Framework Convention on Climate Change

Statistics: Methods and Applications.

Spaceborne differential synthetic aperture radar interferometry (DInSAR) has already proven its potential for mapping ground deformation phenomena, e.g. volcano dynamics. However, atmospheric disturbances as well as phase decorrelation have prevented hitherto this technique from achieving full operational capability. These drawbacks are overcome by carrying out measurements on a subset of image pixels corresponding to pointwise stable reflectors (Permanent Scatterers, PS) and exploiting long temporal series of interferometric data. Results obtained by processing 55 images acquired by the European Space Agency (ESA) ERS SAR sensors over Southern California show that the PS approach pushes measurement accuracy very close to its theoretical limit (about 1 mm), allowing the description of millimetric deformation phenomena occurring in a complex fault system. A comparison with corresponding displacement time series relative to permanent GPS stations of the Southern California Integrated GPS network (SCIGN) is carried out. Moreover, the pixel-by-pixel character of the PS analysis allows the exploitation of individual phase stable radar targets in low-coherence areas. This makes spaceborne interferometric measurements possible in vegetated areas, as long as a sufficient spatial density of individual isolated man-made structures or exposed rocks is available. The evolution of the Ancona landslide (central Italy) was analysed by processing 61 ERS images acquired in the time span between June 1992 and December 2000. The results have been compared with deformation values detected during optical levelling campaigns ordered by the Municipality of Ancona. The characteristics of PS, GPS and optical levelling surveying are to some extent complementary: a synergistic use of the three techniques could strongly enhance quality and reliability of ground deformation monitoring. D 2002 Elsevier Science B.V. All rights reserved.

Monitoring Landslides and Tectonic Motions with the Permanent Scatterers Technique

Emerging technologies such as Internet of Things (IoT) can provide significant potential in Smart Farming and Precision Agriculture applications, enabling the acquisition of real-time environmental data. IoT devices such as Unmanned Aerial Vehicles (UAVs) can be exploited in a variety of applications related to crops management, by capturing high spatial and temporal resolution images. These technologies are expected to revolutionize agriculture, enabling decision-making in days instead of weeks, promising significant reduction in cost and increase in the yield. Such decisions enable the effective application of farm inputs, supporting the four pillars of precision agriculture, i.e., apply the right practice, at the right place, at the right time and with the right quantity. However, the actual proliferation and exploitation of UAVs in Smart Farming has not been as robust as expected mainly due to the challenges confronted when selecting and deploying the relevant technologies, including the data acquisition and image processing methods. The main problem is that still there is no standardized workflow for the use of UAVs in such applications, as it is a relatively new area. In this article, we review the most recent applications of UAVs for Precision Agriculture. We discuss the most common applications, the types of UAVs exploited and then we focus on the data acquisition methods and technologies, appointing the benefits and drawbacks of each one. We also point out the most popular processing methods of aerial imagery and discuss the outcomes of each method and the potential applications of each one in the farming operations.

https://www.mdpi.com/2078-2489/10/11/349/pdf

A Review on UAV-Based Applications for Precision Agriculture

There has been a dramatic growth in research on biological invasions over the past 20 years, but a mature understanding of the field has been hampered because invasion biologists concerned with different taxa and different environments have largely adopted different model frameworks for the invasion process, resulting in a confusing range of concepts, terms and definitions. In this review, we propose a unified framework for biological invasions that reconciles and integrates the key features of the most commonly used invasion frameworks into a single conceptual model that can be applied to all human-mediated invasions. The unified framework combines previous stage-based and barrier models, and provides a terminology and categorisation for populations at different points in the invasion process.

A proposed unified framework for biological invasions.

