Empleo de VANT para determinar fallas superficiales en pavimentos flexibles

The objective of this study is to evaluate the surface faults present in a flexible pavement of approximately 500 m  length through the use of UAV and to demonstrate the capacity of the images captured with it. This is an applied technological research and the quantitative approach was usedfor its development  The methodology focused on three stages, planning the study area, data collection and processing, and analysis of results. The sample was random, with 13 types of potholes and fissures. Data were obtained at an elevation of 40 meters high, capturing images every 2 seconds. The processing was done through photogrammetric software based on the SfM (Structure from Motion) algorithm. As a result, it is observed that the difference between measurements ranges from 0.17 to 5 cm. The standard deviation of the set of samples was 2.32 cm. The ability of UAV imaging for surface fault extraction was demonstrated. This system provides precise measurements of deterioration geometry, which  allows the improvement of road condition monitoring.El objetivo de este estudio fue evaluar las fallas superficiales presentes en un pavimento flexible de aproximadamente 500 m de longitud mediante el uso de VANT y demostrar la capacidad de las imágenes capturadas. La investigación es de tipo aplicada tecnológica y el enfoque que se utilizó fue cuantitativo. La metodología se centró en tres etapas: inicialmente, la planificación de la zona de estudio, luego la recolección y procesamiento de datos, para culminar con el análisis de resultados. La muestra de estudio fue aleatoria, con 13 tipos de baches y fisuras sobre el pavimento. Los datos se obtuvieron a una elevación de 40 m capturando imágenes cada 2 s. El procesamiento se hizo a través de un software fotogramétrico basado en el algoritmo SfM. Como resultado, se aprecia que la diferencia entre las mediciones visuales y las obtenidas por SIG oscila entre 0,17 y 5 cm. La desviación estándar del conjunto de muestras fue de 2,32 cm. Se demostró la capacidad de la imagen capturada con VANT para la extracción de distintas fallas superficiales. Este sistema proporciona una medición detallada y precisa de la ruta de la carretera y de la geometría del bache, y, por lo tanto, mejora la eficiencia del monitoreo del estado de la carretera

Evaluating Changes in Vegetation and Non-Vegetation Patterns of Lidder Valley, Kashmir, India by Using Remote Sensing and GIS

The main goal of this study is to reveal the changes in the vegetation and non-vegetation land cover classes over the study region (Lidder Valley) from 1998 to 2020. Remote sensed data in the form of multi-spectral imagery was used to compute Normalized Difference Vegetation Indices. It provided the base for calculating changes in the land cover categories (Vegetation and non-vegetation). It has been analysed that a large area of the vegetation and non-vegetation classes of the study region had remained the same over 24 years, i.e., no change was noticed among them. About 06 % and 05% of the total area of the study region have witnessed afforestation and deforestation, respectively. Many studies have found that increasing horticultural area at the cost of agriculture is an important reason for increasing vegetation cover. In contrast, increasing population and tourism are the leading causes behind decreased vegetation cover. The large area under non-vegetation should be converted into vegetation other than horticulture because it has been found that increasing horticulture has created many problems in the region. Moreover, it is the need of the hour that government should restrict deforestation practices in the area

Comparison of four UAV georeferencing methods for environmental monitoring purposes focusing on the combined use with airborne and satellite remote sensing platforms

Altres ajuts: C.P. is a recipient of a FI-DGR scholarship grant (2016B_00410). X.P. is a recipient of an ICREA Academia Excellence in Research Grant ().This work is aimed at the environmental remote sensing community that uses UAV optical frame imagery in combination with airborne and satellite data. Taking into account the economic costs involved and the time investment, we evaluated the fit-for-purpose accuracy of four positioning methods of UAV-acquired imagery: 1) direct georeferencing using the onboard raw GNSS (GNSSNAV) data, 2) direct georeferencing using Post-Processed Kinematic single-frequency carrier-phase without in situ ground support (PPK1), 3) direct georeferencing using Post-Processed Kinematic double-frequency carrier-phase GNSS data with in situ ground support (PPK2), and 4) indirect georeferencing using Ground Control Points (GCP). We tested a multispectral sensor and an RGB sensor, onboard multicopter platforms. Orthophotomosaics at <0.05 m spatial resolution were generated with photogrammetric software. The UAV image absolute accuracy was evaluated according to the ASPRS standards, wherein we used a set of GCPs as reference coordinates, which we surveyed with a differential GNSS static receiver. The raw onboard GNSSNAV solution yielded horizontal (radial) accuracies of RMSEr≤1.062 m and vertical accuracies of RMSEz≤4.209 m; PPK1 solution gave decimetric accuracies of RMSEr≤0.256 m and RMSEz≤0.238 m; PPK2 solution, gave centimetric accuracies of RMSEr≤0.036 m and RMSEz≤0.036 m. These results were further improved by using the GCP solution, which yielded accuracies of RMSEr≤0.023 m and RMSEz≤0.030 m. GNSSNAV solution is a fast and low-cost option that is useful for UAV imagery in combination with remote sensing products, such as Sentinel-2 satellite data. PPK1, which can register UAV imagery with remote sensing products up to 0.25 m pixel size, as WorldView-like satellite imagery, airborne lidar or orthoimagery, has a higher economic cost than the GNSSNAV solution. PPK2 is an acceptable option for registering remote sensing products of up to 0.05 m pixel size, as with other UAV images. Moreover, PPK2 can obtain accuracies that are approximate to the usual UAV pixel size (e.g. co-register in multitemporal studies), but it is more expensive than PPK1. Although indirect georeferencing can obtain the highest accuracy, it is nevertheless a time-consuming task, particularly if many GCPs have to be placed. The paper also provides the approximate cost of each solution

