Image registration is an indispensable component in multi-source remote sensing image processing. In this paper, we put forward a remote sensing image registration method by including an improved multi-scale and multi...Image registration is an indispensable component in multi-source remote sensing image processing. In this paper, we put forward a remote sensing image registration method by including an improved multi-scale and multi-direction Harris algorithm and a novel compound feature. Multi-scale circle Gaussian combined invariant moments and multi-direction gray level co-occurrence matrix are extracted as features for image matching. The proposed algorithm is evaluated on numerous multi-source remote sensor images with noise and illumination changes. Extensive experimental studies prove that our proposed method is capable of receiving stable and even distribution of key points as well as obtaining robust and accurate correspondence matches. It is a promising scheme in multi-source remote sensing image registration.展开更多
An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyram...An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyramid network(FPN)structure of the original YOLOv8 mode is replaced by the generalized-FPN(GFPN)structure in GiraffeDet to realize the"cross-layer"and"cross-scale"adaptive feature fusion,to enrich the semantic information and spatial information on the feature map to improve the target detection ability of the model.Secondly,a pyramid-pool module of multi atrous spatial pyramid pooling(MASPP)is designed by using the idea of atrous convolution and feature pyramid structure to extract multi-scale features,so as to improve the processing ability of the model for multi-scale objects.The experimental results show that the detection accuracy of the improved YOLOv8 model on DIOR dataset is 92%and mean average precision(mAP)is 87.9%,respectively 3.5%and 1.7%higher than those of the original model.It is proved the detection and classification ability of the proposed model on multi-dimensional optical remote sensing target has been improved.展开更多
The automatic registration of multi-source remote sensing images (RSI) is a research hotspot of remote sensing image preprocessing currently. A special automatic image registration module named the Image Autosync has ...The automatic registration of multi-source remote sensing images (RSI) is a research hotspot of remote sensing image preprocessing currently. A special automatic image registration module named the Image Autosync has been embedded into the ERDAS IMAGINE software of version 9.0 and above. The registration accuracies of the module verified for the remote sensing images obtained from different platforms or their different spatial resolution. Four tested registration experiments are discussed in this article to analyze the accuracy differences based on the remote sensing data which have different spatial resolution. The impact factors inducing the differences of registration accuracy are also analyzed.展开更多
[Objective] The aim was to extract red tide information in Haizhou Bay on the basis of multi-source remote sensing data.[Method] Red tide in Haizhou Bay was studied based on multi-source remote sensing data,such as IR...[Objective] The aim was to extract red tide information in Haizhou Bay on the basis of multi-source remote sensing data.[Method] Red tide in Haizhou Bay was studied based on multi-source remote sensing data,such as IRS-P6 data on October 8,2005,Landsat 5-TM data on May 20,2006,MODIS 1B data on October 6,2006 and HY-1B second-grade data on April 22,2009,which were firstly preprocessed through geometric correction,atmospheric correction,image resizing and so on.At the same time,the synchronous environment monitoring data of red tide water were acquired.Then,band ratio method,chlorophyll-a concentration method and secondary filtering method were adopted to extract red tide information.[Result] On October 8,2005,the area of red tide was about 20.0 km2 in Haizhou Bay.There was no red tide in Haizhou bay on May 20,2006.On October 6,2006,large areas of red tide occurred in Haizhou bay,with area of 436.5 km2.On April 22,2009,red tide scattered in Haizhou bay,and its area was about 10.8 km2.[Conclusion] The research would provide technical ideas for the environmental monitoring department of Lianyungang to implement red tide forecast and warning effectively.展开更多
Dust storms in arid and desert areas affect radiation budget,air quality,visibility,enzymatic activities,agricultural products and human health.Due to increased drought and land use changes in recent years,the frequen...Dust storms in arid and desert areas affect radiation budget,air quality,visibility,enzymatic activities,agricultural products and human health.Due to increased drought and land use changes in recent years,the frequency of dust storms occurrence in Iran has been increased.This study aims to identify dust source areas in the Sistan watershed(Iran-Afghanistan borders)-an important regional source for dust storms in southwestern Asia,using remote sensing(RS)and bivariate statistical models.Furthermore,this study determines the relative importance of factors controlling dust emissions using frequency ratio(FR)and weights of evidence(WOE)models and interpretability of predictive models using game theory.For this purpose,we identified 211 dust sources in the study area and generated a dust source distribution map-inventory map-by dust source potential index based on RS data.In addition,spatial maps of topographic factors affecting dust source areas including soil,lithology,slope,Normalized difference vegetation index(NDVI),geomorphology and land use were prepared.The performance of two models(WOE and FR)was evaluated using the area under curve(AUC)of the receiver operating characteristic curve.The results showed that soil,geomorphology and slope exhibited the greatest influence in the dust source areas.The 55.3%(according to FR)and 62.6%(according to WOE)of the total area were classified as high and very high potential dust sources,while both models displayed acceptable accuracy with subsurface levels of 0.704 for FR and 0.751 for WOE,although they predict different fractions of dust potential classes.Based on Shapley additive explanations(SHAP),three factors,i.e.,soil,slope and NDVI have the highest impact on the model's output.Overall,combination of statistic-based predictive models(or data mining models),RS and game theory techniques can provide accurate maps of dust source areas in arid and semi-arid regions,which can be helpful for mitigation of negative effects of dust storms.展开更多
One of the crucial elements that is directly tied to the quality of living organisms is the quality of the water.How-ever,water quality has been adversely affected by plastic pollution,a global environmental disaster ...One of the crucial elements that is directly tied to the quality of living organisms is the quality of the water.How-ever,water quality has been adversely affected by plastic pollution,a global environmental disaster that has an effect on aquatic life,wildlife,and human health.To prevent these effects,better monitoring,detection,characterisation,quanti-fication,and tracking of aquatic plastic pollution at regional and global scales is urgently needed.Remote sensing tech-nology is regarded as a useful technique,as it offers a promising new and less labour-intensive tool for the detection,quantification,and characterisation of aquatic plastic pollution.The study seeks to supplement to the body of scientific literature by compiling original data on the monitoring of plastic pollution in aquatic environments using remote sensing technology,which can function as a cost saving method for water pollution and risk management in developing nations.This article provides a profound analysis of plastic pollution,including its categories,sources,distribution,chemical properties,and potential risks.It also provides an in-depth review of remote sensing technologies,satellite-derived in-dices,and research trends related to their applicability.Additionally,the study clarifies the difficulties in using remote sensing technologies for aquatic plastic monitoring and practical ways to reduce aquatic plastic pollution.The study will improve the understanding of aquatic plastic pollution,health hazards,and the suitability of remote sensing technology for aquatic plastic contamination monitoring studies among researchers and interested parties.展开更多
