Digital image correlation (DIC) analysis of the 3 December 2013 Montescaglioso landslide (Basilicata, Southern Italy). Results from a multi-dataset investigation

Image correlation remote sensing monitoring techniques are becoming key tools for providing effective qualitative and quantitative information suitable for natural hazard assessments, specifically for landslide investigation and monitoring. In recent years, these techniques have been successfully integrated and shown to be complementary and competitive with more standard remote sensing techniques, such as satellite or terrestrial Synthetic Aperture Radar interferometry. The objective of this article is to apply the proposed in-depth calibration and validation analysis, referred to as the Digital Image Correlation technique, to measure landslide displacement. The availability of a multi-dataset for the 3 December 2013 Montescaglioso landslide, characterized by different types of imagery, such as LANDSAT 8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor), high-resolution airborne optical orthophotos, Digital Terrain Models and COSMO-SkyMed Synthetic Aperture Radar, allows for the retrieval of the actual landslide displacement field at values ranging from a few meters (2–3 m in the north-eastern sector of the landslide) to 20–21 m (local peaks on the central body of the landslide). Furthermore, comprehensive sensitivity analyses and statistics-based processing approaches are used to identify the role of the background noise that affects the whole dataset. This noise has a directly proportional relationship to the different geometric and temporal resolutions of the processed imagery. Moreover, the accuracy of the environmental-instrumental background noise evaluation allowed the actual displacement measurements to be correctly calibrated and validated, thereby leading to a better definition of the threshold values of the maximum Digital Image Correlation sub-pixel accuracy and reliability (ranging from 1/10 to 8/10 pixel) for each processed dataset

The role of combining national official statistics with global monitoring to close the data gaps in the environmental SDGs

The Sustainable Development Goals (SDGs) have elevated the profile of the environmental dimension of development – and how we monitor this dimension. However, they have also challenged national statistical systems and the global statistical community to put in place both the methodologies and mechanisms for data collection and reporting on environmental indicators. According to a recent analysis, there is too little data to formally assess the status of 68% of the environment-related SDGs [1]. Many environment-related indicators were not part of the purview of national statistical systems and did not have a methodology or data collection system in place prior to the adoption of the SDG indicator framework [2]. Moderate improvements have been made, as evidenced by the reduced proportion of environment-related SDG indicators classified as Tier III between the original classification in 2016 and May 2019 – dropping from 50% to 28% [3]. As of March 2020, there are currently no Tier III indicators; however, as many of the SDG indicators have been recently reclassified the data availability and experience in compiling these indicators is severely limited. Socioeconomic indicators have far outpaced environmental indicators in this shift, with only 7% of non-environmental indicators classified as Tier III in May 2019 [1,4,5]. As the custodian agency for 26 of the environment-related SDG indicators, UN Environment is establishing methodologies and mechanisms to collect country-level data. However, many countries currently do not have national systems in place for monitoring these environmental indicators and thus there is a risk that much of the environmental dimension of development cannot be captured by using reporting mechanisms which only include traditionally collected national official statistics. For many of these indicators, UN Environment is exploring new data sources, such as data from citizen science. Citizen science has the potential to contribute to global and local level SDG monitoring. Realizing its full potential however, would require building key partnerships around citizen science data and creating an enabling environment. Global modelling is another approach to fill data gaps. These new types of data could not only improve global estimations but could be incorporated in national official statistics in order to improve nationally relevant data and analysis [6]. The Global Material Flow database, which estimates Domestic Material Consumption (covering SDG indicators 8.4.2 and 12.2.2), and the Global Surface Water Explorer application (covering SDG indicator 6.6.1) are a couple of examples of where UN Environment is complementing national data with global data products in the official SDG reporting process. In these cases the use of globally-derived data has been agreed by the Inter-Agency and Expert Group on SDG Indicators (IAEG-SDGs) [7]. Expanding globally-estimated or -modelled data to cover environment-related SDG indicators could build the foundation for a digital ecosystem for the planet, which would provide a basis for developing integrated analysis and insights. A Sustainability Gap Index could be one mechanism to bring together the environmental dimension of development into a single metric, which could inform the achievement of the SDGs, environmental assessments and national policy. This paper presents a summary of how the world is faring in terms of measuring the environmental dimension of the SDGs