Land Cover Classification Based on UAV Photogrammetry and Deep Learning for Supporting Mine Reclamation: A Case Study of Mae Moh Mine in Lampang Province, Thailand

Detailed, accurate, and frequent mapping of land cover are the prerequisite regarding areas of reclaimed mines and the development of sustainable project-level for goals. Mine reclamation is essential as the extractive organizations are bounded by-laws that have been established by stakeholders to ensure that the mined areas are properly restored. As databases at the mines area become outdated, an automated process of upgrading is needed. Currently, there are only few studies regarding mine reclamation which has less potential of land cover classification using Unmanned Aerial Vehicle (UAV) photogrammetry with Deep learning (DL). This paper aims to employ the classification of land cover for monitoring mine reclamation using DL from the UAV photogrammetric results. The land cover was classified into five classes, comprising: 1) trees, 2) shadow, 3) grassland, 4) barren land, and 5) others (as undefined). To perform the classification using DL, the UAV photogrammetric results, orthophoto and Digital Surface Model (DSM) were used. The effectiveness of both results was examined to verify the potential of land cover classification. The experimental findings showed that effective results for land cover classification over test area were obtained by DL through the combination of orthophoto and DSM with an Overall Accuracy of 0.904, Average Accuracy of 0.681, and Kappa index of 0.937. Our experiments showed that land cover classification from combination orthophoto with DSM was more precise than using orthophoto only. This research provides framework for conducting an analytical process, a UAV approach with DL based evaluation of mine reclamation with safety, also providing a time series information for future efforts to evaluate reclamation. The procedure resulting from this research constitutes approach that is intended to be adopted by government organizations and private corporations so that it will provide accurate evaluation of reclamation in timely manner with reasonable budget

Using Unmanned Aerial Vehicles in Postfire Vegetation Survey Campaigns through Large and Heterogeneous Areas: Opportunities and Challenges

17 p.This study evaluated the opportunities and challenges of using drones to obtain multispectral orthomosaics at ultra-high resolution that could be useful for monitoring large and heterogeneous burned areas. We conducted a survey using an octocopter equipped with a Parrot SEQUOIA multispectral camera in a 3000 ha framework located within the perimeter of a megafire in Spain. We assessed the quality of both the camera raw imagery and the multispectral orthomosaic obtained, as well as the required processing capability. Additionally, we compared the spatial information provided by the drone orthomosaic at ultra-high spatial resolution with another image provided by theWorldView-2 satellite at high spatial resolution. The drone raw imagery presented some anomalies, such as horizontal banding noise and non-homogeneous radiometry. Camera locations showed a lack of synchrony of the single frequency GPS receiver. The georeferencing process based on ground control points achieved an error lower than 30 cm in X-Y and lower than 55 cm in Z. The drone orthomosaic provided more information in terms of spatial variability in heterogeneous burned areas in comparison with theWorldView-2 satellite imagery. The drone orthomosaic could constitute a viable alternative for the evaluation of post-fire vegetation regeneration in large and heterogeneous burned areasS

Evaluation of LAI Estimation of Mangrove Communities Using DLR and ELR Algorithms With UAV, Hyperspectral, and SAR Images

