Maedeh Behifar; Hossein Aghighi; Aliakbar Matkan; Hamid Salehi shahrabi
Abstract
Leaf area index (LAI) derived from remotely sensed images is considered as an important index for spatial modelling of vegetation productivity. Traditionally, the spectral vegetation indices (VIs) derived from the red (R) and near infrared (NIR) reflectance values have been utilized to statistically ...
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Leaf area index (LAI) derived from remotely sensed images is considered as an important index for spatial modelling of vegetation productivity. Traditionally, the spectral vegetation indices (VIs) derived from the red (R) and near infrared (NIR) reflectance values have been utilized to statistically estimate LAI. However, most of these VIs saturate at some level of LAI. This limitation was over-come by using the reflectance spectra in the red-edge region. Therefore, it is necessary to evaluate the capability of different VIs derived from RS data to estimate the LAI of silage maize. For this purpose, five field sampling campaigns which were near-simultaneous with Sentinel II over-passes were conducted by the Space Research Center, Iranian Space Research Center and totally 234 samples were collected from the silage maize fields, in Magsal, Qazvin. Then, 13 VIs from the time series of Sentinel-2 imagery were computed and employed to statistically estimate the LAI values. The results showed that Enhanced vegetation index (EVI) with outperformed other VIs to estimate LAI of silage maize. Moreover, the values of non-linear regression models were higher that the liner ones.
Mohammad Tavosi; Mehdi Vafakhah; Vahdi Moosavi
Abstract
Due to the importance of meteorological data and limitations of data gathering from ground stations, remote sensing can play an important role in the preparation of these data. The purpose of this study was to quantitatively evaluate the Land Surface Temperature (LST) obtained from Moderate Resolution ...
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Due to the importance of meteorological data and limitations of data gathering from ground stations, remote sensing can play an important role in the preparation of these data. The purpose of this study was to quantitatively evaluate the Land Surface Temperature (LST) obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor images for estimating the maximum and minimum daily air temperature in the Taleghan watershed. For this purpose, the maximum and minimum daily air temperature data of three existing ground stations for the period 2009 to 2015 were obtained. Day and night LST and Normalized Difference Vegetation Index (NDVI) values of MODIS were also prepared. The relationships between each of the effective variables and maximum and minimum daily air temperature in ground stations have been extracted using multiple linear regression method. The results showed that there was a significant correlation between maximum and minimum daily temperature of ground stations with day and night LST and NDVI from MODIS sensor. Therefore, these variables were used in regression relationships. The results of validation showed that the established relationships with all effective variables had the most accuracy. Therefore, the best model for estimating the maximum daily air temperature had , NSE and RMSE values of 0.74, 0.74, and +4.7, respectively and for estimating the minimum daily air temperature had 0.71, 0.72 and +2.9, respectively. Therefore, by converting the surface temperature obtained from MODIS sensor images, the air temperature can be estimated with high accuracy on a daily and monthly scales for various studies.
Mohammadreza Negahdarsaber; Shohreh Didari; Mojtaba Pakparvar; Alireza Abbasi
Abstract
Iranian oak has been affected by oak canopy level dieback in recent years. This phenomenon has led to damage a vast part of the oak forests in the Zagros arena. As to the suitable temporal and spacial resolution of the recent satellite images, it seems promising to detect the forest dieback by remote ...
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Iranian oak has been affected by oak canopy level dieback in recent years. This phenomenon has led to damage a vast part of the oak forests in the Zagros arena. As to the suitable temporal and spacial resolution of the recent satellite images, it seems promising to detect the forest dieback by remote sensing. The spatial capabilities of Spot6 images with pan and spectral resolution of 1.5 and 6 m, respectively in detecting the drying of oak trees was investigated. The forest area was located on Kuhmareh district of Shiraz in Fars province. The values of different indices such NDVI, EVI, TDVI, SAVI, RNDVI, OSAVI, DVI, MSR of each tree stocks was obtained and the corresponding quantity of dryness was determined at the filed. The best correlation was obtained between TDVI and the observed data. A non-linear function was built based on TDVI standard deviation to predict the dryness of more than 30% as y=17.92(x-0.06)-0.32 with an R2 = 82%. Monitoring forest areas to understand the decline or recovery of trees will be of great help to the forest management community. Therefore, using the results of this study can be a proof to compare the current situation with future periods.
