SOME RESULTS OF CROP STRESS MONITORING BY REMOTE SENSING IN NORTHERN MONGOLIA

2018 ◽  
Vol 21 (02) ◽  
pp. 59-63 ◽  
Author(s):  
Tuvshinbayar D ◽  
Erdenetuya B ◽  
Erkhembayar E ◽  
Batbileg B ◽  
Sarangerel J
Keyword(s):  
Remote Sensing ◽  
Stress Field ◽  
Satellite Imagery ◽  
Satellite Images ◽  
Satellite Image ◽  
Stress Monitoring ◽  
Wheat Growth ◽  
Northern Mongolia ◽  
Middle Period ◽  
Farming Industry

This paper presents the spatiotemporal monitoring crop stress in the first period of wheat phenology by satellite image in northern Mongolia. We used 2 satellite images Landsat8 that are dated June 23rd and July 12th of this year. Also calculated are same ratio-based indices such as NDVI, LAI and GNDVI of 2 images in the middle period of wheat phrenology, which are indicated crop stress field reports. NDVI and LAI, derived from satellite imagery are the most important characteristics of wheat stress monitoring. According to our result, as shown satellite image, wheat growth is critical and fuzzily, which is predicted necessary some management for farming. Our results show the ability of pre-processing image to analyze and visualize agricultural environments and workflows has proven to be beneficial to those involved in the farming industry.

scholarly journals LAND COVER / LAND USE MONITORING FOR AGRICULTURE FEATURES CLASSIFICATION

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 61-65
Author(s):  
S. H. Guliyeva
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Satellite Imagery ◽  
Satellite Image ◽  
Research Work ◽  
Mountain Range ◽  
Land Use Land Cover ◽  
Stress Monitoring ◽  
Sensing Applications

Abstract. Remote sensing applications are directed to agricultural observation and monitoring. It has been huge of scientific papers are dedicated to the research of the contribution of remote sensing for agriculture studies. There are several global challenges needed to be considered within agriculture activities. It can be embraced by the main agriculture sector facing the obstacles impacting the production and productivity of the sector. These are the following options that can be pointed out: biomass and yield estimation; vegetation vigor and drought stress monitoring; assessment of crop phenological development; crop acreage estimation and cropland mapping; and mapping of disturbances and Land Use/Land Cover changes. In this study has been undertaken the realization of satellite-based Land Use/Land Cover monitoring based on various optical satellite data. It has been used satellite images taken from satellites AZERSKY, RapidEye, Sentinel-2B and further processed for Land Use/Land Cover classification. Following the complex approach of the supervised and unsupervised classification, the methodology has been used for satellite image processing. As the main satellite imagery for monitoring crop condition were AZERSKY taken during the growing season, from May to June of 2019 year. The study area was some part of the Sheki region, which covers the central part of the southern slope of the Greater Caucasus Mountain Range within Azerbaijan Republic. In this research work satellite imagery processing and mapping has been carried out on the basis of software application of ArcGIS Pro 2.5.

scholarly journals Segmentation of Satellite Imagery of Amedi Site Using Chan–Vese Model with Saliency Estimation

2020 ◽  
Vol 1 (4) ◽  
pp. 125-134
Author(s):  
Pawan Rachee
Keyword(s):  
Satellite Imagery ◽  
Satellite Images ◽  
Object Segmentation ◽  
Satellite Image ◽  
Bilateral Filter ◽  
Histogram Equalization ◽  
Saliency Map ◽  
Enhancement Technique ◽  
Map Segmentation ◽  
Quantitative Manner

The images that have been taken from space satellites are described by satellite imagery. The presence of the earth's surface is detected by remote sensing. Normally the source of the satellite image is barely seen, because many points in the sky are obscured with cloud shadows. Therefore, one of the most important and ubiquitous tasks in image analysis is segmentation. Segmentation is the method of dividing a image into a collection of specific regions that vary in some essential qualitative or quantitative manner. In this paper we will focus on a method for segmenting images that was developed   Three different methods to detect the location of the satellite images have been studied, implemented, and tested; these are based on Chan-Vese and saliency map segmentation, and multi-resolution segmentation to obtain a proper object segmentation. In this study, the combination of the proposed segmentation automatic detection and image enhancement technique has been performed to reduce the noise of the original image. In addition, the Bilateral filter, and histogram equalization are used in these proposed techniques. Experimental results demonstrate that the suggested method can precisely extract the objective of Amedi site from the satellite images with difficult backgrounds and overlapping regions.

