2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)最新文献_第2页

Deep Convolutional Neural Networks for Weed Detection in Agricultural Crops Using Optical Aerial Images 基于光学航空图像的农作物杂草检测的深度卷积神经网络

2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS) Pub Date : 2020-03-01 DOI: 10.1109/LAGIRS48042.2020.9165562

W. Ramirez, P. Achanccaray, Leonardo A. F. Mendoza, Marco Aurélio Cavalcanti Pacheco

引用次数: 16

Identification Of Affected High-Altitude Wetlands In The North Chile Using Large Landsat Time Series 利用大Landsat时间序列识别智利北部受影响的高海拔湿地

2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS) Pub Date : 2020-03-01 DOI: 10.1109/LAGIRS48042.2020.9165678

D. Castillo, A. Russell, V. Caquilpan, S. Elgueta

{"title":"Identification Of Affected High-Altitude Wetlands In The North Chile Using Large Landsat Time Series","authors":"D. Castillo, A. Russell, V. Caquilpan, S. Elgueta","doi":"10.1109/LAGIRS48042.2020.9165678","DOIUrl":"https://doi.org/10.1109/LAGIRS48042.2020.9165678","url":null,"abstract":"High-Andean wetlands from northern Chile are considered worldwide biodiversity hot spots, however, they are subdued to high anthropic pressure. The monitoring of state variables, such as vegetation, allows to know the ecosystem’s global condition, which could be assessed by the analysis of spectral vegetation indices. The main goal of this paper was to detect changes in the high-Andean wetland vegetation, with remote sensing tools, to focalize surveillance efforts and the use of resources from environmental agencies. NDVI time series were constructed spanning from 1986 to 2019 based on Landsat data, which were analyzed based on the vegetation change detection using BFAST Monitor method. Detected changes were categorized to highlight certain types of changes that were considered more relevant. Wetlands were separated in two rankings (A and B) based on detected changes and territorial context. From 5,622 wetlands, 81 were categorized into group A and 510 into group B. One affected wetland was used as study case to assess the method’s efficiency, being able to detect changes and assign a relative importance to the case. It is shown that the proposed method has the capacity to detect vegetation degradation processes in high-Andean wetlands and could improve in the efficiency and effectiveness of the environmental agencies control labors over these ecosystems.","PeriodicalId":111863,"journal":{"name":"2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115058072","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

Importance of Remote Sensing for the Study of Spatial Dynamics of Estuarine Neuston from Southern Chile 遥感对智利南部纽斯顿河口空间动力学研究的重要性

2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS) Pub Date : 2020-03-01 DOI: 10.1109/LAGIRS48042.2020.9165571

J. Cañete, C. Cárdenas, M. Frangópulos, X. Aguilar, J. Díaz-Ochoa

{"title":"Importance of Remote Sensing for the Study of Spatial Dynamics of Estuarine Neuston from Southern Chile","authors":"J. Cañete, C. Cárdenas, M. Frangópulos, X. Aguilar, J. Díaz-Ochoa","doi":"10.1109/LAGIRS48042.2020.9165571","DOIUrl":"https://doi.org/10.1109/LAGIRS48042.2020.9165571","url":null,"abstract":"Zooplankton aggregation, hydrographic and remote sensing data were employed to relate the spatial dynamics of neustonic communities with chlorophyll a (Chl a) and suspended organic matter (SOM) at a spatial mesoscale (10 to 1000 km) in the southem Chilean fjords system along Magellan Strait, Chile (CIMAR 16: October/November 2010 and CIMAR 25; September/October 2019) in order to identify oceanographic process producing aggregation of neuston. Preliminary evidence of CIMAR 25 shows significant concentrations of Chl a and SOM around Dawson Island (DI), Magellan Strait. During CIMAR 16 important aggregation of specific neustonic taxa (copepodites of Microsetella rosea, larvae of the polychaete Polygordius sp and cyphonautes of the bryozoan Membranipora isabelleana) was observed around DI, Magellan Strait. Satelital images in the area of CIMAR 16 provide evidence of important aggregation of chlorophyll a/SOM around DI. CIMAR Cimar 25 showed that the Chl a and SOM aggregation around DI is recurrent and could to explain the high concentration of neuston around this island to spite of mesotrophic conditions. Remote sensing in this study area provides a tool to understanding oceanographic and topographic factors that potentially regulate the abundance and spatial distribution of surface zooplankton to spatial meso-scale along Magellan Strait.","PeriodicalId":111863,"journal":{"name":"2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117042306","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

