Mangrove Extent Research Articles

Evaluation of the Continuous Monitoring of Land Disturbance Algorithm for Large-Scale Mangrove Classification

Mangrove forests are of high biological, economic, and ecological importance globally. Growing within the intertidal zone, they are particularly vulnerable to the effects of climate change in addition to being threatened on local scales by over-exploitation and aquaculture expansion. Long-term monitoring of global mangrove populations is therefore highly important to understanding the impact of these threats. However, data availability from satellites is often limited due to cloud cover. This problem can be mitigated using a season-trend modelling approach such as Continuous Monitoring of Land Disturbance (COLD). COLD operates by using every available observation on a pixel-wise basis, removing the need for whole cloud free images. The approach can be used to better classify land cover by taking into account the underlying seasonal variability, and can also be used to extrapolate between data points to obtain more accurate long term trends. To demonstrate the utility of COLD for global mangrove monitoring, we applied it to five study sites chosen to represent a range of mangrove species, forest types, and quantities of available data. The COLD classifier was trained on the Global Mangrove Watch 2010 dataset and applied to 30 years of Landsat data for each site. By increasing the period between model updates, COLD was successfully applied to all five sites (2253 scenes) in less than four days. The method achieved an overall accuracy of 92% with a User’s accuracy of 77% and a Dice score of 0.84 for the mangrove class. The lowest User’s accuracy was for North Kalimantan (49.9%) due to confusion with mangrove palms. However, the method performed extremely well for the Niger Delta from the 2000s onwards (93.6%) despite the absence of any Landsat 5 data. Observation of trends in mangrove extent over time suggests that the method was able to accurately capture changes in extent caused by the 2014/15 mangrove die-back event in the Gulf of Carpentaria and highlighted a net loss of mangroves in the Matang Forest Reserve over the last two decades, despite ongoing management. COLD is therefore a promising methodology for global, long-term monitoring of mangrove extent and trends.

Development of a Rapid Mangrove Zonation Mapping Workflow Using Sentinel 2-Derived Indices and Biophysical Dataset

Moderate to high resolution satellite imageries are commonly used in mapping mangrove cover from local to global scales. In addition to extent information, studies such as mangrove composition, ecology, and distribution analysis require further information on mangrove zonation. Mangrove zonation refers to unique sections within a mangrove forest being dominated by a similar family, genus, or species. This can be observed both in natural and planted mangrove forests. In this study, a mapping workflow was developed to detect zonation in test mangrove forest sites in Katunggan-It Ibajay (KII) Ecopark (Aklan), Bintuan (Coron), Bogtong, and Sagrada (Busuanga) in the Philippines and Fukido Mangrove Park (Ishigaki, Japan) using Sentinel-2 imagery. The methodology was then applied to generate a nationwide mangrove zonation map of the Philippines for year 2020. Combination of biophysical products, water, and vegetation indices were used as classification inputs including leaf area index (LAI), fractional vegetation cover (FVC), fraction of photosynthetically-active radiation (FAPAR), Canopy chlorophyll content (Cab), canopy water content (Cw), Normalized Difference Vegetation Index (NDVI), modified normalized difference water index (MNDWI), modified chlorophyll absorption in reflectance index (MCARI), and red-edge inflection point (REIP). Mangrove extents were first mapped using either the Maximum Likelihood Classification (MLC) algorithm or the Mangrove Vegetation Index (MVI)-based methodology. The biophysical and vegetation indices within these areas were stacked and transformed through Principal Component Analysis (PCA). Regions of Interest (ROIs) were selected on the PCA bands as training input to the MLC. Results show that mangrove zonation maps can highlight the major mangrove zones in the study sites, commonly limited up to genera level only except for genera with only one known species thriving in the area. Four zones were detected in KII Ecopark: Avicennia zone, Nypa zone, Avicennia mixed with Nypa zone, and mixed mangroves zones. For Coron and Busuanga, the mapped mangrove zones are mixed mangroves, Rhizophora zone and sparse/damaged zones. Three zones were detected in Fukido site: Rhizophora stylosa-dominant zone, Bruguiera gymnorrhiza-dominant zone, and mixed mangrove zones. The zonation maps were validated using field plot data and orthophotos generated from Unmanned Aerial System (UAS) surveys, with accuracies ranging from 75 to 100%.

