Infrared Percentage Vegetation Index Research Articles

Use of different vegetation indices for the evaluation of the kinetics of the cherry tomato (Solanum lycopersicum var. cerasiforme) growth based on multispectral images by UAV

Abstract This study evaluated seven vegetation indices for the monitoring of a cherry tomato crop using an unmanned aerial vehicle with a multispectral camera that measures in the green, red, and near-infrared spectral bands. A photogrammetric flight plan was designed to capture the spectral images every 2 weeks in two agricultural parcels identified as Treatment 1 ( T 1 {T}_{1} ) and Treatment 2 ( T 2 {T}_{2} ). The corresponding orthophotographs were obtained using digital photogrammetry techniques. Subsequently, vegetation indices were calculated for these orthophotographs. The mean and standard deviation of these indices were extracted, and a statistical analysis was performed to compare the vegetation indices and to analyze their behavior over time. Analysis of variance showed that the ratio vegetation index (RVI), green vegetation index (GVI), normalized difference vegetation index (NDVI), infrared percentage vegetation index (IPVI), green normalized difference vegetation index (GNDVI), and optimized soil-adjusted vegetation index (OSAVI) indices showed significant variation (P-value <0.05) over time. No statistically significant differences between the two treatments were found. IPVI, NDVI, and OSAVI showed less variation in pixel values. RVI, GVI, NDVI, IPVI, GNDVI, and OSAVI proved to be valuable tools for monitoring field crops since these indices responded to the crop growth kinetics.

Pre-Harvest Corn Grain Moisture Estimation Using Aerial Multispectral Imagery and Machine Learning Techniques

Corn grain moisture (CGM) is critical to estimate grain maturity status and schedule harvest. Traditional methods for determining CGM range from manual scouting, destructive laboratory analyses, and weather-based dry down estimates. Such methods are either time consuming, expensive, spatially inaccurate, or subjective, therefore they are prone to errors or limitations. Realizing that precision harvest management could be critical for extracting the maximum crop value, this study evaluates the estimation of CGM at a pre-harvest stage using high-resolution (1.3 cm/pixel) multispectral imagery and machine learning techniques. Aerial imagery data were collected in the 2022 cropping season over 116 experimental corn planted plots. A total of 24 vegetation indices (VIs) were derived from imagery data along with reflectance (REF) information in the blue, green, red, red-edge, and near-infrared imaging spectrum that was initially evaluated for inter-correlations as well as subject to principal component analysis (PCA). VIs including the Green Normalized Difference Index (GNDVI), Green Chlorophyll Index (GCI), Infrared Percentage Vegetation Index (IPVI), Simple Ratio Index (SR), Normalized Difference Red-Edge Index (NDRE), and Visible Atmospherically Resistant Index (VARI) had the highest correlations with CGM (r: 0.68–0.80). Next, two state-of-the-art statistical and four machine learning (ML) models (Stepwise Linear Regression (SLR), Partial Least Squares Regression (PLSR), Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), and K-nearest neighbor (KNN)), and their 120 derivates (six ML models × two input groups (REFs and REFs+VIs) × 10 train–test data split ratios (starting 50:50)) were formulated and evaluated for CGM estimation. The CGM estimation accuracy was impacted by the ML model and train-test data split ratio. However, the impact was not significant for the input groups. For validation over the train and entire dataset, RF performed the best at a 95:5 split ratio, and REFs+VIs as the input variables (rtrain: 0.97, rRMSEtrain: 1.17%, rentire: 0.95, rRMSEentire: 1.37%). However, when validated for the test dataset, an increase in the train–test split ratio decreased the performances of the other ML models where SVM performed the best at a 50:50 split ratio (r = 0.70, rRMSE = 2.58%) and with REFs+VIs as the input variables. The 95:5 train–test ratio showed the best performance across all the models, which may be a suitable ratio for relatively smaller or medium-sized datasets. RF was identified to be the most stable and consistent ML model (r: 0.95, rRMSE: 1.37%). Findings in the study indicate that the integration of aerial remote sensing and ML-based data-run techniques could be useful for reliably predicting CGM at the pre-harvest stage, and developing precision corn harvest scheduling and management strategies for the growers.

