Analyzing land conversion in South East Minahasa, Indonesia, under Landsat Satellite image data using the supervised classification method

2016 ◽  
Author(s):  
Eko Sediyono ◽  
Gregorius Anung Hanindito ◽  
Adi Setiawan
Keyword(s):  
Supervised Classification ◽  
Satellite Image ◽  
Image Data ◽  
Classification Method ◽  
Land Conversion ◽  
Satellite Image Data ◽  
Landsat Satellite

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 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 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.

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 Verification of multispectral data processing for the Sentinel-2A bands, field ASD FieldSpec® 3FR and UAV with the DJI STS-VIS

2019 ◽  
pp. 29-39 ◽  
Author(s):  
Stanislav Dugin ◽  
Oksana Sybirtseva ◽  
Stanislav Golubov ◽  
Yelizaveta Dorofey
Keyword(s):  
Pearson Correlation ◽  
Plant Cover ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Image Data ◽  
Multispectral Data ◽  
Maximum Value ◽  
Substantial Correlation ◽  
Satellite Image Data ◽  
Sentinel 2A

The study of plant cover have been performed by the hyperspectral remote sensing method using ASD FieldSpec® 3FR and DJI STS-VIS measurements. The orthophotoplans are compiled for the test plots of interest at the spatial resolution of 2.5 cm. The substantial correlation for the results of terrestrial verification for the satellite image data in the range of Sentinel-2A bands are confirmed. 15 vegetation indices for the Sentinel-2А wavelength bands were drawn at the Pearson correlation coefficient r > 0.97, with a maximum value of the correlation error of 0.07.

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