最新刊期
卷 26 ， 期 12 ， 2022
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摘要：The development of Mobile Geographic Information System (MGIS), which is driven by the change of information technology, is clarified and divided into three eras in this work: embedded, mobile Internet, and intelligent Internet Of Things (IOT) era.At the end of 1990s, with the completion of Global Positioning System (GPS) deployment in the United States, the functions related to information acquisition of desktop GIS system were transplanted to Personal Digital Assistant (PDA) and other embedded devices to facilitate field data acquisition. MGIS entered the “embedded era” combined with GPS. In this period, the stand-alone version of MGIS has been successfully applied in the field data collection of land, forestry, surveying, mapping, and other industries. Although MGIS had some online functions at that time, the bandwidth of mobile network was not enough to support high-frequency network GIS services.MGIS has gradually entered the “mobile Internet era” with the rise of 3G/4G and other broadband mobile networks and the popularity of intelligent mobile terminals (especially Android mobile phones). In this period, the core module of MGIS was translated from Global Navigation Satellite System (GNSS) to wireless communication network. The most typical applications in this era are the map APP developed by Google, Baidu, and other electronic map service providers and its related location-based services APP. At this time, MGIS has extended to the entire geographic information industry chains, involving data collection, data processing, platform software, industry applications, etc. “Cloud+End” constitutes a new ecosystem of geographic information. However, due to the problems of cloud computing, such as less real-time, insufficient bandwidth, and large energy consumption, which are not conducive to data security and privacy, MGIS in this era is still in the traditional artificial ground operation stage, making real-time spatial analysis, target recognition, and other intelligent processing difficult.The MGIS technology gradually enters the “intelligent IOT era” around 2019 with the ubiquitous development of IOT, especially the development of Computer Vision (CV), Artificial Intelligence (AI), 5G mobile communication, edge computing, and other technologies. The main technical features of this stage are intelligent, real-time, and ubiquitous GIS, and the system architecture evolves into “cloud+edge+end”. In this era, everyone is a sensor and plotter. A large number of intelligent sensors, such as cameras and radars integrated on ground mobile platforms (wearable devices, vehicles, etc.) and air mobile platforms (unmanned aerial vehicles, etc.) have emerged, which can help us locate and provide holographic map information, such as acoustic-optic-magnetic information. These IOT terminals can be used as carriers of MGIS. The massive raw data of the collected images, videos, locations, and other data are no longer uploaded to the cloud, but are analyzed and processed in real time by AI and other technologies on the network edge devices. Only the results are transmitted to the cloud. This mode greatly reduces the pressure of network bandwidth, data center power consumption, and system delay and enhances the service response ability. Furthermore, the risk of network data leakage is greatly reduced, and user data security and privacy are protected because users no longer upload privacy or sensitive data (only stored on network edge devices).On this basis, a new generation of MGIS is proposed, which integrates GNSS, 5G, AI, CV, and other information technologies. The ubiquitous, real-time, and intelligent features of this technology are analyzed, and three core technologies, including cross-platform kernel, simultaneous localization and mapping, pan-information-based high-precision navigation map, semantic map, and intelligent decision-making, are discussed. The development trend and direction are also predicted.关键词：mobile GIS;artificial intelligence;UAV remote sensing;internet of things;cloud computing;edge computing;smart industry- 3084
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发布时间：2024-03-27 -
摘要：Pansharpening is a fundamental problem in the field of remote sensing data processing. It has important research significance and application value in ground object classification and ground surface change detection. In recent years, Deep Learning (DL) has made great progress in natural language processing, computer vision, etc. and has promoted the development of pixel-level pansharpening technology. This work presents a systematic review of the research of DL in pansharpening from two aspects (classical and collaborative approaches) and makes a prospect on this basis. First, the common datasets of pansharpening and the objective evaluation indexes of pansharpening, including reference and non-reference quality evaluation indexes, are provided. Second, the latest research results of DL-based pansharpening are introduced in two different categories from the classical and collaborative methods, and the performance of their algorithms is compared, analyzed, and summarized. The classical methods mainly include AE-based pansharpening, CNN-based pansharpening, DRN-based pansharpening, and GAN-based pansharpening methods. Meanwhile, the collaborative methods mainly include DL+CS-based pansharpening, DL+MRA-based pansharpening, DL+MB-based pansharpening, DL+injection model-based pansharpening, CNN+DRN-based pansharpening, and RNN+CNN-based pansharpening methods. In the comparative analysis of the classical and collaborative methods, the common point is that the DL technology can automatically learn the advantages of complex data features and extract the feature information of the MS or PAN image (i.e., the information that needs to be retained in the HRMS fusion image). The difference is that the structure of the classical mode is more concise, while that of the collaborative mode is more complex because it is the combination of multiple methods or frameworks. In addition, most early DL-based pansharpening methods utilized the powerful data fitting ability of the DL model and seldom paid attention to the field of pansharpening problems. With the gradual deepening of research, such as using DL methods combined with traditional pansharpening methods, this designed fusion model considers spectral and spatial distortions. Accordingly, the DL methods can further enhance the pansharpening effect. Thirdly, the three main application fields of pansharpening are analyzed, such as object classification, target recognition, and surface change detection. Finally, this work discusses the future research direction of DL-based pansharpening in combination with remote sensing knowledge to fully tap the potential of DL to obtain fused images with richer details and more natural spectra. For example, for the evaluation of pansharpening application, the performance of pansharpening in a certain application is related not only to the high quality of fusion image but also to the knowledge of a specific application field. Accordingly, the application-oriented pansharpening evaluation algorithms will be the focus of future study. Furthermore, DL-based pansharpening needs to train a large number of network parameters, resulting in a longer training time for the pansharpening model. The lightweight depth model has a smaller network capacity, lower time complexity, and lower hardware requirements. Therefore, constructing a lightweight pansharpening model is a promising future direction.关键词：pansharpening;deep learning;classical mode;collaborative mode;quality evaluation;remote sensing image fusion- 2266
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发布时间：2024-03-27 -