Application of Polarization Remote Sensing to Identify Activity of Yitong-Shulan Fault, Jilin Province, China

2014 ◽  
Vol 1065-1069 ◽  
pp. 2246-2250
Author(s):  
Jian Sheng ◽  
Guang Yuan Yu ◽  
Yu Meng Wang ◽  
Han Lv
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Eastern China ◽  
Satellite Image ◽  
Jilin Province ◽  
Geophysical Method ◽  
Fault Activity ◽  
Shitoukoumen Reservoir ◽  
Observation Method ◽  
Buried Fault

Yitong-Shulan fault, one north section of the famed Tanlu grand fault zone in eastern China, is NNE-trending though the Jilin Province, China. In October 2010, Heilongjiang segment of this fault was discovered the evidence of its activity in Holonce, and further inferred it is associated with a paleoearthquake event. So the recognize of Yitong-Shulan fault Jilin section active in the early Quaternary capable of generating moderate quakes is doubted. Yitong-Shulan fault is almost covered by Quaternary strata in Jilin Province. Traditional method is difficult to explore buried fault, and geophysical method is partial and expensive. The polarization remote sensing is a kind of emerging earth observation method, which has high terrain-recognization resolution. The polarization remote sensing method can to indentify the scarps and displaced geomorphic objects along the fault though satellite images. It even can to discover the high of scarps, displacement of geomorphic objects, and so on. The fault activity can be indicated well by the interpretation of polarization remote sensing. In this paper, use the polarization remote sensing method to study the activity of Yitong-Shulan fault Jilin section. Satellite image near the Shulan City, Jilin Province interpreted by polarization remote sensing reveals that the obviously linear scarps which extend long the fault is 1-3m high. Along the fault various kinds of geomorphic objects are displaced. This interpretation result indicated the Shulan-Shitoukoumen Reservoir segment of the fault is active since Holocene. The fault activity also is proved by geophysical method.

scholarly journals Spatial and temporal changes in household structure locations using high-resolution satellite imagery for population assessment: an analysis in southern Zambia, 2006-2011

2016 ◽  
Vol 11 (2) ◽  
Author(s):  
Timothy Shields ◽  
Jessie Pinchoff ◽  
Jailos Lubinda ◽  
Harry Hamapumbu ◽  
Kelly Searle ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Satellite Images ◽  
Population Distribution ◽  
Satellite Image ◽  
Public Health Research ◽  
Cluster Detection ◽  
Practical Significance ◽  
Programme Implementation ◽  
Detection Analysis ◽  
High Resolution Satellite Imagery

Satellite imagery is increasingly available at high spatial resolution and can be used for various purposes in public health research and programme implementation. Comparing a census generated from two satellite images of the same region in rural southern Zambia obtained four and a half years apart identified patterns of household locations and change over time. The length of time that a satellite image-based census is accurate determines its utility. Households were enumerated manually from satellite images obtained in 2006 and 2011 of the same area. Spatial statistics were used to describe clustering, cluster detection, and spatial variation in the location of households. A total of 3821 household locations were enumerated in 2006 and 4256 in 2011, a net change of 435 houses (11.4% increase). Comparison of the images indicated that 971 (25.4%) structures were added and 536 (14.0%) removed. Further analysis suggested similar household clustering in the two images and no substantial difference in concentration of households across the study area. Cluster detection analysis identified a small area where significantly more household structures were removed than expected; however, the amount of change was of limited practical significance. These findings suggest that random sampling of households for study participation would not induce geographic bias if based on a 4.5-year-old image in this region. Application of spatial statistical methods provides insights into the population distribution changes between two time periods and can be helpful in assessing the accuracy of satellite imagery.

