Agricultural Land Monitoring Research Articles

THE CURRENT STATE OF AGRICULTURAL LAND USE IN THE CONTEXT OF SUSTAINABLE DEVELOPMENT

This article discusses the rational use of agricultural land, taking into account structural and resource indicators for sustainable development. The effectiveness of land use in farms with different forms of land ownership depends on increasing labor productivity, strengthening the economy regime, increasing the intensification of production, using internal reserves and opportunities for agricultural production and, especially, rational use of land. The basis for the formation of sustainable land use should be strict observance of the established relationships between disturbed and undisturbed territories. Undisturbed territories should be considered as a stabilizing factor neutralizing anthropogenic impacts on the landscape. In this regard, the tasks of preserving productive agricultural land, optimizing arable land and acreage in terms of quantitative and qualitative characteristics of land are becoming a priority. The solution of these tasks is connected with the improvement of technologies for maintaining and increasing the bioproductivity of agricultural lands, the development of technologies for rational land management, land use and land protection, the creation of effective organizational and legal mechanisms for managing agricultural lands, as well as the development of state monitoring of agricultural lands. In order to approach the practice of determining the efficiency of land use, it is necessary to focus in more detail on its factors according to the selected functional subsystems and types of efficiency.

Near surface camera informed agricultural land monitoring for climate smart agriculture

Continuous and accurate monitoring of agricultural landscapes is crucial for understanding crop phenology and responding to climatic and anthropogenic changes. However, the widely used optical satellite remote sensing is limited by revisit cycles and weather conditions, leading to gaps in agricultural monitoring. To address these limitations, we designed and deployed a Near Surface Camera (NSCam) Network across China, and explored its application in agricultural land monitoring and achieving climate-smart agriculture (CSA). By analyzing the image data captured by the NSCam Network, we can accurately assess long-term or abrupt agricultural land changes. According to the preliminary monitoring results, integrating NSCam data with remote sensing imagery greatly enhances the temporal details and accuracy of agricultural monitoring, aiding agricultural managers in making informed decisions. The impacts of abnormal weather conditions and human activities on agricultural land, which are not captured by remote sensing imagery, can be complemented by incorporating our NSCam Network. The successful implementation of this method underscores its potential for broader application in CSA, promoting resilient and sustainable agricultural practices.

ЯКІСНА ОЦІНКА ГРУНТІВ ІРШАВСЬКОГО РАЙОНУ ЗАКАРПАТСЬКОЇ ОБЛАСТІ

The materials of the main fertility indicators of the grounds of the Irshava district based on the results of the monitoring of agricultural lands in the XI round of agrochemical certification are highlighted and their qualitative assessment is established. Key words: agrochemical certification, acidity, humus, nitrogen, phosphorus, potassium, sulfur, ecological and agrochemical assessment

Mapping changes of grassland to arable land using automatic machine learning of stacked ensembles and H2O library

ABSTRACT Permanent grasslands play a very important role in the landscape. The loss of permanent grasslands and their subsequent conversion into arable land create erosion-prone agricultural areas in the landscape and have a negative impact on the biodiversity. From this point of view, there is a need for the accurate and effective monitoring of changes in the agricultural landscape along with an assessment of the influence of the agricultural policies on the landscape. Sentinel-2 from the Copernicus programme has improved options for the implementation of remote sensing data into the monitoring of agricultural land. The aim of this study was to evaluate the potential of H2O library and within implemented Automachine learning function (AutoML) and its stacked ensembles for mapping changes from grasslands to arable lands. All results show high overall accuracy from 93.5% to 96.6% and high values of area under the ROC curve (0.94–0.98). Stacked ensembles appear to be the most accurate machine learning models for mapping changes from grasslands to arable lands. The importance of several biological predictors has been tested (FAPAR, FCOVER, LAI, NDVI, etc.) with the help of a heatmap that is part of AutoML function of H2O library.

