEM), which provides terrain data for supporting the analysis of road design features. The resolution and accuracy of the DEM in representing the terrain structures vary depending on the preferred dataset, which then reflects some of the essential road features such as alignment, road slope, and earthwork. In this study, three forest road sections were designed by using high-resolution DEMs generated from UAV photogrammetry data, GNSS-GPS data and Total Station data. NetCAD 7.6 software, developed in Turkey and mostly used in road design applications, was used to perform the road design while calculating horizontal profiles, vertical profiles, curves, cross sections, and earthwork. The DEM generation capabilities for three datasets were compared based on spatial resolution, data collection and data processing stage. Then, the differences between three road sections were evaluated by considering specified road features such as alignment properties, road slope, and earthwork. The results indicated that the UAV (Unmanned Aerial Vehicles) based DEM generation method provided the highest resolution (10 cm), followed by the Total Station (56 cm) and GNSS-GPS (61 cm) based methods. When comparing the time for data collection procedure, it took 14 minutes, 70 minutes, and 110 minutes for UAV data, GNSS-GPS data, and Total Station data, respectively. On the other hand, UAV based method falls into a disadvantageous situation in data processing stage, due to high data processing time (3 hours). However, GNSS-GPS and Total Station based methods work only with spatial point data, so they require less processing time of 15 minutes and 25 minutes, respectively. The results indicated that road lengths were 294.8, 272.4 and 282.1 m and the average road slopes were 3.41%, 3.39%, and 3.31% for the road sections designed by using UAV, GNSS-GPS, and Total Station based DEMs, respectively. The excavation and landfill volumes were 369.16 m3 and 166.98 m3, 285.86 m3 and 201.83 m3, and 433.17 m3 and 183.95 m3, respectively. The results indicated that UAV photogrammetry data generates high-resolution DEMs that can be effectively used to design forest roads.

A geodesic distance-based routing scheme for sensor networks with irregular terrain structure

A geodesic distance-based routing scheme for sensor networks with irregular terrain structure

Quantification of Surface Pattern Based on the Binary Terrain Structure in Mountainous Areas

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.

Environmental selection, rather than neutral processes, best explain regional patterns of diversity in a tropical rainforest fish

To conserve the high functional and genetic variation in hotspots such as tropical rainforests, it is essential to understand the forces driving and maintaining biodiversity. We asked to what extent environmental gradients and terrain structure affect morphological and genomic variation across the wet tropical distribution of an Australian rainbowfish, Melanotaenia splendida splendida. We used an integrative riverscape genomics and morphometrics framework to assess the influence of these factors on both putative adaptive and non-adaptive spatial divergence. We found that neutral genetic population structure was largely explainable by restricted gene flow among drainages. However, environmental associations revealed that ecological variables had a similar power to explain overall genetic variation, and greater power to explain body shape variation, than the included neutral covariables. Hydrological and thermal variables were the strongest environmental predictors and were correlated with traits previously linked to heritable habitat-associated dimorphism in rainbowfishes. In addition, climate-associated genetic variation was significantly associated with morphology, supporting heritability of shape variation. These results support the inference of evolved functional differences among localities, and the importance of hydroclimate in early stages of diversification. We expect that substantial evolutionary responses will be required in tropical rainforest endemics to mitigate local fitness losses due to changing climates.

Cultivation and resource of Artemisia schrenkiana L. for increased pharmaceutical perspective​

Studying the source and cultivation conditions of medicinal plants, growing them in the field of agriculture, and determining new prospects for production is one of the topical topics. This research was conducted in 2022 in the south-eastern territory of the Republic of Kazakhstan. The research work was carried out using geobotanical and biological methods. In this article, the authors describe that contains opposing points of view on the problem considered in the importance of cultivation and resource of agricultural systems medical plant, Artemisia schrenkiana L.. The article presents the results of the plant community of the area where the A. schrenkiana Ledeb plant grows in the south-east of Kazakhstan and laboratory seed productivity. As a result of the research, the authors have described 163 plant species belonging to 31 families and 116 genera from the plant community, making a comprehensive analysis of the microclimatic conditions, terrain structure and floral composition of the area where the A. schrenkiana Ledeb plant grows. Laboratory experiments showed high germination of A. schrenkiana seeds, but the germination of seeds collected from different populations was different: for example, № 2 population seeds had the highest laboratory germination. This was probably due to the remoteness of these populations from anthropogenic factors. because 1 and № 3 populations of A. schrenkiana were found in the vicinity of settlements, while № 2 populations were located in pastures far from villages. According to the natural resource analysis, the following five genera dominated by the number of species in the places where A. schrenkiana Ledeb grows: Gramineae Juss. Poaceae, Compositae Giseke., Chenopodiaceae Vent., Leguminosae Juss. and Boraginaceae Juss. The plants in the studied community were classified according to their useful properties as follows: 157 species value plants, 38 species of medicinal plants, five species of poisonous plants, 10 species of essential oil plants, three species of succulent plants and three species of ornamental plants. Currently, no external factor had a strong negative impact on the plant community where A. schrenkiana Ledeb, a plant with medicinal potential and important in agriculture.