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 Use of Satellite Imagery for Pastoral Resources Monitoring in Kossi Province (Burkina Faso)

2019 ◽  
Vol 7 (2) ◽  
pp. 1
Author(s):  
Sieza Yssouf ◽  
Gomgnimbou P. K Alain ◽  
Belem Adama ◽  
Serme Idriss
Keyword(s):  
Remote Sensing ◽  
Surface Water ◽  
Burkina Faso ◽  
Satellite Imagery ◽  
Field Data ◽  
Satellite Images ◽  
Temporal Evolution ◽  
Livestock Farming ◽  
Important Place ◽  
Livestock Sector

In Burkina Faso, livestock sector has an important place in the country's economy. Essentially extensive, this livestock farming is characterized by transhumance system, which consists of leading livestock sometimes over long distances in search of good pastures and water.Satellite images from different periods can be used to monitor the evolution of pastoral resources (pasture areas and surface water points) in a given area. Field data, coupled with satellite images, provide a better understanding of livestock transhumance movements in the study area. The objective of this study was to monitor the spatial and temporal evolution of pastoral resources using remote sensing tools in Kossi province. Field data, coupled with satellite images, provide a better understanding of livestock transhumance movements in the study area.

scholarly journals STUDY OF DUST POLLUTION DETECTING ABILITY IN URBAN AREAS BY REMOTE SENSING TECHNOLOGY TO SUPPORT AIR ENVIRONMENT OBSERVATION

2012 ◽  
Vol 15 (4) ◽  
pp. 33-47
Author(s):  
Van Thi Tran ◽  
Binh Thi Trinh ◽  
Bao Duong Xuan Ha
Keyword(s):  
Remote Sensing ◽  
Satellite Imagery ◽  
Urban Areas ◽  
Satellite Image ◽  
Regression Function ◽  
Statistical Correlation ◽  
Step Test ◽  
Residential Areas ◽  
Remote Sensing Technology ◽  
Sensing Technology

This paper presents the approach towards application of remote sensing technology to monitor the air environemnt. Specific inital research is findings PM10 dust from SPOT 5 satellite image. The calculation based on reflectance value on remote sensing satellite images. The main method is to calculate statistical correlation regression between the PM10 concentration from ground station observations and reflectance value on each image band and the main components of satellite imagery in 2003 to find the best regression function, applied then to images 2011 where its radiance value was relatively normalized under atmospheric, geometric, environmental conditions of image 2003. The results showed the best correlation in nonlinear regression case. Spatial distribution of PM10 concentrations > 200μg/m3 found on most main roads, industrial parks and residential areas. This study is a first step test, but the results have demonstrated that satellite imagery can be used as a useful, effective tool, to monitor air environment in cities.

scholarly journals ALGORITHM AND APPLICATION OF GCP-INDEPENDENT BLOCK ADJUSTMENT FOR SUPER LARGE-SCALE DOMESTIC HIGH RESOLUTION OPTICAL SATELLITE IMAGERY

2018 ◽  
Vol XLII-3 ◽  
pp. 1607-1615
Author(s):  
Y. S. Sun ◽  
L. Zhang ◽  
B. Xu ◽  
Y. Zhang
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Large Scale ◽  
Satellite Images ◽  
Satellite Image ◽  
Optimization Method ◽  
Adjustment Process ◽  
Large Area ◽  
Rank Defect ◽  
And Performance

The accurate positioning of optical satellite image without control is the precondition for remote sensing application and small/medium scale mapping in large abroad areas or with large-scale images. In this paper, aiming at the geometric features of optical satellite image, based on a widely used optimization method of constraint problem which is called Alternating Direction Method of Multipliers (ADMM) and RFM least-squares block adjustment, we propose a GCP independent block adjustment method for the large-scale domestic high resolution optical satellite image – GISIBA (GCP-Independent Satellite Imagery Block Adjustment), which is easy to parallelize and highly efficient. In this method, the virtual "average" control points are built to solve the rank defect problem and qualitative and quantitative analysis in block adjustment without control. The test results prove that the horizontal and vertical accuracy of multi-covered and multi-temporal satellite images are better than 10 m and 6 m. Meanwhile the mosaic problem of the adjacent areas in large area DOM production can be solved if the public geographic information data is introduced as horizontal and vertical constraints in the block adjustment process. Finally, through the experiments by using GF-1 and ZY-3 satellite images over several typical test areas, the reliability, accuracy and performance of our developed procedure will be presented and studied in this paper.

scholarly journals Monitoring of specially protected natural territories of forest-steppe landscapes of the Stavropol upland by means of remote sensing data

2021 ◽  
Vol 27 (1) ◽  
pp. 364-376
Author(s):  
Tigran Shahbazyan
Keyword(s):  
Remote Sensing ◽  
Satellite Imagery ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Anthropogenic Factors ◽  
Forest Steppe ◽  
Landsat 8 ◽  
Sensing Data ◽  
Ndvi Index ◽  
The Impact