Cerrado Knowledge Platform: A Social And Environmental Management Tool To Conserve Brazilian Savannas 塞拉多知识平台:保护巴西稀树草原的社会和环境管理工具

2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS) Pub Date : 2020-03-01 DOI: 10.1109/LAGIRS48042.2020.9165679

M. E. Ferreira, E. B. Silva, F. Malaquias, L. M. S. Teixeira, L. M. Pascoal, N. B. Santos, T. F. Oliveira

{"title":"Cerrado Knowledge Platform: A Social And Environmental Management Tool To Conserve Brazilian Savannas","authors":"M. E. Ferreira, E. B. Silva, F. Malaquias, L. M. S. Teixeira, L. M. Pascoal, N. B. Santos, T. F. Oliveira","doi":"10.1109/LAGIRS48042.2020.9165679","DOIUrl":"https://doi.org/10.1109/LAGIRS48042.2020.9165679","url":null,"abstract":"In the last decade, the access to geographic information through Web platforms has grown substantially in Brazil, and is now a strategic condition for the social and environmental governance of large biomes such as Cerrado (savanna) and Amazonia. This paper aims to present the first version of the Cerrado Knowledge Platform, designed within the scope of the Critical Ecosystem Partnership Fund (CEPF) for the Brazilian savanna. The Platform aims to provide geospatial and census data, organize and systematize the accumulated knowledge about the Cerrado, and also highlight the actions of researches and social networks in this region. It was developed based on three components: (1) protocols and data formats; (2) adaptation of computational tools for social and environmental analysis and monitoring, in order to notify possible threats to the ecosystem (e.g. burning and deforestation); (3) training and maintenance database component. Although still in a beta version, our platform already has some active features, including access to dynamic land use maps, deforestation, and aerial imagery provided by Unmanned Aerial Vehicles (UAVs). With its enhancement and constant data input from partners, we expect the Cerrado Knowledge Platform can better assist the management of land use and land cover of Cerrado, with a perspective of maintaining key areas for biodiversity conservation.","PeriodicalId":111863,"journal":{"name":"2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128907377","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

Semantic Segmentation Of Endangered Tree Species In Brazilian Savanna Using Deeplabv3+ Variants 基于Deeplabv3+变体的巴西热带草原濒危树种语义分割

2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS) Pub Date : 2020-03-01 DOI: 10.1109/LAGIRS48042.2020.9165625

D. L. Torres, R. Feitosa, L. L. la Rosa, P. Happ, J. Marcato, W. Gonçalves, J. Martins, V. Liesenberg

{"title":"Semantic Segmentation Of Endangered Tree Species In Brazilian Savanna Using Deeplabv3+ Variants","authors":"D. L. Torres, R. Feitosa, L. L. la Rosa, P. Happ, J. Marcato, W. Gonçalves, J. Martins, V. Liesenberg","doi":"10.1109/LAGIRS48042.2020.9165625","DOIUrl":"https://doi.org/10.1109/LAGIRS48042.2020.9165625","url":null,"abstract":"Knowing the spatial distribution of endangered tree species in a forest ecosystem or forest remnants is a valuable information to support environmental conservation practices. The use of Unmanned Aerial Vehicles (UAVs) offers a suitable alternative for this task, providing very high-resolution images at low costs. In parallel, recent advances in the computer vision field have led to the development of effective deep learning techniques for end-to-end semantic image segmentation. In this scenario, the DeepLabv3+ is well established as the state-of-the-art deep learning method for semantic segmentation tasks. The present paper proposes and assesses the use of DeepLabv3+ for mapping the threatened Dipteryx alata Vogel tree, popularly also known as cumbaru. We also compare two backbone networks for feature extraction in the DeepLabv3+ architecture: the Xception and MobileNetv2. Experiments carried out on a dataset consisting of 225 UAV/RGB images of an urban area in Midwest Brazil demonstrated that DeepLabv3+ was able to achieve in mean overall accuracy and Fl-score above 90%, and IoU above 80%. The experimental analysis also pointed out that the MobileNetv2 backbone overcame its counterpart by a wide margin due to its comparatively simpler architecture in view of the available training data.","PeriodicalId":111863,"journal":{"name":"2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132032681","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: 5

Deforestation Monitoring in Different Brazilian Biomes: Challenges and Lessons 巴西不同生物群落的森林砍伐监测:挑战和教训

2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS) Pub Date : 2020-03-01 DOI: 10.1109/LAGIRS48042.2020.9285976

