|
|
|
|
|
|
| Monitoring and Assessing of Biodiversity in China Based on Multispectral
Remote Sensing Data |
| YANG Wen-fu1, 2, 3, LIU Jun4*, WANG Wen-wen2, 3, LIU Xiao-song2, 3, HAO Xiao-yang2, 3 |
1. School of Land Science and Technology, China University of Geosciences, Beijing, Beijing 100083, China
2. Key Laboratory of Monitoring and Protection of Natural Resources in Mining Cities, Ministry of Natural Resources, Jinzhong 030600, China
3. Shanxi Provincial Key Lab of Resources, Shanxi Coal Geology Geophysical Surveying Exploration Institute, Jinzhong 030600, China
4. College of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China
|
|
|
|
|
Abstract Biodiversity is the basis of human survival. Affected by the environment and climate change, the decrease in global biodiversity is becoming increasingly serious. Therefore, the study of regional biodiversity has great significance in protecting endangered species’ habitat, planning and utilising the regional resources reasonably. Three dynamic habitat indices (DHI) of cumulative, minimum and difference were constructed by datasets of NDVI, EVI, FPAR, LAI and GPP of multispectral remote sensing vegetation products from 2002 to 2018. The multiple regression analysis was used to study ① the applicability of DHIs constructed by different multispectral remote sensing indices (NDVI, EVI, FPAR, LAI and GPP); ②the complementarity of species diversity expressed by cumulative, minimum and difference DHI models; ③the impact of climate change on biodiversity in China; ④the ability of cumulative, minimum and difference DHIs to express species richness. The research shows that ① there is a strong correlation between corresponding DHIs based on the same MODIS multispectral vegetation indices (correlation coefficient from 0.77 to 0.98), so they can substitute for each other; There is a certain correlation among these three DHIs, while they cannot replace each other. ②Compared with DHIs constructed by NDVI, EVI, FPAR and LAI product data, GPP-DHIs have the strongest ability to monitor biodiversity in China and have a good correlation with species richness (correlation coefficient from 0.32 to 0.84). ③Continuous climate change will affect the total productivity of vegetation significantly in the large region, and extreme climate has little impact on the large region; Evapotranspiration has a more significant impact on the total productivity of vegetation in large-scale regions than temperature, and precipitation. ④Environmental change has the greatest impact on amphibian species richness, followed by birds and mammals. ⑤The cumulative DHI and minimum DHI in China gradually increase from the northwest inland to the southeast coastal area. The minimum DHI in the northwest, north China, high altitude area, high latitudes, and northwest desert areas are very small, which indicate the ecological environment in the southeast coastal area is more suitable for biological survival, and the harsh environment affects biodiversity seriously. The difference in DHI shows a higher spatial pattern in Northeast and North China, and a lower spatial pattern in Central and South China, which indicates that the living environment of species in Northeast and North China changed greatly, and the living environment of species in Central and South China was relatively stable.
|
|
Received: 2022-03-16
Accepted: 2022-07-21
|
|
|
|
Corresponding Authors:
LIU Jun
E-mail: 8886355@163.com
|
|
[1] Knudby A, Ledrew E, Brenning A. Remote Sensing of Environment, 2010, 114(6): 1230.
[2] WU Hao, XU Yao-zhan, JIANG Ming-xi(吴 浩, 徐耀粘, 江明喜). Resources and Environment in the Yangtze Basin(长江流域资源与环境), 2021, 30(6): 1384.
[3] FU Bo-jie, YU Dan-dan, LÜ Nan(傅伯杰, 于丹丹, 吕 楠). Acta Ecologica Sinica(生态学报), 2017, 37(2): 341.
[4] Ministry of Environmental Protection of the People’s Republic of China(中华人民共和国环境保护部). Standard for the Assessment of Regional Biodiversity HJ 623—2011(区域生物多样化评价标准HJ 623—2011). Beijing: China Environmental Science Press(北京：中国环境科学出版社), 2011.
[5] Ceballos G, Ehrlich P R, Barnosky A D, et al. Science Advances, 2015, 1(5): e1400253.
[6] LIU Shao-yang, BAI Jian-jun, CHEN Jun(刘少阳, 白建军, 陈 军). Resouces and Environment in the Yangtze Basin(长江流域资源与环境), 2020, 29(1): 232.
[7] HE Cheng, FENG Zhong-ke, YUAN Jin-jun, et al(何 诚, 冯仲科, 袁进军, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2012, 32(6): 1628.
[8] Saatchi S, Buermann W, Steege H T, et al. Remote Sensing of Environment, 2008, 112(5): 2000.
[9] Leyequien E，Verrelst J，Slot M，et al. International Journal of Applied Earth Observational Geoinformation，2007, 9(1): 1.
[10] Li J, Zhao H, Yang X. African Journal of Agricultural Research, 2011, 6(17): 4019.
[11] Radeloff V C, Dubinin M, Coops N C, et al. Remote Sensing of Environment, 2019, 222: 204.
[12] Berry S, Mackey B G, Brown T. Pacific Conservation Biology, 2007, 13(2): 120.
[13] Coops N C, Waring R H，Wulder M A, et al. J. Biogeogr., 2009, 36(5): 905.
[14] Coops N C, Wulder M A, Iwanicka D. Remote Sensing of Environment, 2009, 113(3): 1250.
[15] Skidmore A K, Pettorelli N, Coops N C, et al. Nature, 2015, 523(7561): 403.
[16] Knudby A, Ledrew E, Brenning A. Remote Sensing of Environment, 2010, 114(6)：1230.
