Determination of differences in crop injury from aerial application of glyphosate using vegetation indices

2011 ◽  
Vol 77 (2) ◽  
pp. 204-213 ◽  
Author(s):  
B.V. Ortiz ◽  
S.J. Thomson ◽  
Y. Huang ◽  
K.N. Reddy ◽  
W. Ding
Keyword(s):  
Vegetation Indices ◽  
Aerial Application ◽  
Crop Injury

Determination of sufficiency values of canopy reflectance vegetation indices for maximum growth and yield of cucumber

2017 ◽  
Vol 84 ◽  
pp. 1-15 ◽  
Author(s):  
Francisco M. Padilla ◽  
M. Teresa Peña-Fleitas ◽  
Marisa Gallardo ◽  
Rodney B. Thompson
Keyword(s):  
Vegetation Indices ◽  
Maximum Growth ◽  
Growth And Yield ◽  
Canopy Reflectance

scholarly journals Spatio-Temporal Determination of Irrigated Paddy Rice Pixels Using Evapotranspiration and Vegetation Indices: A Case Study for Ca River Basin in Vietnam

2020 ◽  
Vol 08 (12) ◽  
pp. 94-107
Author(s):  
Chung N. Luong ◽  
Lan T. Ha ◽  
Thanh C. Pham ◽  
Hung X. Dinh ◽  
Thanh T. Hoang ◽  
...  
Keyword(s):  
River Basin ◽  
Vegetation Indices ◽  
Paddy Rice ◽  
Spatio Temporal ◽  
Ca River

scholarly journals Reflectance-Based Vegetation Index Assessment of Four Plant Species Exposed to Lithium Chloride

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2750
Author(s):  
Nicole Martinez ◽  
Julia Sharp ◽  
Thomas Johnson ◽  
Wendy Kuhne ◽  
Clay Stafford ◽  
...  
Keyword(s):  
Plant Species ◽  
Lithium Chloride ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Reflectance Spectra ◽  
Treatment Level ◽  
Length Of Treatment ◽  
Contact Probe ◽  
Relative Water

This study considers whether a relationship exists between response to lithium (Li) exposure and select vegetation indices (VI) determined from reflectance spectra in each of four plant species: Arabidopsis thaliana, Helianthus annuus (sunflower), Brassica napus (rape), and Zea mays (corn). Reflectance spectra were collected every week for three weeks using an ASD FieldSpec Pro spectroradiometer with both a contact probe (CP) and a field of view probe (FOV) for plants treated twice weekly in a laboratory setting with 0 mM (control) or 15 mM of lithium chloride (LiCl) solution. Plants were harvested each week after spectra collection for determination of relevant physical endpoints such as relative water content and chlorophyll content. Mixed effects analyses were conducted on selected endpoints and vegetation indices (VI) to determine the significance of the effects of treatment level and length of treatment as well as to determine which VI would be appropriate predictors of treatment-dependent endpoints. Of the species considered, A. thaliana exhibited the most significant effects and corresponding shifts in reflectance spectra. Depending on the species and endpoint, the most relevant VIs in this study were NDVI, PSND, YI, R1676/R1933, R750/R550, and R950/R750.

scholarly journals Monitoring Maize Lodging Grades via Unmanned Aerial Vehicle Multispectral Image

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Qian Sun ◽  
Lin Sun ◽  
Meiyan Shu ◽  
Xiaohe Gu ◽  
Guijun Yang ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Near Infrared ◽  
Confusion Matrix ◽  
Accurate Determination ◽  
Vegetation Indices ◽  
Texture Features ◽  
Multispectral Image ◽  
Visual Interpretation ◽  
Aerial Vehicle

Lodging is one of the main factors affecting the quality and yield of crops. Timely and accurate determination of crop lodging grade is of great significance for the quantitative and objective evaluation of yield losses. The purpose of this study was to analyze the monitoring ability of a multispectral image obtained by an unmanned aerial vehicle (UAV) for determination of the maize lodging grade. A multispectral Parrot Sequoia camera is specially designed for agricultural applications and provides new information that is useful in agricultural decision-making. Indeed, a near-infrared image which cannot be seen with the naked eye can be used to make a highly precise diagnosis of the vegetation condition. The images obtained constitute a highly effective tool for analyzing plant health. Maize samples with different lodging grades were obtained by visual interpretation, and the spectral reflectance, texture feature parameters, and vegetation indices of the training samples were extracted. Different feature transformations were performed, texture features and vegetation indices were combined, and various feature images were classified by maximum likelihood classification (MLC) to extract four lodging grades. Classification accuracy was evaluated using a confusion matrix based on the verification samples, and the features suitable for monitoring the maize lodging grade were screened. The results showed that compared with a multispectral image, the principal components, texture features, and combination of texture features and vegetation indices were improved by varying degrees. The overall accuracy of the combination of texture features and vegetation indices is 86.61%, and the Kappa coefficient is 0.8327, which is higher than that of other features. Therefore, the classification result based on the feature combinations of the UAV multispectral image is useful for monitoring of maize lodging grades.