The high-precision estimation of mangrove leaf area index (LAI) using a deep learning regression algorithm (DLR) always requires a large amount of training sample data. However, it is difficult for LAI field measurements to collect a sufficient amount of sample data in mangrove wetlands. To tackle this challenge, this paper proposed an approach for expanding training samples and quantitatively evaluated the performance of estimating LAI for mangrove communities using Deep Neural Networks (DNN) and Transformer algorithms. This study also explored the effects of unmanned aerial vehicle (UAV) and Sentinel-2A multispectral, orbital hyper spectral (OHS), and GF-3 SAR images on LAI estimation of different mangrove communities. Finally, this paper evaluated the LAI estimation ability of mangrove communities using ensemble learning regression (ELR) and DLR algorithms. The results showed that: (1) the UAV images achieved the better LAI estimation of different mangrove communities (R2 = 0.5974–0.6186), and GF-3 SAR images were better for LAI estimation of Avicennia marina with high coverage (R2 = 0.567). The optimal spectral range for estimating LAI for mangroves in the optical images was between 650–680 nm. (2) The ELR model outperformed single base model, and produced the high-accuracy LAI estimation (R2 = 0.5266–0.713) for different mangrove communities. (3) The average accuracy (R2) of the ELR model was higher by 0.0019–0.149 than the DLR models, which demonstrated that the ELR model had a better capability (R2 = 0.5865–0.6416) in LAI estimation. The Transformer-based LAI estimation of A. marina (R2 = 0.6355) was better than the DNN model, while the DNN model produced higher accuracy for Kandelia candel (KC) (R2 = 0.5577). (4) With the increase in the expansion ratio of the training sample (10–50%), the LAI estimation accuracy (R2) of DNN and Transformer models for different mangrove communities increased by 0.1166–0.2037 and 0.1037–0.1644, respectively. Under the same estimation accuracy, the sample enhancement method in this paper could reduce the number of filed measurements by 20–40%

Análise da deriva simulada de herbicidas auxínicos em soja através de índices de vegetação RGB obtidos por VANT

Objetivou-se avaliar a fitotoxicidade e o efeito no rendimento de grãos da soja causados pela deriva de herbicidas auxínicos através da aplicação de índices de vegetação (IV) em imagens obtidas por um sensor RGB embarcado em Veículo Aéreo Não-Tripulado (VANT). Verificou-se ainda a relação do rendimento de grãos com o grau de fitotoxicidade produzido pelas diferentes doses dos herbicidas avaliados. Para isso, foram administrados tratamentos com Dicamba e duas formulações 2,4-D (sal de dimetilamina e sal de colina) simulando a ocorrência de deriva em lavoura de soja sensível. Os resultados indicaram que as doses recomendadas para cultivares de soja resistentes a Dicamba; 2,4-D sal de dimetilamina e 2,4-D sal de colina capazes de reduzir em 50% o rendimento de grãos foram de 73,81 (R2 = 0,99); 556,77 (R2 = 0,87) e 485,31 g e.a. ha-1 (R2 = 0,94), respectivamente. Fitotoxicidade de 4% aos 8 dias após o tratamento (DAT) com Dicamba, nas doses de 1 e 5%, resultou em reduções de 12 e 13% no rendimento de grãos, respectivamente. Tratamentos com 2,4-D não produziram fitotoxicidade na dose de 1%, indicando um baixo acréscimo no rendimento de grãos. Tratamentos com Dicamba nas doses acima de 40% reduziram o rendimento da soja a zero. Em contrate, apenas a dose de 100% das formulações 2,4-D produziu resultado semelhante. Os índices MGRVI e MPRI apresentaram performances semelhantes às avaliações de fitotoxicidade realizadas visualmente para a estimativa do impacto causados pelos herbicidas no rendimento de grãos da soja. Cinco dos seis índices RGB (ExG, ExGR, MGRVI, MPRI e RGBVI) avaliados demonstraram alta relação com as perdas em rendimento de grãos produzidas pelas doses de deriva simulada do Dicamba e formulações 2,4-D. A aplicação de IVs em imagens obtidas pelo sensor RGB forneceu uma maneira simples e direta de detecção de sintomas de fitotoxicidade na cultura da soja, possibilitando ainda estimar o efeito no rendimento de grãos. A metodologia aplicada neste estudo pode ser considerada uma técnica simples e de baixo custo, que pode ser facilmente replicada, além de permitir repetidas e alta frequência de observações.We evaluated the auxin herbicide drift effect on soybean injury and yield loss first by vegetation indices (VI) applied on images from an RGB sensor on-board an Unmanned Aerial Vehicle (UAV), second by visual injury degree analysis. Dicamba treatments and two forms (dimethylamine salt and choline salt) of 2,4-D were performed, simulating the occurrence of drift in susceptible-soybean crops. The results indicated that using the herbicides recommended doses for soybean resistant cultivars of Dicamba; 2,4-D dimethylamine salt and 2,4-D choline salt doses are capable of reducing by 50% yield were 73.81 (R2 = 0.99); 556.77 (R2 = 0.87) e 485.31 g e.a. ha-1 (R2 = 0.94), respectively. Visual injury of 4% at 8 days after treatment (DAT) with Dicamba, doses of 1 and 5%, indicated 12 and 13% yield loss, respectively. Treatments with 2,4-D produced no injury at the dose of 1%, indicating a subtle increase in grain yield. Dicamba treatments at doses above 40% reduced soybean yield to zero. In contrast, only the 100% dose of 2,4-D formulations shown similar results. The MGRVI and MPRI indices showed similar performances to the phytotoxicity assessments (visual injury) for estimating the herbicides effects on soybean yield losses. Five RGB indices (ExG, ExGR, MGRVI, MPRI e RGBVI) evaluated were substantially correlated with yield losses produced by the simulated drift of Dicamba and 2,4-D formulations. The spectral ratio method by RGB indices, provided a simple yet straightforward way to detect soybean injury and was substantially correlated with yield loss from Dicamba and 2,4-D, which is relatively easy to use. This tool provides a low-cost and simple way to replication with repeatable observations and high frequency

