ArticlePublisher preview available

Monitoring the tropical cyclone ‘Yass’ and ‘Amphan’ affected flood inundation using Sentinel‑1/2 data and Google Earth Engine

To read the full-text of this research, you can request a copy directly from the authors.

Abstract and Figures

The tropical cyclone is infuenced the natural environmental conditions, biodiversity, mangrove forest degradation; salinity increased, and flood inundation at the global landscape and frequently in the coastal regions. Flood inundation is increase due to global sea-level rise and shoreline shifting. In India, several tropical cyclones are infuenced by the natural environ- ment, like Fani, Bulbul, Amphan, and Yass. This study is to identify the flood inundation and land use land cover (LULC) affected area due to tropical cyclone Amphan and Yass. Cloud computing-based Google Earth Engine (GEE) platform was used for the development of flood inundation area calculation in affected Indian coast. Random forest algorithm was used for the LULC classifcation and Sentinel-1 Synthetic Aperture Rader (SAR) of pre and post-cyclone was used for mapping and monitoring the cyclonic effect. The tropical cyclone Amphan and Yass have infuenced the agricultural land and mangrove forest areas, like Sundarban and Bhitarkanika, and massive economic losses. Saltwater intrusion in the coastal area has been increased the salinity and frequently those types of cyclonic activities are triggering the coastal vulnerability. Total 11,405.21 sq. km of West Bengal and 10,437.58 sq. km Odisha coastal regions are inundated due to Yass and 22,735.29 sq. km areas are flooded due to Amphan cyclone. Mostly agricultural lands are affected because most of the areas are covered by agricultural land. This study results could be useful for the post-cyclone emergency response, coastal planners, and administrators for sustainable development of people life.
This content is subject to copyright. Terms and conditions apply.
1 3
Modeling Earth Systems and Environment (2022) 8:4317–4332
Monitoring thetropical cyclone ‘Yass’ andAmphan’ affected flood
inundation using Sentinel‑1/2 data andGoogle Earth Engine
BijayHalder1 · JatisankarBandyopadhyay1
Received: 9 October 2021 / Accepted: 31 January 2022 / Published online: 5 March 2022
© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022
The tropical cyclone is influenced the natural environmental conditions, biodiversity, mangrove forest degradation; salinity
increased, and flood inundation at the global landscape and frequently in the coastal regions. Flood inundation is increase
due to global sea-level rise and shoreline shifting. In India, several tropical cyclones are influenced by the natural environ-
ment, like Fani, Bulbul, Amphan, and Yass. This study is to identify the flood inundation and land use land cover (LULC)
affected area due to tropical cyclone Amphan and Yass. Cloud computing-based Google Earth Engine (GEE) platform was
used for the development of flood inundation area calculation in affected Indian coast. Random forest algorithm was used for
the LULC classification and Sentinel-1 Synthetic Aperture Rader (SAR) of pre and post-cyclone was used for mapping and
monitoring the cyclonic effect. The tropical cyclone Amphan and Yass have influenced the agricultural land and mangrove
forest areas, like Sundarban and Bhitarkanika, and massive economic losses. Saltwater intrusion in the coastal area has been
increased the salinity and frequently those types of cyclonic activities are triggering the coastal vulnerability. Total 11,405.21
sq. km of West Bengal and 10,437.58 sq. km Odisha coastal regions are inundated due to Yass and 22,735.29 sq. km areas are
flooded due to Amphan cyclone. Mostly agricultural lands are affected because most of the areas are covered by agricultural
land. This study results could be useful for the post-cyclone emergency response, coastal planners, and administrators for
sustainable development of people life.
Keywords Flood inundation· Tropical cyclone· Cloud computing· Land transformation· RGB clustering
Cyclonic activities are increased in the tropical region in
some last decades. Due to climate change, sea-level rise and
temperature variation flood inundation is a common phe-
nomenon in the coastal regions (Sadat-Noori etal. 2021).
