Applied Geomatics最新文献

Many engineering structures require high measurement accuracy. Their displacement and deformation are determined from the results of special measurements. For the measurements to be accurate, a properly constructed and marked survey network is necessary. The long-term stability of survey points can be ensured by marking (installing) them on solid rock or special triangulation pillars. Accurate and repeatable instrument positioning and premarking over the points is ensured by centring plates. Centring plates with eccentric points can be used when a survey involves several instruments. The article presents the results of measurements and computations done using centring plates with eccentric points. The measurements were conducted in a metrology laboratory. The sought points were premarked with prisms and reflective targets. The measuring methods were angular intersection, linear intersection, and linear-angular measurements. We computed coordinates for the measured points, and the results were compared to their known directory values. The results demonstrate that centring plates with eccentric points can be employed in engineering surveys.

Quantity and distribution of Ground Control Points (GCPs) play a significant role in determining the positional accuracy of UAV photogrammetry. A dense GCP network helps in achieving good accuracy. However, the cost, time, and feasibility of setting up a dense network are challenging. Therefore, it is crucial to assess whether high accuracy can be achieved using minimal GCPs and its optimal distribution. This study investigated the effects of quantity, quality, horizontal, and vertical distribution using 0, 3–11 GCPs to identify a suitable configuration for a sparse GCP network. Thirty-eight configurations were experimented by distributing GCPs in the corners, edges, centre and vertically. Also, another sixteen configurations were used to understand the influence of incorrectly surveyed GCPs on positional accuracy. Horizontal and vertical Root Mean Square Error (RMSE) values were calculated from 79 Check Points for accuracy assessment. Initially, on assessing the effect of quantity, a higher count of GCPs produced high accuracy, but specific configurations using 4–5 GCPs rendered accuracy levels similar to 9–11 GCPs. On further investigation, configurations with few GCPs at the corners showed better accuracy than GCPs distributed only in the edge or centre. A significant reduction in RMSE_z of ± 1.5 cm was witnessed by adding vertically distributed GCPs. Based on the results, configurations using 4–5 GCPs distributed vertically and at corners equalled the RMSE values of configurations using 8–11 GCPs, proving it to be an ideal distribution while using fewer GCPs. The poor quality of GCP resulted in low positional accuracy when a sparse number of GCPs were used.

Urban areas are gaining attention globally with the implementation of the United Nations sustainable development agenda 2030 where more emphasis is given on making cities inclusive, resilient, safe, and sustainable. Hence, it is crucial to have precise data of urban built-up areas such as the shape, size, and spatial context. It is a challenging task to extract urban built-up features due to continuous modifications in land as well as heterogeneity in spatial and spectral extent of the urban surfaces. The present research attempts to extract urban built up structures using rule-based object-oriented classification. SEaTH, a tool used for feature analysis in eCognition software was applied to select the discrete features and optimum thresholds that allow more and more separability during classification. With respect to diversity in urban areas, two urban patches of Pune city were selected where one patch is the core part of the city with a congested network of roads and buildings and another patch is located in the outskirts comprises of modern multi-story buildings and relatively broad roads. Multiresolution segmentation with scale parameter of 5 with a shape 0.1 and compactness of 0.5 was finally accepted after a lot of trial iterations for both the areas. Using the SEaTH tool, some of the best object features such as shape properties, spectral bands, and indices (NDVI) were selected for the assessment of the separability and threshold. A rule-based classification was performed to acquire land use/land cover with an overall accuracy of 92% for the city core and 91% for the suburb. The k-hat value obtained was 0.81 and 0.88 for the city core and suburb area, respectively. With incorporating shape parameters in image classification, the SEaTH method applied hierarchically the shape features such as density, compactness, and shape index as the best features to separate the buildings and roads. The NDVI spectral index demonstrated in this study proved beneficial to classify vegetation features from other land use types. As a result of the present study, it has been concluded that rule-based object-oriented classification can help improve the classification of dynamic urban areas and update land use maps effectively.