Computing Vegetation Indices from the Satellite Images Using GRASS GIS Scripts for Monitoring Mangrove Forests in the Coastal Landscapes of Niger Delta, Nigeria

This paper addresses the issue of the satellite image processing using GRASS GIS in the mangrove forests of the Niger River Delta, southern Nigeria. The estuary of the Niger River Delta in the Gulf of Guinea is an essential hotspot of biodiversity on the western coast of Africa. At the same time, climate issues and anthropogenic factors affect vulnerable coastal ecosystems and result in the rapid decline of mangrove habitats. This motivates monitoring of the vegetation patterns using advanced cartographic methods and data analysis. As a response to this need, this study aimed to calculate and map several vegetation indices (VI) using scripts as advanced programming methods integrated in geospatial studies. The data include four Landsat 8-9 OLI/TIRS images covering the western segment of the Niger River Delta in the Bight of Benin for 2013, 2015, 2021, and 2022. The techniques included the ’i.vi’, ’i.landsat.toar’ and other modules of the GRASS GIS. Based on the GRASS GIS ’i.vi’ module, ten VI were computed and mapped for the western segment of the Niger River Delta estuary: Atmospherically Resistant Vegetation Index (ARVI), Green Atmospherically Resistant Vegetation Index (GARI), Green Vegetation Index (GVI), Difference Vegetation Index (DVI), Perpendicular Vegetation Index (PVI), Global Environmental Monitoring Index (GEMI), Normalized Difference Water Index (NDWI), Second Modified Soil Adjusted Vegetation Index (MSAVI2), Infrared Percentage Vegetation Index (IPVI), and Enhanced Vegetation Index (EVI). The results showed variations in the vegetation patterns in mangrove habitats situated in the Niger River Delta over the last decade as well as the increase in urban areas (Onitsha, Sapele, Warri and Benin City) and settlements in the Delta State due to urbanization. The advanced techniques of the GRASS GIS of satellite image processing and analysis enabled us to identify and visualize changes in vegetation patterns. The technical excellence of the GRASS GIS in image processing and analysis was demonstrated in the scripts used in this study.

Paddy Yield Estimation Models Using Satellite Data: A Case Study in Kamburupitiya DS Division

In Sri Lanka, the crop cutting survey method is used to estimate the average paddy yield during the harvesting period, and it has failed to forecast the yield prior to harvesting. This method is time-consuming, expensive and make various errors. The yield prediction is worth to estimate yield before harvesting thousand tonnes of paddy. Satellite Remote Sensing is used to forecast crop yield using images during vegetative growth. The research was conducted in Kamburupitiya Divisional Secretariat (DS) division, Matara District, Sri Lanka, during <em>Yala </em>and <em>Maha </em>paddy growing seasons. Landsat 8 OLI/TIRS images with 30 m resolution were utilized to develop the paddy yield models using paddy yield records during 2016 to 2020. The simple linear regression models were developed using recorded average paddy yield data of the Kamburupitiya DS division and the average values of respective vegetation indices (Normalized Difference Vegetation Index (NDVI); Green Vegetation Index (GVI); Difference Vegetation Index (DVI); Infrared Percentage Vegetation Index (IPVI) and Ratio Vegetation Index (RVI) and reflectance values of three spectral bands (Green (G), Red (R) and near-infrared (NIR)). The validation process was carried out using Mean Absolute Percentage Error (MAPE) as the statistical indicator between modeled and predicted yield. When considering coefficient of determination (R₂) value, MAPE percentage, Durbin Watson statistics values, and Pearson correlation values, the yield model based on the GVI for <em>Maha</em> season and NDVI based model for <em>Yala </em>season are selected as the best models for paddy yield prediction using Landsat 8 OLI/TIRS imagery. The models will allow authorities to make effective decisions in paddy supply management.

