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Optical satellite remote sensing has become an important means of large-scale targets detection. However, due to the small perspective of satellite remote sensing, most of the structural information of slender targets, such as power transmission towers, is compressed during the imaging process. Experiments have found that the existing network does not work well in this situation. In order to tackle this problem, we proposed a shadow information-based slender targets detection (SI-STD) method. First, the shadow of the tower is used to compensate for the structure information lost during imaging. Second, the candidate regions containing towers and shadows are proposed by a modified regional proposal network (MRPN), which also classifies these regions at the same time. Third, the tower targets are regressed from candidate regions by TowerHead. Finally, by fusing the results of MRPN and TowerHead, we get the final results. The framework can comprehensively use the tower and its shadow to complete the detection task, and it has also shown excellent performance in the test.

With the development of space technology in recent years, various spacecraft with different sensors have been launched one after another, and there are more and more satellite remote sensing images in different situations. How to obtain better quality images has become the main research direction in the field of image fusion. Image fusion is an important branch and main research object of information fusion. Generally speaking, with the rapid development of the information society, people have higher and higher quality requirements for a variety of images, so There are serious challenges in storing, transmission and signal sampling. Nowadays, with the development of compressed sensing (CS) theory, a new sampling method has been extensively studied by many scholars. Compared with traditional Nyquist sampling theorem, Compression sensing can respond to the original signal with fewer sampling points. This paper is introduced the fusion algorithm of satellite remote sensing image based on compressed sensing. Firstly, the basic theory of compressed perceptual discretization signal is introduced. Secondly, a sparse CS remote sensing image fusion algorithm based on wavelet transform is proposed. Finally, through simulation verification, comparing the widely used IHS fusion and PCA fusion image methods, the method in this paper can get higher correlation coefficient and lower interaction entropy and spectral distortion after fusion. Compared with other methods in this paper, the fusion image can carry more space information and the original image is more similar.

Industrial enterprises are one of the main causes of air pollution in eastern China. How to monitor them efficiently is directly related to the result of air pollution control in China. However, the monitoring of enterprises now mainly relies on the ground online monitoring technology, and only for some large enterprises, some small and medium-sized enterprises will be missed which also contribute a lot to the air pollution. Based on the satellite remote sensing technology, this paper explores an operational method of monitoring the whole coverage of pollution enterprises from macro to micro, focusing on monitoring the regional pollution status results of the pollution enterprises and identifying the key monitoring objects including the small and medium-sized ones. Based on the air pollutants retrieval results of satellite remote sensing, a remote sensing high-occurrence index model of air pollution is built. By using the pollution focused grid extract method and high-resolution satellite image identification technology, the key monitoring objects of pollution enterprises can be extracted. The result of the application cases in eastern China and the typical districts show that the remote sensing high-occurrence index model can clearly reflect the comprehensive pollution status by the pollution enterprises; validation of the identification rate of the small and middle-sized enterprises can reach over 77%, which has made a great progress compared with the inspection only relying on the supervision personnel in the absence of specific objects. The method proposed in this paper can greatly improve the scientific and accurate supervision level of environmental management.

Oil spill is an environmental challenthat influences various aspects of coastal zones including humans, health, economy, and coastal ecosystems. The source of the oil spill can be both natural and ...

Mapping the topography of intertidal zones through in situ methods has many challenges associated with it and airborne techniques for determining the elevation profile are expensive. High temporal and spatial coverage provided by satellite remote sensing makes it highly efficient in assessing the extent and profile of intertidal zone even in remote and inaccessible areas. In this study, a semi-automated and scalable methodology is presented for generating the digital elevation model of the intertidal zone of Gulf of Kutch, India, thus bridging the existing gaps between terrestrial and marine elevation datasets. Time series multispectral data from the Landsat series are explored in the tidal height realm, and a median composting approach is used with NDWI and subsequent thresholding to effectively determine the extent of the intertidal zone. The median composite is used to handle the presence of noise or artefacts, thus ensuring the use of high-quality data and eradicating the problems associated with single-scene approaches previously used for such analysis. Using the capabilities of cloud-based computing platforms and openly accessible satellite data, this study aims to map the extent and 3D distribution of intertidal flats in a hostile coastal environment.

Since DCNNs (deep convolutional neural networks) have been successfully applied to various academic and industrial fields, semantic segmentation methods, based on DCNNs, are increasingly explored f...

