Assessment Of Natural Resources Research Articles

Assessment of natural resources for energy transition in Rivers state, Nigeria

The increasing problems associated with global climate change due to the use of fossil fuels and greenhouse gases in the atmosphere call for serious concerns. This scenario has necessitated the need for a shift from the use of fossil fuels to renewable energy to curtail the impact of climate change on the environment. The study was conducted to assess the potential and variability of climatic and other natural resources for renewable energy and evaluation of public perception of the energy transition process in Rivers State Nigeria. The climate data was obtained from the Nigeria Meteorological Agency (NIMET) from 2000 and 2023 and analyzed using the Linear Regression Model while data on public perception was generated from structured questionnaires and analyzed using descriptive statistics. The outcome of the Regression and ANOVA analysis were generally significant for both the natural resources and residents of the study area. Also, 65.7% of the respondents affirmed that the available natural resources are viable and suitable for energy transition. The significant and positive relationship of the different climatic elements in the study signposts huge potential for the renewable energy transition in the study area. The perception of residents was positive and showed excellent knowledge of the abundance of natural resources for the energy transition. It is therefore recommended that the Rivers State government should partner with the private sector to increase investment in energy infrastructure for improved environmental sustainability.

Analyses of LULC dynamics in a socio-ecological system of the Bale Mountains Eco Region of Southeast Ethiopia.

Analysis of land use and land cover (LULC) change and its drivers and impacts in the biodiversity hotspot of Bale Mountain's socio-ecological system is crucial for formulating plausible policies and strategies that can enhance sustainable development. The study aimed to analyze spatio-temporal LULC changes and their trends, extents, drives, and impacts over the last 48 years in the Bale Mountain social-ecological system. Landsat imagery data from the years 1973, 1986, 1996, 2014, and 2021 together with qualitative data were used. LULC classification scheme employed a supervised classification method with the application of the maximum likelihood algorithm technique. In the period between 1973 and 2021, agriculture, bare land, and settlement showed areal increment by 153.13%, 295.57%, and 49.03% with the corresponding increased annual rate of 1.93%, 2.86%, and 0.83%, respectively. On the contrary, forest, wood land, bushland, grass land, and water body decreased by 29.97%, 1.36%, 28.16%, 8.63%, and 84.36% during the study period, respectively. During the period, major LULC change dynamics were also observed; the majority of woodland was converted to agriculture (757.8 km2) and grassland (531.3 km2); and forests were converted to other LULC classes, namely woodland (766.5 km2), agriculture (706.1 km2), grassland (34.6 km2), bushland (31.9 km2), settlement (20.5 km2), and bare land (14.3 km2). LULC changes were caused by the expansion of agriculture, settlement, overgrazing, infrastructure development, and fire that were driven by population growth and climate change, and supplemented by inadequate policy and institutional factors. Social and environmental importance and values of land uses and land covers in the study area necessitate further assessment of potential natural resources' user groups and valuation of ecosystem services in the study area. Hence, we suggest the identification of potential natural resource-based user groups, and assessment of the influence of LULC changes on ecosystem services in Bale Mountains Eco Region (BMER) for the sustainable use and managements of land resources.

How do Porosity, Size and Interconnectivity of Pores Influence The Values of the Archie Equation Exponents in Carbonate and Sandy Rocks?

Objective: The aim of this study is to investigate the exponents of the Archie equation in order to support the evaluation of hydrocarbon or groundwater reserves through electrical resistivity measurements of subsurface rocks. Theoretical Framework: The Archie equation describes the relationship between the electrical resistivity of a rock and its water saturation and depends on the cementation (m) and saturation (n) exponents. Several authors state that electrical resistivity and the exponents m and n depend on the size, geometry, and connectivity of the pores. Method: This article applies the finite element method to simulate the propagation of the electric field in digital rock models and thus evaluate the Archie exponents. Results and Discussion: Our results indicate that the relationship between these exponents and pore attributes is different for values below or above a threshold. The variables with the greatest influence on m are the porosity of macropores and their interconnectivity, as well as the volumetric fraction of the microporous phase. As for the saturation exponent, the variables with the greatest influence are the porosity of macropores and the interconnectivity of both potentially conductive domains. Research Implications: The practical and theoretical implications of this research are discussed, providing insights into how the results can be applied or influence practices in the field of underground natural resource assessment. These implications may cover the sectors of hydrocarbon production, groundwater, geo-environmental investigations, and geotechnical studies. Originality/Value: This study contributes to the literature by presenting an innovative method for determining the exponents of the Archie equation. The relevance and value of this research are evidenced by its high economic impact on the aforementioned sectors and by its applicability anywhere.

