作者机构：
[Xu, Liangliang; Ma, Kaisen; Jiang, Fugen; Yi, Jing; Sun, Hua] Cent South Univ Forestry & Technol, Res Ctr Forestry Remote Sensing & Informat Engn, Changsha 410004, Peoples R China.;[Xu, Liangliang; Ma, Kaisen; Jiang, Fugen; Yi, Jing; Sun, Hua] Key Lab Forestry Remote Sensing Based Big Data & E, Changsha 410004, Peoples R China.;[Xu, Liangliang; Ma, Kaisen; Jiang, Fugen; Yi, Jing; Sun, Hua] Key Lab Natl Forestry & Grassland Adm Forest Resou, Changsha 410004, Peoples R China.;[Li, Chaokui] Hunan Univ Sci & Technol, Natl Local Joint Engn Lab Geo Spatial Informat Tec, Xiangtan 411100, Peoples R China.;[Huang, Heqin] Hunan Software Vocat & Tech Univ, Architectural Engn Inst, Xiangtan 411100, Peoples R China.
通讯机构：
[Hua Sun] R;Research Center of Forestry Remote Sensing & Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China<&wdkj&>Key Laboratory of National Forestry & Grassland 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<&wdkj&>Author to whom correspondence should be addressed.
关键词：
UAV-LiDAR;forest remote sensing;normalized point cloud;individual tree detection;treetop displacement
摘要：
Normalized point clouds (NPCs) derived from unmanned aerial vehicle-light detection and ranging (UAV-LiDAR) data have been applied to extract relevant forest inventory information. However, detecting treetops from topographically normalized LiDAR points is challenging if the trees are located in steep terrain areas. In this study, a novel point cloud normalization method based on the imitated terrain (NPCIT) method was proposed to reduce the effect of vegetation point cloud normalization on crown deformation in regions with high slope gradients, and the ability of the treetop detection displacement model to quantify treetop displacements and tree height changes was improved, although the model did not consider the crown shape or angle. A forest farm in the mountainous region of south-central China was used as the study area, and the sample data showed that the detected treetop displacement increased rapidly in steep areas. With this work, we made an important contribution to theoretical analyses using the treetop detection displacement model with UAV-LiDAR NPCs at three levels: the method, model, and example levels. Our findings contribute to the development of more accurate treetop position identification and tree height parameter extraction methods involving LiDAR data.
作者机构：
[Long, Yi; Deng, Muli; Jiang, Fugen; Wang, Tianhong; Sun, Hua] Cent South Univ Forestry & Technol, Res Ctr Forestry Remote Sensing & Informat Engn, Changsha 410004, Peoples R China.;[Long, Yi; Deng, Muli; Jiang, Fugen; Wang, Tianhong; Sun, Hua] Key Lab Forestry Remote Sensing Based Big Data & E, Changsha 410004, Peoples R China.;[Long, Yi; Deng, Muli; Jiang, Fugen; Wang, Tianhong; Sun, Hua] Grassland Adm Forest Resources Management & Monito, Key Lab Natl Forestry, Changsha 410004, Peoples R China.