摘要：Remotely sensed Land Surface Temperature (LST) from a single source rarely has high temporal and spatial resolutions due to the sensors’ optical characteristics. Spatiotemporal fusion uses data from multiple sources to retrieve LST with high temporal frequency and spatial detail, and the spatiotemporal contradiction is disentangled in the fusion process. According to an in-depth study of spatiotemporal fusion, LST exhibits unique features distinct from other land surface variables. However, the inherent mechanism and potential application of LST spatio-temporal fusion have yet to be compiled and extensively explored. Based on the intersection between LST and spatiotemporal fusion, this work collects, analyzes, and summarizes the state-of-the-art developments in LST spatio-temporal fusion. The research background, principles, methods, and applications of this field are systematically elaborated. In particular, the relations and differences with the ubiquitous spatiotemporal fusion technology are emphasized.In essence, spatiotemporal fusion methods extract exquisite temporal variation of pixels from the low spatial resolution images and obtain spatial correspondence from images at various scales to predict high spatial resolution images. The spatiotemporal fusion shows great promise over homogeneous and stable land surface, but has an unsatisfactory performance over heterogeneous landscapes with unstable thermal conditions. In comparison with Land Surface Reflectance (LSR), the spatiotemporal fusion for LST can be less sensitive to the land cover classification uncertainties because of its lower spatial resolution and lower diversity among different land types, but it is difficult to achieve using the general laws for accurate prediction due to the drastic temporal variation of LST.After spatiotemporal fusion was successfully implemented in LSR, several studies adapted it to LST with some improvements based on the thermal characteristics. In the existing five categories of spatiotemporal fusion models based on weight and learning, Bayesian and hybrid models have been applied to LST. Among these models, the weight models are more mature, robust, and effective, but they cannot easily capture the temporal change of LST. Furthermore, the improvement is relatively limited based on STARFM, ESTARFM, or other classical weight models. Learning models can realize a nonlinear prediction based on the structural similarity of training data when supported by reliable network architecture and abundant training. In particular, the deep learning models have more superior ability to depict and extract the LST with weak spectral characteristics, but suitable neural networks and model parameters must be selected and optimized. Although fusion studies based on the Bayesian framework (including maximum a posterior and Bayesian maximum entropy) are relatively rare, they have shown great potential for achieving unbiased and nonlinear predictions and low-quality requirements for the initial data as LST. The hybrid models can integrate the preponderances of the above-mentioned models and acquire more flexible, efficient, and accurate prediction results compared with a single fusion model, which could be the mainstream of the future spatiotemporal fusion model.Although the spatiotemporal fusion models are consistently developed, most of them only focus on generating fused products, with a lack of quantitative and qualitative analysis with respect to the practical applications of the fused LST products, such as agriculture and ecology. In this work, the applications in this field are divided into six aspects: land temperature, sea surface temperature, agroforestry, urban heat island, public health, and others, which cover the majority of remote sensing service fields. However, the breadth and depth of the application of the LST fusion products are less than those of LSR fusion products. The mutual development between theoretical research and application demand is urgently needed.The primary impediment to the application and dissemination of spatiotemporal fusion is the data itself, as evidenced by the diversity of multi-source data, the spatial continuity of image, and the sensitivity of temperature in time series. The angular effect, unstable inversion accuracy, and dramatical diurnal variation significantly constrain their potential applications. Considering these characteristics of LST and existing defects of the spatiotemporal fusion model, this work proposed the future work prospects, such as improving LST inversion accuracy, complementing the strengths of multi-source data, employing a deep learning model, enhancing algorithm flexibility, and constructing a spatiotemporal fusion integrated procedure. The implementation of these strategies will propel the development of theoretical research and operational application of LST with the spatiotemporal fusion technology.关键词：remote sensing;land surface temperature;spatio-temporal fusion;dense time series;spatial downscaling- 2013
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发布时间：2024-03-27 -
摘要：Leaf Area Index (LAI), an essential climate variable that characterizes vegetation canopy structure, is essential in ecological and hydrological processes. Global scale LAI remote sensing products had been generated and widely used in the research of ecological environment. Most existing LAI retrieval algorithms assume that the land surface is flat and homogeneous, thereby demonstrating good performance in a homogeneous land surface. However, many studies have demonstrated that neglecting the influence of topography may cause large biases and uncertainties of the estimated LAI in a mountain area. A rugged terrain can not only distort radiation in different slopes and aspects but also cause shadows due to neighboring topographic effects. Forest occupies a large proportion of the land surface and has the most complex structure over rugged terrain, attracting greater attention to estimate accurate LAI due to its great contributions to the ecological environment. In this work, we systematically summarized the LAI retrieval algorithms and global remote sensing products and investigated the major challenges when applying those algorithms to LAI inversion over rugged terrain. Thereafter, we reviewed the main LAI retrieval methods, including topographic correction methods and the mountain radiative transfer models. Finally, the topographic and scale effects of the field in situ LAI data over a rugged terrain were discussed. The comprehensive summary and prospects show that great advances in remote sensing observations, radiation transfer modeling, machine learning techniques, etc. provide a promising way toward accurate LAI estimations and reliable validation over a rugged terrain.关键词：leaf area index;topography;remote sensing;retrievel;statistical model;canopy reflectance model;validation- 2132
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发布时间：2024-03-27 -
摘要：Merged satellite altimeter products have high scientific and social values and are widely used in many ocean related fields, such as marine environmental monitoring, ocean meso-scale structure research, and ocean numerical forecast. At present, 5—6 altimeter satellites are in orbit and simultaneously publishing along-track data. We focused on the main question of extracting effective information as much as possible from the existing satellite altimeters of constant number and improving the effective resolution and product quality. The development status of the existing merged satellite altimeter products were introduced in detail.We introduced DT series merged products made by DUACS in detail based on the widely used AVISO products. Moreover, we analyzed the influence of different merged methods on the product quality starting from the merged method and expounded the decisive role of the selection of background field in the merged method on the effective resolution of the merged product. When combined with sentinel-3a (S3a) and J3 satellites that have recently released data and CryoSat, Saral/Altika, and HY-2a satellites, the observation density of satellite altimeters along the orbit is significantly improved. Meanwhile, the effective resolution of DT2018 produced by DUACS is not significantly improved compared with DT2014 and DT2010. The main reason is that the 7, 20, and 25 year average MSS statistical fields were selected for the background field, which includes more large-scale signals in the