APPLICATION OF VEGETATION INDEXES TO ASSESS CARBON STOCK

This was objectively conducted to estimate the carbon stock, to show the relation between carbon stock and indices and to assess the factors affecting carbon stock in community forests. Three community forests (CFs) namely Gumalchowki, Mahakalsthan and Mahalaxmi of Chandragiri Municipality in Nepal were selected as research sites. Altogether 135 plots were randomly established to collect data from the field. The biomass was calculated using Chave et al., equation which was converted into carbon stock multiplying by default value 0.47. The values of Normalized Difference Vegetation Index (NDVI), Difference Vegetation Index (DVI) and Infrared Percentage Vegetation Index (IPVI) were calculated and regression equation between the indices and carbon stock was performed. The result showed that total above ground carbon stock was highest in Mahalaxmi CF with 30.42 ton/ha, followed by Mahakalsthan CF with 22.62 ton/ha and comparatively lowest with 21.55 ton/ha in Gumalchowki CF. The regression analysis between carbon stock and indices showed significantly and positive correlation. The R2 value of NDVI of Gumalchowki, Mahakalsthan and Mahalaxmi CF were found to be 0.51, 0.54 and 0.58, also, RMSE value of CFs were 1.41, 1.36 and 1.91 respectively. Principal component analysis showed that road construction, transmission line expansion, soil erosion, encroachment, disease, weeds, recreation, illegal logging are the major factors affecting carbon stock in all three community forests. Keywords: Carbon, Nepal, Community Forests.

Examining the Integration of Landsat Operational Land Imager with Sentinel-1 and Vegetation Indices in Mapping Southern Yellow Pines (Loblolly, Shortleaf, and Virginia Pines)

The mapping of southern yellow pines (loblolly, shortleaf, and Virginia pines) is important to supporting forest inventory and the management of forest resources. The overall aim of this study was to examine the integration of Landsat Operational Land Imager (OLI ) optical data with Sentinel-1 microwave C-band satellite data and vegetation indices in mapping the canopy cover of southern yellow pines. Specifically, this study assessed the overall mapping accuracies of the canopy cover classification of southern yellow pines derived using four data-integration scenarios: Landsat OLI alone; Landsat OLI and Sentinel-1; Landsat OLI with vegetation indices derived from satellite data—normalized difference vegetation index, soil-adjusted vegetation index, modified soil-adjusted vegetation index, transformed soil-adjusted vegetation index, and infrared percentage vegetation index; and 4) Landsat OLI with Sentinel-1 and vegetation indices. The results showed that the integration of Landsat OLI reflectance bands with Sentinel-1 backscattering coefficients and vegetation indices yielded the best overall classification accuracy, about 77%, and standalone Landsat OLI the weakest accuracy, approximately 67%. The findings in this study demonstrate that the addition of backscattering coefficients from Sentinel-1 and vegetation indices positively contributed to the mapping of southern yellow pines.

Modelling the Behavior of DVI and IPVI Vegetation Indices Using Multi-Temporal Remotely Sensed Data

Remote sensing techniques are widely used to detect and analyze land cover changes due to their accuracy and cost-effectiveness. Among the various spectral indices derived from the satellite data Difference Vegetation Index (DVI) and Infrared Percentage Vegetation Index (IPVI) vegetation indices applied to model the behavior of the indices in the study of suburb ecosystem vegetation cover over twenty years. To achieve the aim of the study two objectives were formulated; detect Spatial-temporal variations in urban vegetation and how suitable the selected algorithms to study urban ecosystem vegetation. The study area is a rapidly developing area consists of several suburbs including Battaramulla, Malabe, and Kaduwela, Sri Lanka. The study used Landsat data and pre-processing, processing, geometric and atmospheric corrections were performed using ERDAS imagine mapping software and all the mappings were carried out via Arc GIS software. The results show Infrared Percentage Vegetation Index (IPVI) algorithm as the most suitable vegetation index to study suburb ecosystem vegetation than Difference Vegetation Index (DVI) in the study area. Therefore, the study recommends IPVI than DVI to study ecosystem vegetation in sub-urban areas.

