Parcel-Level Mapping of Horticultural Crops in Mountain Areas Using Irregular Time Series and VHR Images Taking Qixia, China as An Example
Abstract
:1. Introduction
2. Study Area and Dataset
2.1. Study Area
2.2. Remote Sensing Data
2.2.1. Regular Time Series Image
2.2.2. Irregular Time Series Image
2.3. Field Survey Data
3. Method
- Parcel extraction framework based on VHR images. Taking Google Earth images as the data source, the candidate parcels in the study area are extracted using the geographical idea of zoning and hierarchical strategies and the deep learning algorithms.
- Crop classification based on irregular time series. Based on the Sentinel-2 images, the irregular time series growth characteristics of crops are constructed, and the 2DCNN algorithm is used to classify the horticultural crops;
- Parcel-type filling. In view of the complex agricultural planting situation in the study area, a parcel-type filling strategy is designed. According to the filling strategy, the optimal classification result obtained in the second part is used to obtain the parcel category, and finally the parcel-level orchard crop planting structure mapping is realized.
3.1. Parcel Extraction Based on Hierarchical Extraction Scheme
3.2. Classification Method Based on Irregular Time Series
3.2.1. Classification Feature Selection
3.2.2. Irregular Time Series
- Overlapping area between different strips. The observation range of remote sensing satellites generally overlaps between different strips. The number of images in the overlapping area is higher than that in the non-overlapping area;
- Cloud cover. Clouds on optical images will seriously affect the image quality, and the number of effective observations will be different in areas covered by cloud and those without cloud;
- Scale of the study area. For study areas with different scales, the number of images that need to be covered is different. If these images are in different bands, image acquisition dates will vary.
- The number of time series images in different grids is the same, but the image acquisition date is different, such as A and B in Figure 6;
- The image acquisition date is the same, but the total number is different, such as A and E in Figure 6;
- The number of time series images is different, and the image acquisition date is also different, such as A and C, D and F.
3.2.3. 2DCNN for Irregular Time Series
3.2.4. Contrast Experiment
3.3. Classification Result Filling Strategy
- Typical mixing in the study area. The study area is a typical small farm planting area, and the orchard does not have the capacity for scientific planting planning. The market value of cherries is higher than that of apples. Farmers take economic benefits as the leading factor in the adjustment of the planting structure, and they gradually plant many cherry trees in apple orchards. However, there is no obvious regular boundary between cherry trees and apple trees, which leads to the phenomenon of mixed parcels.
- In VHR images, the texture of apple orchards and cherry orchards is very similar. When extracting parcels from slope areas to ensure the correctness of parcel attributes, and taking these two orchards as one type, the parcel extraction experiment based on texture structure was carried out. DABNet can only obtain orchard parcels from VHR images; it cannot obtain detailed apple orchards or cherry orchards. If the model is trained with apple and cherry orchards as sample categories, the effect of parcel extraction will be greatly compromised, and many slope parcels will be over-zoned, making them very different from the actual parcels, and they will lose semantic information.
3.4. Accuracy Evaluation
3.4.1. Precision Evaluation of Parcel Extraction Results
3.4.2. Precision Evaluation of Classification Results
4. Result
4.1. Candidate Parcel Extraction Results
4.2. Analysis of Classification Experiment Results
4.3. Parcel Filling Result
5. Discussion
- Based on the method proposed in this study, parcel-level orchard mapping experiments were successfully carried out in smallholder agricultural areas, and it was demonstrated that parcel-level mapping results can provide more abundant crop planting information than pixel-level classification results.
- Irregular time series are used to construct crop growth characteristics. The classification results show that this method can make full use of optical image information and can solve the problem of missing data in key growth periods in regular time series. The feature mining of time series data is promoted from the first layer to the third layer, and the dependence between time series can be deeply mined.
- The extraction result of the parcels is only used as the spatial constraint of the pixel-level result, to avoid the impact of the error of parcel extraction on the final mapping result. Considering the possible mixed planting situation in parcels, the obtained statistical information has higher accuracy.