A Contour Line Group Simplification Method Based on Classified Terrain Features

Contour line group simplification methods can effectively preserve terrain features during map making and producing. This process involves two main steps, namely terrain feature line extraction and contour bend selection. The terrain feature line extraction includes two steps, that is, terrain feature point extraction and classification, and terrain feature line connection. However, to date, many similar studies have not considered the hierarchy of terrain features. Therefore, we proposed a group simplification method for contour lines based on classified terrain features and tested it on a study area with mainly positive landforms. In accordance with geomorphological theory, we divided valleys into either gradually descending or ordinary. Valley points were extracted based on the constrained Delaunay triangulation method and then classified into the two categories. Gradually descending and ordinary valley lines were then connected. The contour bends were grouped based on the valley lines extracted and then selected according to the geometric indicators of the bend group. The results have demonstrated that the valley lines extracted closely matched human perception in integrity and structure. Contour lines simplified by our method achieved effective reduction of map information and adequate retention of the main terrain structures, which are similar to those from manual simplification.

Real-Time Neural Dense Elevation Mapping for Urban Terrain With Uncertainty Estimations

Having good knowledge of terrain information is essential for improving the performance of various downstream tasks on complex terrains, especially for the locomotion and navigation of legged robots. We present a novel framework for neural urban terrain reconstruction with uncertainty estimations. It generates dense robot-centric elevation maps online from sparse LiDAR observations. We design a novel pre-processing and point features representation approach that ensures high robustness and computational efficiency when integrating multiple point cloud frames. A generative Bayesian model then recovers the detailed terrain structures while simultaneously providing the pixel-wise reconstruction uncertainty. We evaluate the proposed pipeline through both simulation and real-world experiments. Our approach achieves high-quality terrain reconstruction with real-time performance on a mobile platform, and the uncertainty estimates may further benefit the downstream tasks of legged robots.

Remote sensing identification of early planting information of rapeseed in mountainous areas

As a major oilseed crop with high ornamental value, Accurate and timely determination of their distribution and area under cultivation is essential to ensure food security and achieve sustainable development goals. The bright yellow flowers are a unique feature of rapeseed compared to other crops. Therefore, the yellow flower index was previously used to detect rapeseed on aerial images or medium-resolution satellites. However, the impact of its unique topographic terrain structure confuses crop planting structure, while the yellow flower signal of rapeseed is weak in the early stage of rapeseed growth. Therefore, it remains challenging to accurately identify early rapeseed in the southwest mountains. In this study, a new mountain rape index (MWRI) is proposed based on Sentinel-2 time series images. First, the NDVI characteristics of vegetation were used to filter out non-vegetated areas. Secondly, the weak rapeseed signal of non-pure image elements was enhanced by combining the time series reflection variation curves of rapeseed in red, green, NIR, and SWIR bands. The MRM method was used to extract the rapeseed cultivation in Chongqing, a typical mountainous rapeseed growing area in China. Three different previously proposed rapeseed indices: normalized difference yellowness index (NDYI), and yellowness index (RYI) were also calculated for comparison, and validation using high-resolution image interpretation samples in Google earth showed that MWRI has higher rapeseed recognition accuracy OA above 0.97, while other rapeseed indices OA between 0.9 and 0.95. The results indicate that MWRI is an effective index to distinguish mountain rapeseed from other crops.

Satellite Image Processing by Python and R Using Landsat 9 OLI/TIRS and SRTM DEM Data on Côte d’Ivoire, West Africa

In this paper, we propose an advanced scripting approach using Python and R for satellite image processing and modelling terrain in Côte d'Ivoire, West Africa. Data include Landsat 9 OLI/TIRS C2 L1 and the SRTM digital elevation model (DEM). The EarthPy library of Python and 'raster' and 'terra' packages of R are used as tools for data processing. The methodology includes computing vegetation indices to derive information on vegetation coverage and terrain modelling. Four vegetation indices were computed and visualised using R: the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index 2 (EVI2), Soil-Adjusted Vegetation Index (SAVI) and Atmospherically Resistant Vegetation Index 2 (ARVI2). The SAVI index is demonstrated to be more suitable and better adjusted to the vegetation analysis, which is beneficial for agricultural monitoring in Côte d'Ivoire. The terrain analysis is performed using Python and includes slope, aspect, hillshade and relief modelling with changed parameters for the sun azimuth and angle. The vegetation pattern in Côte d'Ivoire is heterogeneous, which reflects the complexity of the terrain structure. Therefore, the terrain and vegetation data modelling is aimed at the analysis of the relationship between the regional topography and environmental setting in the study area. The upscaled mapping is performed as regional environmental analysis of the Yamoussoukro surroundings and local topographic modelling of the Kossou Lake. The algorithms of the data processing include image resampling, band composition, statistical analysis and map algebra used for calculation of the vegetation indices in Côte d'Ivoire. This study demonstrates the effective application of the advanced programming algorithms in Python and R for satellite image processing.