scholarly journals IDENTIFICATION OF MANGROVE FORESTS USING MULTISPECTRAL SATELLITE IMAGERIES

2019 ◽  
Vol 16 (1) ◽  
pp. 63
Author(s):  
Anang Dwi Purwanto ◽  
Wikanti Asriningrum
Keyword(s):  
Mangrove Forest ◽  
Near Infrared ◽  
Image Data ◽  
Short Wave ◽  
Landsat 8 ◽  
Mangrove Forests ◽  
Visual Identification ◽  
Red Edge ◽  
Swir Band ◽  
Sentinel 2A

The visual identification of mangrove forests is greatly constrained by combinations of RGB composite. This research aims to determine the best combination of RGB composite for identifying mangrove forest in Segara Anakan, Cilacap using the Optimum Index Factor (OIF) method. The OIF method uses the standard deviation value and correlation coefficient from a combination of three image bands. The image data comprise Landsat 8 imagery acquired on 30 May 2013, Sentinel 2A imagery acquired on 18 March 2018 and images from SPOT 6 acquired on 10 January 2015. The results show that the band composites of 564 (NIR+SWIR+Red) from Landsat 8 and 8a114 (Vegetation Red Edge+SWIR+Red) from Sentinel 2A are the best RGB composites for identifying mangrove forest, in addition to those of 341 (Red+NIR+Blue) from SPOT 6. The near-infrared (NIR) and short-wave infrared (SWIR) bands play an important role in determining mangrove forests. The properties of vegetation are reflected strongly at the NIR wavelength and the SWIR band is very sensitive to evaporation and the identification of wetlands.

scholarly journals IDENTIFICATION OF MANGROVE FORESTS USING MULTI-RESOLUTION SATELLITE IMAGERY

Jurnal Segara ◽  
2020 ◽  
Vol 16 (2) ◽  
Author(s):  
Anang Dwi Purwanto
Keyword(s):  
Remote Sensing ◽  
Mangrove Forest ◽  
Image Data ◽  
Field Measurements ◽  
Landsat 8 ◽  
Mangrove Forests ◽  
Remote Sensing Technology ◽  
Red Edge ◽  
Sensing Technology ◽  
Sentinel 2A

The development of remote sensing technology for identifying various of coastal and marine ecosystems which one of them is mangrove forest increasing rapidly. Identification of mangrove forests visually is constrained by much of combinations of RGB composite. The aims of this research is to determine the best combination of RGB composite for identifying mangrove forest in Segara Anakan, Cilacap using Optimum Index Factor (OIF) method. The image data used represents 3 levels of intermediate to high resolution spatial resolution including Landsat 8 imagery (30 m) acquisition on 30 May 2013, Sentinel 2A image (10 m) acquisition on 18 March 2018 and SPOT 6 image (6 m) acquisition on 10 January 2015. Data of mangrove distributions used were the results of field measurements in the period 2013-2015. The results showed that the band composites of 564 (NIR+SWIR+Red) of Landsat 8 image and the band composites of 8a114 (Vegetation Red Edge+SWIR+Red) of Sentinel 2A are the best RGB composites for identifying mangrove forest, while the band composites of 341 (Red+NIR+Blue) of SPOT 6 image is  also the best colour composites (R-G-B) for identifying mangrove forest in Segara Anakan, Cilacap. The RGB composites of images developed from Landsat 8 and Sentinel 2A image are able to distinguish objects of mangrove forest from surrounding objects more clearly, but image composites from SPOT 6 image still require additional of association elements to identify mangrove objects.The development of remote sensing technology for identifying various of coastal and marine ecosystems which one of them is mangrove forest increasing rapidly. Identification of mangrove forests visually is constrained by much of combinations of RGB composite. The aims of this research is to determine the best combination of RGB composite for identifying mangrove forest in Segara Anakan, Cilacap using Optimum Index Factor (OIF) method. The image data used represents 3 levels of intermediate to high resolution spatial resolution including Landsat 8 imagery (30 m) acquisition on 30 May 2013, Sentinel 2A image (10 m) acquisition on 18 March 2018 and SPOT 6 image (6 m) acquisition on 10 January 2015. Data of mangrove distributions used were the results of field measurements in the period 2013-2015.The results showed that the band composites of 564 (NIR+SWIR+Red) of Landsat 8 image and the band composites of 8a114 (Vegetation Red Edge+SWIR+Red) of Sentinel 2A are the best RGB composites for identifying mangrove forest, while the band composites of 341 (Red+NIR+Blue) of SPOT 6 image is  also the best colour composites(R-G-B) for identifying mangrove forest in Segara Anakan, Cilacap. The RGB composites of images developed from Landsat 8 and Sentinel 2A image are able to distinguish objects of mangrove forest from surrounding objects more clearly, but imagecomposites from SPOT 6 image still require additional of association elements to identify mangrove objects.

