Vegetation damage assessment due to Hudhud cyclone based on NDVI using Landsat-8 satellite imagery

Konda, Venkata Giri Raj Kumar; Reddy, Chejarla Venkatesh; Mandla, Venkata Ravibabu; Voleti, Vani; Chokkavarapu, Nagaveni

doi:10.1007/s12517-017-3371-8

Cited by 20 publications

(9 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The NDVI designed to monitor biomass [39,40] and makes use of the reflection near-infrared (NIR) and red spectrum [41]. The NIR is between 0.76 nm and 0.90 nm in the TM sensor and 0.77-0.90 in the ETM sensor.…”

Section: Vegetation Indicesmentioning

confidence: 99%

A linear approach for wheat yield prediction by using different spectral vegetation indices

Kaya

Polat

2023

International Journal of Engineering and Geosciences

View full text Add to dashboard Cite

Yield prediction before harvest is one of the important issues in terms of managing agricultural policies and making the right decisions for the future. Using remote sensing techniques in yield estimation studies is one of the important steps for many countries to reach their 21st-century agricultural targets. The aim of this study is to develop a wheat yield model using Landsat-8 and Sentinel-2 satellite data. In this study, the development stages of winter wheat were examined with the help of satellite images obtained between the years 2015-2018 of a selected region in Sanliurfa, Turkey, and it was aimed to predict the yields for other years by establishing a yield estimation model. The yield estimation model was established with the help of Normalized Difference Vegetation Index (NDVI), Soil-adjusted Vegetation Index (SAVI), Green Normalized Difference Vegetation Index (GNDVI) and Modified Soil-adjusted Vegetation Index (MSAVI) obtained from remote sensing satellite images. Linear regression analysis was established between calculated NDVI, SAVI, GNDVI, MSAVI indices, and actual yield values on the pre-flowering, flowering stage, and postflowering stage. As a result of the study, the highest correlation coefficient was found in the flowering stage between the vegetation indices values and the actual yield values. The values of NDVI, SAVI, GNDVI, and MSAVI and correlation coefficients are obtained in the flowering stage were 0.82, 0.80, 0.86, and 0.87, respectively. With the established model, yield values in 2019 were tried to be estimated for three different fields. The highest correlations were seen in the flowering stage for MSAVI and GNDVI, pre-flowering stage for NDVI and post-flowering stage for SAVI. This clearly shows that the satellite images can be used in yield estimation studies with a remarkable correlation between vegetation indices and actual yield values.

show abstract

Section: Vegetation Indicesmentioning

confidence: 99%

A linear approach for wheat yield prediction by using different spectral vegetation indices

Kaya

Polat

2023

International Journal of Engineering and Geosciences

View full text Add to dashboard Cite

show abstract

“…Crop identification and discernment are based on each crop's detail with a matchless spectral signature. The maximum likelihood classification technique was first used to mask uncultivated areas, including sand, plantations, built-up forests, and water bodies [40], a technique called dynamic cropland masking. It results in a binary map that separates the annual target crop area from other areas [41].…”

Section: Acreage Estimationmentioning

confidence: 99%

Assessing the Yield of Wheat Using Satellite Remote Sensing-Based Machine Learning Algorithms and Simulation Modeling

et al. 2022

View full text Add to dashboard Cite

Globally, estimating crop acreage and yield is one of the most critical issues that policy and decision makers need for assessing annual crop productivity and food supply. Nowadays, satellite remote sensing and geographic information system (GIS) can enable the estimation of these crop production parameters over large geographic areas. The present work aims to estimate the wheat (Triticum aestivum) acreage and yield of Maharajganj, Uttar Pradesh, India, using satellite-based data products and the Carnegie-Ames-Stanford Approach (CASA) model. Uttar Pradesh is the largest wheat-producing state in India, and this district is well known for its quality organic wheat. India is the leader in wheat grain export, and, hence, its monitoring of growth and yield is one of the top economic priorities of the country. For the calculation of wheat acreage, we performed supervised classification using the Random Forest (RF) and Support Vector Machine classifiers and compared their classification accuracy based on ground-truthing. We found that RF performed a significantly accurate acreage assessment (kappa coefficient 0.84) compared to SVM (0.68). The CASA model was then used to calculate the winter crop (Rabi, winter-sown, and summer harvested) wheat net primary productivity (NPP) in the study area for the 2020–2021 growth season using the RF-based acreage product. The model used for wheat NPP-yield conversion (CASA) showed 3100.27 to 5000.44 kg/ha over 148,866 ha of the total wheat area. The results showed that in the 2020–2021 growing season, all the districts of Uttar Pradesh had similar wheat growth trends. A total of 30 observational data points were used to verify the CASA model-based estimates of wheat yield. Field-based verification shows that the estimated yield correlates well with the observed yield (R2 = 0.554, RMSE = 3.36 Q/ha, MAE −0.56 t ha−1, and MRE = −4.61%). Such an accuracy for assessing regional wheat yield can prove to be one of the promising methods for calculating the whole region’s agricultural yield. The study concludes that RF classifier-based yield estimation has shown more accurate results and can meet the requirements of a regional-scale wheat grain yield estimation and, thus, can prove highly beneficial in policy and decision making.

