Monitoring Spatial and Temporal Scales of Shoreline Changes in Lahou-Kpanda (Southern Ivory Coast) Using Landsat Data Series (TM, ETM+ and OLI)

Shoreline changes are crucial for assessing human-ecosystem interactions in coastal environments. They are a valuable tool for determining the environmental costs of socioeconomic growth along coasts. In this research, we present an assessment of shoreline changes along the eastern coast of Lahou-Kpanda of the Ivory Coast during the period from 1980 to 2020 by applying Digital Shoreline Analysis System method using Landsat Data Series. The measurement of the shoreline dynamics of the Lahou-Kpanda coastline is mainly described in three parts: the west straight cordon, the dynamics at the mouth and the east straight cordon. The findings show a drastic reduction in natural shorelines. The greatest transition occurred along the mouth segment of the coast, where the average erosive velocity approaches 90 meters each year and the average distance has decreased by around 2 kilometers. The Ivory Coast lost more than 40% of its biological shorelines between 1980 and 2020, according to this report, a worrying development because these are regions that were once biologically abundant and highly rich. In general, human operations on the Ivory Coast’s shorelines have never had such an impact. The effects of these changes on habitats, as well as the vulnerability of new shoreline investments to increased human activity and sea-level rise, must be measured.

The Development of Multi-scale Data Management for CityGML-based 3D Buildings

The CityGML model is now the norm for smart city or digital twin city development for better planning, management, risk-related modelling and other applications. CityGML comes with five levels of detail (LoD), mainly constructed from point cloud measurements and images of several systems, resulting in a variety of accuracies and detailed models. The LoDs, also known as pre-defined multi-scale models, require large storage-memory-graphic consumption compared to single scale models. Furthermore, these multi-scales have redundancy in geometries, attributes, are costly in terms of time and workload in updating tasks, and are difficult to view in a single viewer. It is essential for data owners to engage with a suitable multi-scale spatial management solution in minimizes the drawbacks of the current implementation. The proper construction, control and management of multi-scale models are needed to encourage and expedite data sharing among data owners, agencies, stakeholders and public users for efficient information retrieval and analyses. This paper discusses the construction of the CityGML model with different LoDs using several datasets. A scale unique ID is introduced to connect all respective LoDs for cross-LoD information queries within a single viewer. The paper also highlights the benefits of intermediate outputs and limitations of the proposed solution, as well as suggestions for the future.

Detection of Outlier Observations in Piezometric Measurements: A Case Study in the Southern Region of Poland

One of the main modes of monitoring the geotechnical conditions of earth dams is piezometric measurement, which measures water levels in an open piezometer or water pressure in a closed piezometer. During piezometric measurements, various types of factors can cause disturbances in these measurements that take the form of systematic, accidental, or obvious mistakes. Before measurements from open or closed piezometers are analyzed, outliers due to coarse errors should be detected and rejected. Such observations may significantly influence the result of the analysis and cause erroneous assessment and interpretation of the phenomenon studied. To do this, statistical tests must be applied so that the doubtful measurement can be accepted or rejected at the assumed significance level. This paper uses five statistical tests for identifying and rejecting outliers: the Q-Dixon test, the Grubbs test, as well as the Hampel test, the Iglewicz and Hoaglin test, and the Rosner test. The aim of this article is to try to identify the most suitable test for periodic piezometric measurements. The scope of the study includes the analysis of piezometric measurements for the Czaniec Dam for the multi-year period 2017–2020.

