Journal of Geo-information Science ›› 2023, Vol. 25 ›› Issue (5): 935-952.doi: 10.12082/dqxxkx.2023.220766
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LI Linye(), LI Yanyan(
), CHEN Chuanfa, LIU Yan, LIU Yating, LIU Panpan
Received:
2022-10-09
Revised:
2023-01-03
Online:
2023-05-25
Published:
2023-04-27
Contact:
LI Yanyan
E-mail:lilinye2022@163.com;yylee@whu.edu.cn
Supported by:
LI Linye, LI Yanyan, CHEN Chuanfa, LIU Yan, LIU Yating, LIU Panpan. Method for the Correction of Digital Elevation Models Over Forested Areas: Back Propagation Neural Network with the Consideration of Spatial Autocorrelation[J].Journal of Geo-information Science, 2023, 25(5): 935-952.DOI:10.12082/dqxxkx.2023.220766
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Tab. 1
LiDAR point cloud data acquisition parameter description
林区类型 | 扫描设备 | 飞行高度 /m | 点密度 /(pts/m2) | 地面点密度 /(pts/m2) | 脉冲频率 /(KHZ) | 垂直精度 /cm | 水平精度 /cm | DOI |
---|---|---|---|---|---|---|---|---|
常绿阔叶林 | Optech Titan (14SEN340) | 650~1250 | 48.77 | 1.32 | 50~300 | 12~23 | 5~15 | https://doi.org/10.5069/G9FN14B1 |
常绿针叶林 | RIEGL VQ-480i | 500 | 11.53 | 2.29 | 100 | 9 | 5~10 | https://doi.org/10.5069/G9M043H0 |
混交林 | ALTM(06SEN195) | 80~4000 | 6.16 | 1.42 | 33~167 | 1~36 | 5~10 | https://doi.org/10.5069/G9M61H5D |
落叶阔叶林 | ALTM (06SEN195) | 150~4000 | 8.91 | 0.47 | 33~167 | 3~73 | 5~30 | https://doi.org/10.5069/G9HT2M76 OT |
Tab. 2
Statistics for the training data and testing data
林区类型 | 区域 | 面积/km2 | 点数/个 | 平均高程/m | 平均坡度/° | 平均植被高度/m | 平均植被覆盖度/% |
---|---|---|---|---|---|---|---|
常绿 阔叶林 | T1 | 12 | 10 792 | 366 | 19 | 30 | 68 |
S1 | 12 | 10 792 | 396 | 22 | 29 | 70 | |
常绿 针叶林 | T2 | 12 | 11 220 | 874 | 24 | 14 | 65 |
S2 | 12 | 11 220 | 974 | 22 | 21 | 68 | |
混交林 | T3 | 12 | 11 352 | 625 | 14 | 14 | 75 |
S3 | 12 | 11 352 | 684 | 12 | 14 | 70 | |
落叶 阔叶林 | T4 | 8 | 6739 | 732 | 15 | 22 | 82 |
S4 | 6 | 5700 | 995 | 20 | 21 | 78 |
Tab.3
Fitting results and parameters of Gaussian theory model for four forest training areas
块金值/m2 | 基台值/m2 | 变程/m | R2 | |
---|---|---|---|---|
常绿阔叶林(T1) | ||||
SRTM1 | 0.65 | 652.23 | 147.00 | 1.00 |
AW3D30 | 0.63 | 633.73 | 131.68 | 1.00 |
TDX90 | 2.30 | 2299.51 | 361.23 | 1.00 |
常绿针叶林(T2) | ||||
SRTM1 | 1.02 | 1021.48 | 148.53 | 1.00 |
AW3D30 | 0.90 | 900.71 | 127.04 | 1.00 |
TDX90 | 4.39 | 4398.87 | 359.44 | 1.00 |
混交林(T3) | ||||
SRTM1 | 0.50 | 495.51 | 169.78 | 1.00 |
