The influence of soil salinization on land use changes in azov district of Rostov oblast

Рухович, Д. И.; Симакова, М. С.; Kulyanitsa, A. L.; Bryzzhev, A. V.; Королева, П. В.; Калинина, Н. В.; Черноусенко, Г. И.; Vil’chevskaya, E. V.; Dolinina, E. A.; Rukhovich, S. V.

doi:10.1134/s1064229317010136

Cited by 18 publications

(12 citation statements)

References 14 publications

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“…The actual distribution of soil degradation can be determined based on manual interpretation of large arrays of remote sensing data for the period from 1968 to 2020. Such work was and is being performed on the territory of the Rostov region of Russia [14][15][16][17][18]. In contrast to soil maps, where areas of potential degradation are determined according to relief factors, the interpretation of multi-temporal remote sensing data allows detecting areas of actual distribution of soil degradation.…”

Section: Sources and Methods Of Soil Erosion Detectionmentioning

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%

“…Degradation can be detected based on the analysis of big satellite data in the manual interpretation mode [14][15][16][17][18]. Retrospective monitoring of land use and soil cover allows to identify areas of formation of degraded lands for a period of 50 years or more [19].…”

Section: Introductionmentioning

confidence: 99%

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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“…Big remote sensing data [27] open perspectives for constructing soil degradation maps. Partially big RSD can be processed by manual interpretation [28][29][30][31][32]. These methods include the method of retrospective monitoring of soil and land cover, which allows one to construct maps using RSD for a period of more than 50 years [33].…”

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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“…Soil salinity is one of the rising environmental issues causing considerable yield losses worldwide especially in arid and semiarid regions 1 . It damages the soil structure, reduces soil quality and limits the growing of crops 2 . Halophytes, such as Suaeda glauca (Bunge), Puccinellia tenui ora, Tamarix chinensis Lour and Leymus chinensis, are plant species that grow well in saline soil due to their saline-alkali tolerance features [3][4] .…”

Section: Introductionmentioning

confidence: 99%

Drivers on The Halophytes' Rhizosphere Bacteria Community and Functions in North China Salinized Areas

Yin

Zhang

Cheng

et al. 2021

Preprint

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Soil salinity is a serious environmental issue in arid China. Soil bacteria play a fundamental role in soil systems and respond rapidly to environmental changes. However, the responses of soil bacterial community to the different halophytes remains poorly understood. We investigated rhizosphere soil bacterial community changes under different halophytes in north China using high-throughput sequencing. Three typical halophytes were Leymus chinensis (LC), Puccinellia tenuiflora (PT), Suaeda glauca (SG). The dominant phyla were Proteobacteria, Actinobacteria, and Chloroflexi across three halophytic vegetation. These bacteria have important assistance for halophytes adapt to saline soil. PICRUSt forecasts demonstrated that energy metabolism, amino acid metabolism and carbohydrate metabolism are main bacterial functions in halophyte vegetation soil, and the abundance of metabolism these bacterial functions in SG was significantly higher than that in LC and PT. The pH value of different halophyte rhizosphere soils has a more significant effect on bacterial diversity than EC and soil trophic status, and soil water content (SWC) was important effect factors leading to differences in bacterial functions. These results give us a deeper understanding of the diversity and functional differences of rhizosphere soil bacterial communities in the typical halophytic vegetation of northern China.

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The influence of soil salinization on land use changes in azov district of Rostov oblast

Cited by 18 publications

References 14 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

Drivers on The Halophytes' Rhizosphere Bacteria Community and Functions in North China Salinized Areas

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