Applied Geomatics最新文献

Exploiting photogrammetric computer vision techniques to generate point cloud data for 3D scene understanding has seen many research improvements in the last decade. Open-source research and algorithm development have provided benefits and intellectual capacity to researchers and developers for understanding and providing multiple solutions to problems from different perspectives. This study focuses on the open-source domain for photogrammetry and is trying to provide a walkthrough for the recent developments in extracting 3D information from 2D images with the context of point clouds. Four different free and open-source software (VisualSFM, WebODM, Colmap, Meshroom) were studied from the perspective of their point cloud generation capability and photogrammetric workflow to provide a comparative assessment in this research. Each software is also assessed for their usability and workflow functions. UAV-based photographs were acquired for the study area and using the same datasets and default parameters in each software, dense photogrammetric point clouds were generated using their own photogrammetric workflow. For each of these dense point clouds, an assessment of their quality and enriched information based on some robust parameters is done.

Adama is one of the fastest growing and second-most populous cities in Ethiopia. It is highly prone to flooding due to its location and rapid urbanization. The city is densely populated in the floodplain areas. It is a low-lying flat terrain surrounded by mountains and ridge topography. The objective of this study was to identify and map flood-prone areas in Adama City’s watershed using a geographic information system (GIS)-based multi-criteria decision-making (MCDM) and analytical hierarchy process (AHP). Distance from sewer drainage, topographic wetness index (TWI), elevation, slope, rainfall, land cover, normalized difference vegetation index (NDVI), distance from the river, distance from the road, drainage density, and soil types data sets were combined to meet the objective of the study. The result of the present study revealed that about 98.69% of the study area is moderate to very highly prone to flooding, whereas the other 1.31% of the area is at low risk. The model-generated flood-prone map matched with the ground control points (GCPs) collected by handheld GPS, Google Earth Satellite Imagery, experts’ opinions, and local community reports. Thus, this model has important implications for decision-makers and professionals in early warning and sustainable flood management systems.

The applicability of a machine learning algorithm can vary across regions due to disparities in image data sources, preprocessing techniques, and model training. To enhance the classification accuracy of ground surface structures, it is crucial to select an appropriate method tailored to the specific region. This study used highly-efficient UAV remote sensing photography and conducted training and tests using three supervised machine learning techniques, namely support vector machine (SVM), random forest (RF), and maximum likelihood (ML) as well as performed a cluster analysis using an unsupervised machine learning technique. The main objective of this study was to evaluate the effectiveness of four machine learning methods for classifying five distinct structures (forest, grassland, bare land, built-up area, and road) in UAV images. The machine learning methods will be trained using sample features extracted from the UAV images, and test classifications will be conducted for the five ground surface structures. The results demonstrated that the RF classifier outperformed the other methods, achieving performance metrics, including an accuracy of 91.78%, an area under the curve (AUC) of 0.93, a Kappa coefficient of 0.88, and a gain of 100%. The RF classifier showcased its capability to accurately differentiate between various ground surface structures by examining spectral composition, encompassing both natural and artificial elements, and making precise judgments based on factors such as color, color tone, and texture observed in the images.

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.