scholarly journals Drought monitoring over India through Normalized Difference Vegetation Index (NDVI)

MAUSAM ◽  
2021 ◽  
Vol 61 (4) ◽  
pp. 537-546
Author(s):  
M. V. KAMBLE ◽  
K. GHOSH ◽  
M. RAJEEVAN ◽  
R. P. SAMUI
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Central India ◽  
Vegetation Pattern ◽  
Drought Monitoring ◽  
Significant Relation ◽  
Peninsular India ◽  
Simple Index ◽  
Cumulative Rainfall ◽  
Dry Farming

Normalized Difference Vegetation Index (NDVI) is a simple index to monitor the state of vegetation (stressed/unstressed) which can be derived from satellite data. Hence an attempt is made to find out the vegetation responses to rainfall through NDVI over the study area. Applicability of NDVI in drought monitoring is discussed using the NDVI and rainfall data for the period 1982-2003. The anomaly of NDVI is compared with the percentage departure of rainfall of corresponding years. Results showed a significant relation between the NDVI with the percentage departure of rainfall. The time series plots of averaged NDVI and seasonal rainfall (June-September) are done for NW India (21° N - 31° N, 68° E - 78° E), Central India (22° N - 27° N, 70° E - 77° E) and Peninsular India (16° N - 21° N, 74° E - 79° E) over the period 1982-2003 to analyze changes in vegetation pattern of India during the last two decades. Results indicated a clear linear relationship over NW and Central India. NDVI anomalies and the corresponding cumulative rainfall showed significantly linear correlation of 0.69 over NW India and 0.57 over Central India significant at 1% level but the correlation is found to be insignificant over Peninsular India which was only 0.04. Trend analysis of averaged NDVI over India showed that during last two decades the vegetation status had quite improved over the dry farming tracts of India.

scholarly journals Multispectral Image-Based Estimation of Drought Patterns and Intensity around Lake Chad, Africa

2019 ◽  
Vol 11 (21) ◽  
pp. 2534 ◽  
Author(s):  
Willibroad Gabila Buma ◽  
Sang-Il Lee
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Meteorological Data ◽  
Condition Index ◽  
Drought Monitoring ◽  
World Population ◽  
Lake Chad ◽  
In Situ Data

As the world population keeps increasing and cultivating more land, the extraction of vegetation conditions using remote sensing is important for monitoring land changes in areas with limited ground observations. Water supply in wetlands directly affects plant growth and biodiversity, which makes monitoring drought an important aspect in such areas. Vegetation Temperature Condition Index (VTCI) which depends on thermal stress and vegetation state, is widely used as an indicator for drought monitoring using satellite data. In this study, using clear-sky Landsat multispectral images, VTCI was derived from Land Surface Temperature (LST) and the Normalized Difference Vegetation Index (NDVI). Derived VTCI was used to observe the drought patterns of the wetlands in Lake Chad between 1999 and 2018. The proportion of vegetation from WorldView-3 images was later introduced to evaluate the methods used. With an overall accuracy exceeding 90% and a kappa coefficient greater than 0.8, these methods accurately acquired vegetation training samples and adaptive thresholds, allowing for accurate estimations of the spatially distributed VTCI. The results obtained present a coherent spatial distribution of VTCI values estimated using LST and NDVI. Most areas during the study period experienced mild drought conditions, though severe cases were often seen around the northern part of the lake. With limited in-situ data in this area, this study presents how VTCI estimations can be developed for drought monitoring using satellite observations. This further shows the usefulness of remote sensing to improve the information about areas that are difficult to access or with poor availability of conventional meteorological data.

scholarly journals Time Varying Spatial Downscaling of Satellite-Based Drought Index

2021 ◽  
Vol 13 (18) ◽  
pp. 3693
Author(s):  
Hone-Jay Chu ◽  
Regita Faridatunisa Wijayanti ◽  
Lalu Muhamad Jaelani ◽  
Hui-Ping Tsai
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Standardized Precipitation Index ◽  
Environmental Data ◽  
Drought Monitoring ◽  
Drought Severity ◽  
Time Varying ◽  
Fine Resolution ◽  
Spatial Downscaling