The Austrian node of the Natural Resources Satellite Remote Sensing Cloud Service Platform was established in 2016 through a cooperation agreement between the Land Satellite Remote Sensing Application Center (LASAC), Ministry of Natural Resources of the Peoples Republic of China and the University of Vienna, Austria. Under this agreement panchromatic and multispectral data of the Chinese ZY-3 satellite are pushed to the server at the University of Vienna for use in education and research. So far, nearly 500 GB of data have been uploaded to the server. This technical note briefly introduces the ZY-3 system and illustrates the implementation of the agreement by the first China-Sat Workshop and several case studies. Some of them are already completed, others are still ongoing. They include a geometric accuracy validation of ZY-3 data, an animated visualization of image quick views on a spherical display to demonstrate the time series of the image coverage for Austria and Laos, and the use of ZY-3 data to study the spread of bark beetle in the province of Lower Austria. An accuracy study of DTMs from ZY-3 stereo data, as well as a land cover analysis and comparison of Austria with ZY-3 and other sensors are still ongoing.

Drought and heat events affect the uptake and sequestration of carbon in terrestrial ecosystems. Factors such as the duration, timing, and intensity of extreme events influence the magnitude of impacts on ecosystem processes such as gross primary production (GPP), i.e., the ecosystem uptake of CO2. Preceding soil moisture depletion may exacerbate these impacts. However, some vegetation types may be more resilient to climate extremes than others. This effect is insufficiently understood at the global scale and is the focus of this study. Using a global upscaled product of GPP that scales up in situ land CO2 flux observations with global satellite remote sensing, we study the impact of climate extremes at the global scale. We find that GPP in grasslands and agricultural areas is generally reduced during heat and drought events. However, we also find that forests, if considered globally, appear in general to not be particularly sensitive to droughts and heat events that occurred during the analyzed period or even show increased GPP values during these events. On the one hand, normal-to-increased GPP values are in many cases plausible, e.g., when conditions prior to the event have been particularly positive. On the other hand, however, normal-to-increased GPP values in forests may also reflect a lack of sensitivity in current remote-sensing-derived GPP products to the effects of droughts and heatwaves. The overall picture calls for a differentiated consideration of different land cover types in the assessments of risks of climate extremes for ecosystem functioning.

Epidemiological studies have reported the associations of adverse health outcomes with ambient particulate matter with aerodynamic diameter ≤2.5 μm (PM2.5). While these studies have accumulated increasingly refined evidence on PM2.5-health associations, the needs for more advanced PM2.5 exposure models have also grown. For the last two decades, PM2.5 estimation approaches using satellite remote sensing have been developed and advanced, taking advantage of quantitative aerosol data (e.g., aerosol optical depth; AOD), the development of satellite instruments and data retrieval algorithms, and the application of statistical and machine learning techniques. Subsequently, the satellite-based PM2.5 concentrations have contributed to health effect studies by providing spatially resolved exposure estimates of ambient PM2.5 and thus reducing exposure misclassification. This article summarizes previous development and recent advancement of satellite-based PM2.5 exposure assessment in the context of satellite aerosol products and PM2.5 estimation methodologies. Furthermore, this article deals with enhanced satellite capabilities of generating the exposure estimates of PM2.5 composition and time-resolved PM2.5. Finally, the future directions of satellite-based exposure assessment are discussed based on research needs and the satellite remote sensing technology of addressing them.

The estimation of the PM2.5 concentration based on satellite remote sensing is currently a hot topic for research, with machine learning algorithms starting to be applied to the estimation model. However, there are few comparisons between different machine learning algorithms, and few scholars objectively evaluate the estimation performance of machine learning algorithms under different weather conditions. In this study, PM2.5 concentration was estimated by six commonly used machine learning algorithms, and their performances were compared in four different weather conditions in the Beijing–Tianjin–Hebei (BTH) region. The results showed that decision tree and random forest consistently performed well in different weather conditions, while SVM performed poorly. When the PM2.5 concentration was greater than 150 μg/m3, the R and RMSE values for decision tree were 0.854 and 31.53 μg/m3, respectively, while the evaluation coefficient of SVM algorithm was only 0.597 and 49.31 μg/m3, it was worth noting that all algorithms performed better in this interval than in others. This study also focused on the development of an optimal combination algorithm to estimate PM2.5 concentration under different weather conditions and got a good application effect. The results of this research may provide a theoretical basis and an important reference for the application of machine learning algorithms in the field of remote sensing.

Compared with satellite remote sensing images, ground-based invisible images have limited swath, but featured in higher resolution, more distinct cloud features, and the cost is greatly reduced, conductive to continuous meteorological observation of local areas. For the first time, this paper proposed a high-resolution cloud image classification method based on deep learning and transfer learning technology for ground-based invisible images. Due to the limited amount of samples, traditional classifiers such as support vector machine can't effectively extract the unique features of different types of clouds, and directly training deep convolutional neural networks leads to over-fitting. In order to prevent the network from over-fitting, this paper proposed applying transfer learning method to fine-tune the pre-training model. The proposed network achieved as high as 85.19% test accuracy on 6-type cloud images classification task. The networks proposed in this paper can be applied to classify digital photos captured by cameras directly, which will reduce the cost of system greatly.

