scholarly journals Research of rice crops in Krasnodar region by remote sensing data

2020 ◽  
Vol 175 ◽  
pp. 01004
Author(s):  
Sergey Garkusha ◽  
Mikhail Skazhennik ◽  
Evgeny Kiselev ◽  
Vitaliy Chizhikov ◽  
Alexey Petrushin
Keyword(s):  
Remote Sensing ◽  
Crop Production ◽  
Remote Sensing Data ◽  
Test Site ◽  
Experimental Methodology ◽  
Federal State ◽  
Automated Mapping ◽  
Yield Information ◽  
Geoinformation Modeling ◽  
Sentinel 2A

The concept of digitalization of agricultural production in the Russian Federation provides for the implementation of measures to develop and create a system of geographic information monitoring and decision support in crop production. The aim of the research was to conduct geoinformation monitoring of rice crops to develop methods for automated mapping of their condition and yield forecasting. The studies were carried out on a test site of the Federal State Budgetary Scientific Institution “Federal Scientific Rice Centre” with an area of 274 hectares. The survey was performed by a quadcopter with a MicaSense RedEdge-M multispectral camera mounted on a fixed suspension. The shooting period using an unmanned aerial vehicle (UAV) was limited to early June and additionally used the Sentinel-2A satellite. To assess the state of rice crops, the normalized relative vegetative index NDVI was used. Based on the NDVI distribution and yield information from the combine TUCANO 580 (CLAAS), a statistical analysis was carried out in fields 7 and 9. Testing of the experimental methodology for monitoring crops in 2019 on the basis of remote sensing of test plots and geoinformation modeling and the statistical apparatus should be considered satisfactory.

scholarly journals Rice crops research according to remote sensing data (overview)

2021 ◽  
Vol 285 ◽  
pp. 02038
Author(s):  
Michael Skazhennik ◽  
Vitaly Chizhikov ◽  
Anna Shevchenko ◽  
Andrey Migachev
Keyword(s):  
Remote Sensing ◽  
Rice Variety ◽  
Soil Layer ◽  
Remote Sensing Data ◽  
Test Site ◽  
The State ◽  
Experimental Methodology ◽  
Federal State ◽  
Agricultural Producers ◽  
Sentinel 2A

The introduction of precision farming technologies using hightech equipment will increase the productivity of rice, reduce its cost, and improve the environment. The use of digital technologies in agriculture is especially relevant in the face of rising prices for seeds, fertilizers and fuel, as it helps to significantly reduce costs and increase the profitability of agribusiness. The paper reviews the use of unmanned aerial vehicles (UAV) in rice cultivation and describes methods for assessing the state of rice crops. Drones are a more versatile and efficient tool for obtaining data on the state of crops of crops compared to information obtained from satellite images. They allow real-time monitoring of the most important indicators of the state of crops, which allows agricultural producers to make timely decisions. The UAV was used to determine the boundaries of the rice system, terrain, microreliefs of checks, moisture of the surface soil layer and the state of rice crops. The studies were carried out on a test site of the Federal State Budgetary Scientific Institution “Federal Scientific Rice Centre” with an area of 327 hectares. The main cultivated crop is rice variety Flagman. The survey was performed by a quadcopter with a Mica Sense Red Edge-M multispectral camera mounted on a fixed suspension. The shooting period using an unmanned aerial vehicle (UAV) was limited to early June and additionally used the Sentinel-2A satellite data covering the entire analyzed period (06.05.2019 – 08.29.2019). To assess the state of rice crops, the normalized relative vegetative index NDVI was used. Based on the NDVI distribution and yield information from the combine TUCANO 580 (CLAAS), a statistical analysis was carried out in fields 7 and 9. Testing of the experimental methodology for monitoring crops in 2019 on the basis of remote sensing of test plots and geoinformation modeling and the statistical apparatus should be considered satisfactory.

scholarly journals Rapid Evaluation and Validation Method of Above Ground Forest Biomass Estimation Using Optical Remote Sensing in Tundi Reserved Forest Area, India

2021 ◽  
Vol 10 (1) ◽  
pp. 29
Author(s):  
Praveen Kumar ◽  
Akhouri P. Krishna ◽  
Thorkild M. Rasmussen ◽  
Mahendra K. Pal
Keyword(s):  
Remote Sensing ◽  
Vegetation Cover ◽  
Remote Sensing Data ◽  
Test Site ◽  
Forest Area ◽  
Relational Model ◽  
Biomass Estimation ◽  
Optical Remote Sensing ◽  
Ground Sample ◽  
Sensing Data

