Early detection of pine wilt disease in Pinus tabuliformis in North China using a field portable spectrometer and UAV-based hyperspectral imagery

Background Pine wilt disease (PWD) is a major ecological concern in China that has caused severe damage to millions of Chinese pines (Pinus tabulaeformis). To control the spread of PWD, it is necessary to develop an effective approach to detect its presence in the early stage of infection. One potential solution is the use of Unmanned Airborne Vehicle (UAV) based hyperspectral images (HIs). UAV-based HIs have high spatial and spectral resolution and can gather data rapidly, potentially enabling the effective monitoring of large forests. Despite this, few studies examine the feasibility of HI data use in assessing the stage and severity of PWD infection in Chinese pine. Method To fill this gap, we used a Random Forest (RF) algorithm to estimate the stage of PWD infection of trees sampled using UAV-based HI data and ground-based data (data directly collected from trees in the field). We compared relative accuracy of each of these data collection methods. We built our RF model using vegetation indices (VIs), red edge parameters (REPs), moisture indices (MIs), and their combination. Results We report several key results. For ground data, the model that combined all parameters (OA: 80.17%, Kappa: 0.73) performed better than VIs (OA: 75.21%, Kappa: 0.66), REPs (OA: 79.34%, Kappa: 0.67), and MIs (OA: 74.38%, Kappa: 0.65) in predicting the PWD stage of individual pine tree infection. REPs had the highest accuracy (OA: 80.33%, Kappa: 0.58) in distinguishing trees at the early stage of PWD from healthy trees. UAV-based HI data yielded similar results: the model combined VIs, REPs and MIs (OA: 74.38%, Kappa: 0.66) exhibited the highest accuracy in estimating the PWD stage of sampled trees, and REPs performed best in distinguishing healthy trees from trees at early stage of PWD (OA: 71.67%, Kappa: 0.40). Conclusion Overall, our results confirm the validity of using HI data to identify pine trees infected with PWD in its early stage, although its accuracy must be improved before widespread use is practical. We also show UAV-based data PWD classifications are less accurate but comparable to those of ground-based data. We believe that these results can be used to improve preventative measures in the control of PWD.

Describing law of motion of flexible inextensional shell in gravity force field

This work makes use of Navier-Stokes equations to describe an analytical method of finding the motion speed of a flexible inextensional shell falling down to the ground from a preset height and determines the duration of this fall. The soft shell in question is a fabric body of aerodynamic shape or an item of clothes, an airborne vehicle element, etc. Analytical relations are presented for the speed at which the shell moves in the air, taking account of the air resistance and the shell fall duration. The boundary problem of the soft shell vertically falling in the air is solved.

First Evidence of Peat Domes in the Congo Basin using LiDAR from a Fixed-Wing Drone

The world’s most extensive tropical peatlands occur in the Cuvette Centrale depression in the Congo Basin, which stores 30.6 petagrams of carbon (95% CI, 6.3–46.8). Improving our understanding of the genesis, development and functioning of these under-studied peatlands requires knowledge of their topography and, in particular, whether the peat surface is domed, as this implies a rain-fed system. Here we use a laser altimeter mounted on an unmanned airborne vehicle (UAV) to measure peat surface elevation along two transects at the edges of a peatland, in the northern Republic of Congo, to centimetre accuracy and compare the results with an analysis of nearby satellite LiDAR data (ICESat and ICESat-2). The LiDAR elevations on both transects show an upward slope from the peatland edge, suggesting a surface elevation peak of around 1.8 m over ~20 km. While modest, this domed shape is consistent with the peatland being rainfed. In-situ peat depth measurements and our LiDAR results indicate that this peatland likely formed at least 10,000 years BP in a large shallow basin ~40 km wide and ~3 m deep.

