Mangrove Forest Changes Research Articles

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.

A Novel Approach to Modelling Mangrove Phenology from Satellite Images: A Case Study from Northern Australia

Around the world, the effects of changing plant phenology are evident in many ways: from earlier and longer growing seasons to altering the relationships between plants and their natural pollinators. Plant phenology is often monitored using satellite images and parametric methods. Parametric methods assume that ecosystems have unimodal phenologies and that the phenology model is invariant through space and time. In evergreen ecosystems such as mangrove forests, these assumptions may not hold true. Here we present a novel, data-driven approach to extract plant phenology from Landsat imagery using Generalized Additive Models (GAMs). Using GAMs, we created models for six different mangrove forests across Australia. In contrast to parametric methods, GAMs let the data define the shape of the phenological curve, hence showing the unique characteristics of each study site. We found that the Enhanced Vegetation Index (EVI) model is related to leaf production rate (from in situ data), leaf gain and net leaf production (from the published literature). We also found that EVI does not respond immediately to leaf gain in most cases, but has a two- to three-month lag. We also identified the start of season and peak growing season dates at our field site. The former occurs between September and October and the latter May and July. The GAMs allowed us to identify dual phenology events in our study sites, indicated by two instances of high EVI and two instances of low EVI values throughout the year. We contribute to a better understanding of mangrove phenology by presenting a data-driven method that allows us to link physical changes of mangrove forests with satellite imagery. In the future, we will use GAMs to (1) relate phenology to environmental variables (e.g., temperature and rainfall) and (2) predict phenological changes.

Gap-Free Monitoring of Annual Mangrove Forest Dynamics in Ca Mau Province, Vietnamese Mekong Delta, Using the Landsat-7-8 Archives and Post-Classification Temporal Optimization

Ecosystem services offered by mangrove forests are facing severe risks, particularly through land use change driven by human development. Remote sensing has become a primary instrument to monitor the land use dynamics surrounding mangrove ecosystems. Where studies formerly relied on bi-temporal assessments of change, the practical limitations concerning data-availability and processing power are slowly disappearing with the onset of high-performance computing (HPC) and cloud-computing services, such as in the Google Earth Engine (GEE). This paper combines the capabilities of GEE, including its entire Landsat-7 and Landsat-8 archives and state-of-the-art classification approaches, with a post-classification temporal analysis to optimize land use classification results into gap-free and consistent information. The results demonstrate its application and value to uncover the spatio-temporal dynamics of mangrove forests and land use changes in Ngoc Hien District, Ca Mau province, Vietnamese Mekong delta. The combination of repeated GEE classification output and post-classification optimization provides valid spatial classification (94–96% accuracy) and temporal interpolation (87–92% accuracy). The findings reveal that the net change of mangroves forests over the 2001–2019 period equals −0.01% annually. The annual gap-free maps enable spatial identification of hotspots of mangrove forest changes, including deforestation and degradation. Post-classification temporal optimization allows for an exploitation of temporal patterns to synthesize and enhance independent classifications towards more robust gap-free spatial maps that are temporally consistent with logical land use transitions. The study contributes to a growing body of work advocating full exploitation of temporal information in optimizing land cover classification and demonstrates its use for mangrove forest monitoring.

Identification before-after Forest Fire and Prediction of Mangrove Forest Based on Markov-Cellular Automata in Part of Sembilang National Park, Banyuasin, South Sumatra, Indonesia

In 1997, the worst forest fire in Indonesia occurred and hit mangrove forest areas including in Sembilang National Park Banyuasin Regency, South Sumatra. Therefore, the Indonesian government keeps in trying to rehabilitate the mangrove forest in Sembilang National Park. This study aimed to identify the mangrove forest changing and to predict on the future year. The situations before and after forest fire were analyzed. This study applied an integrated Markov Chain and Cellular Automata model to identify mangrove forest change in the interval years of 1989–2015 and predict it in 2028. Remote sensing technology is used based on Landsat satellite imagery (1989, 1998, 2002, and 2015). The results showed mangrove forest has decreased around 9.6% from 1989 to 1998 due to forest fire, and has increased by 8.4% between 1998 and 2002, and 2.3% in 2002–2015. Other results show that mangroves area has continued to increase from 2015 to 2028 by 27.4% to 31% (7974.8 ha). It shows that the mangrove ecosystem is periodically changing due to good management by the Indonesian government.

