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Data Details

Yearly Remote Sensing Rao's Q Index Dataset of Forests in China (2000-2017)


JIANG Xiao1,2CAI Hongyan1,2YANG Xiaohuan*1,2
1 State Key Laboratory of Resources and Environmental Information System，Institute of Geographic Sciences and Natural Resources Research，Chinese Academy of Sciences，Beijing 100101，China2 University of Chinese Academy of Sciences，Beijing 100049，China

DOI:10.3974/geodb.2024.03.08.V1

Published:Mar. 2024

Visitors：3653       Data Files Downloaded：29      
Data Downloaded：150.11 MB      Citations：

Key Words：

remote sensing,forest,Rao’s Q index,2000-2017

Abstract：

The yearly remote sensing Rao’s Q index dataset of forests in China (2000-2017) was developed based on MOD09A1 data, integrated with the formula for Rao’s Q index. The temporal resolution of the data was one year, the spatial resolution was 5 km. The dataset is archived in .tif format, and consists of 72 data files with data size of 58.2 MB (Compressed into one file with 5.17 MB). The research paper based on this dataset was published in Acta Ecologica Sinica, Vol. 43, No. 8, 2023.

Foundation Item：

Data Citation：

JIANG Xiao, CAI Hongyan, YANG Xiaohuan*. Yearly Remote Sensing Rao's Q Index Dataset of Forests in China (2000-2017)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2024. https://doi.org/10.3974/geodb.2024.03.08.V1.

References：


     [1] Pimm, S. L., Russell, G. J., Gittleman, J. L., et al. The future of biodiversity [J]. Science, 1995, 269(5222): 347-350.
     [2] Féret, J. B., Asner, G. P. Mapping tropical forest canopy diversity using high-fidelity imaging spectroscopy [J]. Ecological Applications, 2014, 24(6): 1289-1296.
     [3] Wang, R., Gamon, J. A. Remote sensing of terrestrial plant biodiversity [J]. Remote Sensing of Environment, 2019, 231: 111218.
     [4] Palmer, M. W., Earls, P. G., Hoagland, B. W., et al. Quantitative tools for perfecting species lists [J]. Environmetrics, 2002, 13(2): 121-137.
     [5] Rocchini, D., Marcantonio, M., Ricotta, C. Measuring Rao’s Q diversity index from remote sensing: an open source solution [J]. Ecological Indicators, 2017, 72: 234-238.
     [6] Rocchini, D., Marcantonio, M., Re, D. D., et al. Time-lapsing biodiversity: an open source method for measuring diversity changes by remote sensing [J]. Remote Sensing of Environment, 2019, 231: 111192.
     [7] Khare, S., Latifi, H., Rossi, S. A 15-year spatio-temporal analysis of plant β-diversity using Landsat time series derived Rao's Q index [J]. Ecological Indicators, 2021, 121: 107105.
     [8] Wu, D. Hazy weather research in China in the last decade: a review [J]. Acta Scientiae Circumstantiate, 2012, 32(2): 257‒269.
     [9] Khare, S., Latifi, H., Rossi, S. Forest beta-diversity analysis by remote sensing: How scale and sensors affect the Rao's Q index [J]. Ecological Indicators, 2019, 106: 105520.
     [10] Petchey, O. L., Gaston, K. J. Functional diversity (FD), species richness and community composition [J]. Ecology Letters, 2002, 5(3): 402-411.
     [11] Hernández-Stefanoni, J. L., Gallardo-Cruz, J. A., Meave, J. A., et al. Modeling α- and β-diversity in a tropical forest from remotely sensed and spatial data [J]. International Journal of Applied Earth Observation and Geoinformation, 2012, 19: 359-368.
     

Data Product:

ID Data Name Data Size Operation
1 ChinaForest_Rao.rar 5300.57KB
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