Smart Environmental Health Monitoring System

Pollution is a growing issue these days. It is necessary to analyze environment and keep ichecking for the best future and healthy life. We proposed an Environment Monitoring System that permit us to watch & check live environment in especially areas through Internet of Things (IOT). IoT supports a realtime environmental monitoring system. It plays a crucial role in today’s world through a huge and protracted system of sensor networks concerned to the environment & its parameters. This technique monitors important environmental conditions like temperature, humidity & CO-level using the sensor & then transfer data to the web page. This information is often accessed from anyplace over the internet & then the sensor information is presented as graphical statistics during mobile application. This paper explains & present the implementation & outcome of this environmental system uses the sensors for temperature, humidity, air quality & different environmental parameters of the surrounding space. This data is often used to take remote actions to regulate the conditions. Information is pushed to the distributed storage & android app get to the cloud & present the effect to the end users. The system employs a Node MCU, DHT-11 sensor, MQl35 sensor, which transmits data to WEBPAGE. An Android application is made which accesses the cloud data and displays results to the end users. The sensors interact with microcontroller which processes this information & transmit it over internet. This system is best method for any use in monitoring the environment and handling it because everything is controlled automatically through all the time of the process. The results of this system tells across different field where it was controlled precisely and effectively which further explains that this system easily makes our work easier because of this automatic monitoring system worries about other unexpected climate issues for world

Assessing, valuing and protecting our environment- is there a statistical challenge to be answered?

This short article describes some of the evolution in environmental regulation, management and monitoring and the information needs, closely aligned to the statistical challenges to deliver the evidence base for change and effect

Indicators: tools for informing, monitoring or controlling?

Today, indicators are produced and used worldwide; across all levels and sectors of society; by public, private and civil society actors; for a variety of purposes, ranging from knowledge-provision to administrative control. While the use of quantitative data as policy support, including policy formulation, has a long history, recent decades have seen the rise of what some have called an ‘indicator industry’ (for example, Hezri and Hasan 2004), focused especially on the production of environmental and sustainability indicators, within a framework variously called ‘governance by numbers' (Miller 2001; Lascoumes and Le Galès 2005; Jackson 2011), ‘management by numbers’ in public service (for example, Hood 2007) or ‘numbers discourse’ (Jackson 2011, p. 23). Indicators are generally expected to enhance the rationality of policymaking and public debate by providing a supposedly more objective, robust, and reliable information base. Indicators can operate as ‘boundary objects’ (for example, Turnhout 2009; Star 2010), catering to both technocratic and deliberative ideals, by combining ‘hard facts’ and modelling with collective reasoning and ‘speculation’. Research and development work in the area has hitherto overwhelmingly concentrated on improving the technical quality of indicators, while the fate of indicators in policymaking and the associated sociopolitical aspects have attracted little attention. This chapter focuses on this neglected area of indicator research, by providing an overview of the multiple types of existing indicators, as well as their use and influence in various venues of policymaking. Empirical examples are drawn mainly from the fields of environmental and sustainability indicators

Non-financial reporting challenges in monitoring SDG`s achievement : investment aspects for transition economy

This research were developed by author during participation at post doctoral training programme at Academy of financial management, Kyiv, Ukraine (2017-2019).Purpose: The purpose of the article is to reveal and deepen the investment aspects of the methodology for monitoring the achievement of Sustainable Development Goals (SDG`s) in transition countries. Design/Methodology/Approach: The methodological approach of the paper is based on comparative analysis of core investment indicators proposed by main sustainable reporting initiatives. Conducted analysis helped to identify significant differences in methodological recommendations complicating the process of data comparability for VNR`s compiling purposes. Findings: As a part of SDG`s monitoring process reporting challenges include: the use of so-called “SDG-washing” practices in non-financial reporting; selective presentation of facts through the use of “Cherry-picking” practice in non-financial reporting; the difficulty in measuring progress of the entity's contribution to the achievement of the SDGs on the basis of available indicators in the non-financial reporting; a weak corporate governance culture for reporting as in transition economies; the necessity to develop approaches to assess the materiality of information received for investment purposes. Practical Implications: Sustainability investment indicators in non-financial reporting requirements today do not reflect investing in cost-effectiveness in the context of evaluating the progress of the SDG`s implementation. In order to reveal the entity's attempts to use “SDG-washing” and “Cherry-picking” practices is proposed to include an investment priority ratio to the list of economic indicators. Originality/Value: The paper contains a methodology for a new non-financial reporting indicator allowing to evaluate the purpose of enterpeise`s capital investments policy.peer-reviewe

Agri-environmental and rural development indicators: a proposal

The present work is a proposal of a set of indicators prepared for the Ministry of Agriculture and Forestry. The indicators are to be used in monitoring the implementation of the Ministry's strategy for sustainable use of natural resources. The core of the present work is in setting up an indicator system, which is structured around specific themes. The focus is on the assessment of agricultural and rural development. At the end, an attempt is made to provide a comprehensive picture by considering the mutual inter-linkages between the various indicators