Optical remote sensing data are freely available on a global scale. However, the satellite image processing and analysis for quick, accurate, and precise forest above ground biomass (AGB) evaluation are still challenging and difficult. This paper is aimed to develop a novel method for precise, accurate, and quick evaluation of the forest AGB from optical remote sensing data. Typically, the ground forest AGB was calculated using an empirical model from ground data for biophysical parameters such as tree density, height, and diameter at breast height (DBH) collected from the field at different elevation strata. The ground fraction of vegetation cover (FVC) in each ground sample location was calculated. Then, the fraction of vegetation cover (FVC) from optical remote sensing imagery was calculated. In the first stage of method implementation, the relation model between the ground FVC and ground forest AGB was developed. In the second stage, the relational model was established between image FVC and ground FVC. Finally, both models were fused to derive the relational model between image FVC and forest AGB. The validation of the developed method was demonstrated utilizing Sentinel-2 imagery as test data and the Tundi reserved forest area located in the Dhanbad district of Jharkhand state in eastern India was used as the test site. The result from the developed model was ground validated and also compared with the result from a previously developed crown projected area (CPA)-based forest AGB estimation approach. The results from the developed approach demonstrated superior capabilities in precision compared to the CPA-based method. The average forest AGB estimation of the test site obtained by this approach revealed 463 tons per hectare, which matches the previous estimate from this test site.

scholarly journals Comparing of Landsat 8 and Sentinel 2A using Water Extraction Indexes over Volta River

2017 ◽  
Vol 10 (1) ◽  
pp. 1 ◽  
Author(s):  
Clement Kwang ◽  
Edward Matthew Osei Jnr ◽  
Adwoa Sarpong Amoah
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Unsupervised Classification ◽  
Water Bodies ◽  
Water Extraction ◽  
Landsat 8 ◽  
Sensing Data ◽  
Optimal Classifier ◽  
Sentinel 2A

Remote sensing data are most often used in water bodies’ extraction studies and the type of remote sensing data used also play a crucial role on the accuracy of the extracted water features. The performance of the proposed water indexes among the various satellite images is not well documented in literature. The proposed water indexes were initially developed with a particular type of data and with advancement and introduction of new satellite images especially Landsat 8 and Sentinel, therefore the need to test the level of performance of these water indexes as new image datasets emerged. Landsat 8 and Sentinel 2A image of part Volta River was used. The water indexes were performed and then ISODATA unsupervised classification was done. The overall accuracy and kappa coefficient values range from 98.0% to 99.8% and 0.94 to 0.98 respectively. Most of water bodies enhancement indexes work better on Sentinel 2A than on Landsat 8. Among the Landsat based water bodies enhancement ISODATA unsupervised classification, the modified normalized water difference index (MNDWI) and normalized water difference index (NDWI) were the best classifier while for Sentinel 2A, the MNDWI and the automatic water extraction index (AWEI_nsh) were the optimal classifier. The least performed classifier for both Landsat 8 and Sentinel 2A was the automatic water extraction index (AWEI_sh). The modified normalized water difference index (MNDWI) has proved to be the universal water bodies enhancement index because of its performance on both the Landsat 8 and Sentinel 2A image.

scholarly journals Utilization of Multi-Temporal Microwave Remote Sensing Data within a Geostatistical Regionalization Approach for the Derivation of Soil Texture

2020 ◽  
Vol 12 (16) ◽  
pp. 2660
Author(s):  
Philip Marzahn ◽  
Swen Meyer
Keyword(s):  
Remote Sensing ◽  
Soil Texture ◽  
Land Surface ◽  
Temporal Frequency ◽  
Management Strategies ◽  
Remote Sensing Data ◽  
Microwave Remote Sensing ◽  
Test Site ◽  
Soil Parameters ◽  
Sensing Data

Land Surface Models (LSM) have become indispensable tools to quantify water and nutrient fluxes in support of land management strategies or the prediction of climate change impacts. However, the utilization of LSM requires soil and vegetation parameters, which are seldom available in high spatial distribution or in an appropriate temporal frequency. As shown in recent studies, the quality of these model input parameters, especially the spatial heterogeneity and temporal variability of soil parameters, has a strong effect on LSM simulations. This paper assesses the potential of microwave remote sensing data for retrieving soil physical properties such as soil texture. Microwave remote sensing is able to penetrate in an imaged media (soil, vegetation), thus being capable of retrieving information beneath such a surface. In this study, airborne remote sensing data acquired at 1.3 GHz and in different polarization is utilized in conjunction with geostatistics to retrieve information about soil texture. The developed approach is validated with in-situ data from different field campaigns carried out over the TERENO test-site “North-Eastern German Lowland Observatorium”. With the proposed approach a high accuracy of the retrieved soil texture with a mean RMSE of 2.42 (Mass-%) could be achieved outperforming classical deterministic and geostatistical approaches.

