作者：
Usoltsev, Vladimir A.;Lin, Hui*;Shobairi, Seyed Omid Reza;Tsepordey, Ivan S.;Ye, Zilin
期刊：
Plants-Basel,2020年9(10):1255 ISSN：2223-7747
通讯作者：
Lin, Hui
作者机构：
[Shobairi, Seyed Omid Reza; Usoltsev, Vladimir A.; Ye, Zilin; Lin, Hui] Cent South Univ Forestry & Technol, Res Ctr Forestry Remote Sensing & Informat Engn, Changsha 410004, Peoples R China.;[Shobairi, Seyed Omid Reza; Usoltsev, Vladimir A.] Ural State Forest Engn Univ, Fac Forestry, Sibirskiy Trakt 37, Ekaterinburg 620100, Russia.;[Usoltsev, Vladimir A.; Tsepordey, Ivan S.] RAS, Bot Garden Ural Branch, Dept Forest Prod, Ul 8 Marta,202a, Ekaterinburg 620144, Russia.;[Ye, Zilin; Lin, Hui] Key Lab Forestry Remote Sensing Based Big Data &, Changsha 410004, Peoples R China.;[Ye, Zilin; Lin, Hui] Key Lab State Forestry Adm Forest Resources Manag, Changsha 410004, Peoples R China.
通讯机构：
[Lin, Hui] C;[Lin, Hui] K;Cent South Univ Forestry & Technol, Res Ctr Forestry Remote Sensing & Informat Engn, Changsha 410004, Peoples R China.;Key Lab Forestry Remote Sensing Based Big Data &, Changsha 410004, Peoples R China.;Key Lab State Forestry Adm Forest Resources Manag, Changsha 410004, Peoples R China.
关键词：
January temperature;annual rainfall;biomass equations;hydrothermal gradients;regression models;stand biomass
摘要：
<jats:p>Currently, the problem of the impact of climate change on the productivity of forest ecosystems and their carbon-depositing capacity is far from being solved. Therefore, this paper presents the models for the stand biomass of the two-needled subgenus’ (Pinus spp.) and the genus Picea spp.’s trends along the trans-Eurasian hydrothermal gradients, designed for pure stands in a number of 2110- and 870-sample plots with Pinus and Picea correspondingly. It was found that in the case of an increase in mean winter temperatures by 1 °C, pine and spruce respond by increasing the biomass of most components, and in the case of an increase in the annual sum of precipitation by 100 mm, the total, aboveground, stem and root biomasses of pine and spruce react the same way, but crown biomass reacts in the opposite way. Therefore, all identified trends are species-specific.</jats:p>
作者机构：
[Wang, Guangxing; Long, Jiangping; Yan, Enping; Lin, Hui; Sun, Hua] Cent South Univ Forest & Technol, Res Ctr Forestry Remote Sensing & Informat Engn, Changsha 410004, Hunan, Peoples R China.;[Wang, Guangxing; Long, Jiangping; Yan, Enping; Lin, Hui; Sun, Hua] Key Lab Forestry Remote Sensing Based Big Data &, Changsha 410004, Hunan, Peoples R China.;[Wang, Guangxing; Long, Jiangping; Yan, Enping; Lin, Hui; Sun, Hua] Key Lab State Forestry Adm Forest Resources Manag, Changsha 410004, Hunan, Peoples R China.;[Wang, Guangxing] Southern Illinois Univ, Dept Geog & Environm Resources, Carbondale, IL 62901 USA.
通讯机构：
[Lin, Hui] C;[Lin, Hui] K;Cent South Univ Forest & Technol, Res Ctr Forestry Remote Sensing & Informat Engn, Changsha 410004, Hunan, Peoples R China.;Key Lab Forestry Remote Sensing Based Big Data &, Changsha 410004, Hunan, Peoples R China.;Key Lab State Forestry Adm Forest Resources Manag, Changsha 410004, Hunan, Peoples R China.
