Please wait a minute...
img

官方微信

遥感技术与应用  2020, Vol. 35 Issue (5): 1004-1014    DOI: 10.11873/j.issn.1004-0323.2020.5.1004
LAI专栏     
基于机载LVIS和星载GLAS波形LiDAR数据反演森林LAI
汪垚1,2(),方红亮1,2(),张英慧1,2,李思佳1,2
1.中国科学院地理科学与资源研究所 资源与环境信息系统国家重点实验室,北京 100101
2.中国科学院大学资源与环境学院,北京 100049
Retrieval of Forest LAI Using Airborne LVIS and Spaceborne GLAS Waveform LiDAR Data
Yao Wang1,2(),Hongliang Fang1,2(),Yinghui Zhang1,2,Sijia Li1,2
1.LREIS,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China
2.College of Resources and Environment,University of Chinese Academy of Sciences,Beijing 100049,China
 全文: PDF(5254 KB)   HTML
摘要:

波形激光雷达(Light Detection And Ranging, LiDAR)已经大量用于森林叶面积指数(Leaf Area Index, LAI)估算,但是波形LiDAR数据估算森林LAI易受地形影响。地形坡度引起的波形展宽使得地面回波和植被冠层回波信息混合在一起,难以得到准确的地面回波和冠层回波,进而影响到LAI估算精度。为了估算不同地形坡度条件下的LAI,本文采用一种坡度自适应的方法处理机载LVIS和星载GLAS波形数据。通过坡度自适应的方法得到地面波峰位置,基于高度阈值来区分地面回波和冠层回波,进而得到能量比值用于LAI估算。基于LVIS和GLAS数据,估算了不同森林站点的LAI,并利用实测LAI数据进行检验。结果表明:利用波形LiDAR数据可以估算森林LAI,坡度自适应方法可以改善地形的影响,提高LAI估算精度。对于机载LVIS,估算新英格兰森林LAI精度为R2=0.77和RMSE=0.21;对于星载GLAS,估算塞罕坝森林LAI精度为R2=0.81和RMSE=0.28。无论机载还是星载数据,该方法都有着较高的精度,对于复杂地形估算LAI具有一定潜力。

关键词: 地形坡度激光雷达叶面积指数LVISGLAS    
Abstract:

Estimation of forest Leaf Area Index (LAI) using waveform LiDAR has been performed in many studies. However, the LAI estimation from waveform LiDAR was affected by terrain slope. Terrain slopes can blur the boundary between ground and canopy returns in a waveform LiDAR, and it is difficult to obtain accurate ground return and canopy return. In order to estimate the LAI under different terrain slopes, a slope-adaptive method was used to process the airborne LVIS and spaceborne GLAS waveform data. First, the ground peak position was obtained by slope-adaptive method. Subsequently, the ground return and canopy return were separated based on the height threshold. Finally, the energy ratios were calculated for LAI estimation. For the LVIS and GLAS data, LAI of different forest sites was estimated and validated with the field LAI. The result shows that forest LAI was successfully estimated with waveform LiDAR data, and the slope-adaptive method can overcome the effect of terrain and improve the accuracy of LAI estimation. For airborne LVIS, the accuracy of LAI in New England is R2 = 0.77 and RMSE = 0.21. For spaceborne GLAS, the accuracy of LAI in the Saihanba is R2 = 0.81 and RMSE = 0.28. No matter on the airborne or spaceborne data, the proposed method indicates high accuracy and shows potential for LAI estimation over complex topography.

Key words: Slope    LiDAR    Leaf Area Index (LAI)    LVIS    GLAS
收稿日期: 2020-06-29 出版日期: 2020-11-26
ZTFLH:  S771.8  
基金资助: 国家重点研发计划项目(2016YFA0600201)
通讯作者: 方红亮     E-mail: wangy.18b@igsnrr.ac.cn;fanghl@lreis.ac.cn
作者简介: 汪垚(1993-),男,重庆人,博士研究生,主要从事植被结构参数的激光雷达反演研究。E?mail:wangy.18b@igsnrr.ac.cn
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  
汪垚
方红亮
张英慧
李思佳

引用本文:

汪垚,方红亮,张英慧,李思佳. 基于机载LVIS和星载GLAS波形LiDAR数据反演森林LAI[J]. 遥感技术与应用, 2020, 35(5): 1004-1014.

