Hot spot ($${G}_{i}^{{*}}$$) model for forest vulnerability assessment: a remote sensing-based geo-statistical investigation of the Sundarbans mangrove forest, Bangladesh

Hussain, Nur; Islam, Md. Nazrul

doi:10.1007/s40808-020-00828-4

Cited by 11 publications

(5 citation statements)

References 37 publications

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“…Alongside supervised classification, unsupervised classification, and vegetation indices especially NDVI (as a single index or part of a composite index) were extensively used for forest degradation (Reddy et al, 2016;Sharma et al, 2022), monitoring assessment (Islam et al, 2019;Awty-Carroll., 2019) and ecosystem health assessment (Ishtiaque et al, 2016). In addition, the application of machine learning to analyze satellite data was also observed in several studies to study forest cover mapping and change detection (Redowan et al, 2020;Hussain and Islam, 2020) and forest degradation (Hasan et al, 2021;Rahaman et al, 2022). The algorithms used in those studies include CLASlite (Redowan et al, 2020), Stochastic Gradient Boosting (SGB), Random Forest (RF), Support Vector Machine (SVM), Partial Least Square Regression (PLSR) for leaf carbon ratio analysis (Rahman et al, 2020); Maximum likelihood classification (MLC), support vector machine (SVM), random forest (RF) and artificial neural network (ANN) to assess vegetation degradation (Rahaman et al, 2022); Random Forest (RF) for forest degradation mapping (Hasan et al, 2021).…”

Section: Methods Of Analyses Used In the Studiesmentioning

confidence: 97%

A Review on Remote Sensing Based Forest Vegetation Health Assessment: Bangladesh Perspective

Sumiya,

Hasan,

Nusrat

et al. 2024

Dhaka Univ J Earth Env Sci

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This paper aims to present a critical review of the published scientific papers that have addressed the issue of forest health in Bangladesh using remote sensing techniques. A systematic review approach has been followed in this study where all the available papers on the application of remote sensing to assess the forest health vegetation condition of Bangladesh were considered for review. That search resulted in the selection of 48 papers. The findings indicate that remote-sensing-based studies have focused mostly on forest cover mapping and change, and landcover change detection rather than assessing the overall health condition of those forests. Also, among the major forests of the country, most studies have been conducted on Mangrove (Sundarban) forests whereas the least number of studies were found for the forests in the Chittagong Hill Tracts Forests areas and those studies were mostly conducted after the Rohingya crisis. Landsat satellite products have been most extensively used for their broader temporal resolution and availability while a few studies have worked with other products like MODIS, Sentinel, SPOT, etc. The application of advanced classification approaches incorporating machine learning algorithms and ground validation has shown effectiveness for investigating the forest or overall ecosystem health in a more detailed way. Although the RS techniques are increasingly used to study the forests of Bangladesh, forest health-specific and indicator-based research is yet to be done which can ensure sustainable forest management. The Dhaka University Journal of Earth and Environmental Sciences, Vol. 12(1), 2023, P 107-121

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Section: Methods Of Analyses Used In the Studiesmentioning

confidence: 97%

A Review on Remote Sensing Based Forest Vegetation Health Assessment: Bangladesh Perspective

Sumiya,

Hasan,

Nusrat

et al. 2024

Dhaka Univ J Earth Env Sci

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“…Hotspot analysis was carried out using Getis-Ord Gi Ã statistics to identify the statistically significant hotspots and cold spots in TC-affected and unaffected regions. The z-score of Getis-Ord Gi Ã statistics is helpful for the determination of locations with spatial clusters of high or low values, with a confidence level of 99% (a < 0.01), 95% (a < 0.05), and 99% (a < 0.1) (Das et al 2020;Hussain and Islam 2020). The Getis-Ord Gi Ã statistics can be calculated as follows (Ord and Getis 2010):…”

Section: Spatial Statistics Methodsmentioning

confidence: 99%

Geospatial modeling of the tropical cyclone risk in the Guangdong Province, China

