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This paper aims to analyze the application of remotely piloted aircraft (RPA) for remote oil spill sensing.

This paper is an analysis of RPA strong points.

To increase the accuracy and eliminate potentially false contamination detection, which can be caused by external factors, an oil thickness measurement algorithm is used with the help of the multispectral imaging that provides high accuracy and is versatile for any areas of water and various meteorological and atmospheric conditions.

SWOT analysis of implementation of RPA for remote sensing of oil spills.

The use of RPA will improve the remote sensing of oil spills.

The concept of oil spills monitoring needs to be developed for quality data collection, oil pollution control and emergency response.

The research covers the development of a method and design of a device intended for taking samples and determining the presence of oil contamination in an aquatorium area; the procedure includes taking a sample from the water surface, preparing it for transportation and delivering the sample to a designated location by using the RPA. The objective is to carry out the analysis of remote oil spill sensing using RPA. The RPA provides a reliable sensing of oil pollution with significant advantages over other existing methods. The objective is to analyze the use of RPA employing all of their strong points. In this paper, technical aspects of sensors are analyzed, as well as their advantages and limitations.

Heavy rainfall is one of the main causes of the degradation of historic rammed Earth architecture. For this reason, ensuring the conservation thereof entails understanding the factors involved in these risk situations. The purpose of this study is to research three past events in which rainfall caused damage and collapse to historic rammed Earth fortifications in Andalusia in order to analyse whether it is possible to prevent similar situations from occurring in the future.

The three case studies analysed are located in the south of Spain and occurred between 2017 and 2021. The hazard presented by rainfall within this context has been obtained from Art-Risk 3.0 (Registration No. 201999906530090). The vulnerability of the structures has been assessed with the Art-Risk 1 model. To characterise the strength, duration, and intensity of precipitation events, a workflow for the statistical use of GPM and GSMaP satellite resources has been designed, validated, and tested. The strength of the winds has been evaluated from data from ground-based weather stations.

GSMaP precipitation data is very similar to data from ground-based weather stations. Regarding the three risk events analysed, although they occurred in areas with a torrential rainfall hazard, the damage was caused by non-intense rainfall that did not exceed 5 mm/hour. The continuation of the rainfall for several days and the poor state of conservation of the walls seem to be the factors that triggered the collapses that fundamentally affected the restoration mortars.

A workflow applied to vulnerability and hazard analysis is presented, which validates the large-scale use of satellite images for past and present monitoring of heritage structure risk situations due to rain.

Globally, urban planners and decision makers are pursuing place-based initiatives to develop and enhance urban infrastructure to optimise city performance, competitiveness and sustainability credentials. New discourses associated with big data, Building Information Modelling, SMART cities, green and biophilic thinking inform research, policy and practice agendas to varying extents. However, these discourses remain relatively isolated as much city planning is still pursued within traditional sectoral silos hindering integration. This research explores new conceptual ground at the Smart – Natural City interface within a safe interdisciplinary opportunity space. Using the city of Birmingham UK as a case study, a methodology was developed championing co-design, integration and social learning to develop a conceptual framework to navigate the challenges and opportunities at the Smart-Natural city interface. An innovation workshop and supplementary interviews drew upon the insights and experiences of 25 experts leading to the identification of five key spaces for the conceptualisation and delivery at the Smart-Natural city interface. At the core is the space for connectivity; surrounded by spaces for visioning, place-making, citizen-led participatorylearning and monitoring.The framework provides a starting point for improved discussions, understandings and negotiations to cover all components of this particular interface. Our results show the importance of using all spaces within shared narratives; moving towards ‘silver-green’ and living infrastructure and developing data in response to identified priorities. Whilst the need for vision has dominated traditional urban planning discourses we have identified the need for improved connectivity as a prerequisite. The use of all 5 characteristics collectively takes forward the literature on socio-ecological-technological relationships and heralds significant potential to inform and improve city governance frameworks, including the benefits of a transferable deliberative and co-design method that generates ownership with a real stake in the outcomes.

The present study aims to construct ensemble machine learning (EML) algorithms for groundwater potentiality mapping (GPM) in the Teesta River basin of Bangladesh, including random forest (RF) and random subspace (RSS).

The RF and RSS models have been implemented for integrating 14 selected groundwater condition parametres with groundwater inventories for generating GPMs. The GPM were then validated using the empirical and bionormal receiver operating characteristics (ROC) curve.