Timely crop acreage and distribution information are the basic data which drive many agriculture related applications.For identifying crop types based on remote sensing,methods using only a single image type have sign...Timely crop acreage and distribution information are the basic data which drive many agriculture related applications.For identifying crop types based on remote sensing,methods using only a single image type have significant limitations.Current research that integrates fine and coarser spatial resolution images,using techniques such as unmixing methods,regression models,and others,usually results in coarse resolution abundance without sufficient detail within pixels,and limited attention has been paid to the spatial relationship between the pixels from these two kinds of images.Here we propose a new solution to identify winter wheat by integrating spectral and temporal information derived from multi-resolution remote sensing data and determine the spatial distribution of sub-pixels within the coarse resolution pixels.Firstly,the membership of pixels which belong to winter wheat is calculated using a 25-m resolution resampled Landsat Thematic Mapper(TM)image based on the Bayesian equation.Then,the winter wheat abundance(acreage fraction in a pixel)is assessed by using a multiple regression model based on the unique temporal change features from moderate resolution imaging spectroradiometer(MODIS)time series data.Finally,winter wheat is identified by the proposed Abundance-Membership(AM)model based on the spatial relationship between the two types of pixels.Specifically,winter wheat is identified by comparing the spatially corresponding 10×10 membership pixels of each abundance pixel.In other words,this method takes advantage of the relative size of membership in a local space,rather than the absolute size in the entire study area.This method is tested in the major agricultural area of Yiluo Basin,China,and the results show that acreage accuracy(Aa)is 93.01%and sampling accuracy(As)is 91.40%.Confusion matrix shows that overall accuracy(OA)is 91.4%and the kappa coefficient(Kappa)is 0.755.These values are significantly improved compared to the traditional Maximum Likelihood classification(MLC)and Random Forest classification(RFC)which rely on spectral features.The results demonstrate that the identification accuracy can be improved by integrating spectral and temporal information.Since the identification of winter wheat is performed in the space corresponding to each MODIS pixel,the influence of differences of environmental conditions is greatly reduced.This advantage allows the proposed method to be effectively applied in other places.展开更多
Removal of cloud cover on the satellite remote sensing image can effectively improve the availability of remote sensing images. For thin cloud cover, support vector value contourlet transform is used to achieve multi-...Removal of cloud cover on the satellite remote sensing image can effectively improve the availability of remote sensing images. For thin cloud cover, support vector value contourlet transform is used to achieve multi-scale decomposition of the area of thin cloud cover on remote sensing images. Through enhancing coefficients of high frequency and suppressing coefficients of low frequency, the thin cloud is removed. For thick cloud cover, if the areas of thick cloud cover on multi-source or multi-temporal remote sensing images do not overlap, the multi-output support vector regression learning method is used to remove this kind of thick clouds. If the thick cloud cover areas overlap, by using the multi-output learning of the surrounding areas to predict the surface features of the overlapped thick cloud cover areas, this kind of thick cloud is removed. Experimental results show that the proposed cloud removal method can effectively solve the problems of the cloud overlapping and radiation difference among multi-source images. The cloud removal image is clear and smooth.展开更多
In Burkina Faso, rice consumption is currently common and increasing in both, rural and urban areas. Although several efforts have been made by the state to develop land for rice cultivation, the population’s demand ...In Burkina Faso, rice consumption is currently common and increasing in both, rural and urban areas. Although several efforts have been made by the state to develop land for rice cultivation, the population’s demand is still greater than the supply. Nevertheless, the country has great potential for rice cultivation. This study aims to analyze the suitability of land for rice cropping in the province of Nahouri. Field and satellite data were collected to map the rice crops developed areas. Furthermore, morphopedological, topographic, land use/cover, and accessibility to land data were collected and integrated into a Geographic Information System (GIS) based on simple Multi-Criteria Analysis (MCA). Land suitability for rice crop was performed. The results show that the total suitable area for rice cultivation in the province is 106,373 ha (28% of the province) where the most suitable areas cover 32,614 ha and the suitable areas cover 73,759 ha. Already 3144 ha of the province area had been developed for rice cultivation with 815 ha and 845 ha of most suitable and suitable areas respectively whereas more than 45% of the developed lands were not fit for the rice crop suitable land.展开更多
Mangroves are woody plant communities that appear in tropical and subtropical regions,mainly in intertidal zones along the coastlines.Despite their considerable benefits to humans and the surrounding environment,their...Mangroves are woody plant communities that appear in tropical and subtropical regions,mainly in intertidal zones along the coastlines.Despite their considerable benefits to humans and the surrounding environment,their existence is threatened by anthropogenic activities and natural drivers.Accordingly,it is vital to conduct efficient efforts to increase mangrove plantations by identifying suitable locations.These efforts are required to support conservation and plantation practices and lower the mortality rate of seedlings.Therefore,identifying ecologically potential areas for plantation practices is mandatory to ensure a higher success rate.This study aimed to identify suitable locations for mangrove plantations along the southern coastal frontiers of Hormozgan,Iran.To this end,we applied a hybrid Fuzzy-DEMATEL-ANP(FDANP)model as a Multi-Criteria Decision Making(MCDM)approach to determine the relative importance of different criteria,combined with geospatial and remote sensing data.In this regard,ten relevant sources of environmental criteria,including meteorological,topographical,and geomorphological,were used in the modeling.The statistical evaluation demonstrated the high potential of the developed approach for suitable location identification.Based on the final results,6.10%and 20.80%of the study area were classified as very-high suitable and very-low suitable areas.The obtained values can elucidate the path for decision-makers and managers for better conservation and plantation planning.Moreover,the utility of charge-free remote sensing data allows cost-effective implementation of such an approach for other regions by interested researchers and governing organizations.展开更多
This paper reviewed studies on remote sensing of water depth retrieval. Four water depth retrieval models （single-band, dou- ble-ratio-band, multi-band, and BP network models） were evaluated using TM image and water...This paper reviewed studies on remote sensing of water depth retrieval. Four water depth retrieval models （single-band, dou- ble-ratio-band, multi-band, and BP network models） were evaluated using TM image and water data from Bangong Co Lake, which is located in China＇s Tibet Autonomous Region and Indian Kashmir. Tested by independent data, comparison of these four models demonstrates that BP network model performed better than the multi-band model, with the single-band model performing the worst. To sum up, this study demonstrates that first, BP network model performed better than the traditional model; second, precise atmospheric correction and radiation study, affected by different water level sand sediment, could improve the precision of water depth retrieval.展开更多