Saltwater intrusion, soil fertility decreased, reducing agri-
cultural productivities, life losses, and massive economic
disasters are notified during and after the cyclone. Many
coastal and near-coastal countries are affected by cyclonic
storms, flood inundation, deforestation, and soil salinity
increased (Guzman and Jiang 2021). Temperature variation
and massive development of water, tidal effect, and activi-
ties are increased in the near-shore areas. Also, the global
sea-level rise is prompting cyclone-related activities in the
coastal regions. A tropical cyclone is occurring due to cli-
mate change conditions and water pressure in the sea area.
The cyclonic storm, flood inundation, and massive vegeta-
tion losses are noticed in the tropical regions (Xu and Zhao
2021; Nadimpalli etal. 2021). Indirectly anthropogenic
activities are also triggering the cyclonic scenarios in the
coastal regions.
The cyclonic activities are increased the coastal geo-
morphological change, shoreline shifting, mangrove deg-
radation, ecological imbalance and saltwater intrusion.
Monitoring the disaster-related information and natural
environmental degradation is an important part of coastal
development and planning (Carreño Conde and De Mata
Muñoz 2019; Kwak 2017; Rahman and Thakur 2018). The
flooded area monitoring and identifying the natural haz-
ards related phenomenon are more time consuming and
difficult for identifying the flood inundated area. Satellite
imageries, ground truth data, and skilled persons are need
for this study (Rahman and Thakur 2018). The mangrove
area is more valuable land cover for human life but that
* Bijay Halder
1 Department ofRemote Sensing andGIS, Vidyasagar
University, Midnapore, India
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
... Besides soil erosion, the Sundarban has witnessed several cyclonic events such as "Aila" (May 2009), "Fani" (April 2019), "Bulbul" (November 2019), "Umphun" (May 2020), "Yaas" (May 2021) causing huge damage to the environment and human population (Subhani and Ahmad, 2019;Mishra et al., 2021). Here, the LULC changes include deforestation of mangrove forests for agriculture, coastal aquaculture and expansion of human settlements towards the interior of forests for monetary benefit, which in turn affect the stored carbon removal from the system and increased CH 4 emission (Halder and Bandyopadhyay, 2022;Mitra et al., 2022). ...
Coastal mangroves have been lost to deforestation for anthropogenic activities such as agriculture over the past two decades. The genesis of methane (CH4), a significant greenhouse gas (GHG) with a high potential for global warming, occurs through these mangrove beds. The mangrove forests in the Indian Sundarban deltaic region were studied for pre-monsoonal and post-monsoonal variations of CH4 emission. Considering the importance of CH4 emission, a process-based spatiotemporal (PBS) and an analytical neural network (ANN) model were proposed and used to estimate the amount of CH4 emission from different land use land cover classes (LULC) of mangroves. The field work was performed in 2020, and gas samples of various LULC were directly collected from the mangrove bed using the enclosed box chamber method. Historical climatic data (1960-1989) were used to predict future climate scenarios and associated CH4 emissions. The analysis and estimation activities were carried out utilizing satellite images from the pre-monsoonal and post-monsoonal seasons of the same year. The study revealed that pre-monsoonal CH4 emission was higher in the south-west and northern parts of the deforested mangrove of the Indian Sundarban. A sensitivity study of the anticipated models was conducted using a variety of environmental input parameters and related main field observations. The measured precision area under curve of receiver operating characteristics was 0.753 for PBS and 0.718 for ANN models, respectively. The temperature factor (Tf) was the most crucial variable for CH4 emissions. Based on the PBS model with coupled model intercomparison project-6 temperature data, a global circulation model was run to predict increasing CH4 emissions up to 2100. The model revealed that the agricultural lands were the prime emitters of CH4 in the Sundarban mangrove ecosystem.
... The original image reflectance was converted into atmospheric top reflectance or surface reflectance [37]. Based on these advantages, the GEE platform can more quickly perform long-term and large-scale ecological environment remote-sensing detection [38]. ...
