最新刊期
卷 24 ， 期 5 ， 2020
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摘要：Over the past 40 years, China has attained remarkable achievements in the development of earth observation remote sensing satellite technology. At present, the country has established three main satellite systems, including terrestrial, meteorological, and marine systems, which have been widely used in numerous applications, such as natural resource investigation, marine environmental protection, weather disaster prediction, and other major national projects. This study reviews the development history of the three major satellite systems, analyzes the development status and inherent characteristics of China’s Earth observation remote sensing satellites, and implements the CiteSpace software to summarize the research hotspot literature for all in-orbit remote sensing satellites. The terrestrial remote sensing satellite system has developed rapidly, especially in terms of small commercial satellites. Terrestrial remote sensing satellites comprise four series, including the ZiYuan, GaoFen, HuanJing/ShiJian, and other small satellites. Satellite sensors are rich, and their high spatiotemporal resolution can reach up to 0.5 m. However, they can encounter typical problems, such as uneven development, close-proximity orbital heights, and overlapping spectral ranges in similar sensors. The development of the meteorological remote sensing satellite system is the most mature among the three satellite systems. Two series of polar orbiting and stationary satellites can well detect most atmospheric elements. However, meteorological satellites are few, the spatiotemporal resolution of their sensors is relatively low, and current sensors cannot finely detect certain critical elements, such as the atmospheric wind field. The marine remote sensing satellite system has likewise made significant progress. It consists of three major satellite patterns, namely, marine water color, marine dynamic environment, and marine surveillance and monitoring satellites, which can achieve large-scale simultaneous observation of Chinese marine environments. However, this system also consists of few marine satellites, limited sensor observation elements, and low satellite sensor spatiotemporal resolution. Moreover, hotspot literature analysis shows that the total number of studies on China’s Earth observation satellites is relatively small. The disproportional ratio of articles indexed by SCI and CNKI is serious, especially on the GaoFen and ZiYuan terrestrial satellites. Numerous studies on China’s Earth satellites focus on data processing, but the application aspect is relatively weak and uneven.Therefore, the future launching of terrestrial remote sensing satellites to develop new sensors, such as lidars, is suggested. Furthermore, different orbital heights as well as the complementarity of the spectral range of different sensors should be considered. The meteorological system should launch additional satellites to carry out networking observations, improve detection capabilities for all meteorological elements, and promote the spatiotemporal resolution of new sensors to meet the refined requirements of weather forecasting and disaster monitoring. The marine remote sensing satellite system should likewise launch more satellites and shorten the launching cycle of similar satellites, improve detection capabilities for marine elements, promote the spatiotemporal resolution of new sensors, and accelerate its transformation from scientific to business-oriented research. Finally, researchers should be encouraged to utilize domestic satellite data and explore relevant studies to promote the advanced techniques of China’s Earth observation satellites.关键词：China’s earth observation;remote sensing;terrestrial satellite;meteorological satellite;marine satellite;literature analysis- 1821
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发布时间：2020-08-31
Review
- 摘要：Four levels of stereoscopic forest fire monitoring have been established in China, namely, ground patrol, near-ground monitoring, aviation patrol, and satellite monitoring. Forest fire remains the main forest disaster that causes loss of forest resources, threatens the safety of forest ecological environment, and results in personal injuries. This study aims to provide technical reference for the study of forest fire early warning and monitoring technology in the new period of China. The research progress, existing problems, and development trend of forest fire early warning and fire monitoring methods with satellite remote sensing technique in the past 20 years are investigated on the basis of eight fields, namely, fuel parameter evaluation, smoke identification, active fire point monitoring, combustion dynamic monitoring of large forest fire, burned forest area identification and mapping, damage assessment of forest fire, burned forest biomass estimation, and burned vegetation recovery. The design of forest fire early warning and monitoring technology system has been discussed using the integrated information of satellite, aviation, and ground monitoring techniques to promote Chinese ecological civilization.关键词：satellite remote sensing technique;forest fire early warning;forest fire monitoring;forest fire prevention
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发布时间：2020-08-31 -
