indices

Using drones to assess above-ground carbon stock

3D models of mangrove islands created by applying photogrammetric techniques on UAV imagery

3D models of mangrove islands created by applying photogrammetric techniques on UAV imagery

In our previous posts about assessing ecosystem carbon stocks through remote sensing, we discussed carbon estimation methods which involved the use of vegetation indices applied to satellite imagery, as well as those that utilized volumetric assessments of biomass derived from aerial surveys using LiDAR and RGB sensors. Several papers discussed the potential of using Unmanned Aerial Vehicles (UAVs) to study forest health and carbon estimation, which we take a closer look at in this post.

 

Methods

1.      Carbon estimation through Above Ground Biomass (AGB) derived from Canopy Height Model (CHM)

This method uses RGB data collected from UAVs to create 3D models of tree clusters. From this information, a Digital Terrain Model (DTM) and Digital Surface Model (DSM) are computed, and a Canopy Height Model (CHM) is created. This CHM can be used to deduce median values for height and Diameter at Breast Height (DBH), from which AGB and subsequently carbon stock can be calculated.

Based on existing literature (Ota et al. 2015; González-Jaramillo, Fries, & Bendix 2019), it appears that using CHMs and hence AGB values derived completely from RGB data is not very accurate, primarily because imagery from UAVs cannot be used to build accurate DTMs. Since forest canopies are opaque to RGB imagery, getting good information on the varying height of the terrain at ground level is difficult. González-Jaramillo et al. (2019) found that the correlation between a CHM derived from RGB data and a CHM derived from LiDAR data was very low. Both these studies thus proceeded to use a DTM derived through LiDAR surveys of the area along with RGB based DSMs to derive CHMs. Values for AGB derived from this combination of sensors showed high correlation with those derived only by LiDAR, as well as with those derived through more traditional allometric methods.

It’s interesting to note that González-Jaramillo et al. (2019), did not conduct LiDAR based data collection for this study. They compared the accuracy of UAV based methods against results from a previous study in the same area done using LiDAR. Having access to this data allowed them to create a CHM model that combined LiDAR and RGB imagery. Due to the high costs and computational power required for LiDAR, they recommend that having a single accurate LiDAR-based DTM for an area would be sufficient to do subsequent studies using only UAVs. This is especially useful for forests that require repeated volume estimations, such as for REDD+ or carbon credit programs. Based on these studies, this method seems like a useful application to derive UAV based carbon estimates, provided that a high resolution DTM is available for the area.

 

2.      REDD+ Protocol, as developed by Goslee et al. (2016).

 

This method is based on field work and allometric equations. Sample circular plots are identified in the area for which carbon is to be estimated. The area of these plots is calculated and used to derive a scaling factor. Field work is then conducted in accordance with established protocols to estimate the AGB of sample trees using allometric equations. The scaling factor is used to extrapolate to the plot and entire area of concern. It may be possible to replace some of the tedious fieldwork with data obtained from UAV imagery.

The basic formula is:
𝐶𝑝 = 𝐷𝑀 ∗ 𝐶𝐹 
where
𝐶𝑝 = carbon stock in plot 
DM = dry biomass in plot
CF = carbon fraction



Based on this formula, it would be interesting to test how dry biomass values derived from UAV imagery would compare to results based on field work. RGB images from a UAV could be used to generate a 3D model of the relevant forest plot, which could then be used to derive volume and hence mass in the plot. Estimating dry biomass from this could be done based on the wet vs dry ratio of the tree species. Standardised carbon fractions are publicly available for most tree species.

 

Since this method specifies which field work protocols are to be used, replacing variables by deriving them through UAV-based RGB data would need to be tested adequately. Results from both methods on the same plots would need to be correlated to check accuracy and determine whether this is truly an option. The scale of the area used in this method varies. Especially for larger areas, using UAVs could potentially increase  both the speed and accuracy of the surveys, since it would reduce the time spent on field work as well as provide estimates on the volume of entire plots, rather than having to sample individual trees per plot for extrapolation.

