Analysis Of Remote Sensing Data Research Articles

An improved coverage-oriented retrieval algorithm for large-area remote sensing data

ABSTRACT With the rapid development of satellite technology, the amount of remote sensing data and demand for remote sensing data analysis over large areas are greatly increasing. Hence, it is necessary to quickly filter out an optimal dataset from massive dataset to support various remote sensing applications. However, with the improvements in temporal and spatial resolution, remote sensing data have become fragmented, which brings challenges to data retrieval. At present, most data service platforms rely on the query engines to retrieve data. Retrieval results still have a large amount of data with a high degree of overlap, which must be manually selected for further processing. This process is very labour-intensive and time-consuming. This paper proposes an improved coverage-oriented retrieval algorithm that aims to retrieve an optimal image combination with the minimum number of images closest to the imaging time of interest while maximized covering the target area. The retrieval efficiency of this algorithm was analysed by applying different implementation practices: Arcpy, PyQGIS, and GeoPandas. The experimental results confirm the effectiveness of the algorithm and suggest that the GeoPandas-based approach is most advantageous when processing large-area data.

Temporal Monitoring and Predicting of the Abundance of Malaria Vectors Using Time Series Analysis of Remote Sensing Data through Google Earth Engine.

In many studies regarding the field of malaria, environmental factors have been acquired in single-time, multi-time or a short-time series using remote sensing and meteorological data. Selecting the best periods of the year to monitor the habitats of Anopheles larvae can be effective in better and faster control of malaria outbreaks. In this article, high-risk times for three regions in Iran, including Qaleh-Ganj, Sarbaz and Bashagard counties with a history of malaria prevalence were estimated. For this purpose, a series of environmental factors affecting the growth and survival of Anopheles were used over a seven-year period through the Google Earth Engine. The results of this study indicated two high-risk times for Qaleh-Ganj and Bashagard counties and three high-risk times for Sarbaz county over the course of a year observing an increase in the abundance of Anopheles mosquitoes. Further evaluation of the results against the entomological data available in previous studies showed that the high-risk times predicted in this study were consistent with an increase in the abundance of Anopheles mosquitoes in the study areas. The proposed method is extremely useful for temporal prediction of the increase in abundance of Anopheles mosquitoes in addition to the use of optimal data aimed at monitoring the exact location of Anopheles habitats.

Information system of remote sensing data analysis

The information system is designed to solve the problems of analyzing remote sensing data and making decisions using non-parametric pattern recognition algorithms, the synthesis of which is based on kernel estimates of probability density. Initial information is compiled by training samples on the states of the investigated natural objects and their corresponding spectral data, which are presented in Microsoft Excel files. The application of the software provides the following functions: primary processing of remote sensing data to evaluate the main quantitative characteristics of the laws of distribution of random variables; testing of the hypothesis on distributions of multivariate random variables based on non-parametric pattern recognition algorithms; estimating the state of natural objects by spectral data; mapping of territories by state of natural objects based on spectral data of earth surface elements and their corresponding spatial coordinates. Results of the presented functional tasks are saved in the form of reports in graphic files and spreadsheets of Microsoft Excel. Software tools can be used in the study of various nature objects from remote sensing data.

Fires on Ice: Emerging Permafrost Peatlands Fire Regimes in Russia’s Subarctic Taiga

Wildfires in permafrost areas, including smoldering fires (e.g., “zombie fires”), have increasingly become a concern in the Arctic and subarctic. Their detection is difficult and requires ground truthing. Local and Indigenous knowledge are becoming useful sources of information that could guide future research and wildfire management. This paper focuses on permafrost peatland fires in the Siberian subarctic taiga linked to local communities and their infrastructure. It presents the results of field studies in Evenki and old-settler communities of Tokma and Khanda in the Irkutsk region of Russia in conjunction with concurrent remote sensing data analysis. The study areas located in the discontinuous permafrost zone allow examination of the dynamics of wildfires in permafrost peatlands and adjacent forested areas. Interviews revealed an unusual prevalence and witness-observed characteristics of smoldering peatland fires over permafrost, such as longer than expected fire risk periods, impacts on community infrastructure, changes in migration of wild animals, and an increasing number of smoldering wildfires including overwintering “zombie fires” in the last five years. The analysis of concurrent satellite remote sensing data confirmed observations from communities, but demonstrated a limited capacity of satellite imagery to accurately capture changing wildfire activity in permafrost peatlands, which may have significant implications for global climate.

