scholarly journals Analisis Perubahan Luasan Hutan Mangrove Di Kecamatan Brebes Dan Wanasari, Kabupaten Brebes Menggunakan Citra Satelit Landsat Tahun 2008, 2013 Dan 2018

2019 ◽  
Vol 8 (1) ◽  
pp. 27-35
Author(s):  
Amin Yunita Nur Annisa ◽  
Rudhi Pribadi ◽  
Ibnu Pratikto
Keyword(s):  
Satellite Image ◽  
Image Data ◽  
Landsat 8 ◽  
Mangrove Forests ◽  
Sensing Systems ◽  
Remote Sensing Systems ◽  
Satellite Image Data ◽  
June July ◽  
Landsat Satellite ◽  
Rehabilitation Activity

Mangrove merupakan ekosistem daerah peralihan yang memiliki beberapa fungsi diantaranya ekologis, fisik maupun ekonomi. Kerusakan mangrove sering terjadi di beberapa daerah sehingga kelestarian mangrove sangat perlu dijaga. Salah satu upaya untuk mengurangi kerusakan tersebut dengan kegiatan rehabilitasi. Kegiatan rehabilitasi ini bertujuan untuk memulihkan kondisi mangrove seperti keadaan semula. Keberhasilan dari kegiatan rehabilitasi ini dapat dipantau dengan sistem penginderaan jauh menggunakan citra Satelit Landsat. Penelitian ini dilakukan pada bulan Juni- Juli 2018. Metode penelitian ini menggunakan metode deskriptif bersifat eksploratif. Materi dalam penelitian ini adalah data citra satelit Landsat 5 untuk tahun 2008 dan Landsat 8 untuk tahun 2018. Berdasarkan hasil penelitian didapatkan nilai perubahan luasan hutan mangrove di Desa Kaliwlingi, Kecamatan Brebes dan Desa Sawojajar, Kecamatan Wanasari tahun 2008, 2013 dan 2018. Luas mangrove di Desa Kaliwlingi Kecamatan Brebes pada tahun 2008-2013 bertambah sebesar 101,25 ha yaitu 48,42 ha pada tahun 2008 dan 149,67 ha pada tahun 2013. Pada tahun 2013-2018 juga bertambah 184,23 ha yakni 333,9 ha pada tahun 2018. Pada Desa Sawojajar Kecamatan Wanasari, luas mangrove juga bertambah sebesar 0,09 ha yakni 24,39 ha pada tahun 2008 bertambah menjadi 24,48 ha pada tahun 2013. Tahun 2013-2018 juga bertambah sebesar 12,24 ha sehingga menjadi 36,72 ha di tahun 2018. Luas mangrove di Desa Kaliwlingi dan Sawojajar bertambah dalam kurun waktu sepuluh tahun.] Mangroves are transitional ecosystems that have several functions including ecological, physical and economic. Mangrove damage often occurs in several areas so that the preservation of mangroves is very important. One effort to reduce this damage is through rehabilitation activities. This rehabilitation activity aims to restore the condition of mangroves as they were before. The success of these rehabilitation activities can be monitored by remote sensing systems using Landsat Satellite imagery. This research was conducted in June-July 2018. This research method uses descriptive methods that are alternative. The material in this study is Landsat 5 satellite image data for 2008 and Landsat 8 for 2018. Based on the results of the study, the value of changes in a mangrove forests in Kaliwlingi Village, Brebes and Sawojajar Villages, Wanasari District in 2008, 2013 and 2018. The area of mangroves in Kaliwlingi Village, Brebes Subdistrict in 2008-2013 it increased by 101.25 ha, which was 48.42 ha in 2008 and 149.67 ha in 2013. In 2013-2018 it also increased by 184.23 ha, namely 333.9 ha in 2018. In Sawojajar Village, Wanasari Subdistrict, the area of mangroves also increased by 0.09 ha, which was 24.39 ha in 2008 which increased to 24.48 ha in 2013. 2013-2018 also increased by 12.24 ha to 36.72 ha in 2018. The area of mangrove in Kaliwlingi and Sawojajar villages has increased in ten years.

scholarly journals Preliminary Study of Total Suspended Solid Distribution in Coastal Ujung Pangkah Gresik Based Reflectance Value of Landsat Satellite Imagery

2019 ◽  
Vol 51 (1) ◽  
pp. 42
Author(s):  
Hendrata Wibisana ◽  
Bangun Muljo Soekotjo ◽  
Umboro Lasminto
Keyword(s):  
Satellite Image ◽  
Image Data ◽  
Total Suspended Solid ◽  
Suspended Solid ◽  
Landsat 8 ◽  
Time Period ◽  
Satellite Image Data ◽  
Landsat Satellite ◽  
Solid Distribution

Total suspended solid (TSS) is one of the parameters that uses for detecting health in aquatic environments. The distribution of the TSS value in the water body will affect the aquatic ecosystem. In this research will be analyzed the distribution value of TSS during 5 year period by utilizing Landsat 8 satellite image data, where the developed method is extraction of reflectance value from Landsat 8 satellite image for 5 years using SEADASS and then compiled the TSS algorithm with reflectance value that already obtained on the existing conditions, the algorithm obtained is estimated over 5 years back to get a picture of change and distribution of TSS value. As a case study , the coast of Ujung Pangkah Gresik was taken which has the mouth of the river Bengawan Solo. The results obtained from this study illustrate the decrease of TSS value during that time period, so that with this decrease can be concluded that at the point of field coordinate, TSS value was decreasing and causing the erosion in the environment.

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.

