{"paper_id":"4c55fcda-83b8-405c-aefe-5905d2653368","body_text":"Post-Cyclone Vegetation Recovery and Change Detection in the Sundarbans Mangrove Forest Using Landsat-Derived NDVI and SAVI Indices | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Post-Cyclone Vegetation Recovery and Change Detection in the Sundarbans Mangrove Forest Using Landsat-Derived NDVI and SAVI Indices Md. Saifur Rahman, Fataha Hossen, Md. Ariful Islam Arif, Mawya Siddeqa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6950710/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study investigates the long-term impact of Cyclone Sidr (2007) on the vegetation dynamics of the Sundarbans mangrove forest in Bangladesh. Using multi-temporal Landsat 7 ETM + imagery from 2007, 2008, and 2023, vegetation cover changes were analyzed through Normalized Difference Vegetation Index (NDVI) and Soil-Adjusted Vegetation Index (SAVI). Five vegetation classes namely water bodies, bare soil, sparse, intermediate and dense vegetation were derived using NDVI thresholds and change detection analysis. Results indicate a substantial decrease in dense vegetation from 77.07% in 2007 to 57.37% in 2008, followed by gradual recovery to 72.53% by 2023. Cyclone Sidr caused a dramatic increase in sparse vegetation and water bodies due to flooding and forest damage. Accuracy assessment using ground-truth data and Google Earth observations yielded kappa coefficients of 0.81, 0.87, and 0.76 for 2007, 2008, and 2023 respectively. The findings demonstrate the resilience and regeneration capacity of mangrove ecosystems but also emphasize anthropogenic stressors, including land use change. These insights inform conservation strategies, disaster risk reduction, and forest monitoring efforts in cyclone-prone coastal zones. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Mangrove ecosystems are globally recognized for their ecological significance, particularly in carbon sequestration, shoreline stabilization, and biodiversity conservation. Among them, the Sundarbans—the largest contiguous mangrove forest in the world—straddles southern Bangladesh and eastern India, forming a crucial buffer against tropical cyclones and tidal surges in the Bay of Bengal (Giri et al., 2011 ; Rahman et al., 2010 ). The forest, shaped by the confluence of the Ganges, Brahmaputra, and Meghna river systems, spans approximately 6,017 km² on the Bangladesh side, with about 4,143 km² of land and 1,874 km² of water bodies (Iftekhar & Saenger, 2008 ). Due to its low elevation and geographic location, Bangladesh is highly vulnerable to climate-induced natural disasters, including cyclones and coastal flooding (Ali, 1999 ; Dasgupta et al., 2010 ). Cyclone Sidr, which struck in November 2007, was among the most devastating in recent history, inflicting extensive damage on the Sundarbans' vegetative cover (Giri et al., 2011 ). Mangrove forests such as the Sundarbans play a vital role in mitigating such impacts through their dense root networks and structural complexity (Alongi, 2008 ). However, recurrent cyclones, sea-level rise, and anthropogenic pressures continue to threaten the forest's integrity (Das & Kundu, 2021 ; Hossain & Ahsan 2019 ). Monitoring changes in forest structure and health is essential for sustainable management, particularly in post-disaster scenarios (Chen, 2023 ). Remote sensing (RS) and Geographic Information System (GIS) technologies provide cost-effective, large-scale tools for temporal analysis of vegetation change (Jensen, 2007 ). Among vegetation indices, the Normalized Difference Vegetation Index (NDVI) is widely used to detect variations in canopy greenness and density (Tucker, 1979 ). The Soil-Adjusted Vegetation Index (SAVI) enhances interpretation in areas with sparse vegetation or exposed soil by minimizing the influence of soil brightness on vegetation measurements (Huete, 1988 ). This study utilizes NDVI and SAVI derived from Landsat 7 ETM + imagery to assess vegetation cover changes in the Sundarbans before and after Cyclone Sidr, and to evaluate recovery trends up to 2023. By classifying satellite imagery into vegetation categories and applying change detection analysis, we aim to quantify the cyclone's impact, monitor regeneration, and inform conservation and climate resilience strategies. The study addresses a critical gap in understanding long-term vegetation dynamics in cyclone-affected mangrove ecosystems using freely available remote sensing data. 2. Materials and Methods 2.1 Study Area The Sundarbans mangrove forest, located between 21º30'N and 22º30'N latitude and 89º00'E to 89º55'E longitude, spans parts of Khulna, Satkhira, and Bagerhat districts in southwestern Bangladesh (Iftekhar & Saenger, 2008 ) (Fig. 1 ). The Sundarbans mangrove forest in Bangladesh spans approximately 6,017 km², comprising about 4,143 km² of land and 1,874 km² of water bodies. This area accounts for approximately 44% of the country's total forest coverage and about 4.2% of its total land area (Ortolano et al., 2017 ).​ The elevation ranges from 0.9 to 2.1 meters above mean sea level, making the region highly sensitive to cyclonic storm surges and sea-level rise (Giri et al., 2007 ). 2.2 Satellite Data and Preprocessing Landsat 7 ETM + satellite imagery from three time periods—pre-cyclone (October 2007), post-cyclone (January 2008), and a recent assessment (February 2023) (Table 1 )—was used to analyze vegetation change. All images were obtained from the USGS EarthExplorer portal, ensuring minimal cloud cover and temporal consistency. Each image set included two overlapping scenes to fully cover the Bangladeshi Sundarbans. Table 1 Features of the Landsat 7 ETM + images used to conduct this study Periods Images Dates Sun elevation (°) Sun azimuth (°) Land cloud cover Scene cloud cover Geometric RMSE model Pre- cyclone (a) October 29, 2007 48.74 146.058 0.00 0.00 4.563 m (b) October 20, 2007 51.18 142.994 1.00 7.00 3.807 m Post-cyclone (a) January 8, 2008 38.327 146.64 0.00 0.00 4.005 m (b) January 1, 2008 38.111 147.938 0.00 4.00 4.315 m Present condition (a) February 3, 2023 43.459 143.162 0.04 0.02 6.349 m (b) February 2, 2023 43.249 143.444 0.10 6.63 5.330 m The images were radiometrically and geometrically corrected. Scan line errors in the ETM + data were addressed using interpolation techniques in QGIS. All datasets were co-registered to UTM Zone 45N using the WGS84 datum. Layer stacking and false color composites (FCC) were created to visualize vegetation features. A nearest-neighbor resampling method was applied to maintain the integrity of reflectance values. 2.3 NDVI and SAVI Computation Normalized Difference Vegetation Index (NDVI) was calculated using the standard formula: NDVI = (NIR - Red) / (NIR + Red) (Tucker, 1979 )…………… (I) Where, NIR = reflectance in the near infrared band RED = reflectance in the red (Visible) ArcGIS 10.5 software has been used to calculate the NDVI values of the images. For Landsat 7 ETM+, Band 4 (NIR) and Band 3 (Red) were used, while for Landsat 8 and 9, Band 5 (NIR) and Band 4 (Red) were utilized. NDVI values were reclassified into five vegetation classes: water bodies, bare soil, sparse vegetation, intermediate vegetation, and dense vegetation. SAVI was calculated to minimize soil brightness effects using the formula: SAVI = [(NIR - Red) / (NIR + Red + L)] × (1 + L) (Huete, 1988 ) ……………… ( II) Where, L is a canopy background adjustment factor set to 0.5 for intermediate vegetation density. The L-factor should be applied according to the density of the vegetation being observed. In situations where the vegetation cover is dense, an appropriate L-factor is 0.25; for intermediate density, 0.5 should be used, while for very low density, an L-factor of 1.0 is recommended (Huete, 1988 ; Baret & Guyot, 1991 ). 2.4 Change Detection Analysis Post-classification change detection was performed by comparing NDVI-derived maps from 2007, 2008, and 2023. Pixel-wise transitions between vegetation classes were analyzed to detect trends in deforestation, regeneration, and land transformation. ArcGIS 10.5 was used for raster reclassification, area computation, and change matrix generation. 2.5 Area Estimation Area calculations were performed using the formula: Area (km²) = Pixel Count × 30 × 30 / 1,000,000 (USGS, 2019) ……………….. (III) This provided vegetation class coverage in square kilometers for each time period. 2.6 Accuracy Assessment Accuracy was evaluated using confusion matrices based on 56–81 control points per image year, derived from visual interpretation and Google Earth data. Overall accuracy and kappa coefficients were computed to assess classification reliability. User’s and producer’s accuracies were calculated for each class. Results indicated strong classification agreement, with overall accuracy ranging from 82.71–89.65%. 