通讯机构：
[Hua Sun] R;Research Center of Forestry Remote Sensing & Information Engineering, Central South University of Forestry and Technology, Changsha, China<&wdkj&>Key Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security for Hunan Province, Changsha, China<&wdkj&>Key Laboratory of National Forestry & Grassland Administration on Forest Resources Management and Monitoring in Southern Area, Changsha, China
关键词：
eco-environmental quality;remote sensing ecological index;Google Earth Engine;Hurst exponent;geographical detector;Three-North region of China
摘要：
Eco-environmental quality is a measure of the suitability of the ecological environment for human survival and socioeconomic development. Understanding the spatial-temporal distribution and variation trend of eco-environmental quality is essential for environmental protection and ecological balance. The remote sensing ecological index (RSEI) can quickly and objectively quantify eco-environmental quality and has been extensively utilized in regional ecological environment assessment. In this paper, Moderate Resolution Imaging Spectroradiometer (MODIS) images during the growing period (July-September) from 2000 to 2020 were obtained from the Google Earth Engine (GEE) platform to calculate the RSEI in the three northern regions of China (the Three-North region). The Theil-Sen median trend method combined with the Mann-Kendall test was used to analyze the spatial-temporal variation trend of eco-environmental quality, and the Hurst exponent and the Theil-Sen median trend were superimposed to predict the future evolution trend of eco-environmental quality. In addition, ten variables from two categories of natural and anthropogenic factors were analyzed to determine the drivers of the spatial differentiation of eco-environmental quality by the geographical detector. The results showed that from 2000 to 2020, the RSEI in the Three-North region exhibited obvious regional characteristics: the RSEI values in Northwest China were generally between 0.2 and 0.4; the RSEI values in North China gradually increased from north to south, ranging from 0.2 to 0.8; and the RSEI values in Northeast China were mostly above 0.6. The average RSEI value in the Three-North region increased at an average growth rate of 0.0016/a, showing the spatial distribution characteristics of overall improvement and local degradation in eco-environmental quality, of which the areas with improved, basically stable and degraded eco-environmental quality accounted for 65.39%, 26.82% and 7.79% of the total study area, respectively. The Hurst exponent of the RSEI ranged from 0.20 to 0.76 and the future trend of eco-environmental quality was generally consistent with the trend over the past 21 years. However, the areas exhibiting an improvement trend in eco-environmental quality mainly had weak persistence, and there was a possibility of degradation in eco-environmental quality without strengthening ecological protection. Average relative humidity, accumulated precipitation and land use type were the dominant factors driving the spatial distribution of eco-environmental quality in the Three-North region, and two-factor interaction also had a greater influence on eco-environmental quality than single factors. The explanatory power of meteorological factors on the spatial distribution of eco-environmental quality was stronger than that of topographic factors. The effect of anthropogenic factors (such as population density and land use type) on eco-environmental quality gradually increased over time. This study can serve as a reference to protect the ecological environment in arid and semi-arid regions.
作者机构：
[Xie, Lu; Fu, Liyong; Sun, Hua] Cent South Univ Forestry & Technol, Res Ctr Forestry Remote Sensing & Informat Engn, Changsha 410004, Peoples R China.;[Fu, Liyong; Meng, Xiang] Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China.;[Zhao, Xiaodi] Chinese Acad Forestry, Res Inst Forestry Policy & Informat, Beijing 100091, Peoples R China.;[Sharma, Ram P.] Tribhuwan Univ, Inst Forestry, Kritipur 44600, Nepal.
通讯机构：
[Hua Sun] R;Research Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China<&wdkj&>Author to whom correspondence should be addressed.
摘要：
Fractional vegetation cover (FVC) is an important indicator of ecosystem changes. Both satellite remote sensing and ground measurements are common methods for estimating FVC. However, desert vegetation grows sparsely and scantly and spreads widely in desert regions, making it challenging to accurately estimate its vegetation cover using satellite data. In this study, we used RGB images from two periods: images from 2006 captured with a small, light manned aircraft with a resolution of 0.1 m and images from 2019 captured with an unmanned aerial vehicle (UAV) with a resolution of 0.02 m. Three pixel-based machine learning algorithms, namely gradient enhancement decision tree (GBDT), k-nearest neighbor (KNN) and random forest (RF), were used to classify the main vegetation (woody and grass species) and calculate the coverage. An independent data set was used to evaluate the accuracy of the algorithms. Overall accuracies of GBDT, KNN and RF for 2006 image classification were 0.9140, 0.9190 and 0.9478, respectively, with RF achieving the best classification results. Overall accuracies of GBDT, KNN and RF for 2019 images were 0.8466, 0.8627 and 0.8569, respectively, with the KNN algorithm achieving the best results for vegetation cover classification. The vegetation coverage in the study area changed significantly from 2006 to 2019, with an increase in grass coverage from 15.47 +/- 1.49% to 27.90 +/- 2.79%. The results show that RGB images are suitable for mapping FVC. Determining the best spatial resolution for different vegetation features may make estimation of desert vegetation coverage more accurate. Vegetation cover changes are also important in terms of understanding the evolution of desert ecosystems.
作者机构：
[Jiang, Fugen; Wang, Tianhong; Cao, Yaling; Sun, Hua] Cent South Univ Forestry & Technol, Res Ctr Forestry Remote Sensing & Informat Engn, Changsha 410004, Peoples R China.;[Jiang, Fugen; Chen, Erxue; Liu, Qingwang] Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China.;[Jiang, Fugen; Chen, Erxue; Liu, Qingwang] Natl Forestry & Grassland Adm, Key Lab Forestry Remote Sensing & Informat Syst, Beijing 100091, Peoples R China.
