利用ICESat-2 ATLAS&JL-1反演输电线走廊附近森林冠层高度

doi:10.3969/j.issn.0255-8297.2022.05.010

Abstract

Abstract: Aiming at the problem that ICESat-2 ATL08 product contains a lot of noise photons and gross errors of tree height, a method of photon filtering and gross errors elimination was designed by using a series of characteristics such as photon number, beam energy and ground coverage; The correlations of reflection parameters in each band with NDVI, RVI, SAVI, MSAVI, PVI vegetation indexes extracted from high-resolution multispectral data and with laser altimetry data were studied, and the effectiveness and accuracy of multiple regression and random forest model inversion were compared and analyzed. Study results showed that the random forest model can obtain higher inversion accuracy of tree height. The inversion experiment carried out in the transmission corridor of Lin' an District, Hangzhou City, Zhejiang Province obtained the result of RMSE=2.84 m. The tree height hidden danger map produced by combining the power data can provide auxiliary decision-making for the hidden danger investigation of transmission facilities.

Key words: ICESat-2, ATL08, JL-1, transmission corridor, forest canopy height

CLC Number:

LIN Haoyu, WU Zhaocong, SUN Xiaohu. Inversion of Forest Canopy Height in Transmission Line Corridor Using ICESat-2 ATLAS & JL-1[J]. Journal of Applied Sciences, 2022, 40(5): 809-819.

References

[1] 邱赛, 邢艳秋, 田静, 等. 星载LiDAR与HJ-1A/HSI高光谱数据联合估测区域森林冠层高度[J]. 林业科学, 2016, 52(5):142-149. Qiu S, Xing Y Q, Tian J, et al. Forest canopy height estimation of large area using spaceborne LIDAR and HJ-1A/HSI hyperspectral imageries[J]. Scientia Silvae Sinicae, 2016, 52(5):142-149. (in Chinese)
[2] 于旭宅. 基于LiDAR数据的输电线路通道危险区域提取方法研究[D]. 北京:北京林业大学, 2018.
[3] Lefsky M A. A global forest canopy height map from the moderate resolution imaging spectroradiometer and the geoscience laser altimeter system[J]. Geophysical Research Letters, 2010, 37(15):L15401.
[4] 董立新, 吴炳方, 唐世浩. 激光雷达GLAS与ETM联合反演森林地上生物量研究[J]. 北京大学学报(自然科学版), 2011, 47(4):703-710. Dong L X, Wu B F, Tang S H. Estimation of forest aboveground biomass by integrating GLAS and ETM data[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2011, 47(4):703-710. (in Chinese)
[5] 董立新, 李贵才, 唐世浩. 中国南方森林冠顶高度Lidar反演——以江西省为例[J]. 遥感学报, 2011, 15(6):1301-1314. Dong L X, Li G C, Tang S H. Inversion of forest canopy height in south of China by integrating GLAS and MERSI:the case of Jiangxi Province in China[J]. Journal of Remote Sensing, 2011, 15(6):1301-1314. (in Chinese)
[6] 廖凯涛. 基于大光斑雷达数据与光学遥感数据估算江西省森林树高和森林生物量[D]. 南昌:江西师范大学, 2015.
[7] 崔成玲, 李国元, 闫志刚, 等. 国外激光测高卫星ICESat-2最新进展综述[J]. 测绘科学, 2015, 10(40):129-131. Cui C L, Li G Y, Yan Z G, et al. A review of the latest development of foreign laser altimetry satellite ICESat-2[J]. Science of Surveying and Mapping, 2015, 10(40):129-131. (in Chinese)
[8] Neuenschwander A, Pitts K. The ATL08 land and vegetation product for the ICESat-2 Mission[J]. Remote Sensing of Environment, 2019, 221:247-259.
[9] Neuenschwander A, Pitts K, Jelley B, et al. Ice, cloud, and land elevation satellite 2(ICESat-2) algorithm theoretical basis document (ATBD) for land-vegetation along-track products (ATL08)[EB/OL]. (2020-01-15)[2021-06-11]. https://nsidc.org/sites/nsidc.org/files/technical-references/ICESat2_ATL08_ATBD_r003.pdf.
[10] Zhu X X, Nie S, Wang C, et al. The performance of ICESat-2's strong and weak beams in estimating ground elevation and forest height[C]//2020 International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2020:6073-6076.
[11] Neuenschwander A L, Magruder L A. Canopy and terrain height retrievals with ICESat-2:a first look[J]. Remote Sensing, 2019, 11(14):1721.
[12] Hill R A, Boydj D S, Hopkinson C. Relationship between canopy height and Landsat ETM+ response in lowland Amazonian rainforest[J]. Remote Sensing Letters, 2011, 2(3):203-212.
[13] 汤旭光. 基于激光雷达与多光谱遥感数据的森林地上生物量反演研究[D]. 哈尔滨:中国科学院研究生院(东北地理与农业生态研究所), 2013.
[14] 胡凯龙, 刘清旺, 崔希民, 等. 多源遥感数据支持下的区域性森林冠层高度估测[J]. 武汉大学学报(信息科学版), 2018, 43(2):289-296, 303. Hu K L, Liu Q W, Cui X M, et al. Regional forest canopy height estimation using multi-source remote sensing data[J]. Geomatics and Information Science of Wuhan University, 2018, 43(2):289-296, 303. (in Chinese)
[15] Li W, Niu Z, Shang R, et al. High-resolution mapping of forest canopy height using machine learning by coupling ICESat-2 LiDAR with Sentinel-1, Sentinel-2 and Landsat-8 data[J]. International Journal of Applied Earth Observation and Geoinformation, 2020, 92:102163.
[16] 王奕森, 夏树涛. 集成学习之随机森林算法综述[J]. 信息通信技术, 2018, 12(1):49-55. Wang Y S, Xia S T. A survey of random forests algorithms[J]. Information and Communications Technologies, 2018, 12(1):49-55. (in Chinese)

