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纸质出版日期： 2018-9 ，
录用日期： 2018-2-5
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董文雪, 曾源, 赵玉金, 赵旦, 郑朝菊, 衣海燕. 2018. 机载激光雷达及高光谱的森林乔木物种多样性遥感监测. 遥感学报, 22(5): 833–847
Dong W X, Zeng Y, Zhao Y J, Zhao D, Zheng Z J and Yi H Y. 2018. Forest species diversity mapping using airborne LiDAR and hyperspectral data. Journal of Remote Sensing, 22(5): 833–847
利用机载LiDAR和高光谱数据并结合37个地面调查样本数据，基于结构差异与光谱变异理论，通过相关分析法分别筛选了3个最优林冠结构参数和6个最优光谱指数，在单木尺度上利用自适应C均值模糊聚类算法，在神农架国家自然保护区开展森林乔木物种多样性监测，实现了森林乔木物种多样性的区域成图。研究结果表明，(1)基于结合形态学冠层控制的分水岭算法可以获得较高精度的单木分割结果(
R
2
=0.88，RMSE=13.17，
P
<
0.001)；(2)基于LiDAR数据提取的9个结构参数中，95%百分位高度、冠层盖度和植被穿透率为最优结构参数，与Shannon-Wiener指数的相关性达到
R
2
=0.39—0.42(
P
<
0.01)；(3)基于机载高光谱数据筛选的16个常用的植被指数中，CRI、OSAVI、Narrow band NDVI、SR、Vogelmann index1、PRI与Shannon-Wiener指数的相关性最高(
R
2
=0.37—0.45，
P
<
0.01)；(4)在研究区，利用以30 m×30 m为窗口的自适应模糊C均值聚类算法可预测的最大森林乔木物种数为20，物种丰富度的预测精度为
R
2
=0.69，RMSE=3.11，Shannon-Wiener指数的预测精度为
R
2
=0.70，RMSE=0.32。该研究在亚热带森林开展乔木物种多样性监测，是在区域尺度上进行物种多样性成图的重要实践，可有效补充森林生物多样性本底数据的调查手段，有助于实现生物多样性的长期动态监测及科学分析森林物种多样性的现状和变化趋势。
Forest species diversity
as a key component of biodiversity
plays an irreplaceable role in maintaining ecological balance
processes
and services. In recent years
forest tree species diversity is facing a serious threat with intensifying human activities and influence of climate change. The status and trends of forest tree species diversity must be dynamically monitored to develop effective forest biodiversity conservation approaches. In this study
an airborne light detection and ranging (LiDAR) (
>
4 points/m
2
) and hyperspectral (PHI-3 sensor with spatial resolution of 1 m) data combined with 37 field sample data are used to detect tree species variation in the structural and spectral properties in the Shennongjia Forest Nature Reserve of China. First
we use the morphological crown control method based on a watershed algorithm to isolate individual tree crowns by using LiDAR. We select optimal structural indices from nine commonly used structural indices derived using LiDAR based on the theory of structural and spectral variation hypothesis. Meanwhile
we select optimal vegetation indices from 16 VIs based on the hyperspectral data by conducting a correlation analysis with the field samples. Second
a self-adaptive fuzzy C-means clustering algorithm is applied to map the species diversity (i.e.
richness and Shannon-Wiener index) in the study area for each 30 m×30 m window at the individual tree crown scale. Result indicates that the individual tree isolation by using the watershed algorithm can obtain a high accuracy (
R
2
=0.88
RMSE=13.17
P
<
0.001). The 95th quintile height
canopy cover
and vegetation permeability are the optimal structural indices
and their correlation with the Shannon–Wiener index are from 0.39 to 0.42 (
R
2
=0.39—0.42
P
<
0.01). The correlation among RI
OSAVI
narrow band NDVI
SR
Vogelmann index 1
PRI
and field inventory Shannon–Wiener index are relatively high based on the airborne hyperspectral data (
R
2
=0.37—0.45
P
<
0.01). Finally
we use the selected three structural and six vegetation indices to predict the optimal clustering numbers (species richness) and the Shannon–Wiener index by using the self-adaptive fuzzy C-means clustering algorithm. The result shows that the maximum tree species that can be predicted is 20. The prediction accuracy of species richness is
R
2
=0.69
RMSE=3.11
and the Shannon–Wiener index is
R
2
=0.70
RMSE=0.32. This method shows the potential of LiDAR combined with hyperspectral data in mapping species diversity of a subtropical forest. Moreover
it could provide an effective method for analyzing the current situation and its changing trend of forest biodiversity at a regional scale.