 

3.      Biomass Expansion Factor (BEF) Method:

In this method, above-ground biomass density is derived from the volume of biomass, wood density and BEF (Brown 1997).  This method is generally based on data from National Forest Inventories (NFI). These are records of forest attributes such as species, DBH, age, class, etc generally collected by governments which is used to estimate volume of the biomass of the given area. This is then combined with the species-specific wood density (standardised and available for different tree species) as well as BEF to estimate Aboveground Biomass and then carbon. BEF is the ratio of aboveground oven-dry biomass of trees to oven-dry biomass of inventoried volume. Standardised constants of BEF have been developed by the IPCC (2014) for use across various forest types.

Volumetric assessments derived through NFI data have been cited as one of the shortcomings of this method (Shi & Liu 2017) since the resolution and relevance of the NFI data varies greatly across the world. We see merit in experimenting to see whether using UAVs could fill this gap. Deriving volume data of forests by creating 3D models with UAVs could be useful in situations that require updated and accurate carbon estimations of relatively smaller forested areas that can be mapped by UAVs. For such areas, this could be an efficient way to estimate carbon depending on the accuracy of the results.

 

4. Normalized Difference Vegetation Index (NDVI) Method:

Carbon estimation through satellite-derived vegetation indices are well documented. The same methods can be applied using multispectral UAVs for higher resolution and accuracy. Tian et al. (2017) tested the correlation of AGB derived by various vegetation indices using a multispectral UAV and found that NDVI showed the highest correlation with AGB. However, even their best model only showed a moderate correlation with the AGB (r^2=0.67). Following this, Gonzalez et al (2019) compared results of AGB derived through a UAV based NDVI with that derived from LiDAR for the same area, and showed no correlation. According to them, this was because the UAV-based sensors they were using were saturated, and the resulting NDVI wasn’t suitable to provide forest structure information. Based on this, it seems possible to use UAVs to derive carbon estimates through vegetation indices in contexts where the NDVI varies significantly within the plot.

 

Experiment

Recently, we’ve been experimenting with collecting data on above-ground biomass from UAV based volumetric assessments over mangrove forests, and have some preliminary results. On the mangrove islands indicated below, we’ve calculated that there’s approximately 57,000 cubic metres of above-ground biomass over an area of 3 acres.

Three acres of mangrove forest across two islands, sequestering ~ 27,000 tons of carbon (with huge caveats!).

Three acres of mangrove forest across two islands, sequestering ~ 27,000 tons of carbon (with huge caveats!).

Using the REDD+ protocol, and standard values for the carbon fraction, we estimate that there’s approximately 27,000 tons of above-ground carbon in this plot (calculated value of 8,837 tons/acre). This is definitely an overestimation, possibly by an order of magnitude; the two main factors being due to all the empty space being accounted for in the canopy, as well as because these equations use dry biomass values. With more field work and research, we’ll be able to obtain site-specific scaling factors that we can use to estimate a stronger relationship between the volume of a mangrove forest and the carbon sequestered within it.

In conclusion, with UAV technology more accessible, it seems like the right time to test what is possible to better understand forest health using this platform. Using UAV data in combination with other data sources and testing various methods may produce interesting results and take us a few steps closer to efficient and accessible monitoring of forest carbon and health through remote sensing. If you have any comments or suggestions regarding the suitability of these methods, or could recommend additional methods, please let us know!

 

References

 

·         Ota, T., Ogawa, M., Shimizu, K., Kajisa, T., Mizoue, N., Yoshida, S., … Ket, N. (2015). Aboveground Biomass Estimation Using Structure  from Motion Approach with Aerial Photographs in a  Seasonal Tropical Forest. Forests6(12), 3882–3898. https://doi.org/10.3390/f6113882

·         González-Jaramillo, V., Fries, A., & Bendix, J. (2019). AGB Estimation in a Tropical Mountain Forest (TMF) by Means of RGB and Multispectral Images Using an Unmanned Aerial Vehicle (UAV). Remote Sensing11(12), 1413. https://doi.org/10.3390/rs11121413

·         Lei Shi and Shirong Liu (February 22nd 2017). Methods of Estimating Forest Biomass: A Review, Biomass Volume Estimation and Valorization for Energy, Jaya Shankar Tumuluru, IntechOpen, DOI: 10.5772/65733.

