scholarly journals Mapping of mangrove forest tree density using SENTINEL 2A satelit image in remained natural mangrove forest of Sumatra eastern coastal

2021 ◽  
Vol 912 (1) ◽  
pp. 012001
Author(s):  
Samsuri ◽  
A Zaitunah ◽  
S Meliani ◽  
O K Syahputra ◽  
S Budiharta ◽  
...  
Keyword(s):  
Density Distribution ◽  
Mangrove Forest ◽  
Vegetation Index ◽  
Pearson Correlation ◽  
Distribution Model ◽  
Coefficient Of Determination ◽  
Density Estimator ◽  
Mangrove Forests ◽  
Vegetation Density ◽  
Index Value

Abstract The mangrove ecosystem in Forest Managemen Unit - VII (FMU) Sumatera Utara is a natural forest. FMU has not managed and utilizes mangrove forests optimally. It can open up opportunities for illegal loggers and trigger damage to these natural ecosystems. This condition requires prevention and mitigation so that severe damage to mangrove forests does not occur. This study aims to determine the relationship between vegetation index and mangrove density in the field and map the mangrove density distribution based on the image vegetation index value. The density distribution mapping was carried out by compiling a vegetation density estimator model NDVI, GNDVI, and TVI as independent variables. Correlation test and regression analysis between the vegetation index value (NDVI, GNDVI, and TVI) to the number of trees per unit area. The distribution model for the density of mangrove stands was chosen based on the coefficient of determination (R2). The study resulted from NDVI selected as the vegetation index used to map the distribution of mangrove density with a Pearson correlation coefficient (R) of 0.738. The selected model is Y = 2.48e2.8667x, which is an exponential equation with a coefficient of determination (R2) of 61.3%. Based on this model, the distribution of mangrove density has the lowest density reaching 400, and the highest density is 2,200 trees per hectare

scholarly journals IDENTIFIKASI POTENSI HUTAN MANGROVE SEBAGAI PENGUAT KAWASAN EKOWISATA DI PESISIR BALE-BALE

Jurnal IPTA ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 25
Author(s):  
Wenang Anugoro ◽  
Muhammad Zainuddin ◽  
Andi Andi
Keyword(s):  
Mangrove Forest ◽  
Vegetation Index ◽  
Marine Biota ◽  
Mangrove Forests ◽  
Vegetation Density ◽  
Mangrove Species ◽  
Dominant Type ◽  
Technological Advances ◽  
Potential Condition ◽  
The Relationship

Technological advances in UAV (Unmanned Arial Vehicle) photogrammetry have been more efficient and accurate in the field of mapping and monitoring surveys. This study aims to determine the level of potential mangrove forests seen from the density of its vegetation, mangrove species and know how the relationship to marine biota contained in coastal areas bale-bale Batam. The recording data was taken on 26-08-2017. The method used to determine the density is the transformation of the NDVI vegetation index combined with the field transect. the field transect was conducted to see the species and biota of its association contained in each type of mangrove forest vegetation. The results of this study indicate that mangrove in coastal bale-bale has an area of 4.915 Ha, with the potential of mangrove forest area is still in potential condition seen from the extraction vegetation density from the transformation of vegetation index used and with the identification of mangrove species that is Avecennia and Rhizopora, relationship with the type of biota association Ocypodidae, Coenobitadae, and Gobiidae especially for Rhizopora mangrove species, it is because rhizopora is the most dominant type of mangrove in the research location.

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 KEANEKARAGAMAN JENIS GASTROPODA DI HUTAN MANGROVE SUNGAI KAKAP KABUPATEN KUBU RAYA

2019 ◽  
Vol 7 (2) ◽  
Author(s):  
Emanuel Luis Rosario ◽  
M Sofwan Anwari ◽  
Slamet Rifanjani ◽  
Herlina Darwati
Keyword(s):  
Mangrove Forest ◽  
Local Community ◽  
Diversity Index ◽  
Similarity Index ◽  
Dense Medium ◽  
Mangrove Forests ◽  
Vegetation Density ◽  
Sparse Vegetation ◽  
Mangrove Tree ◽  
Observation Method

Gastropod is one of the mollusk phylum that can adapt and decompose in mangrove forests. The mangrove forest in Sungai Kakap is an area that is slowly being opened due to the local community people’s activities. They opened land to build buildings, farms, and villages. This research is aimed at studying the diversity of Gastropod and the influence of the mangrove tree density on the gastropod diversity in the mangrove forest of Sungai Kakap, Sungai Kakap Village of Kubu Raya Regency. The data were taken in May 2018. The analysis was conducted using the observation method by making six observation lines. The lines were differentiated based on the density of the vegetations: dense, medium, and sparse. The number of Gastropods found in the Kakap River mangrove forest is 4 types of gastropods with a total of 252 individuals. Respectively from dense, medium, and sparse vegetation, the gastropod dominance index is 0.68, 0.37, and 0.51; the diversity index is 0.25, 0.49, and 0.33; the species average index is 0.27, 0.97, and 0.4; and the gastropod species richness index is 4.03, 1.18, and 0.7. Lastly, the species similarity index is respectively 86%, 66%, and 86%. Keywords: Gastropod, Mangrove Forests, Vegetation density.