植物多样性物种丰富度激光雷达高光谱单木分离聚类
forest diversityspecies richnessLiDARhyperspectralindividual tree crown isolationclustering
Antonarakis A S, Richards K S and Brasington J. 2008. Object-based land cover classification using airborne LiDAR. Remote Sensing of Environment, 112(6): 2988–2998
Asner G P. 2008. Hyperspectral remote sensing of canopy chemistry, physiology, and biodiversity in tropical rainforests // Hyperspectral Remote Sensing of Tropical and Sub-Tropical Forests. Boca Raton: CRC Press: 261–296 [DOI: 10.1201/9781420053432.ch12]
Asner G P and Martin R E. 2009. Airborne spectranomics: mapping canopy chemical and taxonomic diversity in tropical forests. Frontiers in Ecology and the Environment, 7(5): 269–276
Asner G P, Martin R E, Knapp D E, Tupayachi R, Anderson C B, Sinca F, Vaughn N R and Llactayo W. 2017. Airborne laser-guided imaging spectroscopy to map forest trait diversity and guide conservation. Science, 355(6323): 385–389
Ceballos A, Hernández J, Corvalán P and Galleguillos M. 2015. Comparison of airborne LiDAR and satellite hyperspectral remote sensing to estimate vascular plant richness in deciduous mediterranean forests of central chile. Remote Sensing, 7(3): 2692–2714
Cho M A, Debba P, Mathieu R, Naidoo L, Aardt J V and Asner G. 2010. Improving discrimination of savanna tree species through a multiple-endmember spectral angle mapper approach: Canopy-Level analysis. IEEE Transactions on Geosciences & Remote sensing, 48: 4133–4142
Clark M L, Roberts D A and Clark D B. 2005. Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales. Remote Sensing of Environment, 96(3/4): 375–398
Clawges R, Vierling K, Vierling L and Rowell E. 2008. The use of airborne lidar to assess avian species diversity, density, and occurrence in a pine/aspen forest. Remote Sensing of Environment, 112(5): 2064–2073
Coops N C, Tompaski P, Nijland W, Rickbeil G J M, Nielsen S E, Bater C W and Stadt J J. 2016. A forest structure habitat index based on airborne laser scanning data. Ecological Indicators, 67: 346–357
Daughtry C S T, Walthall C L, Kim M S, de Colstoun E B and EMcMurtrey J E III. 2000. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, 74(2): 229–239
Davis F W, Stoms D M, Estes J E, Scepan J and Michael S J. 1990. An information systems approach to the preservation of biological diversity. International Journal of Geographical Information Systems, 4(1): 55–78
Fassnacht F E, Latifi H, Stereńczak K, Modzelewska A, Lefsky M, Waser L T, Straub C and Ghosh A. 2016. Review of studies on tree species classification from remotely sensed data. Remote Sensing of Environment, 186: 64–87
Feret J B and Asner G P. 2013. Tree species discrimination in tropical forests using airborne imaging spectroscopy. IEEE Transactions on Geoscience and Remote Sensing, 51(1): 73–84
Gamon J A, Serrano L and Surfus J S. 1997. The photochemical reflectance index: an optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels. Oecologia, 112(4): 492–501
Gitelson A and Merzlyak M N. 1994. Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation. Journal of Plant Physiology, 143(3): 286–292
Gitelson A A, Zur Y, Chivkunova O B and Merzlyak M N. 2002. Assessing carotenoid content in plant leaves with reflectance spectroscopy. Photochemistry and Photobiology, 75(3): 272–281
Guyot G, Baret F and Major D J. 1988. High spectral resolution: determination of spectral shifts between the red and the near infrared. International Archives of Photogrammetry and Remote Sensing, 11: VII-750–VII-760
Heinzel J and Koch B. 2011. Exploring full-waveform LiDAR parameters for tree species classification. International Journal of Applied Earth Observation and Geoinformation, 13(1): 152–160
