Dry-season vegetation mass and cover fraction from SWIR1.6 and SWIR2.1 band ratio: Ground-radiometer and MODIS data in the Sahel

Kergoat, Laurent; Hiernaux, Pierre; Dardel, Cécile; Pierre, Caroline; Guichard, Françoise; Kalilou, Amadou Adamou

doi:10.1016/j.jag.2015.02.011

Cited by 22 publications

(18 citation statements)

References 22 publications

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“…STI is calculated with MODIS nadir bidirectional reflectance distribution function‐adjusted surface reflectance, which is available every 8 days at a 5 km spatial resolution, starting in 2000 for MODIS onboard Terra and 2002 for MODIS onboard Aqua. STI is converted into vegetation cover and mass using the linear equations (3) and (5) from Kergoat et al []. NDVI is used to characterize green vegetation during the rainy season.…”

Section: Methodsmentioning

confidence: 99%

“…Dry‐season vegetation cover is derived from the Soil Tillage Index [ Guerschman et al , ], hereafter STI, which is the ratio of MODIS short‐wave infrared bands at 1.6 and 2.1 μm. Jacques et al [] and Kergoat et al [] have shown that the mass of herbaceous plants in Sahelian rangelands is linearly related to STI up to 1500 kg dry matter/ha in both rainy and dry seasons. Kergoat et al [] further found that the fraction of dry‐vegetation cover for rangelands and millet residues is linearly related to STI up to 20% cover.…”

Section: Methodsmentioning

confidence: 99%

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Influence of dry‐season vegetation variability on Sahelian dust during 2002–2015

Kergoat

Guichard

Pierre

et al. 2017

Geophysical Research Letters

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The drivers of dust emission interannual variability in North Africa, the largest dust source on Earth, are still debated. Early studies outlined the role of previous‐season rainfall and vegetation growth, while some recent studies emphasize the role of wind variability. Here we use a newly developed estimation of dry‐season nonphotosynthetic vegetation cover in the Sahel based on Moderate Resolution Imaging Spectroradiometer (MODIS) short‐wave infrared bands over the 2002–2015 period. The vegetation growth anomalies caused by variability of rainfall in June–September translate to anomalies of dry vegetation cover that persist throughout the dry season until May. These vegetation anomalies explain 43% (50%) of the year‐to‐year variance in Sahelian‐mean dry‐season aerosol optical depth (AOD) as derived from MODIS Deep Blue (Sun photometers). Similar explained variance is found with 10 m wind speed and dust uplift potential. The central Sahel proves more important than the western Sahel for dry‐season AOD variability.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Influence of dry‐season vegetation variability on Sahelian dust during 2002–2015

Kergoat

Guichard

Pierre

et al. 2017

Geophysical Research Letters

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“…A shortwave infrared channel is present in many land monitoring optical sensors, such as the Operational Land Imager (OLI), which has the SWIR 1 and SWIR 2 (1560-1660 nm and 2100-2300 nm, respectively) channels [22]. In vegetation studies, these channels have shown lower reflectance than other spectral regions due to absorption caused by water and biochemical content of vegetation [23], which has motivated their use in the dry-season [24,25] and in wetland vegetation [26]. Although some studies have proposed using the shortwave infrared channel from Moderate Resolution Imaging Spectroradiometer (MODIS) to map forest cover loss [27] and a ratio-based computation to monitor forest damage [28], the SWIR 2 channel from the Landsat series has been rarely used in Cerrado change detection.…”

Section: Introductionmentioning

confidence: 99%

Object-Based Change Detection in the Cerrado Biome Using Landsat Time Series

et al. 2019

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Change detection methods are often incapable of accurately detecting changes within time series that are heavily influenced by seasonal variations. Techniques for de-seasoning time series or methods that apply the spatial context have been used to improve the results of change detection. However, few studies have explored Landsat’s shortwave infrared channel (SWIR 2) to discriminate between seasonal changes and land use/land cover changes (LULCC). Here, we explored the effectiveness of Operational Land Imager (OLI) spectral bands and vegetation indices for detecting deforestation in highly seasonal areas of Brazilian savannas. We adopted object-based image analysis (OBIA), applying a multidate segmentation to an OLI time series to generate input data for discrimination of deforestation from seasonal changes using the Random Forest (RF) algorithm. We found adequate separability between deforested objects and seasonal changes using SWIR 2. Using spectral indices computed from SWIR 2, the RF algorithm generated a change map with an overall accuracy of 88.3%. For deforestation, the producer’s accuracy was 88.0% and the user’s accuracy was 84.6%. The SWIR 2 channel as well as the mid-infrared burn index presented the highest importance among spectral variables computed by the RF average impurity decrease measure. Our results give support to further change detection studies regarding to suitable spectral channels and provided a useful foundation for savanna change detection using an object-based method applied to Landsat time series.

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“…Karlson et al [36] showed that SWIR-based VIs were better at describing vegetation structure and aboveground biomass than red/NIR-based VIs in Sudano-Sahelian woodlands. Kergoat et al [37] demonstrated that SWIR-based VIs were associated with dry-season vegetation biomass and vegetation cover fraction in the Sahel. Caccamo et al [38] found that SWIR-based moisture indices were better at detecting vegetation change than red/NIR-based greenness indices because vegetation water content was more dynamic than changes in greenness properties.…”

Section: Introductionmentioning

confidence: 99%

Vegetation Dynamics in the Upper Guinean Forest Region of West Africa from 2001 to 2015

2016

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Abstract:The Upper Guinea Forest (UGF) region of West Africa is one of the most climatically marginal and human-impacted tropical forest regions in the world. Research on the patterns and drivers of vegetation change is critical for developing strategies to sustain ecosystem services in the region and to understand how climate and land use change will affect other tropical forests around the globe. We compared six spectral indices calculated from the 2001-2015 MODIS optical-infrared reflectance data with manually-interpreted measurements of woody vegetation cover from high resolution imagery. The tasseled cap wetness (TCW) index was found to have the strongest association with woody vegetation cover, whereas greenness indices, such as the enhanced vegetation index (EVI), had relatively weak associations with woody cover. Trends in woody vegetation cover measured with the TCW index were analyzed using Mann-Kendall statistics and were contrasted with trends in vegetation greenness measured with EVI. In the drier West Sudanian Savanna and Guinean Forest-Savanna Mosaic ecoregions, EVI trends were primarily positive, and TCW trends were primarily negative, suggesting that woody vegetation cover was decreasing, while herbaceous vegetation cover is increasing. In the wettest tropical forests in the Western Guinean Lowland Forest ecoregion, declining trends in both TCW and EVI were indicative of widespread forest degradation resulting from human activities. Across all ecoregions, declines in woody cover were less prevalent in protected areas where human activities were restricted. Multiple lines of evidence suggested that human land use and resource extraction, rather than climate trends or short-term climatic anomalies, were the predominant drivers of recent vegetation change in the UGF region of West Africa.

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Dry-season vegetation mass and cover fraction from SWIR1.6 and SWIR2.1 band ratio: Ground-radiometer and MODIS data in the Sahel

Cited by 22 publications

References 22 publications

Influence of dry‐season vegetation variability on Sahelian dust during 2002–2015

Influence of dry‐season vegetation variability on Sahelian dust during 2002–2015

Object-Based Change Detection in the Cerrado Biome Using Landsat Time Series

Vegetation Dynamics in the Upper Guinean Forest Region of West Africa from 2001 to 2015

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