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 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 Ecosystem Drought Response Timescales from Thermal Emission versus Shortwave Remote Sensing

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Erika Andujar ◽  
Nir Y. Krakauer ◽  
Chuixiang Yi ◽  
Felix Kogan
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Thermal Emission ◽  
Plant Stress ◽  
Meteorological Drought ◽  
Condition Index ◽  
Vegetation Health ◽  
Vegetation Health Index

Remote sensing is used for monitoring the impacts of meteorological drought on ecosystems, but few large-scale comparisons of the response timescale to drought of different vegetation remote sensing products are available. We correlated vegetation health products derived from polar-orbiting radiometer observations with a meteorological drought indicator available at different aggregation timescales, the Standardized Precipitation Evapotranspiration Index (SPEI), to evaluate responses averaged globally and over latitude and biome. The remote sensing products are Vegetation Condition Index (VCI), which uses normalized difference vegetation index (NDVI) to identify plant stress, Temperature Condition Index (TCI), based on thermal emission as a measure of surface temperature, and Vegetation Health Index (VHI), the average of VCI and TCI. Globally, TCI correlated best with 2-month timescale SPEI, VCI correlated best with longer timescale droughts (peak mean correlation at 13 months), and VHI correlated best at an intermediate timescale of 4 months. Our results suggest that thermal emission (TCI) may better detect incipient drought than vegetation color (VCI). VHI had the highest correlations with SPEI at aggregation times greater than 3 months and hence may be the most suitable product for monitoring the effects of long droughts.

scholarly journals Identification of Drought and Performance Evaluation of MODIS and TRMM through Remote Sensing: A Case Study in North and West Africa during 2002–2018

2020 ◽  
Author(s):  
Malak Henchiri ◽  
Qi Liu ◽  
Bouajila Essifi ◽  
Shahzad Ali ◽  
Wilson Kalisa ◽  
...  
Keyword(s):  
Remote Sensing ◽  
West Africa ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Severity Index ◽  
Precipitation Anomaly ◽  
Drought Severity ◽  
Monthly Precipitation ◽  
Water Index ◽  
And Performance

North and West Africa are the most vulnerable regions to drought, due to the high variation in monthly precipitation. An accurate and efficient monitoring of drought is essential. In this study, we use TRMM data with remote sensing tools for effective monitoring of drought. The Drought Severity Index (DSI), Temperature Vegetation Drought Index (TVDI), Normalized Difference Vegetation Index (NDVI), and Normalized Vegetation Supply Water Index (NVSWI) are more useful for monitoring the drought over North and West Africa. To classify the areas affected by drought, we used the TRMM spatial maps to verify the TVDI, DSI and NVSWI indexes derived from MODIS. The DSI, TVDI, NVSWI and Monthly Precipitation Anomaly (NPA) indexes with the employ of MODIS-derived ET/PET and NDVI were chosen for monitoring the drought in the study area. The seasonal spatial correlation between the DSI, NPA, NVWSI, NDVI, TVDI and TCI indicates that NVSWI, NDVI and DSI present an excellent monitor of drought indexes. The change trend of drought from 2002 to 2018 was also characterized. The frequency of drought showed a decrease during this period.

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 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.

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 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 Aplicação do Índice de Vegetação por Diferença Normalizada (NDVI) na Caracterização da Cobertura Vegetativa de Juazeiro Do Norte – CE

2020 ◽  
Vol 13 (6) ◽  
pp. 2885
Author(s):  
Antônio Soares Barros ◽  
Lucas Menezes de Farias ◽  
Jefferson Luiz Alves Marinho
Keyword(s):  
Remote Sensing ◽  
Vegetation Cover ◽  
Public Management ◽  
Environmental Planning ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Surface Characteristics ◽  
Multispectral Images ◽  
Vegetative Cover

