Use of Optical Remote Sensing for Detecting Herbicide Injury in Soybean

2004 ◽  
Vol 18 (2) ◽  
pp. 292-297 ◽  
Author(s):  
Kurt D. Thelen ◽  
A. N. Kravchenko ◽  
Chad D. Lee
Keyword(s):  
Remote Sensing ◽  
Vegetation Indices ◽  
Weather Conditions ◽  
Aerial Imagery ◽  
Optical Remote Sensing ◽  
Actual Application ◽  
Application Rates ◽  
Temporal And Spatial Variability ◽  
Untreated Check ◽  
Temporal And Spatial

Experiments were conducted from 2000 to 2002 at two locations each year to determine if lactofen and imazethapyr injury to soybean could be detected using digital aerial imagery and ground-based optical remote sensing. Lactofen and imazethapyr were applied at base rates of 105 and 71 g/ha, respectively, and at 0, 2X, and 4X rates. Treated plots were evaluated between 7 and 21 d after treatment for crop injury using a ground-based radiometer and a system using computer analysis of digital aerial imagery. Both the ground-based radiometer and the digital aerial imagery were effective in detecting herbicide injury under most conditions. The digital aerial imagery system was found to be more sensitive in detecting herbicide injury than the ground-based radiometer system. Herbicide or herbicide rate had a significant effect on normalized differential vegetation indices (NDVI) derived from digital aerial imagery in four of four site-years. NDVI values derived from a multispectral ground-based radiometer were significant for herbicide or herbicide rate in four of six site-years. NDVI values from treated plots were subtracted from the NDVI value of the untreated check to generate a ΔNDVI. The resulting ΔNDVI values from the ground-based radiometer system were significant for herbicide or herbicide rate in six of six site-years. Neither optical remote-sensing system was effective at estimating actual application rates of lactofen and imazethapyr across a broad range of field and weather conditions due to temporal and spatial variability in crop response to the herbicides.

Temporal and Spatial Variability and Trend Investigation of Total Ozone Column over Iraq Employing Remote Sensing Data: 1979-2012

2015 ◽  
Vol 53 ◽  
pp. 1-18
Author(s):  
Ali M. Al-Salihi ◽  
Zehraa M. Hassan
Keyword(s):  
Remote Sensing ◽  
Spatial Variability ◽  
Total Ozone ◽  
Positive Trend ◽  
Climate Change Research ◽  
Total Ozone Column ◽  
Temporal And Spatial Variability ◽  
Temporal And Spatial ◽  
Ozone Column

The objective of this paper is to analyze the temporal and spatial variability of the total ozone column (TOC) distributions and trends over Iraq, during the last 30 years (1979–2012) using remote sensing-derived TOC data. Due to shortage of ground-based TOC measurements. TOC data derived from the Total Ozone Mapping Spectrometer (TOMS) for the period 1979–2004 and Ozone Monitoring Instrument (OMI) for the period 2005–2012 with spatial resolution (1o×1o) were used in present study. The spatial, long-term, monthly variations of TOC over Iraq were analysed. For the spatial variability, the latitudinal variability has a large range between (45 to 55) DU in winter and spring whereas during summer and autumn months ranged between (6 to 10) DU. Also represents an annual cycle with maximum in March and minimum in October. In contrast, the longitudinal variability is not significant. The long-term variability represented a notable decline for the period 1979–2012. The ozone negative trend was observed significantly during 1979–2004, for all months with trend ranged between (− 0.3 to 2) DU/year whereas the ozone positive trend was appear clearly during 2005–2007, for all months (0.1 to 2.3) DU/year ,except February and September which presented negative trends. The results can provide comprehensive descriptions of the TOC variations in Iraq and benefit climate change research in this region.

scholarly journals Radiometric Calibration for Multispectral Camera of Different Imaging Conditions Mounted on a UAV Platform

2019 ◽  
Vol 11 (4) ◽  
pp. 978 ◽  
Author(s):  
Yahui Guo ◽  
J. Senthilnath ◽  
Wenxiang Wu ◽  
Xueqin Zhang ◽  
Zhaoqi Zeng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Indices ◽  
Weather Conditions ◽  
Leaf Biomass ◽  
Atmospheric Effects ◽  
Multispectral Images ◽  
Radiometric Calibration ◽  
Calibration Equation ◽  
Mosaic Images ◽  
Universal Calibration

Unmanned aerial vehicle (UAV) equipped with multispectral cameras for remote sensing (RS) has provided new opportunities for ecological and agricultural related applications for modelling, mapping, and monitoring. However, when the multispectral images are used for the quantitative study, they should be radiometrically calibrated, which accounts for atmospheric and solar conditions by converting the digital number into a unit of scene reflectance that can be directly used in quantitative remote sensing (QRS). Indeed, some of the present applications using multispectral images are processed without precise calibration or with coarse calibration. The radiometric calibration of images from the UAV platform is quite difficult to perform, as the imaging condition is different for every single image. Thus, a standard procedure is necessary for a systematical radiometric calibration method to generate multispectral images with unit reflectance. Further, these images can be used to calculate vegetation indices, which are useful in monitoring vegetation phenology. These vegetation indices are considered as a potential screening tool to know the plant status, such as nitrogen, chlorophyll content, green leaf biomass, etc. This study focuses on a series of radiometric calibrations for multispectral images acquired from different flight altitudes, time instants, and weather conditions. Radiometric calibration for multispectral images is performed using the linear regression method (LRM). The main contribution involves (1) affirming the optimal calibration targets and assessing the atmospheric effects of different flights using the single scene of images; (2) to evaluate the effects of mosaic images with the LRM; (3) to propose and validate a universal calibration equation for the Mini Multiple Camera Array (MCA) 6 camera. The obtained results show that the three calibration targets, such as the dark, moderate, and white, are better for the Mini MCA 6 camera. The atmospheric effects increase with the increase of flight altitudes for each band, and the camera effect is of a fixed number. However, the camera effect and atmospheric attenuation to reflectance from different altitudes were relatively low considering the accuracy assessment. The performance measures namely, mean absolute deviation (indicated as V) and root mean square error (RMSE) between single and mosaic images show that the mosaic will not influence too much reflectance. The LRM performs well in all weather conditions. The universal calibration equation is suitable to apply to the images acquired during a sunny day and even with a little cloud.

