摘要
针对传统遥感解译效率低、区域高精度数据获取困难以及全国尺度产品难以满足小范围地类精细分析等问题,依托谷歌地球引擎平台,融合Landsat遥感影像与多源分类产品,构建2014—2024年呼伦贝尔市多时相土地利用/覆被数据集。在此基础上,采用FCN、Deeplabv3+、U-Net和VM-Unet等多种深度学习模型开展分类实验,并进一步分析研究区土地利用的时空演变特征以及自然与社会多重因子对变化过程的驱动作用。研究结果表明:(1)所构建的数据集能够有效支撑区域尺度分类任务,深度学习模型分类精度达到87.44%,Kappa系数为0.823,能够较为准确地反映土地利用/覆被动态特征;(2)近十年来,草地面积呈持续减少趋势,而耕地与不透水地表面积明显扩张,林地和水体总体保持相对稳定;(3)自然因子如DEM与坡度对耕地、林地与水体变化具有主导作用,而人口增长与经济发展则明显推动了不透水地表的扩张,同时多因子交互的综合驱动效应十分明显。
This study addressed the issues of low efficiency in traditional remote sensing interpretation,difficulties in acquiring high-precision regional data,and the challenge of nationwide products failing to meet the needs for fine-scale land classification analysis.Relying on the Google Earth Engine platform,this study integrated Landsat remote sensing images and multi-source classification products to construct a multi-temporal land use/land cover dataset for Hulun Buir City from 2014 to 2024.Based on this dataset,classification experiments were conducted using several deep learning models,including FCN,Deeplabv3+,U-Net,and VM-Unet.Additionally,the study further analyzed the spatial and temporal evolution characteristics of land use in the study area and the driving effects of natural and social factors on the change process.The results are as follows.(1)The constructed dataset effectively supports regional-scale classification tasks.The classification accuracy of the deep learning models reaches 87.44%,with a Kappa coefficient of 0.823,accurately reflecting the dynamic features of land use/land cover.(2)Over the past decade,the grassland area has shown a continuous decreasing trend,while the areas of arable land and impervious surface have significantly expanded.Forest land and water bodies have remained relatively stable overall.(3)Natural factors such as DEM and slope play a dominant role in the changes of arable land,forest land,and water bodies,while population growth and economic development have notably driven the expansion of impervious surfaces.Additionally,the comprehensive driving effects of multiple interacting factors are particularly evident.
作者
王鹏宇
包正义
白双成
魏薇
WANG Pengyu;BAO Zhengyi;BAI Shuangcheng;WEI Wei(College of Computer Science and Technology,Inner Mongolia Normal University,Hohhot 010022,China)
出处
《内蒙古师范大学学报(自然科学版)》
2026年第2期195-204,共10页
Journal of Inner Mongolia Normal University(Natural Science Edition)
基金
国家自然科学基金资助项目“基于‘天-空-地’多源数据融合的三维大气CO_(2)浓度模拟研究”(42467062)
内蒙古自治区自然科学重点资助项目“基于无穷维哈密顿系统及其算法的蒙古文智能信息处理模型与算法研究”(2023ZD10)
内蒙古自治区自然科学基金资助项目“蒙古高原大气CO_(2)浓度时空变化及其与气象要素的响应关系研究”(2022LHQN04002)
内蒙古师范大学课题基金资助项目“内蒙古地区XCO_(2)时空变化特征研究”(2021YJRC012)。
关键词
深度学习
土地利用/覆盖变化
转移矩阵
地理探测器
语义分割
deep learning
land use/cover change
transition matrix
geographic detector
semantic segmentation