关键词：
polarimetric SAR;Yamaguchi decomposition;growing stem volume;saturation-based multivariate method;Chinese fir plantation
摘要：
For the planning and sustainable management of forest resources, well-managed plantations are of great significance to mitigate the decrease of forested areas. Monitoring these planted forests is essential for forest resource inventories. In this study, two ALOS PALSAR-2 quad-polarimetric synthetic aperture radar (SAR) images and ground measurements were employed to estimate growing stem volume (GSV) of Chinese fir plantations located in a hilly area of southern China. To investigate the relationships between forest GSV and polarization characteristics, single and fused variables were derived by the Yamaguchi decomposition and the saturation value of GSV was estimated using a semi-exponential empirical model as a base model. Based on the estimated saturation values and relative root mean square error (RRMSE), the single and fused characteristics and corresponding models were selected and integrated, which led to a novel saturation-based multivariate method used to improve the GSV estimation and mapping of Chinese fir plantations. The new findings included: (1) All the original polarimetric characteristics, statistically, were not significantly correlated with the forest GSV, and their logarithm and ratio transformation fused variables greatly improved the correlations, thus the estimation accuracy of the forest GSV. (2) The logarithm transformation of surface scattering resulted in the greatest saturation, value but the logarithm transformation of double-bounce scattering resulted in the smallest RRMSE of the GSV estimates. (3) Compared with the single transformations, the fused variables led to more reasonable saturation values and obviously reduced the values of RRMSE. (4) The saturation-based multivariate method led to more accurate estimates of the forest GSV than the univariate method, with the smallest value (29.64%) of RRMSE achieved using the set of six variables. This implied that the novel saturation-based multivariate method provided greater potential to improve the estimation and mapping of the forest GSV.
作者：
Meng Zhang 0016;Hui Lin 0004;Hua Sun 0002;Yaotong Cai
作者机构：
Research Center of Forest Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha, 410004, China;Key Laboratory of Forestry Remote Sensing Based Big Data and Ecological Security for Hunan Province, Changsha, 410004, China;Key Laboratory of State Forestry Administration on Forest Resources Management and Monitoring in Southern Area, Changsha, 410004, China
期刊：
Advances in Space Research,2019年64(11):2233-2244 ISSN：0273-1177
通讯作者：
Zhang, Meng
作者机构：
[Zhang, Meng] Cent South Univ Forestry & Technol, Res Ctr Forestry Remote Sensing & Informat Engn, Changsha 410004, Hunan, Peoples R China.;Key Lab Forestry Remote Sensing Based Big Data &, Changsha 410004, Hunan, Peoples R China.;State Forestry Adm Forest Resources Management &, Key Lab, Changsha 410004, Hunan, Peoples R China.
通讯机构：
[Zhang, Meng] C;Cent South Univ Forestry & Technol, Res Ctr Forestry Remote Sensing & Informat Engn, Changsha 410004, Hunan, Peoples R China.
关键词：
Object-based;Paddy rice;RASTFM;Sentinel-1;Sentinel-2;Time series
摘要：
Rice is one of the world’s major staple foods, especially in China. In this study, we proposed an object-based random forest (RF) method for paddy rice mapping using time series Sentinel-1 and Sentinel-2 data. Firstly, the Robust Adaptive Spatial Temporal Fusion Model (RASTFM) was used to blend MODIS and Sentinel-2 data for achieving multi-temporal Sentinel-2 data. Subsequently, the Savitzky-Golay filter (S-G) was applied to smooth the time series Sentinel-2 NDVI data. And the phenological parameters were derived from the filtered time series NDVI using the threshold method. Then, the optimum feature combination for paddy mapping was formed on the basis of Sentinel-2 MSI images, time series Sentinel-2 NDVI, phenology data and time series Sentinel-1 SAR backscattering images by using the JBh distance. Finally, an object-based Random Forest classifier was used to extract paddy rice with the optimum feature combination. The result showed that fused Sentinel-2 NDVI time series using RASTFM has a high correlation with the original Sentinel-2 image. The overall accuracy and Kappa coefficient of the classification results are higher than 95% and 0.93, respectively, when use the optimum feature combination and object-based RF method. The proposed method can provide technology support for rice mapping in areas with a lot of cloudy and rainy weathers.
作者机构：
[Zeng, Wen; Yan, Enping; Jiang, Qian; Lin, Hui] Cent South Univ & Technol, Res Ctr Forestry Remote Sensing & Informat Engn, Changsha, Hunan, Peoples R China.;[Zeng, Wen; Yan, Enping; Jiang, Qian; Lin, Hui] Key Lab Forestry Remote Sensing Based Big Data &, Changsha, Hunan, Peoples R China.;[Zeng, Wen; Yan, Enping; Jiang, Qian; Lin, Hui] Key Lab State Forestry Adm Forest Resources Manag, Changsha, Hunan, Peoples R China.;[Lu, Hongwang; Wu, Simin] Cent South Univ Forestry & Technol, Coll Forestry, Changsha, Hunan, Peoples R China.
