Cargando…
Detecting Key Factors of Grasshopper Occurrence in Typical Steppe and Meadow Steppe by Integrating Machine Learning Model and Remote Sensing Data
SIMPLE SUMMARY: Grasshoppers are among the most dangerous agricultural pests of China. However, the monitoring, prediction and control of grasshoppers are complex and difficult. Therefore, it is crucial to detect the key factors affecting the spatial distribution of grasshopper occurrence, understan...
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9603866/ https://www.ncbi.nlm.nih.gov/pubmed/36292842 http://dx.doi.org/10.3390/insects13100894 |
_version_ | 1784817664120586240 |
---|---|
author | Lu, Longhui Kong, Weiping Eerdengqimuge, Ye, Huichun Sun, Zhongxiang Wang, Ning Du, Bobo Zhou, Yantao Weijun, Huang, Wenjiang |
author_facet | Lu, Longhui Kong, Weiping Eerdengqimuge, Ye, Huichun Sun, Zhongxiang Wang, Ning Du, Bobo Zhou, Yantao Weijun, Huang, Wenjiang |
author_sort | Lu, Longhui |
collection | PubMed |
description | SIMPLE SUMMARY: Grasshoppers are among the most dangerous agricultural pests of China. However, the monitoring, prediction and control of grasshoppers are complex and difficult. Therefore, it is crucial to detect the key factors affecting the spatial distribution of grasshopper occurrence, understand the role of the environmental factors in grasshopper occurrence, and study whether different laws exist between different grass types. Here we conduct a species–environmental matching model integrated by Maxent model and remote sensing data to identify the spatial distribution of grasshopper occurrence in Inner Mongolia of China, analyze the related environmental variables and detect the most relevant environmental factors for grasshopper occurrence both in typical steppe and meadow steppe. ABSTRACT: Grasshoppers mainly threaten natural grassland vegetation and crops. Therefore, it is of great significance to understand the relationship between environmental factors and grasshopper occurrence. This paper studies the spatial distribution and key factors of grasshopper occurrence in two grass types by integrating a machine learning model (Maxent) and remote sensing data within the major grasshopper occurrence areas of Inner Mongolia, China. The modelling results demonstrate that the typical steppe has larger suitable area and more proportion for grasshopper living than meadow steppe. The soil type, above biomass, altitude and temperature mainly determine the grasshopper occurrence in typical steppe and meadow steppe. However, the contribution of these factors in the two grass types is significantly different. In addition, related vegetation and meteorological factors affect the different growing stages of grasshoppers between the two grass types. This study clearly defines the different effects of key environmental factors (meteorology, vegetation, soil and topography) for grasshopper occurrence in typical steppe and meadow steppe. It also provides a methodology to guide early warning and precautions for grasshopper pest prevention. The findings of this study will be helpful for future management measures, to ensure grass ecological environment security and the sustainable development of grassland. |
format | Online Article Text |
id | pubmed-9603866 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96038662022-10-27 Detecting Key Factors of Grasshopper Occurrence in Typical Steppe and Meadow Steppe by Integrating Machine Learning Model and Remote Sensing Data Lu, Longhui Kong, Weiping Eerdengqimuge, Ye, Huichun Sun, Zhongxiang Wang, Ning Du, Bobo Zhou, Yantao Weijun, Huang, Wenjiang Insects Article SIMPLE SUMMARY: Grasshoppers are among the most dangerous agricultural pests of China. However, the monitoring, prediction and control of grasshoppers are complex and difficult. Therefore, it is crucial to detect the key factors affecting the spatial distribution of grasshopper occurrence, understand the role of the environmental factors in grasshopper occurrence, and study whether different laws exist between different grass types. Here we conduct a species–environmental matching model integrated by Maxent model and remote sensing data to identify the spatial distribution of grasshopper occurrence in Inner Mongolia of China, analyze the related environmental variables and detect the most relevant environmental factors for grasshopper occurrence both in typical steppe