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

2021 ◽  
Vol 24 (2) ◽  
pp. 15-32
Author(s):  
KMM Uzzaman ◽  
MG Miah ◽  
HM Abdullah ◽  
MR Islam ◽  
MSI Afrad ◽  
...  
Keyword(s):  
Mangrove Forest ◽  
Forest Cover ◽  
Water Bodies ◽  
Forest Monitoring ◽  
Continuous Change ◽  
Forest Cover Change ◽  
Subsequent Increase ◽  
Stable Pattern ◽  
Sundarban Mangrove ◽  
Conservation And Management

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

scholarly journals Mapping Mangrove Forest Change in Nijhum Dwip Island

2019 ◽  
Vol 11 (1-2) ◽  
pp. 217-225
Author(s):  
MM Rahman ◽  
MAT Pramanik ◽  
MI Islam ◽  
S Razia
Keyword(s):  
Mangrove Forest ◽  
Forest Cover ◽  
Storm Surges ◽  
Forest Loss ◽  
Forest Conversion ◽  
Forest Cover Change ◽  
Forest Change ◽  
Change Analysis ◽  
Coastal Belt ◽  
Land Covers

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

scholarly journals Forest Cover Change Analysis Using Remote Sensing Techniques in Madhupur Sal Forest of Bangladesh

2018 ◽  
Vol 10 (2) ◽  
pp. 73-78
Author(s):  
MA Salam ◽  
MAT Pramanik
Keyword(s):  
Remote Sensing ◽  
Forest Cover ◽  
Driving Forces ◽  
Forest Resources ◽  
Forest Monitoring ◽  
Forest Damage ◽  
Landsat 8 ◽  
Forest Cover Change ◽  
Change Analysis ◽  
Sal Forest

Deforestation, degradation, damages, transformation and over exploitation of forests are the common problem in different parts of the world. Timely monitoring and assessment of forest resources may help to address and identify the above mentioned problems and thus proper guidance may be given the forest resources manager for rational planning and management of forests. Apart from the conventional methods of forest monitoring, remote sensing with its unique capability of synoptic viewing, real time and repetitive nature offers a potential tool for monitoring and evaluation of forest resources and hence remote sensing technology has been successfully used in various studies like forest inventory, monitoring of forest cover changes and forest damage assessment. In the present research forest cover change analysis in ‘Madhupur Sal Forest’ located in central part of Bangladesh has been investigated using satellite remote sensing data and spatial analysis. Transformation of ‘Sal forest’ to other landuse has been studied using the Landsat MSS (Multi Spectral Scanner) data of 1973 and Landsat 8 OLI (Operational Land Imager) data of 2015. Driving forces behind the transformation of ‘Sal forest’ has also been investigated through GPS (Global Positioning System) based ground verification and interview with the people living in the locality.J. Environ. Sci. & Natural Resources, 10(2): 73-78 2017

Importance of the Sundarban mangrove forest as a fisheries resource.

2020 ◽  
Author(s):  
Khandaker Huq ◽  
Shafiqur Rahman
Keyword(s):  
Mangrove Forest ◽  
Significant Contribution ◽  
Water Bodies ◽  
Nursery Ground ◽  
Sustainable Utilization ◽  
Land Area ◽  
Fisheries Resources ◽  
Sundarban Mangrove ◽  
Points Of View ◽  
Management And Conservation

Abstract The Sundarban mangrove forest has a significant contribution to the fisheries resources of Bangladesh from the ecological and economic points of view. Information regarding the topic has been collected from GOs and NGOs. The total land area of the Sundarbans is 577,285 ha, of which 401,685 ha is land area and 175,600 ha is water bodies. The water bodies support a diversified fisheries resource and, in the case of some fisheries species, serves as an obligatory spawning and nursery ground. Among the fisheries resources, 400 species of 63 families of fishes, 5 families of shrimp, 4 families of crabs, 15 families of mollusks, and some reptiles and mammals of fisheries importance have been recorded. The production and income from Sundarban fisheries resources in 1999-2000 was 3,355 t and Tk 9,136,870, respectively. These resources are, howver, now under threat due to improper management and conservation. Realizing its importance, steps should be taken for its sustainable utilization and proper management.

