Remote-sensing Methods Research Articles

Research on UAV Remote Sensing Method of Mold Detection Suitable for Pericarp of Citri Reticulatae ‘Chachi’ Warehouses

Once the Pericarp of Citri Reticulata ‘Chachi’ (PCRC) develops mildew while in storage, the rapid spread of the flora after the occurrence of mold can cause huge losses. As such, inspecting whether the PCRC is moldy is important. In this paper, we propose an alternative inspection method, namely that of utilizing a small UAV with a camera to inspect the PCRC mildew in the top stacks under consideration. Specifically, we first address the light problem in the collected images with different lights via a multi-spectral method, and find that 625–740 nm of lighting has a significant effect on mildew. Second, we utilize the ultrared 1.4R-G method to extract the features of mildew with Otus binarization. We can see that the mold-free area is less than 95% in an image categorized as having mildew. The proposed mildew inspection method achieved 93.3% accuracy. Our method could send the inspection information to a control system, achieving rapid closed-loop automatic control and reducing the mildew-related loss.

Quantifying Emissions from Fugitive Area Sources Using a Hybrid Method of Multi-Path Optical Remote Sensing and Tomographic Inverse-Dispersion Techniques

Reducing methane (CH4) emissions from anthropogenic activities is critical to climate change mitigation efforts. However, there is still considerable uncertainty over the amount of fugitive CH4 emissions due to large-scale area sources and heterogeneous emission distributions. To reduce the uncertainty and improve the spatial and temporal resolutions, a new hybrid method was developed combining optical remote sensing (ORS), computed tomography (CT), and inverse-dispersion modeling techniques on the basis of which a multi-path scanning system was developed. It uses a horizontal radial plume mapping path configuration and adapts a Lagrangian stochastic dispersion mode into CT reconstruction. The emission map is finally calculated by using a minimal curvature tomographic reconstruction algorithm, which introduces smooth constraints at each pixel. Two controlled-release experiments of CH4 were conducted with different configurations, showing relative errors of only 2% and 3%. Compared with results from the single-path inverse-dispersion method (5–175%), the new method can not only derive the emission distribution but also obtain a more accurate emission rate. The outcome of this research would bring broad application of the ORS-CT and inverse-dispersion techniques to other gases and sources.

Mapping Vegetation Index-Derived Actual Evapotranspiration across Croplands Using the Google Earth Engine Platform

Precise knowledge of crop water consumption is essential to better manage agricultural water use, particularly in regions where most countries struggle with increasing water and food insecurity. Approaches such as cloud computing and remote sensing (RS) have facilitated access, process, and visualization of big geospatial data to map and monitor crop water requirements. To find the most reliable Vegetation Index (VI)-based evapotranspiration (ETa) for croplands in drylands, we modeled and mapped ETa using empirical RS methods across the Zayandehrud river basin in Iran for two decades (2000–2019) on the Google Earth Engine platform using the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index 2 (EVI2). Developed ET-VI products in this study comprise three NDVI-based ETa (ET-NDVI*, ET-NDVI*scaled, and ET-NDVIKc) and an EVI2-based ETa (ET-EVI2). We (a) applied, for the first time, the ET-NDVI* method to croplands as a crop-independent index and then compared its performance with the ET-EVI2 and crop ET, and (b) assessed the ease and feasibility of the transferability of these methods to other regions. Comparing four ET-VI products showed that annual ET-EVI2 and ET-NDVI*scaled estimations were close. ET-NDVIKc consistently overestimated ETa. Our findings indicate that ET-EVI2 and ET-NDVIKc were easy to parametrize and adopt to other regions, while ET-NDVI* and ET-NDVI*scaled are site-dependent and sensitive to image acquisition time. ET-EVI2 performed robustly in arid and semi-arid regions making it a better tool. Future research should further develop and confirm these findings by characterizing the accuracy of VI-based ETa over croplands in drylands by comparing them with available ETa products and examining their performance using crop-specific comparisons.