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.

scholarly journals Perubahan Lahan Mangrove di Pesisir Muara Gembong, Bekasi, Jawa Barat

2021 ◽  
Vol 10 (1) ◽  
pp. 55-63
Author(s):  
Alin Maulani ◽  
Nur Taufiq-SPJ ◽  
Ibnu Pratikto
Keyword(s):  
Data Processing ◽  
Satellite Image ◽  
Image Data ◽  
Mangrove Ecosystem ◽  
Landsat 8 ◽  
Mangrove Forests ◽  
Mangrove Ecosystems ◽  
Satellite Image Data ◽  
Sentinel 2A ◽  
Land Changes

Kecamatan Muara Gembong adalah wilayah dengan ekosistem mangrove yang cukup luas dan tersebar. Mangrove adalah kelompok jenis tumbuhan yang tumbuh di sepanjang garis pantai tropis sampai subtropis di suatu lingkungan yang mengandung garam dan bentuk lahan berupa pantai dengan reaksi tanah anaerob. Kondisi ekosistem mangrove sangat peka terhadap gangguan dari luar terutama dari kegiatan pencemaran, konversi hutan mangrove menjadi kawasan non-hutan, ekploitasi hasil mangrove yang berlebihan sehingga terjadi dinamika pada luasan lahannya. Perubahan yang terjadi pada ekosistem mangrove ini dapat berupa penambahan, pengurangan, dan lahan yang tetap. Metode yang dilakukan pada penelitian ini berupa pengolahan data satelit citra Sentinel 2A, Landsat 8, dan Landsat 5 untuk menganalisa sebaran mangrove pada tahun 2009, 2014, dan 2019, serta perubahan yang terjadi. Validasi data dilakukan dengan pengamatan kawasan langsung di lokasi penelitian berdasarkan pengolahan data yang telah dilakukan. Hasil pengolahan data menunjukan di Kecamatan Muara Gembong pada tahun 2009-2019 diketahui terjadi penambahan luasan lahan mangrove sebesar 1017,746 ha dan pengurangan luasan mangrove sebesar 275,37 ha. Selain itu, terdapat pula lahan mangrove yang tetap bertahan pada kurun waktu 2009-2019 seluas 255,057 ha. Sehingga perubahan lahan mangrove yang terjadi di Kecamatan Muara Gembong cenderung mengalami pertambahan luasan lahan mangrove, yaitu sebesar 66% lahan mangrove yang bertambah. Muara Gembong Subdistrict is an area with a wide and scattered mangrove ecosystem. Mangroves are a group of plant species that grow along tropical to subtropical coastlines in an environment that contains salt and landforms in the form of beaches with anaerobic soil reactions. The condition of mangrove ecosystems is very sensitive to outside disturbances, especially from pollution activities, conversion of mangrove forests to non-forest areas, excessive exploitation of mangrove products resulting in dynamics in the area of land. Changes that occur in this mangrove ecosystem can be in the form of addition, subtraction, and permanent land. The method used in this research is the processing of Sentinel 2A, Landsat 8, and Landsat 5 satellite image data to analyze the distribution of mangroves in 2009, 2014 and 2019, and the changes that occur. Data validation is done by direct observation of the area at the research location based on data processing that has been done. The results of data processing showed that in Muara Gembong Subdistrict in 2009-2019 it was known that there was an increase in the area of mangrove land by 1017, 746 ha and reduction in mangrove area by 275.37 ha. In addition, there are also mangrove lands that have survived in the period 2009-2019 covering 255,057 ha. So that changes in mangrove land that occur in Muara Gembong District tend to experience an increase in the area of mangrove land, which is equal to 66% of the mangrove land that is increasing.

