最新刊期
卷 24 ， 期 12 ， 2020
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摘要：Leaf Area Index (LAI) is an important vegetation parameter that represents leaf density and canopy structure characteristics. This parameter plays an important role in climate change, crop growth model, and carbon and water cycle studies. Remote sensing is an important means to estimate LAI on regional and global scales. LAI products are currently mainly obtained by remote sensing retrieval. However, most LAI product algorithms ignore the effect of topographic features, which results in the great uncertainty in the accuracy of retrieved LAI in mountainous areas. The influence of topographic factors on the canopy reflectance needs to be considered to improve the accuracy of mountain LAI retrieval. Generally, there are mainly two methods to eliminate the influence of topography on mountain LAI retrieval. One method is to use the mountain canopy reflectance model to simulate reflectance, and the other method is to perform topographic correction on remote sensing data.In this paper, the research progress of mountain canopy reflectance model and topographic correction method were comprehensively analyzed on the basis of the theories and methods of LAI retrieval in mountainous areas. For mountain LAI retrieval method based on mountain canopy reflectance simulation, some mountain canopy reflectance models simplify the influence of topographic factors on atmospheric scattering and adjacent terrain scattering, resulting in poor model simulation and low LAI retrieval accuracy. Some complex mountain canopy reflectance models, such as geometric-optical hybrid model or computer simulation model, can accurately simulate topographic effect on reflectance, but it is difficult to invert due to complex input parameters. For mountain LAI retrieval method based on image topographic correction, it is difficult to choose suitable topographic correction method, because the generality of the existing models is poor that a single topographic correction model may only be applicable to a certain terrain condition, a certain area, a certain sensor or a certain waveband. In addition to the above two methods, some studies directly add topographic factors into the statistical regression equation of LAI as a control variable, so as to retrieve mountain LAI. However, this method may cause over fitting phenomenon and does not have robustness and portability.Based on the existing problems of mountain canopy reflectance model, topographic correction method and mountain LAI retrieval method, this paper summarizes and discusses the development trend of future research of mountain LAI retrieval. For mountain canopy reflectance model, it is necessary to develop a model that takes into account the non-Lambertian characteristics of the surface, the geotropic growth of trees, and diffuse radiation and other factors to improve the accuracy of model simulation. In addition, the parameter optimization and retrievability of the model should also be considered. For topographic correction method, it can be combined with BRDF correction or atmospheric correction in the future, especially for complex terrain. To accurately and efficiently retrieve mountain LAI, it is necessary to comprehensively consider factors such as the size of the study area, the heterogeneity of the ground surface, and the degree of terrain undulations, and choose an appropriate topographic correction method or mountain canopy reflectance model. Moreover, it is necessary to carry out more in-depth research on the validation of LAI retrieval accuracy in mountainous areas.关键词：remote sensing;optical remote sensing;LAI;topographic correction;mountain canopy reflectance model;DEM- 924
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发布时间：2021-01-14 -
摘要：Canopy gap fraction and extinction coefficient are two primary variables to retrieve Leaf Area Index (LAI) from light transmittance-based model. Currently, for the difficulty of calculating gap fraction from discrete LiDAR Point Cloud Data (PCD), LiDAR Penetration Index (LPI) is used as the alternative of gap fraction to estimate LAI. However, LPI ignores the target spectral difference which is an important factor affecting the number of canopy and background echoes. Therefore, the backscattering coefficient of the background and canopy,- 680
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发布时间：2021-01-14
Doctor's Voice
