Shallow water bathymetry based on a back propagation neural network and ensemble learning using multispectral satellite imagery

The back propagation(BP)neural network method is widely used in bathymetry based on multispectral satellite imagery.However,the classical BP neural network method faces a potential problem because it easily falls into a local minimum,leading to model training failure.This study confirmed that the local minimum problem of the BP neural network method exists in the bathymetry field and cannot be ignored.Furthermore,to solve the local minimum problem of the BP neural network method,a bathymetry method based on a BP neural network and ensemble learning(BPEL)is proposed.First,the remote sensing imagery and training sample were used as input datasets,and the BP method was used as the base learner to produce multiple water depth inversion results.Then,a new ensemble strategy,namely the minimum outlying degree method,was proposed and used to integrate the water depth inversion results.Finally,an ensemble bathymetric map was acquired.Anda Reef,northeastern Jiuzhang Atoll,and Pingtan coastal zone were selected as test cases to validate the proposed method.Compared with the BP neural network method,the root-mean-square error and the average relative error of the BPEL method can reduce by 0.65–2.84 m and 16%–46%in the three test cases at most.The results showed that the proposed BPEL method could solve the local minimum problem of the BP neural network method and obtain highly robust and accurate bathymetric maps.

Arbitrary Scale Super Resolution Network for Satellite Imagery

Recently,satellite imagery has been widely applied in many areas.However,due to the limitations of hardware equipment and transmission bandwidth,the images received on the ground have low resolution and weak texture.In addition,since ground terminals have various resolutions and real-time playing requirements,it is essential to achieve arbitrary scale super-resolution(SR)of satellite images.In this paper,we propose an arbitrary scale SR network for satellite image reconstruction.First,we propose an arbitrary upscale module for satellite imagery that can map low-resolution satellite image features to arbitrary scale enlarged SR outputs.Second,we design an edge reinforcement module to enhance the highfrequency details in satellite images through a twobranch network.Finally,extensive upsample experiments on WHU-RS19 and NWPU-RESISC45 datasets and subsequent image segmentation experiments both show the superiority of our method over the counterparts.

Change Detection of Lake Chad Water Surface Area Using Remote Sensing and Satellite Imagery

The Lake Chad located in the west-central Africa in the Sahel region at the edge of the Sahara experienced severe drought during 1970s and 1980s and overexploitation (unintegrated and unsustainable use), which is a result of variant land uses and water management practices during the last 50 years. This resulted in a decline of the water level in the Lake and surrounding rivers. The present study analyzed satellite images of Lake Chad from Landsat-MSS, Landsat-OLI to investigate the change of the open water surface area during the years of 1973, 1987, 2001, 2013, and 2017. Supervised classifications were performed for the land cover analysis. The open water area in 1973 was covering 16,157.34 km2 approximately, and that was 64.6% of the total lake area in the 1960s. As an ultimate result of the extreme drought that the study area witnessed through 1970s-1980s, the open water area has decreased to 1831.44 km2, i.e. around 11.33%, compared to that in 1973. The dilemma that the study area is suffering from is believed to be a catastrophic complication of the aforementioned drought crisis, which arose as an ultimate result the climate change, global warming, and the unintegrated and unsustainable use of water challenges the study area is still encountering.

Identification of climatic and environmental factors associated with incidence of cutaneous leishmaniasis in Central Iran using satellite imagery

Objective:To determine the effect of climatic and environmental factors on the incidence of cutaneous leishmaniasis in Qom province in 2018.Methods:In this cross-sectional study,the data on cutaneous leishmaniasis incidence were collected from the Disease Control and Prevention Center in Qom province.Climatic and environmental data including Normalized Difference Vegetation Index(NDVI),Land Surface Temperature(LST),and soil moisture were extracted using satellite images.Data of altitude and sunny hours were provided based on shuttle radar topography mission digital elevation model and hemispherical viewshed algorithm,respectively.The associations of climatic and environmental variables with the incidence of the disease were analyzed by Pearson correlation method.The Arc GIS 10.3 software was used to determine the geographical distribution of these factors.Results:There were positive correlations between cutaneous leishmaniasis incidence and the two climatic factors:LST and sunny hours per day(P=0.041,P=0.016),and it had weak negative correlations with the digital elevation model(P=0.27),soil moisture(P=0.54),and NDVI(P=0.62).The time delay analysis showed that in one-,two-,and three month periods,the correlations increased with a 95%confidence interval.Accordingly,the correlation with the three-month time delay was positive and relatively strong between the cutaneous leishmaniasis incidence and LST and sunny hours(P=0.027,P=0.02);nevertheless,there were negative correlations between the cutaneous leishmaniasis incidence and the soil moisture(P=0.27)and NDVI(P=0.62).Conclusions:As Qom is located in one of the semi-arid climate zones,topography and solar energy are important factors affecting the incidence of cutaneous leishmaniasis in autumn.Therefore,appropriate disease control programs are recommended.