[17] Lucas K L, Raber G T, Carter G A. Journal of Applied Remote Sensing, 2010, 4(1): 043545.
[18] TIAN Yi-chao, HUANG Yuan-lin, ZHANG Qiang, et al(田义超, 黄远林, 张 强, 等). China Environmental Science(中国环境科学), 2020, 40(3): 1320.
[19] PENG Yu, QING Feng-ting, MI Kai, et al(彭 羽, 卿凤婷, 米 凯, 等). Acta Ecologica Sinica(生态学报), 2015, 35(2): 577.
|
| [1] |
LI Hu1, ZHONG Yun1, 2, FENG Ya-ting1, LIN Zhen1, ZHU Shi-jiang1, 2*. Multi-Vegetation Index Soil Moisture Inversion Model Based on UAV
Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 207-214. |
| [2] |
LI Ai-min1, FAN Meng2*, QIN Guang-duo2, WANG Hai-long2, XU You-cheng2. Water Quality Parameter COD Retrieved From Remote Sensing Based on Convolutional Neural Network Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(02): 651-656. |
| [3] |
LIU Hong-jun1, NIU Teng1, YU Qiang1*, SU Kai2, YANG Lin-zhe1, LIU Wei1, WANG Hui-yuan1. Inversion and Estimation of Heavy Metal Element Content in Peach Forest Soil in Pinggu District of Beijing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3552-3558. |
| [4] |
LUO Li-qiang, SHEN Ya-ting. Advantages of X-Ray Spectrometry in Origin of Life, Earth Life on Earth and Global Climate Change[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(03): 665-674. |
| [5] |
LÜ Jing-jing1,2, GONG Wei-jin1, DOU Yan-yan1, DUAN Xue-jun1, LIU Hai-fang1, ZHANG Lie-yu3, XI Bei-dou3, YU Shui-li2, HOU Li-an2,4. Spectroscopy and Microbiological Analysis of Soil Infiltration Clogging in Treating Aged Swine Wastewater[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(05): 1541-1546. |
| [6] |
WANG Teng-jun1, 2, ZHAO Ming-hai3, YANG Yun1*, ZHANG Yang2, 4, CUI Qin-fang1, LI Long-tong1. Inversion of Heavy Metals Content in Soil Using Multispectral Remote Sensing Imagery in Daxigou Mining Area of Shaanxi[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(12): 3880-3887. |
| [7] |
YANG Wei-shan1,2,3, LI Meng4*, SUN Xiao-lei2, HU Hua-ling4, HUANG Li-juan2. Fluorescence Spectral Characteristics of Dissolved Organic Matter in Meadow Soils in Qinghai under Different Altitudes[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(05): 1477-1482. |
| [8] |
MAO Zhi-hui1, DENG Lei1*, SUN Jie1, ZHANG Ai-wu1, CHEN Xiang-yang2, ZHAO Yun1. Research on the Application of UAV Multispectral Remote Sensing in the Maize Chlorophyll Prediction[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(09): 2923-2931. |
| [9] |
WANG Fu-tao1，3, WANG Shi-xin1, ZHOU Yi1*, WANG Li-tao1, YAN Fu-li1, LI Wen-jun1，2, LIU Xiong-fei1，2 . High Resolution Remote Sensing Monitoring and Assessment of Secondary Geological Disasters Triggered by the Lushan Earthquake [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2016, 36(01): 181-185. |
| [10] |
ZHAO Shao-hua1, 2, ZHANG Feng1, 2*, WANG Qiao1, 2, YAO Yun-jun3, WANG Zhong-ting1, 2, YOU Dai-an1, 2 . Application of Hyper-Spectral Remote Sensing Technology in Environmental Protection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2013, 33(12): 3343-3348. |
| [11] |
GONG Yin-xi1, HE Cheng2*, YAN Fei3, FENG Zhong-ke3, CAO Meng-lei3, GAO Yuan3, MIAO Jie3, ZHAO Jin-long4 . Study on Artificial Neural Network Combined with Multispectral Remote Sensing Imagery for Forest Site Evaluation [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2013, 33(10): 2815-2822. |
| [12] |
CHEN Bei-bei1, 2, 3, GONG Hui-li1, 2, 3*, LI Xiao-juan1, 2, 3, LEI Kun-chao1，4, ZHU Lin1, 2, 3, WANG Yan-bing1, 2, 3 . The Impact of Load Density Differences on Land Subsidence Based on Build-Up Index and PS-InSAR Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2013, 33(08): 2198-2202. |
| [13] |
WANG Yan1, 2, TIAN Qing-jiu1, 2*, HUANG Yan1, 2, WEI Hong-wei1, 2. NDVI Difference Rate Recognition Model of Deciduous Broad-Leaved Forest Based on HJ-CCD Remote Sensing Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2013, 33(04): 1018-1022. |
| [14] |
HU Tan-gao, XU Jun-feng*, ZHANG Deng-rong, WANG Jie, ZHANG Yu-zhou . Hard and Soft Classification Method of Multi-Spectral Remote Sensing Image Based on Adaptive Thresholds [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2013, 33(04): 1038-1042. |
| [15] |
HE Cheng1, FENG Zhong-ke1*, YUAN Jin-jun2, WANG Jia1，GONG Yin-xi1, DONG Zhi-hai1, 3 . Advances in the Research on Hyperspectral Remote Sensing in Biodiversity and Conservation [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2012, 32(06): 1628-1632. |
|
|
|
|