USING GEO-SPATIAL DATA AND DATASET FOR CROPLAND MONITORING IN DARKHAN-UUL AND SELENGE PROVINCES, MONGOLIA

Abstract. The central agricultural region is relatively pleasant in terms of nature and climate of our country, and due to the use of land for multiple purposes, the ecological status of the land has been greatly changed. It is necessary to use many types of layered data and use many types of thematic image results in agricultural monitoring and research, which is a necessary land use. These data will be carried out by various branches of research, and the modelling of each data into a unified database with geographical connections will allow for integrated evaluation and thematic drawing, and the use of geo-spatial analysis decision-making system is required. The purpose of this work is to carry out agricultural land monitoring research using remote sensing methods and advanced technology, to develop a unified methodology, to enrich the data of the points of the monitoring network database, to create an interdisciplinary geo-database, and to develop recommendations for further measures to be taken. It will cover 304,000 hectares of 17 soums of Selenge province and 36,000 agricultural lands of 3 soums of Darkhan-Uul province. A geodatabase was created for the monitoring survey of agricultural land, and a map and route for the field survey was prepared before starting the field survey. The database includes 21 sub-district boundaries, team boundaries and agricultural monitoring points, cadastral data of cultivated and agricultural land, agricultural soil data, satellite data and topography data.

ADMINISTRATION OF MEASURES FOR THE RESTORATION OF SOILS TRANSFORMED AS A RESULT OF MILITARY OPERATIONS

The Earth is a wealth of society. Its role in addressing the food problem cannot be overstated. It is known that the population's demand for staple food products doubles on average every thirty years. Therefore, in order to provide enough food for all the people living in the years 2010-2020, the productivity of agriculture needs to be doubled. The solution to this incredibly complex task primarily depends on the efficiency of land utilization. Observations are conducted using ground-based methods, primarily field-based techniques, and remote sensing tools. The establishment of correlation relationships between ground-based and remote sensing methods is carried out on dedicated test sites. The purpose of the article is to provide proposals for implementing measures for the restoration of lands transformed as a result of military actions. The methodology should include modern mathematical support, including principles of database creation, automated data processing and retrieval systems, and methods for real-time and long-term forecasting. For an accurate assessment of such transformations and the implementation of targeted regulation of soil processes, there is a need to organize systematic monitoring services to observe them. The absence of such monitoring can lead to irreversible degradation of the soil cover, which would later require enormous costs and time for restoration. It is clear that from both economic and socio-environmental perspectives, it is more practical and advantageous to prevent adverse changes rather than trying to remedy them later. By processing such information, it is possible to identify optimal natural conditions for various activities, predict both positive and negative factors for agricultural operations, and take measures to reduce the impact of negative factors on human life and activities. It is known that for a long time, observations were only conducted on changes in the state of the natural environment caused by natural factors. Indeed, the state of the biosphere changes under the influence of both natural and anthropogenic factors. However, there is a significant difference in the outcomes of these influences: the state of the biosphere, which constantly changes due to natural causes, usually returns to its previous state. Large balanced ecological systems, geosystems, change extremely slowly under the influence of natural processes. These gradual evolutionary changes occur only over time measured by historical epochs. The deteriorating ecological situation in Ukraine, including the agroecological state of the soil cover, emphasizes the need for the State Agrochemical Service to transition from soil agrochemical surveys to comprehensive soil-agrochemical monitoring of agricultural lands. Such monitoring belongs to the ecological monitoring type and consists of three main components: soil surveys (including laboratory analysis), assessment of their agro-ecological condition, prediction of changes in relevant indicators, and soil fertility management, which refers to the specific properties of soils that distinguish them qualitatively from their parent geological formations.

Applicability of UAVs in detecting and monitoring burning residue of paddy crops with IoT Integration: A step towards greener environment

The disposal of paddy-based residue in some parts of Indian north-western states, particularly in Punjab and Haryana, has emerged as a significant issue, leading farmers to resort to on-site burning. This practice wastes valuable resources, has detrimental effects on the local economy, and poses a global environmental challenge. The consequences of burning agricultural residues include soil erosion, carbon sequestration problems, and a negative impact on the ecosystem. Recognizing the importance of managing paddy residue due to its nutrient content and its contribution to soil–plant-atmosphere continuity, this article aims to quantify the amount of residue generated and shed light on the adverse effects of residue burning on human health, soil quality, and the environment in the region. Employing drone technology, the study presents an experimental case based on data collected from selected states in India. Real-time monitoring of agricultural land using unmanned aerial vehicles (UAVs) enables the acquisition of crucial data on paddy residue. The collected images are then transferred to the cloud for analysis and decision-making purposes. By leveraging this approach, the study achieves an accuracy of 96.67% in analysing paddy crop waste and identifying areas where residue burning occurs. The proposed work offers timely assistance to state authorities in pinpointing burning locations promptly, and taking necessary measures accordingly. This monitoring initiative not only contributes to climate control efforts but also aids in combating the rising AQI levels of air pollution, and potentially exploring other means of monitoring various environmental factors.