Structurally-controlled Gold Mineralization in the Southern Zuru Schist Belt NW Nigeria: Application of Remote Sensing and Geophysical Methods

A significant number of gold occurrences are hosted by hydrothermal alteration zones in the Zuru Schist Belt, Northwestern Nigeria. The aim of this study is to identify the spatial correlation between faults, sheared, hydrothermally altered zones, and gold mineralized areas using geophysical and remote sensing data. The main structural trends of the schist belt as interpreted from the application of total gradient amplitude and local wavenumber techniques on the aeromagnetic data are in the E-W and NE-SW directions. These trends align with the locations of gold mineralization and key pathfinders such as faults in the Anka-Yauri-Iseyin shear zone. In the radiometric data, magnetic faults occurred in areas with elevated potassium enrichment within the Anka-Yauri-Iseyin shear zone and present evidence for structurally controlled hydrothermal processes. The mapped Anka-Yauri-Iseyin shear zone from the aeromagnetic data further coincides with morphological features such as ridges, valleys, and highlands, signifying that the shear zone is represented by topographical linear indicators. Importantly, the integrated approach and results presented in this work show that the Southern Zuru Schist Belt is fragmented by a NE-SW oriented AYI shear complex. Faults and hydrothermally altered zones in the AYI shear complex are new target areas for direct exploration of gold in the study area.

Application of Satellite Remote Sensing, UAV-Geological Mapping, and Machine Learning Methods in the Exploration of Podiform Chromite Deposits

The irregular and sporadic occurrence of chromite pods in podiform chromite deposits (PCD), especially in mountainous terranes with rough topography, necessitates finding innovative methods for reconnaissance and prospecting. This research combines several remote sensing methods to discriminate the highly serpentinized peridotites hosting chromite pods from the other barren ultramafic and mafic cumulates. The case study is the area of the Sabzevar Ophiolite (NE Iran), which hosts several known chromite and other mineral deposits. The integration of satellite images [e.g., Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite sensor, Landsat series, and Sentinel-2] coupled with change detection, band rationing, and target detection algorithms [including the Spectral Angle Mapper (SAM)] were used to distinguish potential lithological units hosting chromites. Results have been verified by an initial on-field checking and compared with the high-resolution (GSD ~6 cm) orthomosaic images obtained by the processing of photographs taken from an Unmanned Aerial Vehicle (UAV) at a promising area of 35 km2. The combination of visual interpretation and supervised classification by machine learning methods [Support Vector Machine (SVM)] yielded the production of a geological map, in which the lithological units and structures are outlined, including the crust-mantle transition zone units, mafic cumulates, crosscutting dykes, and mantle sequences. The validation of the results was performed through a second phase, made up of field mapping, sampling, chemical analysis, and microscopic studies, leading to the discovery of new chromite occurrences and mineralized zones. All ultramafic units were classified into four groups based on the degree of serpentinization, represented by the intensity of their average spectral reflectance. Based on their presumed protolith, the highly serpentinized ultramafics and serpentinites were classified into two main categories (dunite or harzburgite). The serpentinite with probable dunitic protolith, discriminated for a peculiar Fe-rich Ni-bearing lateritic crust, is more productive for chromite prospecting. This is particularly true at the contact with mafic dykes, akin to some worldwide chromite deposits. The results of our work highlight the potential of multi-scale satellite and UAV-based remote sensing to find footprints of some chromite mineral deposits.

Determination of the Leaf Inclination Angle (LIA) through Field and Remote Sensing Methods: Current Status and Future Prospects

The leaf inclination angle (LIA), defined as the leaf or needle inclination angle to the horizontal plane, is vital in radiative transfer, precipitation interception, evapotranspiration, photosynthesis, and hydrological processes. This paper reviews the field and remote sensing methods to determine LIA. In the field, LIA is determined using direct and indirect methods. The direct methods include direct contact, photographic, and light detection and ranging (LiDAR) methods, while the indirect methods are composed of the gap fraction, four-component, and polarization measurement methods. The direct methods can obtain LIA accurately at individual leaves, crown, and plot scales, whereas the indirect methods work well for crops at the plot level. The remote sensing methods to estimate LIA are mainly based on the empirical, radiative transfer model, and gap fraction methods. More advanced inversion strategies and validation studies are necessary to improve the robustness of LIA remote sensing estimation. In future studies, automated observation systems can be developed and the LIA measurement can be incorporated into existing ground observation networks to enhance spatial coverage.

