ABSTRACT:Lightweight unmanned aerial vehicle (UAV) loaded with novel sensors offers a low cost and minimum risk solution for data acquisition in complex environment. This study assessed the performance of UAV-based hyperspectral image and digital surface model (DSM) derived from photogrammetric point clouds for 13 species classification in wetland area of Hong Kong. Multiple feature reduction methods and different classifiers were compared. The best result was obtained when transformed components from minimum noise fraction (MNF) and DSM were combined in support vector machine (SVM) classifier. Wavelength regions at chlorophyll absorption green peak, red, red edge and Oxygen absorption at near infrared were identified for better species discrimination. In addition, input of DSM data reduces overestimation of low plant species and misclassification due to the shadow effect and inter-species morphological variation. This study establishes a framework for quick survey and update on wetland environment using UAV system. The findings indicate that the utility of UAV-borne hyperspectral and derived tree height information provides a solid foundation for further researches such as biological invasion monitoring and bio-parameters modelling in wetland.
Mangroves have significant social, economic, environmental, and ecological values but they are under threat due to human activities. An accurate map of mangrove species distribution is required to effectively conserve mangrove ecosystem. This study evaluates the synergy of WorldView-3 (WV-3) spectral bands and high return density LiDAR-derived elevation metrics for classifying seven species in mangrove habitat in Mai Po Nature Reserve in Hong Kong, China. A recursive feature elimination algorithm was carried out to identify important spectral bands and LiDAR (Airborne Light Detection and Ranging) metrics whilst appropriate spatial resolution for pixel-based classification was investigated for discriminating different mangrove species. Two classifiers, support vector machine (SVM) and random forest (RF) were compared. The results indicated that the combination of 2 m resolution WV-3 and LiDAR data yielded the best overall accuracy of 0.88 by SVM classifier comparing with WV-3 (0.72) and LiDAR (0.79). Important features were identified as green (510–581 nm), red edge (705–745 nm), red (630–690 nm), yellow (585–625 nm), NIR (770–895 nm) bands of WV-3, and LiDAR metrics relevant to canopy height (e.g., canopy height model), canopy shape (e.g., canopy relief ratio), and the variation of height (e.g., variation and standard deviation of height). LiDAR features contributed more information than spectral features. The significance of this study is that a mangrove species distribution map with satisfactory accuracy can be acquired by the proposed classification scheme. Meanwhile, with LiDAR data, vertical stratification of mangrove forests in Mai Po was firstly mapped, which is significant to bio-parameter estimation and ecosystem service evaluation in future studies.
Continuous monitoring of coastal water qualities is critical for water resource management and marine ecosystem sustainability. While remote sensing data such as Sentinel-2 satellite imagery routinely provide high-resolution observations for time-series analysis, the cloud-based Google Earth Engine (GEE) platform supports simple image retrieval and large-scale processing. Using coastal waters of Hong Kong as the study area, this study utilized GEE to (i) query and pre-process all Sentinel-2 observations that coincided with in situ measurements; (ii) extract the spectra to develop empirical models for water quality parameters using artificial neural networks; and (iii) visualize the results using spatial distribution maps, time-series charts and an online application. The modeling workflow was applied to 22 water quality parameters and the results suggested the potential to predict the levels of several nutrients and inorganic constituents. In-depth analyses were conducted for chlorophyll-a, suspended solids and turbidity which produced high correlations between the predicted and observed values when validated with an independent dataset. The selected input variables followed spectral characteristics of the optical constituents. The results were considered more robust compared to previous works in the same region due to the automatic extraction of all available images and larger number of observations from different years and months. Besides visualizing long-term spatial and temporal variabilities through distribution maps and time-series charts, potential anomalies in the monitoring period including algal bloom could also be captured using the models developed from historical data. An online application was created to allow novice users to explore and analyze water quality trends with a simple web interface. The integrated use of remotely-sensed images, in situ measurements and cloud computing can offer new opportunities for implementing effective monitoring programs and understanding water quality dynamics. Although the obtained levels of accuracies were below the desired standard, the end-to-end cloud computing workflow demonstrated in this study should be further investigated considering the cost and computational efficiency for timely information delivery.
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