Remote sensing and its use in detection and monitoring plant diseases: A review

2018 ◽  
Author(s):  
N.K. Gogoi ◽  
B. Deka ◽  
L.C. Bora
Keyword(s):  
Remote Sensing ◽  
Crop Production ◽  
Imaging Techniques ◽  
Crop Protection ◽  
Remote Sensing Data ◽  
Modern Technology ◽  
Plant Diseases ◽  
Resonance Spectroscopy ◽  
Agricultural Crop ◽  
Non Invasive

Remote sensing is a rapid, non-invasive and efficient technique which can acquire and analyze spectral properties of earth surfaces from various distances, ranging from satellites to ground-based platforms. This modern technology holds promise in agricultural crop production including crop protection. Variability in the reflectance spectra of plants resulting from occurrence of disease and pests, allows their identification using remote sensing data. Various spectroscopic and imaging techniques like visible, infrared, multiband and fluorescence spectroscopy, fluorescence imaging, multispectral and hyperspectral imaging, thermography, nuclear magnetic resonance spectroscopy etc. have been studied for the detection of plant diseases. Several of these techniques have great potential in phytopathometry. Remote sensing technologies will be extremely helpful to greatly spatialize diagnostic results and thereby rendering agriculture more sustainable and safe, avoiding expensive use of pesticides in crop protection.

scholarly journals Evaluation of Soil Degradation Based on High Resolution Remote Sensing Data

2005 ◽  
pp. 145-148
Author(s):  
Péter Burai ◽  
János Tamás
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Soil Salinity ◽  
Strong Correlation ◽  
Soil Degradation ◽  
Vegetation Index ◽  
Near Infrared ◽  
Salt Content ◽  
Remote Sensing Data ◽  
Test Site

Soil salinity is the main problem of soil degradation in the Grate Plain with cultivated area of 20% affected. Its influence is accelerated on the water managed and irrigated lands. Remote sensing can significantly contribute to detecting temporal changes of salt-related surface features. We have chosen a farm where intensive crop cultivation takes place as a test site as soil degradation can be intensive as a result of land use and irrigation. In order to evaluate soil salt content and biomass analysis, we gathered detailed data from an 100x250 m area. We analyzed the salinity property of the samples. In our research we used a TETRACAM ADC multispectral camera to take high resolution images (0,2-0,5 m) of low altitude (300-500 m). A Normalized Vegetation Index was computed from near infrared (750-950 nm) and red (620-750 nm) bands. This data was compared with the samples of investigated area. Analyzing the images, we evaluated image reliability, and the connection between the bands and the soil properties (pH, salt content). A strong correlation observed between NDVI and soil salinity (EC) makes the multispectral images suitable for construction of salinity map. A further strong correlation was determined between NDVI and yield.

scholarly journals USING REMOTE SENSING DATA TO CREATE MAPS OF VEGETATION AND RELIEF FOR NATURAL RESOURCE MANAGEMENT OF A LARGE ADMINISTRATIVE REGION

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 103-109
Author(s):  
O. V. Artemeva ◽  
S. Zareie ◽  
Y. Elhaei ◽  
N. A. Pozdnyakova ◽  
N. D. Vasilev
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Mapping Method ◽  
Thematic Maps ◽  
Automated Mapping ◽  
Sensing Data ◽  
Visual Models ◽  
Images Processing ◽  
The Moment ◽  
Administrative Structures

Abstract. The authors offer methods for mapping nature, in particular, vegetation and relief maps using remote sensing data. These thematic maps are most often used by administrators of different levels for environmental and territorial management. In the Russian Federation administrative territories occupied large areas. The algorithm for constructing visual models using remote sensing data for large administrative areas differs from the algorithms for working with small territories. Automated mapping method includes the analysis of the territory by indicators of topography and dominant vegetation, the selection of satellite images, processing, composing mosaics, composites, classification of plant objects, post-processing. The authors offer to use a specific data source, because the quality of the materials is sufficient for working with large areas. Classifications – the most complicated section. At the moment, scientists have not proposed an unambiguous solution to the choice of algorithm. However, the authors of this study experimentally came to the most convenient algorithm, which we characterize as the main one precisely for the purposes of managing natural resources of large administrative structures (regions with legally fixed boundaries). Examples of the thematic maps fragments and results of intermediate versions of visual models built by automated methods are given. The potential use of methods by municipal employees, rather than narrow specialists, was taken into account. In this regard, the value of the study is an exclusively applied nature and can be used in the administrative structures of the executive authorities.

