scholarly journals Application of Remote Sensing in Mangroves at the Surrounding of Sungai Selangor Estuary in Kuala Selangor

2021 ◽  
Vol 18 (2) ◽  
pp. 38
Author(s):  
Syed Muhammad Iqbal Sayad Romli ◽  
Illyani Ibrahim ◽  
MZainora Asmawi ◽  
Azizan Abu Samah
Keyword(s):  
Remote Sensing ◽  
River Basin ◽  
Mangrove Forest ◽  
Remote Sensing Data ◽  
Coastal Areas ◽  
Food Sources ◽  
Mangrove Forests ◽  
Sediment Retention ◽  
Management Policies ◽  
Basin Area

The mangrove forest ecosystem protects the land area from the tidal wave hence preventing the coastal areas and properties from severe damage. Mangroves provide valuable ecological services and goods, sediment retention, food sources of some animals, and stabilisation of the coastal areas. Unfortunately, the species have been experiencing an extensive loss in many parts of the world. This paper aims to detect the changes in mangrove forests and possible changes in the Selangor river basin area. The methodology uses remote sensing data via supervised classification on a maximum likelihood algorithm to analyse the distribution of mangrove forests at the Selangor River basin for a thirty-two-year period, from 1989 to 2021. The findings indicate that the percentage of mangroves in the study area has reduced over the study period. The coverage of mangroves has reduced from 24.29 percent (1989) to 15.57 percent in 2008, and continued to reduce to 13.12 percent in 2021. The research finding indicates a decrease in mangroves due to aquaculture, tourism, agriculture, and other human activities. Such a trend may risk coastal and river erosion, thus necessitating a revision of the management policies for environmental protection. Keywords: mangrove, forest, remote sensing, Selangor river basin

scholarly journals Mapping the Global Mangrove Forest Aboveground Biomass Using Multisource Remote Sensing Data

2020 ◽  
Vol 12 (10) ◽  
pp. 1690 ◽  
Author(s):  
Tianyu Hu ◽  
YingYing Zhang ◽  
Yanjun Su ◽  
Yi Zheng ◽  
Guanghui Lin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Aboveground Biomass ◽  
Mangrove Forest ◽  
Remote Sensing Data ◽  
Dimensional Structure ◽  
Coefficient Of Determination ◽  
Free Access ◽  
Mangrove Forests ◽  
Estimation Model ◽  
Mangrove Biomass

Mangrove forest ecosystems are distributed at the land–sea interface in tropical and subtropical regions and play an important role in carbon cycles and biodiversity. Accurately mapping global mangrove aboveground biomass (AGB) will help us understand how mangrove ecosystems are affected by the impacts of climatic change and human activities. Light detection and ranging (LiDAR) techniques have been proven to accurately capture the three-dimensional structure of mangroves and LiDAR can estimate forest AGB with high accuracy. In this study, we produced a global mangrove forest AGB map for 2004 at a 250-m resolution by combining ground inventory data, spaceborne LiDAR, optical imagery, climate surfaces, and topographic data with random forest, a machine learning method. From the published literature and free-access datasets of mangrove biomass, we selected 342 surface observations to train and validate the mangrove AGB estimation model. Our global mangrove AGB map showed that average global mangrove AGB density was 115.23 Mg/ha, with a standard deviation of 48.89 Mg/ha. Total global AGB storage within mangrove forests was 1.52 Pg. Cross-validation with observed data demonstrated that our mangrove AGB estimates were reliable. The adjusted coefficient of determination (R2) and root-mean-square error (RMSE) were 0.48 and 75.85 Mg/ha, respectively. Our estimated global mangrove AGB storage was similar to that predicted by previous remote sensing methods, and remote sensing approaches can overcome overestimates from climate-based models. This new biomass map provides information that can help us understand the global mangrove distribution, while also serving as a baseline to monitor trends in global mangrove biomass.

scholarly journals Rapid detection of land cover change in tropical savanna environment using conditional change vector analysis on remote sensing data in Moyo watershed, Sumbawa Regency, West Nusa Tenggara Province, Indonesia

2021 ◽  
Vol 8 (3) ◽  
pp. 2731-2741
Author(s):  
Gatot Nugroho ◽  
Galdita Aruba Chulafak ◽  
Fajar Yulianto
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Land Cover Change ◽  
River Basin ◽  
Remote Sensing Data ◽  
Vector Analysis ◽  
Tropical Savanna ◽  
Change Vector Analysis ◽  
Change Vector ◽  
Basin Area

