Using ontology and rules to retrieve the semantics of disaster remote sensing data

Remote sensing data plays an important role in natural disaster management.However,with the increase of the variety and quantity of remote sensors,the problem of“knowledge barriers”arises when data users in disaster field retrieve remote sensing data.To improve this problem,this paper proposes an ontology and rule based retrieval(ORR)method to retrieve disaster remote sensing data,and this method introduces ontology technology to express earthquake disaster and remote sensing knowledge,on this basis,and realizes the task suitability reasoning of earthquake disaster remote sensing data,mining the semantic relationship between remote sensing metadata and disasters.The prototype system is built according to the ORR method,which is compared with the traditional method,using the ORR method to retrieve disaster remote sensing data can reduce the knowledge requirements of data users in the retrieval process and improve data retrieval efficiency.

Dynamic of Chinas cultivated land and landcover changes of its typical regions based on remote sensing data

Using the multi-temporal Landsat data and survey data of national resources, the authors studied the dynamics of cultivated land and landcover changes of typical ecological regions in China. The results of investigation showed that the whole distribution of the cultivated land shifted to Northeast and Northwest China, and as a result, the ecological quality of cultivated land dropped down. The seacoast and cultivated land in the area of Yellow River Mouth expanded by an increasing rate of 0.73 kma-1, with a depositing rate of 2.1 kma-1. The desertification area of the dynamic of Horqin Sandy Land increased from 60.02% of the total land area in1970s to 64.82% in1980s but decreased to 54.90% in early 1990s. As to the change of North Tibet lakes, the water area of the Namu Lake decreased by 38.58 km2 from year 1970 to 1988, with a decreasing rate of 2.14 km2a-1.

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）

Progress of Geological Survey Using Airborne Hyperspectral Remote Sensing Data in the Gansu and Qinghai Regions

Hyperspectral remote sensing is now a frontier of the remote sensing technology. Airborne hyperspectral remote sensing data have hundreds of narrow bands to obtain complete and continuous ground-object spectra. Therefore, they can be effectively used to identify these grotmd objects which are difficult to discriminate by using wide-band data, and show much promise in geological survey. At the height of 1500 m, have 36 bands in visible to the CASI hyperspectral data near-infrared spectral range, with a spectral resolution of 19 nm and a space resolution of 0.9 m. The SASI data have 101 bands in the shortwave infrared spectral range, with a spectral resolution of 15 nm and a space resolution of 2.25 m. In 2010, China Geological Survey deployed an airborne CASI/SASI hyperspectral measurement project, and selected the Liuyuan and Fangshankou areas in the Beishan metallogenic belt of Gansu Province, and the Nachitai area of East Kunlun metallogenic belt in Qinghai Province to conduct geological survey. The work period of this project was three years.

Modeling and predicting dengue fever cases in key regions of the Philippines using remote sensing data

Objective: To correlate climatic and environmental factors such as land surface temperature, rainfall, humidity and normalized difference vegetation index with the incidence of dengue to develop prediction models for the Philippines using remote-sensing data.Methods: Timeseries analysis was performed using dengue cases in four regions of the Philippines and monthly climatic variables extracted from Global Satellite Mapping of Precipitation for rainfall, and MODIS for the land surface temperature and normalized difference vegetation index from 2008-2015.Consistent dataset during the period of study was utilized in Autoregressive Integrated Moving Average models to predict dengue incidence in the four regions being studied.Results: The best-fitting models were selected to characterize the relationship between dengue incidence and climate variables.The predicted cases of dengue for January to December 2015 period fitted well with the actual dengue cases of the same timeframe.It also showed significantly good linear regression with a square of correlation of 0.869 5 for the four regions combined.Conclusion: Climatic and environmental variables are positively associated with dengue incidence and suit best as predictor factors using Autoregressive Integrated Moving Average models.This finding could be a meaningful tool in developing an early warning model based on weather forecasts to deliver effective public health prevention and mitigation programs.

The Identification and Geological Significance of Fault Buried in the Gasikule Salt Lake in China based on the Multi-source Remote Sensing Data

The salinity of the salt lake is an important factor to evaluate whether it contains some mineral resources or not,the fault buried in the salt lake could control the abundance of the salinity.Therefore,it is of great geological importance to identify the fault buried in the salt lake.Taking the Gasikule Salt Lake in China for example,the paper established a new method to identify the fault buried in the salt lake based on the multi-source remote sensing data including Landsat TM,SPOT-5 and ASTER data.It includes the acquisition and selection of the multi-source remote sensing data,data preprocessing,lake waterfront extraction,spectrum extraction of brine with different salinity,salinity index construction,salinity separation,analysis of the abnormal salinity and identification of the fault buried in salt lake,temperature inversion of brine and the fault verification.As a result,the study identified an important fault buried in the east of the Gasikule Salt Lake that controls the highest salinity abnormal.Because the level of the salinity is positively correlated to the mineral abundance,the result provides the important reference to identify the water body rich in mineral resources in the salt lake.

