scholarly journals Assessing Agricultural Vulnerability to Drought in a Heterogeneous Environment: A Remote Sensing-Based Approach

2020 ◽  
Vol 12 (20) ◽  
pp. 3363 ◽  
Author(s):  
Mst Ilme Faridatul ◽  
Bayes Ahmed
Keyword(s):  
Large Scale ◽  
Vegetation Index ◽  
Crop Yields ◽  
Hazard Index ◽  
Condition Index ◽  
Agricultural Drought ◽  
Heterogeneous Environment ◽  
Yield Data ◽  
Vegetation Condition ◽  
Vegetation Condition Index

Agriculture is one of the fundamental economic activities in most countries; however, this sector suffers from various natural hazards including flood and drought. The determination of drought-prone areas is essential to select drought-tolerant crops in climate sensitive vulnerable areas. This study aims to enhance the detection of agricultural areas with vulnerability to drought conditions in a heterogeneous environment, taking Bangladesh as a case study. The normalized difference vegetation index (NDVI) and land cover products from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images have been incorporated to compute the vegetation index. In this study, a modified vegetation condition index (mVCI) is proposed to enhance the estimation of agricultural drought. The NDVI values ranging between 0.44 to 0.66 for croplands are utilized for the mVCI. The outcomes of the mVCI are compared with the traditional vegetation condition index (VCI). Precipitation and crop yield data are used for the evaluation. The mVCI maps from multiple years (2006–2018) have been produced to compute the drought hazard index (DHI) using a weighted sum overlay method. The results show that the proposed mVCI enhances the detection of agricultural drought compared to the traditional VCI in a heterogeneous environment. The “Aus” rice-growing season (sown in mid-March to mid-April and harvested in mid-July to early August) receives the highest average precipitation (>400 mm), and thereby this season is less vulnerable to drought. A comparison of crop yields reveals the lowest productivity in the drought year (2006) compared to the non-drought year (2018), and the DHI map presents that the north-west region of Bangladesh is highly vulnerable to agricultural drought. This study has undertaken a large-scale analysis that is important to prioritize agricultural zones and initiate development projects based on the associated level of vulnerability.

scholarly journals Drought assessment for kharif rice using standardized precipitation index (SPI) and vegetation condition index (VCI)

2021 ◽  
Vol 21 (2) ◽  
pp. 182-187
Author(s):  
DEEPA B. KAMBLE ◽  
SHWETA GAUTAM ◽  
HIMANI BISHT ◽  
SHRADDHA RAWAT ◽  
ARNAB KUNDU
Keyword(s):  
Standardized Precipitation Index ◽  
Condition Index ◽  
Precipitation Index ◽  
Agricultural Drought ◽  
Productivity Index ◽  
Weather Data ◽  
Multiple Regression Equation ◽  
Drought Assessment ◽  
Vegetation Condition ◽  
Vegetation Condition Index

The monthly weather data for 31 years from 1985-2015 was used to analyze the extent of meteorological drought using standardized precipitation index (SPI) over Allahabad, Kanpur and Lucknow. MODIS NDVI data from 2000-2015 was used for monitoring of agricultural drought through NDVI based vegetation condition index (VCI) for all the three districts. The monthly SPI and VCI values from July to October were correlated with productivity index (PI) of kharif rice.Both the indices (SPI and VCI) were positively correlated with PI for all the districts. In Allahabad SPI and VCI during September month showed a significant correlation (0.70**& 0.61*) while in Kanpur VCI during October and SPI of July and August were significantly correlated with PI of kharif Rice. The multiple regression equation developed for predicting kharif rice PI in Allahabad, Kanpur and Lucknow districts explained 69 to 76 per cent variabilityin PI. 

scholarly journals Comparison of Multi-Year Reanalysis, Models, and Satellite Remote Sensing Products for Agricultural Drought Monitoring over South Asian Countries

2021 ◽  
Vol 13 (16) ◽  
pp. 3294
Author(s):  
Muhammad Shahzaman ◽  
Weijun Zhu ◽  
Irfan Ullah ◽  
Farhan Mustafa ◽  
Muhammad Bilal ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
South Asia ◽  
Satellite Remote Sensing ◽  
Crop Production ◽  
Condition Index ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Vegetation Condition ◽  
Vegetation Condition Index

