Influences of Atmospheric Turbulence on Image Resolution of Airborne and Space-Borne Optical Remote Sensing System

A new way is proposed to evaluate the influence of atmospheric turbulence on image resolution of airborne and space-borne optical remote sensing system, which is called as arrival angle-method. Applying this method, some engineering examples are selected to analyze the turbulence influences on image resolution based on three different atmospheric turbulence models quantificationally, for the airborne remote sensing system, the resolution errors caused by the atmospheric turbulence are less than 1 cm, and for the space-borne remote sensing system, the errors are around 1 cm. The results are similar to that obtained by the previous Friedmethod. Compared with the Fried-method, the arrival angle-method is rather simple and can be easily used in engineering fields.

Technical foundation research on high resolution remote sensing system of China's coastal zone

China's coastal zone is a region with a highly developed economy that contrasts clearly with the slow paced regular investigation on its natural environment,which cannot keep pace with the requirement of economic development and modern management.Laying a theoretical foundation for the modern management of China's costal zone is aimed at. This research focuses on the following processing and analyzing technologies for coastal zone high-resolution remote sensing data: organization and management of large amounts of high-resolution remote sensing data, quick and precise spatial positioning system,algorithms for image fusion in feature level and coastal zone feature extraction. They will form a technical foundation of the system. And, if combined with other research results such as coastal zone remote sensing classification system and its mapping subsystem, an advanced technical frame for remote sensing investigation of coastal zone resource will be constructed.

An operational satellite remote sensing system for ocean fishery

Ocean environmental information is very important to supporting the fishermen in fishing and satellite remote sensing technology can provide it in large scale and in near real-time. Ocean fishery locations are always far away beyond the coverage of the satellite data received by a land-based satellite receiving station. A nice idea is to install the satellite ground station on a fishing boat. When the boat moves to a fishery location, the station can receive the satellite data to cover the fishery areas. One satellite remote sensing system was once installed in a fishing boat and served fishing in the North Pacific fishery areas when the boat stayed there. The system can provide some oceanic environmental charts such as sea surface temperature (SST) and relevant derived products which are in most popular use in fishery industry. The accuracy of SST is the most important and affects the performance of the operational system, which is found to be dissatisfactory. Many factors affect the accuracy of SST and it is difficult to increase the accuracy by SST retrieval algorithms and clouds detection technology. A new technology of temperature error control is developed to detect the abnormity of satellite-measured SST. The performance of the technology is evaluated to change the temperature bias from -3.04 to 0.05 ℃ and the root mean square (RMS) from 5.71 to 1.75℃. It is suitable for employing in an operational satellite-measured SST system and improves the performance of the system in fishery applications. The system has been running for 3 a and proved to be very useful in fishing. It can help to locate the candidates of the fishery areas and monitor the typhoon which is very dangerous to the safety of fishing boats.

A remote sensing system of vehicle emissions based on tunable diode laser technology

As being an effective real-time method of monitoring vehicle emissions on-road, a remote sensing system based on the tunable diode laser （TDL） technology was presented, and the key technologies were discussed. A field test in Guangzhou（Guangdong, China） was performed and was found that the factors, such as slope, instantaneous speed and acceleration, had significant influence on the detectable rate of the system. Based on the results, the proposal choice of testing site was presented.

Remote sensing of quality traits in cereal and arable production systems:A review

Cereal is an essential source of calories and protein for the global population.Accurately predicting cereal quality before harvest is highly desirable in order to optimise management for farmers,grading harvest and categorised storage for enterprises,future trading prices,and policy planning.The use of remote sensing data with extensive spatial coverage demonstrates some potential in predicting crop quality traits.Many studies have also proposed models and methods for predicting such traits based on multiplatform remote sensing data.In this paper,the key quality traits that are of interest to producers and consumers are introduced.The literature related to grain quality prediction was analyzed in detail,and a review was conducted on remote sensing platforms,commonly used methods,potential gaps,and future trends in crop quality prediction.This review recommends new research directions that go beyond the traditional methods and discusses grain quality retrieval and the associated challenges from the perspective of remote sensing data.

