The Synthetic Aperture Radar operating in P-band and performing a helical flight pattern promises to be a potential tool for underground tomography, featuring high resolution and penetration. Based on the success of detecting ant nests beneath the industrial forest, a new experiment was carried out to detect buried bodies. Two sets of flights were performed and validated. Six bodies buried 0.5 m to 1.5 m deep, containing the remains of cow and pork, were located with a detection rate of 100% and a false alarm rate of 0%. Also, the ability to detect change across two sets of flights was verified by locating a bucket filled up with a large tarpaulin piece in the first set of flights.

The synthetic aperture radar (SAR) has long been used from satellites for forest monitoring at global level. The boreal forests in Sweden are well described wall-to-wall from airborne laser scanning and airborne photography. Hence, in Sweden, SAR can often only provide a limited added value, due to the low resolution compared to other sensors, despite its all-weather acquisition capabilities. By accounting for interfering effects that currently degrade the useful information in SAR images, it can be extremely valuable for both vegetation mapping and belowground mapping (e.g., soil conditions and tree roots). In the current work, we present the configuration of the first drone-based SAR experiment that allows us to image the forest in 3D with very high spatial resolution. We have started the analyses by using tomography to derive reflectivity for the roots of single trees, and comparing these with reference root biomass. The linear relationship indicates a potential for using SAR to derive forest variables that were yet neglected or little researched. Moreover, extensive additional remote sensing data have been collected from both airborne and spaceborne platforms, and reference data for both the vegetation and soil have been inventoried in-situ using complementary measurements and sensors. Hence, this unique experimental setup enables many unprecedented analyses about SAR applied to boreal forests.

The presence of beaver burrows, primarily found on riverbanks, poses significant risks due to the increased probability of subsidence, which can have a detrimental impact on infrastructures such as railways, bridges, flood embankments, and footpaths. Currently, identifying these structures is a complex and tedious job because they are natural underground formations, located in hard-to-reach areas. Alternative methods such as remote sensing based on Synthetic Aperture Radar (SAR) systems, offer great promise option for burrow detection. This work presents a set of software-based electromagnetic simulations of radar signals to evaluate the effectiveness of SAR technology to detect different beaver burrow formations. Beaver burrows in bare soil cases with varying dielectric constants were considered, achieving results that encourage us to test the beaver burrow detection with real data from SAR drone-borne survey.

The presence of leaf-cutting Acromyrmerx ants in industrial forest plantations is one of the main causes of the loss of biomass and productivity that affect the large pulp producers in Brazil. Thus, the development of monitoring tools that allow the extraction of information below the surface in large areas, such as Synthetic Aperture Radar (SAR) systems, is of crucial importance. This work presents a set of unprecedented electromagnetic simulations for the detection of Acromyrmex ant nests with 1 up to 13 chambers in industrial forests. Ant nests in bare soil and under forest cases were considered for simulations, obtaining promising results that can be used in subsequent tests with real data based on SAR circular and helical survey.

Defoliation by leaf-cutting ants in commercial forest plantations is one of the leading causes of biomass and productivity losses affecting all of Brazilian industrial forest. Thus, the development of monitoring tools that allow extracting the information below the surface in large areas, such as synthetic aperture radar (SAR) systems, is crucial. This work presents a method for ant nest size estimation in industrial forest based on SAR images. A field study is carried out using a drone-borne SAR system to survey a commercial eucalyptus forest by using a helical flight pattern and P band transmitting frequency and finally generating a ground tomography. A convolutional neural network (CNN) is employed for the ant nests size estimation from the tomograms. A mean error of 5 % and 21 % was achieved for a training a validation dataset, respectively.

Smart counties monitoring demands the use of multiple sensors. This article explores the importance of using a class 3 drone-borne electromagnetic sensor suite. The areas most benefited are city management including infrastructure and agriculture. The set of electromagnetic sensors consists of a multi-band synthetic aperture radar, a visible camera and a thermal camera. Monitoring would occur periodically and automatically, covering a set of pre-defined flight lines. Data processing and analysis would take place automatically providing alerts, action lists, forecasts and statistics. Such a system is economically self-sustaining.

The presence of leaf-cutting ants in commercial forest plantations is one of the main causes of the loss of biomass and productivity that affect the large pulp producers in Brazil. Thus, the development of monitoring tools that allow the extraction of information below the surface in large areas, such as SAR systems, is of crucial importance. This work presents a set of unprecedented electromagnetic simulations for the detection of leaf-cutting ant nests with 6 up to 385 chambers in industrial forests. The different tests range from the one-signal case to a tomographic processing based on SAR imaging, obtaining promising results that can be used in subsequent tests with real data based on SAR mapping.

This work presents a remote sensing solution for sugarcane precision agriculture based on a drone-borne differential interferometric synthetic aperture radar (DlnSAR) operating in the P-, L-, and C-bands. With one flight pass, the system can estimate the soil moisture, the plantation height, and the above-ground biomass map; and predict the harvest date and the respective productivity. With two flight passes, it assesses the crop growth via differential interferometry. A new methodology dedicated to sugarcane plantation was developed based on the existing methodologies for soil moisture and biomass measurement. The image information from the three bands, plus the C-band InSAR and P-band DInSAR information, show immense potential for efficient and low-cost monitoring. The results validated the methodology in a large sugarcane mill.