Percorrer por autor "Pádua, Luís"
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- Assessing the Impact of Clearing and Grazing on Fuel Management in a Mediterranean Oak Forest through Unmanned Aerial Vehicle Multispectral DataPublication . Pádua, Luís; Castro, João Paulo; Castro, José; Sousa, Joaquim J.; Castro, MarinaClimate change has intensified the need for robust fire prevention strategies. Sustainableforest fuel management is crucial in mitigating the occurrence and rapid spread of forest fires. Thisstudy assessed the impact of vegetation clearing and/or grazing over a three-year period in theherbaceous and shrub parts of a Mediterranean oak forest. Using high-resolution multispectral datafrom an unmanned aerial vehicle (UAV), four flight surveys were conducted from 2019 (pre- and post-clearing) to 2021. These data were used to evaluate different scenarios: combined vegetation clearingand grazing, the individual application of each method, and a control scenario that was neithercleared nor purposely grazed. The UAV data allowed for the detailed monitoring of vegetationdynamics, enabling the classification into arboreal, shrubs, herbaceous, and soil categories. Grazingpressure was estimated through GPS collars on the sheep flock. Additionally, a good correlation(r = 0.91) was observed between UAV-derived vegetation volume estimates and field measurements.These practices proved to be efficient in fuel management, with cleared and grazed areas showing alower vegetation regrowth, followed by areas only subjected to vegetation clearing. On the otherhand, areas not subjected to any of these treatments presented rapid vegetation growth.
- Characterization of seaweed communities using deep learning applied to UAV-based hyperspectral imagesPublication . Gomes, João Pedro; Sousa, Joaquim J.; Pádua, Luís; Cunha, Carlos R.; Cunha, AntónioMacroalgal communities are generally found in coastal regions, close to rocks or other hard surfaces. They provide shelter and food for many organisms and are of interest to the food industry, pharmaceutical, and agriculture. They are also an indicator of environmental change. Traditionally, the process of identification and monitoring of these communities and their constituent species is based on manual methods. The use of unmanned aerial vehicles (UAVs) allows the remote collection of images with high spatial and spectral resolution and adjustable time scales. The development of methodologies allowing the processing and the analysis of UAV-based high-resolution imagery would be of economic and environmental importance. That would allow to streamline the identification of species with economic potential, the evaluation of the seasonal and spatial variation of the available biomass, and the monitoring of the coastal ecological status and its evolution. Recent technological developments in the areas of remote sensing and artificial intelligence make it possible to provide tools with great potential for these applications. Indeed, hyperspectral sensors can nowadays be coupled in UAVs allowing for high spatial and spectral resolution imagery. The data processing powered by deep learning and its increasing diversity of models and architectures is the ideal way to handle and analyze the huge volume of data acquired. In this paper, we describe a methodology to be implemented in a system to be developed to make the automatic classification of existing species in macroalgal communities, using deep learning models applied to hyperspectral images collected by UAVs.
- Combining UAV-Based Multispectral and Thermal Infrared Data with Regression Modeling and SHAP Analysis for Predicting Stomatal Conductance in Almond OrchardsPublication . Guimarães, Nathalie; Sousa, Joaquim J.; Couto, Pedro; Bento, Albino; Pádua, LuísUnderstanding and accurately predicting stomatal conductance in almond orchards is critical for effective water-management strategies, especially under challenging climatic conditions. In this study, machine-learning (ML) regression models trained on multispectral (MSP) and thermal infrared (TIR) data acquired from unmanned aerial vehicles (UAVs) are used to address this challenge. Through an analysis of spectral indices calculated from UAV-based data and feature-selection methods, this study investigates the predictive performance of three ML models (extra trees, ET; stochastic gradient descent, SGD; and extreme gradient boosting, XGBoost) in predicting stomatal conductance. The results show that the XGBoost model trained with both MSP and TIR data had the best performance (R2 = 0.87) and highlight the importance of integrating surface-temperature information in addition to other spectral indices to improve prediction accuracy, up to 11% more when compared to the use of only MSP data. Key features, such as the green–red vegetation index, chlorophyll red-edge index, and the ratio between canopy temperature and air temperature (Tc-Ta), prove to be relevant features for model performance and highlight their importance for the assessment of water stress dynamics. Furthermore, the implementation of Shapley additive explanations (SHAP) values facilitates the interpretation of model decisions and provides valuable insights into the contributions of the features. This study contributes to the advancement of precision agriculture by providing a novel approach for stomatal conductance prediction in almond orchards, supporting efforts towards sustainable water management in changing environmental conditions.