Using Historical Archives and Landsat Imagery to Explore Changes in the Mangrove Cover of Peninsular Malaysia between 1853 and 2018

Archive records such as maps, journals, books, sketches, cadastre and notarial documents have been underutilised in describing past and present changes in ecological systems, such as mangrove forests. Historical records can be invaluable information sources for baseline establishment, to undertake long-term study on mangrove dynamics and enhance the historical land cover and land-use dynamics of a country. In this study, we explore these untapped information reservoirs, used complementarily with remote sensing techniques, to explain the dynamics of the mangrove systems in Peninsular Malaysia. The archives in the United Kingdom, the Netherlands, Malaysia and Singapore were explored and mined for related information on the mangrove systems in Peninsular Malaysia from past centuries. Most historical records found in this study were used to validate the mangrove presence in Peninsular Malaysia since 1853 while two records from 1944 and 1954 were used to quantify the mangrove cover extent. A significant finding of this study was the oldest record found in 1853 that attested to the presence of a mangrove system on the mainland Penang of Peninsular Malaysia which was not identified again as such in records post-1853. Remote sensing data, specifically Landsat images, were used to determine the mangrove extent in Peninsular Malaysia for the years 1988, 1992, 2002, 2012 and 2018. By complementing the historical records with remote sensing information, we were able to validate the mangrove presence in Peninsular Malaysia since 1853 and determine the gain/loss of mangrove systems over the last 74 years. Peninsular Malaysia has lost over 400 km2 of mangrove forests, equivalent to 31% of its original extent between 1944 and 2018. This is a significant loss for Peninsular Malaysia which has less than 1% mangrove cover of its total land area presently.

Developing a grid-based association rules mining approach to quantify the impacts of urbanization on the spatial extent of mangroves in China

Being significant to the coastal sustainability, mangrove forests have decreased at an alarming rate over the past decades, under various pressures from both natural and anthropogenic forces, while urbanization has been widely recognized as one major factor for the dramatic areal mangrove loss. China has experienced rapid urbanization, particularly in coastal zones, which revealed negative impacts towards mangrove loss in previous literature. However, there is a lack of quantitative understanding between urbanization and mangroves loss at large spatial and temporal scales. Given its complexity at a large scale, the relationship between urbanization and mangroves changes remains under-explored, and satellite remote sensing provides a unique tool for addressing this imperative issue quantitatively. To this end, this study developed a grid-based association analysis (GBAA) method for quantifying and investigating the relationship between urbanization and mangroves changes using satellite-derived datasets in China during 1973–2015. Experimental results indicated that conservation policies in nature reserves have made significant contributions to mangrove conservation in China. The impact of urbanization on the spatial extent of mangrove forests weakened gradually outside nature reserves during 1973–2015, indicating that urbanization does not necessarily damage mangroves in recent decades. These findings provide important clues for understanding the relationship between urbanization and mangroves to support better and wiser policy-making for future mangrove conservation and urban management.

Global Sensitivity Analysis for Canopy Reflectance and Vegetation Indices of Mangroves

Remote sensing has been applied to map the extent and biophysical properties of mangroves. However, the impact of several critical factors, such as the fractional cover and leaf-to-total area ratio of mangroves, on their canopy reflectance have rarely been reported. In this study, a systematic global sensitivity analysis was performed for mangroves based on a one-dimensional canopy reflectance model. Different scenarios such as sparse or dense canopies were set up to evaluate the impact of various biophysical and environmental factors, together with their ranges and probability distributions, on simulated canopy reflectance spectra and selected Sentinel-2A vegetation indices of mangroves. A variance-based method and a density-based method were adopted to compare the computed sensitivity indices. Our results showed that the fractional cover and leaf-to-total area ratio of mangrove crowns were among the most influential factors for all examined scenarios. As for other factors, plant area index and water depth were influential for sparse canopies while leaf biochemical properties and inclination angles were more influential for dense canopies. Therefore, these influential factors may need attention when mapping the biophysical properties of mangroves such as leaf area index. Moreover, a tailored sensitivity analysis is recommended for a specific mapping application as the computed sensitivity indices may be different if a specific input configuration and sensitivity analysis method are adopted.

