Monitoring sustainable development by means of earth observation data and machine learning: a review

Ferreira, Bruno; Iten, Muriel; Silva, Rui G.

doi:10.1186/s12302-020-00397-4

Environmental Sciences Europe

Table 5 Examples of application of dimension reduction methods towards SDGs using EO data

From: Monitoring sustainable development by means of earth observation data and machine learning: a review

SDGs	Field	Main finding	References
SGD 2 (Zero Hunger)	Agriculture	Partial Least Square Regression was applied with success, as a FS method, on crop yield estimation	[115]
SGD 2 (Zero Hunger)	Agriculture	The FS results demonstrated that the proposed Maximum Separability and Minimum Dependency method was more accurate than filter methods	[116]
SGD 6 (Clean Water and Sanitation)	Water resources	The proposed approach proved to be effective and accurate to assess water resources at catchment scale	[117]
SGD 6 (Clean Water and Sanitation)	Water Sources	Stepwise Discriminant Analysis and PCA improved the accuracy of water source recognition	[118]
SDG 7 (Affordable and Clean Energy)	Electricity	The proposed method improved the forecasting of electricity price and it was more accurate than the Independent Electricity System Operator prediction	[119]
SGD 9 (Industry, Innovation and Infrastructure)	Structural Reliability	The Bivariate Dimension Reduction Method proved to be effective for structural reliability analysis	[120]
SGD 11 (Sustainable Cities and Communities)	Land Cover	The results demonstrated that FS improves the classification accuracy of land cover classification	[121]
		The proposed method demonstrates better results compared to other methods for land cover classification in almost all tests	[122]
		Dimensionality Reduction was considered a key step in the land cover classification process	[123]
		The experiments shown that the impervious surface extraction accuracy of Classification and Regression Tree was higher than Seperability and Thresholds algorithm	[124]
	Land use	FS with Classification Optimisation Score metric reduces the processing time and produces higher classification accuracy for land use and land cover classification using VHR data	[125]
SDG 13 (Climate Action)	Pollution	The new Dimension Reduction method demonstrated to be a powerful approach to optimise the knowledge that emerges from atmospheric observations of N₂O	[126]
SGD 15 (Life on Land)	Forest	Proposed a FS SVM-Recursive Feature Elimination method to explore the relationship between the biomass and parameters derived from Landsat-8 imagery. The results demonstrated that this method was able to accurately estimate the aboveground biomass.	[127]
SGD 15 (Life on Land)	Terrestrial ecosystem	FS methods allow the extraction of valuable information to create accurate maps of areas infested by invasive plant species	[128]

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