Mapping and dynamic analysis of mangrove forest during 2009–2019 using landsat–5 and sentinel–2 satellite data along Odisha Coast

Mangrove forests in India are situated along the tidal sea edge of the Arabian Sea and Bay of Bengal which is under threat from both natural and human-induced land-use changes. The multi-temporal satellite data and image processing techniques are usually employed to monitor changes in vegetation dynamics. In this study, the decadal changes of mangrove forests were accomplished along the Odisha coast, India using the Landsat–5 and Sentinel–2A satellite data for 2009 and 2019, respectively. The satellite data were obtained and processed in the Google Earth Engine (GEE) platform. This study aims to derive the spatial extent of mangrove using the high-resolution satellite data and support vector machine (SVM) classifier. The result reveals that the total mangrove extent increased from 222.43 km2 (2009) to 252.47 km2 (2019) which indicates an increase in area by 30.04 km2 (or 13.5%) during the last one decade. The highest increase in mangrove area was in Hatamudia Reserve Forest (15.54 km2) and coastal belt of Bhadrak (9.46 km2) followed by Bhitarkanika National Park in Kendrapara (3.05 km2), Baranaula and Akumi River estuary in Jagatsinghpur (1.12 km2), and Subarnarekha River intertidal zone in Baleshwar (0.87 km2). A significant increase in mangrove forests occurred due to plantation, awareness, restoration, and coastal zone management plan. This study demonstrates the potential of high-resolution satellite data to produce an accurate map in monitoring changes in mangrove forests at a decadal time-scale by using the GEE platform which can help in planning conservation strategies and priorities.

Spatiotemporal dynamics of mangrove forests in the Andaman and Nicobar Islands (India)

Mangrove forests provide multiple ecosystem services, such as primary production, coastal area protection, carbon fixation, and function as habitat for various plant and animal species. Despite the fact that these plant communities have proven effective in coastal area protection against typhoons and tsunamis, the 2004 Indian Ocean Tsunami caused the destruction of a large areas of mangrove forests in the Andaman and Nicobar islands in the Bay of Bengal. In this study, we investigated the decadal changes in mangrove forests in this region, including the decline in the mangrove areas before and after the 2004 Tsunami and the recent changes in the tsunami devastated mangrove areas since then, using Landsat series and Sentinel 2 data. In order to understand how these mangrove forests survive under the sea level rising scenarios, we estimated the possible changes in the land cover when a 1m rise in the current sea level occurs. It is estimated that 15% of the mangrove areas in this region were lost between 1988 and 2018. Even though the overall mangrove areas were shrinking, its causes are relatively different from the north to the south of the Andaman Islands. While the mangrove forests in the southern regions can be affected by natural phenomena such as tsunamis and sea level rise, those in the northern region are being depleted due to logging and land use changes. It is estimated that a 1m rise in the current sea level will result in the loss of 17% (117 km2) of the mangrove areas (the inward migration of mangrove vegetation was not considered).

Study of Mangrove Forest Change towards the Diversity and Carbon Stock of Mangroves in Segara Anakan, Cilacap

Segara Anakan Cilacap, being the largest mangrove area in Java Island, is one example of a degraded area which is mostly caused by land conversion to farming lands, ponds, housing, and industrial properties affecting the mangrove community in various ways. The objective of this research is to know the diversity and carbon stock of mangroves in different types of environmental change in Segara Anakan, Cilacap, as well as to know the correlation between environmental factors in various types of environmental change with diversity and carbon stock of mangroves in Segara Anakan, Cilacap. This research was conducted using clustered sampling from 8 stations located at the ex rice field, ex shrimp pond, illegal cutting area, and forest. The highest diversity and carbon stock value belong to stations with forest habitats with a total of 34 tons/ha of carbon stock and the highest diversity of saplings in station F1 (1.80). Correlation values between diversity and environmental factors ranged from 0.172–0.449, whereas results between carbon stock and environmental factors range from 0.065-0.724.

