最新刊期
卷 24 ， 期 6 ， 2020
- 摘要：Remote sensing for ecosystem mainly focuses on types and patterns identification, functions monitoring, services assessment and processes analysis of ecosystem by remote sensing based methods. The new generation of satellites and sensors provide additional earth observation data sources for ecosystem monitoring. However, the identification capability of ecosystem types has yet to be improved, which put forward higher request to intelligent information extraction (in the Big Data Era). For ecosystem function monitoring, it is necessary to fully exploit the hidden features of remote sensing data and develop new indicators that are easy to process and reflect the functional characteristics of ecosystem. In addition, advanced models are needed to better assess ecosystem services by analyzing the implicit process and performance of ecosystem. Combination with the cloud platform is the future trends of remote sensing for ecosystem, which will provide opportunities for the public participation in ecological protection, and will provide more data support for ecological effects assessment of key projects.关键词：ecosystem;remote sensing;ecosystem functions;ecosystem services;ecological cloud
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发布时间：2021-10-18
Scholar's View Point
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摘要：In order to better understand the development of quantitative remote sensing in China and strengthen the exchange of information among peers, this paper summarizes the core parts of quantitative remote sensing over land surface based on the SCI (Scientific Citation Index) indexed papers and some Chinese papers published by Chinese scholars in 2019, including pre-processing methods (cloud and shadow detection, atmospheric correction and terrain correction, etc.); land surface radiative transfer modeling；inversion methods；tproduct production, evaluation，accuracy validation and applications. Surface products include directional reflectance, downward solar radiation, albedo, surface temperature, long wave radiation, net radiation, fluorescence, remote sensing vegetation biochemical parameters, leaf area index, fraction of the absorbed photosynthetic active radiation, vegetation coverage, forest height, forest biomass, vegetation productivity, soil moisture, snow water equivalent, snow cover, evaporation, surface and underground water, etc. The related research projects, professional symposium and summer training courses are also introduced.关键词：quantitative remote sensing;land;review;China- 1311
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发布时间：2021-10-18 -
摘要：In the demand of the quantitative use of satellite data, the accuracy of instrument calibration improved significantly because of the efforts of major satellite operators. However after decades of development, the current premium uncertainty of radiometric calibration of remote sensing satellites stays at 2% in visible, 0.5 k in infrared spectrum, due to the design restrains of on-board calibration devices, and also the theoretical limitation of vicarious techniques. Since the beginning of 21 st century, global climate change has become the common concern of international community. Global climate research placed an unprecedented need on the accuracy of radiometric calibration for remote sensing satellites. According to ASIC3 report, in order to detect climate change signals effectively and make accurate predictions, the acceptable uncertainty of radiometric calibration is 0.3% for solar reflective spectrum, 0.1 k for infrared spectrum, and 0.01% for total solar irradiance. Living up to the challenge, EU and US proposed TRUTHS and CLARREO plans, with the common goal of launching benchmark satellites. The benchmark satellites, with their extreme accuracy, designed to monitor climate change, could also calibrate other remote sensing satellites in orbit, raising the observation accuracy of the whole global satellite system to a new level. Meanwhile, China also advocated the concept of space radiometric observation benchmark system and launched two five-years projects to develop cutting-edge techniques such as phase-change warm load blackbody and on-orbit cryogenic absolute radiometer etc., aiming at establishing the absolute radiometric reference in space. With the support of these projects, the spaceborne SI-traceable calibrators for Reflective Solar Bands (RSBs) and Thermal Emissive Bands (TEBs) is developing. In the middle of the next five years, we can complete the development of the principle prototype of the radiometric benchmark instruments, and then achieve a comprehensive performance verification.Comparing to its counterparts, China could more likely become the first country to launch radiometric benchmark satellite, which leads remote sensing satellites to a SI-traceable era.关键词：space radiometric measurement;benchmark;satellite;traceable;technique- 957
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发布时间：2021-10-18 -
