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In the conditions of degrading resources of fertile arable land, pressing demand for food from a growing world population, and progressing urbanization and industrialization, agricultural land distribution patterns are becoming more vulnerable to a variety of socioeconomic, environmental, and food security challenges. In the context of this trilemma, there is a need to understand the extent to which farming systems will be able to cope with increasing competition for land with other uses. In this study, the authors developed an approach for predicting the likely influences of non-agricultural lands on agricultural landscapes. In the case of diverse agricultural landscapes in Russia, farming systems were mapped based on a share of agricultural land categories in the land fund across 82 administrative entities. The establishment of a rating system and application of correlation analysis allowed revealing the mismatches between the cadasterbased spatial distribution of farming systems and actual inter-category relationships. The proposed framework is applicable internationally for the study of land-use patterns and simulation of agricultural land distribution systems under the influence of non-agricultural land uses.

In the conditions of degrading resources of fertile arable land, pressing demand for food from a growing world population, and progressing urbanization and industrialization, agricultural land distribution patterns are becoming more vulnerable to a variety of socioeconomic, environmental, and food security challenges. In the context of this trilemma, there is a need to understand the extent to which farming systems will be able to cope with increasing competition for land with other uses. In this study, the authors developed an approach for predicting the likely influences of non-agricultural lands on agricultural landscapes. In the case of diverse agricultural landscapes in Russia, farming systems were mapped based on a share of agricultural land categories in the land fund across 82 administrative entities. The establishment of a rating system and application of correlation analysis allowed revealing the mismatches between the cadaster based spatial distribution of farming systems and actual inter-category relationships. The proposed framework is applicable internationally for the study of land-use patterns and simulation of agricultural land distribution systems under the influence of non-agricultural land uses.

In modern conditions of falling business activity of enterprises and falling incomes of the population, the growing trend of electricity shortages persists. This need may be aggravated by the high level of deterioration of equipment at Russian thermal power plants. All world practice shifts the guidelines for expanding electricity production towards the creation of a market for pre-fabricated renewable energy sources. In the article, the authors focused on justifying the need for the massive construction of wind power plants as an alternative source of energy. Today, «traditional» energy sources cannot meet the growing demand in remote areas of the Tyumen region with districts in the context of the spread of modern information technologies and the process of digitalization of public services. The current situation also imposes significant restrictions on the intensity of the development of the process of educational technologies in remote settlements. In this connection, the authors of the article decided to conduct an analytical study to substantiate the ways of territorial development through the launch of projects for the construction of compact wind power plants as an alternative source of energy in providing resources to remote settlements.

In recent decades, Russia has experienced substantial transformations in agricultural land tenure. Post-Soviet reforms have shaped land distribution patterns but the impacts of these on agricultural use of land remain under-investigated. On a regional scale, there is still a knowledge gap in terms of knowing to what extent the variations in the compositions of agricultural land funds may be explained by changes in the acreage of other land categories. Using a case analysis of 82 of Russia’s territories from 2010 to 2018, the authors attempted to study the structural variations by picturing the compositions of regional land funds and mapping agricultural land distributions based on ranking “land activity”. Correlation analysis of centered log-ratio transformed compositional data revealed that in agriculture-oriented regions, the proportion of cropland was depressed by agriculture-to-urban and agriculture-to-industry land loss. In urbanized territories, the compositions of agricultural land funds were predominantly affected by changes in the acreage of industrial, transportation, and communication lands. In underpopulated territories in the north and far east of Russia, the acreages of cropland and perennial planting were strongly correlated with those of disturbed and barren lands. As the first attempt at such analysis in Russia, the conversion of cadastral classification data into land-rating values enabled the identification of region-to-region mismatches between the cadaster-based mapping and ranking-based distribution of agricultural lands.

