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 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 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 ESTIMASI PRODUKSI PADI DENGAN ANALISIS CITRA SATELIT LANDSAT 8 DI KABUPATEN KLUNGKUNG PROVINSI BALI

2018 ◽  
Vol 12 (1) ◽  
pp. 93
Author(s):  
Made Arya Bhaskara Putra ◽  
I Wayan Nuarsa ◽  
I Wayan Sandi Adnyana
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Image Data ◽  
Research Area ◽  
Rice Production ◽  
Landsat 8 ◽  
Estimation Model ◽  
Accuracy Test ◽  
Accuracy Level

Rice crop is one of the important commodities that must always be available, so estimation of rice production becomes very important to do before harvesting time to know the food availability. The technology that can be used is remote sensing technology using Landsat 8 Satellite. The aims of this study were (1) to obtain the model of estimation of rice production with Landsat 8 image analysis, and (2) to know the accuracy of the model that obtained by Landsat 8. The research area is located in three sub-districts in Klungkung regency. Analysis in this research was conducted by single band analysis and analysis of vegetation index of satellite image of Landsat 8. Estimation model of rice production was developed by finding the relationship between satellite image data and rice production data. The final stage is the accuracy test of the rice production estimation model, with t test and regression analysis. The results showed: (1) estimation of rice production can be calculated between 67 to 77 days after planting; (2) there was a positive correlation between NDVI (Normalized Difference Vegetation Index) vegetation index value with rice yield; (3) the model of rice production estimation is y = 2.0442e1.8787x (x is NDVI value of Landsat 8 and y is rice production); (4) The results of the model accuracy test showed that the obtained model is suitable to predict rice production with accuracy level is 89.29% and standard error of production estimation is + 0.443 ton/ha. Based on research results, it can be concluded that Landsat 8 Satellite image can be used to estimate rice production and the accuracy level is 89.29%. The results are expected to be a reference in estimating rice production in Klungkung Regency.

scholarly journals MACHINE LEARNING-BASED MANGROVE LAND CLASSIFICATION ON WORLDVIEW-2 SATELLITE IMAGE IN NUSA LEMBONGAN ISLAND

2018 ◽  
Vol 14 (2) ◽  
pp. 159 ◽  
Author(s):  
Aulia Ilham ◽  
Marza Ihsan Marzuki
Keyword(s):  
Machine Learning ◽  
Satellite Image ◽  
Image Data ◽  
Information Criterion ◽  
Mangrove Forests ◽  
Mangrove Species ◽  
Rhizophora Apiculata ◽  
Distribution Type ◽  
Non Linear System ◽  
Image Data Classification

Machine learning is an empirical approach for regressions, clustering and/or classifying (supervised or unsupervised) on a non-linear system. This method is mainly used to analyze a complex system for  wide data observation. In remote sensing, machine learning method could be  used for image data classification with software tools independence. This research aims to classify the distribution, type, and area of mangroves using Akaike Information Criterion approach for case study in Nusa Lembongan Island. This study is important because mangrove forests have an important role ecologically, economically, and socially. For example is as a green belt for protection of coastline from storm and tsunami wave. Using satellite images Worldview-2 with data resolution of 0.46 meters, this method could identify automatically land class, sea class/water, and mangroves class. Three types of mangrove have been identified namely: Rhizophora apiculata, Sonnetaria alba, and other mangrove species. The result showed that the accuracy of classification was about 68.32%.

scholarly journals Pansformers: Transformer-Based Self-Attention Network for Pansharpening

2021 ◽  
Author(s):  
Nithin G R ◽  
Nitish Kumar M ◽  
Venkateswaran Narasimhan ◽  
Rajanikanth Kakani ◽  
Ujjwal Gupta ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Performance Metrics ◽  
Satellite Image ◽  
Image Data ◽  
Landsat 8 ◽  
Learning Models ◽  
Attention Network ◽  
Panchromatic Image ◽  
Recent Success