Comparison among normalized vegetation indices for the determination of LAI

2003 ◽  
Author(s):  
Luca Bernasconi ◽  
Ivan Pippi ◽  
Sabrina Raddi
Keyword(s):  
Vegetation Indices

scholarly journals The Use of Deep Machine Learning for the Automated Selection of Remote Sensing Data for the Determination of Areas of Arable Land Degradation Processes Distribution

2021 ◽  
Vol 13 (1) ◽  
pp. 155
Author(s):  
Dmitry I. Rukhovich ◽  
Polina V. Koroleva ◽  
Danila D. Rukhovich ◽  
Natalia V. Kalinina
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Soil Degradation ◽  
Arable Land ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Agricultural Field ◽  
Sensing Data ◽  
Selection Of

Soil degradation processes are widespread on agricultural land. Ground-based methods for detecting degradation require a lot of labor and time. Remote methods based on the analysis of vegetation indices can significantly reduce the volume of ground surveys. Currently, machine learning methods are increasingly being used to analyze remote sensing data. In this paper, the task is set to apply deep machine learning methods and methods of vegetation indices calculation to automate the detection of areas of soil degradation development on arable land. In the course of the work, a method was developed for determining the location of degraded areas of soil cover on arable fields. The method is based on the use of multi-temporal remote sensing data. The selection of suitable remote sensing data scenes is based on deep machine learning. Deep machine learning was based on an analysis of 1028 scenes of Landsats 4, 5, 7 and 8 on 530 agricultural fields. Landsat data from 1984 to 2019 was analyzed. Dataset was created manually for each pair of “Landsat scene”/“agricultural field number”(for each agricultural field, the suitability of each Landsat scene was assessed). Areas of soil degradation were calculated based on the frequency of occurrence of low NDVI values over 35 years. Low NDVI values were calculated separately for each suitable fragment of the satellite image within the boundaries of each agricultural field. NDVI values of one-third of the field area and lower than the other two-thirds were considered low. During testing, the method gave 12.5% of type I errors (false positive) and 3.8% of type II errors (false negative). Independent verification of the method was carried out on six agricultural fields on an area of 713.3 hectares. Humus content and thickness of the humus horizon were determined in 42 ground-based points. In arable land degradation areas identified by the proposed method, the probability of detecting soil degradation by field methods was 87.5%. The probability of detecting soil degradation by ground-based methods outside the predicted regions was 3.8%. The results indicate that deep machine learning is feasible for remote sensing data selection based on a binary dataset. This eliminates the need for intermediate filtering systems in the selection of satellite imagery (determination of clouds, shadows from clouds, open soil surface, etc.). Direct selection of Landsat scenes suitable for calculations has been made. It allows automating the process of constructing soil degradation maps.

scholarly journals CHANGE DETECTION IN FORESTS AND SAVANNAS USING STATISTICAL ANALYSIS BASED ON GEOGRAPHICAL OBJECTS

2017 ◽  
Vol 23 (2) ◽  
pp. 284-295 ◽  
Author(s):  
Lucilia Rezende Leite ◽  
Luis Marcelo Tavares de Carvalho ◽  
Fortunato Menezes da Silva
Keyword(s):  
Change Detection ◽  
Operating Characteristic ◽  
Visual Detection ◽  
Vegetation Indices ◽  
Descriptive Statistics ◽  
Chi Square ◽  
Segmentation Of Images ◽  
Different Levels ◽  
Reflectance Data

The aim of this work was to assess techniques of land cover change detection in areas of Brazilian Forest and Savanna, using Landsat 5/TM images, and two iterative statistical methodologies based on geographical objects. The sensitivity of the methodologies was assessed in relation to the heterogeneity of the input data, the use of reflectance data and vegetation indices, and the use of different levels of confidence. The periods analyzed were from 2000 to 2006, and from 2006 to 2010. After the segmentation of images, the descriptive statistics average and standard deviation of each object were extracted. The determination of change objects was realized in an iterative way based on the Mahalanobis Distance and the chi-square distribution. The results were validated with an early visual detection and analyzed according to Receiver Operating Characteristic (ROC) Curve. Significant gains were obtained by using vegetation masks and bands 3 and 4 for both areas tested with 94,67% and 95,02% of the objects correctly detected as changes, respectively for the areas of Forest and Savanna. The use of the NDVI and different images were not satisfactory in this study.

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