Density Estimates as Representations of Agricultural Fields for Remote Sensing-Based Monitoring of Tillage and Vegetation Cover

We consider the use of remote sensing for large-scale monitoring of agricultural land use, focusing on classification of tillage and vegetation cover for individual field parcels across large spatial areas. From the perspective of remote sensing and modelling, field parcels are challenging as objects of interest due to highly varying shape and size but relatively uniform pixel content and texture. To model such areas we need representations that can be reliably estimated already for small parcels and that are invariant to the size of the parcel. We propose representing the parcels using density estimates of remote imaging pixels and provide a computational pipeline that combines the representation with arbitrary supervised learning algorithms, while allowing easy integration of multiple imaging sources. We demonstrate the method in the task of the automatic monitoring of autumn tillage method and vegetation cover of Finnish crop fields, based on the integrated analysis of intensity of Synthetic Aperture Radar (SAR) polarity bands of the Sentinel-1 satellite and spectral indices calculated from Sentinel-2 multispectral image data. We use a collection of 127,757 field parcels monitored in April 2018 and annotated to six tillage method and vegetation cover classes, reaching 70% classification accuracy for test parcels when using both SAR and multispectral data. Besides this task, the method could also directly be applied for other agricultural monitoring tasks, such as crop yield prediction.

IBEX Ribbon Separation Using Spherical Harmonic Decomposition of the Globally Distributed Flux

Remote imaging of plasmas in the heliosphere and very local interstellar medium is possible with energetic neutral atoms (ENAs), created through the charge exchange of protons with interstellar neutral atoms. ENA observations collected by the Interstellar Boundary Explorer (IBEX) revealed two distinctive sources. One source is the globally distributed flux (GDF), which extends over the entire sky and varies over large spatial scales. The other source encompasses only a narrow circular band in the sky and is called the IBEX ribbon. Here, we utilize the observed difference in spatial scales of these two ENA sources to separate them. We find that linear combinations of spherical harmonics up to degree ℓ max = 3 can reproduce most of the ENA fluxes observed outside the ribbon region. We use these combinations to model the GDF and the difference between the observed fluxes and the GDF yields estimation of the ribbon emission. The separated ribbon responds with a longer time delay to the solar wind changes than the GDF, suggesting a more distant source of the ribbon ENAs. Moreover, we locate the direction of the maximum plasma pressure based on the GDF. This direction is 17°.2 ± 0°.5 away from the upwind direction within the plane containing the interstellar flow and interstellar magnetic field vectors. This deflection is consistent with the expected position of the maximum external pressure at the heliopause. The maps with separated ribbon and GDF are posted concurrently with this paper and can be used to further study these two sources.

Mesoscale Structure in the Solar Wind

Structures in the solar wind result from two basic mechanisms: structures injected or imposed directly by the Sun, and structures formed through processing en route as the solar wind advects outward and fills the heliosphere. On the largest scales, solar structures directly impose heliospheric structures, such as coronal holes imposing high speed streams of solar wind. Transient solar processes can inject large-scale structure directly into the heliosphere as well, such as coronal mass ejections. At the smallest, kinetic scales, the solar wind plasma continually evolves, converting energy into heat, and all structure at these scales is formed en route. “Mesoscale” structures, with scales at 1 AU in the approximate spatial range of 5–10,000 Mm and temporal range of 10 s–7 h, lie in the orders of magnitude gap between the two size-scale extremes. Structures of this size regime are created through both mechanisms. Competition between the imposed and injected structures with turbulent and other evolution leads to complex structuring and dynamics. The goal is to understand this interplay and to determine which type of mesoscale structures dominate the solar wind under which conditions. However, the mesoscale regime is also the region of observation space that is grossly under-sampled. The sparse in situ measurements that currently exist are only able to measure individual instances of discrete structures, and are not capable of following their evolution or spatial extent. Remote imaging has captured global and large scale features and their evolution, but does not yet have the sensitivity to measure most mesoscale structures and their evolution. Similarly, simulations cannot model the global system while simultaneously resolving kinetic effects. It is important to understand the source and evolution of solar wind mesoscale structures because they contain information on how the Sun forms the solar wind, and constrains the physics of turbulent processes. Mesoscale structures also comprise the ground state of space weather, continually buffeting planetary magnetospheres. In this paper we describe the current understanding of the formation and evolution mechanisms of mesoscale structures in the solar wind, their characteristics, implications, and future steps for research progress on this topic.