Delineation of erosion classes in semi-arid southern African grasslands using vegetation indices from optical remote sensing data

2003 ◽  
Vol 17 (5) ◽  
pp. 917-928 ◽  
Author(s):  
Volker Hochschild ◽  
Michael Märker ◽  
Giuliano Rodolfi ◽  
Helmut Staudenrausch
Keyword(s):  
Remote Sensing ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Optical Remote Sensing ◽  
Sensing Data ◽  
Semi Arid

scholarly journals Aerial Biomass and Nitrogen Estimation of Emerged and Floating Macrophytes using Optical Remote Sensing: A Non-Destructive Method

2021 ◽  
Author(s):  
Jessica Cristina Carvalho Medeiros ◽  
Maurício Perine ◽  
Marcelo Pompêo ◽  
Marisa Bitencourt
Keyword(s):  
Remote Sensing ◽  
Aquatic Plants ◽  
Spectral Response ◽  
Nitrogen Concentration ◽  
Vegetation Indices ◽  
Field Measurements ◽  
Pistia Stratiotes ◽  
Optical Remote Sensing ◽  
Eichhornia Azurea ◽  
Sentinel 2

Abstract Freshwater resources faces threats with aquatic plants invasion, considered biological pollution with deep effects on water quality and nutrients cycling due to their rapid growth. Orbital remote sensing has been an effective instrument of monitoring large water bodies. Thus, the aim of this study was to analyze the relation between reflectance and field measurements (biomass and nitrogen concentration) of aquatic plants to develop estimation equations and to test vegetation indices to use in orbital remote sensing. The most common tropical infesting species (Salvinia auriculata, Pistia stratiotes, Eichhornia crassipes and Eichhornia azurea) were collected during a year, measured their spectral response to simulate satellite bands, and the biomass and nitrogen concentration measurements. The bands intervals of Sentinel-2 satellite were choosing to the simulation due to their narrow bands and the RedEdge new band. The obtained field data were correlated with the reflectance obtained from spectroradiometry of each species and the equations showed R² = 0.64 to estimate biomass and R² = 0.60 to estimate nitrogen using the entire spectrum. Several indices described in the literature were tested with different Sentinel-2 bands but with no significant results. The NDVI index showed a separation among species using RedEdge band and can be used to identify the species, but not to estimate their biomass.

SEA ICE AND ITS INFLUENCE TO COASTAL PROCESSES – BALTIC SEA, ESTONIA

2018 ◽  
Author(s):  
К Орвикку ◽  
K Orvikku ◽  
Х. Тониссон ◽  
H. Tonisson
Keyword(s):  
Sea Ice ◽  
Spatial Variability ◽  
Baltic Sea ◽  
Weather Conditions ◽  
Coastal Processes ◽  
The Baltic Sea ◽  
Temporal And Spatial Variability ◽  
Baltic Sea Region ◽  
Temporal And Spatial ◽  
The Baltic

The Baltic Sea region is characterized by variable winter weather conditions. Sea ice forms near the Estonian coast almost every winter and is characterized by large temporal and spatial variability [1, 2].

scholarly journals Aerial Imagery Based on Commercial Flights as Remote Sensing Platform

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1658 ◽  
Author(s):  
Toni Mastelic ◽  
Josip Lorincz ◽  
Ivan Ivandic ◽  
Matea Boban
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Aerial Photography ◽  
Storage Capacity ◽  
Image Data ◽  
Aerial Imagery ◽  
Unmanned Aerial Systems ◽  
Sensing Platform ◽  
Temporal And Spatial ◽  
Aerial Systems

Remote sensing is commonly performed via airborne platforms such as satellites, specialized aircraft, and unmanned aerial systems (UASs), which perform airborne photography using mounted cameras. However, they are limited by their coverage (UASs), irregular flyover frequency (aircraft), and/or low spatial resolution (satellites) due to their high altitude. In this paper, we examine the utilization of commercial flights as an airborne platform for remote sensing. Namely, we simulate a situation where all aircraft on commercial flights are equipped with a mounted camera used for airborne photography. The simulation is used to estimate coverage, the temporal and spatial resolution of aerial imagery acquired this way, as well as the storage capacity required for storing all imagery data. The results show that Europe is 83.28 percent covered with an average of one aerial photography every half an hour and a ground sampling distance of 0.96 meters per pixel. Capturing such imagery results in 20 million images or four petabytes of image data per day. More detailed results are given in the paper for separate countries/territories in Europe, individual commercial airlines and alliances, as well as three different cameras.

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