Development of a convolutional neural network to accurately detect land use and land cover

The detection and modeling of Land Use and Land Cover (LULC) play pivotal roles in natural resource management, environmental modeling and assessment, and ecological connectivity management. However, addressing LULCC detection and modeling constitutes a complex data-driven process. In the present study, a Convolutional Neural Network (CNN) is employed due to its great potential in image classification. The development of these tools applies the deep learning method. A methodology has been developed that classifies the set of land uses in a natural area of special protection. This study area covers the Sierra del Cando (Galicia, northwest Spain), considered by the European Union as a Site of Community Interest and integrated in the Natura 2000 Network. The results of the CNN model developed show an accuracy of 91 % on training dataset and 88 % on test dataset. In addition, the model was tested on images of the study area, both from Sentinel-2 and PNOA. Despite some confusion especially in the residential class due to the characteristics in this area, CNNs prove to be a powerful classification tool.•Classifications based on a CNN model•LULC are classified into 10 different classes•Training and test accuracy are 91 % and 88 %, respectively

ЦЕНА ПРИРОДЫ: ЭКОНОМИЧЕСКАЯ ОЦЕНКА ЭКОЛОГИЧЕСКИХ РЕСУРСОВ И ЕЕ РОЛЬ В УСТОЙЧИВОМ РАЗВИТИИ

The article is devoted to the assessment of environmental resources and their role in sustainable development. In the modern world, awareness of the importance of nature conservation and sustainable use of its resources is becoming increasingly relevant. The features of the economic assessment of natural resources and ecosystem services are considered, as well as the principles and methods of determining their value are analyzed. Special attention is paid to the problems related to the need to integrate the economic assessment of nature into decision-making processes at the level of public policy and business. The importance of taking into account the ecological value of natural resources and ecosystem services in economic models and management practices is emphasized. It is concluded that it is necessary to develop mechanisms that contribute to a more accurate and objective assessment of the value of nature and its consideration in sustainable development.

Multi-source deep-learning approach for automatic geomorphological mapping: the case of glacial moraines

ABSTRACT Landform mapping is the initial step of many geomorphological analyses (e.g. assessment of natural hazards and natural resources) and requires vast resources to be applied to wide areas at high-resolution. Among geomorphological objects, we focus on glacial moraine mapping, since it is a task relevant to many fields (e.g. paleoclimate and glacial geomorphology). Here we proposed to exploit the potential of Deep Learning-based approaches to map moraine landforms by exploiting multi-source remote sensing imagery. To this end, we propose the first Deep Learning model to map glacial moraines, namely MorNet. As multi-source remote sensing information, we combine together three different sources: Topographic (Pleiades-derived DSM), Multispectral (Sentinel-2), and SAR (Sentinel-1) data. To cope with such heterogeneous information, the proposed model has a dedicated branch for each input source and, a late fusion mechanism is leveraged to combine them with the aim to provide the final mapping. The performance of the MorNet model is evaluated on several glacier valleys in China in the Himalayan range. This area contains minimally eroded moraines, so they are well-defined and of varied morphology. The behavior of the proposed method is compared to models using individual mono-source models in order to highlight the benefit to simultaneously leverage multi-source information. The use of multi-source data allows MorNet to exploit the complementarity of the three input sources and improve its performance from an f1-score of about 41.6 using a single source to 52.8 using three sources. MorNet provides a first-order moraine map through its ability to identify well-defined moraines. Consequently, MorNet can identify areas likely to contain moraines and intends to be used as a tool by experts to facilitate and support large-scale mapping.

