Mangrove Mapping Research Articles

Google Earth Engine-based Mangrove Mapping and Change Detections for Sustainable Development in Tien Yen District, Quang Ninh Province, Vietnam

Abstract. Vietnam secures a place among the top countries that possess the largest mangrove areas worldwide. The mangrove forests are mainly found in the Northern, Northeast, Central and Southern Delta, providing goods to habitants, and make significant help mitigate global climate change. Despite this, mangroves are severely threatened because of extensive deforestation in Vietnam. Recent advances have utilized remotely sensed imagery to present the spatial and temporal distribution of mangroves. Nevertheless, the approach is limited by imagery availability and computing resources, and difficult to share within the community. Therefore, a shareable Web-based tool that reinforces coastal managers to monitor the changes in mangroves is needed. Recently, Google Earth Engine (GEE) is a cloud-based geospatial analysis platform, which allows users to freely exploit the availability of satellite imagery and harness their computing capacity. This research aims to use GEE to detect mangrove changes, a case study in Tien Yen district, Quang Ninh, over a period from 2010 to 2020. Four supervised classification algorithms, including Random Forest (RF), Support Vector Machine (SVM), Naïve Bayes classifier, and Classification and Regression Trees (CART) have been implemented on GEE platform to select the best algorithm to produce spatial-temporal mangrove maps, then change detection of mangroves is performed. The results showed that RF demonstrated the highest accuracy with overall accuracy and kappa values of 91.04% and 0.75 respectively. The mangrove areas statistically reported by the GEE-based platform are in line with the governmental statistic report annually, marking the capabilities of GEE in natural resource management.

Validating The Philippine Mangrove Map 2023 through Capacity Building and Crowdsourcing

Abstract. Mangroves are crucial coastal ecosystems. They provide numerous ecological and socio-economic benefits – shoreline protection, carbon sequestration, food security, and biodiversity conservation. The Philippine Mangrove Map 2023, generated through Machine Learning, improves understanding, accounting, and conserving these vital habitats. However, ensuring the accuracy and reliability of maps is imperative for informed decision-making and effective conservation efforts. Various validation methods were reviewed to develop a nationwide approach integrating practicality and statistical rigor, incorporating multiple data sources such as satellite and airborne imagery and GPS points from the government, civil society, and citizens. Capacity building enabled large-scale citizen science collaboration to gather quality datasets and foster reciprocal learning between scientists and local citizens. This collaborative effort aims to improve the quality of datasets and ultimately enhance the accuracy of the Philippine Mangrove Map 2023 and succeeding mangrove extents maps.

Sustainable Development and Ethno-Inhabitant in Mangrove Ecology of India: A Case Study on Governance Policy in Sundarban

Criticality: Sundarban is a world heritage Mangrove area in the eastern part of India and the southern part of Bangladesh. My focus area is the rest of the rest of India. The 54 islands in this region are inhabited by people and the Reserve Forest. Strong ethnic feelings are belonging to Schedule Cast, Schedule Tribe, Other-Backward Class-A & B. Sundarban is a bountiful natural resource region of tropical, marine, and other aquatic organisms, endangered Royal Bengal tigers, crocodiles, and other flora-faunas. Statement of the Problem: Agriculture is the mainstay of the economy, catering to about 89% of the total population of this region; even the high percentage is landless. They have popular common occupations like farming, honey collection, fishing, crab hunting, etc. A unique occupation is Tiger Charmer (Bauley). There is a broad area of Tiger Widow. Tourism could be another economic activity. However, the infrastructure is still inadequate. Despite the rapid development of science and technology in India, it has avoided traditional and peripheral beings. It has to face tremendous loss of life and property due to natural hazards. Objectives of the Study: The study wants to focus on how these ethnic groups take self-initiative for the ecological preservation of the Sundarban. This academic study will possibly try to recommend advanced policies to trim down natural disasters, require realistic and sensible guidelines, illegal approaches, and corruption in mangrove mapping platforms. This study assesses the attitude of local stakeholders towards sustainable management of ethnobotany, permaculture, Mangrove-friendly ecology, etc. Method: This study follows the geomorphologic approach, and it adopts the qual-quant survey method on 1220 households and Tiger Widow Village regarding field study. Findings: Anthropogenic pressure, political corruption, and maladministration are obstacles to achieving the Sustainable Development Goals. Institutions like local governance and the Panchayat Raj system are run by public representatives, most of whom are illiterate. Ecological engineering finds an asymmetric relationship between increasing populations and the decline in mangrove families.