A Python Library for the Jupyteo IDE Earth Observation Processing Tool Enabling Interoperability with the QGIS System for Use in Data Science

This paper describes JupyQgis – a new Python library for Jupyteo IDE enabling interoperability with the QGIS system. Jupyteo is an online integrated development environment for earth observation data processing and is available on a cloud platform. It is targeted at remote sensing experts, scientists and users who can develop the Jupyter notebook by reusing embedded open-source tools, WPS interfaces and existing notebooks. In recent years, there has been an increasing popularity of data science methods that have become the focus of many organizations. Many scientific disciplines are facing a significant transformation due to data-driven solutions. This is especially true of geodesy, environmental sciences, and Earth sciences, where large data sets, such as Earth observation satellite data (EO data) and GIS data are used. The previous experience in using Jupyteo, both among the users of this platform and its creators, indicates the need to supplement its functionality with GIS analytical tools. This study analyzed the most efficient way to combine the functionality of the QGIS system with the functionality of the Jupyteo platform in one tool. It was found that the most suitable solution is to create a custom library providing an API for collaboration between both environments. The resulting library makes the work much easier and simplifies the source code of the created Python scripts. The functionality of the developed solution was illustrated with a test use case.

Identification of Potential Groundwater Recharge Sites in a Semi-arid Region of Pakistan Using Saaty’s Analytical Hierarchical Process (AHP)

Groundwater is a precious source of fresh water and a major component of the entire water supply. Both water quality and quantity could be satisfied by evaluating the groundwater potential sites (GWPS). This paper analyzes the ground-water potentials in a semi-arid region of Khyber Pakhtunkhwa, Pakistan. It describes a standard methodology to identify and map GWPS using integrated Geographical Information System (GIS) and remote sensing (RS) methods. Eight parameters including elevation, slope, drainage density, lineaments density, soil, geology, land use/land cover and rainfall were integrated to explore areas with groundwater holding capability. GWPS were delineated through subjective weights assigned after coupling various thematic layers using Saaty’s Analytical Hierarchical Process (AHP). The modelled GWPS were cross-checked with tube wells data. The result indicates that the central part of the study area has good potential for groundwater reserves/exploitation, where the factors i.e. moderate to high drainage density, sedimentary sequence of alluvial plain, low elevation etc. discern the central portion of the study area as a suitable site for groundwater. This study suggests that the applied method proves to be very significant and reliable tool for timely assessment of quality assured evaluation of groundwater resources. This study could be a systematic guide for future investigations for water related explorations, especially in semi-arid environments.

Future-oriented Waste Management Technology for Ward-6, Bogura, Bangladesh – A Step Towards Sustainability

Rapid urbanization combined with high economic growth, industrialization, and changes in socio-economic conditions increase the quantity of municipal solid waste. Cities located in South-Asia are facing serious issues due to waste, with countries like India, Bangladesh, and Pakistan top of the list of bad waste management. The increasing generation of solid waste and also the improper management of waste in Bangladesh leads to environmental degradation. Current waste management practice in Bangladesh is so weak that day by day it is harming the climate and creating a lot of unwanted situations. This research consists of an examination of the current administrative measures and presents another proposition for the executive cycle to decrease ecological contamination. The research study aims to decrease the amount of waste being dumped into municipal sanitary landfill sites & converting the waste into energy which is both financially and environmentally suitable by involving unemployed people in the management system. The results of this study will give an idea of how waste can be utilized as a resource and how this resource can be a capital good as well as how the local level problems can be solved by taking some strategies and making our environment suitable for future generations.

The Application of Regression Analysis for Estimating the Market Value of Commercial Real Estate

The transaction price of a land property with commercial buildings depends on both its quantitative and qualitative attributes. Quantitative attributes include surface areas of plots of land and usable floor spaces of premises and buildings with various intended purposes, as well as values of rents. Qualitative attributes are represented by the global attributes of these properties.In the analysis of the land property market with commercial buildings, all pairs that relate a transaction price to individual attributes are considered. The market value prediction is based on multiple regression analysis for a two-dimensional random variable, represented by the price and the predetermined attribute. The final market value of the property being valued is calculated as the weighted average of the market values predicted for each attribute.This research paper presents the procedure for determining the market value of land with commercial buildings, which falls within the method of statistical analysis of the market. The derived formulas and substantively justified algorithms may be the basis for market analysis and estimation of the market value of such land. This procedure has been thoroughly verified using two practical numerical examples.