AW3D30 | 0.37 | 368.63 | 146.33 | 1.00 |
TDX90 | 2.21 | 2214.78 | 369.60 | 1.00 |
落叶阔叶林(T4) | ||||
SRTM1 | 0.95 | 948.57 | 153.81 | 1.00 |
AW3D30 | 0.82 | 815.88 | 130.67 | 1.00 |
TDX90 | 3.27 | 3272.92 | 360.10 | 1.00 |
Tab. 4
Effects of different vegetation heights on DEM errors before and after correction in test sites
植被 高度/m | SRTM1 | AW3D30 | TDX90 | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
ME/m | RMSE/m | ME/m | RMSE/m | ME/m | RMSE/m | |||||||
修正前 | 修正后 | 修正前 | 修正后 | 修正前 | 修正后 | 修正前 | 修正后 | 修正前 | 修正后 | 修正前 | 修正后 | |
常绿阔叶林 | ||||||||||||
0~6 | 2.35 | -0.72 | 6.15 | 5.78 | 3.59 | -1.37 | 5.00 | 2.21 | 1.35 | -0.46 | 8.98 | 3.03 |
6~10 | 2.48 | -0.57 | 8.82 | 6.84 | 5.65 | -4.34 | 8.05 | 4.81 | 5.50 | -3.97 | 5.50 | 3.97 |
10~14 | 7.68 | -1.82 | 13.96 | 13.29 | 7.52 | -7.90 | 11.36 | 9.18 | 10.36 | -2.26 | 6.01 | 4.91 |
14~18 | 15.51 | -2.81 | 21.55 | 11.90 | 16.12 | -10.56 | 20.84 | 14.75 | 14.66 | -1.74 | 20.39 | 9.58 |
18~20 | 21.23 | -3.00 | 26.39 | 11.78 | 23.56 | -5.47 | 27.90 | 11.35 | 22.65 | -3.56 | 27.38 | 12.38 |
>20 | 26.14 | 1.01 | 29.14 | 10.95 | 30.70 | 0.46 | 32.89 | 9.26 | 30.19 | -0.87 | 34.90 | 15.80 |
常绿针叶林 | ||||||||||||
0~6 | 1.95 | -0.32 | 8.84 | 6.18 | -0.22 | -0.23 | 6.38 | 1.33 | 17.33 | 9.07 | 16.58 | 12.75 |
6~10 | 6.02 | -4.10 | 12.39 | 11.42 | 4.98 | -1.56 | 9.18 | 1.69 | 8.88 | 7.32 | 21.25 | 10.77 |
10~14 | 7.76 | -4.14 | 14.21 | 11.37 | 7.73 | -4.85 | 12.15 | 10.36 | 15.40 | 5.63 | 27.87 | 20.63 |
14~18 | 13.71 | -2.89 | 16.56 | 8.35 | 16.89 | -1.32 | 19.80 | 8.69 | 20.53 | -1.26 | 28.45 | 16.89 |
18~20 | 16.03 | 0.64 | 18.57 | 8.15 | 19.53 | 0.42 | 21.92 | 8.98 | 24.24 | -2.25 | 29.43 | 13.60 |
>20 | 20.84 | 3.80 | 24.42 | 11.01 | 25.47 | 2.13 | 28.59 | 11.10 | 19.60 | -1.25 | 30.71 | 15.11 |
混交林 | ||||||||||||
0~6 | 1.03 | -0.36 | 1.85 | 1.03 | 2.60 | -0.85 | 3.98 | 2.49 | 1.56 | 0.16 | 5.43 | 1.14 |
6~10 | 1.32 | -0.12 | 2.80 | 2.23 | 5.06 | -0.81 | 6.33 | 2.80 | 2.13 | -1.45 | 8.19 | 2.98 |
10~14 | 1.96 | -0.27 | 4.16 | 2.46 | 7.41 | 0.23 | 8.69 | 3.24 | 4.09 | -0.15 | 11.76 | 3.75 |
14~18 | 6.10 | 0.49 | 7.27 | 2.48 | 7.92 | 0.86 | 8.73 | 3.40 | 5.80 | 1.06 | 12.25 | 6.16 |
18~20 | 6.75 | -0.15 | 7.45 | 2.60 | 8.09 | -1.20 | 8.91 | 3.80 | 10.94 | 1.38 | 12.35 | 6.43 |
>20 | 7.48 | 0.72 | 8.20 | 3.20 | 8.77 | -1.08 | 9.36 | 4.11 | 17.34 | 1.64 | 17.35 | 6.57 |