通讯机构：
[Qingwang Liu] K;Key Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration, Beijing 100091, China<&wdkj&>Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China<&wdkj&>Author to whom correspondence should be addressed.
摘要：
Accurate estimation of forest above-ground biomass (AGB) is critical for assessing forest quality and carbon stocks, which can improve understanding of the vegetation growth processes and the global carbon cycle. Landsat 9, the latest launched Landsat satellite, is the successor and continuation of Landsat 8, providing a highly promising data resource for land cover change, forest surveys, and terrestrial ecosystem monitoring. Regression kriging was developed in the study to improve the AGB estimation and mapping using the Landsat 9 image in Wangyedian forest farm, northern China. Multiple linear regression (MLR), support vector machine (SVM), back propagation neural network (BPNN), and random forest (RF) were used as the original models to predict the AGB trends, and the optimal model was used to overlay the results of kriging interpolation based on the residuals to obtain the new AGB predictions. In addition, Landsat 8 images in Wangyedian were used for comparison and verification with Landsat 9. The results showed that all bands of Landsat 8 and Landsat 9 maintained a high degree of uniformity, with positive correlation coefficients ranging from 0.77 to 0.89 (p < 0.01). RF achieved the highest estimation accuracy among all the original models based on the two data sources. However, kriging regression can significantly reduce the estimation error, with the root mean square error (RMSE) decreasing by 55.4% and 51.1%, for Landsat 8 and Landsat 9, respectively, compared to the original RF. Further, the R-2 and the lowest RMSE for Landsat 8 were 0.88 and 16.83 t/ha, while, for Landsat 9, they were 0.87 and 17.91 t/ha. The use of regression kriging combined with Landsat 9 imagery has great potential for achieving efficient and highly accurate forest AGB estimates, providing a new reference for long-term monitoring of forest resource dynamics.
期刊：
FRONTIERS IN PLANT SCIENCE,2022年13:892625 ISSN：1664-462X
通讯作者：
Sun, H
作者机构：
[Deng, Muli; Long, Yi; Sun, Hua; Jiang, Fugen] Cent South Univ Forestry & Technol, Res Ctr Forestry Remote Sensing & Informat Engn, Changsha, Peoples R China.;[Deng, Muli; Long, Yi; Sun, Hua; Jiang, Fugen] Key Lab Forestry Remote Sensing Based Big Data & E, Changsha, Peoples R China.;[Deng, Muli; Long, Yi; Sun, Hua; Jiang, Fugen] Key Lab State Forestry Adm Forest Resources Manage, Changsha, Peoples R China.
通讯机构：
[Sun, H ] C;Cent South Univ Forestry & Technol, Res Ctr Forestry Remote Sensing & Informat Engn, Changsha, Peoples R China.;Key Lab Forestry Remote Sensing Based Big Data & E, Changsha, Peoples R China.;Key Lab State Forestry Adm Forest Resources Manage, Changsha, Peoples R China.
作者机构：
[Long, Yi; Sun, Hua; Jiang, Fugen; Fu, Liyong; Tang, Jie] Cent South Univ Forestry & Technol, Res Ctr Forestry Remote Sensing & Informat Engn, Changsha 410004, Peoples R China.;[Long, Yi; Sun, Hua; Jiang, Fugen; Tang, Jie] Key Lab Forestry Remote Sensing Based Big Data &, Changsha 410004, Peoples R China.;[Long, Yi; Sun, Hua; Jiang, Fugen; Tang, Jie] Key Lab State Forestry Adm Forest Resources Manag, Changsha 410004, Peoples R China.;[Fu, Liyong] Res Inst Forest Resource Informat Tech, Chinese Acad Forestry, Beijing 100091, Peoples R China.