Mikania micrantha Kunth is an invasive alien weed and known as a plant killer around the world. Accurately and rapidly identifying M. micrantha in the wild is important for monitoring its growth status, as this helps management officials to take the necessary steps to devise a comprehensive strategy to control the invasive weed in the identified area. However, this approach still mainly depends on satellite remote sensing and manual inspection. The cost is high and the accuracy rate and efficiency are low. We acquired color images of the monitoring area in the wild environment using an Unmanned Aerial Vehicle (UAV) and proposed a novel network —MmNet— based on a deep Convolutional Neural Network (CNN) to identify M. micrantha in the images. The network consists of AlexNet Local Response Normalization (LRN), along with the GoogLeNet and continuous convolution of VGG inception models. After training and testing, the identification of 400 testing samples by MmNet is very good, with accuracy of 94.50% and time cost of 10.369 s. Moreover, in quantitative comparative analysis, the proposed MmNet not only has high accuracy and efficiency but also simple construction and outstanding repeatability. Compared with recently popular CNNs, MmNet is more suitable for the identification of M. micrantha in the wild. However, to meet the challenge of wild environments, more M. micrantha images need to be acquired for MmNet training. In addition, the classification labels need to be sorted in more detail. Altogether, this research provides some theoretical and scientific basis for the development of intelligent monitoring and early warning systems for M. micrantha and other invasive species.

Thick cloud and its shadow severely reduce the data usability of optical satellite remote sensing data. Although many approaches have been presented for cloud and cloud shadow removal, most of these approaches are still inadequate in terms of dealing with the following three issues: (1) thick cloud cover with large-scale areas, (2) all the temporal images included cloud or shadow, and (3) deficient utilization of only single temporal images. A novel spatio-temporal patch group deep learning framework for gap-filling through multiple temporal cloudy images is proposed to overcome these issues. The global-local loss function is presented to optimize the training model through cloud-covered and free regions, considering both the global consistency and local particularity. In addition, weighted aggregation and progressive iteration are utilized for reconstructing the holistic results. A series of simulated and real experiments are then performed to validate the effectiveness of the proposed method. Especially on Sentinel-2 MSI and Landsat-8 OLI with single/multitemporal images, under small/large scale regions, respectively.

Precision agriculture has benefited from the development of emerging technologies like Internet of Things (IoT), big data, and artificial intelligence (AI). The huge amount of high-resolution remotely sensed data, the development of frameworks, and machine learning (ML) algorithms have made the analysis of raw data more advanced and precise. Artificial intelligence had unlocked a new perspective to solve sophisticated challenges in agriculture. The goal of this paper is to present recent techniques, algorithms, and methodologies using artificial intelligence (AI) in precision agriculture (PA) using satellite remote sensing, and concern recent studies were conducted in the latest years 2019–2020. The accent was also pointed to the potential of AI in precision agriculture, the challenges, future needs, and trends in the field.

Archaeologists study many phenomena that scale beyond even our most geographically expansive field methodologies. The promise of collecting archaeologically relevant data beyond the scale of region...

Satellite remote sensing has been increasingly employed for the estimation of ground-level atmospheric PM2.5. There have been several cross-validation (CV) approaches applied for the validation of satellite-based PM2.5 estimation models. However, these validation approaches often lead to confusion, due to the unclear applicable conditions. For this, we fully analyze and assess the existing validation approaches, and provide suggestions on applicable conditions for them. Furthermore, the existing validation approaches still have limitations to disregard the uneven distribution of ground stations, and tend to overestimate the performance of the PM2.5 estimation models. To this end, a CV-based validation approach considering the uneven spatial distribution of monitoring stations (denoted as SDCV) is proposed. SDCV introduces the spatial distance between validation station and modeling station into the CV process, and evaluates the spatial performance through a strategy of excluding modeling stations within a specific distance. Meanwhile, this approach has designed reasonable evaluation indices for the model validation. Taking China as a case study, the results indicate that SDCV can yield a more complete and effective evaluation for the popular PM2.5 estimation models than the traditional validation approaches.

