Remote Sensing

Index name: Enhanced Vegetation Index (EVI)

Ease of use: Green

Origins: Originated from work done by Huete and a team from Brazil and the University of Arizona, United States, who developed a moderate resolution imaging spectroradiometer (MODIS)-based tool for assessing vegetation conditions.

Characteristics: Vegetation monitoring from satellite platforms using the Advanced Very High-Resolution Radiometer (AVHRR) to compute the Normalized Difference Vegetation Index (NDVI) is quite useful. EVI uses some of the same techniques as NDVI, but with the input data from a MODIS- based satellite. Both EVI and NDVI are calculated using the MODIS platform and analyzed on how they perform compared to AVHRR platforms. EVI is more responsive to canopy variations, canopy type and architecture, and plant physiognomy. EVI can be associated with stress and changes related to drought.

Input parameters: MODIS-based satellite information.

Applications: Used to identify stress related to drought over different landscapes. Mainly associated with the development of droughts affecting agriculture.

Strengths: High resolution and good spatial coverage over all terrains.

Weaknesses: Stress to plant canopies could be caused by impacts other than drought, and it is difficult to discern them using only EVI. The period of record for satellite data is short, with climatic studies being difficult.

Resources: Methodology and calculations are provided in the literature, and online resources of products exist: http://www.star.nesdis.noaa.gov/smcd/ emb/vci/VH/vh_browse.php.

Reference: Huete et al. (2002).

Index name: Evaporative Stress Index (ESI)

Ease of use: Green

Origins: Developed by a team led by Anderson, in which remotely sensed data were used to compute evapotranspiration over the United States. The team was composed of scientists from the US Department of Agriculture, the University of Alabama-Huntsville, and the University of Nebraska-Lincoln.

Characteristics: Established as a new drought index in which evapotrans- piration is compared to potential evapotranspiration using geostationary satellites. Analyses suggest that it performs similarly to short-term precipitation-based indices, but can be produced at a much higher resolution and without the need for precipitation data.

Input parameters: Remotely sensed potential evapotranspiration.

Applications: Especially useful for identifying and monitoring droughts that have multiple impacts.

Strengths: Very high resolution with a spatial coverage of any area.

Weaknesses: Cloud cover can contaminate and affect results. There is not a long period of record for climatological studies.

Resources: Calculations of the index are provided in the literature: http:// hrsl.arsusda.gov/drought/.

Reference: Anderson et al. (2011).

Index name: Normalized Difference Vegetation Index (NDVI)

Ease of use: Green

Origins: Developed from work done by Tarpley et al. and Kogan with the National Oceanic and Atmospheric Administration (NOAA) in the United States.

Characteristics: Uses the global vegetation index data, which are produced by mapping 4 km daily radiance. Radiance values measured in both the visible and near-infrared channels are used to calculate NDVI. It measures greenness and vigor of vegetation over a 7-day period as a way of reducing cloud contamination and can identify drought-related stress to vegetation.

Input parameters: NOAA AVHRR satellite data.

Applications: Used for identifying and monitoring droughts affecting agriculture.

Strengths: Innovative in the use of satellite data to monitor the health of vegetation in relation to drought episodes. Very high resolution and great spatial coverage.

Weaknesses: Data processing is vital to NDVI, and a robust system is needed for this step. Satellite data do not have a long history.

Resources: The literature describes the methodology and calculations. NDVI products are available online: http://www.star.nesdis.noaa.gov/smcd/emb/ vci/VH/vh_browse.php.

Index name: Temperature Condition Index (TCI)

Ease of use: Green

Origins: Developed from work done by Kogan with NOAA in the United States.

Characteristics: Using AVHRR thermal bands, TCI is used to determine stress on vegetation caused by temperatures and excessive wetness. Conditions are estimated relative to the maximum and minimum temperatures and modified to reflect different vegetation responses to temperature.

Input parameters: AVHRR satellite data.

Applications: Used in conjunction with NDVI and the Vegetation Condition Index (VCI) for drought assessment of vegetation in situations where agricultural impacts are the primary concern.

Strengths: High resolution and good spatial coverage.

Weaknesses: Potential for cloud contamination as well as a short period of record.

Resources: Methodology and calculations are provided in the literature, and online resources of products exist: http://www.star.nesdis.noaa.gov/smcd/ emb/vci/VH/vh_browse.php.

Reference: Kogan (1995b).

Index name: Vegetation Condition Index (VCI)

Ease of use: Green

Origins: Developed from work done by Kogan with NOAA in the United States.

Characteristics: Using AVHRR thermal bands, VCI is used to identify drought situations and determine the onset, especially in areas where drought episodes are localized and ill defined. It focuses on the impact of drought on vegetation and can provide information on the onset, duration, and severity of drought by noting vegetation changes and comparing them with historical values.

Input parameters: AVHRR satellite data.

Applications: Used in conjunction with NDVI and TCI for assessment of vegetation in drought situations affecting agriculture.

Strengths: High resolution and good spatial coverage.

Weaknesses: Potential for cloud contamination as well as a short period of record.

Resources: Methodology and calculations are provided in the literature, and online resources of products exist: http://www.star.nesdis.noaa.gov/smcd/ emb/vci/VH/vh_browse.php.