Spectral Complexity of Hyperspectral Images: A New Approach for Mangrove Classification

Hyperspectral remote sensing across multiple spatio-temporal scales allows for mapping and monitoring mangrove habitats to support urgent conservation efforts. The use of hyperspectral imagery for assessing mangroves is less common than for terrestrial forest ecosystems. In this study, two well-known measures in statistical physics, Mean Information Gain (MIG) and Marginal Entropy (ME), have been adapted to high spatial resolution (2.5 m) full range (Visible-Shortwave-Infrared) airborne hyperspectral imagery. These two spectral complexity metrics describe the spatial heterogeneity and the aspatial heterogeneity of the reflectance. In this study, we compare MIG and ME with surface reflectance for mapping mangrove extent and species composition in the Sierpe mangroves in Costa Rica. The highest accuracy for separating mangroves from forest was achieved with visible-near infrared (VNIR) reflectance (98.8% overall accuracy), following by shortwave infrared (SWIR) MIG and ME (98%). Our results also show that MIG and ME can discriminate dominant mangrove species with higher accuracy than surface reflectance alone (e.g., MIG–VNIR = 93.6% vs. VNIR Reflectance = 89.7%).

POST-DISASTER ASSESSMENT OF MANGROVE FOREST RECOVERY IN LAWAAN-BALANGIGA, EASTERN SAMAR USING NDVI TIME SERIES ANALYSIS

Abstract. In 2013, Typhoon Haiyan (Yolanda) struck the Eastern Philippines. Mangrove forests in the area were destroyed and were estimated to have at least 86% of damage. Some studies done on the typhoon-stricken mangroves had collected data such as measurements of mangrove trunk, height, roots, and seedlings to investigate the extent of damage and recovery. While these studies were proven to effectively identify mangrove gains and losses, these methods are only applicable in sites that are relatively accessible. This paper highlights the relevance of effective remote monitoring of mangrove forests that are vulnerable to typhoons including post-typhoon recovery. In this study, a Time Series Analysis using Google Earth Engine (GEE) was applied in assessing the damages and recovery of mangroves struck by Super Typhoon Haiyan in Lawaan and Balangiga, Samar (Eastern Philippines). The changes in mangrove extent followed the changes in NDVI; however, there were significant site-specific differences. Based on NDVI values, it was estimated that 83% of the mangrove area was damaged. After three years, regeneration from 2014–2017 was about 144%. Mangroves steadily developed but with a minimal change of 2.83% from 2017–2019. Most villages followed the general recovery trends in Lawaan and Balangiga. However, based on the time series analysis, some villages have minimal recovery than others. It suggests that the recovery of mangroves may be a function of the pre-typhoon mangrove extent and possibly vegetation condition. Even if there were new spaces for mangroves to colonize, some of the sites may not be conducive for plant regrowth.

Mapping the Extent of Mangrove Ecosystem Degradation by Integrating an Ecological Conceptual Model with Satellite Data

Anthropogenic and natural disturbances can cause degradation of ecosystems, reducing their capacity to sustain biodiversity and provide ecosystem services. Understanding the extent of ecosystem degradation is critical for estimating risks to ecosystems, yet there are few existing methods to map degradation at the ecosystem scale and none using freely available satellite data for mangrove ecosystems. In this study, we developed a quantitative classification model of mangrove ecosystem degradation using freely available earth observation data. Crucially, a conceptual model of mangrove ecosystem degradation was established to identify suitable remote sensing variables that support the quantitative classification model, bridging the gap between satellite-derived variables and ecosystem degradation with explicit ecological links. We applied our degradation model to two case-studies, the mangroves of Rakhine State, Myanmar, which are severely threatened by anthropogenic disturbances, and Shark River within the Everglades National Park, USA, which is periodically disturbed by severe tropical storms. Our model suggested that 40% (597 km2) of the extent of mangroves in Rakhine showed evidence of degradation. In the Everglades, the model suggested that the extent of degraded mangrove forest increased from 5.1% to 97.4% following the Category 4 Hurricane Irma in 2017. Quantitative accuracy assessments indicated the model achieved overall accuracies of 77.6% and 79.1% for the Rakhine and the Everglades, respectively. We highlight that using an ecological conceptual model as the basis for building quantitative classification models to estimate the extent of ecosystem degradation ensures the ecological relevance of the classification models. Our developed method enables researchers to move beyond only mapping ecosystem distribution to condition and degradation as well. These results can help support ecosystem risk assessments, natural capital accounting, and restoration planning and provide quantitative estimates of ecosystem degradation for new global biodiversity targets.