Mapping of Aquifer Potential in the Northeast Basement Rock of Burkina Faso: Using Remote Sensing and Geophysical Methods

The North-East of Burkina Faso is made up of formations of the Birimian basement rock. The region has a Sahelian climate where surface water resources are very limited. In order to assess the hydraulic potential of the existing aquifers, remote sensing and geophysical techniques (aeromagnetic and ground electric) were used. Satellite image processing and aeromagnetic survey data were used to generate a lineament map that was subsequently validated on the basis of existing geological, hydrogeological and topographical data, geomorphological field surveys and ground geophysical work. The resulting map displays a more or less dense network of 405 magnetic lineaments with a cumulative length of 1,787,676 meters as opposed to 94 lineaments from Landsat 8 images with a cumulative length of 970,054 meters. The average length of the magnetic lineaments is 4,414.01 meters while the average remote sensing length is 10,317.72 meters. The identified NE-SW and WNW-ESE fracture corridors are propitious for the presence of fracture aquifers. In addition to the lineament validation, the ground geophysics performed through electrical surveys allowed the characterization of the aquifers in the study area. Alteration thicknesses are generally less than 20 meters deep. The fractured horizon can reach 100 meters thickness under the alterites. The kernel density map highlights areas of good aquifer potential that could constitute significant groundwater reserves. Mapping aquifer potential is a crucial step in ensuring sustainable and efficient use of groundwater resources.

Remote Sensing of Primary Producers in the Bay-Delta

Remote-sensing methods are being used to study a growing number of issues in the San Francisco Estuary, such as (1) detecting the optical properties of chlorophyll-a concentrations and dissolved organic matter to assess productivity and the nature of carbon inputs, (2) creating historical records of invasive aquatic vegetation expansion through space and time, (3) identifying origins and expansions of invasions, and (4) supporting models of greenhouse-gas sequestration by expanding restoration projects. Technological capabilities of remote sensing have likewise expanded to include a wide array of opportunities: from boat-mounted sensors, human-operated low-flying planes, and aerial drones, to freely accessible satellite imagery. Growing interest in coordinating these monitoring methods in the name of collaboration and cost-efficiency has led to the creation of diverse expert teams such as the Remote Imagery Collaborative, and monitoring frameworks such as the Interagency Ecological Program Aquatic Vegetation Monitoring Framework and Wetland Regional Monitoring Program. This paper explores the emerging technologies and applications of various methods for studying primary producers, with an emphasis on remote sensing.

Determining Land Use/Land Cover (LULC) Changes Using Remote Sensing Method in Lüleburgaz and LULC Change's Impacts on SDGs

Urbanization is among the human activities that affect the quality of urban life and sustainable urban development at a global scale. For monitoring human-induced changes in cities, studies on the changes in land use/land cover (LULC) based on a series of satellite images along with geographic information systems (GIS) are widely used. This study aims to evaluate the LULC changes in Lüleburgaz between 2001 and 2021. For this purpose, the impacts of urban growth on natural areas and how these areas are handled in the context of sustainable development goals are examined, and the effects of urbanization on land use change are evaluated. The accuracy of the LULC maps was evaluated by means of the general accuracy index and the Kappa index. The overall accuracy rates of the land use maps for 2001, 2010, and 2021 were 96.32%, 93.66%, and 95.91%, respectively, while Kappa values were 0.95, 0.91, 0.94. These results indicate that, due to the effect of rapid urbanization and population growth over the last two decades, the built environment increased by 4.82% and the agricultural land has decreased by 4.88%. This result is a testament to the upward trend in the built environment at the expense of depleting a significant amount of agricultural land. The findings of the study provide useful data that can aid authorities in making conscious decisions to achieve sustainable urban planning and to improve environmental conditions.
 Keywords: Urbanization, Land use/land cover change, Agricultural land, Sustainable development goals (SDGs), Remote sensing

Spectrophotometry and Other Remote-Sensing Methods to Study Asteroids: Achievements and New Approaches

Spectrophotometry and Other Remote-Sensing Methods to Study Asteroids: Achievements and New Approaches

Plasma-neutral interactions in the lower thermosphere-ionosphere: The need for in situ measurements to address focused questions

The lower thermosphere-ionosphere (LTI) is a key transition region between Earth’s atmosphere and space. Interactions between ions and neutrals maximize within the LTI and in particular at altitudes from 100 to 200 km, which is the least visited region of the near-Earth environment. The lack of in situ co-temporal and co-spatial measurements of all relevant parameters and their elusiveness to most remote-sensing methods means that the complex interactions between its neutral and charged constituents remain poorly characterized to this date. This lack of measurements, together with the ambiguity in the quantification of key processes in the 100–200 km altitude range affect current modeling efforts to expand atmospheric models upward to include the LTI and limit current space weather prediction capabilities. We present focused questions in the LTI that are related to the complex interactions between its neutral and charged constituents. These questions concern core physical processes that govern the energetics, dynamics, and chemistry of the LTI and need to be addressed as fundamental and long-standing questions in this critically unexplored boundary region. We also outline the range of in situ measurements that are needed to unambiguously quantify key LTI processes within this region, and present elements of an in situ concept based on past proposed mission concepts.

