Analysis of the spectrum and vegetation index of rice under different nitrogen levels based on unmanned aerial vehicle remote sensing

2018 ◽  
Vol 11 (10) ◽  
pp. 832-840
Author(s):  
裴信彪 PEI Xin-biao ◽  
吴和龙 WU He-long ◽  
马 萍 MA Ping ◽  
严永峰 YAN Yong-feng ◽  
彭 程 PENG Cheng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Vegetation Index ◽  
Nitrogen Levels ◽  
Aerial Vehicle

Analysis of the spectrum and vegetation index of rice under different nitrogen levels based on unmanned aerial vehicle remote sensing

2018 ◽  
Vol 11 (5) ◽  
pp. 832-840
Author(s):  
裴信彪 PEI Xin-biao ◽  
吴和龙 WU He-long ◽  
马 萍 MA Ping ◽  
严永峰 YAN Yong-feng ◽  
彭 程 PENG Cheng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Vegetation Index ◽  
Nitrogen Levels ◽  
Aerial Vehicle

UAV Remote Sensing Assessment of Crop Growth

2021 ◽  
Vol 87 (12) ◽  
pp. 891-899
Author(s):  
Freda Elikem Dorbu ◽  
Leila Hashemi-Beni ◽  
Ali Karimoddini ◽  
Abolghasem Shahbazi
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Crop Growth ◽  
Infrared Sensors ◽  
Digital Elevation ◽  
Aerial Vehicle ◽  
Growth Indicators

The introduction of unmanned-aerial-vehicle remote sensing for collecting high-spatial- and temporal-resolution imagery to derive crop-growth indicators and analyze and present timely results could potentially improve the management of agricultural businesses and enable farmers to apply appropriate solution, leading to a better food-security framework. This study aimed to analyze crop-growth indicators such as the normalized difference vegetation index (NDVI), crop height, and vegetated surface roughness to determine the growth of corn crops from planting to harvest. Digital elevation models and orthophotos generated from the data captured using multispectral, red/green/blue, and near-infrared sensors mounted on an unmanned aerial vehicle were processed and analyzed to calculate the various crop-growth indicators. The results suggest that remote sensing-based growth indicators can effectively determine crop growth over time, and that there are similarities and correlations between the indicators.

scholarly journals IMAGENS DE VEÍCULO AÉREO NÃO TRIPULADO APLICADAS NA OBTENÇÃO DO ÍNDICE DE VEGETAÇÃO POR DIFERENÇA NORMALIZADA

2021 ◽  
Vol 36 (1) ◽  
pp. 111-122
Author(s):  
Felipe de Souza Nogueira Tagliarini ◽  
Mikael Timóteo Rodrigues ◽  
Bruno Timóteo Rodrigues ◽  
Yara Manfrin Garcia ◽  
Sérgio Campos
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Spatial Resolution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
High Spatial Resolution ◽  
Spectral Response ◽  
Infrared Sensors ◽  
Aerial Vehicle ◽  
E Mail