Spatio-Temporal Analysis of Landuse/Landcover Change of District Pishin Using Satellite Imagery and GIS

Detecting change on the face of the globe using GIS (Geographic Information System) aided by remotely sensed imagery is now becoming an indispensable tool in managing the resources of our planet. The present study with the help of GIS and remote sensing (RS) is also a similar attempt in recording and quantifying change in land use and land cover in district Pishin both in spatial and temporal extents. Satellite imagery was acquired from the USGS official website from three LANDSAT satellites. Theses satellites are LANDSAT 5, LANDSAT7 and LANDSAT 8. The data were acquired for the years 1992, 2003 and 2013. Satellite imagery was processed in ArcMap 10.1 and maximum likelihood supervised image classification was applied in reaching the goal of detecting change. The result of the analysis revealed that built-up area was increased by 5.84%;vegetation was increased by 3.89%;water bodies were increased by 0.05% and bare surfaces were decreased by 9.78%. The decrease in the barren surfaces was attributed to the increase in vegetation and built-up area which replaced the barren land in the study area. This paper also shows the significance and potential of digital change detection methods in managing the resources of our environment and keeping an eye on the land use and land cover of our Earth.

Mapping common and glossy buckthorns (Frangula alnus and Rhamnus cathartica) using multi-date satellite imagery WorldView-3, GeoEye-1 and SPOT-7

Buckthorns(Glossy buckthorn,Frangula alnus and common buckthorn,Rhamnus cathartica)represent a threat to biodiversity.Their high competitivity lead to the replacement of native species and the inhibition of forest regeneration.Early detection strategies are therefore necessary to limit invasive alien plant species’impacts,and remote sensing is one of the techniques for early invasion detection.Few studies have used phenological remote sensing approaches to map buckthorn distribution from medium spatial resolution images.Those studies highlighted the difficulty of detecting buckthorns in low densities and in understory using this category of images.The main objective of this study was to develop an approach using multi-date very high spatial resolution satellite imagery to map buckthorns in low densities and in the understory in the Québec city area.Three machine learning classifiers(Support Vector Machines,Random Forest and Extreme Gradient Boosting)were applied to WorldView-3,GeoEye-1 and SPOT-7 satellite imagery.The Random Forest classifier performed well(Kappa=0.72).The SVM and XGBoost’s coefficient Kappa were 0.69 and 0.66,respectively.However,buckthorn distribution in understory was identified as the main limit to this approach,and LiDAR data could be used to improve buckthorn mapping in similar environments.

IMPROVED MAN-COMPUTER INTERACTIVE CLASSIFICATION OF CLOUDS BASED ON BISPECTRAL SATELLITE IMAGERY

In this paper,improvement on man-computer interactive classification of clouds based on hispeetral satellite imagery has been synthesized by using the maximum likelihood automatic clustering(MLAC)and the unit feature space classification(UFSC)approaches.The improved classification not only shortens the time of sample-training in UFSC method,but also eliminates the inevitable shortcomings of the MLAC method.(e.g.,1.sample selecting and training is confined only to one cloud image:2.the result of clustering is pretty sensitive to the selection of initial cluster center:3.the actual classification basically can not satisfy the supposition of normal distribution required by MLAC method;4.errors in classification are difficult to be modified.) Moreover,it makes full use of the professionals'accumulated knowledge and experience of visual cloud classifications and the cloud report of ground observation,having ensured both the higher accuracy of classification and its wide application as well.