摘要：The preparation for the occurrence of an earthquake is a complicated process. This process is usually accompanied with material migration, energy release, and information exchange, which can disturb the radiation balance in the seismogenic zone. Obtaining the changing information of the coversphere, atmosphere, and ionosphere through satellite remote sensing; analyzing seismic anomalies; and identifying earthquake precursors, have become the interactive hotspots of the remote sensing and seismology fields. As a typical case where numerous anomalies precede the main shock, this study investigated the Ms 8.0 Wenchuan earthquake and its physical mechanism of the relevant seismic anomalies.On the basis of published research results, this study systematically collected and filtered the possible remote sensing anomalies of the Wenchuan earthquake under certain criteria and summarized the abnormal features of 20 remote sensing parameters related to the coversphere (five parameters), atmosphere (eight parameters), and ionosphere (seven parameters). By mapping the anomalies in a unified framework, the spatiotemporal correlations among the anomaly manifestations were analyzed, and the overall spatiotemporal characteristics of the short-term manifestations of multiple anomalies were revealed.The results of this study can be summarized as follows. (1) The manifestations of remote sensing anomalies gradually increased, enhanced, and congested before the Wenchuan earthquake, and prominent impending earthquake precursors were observed. (2) Remote sensing anomalies developed in a bottom–up manner from the coversphere and atmosphere to the ionosphere three months before the shock, which is in accordance with the Lithosphere–Coversphere–Atmosphere–Ionosphere (LCAI) coupling paradigm. (3) Strong spatial correlations were present among the seismic faults and manifestation positions of short-term-to-impending remote sensing anomalies, which congested along the Longmenshan faults and its nearby region. (4) Multiple short-term-to-impending remote sensing and strip-shaped anomalies covered the epicenter of the main shock and developed along the Longmenshan faults, respectively, thereby reflecting the local effect of the LCAI coupling in the late stage of the seismogenous process. The clustering multi-parameter remote sensing anomalies before May 12 can be regarded as Wenchuan earthquake anomalies with precursory significance.At the macroscopic scale, the seismic response driven by the deep part of the Earth can be explained using the knowledge on the multiple geosphere coupling of the entire planet system. The in-depth analysis of the individual characteristics, spatiotemporal correlations, and overall laws of the Wenchuan earthquake remote sensing anomalies during the earthquake preparation process is critical to the investigation of the physical mechanism of seismic anomalies. This retrospective research provides heuristic clues about the energy exchange process of the Wenchuan earthquake and confirms the great potential of multi-parameter earthquake precursor research. Furthermore, this study benefits the satellite monitoring and synergic analysis of strong inland earthquakes during the late stage of earthquake preparation and provides reference for earthquake prediction studies.关键词：Wenchuan earthquake;remote sensing anomaly;anomaly manifestation;spatio-temporal correlation;multiple geospheres- 676
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发布时间：2021-10-18 -
摘要：The spectral characteristics of ground objects are the theoretical basis for target detection and identification using remote sensing. The spectral library plays an important role in improving the level of remote sensing applications. In this study, we established the “Ground Object Background Spectral Library for Surveying and Mapping (GOSPEL)” under the National Science and Technology Foundation Project to standardize and enrich China’s spectral database and promote its application in the fields of surveying and mapping. Notable progress was observed in the construction of the ground feature spectrum library, warehousing, and preliminary exploration of application demonstrations. Moreover, the Ground Spectral Spectrum and Supporting Non-spectral Data Collection and Summary Standards, Spectral and Supporting Non-spectral Parameter Test Technical Specifications, and Feature Classification Code Standards were established on the basis of the existing spectrum acquisition and processing specifications. Since the implementation of the project, more than 14,000 existing spectral data were compiled and stored in accordance with the data norms and standards formulated by the project. In addition, nationwide spectrum collection experiments were organized and implemented involving the major regions in China, which acquired more than 17,000 ground feature spectra. Full-band experiments (visible–near infrared, infrared, and microwave bands) were performed in north and northeast China to obtain full-band data of snow, soil, vegetation canopy, and artificial target. To facilitate the application and sharing of spectral data, a ground feature spectral database (i.e., GOSPEL) and a data sharing platform containing more than 30,000 data were constructed. Twenty-five datasets were formed according to the type of ground feature spectral data and application requirements, including full-band typical feature spectrum dataset, multi-angle spectral reflectance dataset, multiscale typical feature reflectance dataset, and long-term series spectral dataset. We also performed application demonstrations on ground feature classification, remote sensing mechanism simulation, remote sensing quantitative inversion, and remote sensing product validation based on the current ground feature spectrum from GOSPEL.关键词：spectral library;object spectrum;surveying and mapping;remote sensing;system;data sharing- 843
