scholarly journals PEAT FOREST HEALTH ANALYSIS ON LANDSAT 8 OLI / TIRS IMAGERY USING NDVI METHOD IN KOTAWARINGIN TIMUR REGENCY

2020 ◽  
Vol 21 (2) ◽  
pp. 209-217
Author(s):  
Rois Saida Sanjaya ◽  
Mitha Fitria Anggraini ◽  
Mahendra Zhafir Pratama
Keyword(s):  
Forest Fires ◽  
Satellite Image ◽  
Forest Health ◽  
Local Environment ◽  
Pilot Project ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Dome Collapse ◽  
Gis Software ◽  
Peat Area

The degradation of peatland ecosystem has been a mayor impact on the local environment as well as its surroundings such as fires, irriversible drying and dome collapse. This paper presents the application of satellite remote sensing dan GIS techniques to detect and identify peat forest healt in Kotawaringin Timur, Cental Kalimantan Province, Indonesia. Mapping the spasial distribution of peat forest healt is important for making in land management and mitigation of peatland forest fires. This study uses the integration beetwen GIS software and Landsat 8 OLI/TIRS satellite data to identify peatland healt using NDVI in Kotawaringin Timur Regency. This area were picked up as pilot project area for this research because these areas historically had many fire spots on last few years. The result data processing of landsat 8 satellite image shows that 116586,4 hectares of Kotawaringin Timur area is disturbed peatland. Base on the result of landsat 8 image processing data can be seen some areas of Kotawaringin Timur indicate green color means the peat area, and the health level of the peat forest in Kotawaringin Timur Regency is already in the damaged category. Keywords: Peat forest healt, NDVI, GIS, Landsat 8

Making Of Classification Land Cover Through Result Of Visual Data Satellite Image Analysis Landsat 8 OLI : Case Study in Tapaktuan District, South Aceh District

2021 ◽  
Vol 6 (1) ◽  
pp. 59-65
Author(s):  
Safridatul Audah ◽  
Muharratul Mina Rizky ◽  
Lindawati
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Image Classification ◽  
Satellite Image ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Sensing Technology ◽  
Open Land ◽  
Cover Class ◽  
Satellite Image Analysis

Tapaktuan is the capital and administrative center of South Aceh Regency, which is a sub-district level city area known as Naga City. Tapaktuan is designated as a sub-district to be used for the expansion of the capital's land. Consideration of land suitability is needed so that the development of settlements in Tapaktuan District is directed. The purpose of this study is to determine the level of land use change from 2014 to 2018 by using remote sensing technology in the form of Landsat-8 OLI satellite data through image classification methods by determining the training area of the image which then automatically categorizes all pixels in the image into land cover class. The results obtained are the results of the two image classification tests stating the accuracy of the interpretation of more than 80% and the results of the classification of land cover divided into seven forms of land use, namely plantations, forests, settlements, open land, and clouds. From these classes, the area of land cover change in Tapaktuan is increasing in size from year to year.

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 Climate-Triggered Insect Defoliators and Forest Fires Using Multitemporal Landsat and TerraClimate Data in NE Iran: An Application of GEOBIA TreeNet and Panel Data Analysis

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3965 ◽  
Author(s):  
Omid Abdi
Keyword(s):  
Panel Data ◽  
Forest Fires ◽  
Panel Data Analysis ◽  
Data Models ◽  
Landsat 8 ◽  
Panel Data Models ◽  
Landsat 8 Oli ◽  
The Mean ◽  
Maximum Temperatures ◽  
Object Features

Despite increasing the number of studies for mapping remote sensing insect-induced forest infestations, applying novel approaches for mapping and identifying its triggers are still developing. This study was accomplished to test the performance of Geographic Object-Based Image Analysis (GEOBIA) TreeNet for discerning insect-infested forests induced by defoliators from healthy forests using Landsat 8 OLI and ancillary data in the broadleaved mixed Hyrcanian forests. Moreover, it has studied mutual associations between the intensity of forest defoliation and the severity of forest fires under TerraClimate-derived climate hazards by analyzing panel data models within the TreeNet-derived insect-infested forest objects. The TreeNet optimal performance was obtained after building 333 trees with a sensitivity of 93.7% for detecting insect-infested objects with the contribution of the top 22 influential variables from 95 input object features. Accordingly, top image-derived features were the mean of the second principal component (PC2), the mean of the red channel derived from the gray-level co-occurrence matrix (GLCM), and the mean values of the normalized difference water index (NDWI) and the global environment monitoring index (GEMI). However, tree species type has been considered as the second rank for discriminating forest-infested objects from non-forest-infested objects. The panel data models using random effects indicated that the intensity of maximum temperatures of the current and previous years, the drought and soil-moisture deficiency of the current year, and the severity of forest fires of the previous year could significantly trigger the insect outbreaks. However, maximum temperatures were the only significant triggers of forest fires. This research proposes testing the combination of object features of Landsat 8 OLI with other data for monitoring near-real-time defoliation and pathogens in forests.