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摘要：With the significant improving for the spatial resolution of remote sensing imagery, the limitation of the traditional pixel-based methods for medium and low resolution remote sensing image have become obvious. In recent decades, the Object Based Image Analysis (OBIA) has become the most popular information extraction method for the high spatial resolution remote sensing imagery. The object is the based processing units in the OBIA method, so that the segmentation method obtaining the objects is a key step in the OBIA, because the classification accuracy is directly affected by the quality of segmentation results. However, the objects in high-resolution remote sensing images show multi-scale characteristics, and it is difficult to accurately obtain the optimal segmentation results using the single segmentation scale, so that he multi-scale segmentation method have become an inevitable choice. But the multi-scale segmentation algorithms proposed in previous literatures are difficult to achieve global and local optimization. In this paper, a new multiscale segmentation optimized algorithm was proposed. The algorithm mainly includes following steps: (1) the global optimal scale in multiscale segmentation was obtained for using the local variance criterion; (2) the over-segmentation and under-segmentation objects in global optimized scale were respectively optimized to obtain the local optimization results; (3) the global and local optimized results were fused to obtain the finishing optimized results. In this paper, the two high spatial resolution remote sensing images which respectively located in Dongguan, China and Florida, USA were used to verify the effectiveness of the proposed algorithm, and the experimental results were analyzed by the qualitative and quantitative evaluation .The results were shown following as: (1) From the perspective of visual effects, the more accurate segmentation boundary were obtained in the optimized results, and the objects (such as road, farmland, and water) in the large scale maintain better regional feature, and the objects (such as tree, house, shadow)in the small scale have more detail information. (2) From the perspective of quantitative analysis by the evaluation indicators(RR, RI and ARI), the presented algorithm increased the RR, RI and ARI by 2.1%, 2.4 and 30.2% in comparison with the global optimized segmentation scale, and by 8.3%，0.1% and 8.1% in comparison with the k-means optimized method, and by 0.7%，0.4% and 17.6% in comparison with the fused boundary optimized method in the test 1, and increased the RR,RI and ARI by 4.5%，2.7% and 29.3% in comparison with the global optimized segmentation scale, and by 17%，0.8% and 8.4% in comparison with the k-means optimized method, and by 1.7%，2.5% and 17.2% in comparison with the fused boundary optimized method in the test 2. In summary, compared with classical segmentation algorithms, the proposed algorithm obtained the best segmentation results by both local and global optimization, and reduced over-segmentation and under-segmentation objects in the segmentation results. Meanwhile, the heterogeneity of objects is different in the different types of scenes, for example, the objects in the city scenes are high heterogeneity, but the objects of the rural scenes are high homogeneity. So that, the optimal segmentation parameters in multi-scale segmentation is difficult to other scenes.关键词：remote sensing;high spatial resolution remote sensing imagery;multi-scale segmentation;Local Moran’s I;spatial statistic index;optimized algorithm- 778
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发布时间：2021-01-14 -
摘要：The ice, cloud and land elevation satellite-2 (ICESat-2) is equipped with advanced photon counting lidar. The system is a multi-beam micro pulse photon counting radar, which has the advantages of low energy consumption, high measurement sensitivity, high repetition rate and high space operation altitude. However, due to the characteristics of the lidar, the data returned is the elevation profile photon cloud data. Due to the nature of the instrument, the data is easily affected by noise photons, observation time, observation area and so on. The photon cloud data contains a lot of background light noise. Before using the photon cloud data for canopy extraction, the efficient and high-precision photon denoising and classification algorithm is as follows It's very necessary.Based on the above problems, this paper proposes an improved triple denoising algorithm. Firstly, DBSCAN clustering algorithm is selected for the coarse denoising of photons. The eps parameters of clustering algorithm have a great influence on it. In this paper, by analyzing the correlation between the density of photon cloud and the parameters of the algorithm and the denoising results, it is proposed to select the optimal eps parameters adaptively for rough denoising according to the internal characteristics of the photon cloud The signal photons are not lost and the noise photons are removed effectively. Then, two fine denoising algorithms are carried out to remove the noise photons located at the top of the canopy and below the ground line. Finally, the optical cloud is classified and fitted to the ground line and the canopy top line. Finally, the remaining noise photons are removed according to the fitting ground line and canopy top line interval.In this paper, the algorithm is applied to ICESat-2 airborne test data (MABEL). The experimental results show that the average de-noising accuracy of the algorithm is 94.5% for nighttime data, 96.3% for F1-score, 86.7% for daytime data and 91.7% for F1-score. The results show that the denoising parameters can be selected adaptively according to the photon density of the data However, the results also show that