3. Results 3.1 Vegetation Classification and Coverage Vegetation in the Sundarbans was classified into five categories based on NDVI and SAVI indices: dense, intermediate, sparse vegetation, bare soil, and water bodies which showed in Table 2 . Pre-cyclone (2007) analysis indicated that dense vegetation accounted for 77.07% (3358 km²) of the area. Following Cyclone Sidr in 2008, this dropped sharply to 57.37% (2496 km²), highlighting the immediate impact of the cyclone. Intermediate vegetation also declined from 8.58–7.63%, while sparse vegetation increased significantly from 2.54–18.55%, indicating damage to mature forest cover and regrowth of understory or broken canopy structure. Table 2 Vegetation cover classes definition Category Characterization Scientific name Bangla name Distribution Dense Vegetation Tress height above 65ft and more Heritiera fomes Sundari Az, Ar Sonneratia apetala Kewra Az; Ar Bruguiera gymnorhiza kakra Mz; Pz; Kh B. sexangula Lalkakra Mz; Pz; Ch X. moluccensis Poshur etc. Az; Ar Intermediate vegetation Height between 40 to 65ft S. caseolaris Choila/ora Mz; Sr, Ch Rhizophora apiculata Bhorjhana Mz; Pz; Kh R. mucronata Lam. Jhana Mz; Pz; Kh A. officinalis Baen Az; Ar Xylocarpus granatum Dhundal etc Mz, Kh, St Sparse Vegetation Height below 40ft but above 10ft Phoenix paludosa Hental Az; Ar Ceriops decandra Goran Az; Ar Excoecaria agallocha L. Gewa Az; Ar Nypa frutican Golpata Az; Ar Acanthus ilicifolius Hargoza etc Az; Ar Here, Distribution: Ch = Chandpai Range, Kh = Khulna Range, Sr = Sarankhola Range, St = Satkhira Range; Ar = all range, Az = all zones, Mz = mesohaline zone, Oz = oligohaline zone, and Pz = polyhaline zone. 3.2 Vegetation Recovery and Current Status (2023) Analysis of 2023 satellite imagery shows significant regeneration. Dense vegetation now covers approximately 72.53% (3156 km²), indicating a recovery of over 660 km² since 2008. Intermediate vegetation increased to 9.44% (411 km²), while sparse vegetation declined to 2.52% (110 km²). The recovery trend suggests the ecosystem's resilience, though not all areas have returned to pre-cyclone conditions. Bare soil increased to 2.96% (129 km²), possibly due to land use conversion or slow regrowth. Water bodies, while initially expanded post-cyclone, stabilized at 12.52% (Figs. 2 , 3 and 4 ). 3.3 SAVI-Derived Vegetation Analysis SAVI analysis mirrored NDVI trends, confirming vegetation class transitions. Pre-cyclone SAVI-based dense vegetation was 3216 km² (Figs. 5 and 6 ), decreasing to 2503 km² post-cyclone (Figs. 5 and 7 ), and increasing again by 2023. Intermediate vegetation declined from 639 to 460 km² (Figs. 6 and 7 ) while sparse vegetation increased from 140 to 678 km² (Figs. 6 and 7 ). The SAVI index provided enhanced detection of vegetation recovery in sparse zones where soil exposure was significant. 3.4 Change Detection Analysis Analysis of Fig. 8 , as detailed in Table 3 , the NDVI classification indicates substantial vegetation degradation following Cyclone Sidr. The total vegetated area—including sparse, intermediate, and dense vegetation—declined markedly from 3843 km² in October 2007 to 3635 km² by January 2008. Among the three vegetation classes, dense vegetation experienced the most significant loss, decreasing from 3358 km² (77.07%) to 2496 km² (57.37%). This represents an approximate 45% reduction, highlighting the cyclone's severe impact on mature forest stands. The sharp decline in dense canopy coverage reflects not only physical uprooting and breakage of large trees but also the sensitivity of NDVI in detecting canopy disruption and biomass loss in mangrove ecosystems (Asbridge et al., 2016 ). Intermediate vegetation declined from 8.58–7.63%. Cyclone Sidr’s peak wind speeds (215 km/h) significantly impacted these vegetation types. Larger trees absorbed much of the cyclone’s energy, protecting smaller vegetation. Uprooted and broken trees were classified as sparse vegetation, explaining its apparent increase from 2.54–18.55%. Table 3 Based on NDVI and SAVI, Sundarbans forest statistics in tabulated form (km 2 and percentage) for the years (2007, 2008, and 2023). Features 2007(Pre Sidr) 2008 (Post Sidr) 2023 (Recent) NDVI Class range NDVI area Sq.km % SAVI area Sq.km % NDVI class range NDVI area Sq.km % SAVI area Sq.km % NDVI class range NDVI area Sq.km % Water bodies (-) value 436 10.06 362 8.30 (-) value 689 15.83 670 15.37 (-) value 545 12.52 Bare soil 0.01 78 1.79 100 2.29 0.004 26 0.59 47 1.07 0.10 129 2.96 Sparse Vegetation 0.15 111 2.54 140 3.21 0.08 807 18.55 678 15.55 0.19 110 2.52 Intermediate vegetation 0.32 374 8.58 639 14.66 0.12 332 7.63 460 10.55 0.25 411 9.44 Dense vegetation 0.78 3358 77.07 3116 71.07 0.41 2496 57.37 2503 57.43 0.44 3156 72.53 Flooding also increased water body coverage from 10.06–15.83%, while bare soil decreased from 1.79–0.59%. Current NDVI ranges from − 0.15 to + 0.44, while SAVI ranges from − 0.73 to + 0.65. By 2023, dense vegetation had recovered to 3156 km² (72.53%), though still slightly below the pre-cyclone level. Bare soil has risen to about 3%, while intermediate vegetation has also shown growth. 3.5 Accuracy Assessment Results Accuracy assessment is vital for verifying satellite classification. Confusion matrices were constructed using reference data (Google Earth, visual interpretation, etc.). Overall accuracy and kappa statistics were calculated for 2007, 2008, and 2023. Classification accuracy remained consistently high across all three years. As observed from Tables 4 , 5 , and 6 , the 2007 classification achieved an overall accuracy of 87.5% (Kappa = 0.81), 2008 showed a slightly higher accuracy of 89.65% (Kappa = 0.87), and 2023 maintained strong performance with 82.71% (Kappa = 0.76). These results confirm the robustness and reliability of the NDVI-based classification method, particularly in detecting dense and intermediate vegetation classes with a high degree of confidence. Table 4 , 5 , 6 : Represent the accuracy assessment for the years 2007, 2008, and 2023 respectively Table 4 (2007): Class name Water bodies Bare soil Sparse vegetation Intermediate vegetation Dense vegetation Total User accuracy (%) Water bodies 10 0 0 1 0 11 90 Bare soil 0 2 0 0 0 2 100 Sparse vegetation 0 1 1 0 0 2 50 Intermediate vegetation 0 1 0 7 0 8 90 Dense vegetation 2 0 0 2 29 33 87.87 Total 12 4 1 10 29 56 Producer accuracy (%) 83.33 50 100 70 100 Overall accuracy 87.5% Kappa coefficient 0.81% Table 5 (2008): Class name Water bodies Bare soil Sparse vegetation Intermediate vegetation Dense vegetation Total User accuracy (%) Water bodies 18 0 0 1 0 18 100 Bare soil 0 2 1 0 1 4 50 Sparse vegetation 0 0 6 0 3 9 66.66 Intermediate vegetation 1 0 0 8 0 9 88.89 Dense vegetation 0 0 0 0 18 18 100 Total 19 2 7 8 22 58 Producer accuracy (%) 94.73 100 85.71 100 81.81 Overall accuracy 89.65% Kappa coefficient 0.87% Table 6 (2023): Class name Water bodies Bare soil Sparse vegetation Intermediate vegetation Dense vegetation Total User accuracy (%) Water bodies 20 0 0 0 0 20 100 Bare soil 0 6 3 0 0 9 66.66 Sparse vegetation 0 1 4 0 0 5 80 Intermediate vegetation 0 1 1 11 2 15 73.33 Dense vegetation 2 0 0 4 26 32 81.25 Total 22 8 8 15 28 81 Producer accuracy (%) 90.90 75 75 73.33 92.85 Overall accuracy 82.71% Kappa coefficient 0.76% 4. Discussion Cyclone Sidr (2007) caused extensive damage to the Sundarbans, an ecologically significant mangrove forest. As a natural buffer, the Sundarbans absorbed much of the cyclone's force, resulting in severe vegetation loss. Change detection analysis confirmed widespread flooding and a reduction in dense vegetation. NDVI values showed an increase in water bodies from 10.06–15.83% post-cyclone. Total vegetation cover dropped from 3843 sq.km to 3635 sq.km, with dense vegetation dropping by ~ 45%. Intermediate vegetation also declined, while sparse vegetation temporarily increased due to broken trees being misclassified. The natural shielding by taller vegetation allowed smaller trees to survive. Between 2008 and 2023, significant regrowth occurred. Dense vegetation recovered to 72.53%, and intermediate vegetation increased to 9.44%. The Sundarbans demonstrated ecological resilience, although full restoration has not been achieved. However, anthropogenic pressures, including deforestation and expanding settlements, remain threats. Bare soil increased from 0.59% post-cyclone to 3% by 2023, highlighting ongoing land degradation. This research offers valuable insights into cyclone impact assessment, post-disaster forest recovery, and long-term vegetation monitoring. Findings can inform disaster preparedness, ecological conservation, infrastructure planning, and agricultural decision-making. The Sundarbans' capacity to recover emphasizes the importance of preserving this vital ecosystem against future environmental and human-induced stresses. Limitations and Future Work Despite promising results, this study has limitations. First, classification accuracy may be affected by seasonal variations and residual scan line artifacts in Landsat 7 imagery. Second, field data for biomass estimation or species-level validation were not incorporated, which limits ecological interpretation. Third, while NDVI and SAVI are effective for vegetation detection, their sensitivity may saturate in dense canopy conditions. Future studies could benefit from the integration of higher-resolution datasets (e.g., Sentinel-2 or PlanetScope), LiDAR or radar-based canopy height metrics, and field-based biomass measurements. Time-series analysis using Google Earth Engine would also facilitate continuous monitoring and reduce cloud-cover issues. 5. Conclusion This study provides a detailed assessment of vegetation change in the Sundarbans mangrove forest in response to Cyclone Sidr and subsequent recovery, using NDVI and SAVI derived from multi-temporal Landsat imagery. The findings reveal both the short-term devastation and the long-term regenerative trends of the ecosystem, with implications for mangrove conservation and climate resilience planning. By combining satellite-based indices with change detection and accuracy validation, this research offers a scalable and replicable method for vegetation monitoring in cyclone-affected coastal zones. It emphasizes the urgent need for proactive management strategies to mitigate anthropogenic threats and enhance the adaptive capacity of vulnerable mangrove ecosystems in the face of climate change. Declarations Funding Declaration : There was no Funding Ethics declaration : not applicable. Consent to Publish declaration : not applicable Consent to Participate declaration : not applicable Data Availability Statement: The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request. Author Contribution Md. Saifur Rahman* conceptualized the study, led the manuscript writing and supervision. Fataha Hossen contributed to remote sensing data processing, analysis and visualization. Md. Ariful Islam Arif assisted in literature review, data interpretation, and manuscript editing. Mawya Siddeqa contributed to methodological development and overall supervision. All authors reviewed and approved the final manuscript. References Ali, A. (1999). Climate change impacts and adaptation assessment in Bangladesh. Climate Research, 12(2-3), 109–116. https://doi.org/10.3354/cr012109 Alongi, D. M. (2008). Mangrove forests: Resilience, protection from tsunamis, and responses to global climate change. Estuarine, Coastal and Shelf Science, 76(1), 1–13. https://doi.org/10.1016/j.ecss.2007.08.024 Asbridge, E., Lucas, R., Accad, A., & Dowling, R. (2016). Mangrove response to environmental change in Australia's Gulf of Carpentaria. Ecology and Evolution , 6(11), 3523–3539. https://doi.org/10.1002/ece3.2137 Baret, F., & Guyot, G. (1991). Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment, 35 (2–3), 161–173. https://doi.org/10.1016/0034-4257(91)90009-U Chen, X. (2023). An exploration of forest fires and post-disaster recovery . Frontiers in Forests and Global Change, 6, Article 1223934. https://doi.org/10.3389/ffgc.2023.1223934 Das, S., & Kundu, A. (2021). Assessment and attribution of mangrove forest changes in the Indian Sundarbans from 2000 to 2020 . Remote Sensing, 13(24), 4957. https://doi.org/10.3390/rs13244957 Dasgupta, S., Laplante, B., Murray, S., & Wheeler, D. (2010). Exposure of developing countries to sea-level rise and storm surges. Climatic Change, 106(4), 567–579. https://doi.org/10.1007/s10584-010-9959-6 Giri, C., Ochieng, E., Tieszen, L.L., Zhu, Z., Singh, A., Loveland, T., ... & Duke, N. (2011). Status and distribution of mangrove forests of the world using earth observation satellite data. Global Ecology and Biogeography, 20(1), 154–159. https://doi.org/10.1111/j.1466-8238.2010.00584.x Giri, C., Zhu, Z., Tieszen, L. L., Singh, A., Gillette, S., & Kelmelis, J. A. (2007). Mangrove forest distributions and dynamics (1975–2005) of the tsunami-affected region of Asia. Journal of Biogeography, 35(3), 519–528. https://doi.org/10.1111/j.1365-2699.2007.01806.x Hossain, M. S., & Ahsan, M. A. (2019). Impacts of climate change on the Sundarbans mangrove ecosystem: Challenges and future conservation strategies. Environmental Science and Pollution Research, 26(31), 31440–31457. https://doi.org/10.1007/s11356-019-06181-x Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25 (3), 295–309. https://doi.org/10.1016/0034-4257(88)90106-X Iftekhar, M. S., & Saenger, P. (2008). Vegetation dynamics in the Bangladesh Sundarbans mangroves: A review of forest inventories. Wetlands Ecology and Management, 16(4), 291–312. https://doi.org/10.1007/s11273-007-9063-5 Jensen, J. R. (2007). Remote sensing of the environment: An Earth resource perspective (2nd ed.). Pearson Prentice Hall. Ortolano, L., et al. (2017). Bangladesh Sundarbans: Present Status of the Environment and Biota. ResearchGate . Retrieved from https://www.researchgate.net/publication/281889734_Bangladesh_Sundarbans_Present_Status_of_the_Environment_and_Biota. Rahman, M.M., Asaduzzaman, M., & Islam, M.S. (2010). Ecosystem-based adaptation to climate change: a case study of the Sundarbans mangrove forest, Bangladesh. Environment and Natural Resources Research, 1(1), 1–10. https://doi.org/10.5539/enrr.v1n1p1 Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127-150. https://doi.org/10.1016/0034-4257(79)90013-0 U.S. Geological Survey (USGS). (2019). Using the USGS Landsat Surface Reflectance Data Products . U.S. Department of the Interior. Retrieved from https://www.usgs.gov/ Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-6950710\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":484760728,\"identity\":\"1f698227-3748-4e89-b2aa-af829370cf49\",\"order_by\":0,\"name\":\"Md. Saifur Rahman\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYFACxoYPQFIOzjdgSCCopXEGkDRmYGAmWgsDI0hLYgPRWuTbDzc2fqmwSd/Of/6YBEONHYM5OwEtBmcSG5tlzqTl7pyRzCbBcCyZwbLnAQEtDIntjyXbDuduuMEM1MJ2gMHgBiGH9T9sbJb8dzjd4PxhoJZ/RGhhuJHY2Pix4XCCwQGgwxjbiNBicANoC8OxNEOgX4wtEvuSeQj6Rb4//WHjjxobeXP+gw9vfPhmJ0cwxECAmQdkHYgFVMxDWD0QMP6AaRkFo2AUjIJRgA0AAPXLRp7wHJ+gAAAAAElFTkSuQmCC\",\"orcid\":\"\",\"institution\":\"Patuakhali Science and Technology University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Md.\",\"middleName\":\"Saifur\",\"lastName\":\"Rahman\",\"suffix\":\"\"},{\"id\":484760729,\"identity\":\"d8e9f674-e753-4f11-ac68-6ad042f39afe\",\"order_by\":1,\"name\":\"Fataha Hossen\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Patuakhali Science and Technology University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Fataha\",\"middleName\":\"\",\"lastName\":\"Hossen\",\"suffix\":\"\"},{\"id\":484760730,\"identity\":\"16b6b04a-86b7-40c7-a4d9-729bd75e73bb\",\"order_by\":2,\"name\":\"Md. Ariful Islam Arif\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Patuakhali Science and Technology University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Md.\",\"middleName\":\"Ariful Islam\",\"lastName\":\"Arif\",\"suffix\":\"\"},{\"id\":484760731,\"identity\":\"06077e40-3053-4a34-a019-998f7e6f5d4a\",\"order_by\":3,\"name\":\"Mawya Siddeqa\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Patuakhali Science and Technology University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Mawya\",\"middleName\":\"\",\"lastName\":\"Siddeqa\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-06-22 16:53:12\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-6950710/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-6950710/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":86765953,\"identity\":\"f5f30687-b283-4ced-8abb-97ad203ba10a\",\"added_by\":\"auto\",\"created_at\":\"2025-07-15 11:04:22\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":316684,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eMap of the study area (Sundarbans)\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6950710/v1/33ab46e987a92defc628785f.png\"},{\"id\":86765061,\"identity\":\"9cf7472d-fdd8-4d1d-b1aa-62dc3a62b7a3\",\"added_by\":\"auto\",\"created_at\":\"2025-07-15 10:56:22\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":26905,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eCalculated area of features in Sundarbans (2007, 2008 \\u0026amp; 2023)\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6950710/v1/310648b638daec72f7ba21ca.png\"},{\"id\":86765950,\"identity\":\"6c2fd04b-c25a-43d2-b32b-0081c3ec3bbd\",\"added_by\":\"auto\",\"created_at\":\"2025-07-15 11:04:22\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":33583,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003ePercentage of NDVI classes in Sundarbans (2007, 2008 \\u0026amp; 2023)\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6950710/v1/f6f54c4612b5a6495b153e0d.png\"},{\"id\":86767446,\"identity\":\"5d1676c5-990f-4e4b-ac34-103ce104b346\",\"added_by\":\"auto\",\"created_at\":\"2025-07-15 11:12:22\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":276113,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eNDVI map of Sundarbans (Pre: 2007, Post: 2008, Recent: 2023)\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6950710/v1/bffa9c476681b197b05f8d4c.png\"},{\"id\":86765068,\"identity\":\"e75b6f72-fc0a-439e-bf92-57c0b39e1d1c\",\"added_by\":\"auto\",\"created_at\":\"2025-07-15 10:56:22\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":274577,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSAVI map of pre and post cyclone Sidr\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6950710/v1/cfc74affe2b0f8b9eab135a5.png\"},{\"id\":86765951,\"identity\":\"4a918a75-ecba-4f36-ad28-2d86e2ceab69\",\"added_by\":\"auto\",\"created_at\":\"2025-07-15 11:04:22\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":23168,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eCalculated NDVI \\u0026amp; SAVI surface features in Sundarbans (Before Sidr)\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6950710/v1/a6008b288281d7d949edff0a.png\"},{\"id\":86765063,\"identity\":\"4b2e607e-5aa8-454f-8670-ddb1d7698de2\",\"added_by\":\"auto\",\"created_at\":\"2025-07-15 10:56:22\",\"extension\":\"png\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":24311,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eCalculated NDVI \\u0026amp; SAVI surface features in Sundarbans (After Sidr)\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6950710/v1/01ac60e1936bdede2a9d7551.png\"},{\"id\":86765954,\"identity\":\"3a9030dd-df8c-4cb6-8b2a-977953cd7d9b\",\"added_by\":\"auto\",\"created_at\":\"2025-07-15 11:04:22\",\"extension\":\"png\",\"order_by\":8,\"title\":\"Figure 8\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":482567,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eChange Detection map of Sundarbans (2007-2008) and (2008 to 2023).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"8.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6950710/v1/d544d284219b910959308e4e.png\"},{\"id\":88398853,\"identity\":\"fa2e3226-f533-4334-af12-cadc0001ce07\",\"added_by\":\"auto\",\"created_at\":\"2025-08-06 06:40:11\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":2497153,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6950710/v1/c9756849-e3a2-4dbd-aa10-423ac226d92b.