Landsat 8 Oli,ASTER,and Sentinel 2A satellite images processing was used to map geological formations,lineaments and hydrothermal alteration minerals in the Aouli inlier,as a case study to illustrate the application o...Landsat 8 Oli,ASTER,and Sentinel 2A satellite images processing was used to map geological formations,lineaments and hydrothermal alteration minerals in the Aouli inlier,as a case study to illustrate the application of digital images processing and Geographic Information System(GIS)in geological mapping and mining prospecting.Principal Component Analysis(PCA)applied to the Landsat images allowed good lithological discrimination and contributed to the updating of available geological maps.The Automatic lineament extraction from Sentinel images revealed the main tectonic structures affecting Aouli inlier.The ratio bands(b5+b7)/b6 and the false color composite(b4/b6,b2/b1,b3/b2)allowed the hydrothermal alteration minerals mapping from Aster images.Combined with available geological data and field observations,the satellite derived data were integrated and analyzed in a GIS software to establish mining prospecting guides.The results showed that the anomaly zones are intimately linked to NNE-SSW and NW-SE oriented faults and to highly fractured areas developing argillic and Fe rich alterations.Verified via field survey,this approach was successfully applied to the Aouli inlier to rapidly target potential areas to be explored in the tactical phase.This provides a model for future prospecting efforts for similar mineral deposits in other areas.展开更多
Remote sensing, in particular satellite imagery, has been widely used to map cropland, analyze cropping systems, monitor crop changes, and estimate yield and production. However, although satellite imagery is useful w...Remote sensing, in particular satellite imagery, has been widely used to map cropland, analyze cropping systems, monitor crop changes, and estimate yield and production. However, although satellite imagery is useful within large scale agriculture applications （such as on a national or provincial scale）, it may not supply sufifcient information with adequate resolution, accurate geo-referencing, and specialized biological parameters for use in relation to the rapid developments being made in modern agriculture. Information that is more sophisticated and accurate is required to support reliable decision-making, thereby guaranteeing agricultural sustainability and national food security. To achieve this, strong integration of information is needed from multi-sources, multi-sensors, and multi-scales. In this paper, we propose a new framework of satellite, aerial, and ground-integrated （SAGI） agricultural remote sensing for use in comprehensive agricultural monitoring, modeling, and management. The prototypes of SAGI agriculture remote sensing are ifrst described, followed by a discussion of the key techniques used in joint data processing, image sequence registration and data assimilation. Finally, the possible applications of the SAGI system in supporting national food security are discussed.展开更多
Forests account for 80%of the total carbon exchange between the atmosphere and terrestrial ecosystems.Thus,to better manage our responses to global warming,it is important to monitor and assess forest aboveground carb...Forests account for 80%of the total carbon exchange between the atmosphere and terrestrial ecosystems.Thus,to better manage our responses to global warming,it is important to monitor and assess forest aboveground carbon and forest aboveground biomass(FAGB).Different levels of detail are needed to estimate FAGB at local,regional and national scales.Multi-scale remote sensing analysis from high,medium and coarse spatial resolution data,along with field sampling,is one approach often used.However,the methods developed are still time consuming,expensive,and inconvenient for systematic monitoring,especially for developing countries,as they require vast numbers of field samples for upscaling.Here,we recommend a convenient two-scale approach to estimate FAGB that was tested in our study sites.The study was conducted in the Chitwan district of Nepal using GeoEye-1(0.5 m),Landsat(30 m)and Google Earth very high resolution(GEVHR)Quickbird(0.65 m)images.For the local scale(Kayerkhola watershed),tree crowns of the area were delineated by the object-based image analysis technique on GeoEye images.An overall accuracy of 83%was obtained in the delineation of tree canopy cover(TCC)per plot.A TCC vs.FAGB model was developed based on the TCC estimations from GeoEye and FAGB measurements from field sample plots.A coefficient of determination(R2)of 0.76 was obtained in the modelling,and a value of 0.83 was obtained in the validation of the model.To upscale FAGB to the entire district,open source GEVHR images were used as virtual field plots.We delineated their TCC values and then calculated FAGB based on a TCC versus FAGB model.Using the multivariate adaptive regression splines machine learning algorithm,we developed a model from the relationship between the FAGB of GEVHR virtual plots with predictor parameters from Landsat 8 bands and vegetation indices.The model was then used to extrapolate FAGB to the entire district.This approach considerably reduced the need for field data and commercial very high resolution imagery while achieving two-scale forest information and FAGB estimates at high resolution(30 m)and accuracy(R2=0.76 and 0.7)with minimal error(RMSE=64 and 38 tons ha-1)at local and regional scales.This methodology is a promising technique for cost-effective FAGB and carbon estimations and can be replicated with limited resources and time.The method is especially applicable for developing countries that have low budgets for carbon estimations,and it is also applicable to the Reducing Emissions from Deforestation and Forest Degradation(REDD?)monitoring reporting and verification processes.展开更多
Quantitative application on remote sensing of suspended sediment is an important aspect of the engineering application of remote sensing study. In this paper, the Xiamen Bay is chosen as the study area. Eleven differe...Quantitative application on remote sensing of suspended sediment is an important aspect of the engineering application of remote sensing study. In this paper, the Xiamen Bay is chosen as the study area. Eleven different phases of the remote sensing data are selected to establish a quantitative remote sensing model to map suspended sediment by using remote sensing images and the quasi-synchronous measured sediment data. Based on empirical statistics developed are the conversion models between instantaneous suspended sediment concentration and tidally-averaged suspended sediment concentration as well as the conversion models between surface layer suspended sediment concentration and＂ the depth-averaged suspended sediment concentration. On this basis, the quantitative application integrated model on remote sensing of suspended sediment is developed. By using this model as well as multi-temporal remote sensing images, multi-year averaged suspended sediment concentration of the Xiamen Bay are predicted. The comparison between model prediction and observed data shows that the multi-year averaged suspended sediment concentration of studied sites as well as the concentration difference of neighboring sites can be well predicted by the remote sensing model with an error rate of 21.61% or less, which can satisfy the engineering requirements of channel deposition calculation.展开更多
