Matang Mangrove Forest Reserve (MMFR) in Peninsular Malaysia is under systematic management since 1902 and still considered as the best managed mangrove forest in the world. The present study on silvimetrics assessed the ongoing MMFR forest management, which includes a first thinning after 15 years, a second thinning after 20 years and clear-felling of 30-year old forest blocks, for its efficiency and productivity in comparison to natural mangroves. The estimated tree structural parameters (e.g. density, frequency) from three different-aged mangrove blocks of fifteen (MF15), twenty (MF20), and thirty (MF30) years old indicated that Bruguiera and Excoecaria spp. did not constitute a significant proportion of the vegetation (<5%), and hence the results focused majorly on Rhizophora apiculata. The density of R. apiculata at MF15, MF20 and MF30 was 4,331, 2,753 and 1,767 stems ha−1, respectively. In relation to ongoing practices of the artificial thinnings at MMFR, the present study suggests that the first thinning could be made earlier to limit the loss of exploitable wood due to natural thinning. In fact, the initial density at MF15 was expected to drop down from 6,726 to 1,858 trees ha−1 before the first thinning. Therefore the trees likely to qualify for natural thinning, though having a smaller stem diameter, should be exploited for domestic/commercial purposes at an earlier stage. The clear-felling block (MF30) with a maximum stem diameter of 30 cm was estimated to yield 372 t ha−1 of the above-ground biomass and suggests that the mangrove management based on a 30-year rotation is appropriate for the MMFR. Since Matang is the only iconic site that practicing sustainable wood production, it could be an exemplary to other mangrove locations for their improved management.

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Although mangroves dominated by Avicennia germinans and Rhizophora mangle are extending over 6000 ha in the Tanbi Wetland National Park (TWNP) (The Gambia), their importance for local populations (both periurban and urban) is not well documented. For the first time, this study evaluates the different mangrove resources in and around Banjul (i.e., timber, non-timber, edible, and ethnomedicinal products) and their utilization patterns, including the possibility of ecotourism development. The questionnaire-based results have indicated that more than 80% of peri-urban population rely on mangroves for timber and non-timber products and consider them as very important for their livelihoods. However, at the same time, urban households demonstrate limited knowledge on mangrove species and their ecological/economic benefits. Among others, fishing (including the oyster-Crassostrea cf. gasar collection) and tourism are the major incomegenerating activities found in the TWNP. The age-old practices of agriculture in some parts of the TWNP are due to scarcity of land available for agriculture, increased family size, and alternative sources of income. The recent focus on ecotourism (i.e., boardwalk construction inside the mangroves near Banjul city) received a positive response from the local stakeholders (i.e., users, government, and non-government organizations), with their appropriate roles in sharing the revenue, rights, and responsibilities of this project. Though the guidelines for conservation and management of the TWNP seem to be compatible, the harmony between local people and sustainable resource utilization should be ascertained.

Satellite data and aerial photos have proved to be useful in efficient conservation and management of mangrove ecosystems. However, there have been only very few attempts to demonstrate the ability of drone images, and none so far to observe vegetation (species-level) mapping. The present study compares the utility of drone images (DJI-Phantom-2 with SJ4000 RGB and IR cameras, spatial resolution: 5cm) and satellite images (Pleiades-1B, spatial resolution: 50cm) for mangrove mapping—specifically in terms of image quality, efficiency and classification accuracy, at the Setiu Wetland in Malaysia. Both object- and pixel-based classification approaches were tested (QGIS v.2.12.3 with Orfeo Toolbox). The object-based classification (using a manual rule-set algorithm) of drone imagery with dominant land-cover features (i.e. water, land, Avicennia alba, Nypa fruticans, Rhizophora apiculata and Casuarina equisetifolia) provided the highest accuracy (overall accuracy (OA): 94.0±0.5% and specific producer accuracy (SPA): 97.0±9.3%) as compared to the Pleiades imagery (OA: 72.2±2.7% and SPA: 51.9±22.7%). In addition, the pixel-based classification (using a maximum likelihood algorithm) of drone imagery provided better accuracy (OA: 90.0±1.9% and SPA: 87.2±5.1%) compared to the Pleiades (OA: 82.8±3.5% and SPA: 80.4±14.3%). Nevertheless, the drone provided higher temporal resolution images, even on cloudy days, an exceptional benefit when working in a humid tropical climate. In terms of the user-costs, drone costs are much higher, but this becomes advantageous over satellite data for long-term monitoring of a small area. Due to the large data size of the drone imagery, its processing time was about ten times greater than that of the satellite image, and varied according to the various image processing techniques employed (in pixel-based classification, drone >50 hours, Pleiades <5 hours), constituting the main disadvantage of UAV remote sensing. However, the mangrove mapping based on the drone aerial photos provided unprecedented results for Setiu, and was proven to be a viable alternative to satellite-based monitoring/management of these ecosystems. The improvements of drone technology will help to make drone use even more competitive in the future.

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Time series of satellite sensor data have been used to quantify mangrove cover changes at regional and global levels. Although mangrove forests have been monitored using remote sensing techniques, the use of time series to quantify the regeneration of these forests still remains limited. In this study, we focus on the Matang Mangrove Forest Reserve (MMFR) located in Peninsular Malaysia, which has been under silvicultural management since 1902 and provided the opportunity to investigate the use of Landsat annual time series (1988–2015) for (i) detecting clear-felling events that take place in the reserve as part of the local management, and (ii) tracing back and quantifying the early regeneration of mangrove forest patches after clear-felling. Clear-felling events were detected for each year using the Normalized Difference Moisture Index (NDMI) derived from single date (cloud-free) or multi-date composites of Landsat sensor data. From this series, we found that the average period for the NDMI to recover to values observed prior to the clear-felling event between 1988 and 2015 was 5.9 ± 2.7 years. The maps created in this study can be used to guide the replantation strategies, the clear-felling planning, and the management and monitoring activities of the MMFR.