scholarly journals DenseResUNet: An Architecture to Assess Water-Stressed Sugarcane Crops from Sentinel-2 Satellite Imagery

2021 ◽  
Vol 38 (4) ◽  
pp. 1131-1139
Author(s):  
Shyamal S. Virnodkar ◽  
Vinod K. Pachghare ◽  
Virupakshagouda C. Patil ◽  
Sunil Kumar Jha
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Crop Production ◽  
Vegetation Indices ◽  
Detection Threshold ◽  
Remote Sensing Data ◽  
Remotely Sensed ◽  
Visual Signal ◽  
Agricultural Crop ◽  
Remotely Sensed Data

A single most immense abiotic stress globally affecting the productivity of all the crops is water stress. Hence, timely and accurate detection of the water-stressed crops is a necessary task for high productivity. Agricultural crop production can be managed and enhanced by spatial and temporal evaluation of water-stressed crops through remotely sensed data. However, detecting water-stressed crops from remote sensing images is a challenging task as various factors impacting spectral bands, vegetation indices (VIs) at the canopy and landscape scales, as well as the fact that the water stress detection threshold is crop-specific, there has yet to be substantial agreement on their usage as a pre-visual signal of water stress. This research takes the benefits of freely available remote sensing data and convolutional neural networks to perform semantic segmentation of water-stressed sugarcane crops. Here an architecture ‘DenseResUNet’ is proposed for water-stressed sugarcane crops using segmentation based on encoder-decoder approach. The novelty of the proposed approach lies in the replacement of classical convolution operation in the UNet with the dense block. The layers of a dense block are residual modules with a dense connection. The proposed model achieved 61.91% mIoU, and 80.53% accuracy on segmenting the water-stressed sugarcane fields. This study compares the proposed architecture with the UNet, ResUNet, and DenseUNet models achieving mIoU of 32.20%, 58.34%, and 53.15%, respectively. The results of this study reveal that the model has the potential to identify water-stressed crops from remotely sensed data through deep learning techniques.

scholarly journals Modeling of Percentage of Canopy in Merawu Catchment Derived From Various Vegetation Indices of Remotely Sensed Data

2013 ◽  
Vol 27 (1) ◽  
pp. 23
Author(s):  
Bambang Sulistyo ◽  
Totok Gunawan ◽  
H Hartono ◽  
Projo Danoedoro
Keyword(s):  
Remote Sensing ◽  
Vegetation Indices ◽  
Research Result ◽  
Remote Sensing Data ◽  
Remotely Sensed ◽  
High Accuracy ◽  
Remotely Sensed Data ◽  
High Coefficient ◽  
Coefficient Of Correlation ◽  
Landsat 7

The research was aimed at studying Percentage of Canopy mapping derived from various vegetation indices of remotely-sensed data int Merawu Catchment. Methodology applied was by analyzing remote sensing data of Landsat 7 ETM+ image to obtain various vegetation indices for correlation analysis with Percentage of Canopy measured directly on the field (PTactual) at 48 locations. These research used 11 (eleven) vegetation indices of remotely-sensed data, namely ARVI, MSAVI, TVI, VIF, NDVI, TSAVI, SAVI, EVI, RVI, DVI and PVI. The analysis resulted models (PTmodel) for Percentage of Canopy mapping. The vegetation indices selected are those having high coefficient of correlation (>=0.80) to PTactual. Percentage of Canopy maps were validated using 39 locations on the field to know their accuracies. Percentage of Canopy map (PTmodel) is said to be accurate when its coefficient of correlation value to PTactual is high (>=0.80). The research result in Merawu Catchment showed that from 11 vegetation indices under studied, there were 6 vegetation indices resulted high accuracy of Percentage of Canopy maps (as shown in the value of coefficient of correlation as >=0.80), i.e. TVI, VIF, NDVI, TSAVI, RVI dan SAVI, while the rest, namely ARVI, PVI, DVI, EVI and MSAVI, have r values of < 0.80.