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Post-Cyclone Vegetation Recovery and Change Detection in the Sundarbans Mangrove Forest Using Landsat-Derived NDVI and SAVI Indices\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eMangrove ecosystems are globally recognized for their ecological significance, particularly in carbon sequestration, shoreline stabilization, and biodiversity conservation. Among them, the Sundarbans\\u0026mdash;the largest contiguous mangrove forest in the world\\u0026mdash;straddles southern Bangladesh and eastern India, forming a crucial buffer against tropical cyclones and tidal surges in the Bay of Bengal (Giri et al., \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e; Rahman et al., \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e). The forest, shaped by the confluence of the Ganges, Brahmaputra, and Meghna river systems, spans approximately 6,017 km\\u0026sup2; on the Bangladesh side, with about 4,143 km\\u0026sup2; of land and 1,874 km\\u0026sup2; of water bodies (Iftekhar \\u0026amp; Saenger, \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eDue to its low elevation and geographic location, Bangladesh is highly vulnerable to climate-induced natural disasters, including cyclones and coastal flooding (Ali, \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1999\\u003c/span\\u003e; Dasgupta et al., \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e). Cyclone Sidr, which struck in November 2007, was among the most devastating in recent history, inflicting extensive damage on the Sundarbans' vegetative cover (Giri et al., \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e). Mangrove forests such as the Sundarbans play a vital role in mitigating such impacts through their dense root networks and structural complexity (Alongi, \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e). However, recurrent cyclones, sea-level rise, and anthropogenic pressures continue to threaten the forest's integrity (Das \\u0026amp; Kundu, \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Hossain \\u0026amp; Ahsan \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eMonitoring changes in forest structure and health is essential for sustainable management, particularly in post-disaster scenarios (Chen, \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Remote sensing (RS) and Geographic Information System (GIS) technologies provide cost-effective, large-scale tools for temporal analysis of vegetation change (Jensen, \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e). Among vegetation indices, the Normalized Difference Vegetation Index (NDVI) is widely used to detect variations in canopy greenness and density (Tucker, \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e1979\\u003c/span\\u003e). The Soil-Adjusted Vegetation Index (SAVI) enhances interpretation in areas with sparse vegetation or exposed soil by minimizing the influence of soil brightness on vegetation measurements (Huete, \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e1988\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eThis study utilizes NDVI and SAVI derived from Landsat 7 ETM\\u0026thinsp;+\\u0026thinsp;imagery to assess vegetation cover changes in the Sundarbans before and after Cyclone Sidr, and to evaluate recovery trends up to 2023. By classifying satellite imagery into vegetation categories and applying change detection analysis, we aim to quantify the cyclone's impact, monitor regeneration, and inform conservation and climate resilience strategies. The study addresses a critical gap in understanding long-term vegetation dynamics in cyclone-affected mangrove ecosystems using freely available remote sensing data.\\u003c/p\\u003e\"},{\"header\":\"2. Materials and Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.1 Study Area\\u003c/h2\\u003e\\u003cp\\u003eThe Sundarbans mangrove forest, located between 21\\u0026ordm;30'N and 22\\u0026ordm;30'N latitude and 89\\u0026ordm;00'E to 89\\u0026ordm;55'E longitude, spans parts of Khulna, Satkhira, and Bagerhat districts in southwestern Bangladesh (Iftekhar \\u0026amp; Saenger, \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). The Sundarbans mangrove forest in Bangladesh spans approximately 6,017 km\\u0026sup2;, comprising about 4,143 km\\u0026sup2; of land and 1,874 km\\u0026sup2; of water bodies. This area accounts for approximately 44% of the country's total forest coverage and about 4.2% of its total land area (Ortolano et al., \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e).​ The elevation ranges from 0.9 to 2.1 meters above mean sea level, making the region highly sensitive to cyclonic storm surges and sea-level rise (Giri et al., \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.2 Satellite Data and Preprocessing\\u003c/h2\\u003e\\u003cp\\u003eLandsat 7 ETM\\u0026thinsp;+\\u0026thinsp;satellite imagery from three time periods\\u0026mdash;pre-cyclone (October 2007), post-cyclone (January 2008), and a recent assessment (February 2023) (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e)\\u0026mdash;was used to analyze vegetation change. All images were obtained from the USGS EarthExplorer portal, ensuring minimal cloud cover and temporal consistency. Each image set included two overlapping scenes to fully cover the Bangladeshi Sundarbans.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eFeatures of the Landsat 7 ETM\\u0026thinsp;+\\u0026thinsp;images used to conduct this study\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"9\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003ePeriods\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eImages\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eDates\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eSun elevation (\\u0026deg;)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eSun azimuth (\\u0026deg;)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003eLand cloud cover\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003eScene cloud cover\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003eGeometric RMSE model\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" morerows=\\\"1\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003ePre- cyclone\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e(a)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eOctober 29, 2007\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e48.74\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e146.058\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e4.563 m\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e(b)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eOctober 20, 2007\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e51.18\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e142.994\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e7.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e3.807 m\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" morerows=\\\"1\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003ePost-cyclone\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e(a)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eJanuary 8, 2008\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e38.327\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e146.64\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e4.005 m\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e(b)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eJanuary 1, 2008\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e38.111\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e147.938\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e4.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e4.315 m\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003ePresent condition\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e\\u003cp\\u003e(a)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eFebruary 3, 2023\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e43.459\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e143.162\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.04\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.02\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e6.349 m\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e\\u003cp\\u003e(b)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eFebruary 2, 2023\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e43.249\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e143.444\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.10\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e6.63\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e5.330 m\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe images were radiometrically and geometrically corrected. Scan line errors in the ETM\\u0026thinsp;+\\u0026thinsp;data were addressed using interpolation techniques in QGIS. All datasets were co-registered to UTM Zone 45N using the WGS84 datum. Layer stacking and false color composites (FCC) were created to visualize vegetation features. A nearest-neighbor resampling method was applied to maintain the integrity of reflectance values.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.3 NDVI and SAVI Computation\\u003c/h2\\u003e\\u003cp\\u003eNormalized Difference Vegetation Index (NDVI) was calculated using the standard formula:\\u003c/p\\u003e\\u003cp\\u003eNDVI = (NIR - Red) / (NIR\\u0026thinsp;+\\u0026thinsp;Red) (Tucker, \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e1979\\u003c/span\\u003e)\\u0026hellip;\\u0026hellip;\\u0026hellip;\\u0026hellip;\\u0026hellip; (I)\\u003c/p\\u003e\\u003cp\\u003eWhere,\\u003c/p\\u003e\\u003cp\\u003eNIR\\u0026thinsp;=\\u0026thinsp;reflectance in the near infrared band\\u003c/p\\u003e\\u003cp\\u003eRED\\u0026thinsp;=\\u0026thinsp;reflectance in the red (Visible)\\u003c/p\\u003e\\u003cp\\u003eArcGIS 10.5 software has been used to calculate the NDVI values of the images. For Landsat 7 ETM+, Band 4 (NIR) and Band 3 (Red) were used, while for Landsat 8 and 9, Band 5 (NIR) and Band 4 (Red) were utilized. NDVI values were reclassified into five vegetation classes: water bodies, bare soil, sparse vegetation, intermediate vegetation, and dense vegetation.