A combined image matching method for Chinese optical satellite imagery

Image matching is one of the key technologies for digital Earth.This paper presents a combined image matching method for Chinese satellite images.This method includes the following four steps:(1)a modified Wallis-type filter is proposed to determine parameters adaptively while avoiding over-enhancement;(2)a mismatch detection procedure based on a global-local strategy is introduced to remove outliers generated by the Scale-invariant feature transform algorithm,and geometric orientation with bundle block adjustment is employed to compensate for the systematic errors of the position and attitude observations;(3)we design a novel similarity measure(distance,angle and the Normalized Cross-Correlation similarities,DANCC)which considers geometric similarity and textural similarity;and(4)we introduce a hierarchical matching strategy to refine the matching result level by level.Four typical image pairs acquired from Mapping Satellite-1,ZY-102C,ZY-3 and GeoEye-1,respectively,are used for experimental analysis.A comparison with the two current main matching algorithms for satellite imagery confirms that the proposed method is capable of producing reliable and accurate matching results on different terrains from not only Chinese satellite images,but also foreign satellite images.

Computer Identification of Multispectral Satellite Cloud Imagery

A dynamic clustering method based on multispectral satellite imagery to identify the different features is described. The channel combinations selected are for the different purposes in classification. Several cases are presented using the polar-orbiting satellite imageries.

Characteristics of an Explosive Cyclone over Northeast China Revealed by Satellite Water Vapor Imagery

In this paper,an explosive cyclone(EC)that occurred over Northeast China in the spring of 2016 is studied by using 6.7μm FY satellite water vapor(WV)imagery and NCEP(1°×1°)reanalysis data.Moreover,the evolutions of the upper-level jet stream(ULJ),the vertical motions,and the potential vorticity(PV)are analyzed in detail.Results show that different shapes of the WV image dark zones could reflect different stages of the EC.At the pre-explosion stage,a small dark zone and an S-shaped baroclinic leaf cloud can be found on the WV imagery.Then the dark zone expands and the leaf cloud grows into a comma-shaped cloud at the explosively developing stage.At the post-explosion stage,the dark zone brightens,and the spiral cloud forms.The whole process can be well described by the WV imagery.The dynamic dry band associated with the sinking motion and the ULJ can develop into the dry intrusion later,which is an important signal in forecasting the EC and should be paid attention to when analyzing the WV imagery.Furthermore,the mechanism is also analyzed in detail in this article.EC usually occurs in the left-exit region of the 200-h Pa jet and the region ahead of the 500-h Pa trough where there is significant positive vorticity advection(PVA).When the EC moves onto the sea surface,the decreased friction would favour the development of the EC.The upper-level PVA,the strong convergence at low level,and the divergence at high levels can maintain the strong updraft.Meanwhile,the high PV zone from the upper levels extends downward,approaching the cyclone.Together,they keep the cyclone deepening continuously.

Imagery Data Quality of ZY Satellite Reached International Level

The in-orbit commissioning of ZY-1 02C satellite is proceeding smoothly. According to the relevant experts in this field, the imagery quality of the satellite has reached or nearly reached the level of international satellites of the same kind. ZY-1 02C satellite and ZY-3 satellite were successfully launched on December 22, 2011 and January 9, 2012 respectively. China Centre for Resources Satellite Data andApplication (CRSDA) was responsible for the building of a ground

Mapping Air Quality Using Remote Sensing Technology: A Case Study of Nairobi County

Nairobi County experiences rapid industrialization and urbanization that contributes to the deteriorating state of air quality, posing a potential health risk to its growing population. Currently, in Nairobi County, most air quality monitoring stations use low-cost, inaccurate monitors prone to defects. The study’s objective was to map Nairobi County’s air quality using freely available remotely sensed imagery. The Air Pollution Index (API) formula was used to characterize the air quality from cloud-free Landsat satellite images i.e., Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI from Google Earth Engine. The API values were computed based on vegetation indices namely NDVI, TVI, DVI, and the SWIR1 and NIR bands on the QGIS platform. Qualitative accuracy assessment was done using sample points drawn from residential, industrial, green spaces, and traffic hotspot categories, based on a passive-random sampling technique. In this study, Landsat 5 API imagery for 2010 provided a reliable representation of local conditions but indicated significant pollution in green spaces, with recorded values ranging from -143 to 334. The study found that Landsat 7 API imagery in 2002 showed expected results with the range of values being -55 to 287, while Landsat 8 indicated high pollution levels in Nairobi. The results emphasized the importance of air quality factors in API calibration and the unmatched spatial coverage of satellite observations over ground-based monitoring techniques. The study recommends the recalibration of the API formula for characteristic regions, exploring newer satellite sensors like those onboard Landsat 9 and Sentinel 2, and involving key stakeholders in a discourse to develop a suitable Kenyan air quality index.