Integration of Multiscale Sensing Data for Phenomics Applications

Sensing technologies can be a powerful tool for phenotyping in breeding programs. Plant phenotypes can be assessed non-invasively and repeatedly across the whole population and throughout the plant development period utilizing advanced sensors and remote sensing platforms. In this study, multiscale sensing platforms—satellite, unmanned aerial vehicle (UAV), proximal sensing system, and Internet of Things (IoT) based sensing systems—equipped with sensors such as visible/RGB, multispectral, and hyperspectral systems were utilized for field-based phenomics applications. The applicability of a suitable sensing technology depends on the area of study, specific phenomics application, sensor specification, and data acquisition conditions. Three main phenomics applications were explored: (i) pasture crop health status evaluation, (ii) above-ground biomass quantity and quality evaluation in the field pea, and (iii) evaluating wheat yield potential in winter and spring wheat. The first study demonstrates the reliability of using a high-resolution satellite (ground sampling distance, GSD = 3 m) and UAV imagery for pasture management. The data from multiscale sensing data showed that the grazing density significantly affected pasture biomass (p < 0.05) only in 2019, and the vegetation index (VI) data from the two imagery types were highly correlated (r ≥ 0.78, p < 0.001, 2019). In the second study, the above-ground biomass (AGBM) and biomass quality (12 quality traits) were evaluated using UAV-based RGB and multispectral imaging, and hyperspectral sensing, respectively, in the winter pea breeding program (2019 and 2020 seasons). Three image processing approaches were evaluated for AGBM estimation, where the best results were acquired using the 3D point cloud model at 1.5 alpha shape technique showing high correlation with harvested fresh (r = 0.78–0.81, p < 0.001) and dry (r = 0.70–0.81, p < 0.001) AGBM. Similarly, the selected features from the normalized difference spectral indices and the ratio spectral indices extracted from hyperspectral data with the random forest model provided high predictive accuracy for all 12 biomass quality traits (0.81 < R2 < 0. 93; 0.05 < RMSE (%) < 1.80; 0.03 < MAE (%) < 1.32).In the wheat study, the vegetation indies were highly correlated between satellite (GSD = 0.31 m) and UAV data (0.42 ≤ r ≤ 0.99, p < 0.01) from winter and spring wheat breeding trials (2020 and 2021). The yield prediction using such VIs with the high-resolution satellite imagery (6.26 ≤ RMSE% ≤ 25.49; 5.11 ≤ MAE% ≤ 20.95; 0.17 ≤ r ≤0.78) and UAV imagery (5.53 ≤ RMSE% ≤ 17.20; 4.28 ≤ MAE% ≤ 14.20; 0.43 ≤ r ≤ 0.92) was also high. In addition to these two platforms, an intelligent and compact IoT-based sensor system was developed for independent and automated phenomics applications to measure and monitor plant responses in real-time. The sensor development, improvisation, and implementation encompassed three field seasons (2020, 2021, and 2022 seasons). The developed IoT-based sensor system could be successfully implemented to monitor multiple trials for timely crop management and increased resource efficiency. The system shows a high potential for supporting plant breeding programs for in-field phenotyping applications. All studies demonstrated promising results in monitoring and estimating crop performance and phenotypic traits using multiscale sensing systems