and meadow steppe. ABSTRACT: Grasshoppers mainly threaten natural grassland vegetation and crops. Therefore, it is of great significance to understand the relationship between environmental factors and grasshopper occurrence. This paper studies the spatial distribution and key factors of grasshopper occurrence in two grass types by integrating a machine learning model (Maxent) and remote sensing data within the major grasshopper occurrence areas of Inner Mongolia, China. The modelling results demonstrate that the typical steppe has larger suitable area and more proportion for grasshopper living than meadow steppe. The soil type, above biomass, altitude and temperature mainly determine the grasshopper occurrence in typical steppe and meadow steppe. However, the contribution of these factors in the two grass types is significantly different. In addition, related vegetation and meteorological factors affect the different growing stages of grasshoppers between the two grass types. This study clearly defines the different effects of key environmental factors (meteorology, vegetation, soil and topography) for grasshopper occurrence in typical steppe and meadow steppe. It also provides a methodology to guide early warning and precautions for grasshopper pest prevention. The findings of this study will be helpful for future management measures, to ensure grass ecological environment security and the sustainable development of grassland. MDPI 2022-09-30 /pmc/articles/PMC9603866/ /pubmed/36292842 http://dx.doi.org/10.3390/insects13100894 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Lu, Longhui Kong, Weiping Eerdengqimuge, Ye, Huichun Sun, Zhongxiang Wang, Ning Du, Bobo Zhou, Yantao Weijun, Huang, Wenjiang Detecting Key Factors of Grasshopper Occurrence in Typical Steppe and Meadow Steppe by Integrating Machine Learning Model and Remote Sensing Data |
title | Detecting Key Factors of Grasshopper Occurrence in Typical Steppe and Meadow Steppe by Integrating Machine Learning Model and Remote Sensing Data |
title_full | Detecting Key Factors of Grasshopper Occurrence in Typical Steppe and Meadow Steppe by Integrating Machine Learning Model and Remote Sensing Data |
title_fullStr | Detecting Key Factors of Grasshopper Occurrence in Typical Steppe and Meadow Steppe by Integrating Machine Learning Model and Remote Sensing Data |
title_full_unstemmed | Detecting Key Factors of Grasshopper Occurrence in Typical Steppe and Meadow Steppe by Integrating Machine Learning Model and Remote Sensing Data |
title_short | Detecting Key Factors of Grasshopper Occurrence in Typical Steppe and Meadow Steppe by Integrating Machine Learning Model and Remote Sensing Data |
title_sort | detecting key factors of grasshopper occurrence in typical steppe and meadow steppe by integrating machine learning model and remote sensing data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9603866/ https://www.ncbi.nlm.nih.gov/pubmed/36292842 http://dx.doi.org/10.3390/insects13100894 |
work_keys_str_mv | AT lulonghui detectingkeyfactorsofgrasshopperoccurrenceintypicalsteppeandmeadowsteppebyintegratingmachinelearningmodelandremotesensingdata AT kongweiping detectingkeyfactorsofgrasshopperoccurrenceintypicalsteppeandmeadowsteppebyintegratingmachinelearningmodelandremotesensingdata AT eerdengqimuge detectingkeyfactorsofgrasshopperoccurrenceintypicalsteppeandmeadowsteppebyintegratingmachinelearningmodelandremotesensingdata AT yehuichun detectingkeyfactorsofgrasshopperoccurrenceintypicalsteppeandmeadowsteppebyintegratingmachinelearningmodelandremotesensingdata AT sunzhongxiang detectingkeyfactorsofgrasshopperoccurrenceintypicalsteppeandmeadowsteppebyintegratingmachinelearningmodelandremotesensingdata AT wangning detectingkeyfactorsofgrasshopperoccurrenceintypicalsteppeandmeadowsteppebyintegratingmachinelearningmodelandremotesensingdata AT dubobo detectingkeyfactorsofgrasshopperoccurrenceintypicalsteppeandmeadowsteppebyintegratingmachinelearningmodelandremotesensingdata AT zhouyantao detectingkeyfactorsofgrasshopperoccurrenceintypicalsteppeandmeadowsteppebyintegratingmachinelearningmodelandremotesensingdata AT weijun detectingkeyfactorsofgrasshopperoccurrenceintypicalsteppeandmeadowsteppebyintegratingmachinelearningmodelandremotesensingdata AT huangwenjiang detectingkeyfactorsofgrasshopperoccurrenceintypicalsteppeandmeadowsteppebyintegratingmachinelearningmodelandremotesensingdata |