The Change Detection of Mangrove Forests Using Deep Learning with Medium-Resolution Satellite Imagery: A Case Study of Wunbaik Mangrove Forest in Myanmar

This paper presents the development of a U-Net model using four basic optical bands and SRTM data to analyze changes in mangrove forests from 1990 to 2024, with an emphasis on the impact of restoration programs. The model, which employed supervised learning for binary classification by fusing multi-temporal Landsat 8 and Sentinel-2 imagery, achieved a superior accuracy of 99.73% for the 2020 image classification. It was applied to predict the long-term mangrove maps in Wunbaik Mangrove Forest (WMF) and to detect the changes at five-year intervals. The change detection results revealed significant changes in the mangrove forests, with 29.3% deforestation, 5.75% reforestation, and −224.52 ha/yr of annual rate of changes over 34 years. The large areas of mangrove forests have increased since 2010, primarily due to naturally recovered and artificially planted mangroves. Approximately 30% of the increased mangroves from 2015 to 2024 were attributed to mangrove plantations implemented by the government. This study contributes to developing a deep learning model with multi-temporal and multi-source imagery for long-term mangrove monitoring by providing accurate performance and valuable information for effective conservation strategies and restoration programs.

Space-Based Mapping of Pre- and Post-Hurricane Mangrove Canopy Heights Using Machine Learning with Multi-Sensor Observations

Coastal mangrove forests provide numerous ecosystem services, which can be disrupted by natural disturbances, mainly hurricanes. Canopy height (CH) is a key parameter for estimating carbon storage. Airborne Light Detection and Ranging (LiDAR) is widely viewed as the most accurate method for estimating CH but data are often limited in spatial coverage and are not readily available for rapid impact assessment after hurricane events. Hence, we evaluated the use of systematically acquired space-based Synthetic Aperture Radar (SAR) and optical observations with airborne LiDAR to predict CH across expansive mangrove areas in South Florida that were severely impacted by Category 3 Hurricane Irma in 2017. We used pre- and post-Irma LiDAR-derived canopy height models (CHMs) to train Random Forest regression models that used features of Sentinel-1 SAR time series, Landsat-8 optical, and classified mangrove maps. We evaluated (1) spatial transfer learning to predict regional CH for both time periods and (2) temporal transfer learning coupled with species-specific error correction models to predict post-Irma CH using models trained by pre-Irma data. Model performance of SAR and optical data differed with time period and across height classes. For spatial transfer, SAR data models achieved higher accuracy than optical models for post-Irma, while the opposite was the case for the pre-Irma period. For temporal transfer, SAR models were more accurate for tall trees (>10 m) but optical models were more accurate for short trees. By fusing data of both sensors, spatial and temporal transfer learning achieved the root mean square errors (RMSEs) of 1.9 m and 1.7 m, respectively, for absolute CH. Predicted CH losses were comparable with LiDAR-derived reference values across height and species classes. Spatial and temporal transfer learning techniques applied to readily available spaceborne satellite data can enable conservation managers to assess the impacts of disturbances on regional coastal ecosystems efficiently and within a practical timeframe after a disturbance event.