\\u003c/p\\u003e\\u003cp\\u003eSAVI was calculated to minimize soil brightness effects using the formula:\\u003c/p\\u003e\\u003cp\\u003eSAVI = [(NIR - Red) / (NIR\\u0026thinsp;+\\u0026thinsp;Red\\u0026thinsp;+\\u0026thinsp;L)] \\u0026times; (1\\u0026thinsp;+\\u0026thinsp;L) (Huete, \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e1988\\u003c/span\\u003e)\\u003cem\\u003e\\u0026hellip;\\u0026hellip;\\u0026hellip;\\u0026hellip;\\u0026hellip;\\u0026hellip; (\\u003c/em\\u003eII)\\u003c/p\\u003e\\u003cp\\u003eWhere, L is a canopy background adjustment factor set to 0.5 for intermediate vegetation density.\\u003c/p\\u003e\\u003cp\\u003eThe L-factor should be applied according to the density of the vegetation being observed. In situations where the vegetation cover is dense, an appropriate L-factor is 0.25; for intermediate density, 0.5 should be used, while for very low density, an L-factor of 1.0 is recommended (Huete, \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e1988\\u003c/span\\u003e; Baret \\u0026amp; Guyot, \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e1991\\u003c/span\\u003e).\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.4 Change Detection Analysis\\u003c/h2\\u003e\\u003cp\\u003ePost-classification change detection was performed by comparing NDVI-derived maps from 2007, 2008, and 2023. Pixel-wise transitions between vegetation classes were analyzed to detect trends in deforestation, regeneration, and land transformation. ArcGIS 10.5 was used for raster reclassification, area computation, and change matrix generation.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.5 Area Estimation\\u003c/h2\\u003e\\u003cp\\u003eArea calculations were performed using the formula:\\u003c/p\\u003e\\u003cp\\u003eArea (km\\u0026sup2;)\\u0026thinsp;=\\u0026thinsp;Pixel Count \\u0026times; 30 \\u0026times; 30 / 1,000,000 (USGS, 2019) \\u003cem\\u003e\\u0026hellip;\\u0026hellip;\\u0026hellip;\\u0026hellip;\\u0026hellip;\\u0026hellip;..\\u003c/em\\u003e (III)\\u003c/p\\u003e\\u003cp\\u003eThis provided vegetation class coverage in square kilometers for each time period.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.6 Accuracy Assessment\\u003c/h2\\u003e\\u003cp\\u003eAccuracy was evaluated using confusion matrices based on 56\\u0026ndash;81 control points per image year, derived from visual interpretation and Google Earth data. Overall accuracy and kappa coefficients were computed to assess classification reliability. User\\u0026rsquo;s and producer\\u0026rsquo;s accuracies were calculated for each class. Results indicated strong classification agreement, with overall accuracy ranging from 82.71\\u0026ndash;89.65%.\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"3. Results\",\"content\":\"\\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e3.1 Vegetation Classification and Coverage\\u003c/h2\\u003e\\u003cp\\u003eVegetation in the Sundarbans was classified into five categories based on NDVI and SAVI indices: dense, intermediate, sparse vegetation, bare soil, and water bodies which showed in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e. Pre-cyclone (2007) analysis indicated that dense vegetation accounted for 77.07% (3358 km\\u0026sup2;) of the area. Following Cyclone Sidr in 2008, this dropped sharply to 57.37% (2496 km\\u0026sup2;), highlighting the immediate impact of the cyclone. Intermediate vegetation also declined from 8.58\\u0026ndash;7.63%, while sparse vegetation increased significantly from 2.54\\u0026ndash;18.55%, indicating damage to mature forest cover and regrowth of understory or broken canopy structure.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eVegetation cover classes definition\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"5\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCategory\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eCharacterization\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eScientific name\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eBangla name\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eDistribution\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e\\u003cp\\u003eDense\\u003c/p\\u003e\\u003cp\\u003eVegetation\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e\\u003cp\\u003eTress height above 65ft and more\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eHeritiera fomes\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eSundari\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eAz, Ar\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eSonneratia apetala\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eKewra\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eAz; Ar\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eBruguiera gymnorhiza\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003ekakra\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eMz; Pz; Kh\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eB. sexangula\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eLalkakra\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eMz; Pz; Ch\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eX. moluccensis\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003ePoshur etc.\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eAz; Ar\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e\\u003cp\\u003eIntermediate vegetation\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e\\u003cp\\u003eHeight between 40 to 65ft\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eS. caseolaris\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eChoila/ora\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eMz; Sr, Ch\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eRhizophora apiculata\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eBhorjhana\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eMz; Pz; Kh\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eR. mucronata Lam.\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eJhana\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eMz; Pz; Kh\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eA. officinalis\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eBaen\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eAz; Ar\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eXylocarpus granatum\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eDhundal etc\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eMz, Kh, St\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e\\u003cp\\u003eSparse Vegetation\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e\\u003cp\\u003eHeight below 40ft but above 10ft\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003ePhoenix paludosa\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eHental\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eAz; Ar\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eCeriops decandra\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eGoran\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eAz; Ar\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eExcoecaria agallocha L.\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eGewa\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eAz; Ar\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eNypa frutican\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eGolpata\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eAz; Ar\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eAcanthus ilicifolius\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eHargoza etc\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eAz; Ar\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eHere, Distribution: Ch\\u0026thinsp;=\\u0026thinsp;Chandpai Range, Kh\\u0026thinsp;=\\u0026thinsp;Khulna Range, Sr\\u0026thinsp;=\\u0026thinsp;Sarankhola Range, St\\u0026thinsp;=\\u0026thinsp;Satkhira Range; Ar\\u0026thinsp;=\\u0026thinsp;all range, Az\\u0026thinsp;=\\u0026thinsp;all zones, Mz\\u0026thinsp;=\\u0026thinsp;mesohaline zone, Oz\\u0026thinsp;=\\u0026thinsp;oligohaline zone, and Pz\\u0026thinsp;=\\u0026thinsp;polyhaline zone.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e3.2 Vegetation Recovery and Current Status (2023)\\u003c/h2\\u003e\\u003cp\\u003eAnalysis of 2023 satellite imagery shows significant regeneration. Dense vegetation now covers approximately 72.53% (3156 km\\u0026sup2;), indicating a recovery of over 660 km\\u0026sup2; since 2008. Intermediate vegetation increased to 9.44% (411 km\\u0026sup2;), while sparse vegetation declined to 2.52% (110 km\\u0026sup2;). The recovery trend suggests the ecosystem's resilience, though not all areas have returned to pre-cyclone conditions. Bare soil increased to 2.96% (129 km\\u0026sup2;), possibly due to land use conversion or slow regrowth. Water bodies, while initially expanded post-cyclone, stabilized at 12.52% (Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e, \\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e and \\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e3.3 SAVI-Derived Vegetation Analysis\\u003c/h2\\u003e\\u003cp\\u003eSAVI analysis mirrored NDVI trends, confirming vegetation class transitions. Pre-cyclone SAVI-based dense vegetation was 3216 km\\u0026sup2; (Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e and \\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e), decreasing to 2503 km\\u0026sup2; post-cyclone (Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e and \\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e), and increasing again by 2023. Intermediate vegetation declined from 639 to 460 km\\u0026sup2; (Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e and \\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e) while sparse vegetation increased from 140 to 678 km\\u0026sup2; (Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e and \\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e). The SAVI index provided enhanced detection of vegetation recovery in sparse zones where soil exposure was significant.