Full-text available
The Ulan Mulun River Basin is an essential ecological protective screen of the Mu Us Desert and a necessary energy base in Ordos City. With the acceleration of industrialization and urbanization, human activities have caused enormous challenges to the local ecological environment. To achieve the region’s economic sustainability and make local development plans more objective, it is necessary to evaluate the basin’s ecological environment quality over a period of time. First, in the Landsat historical images, we selected 5 years of data to investigate the changes in this time-period (2000–2020). Second, based on the opened remote sensing database on Google Earth Engine, we calculated the remote-sensing ecological index (RSEI) distribution map. RSEI includes greenness, temperature, humidity, and dryness. Thirdly, we assessed the ecological-environmental distribution and change characteristics in the Ulan Mulun River Basin. Finally, we analyzed the RSEI spatial auto-correlation distribution characteristics in the study area. The mean values of RSEI in 2000, 2005, 2010, 2015, and 2020 were 0.418, 0.421, 0.443, 0.456, and 0.507, respectively, which indicated that the ecological environment quality had gradually improved. The ecological environment quality from 2000 to 2005 had the biggest change, as the area with drastically changed water levels accounted for 78.98% of the total basin. It showed a downward trend in the central and western regions. It showed an upward trend in the eastern region. For 20 years, the area of deterioration decreased by 24.37%, and the slight change area increased by 45.84%. The Global Moran’s I value ranged from 0.324 to 0.568. The results demonstrated that the Ulan Mulun River Basin ecological environment quality spatial distribution was positively correlated, and the clustering degree decreased gradually. Local spatial auto-correlation of RSEI showed that high-high(H-H) was mainly distributed in the basin’s eastern and southern regions, where the population density was low and the vegetation was in good condition. Low-low(L-L) was mainly distributed in the basin’s central regions and western regions, where the population density was high, and the industrial and mining enterprises were concentrated. This study provided a theoretical basis for the sustainable development of the Ulan Mulun River Basin, which is crucial for the local ecological environment and economic development.
... The North 24 Parganas district is generally impacted by natural disasters and environmental degradation every year, e.g., the Odisha super cyclone (1999) [28][29][30][31]. Many cyclone events have occurred like, Aila, Fani, Bulbul, Amphan, and Yash. ...
Full-text available
Extreme climatic conditions and natural hazard-related phenomenon have been affecting coastal regions and riverine areas. Floods, cyclones, and climate change phenomena have hammered the natural environment and increased the land dynamic, socio-economic vulnerability, and food scarcity. Saltwater intrusion has also triggered cropland vulnerability and, therefore, increased the area of inland brackish water fishery. The cropland area has decreased due to low soil fertility; around 252.06 km2 of cropland area has been lost, and 326.58 km2 of water bodies or inland fishery area has been added in just thirty years in the selected blocks of the North 24 Parganas district, West Bengal, India. After saltwater intrusion, soil fertility appears to have been decreased and crop production has been greatly reduced. The cropland areas were 586.52 km2 (1990), 419.92 km2 (2000), 361.67 km2 (2010) and 334.46 km2 (2020). Gradually the water body areas were increased 156.21 km2 (1990), 328.15 km2 (2000), 397.77 km2 (2010) and 482.78 km2 (2020). The vegetated land area also decreased due to it being converted into inland fishery areas, and around 79.15 km2 were degraded during the last thirty years. The super cyclone Aila, along with other super cyclones and other environmental stresses, like water-logging, soil salinity, and irrigation water scarcity were the reasons for the development of the new fishery areas in the selected blocks. There is a need for proper planning for sustainable development of this area.
Full-text available
The inner shelf and coastal region of the Yellow Sea along the Korean peninsula are frequently impacted by Typhoons. The Mokpo coastal region in South Korea has been significantly affected by typhoon Soulik in 2018, the deadliest typhoon strike to the southwestern coast, since Maemi in 2003. Typhoon Soulik overran the region, causing extensive damage to the coast, shoreline, vegetation, and coastal geomorphology. Therefore, it is important to investigate its impact on the coastal ecology, landform, erosion/accretion, suspended sediment concentration (SSC) and associated coastal changes along the Mokpo region. In this study, net shoreline movement (NSM), Normalized Difference Vegetation Index (NDVI)), coastal landform change model, Normalized Difference Suspended Sediment Index (NDSSI), and SSC-reflectance relation have been used to analyze the coastal morphodynamics over the typhoon periods. We used pre- and post-typhoon Sentinel-2B MSI images for mapping and monitoring the typhoon effect. The findings highlighted the significant impacts of typhoons on coastal dynamics, wetland vegetation and sediment resuspension along the Mokpo coast. It has been observed that typhoon-induced SSC influences shoreline and coastal morphology. The outcome of this research may provide databases to manage coastal environments and a long-term plan to restore valuable coastal habitats. In addition, the findings may be useful for post-typhoon emergency response, coastal planners, and administrators involved in the long-term development of human life.