An Innovative Tool for Monitoring Mangrove Forest Dynamics in Cuba Using Remote Sensing and WebGIS Technologies: SIGMEM

The main objective of this work was to develop a viewer with web output, through which the changes experienced by the mangroves of the Gran Humedal del Norte de Ciego de Avila (GHNCA) can be evaluated from remote sensors, contributing to the understanding of the spatiotemporal variability of their vegetative dynamics. The achievement of this objective is supported by the use of open-source technologies such as MapStore, GeoServer and Django, as well as Google Earth Engine, which combine to offer a robust and technologically independent solution to the problem. In this context, it was decided to adopt an action model aimed at automating the workflow steps related to data preprocessing, downloading, and publishing. A visualizer with web output (Geospatial System for Monitoring Mangrove Ecosystems or SIGMEM) is developed for the first time, evaluating changes in an area of central Cuba from different vegetation indices. The evaluation of the machine learning classifiers Random Forest and Naive Bayes for the automated mapping of mangroves highlighted the ability of Random Forest to discriminate between areas occupied by mangroves and other coverages with an Overall Accuracy (OA) of 94.11%, surpassing the 89.85% of Naive Bayes. The estimated net change based on the year 2020 of the areas determined during the classification process showed a decrease of 5138.17 ha in the year 2023 and 2831.76 ha in the year 2022. This tool will be fundamental for researchers, decision makers, and students, contributing to new research proposals and sustainable management of mangroves in Cuba and the Caribbean.

Mangrove mapping in China using Gaussian mixture model with a novel mangrove index (SSMI) derived from optical and SAR imagery

As an important shoreline vegetation and highly productive ecosystem, mangroves play an essential role in the protection of coastlines and ecological diversity. Accurate mapping of the spatial distribution of mangroves is crucial for the protection and restoration of mangrove ecosystems. Supervised classification methods rely on large sample sets and complex classifiers and traditional thresholding methods that require empirical thresholds, given the problems that limit the feasibility and stability of existing mangrove identification and mapping methods on large scales. Thus, this paper develops a novel mangrove index (spectral and SAR mangrove index, SSMI) and Gaussian mixture model (GMM) mangrove mapping method, which does not require training samples and can automatically and accurately map mangrove boundaries by utilizing only single-scene Sentinel-1 and single-scene Sentinel-2 images from the same time period. The SSMI capitalizes on the fact that mangroves are differentiated from other land cover types in terms of optical characteristics (greenness and moisture) and backscattering coefficients of SAR images and ultimately highlights mangrove forest information through the product of three expressions (f(S) = red egde/SWIR1, f(B) = 1/(1 + e-VH), f(W)=(NIR-SWIR1)/(NIR+SWIR1)). The proposed SSMI was tested in six typical mangrove distribution areas in China where climatic conditions and mangrove species vary widely. The results indicated that the SSMI was more capable of mapping mangrove forests than the other mangrove indices (CMRI, NDMI, MVI, and MI), with overall accuracys (OA) higher than 0.90 and F1 scores as high as 0.93 for the other five areas except for the Maowei Gulf (S5). Moreover, the mangrove maps generated by the SSMI were highly consistent with the reference maps (HGMF_2020、LASAC_2018 and IMMA). In addition, the SSMI achieves stable performance, as shown by the mapping results of the other two classification methods (K-means and Otsu’s algorithm). Mangrove mapping in six typical mangrove distribution areas in China for five consecutive years (2019–2023) and experiments in three Southeast Asian countries with major mangrove distributions (Thailand, Vietnam, and Indonesia) demonstrated that the SSMIs constructed in this paper are highly stable across time and space. The SSMI proposed in this paper does not require reference samples or predefined parameters; thus, it has great flexibility and applicability in mapping mangroves on a large scale, especially in cloudy areas.