scholarly journals A Multi-Channel Algorithm for Mapping Volcanic Thermal Anomalies by Means of Sentinel-2 MSI and Landsat-8 OLI Data

2019 ◽  
Vol 11 (23) ◽  
pp. 2876 ◽  
Author(s):  
Francesco Marchese ◽  
Nicola Genzano ◽  
Marco Neri ◽  
Alfredo Falconieri ◽  
Giuseppe Mazzeo ◽  
...  
Keyword(s):  
Near Infrared ◽  
Short Wave ◽  
Global Scale ◽  
Landsat 8 ◽  
Thermal Anomalies ◽  
Volcanic Plumes ◽  
Processing Times ◽  
Multi Temporal ◽  
Sentinel 2A ◽  
Sentinel 2

The Multispectral Instrument (MSI) and the Operational Land Imager (OLI), respectively onboard Sentinel-2A/2B and Landsat 8 satellites, thanks to their features especially in terms of spatial/spectral resolution, represents two important instruments for investigating thermal volcanic activity from space. In this study, we used data from those sensors to test an original multichannel algorithm, which aims at mapping volcanic thermal anomalies at a global scale. The algorithm, named Normalized Hotspot Indices (NHI), combines two normalized indices, analyzing near infrared (NIR) and short wave infrared (SWIR) radiances, to identify hotspot pixels in daylight conditions. Results, achieved studying a number of active volcanoes located in different geographic areas and characterized by a different eruptive behavior, demonstrated the NHI capacity in mapping both subtle and more intense volcanic thermal anomalies despite some limitations (e.g., missed detections because of clouds/volcanic plumes). In addition, the study shows that the performance of NHI might be further increased using some additional spectral/spatial tests, in view of a possible usage of this algorithm within a known multi-temporal scheme of satellite data analysis. The low processing times and the straight forth exportability to data from other sensors make NHI, which is sensitive even to other high temperature sources, suited for mapping hot volcanic targets integrating information provided by current and well-established satellite-based volcanoes monitoring systems.

Assessment of mangrove forest change in Ca Mau province using satellite images in the period of 1988 - 2018

2021 ◽  
Vol 66 (1) ◽  
pp. 175-187
Author(s):  
Duong Phung Thai ◽  
Son Ton
Keyword(s):  
Satellite Images ◽  
Satellite Image ◽  
Image Interpretation ◽  
Image Data ◽  
Vegetation Coverage ◽  
Landsat 8 ◽  
Mangrove Forests ◽  
Forest Change ◽  
Shrimp Ponds ◽  
Area Classification

On the basis of using practical methods, satellite image processing methods, the vegetation coverage classification system of the study area, interpretation key for the study area, classification and post-classification pro cessing, this research introduces how to exploit and process multi-temporal satellite images in evaluating the changes of forest area. Landsat 4, 5 TM and Landsat 8 OLI remote sensing image data were used to evaluate the changes in the area of mangrove forests (RNM) in Ca Mau province in the periods of 1988 - 1998, 1998 - 2013, 2013 - 2018, and 1988 - 2018. The results of the image interpretation in 1988, 1998, 2013, 2018 and the overlapping of the above maps show: In the 30-year period from 1988 to 2018, the total area of mangroves in Ca Mau province was decreased by 28% compared to the beginning, from 71,093.3 ha in 1988 reduced to 51,363.5 ha in 2018, decreasing by 19,729.8 ha. The recovery speed of mangroves is 2 times lower than their disappearance speed. Specifically, from 1988 to 2018, mangroves disappeared on an area of 42,534.9 hectares and appeared on the new area of 22,805 hectares, only 12,154.5 hectares of mangroves remained unchanged. The fluctuation of mangrove area in Ca Mau province is related to the process of deforestation to dig shrimp ponds, coastal erosion, the formation of mangroves on new coastal alluvial lands and soil dunes in estuaries, as well as planting new mangroves in inefficient shrimp ponds.

scholarly journals Road Feature Extraction from LANDSAT-8 and ResourceSat-2 Images

2021 ◽  
Vol 21 (3) ◽  
pp. 1-9
Author(s):  
Sama Lenin Kumar Reddy ◽  
◽  
C. V. Rao ◽  
P. Rajesh Kumar ◽  
◽  
...  
Keyword(s):  
Feature Extraction ◽  
Random Fields ◽  
Markov Random Fields ◽  
Short Wave ◽  
Landsat 8 ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
The Road ◽  
Markov Random ◽  
Swir Band

