林区数字高程模型修正方法：顾及高程自相关的后向传播神经网络模型

doi:10.12082/dqxxkx.2023.220766

Abstract

Abstract:

Due to the limitation of earth observation technology, the existing global Digital Elevation Model (DEM) datasets usually contain information of vegetation, buildings, and other non-ground objects. Especially in forested areas, the DEM data usually cannot describe the bare-earth surface precisely and show large systematic deviations. This study proposes a Back Propagation Neural Network (BPNN) model that takes into account the spatial autocorrelation of elevation to reduce the errors of bare-earth DEM in forested areas. This model first fits the optimal semivariogram to determine the spatial variation of elevation and takes the elevation points within the variation range from a target point as the optimal spatially autocorrelated neighborhood. Then, we train the BPNN model by using the terrain factors (i.e., slope, aspect, and terrain undulation), vegetation factors (i.e., vegetation height and vegetation coverage), and elevation points within the range of variation as the influencing factors, and using the elevation difference between DEM and Light Detection And Ranging (LiDAR) DEM as the predicted value. Finally, the trained model is used to correct the DEM in different forested areas. In order to verify the practicability and efficiency of the model, this paper takes the DEM products including SRTM1, AW3D30, and TanDEM-X (TDX) 90 of four types of forests (evergreen broad-leaved forest, evergreen coniferous forest, mixed forest, and deciduous broad-leaved forest) as the research objects, and trains the BPNN model respectively for each forest type. The correction result is compared with BPNN trained with all four types of forest data (BPNN-T), BPNN trained without terrain factors (BPNN-W), BPNN trained without spatial autocorrelation of elevation (BPNN-R), and multiple linear regression model (MLR). The results show that: (i) The BPNN model significantly improves the accuracy of DEM in the four forests, with the Mean Error (ME) close to 0-1 m and the Root Mean Square Errors (RMSE) reduced by 46%~70%; (ii) The aspect has the largest influence on the DEM correction for TDX90 while has little influence on SRTM1 DEM correction. Before and after correction, the RMSE of each DEM increases with the increase of slope and relief; (iii) The DEM error increases with the increase of vegetation height and vegetation coverage before correction, but this trend disappears after correction, indicating that BPNN effectively eliminates the impact of vegetation on bare ground DEM; (iv) BPNN has the highest prediction accuracy, followed by BPNN-T, MLR, and BPNN-W. And BPNN-R has the worst prediction accuracy. Therefore, the accuracy of DEM can be significantly improved by fully considering terrain factors and spatial autocorrelation of elevation for different forest types.

Key words: DEM, machine learning, error, correction, accuracy assessment, elevation autocorrelation, forest region

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

Figures/Tables 15

Fig. 1

Fig. 2

Fig. 3

Tab. 1

Tab. 2

Tab.3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

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. 4

Fig. 8

Tab. 5

Fig. 9

Fig. 10

References 38

[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

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

	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

模型	SRTM1		AW3D30		TDX90
模型	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

Method for the Correction of Digital Elevation Models Over Forested Areas: Back Propagation Neural Network with the Consideration of Spatial Autocorrelation

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 15

References 38

Related Articles 15

Metrics

Comments

Recommended 0

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
常绿阔叶林	S1	12	10 792	396	22	29	70
常绿针叶林	T2	12	11 220	874	24	14	65
常绿针叶林	S2	12	11 220	974	22	21	68
混交林	T3	12	11 352	625	14	14	75
混交林	S3	12	11 352	684	12	14	70
落叶阔叶林	T4	8	6739	732	15	22	82
落叶阔叶林	S4	6	5700	995	20	21	78