- Comparative Evaluation of Remote Sensing Platforms for Almond Yield PredictionPublication . Guimarães, Nathalie; Fraga, Helder; Sousa, Joaquim J.; Pádua, Luís; Bento, Albino; Couto, PedroAlmonds are becoming a central element in the gastronomic and food industry worldwide. Over the last few years, almond production has increased globally. Portugal has become the third most important producer in Europe, where this increasing trend is particularly evident. However, the susceptibility of almond trees to changing climatic conditions presents substantial risks, encompassing yield reduction and quality deterioration. Hence, yield forecasts become crucial for mitigating potential losses and aiding decisionmakers within the agri-food sector. Recent technological advancements and new data analysis techniques have led to the development of more suitable methods to model crop yields. Herein, an innovative approach to predict almond yields in the Tras-os-Montes region of Portugal was developed, by using machine learning regression models (i.e., the random forest regressor, XGBRegressor, gradient boosting regressor, bagging regressor, and AdaBoost regressor), coupled with remote sensing data obtained from different satellite platforms. Satellite data from both proprietary and free platforms at different spatial resolutions were used as features in the study (i.e., the GSMP: 11.13 km, Terra: 1 km, Landsat 8: 30 m, Sentinel-2: 10 m, and PlanetScope: 3 m). The best possible combination of features was analyzed and hyperparameter tuning was applied to enhance the prediction accuracy. Our results suggest that high-resolution data (PlanetScope) combined with irrigation information, vegetation indices, and climate data significantly improves almond yield prediction. The XGBRegressor model performed best when using PlanetScope data, reaching a coefficient of determination (R2) of 0.80. However, alternative options using freely available data with lower spatial resolution, such as GSMaP and Terra MODIS LST, also showed satisfactory performance (R2 = 0.68). This study highlights the potential of integrating machine learning models and remote sensing data for accurate crop yield prediction, providing valuable insights for informed decision support in the almond sector, contributing to the resilience and sustainability of this crop in the face of evolving climate dynamics.
- GIS application to detect invasive species in aquatic ecosystemsPublication . Duarte, Lia; Castro, João Paulo; Sousa, Joaquim J.; Pádua, LuísThe detection of invasive plant species in aquatic ecosystems is important to help in the control or to mitigate its spread and impacts. Remote sensing (RS) can be explored in this context, helping to monitor this type of plants. This study intends to present a free to use and opensource software application that, through a graphical user interface, can process remote sensed data to monitor the spread of invasive plant species in aquatic environments, enabling a multi-temporal monitoring. Both unmanned aerial vehicle and satellite-based data were used to validate the potential of the proposed application. A site containing water hyacinth (Eichhornia crassipes) was selected as case study. Both RS platforms provided effective data to detect the areas containing water hyacinth. Thus, this tool provides an alternative and user-friendly way to include RS-based data in ecological studies allowing the detection of invasive plants in water channels.
