最新刊期
卷 26 ， 期 3 ， 2022
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摘要：In the context of climate change, vegetation phenology, as a direct manifestation of the ecosystem’s response to environmental changes, has attracted increasing attention from the academic community. Obtaining long-term, continuous, multi-scale vegetation phenology data is the basis of phenological research, and the phenological parameters obtained by satellite remote sensing have become an important indicator of terrestrial ecosystem change. Remote sensing phenological parameters play an important role in the fields of agricultural production management, ecosystem monitoring, land use type mapping, human health, and ecosystem climate change response. In this context, key scientific issues and application fields must be combined to systematically sort out the progress in remote sensing phenological parameter extraction, verification, and product development and to predict to future development trends. First, this article discussed the development of emerging sunlight-induced chlorophyll fluorescence and vegetation optical thickness in addition to the traditional vegetation indices in phenological monitoring. Second, this paper discusses the advantages, disadvantages, and applicability of different time series data preprocessing and phenological metrics retrieval algorithms. Then, this article sorts out the development context of multi-source and -scale verification methods from the development of traditional phenological observations, phenological cameras, flux observations, and unmanned aerial vehicles. Meanwhile, this article introduces the development status of domestic and foreign phenology remote sensing products in recent years, with emphasis on product accuracy. Finally, this article systematically discusses the propagation of errors to the retrieved phenological metrics resulting from different aspects of data preprocessing, parameter extraction methods, and remote sensing data sources. On this basis, this article points out that future research in the field of vegetation phenology remote sensing should focus on the following: (1) better comparability between different research results should be targeted by improving the quality of remote sensing data sources and spatial and temporal consistency; (2) the subjectivity in the phenological retrieval algorithms should be reduced by developing universal algorithms; (3) the complete ground validation scheme should be established by leveraging the development in theory and method of quantitative remote sensing validation field; (4) the experience of using Chinese satellite data for monitoring vegetation phenology should be accumulated by actively extending the application of different Chinese spaceborne sensors. Through the above development, the overarching aim is to meet the demand for high-quality vegetation phenology remote sensing products in various scientific and practical applications.关键词：remote sensing;vegetation phenology;growing season;Vegetation Dynamics;Ecosystem parameters;ground validation- 3693
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发布时间：2022-03-28
Review
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摘要：Alpine treeline is not only an important source in calibrating global climate change but also a fundamental element in scientifically managing forest resources. Furthermore, the location, area size, and change patterns of forest lines are also used as essential information in monitoring and modeling the environment. The alpine forest line of the Qinling Mountains is located in the ecological staggered zone at high altitude, with an obvious distribution of altitudinal spectrum, which is an important north-south geographical dividing line in China. Therefore, a novel approach for the rapid and accurate identification of alpine treeline in the Qinling Mountains must be developed.We propose a remote-sensor-based algorithm for extracting alpine treelines in the Qinling Mountains by combining the high-resolution global forest cover data in 2000 with a digital elevation model and mountain distribution data. Specifically, tree cover is first extracted from the forest cover data. Next, the highest point of the study area is determined from the elevation data. Finally, the 8-connected domain search algorithm is employed to find the boundary between forest and non-forest covers to determine the alpine treeline. The algorithm is validated by high-resolution Google Earth images, GPS ground-based data, and the NDVI dataset. Further, we systematically investigate the relationship between the alpine treeline distribution and geographical factors (elevation, slope, and aspect) in the study area using the elevation data.The distribution of treelines in this paper is consistent with the actual treelines distribution in Google Earth images, further demonstrating the performance of the proposed algorithm. The elevation of treelines in the Qinling Mountains ranges