- 标题
- 摘要
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- 实验方案
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Optical Remote Sensing Potentials for Looting Detection
摘要： Looting of archaeological sites is illegal and considered a major anthropogenic threat for cultural heritage, entailing undesirable and irreversible damage at several levels, such as landscape disturbance, heritage destruction, and adverse social impact. In recent years, the employment of remote sensing technologies using ground-based and/or space-based sensors has assisted in dealing with this issue. Novel remote sensing techniques have tackled heritage destruction occurring in war-conflicted areas, as well as illicit archeological activity in vast areas of archaeological interest with limited surveillance. The damage performed by illegal activities, as well as the scarcity of reliable information are some of the major concerns that local stakeholders are facing today. This study discusses the potential use of remote sensing technologies based on the results obtained for the archaeological landscape of Ayios Mnason in Politiko village, located in Nicosia district, Cyprus. In this area, more than ten looted tombs have been recorded in the last decade, indicating small-scale, but still systematic, looting. The image analysis, including vegetation indices, fusion, automatic extraction after object-oriented classification, etc., was based on high-resolution WorldView-2 multispectral satellite imagery and RGB high-resolution aerial orthorectified images. Google Earth? images were also used to map and diachronically observe the site. The current research also discusses the potential for wider application of the presented methodology, acting as an early warning system, in an effort to establish a systematic monitoring tool for archaeological areas in Cyprus facing similar threats.
关键词： image analysis,satellite data,remote sensing archaeology,looting,Cyprus
更新于2025-09-23 15:23:52
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An assessment of semi-analytical models based on the absorption coefficient in retrieving the chlorophyll-a concentration from a reservoir
摘要： Monitoring chlorophyll-a (Chl-a) concentrations in inland waters is crucial for water quality management, since Chl-a is a proxy for phytoplankton biomass and, thus, for ecological health of a water environment. Chl-a concentration can be retrieved through the inherent optical properties (IOPs) of a water system, which, in turn, can be remotely sensed obtained. Quasi-analytical algorithm (QAA), originally developed for ocean waters, can also retrieve IOPs for inland waters after re-parameterizations. This study is aimed at assessing the performance of sixteen schemes composed by QAA original and re-parameterized versions followed by models that use absorption coefficients as inputs for estimating Chl-a concentration in Ibitinga reservoir, located at Tietê River cascading system, S?o Paulo State, Brazil. It was verified that only QAAV5 based schemes were able to obtain reasonable estimates for image data and that by four models tested presented similar and acceptable results for QAAV5 outputs. The best model were applied to a Ocean and Land Colour Instrument (OLCI) image. Light absorption in the reservoir showed to be dominated by colored dissolved organic matter (CDOM), and wide spatial and temporal variability of optical and water quality properties was observed.
关键词： water quality monitoring,satellite data.,Trophic status,inland water
更新于2025-09-23 15:23:52
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Pléiades Tri-Stereo Data for Glacier Investigations—Examples from the European Alps and the Khumbu Himal
摘要： In this study, we use Pléiades tri-stereo data to generate a digital elevation model (DEM) from the Pléiades images using a workflow employing semi-global matching (SGM). We examine the DEM accuracy in complex mountain glaciated terrain by comparing the new DEMs with an independent high-quality DEM based on airborne laser scanning (ALS) data for a study area in the Austrian Alps, and with ground control points for a study area in the Khumbu Himal of Nepal. The DEMs derived using the SGM algorithm compare well to the independent high-quality ALS DEM, and the workflow produces models of sufficient quality to resolve ground control points, which are based on Pléiades imagery that are of sufficient quality to perform high spatio-temporal resolution assessments of remote areas for which no field data is available. The relative accuracy is sufficient to investigate glacier surface elevation changes below one meter, and can therefore be applied over relatively short periods of time, such as those required for annual and seasonal assessments of change. The annual geodetic mass balance for the Alpine case derived from our DEM compares well to the glaciological mass balance, and multitemporal DEM analysis is used to resolve the seasonal changes of five glaciers in the Khumbu Himal, revealing that glaciological processes such as accumulation, ablation, and glacier movement mainly take place during the summer season, with the winter season being largely inactive in the year sampled.