Editorial: Advanced numerical and spatial analysis of forest and environmental management

The main question and answers in this research topic are 1. How the wind cape in connection with the forest may be analyzed? 2. GIS and RUSLE methods in analyzing land cover are possible and with which precise? 3. Remote Sensing techniques and methods in the analysis of soil erosion are possible to find real coefficients? 4. Can Depopulation Stop Deforestation? 5. SWAT analysis of surface runoff in forest areas, how is possible to find all factors? 6. Testing new ensemble vegetation classification with a machine learning approach, is possible to estimate the k-factor? 7. Estimation of soil change factors connected by forest areas, how is possible to find all coefficients? 8. GIS and Remote sensing methods in estimation of urban forest areas. How the new approaches may speed up the process of estimation?

A Study of the Land Cover of Razzaza Lake during the Past 25 Years Using Remote Sensing Methods

In this study, the Earth's surface was studied in Razzaza Lake for 25 years, using remote sensing methods. Images of the satellites Landsat 5 (TM) and 8 (OLI) were used to study and determine the components of the land cover. The study covered the years 1995-2021 with an interval of 5 years, as this region is uninhabited, so the change in the land cover is slow. The land cover was divided into three main classes and seven subclasses and classified using the maximum likelihood classifier with the help of training sets collected to represent the classes that made up the land cover. The changes detected in the land cover were studied by considering 1995 as a reference year. It was found that there was a significant reduction in the water mass that made up the lake and its transformation into arid land. The vegetation cover was characterized by the relative stability of the crop class. Its constant percent ranged 60% to 80%, unlike the natural plant class, which fluctuates due to its dependence on environmental factors, which is characterized by change. The Kaju presence of continuous change between soil subclasses was due to the different environments affecting the study area. The most affected class was the shallow water class, which disappeared as a result of the drought experienced by the lake.

Comparative Analysis of Machine Learning Algorithms for Soil Erosion Modelling Based on Remotely Sensed Data

Recent years have seen an increase in the use of remote-sensing based methods to assess soil erosion, mainly due to the availability of freely accessible satellite data, with successful results on a consistent basis. There would be valuable benefits from applying these techniques to the Arctic areas, where ground local studies are typically difficult to perform due to hardly accessible roads and lands. At the same time, however, the application of remote-sensing methods comes with its own set of challenges when it comes to the peculiar features of the Arctic: short growing periods, winter storms, wind, and frequent cloud and snow cover. In this study we perform a comparative analysis of three commonly used classification algorithms: Support Vector Machine (SVM), Random Forest (RF) and Multilayer Perceptron (MLP), in combination with ground truth samples from regions all over Iceland, provided by Iceland’s Soil Conservation Service department. The process can be automated to predict soil erosion risk for larger, less accessible areas from Sentinel-2 images. The analysis performed on validation data sets supports the effectiveness of both approaches for modeling soil erosion, albeit differences are highlighted.

Satellite Remote Sensing and Non-Destructive Testing Methods for Transport Infrastructure Monitoring: Advances, Challenges and Perspectives

High-temporal-frequency monitoring of transport infrastructure is crucial to facilitate maintenance and prevent major service disruption or structural failures. Ground-based non-destructive testing (NDT) methods have been successfully applied for decades, reaching very high standards for data quality and accuracy. However, routine campaigns and long inspection times are required for data collection and their implementation into reliable infrastructure management systems (IMSs). On the other hand, satellite remote sensing techniques, such as the Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) method, have proven effective in monitoring ground displacements of transport infrastructure (roads, railways and airfields) with a much higher temporal frequency of investigation and the capability to cover wider areas. Nevertheless, the integration of information from (i) satellite remote sensing and (ii) ground-based NDT methods is a subject that is still to be fully explored in civil engineering. This paper aims to review significant stand-alone and combined applications in these two areas of endeavour for transport infrastructure monitoring. The recent advances, main challenges and future perspectives arising from their mutual integration are also discussed.