落叶阔叶林 | ||||||||||||
0~6 | 27.75 | 5.51 | 28.53 | 8.87 | 26.53 | 19.76 | 26.98 | 8.17 | 27.30 | -1.68 | 29.32 | 9.99 |
6~10 | 32.24 | 7.53 | 32.61 | 9.08 | 28.74 | 5.43 | 28.80 | 7.07 | 36.90 | 11.33 | 36.90 | 11.33 |
10~14 | 28.79 | 5.73 | 29.72 | 9.15 | 27.17 | 5.08 | 27.85 | 8.54 | 23.55 | -3.47 | 23.95 | 7.37 |
14~18 | 28.24 | 3.24 | 29.13 | 8.08 | 27.05 | 2.81 | 27.56 | 6.65 | 34.98 | 7.30 | 36.77 | 12.96 |
18~20 | 24.87 | -0.56 | 25.88 | 6.78 | 26.56 | 1.26 | 27.10 | 6.37 | 30.70 | 3.83 | 32.98 | 11.15 |
>20 | 21.33 | -2.02 | 23.12 | 7.69 | 26.56 | -0.44 | 27.37 | 7.63 | 19.71 | -1.85 | 26.52 | 14.14 |
Tab. 5
Accuracy comparison of five models
模型 | SRTM1 | AW3D30 | TDX90 | |||||
---|---|---|---|---|---|---|---|---|
ME/m | RMSE/m | ME/m | RMSE/m | ME/m | RMSE/m | |||
常绿阔叶林(S1) | ||||||||
BPNN | 0.01 | 11.37 | 0.37 | 10.08 | -0.34 | 16.03 | ||
BPNN-T | -0.61 | 12.12 | -0.69 | 10.52 | -0.61 | 16.09 | ||
BPNN-R | -1.52 | 15.16 | 0.47 | 14.95 | -0.43 | 19.36 | ||
BPNN-W | -0.86 | 13.31 | -1.65 | 14.18 | -0.67 | 18.52 | ||
MLR | -0.39 | 13.26 | -0.21 | 12.16 | -0.98 | 16.72 | ||
常绿针叶林(S2) | ||||||||
BPNN | -0.62 | 10.29 | -0.24 | 10.15 | -0.79 | 15.85 | ||
BPNN-T | -0.24 | 10.61 | -0.41 | 10.75 | 0.57 | 16.95 | ||
BPNN-R | -0.52 | 13.01 | -0.56 | 14.24 | 0.77 | 19.21 | ||
BPNN-W | -0.70 | 12.44 | -0.32 | 14.25 | -0.73 | 18.84 | ||
MLR | -0.13 | 11.03 | -6.42 | 12.67 | 3.76 | 21.22 | ||
混交林(S3) | ||||||||
BPNN | 0.05 | 2.53 | -0.34 | 3.83 | -0.22 | 6.33 | ||
BPNN-T | -0.08 | 2.83 | 0.33 | 4.88 | -0.46 | 6.48 | ||
BPNN-R | 0.34 | 3.59 | -1.43 | 5.07 | -0.91 | 7.96 | ||
BPNN-W | -0.67 | 3.14 | -0.57 | 4.62 | -1.11 | 7.21 | ||
MLR | -1.00 | 2.91 | -1.81 | 4.98 | -4.07 | 7.93 | ||
落叶阔叶林(S4) | ||||||||
BPNN | 0.12 | 7.96 | -0.89 | 8.30 | -0.21 | 13.24 | ||
BPNN-T | -1.65 | 10.58 | -1.59 | 10.28 | -1.00 | 14.17 | ||
BPNN-R | -1.76 | 9.83 | -3.01 | 11.04 | 0.12 | 16.22 | ||
BPNN-W | 1.50 | 11.90 | 0.23 | 12.74 | -0.31 | 17.90 | ||
MLR | -5.02 | 9.53 | -3.10 | 10.42 | -3.36 | 14.39 |
[1] | 卢丽君, 张继贤, 王腾. 一种基于高分辨率雷达影像以及外部DEM辅助的复杂地形制图方法[J]. 测绘学报, 2011, 40(4):459-463. |
[ Lu L J, Zhang J X, Wang T. A DEM mapping method assisted by external DEM with high resolution InSAR data in complex terrain area[J]. Acta Geodaetica et Cartographica Sinica, 2011, 40(4):459-463. ] | |
[2] |
汤国安. 我国数字高程模型与数字地形分析研究进展[J]. 地理学报, 2014, 69(9):1305-1325.