from 2400 m to 3800 m. The treelines are concentrated in steep slope areas ranging from 15° to 55°. The distribution of alpine treeline elevation shows significant slope differences, with the treeline on the south slope being higher than those on the north slope, and the treelines on the east slope being higher than those on the west slope.The treelines obtained by our algorithm match the actual treelines in the Google Earth images of the study area well, showing an outstanding performance in the integrity and boundary connectivity of treelines. Given the capability of remote sensing technology to observe the Earth in a large scale and the high data quality and accessibility of satellite image data, the proposed algorithm for extracting alpine treelines can be further applied to global treeline mapping to provide technical support for global mountain ecosystem monitoring, conservation, and restoration.关键词：alpine treeline;remote sensing;automatic extraction;spatial distribution;global- 1777
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发布时间：2022-03-28 -
摘要：Accurate estimation of Forest Growing Stock Volume (FGSV) is needed to achieve the goal of carbon neutral. Quantitative inversion of FGSV using remote sensing technologies is still a research challenge. Optical remote sensing technology is one of the most important means for FGSV estimation, but cannot provide sufficiently accurate estimates due to lack of canopy structure features and data saturation problem. Although airborne Lidar can overcome the shortcoming of optical sensor data, its high cost in data collection and limited observation area constrain its extensive application. This research employs integration of Sentinel-2, ZY-3 stereo, and airborne Lidar data to explore the performance of FGSV estimation in north subtropical regions, and examines the advantages of using the hierarchical Bayesian approach to develop FGSV estimation models under the condition of small population of sample plots. The objective is to solve low modeling accuracy caused by the single sensor data and insufficient number of sample plots. The results indicate that the hierarchical Bayesian approach based on combination of Sentinel-2 and Canopy Height Model (CHM) data (subtraction of Lidar-derived digital elevation model data from ZY-3 stereo-derived digital surface model data) provides the best estimation results with relative Root Mean Square Error (rRMSE) of 27.6%. The Root Mean Square Error (RMSE) using this approach reduced by 13.6 m3/ha comparing with the RMSE based on Sentinel-2 data alone, and reduced by 7.4 m3/ha based on CHM data alone. The research shows that use of multi-source data can effectively improve the problems of overestimation when FGSV is small and of underestimation when FGSV is relatively high, that is, use of multi-source data can reduce the overestimation by one forth and the underestimation by one third comparing with use of single data source alone. Comparing with traditional modeling approaches such as linear regression and random forest, the hierarchical Bayesian approach can effectively reduce the requirement of number of samples due to use of stratification strategy and reduce the impacts of forest types and terrain differences on FGSV estimation accuracy. This research provides new insights of using integration of different data sources to develop FGSV estimation models to achieve accurate estimates, and provides key technology for FGSV mapping in subtropical regions.关键词：remote sensing;Forest growing stock volume;hierarchical Bayesian approach;Sentinel-2;ZY-3;airborne LiDAR;multi-source data- 1166
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发布时间：2022-03-28 -
摘要：The rate and intensity of forest fire spread can be predicted, and forest fire prevention measures can be formulated according to the classifying results of fuel types. Accurately exploring the types and spatial distribution of fuel is crucial for predicting the occurrence of forest fires, predicting forest fire behavior, commanding fire-fighting, and biological fire prevention. At present, most researches on fuel types classification based on remote sensing in China are carried out in local areas, but research based on the national scale will become one of the trends in this field. To meet the needs of China’s national-scale fuel types mapping, a fuel type classification system was developed combined with the characteristics of vegetation distribution and phenology in Chinabased on the previous research. The fuels in China, including forest, shrub, and grass, were classified and mapped based on MODIS products and the Chinese national vegetation regionalization map using geographical spatial analysis technology. A method for national fuel types classification in forests, shrubs and grasses based on remote sensing and geospatial analysis was explored. Non-tree cover, average vegetation canopy height and area occupied by each fuel types were calculated, using the product datasets of MODIS VCF（Vegetation Continuous Fields）and forest canopy height. The classification results were validated using field survey data and other data products. The results show that the total accuracies of the classification result at levels 1, 2, and 3 are 90.89%, 84.14%, and 68.16%, respectively; the Kappa coefficients of the classification result at levels 1, 2, and 3 are 0.81, 0.74, and 0.6, respectively. The national-scale fuel types, including forest, shrub, and grass, were classified and mapped by using the multi-source data and geographical spatial analysis technology. The study will provide technical support for the prevention and management of forest and grassland fire in China.