Flood disasters always hit densely populated urban areas during the rainy season. The causes of flooding that will examine in this scientific article are the condition of water infiltration into the soil. The case study was conducted in the urban area of Denpasar, Bali, Indonesia. Remote sensing data derived from various satellite images i.e., Sentinel-2 (BSI and NDVI extraction), Alos Palsar Imagery (slope extraction), CHIRPS (annual rainfall), and soil texture by laboratory analysis. Acquisition of remote sensing data using a Cloud Computing platform named Google Earth Engine (GEE). Data analysis using weighted overlay with ArcGIS 10.8 and threshold classification using natural breaks (Jenks). Denpasar City has the potential for water infiltration is good to very critical conditions. The correlation of the water infiltration map was carried out by comparing flood events in Denpasar City. The correlation results show (R2 = 0.84), (r = 0.916), (RMSE = 0.138), and p-value <0.05, these values indicate very high relation. Flood events often occur in zones with very critical water infiltration with high building density and low vegetated land cover. The condition of water infiltration critical to very critical category, spatially at the proportion of land cover vegetation < 1% and built-up area > 37%.
Full-text available
Tropical cyclones (TC) are among one of the deadliest natural disasters which affect millions of people living in coastal areas around the world. In early days limited tools were available to analyze the huge meteorogical data that were generated continuously over time. With the advent of computing power and artificial intelligence based techniques it is now possible to predict the origin, landfall and intensity of the tropical cyclone by collaborative efforts of the resources available in countries around the world. The real time data analysis plays a major role. From early simulation models built upon the hydrological and satellite data to current sophisticated data driven deep learning models are continuously evolving to serve the human civilization to combat cyclones by providing accurate early warning systems and making efficient disaster preparedness. This paper studies the deep learning based systems along with few early Mesoscale systems to predict TC and compared their relative performances.
Full-text available
Extreme weather and global sea-level rise are the concerning factor for environmental disturbances in the coastal regions, where South 24 Parganas Islands are mostly affected by several cyclonic activities shoreline shifting and defenestration. Land alteration, cropland dynamics, soil water intrusion, and ecological disturbances are located due to these natural and anthropogenic activities. Sea-level rise is a triggering factor for coastal erosion and mangrove degradation. Earth observational medium-resolution Landsat datasets were used for vulnerability assessment of Mausuni Island, where north, north-west, and south-western parts are mostly eroded and lost mangrove forest over the decades. Land alteration study recorded 6.60 sq. km of agricultural land was lost due to climatic conditions and 0.32 sq. km of built-up land is increased. Around 2.65 sq. km of mangrove forest increased in the northern parts of the study which is mostly converted into aqua-cultural land to mangrove. The overall study periods (1991–2021) located 2.94 sq. km of eroded land and 0.76 sq. km of accretion land in Mousuni Island. Most shoreline shifting is located high in 1.63 sq. km (2001–2011) and 2.94 sq. km (1991–2021). The average temperature is increased by 0.07 °C throughout the study years. The geospatial indices like NDVI, NDSI, and NDWI are also identified noticeable changes due to climate change and anthropogenic activities. Sundarban Biosphere Reserve (SBR) is mostly affected by several extreme weather conditions and global sea-level rise; those study results help to understand the ecological and anthropogenic effects in Mausuni Island, which is more important for inhabitant and tourism industries.
Full-text available
Theoretical models of the potential intensity of tropical cyclones (TCs) suggest that TC rainfall rates should increase in a warmer environment but limited observational evidence has been studied to test these hypotheses on a global scale. The present study explores the general trends of TC rainfall rates based on a 19-year (1998–2016) time series of continuous observational data collected by the Tropical Rainfall Measuring Mission and the Global Precipitation Measurement mission. Overall, observations exhibit an increasing trend in the average TC rainfall rate of about 1.3% per year, a fact that is contributed mainly by the combined effect of the reduction in the inner-core rainfall rate with the increase in rainfall rate on the rainband region. We found that the increasing trend is more pronounced in the Northwestern Pacific and North Atlantic than in other global basins, and it is relatively uniform for all TC intensities. Further analysis shows that these trends are associated with increases in sea surface temperature and total precipitable water in the TC environment.