Remote sensing image encryption algorithm based on novel hyperchaos and an elliptic curve cryptosystem

Remote sensing images carry crucial ground information,often involving the spatial distribution and spatiotemporal changes of surface elements.To safeguard this sensitive data,image encryption technology is essential.In this paper,a novel Fibonacci sine exponential map is designed,the hyperchaotic performance of which is particularly suitable for image encryption algorithms.An encryption algorithm tailored for handling the multi-band attributes of remote sensing images is proposed.The algorithm combines a three-dimensional synchronized scrambled diffusion operation with chaos to efficiently encrypt multiple images.Moreover,the keys are processed using an elliptic curve cryptosystem,eliminating the need for an additional channel to transmit the keys,thus enhancing security.Experimental results and algorithm analysis demonstrate that the algorithm offers strong security and high efficiency,making it suitable for remote sensing image encryption tasks.

Delineation of groundwater potential zones using remote sensing and Geographic Information Systems(GIS)in Kadaladi region,Southern India

The primary objective of this research is to delineate potential groundwater recharge zones in the Kadaladi taluk of Ramanathapuram,Tamil Nadu,India,using a combination of remote sensing and Geographic Information Systems(GIS)with the Analytical Hierarchical Process(AHP).Various factors such as geology,geomorphology,soil,drainage,density,lineament density,slope,rainfall were analyzed at a specific scale.Thematic layers were evaluated for quality and relevance using Saaty's scale,and then inte-grated using the weighted linear combination technique.The weights assigned to each layer and features were standardized using AHP and the Eigen vector technique,resulting in the final groundwater potential zone map.The AHP method was used to normalize the scores following the assignment of weights to each criterion or factor based on Saaty's 9-point scale.Pair-wise matrix analysis was utilized to calculate the geometric mean and normalized weight for various parameters.The groundwater recharge potential zone map was created by mathematically overlaying the normalized weighted layers.Thematic layers indicating major elements influencing groundwater occurrence and recharge were derived from satellite images.2 Results indicate that approximately 21.8 km of the total area exhibits high potential for groundwater recharge.Groundwater recharge is viable in areas with moderate slopes,particularly in the central and southeastern regions.

Land Use Land Cover Analysis for Godavari Basin in Maharashtra Using Geographical Information System and Remote Sensing

The dynamic transformation of land use and land cover has emerged as a crucial aspect in the effective management of natural resources and the continual monitoring of environmental shifts. This study focused on the land use and land cover (LULC) changes within the catchment area of the Godavari River, assessing the repercussions of land and water resource exploitation. Utilizing LANDSAT satellite images from 2009, 2014, and 2019, this research employed supervised classification through the Quantum Geographic Information System (QGIS) software’s SCP plugin. Maximum likelihood classification algorithm was used for the assessment of supervised land use classification. Seven distinct LULC classes—forest, irrigated cropland, agricultural land (fallow), barren land, shrub land, water, and urban land—are delineated for classification purposes. The study revealed substantial changes in the Godavari basin’s land use patterns over the ten-year period from 2009 to 2019. Spatial and temporal dynamics of land use/cover changes (2009-2019) were quantified using three Satellite/Landsat images, a supervised classification algorithm and the post classification change detection technique in GIS. The total study area of the Godavari basin in Maharashtra encompasses 5138175.48 hectares. Notably, the built-up area increased from 0.14% in 2009 to 1.94% in 2019. The proportion of irrigated cropland, which was 62.32% in 2009, declined to 41.52% in 2019. Shrub land witnessed a noteworthy increase from 0.05% to 2.05% over the last decade. The key findings underscored significant declines in barren land, agricultural land, and irrigated cropland, juxtaposed with an expansion in forest land, shrub land, and urban land. The classification methodology achieved an overall accuracy of 80%, with a Kappa Statistic of 71.9% for the satellite images. The overall classification accuracy along with the Kappa value for 2009, 2014 and 2019 supervised land use land cover classification was good enough to detect the changing scenarios of Godavari River basin under study. These findings provide valuable insights for discerning land utilization across various categories, facilitating the adoption of appropriate strategies for sustainable land use in the region.