Аналіз характеристик та обґрунтування індексів рослинності

In this article there has been given the analyzing of non-fiction sources in order to find out the popular indexes of the vegetable control parameters. There are such parameters as chlorophyll presence, water containing, the efficiency of a light use, nitrogen and carbon presence, foliage pigmentation.&nbsp; The chlorophyll presence control is conducted by such indexes as the difference vegetation index, the global environmental monitoring index, the infrared percentage vegetation index, the modified simple ratio, the modified Soil adjusted vegetation index 2, the non-linear index, the normalized difference vegetation index, the optimized soil adjusted vegetation index, the renormalized difference vegetation index, the soil adjusted vegetation index, the simple ratio etc. The control of water content can be based on the moisture stress index, the normalized difference infrared index, the normalized difference water index, the normalized multi-band drought index, the water band index. There is the photochemical reflectance index, the structure insensitive pigment index, the red green ratio index for the efficiency control of the light use. The nitrogen presence is controlled by the normalized differential nitrogen index. The carbon presence can be detected by the implementing of cellulose absorption index, the lignin cellulose absorption index, the normalized difference lignin index, the plant senescence reflectance index. The foliage pigmentations can be controlled by the means of the anthocyanin reflectance index 1, the anthocyanin reflectance index 2, the carotenoid reflectance index 1, and the carotenoid reflectance index 2. The specificity of the presented indexes is described; the calculation formulas and its main straits are proposed.

Mapping of the Canopy Openings in Mixed Beech–Fir Forest at Sentinel-2 Subpixel Level Using UAV and Machine Learning Approach

The presented study demonstrates a bi-sensor approach suitable for rapid and precise up-to-date mapping of forest canopy gaps for the larger spatial extent. The approach makes use of Unmanned Aerial Vehicle (UAV) red, green and blue (RGB) images on smaller areas for highly precise forest canopy mask creation. Sentinel-2 was used as a scaling platform for transferring information from the UAV to a wider spatial extent. Various approaches to an improvement in the predictive performance were examined: (I) the highest R2 of the single satellite index was 0.57, (II) the highest R2 using multiple features obtained from the single-date, S-2 image was 0.624, and (III) the highest R2 on the multitemporal set of S-2 images was 0.697. Satellite indices such as Atmospherically Resistant Vegetation Index (ARVI), Infrared Percentage Vegetation Index (IPVI), Normalized Difference Index (NDI45), Pigment-Specific Simple Ratio Index (PSSRa), Modified Chlorophyll Absorption Ratio Index (MCARI), Color Index (CI), Redness Index (RI), and Normalized Difference Turbidity Index (NDTI) were the dominant predictors in most of the Machine Learning (ML) algorithms. The more complex ML algorithms such as the Support Vector Machines (SVM), Random Forest (RF), Stochastic Gradient Boosting (GBM), Extreme Gradient Boosting (XGBoost), and Catboost that provided the best performance on the training set exhibited weaker generalization capabilities. Therefore, a simpler and more robust Elastic Net (ENET) algorithm was chosen for the final map creation.