Quantitative Assessment and Driving Force Analysis of Mangrove Forest Changes in China from 1985 to 2018 by Integrating Optical and Radar Imagery

Mangrove ecosystems are valuable, yet vulnerable, and therefore they have been an important subject of protection and restoration in China. Reliable information on long-term China mangrove dynamics is lacking but vital to analyze the driving forces and evaluate the efforts of mangrove conversation. This study aims to quantify the conversions among mangroves and other land covers with high accuracy. The updated mangrove base map for 2018 was produced by integrating Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar 2 (PALSAR-2) yearly mosaics and Landsat imagery with an overall accuracy of 95.23 ± 6.02%. Then, a novel approach combining map-to-image and image-to-image methods was proposed to detect the changed pixels in mangrove forests from 1985 to 2018. The mangrove base map was adopted to mask the images from other years. To determine the changed pixels, the differencing values in the masked area between two images were calculated and compared with the corresponding thresholds. Based on the changed pixels, the possible driving forces were analyzed and associated with socioeconomic development. The resultant mangrove dynamics demonstrated that mangrove forests in China experienced a tendency of loss first and recovery later during the past 30 years. Most mangrove gains came from aquaculture and mudflat, whilst losses were due to the built-up construction and aquaculture reclamation. These conversions indicated that mangrove deforestations were mainly due to human-induced destruction, while the recoveries were strongly associated with conservation and restoration actions.

Coastal environmental changes in Southeast Asia: A study from Quang Nam Province, Central Vietnam

Coastal environments are highly dynamic in nature and are sensitive to natural phenomena such as climate change and anthropogenic activities such as aquaculture or infrastructure development. At the same time, these areas are important in the economic development of the whole country, in which they are situated. Coastal areas in Vietnam are affected by a number of factors, such as climate change, sea level rise, aquaculture, pollution and tourism-related activities. This study investigated the coastal environmental changes in Quang Nam, which is a poorly developed province in Central Vietnam during the 31-year period between 1988 and 2019. For this, we analyzed spaceborne data such as Landsat series and Sentinel 2A, and digital elevation models. The coastal environmental variables analyzed were shoreline changes, changes in overall vegetation and mangrove forests, and the expansion of aquaculture ponds. In order to understand the variations in sea level heights, we analyzed HYCOM data for the period between 1994 and 2014. It has been noted that rapid shoreline erosion occurred along the Cua Dai Beach in recent years and the implementation of soft measures, such as sand bags, helped to reduce shoreline erosion to some extent. The current study also contributes to the global inventory of mangroves as most of the study area was missing in the previously available databases such as the world mangrove atlas.

Dynamics of mangrove forests in Shenzhen Bay in response to natural and anthropogenic factors from 1988 to 2017