Dados de sensoriamento remoto são fundamentais em pesquisas voltadas a estudos do comportamento da vegetação, assim como no monitoramento de fenômenos meteorológicos e ambientais. Nesse contexto, surgem mecanismos capazes de auxiliar estudos que constatem o que acontece no meio ambiente, em que o Índice de Vegetação por Diferença Normalizada (NDVI) é uma dessas ferramentas. O monitoramento preciso e oportuno das características da superfície da Terra fornece a base para uma melhor compreensão das interações e relações entre os fenômenos humanos e naturais visando um melhor uso e gerenciamento de recursos. Em função disso, o objetivo desse artigo é realizar a geração de um mapa temático da situação da cobertura vegetal do município de Juazeiro do Norte-CE a partir do (NDVI). Para a realização deste trabalho foi utilizado o Sistema de Informação Geográfica (SIG QGIS), versão 2.18. O NDVI foi calculado a partir de imagens de satélites obtidas de forma gratuita no site Earth Explorer. Destaca-se como resultados que o NDVI máximo foi 0,60901. Esse valor próximo a 1 (um) indica uma boa quantidade de cobertura vegetal densa. Dessa forma, a aplicação do NDVI foi relevante para identificar como se encontra a atual situação do município em relação à sua vegetação, pois esse índice pode auxiliar nas tomadas de decisões por parte da gestão pública no planejamento ambiental, uma vez que funciona como indicador de áreas verdes. Portanto, essa técnica pode melhorar a detecção de alterações na vegetação em estudos futuros.Application of the Normalized Difference Vegetation Index (NDVI) in the Characterization of the Vegetative Cover of Juazeiro Do Norte – CEA B S T R A C TRemote sensing data are fundamental in research focused on studies of vegetation behavior, as well as in the monitoring of meteorological and environmental phenomena. In this context, the Normalized Difference Vegetation Index (NDVI) has been widely used for monitoring and evaluating vegetation, as it is one of the first analytical products of remote sensing used to simplify the complexities of multispectral images. Thus, accurate and timely monitoring of earth's surface characteristics provides the basis for a better understanding of the interactions and relationships between human and natural phenomena aiming at better use and resource management. In this sense, the objective of this article is to generate a thematic map of the situation of the vegetation cover of the municipality of Juazeiro do Norte-CE from the (NDVI). The Geographic Information System (SIG QGIS), version 2.18, was used to carry out this work. NDVI was calculated from satellite images obtained free of charge on the Earth Explorer website. It is noteworthy as results that the maximum NDVI was 0.60901. This value close to 1 (one) indicates a good amount of dense vegetation cover. Thus, the application of NDVI was relevant to identify how the current situation of the municipality is found in relation to its vegetation, because this index can help in decision-making by public management in environmental planning, since it acts as an indicator of green areas. Therefore, this technique may improve the detection of changes in vegetation in future studies.Keywords: Vegetation Index, Remote sensing, Vegetable Cover.

TEMPERATURA DE SUPERFÍCIE E A RELAÇÃO COM O ÍNDICE DE VEGETAÇÃO POR DIFERENÇA NORMALIZADA (NDVI) NA MICROBACIA DO RIO DA BATATEIRAS, CRATO-CE

2019 ◽  
Vol 21 (2) ◽  
pp. 1310-1320
Author(s):  
Cícera Celiane Januário da Silva ◽  
Vinicius Ferreira Luna ◽  
Joyce Ferreira Gomes ◽  
Juliana Maria Oliveira Silva
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Temperature Measurement ◽  
Rainy Season ◽  
Satellite Images ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Low Density ◽  
Difference Map