This paper presents a methodology of road feature extraction from the different resolutions of Remote Sensing images of Landsat-8 Operational Lander Image (OLI) and ResourceSat-2 of Linear Imaging Self Sensor-3 (LISS-3) and LISS-4 sensors with the spatial resolutions of 15 m, 24 m, and 5 m. In the methodology of road extraction, an index is proposed based on the spectral profile of Roads, also involving Morphological transform (Top-Hat or Bot-Hat) and Markov Random Fields (MRF). In the proposed index, Short Wave Infrared (SWIR) band has a significant role in the detection of roads from sensors, and it is named Normalized Difference Road Index (NDRI). To enhancement of features from the index, Bot-Hat transforms used. To segment the road features from this image, MRF used. The methodology is performed on the OLI, LISS-3 and LISS-4 images, and presented with results.

scholarly journals PENGARUH PERUBAHAN LUAS HUTAN MANGROVE TERHADAP KONSENTRASI TOTAL SUSPENDED MATTER (TSM) DI MUARA PERANCAK, JEMBRANA – BALI

2019 ◽  
Vol 14 (1) ◽  
Author(s):  
Komang Iwan Suniada
Keyword(s):  
Suspended Matter ◽  
Mangrove Forest ◽  
Satellite Image ◽  
River Mouth ◽  
Remote Sensing Data ◽  
Image Data ◽  
Mangrove Ecosystem ◽  
Total Suspended Matter ◽  
Forest Area ◽  
Mangrove Forests

Study of the function of mangrove forests as a sediment trap has been largely undertaken using field measurement methods, but only a few researches that fully utilize remote sensing data to find out the influence of mangrove forest’s area changes against the Total Suspended Matter (TSM) making this study very interesting and important to do.  This research was conducted in Perancak estuary area which is one of mangrove ecosystem area in Bali besides West Bali National Park, Benoa Forest Park and Nusa Lembongan. The data used to generate TSM information and change of mangrove forest area in this research is medium resolution satellite image data, Landsat.  Tidal data and rainfall data were used as a supporting data. The information of TSM concentration obtained by using Budhiman (2004) algorithm, shows that along with the increasing of mangrove forest area has caused the decreasing of TSM concentration at mouth Perancak river. The decline was caused by sediments trapped and settled around trees or mangrove roots, especially the Rhizophora mangroves. In addition to the increasing of mangrove forest area, the tidal oceanography factor also greatly influences the TSM fluctuation around Perancak river mouth. 

scholarly journals MANGROVE FOREST EFFECT ON THE COASTLINE IN RANGSANG ISLAND RIAU PROVINCE

2020 ◽  
Vol 3 (1) ◽  
pp. 29-37
Author(s):  
Paulinus ◽  
Mubarak Mubarak ◽  
Efriyeldi Efriyeldi
Keyword(s):  
Image Data ◽  
Landsat Image ◽  
Sampling Location ◽  
Landsat 8 ◽  
Mangrove Forests ◽  
Bruguiera Gymnorrhiza ◽  
Mangrove Species ◽  
Rhizophora Apiculata ◽  
Xylocarpus Granatum ◽  
Avicennia Alba

The study was conducted in May-June 2019 on Rangsang Island. This study aims to determine the effect of mangrove forests on coastline found on the island of Rangsang in Riau Province. The sampling location was determined by purposive sampling, namely 3 station points, namely Segomeng Village, Tanjung Kedabu Village, and Sungai Gayung Kiri Village. Landsat image data analyzed at the Oceanographic Physics Laboratory of the Department of Marine Sciences, Faculty of Fisheries and Maritime Affairs, University of Riau. To find out the area of ​​mangroves using Landsad 5 TM satellite imagery and Landsat 8 OLI Tirs imagery and analyzed using Er Mapper Software, Envi 4.5 and Arcgis 10.3. Calculation of the structure of the mangrove community is carried out using the line plot plot method. The results of the analysis of the vast landsat image of mangroves in the coastal areas of Rangsang Island in 1997, 2002, 2007, 2013, 2019 were respectively 11,093 ha, 10,807 ha, 10,393 ha, 10,121 ha and 9,971 ha. Changes in the coastline indicate the occurrence of abrasion and accretion, where the highest abrasion occurs at station three with an average of -7.6 m/year and accretion occurs at station one with an average of 2.68 m / year. Mangrove density at station one is 2266.7 ind / ha and at station two that is 1466.7 ind / ha. Mangrove species found were Rhizophora apiculata, Rhizophora mucronata, Avicennia alba, Sonneratia ovata, Bruguiera gymnorrhiza, and Xylocarpus granatum.