会议名称：
2018 Fifth International Workshop on Earth Observation and Remote Sensing Applications (EORSA)
会议时间：
June 2018
会议地点：
Xi'an, China
会议主办单位：
[Zeng, Wen;Lin, Hui;Yan, Enping;Jiang, Qian] Cent South Univ & Technol, Res Ctr Forestry Remote Sensing & Informat Engn, Changsha, Hunan, Peoples R China.^[Zeng, Wen;Lin, Hui;Yan, Enping;Jiang, Qian] Key Lab Forestry Remote Sensing Based Big Data &, Changsha, Hunan, Peoples R China.^[Zeng, Wen;Lin, Hui;Yan, Enping;Jiang, Qian] Key Lab State Forestry Adm Forest Resources Manag, Changsha, Hunan, Peoples R China.^[Lu, Hongwang;Wu, Simin] Cent South Univ Forestry & Technol, Coll Forestry, Changsha, Hunan, Peoples R China.
会议论文集名称：
2018 Fifth International Workshop on Earth Observation and Remote Sensing Applications (EORSA)
关键词：
Remote sensing;Land cover classification;Feature variables selection;JM distance;You County
摘要：
With the abundance of spectral information and texture information of remote sensing images, the feature variables applicable to land cover classification also increase. But the high-dimensional features will deteriorate the classifier performance. In this paper, an improved selection method was proposed by considering separability and redundancy between feature variables. The study area - You County was divided into eight land cover types based on the SPOT-5 remote sensing image in 2009 combined with the continuous inventory data of forest resources during the same period. A total of 3,921 sample plots containing classification information were selected, of which 2/3 were used as training data, 1/3 as testing data. Waveband operations and texture extraction were performed on the images and a total of four bands information, eight texture factors, and nine vegetation indices were extracted as candidate feature variables subset. Calculate the weighted average of the Jeffries-Matusita (JM) distance between different classes of each feature variable, as the measure of class separability, and select the variable ordered first. Then calculate the PEARSON correlation coefficient between the remaining variables and the selected variables as the measure of variables redundancy. The ratio of separability and redundancy is referred to as the improved JM distance, which is successively substituted into the Bayesian classifier in descending order. The results show that (1) From the JM distance, the spectral features are more suitable for the land cover classification in the study area than the texture features. (2) The overall classification accuracy can be improved through the feature variables selected by improved JM distance. (3) The number of feature variables using the improved JM distance to achieve stable classification accuracy is far less than traditional JM distance, which effectively reduce feature dimensions. Therefore, the improved selection method proposed in this paper not only retains the class separability but also avoids the information redundancy, and can effectively select the feature variables.
摘要：
The purpose of this study is to load different formats of tile files through the tile Pyramid model in the Android platform, so as to optimize the storage mode according to the use scene. To compare the compression rate, loading time, loading speed, loading speed, portability and convenience under the given experimental data, according to the four commonly used storage modes (scattered files, ZIP files, SQLite database files and tile flow collection files). The experimental results show that loading SQLite database files loading tiles, the average time consuming 301ms, the average memory consumption 25.8MB, the overall performance is the best, is the preferred format for the Android version of the GIS grid data storage. At the same time, the test results show that the comprehensive advantage of the custom tile flow collection file is significant. If the storage structure of the file is further optimized, it can completely replace the SQLite storage mode and no longer be limited to the limitation of the database itself.
摘要：
Rice is one of the world’s major staple foods, especially in China. Highly accurate monitoring on rice-producing land is, therefore, crucial for assessing food supplies and productivity. Recently, the deep-learning convolutional neural network (CNN) has achieved considerable success in remote-sensing data analysis. A CNN-based paddy-rice mapping method using the multitemporal Landsat 8, phenology data, and land-surface temperature (LST) was developed during this study. First, the spatial–temporal adaptive reflectance fusion model (STARFM) was used to blend the moderate-resolution imaging spectroradiometer (MODIS) and Landsat data for obtaining multitemporal Landsat-like data. Subsequently, the threshold method is applied to derive the phenological variables from the Landsat-like (Normalized difference vegetation index) NDVI time series. Then, a generalized single-channel algorithm was employed to derive LST from the Landsat 8. Finally, multitemporal Landsat 8 spectral images, combined with phenology and LST data, were employed to extract paddy-rice information using a patch-based deep-learning CNN algorithm. The results show that the proposed method achieved an overall accuracy of 97.06% and a Kappa coefficient of 0.91, which are 6.43% and 0.07 higher than that of the support vector machine method, and 7.68% and 0.09 higher than that of the random forest method, respectively. Moreover, the Landsat-derived rice area is strongly correlated (R2 = 0.9945) with government statistical data, demonstrating that the proposed method has potential in large-scale paddy-rice mapping using moderate spatial resolution images.
