scholarly journals CLASSIFICATION OF LANDS OF REMOTE SENSITIVE DATA BY NDVI METHOD IN SMART AGRICULTURE

2021 ◽  
Vol XLVI-4/W5-2021 ◽  
pp. 73-77
Author(s):  
İ. Avcı ◽  
E. Farzaliyev ◽  
E. Kabullar
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Near Infrared ◽  
Threshold Function ◽  
Vegetation Density ◽  
Sensitive Data ◽  
Index Method ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Plant Groups

Abstract. A large share of the earth's surface is observed with remote sensing technology. Thanks to the data obtained from this process, information about the observed lands is obtained. In this study, NDVI (normalized difference), which is developed by applying mathematical operations on the reflection values of plants at different wavelengths from remote sensing technology and different application areas of this technology, electromagnetic rays, and spectral reflection values, and which is used as a method that provides a value expressing vegetation density. Vegetation index) method, NDVI value, and plant groups analyzed according to this value, sample MATLAB applications related to the NDVI method are mentioned. -Green-Blue) image of visible red and infrared regions, histogram graph showing the relationships between the intensities of values in NIR (near-infrared) and Red (visible Red) bands, NDVI image, and threshold function at the end. The NDVI image was obtained by using the direction (to detect areas that may have vegetation) is shown.

scholarly journals SOIL SALINITY MAPPING USING MULTITEMPORAL LANDSAT DATA

2016 ◽  
Vol XLI-B7 ◽  
pp. 3-9 ◽  
Author(s):  
A. Azabdaftari ◽  
F. Sunar
Keyword(s):  
Remote Sensing ◽  
Linear Regression ◽  
Correlation Analysis ◽  
Soil Salinity ◽  
Vegetation Index ◽  
Landsat Images ◽  
Ground Truth Data ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Salinity Index

Soil salinity is one of the most important problems affecting many areas of the world. Saline soils present in agricultural areas reduce the annual yields of most crops. This research deals with the soil salinity mapping of Seyhan plate of Adana district in Turkey from the years 2009 to 2010, using remote sensing technology. In the analysis, multitemporal data acquired from LANDSAT 7-ETM<sup>+</sup> satellite in four different dates (19 April 2009, 12 October 2009, 21 March 2010, 31 October 2010) are used. As a first step, preprocessing of Landsat images is applied. Several salinity indices such as NDSI (Normalized Difference Salinity Index), BI (Brightness Index) and SI (Salinity Index) are used besides some vegetation indices such as NDVI (Normalized Difference Vegetation Index), RVI (Ratio Vegetation Index), SAVI (Soil Adjusted Vegetation Index) and EVI (Enhamced Vegetation Index) for the soil salinity mapping of the study area. The field’s electrical conductivity (EC) measurements done in 2009 and 2010, are used as a ground truth data for the correlation analysis with the original band values and different index image bands values. In the correlation analysis, two regression models, the simple linear regression (SLR) and multiple linear regression (MLR) are considered. According to the highest correlation obtained, the 21st March, 2010 dataset is chosen for production of the soil salinity map in the area. Finally, the efficiency of the remote sensing technology in the soil salinity mapping is outlined.

scholarly journals SOIL SALINITY MAPPING USING MULTITEMPORAL LANDSAT DATA

2016 ◽  
Vol XLI-B7 ◽  
pp. 3-9 ◽  
Author(s):  
A. Azabdaftari ◽  
F. Sunar
Keyword(s):  
Remote Sensing ◽  
Linear Regression ◽  
Correlation Analysis ◽  
Soil Salinity ◽  
Vegetation Index ◽  
Landsat Images ◽  
Ground Truth Data ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Salinity Index

Soil salinity is one of the most important problems affecting many areas of the world. Saline soils present in agricultural areas reduce the annual yields of most crops. This research deals with the soil salinity mapping of Seyhan plate of Adana district in Turkey from the years 2009 to 2010, using remote sensing technology. In the analysis, multitemporal data acquired from LANDSAT 7-ETM<sup>+</sup> satellite in four different dates (19 April 2009, 12 October 2009, 21 March 2010, 31 October 2010) are used. As a first step, preprocessing of Landsat images is applied. Several salinity indices such as NDSI (Normalized Difference Salinity Index), BI (Brightness Index) and SI (Salinity Index) are used besides some vegetation indices such as NDVI (Normalized Difference Vegetation Index), RVI (Ratio Vegetation Index), SAVI (Soil Adjusted Vegetation Index) and EVI (Enhamced Vegetation Index) for the soil salinity mapping of the study area. The field’s electrical conductivity (EC) measurements done in 2009 and 2010, are used as a ground truth data for the correlation analysis with the original band values and different index image bands values. In the correlation analysis, two regression models, the simple linear regression (SLR) and multiple linear regression (MLR) are considered. According to the highest correlation obtained, the 21st March, 2010 dataset is chosen for production of the soil salinity map in the area. Finally, the efficiency of the remote sensing technology in the soil salinity mapping is outlined.

scholarly journals Application of Remote Sensing Technology in Sediment Estimating Entering the Dam Reservoirs due to Floods