Investigation on the Relationship between Population Density and Satellite Image Features——a Deep Learning Based Approach

Timely and accurate population statistic data plays an important role in many fields.To illustrate the demographic characteristics,population density is a crucial factor in evaluating population data.With a dynamic regional migration in population,it is a challenging job to evaluate population density without a census-based survey.We present the approach to classify satellite images in different magnitudes in population density and execute the comparative experiment to discuss the factors that influence the identification to the images with the deep learning approach.In this paper,we use satellite imagery and community population density data.With convolutional neural networks,we evaluated the performance of CNN on population estimation with satellite images,found the features that are important in population estimation,and then perform the sensitive analysis.

Retrieval of High Resolution Satellite Images Using Texture Features

In this research, a content-based image retrieval （CBIR） system for high resolution satellite images has been developed by using texture features. The proposed approach uses the local binary pattern （LBP） texture feature and a block based scheme. The query and database images are divided into equally sized blocks, from which LBP histograms are extracted. The block histograms are then compared by using the Chi-square distance. Experimental results show that the LBP representation provides a powerful tool for high resolution satellite images （HRSI） retrieval.

Overcast sky condition prevails on and influences the biometeorology of the tropical Andean Páramos

Clouds play a major role in modulating the biometeorological processes. We studied the influence of cloudiness on four biometeorological variables:daily air temperature(Tair), relative humidity(RH),reference evapotranspiration(ETr),and photosynthetic active radiation(PAR), recorded at four sites of Andean Páramos in southern Ecuador during 2.5 to 5.5 years. First, we quantified both the cloud cover percentage(Cloud%) creating cloud masks over the visible bands of Landsat 7 images and the sky condition(K_(T)) using the records of solar and extraterrestrial radiation. Second, we estimated KTfrom Cloud%. Finally, we quantified T_(air), RH, ET_(r), and PAR under clear, cloudy, and overcast K_(T) and their dependence on KT. The average Cloud% ranged between 65%–76%, and KTcorroborated the prevailing overcast sky(between 55% and 72.5% of the days) over the páramos. The proposed model performed well in the sites of calibration(R^(2)= 0.80;MBE = 0.00;RMSE = 0.05) and validation(R^(2)= 0.74;MBE =-0.07;RMSE = 0.11). The overcast sky diminished T_(air)(≤ 10℃), ET_(r)(≤ 1.6 mm day-1), and PAR(4 MJ m^(-2)day^(-1)) and increased RH(≥ 88%),while the variables showed the opposite behavior during the uncommon clear sky(≤ 5.5% of the days).Thus, mostly the dynamic of RH(R^(2)≥ 0.62), ETr(R^(2)≥ 0.85), and PAR(R2≥ 0.77) depended on K_(T). Hence,the prevailing overcast sky influenced the biometeorology of the páramos.

Deep Neural Network Based Detection and Segmentation of Ships for Maritime Surveillance

In recent years,computer visionfinds wide applications in maritime surveillance with its sophisticated algorithms and advanced architecture.Auto-matic ship detection with computer vision techniques provide an efficient means to monitor as well as track ships in water bodies.Waterways being an important medium of transport require continuous monitoring for protection of national security.The remote sensing satellite images of ships in harbours and water bodies are the image data that aid the neural network models to localize ships and to facilitate early identification of possible threats at sea.This paper proposes a deep learning based model capable enough to classify between ships and no-ships as well as to localize ships in the original images using bounding box tech-nique.Furthermore,classified ships are again segmented with deep learning based auto-encoder model.The proposed model,in terms of classification,provides suc-cessful results generating 99.5%and 99.2%validation and training accuracy respectively.The auto-encoder model also produces 85.1%and 84.2%validation and training accuracies.Moreover the IoU metric of the segmented images is found to be of 0.77 value.The experimental results reveal that the model is accu-rate and can be implemented for automatic ship detection in water bodies consid-ering remote sensing satellite images as input to the computer vision system.

Monitoring Wildfires in Thailand: A Case Study of the ECSTAR-TeroSpace’s Earth Observation Project

The primary objective of this paper is to present a comprehensive case study on monitoring wildfires in Nakhon Nayok, Thailand, utilizing Earth observation platforms. This initiative project has been undertaken by the Excellence Center of Space Technology and Research (ECSTAR), in partnership with its spin-off startup, TeroSpace. The study aims to provide an in-depth analysis of the wildfire incidents in the region, utilizing advanced technologies such as satellite imagery and data analytics, and to identify ways to improve future wildfire management. In particular, the paper focuses on the wildfires including thermal area comparison that ravaged the land in Nakhon Nayok Province in central Thailand from March to April 18th, 2023. To conduct this study, the ECSTAR-TeroSpace analytic team utilized satellite images from Earth observation platforms: MODIS and Sentinel-2A. By presenting this case study, this paper contributes to the broader understanding of how to monitor and manage wildfires in a changing climate. The findings of this study underscore the importance of proactive and collaborative efforts in mitigating the negative impacts of wildfires in Nakhon Nayok and other regions in Thailand.