Principle and mathematical method for inverting stress state of a medium from the remote sensing data

It has been proved through experiments that the electromagnetic radiation energy of a substance will vary when stress acts on the substance. This moment, the electromagnetic radiation energy (observation value) received by the remote sensor is triggered not only by the substance temperature and also by the stress. Separating quantitatively these two kinds of electromagnetic radiation energy and then inversing the actual temperature state and stress state of a medium is a matter with practical significance in earthquake prediction and stability monitoring for the large-scale geotechnical engineering. In this paper the principle and the mathematical method for inversing stress by using multiband remote sensing data are discussed in detail. A calculation example is listed.

Red Tide Information Extraction Based on Multi-source Remote Sensing Data in Haizhou Bay

[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.

Mapping a Paleodrainage System of the Keriya River Using Remote Sensing Data and Historical Materials

Keriya River, one of the ancient Four Green Corridors in the Tarim Basin, recording the changes of climate-environment and the ancient Silk Road of the region. According to the archaeological data, historical materials and paleoclimates information, its eeo-environment and climate have taken great changes since the 1.09 Ma B.P, especially during the recent 2,000 years, many famous ancient cities having been abandoned and the south route of the Silk Road has been moved southward. This study illustrates the capability of the remote sensing data （radar data, topographic data and optical images） and historical materials, in mapping the ancient drainage networks. A major paleodrainage system of Keriya River has linked the Kunlun Mountains to the Tienshan Mountains, possibly as far back as the early Pleistocene. The Keriya River will have important implications for not only the understanding of the paleoenvironments and paleoclimates of Tarim Basin from the early Pleistocene to the Holocene, but also the changes of the Silk Road.

The Review of Land Use/Land Cover Mapping AI Methodology and Application in the Era of Remote Sensing Big Data

With the increasing number of remote sensing satellites,the diversification of observation modals,and the continuous advancement of artificial intelligence algorithms,historically opportunities have been brought to the applications of earth observation and information retrieval,including climate change monitoring,natural resource investigation,ecological environment protection,and territorial space planning.Over the past decade,artificial intelligence technology represented by deep learning has made significant contributions to the field of Earth observation.Therefore,this review will focus on the bottlenecks and development process of using deep learning methods for land use/land cover mapping of the Earth’s surface.Firstly,it introduces the basic framework of semantic segmentation network models for land use/land cover mapping.Then,we summarize the development of semantic segmentation models in geographical field,focusing on spatial and semantic feature extraction,context relationship perception,multi-scale effects modelling,and the transferability of models under geographical differences.Then,the application of semantic segmentation models in agricultural management,building boundary extraction,single tree segmentation and inter-species classification are reviewed.Finally,we discuss the future development prospects of deep learning technology in the context of remote sensing big data.

Ocean surface currents estimated from satellite remote sensing data based on a global hexagonal grid

Global ocean surface currents estimated from satellite derived data based on a regular global grid are affected by the grid’s shape and placement.Due to different neighbourhood relationships,the rectangular lat/lon grids lose accuracy when interpolating andfitting elevation data.Hexagonal grids have shown to be advantageous due to their isotropic,uniform neighbourhood.Considering these merits,this paper aims to estimate global ocean surface current using a global isotropic hexagonal grid from satellite remote sensing data.First,gridded satellite altimeter data and wind data with different resolutions are interpolated into the centre of the global isotropic hexagonal grid.Then,geostrophic and Ekman currents components are estimated according to the Lagerlof Ocean currents theory.Finally,the inversion results are verified.By analyzing the results,we conclude that the ocean surface currents estimated based on the global isotropic hexagonal grid have considerable accuracy,with improvement over rectangular lat/lon grids.