A Scalable Computing Resources System for Remote Sensing Big Data Processing Using GeoPySpark Based on Spark on K8s

As a result of Earth observation (EO) entering the era of big data, a significant challenge relating to by the storage, analysis, and visualization of a massive amount of remote sensing (RS) data must be addressed. In this paper, we proposed a novel scalable computing resources system to achieve high-speed processing of RS big data in a parallel distributed architecture. To reduce data movement among computing nodes, the Hadoop Distributed File System (HDFS) is established on nodes of K8s, which are also used for computing. In the process of RS data analysis, we innovatively use the tile-oriented programming model instead of the traditional strip-oriented or pixel-oriented approach to better implement parallel computing in a Spark on Kubernetes (K8s) cluster. A large RS raster layer can be abstracted as a user-defined tile format of any size, so that a whole computing task can be divided into multiple distributed parallel tasks. The computing resources applied by users would be immediately assigned in the Spark on K8s cluster by simply configuring and initializing SparkContext through a web-based Jupyter notebook console. Users can easily query, write, or visualize data in any box size from the catalog module in GeoPySpark. In summary, the system proposed in this study can provide a distributed scalable resources system for assembling big data storage, parallel computing, and real-time visualization.

Basement rocks around the eastern sector of Baranis-Aswan Road, Egypt: Remote sensing data analysis and petrology

The basement rocks around Baranis-Aswan Road, South Eastern Desert of Egypt encompasses a variety of the oldest Pan-African rock units including medium-grade hornblende schist, a variety of gneisses and migmatite as well as medium- to low-grade metavolcanics. These were intruded by syn- to late-tectonic tonalite, granodiorite, quartz diorite as well as gabbros. The exposed basement rock units in the area were discriminated with the detailed lithological mapping using the processed data of ASTER, Landsat-8 and Sentinel-2. Spectral signatures of these rocks were achieved by ASD TerraSpec Halo mineral identifier device. Petrography and geochemistry data enabled to put constraints on their petrogenesis. Geochemically, the schist and gneisses imply K-poor mafic igneous precursors that were developed in a primitive juvenile oceanic environment. The migmatite pertains to stromatic type, whereas granitic magma (neosome) was injected in hornblende schist (paleosome) through cleavage planes. The syn-tectonic tonalite to quartz diorite are also low-K, non-mature calc-alkaline rocks and pertain to oceanic plagiogranite. They display a continuous fractionation evolution trend from quartz-diorite to tonalite and granodiorite.

A review on remote sensing imagery augmentation using deep learning

The quantity of remote sensing pictures being captured is rising dramatically due to advancements in earth observation technology, bringing remote sensing in a long and distinct period of history of big data. Discovering how to successfully harvest this enormous intensity of remote sensing data will be a new challenge. With deep learning, remote sensing data analysis may take on a narrative tone. CNNs are a Deep Learning model capable of directly extracting features from large volumes of picture data and are effective at exploiting realistic imagery data characteristics. With regards to computer vision, CNNs have shown to be highly successful. CNNs are a powerful tool. There have been several studies employing CNNs for remote sensing picture classification in the last few years, and these studies show that CNNs can produce fast and accurate results. It is the goal of this study to offer an overview of the most current and advanced applications of deep learning based on CNNs for remote sensing picture enhancement. To begin, the concepts and features of convolutional neural networks (CNNs) will be briefly reviewed in this review. There are new innovations and structural enhancements to neural network models that make them more suited for RS picture enhancement, as well as data for remote sensing image categorization and augmentation approaches that may be obtained easily and quickly.. As a result of the survey, we expect remote sensing scientists will be better equipped to tackle categorization challenges utilizing cutting-edge deep learning algorithms and methodologies. To increase the number of training examples with a considerable reduction in model over-fitting, data augmentation is a well-known approach used in most ML & AI applications.

