This study aims to elucidate the historical justification for protection and restoration efforts in China while also examining the theoretical underpinnings of ecological conservation and restoration. Utilizing a literature review method, logical reasoning method, and inductive summarizing method, the historical and theoretical frameworks of territorial spatial ecological conservation and restoration are elucidated, leading to the following conclusions: (1) The ecological protection and restoration of national land space represents an advanced form of land remediation; however, they differ in terms of objects, principles, goals, and measures. (2) Territorial space ecological protection and restoration is historically unavoidable due to the current phase of societal development, driven by the natural environment and the necessity to establish an ecological civilization. (3) To implement territorial spatial ecological protection and restoration effectively and rationally, it is essential to comprehend its systematicity, territoriality, dynamics, and scientificity. By clarifying its historical and theoretical rationale, we can achieve a deeper understanding of its current relevance. This can effectively inform practical efforts in territorial spatial ecological protection and restoration.

Monitoring dust on plant canopies around open-pit coal mines is crucial to assessing environmental pollution and developing effective dust suppression strategies. This research focuses on the Ha’erwusu open-pit coal mine in Inner Mongolia, China, using measured dust content on plant canopies and UAV-borne VNIR hyperspectral data as the data sources. The study employed five spectral transformation forms—first derivative (FD), second derivative (SD), logarithm transformation (LT), reciprocal transformation (RT), and square root (SR)—alongside the competitive adaptive reweighted sampling (CARS) method to extract characteristic bands associated with canopy dust. Various regression models, including extreme learning machine (ELM), random forest (RF), partial least squares regression (PLSR), and support vector machine (SVM), were utilized to establish dust inversion models. The spatial distribution of canopy dust was then analyzed. The results demonstrate that the geometric and radiometric correction of the UAV-borne VNIR hyperspectral images successfully restored the true spatial information and spectral features. The spectral transformations significantly enhance the feature information for canopy dust. The CARS algorithm extracted characteristic bands representing 20 to 30% of the total spectral bands, evenly spread across the entire range, thereby reducing the estimation model’s computational complexity. Both feature extraction and model selection influence the inversion accuracy, with the LT-CARS and RF combination offering the best predictive performance. Canopy dust content decreases with increasing distance from the dust source. These findings offer valuable insights for canopy dust retention monitoring and offer a solid foundation for dust pollution management and the development of suppression strategies.