Yao Wang,Hongliang Fang,Yinghui Zhang,Sijia Li. Retrieval of Forest LAI Using Airborne LVIS and Spaceborne GLAS Waveform LiDAR Data. Remote Sensing Technology and Application, 2020, 35(5): 1004-1014.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2020.5.1004        http://www.rsta.ac.cn/CN/Y2020/V35/I5/1004

图1  研究区范围与地面实测采样方式(背景图来源于Google Earth?)
森林冠层枝下高/m站点基本观测单元编号LAILVIS 光斑数量/个
Harvard 森林7.5Har01

ALL

CP

NE

NW

SE

SW

3.46

4.58

4.73

4.69

3.99

3.86

62

9

11

14

15

22

Har02

ALL

CP

NE

NW

SE

SW

3.66

4.03

3.4

3.13

3.29

3.47

36

11

10

6

9

11

Bartlett 森林9Bar01

ALL

CP

NE

NW

SE

SW

3.92

4.16

3.7

4.29

3.63

3.8

39

13

10

12

9

8

Bar02

ALL

CP

NE

NW

SE

SW

4.17

4.65

4.37

3.5

4.14

31

5

7

7

6

11

Howland 森林6.5How01

ALL

CP

NE

NW

SE

SW

3.86

3.87

3.8

3.54

3.41

55

11

15

17

11

12

How02

ALL

CP

NN

SS

3.52

3.66

3.22

3.67

18

14

7

4

表1  新英格兰样地调查数据和LVIS数据
样地号记录号光斑号坡度(°)LAI

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

377874826

377874826

377874831

377874831

377874831

377874831

537071457

537071457

537071457

537071457

537071462

537071462

537071462

537071462

537071462

537071462

537071462

19

20

1

2

13

14

14

15

20

21

2

3

13

15

20

21

22

4.26

2.52

3.41

3.09

3.54

11.64

9.00

4.40

3.56

1.77

9.65

3.94

2.69

7.16

1.35

1.86

1.88

2.97

2.05

2.74

2.04

3.61

2.65

3.34

2.64

3.95

3.54

2.48

2.61

4.45

2.57

3.16

3.34

3.19

表2  塞罕坝样地调查数据和GLAS数据
图2  技术流程图
图3  模拟的地面回波与实际波形的吻合性示意图
图4  新英格兰地区的地面回波和冠层回波分离示意图
图5  新英格兰地区不同的森林站点LVIS LAI与实测DHP LAI的回归模型
图6  新英格兰所有站点的实测DHP LAI与LVIS LAI的回归关系(R2=0.77 和 RMSE=0.21)
图7  塞罕坝地区不同坡度下的GLAS地面回波和冠层回波分离示意图
图8  实测LAI和GLAS LAI回归分析
1 Chen J M, Black T A.Defining Leaf-Area Index for Non-Flat Leaves[J].Plant Cell and Environment,1992,15:421-429.
2 Alton P B. The Sensitivity of Models of Gross Primary Productivity to Meteorological and Leaf Area Forcing: A Comparison between a Penman-Monteith Ecophysiological Approach and The MODIS Light-Use Efficiency Algorithm [J]. Agricultural and Forest Meteorology, 2016, 218: 11-24.
3 Asner G P, Braswell B H, Schimel D S, et al. Ecological Research Needs from Multiangle Remote Sensing Data[J]. Remote Sensing of Environment, 1998, 63:155-165.
4 Fang H L, Baret F, Plummer S, et al. An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications[J]. Reviews of Geophysics, 2019,57:739-799.
5 Solberg S, Brunner A, Hanssen K H, et al. Mapping LAI in a Norway Spruce Forest Using Airborne Laser Scanning[J]. Remote Sensing of Environment, 2009, 113: 2317-2327.
6 Tang H, Dubayah R, Swatantran A, et al. Retrieval of Vertical LAI Profiles over Tropical Rain Forests Using Waveform LiDAR at La Selva, Costa Rica[J]. Remote Sensing of Environment, 2012, 124: 242-250.