scholarly journals MANGROVE FOREST CHANGE IN NUSA PENIDA MARINE PROTECTED AREA, BALI - INDONESIA USING LANDSAT SATELLITE IMAGERY

2019 ◽  
Vol 15 (2) ◽  
pp. 141 ◽  
Author(s):  
August Daulat ◽  
Widodo Setiyo Pranowo ◽  
Syahrial Nur Amri
Keyword(s):  
Satellite Imagery ◽  
Vegetation Cover ◽  
Protected Area ◽  
Mangrove Forest ◽  
Vegetation Index ◽  
Marine Protected Area ◽  
Mangrove Forests ◽  
Forest Change ◽  
Landsat Satellite ◽  
The Impact

Nusa Penida, Bali was designated as a Marine Protected Area (MPA) by the Klungkung Local Government in 2010 with support from the Ministry of Marine Affairs and Fisheries, Republic of Indonesia. Mangrove forests located in Nusa Lembongan Island inside the Nusa Penida MPA jurisdiction have decreased in biomass quality and vegetation cover. It’s over the last decades due to influences from natural phenomena and human activities, which obstruct mangrove growth. Study the mangrove forest changes related to the marine protected areas implementation are important to explain the impact of the regulation and its influence on future conservation management in the region. Mangrove forest in Nusa Penida MPA can be monitored using remote sensing technology, specifically Normalized Difference Vegetation Index (NDVI) from Landsat satellite imagery combined with visual and statistical analysis. The NDVI helps in identifying the health of vegetation cover in the region across three different time frames 2003, 2010, and 2017. The results showed that the NDVI decreased slightly between 2003 and 2010. It’s also increased significantly by 2017, where a mostly positive change occurred landwards and adverse change happened in the middle of the mangrove forest towards the sea.

scholarly journals Uji Akurasi Beberapa Indeks Vegetasi dalam Mengestimasi Kerapatan Hutan Mangrove dengan Citra Sentinel-2A di Taman Nasional Bali Barat

2020 ◽  
Vol 3 (2) ◽  
pp. 59
Author(s):  
Maulana Ilham Fahmy Alam ◽  
I Wayan Nuarsa ◽  
Ni Luh Putu Ria Puspitha
Keyword(s):  
Vegetation Index ◽  
Statistical Tests ◽  
Vegetation Indices ◽  
Field Measurements ◽  
Coefficient Of Determination ◽  
Test Model ◽  
Significance Level ◽  
Sentinel 2A ◽  
Index Value ◽  
Density Results

Vegetation Indices is one of the remote sensing parameters that can be used to estimate the mangrove forest density. The purpose of this study is to determine the vegetation index with the best accuracy to estimate the condition of mangrove density, as well as determine the spatial distribution of mangrove density in the TNBB area. This study uses Sentinel-2A satellite imagery data and five different vegetation indices, namely NDVI, NNIR, EVI, mRE-SR, and vegetation index developed in this study. The method of determining samples in the field uses stratified random and proportional sampling. Data collection of canopy density used hemispherical photography method, which is taking vertical photos with a 180o angle of view using a camera with a Fish Eye or Wide lens. Data analysis used in this study is regression analysis, coefficient of determination test, model validation test, and paired t test. From statistical tests conducted on several vegetation indices, the mRE-SR vegetation index value shows the best results on all the accuracy parameters tested.  The R2 value was generated by the mRE-SR vegetation index from the relationship between mangrove density results from field measurements with the vegetation index value and the estimated density results shows that the highest values, namely 0.909 and 0.935. These results show that the mRE-SR vegetation index is the best vegetation index in explaining the variation of mangrove density in the field. The mRE-SR vegetation index also has the lowest deviation of the estimated value, with the resulting SE values in the two linear relationships of 1,592 and 0,999. In addition, the mRE-SR vegetation index has a P (T <= t) two-tail value greater than the significance level (0.05), the results means that two values of the tested variables are not significant different. The calculation results show that the total area of mangroves in TNBB is 409.21 ha. From the percentage of density obtained, the mangrove density class was only distributed in the medium and solid density classes.

scholarly journals Assessment and Attribution of Mangrove Forest Changes in the Indian Sundarbans from 2000 to 2020