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e3.4 Change Detection Analysis\\u003c/h2\\u003e\\u003cp\\u003eAnalysis of Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003e, as detailed in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e, the NDVI classification indicates substantial vegetation degradation following Cyclone Sidr. The total vegetated area\\u0026mdash;including sparse, intermediate, and dense vegetation\\u0026mdash;declined markedly from 3843 km\\u0026sup2; in October 2007 to 3635 km\\u0026sup2; by January 2008. Among the three vegetation classes, dense vegetation experienced the most significant loss, decreasing from 3358 km\\u0026sup2; (77.07%) to 2496 km\\u0026sup2; (57.37%). This represents an approximate 45% reduction, highlighting the cyclone's severe impact on mature forest stands. The sharp decline in dense canopy coverage reflects not only physical uprooting and breakage of large trees but also the sensitivity of NDVI in detecting canopy disruption and biomass loss in mangrove ecosystems (Asbridge et al., \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eIntermediate vegetation declined from 8.58\\u0026ndash;7.63%. Cyclone Sidr\\u0026rsquo;s peak wind speeds (215 km/h) significantly impacted these vegetation types. Larger trees absorbed much of the cyclone\\u0026rsquo;s energy, protecting smaller vegetation. Uprooted and broken trees were classified as sparse vegetation, explaining its apparent increase from 2.54\\u0026ndash;18.55%.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eBased on NDVI and SAVI, Sundarbans forest statistics in tabulated form (km\\u003csup\\u003e2\\u003c/sup\\u003e and percentage) for the years (2007, 2008, and 2023).\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"14\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c10\\\" colnum=\\\"10\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c11\\\" colnum=\\\"11\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c12\\\" colnum=\\\"12\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c13\\\" colnum=\\\"13\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c14\\\" colnum=\\\"14\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eFeatures\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c6\\\" namest=\\\"c2\\\"\\u003e\\u003cp\\u003e2007(Pre Sidr)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c11\\\" namest=\\\"c7\\\"\\u003e\\u003cp\\u003e2008 (Post Sidr)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c14\\\" namest=\\\"c12\\\"\\u003e\\u003cp\\u003e2023 (Recent)\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eNDVI\\u003c/p\\u003e\\u003cp\\u003eClass range\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eNDVI area Sq.km\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e%\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eSAVI area Sq.km\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e%\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003eNDVI class range\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003eNDVI area Sq.km\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e%\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003eSAVI area Sq.km\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e%\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003eNDVI class range\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003eNDVI area Sq.km\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c14\\\"\\u003e\\u003cp\\u003e%\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eWater bodies\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e(-)\\u003c/p\\u003e\\u003cp\\u003evalue\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e436\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e10.06\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e362\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e8.30\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e(-) value\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e689\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e15.83\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e670\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e15.37\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e(-) value\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e545\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c14\\\"\\u003e\\u003cp\\u003e12.52\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eBare soil\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.01\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e78\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.79\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e100\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e2.29\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.004\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e26\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e0.59\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e47\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e1.07\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e0.10\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e129\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c14\\\"\\u003e\\u003cp\\u003e2.96\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSparse Vegetation\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.15\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e111\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2.54\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e140\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e3.21\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.08\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e807\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e18.55\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e678\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e15.55\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e0.19\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e110\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c14\\\"\\u003e\\u003cp\\u003e2.52\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eIntermediate vegetation\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.32\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e374\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e8.58\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e639\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e14.66\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.12\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e332\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e7.63\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e460\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e10.55\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e0.25\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e411\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c14\\\"\\u003e\\u003cp\\u003e9.44\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eDense\\u003c/p\\u003e\\u003cp\\u003evegetation\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.78\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3358\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e77.07\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e3116\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e71.07\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e0.41\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e2496\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u003cp\\u003e57.37\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u003cp\\u003e2503\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u003cp\\u003e57.43\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u003cp\\u003e0.44\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u003cp\\u003e3156\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c14\\\"\\u003e\\u003cp\\u003e72.53\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eFlooding also increased water body coverage from 10.06\\u0026ndash;15.83%, while bare soil decreased from 1.79\\u0026ndash;0.59%. Current NDVI ranges from \\u0026minus;\\u0026thinsp;0.15 to +\\u0026thinsp;0.44, while SAVI ranges from \\u0026minus;\\u0026thinsp;0.73 to +\\u0026thinsp;0.65. By 2023, dense vegetation had recovered to 3156 km\\u0026sup2; (72.53%), though still slightly below the pre-cyclone level. Bare soil has risen to about 3%, while intermediate vegetation has also shown growth.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e3.5 Accuracy Assessment Results\\u003c/h2\\u003e\\u003cp\\u003eAccuracy assessment is vital for verifying satellite classification. Confusion matrices were constructed using reference data (Google Earth, visual interpretation, etc.). Overall accuracy and kappa statistics were calculated for 2007, 2008, and 2023. Classification accuracy remained consistently high across all three years. As observed from Tables\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e, \\u003cspan refid=\\\"Tab5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e, and \\u003cspan refid=\\\"Tab6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e, the 2007 classification achieved an overall accuracy of 87.5% (Kappa\\u0026thinsp;=\\u0026thinsp;0.81), 2008 showed a slightly higher accuracy of 89.65% (Kappa\\u0026thinsp;=\\u0026thinsp;0.87), and 2023 maintained strong performance with 82.71% (Kappa\\u0026thinsp;=\\u0026thinsp;0.76). These results confirm the robustness and reliability of the NDVI-based classification method, particularly in detecting dense and intermediate vegetation classes with a high degree of confidence.