background error, resulting in the background error. The scale of error covariance correlation coefficient is longer. The filtering effect of the observation data is more significant, and the effective resolution cannot be effectively improved. HY-2B and HY-2C satellites were successfully launched in October 2018 and September 2020, respectively, with sentinel-3b (S3b) already in operation. The accuracy and density of satellite altimeter observation data will reach a new stage together with the SWOT altimeter satellite, which will be launched in 2021. How to consider the correlation of the same orbit observation error, accurately estimate the background error, establish a more reasonable expression function of the correlation coefficient of the background error, and provide an effective method for further improving the merged product quality and effective resolution are the directions that scientists continue to explore.Previous studies showed that the quality of merged satellite altimeter products is mainly affected by three aspects(1) Type of satellite selected for the production of products: the space-time accuracy of sea surface height also varies due to the difference of orbit height and period of different types of satellites; (2) Selection of altimeter standards: instrument parameters, geophysical parameters, environmental correction, construction of mean sea level, etc.; (3) Data merged methods: selection of different merged methods in the background field, difference of background error correlation coefficient scale, and processing of observation error.关键词：satellite altimeter;merged data product;effective resolution;variational method;optimal estimation- 2875
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发布时间：2024-03-27
Review
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摘要：This paper used CO2 products inversed from Scanning Imaging Absorption spectrometer for Atmospheric CartograpHY (SCIAMACHY) and Greenhouse Gases Observing Satellite Fourier Transformation Spectrometer (GOSAT FTS), and the linear regression analysis was used to validate the remote sensing data with CO2 concentration data observed at the Waliguan station, then the remote sensing data were corrected. The superposition model of linear and sine function was further used to analyze the temporal trend and periodicity of regional mean CO2 over China from 2003 to 2018, and the CO2 was synthesized at different temporal scales to study the spatial distribution characteristics. Finally, the influencing factors on these temporal and spatial characteristics were analyzed. The results showed that SCIAMACHY and GOSAT CO2 retrievals agree well in general with ground observation CO2, but the satellite remote sensing has obvious systematic errors. The differences between GOSAT and WDCGG CO2 were larger than those between SCIAMACHY and WDCGG CO2, with an average deviation of -3.89 ppm and 1.00 ppm, respectively. The SCIAMACHY CO2 was overestimated at smaller value and underestimated at the larger value. In terms of seasonal variation, the regional monthly mean value of CO2 column concentration appeared a cyclical variation at 12 months and gradually increase with temporal processing. The annual regional average CO2 column concentration in 2003 and 2018 were 374.4 ppm and 413.7 ppm, respectively. The CO2 increased 39.3 ppm (about 10.51%) from 2003 to 2018, with the average annual growth rate of 0.59%. The monthly variation of CO2 column concentration showed significant temporal and spatial differences characterizing by a sinusoidal fluctuation, with the minimum and maximum values appeared in August and April with 407.7 ppm and 416.3 ppm in 2018, respectively. The periodic characteristics of CO2 column concentration are primarily affected by the vegetation growth cycle of terrestrial ecosystems, various chemical processes in the soil, and anthropogenic emissions. The multi-yearly average CO2 column concentration values from 2003 to 2018 varied between 388 and 398 ppm, with the standard deviation ranged from 10 ppm to 15 ppm. Additionally, the high values of CO2 column concentration mainly appeared in the subtropical and temperate regions in the eastern China. The annual average CO2 column concentration in 2018 is up to 417.9 ppm. The lowest value was in northern Inner Mongolia of about 409.5 ppm in 2018. The CO2 column concentration in the entire region of China showed increased significantly from 2003 to 2018, but the growth rate has obvious heterogeneity in space. The increase values from 2003 to 2018 were between 31.0 ppm and 45.4 ppm. Furthermore, the growth rate had obvious heterogeneity in space, which was in range of 8.9%—12.2% in space. There was an obvious shed line for the growth rate of CO2 column concentration, which was from northeast to southwest, coinciding with the geographical dividing line of China (Mohe-Tengchong Line). The largest growth rate occurred at the junction of Liaoning and Jilin Province, with the value of 12.2%. The region with the smaller growth appeared in Central China, with the lowest growth rate of about 8.9%.关键词：SCIAMACHY;GOSAT;CO2 column concentration;remote sensing;Variation coefficient- 1535
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发布时间：2024-03-27 -
摘要：Monitoring the atmospheric particle pollution is one of priorities for environmental protection. To infer the near-surface fine particulate matter (PM2.5) mass concentration, aerosol Fine-Mode Fraction (FMF) is an important parameter, which can separate contributions from smaller and bigger particles in Aerosol Optical Depth (AOD). However, there still have great challenges for the conversion between FMF and remotely sensed optical measurements. As one of the most promising methods of remote sensing, polarimetry is widely employed for atmospheric aerosol monitoring, and has a good potential for improving FMF inversion. In order to investigate the contribution of polarization to the for improved characterization of FMF, an algorithm for FMF retrieval from multispectral intensity and Degree Of Linear Polarization (DOLP) measurements is proposed in this paper.The proposed algorithm is based on the Optimal Estimation (OE) inversion theory. The UNified Linearized Vector Radiative Transfer Model (UNL-VRTM) is adopted as the forward model, and the quasi-Newton approach implemented by the L-BFGS-B code is used to find the minimum of the cost function. In order to test the performance of the algorithm, synthetic data for ground-based measurements of sky light, in the conditions of different aerosol optical depth (AOD, from 0.1 to 3.0) and FMF (from 0.05 to 0.95), are simulated. In addition, near-infrared (NIR) measurements at a wavelength of 1610 nm were introduced to improve the retrieval of coarse mode aerosol. Under the OE inversion framework, the AOD and FMF can be retrieved simultaneously after several iterations.Based on the synthetic data, analysis shows that the DOLP is more sensitive to FMF in the NIR band (centered at 1610 nm) than in the visible band (centered at 490, 550, 670 and 870 nm). Numerical inversion test furtherly show that the algorithm has well self-consistency, the error of retrieved FMF caused by the algorithm itself is 0.014%. In the case of 5% observation error is considered, the average fitting residual, differences between the simulations with best inversion results and the measurements, is 5.2%, which is slightly higher than the intensity observation error (5%). By introducing DOLP measurements into the retrieval, the inversion accuracy improved significantly than only using the intensity measurements. The retrieval error of AOD has decreased from 1% to 0.3%, and the retrieval error of FMF has decreased from 1.4% to 0.18%.These results strongly validate the feasibility and potentiality of the proposed OE inversion method in atmospheric aerosol polarimetric remote sensing. This mechanism is expected to be a new approach to improving the remote sensing capabilities of PM2.5 monitoring.关键词：remote sensing;degree of linear polarization;aerosol;fine mode fraction;optimal estimation retrieval- 940
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发布时间：2024-03-27 -