Pansharpening is the task of creating a High-Resolution Multi-Spectral Image (HRMS) by extracting and infusing pixel details from the High-Resolution Panchromatic Image into the Low-Resolution Multi-Spectral (LRMS). With the boom in the amount of satellite image data, researchers have replaced traditional approaches with deep learning models. However, existing deep learning models are not built to capture intricate pixel-level relationships. Motivated by the recent success of self-attention mechanisms in computer vision tasks, we propose Pansformers, a transformer-based self-attention architecture, that computes band-wise attention. A further improvement is proposed in the attention network by introducing a Multi-Patch Attention mechanism, which operates on non-overlapping, local patches of the image. Our model is successful in infusing relevant local details from the Panchromatic image while preserving the spectral integrity of the MS image. We show that our Pansformer model significantly improves the performance metrics and the output image quality on imagery from two satellite distributions IKONOS and LANDSAT-8.

scholarly journals Analysis of Changes in Area and Density of Mangroves through Landsat 8 Satellite Image Processing

2019 ◽  
Vol 9 (2) ◽  
pp. 16-22
Author(s):  
Nadya Fiqi Nurcahyani
Keyword(s):  
Image Processing ◽  
Satellite Imagery ◽  
Good Accuracy ◽  
Social Values ◽  
Satellite Image ◽  
Landsat 8 ◽  
Mangrove Forests ◽  
Android Application ◽  
Mangrove Area ◽  
Satellite Image Processing

Mangrove forests have high ecological, economic and social values ??which function to maintain shoreline stability, protect beaches and riverbanks, filter and remediate waste, and to withstand floods and waves. The facts show that mangrove damage is everywhere, even the intensity of damage and its area tends to increase significantly. Many roles of mangroves require proper management to maintain the existence of mangroves. One way to determine the area of ??mangroves is by processing Landsat 8 satellite imagery. The stages of mangrove identification are carried out by using 564 RGB band merger, then separating the mangrove and non-mangrove objects. Next step is to analyze the density of mangroves using NDVI formula. To maximize monitoring of mangrove area, an android application was created that provides information on the area and density of mangroves at several locations, namely Clungup, Bangsong Teluk Asmara and Cengkrong from 2015 to 2018.The results showed that Landsat 8 satellite imagery can be used to identify changes in the area of ??mangrove forests with good accuracy, namely in the Clungup area of ??90% and Cengkrong of 86.67%. From processing results, the mangrove area in the Clungup area has also decreased from 2015 to 2017 but has increased in 2018 so that the application provides recommendations for embroidering mangroves in 2016 to 2017 and mangrove recommendations are maintained in 2018. As for Bangsong Teluk area Asmara and Cengkrong have increased the area of ??mangroves every year so that the application provides recommendations to be maintained from 2016 to 2018.

scholarly journals Research of total phosphorus concentration in Hefei Ring River based on landsat-8 satellite image data

2019 ◽  
Vol 136 ◽  
pp. 06032
Author(s):  
Kun Ding ◽  
Chen Yang ◽  
Chuan-hua Zhu ◽  
Yong Zhang ◽  
Hui Zhang ◽  
...  
Keyword(s):  
Total Phosphorus ◽  
Satellite Image ◽  
Water Environment ◽  
Image Data ◽  
Phosphorus Concentration ◽  
Landsat 8 ◽  
Total Phosphorus Concentration ◽  
Important Indicator ◽  
Daily Monitoring ◽  
Sensing Technology

Total phosphorus (TP) in water is an important indicator reflecting water environment and water ecology. If the concentration exceeds the standard, it will directly lead to eutrophication. The daily monitoring of total phosphorus in water bodies has already mentioned the important agenda of environmental protection, while the routine testing has a large workload and heavy tasks. We used satellite remote sensing technology to extract image data and establish a mathematical models, what was used to invert the total phosphorus concentration in water. Taking the Ring River as an example, we selected different time nodes to sample and measure the TP value, and use the landsat-8 image data to establish a semi-empirical regression model. The model structure, the calculation results found that the error with the measured data is within the controllable range. The method is simple in operation, saves resources, manpower and financial resources, and can accurately reflect the actual situation of the water body TP.

scholarly journals Estimation of PM10 Distribution using Landsat5 and Landsat8 Remote Sensing

Proceedings ◽  
2018 ◽  
Vol 2 (23) ◽  
pp. 1430
Author(s):  
V. M. Fernández-Pacheco ◽  
C. A. López-Sánchez ◽  
E. Álvarez-Álvarez ◽  
M. J. Suárez López ◽  
L. García-Expósito ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Air Pollutants ◽  
Satellite Images ◽  
Satellite Image ◽  
Remote Sensing Image ◽  
Regional Studies ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Landsat 5 Tm ◽  
Source Of Information