\\u003c/p\\u003e\\u003cp\\u003eTable\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e, \\u003cspan refid=\\\"Tab5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e, \\u003cspan refid=\\\"Tab6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e: Represent the accuracy assessment for the years 2007, 2008, and 2023 respectively\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003e(2007):\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"8\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eClass name\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eWater bodies\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eBare soil\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eSparse vegetation\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eIntermediate vegetation\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eDense vegetation\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003eTotal\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003eUser accuracy (%)\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eWater bodies\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e10\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e11\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e90\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eBare soil\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e100\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSparse vegetation\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e50\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eIntermediate vegetation\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e90\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eDense vegetation\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e29\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e33\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e87.87\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eTotal\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e12\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e10\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e29\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e56\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eProducer accuracy (%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e83.33\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e50\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e100\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e70\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e100\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eOverall accuracy\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e87.5%\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eKappa coefficient\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.81%\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab5\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 5\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003e(2008):\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"8\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eClass name\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eWater bodies\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eBare soil\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eSparse vegetation\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eIntermediate vegetation\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eDense vegetation\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003eTotal\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003eUser accuracy (%)\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eWater bodies\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e18\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e18\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e100\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eBare soil\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e50\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSparse vegetation\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e66.66\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eIntermediate vegetation\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e88.89\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eDense vegetation\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e18\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e18\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e100\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eTotal\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e19\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e22\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e58\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eProducer accuracy (%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e94.73\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e100\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e85.71\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e100\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e81.81\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eOverall accuracy\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e89.65%\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eKappa coefficient\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.87%\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab6\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 6\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003e(2023):\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"8\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eClass name\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eWater bodies\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eBare soil\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eSparse vegetation\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eIntermediate vegetation\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eDense vegetation\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003eTotal\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003eUser accuracy (%)\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eWater bodies\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e20\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e20\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e100\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eBare soil\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e66.66\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSparse vegetation\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e80\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eIntermediate vegetation\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e11\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e15\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e73.33\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eDense vegetation\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e26\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e32\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e81.25\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eTotal\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e22\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e15\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e28\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e81\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eProducer accuracy (%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e90.90\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e75\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e75\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e73.33\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e92.85\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eOverall accuracy\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e82.71%\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eKappa coefficient\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.76%\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"4. Discussion\",\"content\":\"\\u003cp\\u003eCyclone Sidr (2007) caused extensive damage to the Sundarbans, an ecologically significant mangrove forest. As a natural buffer, the Sundarbans absorbed much of the cyclone's force, resulting in severe vegetation loss. Change detection analysis confirmed widespread flooding and a reduction in dense vegetation. NDVI values showed an increase in water bodies from 10.06\\u0026ndash;15.83% post-cyclone. Total vegetation cover dropped from 3843 sq.km to 3635 sq.km, with dense vegetation dropping by ~\\u0026thinsp;45%. Intermediate vegetation also declined, while sparse vegetation temporarily increased due to broken trees being misclassified. The natural shielding by taller vegetation allowed smaller trees to survive.\\u003c/p\\u003e\\u003cp\\u003eBetween 2008 and 2023, significant regrowth occurred. Dense vegetation recovered to 72.53%, and intermediate vegetation increased to 9.44%. The Sundarbans demonstrated ecological resilience, although full restoration has not been achieved. However, anthropogenic pressures, including deforestation and expanding settlements, remain threats. Bare soil increased from 0.59% post-cyclone to 3% by 2023, highlighting ongoing land degradation.\\u003c/p\\u003e\\u003cp\\u003eThis research offers valuable insights into cyclone impact assessment, post-disaster forest recovery, and long-term vegetation monitoring. Findings can inform disaster preparedness, ecological conservation, infrastructure planning, and agricultural decision-making. The Sundarbans' capacity to recover emphasizes the importance of preserving this vital ecosystem against future environmental and human-induced stresses.