IMAGENS DE VEÍCULO AÉREO NÃO TRIPULADO APLICADAS NA OBTENÇÃO DO ÍNDICE DE VEGETAÇÃO POR DIFERENÇA NORMALIZADA   FELIPE DE SOUZA NOGUEIRA TAGLIARINI1, MIKAEL TIMÓTEO RODRIGUES2-3, BRUNO TIMÓTEO RODRIGUES1; YARA MANFRIN GARCIA1 E SÉRGIO CAMPOS1   1 Departamento de Engenharia Rural, Faculdade de Ciências Agronômicas (FCA) - Universidade Estadual Paulista (UNESP), Avenida Universitária, nº 3780, Altos do Paraíso, CEP: 18610-034, Botucatu, São Paulo, Brasil. E-mail: [email protected]; [email protected]; [email protected]; [email protected] 2 Centro Universitário Dinâmica das Cataratas (UDC), Rua Castelo Branco, nº 440, Centro, CEP: 85852-010, Foz do Iguaçu, Paraná, Brasil. E-mail: [email protected] 3 Parque Tecnológico Itaipu (PTI), Avenida Tancredo Neves, nº 6731, Jardim Itaipu, Caixa Postal: 2039, CEP: 85867-900, Foz do Iguaçu, Paraná, Brasil. E-mail: [email protected].   RESUMO: O advento dos Veículos Aéreos Não Tripulados (VANT) como ferramenta no sensoriamento remoto possibilitou uma plataforma atuante em diferentes áreas para o mapeamento com elevada precisão e resolução. O objetivo deste estudo consistiu na análise do Índice de Vegetação por Diferença Normalizada (NDVI) para elaboração de mapa temático por meio de aerofotogrametria e fotointerpretação, com maior detalhamento da vegetação devido à altíssima resolução espacial alcançada com o uso de imagens coletadas por VANT em trecho do rio Lavapés, dentro dos limites da Fazenda Experimental Lageado no município de Botucatu-SP. As imagens foram obtidas por meio dos sensores MAPIR Survey3W RGB e Survey3W NIR/InfraRED, embarcados em VANT multirrotor 3DR SOLO. Para construção dos ortomosaicos RGB e NDVI, as imagens foram processadas no software Pix4Dmapper 3.0. O resultado do NDVI proporcionou transição bem nítidas entre os alvos bióticos (vegetação) e os alvos abióticos (corpo d'água, solo e edificações), e também entre a própria vegetação, possibilitando a distinção da vegetação de porte arbóreo, com maior vigor vegetativo, em relação a vegetação de porte herbáceo. As imagens com elevada resolução espacial coletadas por VANT, demonstraram flexibilidade de utilização, possuindo elevado potencial para o mapeamento de dinâmica da paisagem e a resposta espectral da vegetação.   Palavras-chaves: drone, índice radiométrico, sensoriamento remoto   IMAGES OF UNMANNED AERIAL VEHICLE APPLIED TO OBTAIN THE NORMALIZED DIFFERENCE VEGETATION INDEX   ABSTRACT: The advent of Unmanned Aerial Vehicle (UAV) as a tool in remote sensing has enabled a platform acting in different areas for mapping with high precision and resolution. This study aimed to analyze the Normalized Difference Vegetation Index (NDVI) for the elaboration of thematic map through aerophotogrammetry and photointerpretation, with greater detail of vegetation due to high spatial resolution achieved with the use of images collected by UAV in a stretch of Lavapés river, inside the domains of Lageado Experimental Farm in the municipality of Botucatu-SP. The images were obtained through MAPIR Survey3W RGB and Survey3W NIR/InfraRED sensors, aboard a 3DR SOLO multirotor UAV. For constructing RGB and NDVI orthomosaics, the images were processed using Pix4Dmapper 3.0 software. The NDVI result provided a clear transition among biotic targets (vegetation) and abiotic targets (water, soil and buildings), and among the vegetation itself, with greater vegetative vigor, making possible the distinction of arboreal vegetation, in relation to herbaceous vegetation. The images with high spatial resolution collected by UAV demonstrated the flexibility of use, having high potential to mapping landscape dynamics and the spectral response of vegetation.   Keywords: drone, radiometric index, remote sensing.

scholarly journals Recent Advances in Unmanned Aerial Vehicle Forest Remote Sensing—A Systematic Review. Part I: A General Framework

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 327 ◽  
Author(s):  
Riccardo Dainelli ◽  
Piero Toscano ◽  
Salvatore Filippo Di Gennaro ◽  
Alessandro Matese
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Relevant Information ◽  
Forest Monitoring ◽  
Machine Learning Techniques ◽  
Planted Forests ◽  
Systematic Analysis ◽  
Aerial Photogrammetry ◽  
Wide Range ◽  
Aerial Vehicle

Natural, semi-natural, and planted forests are a key asset worldwide, providing a broad range of positive externalities. For sustainable forest planning and management, remote sensing (RS) platforms are rapidly going mainstream. In a framework where scientific production is growing exponentially, a systematic analysis of unmanned aerial vehicle (UAV)-based forestry research papers is of paramount importance to understand trends, overlaps and gaps. The present review is organized into two parts (Part I and Part II). Part II inspects specific technical issues regarding the application of UAV-RS in forestry, together with the pros and cons of different UAV solutions and activities where additional effort is needed, such as the technology transfer. Part I systematically analyzes and discusses general aspects of applying UAV in natural, semi-natural and artificial forestry ecosystems in the recent peer-reviewed literature (2018–mid-2020). The specific goals are threefold: (i) create a carefully selected bibliographic dataset that other researchers can draw on for their scientific works; (ii) analyze general and recent trends in RS forest monitoring (iii) reveal gaps in the general research framework where an additional activity is needed. Through double-step filtering of research items found in the Web of Science search engine, the study gathers and analyzes a comprehensive dataset (226 articles). Papers have been categorized into six main topics, and the relevant information has been subsequently extracted. The strong points emerging from this study concern the wide range of topics in the forestry sector and in particular the retrieval of tree inventory parameters often through Digital Aerial Photogrammetry (DAP), RGB sensors, and machine learning techniques. Nevertheless, challenges still exist regarding the promotion of UAV-RS in specific parts of the world, mostly in the tropical and equatorial forests. Much additional research is required for the full exploitation of hyperspectral sensors and for planning long-term monitoring.