Forest Resources Management Information System for Forest Farms Based on Remote Sensing Images and Web GIS

This study was to estabIish the forest resources management information system for forest farms based on the B/S structural WebGIS with trial forest farm of Hunan Academy of Forestry as the research field, forest resources field survey da-ta, ETM＋ remote sensing data and basic geographical information data as research material through the extraction of forest resource data in the forest farm, require-ment analysis on the system function and the estabIishment of required software and hardware environment, with the alm to realize the management, query, editing, analysis, statistics and other functions of forest resources information to manage the forest resources.

Application of Remote Sensing and Geographic Information System in Land Use and Land Cover Change

The integration and application of remote sensing （RS） and geographic in-formation system （GIS） in the study of the Land Use and Land Cover Change （LUCC） were summarized, as wel as researches on the monitoring dynamic changes in LUCC, driving force and application examples of the integration and the application of RS and GIS in simulation research. The methods and technical ap-proaches of RS and GIS in LUCC research were discussed. Views on the existing problems of the integration and the application of RS and GIS were put forward, and the future developing direction of LUCC technology was forecasted.

Remote Sensing Dynamic Monitoring System for Agricultural Disaster in Henan Province Based on Multi-source Satellite Data

Using 3S technology, relying on earth-space three-dimensional agriculture disaster monitoring network, remote sensing monitoring model for agricultural disaster in Henan Province was established, and agricultural disaster monitoring system plat- form of Henan Province based on multi-souroe satellite data was further constructed, which realizes dynamic monitoring of agricultural disasters in Henan Province （drought, flood, snow cover and straw burning）.

Remote sensing of air pollution incorporating integrated-path differential-absorption and coherent-Doppler lidar

An innovative complex lidar system deployed on an airborne rotorcraft platform for remote sensing of atmospheric pollution is proposed and demonstrated.The system incorporates integrated-path differential absorption lidar(DIAL) and coherent-doppler lidar(CDL) techniques using a dual tunable TEA CO_(2)laser in the 9—11 μm band and a 1.55 μm fiber laser.By combining the principles of differential absorption detection and pulsed coherent detection,the system enables agile and remote sensing of atmospheric pollution.Extensive static tests validate the system’s real-time detection capabilities,including the measurement of concentration-path-length product(CL),front distance,and path wind speed of air pollution plumes over long distances exceeding 4 km.Flight experiments is conducted with the helicopter.Scanning of the pollutant concentration and the wind field is carried out in an approximately 1 km slant range over scanning angle ranges from 45°to 65°,with a radial resolution of 30 m and10 s.The test results demonstrate the system’s ability to spatially map atmospheric pollution plumes and predict their motion and dispersion patterns,thereby ensuring the protection of public safety.

Remote Sensing of Ecosystem Services:An Opportunity for Spatially Explicit Assessment

Ecosystem service is an emerging concept that grows to be a hot research area in ecology.Spatially explicit ecosystem service values are important for ecosystem service management.However,it is difficult to quantify ecosystem services.Remote sensing provides images covering Earth surface,which by nature are spatially explicit.Thus,remote sensing can be useful for quantitative assessment of ecosystem services.This paper reviews spatially explicit ecosystem service studies conducted in ecology and remote sensing in order to find out how remote sensing can be used for ecosystem service assessment.Several important areas considered include land cover,biodiversity,and carbon,water and soil related ecosystem services.We found that remote sensing can be used for ecosystem service assessment in three different ways:direct monitoring,indirect monitoring,and combined use with ecosystem models.Some plant and water related ecosystem services can be directly monitored by remote sensing.Most commonly,remote sensing can provide surrogate information on plant and soil characteristics in an ecosystem.For ecosystem process related ecosystem services,remote sensing can help measure spatially explicit parameters.We conclude that acquiring good in-situ measurements and selecting appropriate remote sensor data in terms of resolution are critical for accurate assessment of ecosystem services.

