中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 地理科学与资源研究所
  3. 中国科学院地理科学与资源研究所
An Integrated CNN Model for Reconstructing and Predicting Land Use/Cover Change: A Case Study of the Baicheng Area, Northeast China

文献类型：期刊论文

作者Zhang, Yubo1,2; Yang, Jiuchun2; Wang, Dongyan1; Wang, Jing3; Yu, Lingxue2; Yan, Fengqin4; Chang, Liping2; Zhang, Shuwen2
刊名REMOTE SENSING
出版日期2021-12-01
卷号13期号:23页码:21
关键词land use and land cover change machine learning algorithms convolutional neural networks deep learning
DOI10.3390/rs13234846
通讯作者Yang, Jiuchun(yangjiuchun@iga.ac.cn)
英文摘要Land use and land cover change (LUCC) modeling has continuously been a major research theme in the field of land system science, which interprets the causes and consequences of land use dynamics. In particular, models that can obtain long-term land use data with high precision are of great value in research on global environmental change and climate impact, as land use data are important model input parameters for evaluating the effect of human activity on nature. However, the accuracy of existing reconstruction and prediction models is inadequate. In this context, this study proposes an integrated convolutional neural network (CNN) LUCC reconstruction and prediction model (CLRPM), which meets the demand for fine-scale LUCC reconstruction and prediction. This model applies the deep learning method, which far exceeds the performance of traditional machine learning methods, and uses CNN to extract spatial features and provide greater proximity information. Taking Baicheng city in Northeast China as an example, we verify that CLRPM achieved high-precision annual LUCC reconstruction and prediction, with an overall accuracy rate 9.38% higher than that of the existing models. Additionally, the error rate was reduced by 49.5%. Moreover, this model can perform multilevel LUCC classification category reconstructions and predictions. This study casts light on LUCC models within the high-precision and fine-grained LUCC categories, which will aid LUCC analyses and help decision-makers better understand complex land-use systems and develop better land management strategies.
WOS关键词CELLULAR-AUTOMATA ; LEARNING CLASSIFICATION ; ZHENLAI COUNTY ; TRAINING DATA ; COVER CHANGE ; USE LEGACIES ; PATTERNS ; CLIMATE ; FUTURE ; FOREST
资助项目Strategic Priority Research Program of the Chinese Academy of Sciences[No.XDA2003020103] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA23060405]
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
出版者MDPI
WOS记录号WOS:000734692700001
资助机构Strategic Priority Research Program of the Chinese Academy of Sciences
源URL[http://ir.igsnrr.ac.cn/handle/311030/168941]  
专题中国科学院地理科学与资源研究所
通讯作者Yang, Jiuchun
作者单位1.Jilin Univ, Coll Earth Sci, Changchun 130021, Peoples R China
2.Chinese Acad Sci, Northeast Inst Geog & Agroecol, Changchun 130102, Peoples R China
3.Northeast Agr Univ, Coll Publ Adm & Law, Harbin 150036, Peoples R China
4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Yubo,Yang, Jiuchun,Wang, Dongyan,et al. An Integrated CNN Model for Reconstructing and Predicting Land Use/Cover Change: A Case Study of the Baicheng Area, Northeast China[J]. REMOTE SENSING,2021,13(23):21.
APA Zhang, Yubo.,Yang, Jiuchun.,Wang, Dongyan.,Wang, Jing.,Yu, Lingxue.,...&Zhang, Shuwen.(2021).An Integrated CNN Model for Reconstructing and Predicting Land Use/Cover Change: A Case Study of the Baicheng Area, Northeast China.REMOTE SENSING,13(23),21.
MLA Zhang, Yubo,et al."An Integrated CNN Model for Reconstructing and Predicting Land Use/Cover Change: A Case Study of the Baicheng Area, Northeast China".REMOTE SENSING 13.23(2021):21.

入库方式： OAI收割

来源：地理科学与资源研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。