scholarly journals REQUIREMENTS TO THE MATERIALS OF REMOTE SENSING FOR MONITORING THE STATE OF PASTURES

2019 ◽  
Vol 6 (1) ◽  
pp. 16-23
Author(s):  
Aizhanat Svankulova ◽  
Ekaterina Kulik
Keyword(s):  
Remote Sensing ◽  
Satellite Observation ◽  
Landsat 8 ◽  
Electromagnetic Spectrum ◽  
Observation Data ◽  
Satellite Systems ◽  
Area Of Interest ◽  
Pasture Degradation ◽  
Wide Range

The article is devoted to the role of remote sensing and satellite observation data in the optical and infrared ranges of the electromagnetic spectrum for solving a wide range of pasture monitoring tasks. The article outlines the requirements for images from the Landsat 8 and Sentinel-2 spacecraft for pastures monitoring in the Kosh-Agach district of the Altai Republic. The main characteristics of the imaging equipment of satellite systems data are considered. Data acquisition is recommended from the Earth Explorer geoportal, where the choice of materials includes the following: area of interest, cloudiness percentage, shooting period, shooting system, shooting time. Survey data are involved in the formation of an information model for determining pasture degradation, tracking pasture status and yield dynamics, and planning for their rational use.

scholarly journals Identification and Classification of Complex Agricultural Croplands Using Multi-Temporal ALOS-2/PALSAR-2 Data: A Case Study in Central Java, Indonesia

2018 ◽  
Vol 10 (2) ◽  
pp. 58
Author(s):  
Prima Rizky Mirelva ◽  
Ryota Nagasawa
Keyword(s):  
Remote Sensing ◽  
Paddy Field ◽  
Agricultural Land ◽  
National Income ◽  
Landsat 8 ◽  
Observation Data ◽  
Optical Remote Sensing ◽  
Cropping Pattern ◽  
Central Java ◽  
Multi Temporal

The agriculture sector makes a significant contribution to the Indonesian economy and has become one of the sources of national income. Therefore, precise agricultural mapping is very important to national and regional administrations. Satellite remote sensing provides the most effective tool for identifying a wide expanse of agriculture croplands. However, cloud coverage in tropical regions limits the use of optical remote sensing. SAR is an active remote sensing technique, which offers completely cloud-free observation data. The multi-temporal ALOS-2/PALSAR-2 data were used in this study, complemented by optical multi-temporal remote sensing data, that is, Landsat 8 OLI for classifying complex agricultural croplands. The study area, located in the Klaten Regency, Central Java Province, with 112 km2 coverage, was selected because of its dynamic cropping pattern and complex agricultural land use types. In this study, the RGB composite of HH, HV and HV-HH, derived from ALOS-2/PALSAR-2 polarizations, was found to be effective at separating two types of paddy field cropping pattern: all-year paddy (paddy-I) and paddy upland fields (paddy-II). The multi-temporal Landsat 8 data were also found to be useful for observing the cropping pattern. Moreover, the classification accuracy, which was as high as 85.02% in terms of overall accuracy, with a kappa coefficient of 0.824, from multi-temporal ALOS-2/PALSAR-2 data, was obtained. These results show that multi-temporal ALOS-2/PALSAR-2 data are capable of discriminating between two different paddy field cropping types, as well as beneficial for discriminating between the cropping stage and cropping pattern information for several other land uses.

scholarly journals Observations and Recommendations for the Calibration of Landsat 8 OLI and Sentinel 2 MSI for Improved Data Interoperability

2018 ◽  
Vol 10 (9) ◽  
pp. 1340 ◽  
Author(s):  
Dennis Helder ◽  
Brian Markham ◽  
Ron Morfitt ◽  
Jim Storey ◽  
Julia Barsi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Sensor Calibration ◽  
Landsat 8 ◽  
Data Sets ◽  
Optical Remote Sensing ◽  
Data Interoperability ◽  
Satellite Systems ◽  
Combining Data ◽  
The Impact ◽  
Sentinel 2

Combining data from multiple sensors into a single seamless time series, also known as data interoperability, has the potential for unlocking new understanding of how the Earth functions as a system. However, our ability to produce these advanced data sets is hampered by the differences in design and function of the various optical remote-sensing satellite systems. A key factor is the impact that calibration of these instruments has on data interoperability. To address this issue, a workshop with a panel of experts was convened in conjunction with the Pecora 20 conference to focus on data interoperability between Landsat and the Sentinel 2 sensors. Four major areas of recommendation were the outcome of the workshop. The first was to improve communications between satellite agencies and the remote-sensing community. The second was to adopt a collections-based approach to processing the data. As expected, a third recommendation was to improve calibration methodologies in several specific areas. Lastly, and the most ambitious of the four, was to develop a comprehensive process for validating surface reflectance products produced from the data sets. Collectively, these recommendations have significant potential for improving satellite sensor calibration in a focused manner that can directly catalyze efforts to develop data that are closer to being seamlessly interoperable.