Within-Field Relationships between Satellite-Derived Vegetation Indices, Grain Yield and Spike Number of Winter Wheat and Triticale

The aims of this study were to: (i) evaluate the relationships between vegetation indices (VIs) derived from Sentinel-2 imagery and grain yield (GY) and the number of spikes per square meter (SN) of winter wheat and triticale; (ii) determine the dates and plant growth stages when the above relationships were the strongest at individual field scale, thus allowing for accurate yield prediction. Observations of GY and SN were performed at harvest on six fields (three locations in two seasons: 2017 and 2018) in three regions of Poland, i.e., northeastern (A—Brożówka), central (B—Zdziechów) and southeastern Poland (C—Kryłów). Vegetation indices (Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), modified SAVI (mSAVI), modified SAVI 2 (mSAVI2), Infrared Percentage Vegetation Index (IPVI), Global Environmental Monitoring Index (GEMI), and Ratio Vegetation Index (RVI)) calculated for sampling points from mid-March until mid-July, covering within-field soil and topographical variability, were included in the analysis. Depending on the location, the highest correlation coefficients (of about 0.6–0.9) for most of VIs with GY and SN were obtained about 4–6 weeks before harvest (from the beginning of shooting to milk maturity). Therefore, satellite-derived VIs are useful for the prediction of within-field cereal GY as well as SN variability. Information on GY, predicted together with the results for soil nutrient availability, is the basis for the formulation of variable fertilize rates in precision agriculture. All examined VIs were similarly correlated with GY and SN via the commonly used NDVI. The increase in NDVI by 0.1 unit was related to an average increase in GY by about 2 t ha−1.

Identifying tree health using sentinel-2 images: a case study on Tortrix viridana L. infected oak trees in Western Iran

Forest land has a vital role in our planet ecosystem health. Forest areas are under natural and human pressure worldwide. Pests may have irreparable damages to vegetation cover; Tortrix viridana is one of the most important pests in the western forests of Iran and is mainly hosted by oak trees. In this study the performance of Sentinel-2 images to detect infected oaks by T. viridana in the Zagros forest habitat was considered. Vegetation indices (VIs) were extracted from affected and non-affected areas by T. viridana. The indices indices included normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), infrared percentage vegetation index (IPVI) and inverted red-edge chlorophyll index (IRECI) which were extracted from Sentinel-2 satellite images. The results of the present study show that VIs in affected and non-affected areas of the study site have significant differences at 99% of confidence level. In addition, the Spearman’s correlation coefficients between the VIs values in the affected and non-affected were 0.213, 0.213, 0.168 and 0.121 for IPVI, NDVI, IRECI and SAVI, respectively. This shows that Sentinel-2 images can be used to detect pests in forest areas.

Above-ground carbon stock estimation using pleiades satellite imagery of the secondary forest ecosystem in Ibadan, Nigeria

Secondary forest ecosystem contributes to global climate change mitigation through carbon sequestration. Above-Ground Biomass (AGB) is the major component for monitoring and estimating Carbon Stocks (CS) and fluxes in tropical forests. However, information on Above-Ground Carbon Stock (AGCS) for the International Institute of Tropical Agriculture (IITA), which hosts relics of the undisturbed secondary forest ecosystem in south-western Nigeria, has not been documented. Therefore, AGCS of the secondary forest ecosystem was estimated using remote sensing techniques. Pleiades satellite data were used for this study. One hundred and forty plots of 50m x 50m were laid in IITA secondary forest using systematic sampling technique at 10% sampling intensity. Pleiades satellite imagery was acquired using Remote Sensing (RS) technique and spectral data for each sample plot extracted. The spectral indices used for AGB estimation were: Normalised Difference Vegetation Index (NDVI), Difference Vegetation Index (DVI), Infrared Percentage Vegetation Index (IPVI), Optimised Soil Adjusted Vegetation Index (OSAVI) and Re-normalised Difference Vegetation Index (RDVI). Regression equation was used for the prediction of AGB from where the total CS estimate was obtained. Data were analysed using descriptive statistics and linear regression analysis. The AGB and CS ranged from 101.06 to 881,834.92 kg/ha and 50.53 to 440,917.46 kg/ha, respectively. The DVI had the highest AGB value which ranged from 187 to 15,577 kg/ha, followed by IPVI, RDVI and OSAVI which ranged from 7,561 to 12,324 kg/ha, 64.0591 to 133.178 kg/ha, 0.0134 to 0.5621 kg/ha, respectively, while NDVI had the least values which ranged from -0.01 to 0.48 kg/ha. The best AGB estimation model was AGB = exp. (3,496.61 + 0.99 x (RDVI) 1/2); Coefficient of Determination (R2) = 0.93, Bayesian Information Criterion (BIC) = 82.34). The total carbon stock ranged from 11,035 to 18,774 kg/ha. Model with re-normalized difference vegetation index was most suitable among other indices for estimating above-ground carbon stock. Therefore, effective integration of different sensor data will be an important research topic for improving above-ground biomass estimation performance.