THE ROLE OF REMOTE SENSING DATA FOR COASTAL ZONE MONITIORING AND MANAGEMENT (CASE STUDY FOR THE EAST PART OF GULF OF FINLAND)

2017 ◽  
Author(s):  
Nikifor Ostanin ◽  
Nikifor Ostanin
Keyword(s):  
Remote Sensing ◽  
Coastal Zone ◽  
Environmental Changes ◽  
Rapid Development ◽  
Water Environment ◽  
Remote Sensing Data ◽  
Remotely Sensed ◽  
Gulf Of Finland ◽  
Coastal Protection ◽  
Remotely Sensed Data

Coastal zone of the Eastern Gulf of Finland is subjected to essential natural and anthropogenic impact. The processes of abrasion and accumulation are predominant. While some coastal protection structures are old and ruined the problem of monitoring and coastal management is actual. Remotely sensed data is important component of geospatial information for coastal environment research. Rapid development of modern satellite remote sensing techniques and data processing algorithms made this data essential for monitoring and management. Multispectral imagers of modern high resolution satellites make it possible to produce advanced image processing, such as relative water depths estimation, sea-bottom classification and detection of changes in shallow water environment. In the framework of the project of development of new coast protection plan for the Kurortny District of St.-Petersburg a series of archival and modern satellite images were collected and analyzed. As a result several schemes of underwater parts of coastal zone and schemes of relative bathymetry for the key areas were produced. The comparative analysis of multi-temporal images allow us to reveal trends of environmental changes in the study areas. This information, compared with field observations, shows that remotely sensed data is useful and efficient for geospatial planning and development of new coast protection scheme.

scholarly journals Remote Sensing Approaches and Related Techniques to Map and Study Landslides

2020 ◽  
Author(s):  
Ram L. Ray ◽  
Maurizio Lazzari ◽  
Tolulope Olutimehin
Keyword(s):  
Remote Sensing ◽  
Hazard Assessment ◽  
Remote Sensing Data ◽  
Remotely Sensed ◽  
Landslide Monitoring ◽  
In Situ Observation ◽  
Remotely Sensed Data ◽  
Sensing Data ◽  
Stability Model

Landslide is one of the costliest and fatal geological hazards, threatening and influencing the socioeconomic conditions in many countries globally. Remote sensing approaches are widely used in landslide studies. Landslide threats can also be investigated through slope stability model, susceptibility mapping, hazard assessment, risk analysis, and other methods. Although it is possible to conduct landslide studies using in-situ observation, it is time-consuming, expensive, and sometimes challenging to collect data at inaccessible terrains. Remote sensing data can be used in landslide monitoring, mapping, hazard prediction and assessment, and other investigations. The primary goal of this chapter is to review the existing remote sensing approaches and techniques used to study landslides and explore the possibilities of potential remote sensing tools that can effectively be used in landslide studies in the future. This chapter also provides critical and comprehensive reviews of landslide studies focus¬ing on the role played by remote sensing data and approaches in landslide hazard assessment. Further, the reviews discuss the application of remotely sensed products for landslide detection, mapping, prediction, and evaluation around the world. This systematic review may contribute to better understanding the extensive use of remotely sensed data and spatial analysis techniques to conduct landslide studies at a range of scales.

scholarly journals Mapping Dragon Fruit Croplands from Space Using Remote Sensing of Artificial Light at Night

2020 ◽  
Vol 12 (24) ◽  
pp. 4139
Author(s):  
Ruirui Wang ◽  
Wei Shi ◽  
Pinliang Dong
Keyword(s):  
Remote Sensing ◽  
Infrared Imaging ◽  
Remote Sensing Data ◽  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Important Indicator ◽  
Artificial Lighting ◽  
Sensing Data ◽  
Dragon Fruit ◽  
Positive Correlations

The nighttime light (NTL) on the surface of Earth is an important indicator for the human transformation of the world. NTL remotely sensed data have been widely used in urban development, population estimation, economic activity, resource development and other fields. With the increasing use of artificial lighting technology in agriculture, it has become possible to use NTL remote sensing data for monitoring agricultural activities. In this study, National Polar Partnership (NPP)-Visible Infrared Imaging Radiometer Suite (VIIRS) NTL remote sensing data were used to observe the seasonal variation of artificial lighting in dragon fruit cropland in Binh Thuan Province, Vietnam. Compared with the statistics of planted area, area having products and production of dragon fruit by district in the Statistical Yearbook of Binh Thuan Province 2018, values of the mean and standard deviation of NTL brightness have significant positive correlations with the statistical data. The results suggest that the NTL remotely sensed data could be used to reveal some agricultural productive activities such as dragon fruits production accurately by monitoring the seasonal artificial lighting. This research demonstrates the application potential of NTL remotely sensed data in agriculture.

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.