通讯机构：
[Sun, H ] C;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：
Camellia oleifera;yield identification;terrestrial laser scanning;mean shift;color space
摘要：
Oil tea (Camellia oleifera) is one of the world's major woody edible oil plants and is vital in providing food and raw materials and ensuring water conservation. The yield of oil tea can directly reflect the growth condition of oil tea forests, and rapid and accurate yield measurement is directly beneficial to efficient oil tea forest management. Light detection and ranging (LiDAR), which can penetrate the canopy to acquire the geometric attributes of targets, has become an effective and popular method of yield identification for agricultural products. However, the common geometric attribute information obtained by LiDAR systems is always limited in terms of the accuracy of yield identification. In this study, to improve yield identification efficiency and accuracy, the red-green-blue (RGB) and luminance-bandwidth-chrominance (i.e., YUV color spaces) were used to identify the point clouds of oil tea fruits. An optimized mean shift clustering algorithm was constructed for oil tea fruit point cloud extraction and product identification. The point cloud data of oil tea trees were obtained using terrestrial laser scanning (TLS), and field measurements were conducted in Changsha County, central China. In addition, the common mean shift, density-based spatial clustering of applications with noise (DBSCAN), and maximum-minimum distance clustering were established for comparison and validation. The results showed that the optimized mean shift clustering algorithm achieved the best identification in both the RGB and YUV color spaces, with detection ratios that were 9.02%, 54.53%, and 3.91% and 7.05%, 62.35%, and 10.78% higher than those of the common mean shift clustering, DBSCAN clustering, and maximum-minimum distance clustering algorithms, respectively. In addition, the improved mean shift clustering algorithm achieved a higher recognition rate in the YUV color space, with an average detection rate of 81.73%, which was 2.4% higher than the average detection rate in the RGB color space. Therefore, this method can perform efficient yield identification of oil tea and provide a new reference for agricultural product management.
作者机构：
[Ma, Kaisen; Sun, Hua; Fu, Liyong; Jiang, Fugen; Yi, Jing] Cent South Univ Forestry & Technol, Res Ctr Forestry Remote Sensing & Informat Engn, Changsha 410004, Peoples R China.;[Ma, Kaisen; Sun, Hua; Jiang, Fugen; Yi, Jing] Key Lab Forestry Remote Sensing Based Big Data &, Changsha 410004, Peoples R China.;[Ma, Kaisen; Sun, Hua; Jiang, Fugen; Yi, Jing] Key Lab State Forestry Adm Forest Resources Manag, Changsha 410004, Peoples R China.;[Chen, Zhenxiong; Du, Zhi] Cent South Inventory & Planning Inst Natl Forestr, Dept Forest Inventory & Monitoring, Changsha 410004, Peoples R China.;[Fu, Liyong] Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China.
通讯机构：
[Sun, H ] C;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：
LiDAR;forest investigation;individual tree segmentation;tree detection;tree height extraction
摘要：
Using unmanned aerial vehicles (UAV) as platforms for light detection and ranging (LiDAR) sensors offers the efficient operation and advantages of active remote sensing; hence, UAV-LiDAR plays an important role in forest resource investigations. However, high-precision individual tree segmentation, in which the most appropriate individual tree segmentation method and the optimal algorithm parameter settings must be determined, remains highly challenging when applied to multiple forest types. This article compared the applicability of methods based on a canopy height model (CHM) and a normalized point cloud (NPC) obtained from UAV-LiDAR point cloud data. The watershed algorithm, local maximum method, point cloud-based cluster segmentation, and layer stacking were used to segment individual trees and extract the tree height parameters from nine plots of three forest types. The individual tree segmentation results were evaluated based on experimental field data, and the sensitivity of the parameter settings in the segmentation methods was analyzed. Among all plots, the overall accuracy F of individual tree segmentation was between 0.621 and 1, the average RMSE of tree height extraction was 1.175 m, and the RMSE% was 12.54%. The results indicated that compared with the CHM-based methods, the NPC-based methods exhibited better performance in individual tree segmentation; additionally, the type and complexity of a forest influence the accuracy of individual tree segmentation, and point cloud-based cluster segmentation is the preferred scheme for individual tree segmentation, while layer stacking should be used as a supplement in multilayer forests and extremely complex heterogeneous forests. This research provides important guidance for the use of UAV-LiDAR to accurately obtain forest structure parameters and perform forest resource investigations. In addition, the methods compared in this paper can be employed to extract vegetation indices, such as the canopy height, leaf area index, and vegetation coverage.
作者机构：
[Ma, Kaisen; Jiang, Fugen; Sun, Hua] Cent South Univ Forestry & Technol, Res Ctr Forestry Remote Sensing & Informat Engn, Changsha 410004, Peoples R China.;[Ma, Kaisen; Jiang, Fugen; Sun, Hua] Key Lab Forestry Remote Sensing Based Big Data &, Changsha 410004, Peoples R China.;[Ma, Kaisen; Jiang, Fugen; Sun, Hua] Key Lab State Forestry Adm Forest Resources Manag, Changsha 410004, Peoples R China.;[Zhao, Feng] East China Normal Univ, State Key Lab Estuarine & Coastal Res, Inst Eco Chongming, Shanghai 200241, Peoples R China.;[Li, Dongsheng] Hebei Acad Forestry & Grassland Invest & Planning, Shijiazhuang 050051, Hebei, Peoples R China.