Bones, carnivores, and grassland fires. Actualistic taphonomy of faunal assemblages from two caves in Central Argentina and its implication for the fossil record

ABSTRACT The aim of this paper is to identify the potential accumulator of two modern non-digested bone assemblages recovered from El Laurel (EL) and El Pulgar (EP) caves, both located in Characato (Córdoba province, Central Argentina), as well as to evaluate the role of recent wildfires in its formation. For this purpose, we combined a detailed taphonomic analysis of bones and remote sensing data analysis in GIS. Our results and its comparison with other actualistic studies from different latitudes show that the assemblages are likely to be averaged accumulations produced and modified by different small carnivores (foxes and felids) that inhabit the area, including Lycalopex culpaeus smithersi, Leopardus geoffroyi and/or Lycalopex gymnocercus. On the other hand, remote sensing analyses showed that fires differentially affected the caves – EL was the most affected–, especially due to variations in their surrounding environments and fuel availability. This difference in fire severity between both caves is reflected in the frequency of burnt specimens and the extension of burning in the bone surface of their assemblages.

Mapping of Groundwater potential zones in Lingasugur Taluk in North-eastern part of Karnataka, India using Remote Sensing, GIS and multi-criteria data analysis

Groundwater research has evolved tremendously as presently it is the need of society. Remote Sensing (RS) and Geographical Information System (GIS) are the main methods in finding the potential zones for the groundwater. They help in assessing, exploring, monitoring and conserving groundwater resources. A case study was conducted to find the groundwater potential zones in Lingasugur taluk, Raichur District, Karnataka State, India. Ten thematic maps were prepared for the study area such as geology, hydrogeomorphology, land use/ land cover, soil type, NDVI, NDWI, slope map, lineament density, rainfall and drainage density. A weighted overlay superimposed method was used after converting all the thematic maps in raster format. Thus from analysis, the classes in groundwater potential were made as very good, moderate, poor and very poor zones covering an area of 10.1 sq.km., 169.25 sq.km., 1732.31 sq.km. and 53.66 sq.km. respectively. By taking the present study into consideration, the future plans for urbanization, recharge structures and groundwater exploration sites can be decided.

Research on Urban Land Use Change in Ha Noi, Viet Nam Using Remote Sensing and GIS for Planning Oriented Work

Currently, 55% of the world’s population lives in urban areas, this proportion is expected to increase to 68% by 2050. In the next 30 years, a large amount of the world's population is predictably concentrated in urban areas in the developing world. Ha Noi is the capital and largest city of Viet Nam which has the average growth rate of approximately 3% per year. Urban developmentmanagement has become an important issue in Viet Nam since the negative impacts of the urban sprawl on the environmental sustainability, life quality has been increasing as well. Hence, urban planning and management would be pivotal for creating the effective framework conditions for a sustainable development. The objective of our study is to explore the urban growth of Ha Noi using the Landsat images from 1975 to 2020 compared to the city planning. The volatility analysis information from classified urbanland maps is considered supportive for urban management and planning oriented work. In addition, the remote sensing data analysis is a useful tool to support planners, managers for urban management and decision. This study results showed the urban land area in study site city has been growing about 3 times and the largest rate (4-6 times) for the Dong Anh, Tu Liem, Gia Lam and smallest rate (10-25%) for Ba Dinh and Hai Ba Trung districts.