scholarly journals Direct Measurement of Forest Degradation Rates in Malawi: Toward a National Forest Monitoring System to Support REDD+

Forests ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 426
Author(s):  
David Skole ◽  
Jay Samek ◽  
Cheikh Mbow ◽  
Michael Chirwa ◽  
Dan Ndalowa ◽  
...  
Keyword(s):  
Carbon Emissions ◽  
Forest Cover ◽  
Forest Degradation ◽  
Forest Monitoring ◽  
National Forest ◽  
Forest Cover Change ◽  
Deforestation Rates ◽  
Degradation Rates ◽  
The Government ◽  
Agricultural Areas

Spatial time-series measurements of forest degradation rates are important for estimating national greenhouse gas emissions but have been challenging for open forests and woodlands. This lack of quantitative data on forest degradation rates, location and biomass is an important constraint to developing national REDD+ policy. In Malawi, and in most countries in Africa, most assessments of forest cover change for carbon emissions monitoring tend to report only deforestation in the public forest estate managed by the government, even when important forest degradation also occurs in agricultural areas, such as customary forests and other tree-based systems. This study has resulted in: (a) a new robust forest map for Malawi, (b) spatial and quantitative measurements of both forest degradation and deforestation, and (c) a demonstration of the approach through the introduction of a tool that maps across the broad landscape of forests and trees outside of forests. The results can be used to support REDD+ National Forest Monitoring Systems. This analysis produces new estimates of landscape-wide deforestation rates between 2000–2009 (22,410 ha yr−1) and 2009–2015 (38,937 ha yr−1). We further produce new estimates of the rate of forest degradation between 2000–2009 (42,961 ha yr−1) and 2009–2015 (71,878 ha yr−1). The contribution of these new tools and estimates to capacities for calculating carbon emissions are important, increasing prospects for full REDD+ readiness across semi-arid Africa.

scholarly journals Shedding New Light on Mountainous Forest Growth: A Cross-Scale Evaluation of the Effects of Topographic Illumination Correction on 25 Years of Forest Cover Change across Nepal

2021 ◽  
Vol 13 (11) ◽  
pp. 2131
Author(s):  
Jamon Van Den Hoek ◽  
Alexander C. Smith ◽  
Kaspar Hurni ◽  
Sumeet Saksena ◽  
Jefferson Fox
Keyword(s):  
Remote Sensing ◽  
Geographic Distribution ◽  
Classification Accuracy ◽  
Forest Cover ◽  
Forest Growth ◽  
Forest Cover Change ◽  
Scale Evaluation ◽  
Beneficial Effects ◽  
Affect Detection

Accurate remote sensing of mountainous forest cover change is important for myriad social and ecological reasons, but is challenged by topographic and illumination conditions that can affect detection of forests. Several topographic illumination correction (TIC) approaches have been developed to mitigate these effects, but existing research has focused mostly on whether TIC improves forest cover classification accuracy and has usually found only marginal gains. However, the beneficial effects of TIC may go well beyond accuracy since TIC promises to improve detection of low illuminated forest cover and thereby normalize measurements of the amount, geographic distribution, and rate of forest cover change regardless of illumination. To assess the effects of TIC on the extent and geographic distribution of forest cover change, in addition to classification accuracy, we mapped forest cover across mountainous Nepal using a 25-year (1992–2016) gap-filled Landsat time series in two ways—with and without TIC (i.e., nonTIC)—and classified annual forest cover using a Random Forest classifier. We found that TIC modestly increased classifier accuracy and produced more conservative estimates of net forest cover change across Nepal (−5.2% from 1992–2016) TIC. TIC also resulted in a more even distribution of forest cover gain across Nepal with 3–5% more net gain and 4–6% more regenerated forest in the least illuminated regions. These results show that TIC helped to normalize forest cover change across varying illumination conditions with particular benefits for detecting mountainous forest cover gain. We encourage the use of TIC for satellite remote sensing detection of long-term mountainous forest cover change.

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