7 Zhao K G, Popescu S. -based Mapping of Leaf Area Index and Its Use for Validating GLOBCARBON Satellite LAI Product in a Temperate Forest of the Southern USA[J]. Remote Sensing of Environment, 2009, 113: 1628-1645.
8 Zheng G, Moskal L M, Kim S H. Retrieval of Effective Leaf Area Index in Heterogeneous Forests with Terrestrial Laser Scanning[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51: 777-786.
9 Wang Y, Ni W, Sun G, et al. Slope-adaptive Waveform Metrics of Large Footprint LiDAR for Estimation of Forest Aboveground Biomass[J]. Remote Sensing of Environment, 2019, 224: 386-400.
10 Tang H, Dubayah R, Brolly M, et al. Large-scale Retrieval of Leaf Area Index and Vertical Foliage Profile from the Spaceborne Waveform LiDAR(GLAS/ICESat)[J]. Remote Sensing of Environment, 2014, 154: 8-18.
11 Lefsky M A, Cohen W B, Parker G G. et al. LiDAR Remote Sensing for Ecosystem Studies[J].Bioscience,2002,52:19-30.
12 Zhao F, Yang X Y, Strahler A H, et al. A Comparison of Foliage Profiles in the Sierra National Forest Obtained with a Full-waveform Under-canopy EVI LiDAR System with the Foliage Profiles Obtained with an Airborne Full-waveform LVIS LiDAR System[J]. Remote Sensing of Environment, 2013, 136: 330-341.
13 Blair J B, Rabine D L, Hofton M A. The Laser Vegetation Imaging Sensor: A Medium-altitude, Digitisation-only, Airborne Laser Altimeter for Mapping Vegetation and Topography[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 1999, 54: 115-122.
14 Zwally H J, Schutz B, Abdalati W, et al. ICESat's Laser Measurements of Polar Ice, Atmosphere, Ocean, and Land[J]. Journal of Geodynamics, 2002, 34: 405-445.
15 Garcia M, Popescu S, Riano D,et al. Characterization of Canopy Fuels Using ICESat/GLAS Data[J]. Remote Sensing of Environment, 2012, 123: 81-89.
16 Luo S Z, Wang C, Li G C, et al. Retrieving Leaf Area Index Using ICESat/GLAS Full-waveform Data[J]. Remote Sensing Letters, 2013, 4: 745-753.
17 Ni-Meister W, Jupp D L B, Dubayah R. Modeling LiDAR Waveforms in Heterogeneous and Discrete Canopies[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39: 1943-1958.
18 Tang H, Ganguly S, Zhang G, et al. Characterizing Leaf Area Index(LAI) and Vertical Foliage Profile(VFP) over the United States[J]. Biogeosciences, 2016, 13:239-252.
19 Hofton M A, Minster J B, Blair J B. Decomposition of Laser Altimeter Waveforms[J]. IEEE Transactions on Geoscience and Remote Sensing, 2000, 38: 1989-1996.
20 Lee S, Ni-Meister W, Yang W.Z,et al. Physically based Vertical Vegetation Structure Retrieval from ICESat Data: Validation Using LVIS in White Mountain National Forest, New Hampshire, USA[J]. Remote Sensing of Environment, 2011, 115: 2776-2785.