摘要：Euphotic zone depth (Zeu) is defined as the depth at which photosynthetic available radiation is 1% of its surface value. This zone is in the upper water column, where marine phytoplankton can effectively photosynthesize, which is essential in air–sea interaction through transfer of either gases or heat, especially with reference to greenhouse gases, such as carbon dioxide. Accordingly, the euphotic zone has an important influence on research into marine primary productivity, phytoplankton biomass, and global carbon cycle. Meanwhile, the spatial and temporal variations of Zeu are closely related to the variability of water color elements. Consequently, Zeu is regarded as an indicator of water clarity, which may even have a certain indicative significance for ecosystems. Thus, marine researchers have prioritized Zeu monitoring.In this study, a remote sensing model was proposed to estimate Zeu from the moderate resolution imaging spectroradiometer (MODIS) satellite data based on in situ data collected from several cruises in the Bohai Sea and Yellow Sea. The designed model uses the logarithm of slope of the remote sensing reflectance (Rrs) between 443 nm and 6S67 nm as an input. In situ data validations indicated that the algorithm shows good performance, with 0.86 R2 (coefficient of determination), 4.14 root-mean square error, and 17.2% mean absolute percentage error. The model based on Rrs efficiently performs compared with the current common models.The long term MODIS satellite data (2002—2020) were further used to investigate the spatial and temporal distributions of Zeu in the Bohai Sea and Yellow Sea. Results indicate that: (1) Zeu is low in the coastal regions but high in offshore waters. Meanwhile, clear temporal variability in Zeu was observed, showing that Zeu is typically high in summer but low in winter for most regions. (2) The tongue-shaped structure with a low value in the North of Yangtze River Estuary extends to the northeast in summer and turns to the southeast in early autumn. (3) Zeu monotonously varied in the Bohai Sea, Northern Yellow Sea, and Subei Shoal from 2002 to 2020. In the Bohai Sea and Subei Shoal, Zeu showed a downward trend, while it displayed an upward trend in the Northern Yellow Sea. Meanwhile, Zeu indicated a fluctuating trend in the Southern Yellow Sea, South of Jeju Island, and North of Yangtze River Estuary.Furthermore, the potential driving factors responsible for these spatiotemporal variations were examined based on multi-source satellite data. The results indicate that the spatial and temporal variations of Zeu in the Bohai Sea, Southern Yellow Sea, Northern Yellow Sea, and Subei Shoal are influenced by a variety of driving factors. Zeu is positively driven by the sea surface temperature and photosynthetic active radiation but negatively driven by wind speed and total suspended matter concentration. Specifically, the total suspended matter concentration has a significant effect on the Zeu variations. Meanwhile, Zeu in the North of Yangtze River Estuary is strongly related with the amount of runoff (correlation coefficient R=-0.55).关键词：euphotic zone depth;remote sensing estimation;MODIS;Bohai Sea and Yellow Sea;spatial and temporal variations;driving factors- 1145
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发布时间：2024-03-27
Atmosphere and Ocean
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摘要：Green plastic covers have been widely used as the primary method of dust prevention in construction sites. The rapid acquisition of green plastic cover and spatial–temporal change information has an important guiding significance for the formulation of dust prevention and ecological environmental protection measures. This work extracted the covered area of green plastic cover by using DeepLabv3+ semantic segmentation model based on Sentinel-2 remote sensing data and realized the annual green plastic cover segmentation and extraction in Jinan from 2016 to 2020. The spatial distribution characteristics and spatial–temporal expansion trend of green plastic cover were analyzed by area statistics, landscape pattern analysis, and mean center-standard deviation ellipse. Results show that: (1) According to the accuracy evaluation, the proposed architecture reaches an acceptable accuracy, with 84.05% precision, 80.09% recall, 0.82 F1 score, and 69.72 IoU, which could quickly and accurately extract the urban green plastic cover and realize the large-scale and time series dynamic monitoring and management. (2) The accuracy of DeepLabv3+ model used in this work is the best compared with the traditional remote sensing classification method and other sematic segmentation models, and the extraction results of green plastic cover are more precise. In addition, the extraction experiments in Beijing and Tianjin also confirmed the migration ability of the model. (3) The laying range of green plastic cover significantly expanded from 2016 to 2020. The area and number of green plastic cover patches, fragmentation degree, and shape complexity increased. The average patch area increased, the cohesion and aggregation between patches fluctuated, and the landscape pattern was complex and unstable. The expansion has an obvious direction. The distribution and dynamic expansion of green plastic cover are affected by human factors, such as urban planning and project process. Urban planning determines the distribution of green plastic cover, and the current use of green plastic cover reflects the process of urban construction to a certain extent. Therefore, the dynamic monitoring and management of urban green plastic cover by remote sensing means can provide data and technical support for urban planning, ecological environment construction, and urban accurate management, which are of great significance to the management of urban expansion mode and reconstruction.关键词：green plastic cover;remote sensing;semantic segmentation;DeepLabv3+;Sentinel-2;temporal and spatial variation- 795
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发布时间：2024-03-27 -
摘要：Synthetic Aperture Radar (SAR) data from different polarization channels have distinct responses to ground objects. The surface deformation monitoring capabilities of the conventional single-polarization InSAR technology can be improved by using multi-polarization SAR data. Existing studies have compared the deformation monitoring capabilities of different polarization data (VV-VH) for dual-polarization Sentinel-1 data or optimized a certain scattering target based on a single quality criterion to improve its interference phase quality, which is supposed to have better performance than conventional persistent scatterer interferometry (PSI) approaches. However, the potential of dual-polarized Sentinel-1 data was not fully realized. To this end, this work proposes an adaptive polarimetric persistent scatterer interferometry method (PolPSI) based on dual-polarization Sentinel-1 data. In the PolPSI method, the persistent scatterer target and distributed scattering target are adaptively optimized to obtain optimized interferograms, and they are used as bases to monitor surface deformation. Besides, in this study, we simultaneously applies PSI, DSI, and PolPSI method for Shanghai Pudong International Airport deformation monitoring, by using 34 scenes of dual-polarization (VV-VH) Sentinel-1 images, and combining the information from both VV and VH channels, the polarimetric persistent scatterer interferometry (PolPSI) techniques is supposed to achieve better ground deformation monitoring results than conventional PSI techniques (using only VV channel) and DSI techniques (using MMSE polarization filtering). Based on results obtained, the different characteristics of PolPSI techniques have been discussed. The results show that the use of the PolPSI algorithm can effectively improve the interferometric phase quality of scatterers. Thus, more qualified pixels can be used for ground deformation estimation by PolPSI methods with respect to the PSI technique and the DSI techniques. Specifically, in comparison with the conventional PSI and DSI technologies, the densities of the monitored pixels obtained by the PolPSI technology in this work increased by 103% and 30.8%, respectively, which can better invert the deformation in some local parts of the airport area. What’s more, PSI, and DSI, and PolPSI, the three types of InSAR techniques’ monitoring deformation is consistent. Indicate that the monitoring result of PolPSI is reliable, and it’s the most efficient method among these three methods. On the other hand, all scatterers’ optimal scattering parameter histograms show that PolPSI is the first choice for the area with abundant deterministic scatterers. Therefore, the PolPSI method based on dual-polarization Sentinel-1 data proposed in this work can improve the ability of Sentinel-1 data in the application of surface deformation monitoring by utilizing and mining polarization information.关键词：remote sensing;dual-polarization Sentinel-1 SAR images;ground deformation monitoring;time-series InSAR;interferogram polarimetric optimization- 1385