Hernández-Stefanoni J L and Dupuy J M. 2007. Mapping species density of trees, shrubs and vines in a tropical forest, using field measurements, satellite multiespectral imagery and spatial interpolation. Biodiversity and Conservation, 16(13): 3817–3833
Heywood V H. 1995. Global Biodiversity Assessment. Cambridge: Cambridge University Press
Jordan C F. 1969. Derivation of leaf-area index from quality of light on the forest floor. Ecology, 50(4): 663–666
Kapos V. 2017. Seeing the forest through the trees. Science, 355(6323): 347–349
Kooistra L, Wamelink W, Schaepman-Strub G, Schaepman M, van Dobben H, Aduaka U and Batelaan O. 2008. Assessing and predicting biodiversity in a floodplain ecosystem: assimilation of net primary production derived from imaging spectrometer data into a dynamic vegetation model. Remote Sensing of Environment, 112(5): 2118–2130
Lefsky M A, Cohen W B, Harding D J, Parker G G, Acker S A and Gower S T. 2002. Lidar remote sensing of above-ground biomass in three biomes. Global Ecology and Biogeography, 11(5): 393–399
Li Y and Yu F S. 2009. A new validity function for fuzzy clustering // International Conference on Computational Intelligence and Natural Computing. Wuhan, China: IEEE: 462–465 [DOI: 10.1109/CINC.2009.100]
Lopatin J, Dolos K, Hernández H J, Galleguillos M and Fassnacht F E. 2016. Comparing Generalized Linear Models and random forest to model vascular plant species richness using LiDAR data in a natural forest in central Chile. Remote Sensing of Environment, 173: 200–210
Möckel T, Dalmayne J, Schmid B C, Prentice H C and Hall K. 2016. Airborne hyperspectral data predict fine-scale plant species diversity in grazed dry grasslands. Remote Sensing, 8(2): 133
Moffiet T, Mengersen K, Witte C, King R and Denham R. 2005. Airborne laser scanning: Exploratory data analysis indicates potential variables for classification of individual trees or forest stands according to species. Isprs Journal of Photogrammetry and Remote Sensing, 59(5): 289–309
Mutanga O and Skidmore A K. 2004. Narrow band vegetation indices overcome the saturation problem in biomass estimation. International Journal of Remote Sensing, 25(19): 3999–4014
Naidoo L, Cho M A, Mathieu R and Asner G. 2012. Classification of savanna tree species, in the Greater Kruger National Park region, by integrating hyperspectral and LiDAR data in a Random Forest data mining environment. Isprs Journal of Photogrammetry and Remote Sensing, 69: 167–179
国家测绘地理信息局. 2012. 机载激光雷达数据处理技术规范. 北京: 测绘出版社
National Administration of Surveying, Mapping and Geoinformation. 2012. Technical specification for data processing of airborne laser radar. Beijing: Surveying and Mapping Press
Nelson R, Krabill W and Tonelli J. 1988. Estimating Forest Biomass and Volume Using Airborne Laser Data. Remote Sensing of Environment, 24(2): 247–267
Oindo B O, Skidmore A K and De Salvo P. 2003. Mapping habitat and biological diversity in the Maasai Mara ecosystem. International Journal of Remote Sensing, 24(5): 1053–1069
Pielou E C. 1966. The measurement of diversity in different types of biological collections. Journal of Theoretical Biology, 13: 131–144
Qi J, Chehbouni A, Huete A R, Kerr Y H and Sorooshian S. 1994. A Modified Soil Adjusted Vegetation Index. Remote Sensing of Environment, 48(2): 119–126 ])
Riaño D, Valladares F, Condés S and Chuvieco E. 2004. Estimation of leaf area index and covered ground from airborne laser scanner (Lidar) in two contrasting forests. Agricultural and Forest Meteorology, 124(3/4): 269–275
Richardson A J and Everitt J H. 1992. Using spectral vegetation indices to estimate rangeland productivity. Geocarto International, 7(1): 63–69
Richardson J J, Moskal L M and Kim S H. 2009. Modeling approaches to estimate effective leaf area index from aerial discrete-return LIDAR. Agricultural and Forest Meteorology, 149(6/7): 1152–1160