doi: 10.11821/dlxb201409006 |
[ Tang G A. Progress of DEM and digital terrain analysis in China[J]. Acta Geographica Sinica, 2014, 69(9):1305-1325. ] DOI:10.11821/dlxb201409006
doi: 10.11821/dlxb201409006 |
|
[3] | 李振洪, 李鹏, 丁咚, 等. 全球高分辨率数字高程模型研究进展与展望[J]. 武汉大学学报·信息科学版, 2018, 43(12):1927-1942. |
[ Li Z H, Li P, Ding D, et al. Research progress of global high resolution digital elevation models[J]. Geomatics and Information Science of Wuhan University, 2018, 43(12):1927-1942. ] DOI:10.13203/j.whugis20180295
doi: 10.13203/j.whugis20180295 |
|
[4] |
Slater J A, Garvey G, Johnston C, et al. The SRTM data “finishing” process and products[J]. Photogrammetric Engineering & Remote Sensing, 2006, 72(3):237-247. DOI: 10.14358/pers.72.3.237
doi: 10.14358/pers.72.3.237 |
[5] |
Zink M, Bachmann M, Brautigam B, et al. TanDEM-X: The new global DEM takes shape[J]. IEEE Geoscience and Remote Sensing Magazine, 2014, 2(2):8-23. DOI: 10.1109/MGRS.2014.2318895
doi: 10.1109/MGRS.2014.2318895 |
[6] | 余婷婷, 董有福. 利用随机森林回归算法校正ASTER GDEM高程误差[J]. 武汉大学学报·信息科学版, 2021, 46(7):1098-1105. |
[ Yu T T, Dong Y F. Correcting elevation error of ASTER GDEM using random forest regression algorithm[J]. Geomatics and Information Science of Wuhan University, 2021, 46(7):1098-1105. ] DOI:10.13203/j.whugis20190245
doi: 10.13203/j.whugis20190245 |
|
[7] |
Takaku J, Tadono T, Tsutsui K, et al. Validation of “aw3d” global dsm generated from alos prism[J]. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016, III-4:25-31. DOI:10.5194/isprsannals-iii-4-25-2016
doi: 10.5194/isprsannals-iii-4-25-2016 |
[8] |
Bourgine B, Baghdadi N. Assessment of C-band SRTM DEM in a dense equatorial forest zone[J]. Comptes Rendus Geoscience, 2005, 337(14):1225-1234. DOI:10.1016/j.crte.2005.06.006
doi: 10.1016/j.crte.2005.06.006 |
[9] |
Kugler F, Schulze D, Hajnsek I, et al. TanDEM-X pol-InSAR performance for forest height estimation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(10):6404-6422. DOI:10.1109/TGRS.2013.2296533
doi: 10.1109/TGRS.2013.2296533 |
[10] |
Schlund M, Baron D, Magdon P, et al. Canopy penetration depth estimation with TanDEM-X and its compensation in temperate forests[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 147:232-241. DOI: 10.1016/j.isprsjprs.2018.11.021
doi: 10.1016/j.isprsjprs.2018.11.021 |
[11] | 蔡士雪, 岳林蔚, 尹超, 等. 顾及林区植被穿透率的多源DEM数据精度评价[J]. 遥感学报, 2022, 26(11):2268-2281. |