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发布时间：2021-10-18
Review
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摘要：Scene classification and recognition of remote sensing image is an important task for image interpretation. High-resolution remote sensing images have rich spatial texture features and semantic information, and their scene categories are diversified. As a result, images in the same category have a huge difference and some images in different categories become similar. which makes images difficult to be classified and recognized correctly. Therefore, choosing effective features and classification algorithms can improve classification performance. In this case, high-precision classification can only be achieved by selecting effective features and classifiers.Traditional scene classification algorithms adopt low-level or mid-level handcrafted features. These features have poor ability to represent high-level semantic information of images, which makes it difficult to achieve satisfactory results on massive complex scene images difficult. Deep learning, especially convolutional neural networks, has made great progress in computer vision. Compared with the methods using handcrafted features, deep learning is currently the most effective way for image classification. The application of a convolutional neural network to remote sensing image classification has achieved higher precision than methods using traditional handcrafted features. However, training a deep convolutional neural network that has too many parameters needs many labeled images, and the process of training is complicated and time-consuming. Generally, a deep convolutional neural network would not perform well with only a few images.A method for image classification using an ensemble convolutional neural network is proposed to improve the performance of convolutional neural networks. The method is composed of three main phases, namely, preprocessing, feature extraction, and ensemble learning. Firstly, the preprocessing stage includes geometry normalization, image intensity normalization, and image augmentation. Secondly, the feature extraction phase considers several deep convolutional neural networks, which have been well pre-trained on ImageNet, and are chosen to remove the last classification layer in the network and to extract different deep features of the same image. Thirdly, a stacking model is constructed in the ensemble learning phase. The stacking model consists of base and meta classifiers. The base classifier is composed of several logistic regression modes that are used to train different features extracted by deep convolutional neural networks. The meta classifier is a support vector machine. Finally, the probability distribution predicted by the base classifier is used to construct a new dataset that would be trained by the meta classifier.Experiments were conducted on two datasets named UCMerced_LandUse and NWPU-RESISC45 to verify the effectiveness of the proposed method. Compared with state-of-the-art methods, the proposed method performed better in overall accuracies. The proposed method could greatly improve performance and achieve overall accuracies of 90.74% and 87.21% on the two datasets, respectively, even with only 10% data used for training.With transfer learning, the features extracted by the deep convolutional neural networks are highly abstract and semantic, which have better ability in classification than other handcrafted features. Through feature fusion and model transferring, the advantages of different features and classification methods could be synthetically utilized. In this way, high classification accuracy could be achieved even with very little training data.关键词：remote sensing image;scene classification;convolutional neural network;transfer learning;ensemble learning- 981
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发布时间：2021-10-18 -
摘要：Change detection with single-phase remote sensing image between two different times is widely used in land cover, urban expansion, coral reef health, forest fire events, and deforestation. The most important step in change detection is to determine the change threshold value, which is used to distinguish change and no-change areas. Traditional change detection methods usually determine only one threshold. These methods neglect the difference of spectral value range between different land cover types. Even the same land cover types may have great differences. For example, areas of farmland that have been harvested are different from areas that have not been harvested. Thus, we propose an adaptive multi-threshold value remote sensing image change detection method that is based on land cover type feature.Land cover data for 2015 and two Landsat 8 OLI images for 2013 and 2015 were collected. First, the method used Temporally Invariant Cluster (TIC) to ensure the consistency of the radiometric level of the two images. To avoid salt-and-pepper noise, we segmented the remote sensing image with