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Remote sensing technology is a timely and cost-efficient method for leaf area index (LAI) estimation, especially for less accessible areas such as mangrove forests. Confounded by the poor penetrability of optical images, most previous studies focused on estimating the LAI of the main canopy, ignoring the understory. This study investigated the capability of multispectral Sentinel-2 (S2) imagery, airborne hyperspectral imagery (HSI), and airborne LiDAR data for overstory (OLe) and understory (ULe) LAI estimation of a multi-layered mangrove stand in Mai Po, Hong Kong, China. LiDAR data were employed to stratify the overstory and understory. Vegetation indices (VIs) and LiDAR metrics were generated as predictors to build regression models against the OLe and ULe with multiple parametric and non-parametric methods. The OLe model fitting results were typically better than ULe because of the dominant contribution of the overstory to the remotely sensed signal. A single red-edge VI derived from HSI data delivered the lowest RMSE of 0.12 and the highest R2adj of 0.79 for OLe model fitting. The synergetic use of LiDAR metrics and S2 VIs performed best for ULe model fitting with RMSE = 0.33, R2adj = 0.84. OLe estimation benefited from the high spatial and spectral resolution HSI that was found less confounded by the understory. In addition to their penetration attributes, LiDAR data could separately describe the upper and lower canopy, which reduced the noise from other components, thereby improving the ULe estimation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Understanding species distribution and canopy structure of mangrove forests is imperative for flora and fauna conservation in mangrove habitats. However, most mangrove studies focused on the top canopy layer without exploring the vertical structure of mangroves. This paper presents multi-layered mangrove mapping which considered both overstory and understory detection and species classification using multispectral WorldView-3 (WV-3) data, airborne hyperspectral images (HSI), and LiDAR point cloud. First, LiDAR returns were stratified into the overstory and understory by analyzing the profile of return height, which helped understand the vertical structure of the mangrove stands. Second, three classification algorithms Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN) were compared by applying WV-3, HSI, LiDAR data, and their combinations to map seven vegetative species. Feature selection was conducted to identify important features and the optimal feature size prior to classification tasks. The measured and estimated understory canopy heights reached a high correlation coefficient of 0.71, which demonstrated the effectiveness of using LiDAR data and the proposed procedure to stratify multi-layered canopies. The combined HSI and LiDAR data produced satisfactory results by the three classifiers with overall accuracy (OA) varying from 0.86 to 0.88. And the species was also accurately mapped by integrating WV-3 and LiDAR data using both RF and SVM algorithms with OA attaining between 0.84 and 0.86. The results of this study highlight that (1) LiDAR data provided superior information to map the vertical structure of multi-layered mangroves, which provided valuable information to classify single-layered and dual-layered Kandelia obovata with understory beneath; (2) the combination of spectral and LiDAR features improved mangrove species classification; (3) and species mapping results derived from combined datasets appeared to be more influential by LiDAR features when using RF and SVM, but spectral features played a more important role in CNN. • The overstory and understory of mangroves were correctly stratified by LiDAR data. • Four types of vertical structure in the mangrove stand were classified. • The canopy stratification result contributed to mangrove species identification. • Multiple sources of data and classifiers were compared for species mapping. • Combining optical and LiDAR data improved the species prediction accuracy. [ABSTRACT FROM AUTHOR]

Disturbances such as tropical cyclones and insect pests in mangroves can cause defoliation, tree mortality, and other changes in ecosystem processes. Understanding the resistance and resilience of mangroves to disturbance is critical to developing strategies for conservation. However, most studies apply multi-temporal optical data which have limited power to detect structural changes, especially for forests with complex architectures. We combined multispectral Sentinel-2 (S2) images and airborne LiDAR Scanning (ALS) datasets to assemble a comprehensive view of the effects of two disturbance events (a moth pest and a super-typhoon) on mangroves in Mai Po, Hong Kong. A series of normalized difference vegetation index (NDVI) estimates derived from S2 data indicated changes in greenness before and after the moth pest and typhoon events. An object-based stratification method was applied with ALS data to separate the overstory and understory to distinguish stratum changes. The results showed that moth larvae were more likely to encroach leafy mangroves of Avicennia marina. Double-layered and single-layered short mangroves have better resistance to typhoons than younger tall mangroves without understory beneath. NDVI recovered rapidly after three to six months post-disturbance but significant changes in canopy structures were found from the ALS data. Canopy gaps increased both in size and quantity in mature overstory areas, likely benefitting the growth of the understories beneath. Finally, the understory area grew resulting in a transition from single-layered to double-layered structures. The combination of multi-temporal LiDAR and multispectral data used here highlights the power of complementary remote sensing products in documenting mangrove ecosystem processes. • The study provides a comprehensive view of disturbance processes in mangroves. • NDVI recovered rapidly after three to six months post-disturbances. • LiDAR data reveals the canopy structures in both overstory and understory. • Double-layered or short mangroves have better resistance to typhoons. • Disturbances may facilitate a transition from single to double-layered structures. [ABSTRACT FROM AUTHOR]