作者：
Usoltsev, V. A.;Lin, H.;Shobairi, S. O. R.;Tsepordey, I. S.;Ye, Z.;...
期刊：
Applied Ecology and Environmental Research,2022年20(4):3683-3698 ISSN：1589-1623
作者机构：
[Usoltsev, V. A.] Ural State Forest Engn Univ, Siberian Tract 37, Ekaterinburg 620100, Russia.;[Shobairi, S. O. R.; Lin, H.; Ye, Z.] Cent South Univ Forestry & Technol, Res Ctr Forestry Remote Sensing & Informat Engn, Changsha 410004, Peoples R China.;[Usoltsev, V. A.; Tsepordey, I. S.] Russian Acad Sci, Ural Branch, Bot Garden, Ul 8 Marta,202a, Ekaterinburg 620144, Russia.;[Lin, H.; Ye, Z.] Key Lab Forestry Remote Sensing Based Big Data &, Changsha 410004, Peoples R China.;[Lin, H.; Ye, Z.] State Forestry Adm Forest Resources Management &, Key Lab, Changsha 410004, Peoples R China.
关键词：
hydrothermal gradients;biomass components;allometric models;mean January temperature;annual precipitation
摘要：
Human society faces problems of a global scale today, as a result of which the priorities of environmental research are shifting to the macro level, and ecology has entered the era of "big data". The authors have created a database of 1,717 model trees of Betula spp. with measured indicators of diameter at breast height (DBH), tree height, age, and aboveground biomass growing in the territory of Eurasia. Regression models for aboveground biomass components are calculated, including the dendrological indices mentioned, and two climate indicators as independent variables. Based on the theory of space-for time substitution, the obtained patterns of changes in aboveground biomass in the territorial climatic gradients of Eurasia are used to predict changes in biomass due to climate shifts. In accordance with the law of the limiting factor by Liebig, it is established that in sufficiently moisture-rich climatic zones, an increase in temperature by 1??C with a constant amount of precipitation causes an increase in biomass, and in water-deficient zones ??? its decrease. In warm climatic zones, a decrease in precipitation by 100 mm at a constant average January temperature causes a decrease in biomass, and in cold climatic zones ??? its increase.
作者：
Shobairi, Seyed Omid Reza;Lin, Hui;Usoltsev, Vladimir Andreevich;Osmirko, Anna Andreevna;Tsepordey, Ivan Stepanovich;...
期刊：
CROATIAN JOURNAL OF FOREST ENGINEERING,2022年43(2):457-467 ISSN：1845-5719
通讯作者：
Lin, Hui(linhui@csuft.edu.cn)
作者机构：
[Shobairi, Seyed Omid Reza] Cent South Univ Forestry & Technol, Res Ctr Forestry Remote Sensing & Informat Engn, Changsha 410004, Peoples R China.;[Ye, Zilin; Lin, Hui] Cent South Univ Forestry & Technol, Changsha Changchang Forestry Technol Consulting C, Resources Management & Monitoring Southern Area, Key Lab State Forestry Adm Forest Resources Manag, Changsha 410004, Peoples R China.;[Usoltsev, Vladimir Andreevich; Osmirko, Anna Andreevna] Ural State Forest Engn Univ, Fac Forestry, Sibirskiy Trakt 37, Ekaterinburg 620100, Russia.;[Tsepordey, Ivan Stepanovich; Usoltsev, Vladimir Andreevich] Bot Garden Ural Branch RAS, Dept Forest Product, Ul 8 Marta 202a, Ekaterinburg 620144, Russia.;[Anees, Shoaib Ahmad] Beijing Forestry Univ, Coll Forestry, Beijing Key Lab Precis Forestry, Beijing 100083, Peoples R China.
通讯机构：
[Lin, H.] R;Research Center of Forestry, Remote Sensing & Information Engineering Central South, University of Forestry and Technology, Changsha, China
关键词：
Average annual precipitation;Average January temperature;Forest biomass structure;Regression models;Space-for-time substitution
期刊：
Science of The Total Environment,2021年780:146615 ISSN：0048-9697
通讯作者：
Yaotong Cai
作者机构：
[Long, Xiangren; Zhang, Meng; Cai, Yaotong; Lin, Hui] Cent South Univ Forestry & Technol, Res Ctr Forestry Remote Sensing & Informat Engn, Changsha 410004, Peoples R China.;[Long, Xiangren; Zhang, Meng; Cai, Yaotong; Lin, Hui] Key Lab State Forestry Adm Forest Resources Manag, Changsha 410004, Peoples R China.;[Long, Xiangren; Zhang, Meng; Cai, Yaotong; Lin, Hui] Key Lab Forestry Remote Sensing Based Big Data &, Changsha 410004, Peoples R China.