Monitoring of Mangrove Cover of Western Indus Delta Karachi Pakistan

The coastline of Pakistan comprises of five significant sites comprising of mangroves including Indus Delta which contains extensive mangroves zones and termed as the largest arid mangrove found globally. This study evaluate the current extent of mangroves along the Western edge of Indus Delta and provide the most recent forest cover assessment of mangroves. Moreover, this study identifies the changes occurred in forest cover from the years 2000 to 2020. Landsat 5 Thematic Mapper (TM), 8 Operational Land Imager (OLI) and Landsat 7 ETM data were used for mangroves mapping through supervised classification method. The results displayed that total area of mangrove forest cover was nearly 279.094km², 395.77km², 306.58km² in the years 2000, 2010 and 2020 respectively. This study indicates an increase in area of mangrove cover from 29% to 41% from the year 2000 to 2010. Regeneration of mangrove mostly took place around the southern region of the Indus Delta. The mangrove specie has decreased from 41% to 31% from the year 2010 to 2020. The major causes of this reduction were urban water and industrial pollution, over-fishing in Indus delta, freshwater diversion for agriculture, and overharvesting of mangroves by the local communities, coastal erosion and sedimentation.

STRATEGI PENGEMBANGAN HUTAN MANGROVE DI KECAMATAN JORONG KABUPATEN TANAH LAUT KABUPATEN KALIMANTAN SELATAN

Mangrove forests in South Kalimantan have been converted into fish ponds, settlements, ports and other uses. The condition of damaged mangrove forests is very alarming. The purpose of this study is to formulate a development and conservation strategy for mangrove forests in Jorong District. This research uses interviews, direct observation in the field to analyze the factors that cause mangrove damage, descriptive analysis through the In depth interview approach with key information about to formulate a strategy for developing and conserving mangrove forests in Jorong District. The results of changes in the distribution and extent of mangroves decreased from 2010 (2.324 ha) to 2020 (1.665 ha). The dynamics of mangrove damage are caused by mining activities and oil palm company expansion. The formulation of the strategy is socialization to the community related to the development of mangrove forests that contribute to high biota, the concept of developing mangrove ecotourism to improve the living standards of the poor, applying the pattern of developing mangrove forests for the common good.

Extent change of protected mangrove forest and its relation to wave power exposure on Aldabra Atoll

Mangrove forests, vital for the conservation of biodiversity, protection of coastlines, and carbon capture, are decreasing globally at a rate higher than most other tropical forests. They are threatened by sea level rise, drought and storm surge, especially on low-lying islands where forests are directly exposed to the elements and have limited land area. We investigated changes in the spatial extent of mangroves on Aldabra Atoll, Seychelles – a protected area without direct human pressures, over 21 years using Landsat images. Over the 21-year study period, mean mangrove extent was 1283 ha with an overall net increase of 60 ha (0.23% year-1). The majority of extent changes were small (<2 ha) and contiguous to the existing mangrove extent. We then assessed the relation of mangrove cover change along the lagoon coastline with wave power (rate of energy transfer by waves), using fetch measures and local wind data. We found lower wave power values for stable mangrove areas than for areas that had gained or lost mangroves from 1997 to 2018. We identified wave power thresholds of 2.3 W m-1 for stable mangrove and 7.1 W m-1 for mangrove occurrence. These thresholds might be valuable for assessing threats and sites with the greatest potential for mangrove restoration across similar areas worldwide. Our results highlight the importance of quantifying mangrove extent changes at a local scale to assist with planning for the protection and restoration of this ecologically important habitat, given its vulnerability to the pressures associated with climate change.

