2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS)最新文献_第8页

Wildfire risk assessment based on Light Gradient Boosting Machine model 基于光梯度增强机模型的野火风险评估

2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS) Pub Date : 2022-04-22 DOI: 10.1109/ICGMRS55602.2022.9849276

Feng Xiao, Guanyu Lin, Tianyu Li, Jiaying Li, Jiaqing Zhang

{"title":"Wildfire risk assessment based on Light Gradient Boosting Machine model","authors":"Feng Xiao, Guanyu Lin, Tianyu Li, Jiaying Li, Jiaqing Zhang","doi":"10.1109/ICGMRS55602.2022.9849276","DOIUrl":"https://doi.org/10.1109/ICGMRS55602.2022.9849276","url":null,"abstract":"With the expansion of the power grid and the limitation of the geographical environment, some areas have to adopt the way of crossing the forest area to arrange the transmission lines. Some forest areas are sparsely populated and the vegetation is lush. Once a mountain fire occurs, it is easy to spread to the vicinity of the transmission corridor, resulting in the failure of transmission line tripping and reclosing. In order to effectively predict wildfires, this paper proposes a wildfire risk assessment model based on LightGBM. Combining vegetation factors, meteorological factors, terrain factors, and human factors, the moderately correlated fire point characteristics were screened out based on correlation analysis, and a wildfire risk assessment model was constructed. After that, the fire point products of NPP and MODIS are used as the validation data of the model, and the acracy of the model is predicted by the accuracy, precision, recall, F1-Score and AUC values. A comprehensive evaluation showed that the accuracy of the model was 0.86 and the AUC value was 0.83. The results showed that the model could effectively predict wildfire risk.","PeriodicalId":129909,"journal":{"name":"2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123953054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Quantitative monitoring of sugarcane typhoon disaster based on multi-source remote sensing data 基于多源遥感数据的甘蔗台风灾害定量监测

2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS) Pub Date : 2022-04-22 DOI: 10.1109/ICGMRS55602.2022.9849279

Lisha Qian, Shuisen Chen, Hao Jiang, Xuemei Dai, Kai Jia

{"title":"Quantitative monitoring of sugarcane typhoon disaster based on multi-source remote sensing data","authors":"Lisha Qian, Shuisen Chen, Hao Jiang, Xuemei Dai, Kai Jia","doi":"10.1109/ICGMRS55602.2022.9849279","DOIUrl":"https://doi.org/10.1109/ICGMRS55602.2022.9849279","url":null,"abstract":"With a foreseen increase in the number of agrometeorological disasters due to climate change, especially in the field of crop lodging. This paper presented an innovative monitoring technique to explore the application potential of muti-source remote sensing data. Based on the sugarcane planting area extracted from sentinel-1 time-series data and combination of Landsat-8 and sentinel-2 MSI images before and after Super Typhoon Hato, a vegetation index distance leveling method was come out and then was applied to assess the sugarcane lodging in Dagang Town, Nansha District, Guangzhou City. The region was caused by strong wind and rainstorm on August 23, 2017. The validation results showed that the multi-temporal Sentinel-1 image data can effectively extract the sugarcane planted area before and after lodging with an accuracy of 87.83%. Compared with other vegetation indices (RVI/LSWI/NBR/EVI/DVI), NDVI was the most sensitive in response to sugarcane lodging. The validation accuracy of extracting farmland damage extent reached 71.64%, among them, the affected area of sugarcane reached 711.33 ha. The study further illustrates the capability of the image vegetation index difference method on monitoring of sugarcane lodging degree at the reginal scale.","PeriodicalId":129909,"journal":{"name":"2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124124532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Evaluation of Carbon Sink Capacity of Tree Species in Gansu Province based on Remote Sensing Data 基于遥感数据的甘肃省树种碳汇容量评价

2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS) Pub Date : 2022-04-22 DOI: 10.1109/ICGMRS55602.2022.9849299