关键词：remote sensing;Fuel type;remote sensing classification;spatial analysis technology;MCD12Q1;MOD44B- 2921
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发布时间：2022-03-28
Forestry and Agriculture
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摘要：Aerosol has an extensive impact on Earth’s climate and ecosystems and thus harmful to human health. The distribution of atmospheric aerosols has diurnal variation, and nighttime aerosol concentrations are higher than those during the day. Therefore, the accurate monitoring of AOD at night is significant but challenging. The Visible/Infrared Imager/Radiometer Suite (VIIRS) of Suomi National Polar-orbiting Partnership has a Day Night Band (DNB), which can observe the city light overnight. The artificial night light observed by the DNB of VIIRS reflects the extinction effect of the atmosphere and it is helpful in obtaining the nighttime AOD.To monitor the nighttime AOD, this study obtained the nighttime AOD on the basis of the theory of atmospheric radiation transmission. First, moonless and cloudless DNB data were selected, and the artificial lights during the crescent period were obtained by multi-day DNB data fusion. Second, the nighttime AOD was retrieved based on the extinction effect of aerosol on artificial lights. Finally, the obtained AOD is compared with the AOD from the CE318 and AQI indices from the environmental quality monitoring station to verify the feasibility of the retrieval method.This study focused on the nighttime AOD in North China from March 2016 to February 2017. The artificial lights of North China from four seasons were obtained, indicating that the major cities, especially the JingJinJi urban agglomeration, are very prominent in the artificial lights distribution map, and the traffic network between cities is clearly visible. Meanwhile, the nighttime AOD distribution during two heavily polluted weather processes in July and October 2016 was retrieved. The retrieval results indicate that the nighttime aerosols in North China are mainly distributed in the JingJinJi region and the provincial capital, which have developed industries and a dense population. Moreover, the retrieved and observed AODs are consistent, with an agreement index of up to 0.962, indicating that the retrieval study achieved good results.This study demonstrates the potential of satellite twilight data in monitoring the spatial distribution of urban pollution at night. The distribution characteristics of nighttime urban lights and aerosols are also provided, enriching the understanding of the temporal and spatial distribution of aerosols at night.关键词：nighttime aerosol;Optical depth;NPP VIIRS DNB;city lights;remote sensing retrieval- 1096
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发布时间：2022-03-28 -
摘要：Aerosol is one of the important components of the earth’s atmospheric environment, which has a profound impact on atmospheric transport, climate simulation, environmental research, remote sensing application, pollution monitoring and many other fields. The retrieval of the Aerosol Optical Depth (AOD) over land has always been an important research topic in the study of environment and climate. As an emerging remote sensing method in recent years, multi-angular polarized remote sensing has obvious advantages over traditional optical remote sensing in the problem of land-atmosphere decoupling, which has been rapidly applied and developed in the field of cloud and aerosol. In this study, an Optimal Grouped Residual Method for the aerosol was proposed, which uses the multi-angular polarized data of POLDER Level 1 datasets (Polarization and Directionality of Earth’s Reflectance, France). According to Mie scattering theory, the polarization scattering phase function of atmospheric aerosol were calculated. Then, the polarization reflectance contribution of aerosols was calculated based on the polarization scattering phase function of aerosols, and the polarization reflectance contribution of atmospheric gas molecules and the surface were calculated using empirical formula. Finally, the multi-angular apparent polarization reflectance of the top of the atmosphere under the assumption of single scattering was simulated according to the atmospheric radiative transfer