 

·         Kauppi, P. E., Ausubel, J. H., Fang, J., Mather, A. S., Sedjo, R. A., & Waggoner, P. E. (2006). Returning forests analyzed with the forest identity. Proceedings of the National Academy of Sciences103(46), 17574–17579. https://doi.org/10.1073/pnas.0608343103

 

·         Goslee, K., Walker, S. M., Grais, A., Murray, L., Casarim, F., & Brown, S. (2016). Module C-CS: calculations for estimating carbon stocks. Leaf technical guidance series for the development of a forest carbon monitoring system for REDD+. Winrock International.          https://www.leafasia.org/sites/default/files/tools/Winrock_LEAF_REDD_TechSeries_C-CS_0.pdf

 

·         Inter Governmental Panel on Climate Change. (2014). IPCC Good Practice Guidance for LULUCF (Annex 3A.1 Biomass Default Tables for Section 3.2 Forest Land). Retrieved from https://www.ipcc-nggip.iges.or.jp/public/gpglulucf/gpglulucf_files/Chp3/Anx_3A_1_Data_Tables.pdf

 

·         Brown, S. (1997). Estimating biomass and biomass change of tropical forests: a primer (Vol. 134). Food & Agriculture Org. https://books.google.com/books?hl=en&lr=&id=uv-ISezvitwC&oi=fnd&pg=PA1&dq=Estimating+Biomass+and+Biomass+Change+of+Tropical+Forests:+a+Primer.+(FAO+Forestry+Paper+-+134)&ots=OCwbWs7WzH&sig=xaDzjevMu6Yg5jwbBuciglfkeIQ

 

·         Brown, Sandra., & Lugo, A. E. (1992). Aboveground biomass estimates for tropical moist forests of the Brazilian Amazon. Interciencia. Caracas17(1), 8-18.

 

·         Tian, J., Wang, L., Li, X., Gong, H., Shi, C., Zhong, R., & Liu, X. (2017). Comparison of UAV and WorldView-2 imagery for mapping leaf area index of mangrove forest. International Journal of Applied Earth Observation and Geoinformation61, 22–31. https://doi.org/10.1016/j.jag.2017.05.002

 

 

Analysing Drone and Satellite Imagery using Vegetation Indices

A majority of our ecosystem monitoring work involves acquiring, analysing and visualising satellite and aerial imagery. Creating true-colour composites, using the Red, Green and Blue (RGB) bands, allows us to actually view the land areas we’re studying. However, this is only a first step; creating detailed reports on deforestation, habitat destruction or urban heat islands requires us to extract more detailed information, which we do by conducting mathematical operations on the spectral bands available from any given sensor. For example, we can extract surface temperature from Landsat 8 satellite data, as detailed in a previous blogpost.

A true-colour composite image created using data from Landsat 8 bands 2, 3 and 4.

As you may imagine, understanding how much vegetation is available in any given pixel is essential to many of our projects, and for this purpose, we make use of Vegetation Indices. In remote sensing terms, a Vegetation Index is a single number that quantifies vegetation within a pixel. It is extracted by mathematically combining a number of spectral bands based on the physical parameters of vegetation, primarily the fact that it absorbs more more light in the red (R) than in the near-infrared (NIR) region of the spectrum.  These indices can be used to ascertain information such as vegetation presence, photosynthetic activity and plant health, which in turn can be used to look at climate trends, soil quality, drought monitoring and changes in forest cover. In this blogpost, we’re going to provide a technical overview of some of the vegetation indices available for analysing both aerial and satellite imagery. We’ve included the basic formulae used to calculate the indices, using a bracketing system that allows for the formulae to be copy-pasted directly into the Raster Algebra (ArcMap) and Raster Calculator (QGIS) tools; don’t forget to replace the Bx terms with the relevant band filenames when doing the calculations! We’ve also noted down the relevant band combinations for data from Landsat 8’s Operational Land Imager and both the Sentinel-2’s MultiSpectral Instruments.