Medical and biological assessment of natural resources in the rehabilitation system of victims of the consequences of the large-scale aggression of the russian federation (literature review)

Medical and biological assessment of natural resources in the rehabilitation system of victims of the consequences of the large-scale aggression of the russian federation (literature review)

Inclusive Approach in Higher Education: From the Experience of Pavlo Tychyna Uman State Pedagogical University

Purpose: The aim of the study is to summarise the experience of Pavlo Tychyna Uman State Pedagogical University in creating conditions for people with disabilities to obtain higher education. Design / Methodology / Approach: The following methods were used in the course of the study: analysis of regulatory acts, content analysis of websites, comparative analysis, scientific abstraction, etc. Findings: The article provides a thorough analysis of the educational and scientific activities of the Centre for Social and Pedagogical Integration, Inclusive Rehabilitation and Social Tourism “Barrier-Free”, which introduces new technologies of social and psychological rehabilitation in the context of expanding the life space of students with disabilities, in particular through inclusive social and rehabilitation tourism, volunteering, social and pedagogical practice, etc. The principles of the Centre’s activities are defined, including respect and perception of individual characteristics of a person, observance of his or her best interests, prevention of discrimination and violation of human rights, confidentiality, accessibility of educational services, and interagency cooperation. The main activities of Pavlo Tychyna Uman State Pedagogical University in the context of inclusive higher education are highlighted. Originality / Value: It is proved that the greatest social effect is achieved by involving students with disabilities in the university community and educational activities, creating conditions for their social integration, and professional assistance in overcoming their social exclusion and isolation. Practical Meaning: The practical aspects of inclusion in the educational activities of Pavlo Tychyna Uman State Pedagogical University are as follows: ensuring compliance with the requirements for accessibility to educational facilities for persons with disabilities and other low-mobility groups; Adaptation of the official university website and the website of the Faculty of Social and Psychological Education for people with visual impairments in accordance with WCAG 2.0 global standards; creation of 5 inclusive groups in accordance with the current legislation; introduction of psychological support for applicants under martial law; launching project activities; completion of the research project of young scientists “Social and Psychological Rehabilitation of Children and Youth with Special Educational Needs through Inclusive Tourism”. Further Research: Development of methodological support for social and natural resource assessment of inclusive tourism facilities.

The Analysis of Willingness to Accept and Willingness to Pay of Farmers and Consumers on Organic Horticultural Commodities Through Android Application-Based Digital Marketplace

Purpose: This study aims to analyze farmers' and consumers' willingness to accept and willingness to pay for organic horticultural commodities through an Android application-based digital marketplace. Theoretical Framework: Parameters in the economic assessment of natural resources and the environment involve how much individuals are willing to pay (WTP) and receive (WTA) Economic valuation methods for natural resources and the environment can be grouped into two types, namely the revealed preference approach and the stated preference approach The implied preference approach includes several techniques, such as the Travel Cost Method, Hedonic Pricing Method, and Averting Cost. On the other hand, the techniques included in the expressed preference approach involve the Contingent Valuation Method (CVM) and Conjoint Analysis. Method: This study was conducted in Semarang and Magelang Regencies, Central Java Province. The number of samples interviewed was 110 respondents spread across the two districts. The number of samples in each district was 55 respondents of producer farmers who are accustomed to growing organic horticulture and at the same time as consumers of organic horticulture. The analysis of users’ WTP and WTA of organic horticulture offered to the respondents. Results: The estimated Result of WTP Parameters of the characteristics of the product offered is one of the factors which can affect consumer willingness to pay (WTP). Consumer backgrounds such as income, number of family members, and education can also affect consumers' WTP of a product. In general, the determining factor for consumers’ WTP to be willing to pay more for organic horticulture through a digital marketplace compared to conventional horticulture is better quality. Conclusions: Consumer preference for organic horticulture is positive because the quality and taste are better than horticulture that has been planted before. Organic horticulture has the opportunity to create a market due to the positive response of producers and consumers as indicated by the WTA and WTP.

Efficient air pollution mapping in extensive regions with fully autonomous unmanned aerial vehicles: A numerical perspective

It is noteworthy that comprehensive exploration of atmospheric measurements in the horizontal plane using aerial platforms, necessitating high autonomy, has not been extensively covered in the existing literature. This research presents a systematic numerical approach to effective air pollution mapping achieved through the integration of horizontal and vertical air pollution measurements conducted using a fully autonomous unmanned aerial vehicle (UAV) platform. The developed robust navigation model enables the UAV to efficiently scan the extensive measurement area, which is subdivided into smaller sub-areas using the polygonal decomposition technique, resulting in a comprehensive map of the entire region. Furthermore, technical analysis determines the optimal flight speed, leading to air pollution measurements in up to 30 % more areas and ensuring more consistent results. The simulation results illustrate the effective mapping of the entire area by aggregating air pollution measurements from sub-areas, with seamless transitions emphasizing the accuracy and consistency of the employed air pollution mapping technique. This systematic method offers numerous advantages, including rapid air pollution source identification and swift response capabilities. Moreover, this approach holds potential for various applications, such as forest fire monitoring and natural resources assessment, by equipping UAVs with additional equipment like cameras alongside atmospheric sensors.