theory, and AOD was retrieved.The retrieval results and accuracy were verified by precisely geographic matching and quantitatively comparing with MODIS (Moderate-resolution Imaging Spectroradiometer) aerosol product (MYD04). The results show that the R-square values of the regression analysis between AOD in this study and MYD04 can reach more than 0.68, and the slopes are close to 1, which reveal a good consistency. The AOD results were also compared with AERONET (Aerosol Robotic Network) in two sites, Beijing and Kanpur, revealing that the variation trends of AOD have good consistency. Furthermore, in order to verify the reliability of the method in this study from a broader spatial-temporal dimension, the AOD results were synthesized on a long time series of multi-day. Similarly, the multi-day synthetic AOD results obtained in this study also showed good consistency with the MODIS results. This method can be applied to multi-angular polarized satellite data (not only POLDER) to generate reliable optical depth products for the aerosol over land.关键词：remote sensing;POLDER;Multi-angular Polarized remote sensing;The optical depth of aerosol over land;Optimal Grouped Residual Method- 893
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发布时间：2022-03-28 -
摘要：The rapid detection of ship targets is one of the important applications of spaceborne synthetic aperture radar (SAR) and the first application direction of SAR on-board processing. At present, the constant false alarm (CFAR) detection method is the most commonly used in the rapid detection of SAR ships, and low false alarm has always been the focus of research. In multi-channel SAR, ship targets have multi-channel false targets, further increasing the difficulty of false alarm elimination.This paper proposes a fast detection method for low false alarm CFAR ships according to confidence calculation and SVM discrimination. First, the traditional CFAR algorithm introduces block calculation while designing the calculation method of ship target confidence and at the same time, according to the statistics of the geometric parameters of the current ship’s length, width, area, and so on, and the SVM classifier is used for the false targets with similar geometric parameters. Quickly distinguishing between ship and false targets and designing the overall process of CFAR detection. Finally, the GF-3 satellite dual-channel mode measured data is used to compare the algorithm running time and the target detection performance between the existing and new methods.Experimental results show that on the on-board processing platform based on TX2, the new method runs 30 times shorter than the traditional CFAR method and has good real-time performance. Meanwhile, the detection rate of ship targets reaches 97.8%, and the false alarm rate is controlled at 5.2%, showing a good ability of eliminating false targets.In view of the low real-time performance of traditional CFAR on spaceborne platforms and the existence of multiple false targets in the detection results, a method for quickly calculating the CFAR panoramic threshold is proposed. Experiments show that this algorithm has good real-time and detection performance for the ship detection problem of multi-channel spaceborne SAR data and can quickly and effectively detect ship targets and remove most false targets.关键词：remote sensing;multi-channel spaceborne SAR;ship detection;real-time processing;low false-alarm rate;CFAR (Constant False-Alarm Rate)- 2826
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发布时间：2022-03-28
Atmosphere and Ocean
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摘要：In recent years, the unreasonable development and utilization of mineral resources have been a global concern, and studying the spatial and temporal distribution characteristics of illegal mining resources is of particular importance. To solve the difficulty in obtaining information on the illegal mining of large-scale mineral resources, low accuracy, scattered data, and lack of long time series, this paper proposes a method for extracting the spatiotemporal distribution characteristics of illegal mining in multi-source satellite remote sensing data. First, Hunan Province was used as the research area, and the multi-source satellite remote sensing image data of 2010—2017 were combined with the mining rights data of Hunan Province. The human-machine interactive interpretation method was used to extract the illegal mining data of 2010—2017. The Kriging spatial data interpolation method analyzes the mining illegal mining data for 8 years. Finally, on this basis, the spatial and temporal distribution characteristics of illegal mining in Hunan Province are mainly studied. Results show that from 2010 to 2017, Hunan Province had a total of 2815 illegal mines, which were mainly distributed in Southern Hunan, Southwestern Hunan, Central Hunan, and Eastern Hunan, showing a year-on-year rising trend in time. Xiangnan, Xiangxi, Xiangdong, Xiangbei, and