scholarly journals Satellite remote sensing of Asian aerosols: a case study of clean, polluted and dust storm days

2010 ◽  
Vol 3 (3) ◽  
pp. 2651-2680 ◽  
Author(s):  
K. H. Lee ◽  
Y. J. Kim
Keyword(s):  
Remote Sensing ◽  
East Asia ◽  
Satellite Remote Sensing ◽  
Satellite Observation ◽  
Global Scale ◽  
Observation Data ◽  
Aerosol Composition ◽  
Surface Reflection ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Linear Slope

Abstract. Satellite-based aerosol observation is a useful tool for the estimation of microphysical and optical characteristics of aerosol during more than three decades. Until now, a lot of satellite remote sensing techniques have been developed for aerosol detection. In East Asian region, the role of satellite observation is quite important because aerosols originating from natural and man-made pollution in this region have been recognized as an important source for regional and global scale air pollution. However, it is still difficult to retrieve aerosol over land because of the complexity of the surface reflection and complex aerosol composition, in particular, aerosol absorption. In this study, aerosol retrievals using Look-up Table (LUT) based method was applied to MODerate Resolution Imaging Spectroradiometer (MODIS) Level 1 (L1) calibrated reflectance data to retrieve aerosol optical thickness (AOT) over East Asia. Three case studies show how the methodology works to identify those differences to obtain a better AOT retrieval. The comparison between the MODIS and Aerosol Robotic Network (AERONET) shows better results when the suggested methodology using the cluster based LUTs is applied (linear slope=0.94, R=0.92) than when operational MODIS aerosol products are used (linear slope=0.78, R=0.87). In conclusion, the suggested methodology is shown to work well with aerosol models acquired by statistical clustering the observation data in East Asia.

scholarly journals МАСШТАБНО-ЗАВИСИМЫЕ ЭФФЕКТЫ В СТРУКТУРЕ ЭКОЛОГИЧЕСКОЙ НИШИ ЛЕБЕДЯ-ШИПУНА CYGNUSOLOR (GMELIN, 1803) В ПЕРИОД ЗИМОВКИ В ПРЕДЕЛАХ ЗАЛИВА СИВАШ

2016 ◽  
Vol 6 (3) ◽  
pp. 234-247
Author(s):  
A. Yu. Andrushenko ◽  
A. V. Zhukov
Keyword(s):  
Remote Sensing ◽  
Ecological Niche ◽  
Random Distribution ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Information Value ◽  
Landsat 8 ◽  
Digital Elevation ◽  
Elevation Model

<p>The assessment of the information value of ecogeographical predictors based on remote sensing data from satellites to reflect features of the ecological niche of the Swan-mute <em>Cygnus up</em> (Gmelina, 1803) in wintering within the Gulf Sivash have been presented. Two groups predictors of ecogeographical landscape data have been considered. The first group is assigned digital elevation model and its derivatives. The second set of classified vegetation indices obtained from Landsat 8 image. Ecological niche has been described using ENFA-procedure. The procedure of random distribution of the pseudo-absent points which range from the presence points restricted by some distance has been applied to assess the role of scale in ecological niche. Ecological niche of Swan mute has been shown to be described in terms of landscape ecogeographical variables. The properties of the ecological niche of the Swan-mute have been found to be depends upon the scale of its consideration. Under various boundary ranges we can get an entirely different, but statistically valid, assess the structure of the ecological niche of the Swan-mute based landscape ecogeographical predictors. The role of the various ecogeographical predictors depending on the scale can vary greatly.</p>

scholarly journals APPLICATION OF MULTI-SOURCE REMOTE SENSING IMAGE IN YUNNAN PROVINCE GRASSLAND RESOURCES INVESTIGATION

2018 ◽  
Vol XLII-3 ◽  
pp. 837-841
Author(s):  
J. Li ◽  
G. Wen ◽  
D. Li
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Large Scale ◽  
Yunnan Province ◽  
Remote Sensing Image ◽  
Field Investigation ◽  
Background Information ◽  
Landsat 8 ◽  
Yield Model ◽  
Wide Range

