Search Results (2)

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. Full article

Recognition of tree species and geospatial information on tree species composition is essential for forest management. In this study, tree species recognition was examined using hyperspectral imagery from visible to near-infrared (VNIR) and short-wave infrared (SWIR) camera sensors in combination with a 3D photogrammetric canopy surface model based on RGB camera stereo-imagery. An arboretum with a diverse selection of 26 tree species from 14 genera was used as a test area. Aerial hyperspectral imagery and high spatial resolution photogrammetric color imagery were acquired from the test area using unmanned aerial vehicle (UAV) borne sensors. Hyperspectral imagery was processed to calibrated reflectance mosaics and was tested along with the mosaics based on original image digital number values (DN). Two alternative classifiers, a k nearest neighbor method (k-nn), combined with a genetic algorithm and a random forest method, were tested for predicting the tree species and genus, as well as for selecting an optimal set of remote sensing features for this task. The combination of VNIR, SWIR, and 3D features performed better than any of the data sets individually. Furthermore, the calibrated reflectance values performed better compared to uncorrected DN values. These trends were similar with both tested classifiers. Of the classifiers, the k-nn combined with the genetic algorithm provided consistently better results than the random forest algorithm. The best result was thus achieved using calibrated reflectance features from VNIR and SWIR imagery together with 3D point cloud features; the proportion of correctly-classified trees was 0.823 for tree species and 0.869 for tree genus. Full article