Comparative analysis of forest canopy mapping methods for the Sundarban biosphere reserve, West Bengal, India

Mondal, Ismail; Thakur, Sandeep; Juliev, Mukhiddin; De, Tarun Kumar

doi:10.1007/s10668-021-01291-6

Cited by 22 publications

(19 citation statements)

References 43 publications

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“…Numerous studies have shown that NDVI can be used for monitoring changes in forest coverage [1][2][3][4][5]12,16], and this study also used NDVI to monitor changes in pine forest coverage during the rainy and dry seasons from 2009 to 2020.…”

Section: Methodsmentioning

confidence: 99%

“…2 The NDVI data are presented as the mean ± standard error. 3 Pine damage rate is the victimization rate in Figure 1.…”

Section: Influence Of Pests and Anthropogenic Activity On Changes In ...mentioning

confidence: 99%

“…Vegetation coverage is influenced by climate and other factors such as pests, diseases, and anthropogenic activities. Furthermore, a large number of studies have shown that normalized differential vegetation index (NDVI) has a high correlation with vegetation coverage [1][2][3] and that the higher the NDVI, the higher the vegetation coverage. Therefore, NDVI is an effective indicator for monitoring variation in vegetation, which can reflect the growth of surface vegetation [4,5].…”

Section: Introductionmentioning

confidence: 99%

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Trend Analysis of Coverage Variation in Pinus yunnanensis Franch. Forests under the Influence of Pests and Abiotic Factors

Liu

Zhang

et al. 2022

Forests

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The Yunnan pine (Pinus yunnanensis Franch.) is one of the primary forest vegetation types in Yunnan Province and has prominent economic value and ecological significance. Monitoring changes in Yunnan pine forests contributes to their health management. The normalized differential vegetation index (NDVI) is an essential indicator for studying regional vegetation change. Landsat images were used to analyze the NDVI changes of Yunnan pine forests in rainy and dry seasons as well as the NDVI changes of Yunnan pine forests from 2009 to 2020. The results showed that the NDVI in Yunnan pine forests showed opposite trends in the rainy and dry seasons, with an increase in the rainy season and a decrease in the dry season. The areas of NDVI decline during the rainy season are mainly located at the edges of the forest area, with very few within the forest area. The main reason for the decline in NDVI within the forest area was pests. From 2009 to 2020, the areas where NDVI increased and decreased were 92.23% and 7.77% of the pine forests, respectively. The decreasing areas were mainly located at the edges of the pine forests, with sporadic distribution within the forest areas. The drought led to a significant decline in NDVI in the forest area in 2010 and 2013. Due to the lack of protective measures, the forest area in Xijiekou Town was severely infested with pests, with complex NDVI changes and strong fluctuations, and areas of severe pest infestation could be detected by NDVI imagery. This study is meaningful for forest protection and explores the potential capability of Landsat imagery for pest detection.

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Section: Methodsmentioning

confidence: 99%

“…2 The NDVI data are presented as the mean ± standard error. 3 Pine damage rate is the victimization rate in Figure 1.…”

Section: Influence Of Pests and Anthropogenic Activity On Changes In ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Trend Analysis of Coverage Variation in Pinus yunnanensis Franch. Forests under the Influence of Pests and Abiotic Factors

Liu

Zhang

et al. 2022

Forests

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“…1) and is crisscrossed by many estuaries which are mostly distributaries of the Hooghly estuary (Thakur et al, 2020ab;Mondal, et al, 2016;2021b;Bag et al, 2019;Bandyopadhyay et al, 2014). The delta is an ecologically sensitive region that is impacted by regular tidal ebb and ow of 12 durations each (Mondal et al, 2021a). Water samples were collected at nine widely dispersed sampling stations along the mangrove-dominated banks of the Mooriganga, Saptamukhi, and Hooghly estuaries, three distributaries of the Ganges.…”