Soil Monitoring System Using LoRa-Based ANN Method For Rice Plant Growth

Farmers only use approximation, the application of this approximation can cause the quality of the soil in rice fields to be infertile. In this research, an agricultural land monitoring system was created using an agricultural land monitoring tool to make it easier for farmers to monitor the land to get good crop yields. The test results obtained are data collected on the land according to the parameters needed from rice plants. The research design was made in the form of a monitoring tool which will later display on the website to make it easier for farmers to check the land to be planted with rice remotely. This system also uses the Artificial Neural Network method which functions to predict soil conditions. The result of this system is to make it easier for farmers to get agricultural land that meets the criteria for quality rice plant

Comparison of Areal and Profile Distribution of Magnetic Susceptibility in Steppe Soils of the Russian Plain

A comparative analysis of areal and profile measurements of magnetic susceptibility was carried out on the plots pf 100 m2 in order to improve methods of surface soil sounding for mapping purposes and for identification of anomalies associated with anthropogenic pollution or disturbance of the surface soil layer. Two sites with Haplic Chernozems and one site with Haplic Kastanozem (Endosalic, Cambic) were studied. Additionally, a catena on the Yergeni Upland with different landscape positions (eluvial, transeluvial, transeluvial–accumulative) was studied. A comparison of the areal and profile magnetic susceptibility measured to a depth of 30 cm showed a direct correlation (R2 = 0.7). The areal survey was found to correctly determine the volumetric magnetic susceptibility ({{varkappa }_{{text{s}}}}) to a depth of 30 cm. The {{varkappa }_{{text{s}}}} variation at sites with different types of soils reflects soil–climatic zonality and spatial lithological heterogeneity expressed in different textures and mineralogical compositions of the upper (0–30 cm) soil layer within the test area of 10 × 10 m. The areal magnetic susceptibility of soils can be an important additional indicator capable of reflecting the features of soil-forming, lithological, and geochemical processes occurring in the upper soil layer. The {{varkappa }_{{text{s}}}} variation at sites in different landscape positions is due to the development of sheet erosion and changes in the direction of iron oxidogenesis depending on the position of the soil profile in the relief. The set of areal and profile magnetic susceptibility measurements can be used to study soil inhomogeneities caused by anthropogenic, paleocryogenic, geomorphological, and lithogenic factors. In particular, this approach can be applied to the study of polluted soils and monitoring of agricultural lands.

Comparison of Areal and Profile Distribution of Magnetic Susceptibility in Steppe Soils of the Russian Plain

A comparative analysis of areal and profile measurements of magnetic susceptibility was carried out in order to improve methods of surface sensing of the earth, as well as to take into account small-scale mapping of soils and the identification of anomalies associated with anthropogenic pollution or violation of the surface layer of soils. Two sites with Haplic Chernozems and one Haplic Kastanozems (Endosalic, Cambic) were studied. Additionally, a catena was studied on the territory of the Ergeninsky upland with the inclusion of sites in various positions of the landscape (eluvial, transeluvial, transeluvial-accumulative). The comparison of the areal and profile magnetic susceptibility measured to a depth of 30 cm showed a direct correlation (R2 = 0.7). It was found that the areal type of survey correctly captures the volumetric magnetic susceptibility (\({{\varkappa }_{{\text{s}}}}\)) to a depth of 30 cm. The variation of \({{\varkappa }_{{\text{s}}}}\) at sites with different types of soils reflects soil-climatic zonality and spatial lithological heterogeneity, expressed in different granulometric and mineralogical composition of the 0–30 cm layer on an area of 10 × 10 m. The areal magnetic susceptibility of soils can be an important additional indicator capable of reflecting the features of soil-forming, lithological and landscape geochemical processes occurring in the upper soil layer. Variation of \({{\varkappa }_{{\text{s}}}}\) at sites in different positions of the landscape occurs under the influence of planar flushing and a change in the direction of iron oxidogenesis processes depending on the position of the soil profile in the relief. The complex of measurements of areal and profile magnetic susceptibility can be used to study soil inhomogeneities caused by anthropogenic, paleocryogenic, geomorphological and lithogenic factors. Such an approach can be widely applied to the study of polluted soils and monitoring of agricultural land.