Shufu Lin, Ying Ding, Shenghua Gao

{"title":"Evaluation of Carbon Sink Capacity of Tree Species in Gansu Province based on Remote Sensing Data","authors":"Shufu Lin, Ying Ding, Shenghua Gao","doi":"10.1109/ICGMRS55602.2022.9849299","DOIUrl":"https://doi.org/10.1109/ICGMRS55602.2022.9849299","url":null,"abstract":"With the increase in greenhouse gas emissions, global warming has been accelerating. Aiming at reducing the amount of greenhouse gas in the atmosphere, this paper takes the dominant tree species in Gansu Province of China as the research objects, obtains the data on forests area through remote sensing and GIS technology, and evaluates the carbon sink capacity of forest trees. The research simulates the actual growth of the tree by computer, and compares cumulative carbon sinks between the control group and the experimental group, in order to determine the optimal harvesting ages of the tree. The results show that the optimal harvesting ages of Pinus armandii, Quercus liaotungensis, and Betula platyphylla are 61a, 120a and 61a, respectively. Compared with the control group, the cumulative carbon sinks of Pinus armandii, Quercus liaotungensis and Betula platyphylla are higher by 11.57%, 17.77% and 96.43%, respectively, while those of Abies fabri and Cupressus funebris are lower by 39.74% and 56.49%. In addition, this paper provides a decision-making method for managing tree harvesting from the perspective of carbon sink, which promotes the carbon sink capacity of the tree in a certain number of years. The research in this paper is of great significance to remote sensing applications and scientific decision-making by forest operators.","PeriodicalId":129909,"journal":{"name":"2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124157936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GIS application in environmental monitoring and risk assessment GIS在环境监测与风险评估中的应用

2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS) Pub Date : 2022-04-22 DOI: 10.1109/ICGMRS55602.2022.9849269

Li Chen, Yiying Mao, Ruotong Zhao

{"title":"GIS application in environmental monitoring and risk assessment","authors":"Li Chen, Yiying Mao, Ruotong Zhao","doi":"10.1109/ICGMRS55602.2022.9849269","DOIUrl":"https://doi.org/10.1109/ICGMRS55602.2022.9849269","url":null,"abstract":"GIS techniques are becoming the mainstream tool in different disciplines such as assessments of biomass resources, mineral resource analysis, groundwater, and air quality investigation. This study explored its application in solving environmental problems monitoring and risk assessment. GIS application in environmental monitoring has mainly been divided into three aspects: water, soil, and atmosphere. Based on ArcGIS 10.1 software and ArcGIS 9.3.1 version, GIS has been applied in water supply system monitoring and soil heavy metal concentration monitoring, respectively. In addition, it can achieve real-time geographic location information transmission accurately and monitor in various fields by combining the Alibaba Cloud elastic computing service server, user management development environment, real-time data display, wireless sensor network, Arduino microcontroller, and a series of sensors. Combined with the Radial Basis Function Network model and spatial data, GIS technology could monitor and assess the degree of soil wind erosion hazard by quantifying the various indicators of soil wind erosion. GIS can also be applied to assess the environmental risks from water, land, and atmosphere. Based on geological and geomorphological data, the integration of remote sensing and GIS can complete the assessment of flash flood disasters, groundwater exploration, and groundwater pollution. By using the spatial analysis and data processing capabilities of GIS and combined with other technologies or methods such as digital elevation model and Pollution Index, topographic changes, soil properties, and heavy metal pollution can be assessed. GIS provides the ability to query spatial data and translates existing spatial patterns into measurable targets with its built-in analytical tools. It can be used to predict and assess air quality prediction. Through these approaches, relevant authorities can manage different areas rationally and targeted manner, which has practical and long-term implications.","PeriodicalId":129909,"journal":{"name":"2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126400685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Thick Cloud Removal and Reconstruction for Remote Sensing Images Using Attention-based Deep Neural Networks 基于注意力的深度神经网络遥感图像厚云去除与重建

2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS) Pub Date : 2022-04-22 DOI: 10.1109/ICGMRS55602.2022.9849317

Yidan Wang, Q. Xin, Kun Xiao

{"title":"Thick Cloud Removal and Reconstruction for Remote Sensing Images Using Attention-based Deep Neural Networks","authors":"Yidan Wang, Q. Xin, Kun Xiao","doi":"10.1109/ICGMRS55602.2022.9849317","DOIUrl":"https://doi.org/10.1109/ICGMRS55602.2022.9849317","url":null,"abstract":"Thick cloud removal for remote sensing images is an important and challenging task for researchers. Existed clouds removal methods always have some limitations with a large area of clouds or a long period between the cloudy image and the supplementary cloud-free image. In this paper, we proposed a deep-learning based framework for thick clouds removal. The method added prior spectral information into the model inputs and used deep convolutional neural networks (CNN) with dense connection and channel attention to reconstruct the cloudy areas. The loss function considered both spectral and structure similarity. We designed artificial and observed data experiments to show the performance of the network. Our method achieved the coefficient of determination (R2) of 0.976, structural similarity (SSIM) of 0.937 and root mean squared error (RMSE) of 0.016 in the artificial dataset and can generate reconstruction results with consistent spectral information and clear texture details, indicating that the proposed method is effective for cloud removal and data reconstruction.","PeriodicalId":129909,"journal":{"name":"2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131423323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Experimental study on remote sensing observation of extinction coefficient of smoke aerosols 烟雾气溶胶消光系数的遥感观测实验研究

2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS) Pub Date : 2022-04-22 DOI: 10.1109/ICGMRS55602.2022.9849256