C. Almeida, D. Valeriano, L. Maurano, L. Vinhas, L. Fonseca, D. Silva, C. P. Santos, F. Martins, F. C. B. Lara, J. S. Maia, E. R. Profeta, L. O. Santos, F. Santos, V. Ribeiro

引用次数: 4

LAGIRS 2020 Copyright Page LAGIRS 2020版权页面

2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS) Pub Date : 2020-03-01 DOI: 10.1109/lagirs48042.2020.9165605

引用次数: 0

Vegetation Index Based In Unmanned Aerial Vehicle (Uav) To Improve The Management Of Invasive Plants In Protected Areas, Southern Brazil. 基于无人机的植被指数改进巴西南部保护区入侵植物管理

2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS) Pub Date : 2020-03-01 DOI: 10.1109/LAGIRS48042.2020.9165598

C. L. Mallmann, A. F. Zaninni, W. P. Filho

{"title":"Vegetation Index Based In Unmanned Aerial Vehicle (Uav) To Improve The Management Of Invasive Plants In Protected Areas, Southern Brazil.","authors":"C. L. Mallmann, A. F. Zaninni, W. P. Filho","doi":"10.1109/LAGIRS48042.2020.9165598","DOIUrl":"https://doi.org/10.1109/LAGIRS48042.2020.9165598","url":null,"abstract":"The biological invasion is considered the second largest global threat to the maintenance and conservation of natural ecosystems biodiversity. Strategies and actions that guide the control and monitoring of invasive species in protected areas are still a challenge on the management of these areas. Remote sensing is potential tool to detect and monitoring these species, gaining a timeline scale and allowing the adoption of more effective control methods. In this study, search to evaluate the vegetation index potential by using multispectral images acquired by UAV as a support on detection and monitoring of invasive plants in Quarta Colônia State Park located on the Brazil’s southern region. A sampling area with a density of invasive plants above 80% was evaluated, with predominance of Psidium guajava and Ligustrum lucidum, generating a large data set from the extracted indexes. Among the evaluated index, the ones that showed the most potential in this study were Green Normalized Difference Vegetation Index (GNDVI), Plant Senescence Reflectance Index (PSRI) and Red Green Ratio Index (RGRI). Believe us that the use of UAVs platforms will be an important tool for the management of invasive species in protected areas.","PeriodicalId":111863,"journal":{"name":"2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132604467","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: 4

Depth Retrieval From A Reservoir Using A Conditional-Based Model 基于条件模型的油藏深度检索

2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS) Pub Date : 2020-03-01 DOI: 10.1109/LAGIRS48042.2020.9165636

M. B. Nunes, A. Poz, E. Alcântara, M. Curtarelli

引用次数: 0

FCRN-Based Multi-Task Learning for Automatic Citrus Tree Detection From UAV Images 基于fcrn的无人机柑橘树自动检测多任务学习

2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS) Pub Date : 2020-03-01 DOI: 10.1109/LAGIRS48042.2020.9165654

L. L. la Rosa, M. Zortea, B. H. Gemignani, Dario Augusto Borges Oliveira, R. Feitosa

{"title":"FCRN-Based Multi-Task Learning for Automatic Citrus Tree Detection From UAV Images","authors":"L. L. la Rosa, M. Zortea, B. H. Gemignani, Dario Augusto Borges Oliveira, R. Feitosa","doi":"10.1109/LAGIRS48042.2020.9165654","DOIUrl":"https://doi.org/10.1109/LAGIRS48042.2020.9165654","url":null,"abstract":"Citrus producers need to monitor orchards frequently, and would benefit greatly from having automated tools to analyze aerial images acquired by drones over the plantations. However, analysing large aerial data sets to enable producers to take management decisions that would optimize productivity and sustainability over time and space remains challenging. Motivated by the success of deep learning in computer vision, this work proposes a novel approach based on Fully Convolutional Regression Networks and Multi-Task Learning to detect individual full-grown trees, tree seedlings, and tree gaps in citrus orchards for inventory tracking. We show that the proposal can identify eight-year-old orange trees with accuracy between 95–99% in high-density commercial plantations where adjacent crowns overlap. This quality of detection was achieved on RGB orthomosaics with a pixel size of about 9.5 cm and requires the nominal spacing between adjacent trees as a priori information. Our results also highlight that detecting tree seedlings and tree gaps remains a challenge. For these two categories, classification sensitivity (recall) was between 59–100% and 63–94%, respectively.","PeriodicalId":111863,"journal":{"name":"2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)","volume":"211 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115054920","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: 5