2021 ◽  
Vol 13 (24) ◽  
pp. 4957
Author(s):  
Sourav Samanta ◽  
Sugata Hazra ◽  
Partho P. Mondal ◽  
Abhra Chanda ◽  
Sandip Giri ◽  
...  
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Mangrove Forest ◽  
Vegetation Index ◽  
Health Indicators ◽  
Water Diversion ◽  
Mangrove Forests ◽  
River Channels ◽  
Monotonic Trend ◽  
Indian Sundarbans

The Indian Sundarbans, together with Bangladesh, comprise the largest mangrove forest in the world. Reclamation of the mangroves in this region ceased in the 1930s. However, they are still subject to adverse environmental influences, such as sediment starvation due to migration of the main river channels in the Ganges–Brahmaputra delta over the last few centuries, cyclone landfall, wave action from the Bay of Bengal—changing hydrology due to upstream water diversion—and the pervasive effects of relative sea-level rise. This study builds on earlier work to assess changes from 2000 to 2020 in mangrove extent, genus composition, and mangrove ‘health’ indicators, using various vegetation indices derived from Landsat and MODIS satellite imagery by performing maximum likelihood supervised classification. We show that about 110 km2 of mangroves disappeared within the reserve forest due to erosion, and 81 km2 were gained within the inhabited part of Sundarbans Biosphere Reserve (SBR) through plantation and regeneration. The gains are all outside the contiguous mangroves. However, they partially compensate for the losses of the contiguous mangroves in terms of carbon. Genus composition, analyzed by amalgamating data from published literature and ground-truthing surveys, shows change towards more salt-tolerant genus accompanied by a reduction in the prevalence of freshwater-loving Heiritiera, Nypa, and Sonneratia assemblages. Health indicators, such as the enhanced vegetation index (EVI) and normalized differential vegetation index (NDVI), show a monotonic trend of deterioration over the last two decades, which is more pronounced in the sea-facing parts of the mangrove forests. An increase in salinity, a temperature rise, and rainfall reduction in the pre-monsoon and the post-monsoon periods appear to have led to such degradation. Collectively, these results show a decline in mangrove area and health, which poses an existential threat to the Indian Sundarbans in the long term, especially under scenarios of climate change and sea-level rise. Given its unique values, the policy process should acknowledge and address these threats.

scholarly journals KEANEKARAGAMAN SPESIES BURUNG DI HUTAN MANGROVE PULAU KELAGIAN BESAR PROVINSI LAMPUNG

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Memo Dinda Nugraha ◽  
Agus Setiawan ◽  
Dian Iswandaru ◽  
Yulia Rahma Fitriana
Keyword(s):  
Mangrove Forest ◽  
Local Community ◽  
Diversity Index ◽  
Bird Species ◽  
Index Formula ◽  
Mangrove Forests ◽  
Xema Sabini ◽  
Index Value ◽  
Bird Species Diversity ◽  
White Belly

The existence of mangrove forests is very important in an area because as a habitat for various types of wildlife, especially birds. This study aims to determine the diversity of bird species in the Kelagian Besar Mangrove Forest, Lampung Province. Data on bird species diversity was obtained by the IPA (Indices Ponctual Abundance) method. Data collection of bird species by recording the type and number of individual birds found. Species identification also uses the method of bird recognition directly by people who know the birds in the area such as the local community or an expert on birds. Data obtained in the field are then analyzed using the Shanon-Winner diversity index formula. The results found 27 species of birds from 21 families with a total of 741 individual birds in the Kelagian Besar Mangrove Forest. While the diversity index value is 2.26 with medium index criteria. There are 8 protected bird species, namely the black-necked darautaut (Sterna sumatrana), the white-wing daralaut (Chlidonias leucopterus), the oyster daralaut (Gelochelidon nilotica), the white belly eagle (Haliaeetus leucogaste), the brontok eagle (Spizaetus cirrhatus), oyster belly Rhipidura javanica), sabine seagulls (Xema sabini), and large fissures (Fregata minor).

scholarly journals Diversity of Lichen in Mangrove Forest of Tomoli Village Parigi Moutong Regency

2020 ◽  
Vol 5 (3) ◽  
pp. 140-150
Author(s):  
Ari Pratama ◽  
Manap Trianto
Keyword(s):  
Mangrove Forest ◽  
Diversity Index ◽  
Sampling Technique ◽  
Survey Method ◽  
Symbiotic Association ◽  
Mangrove Forests ◽  
Mutualistic Symbiosis ◽  
Lichen Diversity ◽  
Index Value ◽  
Flavoparmelia Caperata

