期刊：
Science of The Total Environment,2021年774:145067 ISSN：0048-9697
通讯作者：
Li, Jiping(lijiping1602@163.com)
作者机构：
[Tang, Tao; Li, Jiping; Sun, Hua] Cent South Univ Forestry & Technol, Fac Forestry, 498 Southern Shaoshan Rd, Changsha 410004, Hunan, Peoples R China.;[Tang, Tao; Li, Jiping; Sun, Hua] State Forestry Adm Forest Resources Management &, Key Lab, Changsha 410004, Peoples R China.;[Sun, Hua] Key Lab Forestry Remote Sensing Based Big Data &, Changsha 410004, Peoples R China.;[Sun, Hua] Cent South Univ Forestry & Technol, Res Ctr Forest Remote Sensing & Informat Engn, Changsha 410004, Peoples R China.;[Deng, Chao] Forestry Bur Changde City Hunan Prov, Changde 415099, Peoples R China.
通讯机构：
[Jiping Li] F;Faculty of Forestry, Central South University of Forestry and Technology, Changsha 410004, Hunan Province, China<&wdkj&>Key Laboratory of State Forestry Administration on Forest Resources Management and Monitoring in Southern Area, Changsha 410004, China
摘要：
The decline and fragmentation of habitats areas are two main factors that lead to the reduction of biodiversity in landscape ecosystems. As a kind of large carnivores, South China tiger (Panthera tigris amoyensis) is one of the most endangered tiger subspecies and considered to be extinct in the wild. The Chinese government has intended to release a certain number of tigers into two of their historically habitats areas, Hupingshan-Houhe national nature reserves (NNR) in central-south China that provides suitable habitats for P. tigris. Because wild boar (Sus scrofa) is a prey of P. tigris, spatially characterizing the populations of the prey and its habitats is critical for the success of habituating the tigers to the areas. Although there has been effort made to protect the habitats of wild boar, there have been no report that deal with investigation and analysis of the habitat suitability and potential forwild boar, especially in terms of landscape connectivity. Herewe present the novel integration of the habitat suitability index (HSI) and graph-based network to identify the priority areas for wild boar dispersal in and around the NNR. In addition, a novel method to identify the proper connectivity distance to avoid excessive connectivity when the field data are essentially non-existent. Results showed that in summer andwinter, the potential habitat areas were 6848-10,245 and 5984-10,152km(2), respectively. The total area of the priority patches was 1590 km(2), approximately occupying 16% of the suitable habitat area. Our study indicated that the novel integration of the HSI and network analysis led to an effective approach to spatially characterize priority patches to support decision-making for landscape planning. The results shown here also have implications for future efforts for habituating large carnivores into their historical habitat regions. (c) 2021 Elsevier B.V. All rights reserved.
作者机构：
[Jiang, Fugen; Wang, Guangxing; Sun, Hua] Cent South Univ Forestry & Technol, Res Ctr Forestry Remote Sensing & Informat Engn, Changsha 410004, Peoples R China.;[Jiang, Fugen; Sun, Hua] Key Lab Forestry Remote Sensing Based Big Data &, Changsha 410004, Peoples R China.;[Jiang, Fugen; Sun, Hua] Key Lab State Forestry Adm Forest Resources Manag, Changsha 410004, Peoples R China.;[Smith, Andrew R.] Bangor Univ, Sch Nat Sci, Bangor LL57 2UW, Gwynedd, Wales.;[Kutia, Mykola] Bangor Univ, Bangor Coll China, 498 Shaoshan Rd, Changsha 410004, Peoples R China.
