最新刊期
卷 22 ， 期 6 ， 2018
- 摘要：The optical multi-scale effect proposed by Academician Li Xiaowen in the last century has become an important cornerstone of quantitative remote sensing. The high-resolution optical polarization effect is gradually highlighted and directly determines or affects the high-resolution observation system, that is, implementation of quantitative remote sensing effect and quality assurance, given the development of the aerospace high-resolution observation in this century, namely, quantitative remote sensing, after effectively solving the optical multi-scale effect. For example, Chinese scholars found that the vegetation canopy has an extremely weak scattering reflection polarization effect when its model error of 136% is not deducted. Atmospheric attenuation is the atmospheric polarization effect and is the largest error source of remote sensing inversion; the error reaches 5%—30%. Polarization means deducting the atmospheric error can reduce the error by more than half. Observation instrument subtracts multiple scattered transmission polarized light, thereby attaining 5 nm resolution and 0.1—0.3 nm hyperspectral scalability, through polarization-enhanced light noise separation of the central wavelength offset and bandwidth degradation of the root causes of error. The system can achieve 10–8 spherical radiance benchmark observation for remote sensing radiance calibration uncertainty from 7% to 1%—2% across the provision of possibility given the polarization of “strong light weakening, weak light enhancement”.关键词：quantitative remote sensing;polarization effect;surface polarization;atmospheric polarization;polarization instrument;new method of polarization observation
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发布时间：2021-06-07 -
摘要：The development of novel principle photoelectric detection system is an important part of the construction of space situation awareness system. The imaging polarimetry has become a powerful tool to enhance the information available in a variety of space remote sensing applications. This paper presents a new space situational awareness system by imitating the structure and function of biology in nature. Inspired by the information awareness and navigation mechanism of the insect, birds and fish, we explore the science problem about environments information awareness and location measurement by means of model simulation, prototype construction and experimental verification, which based on the bionic polarization vision system. Bionic situational awareness system which fused the polarization image feature and navigation information is suggested. The information fusion and error analysis model based on bionic information perception and navigation is presented. A situational awareness prototype fused space feature acquisition and navigation is built up, the data update rate is higher than 25 Hz, and the angle measurement repeatability is within 0.05°. Polarization imaging situational awareness system function experimental results show that the bionic polarization detection method can reveal more details about the target feature information and enhance the effect of space target detection effectively, which fused the target light intensity, polarization degree and polarization azimuth information.关键词：polarization detection;situational awareness;bionic vision;information perception;position measurement- 541
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发布时间：2021-06-07 - 摘要：This study described the infrared polarization effect, which is one of the polarization effects of high-resolution quantitative remote sensing instruments. The mechanism and new progress in instrument detection are summarized. Light polarization information is missing in the current rapid development of optoelectronics and optical studies. The polarization characteristics, which can be extracted using additional information based on the detected object and the object of study, extend the detection dimension and improve the sensitivity. Furthermore, the measurement technology of infrared polarization effect and some advances in infrared remote sensing mainly from the aspects of basic and applied research are summarized. Then, potential applications from different viewpoints to observe the polarization effect are discussed. In this context, the infrared spectroscopic ellipsometry and its applications in detecting the optical properties of inorganic and organic thin film systems are introduced. Finally, the theory and related progress of infrared polarization optical instruments are summarized, especially their potential applications in space remote sensing.关键词：infrared;polarization;ellipsometric spectra;remoting sensing
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发布时间：2021-06-07 -
摘要：Studying the polarization characteristics of vegetation is important in vegetation monitoring and component quantitative inversion. The reflected radiation from the vegetation canopy possesses a polarization characteristic that is related to the interaction of incident radiation and vegetation canopy and the distribution of leaf inclination angles. This study analyzes the effect of photon–canopy interaction on the polarization characteristics of vegetation and Research Scanning Polarimeter (RSP) utilizes data combined with the Rondeaux and Herman model to study the polarization characteristics of vegetation under different leaf inclination distributions. A systematic comparative analysis is of more significant than a polarization study considering that leaf inclination distribution satisfies spherical distribution. This study presents the mechanism of photon–canopy interaction and some existing results related to the impact of canopy structure on the polarization characteristics of vegetation. As for the impact of leaf inclination, this study utilizes RSP data collected on September 16, 2005 in Oklahoma, USA, after atmospheric correction and dense