通讯机构：
[Yaotong Cai] R;Research Center of Forestry Remote Sensing & Information Engineering Central South University of Forestry & Technology, Changsha 410004, China<&wdkj&>Key Laboratory of State Forestry Administration on Forest Resources Management and Monitoring in Southern Area, Changsha 410004, China<&wdkj&>Key Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security for Hunan Province, Changsha 410004, China
关键词：
Dongting Lake wetland;Quantitative analysis;Vegetation variation;Climate change;Human activities
摘要：
Probing the long-term spatiotemporal patterns of wetland vegetation changes and their response to climate change and human activities is critical to make informed decisions regarding ecosystem protection. Here, the spatiotemporal patterns and factors that drive vegetation changes in the Dongting Lake wetland from 2000 to 2019 were analyzed using monthly normalized difference vegetation index (NDVI) data at a 30 m spatial resolution. First, abrupt vegetation changes were identified using the breaks for additive season and trend approach. Moreover, the relative impacts of climatic factors on monthly vegetation changes were quantified using a partial correlation-based approach, and the effects of three specific climatic factors (temperature, precipitation, and solar radiation) and human factors on vegetation recovery and degradation were determined. Our study found that: 1) the study area is becoming greener, with NDVI increases of 0.006 per year; however, there was a pronounced interannual variation in the vegetation types; 2) more than 50% of the vegetation pixels exhibited at least two breakpoints, with similar to 5% of the vegetation pixels exhibiting eight breakpoints; 3) in the past 20 years, human activities have favored wetland vegetation recovery (58.85%), whereas climate change threatens wetland vegetation (59.19%). Regarding climate factors, the influence of solar radiation on vegetation was found to be stronger than that of temperature and precipitation. (C) 2021 Elsevier B.V. All rights reserved.
作者：
Usoltsev, Vladimir A.;Lin, Hui*;Shobairi, Seyed Omid Reza;Tsepordey, Ivan S.;Ye, Zilin
期刊：
Plants-Basel,2020年9(10) 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：
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.
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
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：
5th International Workshop on Earth Observation and Remote Sensing Applications (EORSA)
会议时间：
JUN 18-20, 2018
会议地点：
Xian, PEOPLES R 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：
International Workshop on Earth Observation and Remote Sensing Applications
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.
作者机构：
[Zeng, Siqi; Wang, Guangxing; Ren, Lanxiang; Xu, Xiaoyu; Li, Jiping; Sun, Hua; Lin, Hui] Cent South Univ Forestry & Technol, Res Ctr Forestry Remote Sensing & Informat Engn, Changsha 410004, Hunan, Peoples R China.;[Zeng, Siqi; Wang, Guangxing; Ren, Lanxiang; Xu, Xiaoyu; Li, Jiping; Sun, Hua; Lin, Hui] Key Lab Forestry Remote Sensing Based Big Data &, Changsha 410004, Hunan, Peoples R China.;[Zeng, Siqi; Wang, Guangxing; Ren, Lanxiang; Xu, Xiaoyu; Li, Jiping; Sun, Hua; Lin, Hui] Key Lab State Forestry Adm Forest Resources Manag, Changsha 410004, Hunan, Peoples R China.;[Wang, Guangxing; Wang, Qing] Southern Illinois Univ, Dept Geog & Environm Resources, Carbondale, IL 62901 USA.;[Luo, Peng] Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China.
通讯机构：
[Wang, Guangxing] C;[Wang, Guangxing] K;[Wang, Guangxing] S;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.
关键词：
land degradation;optimized k-nearest neighbors;landsat image;percentage vegetation cover;Duolun County;Kangbao County
摘要：
Land degradation and desertification in arid and semi-arid areas is of great concern. Accurately mapping percentage vegetation cover (PVC) of the areas is critical but challenging because the areas are often remote, sparsely vegetated, and rarely populated, and it is difficult to collect field observations of PVC. Traditional methods such as regression modeling cannot provide accurate predictions of PVC in the areas. Nonparametric constant k-nearest neighbors (Cons_kNN) has been widely used in estimation of forest parameters and is a good alternative because of its flexibility. However, using a globally constant k value in Cons_kNN limits its ability of increasing prediction accuracy because the spatial variability of PVC in the areas leads to spatially variable k values. In this study, a novel method that spatially optimizes determining the spatially variable k values of Cons_kNN, denoted with Opt_kNN, was proposed to map the PVC in both Duolun and Kangbao County located in Inner Mongolia and Hebei Province of China, respectively, using Landsat 8 images and sample plot data. The Opt_kNN was compared with Cons_kNN, a linear stepwise regression (LSR), a geographically weighted regression (GWR), and random forests (RF) to improve the mapping for the study areas. The results showed that (1) most of the red and near infrared band relevant vegetation indices derived from the Landsat 8 images had significant contributions to improving the mapping accuracy; (2) compared with LSR, GWR, RF and Cons_kNN, Opt_kNN resulted in consistently higher prediction accuracies of PVC and decreased relative root mean square errors by 5%, 11%, 5%, and 3%, respectively, for Duolun, and 12%, 1%, 23%, and 9%, respectively, for Kangbao. The Opt_kNN also led to spatially variable and locally optimal k values, which made it possible to automatically and locally optimize k values; and (3) the RF that has become very popular in recent years did not perform the predictions better than the Opt_kNN for the both areas. Thus, the proposed method is very promising to improve mapping the PVC in the arid and semi-arid areas.
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：
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.
