Key Laboratory of Spatio-temporal Information and Ecological Restoration of Mines of Natural Resources of the People's Republic of China, Henan Polytechnic University, Jiaozuo, China.
Henan Institute of Remote Sensing and Surveying and Mapping, Zhengzhou, China.
PLoS One. 2022 Aug 9;17(8):e0272300. doi: 10.1371/journal.pone.0272300. eCollection 2022.
Annual monitoring of the spatial distribution of cultivated land is important for maintaining the ecological environment, achieving a status quo of land resource management, and guaranteeing agricultural production. With the gradual development of remote sensing technology, it has become a common practice to obtain cultivated land boundary information on a large scale with the help of satellite Earth observation images. Traditional land use classification methods are affected by multiple types of land cover, which leads to a decrease in the accuracy of cultivated land mapping. In contrast, although the current advanced methods (such as deep learning) can obtain more accurate cultivated land mapping results than traditional methods, such methods often require the use of a massive amount of training samples, large computing power, and highly complex model tuning processes, increasing the cost of mapping and requiring the involvement of more professionals. This has hindered the promotion of related methods in mapping institutions. This paper proposes a method based on time series vector features (MTVF), which uses vector thinking to establish the features. The advantage of this method is that the introduction of vector features enlarges the differences between the different land cover types, which overcomes the loss of mapping accuracy caused by the influences of the spectra of different ground objects and ensures the calculation efficiency. Moreover, the MTVF uses a traditional method (random forest) as the classification core, which makes the MTVF less demanding than advanced methods in terms of the number of training samples. Sentinel-2 satellite images were used to carry out cultivated land mapping for 2020 in northern Henan Province, China. The results show that the MTVF has the potential to accurately identify cultivated land. Furthermore, the overall accuracy, producer accuracy, and user accuracy of the overall study area and four sub-study areas were all greater than 90%. In addition, the cultivated land mapping accuracy of the MTVF is significantly better than that of the maximum likelihood, support vector machine, and artificial neural network methods.
年度监测耕地的空间分布对于维护生态环境、实现土地资源管理现状以及保障农业生产至关重要。随着遥感技术的逐步发展，利用卫星对地观测图像获取大面积耕地边界信息已成为一种常见做法。传统的土地利用分类方法受到多种类型的土地覆盖物的影响，这导致耕地制图的准确性降低。相比之下，虽然当前的先进方法（如深度学习）可以获得比传统方法更准确的耕地制图结果，但这些方法通常需要大量的训练样本、大量的计算能力和高度复杂的模型调整过程，增加了制图成本并需要更多专业人员的参与。这阻碍了相关方法在制图机构中的推广。本文提出了一种基于时间序列向量特征（MTVF）的方法，该方法使用向量思维来建立特征。这种方法的优势在于，向量特征的引入扩大了不同土地覆盖类型之间的差异，克服了由于不同地面物体光谱的影响而导致的制图精度损失，保证了计算效率。此外，MTVF 使用传统方法（随机森林）作为分类核心，这使得 MTVF 在训练样本数量方面的要求低于先进方法。本文利用 Sentinel-2 卫星图像，对中国河南省北部 2020 年的耕地进行了制图。结果表明，MTVF 具有准确识别耕地的潜力。此外，整个研究区和四个子研究区的总体精度、生产者精度和用户精度均大于 90%。此外，MTVF 的耕地制图精度明显优于最大似然法、支持向量机和人工神经网络法。