scholarly journals Possibility for Identifying/Extracting Rock Outcrop Using Landsat 8 OLI/TIRS - Case Study of Thua Thien Hue Province

2020 ◽  
Vol 36 (3) ◽  
Author(s):  
Nguyen Quang Tuan ◽  
Do Thi Viet Huong ◽  
Doan Ngoc Nguyen Phong ◽  
Nguyen Dinh Van
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Object Orientation ◽  
Image Method ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Kappa Index ◽  
Exposed Rock ◽  
Ratio Image

This paper approaches the ratio image method to extract the exposed rock information from the Landsat 8 OLI/TIRS satellite image (2019) according to the object orientation classification. Combining automatic interpretation and interpretation through threshold of image index values according to interpretation key the object orientation classification to separate soil object containing exposed rock and no exposed rock in Thua Thien Hue province. Using the Topsoil Grain Size Index (TGSI), the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Built-up Index (NDBI) and other related analytical problems have identified 40 exposed rock storage areas in the study area. The results have been verified in the field and the Kappa index is 85.10%.

scholarly journals Determining PM10 Model in Hanoi Using Landsat 8 Oli and Ground-measured Dust Data

2018 ◽  
Vol 34 (1) ◽  
Author(s):  
Nguyen Nhu Hung ◽  
Tran Van Anh ◽  
Pham Quang Vinh ◽  
Nguyen Thanh Binh ◽  
Vu Van Hoang
Keyword(s):  
Air Pollutants ◽  
Satellite Image ◽  
Calculated Data ◽  
Regression Function ◽  
Measured Data ◽  
Landsat 8 ◽  
Mean Square ◽  
Correlation Model ◽  
Landsat 8 Oli

PM10 (Particulate matter 10 is a dust with aerodynamic diameters of 0.001 ÷ 10μm) is one of the air pollutants affecting human health. In this study, we conducted a modeling study to identify PM10 dust in the air by using Landsat 8 OLI satellite image, along with PM10 ground-measured data using the machine DustTrak II . Conduct regression analysis to determine the correlation model. Here, we used 16 in-situ measurement points. In that, 10 points were used to determine the regression function and 6 other points were used to test the regression model. Results were evaluated based on correlation coefficient (R) and Root Mean Square Error (RMSE) between measured and calculated data.

scholarly journals Landsat 8 OLI Satellite Image Classification using Convolutional Neural Network

2020 ◽  
Vol 167 ◽  
pp. 987-993 ◽  
Author(s):  
Amit Kumar Rai ◽  
Nirupama Mandal ◽  
Akansha Singh ◽  
Krishna Kant Singh
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Satellite Image ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Satellite Image Classification

scholarly journals Preliminary analysis of the forest health state based on multispectral images acquired by Unmanned Aerial Vehicle

2015 ◽  
Vol 57 (3) ◽  
pp. 138-144 ◽  
Author(s):  
Paweł Czapski ◽  
Mariusz Kacprzak ◽  
Jan Kotlarz ◽  
Karol Mrowiec ◽  
Katarzyna Kubiak ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Forest Health ◽  
Growing Season ◽  
Landsat 8 ◽  
Health State ◽  
Multispectral Images ◽  
Landsat 8 Oli ◽  
Aerial Vehicle ◽  
Low Altitude

Abstract The main purpose of this publication is to present the current progress of the work associated with the use of a lightweight unmanned platforms for various environmental studies. Current development in information technology, electronics and sensors miniaturisation allows mounting multispectral cameras and scanners on unmanned aerial vehicle (UAV) that could only be used on board aircraft and satellites. Remote Sensing Division in the Institute of Aviation carries out innovative researches using multisensory platform and lightweight unmanned vehicle to evaluate the health state of forests in Wielkopolska province. In this paper, applicability of multispectral images analysis acquired several times during the growing season from low altitude (up to 800m) is presented. We present remote sensing indicators computed by our software and common methods for assessing state of trees health. The correctness of applied methods is verified using analysis of satellite scenes acquired by Landsat 8 OLI instrument (Operational Land Imager).

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