Remote monitoring of the consequences of hostilities on the territory of the Kharkiv region

With connection with the full-scale Russian military aggression, the issue of clear spatial fixation of violations of natural complexes and their components has become extremely urgent. The consequences of projectiles and rockets, fires that arose as a result of shelling, military fortification activities, etc. led to the direct disturbance of landscapes, as well as their chemical pollution. This, in turn, has an extremely negative impact on the state of the environment and leads to indirect impacts. The quality of drinking water and crop production is deteriorating, soil fertility is decreasing, and the biodiversity of the territories is decreasing. Purpose. The possibilities of fixing and monitoring damage to land resources based on the use of free space images and free geoinformation software. Methods. Space images of the Planet Scope apparatus were taken as the information basis. Viewing, uploading and analysis of images took place in the free geographic information system (GIS) QGIS 3.12 Results. The research was conducted on the territory of Rohan settlement united territorial community (SOTG), which is located in the Kharkiv district of the Kharkiv region. According to the method of monitoring natural and agricultural land, deciphering data from space images shows that in 2022, 916 explosions were formed as a result of shelling of the territory of Rohansk SOTG. A set of maps related to the explosive danger and chemical contamination of the territory of the Rohansk SOTG was created, the decoding of space images of the territory of the Rogansk SOTG in different periods of time was carried out. Following the monitoring of agricultural lands, traces of bomb-trubbing were recorded. Maps of the results of recording the effects of bombing, a thermal map of the density of eddies, a map of the hydrological analysis of the territory that may be polluted as a result of bombing have been created. Conclusions. The methodology for monitoring natural and agricultural lands based on space survey data was developed. The created set of maps related to the explosive danger and chemical pollution of the territory of Rohansk SOTG can be used for planning further studies of the state of the environment and rationalization of economic activity.

Comparison of an Optimised Multiresolution Segmentation Approach with Deep Neural Networks for Delineating Agricultural Fields from Sentinel-2 Images

Effective monitoring of agricultural lands requires accurate spatial information about the locations and boundaries of agricultural fields. Through satellite imagery, such information can be mapped on a large scale at a high temporal frequency. Various methods exist in the literature for segmenting agricultural fields from satellite images. Edge-based, region-based, or hybrid segmentation methods are traditional methods that have widely been used for segmenting agricultural fields. Lately, the use of deep neural networks (DNNs) for various tasks in remote sensing has been gaining traction. Therefore, to identify the optimal method for segmenting agricultural fields from satellite images, we evaluated three state-of-the-art DNNs, namely Mask R-CNN, U-Net, and FracTAL ResUNet against the multiresolution segmentation (MRS) algorithm, which is a region-based and a more traditional segmentation method. Given that the DNNs are supervised methods, we used an optimised version of the MRS algorithm based on supervised Bayesian optimisation. Monotemporal Sentinel-2 (S2) images acquired in Lower Saxony, Germany were used in this study. Based on the agricultural parcels declared by farmers within the European Common Agricultural Policy (CAP) framework, the segmentation results of each method were evaluated using the F-score and intersection over union (IoU) metrics. The respective average F-score and IoU obtained by each method are 0.682 and 0.524 for Mask R-CNN, 0.781 and 0.646 for U-Net, 0.808 and 0.683 for FracTAL ResUNet, and 0.805 and 0.678 for the optimised MRS approach. This study shows that DNNs, particularly FracTAL ResUNet, can be effectively used for large-scale segmentation of agricultural fields from satellite images.

"Double accounting" of agricultural land plots: pros and cons

Since 2022, the state register of agricultural lands has been introduced in our country, and since 1992, monitoring of lands of all categories has been carried out as well. This monitoring, on the one hand, is a part of the state environmental monitoring, and on the other hand, the scope of its data and the system of indicators are changing, becoming more universal and focused on various spheres. Therefore, since 2015, the special rules for monitoring various categories of land have been introduced; in particular, monitoring of agricultural land is carried out not by the Federal Service of State Registration, Cadastre and Cartography, but by the Ministry of Agriculture. Since 2022, the data of such a special monitoring of agricultural lands have become the basis for the formation of the state register of these lands. The article discusses the key features of monitoring and registry of these lands maintained by the Federal Ministry of Agriculture, as well as the pace of formation of such monitoring systems at the regional level.