vegetation extraction, and then combines the Rondeaux and Herman model for a systematic comparative analysis on wavelength, sun observation geometry, and leaf inclination distribution. In this study, we analyzed the impact of photon–canopy interaction on canopy structure and leaf inclination distribution on the polarization characteristics of vegetation. We found that the canopy structure is highly related with polarization characteristics, and the removal of the canopy structure makes polarization a major factor that influences vegetation reflection. We analyzed the stability of the polarimetric reflectance of the vegetation in wavelength range [400, 2500], the distribution of specular reflection in different directions, and the influence of different leaf inclinations and canopy structures on polarimetric radiation. Results of these analyses are clearly presented in the figures in this paper.(1) The effects of canopy structure and leaf polarization on the canopy reflectance of vegetation are closely related. Removing the influence of canopy structure is beneficial to the analysis of polarization characteristics and makes the polarization characteristics a dominant factor affecting canopy reflection. Neglecting the polarization property makes the scattering coefficient (obtained by removing canopy structure impact) produce up to 140% error; this part of the error is almost negligible before removing the canopy structure. (2) The polarimetric effect of multiangle and multitemporal remote sensing on the surface cannot be ignored. Multiangle polarization characteristic analysis and error rejection provide many possibilities for remote-sensing observations under low confinement conditions and help expand the space–time scale of earth observation. (3) Polarization characteristics under a given incident observation geometry is not sensitive to wavelength, and the mirror reflection of the canopy for the incident radiation exists in all directions due to the distribution of the blade inclination angle. Polarization reflectance increases with the increase in phase angle, and polarization characteristics are affected by the distribution of the leaf inclination angle, especially at large phase angles, allowing the use of polarization remote sensing to classify species and obtain canopy structural parameters. In this study, the polarization method is used to represent a high-precision and rich directional information acquisition process in high-resolution quantitative remote sensing and is the further exploration of effective information based on existing remote sensing methods. The effective information contributes to the further description and study of the greenhouse effect and provides a theoretical basis for the basic propositions in high-resolution remote sensing inversion.关键词：sun-sensor geometry;canopy structure;leaf inclination;polarimetric characteristics;RSP;high resolution remote sensing- 509
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发布时间：2021-06-07 - 摘要：In this study, we investigate the relationship between polarization and the structural property of vegetation covers to deepen our understanding of the physical mechanism of multiangle polarized light from natural surfaces, which is basic for describing the properties of Earth’s surface using polarized remote sensing. In this study, we explain the physical mechanism of the polarization properties of vegetation on the basis of the Fresnel equation and its derivative polarized reflectance models and compare them with the polarization measurement results of a single leaf and two vegetation covers, which are measured using a goniometer system. The Fresnel equation is effective for explaining the polarization of vegetation in forward-scattering directions, however, other theories should be considered in explaining polarization in backward-scattering directions. The polarization of vegetation covers can be considered as “noise” when we use the photometric signal, such as the separation of specular portion (which is computed by bidirectional polarized reflectance factor) from the total reflectance factor, which decreases the difference between BRF model results and measured results from 30% to 20%. Moreover, it possesses potentially useful information, such as the relationship between model parameter and vegetation cover roughness, which can characterize the structure of vegetation covers. Investigating the polarization of vegetation contributes to the understanding of the optical property of natural surfaces and potentially provides an additional and effective method for remote-sensing applications of vegetation covers. Our study also potentially provides a method for measuring the polarization of vegetation covers and demonstrates the physical rules of polarization in vegetation covers, such as the flatter the vegetation samples, the more polarization can be measured, and separating polarization from the total reflectance factor contributes to the improvement of the ability of current BRF models to simulate vegetation reflection. These results highlight the efficiency of polarized remote sensing on characterizing natural surfaces such as vegetation covers.关键词：polarized remote sensing;multi-angle;vegetation;bidirectional reflectance distribution model;specular reflection