Network analysis reveals overlapping roles of stakeholders related to seagrass-data provisioning in Indonesia

Seagrass data support conservation areas, carbon budget, natural resource assessment, and other national interests in Indonesia. However, to what extent seagrass-related data are available and how the data are managed are still unknown. In this study, we assess the status of data availability and the roles of stakeholders in seagrass-related data provisioning at a national level in Indonesia. Data were collected in August and September 2022 through Focus Group Discussion (FGD) and a questionnaire. The participants comprised representatives from the central governments (planning, ministerial, research, geospatial agencies), local governments, universities, and non-governmental organizations (NGOs) working in the fields related to the coastal ecosystem. Stakeholders’ roles were analyzed using Stakeholders’ Analysis (SA), while their relationships were examined with Social Network Analysis (SNA). The results showed that the seagrass areal extent (61.7%) was the most crucial data needed, followed by the percent cover (12.77%). Using seagrass data as coastal management was the highest priority among stakeholders. There were overlapping roles between stakeholders related to seagrass data, as producers or users, due to their urgent needs. Almost all stakeholders were key players, and there was a lack of coordination among them relating to seagrass data provisioning. There was an urgent need to strengthen seagrass data governance. The results of this work form an important pathway for Indonesia to develop approaches to map the seagrass areal extent and share knowledge.

Economic reproduction of the natural resource potential of agro-industrial production

Purpose. The goal of the study is to develop scientific and methodological approaches and practical recommendations for improving the economic mechanism of reproduction of the natural resource potential of agro-industrial production (AIP).
 Results. The article substantiates the priorities for improving the reproduction of the natural resource potential of agro-industrial production on the example of Ukraine. The conceptual foundations of forming an information system for ensuring the rational use and reproduction of natural resources in the agrarian sector are highlighted in detail. The conceptual foundations of forming a system of principles, goals and criteria for information support of the processes of reproduction of the natural resource potential of AIP, the environmental and economic mechanism for managing the features of environmental and innovative development of the state are considered in more detail. For optimal redistribution of the resource component of agro-industrial production, methodological recommendations for the economic assessment of natural resources in the agrarian sector are presented. In order to ensure the rational reproduction of natural resources of the agricultural sector, the features of the economic assessment of natural resources of agro-industrial production are analyzed. The essence and content of the concept of the natural resource potential of the agricultural sector is revealed, under which it is proposed to understand a complex system of natural objects, the users of which are enterprises of agro-industrial production with existing technology and socio-economic relations, a component of the environmental and economic potential of the state. This made it possible to develop a system of information support for the reproduction of the natural resource potential of AIP based on a systematic analysis of environmental and economic relations of nature management in the agrarian sector.
 Scientific novelty. The economic category of the natural resource potential of the AIP is developed in the direction of reflecting the structure and relationships between its elements; the definition of the economic assessment of natural resource elements is deepened, which is considered in the study as a criterion indicator of the comparative economic efficiency of the use and reproduction of the natural resource potential of the AIP; scientific and practical foundations of information support for economic processes of reproduction of natural resources in the agricultural sector are improved on the basis of development of an information system for expert assessment of their quality, monitoring, audit, and passport of the environmental management object.
 Practical value. The results of the study are used to improve the economic mechanism of reproduction of natural resources of the agrarian sector in the region. The scientific and practical recommendations are used to determine the indicators of economic assessment of the elements of natural resources of the agrarian sector; to calculate the economic efficiency of conservation and reproduction of the natural resource potential of the agro-industrial complex; to provide financial support for regional reproduction programmes and processes; to develop tools and methods of agro-ecological regulation.