Drought monitoring is essential to detect the presence of drought, and the comprehensive change of drought conditions on a regional or global scale. This study used satellite precipitation data from the Tropical Rainfall Measuring Mission (TRMM), but refined the data for drought monitoring in Java, Indonesia. Firstly, drought analysis was conducted to establish the standardized precipitation index (SPI) of TRMM data for different durations. Time varying SPI spatial downscaling was conducted by selecting the environmental variables, normalized difference vegetation index (NDVI), and land surface temperature (LST) that were highly correlated with precipitation because meteorological drought was associated with vegetation and land drought. This study used time-dependent spatial regression to build the relation among original SPI, auxiliary variables, i.e., NDVI and LST. Results indicated that spatial downscaling was better than nonspatial downscaling (overall RMSEs: 0.25 and 0.46 in spatial and nonspatial downscaling). Spatial downscaling was more suitable for heterogeneous SPI, particularly in the transition time (R: 0.863 and 0.137 in June 2019 for spatial and nonspatial models). The fine resolution (1 km) SPI can be composed of the environmental data. The fine-resolution SPI captured a similar trend of the original SPI. Furthermore, the detailed SPI maps can be used to understand the spatio-temporal pattern of drought severity.

scholarly journals Exploring VIIRS Continuity with MODIS in an Expedited Capability for Monitoring Drought-Related Vegetation Conditions

2021 ◽  
Vol 13 (6) ◽  
pp. 1210
Author(s):  
Trenton D. Benedict ◽  
Jesslyn F. Brown ◽  
Stephen P. Boyte ◽  
Daniel M. Howard ◽  
Brian A. Fuchs ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Infrared Imaging ◽  
Geometric Mean ◽  
Drought Monitoring ◽  
Zero Bias ◽  
Modis Ndvi ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Common Application

Vegetation has been effectively monitored using remote sensing time-series vegetation index (VI) data for several decades. Drought monitoring has been a common application with algorithms tuned to capturing anomalous temporal and spatial vegetation patterns. Drought stress models, such as the Vegetation Drought Response Index (VegDRI), often use VIs like the Normalized Difference Vegetation Index (NDVI). The EROS expedited Moderate Resolution Imaging Spectroradiometer (eMODIS)-based, 7-day NDVI composites are integral to the VegDRI. As MODIS satellite platforms (Terra and Aqua) approach mission end, the Visible Infrared Imaging Radiometer Suite (VIIRS) presents an alternate NDVI source, with daily collection, similar band passes, and moderate spatial resolution. This study provides a statistical comparison between EROS expedited VIIRS (eVIIRS) 375-m and eMODIS 250-m and tests the suitability of replacing MODIS NDVI with VIIRS NDVI for drought monitoring and vegetation anomaly detection. For continuity with MODIS NDVI, we calculated a geometric mean regression adjustment algorithm using 375-m resolution for an eMODIS-like NDVI (eVIIRS’) eVIIRS’ = 0.9887 × eVIIRS − 0.0398. The resulting statistical comparisons (eVIIRS’ vs. eMODIS NDVI) showed correlations consistently greater than 0.84 throughout the three years studied. The eVIIRS’ VegDRI results characterized similar drought patterns and hotspots to the eMODIS-based VegDRI, with near zero bias.