In environmental management, land cover change is a crucial aspect. The area of tropical savanna environments is vulnerable to land degradation. This study aimed to rapidly detect land cover changes in a tropical savanna environment based on remote sensing data. Conditional change detection was performed using the Change Vector Analysis (CVA) with input parameters such as the Enhanced Vegetation Index (EVI) and Normalized Difference Soil Index (NDSI). The results showed that during the period 2015 to 2019, changes were detected in the Moyo watershed every year. From 2015 to 2016, the Moyo River Basin was dominated by changes with a change magnitude of less than 0.088, which was 63% of the Moyo River Basin area. From 2016 to 2017, the changes were dominated by the change magnitude value of 0.063, which was 58.6% of the Moyo River Basin area. From 2017 to 2018, changes were dominated by the change magnitude value of 0.084 of 55.26% of the Moyo watershed area. From 2018 to 2019, the change was dominated by the change magnitude value of 0.057, which was 47.57% of the Moyo watershed area. The direction of land cover change was dominated by Q2 in 2016, Q4 in 2017 and 2018, and Q2 and Q4 in 2019. These changes generally occurred in the Moyo watershed middle and downstream parts, which are grasslands. The use of the Conditional Change Vector Analysis (CCVA) approach in a tropical savanna environment can detect changes and the direction of change with an accuracy of about 70%.

scholarly journals Spatiotemporal Differentiation and the Factors of Ecological Vulnerability in the Toutun River Basin Based on Remote Sensing Data

2019 ◽  
Vol 11 (15) ◽  
pp. 4160 ◽  
Author(s):  
Qin Liu ◽  
Tiange Shi
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
River Basin ◽  
Hot Spot ◽  
Remote Sensing Data ◽  
Cold Spot ◽  
Strengthening Effect ◽  
Land Use Type ◽  
Sensing Data ◽  
Ecological Vulnerability

Ecological vulnerability assessment increases the knowledge of ecological status and contributes to formulating local plans of sustainable development. A methodology based on remote sensing data and spatial principal component analysis was introduced to discuss ecological vulnerability in the Toutun River Basin (TRB). Exploratory spatial data analysis and a geo-detector were employed to evaluate the spatial and temporal distribution characteristics of ecological vulnerability and detect the driving factors. Four results were presented: (1) During 2003 and 2017, the average values of humidity, greenness, and heat in TRB increased by 49.71%, 11.63%, and 6.51% respectively, and the average values of dryness decreased by 165.24%. However, the extreme differences in greenness, dryness, and heat tended to be obvious. (2) The study area was mainly dominated by a high and extreme vulnerability grade, and the ecological vulnerability grades showed the distribution pattern that the northern desert area was more vulnerable than the central artificial oasis, and the central artificial oasis was more vulnerable than the southern mountainous area. (3) Ecological vulnerability in TRB showed significant spatial autocorrelation characteristics, and the trend was enhanced. The spatial distribution of hot/cold spots presented the characteristics of “hot spot—cold spot—secondary hot spot—cold spot” from north to south. (4) The explanatory power of each factor of ecological vulnerability was temperature (0.5955) > land use (0.5701) > precipitation (0.5289) > elevation (0.4879) > slope (0.3660) > administrative division (0.1541). The interactions of any two factors showed a non-linear strengthening effect, among which, land use type ∩ elevation (0.7899), land use type ∩ precipitation (0.7867), and land use type ∩ temperature (0.7791) were the significant interaction for ecological vulnerability. Overall, remote sensing data contribute to realizing a quick and objective evaluation of ecological vulnerability and provide valuable information for decision making concerning ecology management and region development.

scholarly journals Understanding Dynamics of Mangrove Forest on Protected Areas of Hainan Island, China: 30 Years of Evidence from Remote Sensing

2019 ◽  
Vol 11 (19) ◽  
pp. 5356 ◽  
Author(s):  
Liao ◽  
Zhen ◽  
Zhang ◽  
Metternicht
Keyword(s):  
Remote Sensing ◽  
Protected Areas ◽  
Mangrove Forest ◽  
Public Awareness ◽  
Hainan Island ◽  
Current Data ◽  
Support Vector ◽  
Mangrove Forests ◽  
Visual Interpretation ◽  
Wastewater Disposal

Implementation of the UN Sustainable Development Goals requires countries to determine targets for the protection, conservation, or restoration of coastal ecosystems such as mangrove forests by 2030. Satellite remote sensing provides historical and current data on the distribution and dynamics of mangrove forests, essential baseline data that are needed to design suitable policy interventions. In this study, Landsat time series were used to map trends and dynamics of mangrove change over a time span of 30 years (1987–2017) in protected areas of Hainan Island (China). A support vector machine algorithm was combined with visual interpretation of imagery and result showed alternating periods of expansion and loss of mangrove forest at seven selected sites on Hainan Island. Over this period, there was a net decrease in mangrove area of 9.3%, with anthropic activities such as land conversion for aquaculture, wastewater disposal and discharge, and tourism development appearing to be the likely drivers of this decline in cover. Long-term studies examining trends in land use cover change coupled with assessments of drivers of loss or gain enable the development of evidence based on policy and legislation. This forms the basis of financing of natural reserves of management and institutional capacity building, and facilitates public awareness and participation, including co-management.

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