Irrigated pinto bean crop stress and yield assessment using ground based low altitude remote sensing technology

The pinto bean is one of widely consumed legume crop that constitutes over 42% of the U.S dry bean production. However, limited studies have been conducted in past to assess its quantitative and qualitative yield potentials. Emerging remote sensing technologies can help in such assessment. Therefore, this study evaluates the role of ground-based multispectral imagery derived vegetation indices (VIs) for irrigated the pinto bean stress and yield assessments. Studied were eight cultivars of the pinto bean grown under conventional and strip tillage treatments and irrigated at 52% and 100% of required evapotranspiration. Imagery data was acquired using a five-band multispectral imager at early, mid and late growth stages. Commonly used 25 broadband VIs were derived to capture crop stress traits and yield potential. Principal component analysis and Spearman’s rank correlation tests were conducted to identify key VIs and their correlation (rs) with abiotic stress at each growth stage. Transformed difference vegetation index, nonlinear vegetation index (NLI), modified NLI and infrared percentage vegetation index (IPVI) were consistent in accounting the stress response and crop yield at all growth stages (rs > 0.60, coefficient of determination (R2): 0.50–0.56, P < 0.05). Ten other VIs significantly accounted for crop stress at early and late stages. Overall, identified key VIs may be helpful to growers for precise crop management decision making and breeders for crop stress response and yield assessments.

Estimating oak forest parameters in the western mountains of Iran using satellite-based vegetation indices

Remote sensing is an important tool for studying and modeling forest stands. Vegetation indices obtained from Landsat-8 remotely sensed data were used to estimate the forest parameters in the western mountains of Iran. Thirty-four sample points were selected on the map of Bayangan County, Kermanshah province, Iran. At each point, a cluster of five rectangular plots of 8100 m2 and 200 m apart was established. Some clusters were primarily sampled to determine the variance of the forest parameters. The coefficient of variation was used as a criterion for sample allocation. Stand density, canopy cover and basal area per hectare were calculated for each plot. Vegetation indices were extracted from the Landsat-8 images for each plot. Simple linear and nonlinear regressions were conducted to develop the models. The models were validated using an independent data set. Stability of model parameters was statistically evaluated. The results showed that Normalized difference vegetation index (NDVI) and Transformed normalized difference vegetation index (TNDVI) followed by Simple ratio vegetation index (SRVI) were the best predictors, explaining up to 91% of the variations with high precision. For NDVI, Soil adjusted vegetation index 2 (SAVI2) and SRVI, the cubic model was more appropriate than the linear model for predicting the forest parameters. For this model, the R-square value increased while NRMSE decreased significantly. For Infrared percentage vegetation index (IPVI), the quadratic model was better, but, for other vegetation indices, nonlinear models were not superior to linear ones. Finally, it can be concluded that Landsat-8 imagery is suitable for predicting forest parameters in the oak forests of western Iran. Of course, large plots must be selected, and pre-classification is necessary to gain accurate and precise estimations.