We’ve created maps for most of the vegetation indices described below, using data from Landsat 8 acquired over Goa, India on the 28th of December 2018. Each band was clipped to the area of interest and the Digital Numbers were rescaled to calculate Top-of-Atmosphere radiance values. All the index calculations were then executed on these clipped and corrected bands. We used a single min-max stretched red-to-green colour gradient to visualise each index. For actual projects, we’d then classify each image to provide our partners with meaningful information.

The Basic Vegetation Indices

Ratio Vegetation Index

One of the first Vegetation Indices developed was the Ratio Vegetation Index (RVI) (Jordan 1969) which can be used to estimate and monitor above-ground biomass. While the RVI is very effective for the estimation of biomass, especially in densely-vegetated areas, it is sensitive to atmospheric effects when the vegetation cover is less than 50%, (Xue et al. 2017).

RVI = R / NIR

Sentinel 2: B4 / B8

Landsat 8: B4 / B5

 

Difference Vegetation Index

The Difference Vegetation Index (DVI) (Richardson et al. 1977) was developed to distinguish between soil and vegetation, and as the name suggests, is a simple difference equation between the red and near-infrared bands.

DVI = NIR - R

Sentinel 2: B8 - B4

Landsat 8: B5 - B4

Normalised Difference Vegetation Index

The Normalised Difference Vegetation Index (NDVI) (Rouse Jr. et al. 1974) was developed as an index of plant “greenness” and attempts to track photosynthetic activity. It has since become one of the most widely applied indices. Like the RVI and the DVI, it is also based on the principle that well-nourished, living plants absorb red light and reflect near-infrared light. However, it also takes into account the fact that stressed or dead vegetation absorbs comparatively less red light than healthy vegetation, bare soil reflects both red and near-infrared light about equally, and open water absorbs more infrared than red light. The NDVI is a relative value and cannot be used to compare between images taken at different times or from different sensors. NDVI values range from -1 to +1, where higher positive values indicate the presence of greener and healthier plants. The NDVI is widely used due to its simplicity, and several indices have been developed to replicate or improve upon it.

NDVI = NIR - R / NIR + R

Sentinel 2: B8 - B4 / B8 + B4

Landsat 8: B5 - B4 / B5 + B4

 

Synthetic NDVI

Synthetic NDVI

The Synthetic NDVI is an index that attempts to predict NDVI values using only Red and Green bands. Hence it can be applied to imagery collected from any RGB sensor., including those used on consumer-level drones. Like the NDVI, its values also range from -1 to +1, with higher values suggesting the presence of healthier plants. However, it is not as accurate as the NDVI and needs to be calibrated using ground information to be truly useful. It is also known as the Green Red Vegetation Index (GRVI) (Motohka et al. 2010).

Synthetic NDVI = ( G - R ) / ( G + R )

Sentinel 2: ( B3 - B4 ) / ( B3 + B4 )

Landsat 8: ( B3 - B4) / ( B3 + B4 )

 

Visible Difference Vegetation Index

Similarly, the Visible Difference Vegetation Index (VDVI) (Wang et al. 2015) can also be calculated using information from only the visible portion of the electromagnetic spectrum. Some studies indicate that VDVI is better at extracting vegetation information and predicting NDVI than other RGB-only indices,.

VDVI = ( (2*G) - R - B ) / ( (2 * G) + R + B )

Sentinel 2:  ( ( 2 * B3 ) - B4 - B2 ) / ( (2 * B3 ) + B4 + B2 )

Landsat 8: ( ( 2 * B3 ) - B4 - B2 ) / ( ( 2 * B3 ) + B4 + B2 ) 

  

Excess Green Index

The Excess Green Index (ExGI) contrasts the green portion of the spectrum against red and blue to distinguish vegetation from soil, and can also be used to predict NDVI values. It has been shown to outperform other indices (Larrinaga et al. 2019) that work with the visible spectrum to distinguish vegetation.

ExGI = ( 2 * G ) - ( R + B )

Sentinel 2: ( 2 * B3) - ( B4 + B2 )

Landsat 8: ( 2 * B3 ) - ( B4 + B2 )

Green Chromatic Coordinate

The Green Chromatic Coordinate (GCC) is also an RGB index (Sonnentag et al. 2012) which has been used to examine plant phenology in forests.