show abstract

“…NDVI is widely used for measuring vegetation density, health status, regional vegetation condition, and detecting vegetation disturbances (Xu et al, 2021;Mishra et al, 2021b;Wang et al, 2010;Yang et al, 2018). Numerous studies have shown that the NDVI is a reliable indicator of post-typhoon damage detection (Xu et al, 2021;Mishra et al, 2021a;Charrua et al, 2021;Shamsuzzoha et al, 2021;Kumar et al, 2021;Nandi et al, 2020;Wang and Xu, 2018;Konda et al, 2018;Zhang et al, 2013;Rodgers et al, 2009). Therefore, in this study, the vegetation damage before and after Typhoon Soulik has been determined using the NDVI approach.…”

Section: Analyses Of Coastal Vegetation Loss and Disturbancementioning

confidence: 99%

“…The degree of vegetation damage has been determined by comparing the NDVI values of the pre-and post-typhoon periods. Various researchers have frequently used the direct difference of NDVI to determine the damage severity caused by typhoons to natural vegetated land (Wang and Xu, 2018;Konda et al, 2018). It has been calculated on a cell-by-cell basis by subtracting the pre-typhoon NDVI image from that of the post-typhoon, in ArcGIS software using map algebra (Zhang et al, 2013;Cakir et al, 2006).…”

Section: Analyses Of Coastal Vegetation Loss and Disturbancementioning

confidence: 99%

“…Several studies have been carried out in South Asia using various techniques to map the hazard, vulnerability, risk and effects of typhoon disasters (Halder and Bandyopadhyay, 2022;Wang et al, 2021;Shamsuzzoha et al, 2021;Kumar et al, 2021;Sadik et al, 2020;Konda et al, 2018;Parida et al, 2018;Zhang et al, 2013;Yin et al, 2013;Li and Li., 2013;Rodgers et al, 2009). Remote sensing and geospatial technology played a crucial role in monitoring a variety of natural disasters (Wang and Xu, 2018;Mishra et al, 2021b;Charrua et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Assessing Typhoon Soulik-induced morphodynamics over the Mokpo coast region in South Korea based on a geo-spatial approach

Yum

Song

Adhikari

2022

Preprint

View full text Add to dashboard Cite

Abstract. The inner shelf and coastal region of the Yellow Sea along the Korean peninsula are frequently impacted by Typhoons. The Mokpo coastal region in South Korea has been significantly affected by typhoon Soulik in 2018, the deadliest typhoon strike to the southwestern coast, since Maemi in 2003. Typhoon Soulik overran the region, causing extensive damage to the coast, shoreline, vegetation, and coastal geomorphology. Therefore, it is important to investigate its impact on the coastal ecology, landform, erosion/accretion, suspended sediment concentration (SSC) and associated coastal changes along the Mokpo region. In this study, net shoreline movement (NSM), Normalized Difference Vegetation Index (NDVI)), coastal landform change model, Normalized Difference Suspended Sediment Index (NDSSI), and SSC-reflectance relation have been used to analyze the coastal morphodynamics over the typhoon periods. We used pre- and post-typhoon Sentinel-2B MSI images for mapping and monitoring the typhoon effect. The findings highlighted the significant impacts of typhoons on coastal dynamics, wetland vegetation and sediment resuspension along the Mokpo coast. It has been observed that typhoon-induced SSC influences shoreline and coastal morphology. The outcome of this research may provide databases to manage coastal environments and a long-term plan to restore valuable coastal habitats. In addition, the findings may be useful for post-typhoon emergency response, coastal planners, and administrators involved in the long-term development of human life.

show abstract

Vegetation damage assessment due to Hudhud cyclone based on NDVI using Landsat-8 satellite imagery

Cited by 20 publications

References 12 publications

A linear approach for wheat yield prediction by using different spectral vegetation indices

A linear approach for wheat yield prediction by using different spectral vegetation indices

Assessing the Yield of Wheat Using Satellite Remote Sensing-Based Machine Learning Algorithms and Simulation Modeling

Assessing Typhoon Soulik-induced morphodynamics over the Mokpo coast region in South Korea based on a geo-spatial approach

Contact Info

Product

Resources

About