Eleven Years of Mangrove–Mudflat Dynamics on the Mud Volcano-Induced Prograding Delta in East Java, Indonesia: Integrating UAV and Satellite Imagery

This article presents a novel approach to explore mangrove dynamics on a prograding delta by integrating unmanned aerial vehicle (UAV) and satellite imagery. The Porong Delta in Indonesia has a unique geographical setting with rapid delta development and expansion of the mangrove belt. This is due to an unprecedented mud load from the LUSI mud volcanic eruption. The mangrove dynamics analysis combines UAV-based Structure from Motion (SfM) photogrammetry and 11 years (2009–2019) satellite imagery cloud computing analysis by Google Earth Engine (GEE). Our analysis shows unique, high-spatiotemporal-resolution mangrove extent maps. The SfM photogrammetry analysis leads to a 3D representation of the mangrove canopy and an estimate of mangrove biophysical properties with accurate height and individual position of the mangroves stand. GEE derived vegetation indices resulted in high (three-monthly) resolution mangrove coverage dynamics over 11 years (2009–2019), yielding a value of more than 98% for the overall, producer and consumer accuracy. Combining the satellite-derived age maps and the UAV-derived spatial tree structure allowed us to monitor the mangrove dynamics on a rapidly prograding delta along with its structural attributes. This analysis is of essential value to ecologists, coastal managers, and policymakers.

Monitoring the inter-decade spatial–temporal dynamics of the Sundarban mangrove forest of India from 1990 to 2019

The space borne remote sensing satellite systems are increasingly used in ecological monitoring, growth dynamics, and mapping of mangrove extent worldwide. Sundarban is the largest, diverse contiguous productive mangrove ecosystem and is increasingly threatened by both environmental changes and anthropogenic forces. The study focuses on the spatio-temporal dynamics in the Indian Sundarban mangrove ecosystem using time-series Landsat satellite imagery during the periods from 1990 to 1999, 1999 to 2009, and 2009 to 2019. The maximum likelihood classifier approach has been applied for image classification and post-classification comparison techniques for change detection analysis over the study period. The findings revealed a decline of 3.76% areal extent of mangroves forest between 1990 and 2019. The areal extent of the Indian Sundarban mangrove areas has been maintained at a relatively constant despite the high population density in its immediate surrounding region. The overall accuracies of 72%, 83%, 79%, and 85.9% were recorded for the classified satellite image of 1990, 1999, 2009, and 2019, respectively. The present study is of great significance to the restoration and conservation of the mangrove forests in response to global climatic change.

Seascape heterogeneity modifies estuarine fish assemblages in mangrove forests

AbstractMangroves are a dominant structural habitat within tropical and subtropical estuaries that provide a number of ecosystem services, including habitat for a range of crustaceans and fish. However, mangroves are one of the most threatened estuarine habitats globally, having been severely reduced in extent, and replaced by urban structures. Here, we test for the effects of both natural (e.g. seagrass, rock and mangroves habitat extent, and connectivity) and human (e.g. extent of urban area) landscape variables on the number and type of fish inhabiting mangroves forests. We used remote underwater video stations to quantify fish assemblages within mangroves at 150 sites in 30 estuaries across Queensland, Australia. Fish community structure was best explained by the extent of mangroves and seagrasses within an estuary, the distance to the estuary mouth, and the size of the estuary and catchment. Moderate catchment size and proximity to the estuary mouth increased species richness and abundance of harvestable fish at individual mangrove sites. In order to maintain mangrove fish assemblages and the functions they provide, management initiatives should focus on maintaining natural estuarine seascapes that are located closer to the mouth of estuaries, in particular, focusing on estuaries that have lower levels of catchment urbanization.