通讯机构：
[Sun, Hua] C;[Sun, Hua] 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：
Leaf area index;medium-resolution images;characteristic variable selection;modified kNN;dry regions
摘要：
As an important vegetation canopy parameter, the leaf area index (LAI) plays a critical role in forest growth modeling and vegetation health assessment. Estimating LAI is helpful for understanding vegetation growth and global ecological processes. Machine learning methods such as k-nearest neighbors (kNN) and random forest (RF) with remote sensing images have been widely used for mapping LAI. However, the accuracy of mapping LAI in arid and semi-arid areas using these methods is limited due to remote and large areas, the high cost of collecting field data, and the great spatial variability of the vegetation canopy. Here, a novel and modified kNN method was presented for mapping LAI in arid and semi-arid areas of China using Sentinel-2 and Landsat 8 images with field data collected in Ganzhou and Kangbao of China. The modified kNN was developed by integrating the traditional kNN estimation and RF classification. The results were compared with those from kNN and RF regression alone using three sets of input predictors: (i) spectral reflectance bands (input 1); (ii) vegetation indices (input 2); and (iii) a combination of spectral reflectance bands and vegetation indices (input 3). Our analysis showed that in Ganzhou, the red-edge bands of the Sentinel-2 image had a high correlation with LAI. Using the red-edge band-derived vegetation indices increased the accuracy of mapping LAI compared with using other spectral variables. Among the three sets of input predictors, input 3 resulted in the highest prediction accuracy. Based on the combination, the values of RMSE obtained by the traditional kNN, RF, and modified kNN were 0.526, 0.523, and 0.372, respectively, and the modified kNN significantly improved the accuracy of LAI prediction by 29.3% and 28.9% compared with the kNN and RF alone, respectively. A similar improvement was achieved for input 1 and input 2. In Kangbao, the improvement of the prediction accuracy obtained by the modified kNN was 31.4% compared with both the kNN and RF. Therefore, this study implied that the modified kNN provided the potential to improve the accuracy of mapping LAI in arid and semi-arid regions using the images.
作者机构：
[Sun, H.; Jiang, F. G.] Cent South Univ Forestry & Technol, Res Ctr Forestry Remote Sensing & Informat Engn, Changsha 410004, Hunan, Peoples R China.;[Sun, H.; Jiang, F. G.] Key Lab Forestry Remote Sensing Based Big Data &, Changsha 410004, Hunan, Peoples R China.;[Sun, H.; Jiang, F. G.] Key Lab Natl Forestry & Grassland Adm Forest Reso, Changsha 410004, Peoples R China.;[Wang, G. X.; Wang, Q.] Southern Illinois Univ, Dept Geog & Environm Resources, Carbondale, IL 62901 USA.;[Luo, P.] Chinese Acad Forestry, Res Inst Forest Resources Informat Tech, Beijing 100091, Peoples R China.
通讯机构：
[Wang, G. X.] S;Southern Illinois Univ, Dept Geog & Environm Resources, Carbondale, IL 62901 USA.
关键词：
accurate estimation, desertification, geographically weighted logistic regression, Kangbao County, land degradation, northern China, remote sensing, spatial variability, vegetation cover.
摘要：
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Accurately estimating and mapping vegetation cover for monitoring land degradation and desertification of arid and semiarid areas using remotely sensed images is promising but challenging in remote, sparsely vegetated and large areas. In this study, a novel method – geographically weighted logistic regression (GWLR – integrating geographically weighted regression (GWR) and a logistic model) was proposed to improve vegetation cover mapping of Kangbao County, Hebei of China using Landsat 8 image and field data. Additionally, a new method to determine the bandwidth of GWLR is presented. Using cross-validation, GWLR was compared with a globally linear stepwise regression (LSR), a local linear modelling method GWR and a nonparametric method, k-nearest neighbours (kNN) with varying numbers of nearest plots. Results demonstrated (1) the red and near infrared relevant band ratios and vegetation indices significantly improved mapping; (2) the GWLR, GWR and kNN methods led to more accurate predictions than LSR; (3) GWLR reduced overestimations and underestimations compared with LSR, kNN and GWR, and also eliminated negative and very large estimates caused by GWR and LSR; and (4) The maximum distance of spatial autocorrelation could be used to determine the bandwidth for GWLR. Overall, GWLR proved more promising for mapping vegetation cover of arid and semiarid areas.
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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.
作者机构：
[Cui, Yunlei; Wang, Guangxing; Li, Chengjie; Xu, Xiaoyu; Sun, Hua] Cent South Univ Forestry & Technol, Res Ctr Forestry Remote Sensing & Informat Engn, Changsha 410004, Peoples R China.;[Cui, Yunlei; Li, Chengjie; Sun, Hua] Key Lab Forestry Remote Sensing Based Big Data &, Changsha 410004, Peoples R China.;[Cui, Yunlei; Li, Chengjie; Sun, Hua] Key Lab State Forestry & Grassland Adm Forest Res, Changsha 410004, Peoples R China.;[Wang, Guangxing; Xu, Xiaoyu] Southern Illinois Univ, Dept Geog & Environm Resources, Carbondale, IL 62901 USA.