multiscale segmentation algorithm. The segmentation spatial scales 200, 150, and 100 were used for different land cover types. Change vectors of the image objects at different segmentation spatial scales were then constructed. The maximum inter-class variance method is used to determine the change detection in single and multi-threshold values that are based on land cover types. Finally, we collected 500 samples by using visual interpretation and subsequently conducted accuracy assessment on the result of single and multi-threshold value change detection.The experiment outcomes showed that the multi-threshold value change detection method had higher accuracy and greater stability than the single threshold value change detection. The total accuracy of the multi threshold value change detection is 87.2%, whereas the total accuracy of the single threshold value change detection is 79.6%. The Kappa coefficient is 0.741 and 0.601, respectively. To compare the proposed multi- threshold values method with the traditional single threshold value method, we conducted further accuracy assessment with each land cover type. Results showed that the producer’s accuracy of the no-change area in farmland, water, as well as developed and barren land was improved. Similarly, the user’s accuracy of the change area in farmland and water was enhanced. The multi-threshold values change detection method weakened the influence of phenology phase to an extent and has better applicability.The TIC relative radiometric normalization method could overcome the shortcomings of traditional visual interpretation for selecting time-invariant pixels. The method avoids the influence of subject factors and can normalize images accurately and efficiently. In addition, the multiscale segmentation algorithm can provide different spatial segmentation scales to avoid over and under segmentation problems. The proposed method involves the change vector analysis driven by different thresholds based on land cover type rather than a single threshold value. The proposed method has improved the accuracy of the change detection and provided a reference for the application of efficiently updating of land cover data in large-scale area.关键词：remote sensing land cover;change detection;temporally invariant cluster;multiscale segmentation;relative radiometric correction;maximum inter-class variance- 668
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发布时间：2021-10-18 -
摘要：The development and application of three-line array CCD （Charge-coupled Device）sensor is an important direction in remote sensing, surveying, and mapping. The objective of this study is to investigate the geometric calibration technology of the first Chinese self-developed airborne three-line array CCD camera (hereinafter referred to as GFXJ).In this study, a series of innovative research works is conducted on the geometric calibration technology of the domestic GFXJ camera. First, a comparative analysis is made on the imaging characteristics and geometric deformation factors of the GFXJ camera. Upon this camera, a piece-wise self-calibration model based on CCD tilt angle is established. The piece-wise self-calibration model absorbs the influence of various geometric distortion factors by using a segmented mathematical model to avoid over-parameterization and strong inter-correlation. At the CCD segment boundary, the model satisfies the equivalent and the smoothing constraints.Then, an iterative two-step calibration scheme is proposed to achieve stable and reliable calibration values. The aerial triangulation of exterior orientation elements and the calibration of additional self-calibrating parameters are performed independently and iteratively. The iterative two-step calibration process is implemented until the exterior orientation elements and additional calibration parameters reach stability and the changes between the two iterations are less than the threshold.Multiple sets of flight experimental data were obtained from the China Songshan remote sensing comprehensive field and Hegang area of Heilongjiang Province. The proposed iterative two-step calibration scheme was applied to set accurately the tilt angle calibration of each CCD detector in the forward, nadir, and backward CCD arrays. Reliable CCD image pixel coordinate files were generated for forward, nadir, and backward arrays independently.Experiment results showed that after calibration, uncontrolled image positioning precision can be greatly improved. Supported by several control points for bundle block adjustment, the image positioning accuracy of GFXJ camera can meet the 1:1000 scale topographic mapping requirements on aerial triangulation.From experimental results, we draw the following conclusions. First, the proposed piece-wise self-calibration model based on CCD viewing angle and the iterative two-step calibration scheme are suitable and efficient for the GFXJ camera. The geometric distortion factors, such as lens distortion, CCD rotation, and scaling, affect the accuracy of the height and positioning of the GFXJ camera and planar positioning, respectively. The piece-wise self-calibration calibration model based on tilt angles and the iterative two-step calibration scheme proposed in this study can effectively calibrate the inherent lens and CCD distortion errors of the GFXJ camera. Second, the GCPs layout scheme of “four corners” can ensure the aerial triangulation accuracy of GFXJ, but denser GCPs layout scheme had little contribution to accuracy improvement. Third, the calibrated CCD image pixel coordinate files can serve as a qualified and reliable calibration product for subsequent users. At the same time, the calibration method and research results proposed in this study can serve as reference for the geometric calibration research of other airborne three-linear array CCD cameras.关键词：remote sensing;airborne three-line array CCD camera;piece-wise self-calibration model;geometric calibration;China Songshan remote sensing comprehensive field;iterative two-step calibration scheme- 681