The substantial reliance of South Asia (SA) to rain-based agriculture makes the region susceptible to food scarcity due to droughts. Previously, most research on SA has emphasized the meteorological aspects with little consideration of agrarian drought impressions. The insufficient amount of in situ precipitation data across SA has also hindered thorough investigation in the agriculture sector. In recent times, models, satellite remote sensing, and reanalysis products have increased the amount of data. Hence, soil moisture, precipitation, terrestrial water storage (TWS), and vegetation condition index (VCI) products have been employed to illustrate SA droughts from 1982 to 2019 using a standardized index/anomaly approach. Besides, the relationships of these products towards crop production are evaluated using the annual national production of barley, maize, rice, and wheat by computing the yield anomaly index (YAI). Our findings indicate that MERRA-2, CPC, FLDAS (soil moisture), GPCC, and CHIRPS (precipitation) are alike and constant over the entire four regions of South Asia (northwest, southwest, northeast, and southeast). On the other hand, GLDAS and ERA5 remain poor when compared to other soil moisture products and identified drought conditions in regions one (northwest) and three (northeast). Likewise, TWS products such as MERRA-2 TWS and GRACE TWS (2002–2014) followed the patterns of ERA5 and GLDAS and presented divergent and inconsistent drought patterns. Furthermore, the vegetation condition index (VCI) remained less responsive in regions three (northeast) and four (southeast) only. Based on annual crop production data, MERRA-2, CPC, FLDAS, GPCC, and CHIRPS performed fairly well and indicated stronger and more significant associations (0.80 to 0.96) when compared to others. Thus, the current outcomes are imperative for gauging the deficient amount of data in the SA region, as they provide substitutes for agricultural drought monitoring.

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

2012 ◽  
Vol 4 (2) ◽  
pp. 214-219 ◽  
Author(s):  
Kishan Singh Rawat ◽  
Anil Kumar Mishra ◽  
Rakesh Kumar ◽  
Jitendra Singh
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Condition Index ◽  
Geographical Information ◽  
Climatic Region ◽  
Time Data ◽  
Data Set ◽  
Vegetation Condition ◽  
Vegetation Condition Index ◽  
Satellite Sensor

This study describes the Vegetation Condition Index in the near-real-time with help of SPOT based Normalized Difference Vegetation Index (NDVI) for Agro climatic-region of India and gave the development pattern in last six year (2002-2007) over the study area of India using decadal time data set from SPOT satellite sensor for 2002-2007 time periods. The each Agro-climatic region of study, 1°x1° degree in area, part of India agro-climate regions, has been taken for analysis using remote sensing and Geographical Information System (RS and GIS)methods, SPOT satellite sensor NDVI data, and from processed data set (geo-referenced data set), cut out 1°x1° degree of area by preparing a layers representing Agro-climatic region of India as base mapping units (BMU),The results indicated that NDVI index is only water stress over vegetation while VCI is an appropriate index for vegetation pattern monitoring over study area. As satellite observations provide better spatial and temporal coverage, the VCI based system will provide efficient tools for management of the improvement of agricultural planning. This system will serve as a prototype in the other parts of the world where ground observations are limited or not available.

scholarly journals Effects of Droughts on Vegetation Condition and Ecosystem Service Delivery in Data-Poor Areas: A Case of Bobirwa Sub-District, Limpopo Basin and Botswana

2020 ◽  
Vol 12 (19) ◽  
pp. 8185
Author(s):  
Ephias Mugari ◽  
Hillary Masundire ◽  
Maitseo Bolaane
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Moisture Stress ◽  
Condition Index ◽  
Crop Productivity ◽  
Rainfall Anomaly ◽  
Wet Season ◽  
Vegetation Condition ◽  
Vegetation Condition Index ◽  
Drought Occurrence

Understanding the effects of droughts on vegetation and ecosystem services (ES) is important for climate change adaptation. However, drought occurrence varies across space and time. We examined drought dynamics and impacts on vegetation and ES in the semi-arid Limpopo Basin of Botswana. Weather station precipitation, remotely sensed normalized difference vegetation index (NDVI) and participatory mapping exercises provided data for the analyses. Results show that between 1980 and 2015, rainfall anomaly indices of potential drought years ranged between −4.38 and −0.12. The longest spell of below-average rainfall occurred between 1992 and 1996. On average, drought events lasted for 1.9 years and recurred every 2.3 years. Although the overall drought frequency was 3.7 times in every 5 years, drought prevalence increased to 50%, 60% and 70% between 1981–1990, 1991–2000, and 2001–2010, respectively. The wet season average vegetation condition index between 2000 and 2015 revealed the occurrence of severe-to-extreme droughts in 2002–2003, 2005, 2008–2009 and 2012–2013 and light-to-moderate droughts in 2004, 2006–2007 and 2011, giving a drought prevalence of 73.3%. The increased frequency and severity of droughts is diminishing natural vegetation, crop productivity and several provisioning ES through moisture stress and drought-induced agricultural expansions. There exists an urgent need for smallholder irrigation development in Bobirwa sub-district to improve crop productivity and reduce the drought-induced conversion of woodlands to agriculture.

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