The non-spatial information of cadastral maps must be repeatedly updated to monitor recent changes in land property and to detect illegal land registrations by tax evaders. Since non-spatial information, such as land category, is usually updated by field-based surveys, it is time-consuming and only a limited area can be updated at a time. Although land categories can be updated by remote sensing techniques, the update is typically performed through manual analysis, namely through a visually interpreted comparison between the newly generated land information and the existing cadastral maps. A cost-effective, fast alternative to the current surveying methods would improve the efficiency of land management. For this purpose, the present study analyzes the discrepancy between the existing cadastral map and the actual land use. Our proposed method operates in two steps. First, an up-to-date land cover map is generated from hyperspectral unmanned aerial vehicle (UAV) images. These images are effectively classified by a hybrid two- and three-dimensional convolutional neural network. Second, a discrepancy map, which contains the ratio of the area that is being used differently from the registered land use in each parcel, is constructed through a three-stage inconsistency comparison. As a case study, the proposed method was evaluated using hyperspectral UAV images acquired at two sites of Jeonju in South Korea. The overall classification accuracies of six land classes at Sites 1 and 2 were 99.93% and 99.75% and those at Sites 1 and 2 are 39.4% and 34.4%, respectively, which had discrepancy ratios of 50% or higher. Finally, discrepancy maps between the land cover maps and existing cadastral maps were generated and visualized. The method automatically reveals the inconsistent parcels requiring updates of their land category. Although the performance of the proposed method depends on the classification results obtained from UAV imagery, the method allows a flexible modification of the matching criteria between the land categories and land coverage. Therefore, it is generalizable to various cadastral systems and the discrepancy ratios will provide practical information and significantly reduce the time and effort for land monitoring and field surveying.

The author of this article analyzes one of the basic concepts of Russian law of land - the legal regime of land. However, despite unconditional importance of the specified concept, today in the theory of law of land no definition of this concept is provided. This article examines different interpretations of the concept «legal regime of land» provided by Russian law scholars and practitioners. That allows for understanding specific features of legal regime of land used for different purposes.

The article deals with the land categorization with special focus on the definition of category FO­REST in the National Forest Inventory of the Czech Republic (NFI CR). Definitions of land categories used in the first cycle of forest inventory in 2001–2004 are evaluated. The first task is to assess the appropriateness of existing land categorization and definition of category FOREST in terms of suitability of used parameters defining individual categories and their values. Their compatibility with international definitions of category FOREST is also assessed. The second task is, based on data from the first cycle of NFI CR, to calculate the area of category FOREST according to the international definition of European National Forest Inventory Network (ENFIN) and to determine whether the area of category FOREST is the same or varies from the area according to the definition FOREST defined in NFI CR.In the first part there is a list of used land classifications in the Czech Republic and there are also described used international classifications. Land categorization and definitions according ENFIN are presented. Further the parameters are chosen in the national definition of NFI CR, which are compared with analogous parameters defined by ENFIN, indicating differences. Subsequently, the area of category FOREST is calculated according to the parameters of national definition and ENFIN definition. Finally, suggestions are given for the land classification into categories for the second cycle of NFI CR, including the appropriate parameters and their values for the definition of category FOREST. Possible ways of their implementation into the methodology of NFI CR are listed.

Management of land use related to brownfield redevelopment areas offers opportunities to respond to the challenges from rapid urbanization in China. This paper explores the mixed functions of land use in brownfield redevelopment by using the Tiexi District in Shenyang as a case study and extending the current national land-use coding system. Based on examination of the land-use statistics for the Tiexi District, the authors found that the current coding system is not suitable or precise enough for calculating the areas of land use or its mixed-function-based measurement in a way useful for scientific research and local policy-making. Thus, an extended coding system is proposed, and four examples (two residential communities, one commercial business facility, and one industrial cultural plaza) were selected for empirical study. The extended coding system supplies more-detailed information for understanding the land-use functions in brownfield redevelopment and provides a more-valid database for measuring the social, economic, and environmental implications of redevelopment of brownfield lands. Extended land-use categories also should benefit local decision-making regarding long-term sustainable development. [ABSTRACT FROM AUTHOR]

