测绘通报 ›› 2026, Vol. 0 ›› Issue (4): 28-34,64.doi: 10.13474/j.cnki.11-2246.2026.0404

• 测绘地理信息赋能低空经济 • 上一篇    下一篇

低空遥感赋能高标准农田建设:测绘地理信息驱动的数字化管理新模式

于磊1, 张亚红2, 雷倩芳2, 刘艺炫2, 柴成富2, 贾康2   

  1. 1. 中煤数字科技(甘肃)有限公司, 甘肃 兰州 730030;
    2. 中煤航测遥感集团有限公司, 陕西 西安 710199
  • 收稿日期:2025-11-17 发布日期:2026-05-12
  • 作者简介:于磊(1993—),男,硕士,工程师,主要研究方向为无人机航测及三维建模。E-mail:yulei561488@163.com
  • 基金资助:
    2026年度甘肃省重大专项计划(26ZDLA003);中煤航测遥感集团有限公司科技创新资助项目(MHKJ-2025-1)

A surveying,mapping and geographic information-driven digital management framework for high-standard farmland based on low-altitude remote sensing

YU Lei1, ZHANG Yahong2, LEI Qianfang2, LIU Yixuan2, CHAI Chengfu2, JIA Kang2   

  1. 1. China Coal Digital Technology (Gansu)Co., Ltd., Lanzhou 730030, China;
    2. Aerial Photogrammetry and Remote Sensing Group Co., Ltd., Xi'an 710199, China
  • Received:2025-11-17 Published:2026-05-12

摘要: 在低空经济上升为国家战略性新兴产业的背景下,本文探讨了以测绘地理信息技术为核心的低空遥感体系如何为高标准农田建设、管理与使用全流程提供空间信息支持。研究构建了“空-天-地-网”四位一体的低空遥感监测架构,融合北斗卫星导航系统、5G通信、高分卫星、无人机遥感及地面传感器等多源地理空间数据,结合智能解译与时空分析模型,形成测绘地理信息驱动的数字化管理方法。在甘肃省环县的高标准农田监测试点中,该模式实现了农田工程设施自动识别精度超92%,作物长势分类精度达88.4%;在一次灌溉系统故障应急响应中,基于多源空间数据异常分析,仅用3 h即完成故障定位与预警。研究表明,以低空遥感为核心的测绘地理信息技术是实现高标准农田数字化、智能化管理的有效途径,为低空经济在农业领域的规模化应用提供了可复制的实践范式。

关键词: 低空遥感, 高标准农田, 测绘地理信息, 多源数据融合, 数字化管理

Abstract: With the low-altitude economy emerging as a national strategic industry,this paper explores how a low-altitude remote sensing system,driven by surveying,mapping and geographic information,can support the entire lifecycle of high-standard farmland,including construction,management,and utilization.An integrated “air-sky-ground-network” monitoring architecture was established by integrating multi-source geospatial data from BeiDou navigation,5G communication,high-resolution satellites,UAV remote sensing,and ground-based sensors.By incorporating AI-based image interpretation and spatio-temporal analysis models,a geomatics-driven digital management framework for high-standard farmland was developed.In a pilot application conducted in Huanxian county,Gansu province,the proposed framework achieved over 92% accuracy in automatic identification of farmland engineering facilities and 88.4% accuracy in crop growth status classification.In addition,during an emergency response to an irrigation system failure,anomalies in multi-source geospatial data enabled fault localization and early warning to be completed within three hours.The results demonstrate that the integration of low-altitude remote sensing with geomatics provides an effective solution for the digital and intelligent management of high-standard farmland.The proposed framework offers a replicable and scalable paradigm for the large-scale application of the low-altitude economy in agriculture.

Key words: low-altitude remote sensing, high-standard farmland, surveying,mapping and geographic information, multi-source data fusion, digital management

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