GCC = G / ( R + G + B )

Sentinel 2: B3 / ( B4 + B3 + B2 )

Landsat 8: B3 / ( B4 + B3 + B2 )

One of the primary shortcomings of the NDVI is that it is sensitive to atmospheric interference, soil reflectance and cloud- and canopy- shadows. Indices have thus been developed that help address some of these shortcomings.

Indices that address Atmospheric (and other) Effects

Enhanced Vegetation Index

The Enhanced Vegetation Index (EVI) was devised as an improvement over the NDVI (Heute et al. 2002) to be more effective in areas of high biomass, where it is possible for NDVI values to become saturated. The EVI attempts to reduce atmospheric influences, including aerosol scattering, and correct for canopy background signals. In remote sensing terms, a saturated index implies a failure to capture variation due to the maximum values being registered for some pixels. 

EVI = 2.5 * ( ( NIR - R ) / ( NIR + (6 * R) - ( 7.5 * B ) + 1 ) )

Sentinel 2: 2.5 * ( ( B8 - B4) / ( B8 + ( 6 * B4) - ( 7.5 * B2 ) + 1) )

Landsat 8: 2.5 * ( ( B5 - B4) / ( B5 + ( 6 * B4) - ( 7.5 * B2 ) + 1 ) )

 

Atmospheric Reflection Vegetation Index

The Atmospheric Reflection Vegetation Index (ARVI) was developed specifically to eliminate atmospheric disturbances (Kaufman et al. 1992).  However, for a complete elimination of aerosols and the ozone effect, the atmospheric transport model has to be implemented, which is complicated to calculate and for which the data is not always easily available.  Without integrating this model into the calculation, the ARVI is not expected to outperform the NDVI in terms of accounting for atmospheric effects, but can still be useful as an alternative to it.

ARVI (w/o atmospheric transport model) = ( NIR – ( R * B ) ) / ( NIR + (R * B) )

Sentinel 2: ( B8 - ( B4 * B2 ) ) / ( B8 + ( B4 * B2 ) )

Landsat 8: ( B5 - ( B4 * B2) ) / ( B5 + (B4 * B2 ) )

 

Green Atmospherically Resistant Index

The Green Atmospherically Resistant Index (GARI) was also developed to counter the effects of atmospheric interference in satellite imagery. It shows much higher sensitivity to chlorophyll content (Gitelson et al. 1996) and lower sensitivity to atmospheric interference.

GARI = ( NIR – ( G – ( γ * ( B – R ) ) ) ) / ( NIR + ( G – ( γ * ( B – R ) ) ) )

Sentinel 2: ( B8 – ( B3 – ( γ * ( B2 – B4 ) ) ) ) / ( B8 + ( B3 – ( γ * (B2-B4) ) ) )

  Landsat 8: ( B5 – ( B3 – ( γ * ( B2 – B4 ) ) ) ) / ( B5 + [ B3 – ( γ * ( B2 – B4) ) ) )

In the formula above, γ is a constant weighting function that the authors suggested be set at 1.7 (Gitelson et al. 1996, p 296) but may have to be recalibrated in areas of complete canopy coverage. For this image, we used a γ value of 1.

 

Visible Atmospherically Resistant Index

The Visible Atmospherically Resistant Index (VARI) can be used to account for atmospheric effects in RGB imagery.

VARI = ( G - R) / ( G + R - B )

Sentinel 2: ( B3 - B4 ) / ( B3 + B4 - B2 )

Landsat 8: ( B3 - B4 ) / ( B3 + B4 - B2 )

Addressing Soil Reflectance

As in the case of atmospheric effects, indices were also developed to address the effects of varying soil reflectance.

Soil Adjusted Vegetation Index

The Soil Adjusted Vegetation Index is a modified version of the NDVI designed specifically for areas with very little vegetative cover, usually less than 40% by area. Depending on the type and water content, soils reflect varying amounts of red and infrared light. The SAVI accounts for this by suppressing bare soil pixels.