Lichen is an organism resulting from a symbiotic association between fungi and algae in mutualistic symbiosis and eroticism, forming a morphological unity that is different from other species from its constituent components. This study aims to determine the level of lichen species diversity that grows in mangrove forests in Tomoli Village, Parigi Moutong Regency. This research was conducted in July 2019. The method used in this study was a survey method, the sampling technique was purposive sampling, namely by using a 10 cm x 10 cm plot on the mangrove trees in each path at the research location. The research results found ten types of lichen consisting of six genera, five families, seven orders, and four classes divided into two groups based on the kind of thallus, namely lichen crustose and foliose. The crustose lichen group is Aspicilia calcarea, Aspicilia sp, Pyrenula sp, Pyrenula dermatodes, Pyrenula santensis, Cryptothecia striata, Phaeographis sp, Graphis script, Verrucaria sp. Meanwhile, the foliose lichen group is Flavoparmelia caperata. The lichen diversity index value obtained in the mangrove forest in Tomoli Village was 2,225, indicating that the level of diversity is moderate.

scholarly journals ANALYSIS OF LAND COVER CHANGES IN MANGROVE VEGETATION USING LANDSAT IMAGE DATA IN COASTAL, SUNGAI APIT DISTRICT, SIAK REGENCY, RIAU PROVINCE

2021 ◽  
Vol 4 (2) ◽  
pp. 154-162
Author(s):  
Armanda Armanda ◽  
Mubarak Mubarak ◽  
Elizal Elizal
Keyword(s):  
Land Cover ◽  
Mangrove Forest ◽  
Vegetation Index ◽  
Image Data ◽  
Landsat Image ◽  
Survey Method ◽  
Land Cover Changes ◽  
Mangrove Forests ◽  
A Value ◽  
Mangrove Vegetation

This research was conducted in March-April 2021 in the Coastal District of Sungai Apit, Siak Regency, Riau Province. The purpose of this study was to analyze changes in the land cover area of ​​mangrove vegetation and mangrove vegetation index in Sungai Apit District, Siak Regency, Riau Province. The method used in this study is a survey method with the interpretation of Landsat image data recorded in 2000, 2005, 2010, 2015, 2020. The results of the study obtained that mangrove forests with the highest area were in 2000 with an area of ​​mangrove vegetation reaching 7990,586 ha and there was a decline with the lowest number in 2015 with a vegetation area of ​​486,43 ha and in 2020 the mangrove vegetation area of ​​497,511 ha. Overall as much as 79% of the mangrove forest area has been damaged and changed its function within a period of 20 years. The NDVI value in Sungai Apit District is moderate with a value of 0,3-0,5, the category of meeting with a value of 0,5-0,6, and the very dense category of 0,6-0,8

scholarly journals A New Vegetation Index to Detect Periodically Submerged Mangrove Forest Using Single-Tide Sentinel-2 Imagery

2019 ◽  
Vol 11 (17) ◽  
pp. 2043 ◽  
Author(s):  
Jia ◽  
Wang ◽  
Wang ◽  
Mao ◽  
Zhang
Keyword(s):  
Mangrove Forest ◽  
Vegetation Index ◽  
Near Infrared ◽  
Aquatic Vegetation ◽  
Vegetation Indices ◽  
Short Wave ◽  
Mangrove Forests ◽  
Red Edge ◽  
Jensen Shannon Divergence ◽  
Sentinel 2

Mangrove forests are tropical trees and shrubs that grow in sheltered intertidal zones. Accurate mapping of mangrove forests is a great challenge for remote sensing because mangroves are periodically submerged by tidal floods. Traditionally, multi-tides images were needed to remove the influence of water; however, such images are often unavailable due to rainy climates and uncertain local tidal conditions. Therefore, extracting mangrove forests from a single-tide imagery is of great importance. In this study, reflectance of red-edge bands in Sentinel-2 imagery were utilized to establish a new vegetation index that is sensitive to submerged mangrove forests. Specifically, red and short-wave near infrared bands were used to build a linear baseline; the average reflectance value of four red-edge bands above the baseline is defined as the Mangrove Forest Index (MFI). To evaluate MFI, capabilities of detecting mangrove forests were quantitatively assessed between MFI and four widely used vegetation indices (VIs). Additionally, the practical roles of MFI were validated by applying it to three mangrove forest sites globally. Results showed that: (1) theoretically, Jensen–Shannon divergence demonstrated that a submerged mangrove forest and water pixels have the largest distance in MFI compared to other VIs. In addition, the boxplot showed that all submerged mangrove forests could be separated from the water background in the MFI image. Furthermore, in the MFI image, to separate mangrove forests and water, the threshold is a constant that is equal to zero. (2) Practically, after applying the MFI to three global sites, 99–102% of submerged mangrove forests were successfully extracted by MFI. Although there are still some uncertainties and limitations, the MFI offers great benefits in accurately mapping mangrove forests as well as other coastal and aquatic vegetation worldwide.

Sign in / Sign up

Export Citation Format

Share Document