Northwestern Hunan expand and change in stages and regions. The spatial distribution also tends to develop from “high concentration” to “multiple points and wide areas.” Illegal mining is mainly based on non-metallic mines. A certain number of energy and metal mines exists. The illegal mining of non-metal mines shows a significant upward trend; the types of illegal mining are mainly unlicensed and cross-border mining, and the behavior of cross-border mining is on the rise. This study shows that the use of multi-source satellite remote sensing data can objectively, accurately, and long-term extract large-scale mining illegal information and effectively reveal the spatial and temporal distribution characteristics of illegal mining. An in-depth study of the driving force of illegal mining behavior is provided. Scientific and theoretical bases are provided for the country to formulate adaptive policies.关键词：spatiotemporal changes;mine;illegal mining;kriging;multi-source remote sensing;Hunan- 1229
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发布时间：2022-03-28 -
摘要：As an advanced surveying and mapping system, vehicle-borne mobile mapping system has several advantages, such as high precision, high efficiency, active, and non-contact measurement. This system can quickly collect high-precision road 3D point clouds, which are important for road boundary automatic extraction, and has become important in road information acquisition and update.To address the difficult and inaccurate extraction of urban road boundary point clouds in vehicle-borne laser point clouds, this paper introduces the Local Binary Pattern (LBP), which is an efficient and effective image processing method, to automatic point cloud classification. First, to take full advantage of the characteristics of various urban road boundaries, three improved operators were developed, including height, elevation dispersion, and spatial shape LBPs, which make full use of the three-dimensional shape, spatial geometry, and distribution characteristics of curbs. Statistical analysis was also performed on the feature image pixel values of the three LBP improvement operators. The statistical results are consistent with the spatial distribution and geometric characteristics of different objects, such as road boundary and road surface. Then, a diverse LBP features semantic recognition model, which can realize the quantitative expression of the spatial geometry and distribution characteristics curbs and pavements, was built. Finally, the road boundary point clouds were extracted by cluster and denoised with the road direction as the constraint.The point clouds of four different urban sections were tested. Results show that the extraction completeness rate of the experimental data is 92.0%. The method we developed can extract the main road and sidewalk boundary point clouds under different road environments. In terms of accuracy, 95.8% accuracy was achieved from considering the spatial distribution and geometrical characteristics of the curb. The results indicate that our method can accurately extract road boundaries in different environments and has strong adaptability.关键词：remote sensing;mobile measurement system;road boundary;diverse LBP;curb;point clouds classification- 848
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发布时间：2022-03-28
City and Land
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摘要：The quality of ICESat/GLAS satellite laser altimetry data is mainly dependent on the complex relationships between several factors in the path of laser transmission and on the illuminated surface, including clouds, atmospheric aerosol, satellite pointing, laser energy, topography, vegetation, footprint size, shape, and orientation. Although ICESat/GLAS has a high precision of elevation (~10 cm), the horizontal accuracy is relatively poor (~5 m). Therefore, the precise positioning of the laser shoot point has become an urgent problem.In the ideal situation, the energy intensity distribution of the LPA is approximately Gaussian, and its shape is approximately elliptic on the ground. Considering the effect of the attenuation of the transmitted laser energy in the atmosphere, the size of the spot image boundary can be determined by the 1/e2 maximum energy after eliminating the influence of the background noise of the LPA image. Therefore, we extracted the Laser Profile Array (LPA) image centroid and parameters of ICESat/GLAS via the 1/e2 maximum energy distribution and the least square ellipse fitting in different campaigns, respectively.Result shows that the extraction and relative positioning accuracies/frequencies of the LPA centroid can reach 0.9″/40 Hz and 0.37″/40 Hz, respectively, which are better than 0.3 and 0.11 pixels, respectively. The centroid results are similar to the official centroid provided in GLAH05. The parameters, namely, azimuth (computed as the angle between major axis and the LPA x-axis), major axis, eccentricity, and total intensity, were only approximations using the maximum energy distribution in different campaigns.The method in this study can effectively monitor the changes of these parameters. The characteristic extraction and analysis of space-borne laser spot image are of great significance to data processing and quality evaluation. ICESat’s spot image data were utilized to carry out experimental verification, providing reference for the launched of GF-7 and the follow-up domestic Terrestrial Ecosystem Carbon Monitoring Satellite.关键词：satellite laser altimetry;ICESat/GLAS;LPA spot image;Extraction of LPA characteristic parameters;gray weighted matrix method;centroid extraction- 830