References: Kogan (1995b); Liu and Kogan (1996).

Index name: Vegetation Drought Response Index (VegDRI)

Ease of use: Green

Origins: Developed by a team of scientists from NDMC, the United States Geological Survey's Earth Resources Observation and Science Center, and the United States Geological Survey Flagstaff Field Center.

Characteristics: Developed as a drought index that was intended to monitor drought-induced vegetation stress using a combination of remote sensing, climate-based indicators, and other biophysical information and land-use data.

Input parameters: SPI, PDSI, percentage annual seasonal greenness, start of season anomaly, land cover, soil available water capacity, irrigated agriculture, and defined ecological regions. As some of the inputs are derived variables, additional inputs are needed.

Applications: Used mainly as a short-term indicator of drought for agricultural applications.

Strengths: An innovative and integrated technique using both surface and remotely sensed data, and technological advances in data mining.

Weaknesses: Short period of record due to remotely sensed data. Not useful out of season or during periods of little or no vegetation.

Resources: The methods used and a description of the calculations can be found in the reference given below. See also http://vegdri.unl.edu/.

Reference: Brown et al. (2008).

Index name: Vegetation Health Index (VHI)

Ease of use: Green

Origins: The result of work done by Kogan with NOAA in the United States.

Characteristics: One of the first attempts to monitor and identify drought- related agricultural impacts using remotely sensed data. AVHRR data in the visible, infrared, and near-infrared channels are all used to identify and classify stress to vegetation due to drought.

Applications: Used to identify and monitor droughts affecting agriculture around the world.

Strengths: Coverage over the entire globe at a high resolution.

Weaknesses: The period of record for satellite data is short.

Resources: The calculations and sample case studies are given in the literature. VHI maps can be found online at http://www.star.nesdis.noaa.gov/ smcd/emb/vci/VH/vh_browse.php.

References: Kogan (1990, 1997, 2001).

Index name: Water Requirement Satisfaction Index (WRSI) and Geo-spatial WRSI

Ease of use: Green

Origins: Developed by the Food and Agriculture Organization of the United Nations to monitor and investigate crop production in famine-prone parts of the world. Additional work was done by the Famine Early Warning Systems Network.

Characteristics: Used to monitor crop performance during the growing season and based upon how much water is available for the crop. It is a ratio of actual to potential evapotranspiration. These ratios are crop specific, and are based upon crop development and known relationships between yields and drought stress.

Input parameters: Crop development models, crop coefficients, and satellite data.

Applications: Used to monitor crop development progress and stress related to agriculture.

Strengths: High resolution and good spatial coverage over all terrains.

Weaknesses: Stress related to factors other than available water can affect the results. Satellite- based rainfall estimates have a degree of error that will affect the results of the crop models used and the balance of evapotranspiration.

Resources: http://chg.geog.ucsb.edu/tools/geowrsi/index.html http://iridl. ldeo.columbia.edu/documentation/usgs/adds/wrsi/WRSI_readme.pdf.

Reference: Verdin and Klaver (2002).

Index name: Normalized Difference Water Index (NDWI) and Land Surface Water Index (LSWI)

Ease of use: Green

Origins: Developed from work done by Gao in the mid-1990s at the National Aeronautics and Space Administration (NASA) Goddard Space Center in the United States.

Characteristics: Very similar to the NDVI methodology, but uses the near-infrared channel to monitor the water content of the vegetation canopy. Changes in the vegetation canopy are used to identify periods of drought stress.

Input parameters: Satellite information in the various channels of the nearinfrared spectrum.

Applications: Used for monitoring of drought affecting agriculture as a method of stress detection.

Strengths: High resolution and good spatial coverage over all terrains. Different to NDVI, as the two indices look at different signals.

Weaknesses: Stress to plant canopies can be caused by impacts other than drought, and it is difficult to discern them using only NDWI. The period of record for satellite data is short, with climatic studies being difficult.

Resources: The methodology is described in the literature as are the calculations based on the MODIS data being used: http://www.eomf.ou.edu/ modis/visualization/.

References: Chandrasekar et al. (2010); Gao (1996).

Note: The NDWI concept and calculations are very similar to those of the Land Surface Water Index (LSWI).

Index name: Soil Adjusted Vegetation Index (SAVI)

Ease of use: Red

Origins: Developed by Huete at the University of Arizona, United States, in the late 1980s. The idea was to have a global model for monitoring soil and vegetation from remotely sensed data.

Characteristics: SAVI is similar to NDVI—spectral indices may be calibrated in such a way that the variations of soils are normalized and do not influence measurements of the vegetation canopy. These enhancements to NDVI are useful because SAVI accounts for variations in soils.

Input parameters: Remotely sensed data, which are then compared to known surface plots of various vegetation.

Applications: Useful for the monitoring of soils and vegetation.

Strengths: High-resolution and high-density data associated with remotely sensed data allow for very good spatial coverage.

Drought and Water Crises

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Weaknesses: Calculations are complex, as is obtaining data to run operationally. A short period of record associated with the satellite data can hamper climate analyses.

Resources: The methodology and associated calculations are explained well in the literature.

Reference: Huete (1988).

 
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