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Mohammad Hadian ◽  
Abolfazl Mosaedi
Keyword(s):  
Remote Sensing ◽  
Particulate Matter ◽  
Near Infrared ◽  
Spectral Response ◽  
Landsat 8 ◽  
Exponential Relationship ◽  
Particulate Materials ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Dam Reservoirs

The present study aimed to use remote sensing technology to estimate the concentration of particulate materials in the water entering the reservoirs of dams and consequently investigate the possibility of estimating the amount of sediment carried to the reservoir by flood during the life of the dam and its annual estimate. Using an advanced spectrometer device (ASD), the reflectance values of water containing different amounts of particulate sediments were measured in the range of 400–2500 nm; then, these reflectance values were represented for the Landsat 8 satellite OLI bands using their spectral response functions. In the study of interband correlation with the number of particulate materials, band 2 (blue) and band 5 (near-infrared) were identified to prepare a specific and appropriate model. The specificity of the reflectance difference in the two abovementioned bands was presented as an exponential relationship between the concentration of particulate materials and spectral reflectance. In this model, the RMSE parameter for the maximum possible sediment concentration was equal to 1.57 and the parameter R2 was equal to 0.91. In the second step, at the same time as the satellite passed, the area was visited and the sediments of the Ardak dam reservoir were sampled by recording their location. To complete this research, two measures were performed simultaneously, calculating the concentration of particulate materials sampled in the laboratory environment and their location on the image. Then, the number of particulate materials is estimated by taking into account the coordinates recorded from the images on which the relevant corrections have been made. According to the extracted exponential model, the results of estimating the concentration of particulate matter obtained from the model and Landsat satellite images with the concentration of particulate matter obtained from sampling showed its complete compatibility with field surveys to validate this research.

scholarly journals Green Space Assessment and Management in Biscay Province, Spain using Remote Sensing Technology

2021 ◽  
Vol 15 (4) ◽  
pp. 21-43
Author(s):  
Esther O. Makinde ◽  
Cristina M. Andonegui ◽  
Ainhoa A. Vicario
Keyword(s):  
Remote Sensing ◽  
Carbon Stock ◽  
Vegetation Index ◽  
Green Space ◽  
Total Carbon ◽  
Spectral Indices ◽  
Moisture Index ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Forest Lands

Our ecosystem, particularly forest lands, contains huge amounts of carbon storage in the world today. This study estimated the above ground biomass and carbon stock in the green space of Bilbao Spain using remote sensing technology. Landsat ETM+ and OLI satellite images for year 1999, 2009 and 2019 were used to assess its land use land cover (LULC), change detection, spectral indices and model biomass based on linear regression. The result of the LULC showed that there was an increase in forest vegetation by 12.5% from 1999 to 2009 and a further increase by 2.3% in 2019. However, plantation cover had decreased by 3.5% from 1999–2009; while wetlands had also decreased by 9% within the same period. There was, however, an increase in plantation cover from 2009 to 2019 by 2.1% but a further decrease in wetlands of 4.3%. Further results revealed a positive correlation across the three decades between the widely used Normalized Differential Vegetation Index (NDVI) with other spectral indices such as Enhance Vegetation Index (EVI) and Normalized Differential Moisture Index (NDMI) for biomass were: for 1999 EVI (R2 = 0.1826), NDMI (R2 = 0.0117), for 2009 EVI (R2 = 0.2192), NDMI (R2 = 0.3322), for 2019EVI (R2 = 0.1258), NDMI (R2 = 0.8148). A reduction in the total carbon stock from 14,221.94 megatons in 1999 to 10,342.44 megatons 2019 was observed. This study concluded that there has been a reduction in the amount of carbon which the Biscay Forest can sequester.

scholarly journals Multi-Sensor Approach to Improve Bathymetric Lidar Mapping of Semi-Arid Groundwater-Dependent Streams: Devils River, Texas

2020 ◽  
Vol 12 (15) ◽  
pp. 2491 ◽  
Author(s):  
Kutalmis Saylam ◽  
Aaron R. Averett ◽  
Lucie Costard ◽  
Brad D. Wolaver ◽  
Sarah Robertson
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Aquatic Vegetation ◽  
Survey Methods ◽  
Airborne Lidar ◽  
Active Sensors ◽  
Geomorphic Mapping ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Complex Settings

Remote sensing technology enables detecting, acquiring, and recording certain information about objects and locations from distances relative to their geographic locations. Airborne Lidar bathymetry (ALB) is an active, non-imaging, remote sensing technology for measuring the depths of shallow and relatively transparent water bodies using light beams from an airborne platform. In this study, we acquired Lidar datasets using near-infrared and visible (green) wavelength with the Leica Airborne Hydrography AB Chiroptera-I system over the Devils River basin of southwestern Texas. Devils River is a highly groundwater-dependent stream that flows 150 km from source springs to Lake Amistad on the lower Rio Grande. To improve spatially distributed stream bathymetry in aquatic habitats of species of state and federal conservation interest, we conducted supplementary water-depth observations using other remote sensing technologies integrated with the airborne Lidar datasets. Ground penetrating radar (GPR) mapped the river bottom where vegetation impeded other active sensors in attaining depth measurements. We confirmed the accuracy of bathymetric Lidar datasets with a differential global positioning system (GPS) and compared the findings to sonar and GPR measurements. The study revealed that seamless bathymetric and geomorphic mapping of karst environments in complex settings (e.g., aquatic vegetation, entrained air bubbles, riparian zone obstructions) require the integration of a variety of terrestrial and remotely operated survey methods. We apply this approach to Devils River of Texas. However, the methods are applicable to similar streams globally.