Investigation of the ecological state of natural resources at agricultural enterprises of various forms of ownership in the eastern region of Opllia

In order to provide sustainable development of agroecosystems and rational nature management, it is essential to make a transition to alternative farming methods. Organization of agricultural area is the first stage in the process of transforming a natural ecosystem into an agroecosystem. The limits of the types of agricultural area organization form natural boundaries of agroecosystems. The basis of conducting the research is the investigation of the ecological state of natural resources of agricultural enterprises of various forms of ownership in the eastern region of Opillia, namely the agroecosystems of two farms as a comparison of a traditional method of farming and a bioorganic one as well as their influence on the ecosystems on the territory of Berezhany district (investigation of the agroecosystems in TzOV “Zhyva Zemlia Potutory” in the village of Potutory Berezhany district (organic farming method), and TOV “Agroprodservis” in the village of Kozliv, Ternopil region). The research is aimed at the substantiation of ecological approaches and the analysis of practical recommendations on the formation of a mechanism of the development of ecologically-safe farming and at providing the ecological assessment of the agricultural landscapes in order to provide organic crop cultivation. Materials and methods corresponded to the methodology of a continuous soil-agrochemical monitoring of agricultural lands in Ukraine. In most cases, alternative farms cannot compete with traditional farms in terms of crop yield. Satisfactory crop yields absolutely depend on the amount of manure and compost applied as well as on the area that farmers can allocate for growing legumes.

Monitoring agricultural land areas using GIS-online program EOS DA: case study of Andijan region

This research aims to look at the use of Earth observing system data analytics (EOS DA), an interactive online system, for agricultural land monitoring in Uzbekistan. With the increasing relevance of digital technology in agriculture, there is a need to investigate innovative approaches for improving land quality monitoring and benchmarking crop conditions and productivity indicators, especially concerning degraded soil processes. The agricultural sector can benefit greatly from the start-up of interactive online digital technologies. Scientific and practical research can be advanced by leveraging existing crop monitoring and forecasting systems and adding components of digital agricultural production systems. Regular crop monitoring adds to higher production and land productivity, both of which are critical in modern agriculture. To reach these goals, modern technologies and the usage of geo-information and remote sensing data must be integrated. The primary goal of this project is to use EOS DA to assist in the continued improvement of agricultural land monitoring in Uzbekistan. The study intends to provide significant insights and recommendations for optimizing agricultural practices and maximizing land productivity by utilizing this interactive online approach. Integrating EOS DA and other geospatial data can result in more informed decision-making and better resource management in the agricultural industry. Finally, using EOS DA, this work aims to contribute to the continual improvement of agricultural land monitoring in Uzbekistan. The study intends to improve agricultural practices and promote sustainable land management in the country by leveraging the potential of interactive online tools and incorporating geospatial information.

Rational use of agricultural land in Kazakhstan

The purpose of the article is to consider the institutional foundations of the rational use of agricultural land in a multicultural economy. The formation of land use is faced with the acute problem of organizing a sustainable competitive land use, ensuring a high level of marketability of production and a sufficient level of profitability in conditions of the developing land market. This article discusses the rational use of agricultural land in a multicultural economy, taking into account structural and resource indicators. The efficiency of land use in farms with different land ownership depends on increasing labor productivity, strengthening the economic regime, increasing the intensification of production, using internal reserves and agricultural production opportunities, and, especially, rational use of land. The greatest efficiency of production and use of land has been achieved in large agricultural formations, where high-performance equipment is used, crop rotations are observed, and there is greater availability of credit resources, subsidies, and leasing.The monitoring data of the Committee of the Republic of Kazakhstan on Statistics for 1.01.2020 indicate that 93.7% of peasant and farm farms have a land area of up to 500 hectares. To the greatest extent, small-earth peasant farms have become widespread in the southern region, where the share in the total number of up to 50 hectares is 90.1%, while in the northern region only 8.1%, central - 3.9%, and western - 11.3%. In this regard, the tasks of preserving productive agricultural lands, and optimizing arable land and acreage in terms of quantitative and qualitative characteristics of land become a priority. The solution to these tasks is connected with the improvement of technologies for maintaining and increasing the bio-productivity of agricultural lands, the development of technologies for rational land management, land use, and land protection, the creation of effective organizational and legal mechanisms for managing agricultural lands, as well as the development of state monitoring of agricultural lands. Optimization of land use in farms and agricultural enterprises of based on the proposed methodology, taking into account state support measures, will create a basis for a new stage in the development of land reform and will create incentives for the effective use of agricultural land.