scholarly journals Geoinformation Modeling of Flooded Areas in Settlements (in the Example of Lutsk)

2018 ◽  
Author(s):  
Anna Shostak ◽  
Volodymyr Voloshyn ◽  
Oleksandr Melnyk ◽  
Pavlo Manko
Keyword(s):  
Remote Sensing ◽  
Water Level ◽  
Remote Sensing Data ◽  
Water Levels ◽  
Practical Significance ◽  
Natural Phenomenon ◽  
Short Term ◽  
Sensing Data ◽  
Geoinformation Modeling ◽  
Flooded Areas

Object. Flooding in Ukraine is a common natural phenomenon that repeats periodically and in some cases it becomes disastrous. In an average year floods on the rivers of Volyn region take place from one to three times which extend beyond the limits of the floodplain. The floodplain of Styr river is located in the historical center of Lutsk city, that`s why issues of research and forecasting of floods are very important for a given city. Methodology. Using modern technologies of geodesy and remote sensing allows to quickly determine and predict the floodplain area of settlements. Based on the statistical data of the Volyn Regional Center for Hydrometeorology during the 7 year period 2011-2017 about water levels of the river Styr. We conducted mathematical modeling of fluctuations of water levels within the territory of Lutsk, based on creating a partial Fourier series for discrete values of middle-ten-day water levels values. The post hydrological measurements of Styr river water levels in the territory of Lutsk located on the Shevchenko Street comply with an altitude 172.87 meters. Based on the data of short-term flood forecasting in February and March, and relief data from the Department of Architecture and Urban Development of Volyn State Administration, we conducted visualization of the results using geographic information system QGIS. Results. The results of mathematical processing were the basis for geoinformation simulation of flooded areas using remote sensing data that are publicly available. Use of statistical and geospatial data in this article has great potential for further application in modeling the processes of natural and technogenic origin. Scientific novelty. The mathematical model of short-term forecasting of water levels during the flood period on the river Styr with implementation of geoinformation modeling of flooded areas using remote sensing data is proposed. Practical significance. The research results of water level changes on the Styr River and flood zones within the limits of Lutsk is proposed. The spring flood in February-March 2018, with the maximum water level 5.33 m, corresponds to an absolute mark of 178.20 m, which is forecasted in this article.

scholarly journals On the accuracy of glacier outlines derived from remote-sensing data

2013 ◽  
Vol 54 (63) ◽  
pp. 171-182 ◽  
Author(s):  
F. Paul ◽  
N.E. Barrand ◽  
S. Baumann ◽  
E. Berthier ◽  
T. Bolch ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Reference Value ◽  
Remote Sensing Data ◽  
Automated Classification ◽  
Automated Mapping ◽  
Sensing Data ◽  
Medium Resolution ◽  
Debris Cover ◽  
Manual Correction

AbstractDeriving glacier outlines from satellite data has become increasingly popular in the past decade. In particular when glacier outlines are used as a base for change assessment, it is important to know how accurate they are. Calculating the accuracy correctly is challenging, as appropriate reference data (e.g. from higher-resolution sensors) are seldom available. Moreover, after the required manual correction of the raw outlines (e.g. for debris cover), such a comparison would only reveal the accuracy of the analyst rather than of the algorithm applied. Here we compare outlines for clean and debris-covered glaciers, as derived from single and multiple digitizing by different or the same analysts on very high- (1 m) and medium-resolution (30 m) remote-sensing data, against each other and to glacier outlines derived from automated classification of Landsat Thematic Mapper data. Results show a high variability in the interpretation of debris-covered glacier parts, largely independent of the spatial resolution (area differences were up to 30%), and an overall good agreement for clean ice with sufficient contrast to the surrounding terrain (differences ∼5%). The differences of the automatically derived outlines from a reference value are as small as the standard deviation of the manual digitizations from several analysts. Based on these results, we conclude that automated mapping of clean ice is preferable to manual digitization and recommend using the latter method only for required corrections of incorrectly mapped glacier parts (e.g. debris cover, shadow).

Sign in / Sign up

Export Citation Format

Share Document