通讯机构：
[Hua Sun] K;Key Laboratory of State Forestry Administration on Forest Resources Management and Monitoring in Southern Area, Changsha 410004, China<&wdkj&>Research Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China<&wdkj&>Key Laboratory of Forestry Remote Sensing Based Big Data and Ecological Security for Hunan Province, Changsha 410004, China<&wdkj&>Author to whom correspondence should be addressed.
摘要：
The forest canopy height (FCH) plays a critical role in forest quality evaluation and resource management. The accurate and rapid estimation and mapping of the regional forest canopy height is crucial for understanding vegetation growth processes and the internal structure of the ecosystem. A stacking algorithm consisting of multiple linear regression (MLR), support vector machine (SVM), k-nearest neighbor (kNN), and random forest (RF) was used in this paper and demonstrated optimal performance in predicting the forest canopy height by synergizing Sentinel-2 images acquired from the cloud-based computation platform Google Earth Engine (GEE) with data from ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2). This research was conducted to achieve continuous mapping of the canopy height of plantations in Saihanba Mechanical Forest Plantation, which is located in Chengde City, northern Hebei province, China. The results show that stacking achieved the best prediction accuracy for the forest canopy height, with an R-2 of 0.77 and a root mean square error (RMSE) of 1.96 m. Compared with MLR, SVM, kNN, and RF, the RMSE obtained by stacking was reduced by 25.2%, 24.9%, 22.8%, and 18.7%, respectively. Since Sentinel-2 images and ICESat-2 data are publicly available, this opens the door for the accurate mapping of the continuous distribution of the forest canopy height globally in the future.
作者机构：
[Ma, Kaisen; Jiang, Fugen; Chen, Song; Sun, Hua] Cent South Univ Forestry & Technol, Res Ctr Forestry Remote Sensing & Informat Engn, Changsha 410004, Peoples R China.;[Ma, Kaisen; Jiang, Fugen; Chen, Song; Sun, Hua] Key Lab Forestry Remote Sensing Based Big Data &, Changsha 410004, Peoples R China.;[Ma, Kaisen; Jiang, Fugen; Chen, Song; Sun, Hua] Key Lab State Forestry Adm Forest Resources Manag, Changsha 410004, Peoples R China.;[Xiong, Yujiu] Sun Yat Sen Univ, Sch Civil Engn, Guangzhou 510275, Peoples R China.
通讯机构：
[Hua Sun] R;Research Center of Forestry Remote Sensing & Information Engineering, Central South University of Forestry and 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<&wdkj&>Author to whom correspondence should be addressed.
关键词：
single-tree segmentation;UAV;LiDAR;vegetation point cloud density model;improved watershed algorithm
摘要：
Detecting and segmenting individual trees in forest ecosystems with high-density and overlapping crowns often results in bias due to the limitations of the commonly used canopy height model (CHM). To address such limitations, this paper proposes a new method to segment individual trees and extract tree structural parameters. The method involves the following key steps: (1) unmanned aerial vehicle (UAV)-scanned, high-density laser point clouds were classified, and a vegetation point cloud density model (VPCDM) was established by analyzing the spatial density distribution of the classified vegetation point cloud in the plane projection; and (2) a local maximum algorithm with an optimal window size was used to detect tree seed points and to extract tree heights, and an improved watershed algorithm was used to extract the tree crowns. The proposed method was tested at three sites with different canopy coverage rates in a pine-dominated forest in northern China. The results showed that (1) the kappa coefficient between the proposed VPCDM and the commonly used CHM was 0.79, indicating that performance of the VPCDM is comparable to that of the CHM; (2) the local maximum algorithm with the optimal window size could be used to segment individual trees and obtain optimal single-tree segmentation accuracy and detection rate results; and (3) compared with the original watershed algorithm, the improved watershed algorithm significantly increased the accuracy of canopy area extraction. In conclusion, the proposed VPCDM may provide an innovative data segmentation model for light detection and ranging (LiDAR)-based high-density point clouds and enhance the accuracy of parameter extraction.
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.