Urban cooling island vs urban heat island, who is the winner? Study of the green spaces effect in coolingdown of urban heat in Kapanewon Depok, Sleman, Yogyakarta, Indonesia

Urban heat zone are the major problem affecting human health through the microclimatological effect. Green Spaces are like a natural air conditioners that can cool dense urban environments from hot air. The cooling effect of urban heat from the presence of Green Space is called the Urban Cooling Effect (UCE) while the cool air produced is called Urban Cooling Island (UCI). This research aims to analyze the extent to which UCI is able to neutralize UHI by urban cooling. This study of this research was conducted in Kapanewon Depok around the North Ringroad zone. In the research, the Remote Sensing method was used utilizing Landsat-8 and processed with ArcGIS to determine the patterns and dynamics of UCI and UHI and was equipped with field data measurement and interviews data using questionnaires. The results obtained were that the coldest and largest UCI Spot was at Babarsari Camping Ground, 27,777°C in 159,218 m2, with green space types consisting of shrubs, grass, plants, mixed trees and flowing river. Questionnaire results show that the majority of residents still feel hot. It was concluded that although Green Space is useful in cooling cities, but UCI VS UHI is still dominated by UHI’s influence.

Assessing the Relative Performance of GNSS-R Flood Extent Observations: Case Study in South Sudan

Flooding is one of the deadliest and costliest natural disasters. Climate change-induced flooding events are increasing worldwide, disproportionately impacting low-income and developing communities. While early warning systems save lives, satellite-based observation systems are vital for the disaster relief and recovery phases. Current satellite-based operational flood products are largely based on either optical remote-sensing methods, which exhibit a limited ability to detect water through clouds and vegetation, or microwave remote sensing, which provides relatively low spatial and temporal resolution. New small satellite constellations using radar or GNSS reflectometry (GNSS-R) have been shown to enhance our ability to overcome these deficiencies. In this work, we quantify the performance of using GNSS-R measurements from the NASA Cyclone Global Navigation Satellite Systems (CYGNSS) satellite constellation to map surface water in South Sudan and the Sudd wetland in comparison with a set of representative operational products. We make quantitative comparisons of our results with operational flood products based on Visible Infrared Imaging Radiometer Suite (VIIRS) and MODIS and with C-band Sentinel-1 synthetic aperture radar. We find that our method detects 35.4% more surface water than Sentinel-1, while the VIIRS- and MODIS-based products underestimate by 4.8% and 83.7%, respectively. We use several metrics commonly used to evaluate classification performance: precision, true positive rate (TPR), true negative rate (TNR), F2-score, and the Matthews correlation coefficient (MCC) and assess the comparisons in this statistical framework. We discuss the consequences of our findings, including ways CYGNSS data may enhance current flood products and assist decision-makers and emergency managers.

Spectrophotometry and Other Remote-Sensing Methods to Study Asteroids: Achievements and New Approaches

Physical parameters and characteristics of asteroids as solid atmosphereless celestial bodies are traditionally studied with the same methods as those used for investigating most of the other celestial objects, though they have certain specific features. The main attention is paid to spectrophotometry, as the most effective tool to study remotely the composition, evolution, and origin of asteroids. However, very important information about asteroids was also obtained by other observational methods, such as photometry, polarimetry, radiometry, and radar. Because of this, in addition to spectrophotometry, we discuss here photometry, polarimetry, and radiometry, which, on the one hand, are very close in methodology and, on the other hand, there has been a trend to their integrated use. In connection with the discovery of sublimation–dust activity on a number of asteroids and the periodic formation of a dust exosphere around these asteroids near perihe lion (see, e.g., Busarev et al., 2021), we also consider a methodologically new approach to estimating the chemical and mineralogical composition of particles in the exosphere of these asteroids and, indirectly, of their surface material.

Detection of the Mineral Presence and Its Effects on Different Tree Species Using Drone Photogrammetry and Remote Sensing Methods

Detection of the Mineral Presence and Its Effects on Different Tree Species Using Drone Photogrammetry and Remote Sensing Methods

The Role of Drone Photogrammetry, Remote Sensing and GIS Methods in the Detection of Ore Areas and Their Surroundings

The Role of Drone Photogrammetry, Remote Sensing and GIS Methods in the Detection of Ore Areas and Their Surroundings