UAV and Spaceborne LiDAR Gives First Evidence of Peat Domes in the Congo Basin

<p>The peatlands of the Cuvette Centrale depression in the Congo Basin store between 6.3 and 46.8 petagrams of carbon. To improve our understanding of the genesis, development and functioning of these peatlands, we need to know if their surface is domed. Past work using satellite-based instruments has established that if the peatland surface is domed, it is very shallow, below 2‑3 m over a distance of 26km. We used a laser altimeter mounted on an unmanned airborne vehicle (UAV) to measure peat surface elevation along two transects at the edges of a peatland  to centimetre accuracy, and combined the results with an analysis of local ICESat and ICESat-2 returns. The LiDAR elevations show an upward slope inwards from both edges, and the ICESat and ICESat-2 returns suggest a peak around 1.8 m  above the edges. This matches our expectations of a rainfed peatland and, combined with prior measurements of peat depth, indicates that this peatland formed in a 3 m-deep basin.</p>

Differential Target Antenna Coupling (DTAC) EM Surveying with Stationary Transmitter Loop and Moving In-Loop Receivers

Following our previous studies of the Differential Target Antenna Coupling (DTAC) method with horizontal and vertical arrays for EM surveys, in this paper we study the application of the DTAC method to a different configuration, where a large, stationary transmitter loop is on the ground surface. We then run profile lines inside this loop. The DTAC method is effective in eliminating errors due to the large variations in the primary field along profile lines within the transmitting loop. Operational tests show that we obtain more diagnostic DTAC anomalies over buried targets than using just the B x and B y data. The DTAC method also produces smaller false-alarm targets due to background geology variations, compared with B z measurements. The DTAC method can be used with either time- or frequency-domain data and the receiver can be moved on the ground or deployed from an airborne vehicle, such as a drone.

THE SUPERSPECTRAL/HYPERSPATIAL WORLDVIEW-3 AS THE LINK BETWEEN SPACEBORNE HYPERSPECTRAL AND AIRBORNE HYPERSPATIAL SENSORS: THE CASE STUDY OF THE COMPLEX TROPICAL COAST

<p><strong>Abstract.</strong> Earth observation of complex scenes, such as coastal fringes, is based on a plethora of optical sensors constrained by trade-offs between spatial, spectral, temporal and radiometric resolution. The spaceborne hyperspectral EO-1 Hyperion sensor (decommissioned in 2017) was able to acquire imagery with 10&amp;thinsp;nm spectral (220 bands) at 30&amp;thinsp;m spatial resolutions over 1424.5&amp;thinsp;km² scenes. Conversely, the widespread unmanned airborne vehicle (UAV) hyperspatial DJI Mavic Pro camera can collect only natural-coloured imagery of 100&amp;thinsp;nm spectral (3 bands) but at 0.1&amp;thinsp;m spatial resolution over &amp;sim;10&amp;thinsp;km² scenes (with a single battery and calm meteo-marine conditions). The spaceborne WorldView-3 (WV3), featured by 60&amp;thinsp;nm spectral (16 bands) at 0.3&amp;thinsp;m spatial resolution (when pansharpened) over 1489.6&amp;thinsp;km² scenes, has the capacity to bridge both sensors. This study aims at testing the spectral and spatial performances of the WV3 to discriminate 10 complex coastal classes, ranging from ocean, reefs and terrestrial vegetation in Moorea Island (French Polynesia). Our findings show that geometrically- and radiometrically-corrected 0.3-m 16-band WV3 bands competed with (30-m) 167-band Hyperion performance for classifying 10 coastal classes with 2-neuron artificial neural network modelling, while being able to segment objects seized by 0.1-m (3-band) UAV. Unifying superspectral and hyperspatial specificities, the WV3 also leverages hypertemporal resolution, that is to say 1-day temporal resolution, rivalling UAV’s one.</p>

Identification of Ramularia Leaf Blight Cotton Disease Infection Levels by Multispectral, Multiscale UAV Imagery

The reduction of the production cost and negative environmental impacts by pesticide application to control cotton diseases depends on the infection patterns spatialized in the farm scale. Here, we evaluate the potential of three-band multispectral imagery from a multi-rotor unmanned airborne vehicle (UAV) platform for the detection of ramularia leaf blight from different flight heights in an experimental field. Increasing infection levels indicate the progressive degradation of the spectral vegetation signal, however, they were not sufficient to differentiate disease severity levels. At resolutions of ~5 cm (100 m) and ~15 cm (300 m) up to a ground spatial resolution of ~25 cm (500 m flight height), two-scaled infection levels can be detected for the best performing algorithm of four classifiers tested, with an overall accuracy of ~79% and a kappa index of ~0.51. Despite limited classification performance, the results show the potential interest of low-cost multispectral systems to monitor ramularia blight in cotton.