Estimating the Soil Erosion Cover-Management Factor at the European Part of Russia

Mukharamova, S. S.; Saveliev, Anatoly A.; Иванов, М. А.; Gafurov, Artur; Yermolaev, Oleg

doi:10.3390/ijgi10100645

Cited by 19 publications

(13 citation statements)

References 36 publications

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“…In the model, the cover management factor (the C factor) relates to the conditions that can be most easily controlled in reducing erosion for the purpose of landscape restoration, and can therefore be described as one of the most important planning instruments for policies and land use decisions (Maetens et al, 2012;Biddoccu et al, 2016;Mukharamova et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Simulating with a Combination of RUSLE GIS and Sediment Delivery Ratio for Soil Restoration

Öztürk

Özcan

Aytaş

et al. 2023

Preprint

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Erosion by water is the main cause of land degradation. Landscapes degraded by erosion need to be restored in many respects, and particularly in terms of ecosystem services. From an economic and management perspective, care is needed to select priority areas and determine the means to be applied to restore them. Globally, the model most commonly used to produce scenarios for the prevention of soil losses is the Revised Universal Soil Loss Equation (RUSLE). This study of the subbasin of the Sulakyurt Dam Basin in Turkey aims (1) to identify the distribution of soil losses over time and by location, and (2) to grade the priority areas for the prevention of soil losses by means of a simulation. The average potential soil losses in the area under study are estimated at 42.35 t ha−1 year−1, and the average actual losses at 39.49 t ha−1 year−1. According to the simulation, 27.61% of the study area (2782 ha) is of the highest priority for soil restoration. In our study, forests have the highest soil losses, which is contrary to the natural protection that forests provide against erosion; however, this is mainly due to topographic aspects that influence soil losses. Of the forest areas, 41.74% (1766 ha) falls within the areas of highest priority. The study serves as a guide for landscape planning and the determination of erosion risk in restoration efforts, and for identifying the methods to be adopted during the restoration work to reduce the loss of soil.

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Section: Introductionmentioning

confidence: 99%

Simulating with a Combination of RUSLE GIS and Sediment Delivery Ratio for Soil Restoration

Öztürk

Özcan

Aytaş

et al. 2023

Preprint

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“…The emphasis on the precise delineation of these lands is underscored by their significant bearing on numerous operational facets. For instance, meticulous mapping is indispensable for orchestrating effective land use planning and management, fostering an environment in which lands are exclusively earmarked for agricultural undertakings, thereby averting soil degradation and environmental detriment [2].…”

Section: Introductionmentioning

confidence: 99%

Automated Mapping of Cropland Boundaries Using Deep Neural Networks

Gafurov

2023

AgriEngineering

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Accurately identifying the boundaries of agricultural land is critical to the effective management of its resources. This includes the determination of property and land rights, the prevention of non-agricultural activities on agricultural land, and the effective management of natural resources. There are various methods for accurate boundary detection, including traditional measurement methods and remote sensing, and the choice of the best method depends on specific objectives and conditions. This paper proposes the use of convolutional neural networks (CNNs) as an efficient and effective tool for the automatic recognition of agricultural land boundaries. The objective of this research paper is to develop an automated method for the recognition of agricultural land boundaries using deep neural networks and Sentinel 2 multispectral imagery. The Buinsky district of the Republic of Tatarstan, Russia, which is known to be an agricultural region, was chosen for this study because of the importance of the accurate detection of its agricultural land boundaries. Linknet, a deep neural network architecture with skip connections between encoder and decoder, was used for semantic segmentation to extract arable land boundaries, and transfer learning using a pre-trained EfficientNetB3 model was used to improve performance. The Linknet + EfficientNetB3 combination for semantic segmentation achieved an accuracy of 86.3% and an f1 measure of 0.924 on the validation sample. The results showed a high degree of agreement between the predicted field boundaries and the expert-validated boundaries. According to the results, the advantages of the method include its speed, scalability, and ability to detect patterns outside the study area. It is planned to improve the method by using different neural network architectures and prior recognized land use classes.

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“…The study of global land use is an essential area of research, especially with regard to its effect on soil erosion and other exogenous processes [14]. To better understand the impact of land use on erosion, researchers have developed a range of global land-use models derived from remote sensing data from satellites.…”

Section: Introductionmentioning

confidence: 99%

Advancing Agricultural Crop Recognition: The Application of LSTM Networks and Spatial Generalization in Satellite Data Analysis

Gafurov,

Mukharamova,

Saveliev

et al. 2023

Agriculture

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This study addresses the challenge of accurate crop detection using satellite data, focusing on the application of Long Short-Term Memory (LSTM) networks. The research employs a “spatial generalization” approach, where a model trained on one geographic area is applied to another area with similar vegetation conditions during the growing season. LSTM networks, which are capable of learning long-term temporal dependencies, are used to overcome the limitations of traditional machine learning techniques. The results indicate that LSTM networks, although more computationally expensive, provide a more accurate solution for crop recognition compared with other methods such as Multilayer Perceptron (MLP) and Random Forest algorithms. The accuracy of LSTM networks was found to be 93.7%, which is significantly higher than the other methods. Furthermore, the study showed a high correlation between the real and model areas of arable land occupied by different crops in the municipalities of the study area. The main conclusion of this research is that LSTM networks, combined with a spatial generalization approach, hold great promise for future agricultural applications, providing a more efficient and accurate tool for crop recognition, even in the face of limited training data and complex environmental variables.

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Estimating the Soil Erosion Cover-Management Factor at the European Part of Russia

Cited by 19 publications

References 36 publications

Simulating with a Combination of RUSLE GIS and Sediment Delivery Ratio for Soil Restoration

Simulating with a Combination of RUSLE GIS and Sediment Delivery Ratio for Soil Restoration

Automated Mapping of Cropland Boundaries Using Deep Neural Networks

Advancing Agricultural Crop Recognition: The Application of LSTM Networks and Spatial Generalization in Satellite Data Analysis

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