[ Cai S X, Yue L W, Yin C, et al. Accuracy evaluation of multi-source DEM data based on the analysis of vegetation-induced penetration rate in the forest area[J]. National Remote Sensing Bulletin, 2022, 26(11):2268-2281. ] DOI:10.11834/jrs.20210221
doi: 10.11834/jrs.20210221 |
|
[12] |
Wilson M, Bates P, Alsdorf D, et al. Modeling large-scale inundation of Amazonian seasonally flooded wetlands[J]. Geophysical Research Letters, 2007, 34(15):L15404. DOI:10.1029/2007gl030156
doi: 10.1029/2007gl030156 |
[13] |
Baugh C A, Bates P D, Schumann G, et al. SRTM vegetation removal and hydrodynamic modeling accuracy[J]. Water Resources Research, 2013, 49(9):5276-5289. DOI: 10.1002/wrcr.20412
doi: 10.1002/wrcr.20412 |
[14] |
O'Loughlin F E, Paiva R C D, Durand M, et al. A multi-sensor approach towards a global vegetation corrected SRTM DEM product[J]. Remote Sensing of Environment, 2016, 182:49-59. DOI: 10.1016/j.rse.2016.04.018.
doi: 10.1016/j.rse.2016.04.018 |
[15] |
Su Y J, Guo Q H, Ma Q, et al. SRTM DEM correction in vegetated mountain areas through the integration of spaceborne LiDAR, airborne LiDAR, and optical imagery[J]. Remote Sensing, 2015, 7(9):11202-11225. DOI: 10.3390/rs70911202
doi: 10.3390/rs70911202 |
[16] |
Zhao X Q, Su Y J, Hu T Y, et al. A global corrected SRTM DEM product for vegetated areas[J]. Remote Sensing Letters, 2018, 9(4):393-402. DOI: 10.1080/2150704x.2018.1425560
doi: 10.1080/2150704x.2018.1425560 |
[17] |
Wendi D, Liong S Y, Sun Y B, et al. An innovative approach to improve SRTM DEM using multispectral imagery and artificial neural network[J]. Journal of Advances in Modeling Earth Systems, 2016, 8(2):691-702. DOI: 10.1002/2015ms000536
doi: 10.1002/2015ms000536 |
[18] | 张晨, 朱建军, 付海强. 基于ICESat-2数据及TanDEM-X DEM的林下地形反演[J]. 测绘工程, 2021, 30(1):60-65. |
[ Zhang C, Zhu J J, Fu H Q. Sub-canopy topography inversion based on ICESat-2 and TanDEM-X DEM[J]. Engineering of Surveying and Mapping, 2021, 30(1):60-65. ] DOI: 10.19349/j.cnki.issn1006-7949.2021.01.010
doi: 10.19349/j.cnki.issn1006-7949.2021.01.010 |
|
[19] |
杨帅, 杨娜, 陈传法, 等. 顾及数据配准的江西省SRTM DEM精度评价和修正[J]. 地球信息科学学报, 2021, 23(5):869-881.
doi: 10.12082/dqxxkx.2021.200396 |
[ Yang S, Yang N, Chen C F, et al. Accuracy assessment and improvement of SRTM DEM based on ICESat/GLAS under the consideration of data coregistration over Jiangxi Province[J]. Journal of Geo-Information Science, 2021, 23(5):869-881. ] DOI:10.12082/dqxxkx.2021.200396
doi: 10.12082/dqxxkx.2021.200396 |
|
[20] |
李文梁, 汪驰升, 朱武. 中国大陆地区TanDEM-X 90 m DEM误差空间分布特征[J]. 地球信息科学学报, 2020, 22(12):2277-2288.