it can not achieve good results in areas where the density of signal photons and noise photons is excessively similar. However, the overall accuracy evaluation shows that the F1-score of all segments is 91%, 92% and 95% respectively in the three times denoising algorithm. The results show that the following two denoising algorithms can accurately remove most of the remaining noise photons which are not completely removed, and significantly improve the denoising accuracy of photon cloud, which provides a guarantee for the accurate extraction of the subsequent photon categories. The overall experimental results show that the algorithm has good denoising effect and stability for MABEL photon cloud data. In the section data of denoising, the qualitative results show that the photon classification algorithm in this paper can select the canopy vertex, ground point and forest photons from the photon data based on the denoising results. The final photon classification results show that the algorithm can extract the forest profile structure from the complex photon cloud data, and retain most of the signal photons in the canopy, which can provide some reference for the subsequent tree height extraction and biomass calculation of ICESat-2 data.关键词：remote sensing;ICESat-2;MABEL;photon cloud;DBSCAN;denoising;classification- 870
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发布时间：2021-01-14 -
摘要：Existing remote sensing image interpretation methods can obtain high-precision classification results but also have some defects. On the one hand, the problem of disconnection in the road extraction result of remote sensing image reduces the extraction precision. On the other hand, this problem affects the integrity of the road morphology. Thus, the extraction result cannot be directly applied to spatial decision making and analysis. Therefore, this study solves the problem of disconnection in the road extraction results of remote sensing images. A road extraction method for high-resolution remote sensing image of U-Net network with consideration of connectivity is also proposed.On the basis of the advantages of global feature representation of U-Net network in road extraction of high-resolution remote sensing images, this study proposes a broken road repair method that considers connectivity to improve the local features of U-Net network. First, the sample data after data enhancement and data volume expansion are used as an input of the U-Net network to train the model and perform road extraction of the optimal model. Then, the road breakpoint detection, the road breakpoint clustering, and the cubic polynomial curve fitting are organically combined to optimize the result because of the broken road appearing in the extraction result.The proposed method is practical and feasible according to the verification of experimental results. The method is also universal. The experiment shows that the accuracy and shape integrity of the road extraction in this method are improved significantly compared with similar networks. The precision is 86.25%, the recall rate is 85.50%, and F1-score reaches 85.87%.The roads extracted by the proposed method have good connectivity and morphological integrity. The resulting image can be directly applied to geographic decision analysis, especially for post-disaster path planning. The proposed method has certain reference significance for the occurrence of similar disconnection problems in the classification results of linear objects, such as roads, power grids, orbits, and rivers.关键词：remote sensing;the integrity of the road morphology;U-Net network;high-resolution remote sensing images;connectivity;broken road- 881
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发布时间：2021-01-14 -
摘要：GF-4 is a geostationary orbit satellite launched under the support of the major special project of China’s high-resolution earth observation system. Notably, the spatial resolution of the medium-wave infrared channel in GF-4 satellite imagery is much lower than the corresponding visible near-infrared channels of the same scene. The resolution of the medium-wave infrared channel needs to be improved.In this study, a deep learning method termed as GF-4 Super-Resolution Network (GF-4-SRN) is proposed for super-resolution GF-4 satellite imagery. First, a convolutional regression network is designed for preliminary reconstruction of the original image. The convolutional regression network is trained using eight times down-sampled visible near-infrared channels and the original low-resolution medium-wave infrared channel. Thus, the correspondence between the visible near-infrared channels and the medium-wave infrared channel is developed. Second, a convolutional reconstruction network is designed to further reconstruct the preliminary results. This network is trained using the low-resolution preliminary reconstruction results and the original medium-wave infrared image. Notably, the convolutional reconstruction network can build the relationship between the preliminary reconstruction results and the medium-wave infrared image. Finally, the original visible near-infrared channels in