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发布时间：2021-06-07 - 摘要：In optical remote sensing, the strong specular reflectivity and angle selectivity of water lead to detector saturation or to very low reflectivity for extracting effective information. The strong reflection characteristics and surface sensitivity of the snow limit the capability of the sensor to detect directly. Thus, water and snow are problems of passive remote sensing. The sensitivity of vegetation index under different reflection intensities in high-resolution quantitative remote sensing also challenges the accuracy and effectiveness of classical vegetation monitoring methods. This study aims to solve the bottleneck of water and snow that cannot be measured by optical remote sensing. This study uses the fourth law of remote sensing polarization effect, that is, high information–background ratio filtering characteristics. The polarization information can be obtained by adding the polarizer to the sensor in any direction. The Fessenkov method can be used to calculate the Degree Of Polarization (DOP) according to the data of different polarization angles and thus provide a new solution to the abovementioned remote sensing problems. The polarizing method can effectively enhance the information of the water-background ratio, which strips more than 70% of the glitter, provides the necessary method for the remote sensing of snow, and reduce up to 78% of error in vegetation monitoring. The mechanism of high-information-background ratio filtering is proved for the first time. Under the theoretical guidance and deepening of the experiment, the problems that water and snow can hardly be measured, and the bottleneck that vegetation cannot be accurately measured under strong reflection are solved.关键词：polarization;information-background ratio;filter characteristics;water;snow;vegetation
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发布时间：2021-06-07 -
摘要：Skylight polarization field patterns are not only used in high-resolution quantitative remote sensing but are also known as the source of polarization navigation information. Polarization navigation technology for biomimetic insects is a hotspot in navigation and bionic fields. Scientists have found a stable polarization distribution in the sky with the height of the sun. In addition, biologists have found that various insects can use their compound eyes to detect polarization information and achieve navigation and positioning function. Hence, domestic and foreign scholars have conducted extensive research on the mechanism of polarized light navigation sensor of bionic insect compound eyes. Further research into the precision mapping rules between polarization field patterns and polarization sensors under different space–time conditions, the accurate acquisition and modeling of skylight polarization field patterns, and the measurement accuracy and error analysis for information integration of skylight polarization field patterns under multi-space–time conditions is important. In this study, the research progress of polarized navigation is introduced from the aspects of atmospheric polarization field distribution and polarization navigation technique. The theoretical basis for describing the polarization distribution is introduced. Rayleigh scattering theory is similar to the clear cloudless atmospheric polarization model. Mie scattering theory and the vector radiative transfer equation consider the effects of cloud scattering, and they are closer to the true atmospheric polarization. The effect of polarization distribution test under different weather conditions is then described in the background of land, ocean, and sunlight and moonlight to compare the differences from neutral points under different conditions. The variation law and physical characteristics of the polarization vector field are summarized. The polarization distribution pattern of the whole weather is proved, which shows that the whole weather has the same variation law but is different in the polarization index. At the same time, the detectable and information utilization of the atmospheric polarized vector field during the day is higher than that at night. Meanwhile, the measurement error analysis based on cloud computing is presented. Finally, the development history and research results about accuracy assessment of polarized navigation devices at home and abroad are introduced. The broad application of polarization navigation in integrated navigation is pointed out. At present, polarized light navigation sensors can be divided into two types, namely, point-source and imaging types. The point-source type has good real-time performance and imaging robustness. However, its anti-interference ability is poor, making it difficult to achieve real-time measurement. In precision measurement, the point-source type is higher than the imaging type, and the highest accuracy it can reach is 0.1 degrees of navigation accuracy without drift error. A polarized light navigation system is characterized by autonomy with non-accumulative errors. In this study, the combined applications of polarized light navigation sensors and GPS, gyroscopes, and other navigation devices are introduced, and the research direction of polarized light navigation sensors is pointed out. Polarized light navigation is one of the practical applications of atmospheric polarization vector fields. The objective validity of skylight polarization field patterns is verified, and a practical guidance for high-resolution quantitative remote sensing technology is provided.关键词：atmospheric optics;polarized light field;bionic navigation;polarized navigation;combined navigation- 876
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发布时间：2021-06-07 - 摘要：Presently, polarized remote sensing, along with numerous polarized remote sensors equipped for satellites emission in China, becomes a new growth point and research hot spot in the field of earth observation. The polarization information of land objects is a valuable source for remote sensing. However, land surface object polarization information received by the polarimeter is constantly submerged in the atmospheric polarization effect. Consequently, the primary goal is to find a means to eliminate or reduce the atmospheric polarization effects when using the polarization remote sensing for land surface detection. In this paper, we demonstrated the feasibility of the neutral point separation method for polarized effect between land objects and atmosphere in the polarization remote sensing through the regular