Zhou

Kuang

Zhu

et al. 2021

Geomatics, Natural Hazards and Risk

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Tropical cyclones (TCs) are prominent natural hazards in the Guangdong Province, China; hence, comprehensive risk assessment is vital to reduce potential losses due to TC hazards. A multi-indicator system was developed by incorporating 19 criteria under 3 risk components and a combination of the analytic hierarchy process and index of entropy was adopted to generate a TC risk map. Spatial statistics were applied to determine the spatial association and significant TC risk hotspots. The TC risk map showed that $40% of the study area is classified as a high or extremely high TC risk. Areas with the highest risk are mainly concentrated in the eastern region (ER), western region (WR), and Zhuhai and Shenzhen in the Pearl River Delta (PRD), whereas other areas in the PRD and mountainous region (MR) in the inland are classified as zones with the lowest risk. The results of the spatial autocorrelation analysis showed a significant positive spatial clustering in the spatial pattern of the TC risk, suggesting a varying spatial distribution and clustering pattern of the TC risk level that can be used as preliminary planning framework for tropical cyclone risk management. The results of this study provide theoretical reference for the design of disaster mitigation measures.

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“…The transform of healthy vegetation to unhealthy vegetation was remarkably higher during the 2011-2021 period. The decrease of unhealthy vegetation and subsequent increase of healthy vegetation during 2001-2011 can the explained by the fact that cyclone Sidr extensively damaged mangrove forest in 2007 and rapid regrowth of mangrove occurred from 2007 to 2011 and possibly estimated higher healthy vegetation in 2011 Hussain and Islam (2020). also documented a low-density mangrove area due to cyclone Sidr but observed a high-density mangrove area in 2009.…”

mentioning

confidence: 91%

Thirty-Year Spatiotemporal Change Record of Sundarban Mangrove Forest in Bangladesh

Uzzaman

Miah²,

Abdullah

et al. 2021

Ann. Bangladesh Agric.

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Accurate and realistic forest cover change assessment is essential for the conservation and management of the Sundarban mangrove forest of Bangladesh. With these views, an integrated way of the vegetation cover assessment was conducted using time-series Landsat satellite imageries of 1991, 2001, 2011, and 2021. During the last 30-year (1991-2021), variations in four land cover classes viz. healthy vegetation, unhealthy vegetation, water body, and sandbar were recorded. It showed a decreasing trend of forest vegetation and a subsequent increase of water bodies during the study period. The healthy vegetation and unhealthy vegetation decreased at 1.33 and 1.66%, respectively, whereas water bodies increased 2.55% at the same time. The healthy vegetation consistently decreased over the decades, but unhealthy vegetation decreased during the 2001-2011 period. Conversion from healthy vegetation to unhealthy vegetation and unhealthy vegetation to healthy vegetation during 1991-2001 was similar. Such transform was much higher from unhealthy to healthy vegetation during 2001-2011. Transformation of healthy vegetation to unhealthy vegetation was remarkably higher during the 2011-2021 period. Further continuous change detection and classification algorithm (CCDC) showed a stable pattern over the study period without significant breakpoints. This study reveals the need for regular mangrove forest monitoring. The findings of this study can be used as a reference in the formulation and implementation of sustainable mangrove forest conservation and management. Ann. Bangladesh Agric. (2020) 24(2): 15-32

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Hot spot ($${G}_{i}^{{*}}$$) model for forest vulnerability assessment: a remote sensing-based geo-statistical investigation of the Sundarbans mangrove forest, Bangladesh

Cited by 11 publications

References 37 publications

A Review on Remote Sensing Based Forest Vegetation Health Assessment: Bangladesh Perspective

A Review on Remote Sensing Based Forest Vegetation Health Assessment: Bangladesh Perspective

Geospatial modeling of the tropical cyclone risk in the Guangdong Province, China

Thirty-Year Spatiotemporal Change Record of Sundarban Mangrove Forest in Bangladesh

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