Shiting Sheng, Jun Pan, Lijun Jiang, Yehan Sun

{"title":"Experimental study on remote sensing observation of extinction coefficient of smoke aerosols","authors":"Shiting Sheng, Jun Pan, Lijun Jiang, Yehan Sun","doi":"10.1109/ICGMRS55602.2022.9849256","DOIUrl":"https://doi.org/10.1109/ICGMRS55602.2022.9849256","url":null,"abstract":"Forest fires and other combustions produce many particulate matter smoke aerosols. The study of optical properties is significant for smoke recognition, remote sensing image correction, etc. To obtain the extinction coefficient of biomass combustion, this study adopts the remote sensing simulation experimental observation method, based on scattering-absorption theory and Bouger-Lambert’s law, establishes the correlation between the extinction coefficient and transmittance of smoke aerosols, and conducts experimental observations under different background objects and different field angles of view, realizes the inversion of the extinction coefficient based on the least-squares regression analysis method, and determines the value and change law of the extinction coefficient of smoke aerosol. The results show that the relationship between the transmittance and concentration of smoke aerosols in the visible light-near-infrared band is in line with the Bouger-Lambert law. The extinction coefficients of different observation field angles and material backgrounds have consistent value rules. The extinction coefficient of the visible light band is more significant in general, and the peak occurs near 700nm.","PeriodicalId":129909,"journal":{"name":"2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131226504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Building Instance Change Detection from High Spatial Resolution Remote Sensing Images with Improved Instance Segmentation Architecture 基于改进实例分割体系的高空间分辨率遥感图像建筑实例变化检测

2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS) Pub Date : 2022-04-22 DOI: 10.1007/s12524-022-01601-z

Li Yan, Jianbing Yang, Yi Zhang

引用次数: 1

Construction of basic geospatial information framework in Huludao City 葫芦岛市基础地理空间信息框架建设

2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS) Pub Date : 2022-04-22 DOI: 10.1109/ICGMRS55602.2022.9849378

Songyan Wang, Ye Wen, Zhe Wang

引用次数: 0

Building Segmentation of UAV-based Oblique Photography Point Cloud Using DoPP and DBSCAN 基于DoPP和DBSCAN的无人机斜摄点云建筑分割

2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS) Pub Date : 2022-04-22 DOI: 10.1109/ICGMRS55602.2022.9849376

Guodong Wang, Qiang Wang, R. Zhao, Chao Chen, Yan-xin Lu

引用次数: 1

Spatial-temporal characteristics of vegetation coverage and its relationship with environmental factors in the Three-river headwaters region 三江源区植被覆盖度时空特征及其与环境因子的关系

2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS) Pub Date : 2022-04-22 DOI: 10.1109/ICGMRS55602.2022.9849327

Weiyi Huang, Xiao Chang, Qilin Qu, Jianzhou Bai

{"title":"Spatial-temporal characteristics of vegetation coverage and its relationship with environmental factors in the Three-river headwaters region","authors":"Weiyi Huang, Xiao Chang, Qilin Qu, Jianzhou Bai","doi":"10.1109/ICGMRS55602.2022.9849327","DOIUrl":"https://doi.org/10.1109/ICGMRS55602.2022.9849327","url":null,"abstract":"In order to explore the temporal and spatial characteristics of vegetation coverage and its relationship with environmental factors in the Three-River headwaters region, this study adopted MODIS MYD13Q1 land vegetation data product with a spatial resolution of 250 m and a time interval of 16 days. The vegetation coverage was retrieved based on the improved pixel binary model, and the relationship between the elevation, temperature and precipitation data was analyzed. The results show: 1) From the spatial feature, the vegetation coverage gradually increases from northwest to southeast in the three-river headwaters region, and the boundary is more obvious. The overall vegetation coverage distribution in the three-river source region is as follows: the source region of the Yellow River > the source region of the Yangtze River > the source region of the Lancang River. 2) From the temporal characteristics, In 2019, the monthly normalized vegetation index showed obvious seasonal changes. From January to July, the normalized vegetation index increased from 0.06 to 0.29, and then decreased to 0.09 from August to December. From 2000 to 2019, the average annual vegetation coverage showed a slow upward trend on the whole, with the vegetation coverage between 0.40 and 0.45. In 2015, there was a process of first declining and then rising. 3) Environmental factors: On the whole, the vegetation coverage decreased with the increase of elevation, and the value of normalized vegetation index reached the maximum at 3900-4000 m. Locally, there is a fluctuation at 4500 m; The correlation coefficients between accumulated annual precipitation and accumulated annual mean temperature and NORMALIZED vegetation index are 0.26 and 0.72, respectively, indicating that temperature has a great influence on vegetation coverage.","PeriodicalId":129909,"journal":{"name":"2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124202289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0