polarization distribution of clear sky and nature of the atmospheric polarization neutral points. The polarized effect of the Babinet neutral point was obtained by calculating the atmospheric neutral points in ascending and descending radiations. We discussed the detailed technology of applying the neutral point for polarization remote sensing based on the basic method of using Babinet points for polarization earth observation from two aspects of aerial and satellite remote sensing. The results show that (1) the Babinet neutral point is suitable for the separation method of the polarized effect between land objects and atmosphere in the polarization remote sensing. (2) The aerial remote sensing detection at the Babinet neutral point can eliminate the atmospheric polarization to effectively highlight the polarized information. (3) The solar synchronous orbit, POLDER satellite remote sensing images, can also identify the polarization neutral area effectively. (4) These results are a type of method for reducing the atmospheric error source in the polarization remote sensing to the lowest possible value and then obtaining the polarized reflectance of land object maximized from this atmospheric window of the polarization remote sensing. This approach separates the atmospheric polarization coupling effect in the high-resolution quantitative remote sensing inversion. These research results are practically significant for the atmospheric correction and improvement of the quantification level of polarized remote sensing.关键词：polarized light;remote sensing;atmosphere neutral point;mechanism;earth observation
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发布时间：2021-06-07 - 摘要：This paper discusses the status of full sky polarization pattern and the major factors affecting its strength. These factors are Aerosol Optical Depth (AOD) and aerosol models (described by the single scattering properties of aerosol particles). The different results of AOD and aerosol models are demonstrated. A relatively stable and regular sky polarization pattern is suggested to exist in the sky centering the position of the sun through the comprehensive results of incident solar light with underlying surface and atmosphere. This complex process can be described and calculated by vector radiative transfer models with the output of polarization components. The full sky polarization signals exhibit a certain regular distribution in the sky. The strength and pattern of full sky polarization are affected by the combination of surface reflective properties (including polarized reflectance and bidirectional reflectance), atmosphere molecule-scattering properties, and aerosol optical properties. This study simulated different full sky polarization patterns under various AOD conditions based on vector radiative transfer models. For example, the underlying surface is assumed to be an ocean surface. The land surface is omitted in this study because its basic pattern is similar to that of the ocean surface. The single scattering properties of dust nonspherical and soot nonspherical aerosol particles were calculated using a TMatrix-Geo model. Phase functions and other single scattering properties were then obtained as the basic inputs of vector radiative transfer model. Results demonstrated a relatively stable sky polarization pattern centering the direction of incident solar light. However, different effects were observed between dust and soot aerosol on full sky polarization pattern. The increase in dust-scattering AOD weakens the pattern of sky polarization from a maximum degree of polarization of 50% to less than 30%. Meanwhile, an increase in soot-absorbing AOD strengthens the pattern of sky polarization from the maximum degree of polarization of 50% to >70%. With the bimodel (mixture of dust and soot aerosol) aerosol model, the sky polarization patterns were systematically analyzed with different ratios of soot-to-dust aerosols. The basic sky polarization pattern is suggested to still exist in the sky, but the strength is influenced by both aerosol model and AOD. With increased soot AOD, sky polarization tends to increase despite the total AOD being the same. When retrieving aerosol optic properties with sky polarization pattern, paying attention to the selection of aerosol model information is necessary.关键词：polarization;polarization field;aerosol;sky polarization pattern
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发布时间：2021-06-07 -
摘要：Clouds are important regulators of the ocean–atmosphere coupling system in ocean satellite remote sensing. The results of cloud detection have a significant influence on the retrieval accuracy of cloud microphysical properties over the ocean. Therefore, achieving cloud detection over the ocean and determining methods to improve the processing speed of operational algorithm and the precision of cloud pixel recognition for polarized sensors are urgent concerns. This work proposes an Improved Cloud Detection (ICD) algorithm over the ocean according to operational cloud detection problems in satellite polarized sensor data. A series of continuous processes and tests is used to identify the clear-sky and pixel-by-pixel cloudy area using the data of Polarization and Directionality of Earth’s Reflectances (POLDER3). Such data are loaded by Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a LiDAR (PARASOL) satellite. The pixels are divided into ocean and land parts. Then, the ocean glint pixels are eliminated via the glint angle computing formula and the empirical threshold (see MODIS 40 degrees). Thereafter, the cloudy pixels are identified using the characteristic difference of near-infrared reflectance between cloud and clear-sky regions. Cloudy pixels with low reflectance are also further recognized using a polarized reflectance test according to the polarized sensitive characteristics of cloud particles. Next, the clear-sky pixels are identified by the reflectance