关键词： Khumbu Himal,semi-global matching,Pléiades tri-stereo data,surface change,optical satellite data,DEM,glaciers
更新于2025-09-23 15:22:29
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Télédétection satellitaire des surfaces enneigées et englacées
摘要： This article presents an overview of recent advances in remote sensing applied to the study of snow and glacierized areas, in which the French scientific community has been involved. Whatever the type of satellite data, optical, radar, lidar or gravimetric, these works on seasonal or perennial snow cover, mountain glaciers, ice caps, sea ice, and lake or river ice, aim at documenting both the physical characteristics of these objects and their spatial and temporal variability at local, regional or global scales.
关键词： glaciers,remote sensing,snow,ice,spatial variability,satellite data,temporal variability
更新于2025-09-23 15:21:21
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Delineation and mapping of coal mine fire using remote sensing data – a review
摘要： Various countries around the globe face numerous hazards due to the burning of coal on the surface as well as below ground. Countries like China, India, United States of America (USA), Australia, Indonesia, and many other countries have reported the burning of coal fires, and thus it is the urgent need to control the coal fire propagation to prevent the loss of energy resources. Coal is a fossil fuel that has a tendency to catch fire for many reasons; spontaneous combustion being the most frequent reasons for its burning. Other factors leading to coal fire include forest fires close to coal seams, natural hazards, old mining techniques, and external heat sources. The review work demonstrates the application of various satellite data in fire detection and mapping. The literature reveals that remote sensing plays an important role in facilitating quick and complete delineation of coal mine fires. Many algorithms have been developed around the world for fire detection from different satellite data. A comprehensive demonstration of different algorithms along with their merits and demerits are outlined. Comparative performances of the different algorithms with their case studies are also explained. It can be inferred from the various literature that it is very difficult to select a particular sensor algorithm for generating global fire products. Suggestions are given to further explore the possibility of improvement of fire detection algorithms.
关键词： remote sensing,fire detection algorithms,coal mine fire,satellite data,thermal anomaly
更新于2025-09-23 15:21:21
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[Sustainable Development Goals Series] Remote Sensing for Food Security || Application of Vegetation Health Data and Products for Monitoring Food Security
摘要： The year 2018. Almost one-fifth of the twenty-first century has already past and the Earth has still been continuing the previous tendencies for a rapid population growth, declining stock of natural resources, climate warming, land cover changes, increasing natural disasters, etc., which have intensified considerably world’s concerns about the future food supply/demand and global food security (USDA 2017; FAO 2017, 1999; Heibuch 2011). Most of the indicated problems are related to a deterioration of environmental conditions. As has never been before, decision makers of the world, countries, communities, international organizations, and businesses need reliable and timely information to understand, monitor, and predict impacts of weather/climate and environmentally based Earth’s changes on global food security (FS).
关键词： Environmental Monitoring,Food Security,Vegetation Health,Drought Monitoring,Satellite Data
更新于2025-09-23 15:21:01
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Cloud Masking Technique for High-Resolution Satellite Data: An Artificial Neural Network Classifier Using Spectral & Textural Context
摘要： Cloud masking is a very important application in remote sensing and an essential pre-processing step for any information derivation applications. It helps in estimation of usable portion of the images. Many popular spectral classi?cation techniques rely upon the presence of a short-wave infrared band or bands of even higher wavelength to differentiate between clouds and other land covers. However, these methods are limited to sensors equipped with higher wavelength bands. In this paper, a generic and ef?cient technique is attempted using the Cartosat-2 series (C2S) satellite which is having high-resolution multispectral sensor in the visible and near-infrared bands. The methodology is based on textural features from the available spectral context, and using a feedforward neural network for the classi?cation is proposed. The method was shown to have an overall accuracy of 97.98% for a large manually pre-classi?ed validation dataset with more than 2 million data points. Experimental results and cloud masks generated for various scenes show that the method may be viable as a reasonable cloud masking algorithm for C2S data.