GaoFen-4 satellite imagery are fed into the trained GF-4-SRN. The final super-resolution reconstruction results can be obtained by the relationship between visible near-infrared images and the medium-wave infrared image.Experiments are performed on two real-world GF-4 satellite images acquired from Hubei and Jiangxi regions. Experimental results demonstrate that the proposed GF-4-SRN method can effectively enhance the spatial resolution of the medium-wave infrared image. Compared with the root mean square error of state-of-the-art methods, that of the GF-4-SRN is reduced by at least 7.54. Moreover, the visual effects are much clearer and more natural. Therefore, the GF-4-SRN contributes to expanding the application range of the GF-4 satellite imagery.This paper proposes a deep learning-based super-resolution method for GF-4 satellite imagery. The proposed GF-4-SRN is composed of preliminary reconstruction by convolutional regression network and further reconstruction by convolutional reconstruction network. Experimental results on Hubei and Jiangxi regions demonstrate the effectiveness of the proposed GF-4-SRN. The spatial resolution is increased by fully utilizing the information provided by the visible near-infrared images. The root mean square error, erreur relative globale adimensionnelle de synthèse, and spectral angle mapper are lower than those of other methods. Meanwhile, the peak signal-to-noise ratio is higher than those of the competitors. Moreover, the visual effect of the results provided by the GF-4-SRN is much clearer and the reconstruction results contain more details. Notably, the deep learning-based method has great potential in super-resolution GF-4 satellite imagery.In the future, we will improve the GF-4-SRN by adopting various window sizes in the convolutional kernel. The network connection mode and optimization method can also be changed to improve the speed and performance of the network. The GF4-SRN will also be extended to other application areas, such as disaster monitoring.关键词：GF-4;super-resolution;reconstruction;deep learning;convolutional networks;Hubei;Jiangxi- 748
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发布时间：2021-01-14
Technology and Methodology
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摘要：The information of forest age structure can effectively reflect the carbon sequestration capacity of regional forest communities at different growth stages. This way is important for assessing the health status of forest ecosystems. In this study, the typical dominant tree species Larix gmelinii forest in temperate zone of China is selected as the object, and Sentinel-2 images of its bud germination period, elongating period of leaf, and defoliation period are selected. The retrieval model of Larix gmelinii stand age is constructed using Multiple Linear Regression (MLR), Random Forest (RF), support vector regression, feedforward back propagation neural network, and multiple adaptive regression spline. The optimal phenophase of remote sensing retrieval is first determined through correlation analysis. On this basis, five optimal characteristic variables, namely, Canopy Water Content (CWC), normalized difference water index, leaf area index, fraction of absorbed photosynthetically active radiatio, and fractional vegetation cover, are selected for model retrieval according to the difference in correlation. Results show that the elongating period of leaf is the optimal remote sensing retrieval phenophase. Except for the plant senescence reflectance index and NDVI and RVI in defoliation period, a negative correlation exists between the stand age of Larix gmelinii and each index, among which the correlation between the stand age and (CWC is the closest, and the correlation coefficient of Pearson reaches -0.74 (p<0.01). The results of different model retrievals indicate that RF model is the best model for estimating the age of Larix gmelinii, and its average coefficient of determination (R2) and mean Root Mean Square Error (RMSE) are 0.89 and 2.91 a, respectively. MLR is the worst for estimating Larix gmelinii forest age, and its average R2 and RMSE are 0.57 and 5.69 a, respectively. Nonlinear models can better explain the relationship between stand age and modeling variables.关键词：remote sensing;Sentinel-2;Larix gmelinii;stand age retrieval;biophysical parameters;Random Forest- 869
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发布时间：2021-01-14 -