Improved Agricultural Field Segmentation in Satellite Imagery Using TL-ResUNet Architecture

Currently, there is a growing population around the world, and this is particularly true in developing countries, where food security is becoming a major problem. Therefore, agricultural land monitoring, land use classification and analysis, and achieving high yields through efficient land use are important research topics in precision agriculture. Deep learning-based algorithms for the classification of satellite images provide more reliable and accurate results than traditional classification algorithms. In this study, we propose a transfer learning based residual UNet architecture (TL-ResUNet) model, which is a semantic segmentation deep neural network model of land cover classification and segmentation using satellite images. The proposed model combines the strengths of residual network, transfer learning, and UNet architecture. We tested the model on public datasets such as DeepGlobe, and the results showed that our proposed model outperforms the classic models initiated with random weights and pre-trained ImageNet coefficients. The TL-ResUNet model outperforms other models on several metrics commonly used as accuracy and performance measures for semantic segmentation tasks. Particularly, we obtained an IoU score of 0.81 on the validation subset of the DeepGlobe dataset for the TL-ResUNet model.

Monitoring of Agricultural Land Using Pilotless Aircraft

Monitoring of Agricultural Land Using Pilotless Aircraft

Impact of no-till technology on the fertility of degraded and low-humus soils

The transition to innovative tillage technologies is currently an effective way to preserve and increase the fertility of agricultural land. The purpose of the work is to study the impact of no-till technology on the fertility of degraded and low-humus soils in the Middle Volga region. The main research method was the monitoring of agricultural lands, where traditional (control), minimum and no-till technologies for cultivating grain crops were used as part of field crop rotations. As a result of research conducted over fifteen years on degraded leached chernozems, light gray forest and low-humus soddy-podzolic soil types, it was found that the introduction of no-till technology contributes to an increase in the number of earthworms by 36% and soil invertebrates by 42%. At the same time, the ratio of different groups of soil mesofauna also changes. In particular, an increase in the number of organized predators (ground beetles, rove beetles, spiders, geofields) capable of regulating the number of harmful species of soil animals (for example, wireworm larvae) was revealed. No-till technology has a beneficial effect on agrophysical, agrochemical indicators of fertility and biological activity of degraded and low-humus soils. The crushing of straw and its use as mulch for fifteen years made it possible to form a positive balance of organic matter and increase the content of mobile phosphorus and exchangeable potassium in the soil by an average of 20% and 15%, respectively. Improving the nutritional regime of the soil contributes to a positive impact on the quality characteristics and grain yield of winter and spring wheat, winter rye, barley and corn. With the introduction of no-till technology as the main method of crop production, grain yield increases by 3 and 6% compared to the minimum and traditional technology.

Оценка экологического состояния агроландшафтов в южной части междуречья Тигра и Евфрата с использованием данных дистанционного зондирования и ГИС-технологий: теоретические основы и предпосылки

Identification of the current state and agroforestry arrangement of agricultural landscapes, including agricultural lands, is an urgent task, the solution of which will allow planning work to prevent the destruction of the fertile soil layer and create conditions for the sustainable functioning of natural and territorial complexes. The conducted research allowed us to establish that negative natural factors and the intensification of agricultural production lead to soil depletion; the limited area of land suitable for agriculture requires improving soil quality, returning degraded and abandoned lands to agricultural circulation; constant monitoring of agricultural lands is necessary for an objective assessment of the state and effective restoration of resources; the use of innovative resource-saving technologies, make it possible to prevent degradation and restore soil fertility. As a result of studies of agroforestry landscapes using geoinformation systems, a variant of a modified methodology for the study of agricultural land, the creation of agroforestry protective plantings to prevent soil degradation and deterioration of the conditions of functioning of agricultural land is proposed. The landscapes of the southern part of the interfluve of the Tigris and Euphrates rivers are the standards characteristic of floodplain ecosystems. Therefore, the results obtained using computer mapping and analysis of the state of agricultural lands can be used to form an ecological framework of sustainable landscapes, as well as to identify and map degradation processes in landscapes of analogues.