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发布时间：2021-10-18
Technology and Methodology
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摘要：Remote sensing is an important method used to estimate forest canopy closure in large scale. The three kinds of remote sensing algorithms for canopy closure retrieval are statistical, physical, and mixed models. Although statistical models are commonly used, they lack physical explanation and are limited in local areas. Physical models have clear understanding on mechanism, which can be used in large areas. However, due to higher complexity, physical models are less applied. The Stochastic Radiative Transfer (SRT) model is applicable in simulating forests with horizontally distributed heterogeneity, which may represent different canopy closures. Exploring the inversion method using the SRT model could improve the efficiency and precision of canopy closure inversion.On the basis of the SRT model, an inversion method has been proposed on canopy closure retrieval of Yunnan pine forests. The fundamental step is to determine the quantitative relationship between the canopy closure and the probability of finding foliage elements in SRT model. To match the Yunnan pine crown shape, an equivalent model was used to correct the cylinder shape assumption. Then, a look-up-table was constructed to inverse the canopy closure to obtain the reflectance from GF-1 and Landsat 8 satellite images. The probability of finding foliage elements and leaf area index were determined in the case of a minimum difference between simulated reflectance and satellite observations, and to calculate the canopy closure on the basis of the stochastic Beer–Lambert–Bouguer law. Thirty plots of field data were used to assess the inversion accuracy. A statistical inversion method based on NDVI was conducted for comparison.Results showed that the inversion can accurately map the canopy closure of Yunnan pine forests in the study area (R2=0.8345, RMSE=0.0688). Reflectance of the bands used for retrieval performed sensitively to canopy closure. The use of composite image from GF-1 and Landsat 8 is feasible. The equivalent shape correction model is reasonable, which reduced RMSE by 0.0466, and the algorithm is flexible in different crown cases.This study can support forward models and inversion methods for large-scale forest canopy closure retrieval. The research could be extended to any tree species by changing the model parameter input, and any crown type by crown shape equivalent correction.关键词：remote sensing;stochastic radiative transfer model;Yunnan pine;canopy closure;crown shape correction;GF-1;Landsat 8- 662
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发布时间：2021-10-18 -
摘要：Cloud Liquid Water Content (CLW) links the hydrological and radiative components of the climate system and is an important parameter for research in climate and cloud microphysics. Cloud liquid water is a highly variable target and depends on cloud type. The different cloud types exist on different levels and vary in the satellite sensor view.Cloud liquid water is one the most uncertain factors in climate change research. CLW can be directly measured from the passive microwave measurements on the basis of its spectral and polarization signatures. The all-sky CLW retrieval algorithms for FY-3C and FY-3D microwave imagers (MWRI) are presented in this study. The CRTM rapid radiative transfer and various cloud models, as well as ECMWF short-range forecast profile datasets are utilized for training the retrieval coefficients. Hence, the physical-based algorithm could ensure the adaptability of the CLW products for different seasons and regions. To prevent matching errors between visible and microwave pixels, a novel clear-sky detection method based on O-B (observations minus backgrounds) errors of FY-3 MWRI brightness temperatures is given and proved effective to adjust the coefficients and the scale factor of the retrieval equation. Then, the retrievals under rainy conditions based on the climate statistical features are added in the FY-3 CLW all-sky algorithms.As the validation of satellite derived CLWs is difficult to carry out, the statistical histogram method raised by Remote Sensing System (RSS) is used to estimate the accuracy of FY-3 CLW daily products and DMSP-F16 SSM/I CLW products. Results showed that the RMSE of FY-3C CLWs is 0.028 mm and FY-3D is 0.025 mm. As the RMSE of SSM/I CLWs is 0.025 mm according to the same method, the accuracy of FY-3 CLWs is comparable to that of the RSS operational products. We selected 