摘要：Fengyun/Medium Resolution Spectral Imager-II (FY-3D/MERSI-II) global fire products can be used in real-time monitoring of forest and grassland fires, straw burning, and other biomass burning worldwide. This paper discusses FY-3D/MERSI-II global fire products, including the methods, product contents, and applications.Fire spot discerning considers several conditions, such as cloud contamination, cloud edge, and thin cloud influences. Subpixel size evaluation uses a single channel with fire temperature set to 750 K. Fire intensity level is established on the basis of Fire Radiative Power (FRP) and is calculated using sub-pixel size and fire temperature. Daily global fire products with 0.01° spatial resolution are generated, and global monthly fire spot density maps with 0.25°×0.25° grid points are produced monthly on the basis of the results of automatic global fire spot discerning.We selected the results in several typical areas, including Northeast China, Russian Far East, South America, and South-central Africa, from May to June 2018, and compared them using interactive fire spot discerning as the truth to verify the accuracy of automatic fire spot discerning in Fengyun/Medium Resolution Spectral Imager-II (FY-3D/MERSI-II) global fire monitoring. The statistical analysis results show that the accuracy of the automatic fire monitoring algorithm reaches more than 95. The daily FY-3D/MERSI-II global fire products include fire location, subpixel size (in hectare, temperature, fire intensity level, and FRP. Two application examples are introduced, where the first example is for monitoring the huge wildfire that occurred in California, US in 2018, and the second example is for analyzing the temporal and spatial changing features of wildfire around the Arctic Pole circle in the summer of 2018.The FY-3 global fire spot discerning algorithm considers various weather and underlying conditions, making it suitable for fire monitoring in different regions of the world. The results show that the proposed algorithm has good accuracy. The rich detection information and global observation ability of FY-3 meteorological satellite reach the international advanced level of forest and grassland fire monitoring. FY-3 global fire product can be used for disaster prevention and mitigation, climate change, and ecological environment protection worldwide. In future studies, we will develop methods, such as burned area assessment and estimation of biomass carbon emissions, smoke impact prediction, and forest and grassland fire risk warning. FY-3 meteorological satellite will play a huge role in global wildfire monitoring and early warning and prediction services.关键词：remote sensing;FY-3;fire monitoring;middle infrared channel;global;daily and monthly products- 746
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发布时间：2020-08-31 -
摘要：Satellite remote sensing plays an important role in natural disaster management and response. Fire remote sensing is highly needed with the frequent occurrence of fire disasters. In this study, the demands of fire monitoring from volcanoes, environment, climate, and fire disasters were analyzed, and the needs of industrial users of fire monitoring in China were investigated under different temporal and spatial resolutions. This study aimed to introduce the principles, methods, and applications of fire remote sensing for providing insights into its effective use for remote sensing users and researchers.This study analyzed the application fields and the needs of different users. Satellite data sources were listed on the basis of sensor spectral characteristics and satellite orbit styles. Physical principles and retrieval methods were classified and introduced followed by their developments. The effects and abilities of fire remote sensing were investigated on the basis of the 2019 spring fire disasters in China.The satellite data sources used for fire remote sensing monitoring were summarized, and their characteristics were analyzed. Thermal infrared bands, which are sensitive, are important spectral bands. Satellite orbit is an essential factor that affects fire remote sensing ability. The research progress, advantages, and disadvantages of current fire remote sensing methods were reviewed through the analysis of the physical principles and methods of fire remote sensing. The information supporting ability of fire remote sensing was discussed on the basis of three stages, namely, before, during, and after the fire disaster. Fire remote sensing should be combined with ground-based and other methods to detect the occurrence of a fire disaster.An increasing number of satellites have been launched in the past few years, and fire remote sensing technology has accelerated with many functions and good accuracy. Fire remote sensing contributes in different steps of a fire disaster but cannot detect its occurrence. A national platform of fire remote sensing with multisatellite data and standard service interface should be developed for China.关键词：fire remote sensing;infrared;disaster;monitoring;forest fire;land surface temperature- 1527
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发布时间：2020-08-31 -
摘要：Forest fires are common disasters that seriously endanger human life. Timely and accurate monitoring of forest fires is essential for fighting fires and reducing losses. At present, active forest fire monitoring mainly uses polar or geostationary orbit satellites with low spatial resolution. The spatial resolution is extremely low, making it difficult to detect small-scale fires and control fire conditions. This paper proposes a near-real-time forest fire monitoring system with medium and high spatial resolutions on the basis of the rapid development of medium and high spatial resolution satellite sensors, data sharing policy, and data processing capabilities in recent years.This paper summarizes the research status and related shortage in four aspects, namely, basic principles of forest fire monitoring, currently available medium and high spatial resolution satellite data and their characteristics, active forest fire monitoring algorithms and data sharing, and cloud storage and computation, and analyze the feasibility of a near-real-time forest fire monitoring system with medium and high spatial resolutions. The proposed near-real-time fire monitoring system with medium and high spatial resolutions can serve as an important supplement to existing forest fire monitoring systems with coarse resolution. It can early and accurately detect small-scale forest fires and provide support for forest fire prevention and management because of its high spatial resolution.关键词：remote sensing;forest fire;near-real-time;medium and high spatial resolution satellite;data sharing;cloud storage and computation- 894