Rocchini D, Chiarucci A and Loiselle S A. 2004. Testing the spectral variation hypothesis by using satellite multispectral images. Acta Oecologica, 26(2): 117–120
Rondeaux G, Steven M and Baret F. 1996. Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 55(2): 95–107
Serrano L, Peñuelas J and Ustin S L. 2002. Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data: decomposing biochemical from structural signals. Remote Sensing of Environment, 81(2/3): 355–364
Simonson W D, Allen H D and Coomes D A. 2012. Use of an airborne lidar system to model plant species composition and diversity of mediterranean oak forests. Conservation Biology, 26(5): 840–850
Simpson E H. 1949. Measurement of diversity. Nature, 163(4148): 688–688
Sims D A and Gamon J A. 2002. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and development stages. Remote Sensing of Environment, 81(2-3): 337–354
Tapp P D and Siwak C T. 2006. The canine model of human brain aging: cognition, behavior, and neuropathology // Handbook of Models for Human Aging. New York: Academic Press: 415–434 [DOI: 10.1016/B978-012369391-4/50036-9]
Turner W. 2014. Sensing biodiversity. Science, 346(6207): 301–302
Vogelmann J E, Rock B N and Moss D M. 1993. Red edge spectral measurements from sugar maple leaves. International Journal of Remote Sensing, 14(8): 1563–1575
Warner T A and Shank M C. 1997. Spatial autocorrelation analysis of hyperspectral imagery for feature selection. Remote Sensing of Environment, 60(1): 58–70
魏彦昌, 吴炳方, 张喜旺, 杜鑫. 2008. 生物多样性遥感研究进展. 地球科学进展, 23(9): 924–931
Wei Y C, Wu B F, Zhang X W and Du X. 2008. Advances in remote sensing research for biodiversity monitoring. Advances in Earth Science, 23(9): 924–931 (
Westman W E, Strong L L and Wilcox B A. 1989. Tropical deforestation and species endangerment: the role of remote sensing. Landscape Ecology, 3(2): 97–109
邬伦, 刘瑜, 张晶. 2001. 地理信息系统: 原理、方法和应用. 北京: 科学出版社
Wu L, Liu Y and Zhang J. 2001. Geographic Information System: Principles, Methods and Applications. Beijing: Science Press
徐文婷, 吴炳方. 2005. 遥感用于森林生物多样性监测的进展. 生态学报, 25(5): 1199–1204
Xu W T and Wu B F. 2005. Progress on measuring forest biodiversity with remote sensing technique. Acta Ecologica Sinica, 25(5): 1199–1204 (
Zarco-Tejada P J, Miller J R, Noland T L, Mohammed G H and Sampson P H. 2001. Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 39(7): 1491–1507
Zeng Y, Schaepman M E, Wu B, Clevers J and Bregt A. 2008. Scaling-based forest structural change detection using an inverted geometric-optical model in the Three Gorges region of China. Remote Sensing of Environment, 112(12): 4261–4271
张煜星, 王祝熊. 2007. 遥感技术在森林资源清查中的应用研究. 北京: 中国林业出版社
Zhang Y X and Wang Z X. 2007. Research on the Application of Remote Sensing Technology in Inventory of Forest Resources. Beijing: China Forestry Publishing House
Zhao D, Pang Y, Li Z Y and Liu L J. 2014. Isolating individual trees in a closed coniferous forest using small footprint lidar data. International Journal of Remote Sensing, 35(20): 7199–7218
Zhao D, Pang Y, Li Z Y and Sun G Q. 2013. Filling invalid values in a lidar-derived canopy height model with morphological crown control. International Journal of Remote Sensing, 34(13): 4636–4654
Zhao Y J, Zeng Y, Zhao D, Wu B F and Zhao Q J. 2016. The optimal leaf biochemical selection for mapping species diversity based on imaging spectroscopy. Remote Sensing, 8(3): 216
赵英时. 2003. 遥感应用分析原理与方法. 北京: 科学出版社
Zhao Y S. 2003. Principles and Methods of Remote Sensing Application Analysis. Beijing: Science Press
赵淑清, 方精云, 宗占江, 朱彪, 沈海花. 2004. 长白山北坡植物群落组成、结构及物种多样性的垂直分布. 生物多样性, 12(1): 164–173
Zhao S Q, Fang J Y, Zong Z J, Zhu B and Shen H H. 2004. Composition, structure and species diversity of plant communities along an altitudinal gradient on the northern slope of Mt. Changbai, Northeast China. Chinese Biodiversity, 12(1): 164–173 (
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