EIAGA-S: Rapid Mapping of Mangroves Using Geospatial Data without Ground Truth Samples

Mangrove forests are essential for coastal protection and carbon sequestration, yet accurately mapping their distribution remains challenging due to spectral similarities with other vegetation. This study introduces a novel unsupervised learning method, the Elite Individual Adaptive Genetic Algorithm-Semantic Inference (EIAGA-S), designed for the high-precision semantic segmentation of mangrove forests using remote sensing images without the need for ground truth samples. EIAGA-S integrates an adaptive Genetic Algorithm with an elite individual’s evolution strategy, optimizing the segmentation process. A new Mangrove Enhanced Vegetation Index (MEVI) was developed to better distinguish mangroves from other vegetation types within the spectral feature space. EIAGA-S constructs segmentation rules through iterative rule stacking and enhances boundary information using connected component analysis. The method was evaluated using a multi-source remote sensing dataset covering the Hainan Dongzhai Port Mangrove Nature Reserve in China. The experimental results demonstrate that EIAGA-S achieves a superior overall mIoU (mean intersection over union) of 0.92 and an F1 score of 0.923, outperforming traditional models such as K-means and SVM (Support Vector Machine). A detailed boundary analysis confirms EIAGA-S’s ability to extract fine-grained mangrove patches. The segmentation includes five categories: mangrove canopy, other terrestrial vegetation, buildings and streets, bare land, and water bodies. The proposed EIAGA-S model offers a precise and data-efficient solution for mangrove semantic mapping while eliminating the dependency on extensive field sampling and labeled data. Additionally, the MEVI index facilitates large-scale mangrove monitoring. In future work, EIAGA-S can be integrated with long-term remote sensing data to analyze mangrove forest dynamics under climate change conditions. This innovative approach has potential applications in rapid forest change detection, environmental protection, and beyond.

Semi-automated mangrove mapping at National-Scale using Sentinel-2, Sentinel-1, and SRTM data with Google Earth Engine: A case study in Thailand

Mangroves are a crucial part of the coastal ecosystem; thus, precise and up-to-date monitoring is essential to guide regional policies and inform conservation strategies. This study investigates the capabilities of semi-automated remote sensing approaches within a Google Earth Engine framework for national-scale mangrove mapping in Thailand. Remote sensing data from 2018—10,000 data points acquired from Sentinel-1, Sentinel-2, and the Shuttle Radar Topography Mission (SRTM)—was used to train several machine learning models. The Gradient Tree Boost (GTB) proved to be the most reliable, with the least variation in validity (the lowest IQR) and the highest average Overall Accuracy of 96.75 ± 0.63 % compared to the others—96.64 ± 0.72 % for Random Forest (RF); 96.12 ± 0.80 %for Classification and Regression Trees (CART); and 95.43 ± 0.74 % for Support Vector Machines (SVM). Thus, the GTB was instrumental in mapping mangrove distribution with 10-m spatial resolution across Thailand from 2016 to 2022, the period in which the mangrove areas increased by 11 %, reflecting successful conservation efforts over the past decade. The developed framework establishes the foundation for semi-automated mangrove mapping that can be developed for other geographical contexts.

Enhanced mangrove index: A spectral index for discrimination understorey, nypa, and mangrove trees

Unsupervised classification using vegetation indices has been extensively employed to map mangrove forests using medium-resolution satellite images. However, its capability is restricted to determining the extent of mangroves only. This study introduces a new spectral index called the enhanced mangrove index (EMI) for accurately mapping different components of mangrove vegetation, including mangrove trees, nypa, and understorey. An immediate effort is required to monitor the invasion of nypa and understorey in the mangrove forest of Segara Anakan Lagoon, located in Central Java, Indonesia. This issue may also be prevalent in other mangrove areas worldwide. The development of EMI involved: 1). the analysis of the reflectance exhibited by different types of mangrove vegetation, and 2). The performance of EMI was evaluated by comparing it with spectral indices such as automated mangrove map and index (AMMI), as well as supervised classification models like random forest (RF). The accuracy assessment indicates that the overall accuracy and Kappa coefficient achieved values of 0.87 and 0.84, respectively, surpassing other spectral indices and supervised classification models. AMMI and RF exhibited high overall accuracy, with values of 0.82 and 0.73, respectively. Additionally, they demonstrated a Kappa coefficient of 0.77 and 0.66, respectively.