- The requirements for sample distribution are higher. Irregular time series are built independently in each grid. Within the grid, the time series characteristics (length, date distribution) are consistent. This requires that when preparing sample data, there should be a distribution of the sample data in each grid. Therefore, the question of how to enhance the model to learn more detailed and profound features between the different grid time series is the focus of future research.
- The 2DCNN model is used in the classification. In order to obtain the input format supported by the model, the time series data are stretched into a three-dimensional image. During this process, the sparse matrix is obtained. Although the classification effect has been optimized, there is still room for improvement. It is believed that if the feature matrix can be supplemented with information by fusing SAR data and other multi-source data, better classification results will be achieved.
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Data Attribute | Description | ||
---|---|---|---|
Product Type | Level 2A | ||
Pixel size | 10 m | ||
Spectral Bands | Band | Central wavelength(nm) | Bandwidth (nm) |
B2: Blue | 490 | 65 | |
B3: Green | 560 | 35 | |
B4: Red | 665 | 30 | |
B8: NIR | 842 | 115 |
Parcel Type | Train Data | Validation Data |
---|---|---|
Apple | 26,728 | 6682 |
Seedling | 11,448 | 2862 |
Cherry | 2632 | 658 |
Greenhouse | 3044 | 761 |
Geographic Area | Farmland Type | Features |
---|---|---|
Plain Area | Greenhouse parcels | Regular shape, clear boundary, distributed in plain areas, and different from the surrounding crop background. |
Regular parcels | Plain parcels, regular shape, clear boundaries, uniform internal texture, and uniform area. | |
Mountain Area | Terrace parcels | Long and narrow shape with uniform width, clear boundaries, uniform internal texture, and regular arrangement. |
Slope parcels | Fuzzy boundary, uniform internal texture, irregular shape, irregularly distributed on the hillside, and great difference in area. |
Accuracy Evaluation Index | Radius of Buffer | |
---|---|---|
1 m | 2 m | |
Recall | 0.97 | 0.98 |
Precious | 0.89 | 0.94 |
F1-score | 0.93 | 0.96 |
IoU | 0.872 |
Parcel Type | Number | Area (m2) |
---|---|---|
Apple orchard | 415,233 | 79,9424,375.7 () |
Cherry orchard | 19,455 | 23,766,845.11 () |
Seedling | 4031 | 31,212,521.33 |
Greenhouse | 411 | 822,299.1387 |
Mixed parcel | 30,724 | 42,699,347 () |
33,875,728 () |
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Jiao, S.; Shen, Z.; Kou, W.; Wang, H.; Li, J.; Jiao, Z.; Lei, Y. Parcel-Level Mapping of Horticultural Crops in Mountain Areas Using Irregular Time Series and VHR Images Taking Qixia, China as An Example. Remote Sens. 2023, 15, 175. https://doi.org/10.3390/rs15010175
Jiao S, Shen Z, Kou W, Wang H, Li J, Jiao Z, Lei Y. Parcel-Level Mapping of Horticultural Crops in Mountain Areas Using Irregular Time Series and VHR Images Taking Qixia, China as An Example. Remote Sensing. 2023; 15(1):175. https://doi.org/10.3390/rs15010175
Chicago/Turabian StyleJiao, Shuhui, Zhanfeng Shen, Wenqi Kou, Haoyu Wang, Junli Li, Zhihao Jiao, and Yating Lei. 2023. "Parcel-Level Mapping of Horticultural Crops in Mountain Areas Using Irregular Time Series and VHR Images Taking Qixia, China as An Example" Remote Sensing 15, no. 1: 175. https://doi.org/10.3390/rs15010175
APA StyleJiao, S., Shen, Z., Kou, W., Wang, H., Li, J., Jiao, Z., & Lei, Y. (2023). Parcel-Level Mapping of Horticultural Crops in Mountain Areas Using Irregular Time Series and VHR Images Taking Qixia, China as An Example. Remote Sensing, 15(1), 175. https://doi.org/10.3390/rs15010175