Solid waste dumping is a hectic problem in urban and developing areas due to shortage of land for the purpose. The main objective of this study was to select potential areas for suitable solid waste dumping for Kajiad...Solid waste dumping is a hectic problem in urban and developing areas due to shortage of land for the purpose. The main objective of this study was to select potential areas for suitable solid waste dumping for Kajiado County, Kenya. Eight input map layers including DEM （digital elevation model）, topography, urban settlement, roads, wetlands, rivers, forests and protected areas were prepared and MCDA （Multi Criteria Decision Analysis Methods） were implemented in a GIS （geographic information systems） environment. GIS, RS （remote sensing） and MDCA are powerful tools which can effectively be applied during the planning phase of solid waste management in order to avoid adverse catastrophes in future. The final suitability map was prepared by weighted overlay analyses and leveled as the most suitable, moderate suitable, less suitable and unsuitable areas. The area of each suitability level was calculated using spatial statistics. Polygons representing the most suitable sites were further analyzed in terms of area perimeter ratio in order to investigate the most suitable areas in terms of shape regularity. The leading four polygons considered were marked A, B, C, D respectively in the final map. This study showed that suitable areas for solid waste landfills were limited and scattered in the study area.展开更多
This paper focuses on prediction of change in agricultural lands by using ART2 algorithm. The existing method used ENVI and ARCGIS software to predict the changes in land, which showed less accuracy due to human error...This paper focuses on prediction of change in agricultural lands by using ART2 algorithm. The existing method used ENVI and ARCGIS software to predict the changes in land, which showed less accuracy due to human errors. To overcome this user friendly GUI based ART2 algorithm has been developed in java to obtain more accuracy in prediction of changes in land. The input is satellite temporal images of the years 1990 and 2014. By using the ART2 algorithm, the input images of the years 1990 and 2014 are classified, where the features are identified to form cluster. The clustered image is given as input and pixel to pixel comparison method in ART2 is implemented in java, for detecting the changes in agricultural lands. The comparison results of ENVI and ARCGIS and GUI based ART2 with in situ data show that the prediction of changes in agricultural land is more accurate in the case of GUI based ART2 implementation.展开更多
Precision Agriculture (PA) recognizes and manages intra-field spatial variability to increase profitability and reduced environmental impact. Site Specific Crop Management (SSCM), a form of PA, subdivides a cropping f...Precision Agriculture (PA) recognizes and manages intra-field spatial variability to increase profitability and reduced environmental impact. Site Specific Crop Management (SSCM), a form of PA, subdivides a cropping field into uniformly manageable zones, based on quantitative measurement of yield limiting factors. In Mediterranean environments, the spatial and temporal yield variability of rain-fed cropping system is strongly influenced by the spatial variability of Plant Available Water-holding Capacity (PAWC) and its strong interaction with temporally variable seasonal rainfall. The successful adoption of SSCM depends on the understanding of both spatial and temporal variabilities in cropping fields. Remote sensing phenological metrics provide information about the biophysical growth conditions of crops across fields. In this paper, we examine the potential of phenological metrics to assess the spatial and temporal crop yield variability across a wheat cropping field at Minnipa, South Australia. The Minnipa field was classified into three management zones using prolonged observations including soil assessment and multiple year yield data. The main analytical steps followed in this study were: calculation of the phenological metrics using time series NDVI data from Moderate Resolution Imaging Spectroscope (MODIS) for 15 years (2001-2015);producing spatial trend and temporal variability maps of phenological metrics;and finally, assessment of association between the spatial patterns and temporal variability of the metrics with management zones of the cropping field. The spatial trend of the seasonal peak NDVI metric showed significant association with the management zone pattern. In terms of temporal variability, Time-integrated NDVI (TINDVI) showed higher variability in the “good” zone compared with the “poor” zone. This indicates that the magnitude of the seasonal peak is more sensitive to soil related factors across the field, whereas TINDVI is more sensitive to seasonal variability. The interpretation of the association between phenological metrics and the management zone site conditions was discussed in relation to soil-climate interaction. The results demonstrate the potential of the phenological metrics to assess the spatial and temporal variability across cropping fields and to understand the soil-climate interaction. The approach presented in this paper provides a pathway to utilize phenological metrics for precision agricultural management application.展开更多
Snow depth （SD） is a key parameter for research into global climate changes and land surface processes. A method was developed to obtain daily SD images at a higher 4 km spatial resolution and higher precision with ...Snow depth （SD） is a key parameter for research into global climate changes and land surface processes. A method was developed to obtain daily SD images at a higher 4 km spatial resolution and higher precision with SD measurements from in situ observations and passive microwave remote sensing of Advanced Microwave Scanning Radiometer-EOS （AMSR-E） and snow cover measurements of the Interactive Multisensor Snow and Ice Mapping System （IMS）. AMSR-E SD at 25 km spatial resolution was retrieved from AMSR-E products of snow density and snow water equivalent and then corrected using the SD from in situ observations and IMS snow cover. Corrected AMSR-E SD images were then resampled to act as ＂virtual＂ in situ observations to combine with the real in situ observations to interpolate at 4 km spatial resolution SD using the Cressman method. Finally, daily SD data generation for several regions of China demonstrated that the method is well suited to the generation of higher spatial resolution SD data in regions with a lower Digital Elevation Model （DEM） but not so well suited to regions at high altitude and with an undulating terrain, such as the Tibetan Plateau. Analysis of the longer time period SD data generation for January between 2003 and 2010 in northern Xinjiang also demonstrated the feasibility of the method.展开更多
Dear Editor,Remote sensing data formats are essential for storing,organizing,and managing imagery collected by satellites and sensors.These formats store remote sensing images and their related information,such as geo...Dear Editor,Remote sensing data formats are essential for storing,organizing,and managing imagery collected by satellites and sensors.These formats store remote sensing images and their related information,such as geographic coordinates and band information.It specifies the data storage order,encoding method,header file(which includes the basic information of the image,including the number of rows,columns,bands,and data types),and the organization of the data body.展开更多
基金supported by National Nature Science Foundation of China (Nos. 61462046 and 61762052)Natural Science Foundation of Jiangxi Province (Nos. 20161BAB202049 and 20161BAB204172)+2 种基金the Bidding Project of the Key Laboratory of Watershed Ecology and Geographical Environment Monitoring, NASG (Nos. WE2016003, WE2016013 and WE2016015)the Science and Technology Research Projects of Jiangxi Province Education Department (Nos. GJJ160741, GJJ170632 and GJJ170633)the Art Planning Project of Jiangxi Province (Nos. YG2016250 and YG2017381)
文摘Image registration is an indispensable component in multi-source remote sensing image processing. In this paper, we put forward a remote sensing image registration method by including an improved multi-scale and multi-direction Harris algorithm and a novel compound feature. Multi-scale circle Gaussian combined invariant moments and multi-direction gray level co-occurrence matrix are extracted as features for image matching. The proposed algorithm is evaluated on numerous multi-source remote sensor images with noise and illumination changes. Extensive experimental studies prove that our proposed method is capable of receiving stable and even distribution of key points as well as obtaining robust and accurate correspondence matches. It is a promising scheme in multi-source remote sensing image registration.
