Remote sensing of biodiversity: Soil correction and data dimension reduction methods improve assessment of α-diversity (species richness) in prairie ecosystems

被引:96
作者
Gholizadeh, Hamed [1 ]
Gamon, John A. [1 ,2 ,3 ]
Zygielbaum, Arthur I. [1 ]
Wang, Ran [2 ]
Schweiger, Anna K. [4 ]
Cavender-Bares, Jeannine [4 ]
机构
[1] Univ Nebraska, Sch Nat Resources, Lincoln, NE 68583 USA
[2] Univ Alberta, Dept Earth & Atmospher Sci, Edmonton, AB T6G 2E3, Canada
[3] Univ Alberta, Dept Biol Sci, Edmonton, AB T6G 2R3, Canada
[4] Univ Minnesota, Dept Ecol Evolut & Behav, St Paul, MN 55108 USA
基金
加拿大自然科学与工程研究理事会; 美国国家科学基金会;
关键词
alpha-Diversity; Spectral diversity; Remote sensing; Hyperspectral imaging; Dimension reduction; Soil exposure; Prairie; HYPERSPECTRAL IMAGER; FUNCTIONAL DIVERSITY; CHLOROPHYLL CONTENT; PRODUCTIVITY; VEGETATION; SYSTEM; CLASSIFICATION; RESOLUTION; INDICATORS; SELECTION;
D O I
10.1016/j.rse.2017.12.014
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Hyperspectral data, with their detailed spectral information at different wavelengths, offer multiple ways to assess biodiversity. One approach, known as the "spectral variation hypothesis" (SVH), proposes that biodiversity is linked to spectral diversity. However, SVH-based approaches, which we refer to as "spectral diversity metrics", can be confounded by soil exposure and are sensitive to the spatial resolution of the data. To address these issues, we 1) investigated the impact of soil exposure on spectral diversity, 2) identified optimal bands for mapping biodiversity using a spectral diversity metric based on dimension reduction, and 3) assessed the impact of spatial resolution on spectral diversity metrics. In this study; alpha-diversity (species richness) was used as a measure of plant biodiversity. The study was based on two imaging spectrometry data sets from the Cedar Creek Ecosystem Science Reserve in Central Minnesota, USA, at two levels: proximal and airborne. The data sets included varying degrees of soil background sampled at two different spatial resolutions (1 mm and 0.75 m). We explored five spectral diversity metrics, including the coefficient of variation, convex hull volume, spectral angle mapper, spectral information divergence, and a newly proposed dimension reduction-based metric called "convex hull area." For the proximal data set (pixel size of 1 mm), filtering soil pixels by applying a normalized difference vegetation index (NDVI) threshold improved the performance of all spectral diversity metrics significantly, with the coefficient of variation showing the highest correlation with species richness. In the airborne data set (pixel size of 0.75 m), the convex hull area outperformed other metrics. These findings demonstrate promising approaches for remote sensing of biodiversity, illustrate a confounding effect of soil background on remote diversity measurement, and indicate that the most informative regions of the electromagnetic spectrum for estimating species richness can vary with spatial scale.
引用
收藏
页码:240 / 253
页数:14
相关论文
共 82 条
[1]   Spectral unmixing of vegetation, soil and dry carbon cover in arid regions: comparing multispectral and hyperspectral observations [J].
Asner, GP ;
Heidebrecht, KB .
INTERNATIONAL JOURNAL OF REMOTE SENSING, 2002, 23 (19) :3939-3958
[2]   A biogeophysical approach for automated SWIR unmixing of soils and vegetation [J].
Asner, GP ;
Lobell, DB .
REMOTE SENSING OF ENVIRONMENT, 2000, 74 (01) :99-112
[3]   The Quickhull algorithm for convex hulls [J].
Barber, CB ;
Dobkin, DP ;
Huhdanpaa, H .
ACM TRANSACTIONS ON MATHEMATICAL SOFTWARE, 1996, 22 (04) :469-483
[4]  
Boardman J. W., 2000, P 9 JPL AIRB EARTH S, P195
[5]  
Bonar Scott A., 2011, P11
[6]  
Bro R, 1997, J CHEMOMETR, V11, P393, DOI 10.1002/(SICI)1099-128X(199709/10)11:5<393::AID-CEM483>3.3.CO
[7]  
2-C
[8]   Harnessing plant spectra to integrate the biodiversity sciences across biological and spatial scales [J].
Cavender-Bares, Jeannine ;
Gamon, John A. ;
Hobbie, Sarah E. ;
Madritch, Michael D. ;
Meireles, Jose Eduardo ;
Schweiger, Anna K. ;
Townsend, Philip A. .
AMERICAN JOURNAL OF BOTANY, 2017, 104 (07) :966-969
[9]   Associations of Leaf Spectra with Genetic and Phylogenetic Variation in Oaks: Prospects for Remote Detection of Biodiversity [J].
Cavender-Bares, Jeannine ;
Meireles, Jose Eduardo ;
Couture, John J. ;
Kaproth, Matthew A. ;
Kingdon, Clayton C. ;
Singh, Aditya ;
Serbin, Shawn P. ;
Center, Alyson ;
Zuniga, Esau ;
Pilz, George ;
Townsend, Philip A. .
REMOTE SENSING, 2016, 8 (03)
[10]   An information-theoretic approach to spectral variability, similarity, and discrimination for hyperspectral image analysis [J].
Chang, CI .
IEEE TRANSACTIONS ON INFORMATION THEORY, 2000, 46 (05) :1927-1932