MANGLEE: A Tool for Mapping and Monitoring MANgrove Ecosystem on Google Earth Engine—A Case Study in Ecuador

Mangroves, integral to ecological balance and socioeconomic well-being, are facing a concerning decline worldwide. Remote sensing is essential for monitoring their evolution, yet its effectiveness is hindered in developing countries by economic and technical constraints. In addressing this issue, this paper introduces MANGLEE (Mangrove Mapping and Monitoring Tool in Google Earth Engine), an accessible, adaptable, and multipurpose tool designed to address the challenges associated with sustainable mangrove management. Leveraging remote sensing data, machine learning techniques (Random Forest), and change detection methods, MANGLEE consists of three independent modules. The first module acquires, processes, and calculates indices of optical and Synthetic Aperture Radar (SAR) data, enhancing tracking capabilities in the presence of atmospheric interferences. The second module employs Random Forest to classify mangrove and non-mangrove areas, providing accurate binary maps. The third module identifies changes between two-time mangrove maps, categorizing alterations as losses or gains. To validate MANGLEE’s effectiveness, we conducted a case study in the mangroves of Guayas, Ecuador, a region historically threatened by shrimp farming. Utilizing data from 2018 to 2022, our findings reveal a significant loss of over 2900 hectares, with 46% occurring in legally protected areas. This loss corresponds to the rapid expansion of Ecuador’s shrimp industry, confirming the tool’s efficacy in monitoring mangroves despite cloud cover challenges. MANGLEE demonstrates its potential as a valuable tool for mangrove monitoring, offering insights essential for conservation, management plans, and decision-making processes. Remarkably, it facilitates equal access and the optimal utilization of resources, contributing significantly to the preservation of coastal ecosystems.

Multidecadal mangrove forest change in Macajalar Bay, Northern Mindanao, Philippines (1950-2020) using remote sensing and geographic information systems.

The mangrove forest in Macajalar Bay is regarded as an important coastal ecosystem since it provides numerous ecosystem services. Despite their importance, the clearing of mangroves has been rampant and has reached critical rates. Addressing this problem and further advancing its conservation require accurate mangrove mapping. However, current spatial information related to mangroves is sparse and insufficient to understand the historical change dynamics. In this study, the synergy of 1950 vegetation maps and Landsat images was explored to provide multidecadal monitoring of mangrove forest change dynamics in Macajalar Bay, Philippines. Vegetation maps containing the 1950 mangrove extent and Landsat images were used as input data to monitor the rates of loss over 70years. In 2020, the mangrove forest cover was estimated to be 201.73ha, equivalent to only 61.99% of the 325.43ha that was estimated in 1950. Between 1950 and 2020, net mangrove loss in Macajalar Bay totaled 324.29ha. The highest clearing rates occurred between 1950 and 1990 when it recorded a total of 258.51ha, averaging 6.46ha/year. The original mangrove forest that existed in 1950 only represents 8.56% of the 2020 extent, suggesting that much of the old-growth mangrove had been cleared before 2000 and the existing mangrove forest is mainly composed of secondary mangrove forest stands. Across Macajalar Bay, intensified clearing that happened between 1950 and 1990 has been driven by large-scale aquaculture developments. Mangrove gains on the other hand were evident and have increased the total extent by 79.84ha since 2000 as a result of several afforestation programs. However, approximately half of these gains that were observed since 2010 exhibited low canopy cover. As of writing, approximately 85% of the 2020 mangrove forest stands fall outside the 1950 original mangrove extent. Examining the viability of the original mangrove forest for mangrove reforestation together with promoting site-species matching, and biophysical assessment are necessary undertakings to advance current mangrove conservation initiatives in Macajalar Bay.