Global mangrove forests have exhibited distinct changes in past decades owing to diverse environmental gradients and regional anthropogenic activities. Previous studies have mainly reported alarming mangrove loss. However, few studies have focused on the process of mangrove expansion, and its driving factors remain unclear. By integrating medium-resolution Landsat surface reflectance products and meteorological, hydrological and bathymetric data, the current study investigates the spatiotemporal changes of mangrove forests in Shenzhen Inner Bay (SZIB) of China over the past thirty years, and the relationship between mangrove changes and natural and anthropogenic factors was also identified. The results reveal that the area of mangrove forests in the SZIB increased at a rapid rate of 8.9 ha/yr despite sea level rise, reaching 527.1 ha in 2017. No statistically significant correlation between mangrove area and winter temperature and precipitation was found, whereas a significant positive correlation occurred between mangrove area and sea level rise, in contrast to the common consensus. Human activities affected the SZIB mangroves in two distinct ways. On the one hand, the area of mangrove deforestation occurred on 23.5 ha and 6.8 ha in the Futian and Mai Po reserves, respectively, with reforestation campaigns only compensating for 5.6 ha of the mangrove area lost in the Futian reserve. On the other hand, the reduced tidal prism due to the implementation of the Shenzhen Bay Land Reclamation Project (SZBLRP) resulted in significant deposition in the SZIB, which counterbalanced the negative effect of sea level rise, contributing to 209.9 ha of mangrove expansion by providing suitable accommodation space and accounting for 78.4% of the total mangrove gain. Moreover, the morphological adjustments in the SZIB caused by the Shenzhen River Regulation Project (SZRRP) also had a significant influence on the mangrove extent and distribution, with the mangrove expansion area of 22.7 ha being contributed by the SZRRP, accounting for 8.5% of the total mangrove expansion. The findings of the current study indicate that with the increased sediment supply and accretion rate induced by anthropogenic activities, mangrove forests with limited land retreat space can still expand in the face of sea level rise.

Analysis of mangrove forest changes as a natural beach protection in Surabaya, East Java Indonesia

Surabaya is the most populous city in East Java with high human activities and needs. These human needs include opening new land for residential areas so that some mangrove areas began to be converted into residential areas. This activity can directly cause ecological impacts and threaten the sustainability of the coastal environment. On the other hand, mangrove areas in Surabaya are also developed for conservation and tourism areas. The purpose of this study is to determine the changes in mangrove area in the Surabaya area during the period 2015 to 2019 based on GIS. This study is using maximum likelihood classification method then the area calculation is done by calculate geometric method. The results were obtained from 2015 to 2019 shows an area change every year. So far the exact cause is unknown, but the condition of area changes can occur due to several things, for example from the conversion of mangrove forests to residential areas, drainage needs in rivers or estuaries and stand growth.

Monitoring Changes in Coastal Mangrove Extents Using Multi-Temporal Satellite Data in Selected Communes, Hai Phong City, Vietnam

Mangrove forests are important and known as one of the most productive ecosystems in the tropics. They reduce the impacts of extreme events, provide important breeding grounds for aquatic species and build the resilience of ecosystem-dependent coastal communities. On the contrary, they are also known as one of the most threatened and vulnerable ecosystems worldwide, which have experienced a dramatic decline due to extensive coastal development during the last half-century. Remote sensing techniques have demonstrated a high potential to detect, identify, map, and monitor mangrove conditions and its changes, which is reflected by a large number of scientific papers published on this topic. The aim of this study was to investigate the multi-decadal changes of mangrove forests selected communes in Hai Phong city, North Vietnam, based on using Landsat and Sentinel 2 data from 2000 to 2018. The study used these continuous steps: 1) data pre-processing; 2) image classification using Normalized Difference Vegetation Index; 3) accuracy assessments; and 4) multi-temporal change detection and spatial analysis of mangrove forests. The classification maps in comparison with the ground reference data showed the satisfactory agreement with the overall accuracy was higher than 80.0%. From 2000 to 2018, the areas of mangrove forests in the study regions increased by 584.2 ha in Dai Hop and Bang La communes (Region 1) and by 124.2 ha in Tan Thanh, Ngoc Xuyen and Ngoc Hai communes (Region 2), mainly due to the boom of mangrove planting projects and good mangrove management at the local community level.

Mangrove Forest Classification in Drone Images Using HSV Color Moment and Haralick Features Extraction with K-Nearest Neighbor

Due to the problems with uncontrolled changes in mangrove forests, a forest function management and supervision is required. The form of mangrove forest management carried out in this study is to measure the area of mangrove forests by observing the forests using drones or crewless aircraft. Drones are used to take photos because they can capture vast mangrove forests with high resolution. The drone was flown over above the mangrove forest and took several photos. The method used in this study is extracting color features using mean values, standard deviations, and skewness in the HSV color space and texture feature extraction with Haralick features. The classification method used is the k-nearest neighbor method. This study conducted three tests, namely testing the accuracy of the system, testing the distance method used in the k-nearest neighbor classification method, and testing the k value. Based on the results of the three tests above, three conclusions obtained. The first conclusion is that the classification system produces an accuracy of 84%. The second conclusion is that the distance method used in the k-nearest neighbor classification method influences the accuracy of the system. The distance method that produces the highest accuracy is the Euclidean distance method with an accuracy of 84%. The third conclusion is that the k value used in the k-nearest neighbor classification method influences the accuracy of the system. The k-value that produces the highest accuracy is k = 3, with an accuracy of 84%.