The very high (831–1200 km²) and high groundwater potential areas (521–680 km²) were predicted using EML algorithms. The RSS (AUC-0.892) model outperformed RF model based on ROC's area under curve (AUC).

Two new EML models have been constructed for GPM. These findings will aid in proposing sustainable water resource management plans.

Technology acceptance is a measure of that technology’s usefulness. Oil palm is one of the biggest contributors to Indonesia’s revenues, thus fueling its economy. Using remote sensing would allow a plantation to monitor and forecast its production and the amount of fertilizer used. This review aims to provide a policy recommendation in the form of a strategy to improve the added value of Indonesia’s oil palm and support the government in increasing oil palm production. This recommendation needs to be formulated by determining the users’ acceptance of remote sensing technology (state-owned plantations, private plantation companies and smallholder plantations).

This review’s methodology used sentiment analysis through text mining (bag of words model). The study’s primary data were from focus group discussions (FGDs), questionnaires, observations on participants, audio-visual documentation and focused discussions based on group category. The results of interviews and FGDs were transcribed into text and analyzed to 1) find words that can represent the content of the document; 2) classify and determine the frequency (word cloud); and finally 3) analyze the sentiment.

The result showed that private plantation companies and state-owned plantations had extremely high positive sentiments toward using remote sensing in their oil palm plantations, whereas smallholders had a 60% resistance. However, there is still a possibility for this technology’s adoption by smallholders, provided it is free and easily applied.

Basically, technology is applied to make work easier. However, not everyone is tech-savvy, especially the older generations. One dimension of technology acceptance is user/customer retention. New technology would not be immediately accepted, but there would be user perceptions about its uses and ease. At first, people might be reluctant to accept a new technology due to the perception that it is useless and difficult. Technology acceptance is the gauge of how useful technology is in making work easier compared to conventional ways.

Therefore, technology acceptance needs to be improved among smallholders by intensively socializing the policies, and through dissemination and dedication by academics and the government.

The social implications of using technology are reducing the workforce, but the company will be more profitable and efficient.

Remote sensing is one of the topics that people have not taken up in a large way, especially sentiment analysis. Acceptance of technology that utilizes remote sensing for plantations is very useful and efficient. In the end, company profits can be allocated more toward empowering the community and the environment.

The ecological environment of the Loess Plateau, China, is extremely fragile under the context of global warming. Over the past two decades, the vegetation of the Loess Plateau has undergone great changes. This paper aims to clarify the response mechanisms of vegetation to climate change, to provide support for the restoration and environmental treatment of vegetation on the Loess Plateau.

The Savitsky–Golay (S-G) filtering algorithm was used to reconstruct time series of moderate resolution imaging spectroradiometer (MODIS) 13A2 data. Combined with trend analysis and partial correlation analysis, the influence of climate change on the phenology and enhanced vegetation index (EVI) during the growing season was described.

The S-G filtering algorithm is suitable for EVI reconstruction of the Loess Plateau. The date of start of growing season was found to gradually later along the Southeast–Northwest direction, whereas the date of the end of the growing season showed the opposite pattern and the length of the growing season gradually shortened. Vegetation EVI values decreased gradually from Southeast to Northwest. Vegetation changed significantly and showed clear differentiation according to different topographic factors. Vegetation correlated positively with precipitation from April to July and with temperature from August to November.

This study provides technical support for ecological environmental assessment, restoration of regional vegetation coverage and environmental governance of the Loess Plateau over the past two decades. It also provides theoretical support for the prediction model of vegetation phenology changes based on remote sensing data.

Information and communication technologies (ICT)-presented technological developments, such as soil sensors, remote sensing, artificial intelligence (AI) and big data, have shown the potential to increase crop output and quality while consuming fewer resources and having a smaller environmental impact. The first step in ushering in a new era of technological advancement in the agricultural sector in the Kingdom of Bahrain is evaluating how prepared farmers and farm owners are to adopt these technologies. Therefore, the current study examines how ICT are prepared, accepted and adopted in agriculture in the Kingdom of Bahrain.

The study's goals were attained by using both quantitative and qualitative methodologies. A survey was created to learn more about the present state of ICT usage in agriculture, including its awareness, readiness, acceptance and adoption. To strengthen the conclusions and investigate the current situation related agricultural behavior, production and the use of information technology (IT) to support agriculture in the chosen farms, four exploratory field visits were made. Additionally, a strength-weakness-opportunities-threat (SWOT)-threat, opportunities, weakness, strength (TOWS) analysis was performed to evaluate the Kingdom of Bahrain's readiness and long-term plans for implementing ICT in agriculture. On the basis of secondary data, survey data and interview findings, SWOT-TOWS were created.