doi: 10.12082/dqxxkx.2020.190739 |
[ Li W L, Wang C S, Zhu W. Error spatial distribution characteristics of TanDEM-X 90 m DEM over China[J]. Journal of Geo-Information Science, 2020, 22(12):2277-2288. ] DOI:10.12082/dqxxkx.2020.190739
doi: 10.12082/dqxxkx.2020.190739 |
|
[21] |
Huang X D, Xie H J, Liang T G, et al. Estimating vertical error of SRTM and map-based DEMs using ICESat altimetry data in the eastern Tibetan Plateau[J]. International Journal of Remote Sensing, 2011, 32(18):5177-5196. DOI: 10.1080/01431161.2010.495092
doi: 10.1080/01431161.2010.495092 |
[22] |
Sun G, Ranson K J, Kharuk V I, et al. Validation of surface height from shuttle radar topography mission using shuttle laser altimeter[J]. Remote Sensing of Environment, 2003, 88(4):401-411. DOI:10.1016/j.rse.2003.09.001
doi: 10.1016/j.rse.2003.09.001 |
[23] | 沈焕锋, 刘露, 岳林蔚, 等. 多源DEM融合的高差拟合神经网络方法[J]. 测绘学报, 2018, 47(6):854-863. |
[ Shen H F, Liu L, Yue L W, et al. A multi-source DEM fusion method based on elevation difference fitting neural network[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(6):854-863. ] DOI:10.11947/j.AGCS.2018.20180135
doi: 10.11947/j.AGCS.2018.20180135 |
|
[24] | 刘纪平, 梁恩婕, 徐胜华, 等. 顾及样本优化选择的多核支持向量机滑坡灾害易发性分析评价[J]. 测绘学报, 2022, 51(10):2034-2045. |
[ Liu J P, Liang E J, Xu S H, et al. Multi-kernel support vector machine considering sample optimization selection for analysis and evaluation of landslide disaster susceptibility[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(10):2034-2045. ] DOI:10.11947/j.AGCS.2022.20220326
doi: 10.11947/j.AGCS.2022.20220326 |
|
[25] | 郭子正, 殷坤龙, 付圣, 等. 基于GIS与WOE-BP模型的滑坡易发性评价[J]. 地球科学, 2019, 44(12):4299-4312. |
[ Guo Z Z, Yin K L, Fu S, et al. Evaluation of landslide susceptibility based on GIS and WOE-BP model[J]. Earth Science, 2019, 44(12):4299-4312. ] DOI:10.3799/dqkx.2018.555
doi: 10.3799/dqkx.2018.555 |
|
[26] |
Rodríguez E, Morris C S, Belz J E. A global assessment of the SRTM performance[J]. Photogrammetric Engineering & Remote Sensing, 2006, 72(3):249-260. DOI:10.14358/pers.72.3.249
doi: 10.14358/pers.72.3.249 |
[27] | 金鼎坚, 吴芳, 于坤, 等. 机载激光雷达测深系统大规模应用测试与评估——以中国海岸带为例[J]. 红外与激光工程, 2020, 49(S2):9-23. |
[ Jin D J, Wu F, Yu K, et al. Large-scale application test and evaluation of an airborne lidar bathymetry system—a case study in China’s coastal zone[J]. Infrared and Laser Engineering, 2020, 49(S2):9-23. ] | |
[28] | 刘茜, 杨乐, 柳钦火, 等. 森林地上生物量遥感反演方法综述[J]. 遥感学报, 2015, 19(1):62-74. |
[ Liu Q, Yang L, Liu Q H, et al. Review of forest above ground biomass inversion methods based on remote sensing technology[J]. Journal of Remote Sensing, 2015, 19(1):62-74. ] DOI: 10.11834/jrs.20154108
doi: 10.11834/jrs.20154108 |
|
[29] | 王绚, 范宣梅, 杨帆, 等. 植被茂密山区地质灾害遥感解译方法研究[J]. 武汉大学学报·信息科学版, 2020, 45(11):1771-1781. |
[ Wang X, Fan X M, Yang F, et al. Remote sensing interpretation method of geological hazards in lush mountainous area[J]. Geomatics and Information Science of Wuhan University, 2020, 45(11):1771-1781. ] DOI:10.13203/j.whugis20200044