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发布时间：2020-08-31 -
摘要：Burned area is one of the key parameters for global and regional carbon cycle and climate change research. Satellite remote sensing technologies provide an effective way for obtaining the large-scale spatial distributions of burned areas. In 2018, the Chinese Academy of Sciences released the first GABAM (Global Annual Burned Area Map) based on Landsat series satellite data. The accuracy assessment of remote sensing data products is of great significance for product users. To date, the accuracy of GABAM products is yet to be independently evaluated. In this study, an accuracy validation study was performed on the 2010 global 30 m spatial resolution burned area product (GABAM2010) for the systematic evaluation of GABAM product accuracy. The GABAM2010 product was validated, and accuracy measures were estimated at the global scale and in several terrestrial biomes, and the technical framework for the accuracy assessment of global remote sensing thematic products was explored.Global accuracy was estimated by stratified random sampling and estimation in a weighted manner on the basis of the error matrix of each sampling unit. This method enables the verification of a large number of products, such as the global burned area product. Stratified random sampling was used in selecting 80 non-overlapping TSA (Thiessen Scene Areas), and reference fire perimeters were determined from multitemporal Landsat TM images for each sampled TSA. Error matrices and six accuracy measures were used in satisfying the criteria specified by the end-users of burned area products.The global validation result showed that the overall accuracy of GABAM2010 was 97.85%, and the commission and omission errors were 24.32% and 31.60%, respectively. The extent of a burned area is often underestimated due to the impact of data quality, such as strips, and clouds. The GABAM2010 products showed high precision in biomes, including tropical and subtropical grasslands, which are highly prone to fire. The high-density burned areas within biomes had higher accuracy than low-density ones.A statistically rigorous accuracy verification method, stratified random sampling was used in verifying the GABAM 2010 product. The method is applicable for the accuracy verification of similar remote sensing products, and the results of the evaluation performed on the GABAM 2010 product illustrated the specific applications of sampling design and accuracy analysis. Compared with simple estimatior, combined ratio estimator with stratified random sampling increased the reliability of the accuracy measures. Simple estimatior is ineffective in resolving some problems arising from the small proportion of an area burned and small number of sampling areas, which result in unreliable accuracy estimates. By contrast, stratification ratio estimation uses global ecological and fire behavior data and can obtain reliable accuracy estimate results.关键词：remote sensing;global annual burned area map;biomes;accuracy assessment;error matrix;combined ratio estimator- 640
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发布时间：2020-08-31 -
摘要：Forest fires seriously damage the stability of forest ecology and threaten the safety of human lives and properties. Traditional methods cannot detect fire disasters in time because of their limited observation region and time window. Remote sensing can accurately and continuously detect fire disasters in a large area with improved detection efficiency. Meteorological and polar satellites are mainly used to detect forest fires. However, these satellites cannot detect small fire scars and deal with the influence of cloud and rain. In this study, multisensor remote sensing data were combined to monitor the forest fire spots and progress in Muli Tibetan Autonomous County of Sichuan Province since March 30, 2019.First, we located the fire spots and their time of occurrence using the Chinese Gaofen-4 (GF-4) satellite. This geostationary satellite has a PMS (Panchromatic Multispectral ) camera with a high spatial (50 m) resolution and a mid-infrared sensor (IRS, 400 m) that is sensitive to abnormally high temperatures. Second, we computed the spectral difference between burning and unaffected forest stands using Sentinel-2. Third we classified the fire scars on the basis of Sentinel-2 using the OTSU algorithm to set the threshold of differenced Normalized Burn Ratio (dNBR). Finally, we used the synthetic aperture radar data from Sentinel-1A to relate Normalized Difference Vegetation Index (NDVI) to Polarization Ratio (PR).The results show that: (1) The location of fire spots can be accurately determined using the IRS and PMS data from GF-4. (2) The burning time is March 30 using the PMS data from GF-4 by combining the fire spot location and the sharp reduction in NDVI (from 0.7 to 0.25). (3) A difference is observed in the spectral curves of among the burning forest stands, unaffected areas, and different types of burned areas of Sentinel-2 data at 490 and 2200 nm. (4) The total area of damaged fire scars is 41.56 hm2, with 94.67% accuracy using the dNBR of 0.35 as the threshold from Sentinel-2 derived dNBR map. Lightly damaged fire scars are also classified (66.56 hm2, 90.94%). (5) The PR from Sentinel-1A data increases from 6.6 dB to 10.8 dB after burning. NDVI is linearly related to PR (R2=0.58 for fitting and 0.50 for verification).The above results agree well with the fire spot location and area from local field reports, and the burning time error is less than 12 h. This study provides an efficient method that can ignore the influence of cloud and rain and other complex environments to monitor forest fires. Many details can be obtained to capture the fire progress when GF-4 is used in monitoring. This study provides a reference for the identification of small fire disasters after their occurrences. The research methods and results can provide technical support for forest fire emergency.