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发布时间：2024-03-27 -
摘要：Topographic correction can reduce the problem of uneven solar radiation reception and surface reflectance distortion caused by terrain undulations in complex terrain areas, thus improving the quality of remote sensing images and the accuracy of remote sensing information extraction. However, existing topographic correction models have some problems, such as overcorrection, unstable effect of each band correction, and unsatisfactory correction accuracy.This work proposes a corrected Minnaert topographic correction model, named the CMinnaert topographic correction model, which considers the type of land cover, based on the correlation between the k coefficient of the Minnaert topographic correction model and the bidirectional reflection characteristics of the ground object. Two methods are used in the pre classification of surface features: the first level classification of land cover types and the classification of vegetation density to verify the stability of the CMinnaert model. The best classification scheme of land cover types is proposed. First, a corrected image was pre-classified into land cover types, and the k coefficient was fitted to determine the land cover types in different places. Finally, Minnaert topographic correction was applied to the remote sensing data by using the k coefficient of the land cover type in each area. A Landsat 8/OLI image of Shangcheng County, Henan Province, China was used as experimental data.The cosine correction model, the Sun Canopy Sensor (SCS) correction model, the Minnaert correction model, the Minnaert correction model based on slope, and the CMinnaert correction model were used to perform topographic correction of images in the research area. Visual comparison and statistical data analysis were used to evaluate the topographic correction performance of each algorithm. Results show that the CMinnaert correction model can effectively weaken the influence of the terrain effect on the radiance value of the remote sensing image: the CMinnaert correction model for the first level classification of land cover types can effectively reduce the linear fitting R2 of radiance and cosine of solar incidence angle at each band compared with the original image and the other four topographic correction results, and no over correction phenomenon occurred. Furthermore, the CMinnaert model of the vegetation density classification can effectively weaken the overcorrection problem of other correction models in bands 1 and 5. The linear fitting R2 of the radiance and cosine of the solar incidence angle in the other bands is the lowest of the six models. The CMinnaert model of two pre classification methods is more stable and better than the other four topographic correction models. The results of the visual comparison, cosine correlation analysis of the solar incidence angle, radiance histogram, and spectral characteristic analysis of the sunny and shady slopes are basically consistent.This study recommends that the first level classification should be used in CMinnaert topographic correction considering the algorithm efficiency and practical application ability of the CMinnaert model.关键词：remote sensing;topographic correction;Minnaert model;land cover type;Landsat 8/OLI- 2679
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发布时间：2024-03-27 -
摘要：Global navigation satellite system reflectometry (GNSS-R) is a typical fusion application of the remote sensing and navigation technology and has become a potential research direction. The use of GNSS-R for constructing a passive bistatic synthetic aperture radar (called as GNSS-SAR) has drawn great attention from the research community in recent years. Current investigations on GNSS-SAR focus on the static objects on land. However, few contributions to the moving target imaging can be found in this novel field. Imaging moving target is a long-standing subject for modern SAR systems. However, traditional GNSS-SAR image formation algorithms cannot be directly applied to the moving target due to the unknown motion. Accordingly, the moving target will be smeared and shifted in the static SAR image. To extend the application of GNSS-SAR, this work selects the global positioning system satellite as the illuminator of opportunity and proposes a frequency domain-based moving target image formation algorithm that has a higher processing efficiency than the traditional time domain-based GNSS-SAR algorithm.To image a moving target, frequency domain-based algorithm should solve three main problems: (1) The unknown range cell migration induced by the moving target should be corrected. (2) The velocity of the moving target should be estimated. (3) The azimuth compression derivation should be performed due to the bistatic acquisition geometry. To deal with the main problems, this work selects maritime moving ships as the targets of interest and constructs a bistatic acquisition geometry where the receiver and the satellite are stationary during the observation time. Meanwhile, the trajectory of the moving target perpendicular to the line of sight of the receiver antenna is used as a synthetic aperture. An approximate bistatic range history is first deduced to describe the azimuthal phase variation of the target signal based on the bistatic acquisition geometry. A keystone transform is then employed to address the unknown range cell migration, and a method based on short time Fourier transform and random sample consensus is proposed to estimate the velocity. Finally, a derivation of azimuth compression is conducted to accomplish the moving target imaging.Field experiments were carried out to validate the proposed moving target image formation algorithm. Experimental results show that: (1) The proposed velocity estimation method can obtain the velocity in a low signal-to-noise ratio scene where the least square method cannot work. The fluctuations of the target complex reflectivity will affect the velocity estimation results due to the long observation time, causing errors. However, the errors between the estimated velocities from two groups of the experimental data and the ground truth do not exceed 0.6 m/s. (2) Two targets shown in the SAR image have good accordance with the ground truth in terms of the target-to-receiver vertical distances along the range axis and the ships’ length along the cross-range axis. (3) The designed azimuth matched filter can help in judging the target’s moving direction. Nonetheless, this capability will disappear with the quasi-monostatic configuration. Therefore, the feasibility of the proposed moving image formation algorithm has been confirmed.The proposed algorithm can be used for monitoring the moving ship target and obtain the target’s velocity, length, vertical distance, and moving direction in the future.关键词：GNSS-R;GNSS-SAR;moving target imaging;Keystone transform;short time Fourier transform;random sample consensus- 809
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发布时间：2024-03-27 -