Distribution of density and zoning patterns of mangrove forest in eastern coastal Sumatera, Indonesia

Indonesia has the largest mangrove forest in the world, touching 3.2 million hectares or the equivalent of 22.6% of the total mangrove forests, and the results of the updated national mangrove map in 2021 cover an area of 3,364,080 hectares. One of the mangrove ecosystems located in Wonosari Village, Panai Hilir District, Labuhanbatu Regency, focuses on analyzing each zone’s density distribution and formation. This study aims to analyze the density distribution of mangrove vegetation in 2002 and 2022 and the formation of mangrove vegetation. The method used in this research is NDVI analysis and IVI analysis by conducting vegetation analysis. The results obtained are the density of vegetation in 2002 and 2022, the highest density class value, namely density with an NDVI value range of 0.3 – 0.4 with an area of 415.61 ha in 2002 and 2022 with an area of 495.53 ha. The vegetation density class in 2002 and 2022, which experienced the greatest increase, was the vegetation density class covering an area of 79,92 ha, and the very dense vegetation density decreased by 111.80 ha. The mangrove vegetation formation is divided into three zones: the open zone found Rhizophora mucronata, Bruguiera parviflora, and Avicennia alba. In the middle zone found the species of Rhizophora mucronata and Bruguiera parviflora, and in the back zone, which is on the mainland, found the species of Xylocarpus granatum, Acrostichum aureum, Excoecaria agallocha.

Mangrove mapping and monitoring using remote sensing techniques towards climate change resilience

Mangroves are amongst the richest ecosystems in the world providing valuable goods and services to millions of people while enhancing the resilience of coastal communities against climate change induced hazards, especially island nations. However, these mangroves are severely affected by many anthropogenic activities. Therefore, understanding the spatial variability of mangroves in island nations is highly essential in the events of ongoing climatic change. Thus, this study assessed the use of remote sensing techniques and GIS to map and monitor mangrove cover change at selected sites, namely Le Morne and Ferney, on the tropical island of Mauritius. Freely available 2013 SPOT-5 and 2023 Sentinel 2A images were retrieved and processed using ArcGIS Pro tools and SNAP; mangroves were mapped based on Google Earth Pro historical imagery and ground truthing at the respective sites. Following the application of selected vegetation indices, GLCM and PCA analysis, mosaicked images were classified using the Random Trees algorithm. Kappa values of all the classified images were in the 90 s; Le Morne showed a significant increase in mangrove cover over the decadal scale with main class change from mudflat to mangroves. This study demonstrates how geo-spatial tools are crucial for monitoring mangroves as they provide spatially explicit and time sensitive information. Decision makers, researchers, and relevant stakeholders can utilize this data to bolster tailored mitigation and adaptation strategies at specific sites, thereby enhancing resilience to climate change.

Integration of generative artificial intelligence and Google Earth Engine for mangrove land cover mapping

Mangrove ecosystems, crucial for coastal sustainability, are threatened by human activities, underscoring the need for accurate mapping for effective conservation. This research explores the novel integration of generative artificial intelligence, specifically Microsoft Copilot, with Google Earth Engine (GEE) for mapping mangrove land cover in Kembung River, Bengkalis Island, Indonesia. The methodology leverages Copilot’s natural language processing capabilities to generate GEE JavaScript code, streamlining the process of Sentinel-2 imagery processing and land cover classification using the Random Forest algorithm. Copilot assists in automating complex coding tasks, reducing development time and potential human errors. However, challenges emerge in hyperparameter tuning within GEE’s computational constraints. The results demonstrate an overall accuracy of 84.4% (Kappa = 0.794) in identifying nine land cover classes, with mangroves covering 46.6% of the study area. This innovative approach enhances mangrove mapping efficiency and accuracy, paving the way for improved monitoring and conservation. The study also highlights the potential of AI in environmental science applications, particularly in conservation. Future research should optimize Copilot’s performance for advanced geospatial tasks, address spectral variability challenges, and explore its applicability across diverse ecosystems. This study contributes to mangrove conservation efforts and demonstrates the potential of AI-assisted coding in environmental science applications.