scholarly journals Uso de sensores remotos en la determinación del forraje disponible de Urochloa humidicola cv. Llanero bajo pastoreo en la Altillanura colombiana

2021 ◽  
Vol 9 (3) ◽  
pp. 376-382
Author(s):  
Raúl Alejandro Díaz Giraldo ◽  
Mauricio Álvarez de León ◽  
Otoniel Pérez López
Keyword(s):  
Near Infrared ◽  
Agricultural Research ◽  
Coefficient Of Determination ◽  
Research Centre ◽  
Landsat 8 ◽  
Pastoral Systems ◽  
Forage Availability ◽  
Vegetation Indexes ◽  
Remote Sensing Techniques ◽  
Sentinel 2A

Modernization of pastoral systems based on the use of Urochloa species in the Colombian Eastern Llanos need the use of remote sensing techniques from satellite platforms to estimate amount of offered forage. In the Carimagua Research Centre of the Colombian Corporation for Agricultural Research (Agrosavia), an Urochloa humidicola cv. Llanero pasture was evaluated using Landsat 8 and Sentinel 2A images. The NDVI, SAVI, EVI y GNDVI vegetation indexes determined by using the blue, green, red and near infrared bands; and the results analyzed with the R free software, to relate those indexes with forage availability field measures taken during the dry season. Forage availability ranged between 290 and 656 kg DM ha-1 and the vegetation indexes for the Landsat 8 and Sentinel 2A sensors were: NDVI = 0.67 (±0.037) and 0.69 (±0.061); SAVI = 0.48 (±0.048) and 0.41 (±0.046); EVI = 0.70 (±0.052) and 0.41 (±0.047); y GNDVI = 0.60 (±0.028) and 0.70 (±0.034), respectively. The relationships between vegetation indexes and forage availability were linear. The Coefficient of Determination (R2= 0.56‒0.72) and the Mean Square Error (MSR =63.95‒80.16) of the prediction equations were used. In conclusion, under the conditions of the study, the EVI for Landsat 8 and NDVI for Sentinel 2A were considered adequate for estimating forage availability of Urochloa humidicola cv. Llanero.

scholarly journals Mapping Mangrove Forests of Dongzhaigang Nature Reserve in China Using Landsat 8 and Radarsat-2 Polarimetric SAR Data

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 4012 ◽  
Author(s):  
Jianing Zhen ◽  
Jingjuan Liao ◽  
Guozhuang Shen
Keyword(s):  
Classification Accuracy ◽  
Mangrove Forest ◽  
Nature Reserve ◽  
Radar Data ◽  
Optical Data ◽  
Support Vector ◽  
Accurate Information ◽  
Landsat 8 ◽  
Mangrove Forests ◽  
Sar Data

Mangrove forests are distributed in intertidal regions that act as a “natural barrier” to the coast. They have enormous ecological, economic, and social value. However, the world’s mangrove forests are declining under immense pressure from anthropogenic and natural disturbances. Accurate information regarding mangrove forests is essential for their protection and restoration. The main objective of this study was to develop a method to improve the classification of mangrove forests using C-band quad-pol Synthetic Aperture Radar (SAR) data (Radarsat-2) and optical data (Landsat 8), and to analyze the spectral and backscattering signatures of mangrove forests. We used a support vector machine (SVM) classification method to classify the land use in Hainan Dongzhaigang National Nature Reserve (HDNNR). The results showed that the overall accuracy using only optical information was 83.5%. Classification accuracy was improved to a varying extent by the addition of different radar data. The highest overall accuracy was 95.0% based on a combination of SAR and optical data. The area of mangrove forest in the reserve was found to be 1981.7 ha, as determined from the group with the highest classification accuracy. Combining optical data with SAR data could improve the classification accuracy and be significant for mangrove forest conservation.

Sign in / Sign up

Export Citation Format

Share Document