Study of coastal water zone ecosystem health in Zhejiang Province based on remote sensing data and GIS

The coastal ecosystem health assessment is a field of increasing importance.In this paper,a preliminary assessment of ecosystem health in Zhejiang coastal water zone was made,mainly based on remote sensing data and GIS technique.Its spatial and quantitative evaluation was facilitated by the progress of remote sensing and GIS technique development.Firstly,human activities,hydrology and ecosystem problems in the study area were discussed and analyzed.Secondly,from 4 aspects of human stress,physical,chemical and biological responses to anthropogenic activities and natural stress,several indicators such as water transparency（Secchi Disk Depth,SDD）,suspended substance concentration,dissolved inorganic nitrogen,active phosphate,chlorophyll,harmful algae bloom,as well as distribution of sewage,sea lanes and port were employed.Thirdly,the Analytic Hierarchical Process was used for indicator weight calculation,and the ecosystem health criteria were established according to the integrative analysis of national water quality criteria,similar coastal ecosystem health research in other places or data inherent properties.The results indicated that from 2005 to 2007 the coastal water ecosystem health value in Zhejiang Province was unhealthy and needs ecological restoration by human intervention.

Development and evaluation of low-altitude remote sensing systems for crop production management

Precision agriculture accounts for within-field variability for targeted treatment rather than uniform treatment of an entire field.It is built on agricultural mechanization and state-of-the-art technologies of geographical information systems(GIS),global positioning systems(GPS)and remote sensing,and is used to monitor soil,crop growth,weed infestation,insects,diseases,and water status in farm fields to provide data and information to guide agricultural management practices.Precision agriculture began with mapping of crop fields at different scales to support agricultural planning and decision making.With the development of variable-rate technology,precision agriculture focuses more on tactical actions in controlling variable-rate seeding,fertilizer and pesticide application,and irrigation in real-time or within the crop season instead of mapping a field in one crop season to make decisions for the next crop season.With the development of aerial variable-rate systems,low-altitude airborne systems can provide high-resolution data for prescription variable-rate operations.Airborne systems for multispectral imaging using a number of imaging sensors(cameras)were developed.Unmanned aerial vehicles(UAVs)provide a unique platform for remote sensing of crop fields at slow speeds and low-altitudes,and they are efficient and more flexible than manned agricultural airplanes,which often cannot provide images at both low altitude and low speed for capture of high-quality images.UAVs are also more universal in their applicability than agricultural aircraft since the latter are used only in specific regions.This study presents the low-altitude remote sensing systems developed for detection of crop stress caused by multiple factors.UAVs,as a special platform,were discussed for crop sensing based on the researchers'studies.

RF-CLASS： A remote-sensing-based flood crop loss assessment cyber-service system for supporting crop statistics and insurance decision-making

Floods often cause significant crop loss in the United States. Timely and objective information on flood-related crop loss, such as flooded acreage and degree of crop damage, is very important for crop monitoring and risk management in ag- ricultural and disaster-related decision-making at many concerned agencies. Currently concerned agencies mostly rely on field surveys to obtain crop loss information and compensate farmers＇ loss claim. Such methods are expensive, labor intensive, and time consumptive, especially for a large flood that affects a large geographic area. The results from such methods suffer from inaccuracy, subjectiveness, untimeliness, and lack of reproducibility. Recent studies have demonstrated that Earth observation （EO） data could be used in post-flood crop loss assessment for a large geographic area objectively, timely, accurately, and cost effectively. However, there is no operational decision support system, which employs such EO-based data and algorithms for operational flood-related crop decision-making. This paper describes the development of an EO-based flood crop loss assessment cyber-service system, RF-CLASS, for supporting flood-related crop statistics and insurance decision-making. Based on the service-orientated architecture, RF-CLASS has been implemented with open interoperability specifications to facilitate the interoperability with EO data systems, particularly the National Aeronautics and Space Administration （NASA） Earth Observing System Data and Information System （EOSDIS）, for automatically fetching the input data from the data systems. Validated EO algorithms have been implemented as web services in the system to operationally produce a set of flood-related products from EO data, such as flood frequency, flooded acreage, and degree of crop damage, for supporting decision-making in flood statistics and flood crop insurance policy. The system leverages recent advances in the remote sensing-based flood monitoring and assessment, the near-real-time availability of EO data, the service-oriented architecture, geospatial interoperability standards, and the standard-based geospatial web service technology. The prototypical system has automatically generated the flood crop loss products and demonstrated the feasibility of using such products to improve the agricultural decision-making. Evaluation of system by the end-user agencies indicates that significant improvement on flood-related crop decision-making has been achieved with the system.