doi: 10.13203/j.whugis20200044 |
|
[30] | 杨必胜, 梁福逊, 黄荣刚. 三维激光扫描点云数据处理研究进展、挑战与趋势[J]. 测绘学报, 2017, 46(10):1509-1516. |
[ Yang B S, Liang F X, Huang R G. Progress, challenges and perspectives of 3D LiDAR point cloud processing[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(10):1509-1516. ] DOI:10.11947/j.AGCS.2017.20170351
doi: 10.11947/j.AGCS.2017.20170351 |
|
[31] | 陈传法, 王梦樱, 杨帅, 等. 适用于林区机载LiDAR点云的多分辨率层次插值滤波方法[J]. 山东科技大学学报(自然科学版), 2021, 40(2):12-20. |
[ Chen C F, Wang M Y, Yang S, et al. A multi-resolution hierarchical interpolation-based filtering method for airborne LiDAR point clouds in forest areas[J]. Journal of Shandong University of Science and Technology (Natural Science), 2021, 40(2):12-20. ] DOI:10.16452/j.cnki.sdkjzk.2021.02.002
doi: 10.16452/j.cnki.sdkjzk.2021.02.002 |
|
[32] |
Friedl M A, Sulla-Menashe D, Tan B, et al. MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets[J]. Remote Sensing of Environment, 2010, 114(1):168-182. DOI:10.1016/j.rse.2009.08.016
doi: 10.1016/j.rse.2009.08.016 |
[33] |
Su Y J, Guo Q H. A practical method for SRTM DEM correction over vegetated mountain areas[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 87:216-228. DOI:10.1016/j.isprsjprs.2013.11.009
doi: 10.1016/j.isprsjprs.2013.11.009 |
[34] |
Krieger G, Zink M, Bachmann M, et al. TanDEM-X: A radar interferometer with two formation-flying satellites[J]. Acta Astronautica, 2013, 89:83-98. DOI:10.1016/j.actaastro.2013.03.008
doi: 10.1016/j.actaastro.2013.03.008 |
[35] |
Bhang K J, Schwartz F W, Braun A. Verification of the vertical error in C-band SRTM DEM using ICESat and landsat-7, otter tail County, MN[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(1):36-44. DOI: 10.1109/TGRS.2006.885401
doi: 10.1109/TGRS.2006.885401 |
[36] | 汤国安, 宋佳. 基于DEM坡度图制图中坡度分级方法的比较研究[J]. 水土保持学报, 2006, 20(2):157-160,192. |
Song J. Comparison of slope classification methods in slope mapping from DEMs[J]. Journal of Soil and Water Conservation, 2006, 20(2):157-160,192. ] DOI: 10.13870/j.cnki.stbcxb.2006.02.038
doi: 10.13870/j.cnki.stbcxb.2006.02.038 |
|
[37] | 林荣福, 刘纪平, 徐胜华, 等. 随机森林赋权信息量的滑坡易发性评价方法[J]. 测绘科学, 2020, 45(12):131-138. |
Liu J P, Xu S H, et al. Evaluation method of landslide susceptibility based on random forest weighted information[J]. Science of Surveying and Mapping, 2020, 45(12):131-138. ] DOI:10.16251/j.cnki.1009-2307.2020.12.020.
doi: 10.16251/j.cnki.1009-2307.2020.12.020 |
|
[38] | 朱奇峰, 杨勤科, 师动, 等. 坡度分级方法对坡度制图的影响[J]. 水土保持通报, 2017, 37(3):314-320. |
[ Zhu Q F, Yang Q K, Shi D, et al. Influence of slope classification method on slope mapping[J]. Bulletin of Soil and Water Conservation, 2017, 37(3):314-320. ] DOI:10.13961/j.cnki.stbctb.2017.03.054
doi: 10.13961/j.cnki.stbctb.2017.03.054 |
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