Automated extraction of various vegetative and water body indices from multisensor satellite data: a MATLAB approach

The increasing availability of remote sensing imageries with different spatial, spectral, temporal and radiometric characteristics expands the horizon of our choices in imageries and vegetation mapping. Among the various techniques in vegetation mapping the use of vegetative indices confiscates the simplest and easiest one to understand and calculate. This work proposes automatic extraction of different vegetation and water indices from ResourceSat-1(IRS-P6) images. The concept paves to calculate indices for any ResourceSat image prearranged to the program which is written in MATLAB 2013a. The effortlessly accessible images from its onboard sensors as Medium Resolution Linear Imaging Self-Scanner (LISS-III) and Advanced Wide Field Sensor (AWiFS) were considered to calculate six major indices like Normalized Difference Vegetation Index, Infrared Percentage Vegetation Index, Ratio Vegetation Index, Green Normalized Difference Vegetation Index, Green Red Vegetation Index, and Normalized Difference Water Index automatically. The program was written for any ResourceSat (LISS-III and AWiFS) data so that further analysis as well as calculation of more indices can be done by any individual within MATLAB. The user-friendly and time preserving feature makes MATLAB more powerful that any common user can deal with the analysis without the presence of any GIS experts that other softwares do.

INCREASING ACCURACY VALUE IN THE ESTIMATES OF CARBON STOCK BY USING VEGETATION INDEX FROM ALOS AVNIR 2 SATELLITE IMAGERY

The existence of carbon stock began to be noticed by the public, especially after the global warming phenomenon, because one of the causes of global warming is the increasing amount of carbon in the atmosphere. There are several approaches that can be used to calculate carbon stocks, one of which is through remote sensing. In the study of carbon stocks in Meru Betiri National Park Indonesia, the vegetation index from ALOS-AVNIR satellite imagery is used to estimate carbon reserves by finding an exact equation. If it uses the Modified Soil Adjusted Vegetation Index (MSAVI) only, the correlation value is 0.49. Meanwhile, if Infrared Percentage Vegetation Index (IPVI) is used, the correlation value is 0.47. However, if some vegetation indices such as Soil-Adjusted Vegetation Index (SAVI), Normalize Difference Vegetation Index (NDVI) and Ratio Vegetation Index (RVI) are combined, the correlation value of the equation is 0.63. The comparison showed that by combining several variables of vegetation indices will increase the value of the correlation equation significantly.

Modeling the behavior of Vegetation Indices in the salt dome of Korsia in North-East of Darab, Fars, Iran

The use of remote sensing for rapid and accurate evaluation of phenomena, specially land covers is very important. In this study, for modeling and estimated of salt dome was used visible atmospherically resistant index (VARI), difference vegetation index (DVI), enhanced vegetation index (EVI), green difference vegetation index (GDVI), normalized difference vegetation index (NDVI), optimized soil adjusted vegetation index (OSAVI), soil adjusted vegetation index (SAVI), infrared percentage vegetation index (IPVI) by Landsat 8 ETM+ bands vegetation in the salt dome of Korsia of Darab plain, Iran in 2015 years. By software ENVI preprocessing, processing, geometric and atmospheric corrections were performed, and then vegetation index for the study area was calculated. Also ArcGIS 10.2 software for mapping of area vegetation was applied. Then relationship between Vegetation Indices and salt dome of Korsia were determined. The results show that the value of indices were high in the agriculture field for VARI, DVI, GDVI and IPVI and low value for other indices. Also the results show that cannot used IPVI and OSAVI for determination of soil salinity. DVI and SAVI close to 0.1 and 0.2 is represent soil salinity respectively. Also soil salinity have EVI and GDVI close to 0.14. Finally VARI and NDVI have value of lower that 0 in soil salinity. The comparison of vegetation indices show that change values were same for SAVI, EVI, GDVI and DVI in salt dome. Finally the results show that EVI, GDVI, OSAVI and SAVI are suitable for prediction and modeling of salt dome in the study area.