Establishing evaluation index system for desertification of Keerqin sandy land with remote sensing data

Keerqin sand land is located in the transitional terrain between the Northeast Plain and Inner Mongolia （42°41′-45°15′N, 118°35′-123°30′ E） in Northeast China and it is seriously affected by desertification. According to the configuration and ecotope of the earths surface, the coverage of vegetation, occupation ratio of bare sandy land and the soil texture were selected as evaluation indexes by using the field investigation data. The evaluation index system of Keerqin sandy desertification was established by using Remote Sensing data. and the occupation ratio of bare sandy land was obtained by mixed spectrum model. This index system is validated by the field investioation data and results indicate that it is suitable for the desertification evaluation of Keerqin.Foundation Item： This study is supported by a grant from the National Natural Science Foundation of China （No. 30371192）

Quantifying glacier surging and associated lake dynamics in Amu Darya river basin using UAV and remote sensing data

Glaciers in the Pamir region are experiencing rapid melting and receding due to climate change,which has a significant implication for the Amu Darya river basin.Predominantly,surging glaciers,which undergo unpredictable advances,are potentially leading to the obstruction of high-altitude river channels and also glacial lake outburst floods.decrease of-703.5±30.0 m.There is a substantial increase in the number(from 19 to 75)and area(from 4889.7±0.6 m2 to 15345.5±0.6 m2)of RGS lakes along with supra-glacier ponds based on a comparison of ArcGIS base map in 2011 and high-resolution UAV data in 2023.For M glacier,number of lakes increased from 4 to 22 but the lake area declined from 10715.2±0.6 to 365.6±0.6 m2.It was noted that the largest lake in 2011 with an area of 10406.4±0.6 m2 at the southeastern portion of the glacier was not observed in 2023 due to outburst.Both the glaciers have substantially impacted the river flow(Abdukahor river)by obstructing a significant proportion of river channel in recent years and might cause outburst floods.These findings enhance the understanding of glacier dynamics and their impacts on the surrounding areas,emphasizing the urgent need for continued monitoring and appropriate management strategies,with a specific focus on surging glaciers and the associated risks.

Extensive identification of landslide boundaries using remote sensing images and deep learning method

The frequent occurrence of extreme weather events has rendered numerous landslides to a global natural disaster issue.It is crucial to rapidly and accurately determine the boundaries of landslides for geohazards evaluation and emergency response.Therefore,the Skip Connection DeepLab neural network(SCDnn),a deep learning model based on 770 optical remote sensing images of landslide,is proposed to improve the accuracy of landslide boundary detection.The SCDnn model is optimized for the over-segmentation issue which occurs in conventional deep learning models when there is a significant degree of similarity between topographical geomorphic features.SCDnn exhibits notable improvements in landslide feature extraction and semantic segmentation by combining an enhanced Atrous Spatial Pyramid Convolutional Block(ASPC)with a coding structure that reduces model complexity.The experimental results demonstrate that SCDnn can identify landslide boundaries in 119 images with MIoU values between 0.8and 0.9;while 52 images with MIoU values exceeding 0.9,which exceeds the identification accuracy of existing techniques.This work can offer a novel technique for the automatic extensive identification of landslide boundaries in remote sensing images in addition to establishing the groundwork for future inve stigations and applications in related domains.

Estimation and verification of green tide biomass based on UAV remote sensing

Since 2007,the Yellow Sea green tide has broken out every summer,causing great harm to the environment and society.Although satellite remote sensing(RS)has been used in biomass research,there are several shortcomings,such as mixed pixels,atmospheric interference,and difficult field validation.The biomass of green tide has been lacking a high-precision estimation method.In this study,high-resolution unmanned aerial vehicle(UAV)RS was used to quantitatively map the biomass of green tides.By utilizing experimental data from previous studies,a robust relationship was established to link biomass to the red-green-blue floating algae index(RGB-FAI).Then,the lab-based model for green tide biomass from visible images taken by the UAV camera was developed and validated by field measurements.Re sults show that the accurate and cost-effective method is able to estimate the green tide biomass and its changes in given local waters of the near and far seas.The study provided an effective complement to the traditional satellite RS,as well as high-precision quantitative techniques for decision-making in disaster management.