Comparison between top and bottom of atmosphere Sentinel-2 image for mangrove mapping in Balikpapan Bay, East Kalimantan

Sentinel-2 is high-resolution multispectral imagery that launched by the European Space Agency on June 23, 2015 for Sentinel-2A and March 7, 2017 for Sentinel-2B. The two satellites were launched with the aim of land monitoring studies, including vegetation, soil, and water cover, as well as the observation of inland waterways and coastal areas. In 2018, Sentinel-2 produced bottom-of-atmosphere (L2A) imagery derived from top-of-atmosphere (L1C), which has been atmospherically corrected using Sen2Cor algorithm. However, there is an overcorrection effect due to inaccuracies of digital elevation model, over-detection of clouds over bright targets, and miss-classification of topographic shadows. This research aims to explore the application of Sentinel-2 imagery for mangrove mapping by comparing two levels of data, including L1C and L2A. L2A is divided into two, namely L2A atmospherically corrected using the Sen2Cor method (L2A_Sen2Cor) and dark object subtraction method (L2A_DOS). The classification scheme was built based on in-situ data containing seven objects: water, clouds, built-up, cloud shadows, bare land, mangroves, and land vegetation using random forest classification. The comparison of each level of data is analyzed based on the spectral signature and accuracy assessment using confusion matrix. The result shows that there are differences in the spectral signature between L1C and L2A data because of atmospheric impacts. L2A outperforms L1C, as shown by the higher coefficient of determination (R2). The accuracy is in the range of 93.7 – 95.4%, with the best accuracy shown by L2A_Sen2Cor.

Evaluating Feature Selection Methods and Machine Learning Algorithms for Mapping Mangrove Forests Using Optical and Synthetic Aperture Radar Data

Mangrove forests, mostly found in the intertidal zone, are among the highest-productivity ecosystems and have great ecological and economic value. The accurate mapping of mangrove forests is essential for the scientific management and restoration of mangrove ecosystems. However, it is still challenging to perform the rapid and accurate information mapping of mangrove forests due to the complexity of mangrove forests themselves and their environments. Utilizing multi-source remote sensing data is an effective approach to address this challenge. Feature extraction and selection, as well as the selection of classification models, are crucial for accurate mangrove mapping using multi-source remote sensing data. This study constructs multi-source feature sets based on optical (Sentinel-2) and SAR (synthetic aperture radar) (C-band: Sentinel-1; L-band: ALOS-2) remote sensing data, aiming to compare the impact of three feature selection methods (RFS, random forest; ERT, extremely randomized tree; MIC, maximal information coefficient) and four machine learning algorithms (DT, decision tree; RF, random forest; XGBoost, extreme gradient boosting; LightGBM, light gradient-boosting machine) on classification accuracy, identify sensitive feature variables that contribute to mangrove mapping, and formulate a classification framework for accurately recognizing mangrove forests. The experimental results demonstrated that using the feature combination selected via the ERT method could obtain higher accuracy with fewer features compared to other methods. Among the feature combinations, the visible bands, shortwave infrared bands, and the vegetation indices constructed from these bands contributed the greatest to the classification accuracy. The classification performance of optical data was significantly better than SAR data in terms of data sources. The combination of optical and SAR data could improve the accuracy of mangrove mapping to a certain extent (0.33% to 4.67%), which is essential for the research of mangrove mapping in a larger area. The XGBoost classification model performed optimally in mangrove mapping, with the highest overall accuracy of 95.00% among all the classification models. The results of the study show that combining optical and SAR remote sensing data with the ERT feature selection method and XGBoost classification model has great potential for accurate mangrove mapping at a regional scale, which is important for mangrove restoration and protection and provides a reliable database for mangrove scientific management.