An Attempt to Establish the Scale and Orientation of the “Map of Lobzow with Its Environs”

This work concerns the assessment of the cartometricity of the 19th century plan of the village of Lobzow near Krakow in Poland, known as “Mapa Łob­zowa z przyległościami”. The plan has not been researched in this respect so far. The aim of this study was to establish its scale and orientation in relation to directions of the world. A high resolution raster of the plan provided by the Historical Museum of the City of Krakow was used for the study. At first, an attempt was made to clarify the date of the plan’s origin. An analysis of infor­mation contained in archival documents shows that the plan of Lobzow was most probably created between 1824 and 1852, which corresponds with the period when the large­scale cadastral map was developed in Galicia. Therefore, selected sections of this map, from the area covered by the plan, were used for research. The scale value of the Lobzow plan was calculated using the lengths of sections between selected points on the plan and map. The calculated plan scale is close to the scale of the detailed map section detailed sheet of the ca­dastral map. It was established that the plan of Lobzow has the orientation relative to magnetic north as was common in the first half of the 19th century. The mutual dependencies between the orientation of the plan and the cadastral map in relation to the direction of true geographical north were illustrated. The final result of the work was to show the changes in the topography of Lobzow that have taken place over nearly 200 years.

An Application of the “Traffic Lights” Idea to Crop Control in Integrated Administration Control System

The aim of the paper is to discuss the idea of marking agricultural parcels in the control of direct payments to agriculture. The method of using remote sensing to monitor crops and mark them according to the idea of “traffic lights” is introduced. Classification into a given “traffic lights” color gives clear information about the status of the parcel. The image classification was done on Sentinel-1 and Sentinel-2 datasets by calculating the NDVI and SIGMA time series in the season from autumn 2016 to autumn 2017. Two approaches are presented: semi-automated and automated classifications. Semi-automated classification based on NDVI_index and SIGMA_index. Automated classification was performed on NDVI by Spectral Angle Mapper method and on SIGMA by Artificial Neural Network (Multilayer Perceptron, MLP method). The following overall accuracy was obtained for NDVI_SAM: 70.35%, while for SIGMA_CNN it was: 62.01%. User accuracy (UA) values were adopted for traffic lights analysis, in machine learning: positive predictive value (PPV). The UA/PPV for rapeseed were in NDVI_index method: 88.1% (6,986 plots), NDVI_SAM: 85.0% (199 plots), SIGMA_index: 61.3% (4,165 plots) and in SIGMA_CNN: 88.9% (2,035 plots). In order to present the idea of “traffic lights”, a website was prepared using data from the NDVI_index method, which is a trade-off between the number of plots and UA/PPV accuracy.

Geospatial Analysis of the Impact of Flood and Drought Hazards on Crop Land and Its Relationship with Human Migration at the District Level in Uttar Pradesh, India

The main objective was to explore the connection between flood and drought hazards and their impact on crop land and human migration. The Flood and Drought effect on Cropland Index (FDCI), hot spot analysis and the Global Regression Analysis method was applied for the identification of the relationship between human migration and flood and drought hazards. The spatial pattern and hot and cold spots of FDCI, spatial autocorrelation and Getis-OrdGi* statistic techniques were used respectively. The FDCI was taken as an explanatory variable and human migration was taken as a dependent variable in the environment of the geographically weighted regression (GWR) model which was applied to measure the impact of flood and drought hazards on human migration. FDCI suggests a z-score of 4.9, which shows that the impact of flood and drought frequency on crop land is highly clustered. In the case of the hot spots analysis, out of seventy districts in Uttar Pradesh twenty-one were classified as hot spot and eight were classified as cold spots with a confidence level of 90 to 99%. Hot spot indicate maximum and cold spots show minimum impact of flood and drought hazards on crop land. The impact of flood and drought hazards on human migration show that there are fourteen districts where migration out is far more than predicted while there are ten districts where migration out is far lower.