DETECTING THE AREA DAMAGE DUE TO COAL MINING ACTIVITIES USING LANDSAT MULTITEMPORAL (Case Study: Kutai Kartanegara, East Kalimantan)

Coal is one of the most mining commodities to date, especially to supply both national and international energy needs. Coal mining activities that are not well managed will have an impact on the occurrence of environmental damage. This research tried to utilize the multitemporal Landsat data to analyze the land damage caused by coal mining activities. The research took place at several coal mine sites in East Kalimantan Province. The method developed in this research is the method of change detection. The study tried to know the land damage caused by mining activities using NDVI (Normalized Difference Vegetation Index), NDSI (Normalized Difference Soil Index), NDWI (Normalized Difference Water Index) and GEMI (Global Environment Monitoring Index) parameter based change detection method. The results showed that coal mine area along with the damage that occurred in it can be detected from multitemporal Landsat data using NDSI value-based change detection method. The area damage due to coal mining activities  can be classified into high, moderate, and low classes based on the mean and standard deviation of NDSI changes (ΔNDSI). The results of this study are expected to be used to support government efforts and mining managers in post-mining land reclamation activities

A Hybrid Image Classification Approach to Monitoring LULC Changes in the Mining District of Prestea-Huni Valley, Ghana

Mining and other anthropogenic activities are increasingly destroying forest cover in tropical forest areas of Africa, threating to deplete the entire forest reserves. These depletions not only affect the ecosystems but also have dire implications on global ecological balance and climate. Using Landsat 7 ETM+ satellite images, the study used a combined unsupervised and supervised classification methods to determine the rate of change of the various land use and land cover classes in the mining district of Prestea Huni Valley. The method produced very high accuracies with the least overall accuracy being 95.4272% with a Kappa coefficient of 0.9339. A change detection analysis revealed very significant loss of forest cover as a result of direct mining activities to be 96.78 square kilometres between 2002 and 2015. The results also suggested an overall forest cover loss rate of about 71.63 square kilometres per annum for the periods between 2002 and 2015 which poses a threat to the 493.55 square kilometres of forest cover left in the study area study, if proper monitoring and rehabilitation programmes are not put in place. Keywords: LULC, Degradation, Hybrid Classification, Surface Mining, Forest Cover, Environment, Landsat ETM

South African National Land-Cover Change Map

Globally, countries face a changing environment due to population growth, increase in agricultural production, increasing demand on natural resources, climate change and resultant degradation of the natural environment. One means of monitoring this changing scenario is through land-cover change mapping. Modern Earth Observation (EO) technologies, especially those EO datasets comprising a multi-year data archive, lend themselves to land-cover change studies. This project used a practical and cost-effective approach for monitoring land-cover change at a national scale over time using EO data. The primary objective of this study was to determine the extent of transformed landscape change within South Africa over a 10-year period between 1994 and 2005. The project used three generalised land-cover datasets (for 1994, 2000 and 2005) and quantified the change between these assessment years. The land-cover change was based on five classes: Urban, Mining, Forestry, Cultivation and Other. The standardised five class land-cover datasets representing the three assessment years were compared within a uniform national grid, based on 500 m x 500 m cells. The land-cover allocated to each cell in each year represented the spatially dominant land-cover within that cell, as  determined from the original land-cover datasets. Various spatial modelling procedures were used to ensure compilation of comparable and standardised land-cover class allocations to each cell for each year, prior to any year-on-year change analyses. The results indicate that at a national level there has been a total increase of 1.2% in transformed land  specifically associated with Urban, Cultivation, Plantation Forestry and Mining. This represents an increase from 14.5% transformed land in 1994 to 15.7% in 2005 across South Africa

Pendeteksian Financial Statement Fraud dengan Menggunakan F-Score Model : Perspektif Fraud Pentagon Theory

This study aims to determine the partial effect of financial stability, financial targets, ineffective of monitoring, quality of external audit, change in auditors, change in directors, frequency number of CEO's picture on financial statement fraud in mining companies listed on the IDX. 2021. This study used a quantitative approach and the sample collection method in this study used purposive sampling and obtained as many as 18 companies as samples. The type of data used in this study is secondary data sourced from the company's financial reports and annual reports obtained directly from the Indonesia Stock Exchange (IDX). The data analysis method used is multiple regression analysis which is processed using the SPSS program. Based on the results of research conducted on 18 mining sector companies for the 2020 and 2021 periods it concluded that the financial stability variable as measured by ACHANGE did not have a significant effect on financial statement fraud, the financial target variable as measured by ROA had a significant effect on financial statement fraud, the ineffective variable of monitoring measured by BDOUT has no significant effect on financial statement fraud, the quality of external audit variable as measured by AUD has no significant effect on financial statement fraud, the change in auditor variable as measured by CPA has no significant effect on financial statement fraud, the change in director variable as measured with DCHANGE it has no significant effect on financial statement fraud and the frequency number of CEO's picture variable has no significant effect on financial statement fraud