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发布时间：2022-03-28 -
摘要：Pansharpening aims to sharpen a Low-spatial-Resolution (LR) multispectral (MS) image using a High-spatial-Resolution (HR) panchromatic (PAN) image to obtain a HR MS image. The pansharpened image with high spatial resolution and spectral fidelity had a wide application prospect. However, on the one hand, pansharpening methods usually produce different spatial or spectral distortions; on the other hand, the real HR MS reference image for the quality evaluation of the fused image cannot be obtained due to the limitation of satellite imaging systems. Therefore, the effective evaluation the quality of the fusion image without the real reference image is of great significance.In this paper, we propose a new non-reference quality evaluation method for pansharpened image on the basis of the Multivariate Gaussian Model (MVG). On the basis of a large-scale pansharpening database, this study constructed benchmark, test, and verification data sets, including various satellite sensors and thematic types. Then, a novel benchmark MVG evaluation model was constructed on the basis of the benchmark data set. In the proposed method, the images were first divided into sub-image blocks, and the spatial and spectral sensitive features of each sub-image block were first extracted. Then, many spatial and spectral characteristics were trained to establish the benchmark MVG evaluation model. In addition, in the benchmark MVG training, the sub-image with a high variance was selected for model training to enhance the robustness of the benchmark MVG model. Then, the testing MVG evaluation model for the fused image was established by comprehensively considering the spatial and spectral distortions. Finally, the relative distance between the benchmark and testing MVG models of the fused image was used to calculate the fused image quality.Experimental results show that the proposed method has better performance than the traditional non-reference quality evaluation methods in most satellite- and thematic-based data sets. The proposed method is based on Wald’s protocol and cannot be extended well to the full resolution evaluation of the fused image. Therefore, in future work, we will continue to study the full resolution evaluation method for pansharpening, which is more challenging and meaningful.关键词：remote sensing;image fusion;non-reference quality evaluation;Multivariate Gaussian Model (MVG);panchromatic image;multispectral image- 901
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发布时间：2022-03-28 -
摘要：All kinds of data resources and application methods of High-resolution Earth Observation applications lead to many problems. How to integrate and clustering the different demands, and how to evaluate the demand satisfaction is the key to the construction of the High-resolution Earth Observation system.The present paper study the requirements modeling and evaluation of nature resources investigation using analytic hierarchy process. The demand of nature resources investigation for VNIR are selected as the focus of this study. Firstly, the common demands of nature resources investigation are extracted and then demand model are established using demand for four-element model. Secondly, the condensation level algorithm is used to integrate the nature resources investigation demand for the High-resolution Earth Observation. Thirdly, the demand satisfaction of High-resolution Satellite to the nature resources investigation are evaluated using the fuzzy analytic hierarchy process.The results show that the demand satisfaction of demand cluster except biomass estimation, desertification monitoring and ecological capacity is greater than 8, and the good ratio of demand satisfaction is 88%.The High-resolution Earth Observation system can meet the need of most of the nature resource investigation, but the ability of ecological assessment for the High-resolution Earth Observation system should be enhanced.关键词：high-resolution earth observation;nature resource investigation;requirement integrate;fuzzy analytic hierarchy process;requirement satisfaction ratio- 2857
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发布时间：2022-03-28
Technologies and Methodologies
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