With the exponential growth of the number of vehicles, a series of traffic system problems continue to emerge. Vehicle detection plays an important role in the traffic system. Researchers have invested a lot of energy on it, and have made many achievements. However, there are still some deficiencies in the accuracy and robustness. The development of satellite remote sensing technology and VR panoramic technology provides technical support for this study. In order to detect vehicles more accurately, provide accurate and effective data information to relevant departments, and improve traffic conditions, this study uses deep learning algorithm to detect vehicles in high-resolution satellite remote sensing images. Firstly, the images in the images are classified by using the Alexnet network model, and then the vehicle target detection ability of the Faster R-CNN model algorithm is tested, and the algorithm is optimized by the method of model pruning and quantization, so that the average accuracy rate reaches 70.34%, and has strong robustness. Then the algorithm model is applied to the practical application of vehicle detection at an intersection. The traffic flow and the number of speeding vehicles are detected, and the false detection rate and missing detection rate are calculated. The results showed that the rate of missed detection and false detection was 0% on December 19, and the highest rate was 4.5% and 2.7% respectively. This shows that the vehicle detection based on deep learning algorithm in high resolution satellite remote sensing images has high accuracy, can effectively detect speeding vehicles, and can play a normal role in practical applications.

Cloud detection is an essential and important process in satellite remote sensing. Researchers proposed various methods for cloud detection. This paper reviews recent literature (2004–2018) on cloud detection. Literature reported various techniques to detect the cloud using remote-sensing satellite imagery. Researchers explored various forms of Cloud detection like Cloud/No cloud, Snow/Cloud, and Thin Cloud/Thick Cloud using various approaches of machine learning and classical algorithms. Machine learning methods learn from training data and classical algorithm approaches are implemented using a threshold of different image parameters. Threshold-based methods have poor universality as the values change as per the location. Validation on ground-based estimates is not included in many models. The hybrid approach using machine learning, physical parameter retrieval, and ground-based validation is recommended for model improvement.

A method of labeling aerial and satellite remote sensing images for detecting objects of a priori known shapes is proposed. The implementation of this method uses an algorithm of piecewise linear approximations of the boundary of template objects and an algorithm of transforming the template contour descriptions to fit them to images of objects in the photographs. The developed algorithms are tested using the practical problem of determining the types of aircraft in remote sensing images remote sensing images. It is shown that the proposed method improves the labeling accuracy and is faster than the manual detection of aircraft images in photographs.

Understanding the cross-scale linkages between drought and food security is vital to developing tools to reduce drought impacts and support decision making. This study reviews how drought hazards transfer to food insecurity through changes in physical processes and socio-environmental systems across a wide range of spatial and temporal scales. We propose a multi-scale, integrated framework leveraging modeling advances (e.g. drought and crop monitoring, water-food-energy nexus, decision making) and increased data availability (e.g. satellite remote sensing, food trade) through the lens of the coupled human–natural system to support multidisciplinary approaches and avoid potential policy spillover effects. We discuss current scale-dependent challenges in tackling drought-induced food security whilst minimizing water use conflicts and environmental impacts.

The prediction of PM2.5 concentration is a canonical predictive challenge due to the distribution of $PM_{2.5}$ appears serious spatiotemporal variability at multiple scales. Currently, using satellite-based remote sensing data to estimate ground-level PM2.5 is a promising method for providing spatiotemporal continuous information of PM2.5. In this letter, we proposed a deep neural network (DNN)-based PM2.5 prediction model to capture the spatiotemporal variability of ground-level PM2.5 using the remote sensing aerosol optical depth (AOD) data from the Himawari-8 satellite along with the conventional meteorological observation variables (denoted as PM25-DNN). The PM25-DNN model was trained and tested using the data from Beijing–Tianjin–Hebei region of China in 2017, and we compared the prediction performance between the PM25-DNN and the current state-of-the-art methods in this field. The results show that the PM25-DNN outperforms the other models with the cross-validated coefficient of determination ( $\text{R}^{2}$ ), root-mean-square error (RMSE), mean prediction error (MPE), and relative prediction error (RPE) were 0.84, $19.9~\mu \text{g}/\text{m}^{3}$ , $11.89~\mu \text{g}/\text{m}^{3}$ , and 41.21%, respectively. Then, the trained PM25-DNN model was applied to estimate the hourly gridded PM2.5 with 1-km spatial resolution. Our results indicate that the DNN architecture can capture the essential spatiotemporal distribution associated with PM2.5 only using AOD data and conventional meteorological observational variables without more handcrafted features. The proposed PM25-DNN model can greatly improve the accuracy of PM2.5 estimation, and it provides a new perspective for PM2.5 monitoring using end-to-end deep learning method.

Amazonian tropical forests play a significant role in global water, carbon and energy cycles. Satellite remote sensing is presented as a feasible means in order to monitor these forests. In particu...

This paper proposes a new coal quality exploration method that detects coal quality in coal mining areas and explores and monitors the distribution and change of coal through remote sensing images. First, we collected a large number of coal and noncoal samples such as sandstones, shales, and coal gangues. Second, we measured the actual spectral data of these samples using a spectrometer. For coal mines, we used the chemical analysis method to quantify coal’s fixed carbon and categorize the coal mines into three types based on the fixed carbon content present in coal. Third, we collected satellite remote sensing images of coal mining areas and established spectral data relations between the measured spectral data of the samples and the remote sensing images. Fourth, we proposed an incremental multilayer learning machine algorithm and used the algorithm combined with spectral data to build a coal quality classification model to identify coal quality in remote sensing images. Finally, the model accurately described the distribution map of coal quality. Compared with traditional coal exploration methods, this method has the advantages of high speed, high accuracy, and low price.