基金supported by the National Natural Science Foundation of China(No.62241109)the Tianjin Science and Technology Commissioner Project(No.20YDTPJC01110)。
文摘An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyramid network(FPN)structure of the original YOLOv8 mode is replaced by the generalized-FPN(GFPN)structure in GiraffeDet to realize the"cross-layer"and"cross-scale"adaptive feature fusion,to enrich the semantic information and spatial information on the feature map to improve the target detection ability of the model.Secondly,a pyramid-pool module of multi atrous spatial pyramid pooling(MASPP)is designed by using the idea of atrous convolution and feature pyramid structure to extract multi-scale features,so as to improve the processing ability of the model for multi-scale objects.The experimental results show that the detection accuracy of the improved YOLOv8 model on DIOR dataset is 92%and mean average precision(mAP)is 87.9%,respectively 3.5%and 1.7%higher than those of the original model.It is proved the detection and classification ability of the proposed model on multi-dimensional optical remote sensing target has been improved.
文摘The automatic registration of multi-source remote sensing images (RSI) is a research hotspot of remote sensing image preprocessing currently. A special automatic image registration module named the Image Autosync has been embedded into the ERDAS IMAGINE software of version 9.0 and above. The registration accuracies of the module verified for the remote sensing images obtained from different platforms or their different spatial resolution. Four tested registration experiments are discussed in this article to analyze the accuracy differences based on the remote sensing data which have different spatial resolution. The impact factors inducing the differences of registration accuracy are also analyzed.
基金Supported by Science and Technology Project of Lianyungang City(SH0917)
文摘[Objective] The aim was to extract red tide information in Haizhou Bay on the basis of multi-source remote sensing data.[Method] Red tide in Haizhou Bay was studied based on multi-source remote sensing data,such as IRS-P6 data on October 8,2005,Landsat 5-TM data on May 20,2006,MODIS 1B data on October 6,2006 and HY-1B second-grade data on April 22,2009,which were firstly preprocessed through geometric correction,atmospheric correction,image resizing and so on.At the same time,the synchronous environment monitoring data of red tide water were acquired.Then,band ratio method,chlorophyll-a concentration method and secondary filtering method were adopted to extract red tide information.[Result] On October 8,2005,the area of red tide was about 20.0 km2 in Haizhou Bay.There was no red tide in Haizhou bay on May 20,2006.On October 6,2006,large areas of red tide occurred in Haizhou bay,with area of 436.5 km2.On April 22,2009,red tide scattered in Haizhou bay,and its area was about 10.8 km2.[Conclusion] The research would provide technical ideas for the environmental monitoring department of Lianyungang to implement red tide forecast and warning effectively.
基金The study was financially supported by the Fund for Support of Researchers and Technologists of Iran(97022330)Panhellenic Infrastructure for Atmospheric Composition and Climate Change(PANACEA,MIS 5021516)+1 种基金Competitiveness,Entrepreneurship and Innovation(NSRF 2014-2020)co-financed by Greece and the European Union(European Regional Development Fund).
文摘Dust storms in arid and desert areas affect radiation budget,air quality,visibility,enzymatic activities,agricultural products and human health.Due to increased drought and land use changes in recent years,the frequency of dust storms occurrence in Iran has been increased.This study aims to identify dust source areas in the Sistan watershed(Iran-Afghanistan borders)-an important regional source for dust storms in southwestern Asia,using remote sensing(RS)and bivariate statistical models.Furthermore,this study determines the relative importance of factors controlling dust emissions using frequency ratio(FR)and weights of evidence(WOE)models and interpretability of predictive models using game theory.For this purpose,we identified 211 dust sources in the study area and generated a dust source distribution map-inventory map-by dust source potential index based on RS data.In addition,spatial maps of topographic factors affecting dust source areas including soil,lithology,slope,Normalized difference vegetation index(NDVI),geomorphology and land use were prepared.The performance of two models(WOE and FR)was evaluated using the area under curve(AUC)of the receiver operating characteristic curve.The results showed that soil,geomorphology and slope exhibited the greatest influence in the dust source areas.The 55.3%(according to FR)and 62.6%(according to WOE)of the total area were classified as high and very high potential dust sources,while both models displayed acceptable accuracy with subsurface levels of 0.704 for FR and 0.751 for WOE,although they predict different fractions of dust potential classes.Based on Shapley additive explanations(SHAP),three factors,i.e.,soil,slope and NDVI have the highest impact on the model's output.Overall,combination of statistic-based predictive models(or data mining models),RS and game theory techniques can provide accurate maps of dust source areas in arid and semi-arid regions,which can be helpful for mitigation of negative effects of dust storms.