Application of Google earth engine python API and NAIP imagery for land use and land cover classification: A case study in Florida, USA

The analysis of land use and land cover data provides invaluable support to a variety of land management and conservation activities. However, historically its application has been limited by high costs associated with data acquisition, analysis, and classification. In recent years, freely available satellite imagery and geospatial data sets and rapid advancement in data analysis capabilities provide immense opportunities to understand and solve the real-world environmental problems. Open-source platforms such as Google Earth Engine (GEE) provide a planetary-scale environmental science data and analyses capability at much greater efficiency and accuracy than the traditional workflows. We evaluated the GEE Python API utility for classifying the freely available NAIP aerial imagery of 2017 to derive the land use land cover (LULC) information of a Panhandle area of Florida, USA. We identified eight major LULC classes with an overall accuracy of 86% and Kappa value of 79%. We completed all remote sensing data analyses procedures including data retrieval, classification, and report preparation in the Jupyter notebook, an open-source web application. Computation time for the procedure was less than 15 min. Our results demonstrate the usefulness of this approach for conducting land use and land cover analysis using much less time, money, and human resources. The open-source nature of GEE Python API and its library of remote sensing data could benefit remote sensing projects throughout the world, especially where access to commercial image processing software packages and remote sensing data are limited.

Indices Extraction from Multitemporal Remote Sensing Data for Mapping Urban Built-Up

Remote sensing data analysis in the cloudy area is still a challenging process. Fortunately, remote sensing technology is fast growing. As a result, multitemporal data could be used to overcome the problem of the cloudy area. Using multitemporal data is a common approach to address the cloud problem. However, most methods only use two data, one as the main data and the other as complementary of the cloudy area. In this paper, a method to harness multitemporal remote sensing data for automatically extracting some indices is proposed. In this method, the process of extracting the indices is done without having to mask the cloud. Those indices could be further used for many applications such as the classification of urban built-up. Landsat-8 data that is acquired during 2019 are stacked, therefore each pixel at the same position creates a list. From each list, indices are extracted. In this study, NDVI, NDBI, and NDWI are used to mapping built-up areas. Furthermore, extracted indices are divided into four categories by their value (maximum, quantile 75, median, and mean). Those indices are then combined into a simple formula to mapping built-up to see which produces better accuracy. The Pleiades as high-resolution remote sensing data is used to assist supervised classification for assessment. In this study, the combination of mean NDBI, maximum NDVI, and mean NDWI result highest Kappa coefficient of 0.771.

Analytic and numerical methods for the Abel transform of exponential functions for planetary and cometary atmospheres

Line-of-sight integration of emissions from planetary and cometary atmospheres is the Abel transform of the emission rate, under the spherical symmetry assumption. Indefinite integrals constructed from the Abel transform integral are useful for implementing remote sensing data analysis methods, such as the numerical inverse Abel transform. We propose analytical expressions obtained by a suitable, non-alternating, series development to compute those indefinite integrals. We establish expressions allowing absolute accuracy control of the convergence of these series and illustrate how this accuracy depends on the number of terms involved in the series computation. We compare the analytical method with numerical computation techniques, which are found to be sufficiently accurate as well. Inverse Abel transform fitting is then tested in order to establish that the expected emission rate profiles can be retrieved from the observation of both planetary and cometary atmospheres. We show that the method is robust, i.e. that it can be applied even when the properties of the observed atmosphere depart from the assumed ones, especially when Tikhonov regularization is included. A first application is conducted over observation of comet 46P/Wirtanen, showing some variability, possibly attributable to an evolution of the contamination by dust and icy grains.