Mangrove Changes in Pannikiang Island Barru Regency South Sulawesi

Mangroves play an important role in coastal community livelihoods of South Sulawesi. However, due to excessive and unsustainable use, mangroves experience degradation and deforestation in the past two decades. This study aims to detect mangrove extents, densities, and distributions changes during the periods 1997, 2007, and 2018 in Pannikiang Island, Barru Regency. We used three Landsat satellite imageries with acquisition 1997, 2007, and 2018 and implemented the multispectral classification and NDVI transformation methods, and the results were tested by ground truth. The results showed mangrove extents in 1997, 2007, and 2018 were 94.83 ha, 92.07 ha, and 91.64 ha, respectively. During these periods, mangroves decrease by 3.19 ha or an annual average of 0.15 ha. The decrease is caused by the expansion of settlements and aquaculture ponds. In 1997, the mangroves high-density class about 61.74 ha, moderate-density 14.19 ha, and low-density of 18.6 ha. In 2007, the high and low-density classes decreased to 54.53 ha and 12.29 ha, while the moderate-density increase to 25.35 ha. Finally, in 2018, the high-density class continues to decline by 43.36 ha, while for the low and moderate classes increases 15.34 ha and 32.67 ha, respectively, compared to the previous decade.

Large-Scale High-Resolution Coastal Mangrove Forests Mapping Across West Africa With Machine Learning Ensemble and Satellite Big Data

Coastal mangrove forests provide important ecosystem goods and services, including carbon sequestration, biodiversity conservation, and hazard mitigation. However, they are being destroyed at an alarming rate by human activities. To characterize mangrove forest changes, evaluate their impacts, and support relevant protection and restoration decision making, accurate and up-to-date mangrove extent mapping at large spatial scales is essential. Available large-scale mangrove extent data products use a single machine learning method commonly with 30 m Landsat imagery, and significant inconsistencies remain among these data products. With huge amounts of satellite data involved and the heterogeneity of land surface characteristics across large geographic areas, finding the most suitable method for large-scale high-resolution mangrove mapping is a challenge. The objective of this study is to evaluate the performance of a machine learning ensemble for mangrove forest mapping at 20 m spatial resolution across West Africa using Sentinel-2 (optical) and Sentinel-1 (radar) imagery. The machine learning ensemble integrates three commonly used machine learning methods in land cover and land use mapping, including Random Forest (RF), Gradient Boosting Machine (GBM), and Neural Network (NN). The cloud-based big geospatial data processing platform Google Earth Engine (GEE) was used for pre-processing Sentinel-2 and Sentinel-1 data. Extensive validation has demonstrated that the machine learning ensemble can generate mangrove extent maps at high accuracies for all study regions in West Africa (92%–99% Producer’s Accuracy, 98%–100% User’s Accuracy, 95%–99% Overall Accuracy). This is the first-time that mangrove extent has been mapped at a 20 m spatial resolution across West Africa. The machine learning ensemble has the potential to be applied to other regions of the world and is therefore capable of producing high-resolution mangrove extent maps at global scales periodically.

Mangrove mapping using Sentinel-1 data for improved decision support on sustainable conservation and restoration interventions in the Keta Lagoon Complex Ramsar Site, Ghana

Despite the conservation importance of the Keta Lagoon Complex Ramsar Site (KLCRS), obtaining information on the extent and distribution of mangroves is challenging due to the unavailability of optical satellite data. This research explored Sentinel-1 radar data to provide information on mangrove distribution in the KLCRS. Global positioning system points from 443 training and 196 validation sites were used. In addition, focus group discussions and key informant interviews were used to corroborate information on mangrove distribution. Sentinel-1 data were processed for backscatter coefficients and two backscatter derivatives. These were stacked into a four-layer image composite and classified using a support vector machine. An overall classification accuracy of 89.28% was obtained. In addition, user and producer accuracies of 100 and 97.3% respectively were obtained for the mangrove class. The results show that mangroves occupy a total area of 41.02 km2 in the KLCRS and are mostly found around the Salo, Bomigo, Anyanui and Dzita communities. This study demonstrates the possibility of using Sentinel-1 imagery to map mangroves within the KLCRS. Thus, this study serves as a guideline for other data-constrained mangrove landscapes to map and monitor mangroves for conservation and restoration actions.