15 days of FY-3 CLW orbital product data in March 2015 to compare with the GPM GMI orbital products from RSS under strict matching constraint. The comparison result shows that the two kinds of products are of good consistency, and the correlation coefficient reached 0.9061. The mean deviation and RMSE are 0.0075±0.0325 mm.The global distribution of FY-3 daily CLW was analyzed and compared with the distributions of clouds observed by SSM/I and FY-3D MERSI. According to the analysis, the cloud distribution observed by FY-3 is consistent with SSM/I and MERSI observations. FY-3 CLWs were more sensitive to thin clouds with smaller particles and less water content. The CLW values of thick clouds were slightly underestimated than SSM/I. FY-3 CLWs could depict the detailed structure of typhoons, including eye area, inner cloud wall, and outer spiral rain belt. At present, FY-3C/D CLW products are used in operations. The networking of morning and afternoon orbit satellites could achieve global coverage in one day.关键词：remote sensing;FY-3 MWRI;cloud liquid water;radiative transfer;cloud model;statistical histogram;validation- 720
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发布时间：2021-10-18 -
摘要：Landslides bring great peril to mountainous areas in China. In recent years, catastrophic high-position landslides often occur after the Wenchuan earthquake. The landslides occurred in the mountains that have high or steep terrain and dense vegetation coverage. A typical case in Xinmocun occurred in Diexi Town, Maoxian County, Sichuan Province on June 24, 2017. Given the characteristics of high position and concealment, this type of landslide is difficult to be detected by GPS, InSAR, and other traditional investigation methods. Certain technologies should be promoted and applied to detect and prevent such high concealed landslides at high positions. The optical remote sensing technology, owning a special capability with large-range, non-contact, periodic coverage, and abundant data, has great potential in making up for the limitations of the above methods, which is of great significance to disaster prevention and mitigation. The creep of landslide causes changes in environmental conditions, such as loosening of rock mass, change in soil nutrient, and uneven distribution of water. Changes in environmental conditions cause vegetation growth to vary. In situ investigation is conducted on the anomaly of vegetation growth before landslide. Therefore, on the basis of vegetation anomaly, this study establishes a new indirect landslide monitoring method to prepare for the study of landslide prediction. This method identifies vegetation anomaly on the landslide body using high-resolution optical remote sensing images, establishes the relationship between the creep of landslides and vegetation anomaly, and analyzes the evolution process of landslide creep at the stage of potential landslides.The Xinmocun landslide, which has high vegetation coverage, is selected as an example for conducting experiments. First, according to the comprehensive interpretation of optical remote sensing images and geological data, the Xinmocun landslide area is divided into upper landslide hazard, middle potential impact, and lower human activity. Second, vegetation coverage in each area is calculated using three-time series optical remote sensing images (June 18, 2014; June 21, 2015; and June 28, 2016) before landslides. Finally, the relationship between vegetation growth status and landslide creep is analyzed and verified using the remote sensing images and geological survey after the landslide.Experiments on high-resolution optical remote sensing images, which were conducted at the same period of three years before the landslide, detected changes in vegetation. In the upper landslide hazard area, the main landslide source and the narrow slump areas above the deformable body were affected by the creep of landslide. Hence, vegetation coverage declined evidently from 2014 to 2016. With the increase of distance from the edge of the bare land, the smaller the effect of landslide creep is, and the better the status of vegetation gradually becomes. With the time of landslide approaching, the greater the effect of landslide creep is, and the worse the status of vegetation in the same position becomes. In the middle potential impact area, the vegetation coverage around the springs and gullies declines with the greater effect of landslide creep as the time of landslide approaches. In the lower human activity area, the variation of vegetation coverage has no obvious regularity because of complex factors.Through experiments of optical remote sensing data, the conclusions are drawn as follows. The status of vegetation in the upper landslide hazard area and the middle potential impact area has significant temporal and spatial correlation with landslide creep. This result reflects the inherent relationship between vegetation growth and the creep of landslides, which can be used to predict the occurrence of landslides.关键词：remote sensing;Xinmocun landslide;regional division;vegetation coverage;vegetation anomaly;landslide monitoring;GPS;InSAR- 671
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发布时间：2021-10-18
Remote Sensing Applications
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