关键词：forest fires monitoring;GF-4;SAR;multi-sensor remote sensing data;fire spreading- 1354
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发布时间：2020-08-31 -
摘要：Himawari-8 is suitable in fire detection because of its high spatial resolution, observation frequency, and time efficiency. This paper proposes an improved fire detection algorithm based on the continuous phase change at the 3.9 and 11.2 μm measurements by Himawari-8 satellite. The brightness temperature change is stable and evident on the basis of the results on brightness temperature change in different latitudes under clear sky conditions in one day. The brightness temperature at the 3.9 μm channel changes faster than that at the 11.2 μm channel under the continuous phase change for 10 min when a fire occurs. The proposed improved fire detection algorithm for clear sky conditions considers the visible spectral effect at the 3.9 μm channel during day time. Experiments on this algorithm are conducted in several places, such as the serious explosive fire near a chemical plant in Qiaodong District, Zhangjiakou City, Hebei Province, at 16:40 (UTC) on November 27, 2018 and a fire incident in southwestern Australia on February 28, 2019, and the proposed algorithm quickly and effectively detects the occurrence of fire. Results shows that the proposed algorithm can perform fire detection well during winter, night time, and daytime.关键词：remote sensing;Himawari-8;AHI;fire detection;high-frequency observation;phase change;brightness temperature- 889
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发布时间：2020-08-31
Forest Fire Remote Sensing
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摘要：The multispectral camera carried by the GF-4 satellite was featured by high spatial resolution and high frequency observations. It played an important role in atmospheric aerosol monitoring. The most two mature satellite Aerosol Optical Depth(AOD) retrieval algorithms were Dark Target algorithm(DT) and Deep Blue algorithm(DB).While the former one was limited in the low reflectivity area and 2.1- 663
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发布时间：2020-08-31 -
摘要：Winter wheat is one of the main food crops in China. The accurate spatial distribution information of winter wheat is crucial for yield estimation and food security. However, existing methods for extracting the spatial distribution information of winter wheat using a full convolutional neural network ignore the remote sensing imagery characteristics. The influence of differences among probabilities in the coded class probability vector on the judgment of pixel class attribution can lead to misclassification or missing points at the edge, which can affect the accuracy of the result. In this study, the RefineNet model is coupled with maximum a posteriori probability (MAP), and the WWRSE(Winter Wheat Remote Sensing Extraction) model is developed to create a method for extracting the spatial distribution information of winter wheat from a Gaofen-2 remote sensing image.The WWRSE uses the convolution RefineNet network structure to extract pixel features. An improved SOFTMAX model is used to obtain the pixel category probability vector. With the winter wheat category probability value and other categories as a poor probability vector, differences can be divided into small pixels and differences among large pixels according to the category probability vector. For large differences among pixels, the biggest probability category can be used directly as the pixel category. Small differences among pixels can be combined with the MAP model to determine the type of each pixel. Next, the model is trained using the stochastic gradient method, and the spatial distribution information of winter wheat is extracted from the remote sensing image using the successfully trained model.SegNet, DeepLab, and RefineNet were selected as comparison models. Experimental results showed that WWRSE accuracy improved by 4.2%, 7.6%, and 8.6%, compared with the comparison models. Moreover, overall extraction accuracy reached 93%, thereby indicating that the method proposed in this study has certain advantages in proposing the spatial information distribution of winter wheat.This method deeply explores the information contained in the class probability vector of the output of the full convolutional network and determines that the classification error is closely related to the small component difference in the class probability vector. Based on this finding, the classification result of the full convolution network is revised. The effectiveness and feasibility of the proposed method are proven by the experiments.关键词：GF-2;full convolutional neural networks;RefineNet;maximum aposteriori probability;winter wheat;spatial distribution;Zhangqiu- 1149
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发布时间：2020-08-31
Remote Sensing Applications
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