Consequences of terminating mangrove’s protection in Brazil

The Brazilian government agenda for downgrading and degazettement of protected ecosystems has seen an unprecedented move with the revocation of the mangrove’s protection act in the country. As the second largest country in mangrove extent, with over 11,000 km2 of forests that have been conserved for decades, mangroves may now be open for unsustainable economic development. Shrimp aquaculture and urban development are likely the highest threats to mangrove forests in Brazil. When built over mangrove land, shrimp ponds release greenhouse gases at a rate up to 10-fold larger than cattle pasture conversion on dryland forests. In a scenario of low rates of mangrove loss of 0.1% per year (2-fold current rates), emissions from land use would be close to 1.6 Tg CO2e per year, which is equivalent to 0.01% of emissions from deforestation in the Amazon over an area 1500 times smaller. Urban development and removal of mangroves to human infrastructure also have strong negative effects on pollutant retention, which could further decrease water quality and increase coastal vulnerability near urban centers. The loss of mangroves have also expected negative effects on food provision, which allied to other key ecosystem services provided by mangroves, will be key to a sustainable ocean under a changing climate during the next century.

Driven by Drones: Improving Mangrove Extent Maps Using High-Resolution Remote Sensing

This study investigated how different remote sensing techniques can be combined to accurately monitor mangroves. In this paper, we present a framework to use drone imagery to calculate correction factors which can improve the accuracy of satellite-based mangrove extent. We focus on semi-arid dwarf mangroves of Baja California Sur, Mexico, where the mangroves tend to be stunted in height and found in small patches, as well as larger forests. Using a DJI Phantom 4 Pro, we imaged mangroves and labeled the extent by manual classification in QGIS. Using ArcGIS, we compared satellite-based mangrove extent maps from Global Mangrove Watch (GMW) in 2016 and Mexico’s national government agency (National Commission for the Knowledge and Use of Biodiversity, CONABIO) in 2015, with extent maps generated from in situ drone studies in 2018 and 2019. We found that satellite-based extent maps generally overestimated mangrove coverage compared to that of drone-based maps. To correct this overestimation, we developed a method to derive correction factors for GMW mangrove extent. These correction factors correspond to specific pixel patterns generated from a convolution analysis and mangrove coverage defined from drone imagery. We validated our model by using repeated k-fold cross-validation, producing an accuracy of 98.3% ± 2.1%. Overall, drones and satellites are complementary tools, and the rise of machine learning can help stakeholders further leverage the strengths of the two tools, to better monitor mangroves for local, national, and international management.

High variance in community structure and ecosystem carbon stocks of Fijian mangroves driven by differences in geomorphology and climate

Mangrove ecosystems are particularly important for small island developing states of the Pacific, such as Fiji, which are at the forefront of the impacts of climate change. This is because of the ability of mangroves to mitigate storm surges and floods as well as their high carbon sequestration and storage capacity. However, there are few detailed studies on the spatial variation in mangrove structure and carbon stocks in Fiji, and this information is essential to support decision making by government and communities, enabling the development of effective mitigation and adaptation responses. We assessed mangrove forest structure in contrasting regions around Fiji's largest island, Viti Levu, within sites managed by indigenous (iTaukei) Fijians. Mangroves of the Ba, Nadroga-Navosa, and Rewa and Tailevu regions showed high variance in both structural complexity and ecosystem carbon stocks. Levels of variation were similar to that observed globally due to variable geomorphological and biophysical settings related to orographic rainfall, freshwater influx, tidal amplitude and cyclonic disturbances. High biomass, structurally complex forests occur on the wetter south-east coast (e.g. the Rewa Delta), while structurally uniform scrub mangroves dominate large areas of mangroves along the north-west (e.g. the Ba Delta) and west coast (e.g. the Tuva Delta). Mangroves of the Ba region displayed considerable damage from tropical cyclones, particularly in taller vegetation. All mangrove sites assessed were important reservoirs of carbon, with results when scaled to the spatial extent of mangroves in Fiji revealing that ecosystem carbon storage is disproportionate to area and equates to 73.3% of the carbon held within terrestrial rainforests, despite occupying just 7.3% of the total area. This underscores the importance of mangroves as valuable carbon sinks in Fiji and the need to develop incentives for improved conservation and restoration.