Volumetric Determination of Marine Oil Spills Using Coordinated Airborne and Surface Sampling Data

1973 ◽  
Vol 1973 (1) ◽  
pp. 117-125
Author(s):  
J. E. Estes ◽  
P. G. Mikolaj ◽  
R. R. Thaman ◽  
L. W. Senger
Keyword(s):  
Remote Sensing ◽  
Oil Spills ◽  
Oil Pollution ◽  
Remote Sensing Data ◽  
Remotely Sensed ◽  
Coast Guard ◽  
Surveillance Systems ◽  
Surface Sampling ◽  
Remotely Sensed Data ◽  
Marine Oil

ABSTRACT The detection, measurement, and monitoring of oil pollution in the marine environment are receiving increased attention owing to: I) the growing incidence of oil spills; 2) the associated need for improved cleanup procedures; and, 3) the need for more effective surveillance systems, capable of gathering legal evidence for the prosecution of violators. The Geography Remote Sensing Unit and the Department of Chemical and Nuclear Engineering at the University of California, Santa Barbara for 2 1/2 years has been conducting experiments related to the application of remotely sensed data to these problem areas. As part of a United States Coast Guard test of a high seas oil containment device, a system for estimating the volume of oil loss resulting from oil pollution incidents was developed. This system involved the coordination of remote sensing data acquisition with simultaneous collection of surface sampling data. Results indicate that remotely sensed data, when effectively correlated with surface sampling data, can provide a base for volumetric estimations of a given oil slick. Refinements of these techniques can lead to more efficient, real-time day/night, operational monitoring of marine oil pollution incidents.

scholarly journals Use of remote sensing to understand the terroir of the Niagara Peninsula. Applications in a Riesling vineyard

OENO One ◽  
2015 ◽  
Vol 49 (1) ◽  
pp. 1 ◽  
Author(s):  
Matthieu Marciniak ◽  
Ralph Brown ◽  
Andrew Reynolds ◽  
Marilyne Jollineau
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Water Status ◽  
Remote Sensing Data ◽  
Titratable Acidity ◽  
Weather Conditions ◽  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Berry Composition

<p style="text-align: justify;"><strong>Aim:</strong> The purpose of this study was to determine if multispectral high spatial resolution airborne imagery could be used to segregate zones in vineyards to target fruit of highest quality for premium winemaking. We hypothesized that remotely sensed data would correlate with vine size and leaf water potential (ψ), as well as with yield and berry composition.</p><p style="text-align: justify;"><strong>Methods and results:</strong> Hypotheses were tested in a 10-ha Riesling vineyard [Thirty Bench Winemakers, Beamsville (Ontario)]. The vineyard was delineated using GPS and 519 vines were geo-referenced. Six sub-blocks were delineated for study. Four were identified based on vine canopy size (low, high) with remote sensing in 2005. Airborne images were collected with a four-band digital camera every 3-4 weeks over 3 seasons (2007-2009). Normalized difference vegetation index (NDVI) values (NDVI-red, green) and greenness ratio were calculated from the images. Single-leaf reflectance spectra were collected to compare vegetation indices (VIs) obtained from ground-based and airborne remote-sensing data. Soil moisture, leaf ψ, yield components, vine size, and fruit composition were also measured. Strong positive correlations were observed between VIs and vine size throughout the growing season. Vines with higher VIs during average to dry years had enhanced fruit maturity (higher °Brix and lower titratable acidity). Berry monoterpenes always had the same relationship with remote sensing variables regardless of weather conditions.</p><p style="text-align: justify;"><strong>Conclusions:</strong> Remote sensing images can assist in delineating vineyard zones where fruit will be of different maturity levels, or will have different concentrations of aroma compounds. Those zones could be considered as sub-blocks and processed separately to make wines that reflect those terroir differences. Strongest relationships between remotely sensed VIs and berry composition variables occurred when images were taken around veraison.</p><strong>Significance and impact of the study:</strong> Remote sensing may be effective to quantify spatial variation in grape flavour potential within vineyards, in addition to characteristics such as water status, yield, and vine size. This study was unique by employing remote sensing in cover-cropped vineyards and using protocols for excluding spectral reflectance contributed by inter-row vegetation.

ASSESSMENT OF THE BIOMASS OF SPRING WHEAT SOWING

1970 ◽  
pp. 4-8
Author(s):  
I. M. Mikhaylenko ◽  
V. N. Timoshin ◽  
V. E. Veller
Keyword(s):  
Remote Sensing ◽  
Crop Production ◽  
Vegetation Indices ◽  
Morphological Structure ◽  
Remote Sensing Data ◽  
Estimation Algorithm ◽  
The State ◽  
Approximation Schemes ◽  
Sensing Data ◽  
Estimation Algorithms