Assessment of mangrove forest damage and its recovery in Banda Aceh city post-tsunami disaster

The Indian Ocean Tsunami on 26th December 2004, had caused devastation on land and coastal ecosystems. One of the worst affected ecosystems is the mangrove forest. Many areas of mangrove forest have been destroyed, and it may pose a long-term impact for the region, both in terms of forest and biodiversity conservation and in terms of the ability of the ecosystem to support the livelihoods of the coastal communities. The tragedy of tsunami has requested the rationale for conserving and sustainably managing natural ecosystems among the researchers and scientific community. The research was carried out to make a post-tsunami damage assessment and recovery of mangrove forest in Banda Aceh city area. Several high-resolution satellite imageries data have been effectively used to detect, assess and monitor the changes of mangrove forests in the pre and post-tsunami period using multi-temporal optical satellite data. An on-screen visual interpretation was made to identify the heterogeneous patches for the preliminary classification of fieldwork. A supervised classification was performed to obtain the pre and post Tsunami assessment maps. Accuracy assessment of the classified maps was performed on pixel-level using ground truth. The result highlighted the changes in the spatial extent of the mangrove forests in the study area as a result of Tsunami disaster. The latest condition of the mangrove forest recovery is about 90.3% of the total damage area.

Mapping Mangrove Forest Change in Nijhum Dwip Island

Mangroves have been planting in the coastal belt of Bangladesh to protect the inhabitants of the coastal areas from cyclones and storm surges. Nijhum Dwip is located at the southern part of Hatiya Island. Most part of the island has been planted with the mangroves in the 1970s and 1980s; while parts of the mangroves have been deforested during the past few decades. The objectives of this research were to delineate and quantify the changes in the extent of mangroves in the island. The Landsat data of 1989, 2001, 2010 and 2018 have been utilized in the study. Three major land covers, namely forest, water and other land have been interpreted and delineated by using on-screen digitizing. The quantity of mangrove forest loss in the island is estimated as 1,024 ha, while 395 ha were afforested during 1989-2018. In the decadal change analysis, it was revealed that net forest cover change was higher in 2000s compared to other two decades and it was -425 ha. The result of the study is helpful to understand the extent and pattern of forest conversion in the island and to halt further forest loss and conserve the remaining forest.
 J. Environ. Sci. & Natural Resources, 11(1-2): 217-225 2018

Mangrove forests of Cambodia: Recent changes and future threats

Mangrove forests around the world are critical agents for coastal area protection, ecosystem services and socioeconomic functions. Cambodia is rich is natural resources such as mangrove forests. There are many discrepancies in the estimated area of mangrove forests by different agencies over a few decades in Cambodia. In this study, decadal changes in mangrove forests along the Cambodian coastline were analysed using satellite data (Landsat series). Overall loss of mangrove forests between 1989 and 2017 has been estimated as 42% (1415 ha/year) in the four coastal provinces of Cambodia (Koh Kong, Kampot, Preah Sihanoukville, and Kep). Individual losses of mangrove areas in Koh Kong, Kampot, Sihanoukville and Kep during the study period were 39%, 45%, 52% and 34%, respectively. Three main causes of mangrove forest destruction in Cambodia (salt farming, charcoal production and shrimp farming) have been perceived based on the literature review. Reforestation of mangrove species, banning on illegal charcoal production and deactivation of non-profitable aquaculture ponds are some of the key factors that are believed to have caused a reduction in mangrove loss in recent years in Cambodia.