21 Tang H, Brolly M, Zhao F, et al. Deriving and Validating Leaf Area Index (LAI) at Multiple Spatial Scales through LiDAR Remote Sensing: A Case Study in Sierra National Forest, CA[J].Remote Sensing of Environment,2014,143:131-141.
22 Strahler A H, Schaaf C, Woodcock C, et al. ECHIDNA LIDAR Campaigns: Forest Canopy Imagery and Field Data, U.S.A., 2007-2009[DB]. ORNL Distributed Active Archive Center: 2011; 10.3334/ornldaac/1045.
23 Zhao F, Yang X Y, Schull M A, et al. Measuring Effective Leaf Area Index, Foliage Profile, and Stand Height in New England Forest Stands Using a Full-waveform Ground-based LiDAR[J]. Remote Sensing of Environment, 2011, 115: 2954-2964.
24 Zeng W J, Wang W. Combination of Nitrogen and Phosphorus Fertilization Enhance Ecosystem Carbon Sequestration in a Nitrogen-limited Temperate Plantation of Northern China[J]. Forest Ecology and Management, 2015, 341: 59-66.
25 Song Jinling, Zhu Xiao, Yang Lei, et al. Datasets of LAI and Spectral Observations of the Comprehensive Remote Sensing Experiment of Carbon Cycle, Water Cycle and Energy Balance in Saihanba Region in Xiaoluan Watershed in 2018[DB]. Faculty of Geographical Science, Beijing Normal University, 2018.宋金玲, 朱筱, 杨磊, 等. 碳循环、水循环和能量平衡遥感综合试验:2018年小滦河流域塞罕坝林区LAI及叶片与地面光谱测量数据集[DB]. 北京师范大学地理科学学部遥感科学与工程研究院, 2018.
26 Sun G, Ranson K J, Kimes D S, et al. Forest Vertical Structure from GLAS: An Evaluation Using LVIS and SRTM Data[J]. Remote Sensing of Environment, 2008, 112: 107-117.
27 Chen Q. Retrieving Vegetation Height of Forests and Woodlands over Mountainous Areas in the Pacific Coast Region Using Satellite Laser Altimetry[J]. Remote Sensing of Environment, 2010, 114: 1610-1627.
28 Chi H, Sun G Q, Huang J L, et al. National Forest Aboveground Biomass Mapping from ICESat/GLAS Data and MODIS Imagery in China[J]. Remote Sensing, 2015, 7: 5534-5564.
29 Nie S, Wang C, Zeng H, et al. A Revised Terrain Correction Method for Forest Canopy Height Estimation Using ICESat/GLAS Data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 108: 183-190.
30 Chi H, Sun G, Huang J, et al. Estimation of Forest Aboveground Biomass in Changbai Mountain Region Using ICESat/GLAS and Landsat/TM Data[J]. Remote Sensing, 2017, 9: 707.
31 Liu K L, Wang J D, Zeng W S, et al. Comparison and Evaluation of Three Methods for Estimating Forest above Ground Biomass Using TM and GLAS Data[J]. Remote Sensing, 2017, 9(4):341. doi:10.3390/rs9040341.
doi: 10.3390/rs9040341
32 Wang M J, Sun R, Xiao Z Q. Estimation of Forest Canopy Height and Aboveground Biomass from Spaceborne LiDAR and Landsat Imageries in Maryland[J]. Remote Sensing, 2018, 10(2):344. doi:10.3390/rs10020344.
doi: 10.3390/rs10020344
33 Yu Y, Yang X, Fan W. Estimates of Forest Structure Parameters from GLAS Data and Multi-angle Imaging Spectrometer Data[J]. International Journal of Applied Earth Observation and Geoinformation, 2015, 38: 65-71.