Research and application of flood detention modeling for ponds and small reservoirs based on remote sensing data

This paper proposes a method of small reservoir flood detention modeling that utilizes data from the American land resources satellite Landsat TM/ETM+. Precipitation and potential evapotranspiration are taken as the control conditions in this method on the basis of basin terrain classification. The objective of this method is to solve the question of a small-scale water conservancy project’s influence on flood forecasting precision, which can be used in the basin with multitudinous small reservoirs in the upstream region and can help estimate non-runoff data for small reservoir runoff. Taking the 20060826 flood as an example, the flood detention quantity of 19 small reservoirs is modeled. The results show that the absolute error of the total flood detention quantity is 0.2×10 4 m 3 , and the relative error is 0.12%. The flood detention quantity of small reservoirs in the entire basin is then modeled using this method, and the primary flood forecasting model is adjusted. After adjustment, the precision is significantly improved, with the relative error decreasing from 31.8% to 10.1%.

Monitoring vegetation dynamics in East Rennell Island World Heritage Site using multi-sensor and multi-temporal remote sensing data

East Rennell of Solomon Island is the first natural site under customary law to be inscribed on UNESCO’s World Heritage List.Potential threats due to logging,mining and agriculture led to the site being declared a World Heritage in Danger in 2013.For East Rennell World Heritage Site(ERWHS)to‘shed’its‘Danger’status the management must monitor forest cover both within and outside of ERWHS.We used satellite data from multiple sources to track forest cover changes for the entire East Rennell island since 1998.95%of the island is still covered by undisturbed forests;annual average normalized difference vegetation index(NDVI)for the whole island was above 0.91 in 2015.However,vegetation cover in the island has been slowly decreasing,at a rate of–0.0011 NDVI per year between 2000 and 2015.This decrease less pronounced inside ERWHS compared to areas outside.While potential threats due to forest clearing outside ERWHS remain the forest cover change from 2000 to 2015 has been below 15%.We suggest ways in which the Government of Solomon Islands could use our data as well as unmanned air vehicles and field surveys to monitor forest cover change and ensure the future conservation of ERWHS.

Establishment of Winter Wheat Regional Simulation Model Based on Remote Sensing Data and Its Application

Accurate crop growth monitoring and yield forecasting are significant to the food security and the sustainable development of agriculture. Crop yield estimation by remote sensing and crop growth simulation models have highly potential application in crop growth monitoring and yield forecasting. However, both of them have limitations in mechanism and regional application, respectively. Therefore, approach and methodology study on the combination of remote sensing data and crop growth simulation models are concerned by many researchers. In this paper, adjusted and regionalized WOFOST （World Food Study） in North China and Scattering by Arbitrarily Inclined Leaves-a model of leaf optical PROperties SPECTra （SAIL-PROSFPECT） were coupled through LAI to simulate Soil Adjusted Vegetation Index （SAVI） of crop canopy, by which crop model was re-initialized by minimizing differences between simulated and synthesized SAVI from remote sensing data using an optimization software （FSEOPT）. Thus, a regional remote-sensingcrop-simulation-framework-model （WSPFRS） was established under potential production level （optimal soil water condition）. The results were as follows： after re-initializing regional emergence date by using remote sensing data, anthesis, and maturity dates simulated by WSPFRS model were more close to measured values than simulated results of WOFOST; by re-initializing regional biomass weight at turn-green stage, the spatial distribution of simulated storage organ weight was more consistent with measured yields and the area with high values was nearly consistent with actual high yield area. This research is a basis for developing regional crop model in water stress production level based on remote sensing data.

An improved coverage-oriented retrieval algorithm for large-area remote sensing data

With the rapid development of satellite technology,the amount of remote sensing data and demand for remote sensing data analysis over large areas are greatly increasing.Hence,it is necessary to quickly filter out an optimal dataset from massive dataset to support various remote sensing applications.However,with the improvements in temporal and spatial resolution,remote sensing data have become fragmented,which brings challenges to data retrieval.At present,most data service platforms rely on the query engines to retrieve data.Retrieval results still have a large amount of data with a high degree of overlap,which must be manually selected for further processing.This process is very labour-intensive and time-consuming.This paper proposes an improved coverage-oriented retrieval algorithm that aims to retrieve an optimal image combination with the minimum number of images closest to the imaging time of interest while maximized covering the target area.The retrieval efficiency of this algorithm was analysed by applying different implementation practices:Arcpy,PyQGIS,and GeoPandas.The experimental results confirm the effectiveness of the algorithm and suggest that the GeoPandas-based approach is most advantageous when processing large-area data.

Web service for biodiversity estimation using remote sensing data

This paper presents a technique for the assessment and mapping of land biodiversity by using remote sensing data.The proposed approach uses a fuzzy model that encapsulates different ecological factors influencing biodiversity.We implemented our approach as a web service for the Pre-Black Sea region of the Ukraine.

