地球信息科学学报 ›› 2022, Vol. 24 ›› Issue (2): 391-404.doi: 10.12082/dqxxkx.2022.210138
詹琪琪1,2(), 赵伟1,*(
), 杨梦娇1,2, 付浩1,3, 李昕娟1,2, 熊东红1
收稿日期:
2021-03-17
修回日期:
2021-04-22
出版日期:
2022-02-25
发布日期:
2022-04-25
通讯作者:
*赵 伟(1984— ),男,江西上高人,博士,研究员,主要从事山地地表水热过程遥感监测与应用研究。 E-mail: zhaow@imde.ac.cn作者简介:
詹琪琪(1998— ),女,四川广安人,硕士生,主要从事土地沙化遥感监测研究。E-mail: zhanqq@imde.ac.cn
基金资助:
ZHAN Qiqi1,2(), ZHAO Wei1,*(
), YANG Mengjiao1,2, FU Hao1,3, LI Xinjuan1,2, XIONG Donghong1
Received:
2021-03-17
Revised:
2021-04-22
Online:
2022-02-25
Published:
2022-04-25
Supported by:
摘要:
雅鲁藏布江中部流域长期遭受土地沙化侵蚀,采取有效手段进行沙化土地信息快速识别,跟踪土地沙化现状和动态发展,是土地沙化防治的基本前提。遥感数据因其快速、大范围、高精度监测等特点已被广泛应用于土地沙化监测。为降低该区域沙化土地破碎化分布特征以及广泛分布的稀疏植被地表对沙化土地遥感识别带来的不确定性,本文利用Google Earth Engine平台获取2019年秋季雅鲁藏布江中部流域Landsat无云遥感影像,基于面向对象的分类思想,充分提取沙化土地的光谱、几何和地形特征,根据不同的分类器构建4种分类方案,包括单一分类器(支持向量机、决策树、最近邻)分类以及组合分类法分类,提取雅江中游河谷地区沙化土地信息并验证不同方案的提取精度。结果表明:① 利用面向对象组合分类模型提取的沙化土地信息效果最佳,总体精度高达91.38 %,Kappa系数为0.82;② 相较于采用单一分类器(支持向量机、最近邻和决策树分类)的面向对象分类方法,组合分类模型能更有效地识别破碎化的小面积沙化土地,降低沙化土地与稀疏植被地表的混淆情况,提高分类可靠性;③ 基于面向对象组合分类模型反演得到雅鲁藏布江中部流域2019年沙化土地分布信息,土地沙化面积达299.61 km2,总体上呈现沿河谷的带状不连续分布,且集中分布于河流北岸以及靠近河道的阳坡、低海拔地区。本研究可为土地沙化遥感监测提供新思路,其应用可服务于雅鲁藏布江中部流域土地沙化预防和治理工作。
詹琪琪, 赵伟, 杨梦娇, 付浩, 李昕娟, 熊东红. 雅鲁藏布江中部流域土地沙化遥感识别[J]. 地球信息科学学报, 2022, 24(2): 391-404.DOI:10.12082/dqxxkx.2022.210138
ZHAN Qiqi, ZHAO Wei, YANG Mengjiao, FU Hao, LI Xinjuan, XIONG Donghong. Identification of Sandy Land in the Midstream of the Yarlung Zangbo River[J]. Journal of Geo-information Science, 2022, 24(2): 391-404.DOI:10.12082/dqxxkx.2022.210138
表1
特征数据集信息
特征变量类别 | 变量名称 | 特征说明 |
---|---|---|
光谱特征 | 波段均值(Band Mean) | Landsat-8/OLI波段1—7地表反射率 |
相邻对象的差异(Mean Difference to Neighbors) | Landsat-8/OLI波段1—7地表反射率差异 | |
几何特征 | 面积(Area) | 组成影像对象的像元数目 |
长宽比(Length/Width) | 包围影像对象的最小矩形的长宽比 | |
形状指数(Shape index) | 影像对象的周长与4倍面积平方根的比值 | |
地形特征 | 海拔(Elevation) | 组成影像对象的像元平均海拔 |
坡度(Slope) | 组成影像对象的像元平均坡度 | |
坡向(Aspect) | 组成影像对象的像元平均坡向 | |
专题指数 | 归一化植被指数(Normalized Difference Vegetation Index, NDVI) | |
新型水体指数(New Water Index, NWI) | | |
改进的土壤调节植被指数(Modified Soil Adjusted Vegetation Index, MSAVI) | |
表2
4种分类方法的分类结果精度评价
支持向量机 | 最近邻分类 | 决策树分类 | 组合分类法 | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
沙化 土地 | 非沙化 土地 | 总计 | 沙化 土地 | 非沙化 土地 | 总计 | 沙化 土地 | 非沙化 土地 | 总计 | 沙化 土地 | 非沙化 土地 | 总计 | ||||
沙化土地 | 107 | 26 | 133 | 102 | 31 | 133 | 108 | 25 | 133 | 116 | 17 | 133 | |||
非沙化土地 | 28 | 164 | 192 | 14 | 178 | 192 | 20 | 172 | 192 | 11 | 181 | 192 | |||
总计 | 135 | 190 | 116 | 209 | 128 | 197 | 127 | 198 | |||||||
生产者精度/% | 80.45 | 85.42 | 76.69 | 92.71 | 81.20 | 89.58 | 87.22 | 94.27 | |||||||
用户精度/% | 79.26 | 86.32 | 87.93 | 85.17 | 84.38 | 87.31 | 91.34 | 91.41 | |||||||
总体精度/% | 83.38 | 86.15 | 86.15 | 91.38 | |||||||||||
Kappa系数 | 0.66 | 0.71 | 0.71 | 0.82 |
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