O objetivo do presente trabalho é fazer uma comparação entre a temperatura de superfície e o Índice de Vegetação por Diferença Normalizada (NDVI) na microbacia do rio da Batateiras/Crato-CE em dois períodos do ano de 2017, um chuvoso (abril) e um seco (setembro) como também analisar o mapa de diferença de temperatura nesses dois referidos períodos. Foram utilizadas imagens de satélite LANDSAT 8 (banda 10) para mensuração de temperatura e a banda 4 e 5 para geração do NDVI. As análises demonstram que no mês de abril a temperatura da superfície variou aproximadamente entre 23.2ºC e 31.06ºC, enquanto no mês correspondente a setembro, os valores variaram de 25°C e 40.5°C, sendo que as maiores temperaturas foram encontradas em locais com baixa densidade de vegetação, de acordo com a carta de NDVI desses dois meses. A maior diferença de temperatura desses dois meses foi de 14.2°C indicando que ocorre um aumento da temperatura proporcionado pelo período que corresponde a um dos mais secos da região, diferentemente de abril que está no período de chuvas e tem uma maior umidade, presença de vegetação e corpos d’água que amenizam a temperatura.Palavras-chave: Sensoriamento Remoto; Vegetação; Microbacia.                                                                                  ABSTRACTThe objective of the present work is to compare the surface temperature and the Normalized Difference Vegetation Index (NDVI) in the Batateiras / Crato-CE river basin in two periods of 2017, one rainy (April) and one (September) and to analyze the temperature difference map in these two periods. LANDSAT 8 (band 10) satellite images were used for temperature measurement and band 4 and 5 for NDVI generation. The analyzes show that in April the surface temperature varied approximately between 23.2ºC and 31.06ºC, while in the month corresponding to September, the values ranged from 25ºC and 40.5ºC, and the highest temperatures were found in locations with low density of vegetation, according to the NDVI letter of these two months. The highest difference in temperature for these two months was 14.2 ° C, indicating that there is an increase in temperature provided by the period that corresponds to one of the driest in the region, unlike April that is in the rainy season and has a higher humidity, presence of vegetation and water bodies that soften the temperature.Key-words: Remote sensing; Vegetation; Microbasin.RESUMENEl objetivo del presente trabajo es hacer una comparación entre la temperatura de la superficie y el Índice de Vegetación de Diferencia Normalizada (NDVI) en la cuenca Batateiras / Crato-CE en dos períodos de 2017, uno lluvioso (abril) y uno (Septiembre), así como analizar el mapa de diferencia de temperatura en estos dos períodos. Las imágenes de satélite LANDSAT 8 (banda 10) se utilizaron para la medición de temperatura y las bandas 4 y 5 para la generación de NDVI. Los análisis muestran que en abril la temperatura de la superficie varió aproximadamente entre 23.2ºC y 31.06ºC, mientras que en el mes correspondiente a septiembre, los valores oscilaron entre 25 ° C y 40.5 ° C, y las temperaturas más altas se encontraron en lugares con baja densidad de vegetación, según el gráfico NDVI de estos dos meses. La mayor diferencia de temperatura de estos dos meses fue de 14.2 ° C, lo que indica que hay un aumento en la temperatura proporcionada por el período que corresponde a uno de los más secos de la región, a diferencia de abril que está en la temporada de lluvias y tiene una mayor humedad, presencia de vegetación y cuerpos de agua que suavizan la temperatura.Palabras clave: Detección remota; vegetación; Cuenca.

scholarly journals Detecting Forest Degradation in the Three-North Forest Shelterbelt in China from Multi-Scale Satellite Images

2021 ◽  
Vol 13 (6) ◽  
pp. 1131
Author(s):  
Tao Yu ◽  
Pengju Liu ◽  
Qiang Zhang ◽  
Yi Ren ◽  
Jingning Yao
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Net Primary Production ◽  
Remote Sensing Data ◽  
Forest Degradation ◽  
Observation Data ◽  
Area Index ◽  
Sensing Data ◽  
Multi Scale

Detecting forest degradation from satellite observation data is of great significance in revealing the process of decreasing forest quality and giving a better understanding of regional or global carbon emissions and their feedbacks with climate changes. In this paper, a quick and applicable approach was developed for monitoring forest degradation in the Three-North Forest Shelterbelt in China from multi-scale remote sensing data. Firstly, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Ratio Vegetation Index (RVI), Leaf Area Index (LAI), Fraction of Photosynthetically Active Radiation (FPAR) and Net Primary Production (NPP) from remote sensing data were selected as the indicators to describe forest degradation. Then multi-scale forest degradation maps were obtained by adopting a new classification method using time series MODerate Resolution Imaging Spectroradiometer (MODIS) and Landsat Enhanced Thematic Mapper Plus (ETM+) images, and were validated with ground survey data. At last, the criteria and indicators for monitoring forest degradation from remote sensing data were discussed, and the uncertainly of the method was analyzed. Results of this paper indicated that multi-scale remote sensing data have great potential in detecting regional forest degradation.

Sign in / Sign up

Export Citation Format

Share Document