摘要：
Extraction of tree crown has always been a research hotspot in forestry remote sensing. With the rapid development of UAV technology, aerial imagery can clearly identify the information of ground forest tree crowns. However, Due to the overlapping of tree crown, how to divide the crown size between trees is still a difficult problem. In this paper, the ecological experiment forest of Central South University of Forestry and Technology was selected as the study area, the UAV image in the visible light band was used as experimental data, and the Pinus massoniana as the object of study, the transect sample algorithm was used to extract the size of the individual forest tree crown in the study area. By analyzing the correlations between R, G, and B bands in the visible light band, several major visible vegetation indices and tree crown information, relevant factors suitable for tree crown extraction were selected. Since the aerial imagery used in this study has an ultra-high resolution, the gaps in the crown width of single wood are clearly visible, which is not conducive to transect sample algorithm to extract the crown width. Therefore, the image is smoothed by setting the window size of the 7*7 pixel to reduce the influence of the crown gap on the transect sample algorithm. In the end, the tree crown was extracted by the transect sample algorithm. The results show that: The green band and the CIVE extraction. The total precision of the extraction of Pinus massoniana by transect sample algorithm is 84.1%, and the method can extract the single crown of partial crown to a certain extent, and it can get better effect for the forest with low canopy density.
摘要：
PolInSAR data consist of two SAR images, and the spatial baseline, incident angle, slanting distance, wavelength of the two scene image determine the vertical wavenumber of the data. There are obvious differences in the inversion effects with different vertical wavenumber. This paper defined the impact of vertical wavenumber on forest height inversion. 39 groups of simulated data and 8 groups of real E-SAR data with vertical wavenumber gradient change were used respectively on analyzing the impact of vertical wavenumber on interference coherence and coherent matrix range. And for each groups of data, the forest height inversions were accomplished with three-stage method based on RVoG model. The experimental results show that: 1) When the vertical wavenumber was small (kz<0.06), the interference coherence and the ratio of coherence matrix range of data were relatively small, the data independence was bad, and the decorrelation was serious, so the forest height inversion can not be carried out. 2) When the vertical wavenumber was larger than 0.06, the interference coherence and the ratio of coherence matrix range of data decreased with the increased of vertical wavenumber. The reliability of inversion results decreased gradually, and the decorrelation increased. 3) Optimal vertical wavenumber interval of forest height inversion was 0.06 to 0.10, and the inversion error was less than 15%. When the vertical wavenumber increased further, the inversion error increased significantly, which was not suitable for forest height inversion. We conclude that the data with vertical wavenumber between 0.06 and 0.10 can acquire desirable forest height inversion accuracy.
摘要：
There is a large amount of remote sensing data available for land use and land cover (LULC) classification and thus optimizing selection of remote sensing variables is a great challenge. Although many methods such as Jeffreys-Matusita (JM) distance and random forests (RF) have been developed for this purpose, the existing methods ignore correlation and information duplication among remote sensing variables. In this study, a novel approach was proposed to improve the measures of potential class separability for the selection of remote sensing variables by taking into account correlations among the variables. The proposed method was examined with a total of thirteen spectral variables from a Gaofen-1 image, three class separability measures including JM distance, transformed divergence and B-distance and three classifiers including Bayesian discriminant (BD), Mahalanobis distance (MD) and RF for classification of six LULC types at the East Dongting Lake of Hunan, China. The results showed that (1) The proposed approach selected the first three spectral variables and resulted in statistically stable classification accuracies for three improved class separability measures. That is, the classification accuracies using three or more spectral variables statistically did not significantly differ from each other at a significant level of 0.05; (2) The statistically stable classification accuracies obtained by integrating MD and BD classifiers with the improved class separability measures were also statistically not significantly different from those by RF; (3) The numbers of the selected spectral variables using the improved class separability measures to create the statistically stable classification accuracies by MD and BD classifiers were much smaller than those from the original class separability measures and RF; and (4) Three original class separability measures and RF led to similar ranks of importance of the spectral variables, while the ranks achieved by the improved class separability measures were different due to the consideration of correlations among the variables. This indicated that the proposed method more effectively and quickly selected the spectral variables to produce the statistically stable classification accuracies compared with the original class separability measures and RF and thus improved the selection of the spectral variables for the classification.
作者机构：
[严恩萍; 林辉; 王广兴; 赵运林; 莫登奎] Key Laboratory of Forestry Remote Sensing Big Data &, Ecological Security for Hunan Province, Central South University of Forestry &, Technology, Changsha, 410004, China;[王广兴] Department of Geography, Southern Illinois University, Carbondale, IL, 629012, United States;[严恩萍; 林辉; 王广兴; 赵运林; 莫登奎] College of Forestry, Central South University of Forestry &, Technology, Changsha, 410004, China
通讯机构：
Key Laboratory of Forestry Remote Sensing Big Data & Ecological Security for Hunan Province, Central South University of Forestry & Technology, Changsha, China