摘要：In the remote sensing exploration of oil and gas resources, seabed gas reservoirs are usually detected through the anomaly of methane concentration on the sea surface caused by hydrocarbon seepage. The remote sensing exploration of hydrocarbon seepage in marine gas resources is currently mostly based on methane absorption characteristics and images. Quantitative spectral analysis of methane content on the sea surface is also insufficient. This study designs laboratory spectral response experiments of methane with different concentrations in seawater background to determine the methane anomaly on the sea surface better and improve the accuracy of remote sensing inversion. This study also attempts to establish an inversion model of methane concentration in seawater background.A methane spectra laboratory test platform was designed to obtain hyperspectral data of different methane contents in seawater background. After spectra preprocessing and derivative of ratio spectroscopy, the spectral absorption characteristic parameters (the valley, wave depth, area, wave width, slope, and SAI) were extracted. The correlation between methane content and spectral parameters was analyzed to compare the ability of parameters to distinguish methane content. The correlation between spectral parameters with high correlation of methane content in selected bands was analyzed to further reduce the amount of data. Finally, the spectral parameters that were highly correlated with methane content and lowly correlated with each other were selected as independent variables and methane content was used as dependent variable to construct the methane content inversion model.In the analysis of methane spectra in seawater background, the derivative of ratio spectroscopy can effectively suppress background information of seawater in the spectra and highlight the methane information. Thus, the curve characteristics of the spectra after derivative of ratio spectroscopy are only related to the content of methane. Moreover, higher methane content corresponds to more obvious characteristics. The correlation between spectral parameters in 1650—1664 nm and 2180—2210 nm with methane content is significantly correlated, which is apparently higher than that in 2300—2320 nm and 2350—2380 nm. The valley, wave depth, area, and slope are also significantly correlated with methane content. Meanwhile, wave width is generally correlated with methane content, and SAI is not correlated with methane content. The quadrivariate regression equation (y=-14.356 - 5931.796x1 - 4325.081x2+241.481x3+7531.973x4) in 2180—2210 nm has the best fitting effect, and R2 is 0.9817. The single variable methane inversion model y = 2047.571x - 9.758 is based on wave depth in this band, and R2 is 0.9741, which is better than that of the inversion model based on other spectral characteristic parameters.The corresponding bands of 1650—1664 nm and 2180—2210 nm and corresponding absorption characteristics (valley, wave depth, area, and slope) with significant linear correlation of methane content in sea water background are successfully obtained. The methane content inversion models with good effect and regression significance are established. They can provide theoretical and technical basis for predicting methane concentration on sea surface by multispectral/hyperspectral remote sensing.关键词：remote sensing;hyperspectra;methane content;spectral characteristic parameter;derivative of ratio spectroscopy;inversion model- 688
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发布时间：2021-01-14 -
摘要：The changes in lake level on the Tibetan Plateau are important indicators for the study of climate and ecological environment changes. In-situ gauges can measure high-precision lake level data, but they are costly to maintain and challenging to operate in remote areas. Satellite radar altimetry has now been used successfully for more than two decades to measure lake levels as an addition to gauge measurement. Monitoring the water level changes of more lakes becomes effective with the increase in Cryosat-2 observation data and the improvement in data processing technology.This study presents a high-precision extraction method of lake level time series based on noise removal, improved empirical retracker (ImpMWaPP), and error mixture model. The Cryosat-2 SARIn data were used to obtain water level time series of 133 Tibetan Plateau Lakes from 2010 to 2018, and the spatiotemporal variations of these lake levels were analyzed. The accuracy of lake level extraction was validated using in-situ measurements and Hydroweb water level products.In general, the lake levels on the Tibetan Plateau continue to rise, but the rate of increase is slower than that in the period of 2003—2009. The average annual rate of change is 0.159 m/a. From the geographical distribution, the lake levels in the northern plateau rise most significantly, while the lake levels in the southern plateau tend to be stable. The water levels of most lakes showed a rapid rise in the periods of 2010—2012 and 2016—2018, while the water levels were relatively stable or slightly decreased at other times.The results showed that the accuracy of lake level extraction in this study was higher than that of previous studies, and the change in lake levels on the Tibetan Plateau was similar to those in the previous studies. In the future work, the change in lake levels on the Tibetan Plateau will be further estimated using multi-altimeter data. We will also consider the information of lake extent and study the change in lake volume to support the exploration of climate and environmental changes on the Tibetan Plateau.关键词：remote sensing;lake level- 930
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发布时间：2021-01-14
Remote Sensing Applications
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