文摘One of the crucial elements that is directly tied to the quality of living organisms is the quality of the water.How-ever,water quality has been adversely affected by plastic pollution,a global environmental disaster that has an effect on aquatic life,wildlife,and human health.To prevent these effects,better monitoring,detection,characterisation,quanti-fication,and tracking of aquatic plastic pollution at regional and global scales is urgently needed.Remote sensing tech-nology is regarded as a useful technique,as it offers a promising new and less labour-intensive tool for the detection,quantification,and characterisation of aquatic plastic pollution.The study seeks to supplement to the body of scientific literature by compiling original data on the monitoring of plastic pollution in aquatic environments using remote sensing technology,which can function as a cost saving method for water pollution and risk management in developing nations.This article provides a profound analysis of plastic pollution,including its categories,sources,distribution,chemical properties,and potential risks.It also provides an in-depth review of remote sensing technologies,satellite-derived in-dices,and research trends related to their applicability.Additionally,the study clarifies the difficulties in using remote sensing technologies for aquatic plastic monitoring and practical ways to reduce aquatic plastic pollution.The study will improve the understanding of aquatic plastic pollution,health hazards,and the suitability of remote sensing technology for aquatic plastic contamination monitoring studies among researchers and interested parties.
基金the financial support provided by the National Science & Technology Infrastructure Construction Project of China (2005DKA32300)the Key Science and Technology Project of Henan Province, China (152102110047)+2 种基金the Major Research Project of the Ministry of Education, China(16JJD770019)the Major Scientific and Technological Special Project of Henan Province, China (121100111300)the Cooperation Base Open Fund of the Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River regions and CPGIS (JOF 201602)
文摘Timely crop acreage and distribution information are the basic data which drive many agriculture related applications.For identifying crop types based on remote sensing,methods using only a single image type have significant limitations.Current research that integrates fine and coarser spatial resolution images,using techniques such as unmixing methods,regression models,and others,usually results in coarse resolution abundance without sufficient detail within pixels,and limited attention has been paid to the spatial relationship between the pixels from these two kinds of images.Here we propose a new solution to identify winter wheat by integrating spectral and temporal information derived from multi-resolution remote sensing data and determine the spatial distribution of sub-pixels within the coarse resolution pixels.Firstly,the membership of pixels which belong to winter wheat is calculated using a 25-m resolution resampled Landsat Thematic Mapper(TM)image based on the Bayesian equation.Then,the winter wheat abundance(acreage fraction in a pixel)is assessed by using a multiple regression model based on the unique temporal change features from moderate resolution imaging spectroradiometer(MODIS)time series data.Finally,winter wheat is identified by the proposed Abundance-Membership(AM)model based on the spatial relationship between the two types of pixels.Specifically,winter wheat is identified by comparing the spatially corresponding 10×10 membership pixels of each abundance pixel.In other words,this method takes advantage of the relative size of membership in a local space,rather than the absolute size in the entire study area.This method is tested in the major agricultural area of Yiluo Basin,China,and the results show that acreage accuracy(Aa)is 93.01%and sampling accuracy(As)is 91.40%.Confusion matrix shows that overall accuracy(OA)is 91.4%and the kappa coefficient(Kappa)is 0.755.These values are significantly improved compared to the traditional Maximum Likelihood classification(MLC)and Random Forest classification(RFC)which rely on spectral features.The results demonstrate that the identification accuracy can be improved by integrating spectral and temporal information.Since the identification of winter wheat is performed in the space corresponding to each MODIS pixel,the influence of differences of environmental conditions is greatly reduced.This advantage allows the proposed method to be effectively applied in other places.
基金supported by the National Natural Science Foundation of China(61172127)the Natural Science Foundation of Anhui Province(1408085MF121)
文摘Removal of cloud cover on the satellite remote sensing image can effectively improve the availability of remote sensing images. For thin cloud cover, support vector value contourlet transform is used to achieve multi-scale decomposition of the area of thin cloud cover on remote sensing images. Through enhancing coefficients of high frequency and suppressing coefficients of low frequency, the thin cloud is removed. For thick cloud cover, if the areas of thick cloud cover on multi-source or multi-temporal remote sensing images do not overlap, the multi-output support vector regression learning method is used to remove this kind of thick clouds. If the thick cloud cover areas overlap, by using the multi-output learning of the surrounding areas to predict the surface features of the overlapped thick cloud cover areas, this kind of thick cloud is removed. Experimental results show that the proposed cloud removal method can effectively solve the problems of the cloud overlapping and radiation difference among multi-source images. The cloud removal image is clear and smooth.
文摘In Burkina Faso, rice consumption is currently common and increasing in both, rural and urban areas. Although several efforts have been made by the state to develop land for rice cultivation, the population’s demand is still greater than the supply. Nevertheless, the country has great potential for rice cultivation. This study aims to analyze the suitability of land for rice cropping in the province of Nahouri. Field and satellite data were collected to map the rice crops developed areas. Furthermore, morphopedological, topographic, land use/cover, and accessibility to land data were collected and integrated into a Geographic Information System (GIS) based on simple Multi-Criteria Analysis (MCA). Land suitability for rice crop was performed. The results show that the total suitable area for rice cultivation in the province is 106,373 ha (28% of the province) where the most suitable areas cover 32,614 ha and the suitable areas cover 73,759 ha. Already 3144 ha of the province area had been developed for rice cultivation with 815 ha and 845 ha of most suitable and suitable areas respectively whereas more than 45% of the developed lands were not fit for the rice crop suitable land.
基金funded by Erasmus+ICM programme for a 3-month and 5-month stay at Lund University,Lund,Sweden,and thank the European Union.
文摘Mangroves are woody plant communities that appear in tropical and subtropical regions,mainly in intertidal zones along the coastlines.Despite their considerable benefits to humans and the surrounding environment,their existence is threatened by anthropogenic activities and natural drivers.Accordingly,it is vital to conduct efficient efforts to increase mangrove plantations by identifying suitable locations.These efforts are required to support conservation and plantation practices and lower the mortality rate of seedlings.Therefore,identifying ecologically potential areas for plantation practices is mandatory to ensure a higher success rate.This study aimed to identify suitable locations for mangrove plantations along the southern coastal frontiers of Hormozgan,Iran.To this end,we applied a hybrid Fuzzy-DEMATEL-ANP(FDANP)model as a Multi-Criteria Decision Making(MCDM)approach to determine the relative importance of different criteria,combined with geospatial and remote sensing data.In this regard,ten relevant sources of environmental criteria,including meteorological,topographical,and geomorphological,were used in the modeling.The statistical evaluation demonstrated the high potential of the developed approach for suitable location identification.Based on the final results,6.10%and 20.80%of the study area were classified as very-high suitable and very-low suitable areas.The obtained values can elucidate the path for decision-makers and managers for better conservation and plantation planning.Moreover,the utility of charge-free remote sensing data allows cost-effective implementation of such an approach for other regions by interested researchers and governing organizations.