This article examines the dynamics of an emerging multi-use paradigm for satellite remote sensing (RS). From the beginning of the space age, RS satellites have served as dual-use technologies. The advancements to RS technology since that time have largely been driven by the security and economic interests of States. While these interests are to some extent mutually supporting, they have also created a complex environment where non-governmental actors such as commercial satellite operators are increasingly involved in matters of national security and defence. Furthermore, the growing number of RS data providers globally, and more open and easily accessible data and analysis tools online, are creating a new use paradigm for RS in civil-military relations. These developments are extending the dual-use technology dilemma to one of multi-use, where non-governmental actors, including ordinary individuals, are becoming entangled in government affairs. This article traces the trajectory of these processes, and discusses potential implications for RS capable States.

Drought is a harmful and slow natural phenomenon that has significant effects on the economy, social life,agriculture and environment of the country. Due to its slow process it is difficult to study this phenomenon. RemoteSensing and GIS tools play a key role in studying different hazards like droughts. The main objective of the study wasto investigate drought risk by using GIS and Remote Sensing techniques in district Khushab, Pakistan. Landsat ETMimages for the year 2003, 2009 and 2015 were utilized for spatial and temporal analysis of agricultural andmeteorological drought. Normalized difference vegetation index (NDVI) Standardized Precipitation Index (SPI) andrainfall anomaly indices were calculated to identify the drought prone areas in the study area. To monitormeteorological drought SPI values were used and NDVI was calculated for agricultural drought. These indices wereintegrated to compute the spatial and temporal drought maps. Three zones; no drought, slight drought and moderatedrought were identified. Final drought map shows that 30.21% of the area faces moderate drought, 28.36% faces slightdrought while nearly 41.3% faces no drought situation. Drought prevalence and severity is present more in the southernpart of Khushab district than the northern part. Most of the northern part is not under any type of drought. Thus, anoverall outcome of this study shows that risk areas can be assessed appropriately by integration of various data sourcesand thereby management plans can be prepared to deal with the hazard.

High temporal frequency monitoring of transport infrastructures is crucial for implementing effective maintenance prioritisation strategies and prevent major failures. To this extent, ground-based non-destructive testing (NDT) methods have been successfully applied for decades, reaching very high standards of data quality and accuracy. However, routine and systematic campaigns are required over relatively long inspection times for data collection and 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 infrastructures (roads, railways and airfields), with a much higher temporal frequency of investigation and wider inspection catchment areas. Nevertheless, the integration of information from i) satellite remote sensing and ii) ground-based NDT methods is still an area to be explored in civil engineering. Within this framework, this paper aims to review significant stand-alone applications in these two areas of technology for transport infrastructure monitoring. Furthermore, recent advances, main challenges and future perspectives arising from their integration are discussed. Contents of this paper are organised within the context of an invited keynote talk given at the 2021 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing (AGERS – 2021).

Drought disaster monitoring and evaluation model based on remote sensing image is a hot spot in academic circles at home and abroad. However, the existing models have some problems, such as single drought monitoring index, lack of comprehensive monitoring of multiple drought indicators, and lack of comprehensive methods that take into account the whole process of drought evolution. It has become a bottleneck problem that puzzles the large-scale popularization and application of remote sensing drought monitoring technology. Starting with the understanding of the evolution process of drought disaster, this paper puts forward the “four stages” theory of drought disaster evolution, constructs the framework of Remote Sensing Comprehensive Monitoring Model for the characteristics of drought process, and selects the severe drought in Southwest China from November 2009 to May 2010 as an example to verify the model. The results show that the remote sensing drought monitoring maps are basically consistent with the actual situation of drought disaster. It is considered that the comprehensive monitoring model framework for drought evolution using satellite remote sensing images has good suitability and great application potential.

