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- W4317814797 abstract "Abstract The deployment of scientific and reasonable cultivated land quality (CLQ) monitoring points can provide timely and accurate information on the current situation and changes in CLQ. The conventional method of selecting CLQ monitoring points are based on the CLQ of land use patches including different grades of large patches, which reduces the reliability of monitoring CLQ. Moreover, the conventional method mainly considers only CLQ, resulting in the inaccessibility of some monitoring points. There exist knowledge gaps in deploying reliable CLQ monitoring sites in the present. Thus, to improve the reliability of CLQ monitoring, this study presented a novel approach for deploying CLQ monitoring points. The pixel‐scale CLQ was firstly estimated using the genetic algorithm‐back propagation neural network (GA‐BPNN) model and LANDSAT image‐derived predictors. Then, the stratified sampling model and the improved spatial simulated annealing algorithm (ISSA), considering both slope and road accessibility were applied to deploy monitoring points. The results highlighted that (1) the pixel‐scale CLQ data were more reasonable than the patch‐scale CLQ data with different grades. (2) A total of 132 monitoring points using the stratified sampling model and ISSA were finally identified in the study area, which can effectively avoid the inaccessible places. Thus, the results based on the novel approach proposed in this study provide a scientific basis and technical support for obtaining the optimal CLQ monitoring points." @default.
- W4317814797 created "2023-01-24" @default.
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- W4317814797 date "2023-02-13" @default.
- W4317814797 modified "2023-10-15" @default.
- W4317814797 title "Optimal deployment of cultivated land quality monitoring points based on satellite image‐driven assessment and improved spatial simulated annealing" @default.
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- W4317814797 doi "https://doi.org/10.1002/ldr.4614" @default.
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