Implementação de modelos de espectroscopia hiperespectral e nanossatélite na identificação de cultivares de vitis vinifera e suas variações regionais

O Brasil é destaque na produção mundial de uvas e demonstra uma constante evolução ao longo de sua histórica, desde 1980, com o Estado do Rio Grande do Sul, no topo da lista de produtores. Diversas regiões produtoras de uvas e vinhos no Brasil tem organizado suas atividades no sentido de se tornarem reconhecidas como “Indicações de Procedência” (IP), dando tipicidade e caráter regional aos seus produtos. Esta caracterização requer descrições dos impactos das condições ambientais e do trabalho humano. A utilização de dados adquiridos por sensoriamento remoto, incluindo dados proximais hiperespectrais e de satélites, permitem classificar e caracterizar as variedades de uvas e suas respectivas unidades produtoras de diversas localidades, sob condições climáticas e antrópicas diferenciadas. Esta tese tem como principal objetivo desenvolver uma metodologia para aquisição de dados, treinamento de modelos de hiperespectrais por sensor proximal e imagens via nanossatélite. A área de estudo é composta por oito vinhedos comerciais localizados no Rio Grande do Sul, Brasil. Na primeira fase deste estudo, a unidade de análise foi a folha isolada da videira em diferentes regiões. Posteriormente foi realizado o levantamento dos parâmetros de clorofila, Teor de Sólidos Totais (TST) ou °Brix da uva, espectros de reflectância hiperespectral e imagens de nanossatélite em parcelas de Cabernet Sauvignon em uma vinícola da Serra Gaúcha. Os modelos Light Gradient Booster Machine (LGBM) e Random Forest (RF) obtiveram as melhores acurácias na discriminação espectral em regiões do ultravioleta (UV) e visível (VIS). As estimativas apresentaram elevados R² com o modelo de regressão RF. O índice de Gini teve maiores valores para comprimentos de onda no UV/VIS/NIR e o índice de vegetação Plant Senescence Reflectance Index (PSRI) teve melhor desempenho para predição dos parâmetros de clorofila, e o Triangular Greenness Index (TGI)/Normalized Difference Vegetation Index (NDVI) para o ºBrix da uva, utilizando como dados a reflectância hiperespectral e a reflectância de superfície. Desenvolvimentos futuros incluem o levantamento de dados com maior número de planta e variedades, auxiliando a compreender as assinaturas espectrais de cada variedade como subsídio para um melhor manejo da produção.Brazil has had an increasing prominence in the production of grapes in the world and the country's production history since the 80's demonstrates this constant evolution. At the top of the list of producers is the State of Rio Grande do Sul. Several grape and wine producing regions in Brazil have organized their activities in order to become recognized as “Indications of Origin” (IO), giving their products typicality and regional character. This characterization requires descriptions of environmental conditions and the impacts of these conditions and human work. The use of remote sensing data, including proximal hyperspectral and satellite data, allow us to classify and characterize grape varieties and their respective producing units from various locations, under different climatic and anthropic conditions. The main objective of this thesis is to develop a methodology for data acquisition, training of plant spectroscopy models with a hyperspectral proximal sensor and for nanosatellite imaging. . The study area consists of eight commercial vineyards found in Rio Grande do Sul, Brazil. In the first phase of this study, the unit of analysis was the leaf isolated from the vine in different regions. Subsequently, a survey of chlorophyll parameters, Total Solids Content (°Bx) of the grape, hyperspectral reflectance spectra and nanosatellite images were conducted in Cabernet Sauvignon plots in a Serra Gaúcha winery. Machine learning algorithms were applied in the discrimination of vineyards by region and by variety, and in the estimation of the chlorophyll and Brix parameters of the grape. The Light Gradient Booster Machine (LGBM) and Random Forest (RF) models obtained the best accuracies in spectral discrimination in the ultraviolet (UV) and visible (VIS) regions. The estimates showed high R² with the RF regression model. The Gini index had higher values for UV/VIS/NIR wavelengths, and the Plant Senescence Reflectance Index (PSRI) had better performance for predicting chlorophyll parameters, and the Triangular Greenness Index (TGI)/Normalized Difference Vegetation Index (NDVI) for the degree Brix, using as data the hyperspectral reflectance and the surface reflectance. Future developments include collecting data with a greater number of plants and varieties, helping to understand the spectral signatures of each variety as a subsidy for better production management