scholarly journals ANALYSIS OF CHANGES IN MANGROVE FOREST FERTILITY USING SATELLITE IMAGE DATA AND WILCOXON TEST

CI-TECH ◽  
2021 ◽  
Vol 2 (01) ◽  
pp. 25-29
Author(s):  
Siti Zainab ◽  
Hendrata Wibisana
Keyword(s):  
Regression Model ◽  
Satellite Imagery ◽  
Satellite Image ◽  
Image Data ◽  
Storm Surges ◽  
Wilcoxon Test ◽  
Landsat 8 ◽  
Significant Difference ◽  
Exponential Regression Model ◽  
The City

Gunung Anyar is one of the districts in the city of Surabaya. This district has a height of approximately 3 meters above sea level. Based on data from the Central Statistics Agency (BPS) for the City of Surabaya 2019, Gunung Anyar District has an area of ​​9.2 square kilometers and is divided into four sub-districts. These include the Kelurahan Rungkut Menanggal, Rungkut Tengah, Mount Anyar and Mount Anyar Tambak (AyoSurabaya.com by Rizma Riyandi). The mangrove's robust root system helps form a natural barrier against storm surges and flooding. River and land sediments are trapped by roots, which protect shorelines and slow erosion. This filtering process also prevents harmful sediments from reaching coral reefs and seagrass beds (Anugerah Ayu Sundari 2019). The method used by remote sensing with Landsat 8 satellite imagery was analyzed using SeaDAS software, it was obtained that the comparison value in each band 2,3,4 and band 5 had differences in each reflectance value. The 2015 satellite image map has the largest value in band_4 with the exponential regression model y = 125.06e-22.13x with R2 = 0.0732, while the 2019 satellite image map which has the largest value is band_4 with the logarithmic regression model y = 141.72ln (x) + 326.3 where R2 = 0.0281. Using the Wilcoxon H1 Test Statistics it is accepted that there is a significant difference between the diameter of mangroves from satellite imagery in 2015 and the diameter of mangroves from satellite images in 2019. Because the number of positive rankings from the diameter of mangrove satellite imagery in 2015 is greater than the diameter of mangroves from satellite imagery in 2019. , it can be concluded that the mangrove area of ​​Wonorejo Surabaya is experiencing fertility.

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 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 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.

Post-Disaster Urban Recovery Monitoring in Pisco After the 2007 Peru Earthquake Using Satellite Image

2014 ◽  
Vol 9 (6) ◽  
pp. 1059-1068 ◽  
Author(s):  
Tomoyo Hoshi ◽  
◽  
Osamu Murao ◽  
Kunihiko Yoshino ◽  
Fumio Yamazaki ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Urban Areas ◽  
Satellite Image ◽  
Recovery Process ◽  
Visual Interpretation ◽  
Field Surveys ◽  
Recovery Processes ◽  
The City ◽  
Post Disaster

Pisco was the area most damaged by the 2007 Peru earthquake. The purpose of this research is to develop possibilities of using satellite imagery to monitor postdisaster urban recovery processes, focusing on the urban change in Pisco between 2007 and 2011. To this end, the authors carried out field surveys in the city in 2012 and 2013 and also examined previous surveys to determine that building reconstruction peaked between 2008 and 2009. After analyzing the five-year recovery process, the authors compared its reconstruction conditions by visual interpretation with those by image analysis using satellite image. An accuracy of 71.2% was achieved for the visual interpretation results in congested urban areas, and that for developed districts was about 60%. The result shows that satellite imagery can be a useful tool for monitoring and understanding post-disaster urban recovery processes in the areas in which conducting long-term field survey is difficult.

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 Research Bathymetry Mapping of Shallow Water Areas around Islands in the Truong Sa Archipelago by Deep - Remote sensing technology

2017 ◽  
Vol 33 (4) ◽  
Author(s):  
Phan Quoc Yen ◽  
Dao Khanh Hoai ◽  
Dinh Thi Bao Hoa
Keyword(s):  
Remote Sensing ◽  
Shallow Water ◽  
Satellite Image ◽  
Image Data ◽  
Landsat 8 ◽  
Military Operations ◽  
Sensing Technology ◽  
Remote Sensing Techniques ◽  
Map Data ◽  
The Military

Satellite image data is being researched and applied effectively in the survey and establishment of bathymetry mapping in shallow water areas in both time and human terms. Remote sensing techniques contribute to rapid updating of topography, timely assurance of civil and military operations such as maritime safety, environmental security and rescue, Warfare in the military, especially the ability to remotely monitor disputed areas. The article experiment with the Stumpf et al algorithm to estimate the shallow water depths on the Spratly Island by Landsat 8 image. The correlation coefficient of the model R2 is 0.924; RMSE is 0.99m. In addition, the results are compared with the map data of C-map and use 12 actual test points scores to evaluate the accuracy of the model.

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 Estimation of salt pond area in Madura based on satellite imagery

2021 ◽  
Vol 924 (1) ◽  
pp. 012064
Author(s):  
M F F Mu’tamar ◽  
R A Firmansyah ◽  
M Ulya
Keyword(s):  
Satellite Imagery ◽  
Satellite Remote Sensing ◽  
Satellite Image ◽  
Image Interpretation ◽  
Landsat 8 ◽  
Field Observations ◽  
Salt Ponds ◽  
Classification Methods ◽  
Salt Pond ◽  
Multispectral Data

Abstract Salt is one of the essential commodities in Madura. Still, this commodity is often a problem related to the volume of production that cannot be determined with certainty. Sometimes, the estimation and actual production in the field is much different. The satellite image is a picture of an area photographed by satellite remote sensing of an area according to conditions in the field. Satellite imagery can be used to estimate the area of production of a commodity at a specific location. This study aimed to estimate the total area of salt pond in the Madura Island, specifically Sampang district, using a Landsat 8 satellite image. The method used spectral analysis that extracts multispectral data Landsat 8 to result from different areas. Field observations were conducted to validate the area. The results show that the accuracy of satellite image interpretation of salt ponds and non-salt ponds was 67.5%. Based on the result, it is possible to estimate salt pond area production in the Sampang district using Landsat 8. However, classification results must be improved by using other classification methods.

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