The findings revealed insufficient knowledge and awareness about ICT in agriculture. Despite the high level of digital infrastructure readiness in Bahrain, farmers are not ready to adopt sophisticated devices and complex applications such as crop sensing tools, the internet of things (IoT) and AI; however, there is a strong acceptance among farmers to implement new ideas and agriculture approaches.

The Arabian Gulf Countries, which are characterized by an arid environment, sporadic vegetation, weak soil and a lack of water supplies and arable land, have few studies that explore the crucial role of ICT in growing the agricultural sector. Considering the influence of ICT on the provision of more productive agriculture in a challenging and complicated environment, the study contributes to the body of knowledge by conducting an empirical investigation that addresses an urgent issue. The study is considered one of the few in the countries of the Arabian Gulf to address this subject.

The purpose of this study was to examine the latent part of geographic information systems in inclusive sustainable rural tourism, community-based natural resource management (CBNRM) and community development and empowerment in Southern Africa, Africa generally and many rural areas elsewhere worldwide.

The viewpoint utilizes literature and document reviews to assess African and worldwide agricultural, environmental and tourism resources management scenarios. It thus liaises with CBNRM and geographic information systems in sustainable tourism and local community development applications.

This review viewpoint uncovers a better potential synergetic relationship between tourism and rural (agricultural) activities that geographic information systems along a concept of CBNRM can amplify. Hence, it has poised a need for a decent and integrated tourism strategy to develop and empower the pertinent communities in many rural and marginalized areas within the continent.

Many rural communities in Southern Africa and Africa broadly dwell in low-income areas. Such milieus are rich in natural biodiversity, including tourism destination areas. Geographic information systems, sustainable tourism and CBNRM can form a gestalt of local community development projects within their environs.

We make use of the Volunteered Geographic Information (VGI) data to extract the total extent of the roads using remote sensing images. VGI data is often provided only as vector data represented by lines and not as full extent. Also, high geolocation accuracy is not guaranteed and it is common to observe misalignment with the target road segments by several pixels on the images. In this work, we use the prior information provided by the VGI and extract the full road extent even if there is significant mis-registration between the VGI and the image. The method consists of image segmentation and traversal of multiple agents along available VGI information. First, we perform image segmentation, and then we traverse through the fragmented road segments using autonomous agents to obtain a complete road map in a semi-automatic way once the seed-points are defined. The road center-line in the VGI guides the process and allows us to discover and extract the full extent of the road network based on the image data. The results demonstrate the validity and good performance of the proposed method for road extraction that reflects the actual road width despite the presence of disturbances such as shadows, cars and trees which shows the efficiency of the fusion of the VGI and satellite images.

This study seeks to provide a robust piece of evidence of forest depletion in Ghana and its associated driver intensities to inform national policy decisions towards achieving Sustainable Development Goal (SDG) 15 and beyond.

Using a representative sample size of 733 households, which was obtained with the aid of a structured questionnaire, a descriptive analysis is used to show the evidence of forest depletion. For robustness purposes, the geographic information system (GIS) is used to provide a piece of remote sensing evidence to substantiate the claim. In addition, an ordered probit regression model is estimated given the ranked nature of the responses to determine the drivers of forest depletion.

The results provide evidence that the urban forests in the Greater Accra Region (GAR) of Ghana have been depleted. Overall, 44% argued that the depletion of the forests is high, 30% indicated that the depletion is moderate, while 26% indicated that the depletion is low. Consistent with the literature, the ordered probit regression results show that human behaviour, climate change and institutional failure are the driver intensities of forest depletion in the Region. Besides, the authors find an increasing order effect for all three drivers. Using a descriptive analysis, majority of the respondents posited that human behaviour is the main driver intensity, followed by climate change and then institutional failure. This study recommends the need for education and advocacy, community participation, law enforcement, resource mobilization, modern adaptation strategies and internalization of externalities as a way of controlling the drivers of forest depletion.

The study uses remote sensing techniques to provide empirical evidence of protected forest depletion in the GAR, Ghana. In addition, an ordered probit regression is used to identify the driver intensities that explain the depleted protected forests in the region.