Understanding the Resilience of Sal and Teak Forests to Climate Variability Using NDVI and EVI Time Series

2021 ◽  
Vol 67 (2) ◽  
pp. 192-204
Author(s):  
Maneesh Kumar Patasaraiya ◽  
Rinku Moni Devi ◽  
Bhaskar Sinha ◽  
Jigyasa Bisaria ◽  
Sameer Saran ◽  
...  
Keyword(s):  
Minimum Temperature ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Central India ◽  
Annual Rainfall ◽  
Maximum Temperature ◽  
Spatial And Temporal Scales ◽  
Changing Climate ◽  
Tiger Reserve ◽  
Sal Forest

Abstract This study attempts to understand the climatic resilience of two forest types of central India—that is, Tectona grandis (Teak) forest of Satpura Tiger Reserve and Shorea robusta (Sal) forest of Kanha Tiger Reserve—using normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) extracted from MODIS, and climate variable data sets at highest spatial and temporal scales. Teak and Sal forests within the core area of the selected tiger reserves represent the least anthropogenic disturbances, and therefore, the observed changes in NDVI and EVI over the past 16 years could be analyzed in the context of climate change. The correlation analysis between climatic variables (minimum temperature, maximum temperature, mean temperature, and total annual rainfall) and forest response indicators (NDVI/EVI) at seasonal and annual scales revealed that Teak and Sal forests are more sensitive to change in past temperature as compared with rainfall. Also, the changes in NDVI and EVI of Sal forest are correlated more to minimum temperature, and that of Teak forest to maximum temperature. The analysis of sapling girth class of Sal and Teak further revealed that Sal as compared with Teak is more affected because of the changing climate variables of the recent past. The findings of the study will help manage forests more efficiently in the context of changing climate.

scholarly journals Soil chemical analysis of gangetic delta plain by combined use of multispectral imagery and XRF spectroscopy

2016 ◽  
Vol 4 (2) ◽  
pp. 92
Author(s):  
Neha Singh ◽  
Harshita Asthana ◽  
Chandrasekhar Azad Vishwakarma ◽  
Ratan Sen ◽  
Saumitra Mukherjee
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat Imagery ◽  
Agricultural Productivity ◽  
Principal Component ◽  
Vegetation Pattern ◽  
X Ray Diffraction ◽  
Band Ratio ◽  
Bengal Delta ◽  
Delta Plain

24 Parganas districts of West Bengal are very well known for their agricultural productivity. These districts are the part of the mature delta plain of the Bengal delta which is formed by the deposition of weathered sediments through Himalayan Rivers. The agricultural productivity of an area depends mainly upon the fertility of soil which in turn depends on the presence of essential nutrients in it. Thus, the present study was carried out to assess the types of minerals present in the soil which provide the elements that act as the nutrients to the plant. Band ratio technique using the Landsat imagery and X-Ray Diffraction was carried out for the study of mineral composition. XRF was done for the elemental composition of the soil samples and Principal Component Analysis was carried out to assess the sources of these nutrients in the soil. Normalized Difference Vegetation Index was also calculated using Landsat imagery to study the vegetation pattern in the area. The study suggests that the area is mainly comprised of clay and ferrous minerals and contains nearly all the elements that act as macro-and micro-nutrients. However, the study also shows the accumulation of some of the heavy metals which may be due to the excessive use of fertilizers.

Using Temperature Vegetation Drought Index for Monitoring Drought Based on Remote Sensing Data

2011 ◽  
Vol 356-360 ◽  
pp. 2854-2859
Author(s):  
Lin Sheng Huang ◽  
Qing Song Guan ◽  
Yan Sheng Dong ◽  
Dong Yan Zhang ◽  
Wen Jiang Huang ◽  
...  
Keyword(s):  
Drought Index ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Feature Space ◽  
Drought Monitoring ◽  
Arcgis Engine ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

Drought is one of the major natural disasters in China, it has extremely affected national food security. In this study, Normalized Difference Vegetation Index (NDVI) and surface temperature (Ts) were calculated by using 8-day composite Moderate-Resolution Imaging Spectroradiometer (MODIS) reflectance product data MOD09A1 and MOD11A2, then NDVI-Ts feature space was obtained and dry edge and wet edge equation was fit. According to coefficients of dry edge and wet edge equation, Temperature Vegetation Drought Index (TVDI) will be calculated and refer it as a drought monitoring indicator. In addition, drought monitoring and classification of Shandong province (China) was completed by TVDI from February to May,2011. Furthermore, the drought classification diagram was made and the drought area in each period was counted. The results showed revealed that: NDVI-Ts feature was roughly a triangular shape in the two-dimensional plane, and drought conditions could be better monitored through TVDI. Finally, the desktop demonstration system of drought monitoring was designed and some general functions were realized based on ArcGis Engine.

