基于高分影像特征优选的黄土高原撂荒耕地遥感监测方法

快速、准确地监测撂荒耕地对研究土地利用变化、保障国家粮食安全和制定农业政策有着重要意义，黄土高原地区复杂地貌和耕地破碎性对监测撂荒耕地颇具挑战性。该研究以国产高分卫星遥感影像为主要数据源，使用耦合遗传算法的离散二进制粒子群算法改进最佳指数因子法优选分类特征因子，采用随机森林分类和分类后变化检测方法，获得试验区2011—2022年撂荒耕地时空分布信息，通过影像目视判读和实地调查相结合的方式进行结果验证和精度评价，结果表明：1）经重要性评估发现NDVI平均精度减少（mean decrease accuracy，MDA）得分最高，经改进的优选特征方法优选特征为：绿波段、红波段、近红外波段、蓝绿波段比值指数、坡度、NDVI、方差、对比度，特征优选有效提高了高分影像的土地利用分类精度和效率；2）国产高分卫星数据可实现地块尺度撂荒耕地的精准识别，经验证，撂荒耕地识别总体精度达到92.48%；3）2011—2022年间研究区撂荒耕地总面积为5492.51 hm²，2011—2014年撂荒率最大，达21.09%，2020—2022年撂荒率最小，为0.99%。该研究构建了黄土高原地区地块尺度撂荒耕地遥感监测的方法，并进行验证和评价，对推动撂荒耕地相关研究和国产高分卫星数据的实践应用具有积极意义。

Monitoring method for abandoned farmland on the Loess Plateau based on feature optimization of remote sensing images with high spatial resolution

A great challenge has been found to limit the per capita cultivated land and the scarcity of land resources in China. However, the cultivated land can be abandoned across the country with the rapid growth of industrialization and urbanization. It is crucial to accurately and rapidly monitor the abandoned farmland with high accuracy for farmland protection. Land use can also be evaluated to guarantee national food security for the decision-making on modern agriculture. Among them, there is fragmented farmland with a wide distribution in the Loess Plateau of China. The complex terrain has made some challenge to investigate and monitor the abandoned farmland. This study aims to monitor the abandoned farmland on the loess plateau using feature optimization. Data sources were collected from the Chinese satellite images with high spatial resolution and multi-spectrum. Some feature factors were selected to effectively distinguish the cultivated land from other land types. The prior knowledge was combined with the distinct separability, including the spectral, phenological, topographic and texture features. The spectral feature differences were statistically analyzed from May to October. Spectral time series feature factors were developed to prove the effectiveness and superiority of the schemes. Five types of experimental scheme were also constructed for comparison. The binary particle swarm optimization (BPSO) coupled with the genetic algorithm (GA) was selected to modify the index factor for the optimal selection of feature factors in classification. Random forest (RF) classification and detection were performed post-classification. The spatiotemporal distribution was obtained for the abandoned farmland at field scale between 2011 and 2022. The results showed that the feature optimization improved the accuracy and efficiency in the classification of remotely sensed images with the high spatial resolution. According to an importance assessment for all features, NDVI was found to have the highest MDA score. The final preferred feature was ranked in the order of the green band, red band, near-infrared band, (b1-b2)/(b1+b2), Slope, NDVI, Variance and Contrast. The combination of GA and BPSO exhibited high efficiency in the global search and search speed. The Optimum Index Factor effectively evaluated the overall combination. Feature optimization can be expected to avoid information redundancy and over-fitting issues. The field validation was performed using image interpretation and field investigation. It infers that the abandoned farmland at field scale was recognized from the high spatial resolution images of Chinese satellites with an accuracy as high as 92.48%. There was a 96% overlapping ratio between patches from field investigation and those extracted from images. Among them, the recognition rate of fragmented patches with areas as small as 200-400m² reached 90.11%. The classification showed that there was abandoned farmland with a total area of 5492.51hm² between 2011 and 2022, indicating the maximum abandoned percentage (21.09%) in 2011-2014 and the minimum abandoned percentage (0.99%) in 2020-2022. The large-scale pattern of abandonment of farmland was gradually shifted into a smaller fragmented one by peasant households over this period. As such, the abandoned farmland was monitored at a field scale on the loess plateau region using remote sensing. The finding can provide a strong reference to explore the abandoned farmland using high spatial resolution images of Chinese satellites.