CNN and Transformer Fusion for Remote Sensing Image Semantic Segmentation

Semantic segmentation of remote sensing images has been widely used in environmental protection, geological disaster discovery, and natural resource assessment. With the rapid development of deep learning, convolutional neural networks (CNNs) have dominated semantic segmentation, relying on their powerful local information extraction capabilities. Due to the locality of convolution operation, it can be challenging to obtain global context information directly. However, Transformer has excellent potential in global information modeling. This paper proposes a new hybrid convolutional and Transformer semantic segmentation model called CTFuse, which uses a multi-scale convolutional attention module in the convolutional part. CTFuse is a serial structure composed of a CNN and a Transformer. It first uses convolution to extract small-size target information and then uses Transformer to embed large-size ground target information. Subsequently, we propose a spatial and channel attention module in convolution to enhance the representation ability for global information and local features. In addition, we also propose a spatial and channel attention module in Transformer to improve the ability to capture detailed information. Finally, compared to other models used in the experiments, our CTFuse achieves state-of-the-art results on the International Society of Photogrammetry and Remote Sensing (ISPRS) Vaihingen and ISPRS Potsdam datasets.

Using Landsat-5 for Accurate Historical LULC Classification: A Comparison of Machine Learning Models

This study investigates the application of various machine learning models for land use and land cover (LULC) classification in the Kerch Peninsula. The study utilizes archival field data, cadastral data, and published scientific literature for model training and testing, using Landsat-5 imagery from 1990 as input data. Four machine learning models (deep neural network, Random Forest, support vector machine (SVM), and AdaBoost) are employed, and their hyperparameters are tuned using random search and grid search. Model performance is evaluated through cross-validation and confusion matrices. The deep neural network achieves the highest accuracy (96.2%) and performs well in classifying water, urban lands, open soils, and high vegetation. However, it faces challenges in classifying grasslands, bare lands, and agricultural areas. The Random Forest model achieves an accuracy of 90.5% but struggles with differentiating high vegetation from agricultural lands. The SVM model achieves an accuracy of 86.1%, while the AdaBoost model performs the lowest with an accuracy of 58.4%. The novel contributions of this study include the comparison and evaluation of multiple machine learning models for land use classification in the Kerch Peninsula. The deep neural network and Random Forest models outperform SVM and AdaBoost in terms of accuracy. However, the use of limited data sources such as cadastral data and scientific articles may introduce limitations and potential errors. Future research should consider incorporating field studies and additional data sources for improved accuracy. This study provides valuable insights for land use classification, facilitating the assessment and management of natural resources in the Kerch Peninsula. The findings contribute to informed decision-making processes and lay the groundwork for further research in the field.

Exploring the potential of thermal infrared remote sensing to improve a snowpack model through an observing system simulation experiment

Abstract. The assimilation of data from Earth observation satellites into numerical models is considered to be the path forward to estimate snow cover distribution in mountain catchments, providing accurate information on the mountainous snow water equivalent (SWE). The land surface temperature (LST) can be observed from space, but its potential to improve SWE simulations remains underexplored. This is likely due to the insufficient temporal or spatial resolution offered by the current thermal infrared (TIR) missions. However, three planned missions will provide global-scale TIR data at much higher spatiotemporal resolution in the coming years. To investigate the value of TIR data to improve SWE estimation, we developed a synthetic data assimilation (DA) experiment at five snow-dominated sites covering a latitudinal gradient in the Northern Hemisphere. We generated synthetic true LST and SWE series by forcing an energy balance snowpack model with the ERA5-Land reanalysis. We used this synthetic true LST to recover the synthetic true SWE from a degraded version of ERA5-Land. We defined different observation scenarios to emulate the revisiting times of Landsat 8 (16 d) and the Thermal infraRed Imaging Satellite for High-resolution Natural resource Assessment (TRISHNA) (3 d) while accounting for cloud cover. We replicated the experiments 100 times at each experimental site to assess the robustness of the assimilation process with respect to cloud cover under both revisiting scenarios. We performed the assimilation using two different approaches: a sequential scheme (particle filter) and a smoother (particle batch smoother). The results show that LST DA using the smoother reduced the normalized root mean square error (nRMSE) of the SWE simulations from 61 % (open loop) to 17 % and 13 % for 16 d revisit and 3 d revisit respectively in the absence of clouds. We found similar but higher nRMSE values by removing observations due to cloud cover but with a substantial increase in the standard deviation of the nRMSE of the replicates, highlighting the importance of revisiting times in the stability of the assimilation performance. The smoother largely outperformed the particle filter algorithm, suggesting that the capability of a smoother to propagate the information along the season is key to exploit LST information for snow modelling. Finally, we have compared the benefit of assimilating LST with synthetic observations of fractional snow cover area (FSCA). LST DA performed better than FSCA DA in all the study sites, suggesting that the information provided by LST is not limited to the duration of the snow season. These results suggest that the LST data assimilation has an underappreciated potential to improve snowpack simulations and highlight the value of upcoming TIR missions to advance snow hydrology.