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eLimitations and Future Work\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eDespite promising results, this study has limitations. First, classification accuracy may be affected by seasonal variations and residual scan line artifacts in Landsat 7 imagery. Second, field data for biomass estimation or species-level validation were not incorporated, which limits ecological interpretation. Third, while NDVI and SAVI are effective for vegetation detection, their sensitivity may saturate in dense canopy conditions.\\u003c/p\\u003e\\u003cp\\u003eFuture studies could benefit from the integration of higher-resolution datasets (e.g., Sentinel-2 or PlanetScope), LiDAR or radar-based canopy height metrics, and field-based biomass measurements. Time-series analysis using Google Earth Engine would also facilitate continuous monitoring and reduce cloud-cover issues.\\u003c/p\\u003e\"},{\"header\":\"5. Conclusion\",\"content\":\"\\u003cp\\u003eThis study provides a detailed assessment of vegetation change in the Sundarbans mangrove forest in response to Cyclone Sidr and subsequent recovery, using NDVI and SAVI derived from multi-temporal Landsat imagery. The findings reveal both the short-term devastation and the long-term regenerative trends of the ecosystem, with implications for mangrove conservation and climate resilience planning. By combining satellite-based indices with change detection and accuracy validation, this research offers a scalable and replicable method for vegetation monitoring in cyclone-affected coastal zones. It emphasizes the urgent need for proactive management strategies to mitigate anthropogenic threats and enhance the adaptive capacity of vulnerable mangrove ecosystems in the face of climate change.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eFunding Declaration\\u003c/strong\\u003e: There was no Funding\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthics declaration\\u003c/strong\\u003e: not applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent to Publish declaration\\u003c/strong\\u003e: not applicable\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent to Participate declaration\\u003c/strong\\u003e: not applicable\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData Availability Statement:\\u0026nbsp;\\u003c/strong\\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthor Contribution\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eMd. Saifur Rahman* conceptualized the study, led the manuscript writing and supervision. Fataha Hossen contributed to remote sensing data processing, analysis and visualization. Md. Ariful Islam Arif assisted in literature review, data interpretation, and manuscript editing. Mawya Siddeqa contributed to methodological development and overall supervision. All authors reviewed and approved the final manuscript.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eAli, A. (1999). \\u003cem\\u003eClimate change impacts and adaptation assessment in Bangladesh.\\u003c/em\\u003e Climate Research, 12(2-3), 109\\u0026ndash;116. https://doi.org/10.3354/cr012109\\u003c/li\\u003e\\n\\u003cli\\u003eAlongi, D. M. (2008). \\u003cem\\u003eMangrove forests: Resilience, protection from tsunamis, and responses to global climate change.\\u003c/em\\u003e Estuarine, Coastal and Shelf Science, 76(1), 1\\u0026ndash;13. https://doi.org/10.1016/j.ecss.2007.08.024\\u003c/li\\u003e\\n\\u003cli\\u003eAsbridge, E., Lucas, R., Accad, A., \\u0026amp; Dowling, R. (2016). Mangrove response to environmental change in Australia\\u0026apos;s Gulf of Carpentaria. \\u003cem\\u003eEcology and Evolution\\u003c/em\\u003e, 6(11), 3523\\u0026ndash;3539. https://doi.org/10.1002/ece3.2137\\u003c/li\\u003e\\n\\u003cli\\u003eBaret, F., \\u0026amp; Guyot, G. (1991). Potentials and limits of vegetation indices for LAI and APAR assessment. \\u003cem\\u003eRemote Sensing of Environment, 35\\u003c/em\\u003e(2\\u0026ndash;3), 161\\u0026ndash;173. https://doi.org/10.1016/0034-4257(91)90009-U\\u003c/li\\u003e\\n\\u003cli\\u003eChen, X. (2023). \\u003cem\\u003eAn exploration of forest fires and post-disaster recovery\\u003c/em\\u003e. Frontiers in Forests and Global Change, 6, Article 1223934. https://doi.org/10.3389/ffgc.2023.1223934\\u003c/li\\u003e\\n\\u003cli\\u003eDas, S., \\u0026amp; Kundu, A. (2021). \\u003cem\\u003eAssessment and attribution of mangrove forest changes in the Indian Sundarbans from 2000 to 2020\\u003c/em\\u003e. Remote Sensing, 13(24), 4957. https://doi.org/10.3390/rs13244957\\u003c/li\\u003e\\n\\u003cli\\u003eDasgupta, S., Laplante, B., Murray, S., \\u0026amp; Wheeler, D. (2010). \\u003cem\\u003eExposure of developing countries to sea-level rise and storm surges.\\u003c/em\\u003e Climatic Change, 106(4), 567\\u0026ndash;579. https://doi.org/10.1007/s10584-010-9959-6\\u003c/li\\u003e\\n\\u003cli\\u003eGiri, C., Ochieng, E., Tieszen, L.L., Zhu, Z., Singh, A., Loveland, T., ... \\u0026amp; Duke, N. (2011). \\u003cem\\u003eStatus and distribution of mangrove forests of the world using earth observation satellite data.\\u003c/em\\u003e Global Ecology and Biogeography, 20(1), 154\\u0026ndash;159. https://doi.org/10.1111/j.1466-8238.2010.00584.x\\u003c/li\\u003e\\n\\u003cli\\u003eGiri, C., Zhu, Z., Tieszen, L. L., Singh, A., Gillette, S., \\u0026amp; Kelmelis, J. A. (2007). \\u003cem\\u003eMangrove forest distributions and dynamics (1975\\u0026ndash;2005) of the tsunami-affected region of Asia.\\u003c/em\\u003e Journal of Biogeography, 35(3), 519\\u0026ndash;528. https://doi.org/10.1111/j.1365-2699.2007.01806.x\\u003c/li\\u003e\\n\\u003cli\\u003e\\u003cstrong\\u003eHossain, M. S., \\u0026amp; Ahsan, M. A. (2019).\\u003c/strong\\u003e \\u003cem\\u003eImpacts of climate change on the Sundarbans mangrove ecosystem: Challenges and future conservation strategies.\\u003c/em\\u003e Environmental Science and Pollution Research, 26(31), 31440\\u0026ndash;31457. https://doi.org/10.1007/s11356-019-06181-x\\u003c/li\\u003e\\n\\u003cli\\u003eHuete, A. R. (1988). A soil-adjusted vegetation index (SAVI). \\u003cem\\u003eRemote Sensing of Environment, 25\\u003c/em\\u003e(3), 295\\u0026ndash;309. https://doi.org/10.1016/0034-4257(88)90106-X\\u003c/li\\u003e\\n\\u003cli\\u003eIftekhar, M. S., \\u0026amp; Saenger, P. (2008). \\u003cem\\u003eVegetation dynamics in the Bangladesh Sundarbans mangroves: A review of forest inventories.\\u003c/em\\u003e Wetlands Ecology and Management, 16(4), 291\\u0026ndash;312. https://doi.org/10.1007/s11273-007-9063-5\\u003c/li\\u003e\\n\\u003cli\\u003eJensen, J. R. (2007). \\u003cem\\u003eRemote sensing of the environment: An Earth resource perspective\\u003c/em\\u003e (2nd ed.). Pearson Prentice Hall.\\u003c/li\\u003e\\n\\u003cli\\u003eOrtolano, L., et al. (2017). Bangladesh Sundarbans: Present Status of the Environment and Biota. \\u003cem\\u003eResearchGate\\u003c/em\\u003e. Retrieved from https://www.researchgate.net/publication/281889734_Bangladesh_Sundarbans_Present_Status_of_the_Environment_and_Biota.\\u003c/li\\u003e\\n\\u003cli\\u003eRahman, M.M., Asaduzzaman, M., \\u0026amp; Islam, M.S. (2010). \\u003cem\\u003eEcosystem-based adaptation to climate change: a case study of the Sundarbans mangrove forest, Bangladesh.\\u003c/em\\u003e Environment and Natural Resources Research, 1(1), 1\\u0026ndash;10. https://doi.org/10.5539/enrr.v1n1p1\\u003c/li\\u003e\\n\\u003cli\\u003eTucker, C. J. (1979). \\u003cem\\u003eRed and photographic infrared linear combinations for monitoring vegetation.\\u003c/em\\u003e Remote Sensing of Environment, 8(2), 127-150. https://doi.org/10.1016/0034-4257(79)90013-0\\u003c/li\\u003e\\n\\u003cli\\u003eU.S. Geological Survey (USGS). (2019). \\u003cem\\u003eUsing the USGS Landsat Surface Reflectance Data Products\\u003c/em\\u003e. U.S. Department of the Interior. Retrieved from https://www.usgs.gov/\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-6950710/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-6950710/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eThis study investigates the long-term impact of Cyclone Sidr (2007) on the vegetation dynamics of the Sundarbans mangrove forest in Bangladesh. Using multi-temporal Landsat 7 ETM\\u0026thinsp;+\\u0026thinsp;imagery from 2007, 2008, and 2023, vegetation cover changes were analyzed through Normalized Difference Vegetation Index (NDVI) and Soil-Adjusted Vegetation Index (SAVI). Five vegetation classes namely water bodies, bare soil, sparse, intermediate and dense vegetation were derived using NDVI thresholds and change detection analysis. Results indicate a substantial decrease in dense vegetation from 77.07% in 2007 to 57.37% in 2008, followed by gradual recovery to 72.53% by 2023. Cyclone Sidr caused a dramatic increase in sparse vegetation and water bodies due to flooding and forest damage. Accuracy assessment using ground-truth data and Google Earth observations yielded kappa coefficients of 0.81, 0.87, and 0.76 for 2007, 2008, and 2023 respectively. The findings demonstrate the resilience and regeneration capacity of mangrove ecosystems but also emphasize anthropogenic stressors, including land use change. These insights inform conservation strategies, disaster risk reduction, and forest monitoring efforts in cyclone-prone coastal zones.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Post-Cyclone Vegetation Recovery and Change Detection in the Sundarbans Mangrove Forest Using Landsat-Derived NDVI and SAVI Indices\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-07-15 10:56:17\",\"doi\":\"10.21203/rs.3.rs-6950710/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"f768bdbd-c556-4355-a1f0-adf6ece3c095\",\"owner\":[],\"postedDate\":\"July 15th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-08-06T06:39:37+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-07-15 10:56:17\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-6950710\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-6950710\",\"identity\":\"rs-6950710\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}