关键词： Cloud masking,Feed forward network,High-resolution satellite data,Image classi?cation,Arti?cial neural network,GLCM texture
更新于2025-09-23 15:21:01
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Recent Progress in Quantitative Land Remote Sensing in China
摘要： During the past forty years, since the first book with a title mentioning quantitative and remote sensing was published [1], quantitative land remote sensing has advanced dramatically, and numerous books have been published since then [2–6] although some of them did not use quantitative land remote sensing in their titles. Quantitative land remote sensing has not been explicitly defined in the literature, but we consider it as a sub-discipline of remote sensing including the following components (see Figure 1): radiometric preprocessing, inversion, high-level product generation, and applications. Many inversion algorithms rely on physical models of radiation regimes of landscapes, which link with remotely-sensed data. Generating high-level satellite products of land surface biophysical and biochemical variables create the key bridge between remote sensing science and applications. Conducting in situ measurements for validation of inversion algorithms and satellite products is also a critical component. Application of satellite products to address scientific and societal relevant issues will ultimately decide the future of quantitative land remote sensing.
关键词： inversion algorithms,in situ measurements,land surface biophysical and biochemical variables,satellite data,quantitative land remote sensing
更新于2025-09-23 15:21:01
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Robust Automated Image Co-Registration of Optical Multi-Sensor Time Series Data: Database Generation for Multi-Temporal Landslide Detection
摘要： Reliable multi-temporal landslide detection over longer periods of time requires multi-sensor time series data characterized by high internal geometric stability, as well as high relative and absolute accuracy. For this purpose, a new methodology for fully automated co-registration has been developed allowing efficient and robust spatial alignment of standard orthorectified data products originating from a multitude of optical satellite remote sensing data of varying spatial resolution. Correlation-based co-registration uses world-wide available terrain corrected Landsat Level 1T time series data as the spatial reference, ensuring global applicability. The developed approach has been applied to a multi-sensor time series of 592 remote sensing datasets covering an approximately 12,000 km2 area in Southern Kyrgyzstan (Central Asia) strongly affected by landslides. The database contains images acquired during the last 26 years by Landsat (E)TM, ASTER, SPOT and RapidEye sensors. Analysis of the spatial shifts obtained from co-registration has revealed sensor-specific alignments ranging between 5 m and more than 400 m. Overall accuracy assessment of these alignments has resulted in a high relative image-to-image accuracy of 17 m (RMSE) and a high absolute accuracy of 23 m (RMSE) for the whole co-registered database, making it suitable for multi-temporal landslide detection at a regional scale in Southern Kyrgyzstan.
关键词： SPOT,co-registration,Landsat,optical satellite data,multi-temporal,RapidEye,accuracy,ASTER,landslide,Kyrgyzstan
更新于2025-09-23 15:21:01
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[IEEE 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics) - Hangzhou (2018.8.6-2018.8.9)] 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics) - Mango Grove Relevant Information Extraction Using GF-2 Satellite Data
摘要： The foundation of information extraction based on remote sensing imaging involves spectral band information. Such a method often suffers from the distinctive problem of surface features. In general, artificial orchard planting is relatively regular; thus, it shows textural features that differ from other vegetation types in images with a specific spatial scale. This study used mango groves as research object. By introducing spectral index, texture feature parameters, and by using support vector machine classification method, based on GF-2 satellite images, mango grove information extraction was studied under different combinations of spectra band, vegetation index, and texture feature parameters. The findings show that the information extraction via single spectra band information has lower accuracy. Introduction of a combination of spectra index and spectra band information can improve extraction accuracy of mango groves; however, the overall classification accuracy still remains low. In addition, the introduction of information and spectra band information combination can dramatically improve extraction accuracy. Producer's accuracy and user's accuracy increased to 85.7% and 93.5%, respectively. Under different combination modes, the extracted mango grove accuracy of the combination of integrated spectra band information, textural feature, and vegetation index is optimal. Producer's accuracy and user's accuracy increased to 89.3% and 97.4%, respectively. Relative to the spectra band information, the extraction accuracy improved by 20.6% and 11.0%, respectively. As a result, the support vector machine of integrated spectra and texture can effectively extract the spatial distribution information of mango groves. This method can provide a technical reference for remote sensing extraction of artificial orchards.
关键词： Support vector machine,Information extraction,GF-2 satellite data,Texture information,Mango grove,Classification algorithm
更新于2025-09-11 14:15:04