Оценка перспектив развития предприятий лесного хозяйства Республики Башкортостан

The high level of power supply and technical provision of logging, along with advanced technologies, ensure the enterprise’s competitiveness. The current state of facilities and resources at forestry institutions in the regions is, as a rule, unsatisfactory. Significant engineering and technological backwardness of facilities and resources is due to the specifics of the modern structure of the forest sector. With the purpose of ensuring the production effectiveness of forestry institutions in the regions on the basis of increasing the level of power supply and technical provision for logging, it is necessary to carry out appropriate research. The methods for assessing the forest as an ecosystem are a relevant area of theoretical and applied research. There are 3 basic methods used in world and Russian practice in the assessment of natural resources: comparative, cost, and income. The methodology of cost estimate for natural resources provides a flexible system of indices, which allow assessing natural resources depending on the amount of available information and practical purposes. In terms of spatial economics we propose to develop a pilot project to improve the efficiency of production in the state autonomous forestry institutions of the Republic of Bashkortostan. The project is intended to provide practical guidance on the allocation of 2–3 groups of forestry institutions. We used an expert approach to assess the rating of forestries. Our earlier estimates for forestry enterprises in the Republic of Bashkortostan (state autonomous institutions) were used as the basis for it. The values of quantitative criteria include expert estimates and statistical information from numerous sources, such as reports, handbooks, results of surveys, interviews, etc. The criteria were divided into 2 groups: quantitative (availability of logging equipment, transport accessibility, calculated logging area) and qualitative (state of the logging equipment, repair depot, and management structure). According to the results of the integral assessment, the forestries are divided into 4 groups. Based on the assessment performed, it is advisable to identify the points of growth in the pilot project of modernization the facilities and resources of forestry enterprises. For citation: Konshakova S.A., Bulkhov N.A., Zaikin A.N., Kuznetsov S.G., Sivakov V.V. Assessment of the Development Potential of Forestry Enterprises of the Republic of Bashkortostan. Lesnoy Zhurnal = Russian Forestry Journal, 2023, no. 4, pp. 202–214. (In Russ.). https://doi.org/10.37482/0536-1036-2023-4-202-214

Impact of Urbanization on Landuse/Landcover Change in Old Cuttack and Sikharpur Zone of Cuttack Municipal Corporation

Land use mapping is fundamental for assessment, managing and protection of natural resources of a region . • The information on the existing land use is one of the prime pre-requisites for suggesting better use of terrain. • Land use maps are valuable tools for agricultural and natural resources studies. , With the growth of population and socio-economic activities, natural land cover is being modified for various development purposes. • Land use mapping is an important tool for land management and monitoring. • The rapid expansion of urban areas due to rise in population and economic growth is increasing additional demand on natural resources thereby causing land-use changes especially in megacities. • Therefore, serious problems associated with rapid development such as additional infrastructure, informal settlements, environmental pollution, destruction of ecological structure and scarcity of natural resources has been studied carefully using remote sensing and GIS technologies for a rapidly grown city

Synthesis of ecological and economic aspects as methodological bases for constitutionally motivated land-nature-use in modern Ukraine

An analysis of ecological and economic aspects of land-nature-use was carried out, based on the essence of new theoretical knowledge of constitutional land law acquired by us as a modern need for urgent institutionalization of current constitutional land norms. The purpose of the article is to reveal the main empirical results of the analysis and synthesis of ecological and economic aspects in relation to the methodological foundations of land-nature-use for the application of methods and methods of assessment of natural resources (natural objects) as scientific and practical prerequisites for legal nature use based on current land regulations Constitution of Ukraine. It has been established that the ecological and economic aspects in relation to the methodological foundations of land-nature-use are an integral part of the normative-legal aspects and are completely consistent with each other, since their essence is based on the same land norms of the Constitution of Ukraine. It has been proven that ecological and economic relations are relations that arise between specific subjects of property rights, which are, on the one hand, the Ukrainian people (all citizens of Ukraine) as the absolute owner of all natural resources (objects), and on the other hand — the owner (lessee) of a land plot (object of civil rights) as the «owner» and «user» of natural resources (objects) owned by someone else — the main national wealth, which de jure is under the special protection of the state. Since, in accordance with the Constitution of Ukraine, the ownership of the land and its natural resources by the Ukrainian people obligates and must not be used to the detriment of man and society, it is imperative, first of all, to identify and certify all natural objects that have a nature reserve, nature protection, recreational, historical, cultural and other value. It was established that only under the conditions of implementation of the current constitutional norm of «use» of the property of the Ukrainian people, instead of the imaginary norm in the Land Code of Ukraine (Article 79) and the Civil Code of Ukraine (Article 373) — "distribution", a responsible and effective obligation will arise the obligation to carry out legal nature use and real protection of land and its natural resources as natural objects of someone else’s property for all categories of land, especially agricultural land.