ratio test between near- infrared and visible light. Finally, the spatial registration rule is created with multi-angle cloud detecting results, and all pixels are relabeled to cloudy, clear-sky, and undetermined pixels with this rule. The Indian Ocean is used as an example for experimental analysis. The results of improved cloud detection are compared with those of the Buriez method. The detection accuracy is very close to the Buriez algorithm but is more time-efficient. In the case of clear-sky pixels, the recognition rates of ICD and of the Buriez algorithm are approximately 30. For cloudy pixels, the recognition rate of ICD is approximately 47%. The error range of cloudy and undetermined pixels is approximately 1% compared with the Buriez algorithm. Furthermore, the efficiency of our cloud detection algorithm greatly improved. The data processing speed is improved approximately three times without considering the time consumption of external data acquisition. Results show that the algorithm is highly effective in achieving high-precision results of cloud detection. At the same time, the processing time of cloudy pixels is significantly reduced, and the operational speed of cloud-detecting products is considerably improved. This algorithm can provide real-time and accurate products for the inversion of atmosphere and ocean parameters to meet the high-precision and -efficiency requirements of operational processing in satellite ground systems. This algorithm is also dominant in the cloud detection process for Directional Polarimetric Camera (DPC) in the GF-5 satellite which is planned for launch in 2017.关键词：cloud detection;POLDER3 sensor;polarized reflectance;multidirectional spatial fusion;near infrared- 591
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发布时间：2021-06-07
Polarization Remote Sensing
- 摘要：Spatio-temporally explicit forest disturbance information is regarded as a critical mechanism for net ecosystem productivity. However, quantitative and spatio-temporally explicit information on forest disturbance is currently rare for most regions of the world. Unique among earth observation programs, Landsat data are systematically collected and archived following a global acquisition strategy. The provision of free, robust data products since 2008 has spurred a renaissance of interest in Landsat and resulted in an increasingly widespread use of Landsat Time Series (LTS) for multi-temporal characterizations. The science and application capacity of Landsat have developed steadily since 1972, with the increase in sophistication offered over time incorporated into Landsat processing and analysis practices. With the successful launch of Landsat-8, the continuity of measures at scales of relevance to management and scientific activities is ensured in a short time. The spatial, spectral, and temporal resolution offered by Landsat data is well suited, increasingly established, and operational in usage for forest management and analysis. Particularly, forest change detection algorithms based on LTS stack provide robust tools for detecting near real-time forest ecosystem changes, whereby a baseline of conditions can be determined for both abrupt and gradual changes and attributed to different drivers. Interest in LTS has been further enhanced by the recent introduction of several novel automated data processing techniques suitable for multi-temporal analysis, especially after the successful launch of highly spatio-temporal resolution satellites. The benefits are enabled by data availability, analysis-ready image products, increased computing power and storage, and sophisticated image processing approaches. Thus, change detection research for forest disturbance with time series remote sensing data has entered a brand-new stage. This review systematically summarizes the research progress and application of multi-temporal forest disturbance monitoring methods based on remote sensing data sources. Considering the significance and advantage of applying time series analysis in change detection, data availability selection, and data processing, various spectral indexes and dense time automatic monitoring methods for forest disturbance are analyzed extensively, and the characteristics of multi-source data and algorithms are summarized. Finally, the improvements based on the existing limitations are explored. At the time of this review, three forest disturbance change detection algorithms are commonly used for LTS: spectral variables, classification analysis, and trajectory analysis. Spectral-based techniques range from single-band reflectance to a host of indexes calculated from different algebraic manipulations of the original spectral bands and their derivatives. Classification-based techniques are an extension of traditional change detection techniques based on two-year images. Trajectory-based methods identify trends and breakpoints corresponding to disturbance events, stability, and recovery time, and they are useful for characterizing different disturbance regimes. The strength and limitations of each method prior to conducting an LTS analysis should be understood by forestry and remote sensing communities, who should ideally select a given method depending on their information needs. In summary, unprecedented advances in the assessment of forest disturbance have been realized in recent and future years using LTS data combined with other high spatial and temporal resolution imagery, especially domestic satellites as information sources.关键词：forest disturbance;Landsat;time series;multiple-source data;change detection methodology
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发布时间：2021-06-07
Review