Full-text available
Previous numerical studies have focused on the combined effect of momentum and scalar eddy diffusivity on the intensity and structure of tropical cyclones. The separate impact of eddy diffusivity estimated by planetary boundary layer (PBL) parameterization on the tropical cyclones has not yet been systematically examined. We have examined the impacts of eddy diffusion of moisture on idealized tropical cyclones using the Advanced Research Weather Research and Forecasting model with the Yonsei University PBL scheme. Our results show nonlinear effects of moisture eddy diffusivity on the simulation of idealized tropical cyclones. Increasing the eddy diffusion of moisture increases the moisture content of the PBL, with three different effects on tropical cyclones: (1) an decrease in the depth of the PBL; (2) an increase in convection in the inner rain band and eyewall; and (3) drying of the lowest region of the PBL and then increasing the surface latent heat flux. These three processes have different effects on the intensity and structure of the tropical cyclone through various physical mechanisms. The increased surface latent heat flux is mainly responsible for the decrease in pressure. Results show that moisture eddy diffusivity has clear effects on the pressure in tropical cyclones, but contributes little to the intensity of wind. This largely influences the wind-pressure relationship, which is crucial in tropical cyclones simulation. These results improve our understanding of moisture eddy diffusivity in the PBL and its influence on tropical cyclones, and provides guidance for interpreting the variation of moisture in the PBL for tropical cyclone simulations.
Full-text available
This study assessed two different vortex initialization (VI) and relocation methods for improved prediction of tropical cyclones (TCs) over the Bay of Bengal (BoB) using the triply nested (27/9/3 km) state-of-the-art Hurricane Weather Research and Forecasting (HWRF) model. The first VI method, “cold-start,” obtained the initial TC vortex from the global analysis. The second one, “cyclic-start,” received the initial vortex from the 6-h forecast of the previous forecast cycle of the same model. In both the strategies, the vortex was corrected to the position, strength, and structure defined by the India Meteorological Department. A total of 32 forecast cases (from five cyclones) over the BoB were considered. The cyclic-start experiments yielded better initial structure and asymmetry as compared to the cold-start experiments. The average statistics indicated that the cyclic initialization improved the 24-h track prediction (by 29%), while the cold initialization was better for the 72-h prediction (by ~ 28%). The intensity was consistently improved in the cyclic-start experiment by up to 68%. The number of cyclic initializations depended on the TC duration. On average, the cyclic initialization improved the representation (strength and size) of the initial vortex up to nine cycles after the first cold start and exhibited an improved skill of 25%; beyond nine cycles, the skill improvement was only 12%. Diagnostic analyses of very severe cyclonic storm (VSCS) Phailin (rapidly intensified) and VSCS Lehar (rapidly weakening) revealed that the cyclic initialization realistically represented equivalent potential temperature, upper-level cloud condensate, and moisture intrusion, which improved the model performance. This study brought out the benefit of the (cyclic) VI for improved TC prediction capabilities in the BoB basin.
Full-text available
Flooding is one of the major disasters occurring in various parts of the world. Estimation of economic loss due to flood often becomes necessary for flood damage mitigation. This present practice to carry out post flood survey to estimate damage, which is a laborious and time-consuming task. This paper presents a framework of rapid estimation of flood damage using SAR earth observation satellite data.In Nakhon Si Thammarat, a southern province in Thailand, flooding is a recurrent event affecting the entire province, especially the urban area. Every year, it causes lives and damages to infrastructure, agricultural production and severely affects local economic development. In order to monitor and estimate flood damages in near-real time, numerous techniques can be used, from a simply digitizing on maps, to using detailed surveys or remote sensing techniques. However, when using the last-mentioned technique, the results are conditioned by the time of data acquisition (day or night) as well as by weather conditions. Although, these impediments can be surpassed by using RADAR satellite imagery. The aim of this study is to delineate the land surface of Chian Yai, Pak Phanang and Hua Sai districts of that was affected by floods in December 2018 and January 2019. For this case study, Sentinel-1 C-Band SAR data provided by ESA (European Space Agency) were used. The data sets were taken before and after the flood took place, all within 1 days and were processed using Sentinel Toolbox. Cropland mapping has been carried out to assess the agricultural loss in study area using Sentinel-1 SAR data. The thematic accuracy has been assessed for cropland classification for test site shows encouraging overall accuracy as 82.63 % and kappa coefficients (κ) as 0.78.
