基于遥感的河北省太行山区耕地质量评价与提升分区划定

Cultivated land quality evaluation and improvement zoning delineation in Taihang Mountain area of Hebei Province based on remote sensing

关键词:  耕地质量评价  障碍因子识别  空间自相关分析  河北省太行山区

Key words:  cultivated land quality evaluation  obstacle factor identification  spatial autocorrelation analysis  Taihang Mountain area  Hebei Province

摘要:

耕地质量评价是耕地资源保护、质量提升和农业可持续利用的重要基础。针对山区耕地地形复杂、地块破碎、空间异质性强等特点，文章以河北省太行山区为研究区，基于压力-状态-响应模型，以2022年4—8月覆盖冬小麦抽穗期与收获期、玉米播种期与抽穗期的连续时段Sentinel-2遥感影像为基础，结合DEM、Landsat 8、土壤养分和土壤水分等多源数据，构建耕地质量综合评价指标体系，并结合障碍度模型和局部空间自相关分析，识别耕地质量空间分异特征、主导障碍因素及提升分区。结果表明：（1）河北省太行山区耕地质量等级可划分为五至十等，其中，七等地和八等地为主要类型，合计占比达93.15%，整体处于中下等水平；耕地质量空间上呈现明显“东高西低”格局，并随海拔升高而下降；（2）障碍度分析显示，研究区耕地质量限制因素以土壤养分主导的状态障碍为主，占比90.05%，是制约区域耕地质量提升的关键因素；（3）局部空间自相关结果表明，耕地质量具有显著空间集聚特征，高-高型和低-低型聚类占总体的65.21%。在此基础上，将研究区划分为提升利用区、保护利用区和合理利用区，占比分别为32.28%、29.47%和38.25%。研究结果可为河北省太行山区耕地质量精准提升、差异化管护和土地资源可持续利用提供科学依据。

Abstract:

Background, aim, and scope Cultivated land quality evaluation is an important scientific basis for cultivated land resource protection, quality improvement, and sustainable agricultural utilization. In mountainous areas, cultivated land is commonly characterized by complex terrain, fragmented plots, and strong spatial heterogeneity, which increases the difficulty of accurate quality assessment and targeted management. This study aimed to construct a cultivated land quality evaluation framework suitable for complex mountainous regions, reveal the spatial differentiation characteristics and dominant obstacle factors of cultivated land quality, and provide support for differentiated improvement zoning. Materials and methods Taking the Taihang Mountain area (36°—41°N, 113°—116°E) of Hebei Province, which covers 31 districts and counties belonging to five prefecture-level cities including Shijiazhuang, Baoding, Xingtai, Handan and Zhangjiakou, as the study area, and based on the Pressure-State-Response model, this study integrated multi-source data, including DEM, Sentinel-2 imagery covering the winter wheat heading and harvest stages and the maize sowing and heading stages from April to August 2022, Landsat 8, soil nutrients, and soil moisture data, to construct a comprehensive cultivated land quality evaluation index system. Slope and NDRE were used to represent production pressure, soil nutrients, soil moisture, and NDVI were selected to characterize cultivated land status, and TVDI was introduced as an indirect indicator of irrigation response. The analytic hierarchy process was used to determine indicator weights. The cultivated land quality index was then calculated, and the quality grades were classified. Furthermore, the obstacle degree model was applied to identify the dominant limiting factors, and local spatial autocorrelation analysis was used to reveal the spatial clustering characteristics of cultivated land quality. On this basis, cultivated land quality improvement zones were delineated. Results The cultivated land quality in the Taihang Mountain area of Hebei Province could be classified into grades Ⅴ to Ⅹ. Grade Ⅶ and grade Ⅷ cultivated land were the dominant types, accounting for 93.15% in total, indicating that the overall cultivated land quality was at a medium-low level. Spatially, cultivated land quality showed an obvious pattern of "higher in the east and lower in the west" and decreased with increasing elevation. The obstacle degree analysis showed that the state-type obstacles dominated by soil nutrient deficiency were the main limiting factors, accounting for 90.05%, which constituted the key constraint on the improvement of regional cultivated land quality. The local spatial autocorrelation results indicated that cultivated land quality had significant spatial clustering characteristics, with high-high and low-low clusters accounting for 65.21% of the total. According to the evaluation results, obstacle factors, and spatial clustering characteristics, the study area was divided into improvement utilization zones, protection utilization zones, and rational utilization zones, accounting for 32.28%, 29.47%, and 38.25%, respectively. Discussion The spatial pattern of cultivated land quality was closely related to the geomorphic background of the Taihang Mountain area. The eastern piedmont plain had relatively better natural and production conditions, while the western mountainous and hilly areas were more strongly constrained by elevation, slope, soil erosion risk, and fragmented land distribution. Soil nutrient deficiency was identified as the core driver of the state-type obstacles. Steep slopes and thin topsoil in mountainous terrain trigger severe soil erosion and nutrient depletion, while inadequate agricultural investment under conventional farming hinders nutrient restoration via fertilization and crop rotation in piedmont plains. The spatial clustering results further suggested that cultivated land quality was not randomly distributed, but showed clear spatial continuity, which provides a practical basis for regionalized management and targeted improvement. Conclusions The PSR-based multi-source remote sensing evaluation framework can effectively reveal the cultivated land quality pattern, limiting factors, and improvement direction in complex mountainous areas. The cultivated land quality in the Taihang Mountain area of Hebei Province is generally at a medium-low level, with obvious spatial differentiation and significant clustering characteristics. Soil nutrient deficiency is the primary factor restricting cultivated land quality improvement. The zoning results provide a scientific basis for precise improvement, differentiated management, and sustainable utilization of cultivated land resources. Recommendations and perspectives In the future, the evaluation results should be further connected with national cultivated land quality standards through field sampling and long-term monitoring. More high-resolution and multi-temporal remote sensing data should be introduced to improve the monitoring of crop growth, soil degradation, water stress, and management intensity. In addition, the ecological and economic effects of different improvement measures should be verified through field experiments and localized assessments, so as to promote the application of cultivated land quality evaluation results in land management, high-standard farmland construction, and sustainable agricultural development in mountainous regions.