Using Vegetative Indices to Quantify Agricultural Crop Characteristics

Kokhan, Svitlana; Vostokov, Anatoliy

doi:10.12911/22998993/119808

Cited by 25 publications

(9 citation statements)

References 28 publications

(30 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The test points for the GreenSeeker ground measurements as well as the plant sampling for chemical analysis are the same. The above-mentioned techniques have been also applied in the study of the crop state monitoring [Kokhan and Vostokov 2020].…”

Section: Resultsmentioning

confidence: 99%

Improved Approach to the Development of the Crop Monitoring System Based on the Use of Multi-Source Spatial Data

Kokhan¹,

Dorozhynskyy²,

Burshtynska³

et al. 2020

J. Ecol. Eng.

Self Cite

View full text Add to dashboard Cite

The study describes the stages of conceptual modeling to provide a crop monitoring system based on the multisource spatial data to assess the state of agricultural crops. The process of developing geodatabase models, which is the basis of the crop monitoring system, considered the construction of a set of diagrams of the Unified Modeling Language (UML). The UML Sequence diagrams were developed to describe the specific properties of crop monitoring system components and their behavior. The developed data flow diagram showed the data flow in the crop monitoring system and described the processes involved in the system for the transfer of data from the source files to the geodatabase. The approach presented in the study can be suggested as a methodology that is suitable for a wide range of developers of monitoring systems.

show abstract

Section: Resultsmentioning

confidence: 99%

Improved Approach to the Development of the Crop Monitoring System Based on the Use of Multi-Source Spatial Data

Kokhan¹,

Dorozhynskyy²,

Burshtynska³

et al. 2020

J. Ecol. Eng.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Vegetation Indices are helpful in measuring the plant spectral characteristics based on the reflectance in visible and nearinfrared wavelengths. The amount of green leaf biomass is correlated with the ratio of infra-red to the red band (Kokhan et al, 2020). NDVI and EVI are considered as most common indices used for analysing the vegetation cover.…”

Section: Vegetation Indicesmentioning

confidence: 99%

Impact of Climate on Vegetation Indices Over Rainfed Districts of Uttarakhand, India

Sharma

Ghosh

2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. Due to a change in the landscape, the climate of Uttarakhand state is changing rapidly, impacting the weather, further affecting human beings and vegetation. Nowadays, remote sensing is a favorable tool for monitoring the vegetation condition using NDVI and EVI. Studying the relationship between vegetation and climate more extensively, it is necessary to better understand the anomaly of ecosystems with climate change. This study is carried out to evaluate the vegetation cover dynamics by establishing the association between climate parameters and vegetation indices over the rain-fed districts (Nainital, Bageshwar, Champawat, Dehradun) of Uttarakhand for the period of 20 years. In this study, Google Earth Engine (GEE) is used to extract the MODIS NDVI and EVI at 250 m spatial resolution & 16-day temporal resolution data. The climate parameters for the rain-fed district (study area) are extracted from Indian Meteorological Department (IMD) Pun website for the period from 2000 to 2020. According to the annual vegetation dynamics, the peak attained by both indices is during the monsoon season, and hence they both show identical patterns to each other. Linear Regression Analysis results show a strong impact of climate on vegetation. Both indices shows a positive correlation with climate parameters where minimum temperature and rainfall are strongly correlated with EVI. Thus, the study reveals that EVI is proven to be more appropriate indices for monitoring vegetation cover as compared to NDVI for the study area.

show abstract

“…Commonly, accuracy and Pearson correlation are used to quantify the performance of remote sensing indices [13,14]. However this type of metrics does not take into account either the class ratio nor the shape of the segmentation.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…But none of the presented indices are properly optimized. Thus, in the standard approach, the best index is determined by testing all available indices against the spectral bands of the selected sensor with a Pearson correlation between these indices and a ground truth [13,14]. Furthermore, correlation is not the best estimator because it neither considers the class ratio nor the shape of the obtained segmentation and may again result in a non-optimal solution for a specific segmentation task.…”

Section: Introductionmentioning

confidence: 99%

DeepIndices: Remote Sensing Indices Based on Approximation of Functions through Deep-Learning, Application to Uncalibrated Vegetation Images

et al. 2021

View full text Add to dashboard Cite

The form of a remote sensing index is generally empirically defined, whether by choosing specific reflectance bands, equation forms or its coefficients. These spectral indices are used as preprocessing stage before object detection/classification. But no study seems to search for the best form through function approximation in order to optimize the classification and/or segmentation. The objective of this study is to develop a method to find the optimal index, using a statistical approach by gradient descent on different forms of generic equations. From six wavebands images, five equations have been tested, namely: linear, linear ratio, polynomial, universal function approximator and dense morphological. Few techniques in signal processing and image analysis are also deployed within a deep-learning framework. Performances of standard indices and DeepIndices were evaluated using two metrics, the dice (similar to f1-score) and the mean intersection over union (mIoU) scores. The study focuses on a specific multispectral camera used in near-field acquisition of soil and vegetation surfaces. These DeepIndices are built and compared to 89 common vegetation indices using the same vegetation dataset and metrics. As an illustration the most used index for vegetation, NDVI (Normalized Difference Vegetation Indices) offers a mIoU score of 63.98% whereas our best models gives an analytic solution to reconstruct an index with a mIoU of 82.19%. This difference is significant enough to improve the segmentation and robustness of the index from various external factors, as well as the shape of detected elements.

show abstract

Using Vegetative Indices to Quantify Agricultural Crop Characteristics

Cited by 25 publications

References 28 publications

Improved Approach to the Development of the Crop Monitoring System Based on the Use of Multi-Source Spatial Data

Improved Approach to the Development of the Crop Monitoring System Based on the Use of Multi-Source Spatial Data

Impact of Climate on Vegetation Indices Over Rainfed Districts of Uttarakhand, India

DeepIndices: Remote Sensing Indices Based on Approximation of Functions through Deep-Learning, Application to Uncalibrated Vegetation Images

Contact Info

Product

Resources

About