Identification and Monitoring of Irrigated Areas in Arid Areas Based on Sentinel-2 Time-Series Data and a Machine Learning Algorithm
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area Overview
2.2. Data Sources
2.2.1. Sentinel-2 Imagery
2.2.2. Sample Data
2.2.3. Land Use Classification System
2.2.4. Statistics
2.3. Software Used in This Study
3. Research Methods
3.1. Multi-Scale and Optimal Segmentation Scale Selections
3.2. Feature Data Set Construction
3.3. Object-Oriented Classification Algorithms
3.4. Identification of Irrigation Areas in the Study Area
3.5. Classification Accuracy Evaluation
4. Results and Analysis
4.1. Optimal Scale Optimization Results
4.2. Analysis of Temporal Characteristics of Vegetation Indices
4.3. Accuracy Analysis of Land Use Classification and Optimization of the Classification Algorithm in the Study Area
4.4. Identification of Irrigation Areas and Area Changes in the Study Area
5. Discussion
5.1. Comparison and Analysis of Classification Algorithms
5.2. Analysis of the Effect of Vegetation Index on Extraction of Irrigated Area
5.3. Analysis of the Causes of Changes in Irrigation Area in the Study Area from 2019 to 2023
5.4. Uncertainty Analysis and Prospects
6. Conclusions
- (1)
- The SAVI has more advantages than the OSAVI and NDVI in the identification of agricultural wasteland and abandoned land in arid areas. Therefore, it is best to use the SAVI for the identification of irrigated areas in arid regions.
- (2)
- Compared with the CART decision tree and SVM, RF performed well in classification accuracy, processing speed, user-friendliness, and the prevention of overfitting. In this study, using RF, the overall accuracy of land use classification reached 94.22%, and the kappa coefficient reached 0.92. The overall accuracy of irrigation area identification reached 94.63%, and the kappa coefficient reached 0.92.
- (3)
- From 2019 to 2023, the total irrigated area increased by 4341 hm2, and the forest land increased by 2906 hm2. In 2022, the maximum irrigated area reached 50,686 hm2, and the forest land area reached 9306 hm2.
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Serial Number | Original Date | Serial Number | Original Date | Serial Number | Original Date |
---|---|---|---|---|---|
1 | 19 April 2019 | 16 | 16 August 2020 | 31 | 5 October 2021 |
2 | 28 June 2019 | 17 | 31 August 2020 | 32 | 10 October 2021 |
3 | 28 July 2019 | 18 | 30 September 2020 | 33 | 18 April 2022 |
4 | 27 August 2019 | 19 | 5 October 2020 | 34 | 18 May 2022 |
5 | 21 September 2019 | 20 | 15 October 2020 | 35 | 22 July 2022 |
6 | 6 October 2019 | 21 | 30 October 2020 | 36 | 5 September 2022 |
7 | 16 October 2019 | 22 | 13 April 2021 | 37 | 15 November 2022 |
8 | 31 October 2019 | 23 | 23 May 2021 | 38 | 24 March 2023 |
9 | 8 April 2020 | 24 | 2 July 2021 | 39 | 28 April 2023 |
10 | 28 April 2020 | 25 | 1 August 2021 | 40 | 18 May 2023 |
11 | 8 May 2020 | 26 | 11 August 2021 | 41 | 27 June 2023 |
12 | 28 May 2020 | 27 | 31 August 2021 | 42 | 12 July 2023 |
13 | 2 June 2020 | 28 | 5 September 2021 | 43 | 16 August 2023 |
14 | 22 June 2020 | 29 | 15 September 2021 | 44 | 5 October 2023 |
15 | 17 July 2020 | 30 | 25 September 2021 | 45 | 25 October 2023 |
Typical Sample Point Name | Coordinates |
---|---|
Irrigation point 1 | 43°55′48″ N, 87°1′24″ E |
Irrigation point 2 | 43°59′37″ N, 87°7′1″ E |
Irrigation point 3 | 44°6′39″ N, 87°19′34″ E |
Irrigation point 4 | 44°10′17″ N, 87°14′50″ E |
Non-irrigation point 1 | 44°8′12″ N, 87°3′34″ E |
Non-irrigation point 2 | 44°0′30″ N, 87°11′18″ E |
Non-irrigation point 3 | 44°13′30″ N, 87°11′7″ E |
Non-irrigation point 4 | 44°0′41″ N, 87°13′8″ E |
Types | Description | Sentinal-2A Image | Amap | Google Earth |
---|---|---|---|---|
01 Land under cultivation | The overall hue and color in the remote sensing images of cultivated land usually appears in rich green, brown, and yellow tones based on different plant phenological stages; the texture and structure in images of cultivated land usually exhibit clear continuous block patterns. | |||
03 Woodlands | The overall tone and color in remote sensing images of forest land usually appears in dark green and light green tones; the texture and structure in images of cultivated land usually exhibit a clear stripped pattern. | |||