Industrial storage tanks are widely used in petroleum, chemical industry, metallurgy and other process industries. The use of storage tanks are important in ensuring industrial safety production and product storage. Recently, concerns are rising over leaks and spills which leads to potential environmental and public health risks, for example, air pollution. Thus, there is a need to detect and monitor the tanks, which has become a regulatory requirement in most countries. The monitoring of industrial storage tanks in a city is of great practical significance to the planning and construction of the city. With the recent development of remote sensing technology, computer vision and image processing, it is possible to automatically detect industrial storage tanks from optical remote sensing images. This paper mainly studies the automatic detection of urban industrial storage tanks using deep learning based algorithm, aiming to help people manage and monitor the urban environment and resources. In this paper, we collected optical satellite remote sensing images of the city as a unit and created a city-level dataset including three cities of Guangdong province in south China by utilising satellite image data obtained from Google Earth imagery. To explore the effect of the deep learning object detection algorithm for the industrial storage tanks detection at the city-level, a deep learning model built with SSD (Single-Shot Detector) was trained based on our collected dataset. To further improve the detection accuracy of industrial storage tanks, Hough transform was used to reduce the false alarm in the deep learning results. The experimental results show that the combination of deep learning target detection model and Hough transform is effective in detecting of industrial storage tank on the collected city-level dataset and can achieve promising detection results.

The artificial light at night (ALAN) present in many cities and towns has a negative impact on numerous organisms that live alongside humans, including bats. Therefore, we investigated if the artificial illumination of the historic Wisłoujście Fortress in Gdańsk, Poland (part of the Natura 2000 network), during nighttime events, which included an outdoor electronic dance music (EDM) festival, might be responsible for increased light pollution and the decline in recent years of the pond bat (Myotis dasycneme). An assessment of light pollution levels was made using the methods of geographical information system (GIS) and free-of-charge satellite remote sensing (SRS) technology. Moreover, this paper reviewed the most important approaches for environmental protection of bats in the context of ecological light pollution, including International, European, and Polish regulatory frameworks. The analysis of this interdisciplinary study confirmed the complexity of the problem and highlighted, too, the need for better control of artificial illumination in such sensitive areas. It also revealed that SRS was not the best light pollution assessment method for this particular case study due to several reasons listed in this paper. As a result, the authors’ proposal for improvements also involved practical recommendations for devising suitable strategies for lighting research and practice in the Natura 2000 Wisłoujście Fortress site located adjacent to urban areas to reduce the potential negative impact of ALAN on bats and their natural habitats.

The remembrance of the dead is a ubiquitous dimension of most human societies, and the spatial dimension of mortuary practices actively constitutes an essential element of the cultural significance of certain places in the landscape. The visual prominence of stone-built funerary monuments in dry upland areas is particularly conducive to their multiscalar study through above-ground remote sensing methods. In this paper, we characterize the nature and distribution of Late Holocene drystone funerary monuments in the Dhar Tagant region of south-eastern Mauritania using freely available, very high-resolution (VHR) satellite imagery. We contextualize them in relation to the monumental mortuary records of Senegal and Mali within the West African Sahel, exploring their similarities and differences with other monumental funerary landscapes in semi-arid environments. Ethical considerations and a self-reflective attitude must be at the forefront of archaeological research, and we discuss the ethics of remote sensing research in the study of funerary practices in Africa, as well as the opportunities and challenges for remote collaborative engagement with local communities in the context of fieldwork restrictions.

The recent COVID-19 pandemic affected various aspects of life. Several studies established the consequences of pandemic lockdown on air quality using satellite remote sensing. However, such studies have limitations, including low spatial resolution or incomplete spatial coverage. Therefore, in this paper, we propose a machine learning-based scheme to solve the pre-mentioned limitations by training an optimized space-time extra trees model for each year of the study period. The results have shown that our trained models reach a prediction accuracy up to 95% when predicting the missing values in the MODIS MCD19A2 Aerosol Optical Depth (AOD) product. The outcome of the mentioned scheme was a geo-harmonized atmospheric dataset for aerosol optical depth at 550 nm with 1 km spatial resolution and full coverage over Europe. As an application, we used the proposed machine learning based prediction approach in AOD levels analysis. We compared the mean AOD levels between the lockdown period from March to June in 2020 and the mean AOD values of the same period for the past 5 years. We found that AOD levels dropped over most European countries in 2020 but increased in several eastern and western countries. The Netherlands had the most significant average decrease in AOD levels (19%), while Spain had the highest average increase (10%). Moreover, we analyzed the relationship between the relative percentage difference of AOD and four meteorological variables. We found a positive correlation between AOD and relative humidity and a negative correlation between AOD and wind speed. The value of the proposed prediction scheme is further emphasized by taking into consideration that the reconstructed dataset can be used for future air quality studies concerning Europe.

As a key parameter for studying the movement of the earth, Land Surface Temperature (LST) data has always played an important role. However, since people began to collect LST data purposefully, they have been plagued by the problem of missing data. In recent years, with the development of satellite remote sensing technology, the quality of LST data has been continuously improved. However, due to the limited resolution of the sensor and the complex atmospheric environment, the problem of missing data still occurs in the LST data, and the accuracy and effectiveness of the data are therefore greatly reduced. In this article, we propose a LST data reconstruction method based on deep learning network, and use this method to reconstruct the 9-year (2000–2008) MODIS LST map. In the comparative experiment of selected regions, the ratio of “high-quality” pixels that can be collected in the reconstructed data is between 32% and 41.5%. Compared with traditional methods, the ratio of “high-quality” pixels that can be collected is increased by 10% to 20%, which greatly improves the use value of massive satellite data. Experiments also show that the use of different loss functions will also affect the reconstruction effect of the method.