Spectral wheat yield prediction modeling using SPOT satellite imagery and leaf area index

As wheat represents the main staple food and strategic crop in Egypt and worldwide and since remote sensing satellite imagery is the tool to obtain synoptic, multi-temporal, dynamic, and time-efficient information about any target on the Earth, the main objective of the current study is to use remote sensing satellite imagery to generate remotely sensed empirical preharvest wheat yield prediction models. The main input parameters of these models are spectral data either in the form of spectral reflectance data released from Satellite Pour l’Observation de la Terre (SPOT) 4 satellite imagery or in the form of spectral vegetation indices. The other input factor is leaf area index (LAI) that was measured by LAI Plant Canopy Analyzer. The four spectral bands of SPOT4 imagery are green, red, near-infrared, and middle infrared; the five vegetation indices that are forms of ratios between red and near-infrared bands are normalized difference vegetation index, ratio vegetation index, soil-adjusted vegetation index, difference vegetation index, and infrared percentage vegetation index. Another vegetation index is green vegetation index that is calculated through a ratio between green band and near-infrared band. Each of the above-mentioned factors was used as an input factor against wheat yield to generate wheat yield prediction models. All generated models are site-specific limited to the area and the environment and could be applicable under similar conditions in Egypt. The study was carried out in Sakha experimental station by using the dataset from two wheat season 2007/2008 and 2009/2010. The total wheat area was 1.3 ha cultivated by Sakha 93 cultivar. Modeling and validation process were carried out for each season independently. Modeled yield was tested against reported yield through two common statistical tests; the standard error of estimate between modeled yield and reported yield, and the correlation coefficient for a direct regression analysis between modeled and reported yield with each generated model. Generally, as shown from the correlation coefficient of the generated models, green and middle infrared bands did not show good accuracy to predict wheat yield, while the other spectral bands (red and near-infrared) bands showed high accuracy and sufficiency to predict yield. This was proven through the correlation coefficient of the generated models and through the generated models with the wheat crops for the two seasons. Accordingly, the green vegetation index that is generally calculated from green and near-infrared bands showed relatively lower accuracy than the rest of the vegetation index models that are calculated from red and near-infrared bands. LAI showed high accuracy to predict yield as shown from the statistical analysis. The models are applicable after 90 days from sowing stage and applicable in similar regions with the same conditions.

COMPARISON OF ELEVEN VEGETATION INDICES FOR ESTIMATING PLANT HEIGHT OF ALFALFA AND GRASS

A great variety of vegetation indices, derived from remote sensing measurements, are commonly used to characterize the growth pattern of cropped surfaces. In this study, multispectral canopy reflectance data were obtained from grass (Festuca arundinacea) and alfalfa (Medicago sativa L.) at Kimberly, Idaho, with the purpose of comparing the performance of 11 vegetation indices for estimating plant height of these two structurally different crop canopies. An additional purpose was to develop quantitative relationships between plant height and the different vegetation indices, which could be used to estimate plant height from remote sensing inputs. For alfalfa, good logistic growth relationships between plant height and all the different vegetation indices were found. The relationship resulted in r2 > 0.90 for all the vegetation indices, and r2 > 0.97 for most of them. While all the vegetation indices were very sensitive to changes in plant height at the beginning of the growing cycle, the Normalized Difference Vegetation Index (NDVI), the Infrared Percentage Vegetation Index (IPVI), and the Transformed Vegetation Index (TVI) became insensitive to additional plant growth when alfalfa reached heights of 0.45, 0.40, and 0.45 m, respectively. All the other vegetation indices performed reasonably well for the entire range of alfalfa plant heights considered in this study (< 0.75 m). For grass, on the other hand, only 4 of the 11 vegetation indices, including the Band Ratio (RATIO), TVI, NDVI, and IPVI, resulted in a reasonably good linear relationship with plant height (r² ~ 0.76).

Calculating the vegetation index faster

The near-infrared (NIR) versus red “infrared percentage vegetation index,” NIR/( NIR + Red), is functionally and linearly equivalent to the normalized difference vegetation index, ( NIR- Red/( NIR + Red). Advantageously, it is both computationally faster and never negative.