Applied Geomatics最新文献

The study used remote sensing and GIS techniques for defining the watershed and computing various morphometric characteristics of the Wadi Al-Naft Basins in Diyala Governorate, Iraq. The findings reveal the existence of two sub-basins, each of which was found to have four streams order. The drainage density, a measure of the stream length per unit area, was found to be 0.19 and 0.16 km/km², respectively. They found 153 streams, categorized into different orders based on size and connectivity. First-order streams were found to be 882.71 km in length, totalling 128, while second-order streams totalling 533.12 km in length. Third-order streams numbered 4 with a total length of 199.9 km, while there were two fourth-order streams with a total length of 57 km. These results may be utilised to examine water flow patterns in the area and are crucial for understanding the hydrological features of the Wadi Al-Naft Basin. To effectively manage water resources, the research underlines the value of GIS in obtaining data on water resources, including a range of morphometric factors. A valuable tool for water resource management, the paper outlines a systematic process for identifying and characterising watersheds that may be used in a case study.

This manuscript presents a comprehensive study on soil quality analysis and mapping across various land uses in the city of Madurai, India, leveraging advanced geospatial technology. Soil quality assessment is crucial for sustainable land management and informed decision-making in urban planning and agriculture. The study integrates geospatial data, remote sensing imagery, and ground-truthing techniques to evaluate soil properties and categorize land uses, facilitating a holistic understanding of the city’s soil health. The research begins by collecting soil samples from multiple locations representing diverse land uses, including urban, peri-urban, and agricultural areas within Madurai. Laboratory analyses are performed to measure various soil attributes such as pH, organic matter content, nutrient levels, and texture. Simultaneously, high-resolution satellite imagery and geographic information system (GIS) data are employed to create detailed land use maps, identifying distinct patterns and spatial distributions. The pH, amount of organic matter, amount of nutrients, and texture of the soil were all examined. Based on the significance of these characteristics in determining soil quality, a soil quality index was devised, and maps of soil quality were made for each type of land use. The consistency index map is created to gauge the level of soil contamination. Using statistical and geospatial analyses, the manuscript highlights significant variations in soil properties across different land use types. It explores the impact of urbanization on soil quality, revealing areas of soil degradation and pollution in urban zones. Furthermore, the study identifies regions with fertile soils suitable for agricultural purposes and suggests potential areas for soil improvement and sustainable land management practices.

Quantitative structure models (QSMs) are topological ordered cylinder models of trees which cover the complete branching structure from the stem’s base up to all tips. But the thin branches appear too large in the input point clouds. This leads to a well known problem, the overestimation of the QSM cylinders’ volumes and radii in thin branches. We present here a solution to this problem by introducing two QSM filters correcting the radii of such cylinders. The filters itself are build upon the theoretical fundamentals of allometric scaling theories. For validation we use QSMs produced from an open point cloud data set of tree clouds with the SimpleForest software. We compare the QSM volume against the harvested reference data for 65 felled trees. We also found QSM data of TreeQSM, a competitive and broadly accepted QSM modeling tool utilizing a different filter method. Our method performed more accurate on three different error measures. We quantify the error of our method with a RMSE of 127 (mathtt {dm^{3}}), a (mathtt {r^{2}_{adj.}}) of 0.96 and a CCC of 0.97. With those filters the accuracy of estimating total or partial volume of trees does significantly increase.

This study aims to delineate groundwater potential zones using an integrated approach of remote sensing (RS), geographical information system (GIS), and analytical hierarchy process (AHP) method in the middle and high Cheliff basin, Algeria. Multiple data such as lithology, lineament density, geomorphology, slope, soil, rainfall, drainage density, and land use/land cover were considered for delineating the groundwater potential zones. Spatially distributed maps/thematic layers of all the aforementioned parameters were created using remotely sensed data as well as ground data in a GIS environment. The assigned weights of the thematic layers and their features were then normalized by using the AHP technique. The delineated groundwater potential zones in this study area were categorized as very good, good, moderate, and poor, respectively. The results showed that the area along the Chlef River which is approximately 6% of the total study area was delineated as an area having “very good” potential for groundwater. The “good zone” delineated encompassed approximately 31% of the study area and was found in the pediment-pediplain complex zone. The moderate zones encompassed approximately 58% of the area. The “poor zones” encompassed approximately 5% of the area which included the cities of Ramka, El Hadjadj, Moussadek, and certain parts of Mekhatria. The groundwater potential zones map was compared with the actual discharge data from various wells within the study area and was found reasonable. Overall, this study provides a convenient approach of delineating the potential of groundwater availability which ultimately will aid in better planning and managing of groundwater resources.