[1]	ZHOU Chao, GAN Lulu, WANG Yue, WU Hongyang, YU Jin, CAO Ying, YIN Kunlong. Landslide Susceptibility Prediction based on Non-Landslide Samples Selection and Heterogeneous Ensemble Machine Learning [J]. Journal of Geo-information Science, 2023, 25(8): 1570-1585.
[2]	DONG Yong, ZHOU Liang, GAO Hong, WANG Bao. Detection and Spatial Heterogeneity Analysis of Terrain Fragmentation on the Loess Plateau [J]. Journal of Geo-information Science, 2023, 25(8): 1625-1636.
[3]	YAO Wei, ZHAO Zhiyuan, WU Sheng. Equality of Public Taxi Service in Xiamen City [J]. Journal of Geo-information Science, 2023, 25(8): 1637-1654.
[4]	CAO Yu, FANG Xiuqin, YANG Lulu, JIANG Xinyuan, LIAO Meiyu, REN Liliang. Downscaling of CCI Soil Moisture in the Xiliaohe River Basin based on Random Forest [J]. Journal of Geo-information Science, 2023, 25(8): 1669-1681.
[5]	ZHANG Tong, LIU Renyu, WANG Peixiao, GAO Chulin, LIU Jie, WANG Wangshu. Physics-informed Machine Learning and Its Research Prospects in GeoAI [J]. Journal of Geo-information Science, 2023, 25(7): 1297-1311.
[6]	QI Meng, CHEN Nan, LIN Siwei, ZHOU Qianqian. Geomorphic Recognition of China Considering Complex Network of Catchments [J]. Journal of Geo-information Science, 2023, 25(5): 909-923.
[7]	XIE Jing, CHEN Nan, LIN Siwei. Quantitative Analysis and Spatial Differentiation of Terrain based on Terrain Music: Example of Loess Plateau in Northern Shaanxi [J]. Journal of Geo-information Science, 2023, 25(5): 924-934.
[8]	KE Rihong, WU Sheng, KE Weiwen. A Spatial-temporal Model for Identifying Tidal Shared-bicycle Stops and Bicycle Sharing Demand Prediction based on KNN-LightGBM [J]. Journal of Geo-information Science, 2023, 25(4): 741-753.
[9]	LI Xinyu, YAN Haowen, WANG Zhuo, WANG Bingxuan. Evaluation of Road Environment Safety Perception and Analysis of Influencing Factors Combining Street View Imagery and Machine Learning [J]. Journal of Geo-information Science, 2023, 25(4): 852-865.
[10]	HUANG Shuaiyuan, DONG Youfu, LI Haipeng. Establishment and Comparative Analysis of SRTM1 DEM Error Correction Model in the Loess Plateau [J]. Journal of Geo-information Science, 2023, 25(3): 669-681.
[11]	XIE Qian, LU Ming, XIE Chunshan. High-temporal-frequency Forecast of Tourist Flow for Tourist Attraction based on LBS and Deep Learning [J]. Journal of Geo-information Science, 2023, 25(2): 298-310.
[12]	HUANG Xin, MAO Zhengyuan. Prediction of Passenger Demand for Online Car-hailing based on Spatio-temporal Multi-graph Convolution Network [J]. Journal of Geo-information Science, 2023, 25(2): 311-323.
[13]	JIAO Huaijin, CHEN Chongcheng, HUANG Hongyu. Elevation Accuracy Evaluation and Correction of ASTER GDEM in China Southeast Hilly Region by Combining ICESat-2 and GEDI data [J]. Journal of Geo-information Science, 2023, 25(2): 409-420.
[14]	HUANG Hao, WANG Junchao, WANG Chengfang, XIE Yuanyi, ZHANG Wenchu. An Evaluation Method of Community Supply Support and Resilience Based on Complex Network [J]. Journal of Geo-information Science, 2023, 25(12): 2303-2314.
[15]	MA Yuzhe, WANG Meng, LI Hui, CUI Jiangtao, LIU Junhua, LI Ruimeng. RUG: A Revenue-driven User-based Relocation Approach in One-way Car-sharing Incorporating Public Transportation [J]. Journal of Geo-information Science, 2023, 25(12): 2315-2328.