通讯机构：
[Sun, Hua] C;[Sun, Hua] 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 & Grassland Adm Forest Res, Changsha 410004, Peoples R China.
关键词：
mixed pixel;probability-based method;Landsat 8 image;percentage vegetation cover;Duolun County
摘要：
China has been facing serious land degradation and desertification in its north and northwest arid and semi-arid areas. Monitoring the dynamics of percentage vegetation cover (PVC) using remote sensing imagery in these areas has become critical. However, because these areas are large, remote, and sparsely populated, and also because of the existence of mixed pixels, there have been no accurate and cost-effective methods available for this purpose. Spectral unmixing methods are a good alternative as they do not need field data and are low cost. However, traditional linear spectral unmixing (LSU) methods lack the ability to capture the characteristics of spectral reflectance and scattering from endmembers and their interactions within mixed pixels. Moreover, existing nonlinear spectral unmixing methods, such as random forest (RF) and radial basis function neural network (RBFNN), are often costly because they require field measurements of PVC from a large number of training samples. In this study, a cost-effective approach to mapping PVC in arid and semi-arid areas was proposed. A method for selection and purification of endmembers mainly based on Landsat imagery was first presented. A probability-based spectral unmixing analysis (PBSUA) and a probability-based optimized k nearest-neighbors (PBOkNN) approach were then developed to improve the mapping of PVC in Duolun County in Inner Mongolia, China, using Landsat 8 images and field data from 920 sample plots. The proposed PBSUA and PBOkNN methods were further validated in terms of accuracy and cost-effectiveness by comparison with two LSU methods, with and without purification of endmembers, and two nonlinear approaches, RF and RBFNN. The cost-effectiveness was defined as the reciprocal of cost timing relative root mean square error (RRMSE). The results showed that (1) Probability-based spectral unmixing analysis (PBSUA) was most cost-effective and increased the cost-effectiveness by 29.3% 29.3%, 33.5%, 50.8%, and 53.0% compared with two LSU methods, PBOkNN, RF, and RBFNN, respectively; (2) PBSUA, RF, and RBFNN gave RRMSE values of 22.9%, 21.8%, and 22.8%, respectively, which were not significantly different from each other at the significance level of 0.05. Compatibly, PBOkNN and LSU methods with and without purification of endmembers resulted in significantly greater RRMSE values of 27.5%, 32.4%, and 43.3%, respectively; (3) the average estimates of the sample plots and predicted maps from PBSUA, PBOkNN, RF, and RBFNN fell in the confidence interval of the test plot data, but those from two LSU methods did not, although the LSU with purification of endmembers improved the PVC estimation accuracy by 25.2% compared with the LSU without purification of endmembers. Thus, this study indicated that the proposed PBSUA had great potential for cost-effectively mapping PVC in arid and semi-arid areas.
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：
[Zhang, Meng; Lin, Hui; Cai, Yaotong; Sun, Hua] Cent South Univ Forestry, Res Ctr Forest Remote Sensing & Informat Engn, Changsha 410004, Hunan, Peoples R China.;[Zhang, Meng; Lin, Hui; Cai, Yaotong; Sun, Hua] Key Lab Forestry Remote Sensing Based Big Data &, Changsha 410004, Hunan, Peoples R China.;[Zhang, Meng; Lin, Hui; Cai, Yaotong; Sun, Hua] Key Lab State Forestry Adm Forest Resources Manag, Changsha 410004, Hunan, Peoples R China.
通讯机构：
[Zhang, M; Lin, H] C;[Zhang, M; Lin, H] K;Cent South Univ Forestry, Res Ctr Forest 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.
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：
GF-2 satellite is the highest spatial resolution Remote Sensing Satellite of the development history of China's satellite. In this study, three traditional fusion methods including Brovey, Gram-Schmidt and Color Normalized (CN） were used to compare with the other new fusion method NNDiffuse, which used the qualitative assessment and quantitative fusion quality index, including information entropy, variance, mean gradient, deviation index, spectral correlation coefficient. Analysis results show that NNDiffuse method presented the optimum in qualitative and quantitative analysis. It had more effective for the follow up of remote sensing information extraction and forest, wetland resources monitoring applications.