Unmanned aerial vehicles (UAV) are increasingly used for spatiotemporal monitoring of invasive plants in coastal wetlands. Early identification of invasive species is necessary in planning, restoring, and managing wetlands. This study assessed the effectiveness of UAV technology to identify invasive Phragmites australis in the Old Woman Creek (OWC) estuary using machine learning (ML) algorithms: Neural network (NN), support vector machine (SVM), and k-nearest neighbor (kNN). The ML algorithms were compared with the parametric maximum likelihood classifier (MLC) using pixel- and object-based methods. Pixel-based NN was identified as the best classifier with an overall accuracy of 94.80% and the lowest error of omission of 1.59%, the outcome desirable for effective eradication of Phragmites. The results were reached combining Sequoia multispectral imagery (green, red, red edge, and near-infrared bands) combined with the canopy height model (CHM) acquired in the mid-growing season and normalized difference vegetation index (NDVI) acquired later in the season. The sensitivity analysis, using various vegetation indices, image texture, CHM, and principal components (PC), demonstrated the impact of various feature layers on the classifiers. The study emphasizes the necessity of a suitable sampling and cross-validation methods, as well as the importance of optimum classification parameters.

https://www.mdpi.com/2072-4292/11/11/1380/pdf

Mapping Invasive Phragmites australis in the Old Woman Creek Estuary Using UAV Remote Sensing and Machine Learning Classifiers

The impact of structural parameters of agricultural crops on the retrieval of chlorophyll content presents a real challenge for the remote-sensing community. Canopy reflectance can differ between c...

The importance of leaf area index in mapping chlorophyll content of corn under different agricultural treatments using UAV images

Due to their low light conditions, shadows reduce the accuracy of feature extraction and change detection in remote-sensing images. Unmanned aerial vehicles UAVs are capable of acquiring images that have a resolution of several centimetres and removing shadows is a challenge. In this study, the Maximum Likelihood ML and Support Vector Machine SVM classifiers were used to classify a UAV image acquired using a red–green–blue RGB camera over the Old Woman Creek National Estuarine Research Reserve in Ohio, USA. The impact of shadows on the classification process was explored for different pixel sizes ranging from 0.03 to 1.00 m. The SVM generated higher overall accuracy OA at finer spatial resolution 0.25–0.50 m, while the optimal spatial resolution for the ML classifier was 1.00 m. The percentage of shadow coverage increased with spatial resolution for both classifiers 1.71% for ML and 6.63% for SVM. Shadows were detected and extracted using two approaches: a as a separate class using regions of interests ROIs observed in the image, and b by applying a segmentation threshold of 0.3 to visible atmospherically resistant index VARI. The extracted shadows were separately classified using ROIs selected from shaded surfaces, and then removed using the fusion of RGB reflectance, VARI, and digital surface model DSM images. The OA of classified shadows reached 91.50%. OAs of merged sunlit and shadow classified images improved for 18.48% for SVM, and 17.62% for the ML classifier. VARI accurately captures shadows, and when fused with RGB reflectance and DSM, it intensifies their low signal and enhances classification. Whether used to capture or to remove shadows, VARI serves as an effective ‘shadow index’. Shadows create obstacles to remote-sensing processing; however, their spectral information should not be neglected as both shadows and sunlit areas are important for ecological processes such as photosynthesis, carbon balance, evapotranspiration, fish abundance, and more.

Different colours of shadows: classification of UAV images

Satellite remote-sensing has been widely used to map suspended sediment concentration (SSC) in waterbodies. Current development of the unmanned aerial vehicle (UAV) technology allows mapping of SSC...

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Multi-depth suspended sediment estimation using high-resolution remote-sensing UAV in Maumee River, Ohio

The first generation of platforms for instruments generating remotely sensed data from the surface of the Earth comprised pilot-flown aircraft and the second generation consisted of Earth-orbiting ...

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2018.1523832

Anita Simic Milas

Papers

Mapping Invasive Phragmites australis in the Old Woman Creek Estuary Using UAV Remote Sensing and Machine Learning Classifiers

The importance of leaf area index in mapping chlorophyll content of corn under different agricultural treatments using UAV images

Different colours of shadows: classification of UAV images

Multi-depth suspended sediment estimation using high-resolution remote-sensing UAV in Maumee River, Ohio

Drones – the third generation source of remote sensing data