基金supported by the projection of China Geographic Survey (12120113099800)the projection of "863" (2012AA062601)
文摘This paper reviewed studies on remote sensing of water depth retrieval. Four water depth retrieval models （single-band, dou- ble-ratio-band, multi-band, and BP network models） were evaluated using TM image and water data from Bangong Co Lake, which is located in China＇s Tibet Autonomous Region and Indian Kashmir. Tested by independent data, comparison of these four models demonstrates that BP network model performed better than the multi-band model, with the single-band model performing the worst. To sum up, this study demonstrates that first, BP network model performed better than the traditional model; second, precise atmospheric correction and radiation study, affected by different water level sand sediment, could improve the precision of water depth retrieval.
文摘Landsat 8 Oli,ASTER,and Sentinel 2A satellite images processing was used to map geological formations,lineaments and hydrothermal alteration minerals in the Aouli inlier,as a case study to illustrate the application of digital images processing and Geographic Information System(GIS)in geological mapping and mining prospecting.Principal Component Analysis(PCA)applied to the Landsat images allowed good lithological discrimination and contributed to the updating of available geological maps.The Automatic lineament extraction from Sentinel images revealed the main tectonic structures affecting Aouli inlier.The ratio bands(b5+b7)/b6 and the false color composite(b4/b6,b2/b1,b3/b2)allowed the hydrothermal alteration minerals mapping from Aster images.Combined with available geological data and field observations,the satellite derived data were integrated and analyzed in a GIS software to establish mining prospecting guides.The results showed that the anomaly zones are intimately linked to NNE-SSW and NW-SE oriented faults and to highly fractured areas developing argillic and Fe rich alterations.Verified via field survey,this approach was successfully applied to the Aouli inlier to rapidly target potential areas to be explored in the tactical phase.This provides a model for future prospecting efforts for similar mineral deposits in other areas.
基金supported by the Opening Project of the Key Laboratory of Agri-Informatics,Ministry of Agriculture of China(2012004)the National Basic Research Program of China(973 Program,2010CB951500)+2 种基金the Innovation Project of Chinese Academy of Agricultural Sciencesthe National Natural Science Foundation of China(41301365)the National High-Tech R&D Program of China(863 Program,2013AA12A401)
文摘Remote sensing, in particular satellite imagery, has been widely used to map cropland, analyze cropping systems, monitor crop changes, and estimate yield and production. However, although satellite imagery is useful within large scale agriculture applications （such as on a national or provincial scale）, it may not supply sufifcient information with adequate resolution, accurate geo-referencing, and specialized biological parameters for use in relation to the rapid developments being made in modern agriculture. Information that is more sophisticated and accurate is required to support reliable decision-making, thereby guaranteeing agricultural sustainability and national food security. To achieve this, strong integration of information is needed from multi-sources, multi-sensors, and multi-scales. In this paper, we propose a new framework of satellite, aerial, and ground-integrated （SAGI） agricultural remote sensing for use in comprehensive agricultural monitoring, modeling, and management. The prototypes of SAGI agriculture remote sensing are ifrst described, followed by a discussion of the key techniques used in joint data processing, image sequence registration and data assimilation. Finally, the possible applications of the SAGI system in supporting national food security are discussed.
基金supported by the CAS Strategic Priority Research Program(No.XDA19030402)the National Key Research and Development Program of China(No.2016YFD0300101)+2 种基金the Natural Science Foundation of China(Nos.31571565,31671585)the Key Basic Research Project of the Shandong Natural Science Foundation of China(No.ZR2017ZB0422)Research Funding of Qingdao University(No.41117010153)
文摘Forests account for 80%of the total carbon exchange between the atmosphere and terrestrial ecosystems.Thus,to better manage our responses to global warming,it is important to monitor and assess forest aboveground carbon and forest aboveground biomass(FAGB).Different levels of detail are needed to estimate FAGB at local,regional and national scales.Multi-scale remote sensing analysis from high,medium and coarse spatial resolution data,along with field sampling,is one approach often used.However,the methods developed are still time consuming,expensive,and inconvenient for systematic monitoring,especially for developing countries,as they require vast numbers of field samples for upscaling.Here,we recommend a convenient two-scale approach to estimate FAGB that was tested in our study sites.The study was conducted in the Chitwan district of Nepal using GeoEye-1(0.5 m),Landsat(30 m)and Google Earth very high resolution(GEVHR)Quickbird(0.65 m)images.For the local scale(Kayerkhola watershed),tree crowns of the area were delineated by the object-based image analysis technique on GeoEye images.An overall accuracy of 83%was obtained in the delineation of tree canopy cover(TCC)per plot.A TCC vs.FAGB model was developed based on the TCC estimations from GeoEye and FAGB measurements from field sample plots.A coefficient of determination(R2)of 0.76 was obtained in the modelling,and a value of 0.83 was obtained in the validation of the model.To upscale FAGB to the entire district,open source GEVHR images were used as virtual field plots.We delineated their TCC values and then calculated FAGB based on a TCC versus FAGB model.Using the multivariate adaptive regression splines machine learning algorithm,we developed a model from the relationship between the FAGB of GEVHR virtual plots with predictor parameters from Landsat 8 bands and vegetation indices.The model was then used to extrapolate FAGB to the entire district.This approach considerably reduced the need for field data and commercial very high resolution imagery while achieving two-scale forest information and FAGB estimates at high resolution(30 m)and accuracy(R2=0.76 and 0.7)with minimal error(RMSE=64 and 38 tons ha-1)at local and regional scales.This methodology is a promising technique for cost-effective FAGB and carbon estimations and can be replicated with limited resources and time.The method is especially applicable for developing countries that have low budgets for carbon estimations,and it is also applicable to the Reducing Emissions from Deforestation and Forest Degradation(REDD?)monitoring reporting and verification processes.