The availability of long-term land datasets is fundamental to ensure consistent and accurate national greenhouse gas inventories for land use and land use changes. Estimation of Romania's CO2 removals and emissions over 1970-2010 was assessed comparatively by using National Statistics (as reference dataset), Corine Land Cover data, as well as four additional datasets produced from the reference dataset and assuming some forest data changes according to other available partial datasets (NFI,LUCAS or expert judgment). A spreadsheet, implementing a model of UNFCCC national greenhouse gas inventory, allowed estimation of both net CO2 removals and emissions and gross CO2 fluxes from all carbon pools, for all land subcategories over 1970-2010. The model was run for each dataset. The reference dataset resulted in an average annual gross flux of CO2, twice as large as the corresponding annual net removals of 13 Tg CO2, while annual estimates were more than double in post-1990 compared to pre-1990. Uncertainty of net annual CO2 removals by reference dataset was estimated around 31%, slightly lower than for gross estimate, and just around 55% when based on CLC dataset. Overall, the contribution of 12 broad land sub-categories to CO2 inventory was similar in the national net and gross estimates, for both pre- and post-1990 periods. Land under conversion represented 9% of the country's area in pre- and only 2% in post-1990, corresponding to an annual average of 28% of gross and 6% of net annual CO2 estimates. Among the choices of datasets tested, the reference dataset provided the most conservative estimates of the CO2 inventory. Other datasets generally overestimated annual LULUCF sink and its main contributor forestland, as well as the emissions or removals from land conversion. Compared to pre-1990, when annual sink was rather low, land abandonment and moderating management in the post-1990 period has led to an increase of C stock in all pools, showing the relevance of political changes on land's CO2 emissions/removals. Inconsistency within available land datasets impairs more accurate estimation of national GHG inventory. The development of an improved land use assessment system around National Forest Inventory is therefore suggested as a solution to implement consistent land definitions and to accurately estimate their areas in time. [ABSTRACT FROM AUTHOR]

Abstract: Objective: This study analyzes the spatial distribution of scrub typhus in Korea. Methods: A spatial distribution of Orientia tsutsugamushi occurrence using a geographic information system (GIS) is presented, and analyzed by means of spatial clustering and correlations. Results: The provinces of Gangwon-do and Gyeongsangbuk-do show a low incidence throughout the year. Some districts have almost identical environmental conditions of scrub typhus incidence. The land use change of districts does not directly affect the incidence rate. Conclusion: GIS analysis shows the spatial characteristics of scrub typhus. This research can be used to construct a spatial-temporal model to understand the epidemic tsutsugamushi. [Copyright &y& Elsevier]

The non-spatial information of cadastral maps must be repeatedly updated to monitor recent changes in land property and to detect illegal land registrations by tax evaders. Since non-spatial information, such as land category, is usually updated by field-based surveys, it is time-consuming and only a limited area can be updated at a time. Although land categories can be updated by remote sensing techniques, the update is typically performed through manual analysis, namely through a visually interpreted comparison between the newly generated land information and the existing cadastral maps. A cost-effective, fast alternative to the current surveying methods would improve the efficiency of land management. For this purpose, the present study analyzes the discrepancy between the existing cadastral map and the actual land use. Our proposed method operates in two steps. First, an up-to-date land cover map is generated from hyperspectral unmanned aerial vehicle (UAV) images. These images are effectively classified by a hybrid two- and three-dimensional convolutional neural network. Second, a discrepancy map, which contains the ratio of the area that is being used differently from the registered land use in each parcel, is constructed through a three-stage inconsistency comparison. As a case study, the proposed method was evaluated using hyperspectral UAV images acquired at two sites of Jeonju in South Korea. The overall classification accuracies of six land classes at Sites 1 and 2 were 99.93% and 99.75% and those at Sites 1 and 2 are 39.4% and 34.4%, respectively, which had discrepancy ratios of 50% or higher. Finally, discrepancy maps between the land cover maps and existing cadastral maps were generated and visualized. The method automatically reveals the inconsistent parcels requiring updates of their land category. Although the performance of the proposed method depends on the classification results obtained from UAV imagery, the method allows a flexible modification of the matching criteria between the land categories and land coverage. Therefore, it is generalizable to various cadastral systems and the discrepancy ratios will provide practical information and significantly reduce the time and effort for land monitoring and field surveying. [ABSTRACT FROM AUTHOR]