Optimal deployment of cultivated land quality monitoring points based on satellite image‐driven assessment and improved spatial simulated annealing

Bibliographic Data

ID21649239
AuthorsWenhao Yang (0000-0003-2837-1412, College of Natural Resources and Environment South China Agricultural University Guangzhou PR China), Yiping Peng (0000-0003-4892-9698, College of Natural Resources and Environment South China Agricultural University Guangzhou PR China), Chenjie Lin (College of Natural Resources and Environment South China Agricultural University Guangzhou PR China), Hao Yang (0000-0003-2138-7307, College of Natural Resources and Environment South China Agricultural University Guangzhou PR China), Xinrong Cheng (College of Natural Resources and Environment South China Agricultural University Guangzhou PR China), Xiaofang Wu (0000-0002-3399-2961, College of Natural Resources and Environment South China Agricultural University Guangzhou PR China), Ya Wen (0000-0002-1942-2110, College of Natural Resources and Environment South China Agricultural University Guangzhou PR China), Zhenhua Liu (0009-0006-6126-1708, College of Natural Resources and Environment South China Agricultural University Guangzhou PR China, corresponding author)
Year2023
Volume34
Issue8
Pages2379-2392
Publication date2023-05-15
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueLand Degradation and Development (JOURNAL)
Journal identifiersISSN: 1085-3278 • E-ISSN: 1099-145X
PublisherWiley (PUBLISHER • GB)
DOI10.1002/ldr.4614
OpenAlexW4317814797
LanguageEN
References cited53

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

Algorithm · Cartography · Data mining · Detector · Genetic algorithm · Geography · Machine learning · Pixel · Remote sensing · Satellite · Simulated annealing · Software deployment · Telecommunications · Computer Science · Engineering · Environmental Science · Land Use and Ecosystem Services · Remote Sensing in Agriculture · Soil and Land Suitability Analysis · Artificial Intelligence

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