Full-text available
Landfall of the Amphan (very severe cyclonic storm) occurred at 1730 hrs Indian Standard Time (IST) on May 20, 2020, near the West Bengal (W.B.) coast of India. High wind speed, storm surge, and torrential rainfall-induced flooding caused devastation in W.B. The present study aims to analyse the impacts of Amphan cyclone on land use/land cover (LULC) such as built-up area, cropland, brick-kiln industries and vegetation cover of nine districts of W.B. namely, Barddhaman, Nadia, North 24 Parganas, South 24 Parganas, Purba Medinipur, Paschim Medinipur, Haora, and Kolkata. Flood extent has been mapped using Sentinel-1A and B interferometric wide swath (IW) ground range detected (GRD) VV polarisation images dated May 22, 2020. The total actual flooded area covers 488 km2 of the study area. For the pre-cyclone period, LULC classification and normalised difference vegetation index (NDVI) have been done using Sentinel-2B multispectral instrument (MSI) images dated May 14, 2020. Post-cyclone NDVI has been computed using Sentinel-2B MSI images dated June 3, 2020. Flood-affected cropland covers a large chunk (88.2%) of the total actual flooded area. Mean NDVI values of non-flooded and flooded cropland and vegetation cover have been reduced between May 14, 2020, and June 3, 2020. District, block and pixel-wise changes in pre- and post-cyclone NDVI values have also been analysed. This study helps planners and policy makers to understand the district-wise flooding behavior, severity of damage to cropland and vegetation cover and to plan restriction on high-value land use in flooded low-lying areas.
Full-text available
Bhitarkanika National Park and Mahanadi Delta are the most biologically diverse mangrove patches of India. Due to inadequate representation of the value of the mangroves in decision-making, mangroves of Bhitarkanika and Mahanadi delta have undergone a rapid decline in the last fifty years. Thus, there is a growing need to assess the ecosystem services and multiple values of mangrove ecosystems in the region, and to identify the various anthropogenic and environmental drivers acting upon them. In this paper, we conducted a bibliometric analysis, followed by a synthesis of contemporary knowledge to understand the diverse ecosystem services of the Bhitarkanika mangroves and the existing data gaps for the protection and sustainable management of mangroves. It was observed that Bhitarkanika mangroves primarily serve as a buffer to coastal storms, tsunamis and cyclones, and they also contribute immense provisioning, and cultural ecosystem services for human well-being. The present review provides a comprehensive database projecting important ecosystem services and multiple values (including carbon sink potential, pollinator benefits and protection from storm surges) of Bhitarkanika mangrove forests. We found that there is significant pressure on the mangroves of Bhitarkanika due to increasing aquaculture, environmental pollution, industrialization, storm surges, and frequent cyclones. The present study helps to understand the urgent need of protecting the existing mangroves and to devise a coastal zone policy framework and strengthening participatory approaches for the preservation of dense and intact mangroves of Bhitarkanika and Mahanadi delta.
Full-text available
The canopy density of forests is highly influenced by population pressure which cause forest fragmentations, deforestations, forest degradation and also land reclamation for infrastructure and agriculture. This study was envisaged with the objective of mapping the forest canopy density with two different methods by using Landsat 8 OLI dataset of the year 2016 after mapping the vegetation indices. One of the two methods is the semi-expert FCD mapper model, while the other model is newly developed by us and consists of eight vegetation indices. After running the models, the results of both the models were compared. It was found that for the semi-expert FCD model, the three classes viz. high canopy density, moderate canopy and low canopy covered an area of 81,615.51 ha (40%), 84,474.72 ha (41%) and 38,844.18 ha (19%), respectively. And for the modified FCD model, the same classes covered 69,134.670 ha (37%), 84,062.250 ha (45%) and 32,529.150 ha (18%), respectively. It was observed that the difference between semi-expert FCD model and the modified FCD model’s accuracy is about 1.75% and difference in Kappa statistics is 0.0362. Thus, the modified model is more accurate than the semi-expert FCD model and gives us more detailed canopy density map than the semi-expert FCD map.