摘要：Land surface albedo is a key parameter to describe the surface energy budget. An increasing need for fine-scale albedo products is promoted in regional applications of radiative forcing and coarse-scale albedo product validation. However, the long-term fine-scale albedo products over mountainous areas are currently unavailable. The topographic slope, aspect, and land cover types make the sloping surface more heterogeneous than the flat surface. Existing fine-scale albedo estimation algorithms may carry the uncertainties due to the complex topography. Moreover, the fine-scale albedo observations are often unavailable due to cloud contamination, making it difficult to obtain time series albedo estimations.To overcome these problems, we adopt the improved Angular Bin algorithm and Ensemble Kalman Filter Algorithm in this study to estimate a time-series fine-scale satellite-based albedo over a rugged terrain. The preliminary approach of the new built albedo estimation over mountainous areas was carried out in the Heihe River Basin by using the Chinese GF-4 satellite data.Validation results against ground measurements over various land cover types and topographic slopes show that our algorithm is effective for the selected land surfaces and can achieve root mean square errors of not more than 0.03. When compared with the referenced albedo product retrieved by direct retrieval algorithm, the GF-4 albedo products show a good performance with the RMSE smaller than 0.02.The retrieved long time series GF-4 albedo can improve the understanding of scale effects among different spatial resolution albedo products and help upscale in ground-based albedo measurements to coarse-scale during the multi-scale validation workflow. This algorithm also provides an example for other satellite-based remote sensing product retrieval over a rugged terrain.关键词：land surface albedo;rugged terrain;GF-4 satellite;EnKF;long time series- 2636
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发布时间：2024-03-27 -
摘要：Many airborne LiDAR point cloud filters have been proposed over the past decades. However, these existing filters are incapable of producing satisfactory results in complex landscapes, such as rugged slopes covered with low vegetation and discontinuous terrain. Thus, a point-based multi-scale morphological reconstruction filter (PMMF) is presented in this work to overcome these problems.In contrast with the classical morphological filters, PMMF takes raw point cloud rather than rasterized grids as the basic processing element. First, the potential ground points are obtained by repeatedly dilating the marker point cloud with the k-neighbor structural element and adaptive elevation buffer under the limits of the mask point cloud. Thereafter, the non-ground points mixed in the potential ground points are eliminated by a terrain-adaptive slope filter. Based on the filtering results from the upper scale, PMMF increases the grid scale for selecting ground seeds on the next scale and repeats the filtering process as the upper level until the result converges. The three main contributions of the new algorithm include a point-based morphological method rather than a grid-based one to avoid information loss caused by point cloud rasterization, a multi-scale geodesic dilation with a slope-adaptive elevation buffer to select potential ground points and reduce the omission error on a steep terrain, and a terrain-adaptive slope filter to eliminate commission errors mixed in potential ground points.PMMF was employed to filter the benchmark samples provided by ISPRS, and its results were compared with 15 filtering algorithms proposed in the last 5 years (2016—2020). Results illustrate that PMMF outperforms the other filtering methods on eight out of the 15 samples, and its average total error and Kappa coefficient were 2.71% and 91.08%, respectively. Moreover, PMMF was used to process four high-density airborne LiDAR point clouds with different terrain features, and the filtering results were compared with Progressive Morphological Filter (PMF), Cloth Simulation Filter (CSF), Progressive TIN Densification (PTD), and multiresolution hierarchical filter (MHF). Results show that PMMF with an average total error of 3.24% has the best performance. The total error of PMMF is reduced by 12.0%, 59.1%, 70.1%, and 53.2% compared with those of PMF, CSF, PTD, and MHF, respectively.A large number of experimental results show that PMMF has achieved satisfactory filtering results on various terrains, and the filtering accuracy is significantly higher than those of other conventional filtering algorithms. Experimental verification shows that the three innovations proposed in this work contribute to the higher accuracy of the new algorithm and overcome the imperfection of the existing algorithms.关键词：airborne LiDAR;point cloud filtering;morphological reconstruction;geodesic dilation;adaptive slope threshold- 798
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发布时间：2024-03-27 -
摘要：Hyperspectral image (HSI) and multispectral image (MSI) are two types of images widely used in the field of remote sensing. These images are useful in certain applications, such as environmental monitoring, target detection, and mineral exploration. HSI contains a large amount of spectral information. Photons are typically collected in a larger spatial area on the sensor to ensure a sufficiently high signal-to-noise ratio (SNR). Accordingly, the HSI spatial resolution is much lower compared with MSI. This low spatial resolution greatly affects the practicality of HSI. Accordingly, fusing a low-spatial resolution HSI (LR-HSI) with a high-spatial resolution MSI (HR-MSI) in the same scene to obtain a high-resolution HSI (HR-HSI) is a method for solving such problems, which resolves the contradiction that the spatial resolution and the spectral resolution cannot simultaneously maintain a high level. From the analysis of fusion effect, the spatial and spectral reconstruction errors of the existing algorithms are mainly reflected in the edge and detail areas.The method proposed in this work was a fusion algorithm for dictionary construction and image reconstruction based on detail attention. In terms of maintaining spectral characteristics, the spectral distribution in the detail area is complex and diverse because of the proximity effect of the image. This work proposes to perform dictionary learning on the image and detail layers. The detail perception error terms and a constraint of edge adaptive directional total variation are proposed for spatial characteristic enhancement, which is combined with a local low rank constraint in the same fusion framework to estimate the sparse coefficient.Experiments were conducted on two datasets, namely, Pavia University and Indian Pine, to verify the effectiveness of the proposed method. The quantitative evaluation metrics contain peak SNR, relative dimensionless global error in synthesis, spectral angle map, and universal image quality index. Based on the experimental comparison, the fusion result of the algorithm proposed in this work is significantly improved compared with those of the other algorithms in terms of spatial and spectral characteristics.This work uses dictionary learning to propose a fusion algorithm for dictionary construction and image reconstruction with attention to details through the analysis of the existing hyperspectral and multispectral image fusion algorithms. A hierarchical dictionary learning algorithm is proposed to address the problem of large reconstruction error in the detail part of the existing algorithms. The detail perception error term and the direction adaptive full variational regularization term are used to improve the spectral dictionary solution and coefficient estimation, respectively. The result of the fusion is the error in the spectral characteristics and spatial texture of the detail, which achieves an accurate representation of the edge detail.关键词：remote sensing;hyperspectral image;image fusion;Dictionary Learning;Edge Adaptive Directional Total Variation;Local Low Rank- 1214
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发布时间：2024-03-27 -
