Frontiers of Data and Computing ›› 2026, Vol. 8 ›› Issue (5): 48-60.

CSTR: 32002.14.jfdc.CN10-1649/TP.2026.05.004

doi: 10.11871/jfdc.issn.2096-742X.2026.05.004

• Special Issue: ORISE Supercomputing Application • Previous Articles     Next Articles

Evaluation of Cultivated Land Ecological Elements and Clustering Zoning Protection Based on Multi-Source Data: A Case Study of Hailun Farm

KANG Meiqi1(),WANG Chongchang1,YU Lingxue2,3,YANG Yuewen4,YAN Zhuoran2,3,*()   

  1. 1 College of Geomatics, Liaoning Technical University, Fuxin, Liaoning 123000, China
    2 Key Laboratory of Mollisols Agroecology, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, Jilin 130102, China
    3 Jilin Provincial Key Laboratory of Ecological Environment Remote Sensing Big Data, Changchun, Jilin 130102, China
    4 College of Economics and Management, Southwest Forestry University, Kunming, Yunnan 650224, China
  • Received:2026-04-23 Online:2026-10-20 Published:2026-10-20

Abstract:

[Objective] A scientific understanding of the spatial heterogeneity of multidimensional ecological elements in cultivated land is of great significance for disentangling the trade-offs between agricultural grain production and other ecological services. [Methods] Taking Hailun Farm in Heilongjiang Province as the study area, this research constructed a soil-crop-landscape multidimensional ecological evaluation system based on multi-source data. Hot and cold spot analysis and local bivariate spatial autocorrelation methods were applied to reveal the spatial differentiation patterns and interactions of multidimensional ecological elements, and a self-organizing map (SOM) neural network was employed to delineate comprehensive zones for cultivated land ecological management. [Results] The results indicate that: (1) The grades of multidimensional ecological elements of cultivated land in the study area exhibit distinct differentiation. Specifically, the soil environment is predominantly of medium grade and mostly locally clustered in the western part of the study area; plots with high-grade crop growth are the most numerous and show spatial homogeneity; and plots with low-grade landscape ecology are the most widely distributed, presenting a strong risk of spatial fragmentation. (2) The numbers of plots showing trade-offs and synergies between the landscape and soil dimensions are relatively balanced, whereas the spatial trade-offs between crop growth and other ecological elements are more prominent. The current intensive agricultural model leads to an over-reliance on artificial inputs to maintain excellent crop growth, and the presently fragmented internal connections within the agroecosystem pose a potential threat to the sustainable utilization of soil fertility in Hailun Farm. (3) Following the clustering principle of minimizing intra-group variance and maximizing inter-group variance, the study area is divided into four types of zones: comprehensive remediation zone, ecological optimization zone, quality and efficiency enhancement zone, and ecological conservation zone. Targeted environmental governance and ecological conservation should be implemented in different zones based on their respective limiting factors. [Conclusions] This study provides a scientific reference for the multidimensional ecological assessment and differentiated restoration strategies of cultivated land in Hailun Farm and other intensive agricultural areas.

Key words: cultivated land protection, ecological factors, spatial clustering, artificial neural networks, remote sensing assessment