Research on data mining of salinization information based on phenological characters

Baozhong, 何宝忠 He; Jianli, 丁建丽 Ding; Fei, 王飞 Wang; Zhe, 张喆 Zhang; Bohua, 刘博华 Liu

doi:10.5846/stxb201607201479

Cited by 2 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on the ecosystem types in this study, we selected the following spectral indices: Normalized Difference Vegetation Index (NDVI) [40], Enhanced Vegetation Index (EVI) [41,42], Modified Soil Adjusted Vegetation Index (MSAVI) [43], Spectral Variability Vegetation Index (SVVI) [44], Normalized Difference Built-Up Index (NDBI) [45], Modified Normalized Difference Water Index (MNDWI) [46], Wet Index (WET) [47,48] and Salinity index (SIT) [49,50]. The calculation formulae and significance for these indices are shown in Table 3.…”

Section: Classification Systemmentioning

confidence: 99%

Mapping Dryland Ecosystems Using Google Earth Engine and Random Forest: A Case Study of an Ecologically Critical Area in Northern China

Li,

Guo,

Sun

et al. 2024

Land

View full text Add to dashboard Cite

Drylands are characterized by unique ecosystem types, sparse vegetation, fragile environments, and vital ecosystem services. The accurate mapping of dryland ecosystems is essential for their protection and restoration, but previous approaches primarily relied on modifying land use data derived from remote sensing, lacking the direct utilization of latest remote sensing technologies and methods to map ecosystems, especially failing to effectively identify key ecosystems with sparse vegetation. This study attempts to integrate Google Earth Engine (GEE), random forest (RF) algorithm, multi-source remote sensing data (spectral, radar, terrain, texture), feature optimization, and image segmentation to develop a fine-scale mapping method for an ecologically critical area in northern China. The results showed the following: (1) Incorporating multi-source remote sensing data significantly improved the overall classification accuracy of dryland ecosystems, with radar features contributing the most, followed by terrain and texture features. (2) Optimizing the features set can enhance the classification accuracy, with overall accuracy reaching 91.34% and kappa coefficient 0.90. (3) User’s accuracies exceeded 90% for forest, cropland, and water, and were slightly lower for steppe and shrub-steppe but were still above 85%, demonstrating the efficacy of the GEE and RF algorithm to map sparse vegetation and other dryland ecosystems. Accurate dryland ecosystems mapping requires accounting for regional heterogeneity and optimizing sample data and feature selection based on field surveys to precisely depict ecosystem patterns in complex regions. This study precisely mapped dryland ecosystems in a typical dryland region, and provides baseline data for ecological protection and restoration policies in this region, as well as a methodological reference for ecosystem mapping in similar regions.

show abstract

Section: Classification Systemmentioning

confidence: 99%

Mapping Dryland Ecosystems Using Google Earth Engine and Random Forest: A Case Study of an Ecologically Critical Area in Northern China

Li,

Guo,

Sun

et al. 2024

Land

View full text Add to dashboard Cite

show abstract

“…Therefore, many scholars have also begun to select environmental variables from the perspective of soil genesis to retrieve information on soil salt content [21]. The parent material, biological index, soil index and topographic parameters should be considered as factors for retrieving soil salt content [22,23]. Shahrayini et al [24] observed that topographic factors, such as vertical distance to channel network (VDCN), analytical hill-shading (AH), flow accumulation (FA) and topographic wetness index (TWI), have a strong influence on the prediction of soil salinization.…”

Section: Introductionmentioning

confidence: 99%

Comparing Machine Learning Algorithms for Soil Salinity Mapping Using Topographic Factors and Sentinel-1/2 Data: A Case Study in the Yellow River Delta of China

Zhang

Shao

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

Soil salinization is a critical and global environmental problem. Effectively mapping and monitoring the spatial distribution of soil salinity is essential. The main aim of this work was to map soil salinity in Shandong Province located on the Yellow River Delta of China using Sentinel-1/2 remote sensing data and digital elevation model (DEM) data, coupled with soil sampling data, and combined with four regression models: support vector regression (SVR), stepwise multi-regression (SMR), partial least squares regression (PLSR) and random forest regression (RFR). For these purposes, 60 soil samples were collected during the field survey conducted from 9 to 14 October 2019, corresponding to the Sentinel-1/2 and DEM data. Then we established a soil salinity and feature dataset based on the sampled data and the features extracted from Sentinel-1/2 and DEM data. This study adopted the feature importance of the RF model to screen all features. The results showed that the CRSI index made the greatest contribution in retrieving soil salinity in this region. In this paper, 18 sampling points were used to validate and compare the performance of the four models. The results reveal that, compared with the other regression models, the PLSR model has the best performance (R2 = 0.66, and RMSE = 1.30). Finally, the PLSR method was used to predict the spatial distribution of soil salinity in the Yellow River Delta. We concluded that the model can be used effectively for the quantitative estimation of soil salinity and provides a useful tool for ecological construction.

show abstract

Research on data mining of salinization information based on phenological characters

Cited by 2 publications

References 0 publications

Mapping Dryland Ecosystems Using Google Earth Engine and Random Forest: A Case Study of an Ecologically Critical Area in Northern China

Mapping Dryland Ecosystems Using Google Earth Engine and Random Forest: A Case Study of an Ecologically Critical Area in Northern China

Comparing Machine Learning Algorithms for Soil Salinity Mapping Using Topographic Factors and Sentinel-1/2 Data: A Case Study in the Yellow River Delta of China

Contact Info

Product

Resources

About