Morphometric analysis provides an essential understanding of the geo-hydrological nature of a drainage basin. The advancements in remote sensing products like digital elevation model (DEM) and geographical information system (GIS) have made the assessment of morphometric indices more effective, easier, cheaper, and faster. Many studies have been carried out in different river basins of the country but there are meager works in context of the river basins of Arunachal Pradesh, India. Therefore, in this study, we assessed the morphometric parameters of the Bichom River basin for the first time using the DEM of Advanced Land Observing Satellite-Phased Array-Type L-Band Synthetic Aperture Radar (ALOS-PALSAR) with 12.5m spatial resolution in ArcGIS 10.3. The basin was divided into four sub-watersheds, namely Upper Bichom (SW-1), Dirang-Chu (SW-2), Tenga (SW-3), and Kaya (SW-4), and the linear, areal, and relief parameters have been analyzed. The Bichom River is of 8th order and exhibits dentritic drainage pattern. The results of linear aspects show that the basin is lithologically and geologically controlled with variations in slope and topography. The areal parameters indicate moderately permeable subsoil, moderate to high runoff with steep slope, rapid rainwater discharge, low to moderate permeability or infiltration, mature topography, and semi-circular basin. Finally, the relief attributes of the basin also exhibit steep slope, high runoff with low to moderate infiltration potential, and active erosional processes. The present baseline findings of the morphometric parameters could be effectively used by the decision-makers for prioritizing soil and water resource management at the basin and sub-watershed level.

A novel method for assessing spectral index efficiencies for landscape mapping in tropical wetlands was formulated: spectral indices assessment for mapping tropical wetlands (SIA_MW). SIA_MW consists of three stages: (1) identification of covers that make up the landscape, (2) feature selection consistency assessment, and (3) result validation. These stages are evaluated based on six criteria, each of which contains a decision rule (DR) with their respective rating alternatives. The DRs are integrated into two equations: efficiency of an index for landscape mapping in tropical wetlands (EIM_W) and efficiency of an index for water body mapping in tropical wetlands (EIM_Ww). SIA_MW has been proposed as a novel instrument that allows each of the stages of supervised classification to be developed and evaluated in an orderly and coherent manner. This ensures that the final decision to select an index is supported by a robust process that integrates qualitative and quantitative methods of spectral evaluation. SIA_MW is applicable to multiple remote sensing products and can be used in environments other than wetlands. This is because it is independent of factors such as landscape cover categories, the type of sensor product from which spectral indices are derived, and spectral classification algorithms. For the formulation of SIA_MW, the Bajo Sinú Wetland Complex (BSWC), located in northern Colombia, was selected as a pilot site, and 9 vegetation indices derived from a PlanteScope image were compared and evaluated. The soil-adjusted vegetation and water-adjusted vegetation indices (SAVI and WAVI, respectively) yielded the best results with values for EMI_W of 0.94 and 0.89, respectively. These results indicate SIA_MW was consistent because the covariance between the two best indices was 0.88. Additionally, the correlation between the DR scores of the evaluated indices was low, thus, indicating criteria complementarity.

The use of remote sensing to map land cover and changes in land use has proven to be a practical, reliable, and accessible approach. These images provide precise details about the landscape, utilizing image processing techniques, modeling, and classification algorithms. This study aimed to identify different areas, such as coffee plantations, water bodies, urban areas, forests, exposed soil, and pastures in the Funil reservoir region of Minas Gerais, Brazil. Image data from Landsat-8, Sentinel-1, and Sentinel-2 satellites for June 2021 were used. Different supervised classification algorithms such as rf, rpart1SE, and svmLinear2 were applied based on a large volume of remote sensing data. The analyses and maps were performed using the software RStudio, considering a significance level of 5%. The highest accuracy and kappa index values were found for the rf algorithm, followed by svmLinear2 and rpart1SE. The results showed that the rf algorithm achieved the highest accuracy and kappa index values, followed by svmLinear2 and rpart1SE. However, during the validation phase, the svmLinear2 algorithm outperformed based on the statistical results of the confusion matrix. Therefore, it was considered the most suitable for generating the thematic mapping of the landscape. This is because svmLinear2 identified a more significant number of coffee areas and better-distinguished vegetation areas.