Joaquim J. Sousa

Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry

Traditional imagery—provided, for example, by RGB and/or NIR sensors—has proven to be useful in many agroforestry applications. However, it lacks the spectral range and precision to profile materials and organisms that only hyperspectral sensors can provide. This kind of high-resolution spectroscopy was firstly used in satellites and later in manned aircraft, which are significantly expensive platforms and extremely restrictive due to availability limitations and/or complex logistics. More recently, UAS have emerged as a very popular and cost-effective remote sensing technology, composed of aerial platforms capable of carrying small-sized and lightweight sensors. Meanwhile, hyperspectral technology developments have been consistently resulting in smaller and lighter sensors that can currently be integrated in UAS for either scientific or commercial purposes. The hyperspectral sensors’ ability for measuring hundreds of bands raises complexity when considering the sheer quantity of acquired data, whose usefulness depends on both calibration and corrective tasks occurring in pre- and post-flight stages. Further steps regarding hyperspectral data processing must be performed towards the retrieval of relevant information, which provides the true benefits for assertive interventions in agricultural crops and forested areas. Considering the aforementioned topics and the goal of providing a global view focused on hyperspectral-based remote sensing supported by UAV platforms, a survey including hyperspectral sensors, inherent data processing and applications focusing both on agriculture and forestry—wherein the combination of UAV and hyperspectral sensors plays a center role—is presented in this paper. Firstly, the advantages of hyperspectral data over RGB imagery and multispectral data are highlighted. Then, hyperspectral acquisition devices are addressed, including sensor types, acquisition modes and UAV-compatible sensors that can be used for both research and commercial purposes. Pre-flight operations and post-flight pre-processing are pointed out as necessary to ensure the usefulness of hyperspectral data for further processing towards the retrieval of conclusive information. With the goal of simplifying hyperspectral data processing—by isolating the common user from the processes’ mathematical complexity—several available toolboxes that allow a direct access to level-one hyperspectral data are presented. Moreover, research works focusing the symbiosis between UAV-hyperspectral for agriculture and forestry applications are reviewed, just before the paper’s conclusions.

UAS, sensors, and data processing in agroforestry: a review towards practical applications

ABSTRACT The aim of this study is twofold: first, to present a survey of the actual and most advanced methods related to the use of unmanned aerial systems (UASs) that emerged in the past few years due to the technological advancements that allowed the miniaturization of components, leading to the availability of small-sized unmanned aerial vehicles (UAVs) equipped with Global Navigation Satellite Systems (GNSS) and high quality and cost-effective sensors; second, to advice the target audience – mostly farmers and foresters – how to choose the appropriate UAV and imaging sensor, as well as suitable approaches to get the expected and needed results of using technological tools to extract valuable information about agroforestry systems and its dynamics, according to their parcels’ size and crop’s types.Following this goal, this work goes beyond a survey regarding UAS and their applications, already made by several authors. It also provides recommendations on how to choose both the best sensor and UAV, in according with the required application. Moreover, it presents what can be done with the acquired sensors’ data through theuse of methods, procedures, algorithms and arithmetic operations. Finally, some recent applications in the agroforestry research area are presented, regarding the main goal of each analysed studies, the used UAV, sensors, and the data processing stage to reach conclusions.

Bridge Displacements Monitoring Using Space-Borne X-Band SAR Interferometry

The development of interferometric methodologies for deformation monitoring that are able to deal with long time series of synthetic aperture radar (SAR) images made the detection of seasonal effects possible by decomposing the differential SAR phase. In the case of monitoring of man-made structures, particularly bridges, the use of high-resolution X-band SAR data allows the determination of three major components with significant influence on the SAR phase: the linear deformation trend, the height of structures over terrain, and the thermal expansion. In the case of stable metallic or (reinforced) concrete structures, this last effect can reach a magnitude comparable to or even exceeding the other phase components. In this review, we present two case studies that confirm the feasibility of InSAR techniques for bridge deformation monitoring and our original approach to refine the thermal expansion component.

A Data Mining Approach for Multivariate Outlier Detection in Postprocessing of Multitemporal InSAR Results

Displacement maps from multitemporal InSAR (MTI) are usually noisy and fragmented. Thresholding on ensemble coherence is a common practice for identifying radar scatterers that are less affected by decorrelation noise. Thresholding on coherence might, however, cause loss of information over the areas undergoing more complex deformation scenarios. If the discrepancies in the areas of moderate coherence share similar behavior, it appears important to take into account their spatial correlation for correct inference. The information over low-coherent areas might then be used in a similar way the coherence is used in thematic mapping applications such as change detection. We propose an approach based on data mining and statistical procedures for mitigating the impact of outliers in MTI results. Our approach allows for minimization of outliers in final results while preserving spatial and statistical dependence among observations. Tests from monitoring slope failures and undermined areas performed in this work have shown that this is beneficial: 1) for better evaluation of low-coherent scatterers that are commonly discarded by the standard thresholding procedure, 2) for tackling outlying observations with extremes in any variable, 3) for improving spatial densities of standard persistent scatterers, 4) for the evaluation of areas undergoing more complex deformation scenarios, and 5) for the visualization purposes.