HYPERSPECTRAL IMAGE CLASSIFICATION USING RESIDUAL 2D AND 3D CONVOLUTIONAL NEURAL NETWORK JOINT ATTENTION MODEL

Abstract. Hyperspectral image classification (HSIC) is a challenging task in remote sensing data analysis, which has been applied in many domains for better identification and inspection of the earth surface by extracting spectral and spatial information. The combination of abundant spectral features and accurate spatial information can improve classification accuracy. However, many traditional methods are based on handcrafted features, which brings difficulties for multi-classification tasks due to spectral intra-class heterogeneity and similarity of inter-class. The deep learning algorithm, especially the convolutional neural network (CNN), has been perceived promising feature extractor and classification for processing hyperspectral remote sensing images. Although 2D CNN can extract spatial features, the specific spectral properties are not used effectively. While 3D CNN has the capability for them, but the computational burden increases as stacking layers. To address these issues, we propose a novel HSIC framework based on the residual CNN network by integrating the advantage of 2D and 3D CNN. First, 3D convolutions focus on extracting spectral features with feature recalibration and refinement by channel attention mechanism. The 2D depth-wise separable convolution approach with different size kernels concentrates on obtaining multi-scale spatial features and reducing model parameters. Furthermore, the residual structure optimizes the back-propagation for network training. The results and analysis of extensive HSIC experiments show that the proposed residual 2D-3D CNN network can effectively extract spectral and spatial features and improve classification accuracy.

Hybridizing Cross-Level Contextual and Attentive Representations for Remote Sensing Imagery Semantic Segmentation

Semantic segmentation of remote sensing imagery is a fundamental task in intelligent interpretation. Since deep convolutional neural networks (DCNNs) performed considerable insight in learning implicit representations from data, numerous works in recent years have transferred the DCNN-based model to remote sensing data analysis. However, the wide-range observation areas, complex and diverse objects and illumination and imaging angle influence the pixels easily confused, leading to undesirable results. Therefore, a remote sensing imagery semantic segmentation neural network, named HCANet, is proposed to generate representative and discriminative representations for dense predictions. HCANet hybridizes cross-level contextual and attentive representations to emphasize the distinguishability of learned features. First of all, a cross-level contextual representation module (CCRM) is devised to exploit and harness the superpixel contextual information. Moreover, a hybrid representation enhancement module (HREM) is designed to fuse cross-level contextual and self-attentive representations flexibly. Furthermore, the decoder incorporates DUpsampling operation to boost the efficiency losslessly. The extensive experiments are implemented on the Vaihingen and Potsdam benchmarks. In addition, the results indicate that HCANet achieves excellent performance on overall accuracy and mean intersection over union. In addition, the ablation study further verifies the superiority of CCRM.

Wildfires Vegetation Recovery through Satellite Remote Sensing and Functional Data Analysis

In recent years, wildfires have caused havoc across the world, which are especially aggravated in certain regions due to climate change. Remote sensing has become a powerful tool for monitoring fires, as well as for measuring their effects on vegetation over the following years. We aim to explain the dynamics of wildfires’ effects on a vegetation index (previously estimated by causal inference through synthetic controls) from pre-wildfire available information (mainly proceeding from satellites). For this purpose, we use regression models from Functional Data Analysis, where wildfire effects are considered functional responses, depending on elapsed time after each wildfire, while pre-wildfire information acts as scalar covariates. Our main findings show that vegetation recovery after wildfires is a slow process, affected by many pre-wildfire conditions, among which the richness and diversity of vegetation is one of the best predictors for the recovery.

ANALYSIS OF THE IMPACT OF OIL PRODUCT SPILLS ON NATURAL OBJECTS BASED ON SENTINEL-2 REMOTE SENSING DATA IN EASTERN SIBERIA

The article analyzes the impact of oil spills on natural objects according to the remote sensing system Sentinel-2 in Eastern Siberia. Remote sensing data analysis is used to detect traces of oil products in the accident area. Conclusions about the usage of Sentinel-2 data for detecting traces of oil products were made.

