Differences in Inventories of Waterlogged Territories in Soil Surveys of Different Years and in Land Management Documents

Шаповалов, Д. А.; Королева, П. В.; Калинина, Н. В.; Рухович, Д. И.; Сулейман, Г. А.; Dolinina, E. A.

doi:10.1134/s1064229320010147

Cited by 8 publications

(8 citation statements)

References 17 publications

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“…Approaches to detecting degradation based on vegetation indices [21][22][23] do not require large expenditures of manual labor, do not require the construction of complex DEMs, and make it possible to determine the actual distribution of degradation factors [24][25][26]. The accuracy of work increases when using multi-temporal series [26].…”

Section: Sources and Methods Of Soil Erosion Detectionmentioning

confidence: 99%

“…This method is based on the manual interpretation of satellite imagery of different resolutions over the past 50 years. The method allows to allocate boundaries of agricultural fields with spatial accuracy of topographic maps on a scale of 1:10,000 [19,21] (Figure 2). To find the boundaries of agricultural fields, a dataset for a period of 35 years (from 1984 to 2019) was formed.…”

Section: Setting Boundary Conditions For Interpretationmentioning

confidence: 99%

“…In this case, the accuracy of detection and the accuracy of determination of the boundaries of degraded areas are higher than those during ground-based surveys [18][19][20]. Retrospective monitoring allows to exactly determine the boundaries of agricultural fields and to trace their history [21]. The boundaries of agricultural fields are the boundary conditions for the spread of decryption characteristics during the interpretation of satellite imagery.…”

Section: Introductionmentioning

confidence: 99%

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The Use of Deep Machine Learning for the Automated Selection of Remote Sensing Data for the Determination of Areas of Arable Land Degradation Processes Distribution

et al. 2021

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Soil degradation processes are widespread on agricultural land. Ground-based methods for detecting degradation require a lot of labor and time. Remote methods based on the analysis of vegetation indices can significantly reduce the volume of ground surveys. Currently, machine learning methods are increasingly being used to analyze remote sensing data. In this paper, the task is set to apply deep machine learning methods and methods of vegetation indices calculation to automate the detection of areas of soil degradation development on arable land. In the course of the work, a method was developed for determining the location of degraded areas of soil cover on arable fields. The method is based on the use of multi-temporal remote sensing data. The selection of suitable remote sensing data scenes is based on deep machine learning. Deep machine learning was based on an analysis of 1028 scenes of Landsats 4, 5, 7 and 8 on 530 agricultural fields. Landsat data from 1984 to 2019 was analyzed. Dataset was created manually for each pair of “Landsat scene”/“agricultural field number”(for each agricultural field, the suitability of each Landsat scene was assessed). Areas of soil degradation were calculated based on the frequency of occurrence of low NDVI values over 35 years. Low NDVI values were calculated separately for each suitable fragment of the satellite image within the boundaries of each agricultural field. NDVI values of one-third of the field area and lower than the other two-thirds were considered low. During testing, the method gave 12.5% of type I errors (false positive) and 3.8% of type II errors (false negative). Independent verification of the method was carried out on six agricultural fields on an area of 713.3 hectares. Humus content and thickness of the humus horizon were determined in 42 ground-based points. In arable land degradation areas identified by the proposed method, the probability of detecting soil degradation by field methods was 87.5%. The probability of detecting soil degradation by ground-based methods outside the predicted regions was 3.8%. The results indicate that deep machine learning is feasible for remote sensing data selection based on a binary dataset. This eliminates the need for intermediate filtering systems in the selection of satellite imagery (determination of clouds, shadows from clouds, open soil surface, etc.). Direct selection of Landsat scenes suitable for calculations has been made. It allows automating the process of constructing soil degradation maps.

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Section: Sources and Methods Of Soil Erosion Detectionmentioning

confidence: 99%

Section: Setting Boundary Conditions For Interpretationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Use of Deep Machine Learning for the Automated Selection of Remote Sensing Data for the Determination of Areas of Arable Land Degradation Processes Distribution

et al. 2021

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“…The processing of big remote sensing data in the manual interpretation mode is very laborious. Despite the complexity, the method of retrospective monitoring of the soil and land cover is necessary to determine the boundaries of arable land, which are the boundary conditions for interpretation characteristics [35].…”

