Applications of Machine Learning and Remote Sensing in Soil and Water Conservation

被引:1
|
作者
Kim, Ye Inn [1 ]
Park, Woo Hyeon [1 ]
Shin, Yongchul [2 ]
Park, Jin-Woo [3 ]
Engel, Bernie [4 ]
Yun, Young-Jo [5 ]
Jang, Won Seok [5 ]
机构
[1] Kangwon Natl Univ, Dept Landscape Architecture, Chunchon 24341, South Korea
[2] Kyungpook Natl Univ, Dept Agr Civil Engn, Daegu 41566, South Korea
[3] Kangwon Natl Univ, Coll Forest & Environm Sci, Div Forest Sci, Chunchon 24341, South Korea
[4] Purdue Univ, Dept Agr & Biol Engn, W Lafayette, IN 47907 USA
[5] Kangwon Natl Univ, Dept Ecol Landscape Architecture Design, Chunchon 24341, South Korea
关键词
machine learning; remote sensing; soil conservation; water conservation; environmental analysis; data-driven decision-making; resource management; SPATIAL PREDICTION; ORGANIC-MATTER; LAND-COVER; MOISTURE; QUALITY; AFRICA; IMAGES; LAKES;
D O I
10.3390/hydrology11110183
中图分类号
TV21 [水资源调查与水利规划];
学科分类号
081501 ;
摘要
The application of machine learning (ML) and remote sensing (RS) in soil and water conservation has become a powerful tool. As analytical tools continue to advance, the variety of ML algorithms and RS sources has expanded, providing opportunities for more sophisticated analyses. At the same time, researchers are required to select appropriate technologies based on the research objectives, topic, and scope of the study area. In this paper, we present a comprehensive review of the application of ML algorithms and RS that has been implemented to advance research in soil and water conservation. The key contribution of this review paper is that it provides an overview of current research areas within soil and water conservation and their effectiveness in improving prediction accuracy and resource management in categorized subfields, including soil properties, hydrology and water resources, and wildfire management. We also highlight challenges and future directions based on limitations of ML and RS applications in soil and water conservation. This review aims to serve as a reference for researchers and decision-makers by offering insights into the effectiveness of ML and RS applications in the fields of soil and water conservation.
引用
收藏
页数:26
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