11 Land for water and water conservation facilities | Lakes and rivers usually exhibit naturally curved or locally straight shapes; reservoirs usually exhibit a more regular geometry, and dams exhibit elongated shapes that are perpendicular to the flow of a stream or river. | |||
12 Land for transportation | In remote sensing images, transportation corridors often show an obvious linear or network structure. Railways and roads appear as continuous lines with a relatively fixed width in the image. | |||
20 Towns and villages and industrial and mining land | The hue of urban land is usually relatively uniform, the scale of rural residential land is usually small, and the distribution is relatively scattered; remote sensing images of industrial and mining land usually show regular shapes and structures, such as rectangles and circles. | |||
23 Other land | Other land types typically have a relatively uniform hue, with good uniformity of spectral reflectance, and show distinct shapes and individual structures in texture and structure. |
Feature Name | Characteristic Variable | Description/Calculation Formula |
---|---|---|
Spectral Band | B1, B2, B3, B4, B5, B6, B7, B8, B8A, B9, B10, B11, B12 | Full Sentinel-2 raw band |
Shape Feature | Length/width | Pixel aspect ratio. |
Compactness | Describes the compactness of an image object. | |
Density | Describes the distribution of image objects in the pixel space. | |
Roundness | Describes how similar an image object is to an ellipse. | |
Rectangular Fit | Describes how well an image object matches rectangles of similar size and scale. | |
Texture | Homogeneity | Reflects the consistency or smooth texture in the image. A higher homogeneity index means that the texture of an image is more evenly distributed in space and the details change less. |
Dissimilarity | Used to measure the dissimilarity of textures in an image. Reflects the contrast or grayscale difference between pairs of pixels in an image. | |
Entropy | Reflects the organization structure and arrangement properties of the object surface, which is used to measure the uncertainty or confusion of the pixel gray level in an image. | |
Correlation | A statistic describing the degree of correlation of gray levels between pixels, measuring the linear correlation between the gray levels of pixels in an image, that is, the change trend of gray levels. | |
Vegetation Index | SAVI | Soil-Adjusted Vegetation Index [(B8 − B4) × (1 + L)/(B8 + B4 + L)] |
OSAVI | Optimized Soil-Adjusted Vegetation Index [(B8 − B4) × 1.16/(B8 + B4 + 0.16)] | |
NDVI | Normalized Difference Vegetation Index [(B8 − B3)/(B8 + B3)] | |
NDBI | Normalized Difference Built-Up Index [(B11 − B8)/(B11 + B8)] | |
MNDWI | Modified Normalized Difference Water Index [(B3 − B11)/(B3 + B11)] | |
Red edge index | NDVIre1 | Normalized Vegetation Red Edge1 [(B8A − B5)/(B8A + B5)] |
NDVIre2 | Normalized Vegetation Red Edge2 [(B8A − B6)/(B8A + B6)] | |
NDVIre3 | Normalized Vegetation Red Edge3 [(B8A − B7)(B8A + B7)] |
Year | Calculated Irrigated Area (hm2) | Statistics (hm2) | Difference (hm2) | Value of Error (%) |
---|---|---|---|---|
2023 | 47,900 | 48,867 | 967 | 1.97% |
2022 | 48,908 | 50,686 | 1778 | 3.5% |
2021 | 43,411 | 43,993 | 582 | 1.32% |
2020 | 42,915 | 44,746 | 1831 | 4.09% |
2019 | 44,417 | 44,526 | 109 | 0.24% |
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Yu, L.; Xie, H.; Xu, Y.; Li, Q.; Jiang, Y.; Tao, H.; Aihemaiti, M. Identification and Monitoring of Irrigated Areas in Arid Areas Based on Sentinel-2 Time-Series Data and a Machine Learning Algorithm. Agriculture 2024, 14, 1693. https://doi.org/10.3390/agriculture14101693
Yu L, Xie H, Xu Y, Li Q, Jiang Y, Tao H, Aihemaiti M. Identification and Monitoring of Irrigated Areas in Arid Areas Based on Sentinel-2 Time-Series Data and a Machine Learning Algorithm. Agriculture. 2024; 14(10):1693. https://doi.org/10.3390/agriculture14101693
Chicago/Turabian StyleYu, Lixiran, Hong Xie, Yan Xu, Qiao Li, Youwei Jiang, Hongfei Tao, and Mahemujiang Aihemaiti. 2024. "Identification and Monitoring of Irrigated Areas in Arid Areas Based on Sentinel-2 Time-Series Data and a Machine Learning Algorithm" Agriculture 14, no. 10: 1693. https://doi.org/10.3390/agriculture14101693