In recent studies, object detection of satellite remote sensing images by deep learning method has emerged as a major concern in the fields of environmental detection, military investigation and geography. The current object detection practice, such as Faster RCNN, SSD and YOLO (v1-v5), is using new CNN based deep learning method to replace conventional sliding window and handcraft detectors, and it has achieved impressive success for some specific fields in natural scene images. However, in the task of satellite remote sensing images, it's still a major challenge that the existing models cannot guarantee the multi-scale object detection and real-time monitoring with high accuracy. To address these challenges, in this paper, we proposed SeMo- YOLO, a novel one stage deep learning detector based on the combination of MobileNet, YOLOv3 and channel attention mechanism. Experiment result shows that SeMo-YOLO and it's tiny version can achieve significant improvement in the aspect of accuracy for multiscale remote sensing object detection. It has a mAP (@.5) of 92%, 94%, and 95% on RSOD, HRRSD2019 and SAR-SSDD datasets respectively, which has bring significant improvement in detection precision than the existing state-of-the-art Networks. In addition, the weight parameters of the proposed model are less than the existing YOLOv3 so as to it can achieve relatively higher detection speed near real time.

Over the last decade, the beaching of massive quantities of sargassum in the Caribbean has become a major problem. Relatively few studies have focused on beached sargassum which has numerous negative environmental, social and economic impacts. To aid understanding of the problem and develop approaches to effectively manage it, a satellite-based mapping and monitoring service is being developed on the basis of information generated in a set of user needs workshops. Here, we report on the foundations of this mapping and monitoring service. In particular, user needs obtained from a large and diverse set of stakeholders are used to identify appropriate satellite remote sensing systems to use as data sources. The system will use mainly PlanetScope and Sentinel-2 data to meet the spatio-temporal demands of users. It is planned to develop an operational system that may if desired be extended to the Caribbean as a whole and beyond.

Forest fires are one of the large-scale natural phenomena that humans are not yet able to control (Bowman et al., 2009). Satellite remote sensing is an important tool in all processes involved during natural disasters (Poursanidis et al., 2017), from identifying areas that are most likely to be affected by using mathematical geospatial models and analysis and creating maps, mapping of affected areas in urban and suburban environments (Poursanidis et al., 2015) and use of results in remediation and compensation activities as well as the use of data from satellite image analysis in planning and decision-making systems for targeted firefighting actions or information on evacuations and avoidance of loss of human life (Kochilakis et al., 2017).

Coordinated development of the Beijing-Tianjin-Hebei Region is a national strategy of China, while the most import aspect among the strategy is transportation integration. By using GF-1, ZY-3 satellite data, this paper produced the latest traffic infrastructure dataset, constructed the evaluation system of transportation superiority degree through the technical indicators such as the distribution of transportation infrastructure, the density of traffic network, the influence degree of traffic trunk line, and the location superiority degree. The results show that the transportation superiority degree in Beijing-Tianjin-Hebei Region presents a “bias normal” distribution characteristic. 14.8% of the county-level administrative units have outstanding transportation advantages, with better economic development. Near 9.4% of the county-level administrative units are at an obvious disadvantage in terms of traffic conditions, and about 75.7% units are at the middle or above the middle level of the evaluation samples. The results could be introduced to the transportation planning and supervision of this region.

The protection and monitoring of forest resources has drawn considerable national attention. Traditional deforestation monitoring requires a lot of manpower and material resources through manual visual interpretation and manual change patterns labelling, which has problems of low efficiency and high missed alarm rate. Therefore, this paper explores the detection for deforestation changes from remote sensing images based on deep learning framework, and aims to help forestry department manage and monitor forest resources. In this paper, an U-Net+LSTM framework is used to detect the changes of deforestation from remote sensing images. The evaluation data is Sentinel-2 dataset and the study area is Guangxi Sanjiang Dong Autonomous County in China. The results show that the F1 score of the framework is as high as 0.715, which proves the proposed model can effectively detect the change from forest to bare soil in remote sensing images.

Synthetic Aperture Radar (SAR) is an important means for ship surveillance at sea due to its unique advantages of all-day and all-weather. This paper proposes a ship target detection method in SAR image based on Google Earth Engine remote sensing cloud computing platform. It can realize the real-time and fast detection of ship targets in a large area, the acquisition of ship target information and the batch download of SAR images in the detection area. Compared with the traditional ship target detection methods in SAR images, the proposed method is not limited to the acquisition, storage, utilization and batch processing of SAR satellite remote sensing data, and does not have high requirements on computer performance. Therefore, it has high practical application value for marine ship surveillance.