Amir Hossen Nazemi; Hamed Sabzchi; Aliashrafi Sadraddini; Abolfazl Majnooni Haris
Abstract
Application of the remote sensing methods in crop area mapping on a large spatiotemporal scale serves is as an alternative to costly time-consuming field data gathering methods. So far, some methods have been developed for wheat and rice area mapping using the images from optical and radar sensors. Some ...
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Application of the remote sensing methods in crop area mapping on a large spatiotemporal scale serves is as an alternative to costly time-consuming field data gathering methods. So far, some methods have been developed for wheat and rice area mapping using the images from optical and radar sensors. Some of these methods are appropriate for humid climates with several cloudy days, while others use complex processes in terms of combining both optics and radar images. Meanwhile, methods based on the unique variation of the vegetation index time series belongs to each crop are relatively simple methods that can be used for crop area mapping. The objective of this study is to improve one of the proposed methods for rain-fed wheat area mapping, in which a step-by-step elimination algorithm of non-wheat pixels was applied to MODIS images. The Improved algorithm took advantage of both MODIS and Landsat Images in terms of their high temporal and high spatial resolutions, respectively. The mentioned process could detect rain-fed wheat areas from the pastures and heterogeneous areas with higher accuracy in comparison with the previous algorithm. The overall accuracy, Kapa index, and F1 score for the final rain-fed wheat map was 92.5%, 0.67, and 0.71 respectively.
S.A.R Nouredini; A.A Bonyad
Volume 9, Issue 1 , October 2017, , Pages 93-110
Abstract
Reflectance of different of land surface phenomena on remote sensing data was influenced by different conditions including atmospheric conditions. Variety methods of atmospheric correction have been developed for remove and reduction of its effects. In this study three atmospheric correction methods: ...
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Reflectance of different of land surface phenomena on remote sensing data was influenced by different conditions including atmospheric conditions. Variety methods of atmospheric correction have been developed for remove and reduction of its effects. In this study three atmospheric correction methods: Dark Object Subtraction (DOS), Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubus (FLLASH) and Second Vector Simulation of Satellite Signal in the Solar Spectrum (6SV) have been applied on OLI sensor of Landsat8 inthe forest regions of Guilan province. Numbers of 10 vegetation indices were extracted from each image. Forest area was extracted on various indices detected by global land cover layer. Forest areas segmented on Landsat8 image by object-based method. In the total 91 segments, randomly were selected. Forest canopy density of any segment plot estimated on Google images using 20×20 m network dotted. Person test was used for correlation between indices and training samples and two linear and nonlinear regression models were used for forest canopy density estimation. The results confirmed that 6SV method dominates than other methods in the forest regions of Guilan province. The lowest root means square error (RMSE) with 17.72 was shown in the green atmospherically resistant vegetation index (GARI) extracted from DOS. The results indicated that the lowest RMSE was in atmospherically resistant vegetation index (ARVI) using 6SV, FLAASH and OLI original image with 18.38, 15.87 and 21.78 respectively. The results of this study were shown that use of atmospheric correction methods in preparing vegetation indices is cause of increasing information accuracy from satellite images. Reduction of atmosphere effects in preprocessing before modeling is necessary and suggestible.
A.A Abkar
Volume 7, Issue 2 , November 2015, , Pages 69-88
Abstract
Investigation of various types of vegetation’s characteristics as an effective parameter in the energy exchange between the atmosphere and Earth's surface is very important in environmental, natural resources and agriculture studies. Nowadays, using remote sensing techniques with a wide range of valuable ...
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Investigation of various types of vegetation’s characteristics as an effective parameter in the energy exchange between the atmosphere and Earth's surface is very important in environmental, natural resources and agriculture studies. Nowadays, using remote sensing techniques with a wide range of valuable spectral information facilitate the study of vegetation, especially in estimation of the biophysical parameters. One of the most important biophysical parameters used in the various analyses related to the study of vegetation is Leaf Area Index (LAI). In this study, in addition to the analyzing and modeling of the relationship between LAI and vegetation indices (VIs)via spectrometry observations, the limitations of the mathematical model for estimation of LAI has been explored, some practical guidelines have been provided to improve the accuracy of the model as well as a new vegetation index has been designed. Finally, the results showed that through the conventional vegetation index, Simple Ratio (SR) and Second Soil Adjusted Vegetation Index (SAVI-2) have the minimum RMSE (about 0.08 in LAI unit) and the fitted models using their formulas in comparison with the other indices have the minimum rate of saturation. In other words, these indices are more efficient to estimation of the LAI; especially in high density vegetation area and can be used with high reliability in linear models for LAI estimation.