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 FEATURES OF THE THERMAL REGIME AND WATER SUPPLY FOR STEPPE BURNED AREA BASED ON LANDSAT SATELLITE IMAGES

2020 ◽  
Author(s):  
V.M. Pavleychik ◽  
◽  
K.V. Myachina ◽  
Keyword(s):  
Thermal Regime ◽  
Satellite Images ◽  
Landscape Heterogeneity ◽  
Satellite Image ◽  
Image Data ◽  
Burned Area ◽  
Recovery Processes ◽  
Satellite Image Data ◽  
Landsat Satellite ◽  
Thermal Background

Based on the analysis of Landsat satellite image data, microclimatic features of steppe burned area were identified, consisting in an increased thermal background, reduced depth and duration of snow cover. The duration of recovery processes is estimated taking into account landscape heterogeneity and regularities in the daily and seasonal dynamics of the thermal regime due to uneven insolation are revealed.

scholarly journals THE EFFECT OF DIFFERENT ATMOSPHERIC CORRECTIONS ON BATHYMETRY EXTRACTION USING LANDSAT 8 SATELLITE IMAGERY

2017 ◽  
Vol 12 (1) ◽  
pp. 13 ◽  
Author(s):  
Kuncoro Teguh Setiawan ◽  
Yennie Marini ◽  
Johannes Manalu ◽  
Syarif Budhiman
Keyword(s):  
Remote Sensing ◽  
Satellite Image ◽  
Atmospheric Correction ◽  
Remote Sensing Data ◽  
Image Data ◽  
Landsat 8 ◽  
Calculation Error ◽  
Sensing Technology ◽  
The Difference ◽  
Landsat Satellite

Remote sensing technology can be used to obtain information bathymetry. Bathymetric information plays an important role for fisheries, hydrographic and navigation safety. Bathymetric information derived from remote sensing data is highly dependent on the quality of satellite data use and processing. One of the processing to be done is the atmospheric correction process. The data used in this study is Landsat 8 image obtained on June 19, 2013. The purpose of this study was to determine the effect of different atmospheric correction on bathymetric information extraction from Landsat satellite image data 8. The atmospheric correction methods applied were the minimum radiant, Dark Pixels and ATCOR. Bathymetry extraction result of Landsat 8 uses a third method of atmospheric correction is difficult to distinguish which one is best. The calculation of the difference extraction results was determined from regression models and correlation coefficient value calculation error is generated.

scholarly journals Analysis of Coral Reef Area Based on Landsat 8 Satellite Image Data Processing using the Lyzenga Algorithm

2019 ◽  
Vol 8 (1) ◽  
pp. 18-24
Author(s):  
Laila Martina Azka
Keyword(s):  
Coral Reef ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Image Data ◽  
Correction Method ◽  
Landsat 8 ◽  
Index Method ◽  
Accuracy Level ◽  
Satellite Image Data ◽  
Map Accuracy

Indonesia has the potential of invaluable biological resources from an economic and ecological perspective, one of which is coral reef resources. To determine the condition of the coral reef ecosystem in Indonesian waters as an effort to monitor and monitor using remote sensing data. The purpose of this research is the creation of an information system through a website that can help users access information effectively and efficiently.Utilization of Landsat 8 satellite image data to observe the bottom of the waters directly on coral reef mapping using the Lyzenga Algorithm method, also known as the depthinvariant index method or the water column correction method. This method produces a baseline index that is not affected by depth and works well in clear shallow waters such as in coral reef habitat areas (Maritorena, 1996). The results showed that the image processing data had a map accuracy level for Menjangan Besar and Menjangan Kecil islands of 77.78%, while the map accuracy level for Karimunjawa and Kemujang Islands was 72.23%. And from the processed data, it is known that coral reef cover in general has increased from 2014 to 2018, for Karimunjawa and Kemujan Islands an increase of 17.4% for Menjangan Besar and Menjangan Kecil Islands only a slight increase of 0.27% and for Bunaken Island an increase of 9.1%. And there is one island that is processed has decreased, namely Bunaken Island by 4.5%.

scholarly journals Yield variability prediction by remote sensing sensors with different spatial resolution

2017 ◽  
Vol 31 (2) ◽  
pp. 195-202 ◽  
Author(s):  
Jitka Kumhálová ◽  
Štěpánka Matějková
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Satellite Data ◽  
Satellite Images ◽  
Satellite Image ◽  
Image Data ◽  
Data Availability ◽  
Satellite Image Data ◽  
Landsat Satellite ◽  
Very High

Abstract Currently, remote sensing sensors are very popular for crop monitoring and yield prediction. This paper describes how satellite images with moderate (Landsat satellite data) and very high (QuickBird and WorldView-2 satellite data) spatial resolution, together with GreenSeeker hand held crop sensor, can be used to estimate yield and crop growth variability. Winter barley (2007 and 2015) and winter wheat (2009 and 2011) were chosen because of cloud-free data availability in the same time period for experimental field from Landsat satellite images and QuickBird or WorldView-2 images. Very high spatial resolution images were resampled to worse spatial resolution. Normalised difference vegetation index was derived from each satellite image data sets and it was also measured with GreenSeeker handheld crop sensor for the year 2015 only. Results showed that each satellite image data set can be used for yield and plant variability estimation. Nevertheless, better results, in comparison with crop yield, were obtained for images acquired in later phenological phases, e.g. in 2007 - BBCH 59 - average correlation coefficient 0.856, and in 2011 - BBCH 59-0.784. GreenSeeker handheld crop sensor was not suitable for yield estimation due to different measuring method.

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