Search Results (4)

Climate change (CC) represents an increasing threat to mangroves worldwide and can amplify impacts caused by local anthropogenic activities. The direct effects of CC on mangrove forests have been extensively discussed, but indirect impacts such as the alteration of ecological processes driven by specific functional groups of the biota are poorly investigated. Ecological roles of key functional groups (FGs) in mangroves from the Atlantic–Caribbean–East Pacific (ACEP) and Indo-West Pacific (IWP) regions are reviewed, and impacts from CC mediated by these FGs are explored. Disruption by CC of ecological processes, driven by key FGs, can reinforce direct effects and amplify the loss of ecological functionality and further degradation of mangrove forests. Biogeochemistry mediator microbiotas of the soil, bioturbators, especially semiterrestrial crabs (Ocypodoids and Grapsoids) and herbivores (crustaceans and Insects), would be the most affected FG in both regions. Effects of climate change can vary regionally in the function of the combination of direct and indirect drivers, further eroding biodiversity and mangrove resilience, and impairing the predictability of ecosystem behaviour. This means that public policies to manage and conserve mangroves, as well as rehabilitation/restoration programs, should take into consideration the pressures of CC in specific regions and the response of key FGs to these pressures. Full article

Sembilang National Park, one of the best and largest mangrove areas in Indonesia, is very vulnerable to disturbance by community activities. Changes in the dynamic condition of mangrove forests in Sembilang National Park must be quickly and easily accompanied by mangrove monitoring efforts. One way to monitor mangrove forests is to use remote sensing technology. Recently, machine-learning classification techniques have been widely used to classify mangrove forests. This study aims to investigate the ability of decision tree (DT) and random forest (RF) machine-learning algorithms to determine the mangrove forest distribution in Sembilang National Park. The satellite data used are Landsat-7 ETM+ acquired on 30 June 2002 and Landsat-8 OLI acquired on 9 September 2019, as well as supporting data such as SPOT 6/7 image acquired in 2020–2021, MERIT DEM and an existing mangrove map. The pre-processing includes radiometric and atmospheric corrections performed using the semi-automatic classification plugin contained in Quantum GIS. We applied decision tree and random forest algorithms to classify the mangrove forest. In the DT algorithm, threshold analysis is carried out to obtain the most optimal threshold value in distinguishing mangrove and non-mangrove objects. Here, the use of DT and RF algorithms involves several important parameters, namely, the normalized difference moisture index (NDMI), normalized difference soil index (NDSI), near-infrared (NIR) band, and digital elevation model (DEM) data. The results of DT and RF classification from Landsat-7 ETM+ and Landsat-8 OLI images show similarities regarding mangrove spatial distribution. The DT classification algorithm with the parameter combination NDMI + NDSI + DEM is very effective in classifying Landsat-7 ETM+ image, while the parameter combination NDMI + NIR is very effective in classifying Landsat-8 OLI image. The RF classification algorithm with the parameter Image (6 bands), the number of trees = 100, the number of variables predictor (mtry) is square root ($\surd \mathrm{k}$), and the minimum number of node sizes = 6, provides the highest overall accuracy for Landsat-7 ETM+ image, while combining Image (7 bands) + NDMI + NDSI + DEM parameters with the number of trees = 100, mtry = all variables ($\mathrm{k}$), and the minimum node size = 6 provides the highest overall accuracy for Landsat-8 OLI image. The overall classification accuracy is higher when using the RF algorithm (99.12%) instead of DT (92.82%) for the Landsat-7 ETM+ image, but it is slightly higher when using the DT algorithm (98.34%) instead of the RF algorithm (97.79%) for the Landsat-8 OLI image. The overall RF classification algorithm outperforms DT because all RF classification model parameters provide a higher producer accuracy in mapping mangrove forests. This development of the classification method should support the monitoring and rehabilitation programs of mangroves more quickly and easily, particularly in Indonesia. Full article

Mangroves provide ecosystem services worth billions of dollars worldwide. Although countries with extensive mangrove areas implemented management and conservation programs since the 1980s, the global area is still decreasing. To recuperate this lost area, both restoration and rehabilitation (R/R) projects have been implemented but with limited success, especially at spatial scales needed to restore functional properties. Monitoring mangroves at different spatial scales in the long term (decades) is critical to detect potential threats and select cost-effective management criteria and performance measures to improve R/R program success. Here, we analyze the origin, development, implementation, and outcomes of a country-level mangrove monitoring system in the Neotropics covering >9000 km² over 15 years. The Mexico’s Mangrove Monitoring System (SMMM) considers a spatiotemporal hierarchical approach as a conceptual framework where remote sensing is a key component. We analyze the role of the SMMM’s remote sensing products as a “hub” of multi- and interdisciplinary ecological and social-ecological studies to develop national priorities and inform local and regional mangrove management decisions. We propose that the SMMM products, outcomes, and lessons learned can be used as a blueprint in other developing countries where cost-effective R/R projects are planned as part of mangrove protection, conservation, and management programs. Full article

Mangroves are one of the main considerations that might be used to mitigate the effects of climate change in coastal areas. Mangrove populations can be affected by industrial and civil activities on coasts. According to the Kyoto Protocol, protection and rehabilitation programs may play a pivotal role in conserving mangroves in industrial areas. Therefore, this study was designed to examine and evaluate the possible impact of conservation plans, implemented by the Royal Commission of Yanbu, in preserving mangrove trees’ ability to store carbon in the soil. Soil and plant samples were collected from three distinct locations, including a mangrove conservation site in Yanbu and natural unprotected sites in Umluj and Ar-Rayis. Organic-carbon (OC) stock, in both soil and plants, was calculated. Our results showed that at different depths, soil bulk density (SBD) in Yanbu ranged between 0.32–0.94 g cm⁻³. In Ar-Rayis and Umluj, SBD ranged between 1.43 to 1.99 and 0.90 to 1.57g cm⁻³, respectively. The average SBD values in Yanbu, Umluj, and Ar-Rayis were 0.68, 1.71, and 1.20 g cm⁻³, respectively. Similarly, the average soil OC density in Yanbu, Umluj, and Ar-Rayis was 165.19, 30.82, and 18.90 g C cm⁻³, respectively. Generally, the conserved mangrove tress grown in Yanbu industrial city showed higher (P ≤ 0.001) soil OC stock (0.39 t C ha⁻¹) compared to the unprotected trees grown in Umluj (0.12 t C ha⁻¹) and Ar-Rayis (0.11 t C ha⁻¹) cities. Similarly, the highest (P ≤ 0.001) plant OC stocks (13.93 t C ha⁻¹) were observed in protected mangroves of Yanbu, compared to the plant OC stocks observed in Umluj (8.06 t C ha⁻¹) and Ar-Rayis (8.80 t C ha⁻¹) cities. The results of the current study showed that the protected mangrove trees grown in Yanbu industrial city store more carbon in their sediments than those grown in the Umluj and Ar-Rayis sites without conservation or rehabilitation. These findings may provide evidence for the beneficial role of protecting mangrove forests in mitigating the effects of climate change. Full article