scholarly journals The GA-BPNN-Based Evaluation of Cultivated Land Quality in the PSR Framework Using Gaofen-1 Satellite Data

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5127 ◽  
Author(s):  
Liu ◽  
Peng ◽  
Xia ◽  
Hu ◽  
Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Linear Model ◽  
Vegetation Index ◽  
Regional Scale ◽  
Cultivated Land ◽  
Land Quality ◽  
Remote Sensing Technology ◽  
Evaluation Approach ◽  
Sensing Technology ◽  
State Index

Rapid and efficient assessment of cultivated land quality (CLQ) using remote sensing technology is of great significance for protecting cultivated land. However, it is difficult to obtain accurate CLQ estimates using the current satellite-driven approaches in the pressure-state-response (PSR) framework, owing to the limitations of linear models and CLQ spectral indices. In order to improve the estimation accuracy of CLQ, this study used four evaluation models (the traditional linear model; partial least squares regression, PLSR; back propagation neural network, BPNN; and BPNN with genetic algorithm optimization, GA-BPNN) to evaluate CLQ for determining the accurate evaluation model. In addition, the optimal satellite-derived indicator in the land state index was selected among five vegetation indices (the normalized vegetation index, NDVI; enhanced vegetation index, EVI; modified soil-adjusted vegetation index, MSAVI; perpendicular vegetation index, PVI; and soil-adjusted vegetation index, SAVI) to improve the prediction accuracy of CLQ. This study was conducted in Conghua District of Guangzhou, Guangdong Province, China, based on Gaofen-1 (GF-1) data. The prediction accuracies from the traditional linear model, PLSR, BPNN, and GA-BPNN were compared using observations. The results demonstrated that (1) compared with other models (the traditional linear model: R2 = 0.14 and RMSE = 91.53; PLSR: R2 = 0.33 and RMSE = 74.58; BPNN: R2 = 0.50 and RMSE = 61.75), the GA-BPNN model based on EVI in the land state index provided the most accurate estimates of CLQ, with the R2 of 0.59 and root mean square error (RMSE) of 56.87, indicating a nonlinear relationship between CLQ and the prediction indicator; and (2) the GA-BPNN-based evaluation approach of CLQ in the PSR framework was driven to map CLQ of the study area using the GF-1 data, leading to an RMSE of 61.44 at the regional scale, implying that the GA-BPNN-based evaluation approach has the potential to map CLQ over large areas. This study provides an important reference for the high-accuracy prediction of CLQ based on remote sensing technology.

scholarly journals Mapping mangrove distribution using remote sensing technology in Harapan, Kelapa and Pamegaran Seribu Islands National Park

2021 ◽  
Vol 944 (1) ◽  
pp. 012046
Author(s):  
B F Haikal ◽  
S B Susilo ◽  
S B Agus ◽  
R Z Oktavian
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Rapid Development ◽  
Confusion Matrix ◽  
Mangrove Area ◽  
Remote Sensing Technology ◽  
Mangrove Ecosystems ◽  
Sensing Technology ◽  
Dense Class

Abstract The existence of mangrove ecosystems is increasingly threatened due to the rapid development of tourist destinations and the increasing number of residents in Harapan, Kelapa and Pamegaran island, so that monitoring of mangrove ecosystems is necessary. The purpose of the research is to map the distribution of mangroves using remote sensing technology in Harapan, Kelapa and Pamegaran island. The field survey was conducted on April 1-10, 2021, taking 189 sample points using a hemispherical photography method. The maximum likelihood classification method is used to classify mangrove and non-mangrove vegetation. Normalized Difference Vegetation Index (NDVI) is an algorithm used to calculate vegetation indexes from satellite imagery. The Sentinel-2A image was classified into 3 classes of mangrove density, namely dense, moderate, and rare density classes, with the dominant class being the dense class. The total mangrove area on Pamegaran Island in 2015 amounted to 1.81 ha and the total mangrove area in 2021 amounted to 2.97 ha. The area of mangrove distribution in Harapan and Kelapa Island in 2015 amounted to 4.1 ha and in 2021 amounted to 6.56 ha. Mangrove density classification accuracy test using confusion matrix showed an accuracy of 82.95%.

Hyperspectral Remote Sensing Technology (HRST) program

1997 ◽  
Author(s):  
Tom Wilson ◽  
Rebecca Baugh ◽  
Ron Contillo ◽  
Tom Wilson ◽  
Rebecca Baugh ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Remote Sensing ◽  
Remote Sensing Technology ◽  
Sensing Technology
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