The article considers the methodology of monitoring specially protected natural areas using remote sensing data. The research materials are satellite images of the Landsat 5 and Landsat 8 satellites, obtained from the resource of the US Geological Survey. The key areas of the study were 3 specially protected areas located within the boundaries of the forest-steppe landscapes of the Stavropol upland, the reserves «Alexandrovskiy», «Russkiy Les», «Strizhament». The space survey materials were selected for the period 1991–2020, and the data from the summer seasons were used. The NDVI index is chosen as the method of processing the spectral channels of satellite imagery. To integrate long-term satellite imagery into a single raster image, the method of variance of the variation series for the NDVI index was used. The article describes an algorithm for processing satellite images, which allows us to identify the features of the dynamics of the vegetation state of the studied territory for the period 1991–2020. The bitmap image constructed by means of the variance of the NDVI index was classified by the quantile method, to translate numerical values into classes with qualitative characteristics. There were 4 classes of the territory according to the degree of dynamism of the vegetation state: “stable”, “slightly variable”, “moderately variable”, “highly variable”. The paper highlights the factors of landscape transformation, including natural and anthropogenic ones. In the course of the study, the determining influence of anthropogenic factors of transformation was noted. The greatest impact is on the reserve «Alexandrovskiy», the least on the reserve «Russkiy Les», in the reserve «Strizhament» the impact is expressed locally. The paper identifies the leading anthropogenic factors of vegetation transformation, based on their influence on vegetation.

scholarly journals Contenido y calidad de las imágenes de observación terrestre

2019 ◽  
pp. 15-21
Keyword(s):  
Remote Sensing ◽  
Image Quality ◽  
Information Extraction ◽  
Satellite Images ◽  
Satellite Image ◽  
La Paz ◽  
Sensing System ◽  
Manual Intervention ◽  
Shape Structure ◽  
Important Amount

Contenido y calidad de las imágenes de observación terrestre Earth observation image information content and quality Avid Roman-Gonzalez, Natalia Indira Vargas-Cuentas TELECOM ParisTech, 46 rue Barrault, 75013 – Paris, Francia Escuela Militar de Ingeniería – EMI, La Paz, Bolivia DOI: https://doi.org/10.33017/RevECIPeru2012.0015/ Resumen En el presente artículo describiremos la extracción de información de imágenes satelitales y la importancia de la calidad de las imágenes satelitales. Indagaremos con más detalle en el ámbito de los artefactos y su influencia en la extracción de información de las imágenes satelitales. En un sistema de teledetección, si bien, las imágenes son muy importantes, pero lo más importante es la información que podemos extraer de ellas para interpretar y aplicar esta información en diferentes campos. En ese sentido, la calidad de imagen juega un papel importante. Si queremos obtener la mayor e importante cantidad de información de una imagen, es necesario que la imagen tenga una buena calidad. El principal objetivo de cualquier sistema de teledetección es el uso de la información que se puede extraer de las imágenes, esto incluye la detección, medición, identificación e interpretación de diferentes objetivos de interés. Los objetivos de interés en imágenes de teledetección pueden ser cualquier característica, objeto, textura, forma, estructura, espectro o cobertura superficial que están en la imagen. El proceso de un sistema de teledetección y análisis puede ser realizado manualmente o de manera automática, en realidad, hay muchos grupos de investigación que desarrollan diferentes herramientas para detectar, identificar, interpretar y extraer información de los objetivos de interés sin intervención manual de un intérprete humano. Descriptores: teledetección, imágenes satelitales, detección de artefactos, calidad de las imágenes. Abstract In this article we will describe the information extraction from satellite image, the importance of image quality in satellite image. In this paper we will study in more detail the artifacts and their influence on the information extraction from satellite images. In a remote sensing system, although, the images are very important, but more important is the information that we can extract from them to interpret and apply this information in different fields. In this sense, the image quality plays an important role. If we want to get the biggest and most important amount of information from the image, we need to have a good image quality. The main objective of any remote sensing system is the use of information that we can extract from the images, this includes detection, measurement, identification and interpretation of different targets. Targets in remote sensing images may be any feature, object, texture, shape, structure, spectrum or land covers which are in the image. Remote sensing process and analysis could be performed manually or automatically, actually, there are many research groups that develop different tools for detect, identify, extract information and interpret targets without manual intervention by a human interpreter. Keywords: remote sensing, satellite images, artifacts detection, image quality.

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