Trough mastering background information of Yunnan province grassland resources utilization and ecological conditions to improves grassland elaborating management capacity, it carried out grassland resource investigation work by Yunnan province agriculture department in 2017. The traditional grassland resource investigation method is ground based investigation, which is time-consuming and inefficient, especially not suitable for large scale and hard-to-reach areas. While remote sensing is low cost, wide range and efficient, which can reflect grassland resources present situation objectively. It has become indispensable grassland monitoring technology and data sources and it has got more and more recognition and application in grassland resources monitoring research. This paper researches application of multi-source remote sensing image in Yunnan province grassland resources investigation. First of all, it extracts grassland resources thematic information and conducts field investigation through BJ-2 high space resolution image segmentation. Secondly, it classifies grassland types and evaluates grassland degradation degree through high resolution characteristics of Landsat 8 image. Thirdly, it obtained grass yield model and quality classification through high resolution and wide scanning width characteristics of MODIS images and sample investigate data. Finally, it performs grassland field qualitative analysis through UAV remote sensing image. According to project area implementation, it proves that multi-source remote sensing data can be applied to the grassland resources investigation in Yunnan province and it is indispensable method.

scholarly journals Analysis of Land Use and Land Cover Changes in Urban Areas Using Remote Sensing: Case of Blantyre City

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Jane Ferah Gondwe ◽  
Sun Li ◽  
Rodger Millar Munthali
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Urban Areas ◽  
Agricultural Land ◽  
Landsat 8 ◽  
Lulc Changes ◽  
Wide Range ◽  
Landsat 7 ◽  
Artificial Neural Network Ann

Blantyre City has experienced a wide range of changes in land use and land cover (LULC). This study used Remote Sensing (RS) to detect and quantify LULC changes that occurred in the city throughout a twenty-year study period, using Landsat 7 Enhanced Thematic Mapper (ETM+) images from 1999 and 2010 and Landsat 8 Operational Land Imager (OLI) images from 2019. A supervised classification method using an Artificial Neural Network (ANN) was used to classify and map LULC types. The kappa coefficient and the overall accuracy were used to ascertain the classification accuracy. Using the classified images, a postclassification comparison approach was used to detect LULC changes between 1999 and 2019. The study revealed that built-up land and agricultural land increased in their respective areas by 28.54 km2 (194.81%) and 35.80 km2 (27.16%) with corresponding annual change rates of 1.43 km·year−1 and 1.79 km·year−1. The area of bare land, forest land, herbaceous land, and waterbody, respectively, decreased by 0.05%, 90.52%, 71.67%, and 6.90%. The LULC changes in the study area were attributed to urbanization, population growth, social-economic growth, and climate change. The findings of this study provide information on the changes in LULC and driving factors, which Blantyre City authorities can utilize to develop sustainable development plans.

scholarly journals Identification of Seed Maize Fields With High Spatial Resolution and Multiple Spectral Remote Sensing Using Random Forest Classifier

2020 ◽  
Vol 12 (3) ◽  
pp. 362 ◽  
Author(s):  
Lin Zhang ◽  
Zhe Liu ◽  
Tianwei Ren ◽  
Diyou Liu ◽  
Zhe Ma ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Vegetation Index ◽  
Seed Quality ◽  
Texture Features ◽  
Kappa Coefficient ◽  
Growth Monitoring ◽  
Landsat 8 ◽  
Wide Range ◽  
Sample Data

Seed maize and common maize plots have different planting patterns and variety types. Identification of seed maize is the basis for seed maize growth monitoring, seed quality and common maize seed supply. In this paper, a random forest (RF) classifier is used to develop an approach for seed maize fields’ identification, using the time series vegetation indexes (VIs) calculated from multispectral data acquired from Landsat 8 and Gaofen 1 satellite (GF-1), field sample data, and texture features of Gaofen 2 satellite (GF-2) panchromatic data. Huocheng and Hutubi County in the Xinjiang Uygur Autonomous Region of China were chosen as study area. The results show that RF performs well with the combination of six VIs (normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), triangle vegetation index (TVI), ratio vegetation index (RVI), normalized difference water index (NDWI) and difference vegetation index (DVI)) and texture features based on a grey-level co-occurrence matrix. The classification based on “spectrum + texture” information has higher overall, user and producer accuracies than that of spectral information alone. Using the “spectrum + texture” method, the overall accuracy of classification in Huocheng County is 95.90%, the Kappa coefficient is 0.92, and the producer accuracy for seed maize fields is 93.91%. The overall accuracy of the classification in Hutubi County is 97.79%, the Kappa coefficient is 0.95, and the producer accuracy for seed maize fields is 97.65%. Therefore, RF classifier inputted with high-resolution remote-sensing image features can distinguish two kinds of planting patterns (seed and common) and varieties types (inbred and hybrid) of maize and can be used to identify and map a wide range of seed maize fields. However, this method requires a large amount of sample data, so how to effectively use and improve it in areas lacking samples needs further research.