Comparing Pan-Sharpening Algorithms to Access an Agriculture Area: A Mississippi Case Study

Numerous satellites collect imagery of the Earth’s surface daily, providing information to the public and private sectors. The fusion (pan-sharpening) of high-resolution panchromatic satellite imagery with lower-resolution multispectral satellite imagery has shown promise for monitoring natural resources and farming areas. It results in new imagery with more detail than the original multispectral or panchromatic images. In agricultural areas in Mississippi, landscapes can range from complex mixtures of vegetation and built-up areas to dense vegetative regions. More information is needed on pan-sharpened imagery for assessing landscapes in rural areas of Mississippi. WorldView 3 satellite imagery consisting of landscapes commonly found in rural areas of Mississippi was subjected to 17 pan-sharpening algorithms. The pan-sharpened images were compared qualitatively and quantitatively with three quality indices: 1) Erreur Relative Globale Addimensionelle de Synthese;2) Universal Image Quality Index;3) Bias. à trous wavelet transform with the injection model 3 and hyperspherical color spaced fusion methods were ranked among the best for maintaining image integrity for qualitative and quantitative analyses. The optimized high-pass filter method was often ranked last by the quality indices. The smoothing filter-based intensity modulation algorithm and the gaussian modulation transfer function match filtered with high-pass modulation injection model added artifacts to the images. Pan-sharpened satellite imagery has great potential to enhance the survey of Mississippi’s agricultural areas. The key to success is selecting an image fusion process that increases spatial content while not compromising the image integrity.

Remote Sensing and Precision Agriculture Technologies for Crop Disease Detection and Management with a Practical Application Example

Remote sensing technology has long been used to detect and map crop diseases.Airborne and satellite imagery acquired during growing seasons can be used not only for early detection and within-season management of some crop diseases,but also for the control of recurring diseases in future seasons.With variable rate technology in precision agriculture,site-specific fungicide application can be made to infested areas if the disease is stable,although traditional uniform application is more appropriate for diseases that can spread rapidly across the field.This article provides a brief overview of remote sensing and precision agriculture technologies that have been used for crop disease detection and management.Specifically,the article illustrates how airborne and satellite imagery and variable rate technology have been used for detecting and mapping cotton root rot,a destructive soilborne fungal disease,in cotton fields and how site-specific fungicide application has been implemented using prescription maps derived from the imagery for effective control of the disease.The overview and methodologies presented in this article should provide researchers,extension personnel,growers,crop consultants,and farm equipment and chemical dealers with practical guidelines for remote sensing detection and effective management of some crop diseases.

An evaluation of the role played by remote sensing technology following the World Trade Center attack

Remote sensing technology has been widely recognized for contributing to emergency response efforts after the World Trade Center attack on September 11th, 2001. The need to coordinate activities in the midst of a dense, yet relatively small area, made the combination of imagery and mapped data strategically useful. This paper reviews the role played by aerial photography, satellite imagery, and LIDAR data at Ground Zero. It examines how emergency managers utilized these datasets, and identifies significant problems that were encountered. It goes on to explore additional ways in which imagery could have been used, while presenting recommendations for more effective use in future disasters and Homeland Security applications. To plan adequately for future events, it was important to capture knowledge from individuals who responded to the World Trade Center attack. In recognition, interviews with key emergency management and geographic information system (GIS) personnel provide the basis of this paper. Successful techniques should not be forgotten, or serious problems dismissed. Although widely used after September 11th, it is important to recognize that with better planning, remote sensing and GIS could have played an even greater role. Together with a data acquisition timeline, an expanded discussion of these issues is available in the MCEER/NSF report “Emergency Response in the Wake of the World Trade Center Attack; The Remote Sensing Perspective” (Huyck and Adams, 2002) Keywords World Trade Center (WTC) - terrorism - emergency response - emergency management - ground zero - remote sensing - emergency operations - disasters - geographic information systems (GIS) - satellite imagery - synthetic aperture radar (SAR) - light detection and ranging imagery (LIDAR)