摘要
草原是蒙古高原最主要的植被类型,不仅是蒙古高原生态环境的重要组成部分,而且也是蒙古高原畜牧业发展的重要资源基础。产草量作为草地生产力的评价指标之一,对实现草畜平衡具有指导意义,然而由于长期依赖于人工调查,大范围、高空间分辨率和时间连续的产草量估算产品匮乏。本文以蒙古国为研究区,利用Landsat8遥感影像、MODIS遥感数据及气象数据,结合野外调查的产草量实测样方数据,通过深度神经网络获取实测产草量与植被指数NDVI、地表温度、降水量之间的关系,构建了适宜本区域特点的蒙古国产草量估算模型。建立深度神经网络产草量估算模型,反演获得蒙古国2017−2021年产草量时空分布图。精度验证实验表明,基于深度学习的模型精度较高,RMSE为12.14 g/m^(2),估算精度为81%,可为蒙古国产草量估算提供方法和数据参考。
Grassland is the dominant vegetation type on the Mongolian Plateau.It is not only an important part of the ecological environment of the Mongolian Plateau,but also an important resource base for the development of animal husbandry in the Mongolian Plateau.As one of the evaluation indicators of grassland productivity,the grass yield has guiding significance for striking the balance between grassland and livestock.However,due to the long-term dependence on artificial investigation,there is a shortage of products for estimating grass yield in a large range,high spatial resolution and continuous time.Taking Mongolia as the research area,in this paper,we used Landsat8 remote sensing image,MODIS remote sensing data and meteorological data in combination with the measured sample data of grass yield in the field survey to obtain the relationship between the measured grass yield and the vegetation index NDVI,surface temperature and precipitation through the depth neural network.In this way,we constructed the estimation model of Mongolia's domestic grass yield suitable for the characteristics of the region.Moreover,we establish a deep neural network estimation model for grass yield,and retrieved the temporal and spatial distribution map of grass yield in Mongolia from 2017 to 2021.The precision verification experiment shows that the model based on deep learning has a high precision,with an RMSE of 12.14 g/m^(2)and an estimation accuracy of 81%,which can provide a method and data reference for the estimation of domestic grassland in Mongolia.
作者
李梦晗
王卷乐
李凯
LI Menghan;WANG Juanle;LI Kai(College of Geoscience and Surveying Engineering,China University of Mining&Technology(Beijing),Beijing 100083,P.R.China;State Key Laboratory of Resources and Environmental Information System,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,P.R.China;Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application,Nanjing 210023,P.R.China)
基金
国家重点研发计划资助(2022YFE0119200)
国家自然科学基金(32161143025,41971385)
中国工程科技知识中心建设项目(CKCEST-2022-1-41)
资源与环境信息系统国家重点实验室自主创新项目(KPI006)。
作者简介
李梦晗(1999-),男,河南许昌人,硕士研究生,研究方向为遥感与地图制图。主要承担工作:基础数据处理、产草量估算与论文撰写;通信作者:王卷乐(1976-),男,河南洛阳人,博士,研究员,研究方向为资源生态环境数据集成与共享、GIS和遥感应用。主要承担工作:数据集整体设计、技术方案制定及技术指导。wangjl@igsnrr.ac.cn;李凯(1998-),男,江苏南京人,硕士研究生,研究方向为遥感地表信息提取。主要承担工作:产草量数据处理和精度评估。