Section: Introductionmentioning

confidence: 99%

Recognition of the Bare Soil Using Deep Machine Learning Methods to Create Maps of Arable Soil Degradation Based on the Analysis of Multi-Temporal Remote Sensing Data

et al. 2022

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The detection of degraded soil distribution areas is an urgent task. It is difficult and very time consuming to solve this problem using ground methods. The modeling of degradation processes based on digital elevation models makes it possible to construct maps of potential degradation, which may differ from the actual spatial distribution of degradation. The use of remote sensing data (RSD) for soil degradation detection is very widespread. Most often, vegetation indices (indicative botany) have been used for this purpose. In this paper, we propose a method for constructing soil maps based on a multi-temporal analysis of the bare soil surface (BSS). It is an alternative method to the use of vegetation indices. The detection of the bare soil surface was carried out using the spectral neighborhood of the soil line (SNSL) technology. For the automatic recognition of BSS on each RSD image, computer vision based on deep machine learning (neural networks) was used. A dataset of 244 BSS distribution masks on 244 Landsat 4, 5, 7, and 8 scenes over 37 years was developed. Half of the dataset was used as a training sample (Landsat path/row 173/028). The other half was used as a test sample (Landsat path/row 174/027). Binary masks were sufficient for recognition. For each RSD pixel, value “1” was set when determining the BSS. In the absence of BSS, value “0” was set. The accuracy of the machine prediction of the presence of BSS was 75%. The detection of degradation was based on the average long-term spectral characteristics of the RED and NIR bands. The coefficient Cmean, which is the distance of the point with the average long-term values of RED and NIR from the origin of the spectral plane RED/NIR, was calculated as an integral characteristic of the mean long-term values. Higher long-term average values of spectral brightness served as indicators of the spread of soil degradation. To test the method of constructing soil degradation maps based on deep machine learning, an acceptance sample of 133 Landsat scenes of path/row 173/026 was used. On the territory of the acceptance sample, ground verifications of the maps of the coefficient Cmean were carried out. Ground verification showed that the values of this coefficient make it possible to estimate the content of organic matter in the plow horizon (R2 = 0.841) and the thickness of the humus horizon (R2 = 0.8599). In total, 80 soil pits were analyzed on an area of 649 ha on eight agricultural fields. Type I error (false positive) of degradation detection was 17.5%, and type II error (false negative) was 2.5%. During the determination of the presence of degradation by ground methods, 90% of the ground data coincided with the detection of degradation from RSD. Thus, the quality of machine learning for BSS recognition is sufficient for the construction of soil degradation maps. The SNSL technology allows us to create maps of soil degradation based on the long-term average spectral characteristics of the BSS.

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“…Suppose there are clearly expressed degradation processes in agricultural landscapes that are one of the leading limiting factors for obtaining stable yields; in that case, one should strengthen the negative process monitoring unit in the agroecological monitoring system. At the same time, monitoring data should be sufficient for the environmental and economic assessment of damage from degradation processes [89]. Regarding soil degradation processes, the agroecological monitoring system considers only the area of degraded land.…”

Section: Introductionmentioning

confidence: 99%

Basin-Scale Approach to Integration of Agro- and Hydroecological Monitoring for Sustainable Environmental Management: A Case Study of Belgorod Oblast, European Russia

et al. 2022

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The quantitative and qualitative depletion of water resources (both surface and groundwater) is closely related to the need to protect soils against degradation, rationalization of land use, and regulation of surface water runoff within the watershed area. Belgorod Oblast (27,100 km2), one of the administrative regions of European Russia, was chosen as the study area. It is characterized by a high activity of soil erosion (the share of eroded soils is about 48% of the total area of arable land). The development phase of the River Basin Environmental Management Projects (217 river basins from the fourth to seventh order) allowed for the proceeding of the development of an integrated monitoring system for river systems and river basin systems. The methods used to establish a geoecological network for regional monitoring include the selection and application of GIS techniques to quantify the main indicators of ecological state and predisposition of river basins to soil erosion (the share of cropland and forestland, the share of the south-oriented slopes, soil erodibility, Slope Length and Steepness (LS) factor, erosion index of precipitation, and the river network density) and the method of a hierarchical classification of cluster analysis for the grouping of river basins. An approach considering the typology of river basins is also used to expand the regional network of hydrological gauging stations to rationalize the national hydrological monitoring network. By establishing 16 additional gauging stations on rivers from the fourth to seventh order, this approach allows for an increase in the area of hydro-agroecological monitoring by 1.26 times (i.e., up to 77.5% of the total area of Belgorod Oblast). Some integrated indicators of agroecological (on the watershed surface) and hydroecological (in river water flow) monitoring are proposed to improve basin environmental management projects. Six-year monitoring showed the effectiveness of water quality control measures on an example of a decrease in the concentrations of five major pollutants in river waters.

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Differences in Inventories of Waterlogged Territories in Soil Surveys of Different Years and in Land Management Documents

Cited by 8 publications

References 17 publications

The Use of Deep Machine Learning for the Automated Selection of Remote Sensing Data for the Determination of Areas of Arable Land Degradation Processes Distribution

The Use of Deep Machine Learning for the Automated Selection of Remote Sensing Data for the Determination of Areas of Arable Land Degradation Processes Distribution

Recognition of the Bare Soil Using Deep Machine Learning Methods to Create Maps of Arable Soil Degradation Based on the Analysis of Multi-Temporal Remote Sensing Data

Basin-Scale Approach to Integration of Agro- and Hydroecological Monitoring for Sustainable Environmental Management: A Case Study of Belgorod Oblast, European Russia

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