摘要：The normalized difference snow index (NDSI) is the most commonly used index in snow identification. However, the application of MODIS NDSI products is restricted due to cloud occlusion. This study aims to produce daily cloud-free MODIS NDSI production with high accuracy and determine the optimal NDSI threshold in snow identification.In this work, a cloud removal method based on adjacent similar pixels is presented for MODIS NDSI products. First, MOD10A1 and MYD10A1 on the same day are combined. The rule is that MOD10A1 is updated by MYD10A1 at the same location when MOD10A1 is marked by clouds. However, MYD10A1 is cloud-free. Second, an adjacent temporal composite is created. The mean of the nearest valid NDSI values for the adjacent 2 days to that location was assigned to a cloudy pixel. Finally, the residual cloud pixels are processed based on the removed adjacent similar pixels. A weighted cloud-free similar pixel function is established to predict the cloudy target pixel on the NDSI image. The first n similar pixels in the w by w local window are selected, and a weighting function can be constructed to compute the NDSI value for the target pixel. In this study, n=20 and w=15 are recommended in practice. The cloud removal experiment is carried out with the MODIS NDSI products in the northeast China from October 1, 2017 to April 31, 2018. The optimal NDSI threshold of snow identification is then determined based on the Snow Depth (SD) data of the meteorological stations.The effectiveness of cloud removal was validated through “cloud assumption”. Results showed that the correlation coefficient r between the predicted NDSI value and the true value is 0.95, and the root mean square error is 0.08. The daily cloud free NDSI sequence has good agreement with the SD sequence measured by the meteorological stations. When the measured SD of a meteorological station is greater than or equal to 1 cm, the pixel where the station is located is a snow pixel; otherwise, the pixel is snow free. Accordingly, the true value of the binary snow distribution can be obtained according to the SD measured by meteorological station. Thereafter, the true value is used to analyze the optimal threshold of cloud free NDSI sequence in snow identification. The results show that the accuracy of snow identification is the highest when the NDSI threshold is 0.1 in non-forest areas, which can reach 95.6%; the optimal threshold of NDSI in forest areas is 0, and the corresponding snow identification accuracy is 93.5%.(1) The cloud removal method based on adjacent similar pixels is effective for the generation of daily cloud free MODIS NDSI products. (2) The daily cloud free NDSI sequence has good agreement with the SD sequence measured by the meteorological stations. (3) The optimal threshold of the cloud free NDSI sequence in snow identification is 0.1 in non-forest areas and 0 in forest areas.关键词：remote sensing;Snow;cloud removal;MODIS;NDSI;Northeast China;forest- 1150
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发布时间：2024-03-27
Models and Methods
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摘要：Object detection of remote sensing image is the description of visual features of the object and the expression of the image prior knowledge, and the information obtained by the interpretation has a wide range of applications in both military and civilian fields. A refined multi-scale feature-oriented object detection of remote sensing image is proposed to address the problems of insufficient feature extraction capability of remote sensing image objects in complex scenes, large variations in object scales, arbitrary and closely arranged directions, and difficulties in the accurate orientation of horizontal frames used in traditional object detection.First, a contextual attention network based on dilated convolution is designed, which can capture local and global semantic information by using convolution kernels with different dilated rates and integrate semantic information into the original features utilizing an attention mechanism to enhance feature extraction. Second, a refined feature pyramid network is proposed to reduce the loss of channel information in the feature pyramid by pixel shuffling and strengthen the network’s ability to understand multi-scale object feature information with large variances. Finally, the study uses gliding vertices to regress the oriented rectangular box to represent the location of directed objects within remote sensing images.In this work, the effectiveness of the algorithm is verified by using Fast R-CNN OBB as a baseline on the object detection public datasets DOTA and HRSC2016. Results show that the algorithm in this work improves the mean average precision (mAP) by 22.65% on the DOTA dataset compared with the baseline. The final detection accuracy mAP reaches 76.78%. The final detection accuracy mAP on the HRSC2016 dataset reached 89.95%. In addition, the algorithm in this work has a better improvement compared with the various advanced algorithms.ConclusionFirst, the contextual attention network with dilated convolution is used to strengthen the object features, which enhances the discriminative ability of the convolutional neural network for objects and backgrounds in remote sensing images. Second, the refined feature pyramid is used to solve the problem of large variation of objects in remote sensing images. Finally, the direction factor of gliding vertices is introduced to represent the oriented objects, which reduces the regression boundedness problem that can be brought by angle regression.关键词：remote sensing;deep learning;object detection;feature extraction;multi-scale feature pyramid;oriented bounding box- 1466
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发布时间：2024-03-27 -
摘要：The dry/wet conditions dominated by precipitation, air temperature, and other meteorological factors have globally or regionally changed as a result of global warming. Grasslands cover around 40% of China and are vulnerable to climate change and ecological susceptibility. Accordingly, the dry/wet condition change trend of grasslands in China must be studied. Many studies on drought have been conducted in China, but two defects continue to persist: (1) most studies did not take into account the dry and wet changes of grassland in China as a whole; (2) the time range of research has not been extended to recent years. This study analyzed the temporal variation of drought-wet degree and its causes in this region during 1982—2018 based on the drought index and meteorological factors. The optimal drought index is the maximum correlation coefficient of soil moisture and multi drought index, and the optimal drought index was used for subsequent analysis. The piecewise regression approach was used to examine whether a turning point of the trend of drought index developed, and ordinary least squares was used to test the significance. The least square method was used to estimate the trend of the drought index. Pearson correlation analyses were conducted to quantify the relationship between drought index and climatic factors. Our results indicated the drought index based on the station ratio of precipitation and GLEAM potential evapotranspiration, which can reflect the change of dry/wet degree in China’s grasslands. The drought index had no significant increase trend from 1982 to 2018, and a trend shift occurred in 2005. The drought index of grasslands in China decreased by -0.0005 a-1 from 1982 to 2005 and increased by 0.009 a-1 from 2006 to 2018. The reason is that the increased water consumption causes increased the temperature and enhanced the evapotranspiration from 1982 to 2005. The water consumption of evapotranspiration was alleviated from 2006 to 2018 due to the continuous increase in precipitation and stagnation of temperature increase. The drought index of Mongolia grasslands showed a decreasing trend from 1982 to 2005 and an increasing trend from 2006 to 2018. Meanwhile, the drought index of Northwest grasslands showed an increasing trend from 1982 to 2005 and 2006 to 2018, respectively. The drought index of the Tibetan plateau showed a decreasing trend from 1982 to1994, and an increasing trend from 1995 to 2018. The drought index trend change was positively correlated with precipitation in China grasslands. The drought index was a ratio of precipitation on station and GLEAM PET, which can reflect the temporal dynamics of the dry/wet conditions of grasslands in China. The grasslands in China showed a drought trend from 1982 to 2005 and a wet trend from 2006 to 2018. The turning point year of Mongolia and Northwest grasslands are the same. The Mongolia grasslands and Tibetan plateau transitioned from dry to wet between before and after turning point in two periods. Meanwhile, the Northwest grasslands experienced continuous wetness in two periods. The changes of dry/wet in China grasslands was mainly dominated by Mongolia grasslands. Moreover, the change of trend is mainly dominated by precipitation.关键词：remote sensing;China grasslands;drought index;dynamic change;shift;climate change- 1141