34 Richardson J J, Moskal L M, Kim S H. Modeling Approaches to Estimate Effective Leaf Area Index from Aerial Discrete-return LIDAR[J]. Agricultural and Forest Meteorology, 2009, 149: 1152-1160.
35 Lefsky M A, Cohen W B, Acker S A, et al. LiDAR Remote Sensing of the Canopy Structure and Biophysical Properties of Douglas-fir Western Hemlock Forests[J]. Remote Sensing of Environment, 1999, 70: 339-361.
36 Ni-Meister W, Yang W Z, Lee S, et al. Validating Modeled LiDAR Waveforms in Forest Canopies with Airborne Laser Scanning Data[J]. Remote Sensing of Environment, 2018, 204: 229-243.
37 Yang X, Wang C, Pan F, et al. Retrieving Leaf Area Index in Discontinuous Forest Using ICESat/GLAS Full-waveform data based on Gap Fraction Model[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 148: 54-62.
38 Tachikawa T, Hato M, Kaku M, et al. Characteristics of ASTER GDEM Version 2[C]// In Proceedings of 2011 IEEE International Geoscience and Remote Sensing Symposium, 24-29July2011:3657-3660.
39 Hagensieker R, Roscher R, Rosentreter J, et al. Tropical Land Use Land Cover Mapping in Pará (Brazil) Using Discriminative Markov Random Fields and Multi-temporal TerraSAR-X Data[J]. International Journal of Applied Earth Observation and Geoinformation, 2017, 63: 244-256.
40 Wessel B, Huber M, Wohlfart C, et al. Accuracy Assessment of the Global TanDEM-X Digital Elevation Model with GPS Data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 139: 171-182.
[1] 刘俊,孟庆岩,葛小三,刘顺喜,陈旭,孙云晓. 基于BP神经网络的夏玉米多生育期叶面积指数反演研究[J]. 遥感技术与应用, 2020, 35(1): 174-184.
[2] 徐卫星,薛华柱,靳华安,李爱农. 融合遥感先验信息的叶面积指数反演[J]. 遥感技术与应用, 2019, 34(6): 1235-1244.
[3] 程雪,贺炳彦,黄耀欢,孙志刚,李鼎,朱婉雪. 基于无人机高光谱数据的玉米叶面积指数估算[J]. 遥感技术与应用, 2019, 34(4): 775-784.
[4] 云增鑫, 郑光, 马利霞, 王晓菲, 卢晓曼, 路璐. 联合主被动遥感数据定量评价林下植被对叶面积指数估算的影响[J]. 遥感技术与应用, 2019, 34(3): 583-594.
[5] 林沂, 张萌丹, 张立福, 江淼. 高光谱激光雷达谱位合一的角度效应分析[J]. 遥感技术与应用, 2019, 34(2): 225-231.
[6] 徐凡, 张雪红, 石玉立. 基于激光雷达和航拍影像的城市地物分类研究[J]. 遥感技术与应用, 2019, 34(2): 253-262.
[7] 骆钰波, 黄洪宇, 唐丽玉, 陈崇成, 张浩. 基于地面激光雷达点云数据的森林树高、胸径自动提取与三维重建[J]. 遥感技术与应用, 2019, 34(2): 243-252.
[8] 李伟, 唐伶俐, 吴昊昊, 腾格尔, 周梅. 轻小型无人机载激光雷达系统研制及电力巡线应用[J]. 遥感技术与应用, 2019, 34(2): 269-274.
[9] 林沂, 周国清, 童庆禧. 偏振激光雷达对地观测遥感 [J]. 遥感技术与应用, 2019, 34(2): 232-242.
[10] 刘洁, 李静, 柳钦火, 何彬彬, 于文涛. 全球典型植被叶片光谱特征及其对LAI反演的影响分析[J]. 遥感技术与应用, 2019, 34(1): 155-165.
[11] 皋厦, 申鑫, 代劲松, 曹林. 结合LiDAR单木分割和高光谱特征提取的城市森林树种分类[J]. 遥感技术与应用, 2018, 33(6): 1073-1083.
[12] 廖凯涛,齐述华,王成,王点. 结合GLAS和TM卫星数据的江西省森林高度和生物量制图[J]. 遥感技术与应用, 2018, 33(4): 713-720.
[13] 刘振波,邹娴,葛云健,陈健,曹雨濛. 基于高分一号WFV影像的随机森林算法反演水稻LAI[J]. 遥感技术与应用, 2018, 33(3): 458-464.
[14] 虢韬,沈平,时磊. 机载LiDAR快速定位高压电塔方法研究[J]. 遥感技术与应用, 2018, 33(3): 530-535.
[15] 谢京凯,王福民,王飞龙,张东尼. 面向水稻LAI监测的植被指数土壤调节参数修正[J]. 遥感技术与应用, 2018, 33(2): 342-350.