LANDSAT 9 IMAGERY BASED MANGROVE FOREST MAPPING USING MAXIMUM LIKELIHOOD CLASSIFIER AND SUPPORT VECTOR MACHINE ALGORITHM

The successful launch of Landsat 9 in 2021 emphasizes the commitment to the continuity of the Landsat mission to provide global medium resolution satellite records. The benefit of the availability of the Landsat dataset is the extraction of information related to mangrove forest cover through mapping, especially in Gili Lawang. The main challenge in mangrove mapping is selecting a classification method that provides the most accurate results. This article aims to explore the use of the Maximum Likelihood Classifier (MLC) and Support Vector Machine (SVM) algorithms in the context of mangrove mapping using Landsat 9 imagery on Gili Lawang. The two algorithms show differences in detecting the area of mangrove, non-mangrove and water bodies, respectively for SVM 371.89 ha, 58.11 ha, 3.73 ha; while the MLC algorithm is 328.88 ha, 98.41 ha, and 6.45 ha. The MLC algorithm identifies non-mangrove objects at the outer boundary of Gili Lawang, while SVM identifies the same area as mangrove objects due to the influence of the training area created and low separability. The SVM algorithm has better accuracy with a Kappa statistic of 0.85 compared to MLC with a value of 0.73.
 
 Keywords: Gili Lawang, Landsat 9, Mangrove, Support Vector Machine, Maximum Likelihood Classifier

Klasifikasi Spesies Mangrove Menggunakan Drone di Nusa Lembongan, Bali

Mapping of mangroves using remote sensing technology has been widely carried out, especially in coastal areas of Indonesia. The most used remote sensing technology is satellite imagery. However, there are still some weaknesses in the use of satellite imagery for mapping mangrove, especially at the species level, including taking a long time, high costs, and low spatial resolution. One of the breakthroughs in remote sensing is drone. Drones have advantages, namely time flexibility, relatively low cost, and high spatial resolution. The purpose of this study was to determine the ability to use drones in mapping mangrove up to the species level and distribution of mangrove species in Nusa Lembongan. The method used is classification of GEOBIA (Geographic Object Based Image Analysis). Drone images were obtained with four flight missions in the northern and eastern areas of the Nusa Lembongan mangrove forest. Field data collection was carried out using a systematic random sampling method and high-resolution single band drones. The results showed that drone observations were able to identify two mangrove species in the northern research area and four mangrove species in the eastern Nusa Lembongan research area. In the northern area there are mangrove species Rhizophora apiculata and Bruguiera gymnorrhiza. In the eastern region, mangrove species Rhizophora apiculata, Avicennia alba, Sonneratia alba, and Avicennia lanata were found. The accuracy test in both research areas in the northern and eastern areas of Nusa Lembogan obtained an overall value with an average of 65% and a kappa coefficient of 0.56.

Spatio-temporal dynamics of mangrove extent in coastal Bekasi Regency, Indonesia

One of West Java’s largest mangroves is found alongside the shore of Bekasi Regency. Nevertheless, the primary issues are the high degree of waste contamination, extraction of mangrove products, and conversion of mangrove forests into aquaculture ponds. To determine the present state of mangroves on the coast of Bekasi Regency, research on the dynamics of the mangrove extent spatio-temporally must be conducted. Mangrove mapping has made extensive use of machine learning and satellite images. This study aims to calculate the mangrove area in the villages of Pantaibahagia and Pantaibakti along the coast of Bekasi Regency using Random Forest (RF) classification based on the Google Earth Engine (GEE) Platform. The RF classification results showed a significant loss in mangrove extent over a short period of time (seven years). In 2017, the tidal zone was primarily covered by mangroves. From the linear trend line, it is known that from to 2017-2023, the mangrove area tended to decrease, while in 2023, there was a decrease in the mangrove area, especially in the north coast area. In 2017, the total mangrove area was 305.03 ha. Until 2019, there has been a slight increment, reaching 366.41 ha of mangrove are. The most significant loss was found in 2020, in which the total loss reached 85.58 ha in one year. After 2020, the mangrove area has slightly improved, but it is not significant. We tested the produced map of the RF classification using a confusion matrix and kappa coefficient, which resulted in an Overall Accuracy (OA) of 90.50% and a Kappa coefficient of 0.8105.