scholarly journals Road Extraction from Unmanned Aerial Vehicle Remote Sensing Images Based on Improved Neural Networks

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4115 ◽  
Author(s):  
Yuxia Li ◽  
Bo Peng ◽  
Lei He ◽  
Kunlong Fan ◽  
Zhenxu Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Unmanned Aerial Vehicle ◽  
Computational Efficiency ◽  
Neural Nets ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
Feature Maps ◽  
Aerial Vehicle

Roads are vital components of infrastructure, the extraction of which has become a topic of significant interest in the field of remote sensing. Because deep learning has been a popular method in image processing and information extraction, researchers have paid more attention to extracting road using neural networks. This article proposes the improvement of neural networks to extract roads from Unmanned Aerial Vehicle (UAV) remote sensing images. D-Linknet was first considered for its high performance; however, the huge scale of the net reduced computational efficiency. With a focus on the low computational efficiency problem of the popular D-LinkNet, this article made some improvements: (1) Replace the initial block with a stem block. (2) Rebuild the entire network based on ResNet units with a new structure, allowing for the construction of an improved neural network D-Linknetplus. (3) Add a 1 × 1 convolution layer before DBlock to reduce the input feature maps, reducing parameters and improving computational efficiency. Add another 1 × 1 convolution layer after DBlock to recover the required number of output channels. Accordingly, another improved neural network B-D-LinknetPlus was built. Comparisons were performed between the neural nets, and the verification were made with the Massachusetts Roads Dataset. The results show improved neural networks are helpful in reducing the network size and developing the precision needed for road extraction.

scholarly journals Recent Advances in Unmanned Aerial Vehicles Forest Remote Sensing—A Systematic Review. Part II: Research Applications

Forests ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 397
Author(s):  
Riccardo Dainelli ◽  
Piero Toscano ◽  
Salvatore Filippo Di Gennaro ◽  
Alessandro Matese
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Sustainable Management ◽  
Large Scale ◽  
Spectral Signature ◽  
Accurate Information ◽  
Data Interoperability ◽  
Critical Issues ◽  
Management Context ◽  
Aerial Vehicle

Forest sustainable management aims to maintain the income of woody goods for companies, together with preserving non-productive functions as a benefit for the community. Due to the progress in platforms and sensors and the opening of the dedicated market, unmanned aerial vehicle–remote sensing (UAV–RS) is improving its key role in the forestry sector as a tool for sustainable management. The use of UAV (Unmanned Aerial Vehicle) in precision forestry has exponentially increased in recent years, as demonstrated by more than 600 references published from 2018 until mid-2020 that were found in the Web of Science database by searching for “UAV”+“forest”. This result is even more surprising when compared with similar research for “UAV”+“agriculture”, from which emerge about 470 references. This shows how UAV–RS research forestry is gaining increasing popularity. In Part II of this review, analyzing the main findings of the reviewed papers (227), numerous strengths emerge concerning research technical issues. UAV–RS is fully applicated for obtaining accurate information from practical parameters (height, diameter at breast height (DBH), and biomass). Research effectiveness and soundness demonstrate that UAV–RS is now ready to be applied in a real management context. Some critical issues and barriers in transferring research products are also evident, namely,(1) hyperspectral sensors are poorly used, and their novel applications should be based on the capability of acquiring tree spectral signature especially for pest and diseases detection, (2) automatic processes for image analysis are poorly flexible or based on proprietary software at the expense of flexible and open-source tools that can foster researcher activities and support technology transfer among all forestry stakeholders, and (3) a clear lack exist in sensors and platforms interoperability for large-scale applications and for enabling data interoperability.

Sign in / Sign up

Export Citation Format

Share Document