Impact of soil salinization on the eco-environment quality of coastal wetlands:A case study of Yellow River Delta
ZHANG Zhimei1(), FAN Yanguo1(), JIAO Zhijun2, GUAN Qingchun1
1. College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China 2. School of Geosciences and Info-physics, Central South University, Changsha 410012, China
Soil salinization is an important reason for land degradation and desertification and has a huge impact on the eco-environment. Coastal wetlands are typical areas subjected to a weak eco-environment and severe salinization, and there is an urgent need to investigate the impact of soil salinization on their eco-environment. This study proposed the baseline-based soil salinity index (BSSI), which can effectively suppress the influence of complex features on surface salinization monitoring and improve the accuracy of saline soil extraction by 10% compared to other salinity index models. Furthermore, this study proposed the optimized water benefit-based ecological index (OWBEI) by optimizing the water benefit-based ecological index (WBEI), which can effectively increase the accuracy of eco-environment quality assessment to 87%. Finally, this study explored the mechanical processes of the influence of soil salinization on the eco-environment quality based on the distribution of soil salinization and eco-environment quality obtained from the Yellow River Delta. The results show that the deterioration of soil salinization has led to an increase in the soil vulnerability of coastal wetlands, indirectly resulting in a continuous decrease in eco-environment quality. Although eco-environment protection measures have been continuously proposed, few of them are tailored to the solving of salinization. This leads to the deterioration of the ecological quality, which then yields negative feedback to the soil and eventually forms a vicious circle. This adversely affects local production, life, and social development.
Zhimei ZHANG,Yanguo FAN,Zhijun JIAO, et al. Impact of soil salinization on the eco-environment quality of coastal wetlands:A case study of Yellow River Delta[J]. Remote Sensing for Natural Resources,
2023, 35(4): 226-235.
Zhou X H, Zhang F, Liu C J, et al. Soil salinity inversion based on novel spectral index[J]. Environmental Earth Sciences, 2021, 80(16):501-514.
doi: 10.1007/s12665-021-09752-x
Wang X P, Zhang F, Ding J L, et al. Estimation of soil salt content (SSC) in the Ebinur Lake Wetland National Nature Reserve (ELWNNR),Northwest China,based on a Bootstrap-BP neural network model and optimal spectral indices[J]. Science of The Total Environment, 2018, 615(15):918-930.
doi: 10.1016/j.scitotenv.2017.10.025
url: https://linkinghub.elsevier.com/retrieve/pii/S0048969717327122
Fan X W, Liu Y B, Tao J M, et al. Soil salinity retrieval from advanced multi-spectral sensor with partial least square regression[J]. Remote Sensing, 2015, 7(1):488-511.
doi: 10.3390/rs70100488
url: http://www.mdpi.com/2072-4292/7/1/488
Khan N M, Rastoskuev V V, Shalina E V, et al. Mapping salt-affected soils using remote sensing indicators:A simple approach with the use of GIS IDRISI[C]// 22nd Asian Conference on Remote Sensing.Singapore, 2001:5-9.
Nguyen K A, Liou Y A, Tran H P, et al. Soil salinity assessment by using near-infrared channel and vegetation soil salinity index derived from Landsat8 OLI data:A case study in the Tra Vinh Province,Mekong Delta,Vietnam[J]. Progress in Earth and Planetary Science, 2022, 9(1):46.
doi: 10.1186/s40645-022-00505-3
[15]
Elhag M. Evaluation of different soil salinity mapping using remote sensing techniques in arid ecosystems,Saudi Arabia[J]. Journal of Sensors, 2016:7596175.
Jiao Z J, Sun G Y, Zhang A Z, et al. Water benefit-based ecological index for urban ecological environment quality assessments[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14:7557-7569.
doi: 10.1109/JSTARS.2021.3098667
url: https://ieeexplore.ieee.org/document/9492814/
Piedallu C, Chéret V, Denux J P, et al. Soil and climate differently impact NDVI patterns according to the season and the stand type[J]. Science of the Total Environment, 2019, 651:2874-2885.
doi: 10.1016/j.scitotenv.2018.10.052
Li A H, Bo Y C, Chen L. Bayesian maximum entropy data fusion of field observed LAI and Landsat ETM+ derived LAI[C]// Proceedings of IEEE International Geoscience and Remote Sensing Symposium.Vancouver, 2011:2617-2620.
Zhou D Y, Chen T, Niu R Q, et al. Ecological environment assessment of mining area by using moving window-based remote sensing ecological index[C]// Proceedings of IEEE International Geoscience and Remote Sensing Symposium.Yokohama, 2019:9942-9945.
Firozjaei M K, Fathololoumi S, Weng Q, et al. Remotely sensed urban surface ecological index (RSUSEI):An analytical framework for assessing the surface ecological status in urban environments[J]. Remote Sensing, 2020, 12(12):2029.
doi: 10.3390/rs12122029
url: https://www.mdpi.com/2072-4292/12/12/2029
Yang H. Characteristics and ecosystem health evaluation research of the Yellow River Delta wetland[J]. Zhengzhou:North China University of water Resources and Electric Power, 2019.
Chen L D, Fu B J. Analysis of impact of human activity on landscape structare in Yellow River Delta:A case study of Dongying region[J]. Acta Ecologica Sinica, 1996, 16(4):337-344.
[37]
Barsi J A, Schott J R, Palluconi F D, et al. An atmospheric correction parameter calculator for a single thermal band earth-sensing instrument[C]// Proceedings of IEEE International Geoscience and Remote Sensing Symposium.Toulouse, 2003(5):3014-3016.
[38]
Julia A B, John R S, Frank D P, et al. Validation of a web-based atmospheric correction tool for single thermal band instruments[C]// Proceedings of Spie the International Society for Optical Engineering.San Diego, 2005,58820.
[39]
Jiang H N, Shu H. Optical remote-sensing data based research on detecting soil salinity at different depth in an arid-area oasis,Xinjiang,China[J]. Earth Science Informatics, 2019, 12(1):43-56.
doi: 10.1007/s12145-018-0358-2
Chen L, Li M, Huang F, et al. Relationships of LST to NDBI and NDVI in Wuhan City based on Landsat ETM+ Image[C]// Proceedings of 2013 6th International Congress on Image and Signal Processing (CISP).Hangzhou, 2013:840-845.
[44]
Vibhute A D, Dhumal R, Nagne A, et al. Evaluation of soil conditions using spectral indices from hyperspectral datasets[C]// Proceedings of 2017 2nd International Conference on Man and Machine Interfacing (MAMI).Bhubaneswar, 2017:1-6.
Li X F. TOPSIS model with entropy weight for eco geological environmental carrying capacity assessment[J]. Microprocessors and Microsystems, 2021:103805.
Huang J, Zhao G X, Xi X, et al. Extraction of soil salinization information by combining spectral and texture data in the Yellow River Delta:A case study in Kenli District,Shandong Province[J]. Journal of Agricultural Resources and Environment, 2022, 39(3):594-601.
Cao J R, Liu W Q, Huang C, et al. Analysis on dynamic variation of salinized soil in Yellow River Delta based on Landsat TM/ETM image[J]. Bulletin of Soil and Water Conservation, 2014, 34(6):179-183,371.