Changes in mangrove vegetation, aquaculture and paddy cultivation in the Mekong Delta: A study from Ben Tre Province, southern Vietnam

In this paper, we analyzed the changes in mangrove forests along the coastal districts of Ben Tre Province in Mekong Delta, in southern Vietnam from 1998 to 2015 using Landsat image series (TM and OLI) using vegetation and water indices, and field data. Decadal changes in aquaculture ponds and area of rice crops were also estimated using satellite and field data in order to understand the influence of intensive aquaculture on mangrove deforestation and agriculture in this region. Furthermore, predictions have been made on the changes in mangrove forests, rice crops and aquaculture ponds in case of 1 m and 2 m sea level rise. About 52.5% of the mangrove forests were cleared between 1998 and 2015 for aquaculture and infrastructure developments in Ben Tre during the study period. Thanh Phu District showed highest loss of mangrove forests (59.3%) and increase in the area covered by aquaculture ponds (120%) during this period. Overall reduction in the rice crops areas in Ben Tre was 23.4% whereas this was 31.5% in the coastal districts due to the conversion of freshwater rice fields into brackish shrimp ponds. It is estimated that a 1 m rise in the current sea level would clear 45.2% of the remaining mangrove forests, 60.9% of the current areas planted with rice, 65% of the aquaculture ponds and 46% of the entire province would be under the water. This study shows a clear picture of the coastal environmental changes going on in the Mekong Delta of Vietnam.

Comparing different classification algorithms for monitoring mangrove cover changes in southern Iran

Mangrove forests in Iran are highly productive and complex ecosystems since they represent the interface between land and sea. They are a unique environment for supporting biodiversity, and they provide direct and indirect benefits to humans. Investigating changes in mangrove forests is essential for ecologists and forest managers to improve the assessment and conservation of natural ecosystems. The goals of the present study include: (I) to evaluate and compare four supervised classification algorithms based on Landsat time series imagery to detect mangrove cover in southern Iran, (II) to detect changes in mangrove cover between 1985, 1998, and 2017; and (III) to compare the four different predictions resulting from the applied classification algorithms. An accuracy assessment was conducted using k-fold cross-validation and independent validation, and differences between the classification techniques were analyzed. Although all four algorithms produced high overall accuracy (ranging from 81% to 93%) and Kappa values (from 0.81 to 0.92), visual comparisons of the predictions revealed that Random Forest (RF) performed best. The results of the change analysis showed that mangrove cover areas decreased by approximately 4% from 1985 to 1998 and then increased by approximately 8.9% from 1998 to 2017. A change detection map shows a decrease in mangrove cover in near coastal regions, such as the Tabl and Gavarzin areas, and an increase in mangrove cover at a distance from the Qeshm coastline that involves open spaces between the trees. Rising water levels and human development are important factors in the decline of mangroves. The findings of this research are useful for the management, restoration and conservation planning of mangrove forest in southern Iran.

Landsat data of mangrove forest changes in Hainan Island during 1987–2017

Landsat data of mangrove forest changes in Hainan Island during 1987–2017

Identification of mangrove forest change using object-based analysis in Bontang, East Kalimantan

In past half century, 35-86% of global mangrove forest already loss due to conversion into ponds, urban development and timber harvest. This is become challenging because in some area we don’t have data about distribution and what mangrove species in that forest. We mapping mangrove forest in Bontang city using PlanetScope images acquired at 2017 and 2018. Remote sensing analysis using OBIA method we obtain mangrove extend in Bontang City in 2017 and 2018 are 2,659.2 ha and 3,155.9 ha respectively. With this result, we already loss 491 ha or 13.5% from the initial extend in 1988. Our finding on this research are although have high accuracy, mangrove mapping with 3-meter resolution gave not quite good result. This is because we only obtain mangrove and non-mangrove classification, not until species determination. The factor mainly atmosphere condition which Bontang area have cloud or many aerosols that hamper light transmission. In future need to mapping with species differentiation with this challenge.