The increase in atmospheric greenhouse gas concentrations of CO2 and CH4, due to human activities, is the main driver of the observed increase in surface temperature by more than 1 °C since the pre-industrial era. At the 2015 United Nations Climate Change Conference held in Paris, most nations agreed to reduce greenhouse gas emissions to limit the increase in global surface temperature to 1.5 °C. Satellite remote sensing of CO2 and CH4 is now well established thanks to missions such as NASA’s OCO-2 and the Japanese GOSAT missions, which have allowed us to build a long-term record of atmospheric GHG concentrations from space. They also give us a first glimpse into CO2 and CH4 enhancements related to anthropogenic emission, which helps to pave the way towards the future missions aimed at a Monitoring & Verification Support (MVS) capacity for the global stock take of the Paris agreement. China plays an important role for the global carbon budget as the largest source of anthropogenic carbon emissions but also as a region of increased carbon sequestration as a result of several reforestation projects. Over the last 10 years, a series of projects on mitigation of carbon emission has been started in China, including the development of the first Chinese greenhouse gas monitoring satellite mission, TanSat, which was successfully launched on 22 December 2016. Here, we summarise the results of a collaborative project between European and Chinese teams under the framework of the Dragon-4 programme of ESA and the Ministry of Science and Technology (MOST) to characterize and evaluate the datasets from the TanSat mission by retrieval intercomparisons and ground-based validation and to apply model comparisons and surface flux inversion methods to TanSat and other CO2 missions, with a focus on China.

This study analyzed the effectiveness of temporary emission control measures on air quality of Nanjing, China during the Jiangsu Development Summit (JDS). We employed a regional chemistry model WRF-Chem to simulate air pollutants in Nanjing and compared the results to surface observations and satellite retrievals. During the JDS, air pollutant emissions from industry and transportation sectors largely decreased by 50–67% due to the short-term emission control measures such as reducing coal combustions, shutting down factories, and partially limiting traffic. Benefiting from the emission control, the simulated concentrations of PM2.5, NO2, SO2, CO and VOCs in Nanjing decreased by 17%, 20%, 20%, 19%, and 15% respectively, consistent with the surface and satellite observations. However, both the observed and simulated O3 increased by 3–48% during the JDS, which was mainly due to the remarkable NOx emission reduction (26%) in the downtown of Nanjing where the O3 production regime was mainly VOC-controlled. In addition, the atmospheric oxidation capacity and further the sulfur oxidation ratio, were facilitated by the elevated O3, which led to variable mitigation efficiencies of different secondary PM2.5 compositions. Our study offers an opportunity for understanding the coordinated control of PM2.5 and O3 in typical city clusters, and can provide implications for future mitigation actions.

The rising standard of living has led people to pay more attention to the importance of natural foods. Honey as a nutritional product is beneficial to human health. At present, honey production mainly relies on nectar sites, however, due to temperature, humidity, the flowering period and plant growth status of nectar sites are difficult to be accurately grasped. In this paper, we propose a deep-learning nectar site identification model, which can distinguish nectar sites from non-nectar sites in real time at the macro level, and can accurately determine whether a field monopoly and nectar plants are in flowering stage at the micro level using satellite remote sensing data. It is important for grasping the distribution of nectar sites in real time and promoting the development of smart agriculture.

With the implementation of the 2018–2020 Clean Air Action Plan (CAAP) the and impact from COVID-19 lockdowns in 2020, air pollution emissions in central and eastern China have decreased markedly. Here, by combining satellite remote sensing, re-analysis, and ground-based observational data, we established a machine learning (ML) model to analyze annual and seasonal changes in primary air pollutants in 2020 compared to 2018 and 2019 over central and eastern China. The root mean squared errors (RMSE) for the PM2.5, PM10, O3, and CO validation dataset were 9.027 μg/m3, 20.312 μg/m3, 10.436 μg/m3, and 0.097 mg/m3, respectively. The geographical random forest (RF) model demonstrated good performance for four main air pollutants. Notably, PM2.5, PM10, and CO decreased by 44.1%, 43.2%, and 35.9% in February 2020, which was likely influenced by the COVID-19 lockdown and primarily lasted until May 2020. Furthermore, PM2.5, PM10, O3, and CO decreased by 16.4%, 24.2%, 2.7%, and 19.8% in 2020 relative to the average values in 2018 and 2019. Moreover, the reduction in O3 emissions was not universal, with a significant increase (~20–40%) observed in uncontaminated areas.