The use of Earth remote sensing data (ERS) is a key information technology in modern crop production, as it allows you to assess the parameters of the state of crops of crops, the number of which can reach up to ten. Based on such assessments, managerial decisions are made aimed at obtaining specified final results. It is impossible to assess these parameters by the currently widely used vegetation indices, due to their scalar shape and dimensionless size. The paper develops a classical approach to the problem of assessing the parameters of the state of crops of crops, in which remote sensing data are considered as an indirect measurement of the estimated parameters. The difference of this article from previous works of a similar orientation is that crops with a more complex morphological structure are considered as an object of evaluation, which inevitably leads to a complication of the estimation algorithm. In addition, such an important feature of agricultural objects as their spatial distribution is considered here. To take it into account, a new kind of mathematical models is used in which spatial coordinates are introduced. Due to the significant complication of modeling and estimation algorithms based on such models, approximation schemes that are simpler in terms of software implementation are proposed. The basis of such schemes is the basic estimation algorithms, by which first estimates are formed for 20-30 elementary sowing areas, using which spatial variations of the estimates and reflection parameters are used on the used channels of the remote sensing system, and then linear spatial corrector parameters are constructed from these variations.

Applications of Remotely Sensed Data from the Jornada Basin

2006 ◽  
Author(s):  
Albert Rango ◽  
Jerry Ritchie
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Remotely Sensed ◽  
Data Sets ◽  
Remotely Sensed Data ◽  
Sensing Data ◽  
Laser Altimeters ◽  
Jornada Basin ◽  
Multitemporal Remote Sensing

Like other rangelands, little application of remote sensing data for measurement and monitoring has taken place within the Jornada Basin. Although remote sensing data in the form of aerial photographs were acquired as far back as 1935 over portions of the Jornada Basin, little reliance was placed on these data. With the launch of Earth resources satellites in 1972, a variety of sensors have been available to collect remote sensing data. These sensors are typically satellite-based but can be used from other platforms including ground-based towers and hand-held apparatus, low-altitude aircraft, and high-altitude aircraft with various resolutions (now as good as 0.61 m) and spectral capabilities. A multispectral, multispatial, and multitemporal remote sensing approach would be ideal for extrapolating ground-based point and plot knowledge to large areas or landscape units viewed from satellite-based platforms. This chapter details development and applications of long-term remotely sensed data sets that are used in concert with other long-term data to provide more comprehensive knowledge for management of rangeland across this basin and as a template for their use for rangeland management in other regions. In concert with the ongoing Jornada Basin research program of ground measurements, in 1995 we began to collect remotely sensed data from ground, airborne, and satellite platforms to provide spatial and temporal data on the physical and biological state of basin rangeland. Data on distribution and reflectance of vegetation were measured on the ground along preestablished transects with detailed vegetation surveys (cover, composition, and height); with hand-held and yoke-mounted spectral and thermal radiometers; from aircraft flown at different elevations with spectral and thermal radiometers, infrared thermal radiometers, multispectral video, digital imagers, and laser altimeters; and from space with Landsat Thematic Mapper (TM), IKONOS, QuickBird, Terra/Aqua, and other satellite-based sensors. These different platforms (ground, aircraft, and satellite) allow evaluation of landscape patterns and states at different scales. One general use of these measurements will be to quantify the hydrologic budget and plant response to changes in components in the water and energy balance at different scales and to evaluate techniques of scaling data.

THE ROLE OF REMOTE SENSING DATA FOR COASTAL ZONE MONITIORING AND MANAGEMENT (CASE STUDY FOR THE EAST PART OF GULF OF FINLAND)

2017 ◽  
Author(s):  
Nikifor Ostanin ◽  
Nikifor Ostanin
Keyword(s):  
Remote Sensing ◽  
Coastal Zone ◽  
Environmental Changes ◽  
Rapid Development ◽  
Water Environment ◽  
Remote Sensing Data ◽  
Remotely Sensed ◽  
Gulf Of Finland ◽  
Coastal Protection ◽  
Remotely Sensed Data

Coastal zone of the Eastern Gulf of Finland is subjected to essential natural and anthropogenic impact. The processes of abrasion and accumulation are predominant. While some coastal protection structures are old and ruined the problem of monitoring and coastal management is actual. Remotely sensed data is important component of geospatial information for coastal environment research. Rapid development of modern satellite remote sensing techniques and data processing algorithms made this data essential for monitoring and management. Multispectral imagers of modern high resolution satellites make it possible to produce advanced image processing, such as relative water depths estimation, sea-bottom classification and detection of changes in shallow water environment. In the framework of the project of development of new coast protection plan for the Kurortny District of St.-Petersburg a series of archival and modern satellite images were collected and analyzed. As a result several schemes of underwater parts of coastal zone and schemes of relative bathymetry for the key areas were produced. The comparative analysis of multi-temporal images allow us to reveal trends of environmental changes in the study areas. This information, compared with field observations, shows that remotely sensed data is useful and efficient for geospatial planning and development of new coast protection scheme.

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