scholarly journals Evaluation of Spatial Generalization Characteristics of a Robust Classifier as Applied to Coral Reef Habitats in Remote Islands of the Pacific Ocean

2018 ◽  
Vol 10 (11) ◽  
pp. 1774 ◽  
Author(s):  
Justin Gapper ◽  
Hesham El-Askary ◽  
Erik Linstead ◽  
Thomas Piechota
Keyword(s):  
Remote Sensing ◽  
Pacific Ocean ◽  
Ground Truth ◽  
Temporal Analysis ◽  
Spectral Signature ◽  
Landsat 8 ◽  
Ground Truth Data ◽  
Linear Discriminant ◽  
Area Of Interest

This study was an evaluation of the spectral signature generalization properties of coral across four remote Pacific Ocean reefs. The sites under consideration have not been the subject of previous studies for coral classification using remote sensing data. Previous research regarding using remote sensing to identify reefs has been limited to in-situ assessment, with some researchers also performing temporal analysis of a selected area of interest. This study expanded the previous in-situ analyses by evaluating the ability of a basic predictor, Linear Discriminant Analysis (LDA), trained on Depth Invariant Indices calculated from the spectral signature of coral in one location to generalize to other locations, both within the same scene and in other scenes. Three Landsat 8 scenes were selected and masked for null, land, and obstructed pixels, and corrections for sun glint and atmospheric interference were applied. Depth Invariant Indices (DII) were then calculated according to the method of Lyzenga and an LDA classifier trained on ground truth data from a single scene. The resulting LDA classifier was then applied to other locations and the coral classification accuracy evaluated. When applied to ground truth data from the Palmyra Atoll location in scene path/row 065/056, the initial model achieved an accuracy of 80.3%. However, when applied to ground truth observations from another location within the scene, namely, Kingman Reef, it achieved an accuracy of 78.6%. The model was then applied to two additional scenes (Howland Island and Baker Island Atoll), which yielded an accuracy of 69.2% and 71.4%, respectively. Finally, the algorithm was retrained using data gathered from all four sites, which produced an overall accuracy of 74.1%.

scholarly journals ALBEDO RETRIEVING FROM DSCOVR/EPIC DATA AND PRELIMINARY VALIDATION

2019 ◽  
Vol XLII-3/W9 ◽  
pp. 147-152
Author(s):  
Q. Y. Tian ◽  
Q. Liu ◽  
H. W. Zhang ◽  
Y. H. Che ◽  
Y. N. Wen ◽  
...  
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Time Resolution ◽  
Land Surface ◽  
Surface Albedo ◽  
High Temporal Resolution ◽  
High Time Resolution ◽  
Observation Data ◽  
Climate Change Research ◽  
Wide Range

Abstract. Land surface albedo plays an important role in climate change research. Satellite remote sensing has the characteristic of wide observation range, and it can make repeated observations on the same area. Therefore, using the remote sensing data to retrieve surface albedo becomes a main method to obtain the surface albedo in a wide range or even on a global scale. However, the time resolution of existing albedo products is usually low, which has a great impact on the analysis of rapid changes in surface vegetation and the climate change research. The Deep Space Climate Observatory (DSCOVR) was launched to a sun-earth first Lagrange point (L1) orbit, which is a new and unique vantage point to observe the continuously full, sunlit disk of Earth. DSCOVR can provide observation data with high time resolution, therefore, it is necessary to explore the feasibility of the new sensor DSCOVR/EPIC inversion of the daily albedo product. The relationship between the surface broadband albedo and the surface reflectance was established, and then the surface albedo with high temporal resolution was calculated using the DSCOVR/EPIC data. The Inner Mongolia Autonomous Region and parts of the Sahara Desert were selected to verify the accuracy of DSCOVR albedo compared with MODIS albedo. The results show that the correlation coefficients between DSCOVR albedo and MODIS albedo are greater than 0.7 and RMSE are less than 0.05 both in visible band and shortwave band. It can be seen that this method can be used for the albedo retrieval using DSCOVR/EPIC data.

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