- Identification of aphids using machine learning classifiers on UAV-based multispectral dataPublication . Guimarães, Nathalie; Pádua, Luís; Sousa, Joaquim J.; Bento, Albino; Couto, PedroAlmond trees in Portugal are susceptible to aphid infestation, which can result in reduced fruit production. To effectively tackle this issue, the combination of remote sensing (RS) data and machine learning (ML) classifiers can be used to accurately detect the presence of aphids. This study focuses in the implementation of ML classifiers and RS data analysis to identify aphids on almond trees, using high-resolution multispectral data collected through an unmanned aerial vehicle (UAV) in a Portuguese almond orchard. Four ML classifiers, kNN, SVM, RF and XGBoost, were employed and fine-tuned using vegetation indices derived from spectral data. The results revealed that the SVM classifier achieved an overall accuracy (OA) of 77%, followed by kNN with an OA of 74%, while XGBoost and RF achieved OAs of 71% and 69%, respectively. Consequently, this study demonstrates the viability of employing RS data and ML classifiers for aphid identification in almond orchards.
- Multi-temporal analysis of forestry and coastal environments using UASsPublication . Pádua, Luís; Hruška, Jonáš; Bessa, José; Adão, Telmo; Martins, Luís; Gonçalves, José Alberto; Peres, Emanuel; Sousa, António M.R.; Castro, João Paulo; Sousa, Joaquim J.Due to strong improvements and developments achieved in the last decade, it is clear that applied research using remote sensing technology such as unmanned aerial vehicles (UAVs) can provide a flexible, efficient, non-destructive, and non-invasive means of acquiring geoscientific data, especially aerial imagery. Simultaneously, there has been an exponential increase in the development of sensors and instruments that can be installed in UAV platforms. By combining the aforementioned factors, unmanned aerial system (UAS) setups composed of UAVs, sensors, and ground control stations, have been increasingly used for remote sensing applications, with growing potential and abilities. This paper's overall goal is to identify advantages and challenges related to the use of UAVs for aerial imagery acquisition in forestry and coastal environments for preservation/prevention contexts. Moreover, the importance of monitoring these environments over time will be demonstrated. To achieve these goals, two case studies using UASs were conducted. The first focuses on phytosanitary problem detection and monitoring of chestnut tree health (Padrela region, Valpaços, Portugal). The acquired high-resolution imagery allowed for the identification of tree canopy cover decline by means of multi-temporal analysis. The second case study enabled the rigorous and non-evasive registry process of topographic changes that occurred in the sandspit of Cabedelo (Douro estuary, Porto, Portugal) in different time periods. The obtained results allow us to conclude that the UAS constitutes a low-cost, rigorous, and fairly autonomous form of remote sensing technology, capable of covering large geographical areas and acquiring high precision data to aid decision support systems in forestry preservation and coastal monitoring applications. Its swift evolution makes it a potential big player in remote sensing technologies today and in the near future.
- Remote Sensing Applications in Almond Orchards: A Comprehensive Systematic Review of Current Insights, Research Gaps, and Future ProspectsPublication . Guimarães, Nathalie; Sousa, Joaquim J.; Pádua, Luís; Bento, Albino; Couto, PedroAlmond cultivation is of great socio-economic importance worldwide. With the demand for almonds steadily increasing due to their nutritional value and versatility, optimizing the management of almond orchards becomes crucial to promote sustainable agriculture and ensure food security. The present systematic literature review, conducted according to the PRISMA protocol, is devoted to the applications of remote sensing technologies in almond orchards, a relatively new field of research. The study includes 82 articles published between 2010 and 2023 and provides insights into the predominant remote sensing applications, geographical distribution, and platforms and sensors used. The analysis shows that water management has a pivotal focus regarding the remote sensing application of almond crops, with 34 studies dedicated to this subject. This is followed by image classification, which was covered in 14 studies. Other applications studied include tree segmentation and parameter extraction, health monitoring and disease detection, and other types of applications. Geographically, the United States of America (USA), Australia and Spain, the top 3 world almond producers, are also the countries with the most contributions, spanning all the applications covered in the review. Other studies come from Portugal, Iran, Ecuador, Israel, Turkey, Romania, Greece, and Egypt. The USA and Spain lead water management studies, accounting for 23% and 13% of the total, respectively. As far as remote sensing platforms are concerned, satellites are the most widespread, accounting for 46% of the studies analyzed. Unmanned aerial vehicles follow as the second most used platform with 32% of studies, while manned aerial vehicle platforms are the least common with 22%. This up-to-date snapshot of remote sensing applications in almond orchards provides valuable insights for researchers and practitioners, identifying knowledge gaps that may guide future studies and contribute to the sustainability and optimization of almond crop management.