An investigation of mining impacts on bats in South-West England

The extraction of minerals through open-pit mining can result in sudden and extensive land use change, often posing threats to local biodiversity. Bats are particularly vulnerable to the impacts of mining, but their metapopulation structure and wide-ranging roosting habits can make it challenging to monitor local populations. Here, we investigated the impacts of habitat loss and disturbance at Drakelands open-pit mine, the first new metal mine to be established within Britain in the past 45 years. This was addressed in two parts, firstly by analysing data collected by contracted ecologists at the site, in order to identify potential short-term shifts in bat activity and to evaluate the efficacy of mitigation measures. Secondly, by monitoring bat activity in the wider landscape to identify potential further-ranging impacts of the mine on local bat populations. In conjunction with this work we incorporated a field trial of a novel bat detector designed for long-term monitoring of bat activity. The results highlighted the multitude of factors which influence bat activity at a local level, and may provide a platform for continued research into the impacts of habitat fragmentation and anthropogenic noise at a species/ genus level. The information presented here will help to inform management decision making in regards to bat conservation, both at the Drakelands site and potentially at mining operations elsewhere.Wolf Mineral

Monitoring land use changes using geo-information : possibilities, methods and adapted techniques

Monitoring land use with geographical databases is widely used in decision-making. This report presents the possibilities, methods and adapted techniques using geo-information in monitoring land use changes. The municipality of Soest was chosen as study area and three national land use databases, viz. Top10Vector, CBS land use statistics and LGN, were used. The restrictions of geo-information for monitoring land use changes are indicated. New methods and adapted techniques improve the monitoring result considerably. Providers of geo-information, however, should coordinate on update frequencies, semantic content and spatial resolution to allow better possibilities of monitoring land use by combining data sets

Spatiotemporal variability in Swedish lake ecosystems

Studying ecosystem dynamics is critical to monitoring and managing linked systems of humans and nature. Due to the growth of tools and techniques for collecting data, information on the condition of these systems is more widely available. While there are a variety of approaches for mining and assessing data, there is a need for methods to detect latent characteristics in ecosystems linked to temporal and spatial patterns of change. Resiliencebased approaches have been effective at not only identifying environmental change but also providing warning in advance of critical transitions in social-ecological systems (SES). In this study, we examine the usefulness of one such method, Fisher Information (FI) for spatiotemporal analysis. FI is used to assess patterns in data and has been established as an effective tool for capturing complex system dynamics to include regimes and regime shifts. We employed FI to assess the biophysical condition of eighty-five Swedish lakes from 1996–2018. Results showed that FI captured spatiotemporal changes in the Swedish lakes and identified distinct spatial patterns above and below the Limes Norrlandicus, a hard ecotone boundary which separates northern and southern ecoregions in Sweden. Further, it revealed that spatial variance changed approaching this boundary. Our results demonstrate the utility of this resilience-based approach for spatiotemporal and spatial regimes analyses linked to monitoring and managing critical watersheds and waterbodies impacted by accelerating environmental change

ACon: A learning-based approach to deal with uncertainty in contextual requirements at runtime

Context: Runtime uncertainty such as unpredictable operational environment and failure of sensors that gather environmental data is a well-known challenge for adaptive systems. Objective: To execute requirements that depend on context correctly, the system needs up-to-date knowledge about the context relevant to such requirements. Techniques to cope with uncertainty in contextual requirements are currently underrepresented. In this paper we present ACon (Adaptation of Contextual requirements), a data-mining approach to deal with runtime uncertainty affecting contextual requirements. Method: ACon uses feedback loops to maintain up-to-date knowledge about contextual requirements based on current context information in which contextual requirements are valid at runtime. Upon detecting that contextual requirements are affected by runtime uncertainty, ACon analyses and mines contextual data, to (re-)operationalize context and therefore update the information about contextual requirements. Results: We evaluate ACon in an empirical study of an activity scheduling system used by a crew of 4 rowers in a wild and unpredictable environment using a complex monitoring infrastructure. Our study focused on evaluating the data mining part of ACon and analysed the sensor data collected onboard from 46 sensors and 90,748 measurements per sensor. Conclusion: ACon is an important step in dealing with uncertainty affecting contextual requirements at runtime while considering end-user interaction. ACon supports systems in analysing the environment to adapt contextual requirements and complements existing requirements monitoring approaches by keeping the requirements monitoring specification up-to-date. Consequently, it avoids manual analysis that is usually costly in today’s complex system environments.Peer ReviewedPostprint (author's final draft