Air pollution is one of the major environmental problems, especially in industrial and highly populated areas. Remote sensing image is a rich source of information with many uses. This paper is focused on estimation of air pollutants using Landsat-5 TM and Landsat-8 OLI satellite images. Particulate Matter with particle size less than 10 microns (PM10) is estimated for the study area of Principado de Asturias (Spain). When a satellite records the radiance of the surface received at sensor, does not represent the true radiance of the surface. A noise caused by Aerosol and Particulate Matters attenuate that radiance. In many applications of remote sensing, that noise called path radiance is removed during pre-processing. Instead, path radiance was used to estimate the PM10 concentration in the air. A relationship between the path radiance and PM10 measurements from ground stations has been established using Random Forest (RF) algorithm and a PM10 map was generated for the study area. The results show that PM10 estimation through satellite image is an efficient technique and it is suitable for local and regional studies.

scholarly journals Change in tropical forest cover of Southeast Asia from 1990 to 2010

2013 ◽  
Vol 10 (8) ◽  
pp. 12625-12653 ◽  
Author(s):  
H.-J. Stibig ◽  
F. Achard ◽  
S. Carboni ◽  
R. Raši ◽  
J. Miettinen
Keyword(s):  
Southeast Asia ◽  
Satellite Imagery ◽  
Forest Cover ◽  
Satellite Image ◽  
Image Interpretation ◽  
Selective Logging ◽  
Automated Classification ◽  
Forest Cover Change ◽  
Forest Change ◽  
The One

Abstract. The study assesses the extent and trends of forest cover in Southeast Asia for the period 1990–2000–2010 and provides an overview on the main drivers of forest cover change. A systematic sample of 418 sites (10 km × 10 km size) located at the one-degree geographical confluence points and covered with satellite imagery of 30 m resolution is used for the assessment. Techniques of image segmentation and automated classification are combined with visual satellite image interpretation and quality control, involving forestry experts from Southeast Asian countries. The accuracy of our results is assessed through an independent consistency assessment, performed from a subsample of 1572 mapping units and resulting in an overall agreement of > 85% for the general differentiation of forest cover vs. non-forest cover. The total forest cover of Southeast Asia is estimated at 268 Mha in 1990, dropping to 236 Mha in 2010, with annual change rates of 1.75 Mha (~0.67% and 1.45 Mha (~0.59%) for the periods 1990–2000 and 2000–2010, respectively. The vast majority of forest cover loss (~2/3 for 2000–2010) occurred in insular Southeast Asia. Combining the change patterns visible from satellite imagery with the output of an expert consultation on the main drivers of forest change highlights the high pressure on the region's remaining forests. The conversion of forest cover to cash crop plantations (e.g. oil palm) is ranked as the dominant driver of forest change in Southeast Asia, followed by selective logging and the establishment of tree plantations.

scholarly journals STUDY ON MOSAIC AND UNIFORM COLOR METHOD OF SATELLITE IMAGE FUSION IN LARGE SREA

2018 ◽  
Vol XLII-3 ◽  
pp. 1099-1102
Author(s):  
S. Liu ◽  
H. Li ◽  
X. Wang ◽  
L. Guo ◽  
R. Wang
Keyword(s):  
Information Entropy ◽  
Satellite Images ◽  
Satellite Image ◽  
Image Data ◽  
Texture Features ◽  
Color Difference ◽  
Large Area ◽  
Color Mapping ◽  
Multi Temporal ◽  
Mosaic Images

Due to the improvement of satellite radiometric resolution and the color difference for multi-temporal satellite remote sensing images and the large amount of satellite image data, how to complete the mosaic and uniform color process of satellite images is always an important problem in image processing. First of all using the bundle uniform color method and least squares mosaic method of GXL and the dodging function, the uniform transition of color and brightness can be realized in large area and multi-temporal satellite images. Secondly, using Color Mapping software to color mosaic images of 16bit to mosaic images of 8bit based on uniform color method with low resolution reference images. At last, qualitative and quantitative analytical methods are used respectively to analyse and evaluate satellite image after mosaic and uniformity coloring. The test reflects the correlation of mosaic images before and after coloring is higher than 95 % and image information entropy increases, texture features are enhanced which have been proved by calculation of quantitative indexes such as correlation coefficient and information entropy. Satellite image mosaic and color processing in large area has been well implemented.

Sign in / Sign up

Export Citation Format

Share Document