Vegetation condition index pattern (2002-2007) over Indian agro-climate regions, using of GIS and SPOT sensor NDVI data

Rawat, Kishan Singh; Mishra, Anil Kumar; Kumar, Rakesh; Singh, Jitendra

doi:10.31018/jans.v4i2.252

Cited by 10 publications

(6 citation statements)

References 14 publications

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“…Estimation of SEBAL ET from any region has added advantage as to get spatial distribution of biophysical descriptors namely surface albedo, surface temperature, Normalized Difference Vegetation Index (NDVI, Bala et al, 2015b;Rawat et al 2012) and…”

Section: Introductionmentioning

confidence: 99%

Quantification of wheat crop evapotranspiration and mapping: A case study from Bhiwani District of Haryana, India

Rawat

Bala²,

Singh

et al. 2017

Agricultural Water Management

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In this study an attempt has been made to estimate the actual wheat crop evapotranspiration (ET c ) by Surface Energy Balance Algorithm (SEBAL) and standardized FAO-Penman-Monteith (FAO-PM). Improved knowledge of evapotranspiration (ET) helps in understanding the water balance of any region. The results obtained through measured lysimeter, SEBAL and PM method were evaluated through statistical performance measure tests. ET c estimated from SEBAL was found to correlate significantly as r 2 (0.910) with the measured ET c of lysimeter. ET c estimated by SEBAL was also compared with PM ET c with the help of crop coefficient and was found to correlate significantly as r 2 (0.850). The other statistical parameters (RMSE=0.561, nRMSE=0.090, MAE=0.265, NRMSE=0.2033, R-RMSE=0.268, NSE=1, d=0.870 (≈1)) were also showing a good agreement between SEBAL ET c and PM ET c . The findings of work have suggested that SEBAL model shows a good potential to estimate spatial ET c for the region. Additionally the validation of models results were performed with the analysis of correlation between models ET c and district level wheat production and area under crop of five years. The results of this analysis outline that water availability and good amount of rainfall gives higher wheat yield and resulted into more ET c .

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Section: Introductionmentioning

confidence: 99%

Quantification of wheat crop evapotranspiration and mapping: A case study from Bhiwani District of Haryana, India

Rawat

Bala²,

Singh

et al. 2017

Agricultural Water Management

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“…In which, based on the histogram, maximum area shown LAI as 5, but the Northern part of the district showed LAI between 0 to 2, because of maturity of mustard crop during this period. The highest correlation (r 2 =0.719) between LAI and NDVI was found, as per the imagery of 03 rd March, it is because of the wheat crop was having its pick of greenness and higher density of crop canopy (Rawat et al, 2012).…”

Section: Fig 3 (A) Ndvi and (B) Lai Of Bhiwani From Landsat−7etm+ Imentioning

confidence: 85%

“…In other words, NDVI is strongly influenced by soil reflectance in regions with sparse vegetation cover and with LAI index less than 3. Several studies have shown that how remote sensing can be used for estimating the crop yield (Bouman,1992;Moran et al,1997;Moulin et al, 1998;Layrol et al, 2000;Locke et al, 2000;Yang and Everitt, 2000;Werner et.al., 2000;Rawat et al, 2012). Wiegand et al, 1991 found that the yield differences in different studied fields are mainly related to leaf area index and can be used for estimating the crop yield.…”

Section: Introductionmentioning

confidence: 99%

Vegetation indices mapping for Bhiwani district of Haryana (India) through LANDSAT-7ETM+ and remote sensing techniques

Bala

Rawat

Misra

et al. 2015

JANS

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This study describes the VIs Vegetation Condition Index in term of vegetation health of wheat crop; with help of LANDSAT-7ETM+ data based NDVI and LAI for Bhiwani District of Haryana states (India) and gave the spatial development pattern of wheat crop in year 2005 over the study area of India. NDVI is found to vary from 0.3 to 0.8. In northern and southern parts of study area NDVI varied from 0.6 to 0.7 but in western part of Bhiwani showed NDVI 0.2 to 0.4 due to fertility of soil and well canal destitution. LAI showed variation from 1 to 6 according to the health of crop as the same manner of NDVI because LAI VI is NDVI dependent only change the manner of representation of vegetation health, due to this fact relation curve (r 2 =) between NDVI and LAI of four different growing date of sates are in successively increasing order 0.509, 0.563, 0.577 and 0.719. The study reveals that VIs can be mapped with LANDSAT-7ETM+ through remote sensing, which can be further used for many studies like crop yield or estimating evaptranspiration on regional basis for water management because satellite observations provide better spatial and temporal coverage, the VIs based system will provide efficient tools for monitoring health of crop for improvement of agricultural planning. VIs based monitoring will serve as a prototype in the other parts of the world where ground observations are limited or not available.

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“…The NDVI is one of the best methodical vegetation indices that may be used to assess how land change changes as a result of human activity, such as changes in construction or the extent of the vegetation, among other things (Higginbottom and Symeonakis, 2014). With the use of NDVI and multispectral NIR bands, green can be examined (Rawat et al 2012).…”

Section: Introductionmentioning

confidence: 99%

“…( 1) NDVI method is used for the classi cation of vegetation and crop coverage. Theoretically, NDVI values are lie between − 1.0 and + 1.0 (Rawat et al 2012). Low normalized difference vegetation index quality is common for external resources, and green vegetation quality is for high NDVI.…”

Section: Introductionmentioning

confidence: 99%

Land use and Land cover Analysis using Geographical information system (GIS) and Principal component analysis (PCA)

Raj,

Rawat

2024

Preprint

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Activities of humanoid made on Land Use (LU) indicate an effect on the Land Cover (LC) geographical and for the time being in the presently found in the surroundings. Changes are made continuously made in our study area which is identified by the use of Land-Use/Land-Cover. The purpose of finding areas to identified Land-Use/Land-Cover (LULC) transforms is to start relationships between significant activities, policy declarations, and associated LULC plans. In order to determine the type and extent of changes that have taken place, various dates of satellite photos are connected in a process known as identification of change. The use of LUL Cchange detection in "remote sensing" and “geographic information systems" (GIS) provides crucial learning tools. In order to resolve environmental issues, reliable data planned the evaluation of nature using various methodologies is crucial. By NDVI using as well as PCA parameter to measures land cover changes is main objective of this paper. Landsat 5 (TM) & Landsat 8, OLI/TIRS satellite images are used from 2000 to 2020 prove the thought of LC Change in Muzaffarpur region, Bihar. The four classes of LULC are known to produce the statistics of vegetation area, land area, water bodies, and Grass land. The research analysis revealed that vegetation is reduced about 28% where vegetation, land, Overall accuracy (OA) and kappa coefficient are improved, and Waterland grass and land are improved by 10%, 23%, and 1%, respectively.

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Vegetation condition index pattern (2002-2007) over Indian agro-climate regions, using of GIS and SPOT sensor NDVI data

Cited by 10 publications

References 14 publications

Quantification of wheat crop evapotranspiration and mapping: A case study from Bhiwani District of Haryana, India

Quantification of wheat crop evapotranspiration and mapping: A case study from Bhiwani District of Haryana, India

Vegetation indices mapping for Bhiwani district of Haryana (India) through LANDSAT-7ETM+ and remote sensing techniques

Land use and Land cover Analysis using Geographical information system (GIS) and Principal component analysis (PCA)

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