In-Situ Multi-Spacecraft and Remote Imaging Observations of the First CME Detected by Solar Orbiter and BepiColombo

<p>On April 19th 2020 a CME was detected by Solar Orbiter at a heliocentric distance of 0.8 AU and was also observed in-situ on April 20th by both Wind and BepiColombo. During this time, BepiColombo had just completed a flyby of the Earth and therefore the longitudinal separation between BepiColombo and Wind was just 1.4°. The total longitudinal separation of Solar Orbiter and both spacecraft near the Earth was less than 5°, providing an excellent opportunity for a radial alignment study of the CME. We use the in-situ observations of the magnetic field at Solar Orbiter with those at Wind and BepiColombo to analyse the large-scale properties of the CME and compare results to those predicted using remote observations at STEREO-A, providing a global picture of the CME as it propagated from the Sun to 1 AU.</p>

Analisis Prediksi Sebaran Nilaparvata Lugens (Hama Wereng) Tanaman Padi menggunakan Teknologi Autonomous Drone Mapping dengan Ground Sampling Area

The purpose of this study was to help farmers predict the spread of rice planthopper pests by utilizing Autonomous Drone Mapping technology with Ground Sampling Area. The object under study was rice farming land with an area of ​​64.5 m2 which often experienced disturbances in productivity and quality of rice. Autonomous Drone Mapping Technology with Ground Sampling Area integration. This technology is used to detect the spread of leafhoppers on agricultural land through mapping of the affected land through a map of conditions resulting from shooting and imagery produced by drones flown over agricultural land. This technology can help rice farmers to predict pest attack from an early age by handling and preventive measures so that the level of productivity and quality of rice is not compromised. Prediction analysis can take advantage of remote imaging photos from the use of Autonomous Drone Mapping with Ground Sampling Area by analyzing the spread prediction data with Tren Forecasting Prediction. Based on the analysis of the predictions from the photo of the spread of planthopper pests, the distribution formula is y = 25.396 ln(x) -34.948 with the maximum spread of leafhoppers occurring on the 49th day so that serious handling is needed by farmers.

Spatial Analysis of the Drivers, Characteristics, and Effects of Forest Fragmentation

Building on the existing literature, this study examines whether specific drivers of forest fragmentation cause particular fragmentation characteristics, and how these characteristics can be linked to their effects on forest-dwelling species. This research uses Landsat remote imaging to examine the changing patterns of forests. It focuses on areas which have undergone a high level of a specific fragmentation driver, in particular either agricultural expansion or commodity-driven deforestation. Seven municipalities in the states of Rondônia and Mato Grosso in Brazil are selected as case study areas, as these states experienced a high level of commodity-driven deforestation and agricultural expansion respectively. Land cover maps of each municipality are created using the Geographical Information System software ArcGIS Spatial Analyst extension. The resulting categorical maps are input into Fragstats fragmentation software to calculate quantifiable fragmentation metrics for each municipality. To determine the effects that these characteristics are likely to cause, this study uses a literature review to determine how species traits affect their responses to forest fragmentation. Results indicate that, in areas that underwent agricultural expansion, the remaining forest patches became more complex in shape with longer edges and lost a large amount of core area. This negatively affects species which are either highly dispersive or specialist to core forest habitat. In areas that underwent commodity-driven deforestation, it was more likely that forest patches would become less aggregated and create disjunct core areas. This negatively affects smaller, sedentary animals which do not naturally travel long distances. This study is significant in that it links individual fragmentation drivers to their landscape characteristics, and in turn uses these to predict effects on species with particular traits. This information will prove useful for forest managers, particularly in the case study municipalities examined in this study, in deciding which species require further protection measures. The methodology could be applied to other drivers of forest fragmentation such as forest fires.