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摘要：Using remote sensing technology to accurately obtain the spatial distribution information of crops in a large region is critical in improving the research level of resources and environment, strengthening the reaction capability of climate change, and ensuring national food security. Threshold method is a common method used to extract crop spatial distribution information based on time-series NDVI remote sensing data. However, several subjectivities and uncertainties were observed in traditional threshold methods when the threshold values were determined by humans, which hindered the improvement of threshold method identification accuracy. Considerable studies on determining the significant threshold value have been conducted in recent years. However, the accuracy and automation should be improved when the threshold values are determined. Considering the above problems, Jingxian County of Hengshui City in Hebei Province, which is the main grain producing region in China, was selected as the typical experimental area. A global optimization algorithm was applied to select the threshold value in the threshold model, and a crop distribution mapping method using global optimization algorithms based on threshold detection was developed. In this study, winter wheat was selected as the research crop, and domestic Gaofen-1 satellite images that cover the entire winter wheat growing season were used as major data source. A new spatial distribution mapping method for winter wheat based on time-series NDVI data for the automatic optimization of threshold model parameters was proposed in this study by using the global optimization algorithm Shuffled Complex Evolution-University of Arizona (SCE-UA) and crop area statistics as the total quantity constraint. First, the NDVI curves of winter wheat growth season were established, and the key phenological characteristics of winter wheat were extracted based on crop phenological and training sample data. Second, the general threshold model of winter wheat spatial distribution extraction was established, and the threshold parameters to be optimized were determined based on the seasonal variation and characteristics of time-series winter wheat NDVI at key phenological stages (such as seeding, tillering, jointing, heading, and maturation stages). Third, the optimal parameters of winter wheat spatial distribution model were obtained by the global optimization algorithm SCE-UA by using the crop planting area statistics at county level as total quantity constraint data and objective function. Finally, the optimal parameters of winter wheat threshold model were obtained in the study region, the spatial distribution of winter wheat was extracted by using the optimized threshold parameters, and the winter wheat distribution extraction results were validated by the ground verification samples. The final validation results showed that the accuracy of winter wheat identification results reached 99.99%, which proved that proposed threshold parameter optimization method had a good total quantity constraint effect and other classification accuracies had reached a high level. The overall accuracy and kappa coefficient were 97.03% and 0.94, respectively. Compared with the traditional threshold, support vector machine, and maximum likelihood methods, the overall classification accuracy of the proposed method increased by 4.55%, 2.43%, and 0.15%, respectively. The kappa coefficient of the proposed method increased by 0.12, 0.06, and 0.01, respectively. The above performances indicated that the crop distribution mapping optimization method based on the threshold model parameters of statistical data total quantity constraint and global optimization algorithms was effective and feasible to accurately obtain the spatial distribution mapping results of crops in a large region. This study can serve as basis to improve the accuracy and automation level of crop spatial distribution identification in China and provide several technical support and recommendations for other studies on crop spatial distribution extraction and crop mapping under complex planting pattern at large scale.关键词：remote sensing;crop mapping;threshold method;total quantity constraint;global optimization algorithm;winter wheat- 619
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发布时间：2021-06-07 -
摘要：This study aimed to extract the Leaf Area Index (LAI) of individual trees through multi-return terrestrial laser point cloud data, including single target, first-return waveform, intermediate, and last target data. The species was soapberry planted as street trees. An algorithm based on Beer–Lambert law was developed to divide return waveform, and the LAIs of individual trees with different projection resolutions of 0.01, 0.02, and 0.03 m were obtained. Two-dimensional digital hemispherical image data and LAI-2200 were used to extract the LAIs of corresponding single trees for accuracy comparison. Result shows that the resolutions and multiple return waveforms have significant influence on LAI. The results with 0.02 m and 0.03 m resolutions are similar to those obtained with LAI2200, and the difference is insignificant. However, the single target waveform point cloud data are used for the LAI solution and can be mutually verified with the results of the 2D image data of LAI2200. The LAI value of single target waveform point cloud was calculated with Beer– Lambert at 0.02 m resolution, and the intercept measured by LAI2200 data was subjected to a linear regression with an intercept of 0 and slope of 0.827, which is close to 1. Therefore, for the multi-return waveform ground laser scanning data, the single target waveform point cloud data with a projection resolution of 0.02 m can be used to obtain accurate LAI when using an extinction coefficient method. The algorithm for calculating the LAI based on multi-return waveform ground-based laser data in this study expands the application field of TLS and provides an important technical reference for the accurate extraction of the growth of standing trees and accurate tree modeling.关键词：multi-return waveform;terrestrial laser scan;leaf area index;difference analysis- 514