Spatio-Temporal Analysis of Land Use and Land Cover Changes in Shiekan Locality-North Kordofan State-Sudan Using Remote Sensing

The study was conducted in Shiekan locality in North Kordofan State, Sudan and aimed to identify, assess and mapping of the land use and land cover (LULC) changes and trends in the last three decades (1989 to 2019). Satellite imageries of Landsat 5 TM (1989) and (2009), Landsat 7 ETM+ (2000), and Landsat 8 OLI (2019), covering path and row 174/51 and 175/51 were acquired in dry seasons. Image pre-proccessig, image classification (maximum likelihood) and accuracy assessment were applied. Remotely sensed data were processed and analyzed using ERDAS 9.1 and ArcGIS10.0 software. Results showed that the LULC in Sheikan locality were identified into eight classes (water, trees, shrubs, sandy soil, gardud lands, agricultural lands, rangeland and bare soil, and residential). The trees class showed an increase from 2.36% (2000) to 2.51% (2009) and decreasing in 2019 (1.87%). While, classified imageries indicated an increase of agricultural land from 69.2% in 1989 to 72.2% in 2019 as dominant class, followed by shrub lands increased from 18% in 1989 to 23.1% in 2019 in Sheikan locality. The overall obtained accuracy for classified maps was 87.50%, 89.29%, 90.20%, and 80.77% for 1989, 2000, 2009, and 2019, respectively. The study revealed that expansion of agricultural lands, climate change and illicit felling of trees are the main reseaons for natural resources degradation, mainly forest and rangelands in Shiekan locality. Use of remote sensing and GIS in assessment and monitoring of natural resources in arid and semi-arid area is highly recommended.

A Multi-Pixel Split-Window Approach to Sea Surface Temperature Retrieval from Thermal Imagers with Relatively High Radiometric Noise: Preliminary Studies

In the following decade(s), a set of satellite missions carrying thermal infrared (TIR) imagers with a relatively high noise equivalent differential temperature (NEdT) are expected, e.g., the high resolution TIR imagers flying on the future Thermal infraRed Imaging Satellite for High-resolution Natural resource Assessment (TRISHNA), Land Surface Temperature Monitoring (LSTM) and NASA-JPL/ASI Surface Biology and Geology Thermal (SBG) missions or the secondary payload on board the ESA Earth Explorer 10 Harmony. The instruments on board these missions are expected to be characterized by an NEdT of ⪆0.1 K. In order to reduce the impact of radiometric noise on the retrieved sea surface temperature (SST), this study investigates the possibility of applying a multi-pixel atmospheric correction based on the hypotheses that (i) the spatial variability scales of radiatively active atmospheric variables are, on average, larger than those of the SST and (ii) the effect of atmosphere is accounted for via the split window (SW) difference. Based on 32 Sentinel 3 SLSTR case studies selected in oceanic regions where SST features are mainly driven by meso to sub-mesoscale turbulence (e.g., corresponding to major western boundary currents), this study documents that the local spatial variability of the SW difference term on the scale of ≃3 × 3 km2 is comparable with the noise associated with the SW difference. Similarly, the power spectra of the SW term are shown to have, for small scales, the behavior of white noise spectra. On this basis, we suggest to average the SW term and to use it for the atmospheric correction procedure to reduce the impact of radiometric noise. In principle, this methodology can be applied on proper scales that can be dynamically defined for each pixel. The applicability of our findings to high-resolution TIR missions is discussed and an example of an application to ECOSTRESS data is reported.