基金supported by the Open Foundation of State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering (Grant No. 2009491711)
文摘Quantitative application on remote sensing of suspended sediment is an important aspect of the engineering application of remote sensing study. In this paper, the Xiamen Bay is chosen as the study area. Eleven different phases of the remote sensing data are selected to establish a quantitative remote sensing model to map suspended sediment by using remote sensing images and the quasi-synchronous measured sediment data. Based on empirical statistics developed are the conversion models between instantaneous suspended sediment concentration and tidally-averaged suspended sediment concentration as well as the conversion models between surface layer suspended sediment concentration and＂ the depth-averaged suspended sediment concentration. On this basis, the quantitative application integrated model on remote sensing of suspended sediment is developed. By using this model as well as multi-temporal remote sensing images, multi-year averaged suspended sediment concentration of the Xiamen Bay are predicted. The comparison between model prediction and observed data shows that the multi-year averaged suspended sediment concentration of studied sites as well as the concentration difference of neighboring sites can be well predicted by the remote sensing model with an error rate of 21.61% or less, which can satisfy the engineering requirements of channel deposition calculation.
文摘Solid waste dumping is a hectic problem in urban and developing areas due to shortage of land for the purpose. The main objective of this study was to select potential areas for suitable solid waste dumping for Kajiado County, Kenya. Eight input map layers including DEM （digital elevation model）, topography, urban settlement, roads, wetlands, rivers, forests and protected areas were prepared and MCDA （Multi Criteria Decision Analysis Methods） were implemented in a GIS （geographic information systems） environment. GIS, RS （remote sensing） and MDCA are powerful tools which can effectively be applied during the planning phase of solid waste management in order to avoid adverse catastrophes in future. The final suitability map was prepared by weighted overlay analyses and leveled as the most suitable, moderate suitable, less suitable and unsuitable areas. The area of each suitability level was calculated using spatial statistics. Polygons representing the most suitable sites were further analyzed in terms of area perimeter ratio in order to investigate the most suitable areas in terms of shape regularity. The leading four polygons considered were marked A, B, C, D respectively in the final map. This study showed that suitable areas for solid waste landfills were limited and scattered in the study area.
文摘This paper focuses on prediction of change in agricultural lands by using ART2 algorithm. The existing method used ENVI and ARCGIS software to predict the changes in land, which showed less accuracy due to human errors. To overcome this user friendly GUI based ART2 algorithm has been developed in java to obtain more accuracy in prediction of changes in land. The input is satellite temporal images of the years 1990 and 2014. By using the ART2 algorithm, the input images of the years 1990 and 2014 are classified, where the features are identified to form cluster. The clustered image is given as input and pixel to pixel comparison method in ART2 is implemented in java, for detecting the changes in agricultural lands. The comparison results of ENVI and ARCGIS and GUI based ART2 with in situ data show that the prediction of changes in agricultural land is more accurate in the case of GUI based ART2 implementation.
文摘Precision Agriculture (PA) recognizes and manages intra-field spatial variability to increase profitability and reduced environmental impact. Site Specific Crop Management (SSCM), a form of PA, subdivides a cropping field into uniformly manageable zones, based on quantitative measurement of yield limiting factors. In Mediterranean environments, the spatial and temporal yield variability of rain-fed cropping system is strongly influenced by the spatial variability of Plant Available Water-holding Capacity (PAWC) and its strong interaction with temporally variable seasonal rainfall. The successful adoption of SSCM depends on the understanding of both spatial and temporal variabilities in cropping fields. Remote sensing phenological metrics provide information about the biophysical growth conditions of crops across fields. In this paper, we examine the potential of phenological metrics to assess the spatial and temporal crop yield variability across a wheat cropping field at Minnipa, South Australia. The Minnipa field was classified into three management zones using prolonged observations including soil assessment and multiple year yield data. The main analytical steps followed in this study were: calculation of the phenological metrics using time series NDVI data from Moderate Resolution Imaging Spectroscope (MODIS) for 15 years (2001-2015);producing spatial trend and temporal variability maps of phenological metrics;and finally, assessment of association between the spatial patterns and temporal variability of the metrics with management zones of the cropping field. The spatial trend of the seasonal peak NDVI metric showed significant association with the management zone pattern. In terms of temporal variability, Time-integrated NDVI (TINDVI) showed higher variability in the “good” zone compared with the “poor” zone. This indicates that the magnitude of the seasonal peak is more sensitive to soil related factors across the field, whereas TINDVI is more sensitive to seasonal variability. The interpretation of the association between phenological metrics and the management zone site conditions was discussed in relation to soil-climate interaction. The results demonstrate the potential of the phenological metrics to assess the spatial and temporal variability across cropping fields and to understand the soil-climate interaction. The approach presented in this paper provides a pathway to utilize phenological metrics for precision agricultural management application.
基金Meteorological Research in the Public Interest,No.GYHY201106014Beijing Nova Program,No.2010B037China Special Fund for the National High Technology Research and Development Program of China(863 Program),No.412230
文摘Snow depth （SD） is a key parameter for research into global climate changes and land surface processes. A method was developed to obtain daily SD images at a higher 4 km spatial resolution and higher precision with SD measurements from in situ observations and passive microwave remote sensing of Advanced Microwave Scanning Radiometer-EOS （AMSR-E） and snow cover measurements of the Interactive Multisensor Snow and Ice Mapping System （IMS）. AMSR-E SD at 25 km spatial resolution was retrieved from AMSR-E products of snow density and snow water equivalent and then corrected using the SD from in situ observations and IMS snow cover. Corrected AMSR-E SD images were then resampled to act as ＂virtual＂ in situ observations to combine with the real in situ observations to interpolate at 4 km spatial resolution SD using the Cressman method. Finally, daily SD data generation for several regions of China demonstrated that the method is well suited to the generation of higher spatial resolution SD data in regions with a lower Digital Elevation Model （DEM） but not so well suited to regions at high altitude and with an undulating terrain, such as the Tibetan Plateau. Analysis of the longer time period SD data generation for January between 2003 and 2010 in northern Xinjiang also demonstrated the feasibility of the method.
基金supported by the National Key Research and Development Program of China(grant no.2022YFF0904400)the National Science and Technology Major Project of the Ministry of Science and Technology of China(grant no.2024ZD10021)the Key Program of the National Natural Science Foundation of China(grant no.41830108).
文摘Dear Editor,Remote sensing data formats are essential for storing,organizing,and managing imagery collected by satellites and sensors.These formats store remote sensing images and their related information,such as geographic coordinates and band information.It specifies the data storage order,encoding method,header file(which includes the basic information of the image,including the number of rows,columns,bands,and data types),and the organization of the data body.
