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：
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：
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：
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.
期刊：
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2015年12(9):1993-1997 ISSN：1545-598X
通讯作者：
Sun, Hua
作者机构：
[Wang, Guangxing; Li, Jiping; Sun, Hua; Lin, Hui] Cent South Univ Forestry & Technol, Res Ctr Forest Remote Sensing & Informat Engn, Changsha 410004, Hunan, Peoples R China.;[Wang, Guangxing] South Illinois Univ, Dept Geog, Carbondale, IL 62901 USA.;[Ju, Hongbo; Zhang, Huaiqing] Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China.
通讯机构：
[Sun, Hua] C;Cent South Univ Forestry & Technol, Res Ctr Forest Remote Sensing & Informat Engn, Changsha 410004, Hunan, Peoples R China.
关键词：
Accuracy assessment;estimation;point cloud data;terrestrial laser scanning (TLS);tree and stand parameters
摘要：
Compared with airborne laser scanning, terrestrial laser scanning (TLS) offers ground-based point cloud data of trees and provides greater potential to accurately estimate tree and stand parameters. However, there is a lack of effective methods to accurately identify locations of individual trees from TLS point cloud data. It is also unknown whether the estimation accuracy of the parameters, including tree height (H), diameter at breast height (DBH), and so on, using TLS can meet the requirement of forest management and planning. In this letter, a novel method to effectively process point cloud data and further determine the locations of individual trees in a stand based on the central coordinates of point cloud data on a defined grid according to the largest DBH was developed. Moreover, a point-cloud-data-based convex hull algorithm and the cylinder method were, respectively, used to estimate DBH and H of individual trees. This study was conducted in a pure Chinese fir plantation of 45 trees located in Huang-Feng-Qiao forest farm, You County of Hunan, China. The comparison of the estimated and observed values showed that the obtained tree locations had errors of less than 20 cm, and the relative root mean square errors for the estimates of both DBH and H were less than 5%. This implies that TLS is very promising for the retrieval of tree and stand parameters in forest stands. For the applications of these methods to mixed forests with a structure of multilayer canopies, further examination is needed.
期刊：
International Journal of Remote Sensing,2015年36(2):489-512 ISSN：0143-1161
通讯作者：
Wang, Guangxing
作者机构：
[Wang, Guangxing; Yan, Enping; Lin, Hui; Sun, Hua] Cent South Univ Forestry & Technol, Res Ctr Forest Remote Sensing & Informat Engn, Changsha 410004, Hunan, Peoples R China.;[Wang, Guangxing] So Illinois Univ, Dept Geog & Environm Resources, Carbondale, IL 62901 USA.;[Xia, Chaozong] State Forestry Adm, Acad Forest Inventory & Planning, Beijing 100714, Peoples R China.
通讯机构：
[Wang, Guangxing] C;Cent South Univ Forestry & Technol, Res Ctr Forest Remote Sensing & Informat Engn, Changsha 410004, Hunan, Peoples R China.
摘要：
Remotely sensed data have been widely used for classification of land-use and land-cover (LULC) types. However, classifying different forest types in Northeast China using satellite images is still a great challenge because of the similar spectral reflectances of different tree species. The differences of vegetation phenological characteristics provide the potential of classifying the types using time series of spectral variables derived from images. In this study, time series of the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) from Moderate Resolution Imaging Spectroradiometer (MODIS) images obtained in 2012 for Northeast China were used to calculate various phenological metrics and to further derive amplitude and phase information of harmonic components using Fourier transforms. The separability of eight vegetation cover types plus water and built-up areas was then analysed using phenological metrics, and amplitude and phase of harmonic components. Moreover, a phenology-based decision tree classifier was developed to classify the types in this area. Out of 4900 national forest inventory plots, 3700 plots were used to train the decision tree classifier and 1200 plots to assess the accuracy of classification by combining the plots’ observations with the values of a published LULC map that had a higher spatial resolution and accuracy of classification using a window majority rule. In addition, three data sets from different temporal resolution MODIS NDVI and EVI time series and two similarity measures were compared for separability and classification of the types. The results showed that (1) Fourier transforms of NDVI and EVI time series led to the first four harmonic components (including component 0, average annual NDVI, and EVI) that captured the phenological characteristics of the cover types; (2) compared to those using only NDVI, the separability values of the classes using NDVI, amplitude, and phase increased from 1.71 to 1.95, implying the potential improvement of classification; (3) the data set from 10-day NDVI time series had higher classification accuracy than those from 16-day NDVI and EVI time series, although the EVI time series performed slightly better than the NDVI time series at the same temporal resolution; and (4) a classification accuracy of 83.8% with a kappa coefficient of 0.79 was finally obtained. This study implied that this method is applicable for classification of vegetation cover types for large areas.
通讯机构：
Research Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha, Hunan, China
