Extending the normalized difference vegetation index (NDVI) to short-wave infrared radiation (SWIR) (1- to 2.5-μm)

Author(s):  
Rulon R. Mayer ◽  
Dean A. Scribner
Fire ◽  
2021 ◽  
Vol 4 (4) ◽  
pp. 68
Author(s):  
Sarah A. Lewis ◽  
Peter R. Robichaud ◽  
Andrew T. Hudak ◽  
Eva K. Strand ◽  
Jan U. H. Eitel ◽  
...  

As wildland fires amplify in size in many regions in the western USA, land and water managers are increasingly concerned about the deleterious effects on drinking water supplies. Consequences of severe wildfires include disturbed soils and areas of thick ash cover, which raises the concern of the risk of water contamination via ash. The persistence of ash cover and depth were monitored for up to 90 days post-fire at nearly 100 plots distributed between two wildfires in Idaho and Washington, USA. Our goal was to determine the most ‘cost’ effective, operational method of mapping post-wildfire ash cover in terms of financial, data volume, time, and processing costs. Field measurements were coupled with multi-platform satellite and aerial imagery collected during the same time span. The image types spanned the spatial resolution of 30 m to sub-meter (Landsat-8, Sentinel-2, WorldView-2, and a drone), while the spectral resolution spanned visible through SWIR (short-wave infrared) bands, and they were all collected at various time scales. We that found several common vegetation and post-fire spectral indices were correlated with ash cover (r = 0.6–0.85); however, the blue normalized difference vegetation index (BNDVI) with monthly Sentinel-2 imagery was especially well-suited for monitoring the change in ash cover during its ephemeral period. A map of the ash cover can be used to estimate the ash load, which can then be used as an input into a hydrologic model predicting ash transport and fate, helping to ultimately improve our ability to predict impacts on downstream water resources.


2015 ◽  
Vol 8 (3-4) ◽  
pp. 11-20 ◽  
Author(s):  
András Gulácsi ◽  
Ferenc Kovács

Abstract In this study a new remote sensing drought index called Difference Drought Index (DDI) was introduced. DDI was calculated from the Terra satellite’s MODIS sensor surface reflectance data using visible red, near-infrared and short-wave-infrared spectral bands. To characterize the biophysical state of vegetation, vegetation and water indices were used from which drought indices can be derived. The following spectral indices were examined: Difference Vegetation Index (DVI), Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Difference Water Index (DWI), Normalized Difference Water Index (NDWI), Difference Drought Index (DDI) and Normalized Difference Drought Index (NDDI). Regression analysis with the Pálfai Drought Index (PaDi) and average annual yield of different crops has proven that the Difference Drought Index is applicable in quantifying drought intensity. However, after comparison with reference data NDWI performed better than the other indices examined in this study. It was also confirmed that the water indices are more sensitive to changes in drought conditions than the vegetation ones. In the future we are planning to monitor drought during growing season using high temporal resolution MODIS data products.


IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Yucel Cimtay ◽  
Busra Ozbay ◽  
Gokce Nur Yilmaz ◽  
Eda Bozdemir

2019 ◽  
pp. 25
Author(s):  
L. Hurtado ◽  
I. Lizarazo

<p>Time series analysis of satellite images for detection of deforestation and forest disturbances at specific dates has been a subject of research over the last few years. There are many limitations to identify the exact date of deforestation due mainly to the large volume of data and the criteria required for its correct characterization. A further limitation in the analysis of multispectral time series is the identification of true deforestation considering that forest vegetation may undergo different changes over time. This study analyzes deforestation in a zone within the Colombian Amazon using the Normalized Difference Vegetation Index (NDVI) based on semestral median mosaics generated from Landsat images collected from 2000 to 2017. Several samples representing trends of change over the time series were extracted and classified according to their degree of change and persistence in the series, using four categories: (i) deforestation, (ii) degradation, (iii) forest plantation, and (iv) regeneration. Specific deforestation samples were analyzed in the same way using the soil-adjusted vegetation index (SAVI) to reduce the effect of spectral response variations due to soil reflectance changes. It is concluded that the two indices used, together with the near infrared (NIR) and short-wave infrared (SWIR 1) spectral bands, allow to extract values and intervals where the change produced by deforestation on forest vegetation is identified with acceptable accuracy. The analysis of time series using the Landtrendr algorithm confirmed a reliable change detection in each of the forest disturbance categories.</p>


2009 ◽  
Vol 48 (3) ◽  
pp. 573-582 ◽  
Author(s):  
Pascal Le Bideau ◽  
Jean-Pierre Ploteau ◽  
Patrick Dutournié ◽  
Patrick Glouannec

2009 ◽  
Vol 94 (8) ◽  
pp. 081120 ◽  
Author(s):  
Kristina Driscoll ◽  
Yitao Liao ◽  
Anirban Bhattacharyya ◽  
Lin Zhou ◽  
David J. Smith ◽  
...  

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