Shear-Related Gold Ores in the Wadi Hodein Shear Belt, South Eastern Desert of Egypt: Analysis of Remote Sensing, Field and Structural Data

Space-borne multispectral and radar data were used to comprehensively map geological contacts, lithologies and structural elements controlling gold-bearing quartz veins in the Wadi Hodein area in Egypt. In this study, enhancement algorithms, band combinations, band math (BM), Principal Component Analysis (PCA), decorrelation stretch and mineralogical indices were applied to Landsat-8 OLI, ASTER and ALOS PALSAR following a pre-designed flow chart. Together with the field observations, the results of the image processing techniques were exported to the GIS environment and subsequently fused to generate a potentiality map. The Wadi Hodein shear belt is a ductile shear corridor developed in response to non-coaxial convergence and northward escape tectonics that accompanied the final stages of terrane accretion and cratonization (~680–600 Ma) in the northern part of the Arabian–Nubian Shield. The evolution of this shear belt encompassed a protracted ~E–W shortening and recurrent sinistral transpression as manifested by east-dipping thrusts and high-angle reverse shear zones. Gold-mineralized shear zones cut heterogeneously deformed ophiolites and metavolcaniclastic rocks and attenuate in and around granodioritic intrusions. The gold mineralization event was evidently epigenetic in the metamorphic rocks and was likely attributed to rejuvenated tectonism and circulation of hot fluids during transpressional deformation. The superposition of the NW–SE folds by NNW-trending, kilometer scale tight and reclined folds shaped the overall framework of the Wadi Hodein belt. Shallow NNW- or SSE-plunging mineral and stretching lineations on steeply dipping shear planes depict a considerable simple shear component. The results of image processing complying with field observations and structural analysis suggest that the coincidence of shear zones, hydrothermal alteration and crosscutting dikes in the study area could be considered as a model criterion in exploration for new gold targets.

Vulnerability of the Permafrost Landscapes in the Eastern Chukotka Coastal Plains to Human Impact and Climate Change

Permafrost landscapes are particularly susceptible to the observed climate change due to the presence of ice in the ground. This paper presents the results of the mapping and assessment of landscapes and their vulnerability to potential human impact and further climate change in the remote region of Eastern Chukotka. The combination of field studies and remote sensing data analysis allowed us to identify the distribution of landscapes within the study polygon, reveal the factors determining their stability, and classify them by vulnerability to the external impacts using a hazard index, H. In total, 33 landscapes characterized by unique combinations of vegetation cover, soil type, relief, and ground composition were detected within the 172 km2 study polygon. The most stable landscapes of the study polygon occupy 31.7% of the polygon area; they are the slopes and tops of mountains covered with stony-lichen tundra, alpine meadows, and the leveled summit areas of the fourth glacial-marine terrace. The most unstable areas cover 19.2% of the study area and are represented by depressions, drainage hollows, waterlogged areas, and places of caterpillar vehicle passage within the terraces and water-glacial plain. The methods of assessment and mapping of the landscape vulnerability presented in this study are quite flexible and can be adapted to other permafrost regions.

Remote sensing and structural studies of the Central Asir Shear Zone, Western Arabian Shield: Implications for the late Neoproterozoic E-W Gondwana assembly

The Central Asir Shear Zone (CASZ) is a N- to NNE-oriented inhomogeneous strain zone parallel to the Nabitah shear belt in southern Saudi Arabia. It is ~200 km long and 20 km wide, and traverses the Asir terrane. The deformed terrains in the vicinity of the CASZ have experienced a prolonged history involving three successive phases of deformation: arc-collision, dextral transpression and sinistral strike-slip faulting. Remote-sensing data analysis and field investigation provide insights into the geological and structural features along the CASZ. Image transformation and enhancement reveal faults and folds, and discriminate between granitoids, metavolcanics, gabbros and younger (Ablah Group) sediments. The obtained evidence points to polyphase deformation of the area, characterized by narrow zones of high strain, folding and shearing enclosing slices of less deformed rocks. Lineament analysis shows predominantly NNE, NNW, N and NE trends of folds and shear zones, and NW, NE and E fracture trends. The CASZ has a >100 m.y. long deformation history, including a change from dextral to sinistral shearing. Both shearing senses are found on the older N-S and younger NW-SE fault systems. N-S oriented shearing was accompanied by intrusion of granitoids and localized hydrothermal alteration along N to NNE trending shear zones. Strain analysis shows low strain in the Ablah basin sediments, consistent with Ablah deposition after the tectonic climax in the region.