Inversion of Forest Canopy Height in Transmission Line Corridor Using ICESat-2 ATLAS & JL-1

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	CUI Weihong, LI Jia, LIU Yu. Semi-automatic Extraction and Regularization of Buildings of Different Shapes from High-Resolution Remote Sensing Images [J]. Journal of Applied Sciences, 2022, 40(3): 372-388.
[2]	LU Zhumao, GONG Hao, JIN Qiuheng, HU Qingwu, LI Jiayuan. Tilt Rate Measurement of Power Tower Based on UAV LiDAR Point Cloud [J]. Journal of Applied Sciences, 2022, 40(3): 389-399.
[3]	WANG Wanguo, MU Shiyou, LIU Yue, LIU Guangxiu, LANG Fenling. Research on Insulator Self Exploding Detection in UAV Inspection Based on Deep Learning [J]. Journal of Applied Sciences, 2021, 39(2): 222-231.
[4]	MA Feihu, XU Fadong, SUN Cuiyu. Land-Use Information of Object-Oriented Classification by UAV Image [J]. Journal of Applied Sciences, 2021, 39(2): 312-320.
[5]	ZHANG Chunsen, ZHANG Hui, GUO Bingxuan, DU Shenbin, ZHANG Yueying. Dodging Method Based on Camera Response Functions for UAV Image [J]. Journal of Applied Sciences, 2019, 37(6): 783-794.
[6]	YANG Hui-wen, FANG Jun-yong, ZHAO Dong. Feature Selection in Precise Crop Classification Using Remote Sensing Data Based on Improved SEaTH Algorithm [J]. Journal of Applied Sciences, 2019, 37(1): 51-63.
[7]	MA Fei-hu, JIANG Shan-shan, SUN Cui-yu. Application of PSInSAR in Monitoring Land Subsidence in Yanshan Mining Area [J]. Journal of Applied Sciences, 2018, 36(6): 969-977.
[8]	MO You, ZHONG Ruo-fei, ZHANG Zhen-xin. Acquisition of Indoor Laser Point Clouds Based on Mobile and Terrestrial Scanning [J]. Journal of Applied Sciences, 2018, 36(5): 756-764.
[9]	ZHANG Chun-sen, MU Yan, ZHU Shi-huan, GUO Bing-xuan, QIU Zhen-guo. Least Square Image Matching Algorithm Considering Elevation Plane and Parallax Constraints [J]. Journal of Applied Sciences, 2018, 36(5): 826-836.
[10]	WU Zhao-cong, XIANG Wei, LI Jun, YANG Zhi. Clustering Algorithm for Remote Sensing Application Requirements Based on Satisfaction Evaluation [J]. Journal of Applied Sciences, 2018, 36(4): 635-643.
[11]	PENG Bao-jiang, ZHONG Ruo-fei, SUN Hai-li, GENG Yu-xin. Hybrid Index Method Based on Quad Tree and R-Tree for DEM Reconstruction of Airborne Point Cloud [J]. Journal of Applied Sciences, 2018, 36(4): 644-654.
[12]	ZHANG Sha-sha, WANG Shu-gen. Quality Evaluation Method for the Results of Digital Orthophoto Mosaic [J]. Journal of Applied Sciences, 2018, 36(3): 461-470.
[13]	WU Zhao-cong, LIU Pei, WU Yuan. Synthesis of Requirements in Applications of Multi-spectral Remote Sensing [J]. Journal of Applied Sciences, 2017, 35(5): 658-666.
[14]	LIU Ya-wen, QIN Su-shun. Building Facade Reconstruction on Sparse LiDAR Data Region [J]. Journal of Applied Sciences, 2017, 35(2): 217-225.
[15]	HUANG Jun-jie, TAN Bo, CHEN Xiao-ming, CHEN Jiang-ping, RUAN Ling, FENG Wan-shu, XIONG Yu. Spatiotemporal Distribution of Lightning Disasters of Power Lines in Hubei Province Using Data Mining Based on Apriori Association Rules [J]. Journal of Applied Sciences, 2017, 35(1): 31-41.