Graphical abstract

Hyperspectral remote sensing enables a detailed spectral description of the object’s surface, but it also introduces high redundancy because the narrow contiguous spectral bands are highly correlated. This has two consequences, the Hughes phenomenon and increased processing effort due to the amount of data. In the present study, it is introduced a model that integrates stacked-autoencoders and convolutional neural networks to solve the spectral redundancy problem based on the feature selection approach. Feature selection has a great advantage over feature extraction in that it does not perform any transformation on the original data and avoids the loss of information in such a transformation. The proposed model used a convolutional stacked-autoencoder to learn to represent the input data into an optimized set of high-level features. Once the SAE is learned to represent the optimal features, the decoder part is replaced with regular layers of neurons for reduce redundancy. The advantage of the proposed model is that it allows the automatic selection and extraction of representative features from a dataset preserving the meaningful information of the original bands to improve the thematic classification of hyperspectral images. Several experiments were performed using two hyperspectral data sets (Indian Pines and Salinas) belonging to the AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) sensor to evaluate the performance of the proposed method. The analysis of the results showed precision and effectiveness in the proposed model when compared with other feature selection approaches for dimensionality reduction. This model can therefore be used as an alternative for dimensionality reduction.

Currently, the real-time kinematic (RTK) method is common to be used in the global navigation satellite system (GNSS) positioning solutions, whereas it was primarily used for cadastral measurements, especially measurements of land parcels in Indonesia. In addition, the real-time precise point positioning (RTPPP) method is currently used extensively in Indonesia for positioning applications. Indonesia’s position located in the Asia-Pacific region makes it possible to observe a huge number of multi-GNSS satellite signals from GPS, GLONASS, Galileo, and Beidou which are very favorable for such measures. One particular problem in point positioning in Indonesia is that the measurements are often made in harsh environments covered by vegetation or buildings. This research is aimed at determining the quality of measurement data in static, RTK, and RTPPP methods in harsh environments and determining the contribution of multi-satellite constellations to the measurement of the three methods in harsh areas. Data acquisition of the methods was conducted in various locations covered by vegetation and building obstruction in the baseline distance scheme of 2.5 km, 5 km, 10 km, 20 km, and 50 km. In addition, an analysis of the level of accuracy and precision of static, RTK, and RTPPP measurement methods was conducted. In harsh environments, the accuracy and precision results of the static and RTK methods using multi-satellite constellations may provide solutions that meet the standards of land parcel measurement. Results obtained on a 50-km baseline are still good. However, the results of the baseline distance scheme show that the longer the baseline, the greater tendency for accuracy to decrease. The RTPPP method is not capable of generating data with a fixed solution for all satellite constellation schemes.

Digital elevation models (DEMs) are used for many geosciences studies; hence, their accuracy is essential. Throughout the world, there are many small islands of various sizes and densities; hence, it is important to assess the DEM accuracy on very small islands since DEMs serve as the major data source for many investigations, particularly in geomorphology, land-use planning, and disaster management. Therefore, this paper aims to validate the accuracy of an open-source Indonesian DEM (DEMNAS) in the very small islands of Karimunjawa–Indonesia. Validation was conducted by comparing elevation values from DEMNAS to the true elevation values in four very small islands in Karimunjawa, namely Cemara Besar, Cemara Kecil, Menjangan Besar, and Menjangan Kecil. The true elevation values were obtained by orthorectification of aerial imagery using a DJI Mavic Air-2 Unmanned Aerial Vehicle (UAV). The orthorectification came from ground control points (GCP) from the geodetic Global Positioning System (GPS). In the study area, fourteen GCP were erected; for more significant coverage, they were placed along the edges of the very small islands. After that, Agisoft software analyzed the images to produce a DEM using GCP orthorectification. Based on 280 sampling points, we applied a root-mean-square error (RMSE) to calculate elevation errors, and we performed the linear error 90% (LE90) calculation to judge the average errors with the 90% threshold of absolute values of discrepancies. The DEMNAS RMSE and LE90 calculation results in the Karimunjawa archipelago were 6.33 m and 10.45 m, respectively. Citing Regulation Number 15 of the Head of the Indonesian Geospatial Information Agency of 2014 concerning Technical Guidelines for Basic Map Accuracy, DEMNAS with 10.45 m LE90 can be utilized for producing geomorphological maps with scales of 1:25,000 or smaller. However, detailed geomorphological mapping of a very small island (less than 100 km²) needs better DEM data that is usually produced using aerial photogrammetry. Using UAVs for DEMs creation may benefit small island developing states (SIDS) worldwide.

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.