- Spatio-temporal water hyacinth monitoring in the lower Mondego (Portugal) using remote sensing dataPublication . Pádua, Luís; Duarte, Lia; Geraldes, Ana Maria; Sousa, Joaquim J.; Castro, João PauloMonitoring invasive plant species is a crucial task to assess their presence in affected ecosystems. However, it is a laborious and complex task as it requires vast surface areas, with difficult access, to be surveyed. Remotely sensed data can be a great contribution to such operations, especially for clearly visible and predominant species. In the scope of this study, water hyacinth (Eichhornia crassipes) was monitored in the Lower Mondego region (Portugal). For this purpose, Sentinel-2 satellite data were explored enabling us to follow spatial patterns in three water channels from 2018 to 2021. By applying a straightforward and effective methodology, it was possible to estimate areas that could contain water hyacinth and to obtain the total surface area occupied by this invasive species. The normalized difference vegetation index (NDVI) was used for this purpose. It was verified that the occupation of this invasive species over the study area exponentially increases from May to October. However, this increase was not verified in 2021, which could be a consequence of the adopted mitigation measures. To provide the results of this study, the methodology was applied through a semi-automatic geographic information system (GIS) application. This tool enables researchers and ecologists to apply the same approach in monitoring water hyacinth or any other invasive plant species in similar or different contexts. This methodology proved to be more effective than machine learning approaches when applied to multispectral data acquired with an unmanned aerial vehicle. In fact, a global accuracy greater than 97% was achieved using the NDVI-based approach, versus 93% when using the machine learning approach (above 93%).
- Water hyacinth (Eichhornia crassipes) detection using coarse and high resolution multispectral dataPublication . Pádua, Luís; Geraldes, Ana Maria; Sousa, Joaquim J.; Rodrigues, M.A.; Oliveira, Verónica; Santos, Daniela; Miguens, Filomena; Castro, João PauloEfficient detection and monitoring procedures of invasive plant species are required. It is of crucial importance to deal with such plants in aquatic ecosystems, since they can affect biodiversity and, ultimately, ecosystem function and services. In this study, it is intended to detect water hyacinth (Eichhornia crassipes) using multispectral data with different spatial resolutions. For this purpose, high-resolution data (<0.1 m) acquired from an unmanned aerial vehicle (UAV) and coarse-resolution data (10 m) from Sentinel-2 MSI were used. Three areas with a high incidence of water hyacinth located in the Lower Mondego region (Portugal) were surveyed. Different classifiers were used to perform a pixel-based detection of this invasive species in both datasets. From the different classifiers used, the results were achieved by the random forest classifiers stand-out (overall accuracy (OA): 0.94). On the other hand, support vector machine performed worst (OA: 0.87), followed by Gaussian naive Bayes (OA: 0.88), k-nearest neighbours (OA: 0.90), and artificial neural networks (OA: 0.91). The higher spatial resolution from UAV-based data enabled us to detect small amounts of water hyacinth, which could not be detected in Sentinel-2 data. However, and despite the coarser resolution, satellite data analysis enabled us to identify water hyacinth coverage, compared well with a UAV-based survey. Combining both datasets and even considering the different resolutions, it was possible to observe the temporal and spatial evolution of water hyacinth. This approach proved to be an effective way to assess the effects of the mitigation/control measures taken in the study areas. Thus, this approach can be applied to detect invasive species in aquatic environments and to monitor their changes over time.