A data mining approach for multivariate outlier detection in heterogeneous 2D point clouds: An application to post-processing of multi-temporal InSAR results

Thresholding on coherence is a common practice for identifying the surface scatterers that are less affected by decorrelation noise during post-processing and visualisation of the results from multi-temporal InSAR techniques. Simple selection of the points with coherence greater than a specific value is, however, challenged by the presence of spatial dependence among observations. If the discrepancies in the areas of moderate coherence share similar behaviour, it appears important to take into account their spatial correlation for correct inference. Low coherence areas thus could serve as clear indicators of measurement noise or imperfections in mathematical models. Once exhibiting properties of statistical similarity, they allow for detection of observations that could be considered as outliers and trimmed from the dataset. In this paper we propose an approach based on renowned data mining and exploratory data analysis procedures for mitigating the impact of outlying observations in the final results.

Vineyard properties extraction combining UAS-based RGB imagery with elevation data

ABSTRACT To differentiate between canopy and vegetation cover is particularly challenging. Nonetheless, it is pivotal in obtaining the exact crops’ vegetation when using remote-sensing data. In this article, a method to automatically estimate and extract vineyards’ canopy is proposed. It combines vegetation indices and digital elevation models – derived from high-resolution images, acquired using unmanned aerial vehicles – to differentiate between vines’ canopy and inter-row vegetation cover. This enables the extraction of relevant information from a specific vineyard plot. The proposed method was applied to data acquired from some vineyards located in Portugal’s north-eastern region, and the resulting parameters were validated. It proved to be an effective method when applied with consumer-grade sensors, carried by unmanned aerial vehicles. Moreover, it also proved to be a fast and efficient way to extract vineyard information, enabling vineyard plots mapping for precision viticulture management tasks.

Multi-Temporal Analysis of Forestry and Coastal Environments Using UASs

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, Valpacos, 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.

Factors determining subsidence in urbanized floodplains: evidence from MT-InSAR in Seville (southern Spain): FACTORS DETERMINING SUBSIDENCE IN URBANIZED FLOODPLAINS

Major rivers have traditionally been linked with important human settlements throughout history. The growth of cities over recent river deposits makes necessary the use of multidisciplinary approaches to characterize the evolution of drainage networks in urbanized areas. Since under-consolidated fluvial sediments are especially sensitive to compaction, their spatial distribution, thickness, and mechanical behavior must be studied. Here, we report on subsidence in the city of Seville (Southern Spain) between 2003 and 2010, through the analysis of the results obtained with the Multi-Temporal InSAR (MT-InSAR) technique. In addition, the temporal evolution of the subsidence is correlated with the rainfall, the river water column and the piezometric level. Finally, we characterize the geotechnical parameters of the fluvial sediments and calculate the theoretical settlement in the most representative sectors. Deformation maps clearly indicate that the spatial extent of subsidence is controlled by the distribution of under-consolidated fine-grained fluvial sediments at heights comprised in the range of river level variation. This is clearly evident at the western margin of the river and the surroundings of its tributaries, and differs from rainfall results as consequence of the anthropic regulation of the river. On the other hand, this influence is not detected at the eastern margin due to the shallow presence of coarse-grain consolidated sediments of different terrace levels. The derived results prove valuable for implementing urban planning strategies, and the InSAR technique can therefore be considered as a complementary tool to help unravel the subsidence tendency of cities located over under-consolidated fluvial deposits.

UAS-based photogrammetry of cultural heritage sites: a case study addressing Chapel of Espírito Santo and photogrammetric software comparison

The cost-effectiveness of unmanned aerial systems (UAS) makes them suitable platforms to survey cultural heritage sites. Developments in photogrammetry provide methods capable to generate accurate 3D models out of 2D aerial images. Considering the involved technologies, the purpose of this paper is to document the Chapel of Espiríto Santo: a very relevant monument for Vila Real (Portugal) that is currently located at the campus of the University of Trás-os-Montes and Alto Douro. The UAS based aerial imagery survey approach is presented along with photogrammetric process to build chapels 3D model. Moreover, two photogrammetric software were compared - Pix4Dmapper Pro and Agisoft Photoscan - in terms of modelling accuracy and functionalities ease of use.

Machine learning classification methods in hyperspectral data processing for agricultural applications

In agricultural applications hyperspectral imaging is used in cases where differences in spectral reflectance of the examined objects are small. However, the large amount of data generated by hyperspectral sensors requires advance processing methods. Machine learning approaches may play an important role in this task. They are known for decades, but they need high volume of data to compute accurate results. Until recently, the availability of hyperspectral data was a big drawback. It was first used in satellites, later in manned aircrafts and data availability from those platforms was limited because of logistics complexity and high price. Nowadays, hyperspectral sensors are available for unmanned aerial vehicles, which enabled to reach a high volume of data, thus overcoming these issues. This way, the aim of this paper is to present the status of the usage of machine learning approaches in the hyperspectral data processing, with a focus on agriculture applications. Nevertheless, there are not many studies available applying machine learning approach to hyperspectral data for agricultural applications. This apparent limitation was in fact the inspiration for making this survey. Preliminary results using UAV-based data are presented, showing the suitability of machine learning techniques in remote sensed data.