Section: Study Areamentioning

confidence: 99%

Estimation of Chlorophyll-a, SPM and Salinity in Mangrove Dominated Tropical Estuarine Areas of Hooghly River, North East Coast of Bay of Bengal, India Using Sentinel-3 Data

Mondal

Nandi

et al. 2021

Preprint

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This study aims to explore the variations in spatial/Spatio-temporal characteristics of water quality parameters of three estuaries in the western part of the Indian Sundarbans. Reliable retrieval of near surface concentrations of parameters such as Chlorophyll-a, SST & TSM in various aquatic ecosystems with broad ranges of trophic needs has long been a complex issue. In this study the C2RCC processor has been applied that has been tested for its accuracy across different bio optical regimes in inland & coastal waters. Satellite images for the same period were also collected and analysed using the C2RCC processing sequence to retrieve values of parameters such as the depth of water, surface reflectance, water temperature, inherent optical properties (IOPs), salinity, chlorophyll-a and total suspended matter (TSM) using the SNAP software. During the 2017-2020 season, in situ sampling from specific locations and laboratory water quality analysis were carried out. The OLCI retrieved results were then trained and validated using the in situ datasets. It was observed that the highest amount of TSM was recorded in Diamond Harbour during the pre-monsoon, in the year 2018 (301.40 mgL-1 in-situ value, and 308.54 mg L-1 estimated value). Similarly, chlorophyll-a had higher concentrations during the monsoon season (3.03 mg m-3, in-situ, and 2.96 mg m-3, estimated) in Fraserganj and Sagar south points. Very good fitted correlation results for all seasons between Chl-a, r = 0.829 and TSM, r = 0.924 were found during the comparisons of OLCI and in situ results. The high level of correlation highlights the importance of both primary and secondary data in understanding any dynamic system properly. Finally, the result shows that the water quality model outperforms conventional techniques and OLCI chl-a and TSM products. This paper empirically investigates a reliable remote sensing method for estimating coastal TSM and chl-a concentrations and supports the use of OLCI data in ocean colour remote sensing.

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“…By integrating Geographic Information Science (GIS) and Remote Sensing (RS) techniques, such LULC pattern analysis can be accomplished easily and visualize the affected areas and their impacts on the surrounding environment. Several studies were conducted on LULC changes ( Mondal et al., 2021 , 2022 ; Thakur et al., 2020 ; Thakur et al., 2020 , 2020 ) using various satellite data including Landsat, MODIS, and SPOT, however, Landsat satellite data has more potentiality in terms of LULC change detection studies because of free access of data with moderate spatial resolution and availability of multi-temporal time-series data from the year 1972 ( Lu et al., 2019 ). It is intelligible that the analysis of LULC change dynamics evaluates the numerous changes in the surrounding environment of an area and help to develop an effective management plan which can assist in achieving sustainable development and appease both local and global environmental changes ( Mondal & Bandyopadhyay, 2016 , 2022 ; Chamling and Bera, 2020 ; Lai, 2020 ; Mondal et al., 2019; Mondal et al., 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal variation of land use and land cover changes and their impact on land surface temperature: A case of Kutupalong Refugee Camp, Bangladesh

Bappa¹,

Malaker

Mia

et al. 2022

Heliyon

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Comparative analysis of forest canopy mapping methods for the Sundarban biosphere reserve, West Bengal, India

Cited by 22 publications

References 43 publications

Trend Analysis of Coverage Variation in Pinus yunnanensis Franch. Forests under the Influence of Pests and Abiotic Factors

Trend Analysis of Coverage Variation in Pinus yunnanensis Franch. Forests under the Influence of Pests and Abiotic Factors

Estimation of Chlorophyll-a, SPM and Salinity in Mangrove Dominated Tropical Estuarine Areas of Hooghly River, North East Coast of Bay of Bengal, India Using Sentinel-3 Data

Spatio-temporal variation of land use and land cover changes and their impact on land surface temperature: A case of Kutupalong Refugee Camp, Bangladesh

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