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发布时间：2024-03-27 -
摘要：The airborne LiDAR bathymetry (ALB) is one of the most effective technologies to retrieve the topographic and bathymetric maps of coastal zones. The water depth is typically calculated from the sea surface and seafloor peak positions of the ALB waveforms. However, the seafloor topography slope within the footprint scale causes the seafloor waveform stretching and peak shifting when the blue-green laser beam reaches the seafloor, which induces the timing uncertainly to influence the accuracy of the measured seafloor topography. In this work, a correction method in the airborne LiDAR bathymetric error caused by the effect of seafloor topography slope at the footprint scale is proposed to reduce the influence.In this method, the ALB seafloor waveform simulation model is achieved based on the footprint-scale topography parameters model, taking into account the effect of seafloor topography. The ALB error correction equation is obtained based on the quantitative relationship between seafloor topography slope and peak timing of echo waveforms. The developed method is used to correct the ALB data collected near Ganquan Island in the South China Sea and verified by the topography data captured by a ship-borne multi-beam echo sounder.Results show that the mean absolute error and root mean square error are reduced to 9.4 and 12.3 cm after the correction, respectively. Specifically, MAE and RMSE decreased by 35.6% and 33.5%, respectively.关键词：remote sensing;airborne LiDAR bathymetry (ALB);seafloor topography;seafloor slope;depth correction;laser beam footprint at the seafloor- 923
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发布时间：2024-03-27 -
摘要：The anomaly from infrared remote sensing images, as an important precursor of earthquakes, is influenced by season changes, weather conditions, and geological and human activities all at the same time, so it needs the stable and effective extraction algorithm to discover an earthquake precursor. The relative change of the power spectrum is a common algorithm used in earthquake case studies to extract information about earthquakes from infrared remote sensing data. However, this algorithm has only been verified in a few earthquakes, and the sample size is considerably small to statistically analyze abnormal signals. In addition, the previous research in statistical analysis involves a small space range, making it impossible to observe the abnormal phenomenon with a large area intuitively and completely.This work proposes a statistical method of pre-seismic infrared anomalies based on connected domain identification to solve the above-mentioned problems. First, abnormal points with the time-space continuity are regarded as an abnormal signal sample. The positive predicted value of the abnormal signals and the true positive rate of earthquakes are calculated with different parameters. The significance test is then carried out in different conditions by using the Molchan diagram method to select the optimal parameters with the largest probability gain. Finally, the accuracy and universality of the algorithm are evaluated by analyzing the peak value and the length of the abnormal signal and relevant seismic information, including the time, magnitude, and location of the epicenter.In this work, this method is applied to the relative power spectrum data of the FY-2G satellite infrared remote sensing images. The data in the long-wave infrared band are used to statistically analyze the pre-seismic infrared anomalies in China and the surrounding areas in 2018. Results show that the positive predictive value of 20.37% and the true positive rate of 65.96% could be achieved, and the probability gain is 1.76. The positive predictive value of the abnormal signals with the high value and the wide area is 80%, and the true positive rate of the earthquakes with a magnitude greater than 5.4 is 81.82%. Meanwhile, the true positive rate has an obvious regional difference, which shows that the true positive rate of earthquakes in the Circum-Pacific seismic zone is higher than that in the Mediterranean-Himalayan zone.The statistical method used in this work has verified that the power spectrum relative change method could extract the infrared abnormal signal before most earthquakes and is more sensitive to earthquakes of magnitude 5.4 and above. The positive predictive value is low, and its application potential is limited. The positive predictive value could be improved to a certain extent by raising the threshold value. This method can be used to analyze the characteristics of abnormal signals and evaluate the correlation between abnormal signals and earthquakes and is beneficial to the comparison and improvement of the algorithm.关键词：remote sensing;FY-2G satellite;infrared remote sensing image;relative power spectrum;pre-earthquake anomaly statistics;connected domain identification- 934
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发布时间：2024-03-27 -
摘要：The plantation area of China is the largest in the world. It is very important to precisely monitor plantation structure. The study area is located at Wangyedian forest farm, Chifeng, Inner Mongolia. The dominated tree species include Larix principis-rupprechtii and Pinus tabuliformis. The stand height models of plantation were established using the UAV (Unmanned Aerial Vehicle) LiDAR (Light Detection and Ranging) data and in situ sample plots measurements. The significant independent variables were selected based on the Pearson’s correlations between the six stand heights (Arithmetic mean height, Lorey’s height, Dominated height, Maximum height, Median height and Crown area weighted height) and the statistical metrics of discrete point cloud. The branch-and-bound search of best subset was conducted to fit the estimation models of stand height. The model accuracy was assessed by the cross validation. The results showed that the correlations between the height metrics of LiDAR point cloud and the different stand heights were high. The linear regression obtained the best result for different stand heights. The independent variables of the estimation model were all height metrics. For the six stand heights, the Lorey’s height (R2 = 0.91—0.97, rRMSE = 2.75%—3.96%), dominated height (R2 = 0.86—0.97, rRMSE = 3.72%—3.83%) and Crown area weighted height (R2 =0.86—0.96, rRMSE = 3.81%—4.73%) had the highest accuracy, while arithmetic mean height (R2 =0.85—0.94, rRMSE = 4.52%—6.07%) and median height (R2 =0.80—0.95, rRMSE =5.37%—7.34%) had a lower accuracy, maximum height (R2 = 0.69—0.87, RMSE = 1.30—1.40 m) was the lowest. Considering the forest types, the estimation accuracies of larch plantation stands were better the estimation accuracies of all forest types (ΔR2 = 0—0.05, ΔrRMSE = ‒0.69%—1.97%), which were better than the estimation accuracy of the stand height models of pine stands (ΔR2 = 0.06—0.18, ΔrRMSE = ‒1.90%—1.13%). The UAV LIDAR can be used to estimate the stand height of the northern temperate coniferous forest, and applied for the rapid and accurate investigation of plantation resources.关键词：remote sensing;UAV;lidar;point clouds;plantation;stand height- 1258
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发布时间：2024-03-27
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