Remote sensing based drought monitoring in Zimbabwe

2014 ◽  
Vol 23 (5) ◽  
pp. 649-659 ◽  
Author(s):  
Godfrey Mutowo ◽  
David Chikodzi
Keyword(s):  
Remote Sensing ◽  
Environmental Planning ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Condition Index ◽  
Drought Monitoring ◽  
Drought Severity ◽  
Current Time ◽  
Content Type ◽  
Vegetation Condition

Purpose – Drought monitoring is an important process for national agricultural and environmental planning. Droughts are normal recurring climatic phenomena that affect people and landscapes. They occur at different scales (locally, regionally, and nationally), and for periods of time ranging from weeks to decades. In Zimbabwe drought is increasingly becoming an annual phenomenon, with varying parts of the country being affected. The purpose of this paper is to analyse the spatial variations in the seasonal occurrences of drought in Zimbabwe over a period of five years. Design/methodology/approach – The Vegetation Condition Index (VCI), which shows how close the Normalized Difference Vegetation Index of the current time is to the minimum Normalized Difference Vegetation Index calculated from the long-term record for that given time, was used to monitor drought occurrence in Zimbabwe. A time series of dekadal Normalized Difference Vegetation Index, calculated from SPOT images, was used to compute seasonal VCI maps from 2005 to 2010. The VCI maps were then classified into three drought severity classes (severe, moderate, and mild) based on the relative changes in the vegetation condition from extremely bad to optimal. Findings – The results showed that droughts occur annually in Zimbabwe though, on average, the droughts are mostly mild. The occurrence and the spatial distribution of drought in Zimbabwe was also found to be random affecting different places from season to season thus the authors conclude that most parts of the country are drought prone. Originality/value – Remote sensing technologies utilising such indices as the VCI can be used for drought monitoring in Zimbabwe.

scholarly journals Surface Moisture and Vegetation Cover Analysis for Drought Monitoring in the Southern Kruger National Park Using Sentinel-1, Sentinel-2, and Landsat-8

2018 ◽  
Vol 10 (9) ◽  
pp. 1482 ◽  
Author(s):  
Marcel Urban ◽  
Christian Berger ◽  
Tami Mudau ◽  
Kai Heckel ◽  
John Truckenbrodt ◽  
...  
Keyword(s):  
Vegetation Structure ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Laser Scanner ◽  
Observation Time ◽  
Drought Monitoring ◽  
Landsat 8 ◽  
Moisture Index ◽  
Surface Moisture ◽  
Sentinel 2

During the southern summer season of 2015 and 2016, South Africa experienced one of the most severe meteorological droughts since the start of climate recording, due to an exceptionally strong El Niño event. To investigate spatiotemporal dynamics of surface moisture and vegetation structure, data from ESA’s Copernicus Sentinel-1/-2 and NASA’s Landsat-8 for the period between March 2015 and November 2017 were utilized. In combination, these radar and optical satellite systems provide promising data with high spatial and temporal resolution. Sentinel-1 C-band data was exploited to derive surface moisture based on a hyper-temporal co-polarized (vertical-vertical—VV) radar backscatter change detection approach, describing dynamics between dry and wet seasons. Vegetation information from a TLS (Terrestrial Laser Scanner)-derived canopy height model (CHM), as well as the normalized difference vegetation index (NDVI) from Sentinel-2 and Landsat-8, were utilized to analyze vegetation structure types and dynamics with respect to the surface moisture index (SurfMI). Our results indicate that our combined radar–optical approach allows for a separation and retrieval of surface moisture conditions suitable for drought monitoring. Moreover, we conclude that it is crucial for the development of a drought monitoring system for savanna ecosystems to integrate land cover and vegetation information for analyzing surface moisture dynamics derived from Earth observation time series.

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