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发布时间：2021-06-07 - 摘要：Land-use change is the hotspot in the research on global change and sustainable development. The change of urban land use has attracted the attention of many scholars. Cellular Automata (CA) model, which is characterized by its powerful space–time dynamic simulation capability, is one of the important tools for urban land-use change. However, existing research does not systematically analyze the effect of sampling, neighborhood, structure, and different resolutions in the CA model. Therefore, this paper intends to carry out systematic sensitivity analysis in an urban CA model to obtain the quantitative accuracy effect by different factors, such as sampling and neighborhood structure, and the optimal model simulation results. The critical part of CA models is transition rules, which are usually represented by exogenous impact factors such as roads, highways, and towns. These factors (variables) can be addressed by incorporating multicriteria evaluation (MCE) form into CA, which is transformed from MCE into a logistic form to obtain the parameters with a more objective method. This study applies the Monte Carlo method to create different sample ratios that can be used to obtain the weight of the CA model. We also test the CA model’s sensitivity by using different neighborhood structures. Landscape metrics are adopted to verify the accuracy of simulation results in different spatial resolutions. These methods can help determine the best combination for the CA model. After the model is applied to Panyu, the core area of the Pearl River Delta, simulation results can be obtained by using three combinations and processes. First, different sampling ratios and category proportions are used to study the parameter changes under different sample groups. Second, different neighborhood structures are used to find the relationship between model accuracy and neighborhood structure. Finally, the simulation results and changes in different resolutions, landscape index, and 3×3 micro-neighborhood are analyzed. Different simulation results are determined for different combinations of sample ratio, neighborhood, and spatial resolution. Our findings are as follows: (1) High precision weights can be obtained using high sampling ratios, and the proportion of urban in the sample should be consistent with the change rate in the study area. (2) Regardless of which kind of neighborhood structure is used, the simulation accuracy decreases with low-resolution data. However, the simulation accuracy of the Moore neighborhood will be better than that of the Von Neumann neighborhood. The corner cells have a greater effect than the adjacent cells. (3) The patch number, patch density, concentration, and fractal dimension values fall with low-resolution data. The structure of simulation results becomes simple, and the development density of the Moore neighborhood decreases.关键词：cellular automata;urban expansion;sensitivity;landscape index;neighborhood structure
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发布时间：2021-06-07 -
摘要：Timely updating of wetland map is vital to wetland research and management. However, the highly hydro-dynamic characteristics and great spatial heterogeneity of wetlands pose challenges in updating large-scale wetland thematic maps in a timely manner. General mapping methods, such as supervised or object-oriented classification, are time consuming and can be easily affected by cognitive differences. To address this issue, we propose an automatic updating method of wetland map, namely, iterative interview and reorganization. We aim to design a method that can transfer the knowledge from existing wetland thematic maps into the classification of a new remote sensing image. At the same time, the method should be robust for different geographical conditions. Rather than adapting samples between different domains, Iterative Interview and reorganization (IIR) tries to obtain the precise spatial distribution of ground objects first and then defines the properties of the spatial distributions by matching their spatial features. The method can tackle complex situations caused by changes of ground objects. This automatic method achieves an overall accuracy ranging from 70% to 90%, similar to the results of general supervised classification. In some cases, IIR has better performance in the identification of detailed information than that the support vector machine or maximum likelihood classification, such as for boundaries of ground objects and slender targets. To examine the performance of this method, we choose four wetland reserves with various geographical environments across China, including the Momoge Nature Reserve in high latitude, the Zoige Reserve in high altitude, Poyang Lake in a hot-humid area, and Yellow River Delta along a coastal area. Both overall and individual accuracies of various wetland classes in the four study areas are higher than those of the general supervised classification. Furthermore, IIR can automatically detect new classes such as paddy field. Without extra samples, IIR achieves better classification in four study areas of different landscapes. This method is not only adaptable for eliminating unfavorable factors, such as terrain or clouds, but also more flexible and robust when dealing with different wetlands and phenological changes, demonstrating that the IIR method can be applied in large-scale thematic map updating. IIR can have a consistent interpretation of the same wetland class because all procedures are carried out without expert knowledge. In conclusion, IIR can meet the needs of automatic updating of large-scale wetland thematic maps.关键词：automatic updating;wetland;thematic map;large scale- 521
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发布时间：2021-06-07
Remote Sensing Applications
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