Full-text available
Climate change driven Sea Level Rise (SLR) is creating a major global environmental crisis in coastal ecosystems, however, limited practical solutions are provided to prevent or mitigate the impacts. Here, we propose a novel eco-engineering solution to protect highly valued vegetated intertidal ecosystems. The new ‘Tidal Replicate Method’ involves the creation of a synthetic tidal regime that mimics the desired hydroperiod for intertidal wetlands. This synthetic tidal regime can then be applied via automated tidal control systems, “SmartGates”, at suitable locations. As a proof of concept study, this method was applied at an intertidal wetland with the aim of restabilising saltmarsh vegetation at a location representative of SLR. Results from aerial drone surveys and on-ground vegetation sampling indicated that the Tidal Replicate Method effectively established saltmarsh onsite over a 3-year period of post-restoration, showing the method is able to protect endangered intertidal ecosystems from submersion. If applied globally, this method can protect high value coastal wetlands with similar environmental settings, including over 1,184,000 ha of Ramsar coastal wetlands. This equates to a saving of US$230 billion in ecosystem services per year. This solution can play an important role in the global effort to conserve coastal wetlands under accelerating SLR.
Although studies on species-level classification and mapping using multisource data and machine learning approaches are plenty, the use of data with ideal placement of central wavelength and bandwidth at appropriate spatial resolution, for the classification of mangrove species is underreported. The species composition of a mangrove forest has been estimated utilising the red-edge spectral bands and chlorophyll absorption information from AVIRIS-NG and Sentinel-2 data. In this study, three dominant species, Heritiera fomes, Excoecaria agallocha and Avicennia officinalis, have been classified using the random forest (RF) model for a mangrove forest in Bhitarkanika Wildlife Sanctuary, India. Various combinations of reflectance/backscatter bands and vegetation indices derived from Sentinel-2, AVIRIS-NG, and Sentinel-1 were used for species-level discrimination and mapping. The RF model showed maximum accuracy using Sentinel-2, followed by the AVIRIS-NG, in discriminating three dominant species and two mixed compositions. This study indicates the potential of Sentinel-2 data for discriminating various mangrove species owing to the appropriate placement of central wavelength and bandwidth in Sentinel-2 at ≥10 m spatial resolution. The variable importance plots proved that species-level classification could be attempted using red edge and chlorophyll absorption information. This study has wider applicability in other mangrove forests around the world
The availability of advanced Machine Learning algorithms has made the estimation process of biophysical parameters more efficient. However, the efficiency of those methods seldom compared with the efficiency of already established semi-empirical procedures. Aboveground biomass (AGB) of mangrove forests is a crucial biophysical parameter as it is positively correlated to the carbon stocks and fluxes. The free availability of Sentinel-1 C-band SAR data and machine learning algorithms hold promises in estimating AGB of tropical mangrove forests. We reported high AGB (70 t/ha to 666 t/ha) using 185 field quadrats of 0.04ha each from Bhitarkanika Wildlife Sanctuary, located on the eastern Indian coast that could be attributed to species composition. The AGB maps generated using Interferometric Water Cloud Model (IWCM) and Deep Learning models were different from each other as they rely on different variables. IWCM was more dependent, especially on ground and vegetation components of coherence, while canopy height acted as the most crucial variable in the Deep Learning model. However, the negligible variations in Deep Learning-based AGB maps can be attributed to interpreting the importance of coherence and VH backscatter. Due to low canopy penetration power of C-band SAR, high temporal decorrelation resulting from longer time gap between interferometric image pairs, and high spatial heterogeneity of mangrove forests, IWCM found as an unsuitable method for AGB estimation. Interestingly, a Deep Learning algorithm could translate the exact relationship between predictor variables and mangrove AGB in Bhitarkanika Wildlife Sanctuary. The AGB estimation studies in mangrove forests using Sentinel data should focus more on using machine learning algorithms like Deep Learning rather than semi-empirical models.