A Review of Disentangled Representation Learning for Remote Sensing Data

representation that can identify and isolate different potential variables hidden in the highdimensional observations.Disentangled representation learning can capture information about a single change factor and control it by the corresponding potential subspace,providing a robust representation for complex changes in the data.In this paper,we first introduce and analyze the current status of research on disentangled representation and its causal mechanisms and summarize three crucial properties of disentangled representation.Then,disentangled representation learning algorithms are classified into four categories and outlined in terms of both mathematical description and applicability.Subsequently,the loss functions and objective evaluation metrics commonly used in existing work on disentangled representation are classified.Finally,the paper summarizes representative applications of disentangled representation learning in the field of remote sensing and discusses its future development.

Novel Vegetation Mapping Through Remote Sensing Images Using Deep Meta Fusion Model

Preserving biodiversity and maintaining ecological balance is essential in current environmental conditions.It is challenging to determine vegetation using traditional map classification approaches.The primary issue in detecting vegetation pattern is that it appears with complex spatial structures and similar spectral properties.It is more demandable to determine the multiple spectral ana-lyses for improving the accuracy of vegetation mapping through remotely sensed images.The proposed framework is developed with the idea of ensembling three effective strategies to produce a robust architecture for vegetation mapping.The architecture comprises three approaches,feature-based approach,region-based approach,and texture-based approach for classifying the vegetation area.The novel Deep Meta fusion model(DMFM)is created with a unique fusion frame-work of residual stacking of convolution layers with Unique covariate features(UCF),Intensity features(IF),and Colour features(CF).The overhead issues in GPU utilization during Convolution neural network(CNN)models are reduced here with a lightweight architecture.The system considers detailing feature areas to improve classification accuracy and reduce processing time.The proposed DMFM model achieved 99%accuracy,with a maximum processing time of 130 s.The training,testing,and validation losses are degraded to a significant level that shows the performance quality with the DMFM model.The system acts as a standard analysis platform for dynamic datasets since all three different fea-tures,such as Unique covariate features(UCF),Intensity features(IF),and Colour features(CF),are considered very well.

MIX-RS:A Multi-Indexing System Based on HDFS for Remote Sensing Data Storage

A large volume of Remote Sensing(RS)data has been generated with the deployment of satellite technologies.The data facilitate research in ecological monitoring,land management and desertification,etc.The characteristics of RS data(e.g.,enormous volume,large single-file size,and demanding requirement of fault tolerance)make the Hadoop Distributed File System(HDFS)an ideal choice for RS data storage as it is efficient,scalable,and equipped with a data replication mechanism for failure resilience.To use RS data,one of the most important techniques is geospatial indexing.However,the large data volume makes it time-consuming to efficiently construct and leverage.Considering that most modern geospatial data centres are equipped with HDFS-based big data processing infrastructures,deploying multiple geospatial indices becomes natural to optimise the efficacy.Moreover,because of the reliability introduced by high-quality hardware and the infrequently modified property of the RS data,the use of multi-indexing will not cause large overhead.Therefore,we design a framework called Multi-IndeXing-RS(MIX-RS)that unifies the multi-indexing mechanism on top of the HDFS with data replication enabled for both fault tolerance and geospatial indexing efficiency.Given the fault tolerance provided by the HDFS,RS data are structurally stored inside for faster geospatial indexing.Additionally,multi-indexing enhances efficiency.The proposed technique naturally sits on top of the HDFS to form a holistic framework without incurring severe overhead or sophisticated system implementation efforts.The MIX-RS framework is implemented and evaluated using real remote sensing data provided by the Chinese Academy of Sciences,demonstrating excellent geospatial indexing performance.

Forest type identification by random forest classification combined with SPOT and multitemporal SAR data

We developed a forest type classification technology for the Daxing＇an Mountains of northeast China using multisource remote sensing data.A SPOT-5 image and two temporal images of RADARSAT-2 full-polarization SAR were used to identify forest types in the Pangu Forest Farm of the Daxing＇an Mountains.Forest types were identified using random forest（RF） classification with the following data combination types： SPOT-5 alone,SPOT-5 and SAR images in August or November,and SPOT-5 and two temporal SAR images.We identified many forest types using a combination of multitemporal SAR and SPOT-5 images,including Betula platyphylla,Larix gmelinii,Pinus sylvestris and Picea koraiensis forests.The accuracy of classification exceeded 88% and improved by 12% when compared to the classification results obtained using SPOT data alone.RF classification using a combination of multisource remote sensing data improved classification accuracy compared to that achieved using single-source remote sensing data.