SAVI = [ ( NIR – R ) / ( NIR + R + L ) ] * (1 + L)

Sentinel 2: [ ( B8 – B4 ) / ( B8 + B4 + L ) ] * (1 + L)

Landsat 8: [ ( B5 – B4 ) / (B5 + B4 + L ) ] * (1 + L) 

In the above equations, L is a function of vegetation density; calculating L requires a priori information about vegetation presence in the study area. It ranges from 0-1 (Xue et al. 2017) with higher vegetation coverages resulting values approaching 1.  

 

The Modified Chlorophyll Absorption in Reflectance Index (MCARI) was developed as a vegetation status index. The Chlorophyll Absorption in Reflective Index (Kim 1994) was initially designed to distinguish non-photosynthetic material from photosynthetically active vegetation. The MCARI is a modification of this index and is defined as the depth of chlorophyll absorption (Daughtry et al. 2000) in the Red region of the spectrum relative to the reflectance in the Green and Red-Edge regions.  

MCARI = (Red-Edge - R ) - 0.2 * ( Red-Edge - G) * ( Red-Edge / Red )

Sentinel 2: ( B5 - B4) - 0.2 * ( B5 - B3) * ( B5 / B4)

 Landsat 8: No true equivalent

The Structure Insensitive Pigment Index (SIPI) is also a vegetation status index, with reduced sensitivity to canopy structure and increased sensitivity to pigmentation. Higher SIPI values are strongly correlated with an increase in carotenoid pigments, which in turn indicate vegetation stress. This index is thus very useful in the monitoring of vegetation health.

SIPI = (800nm - 445nm) / (800nm - 680nm)

Sentinel 2: (B8 - B1) / (B8 - B4)

Landsat 8: (B5 - B1 ) /( B5 - B4)

Agricultural Indices

Some indices that were initially designed for agricultural purposes can also be used for the ecological monitoring of vegetation.

Triangular Greenness Index

The Triangular Greenness Index (TGI) was developed to monitor chlorophyll and indirectly, the nitrogen content of leaves (Hunt et al. 2013) to determine fertilizer application regimes for agricultural fields. It can be calculated using RGB imagery and serves as a proxy for chlorophyll content in areas of high leaf cover.

 TGI = 0.5 * ( ( ( λR - λB ) * ( R - G) ) - ( ( λR - λG ) * ( R - B ) ) )

Sentinel 2A: 0.5 * ( ( ( 664.6 - 492.4 ) * ( B4 - B3 ) ) - ( ( 664.6 - 559.8) * ( B4 - B2 ) ) )

Sentinel 2B: 0.5 * ( ( ( 664.9 - 492.1 ) * ( B4 - B3 ) ) - ( ( 664.9 - 559.0 ) * ( B4 - B2 ) ) )

Landsat 8: 0.5 * ( ( ( 654.59 - 482.04 ) * ( B4 - B3 ) ) - ( ( 654.59 - 561.41 ) * ( B4 - B2 ) ) )

In the above equations, λ represents the center wavelengths of the respective bands; the central wavelengths of Sentinel 2A and Sentinel 2B vary slightly.

 

Normalised Difference Infrared Index

The Normalised Difference Infrared Index (NDII) uses a normalized difference formulation instead of a simple ratio. It is a reflectance measurement that is sensitive to changes in the water content of plant canopies, and higher values in the index are associated with increasing water content. The NIDI can be used for agricultural crop management, forest canopy monitoring, and the detection of stressed vegetation.

NDII = ( NIR - SWIR ) / (NIR + SWIR )

Sentinel 2 : ( B8 - B11 ) / ( B8 + B11 )

Landsat 8: ( B5 - B6) / ( B5 + B6 )

Green Leaf Index

The Green Leaf Index (GLI) was originally designed for use with a digital RGB camera to measure wheat cover. It can also be applied to aerial and satellite imagery.

GLI = ( ( G - R ) + ( G - B ) ) / ( ( 2 * G ) + ( B + R ) )

Sentinel 2: ( ( B3 - B4 ) + ( B3 - B2 ) ) / [ ( 2 * B3) + ( B2 + B4 ) )

Landsat 8:  ( ( B3 - B4 ) + ( B3 - B2 ) ) / [ ( 2 * B3) + ( B2 + B4 ) )

 

Task-specific Vegetation Indices

As we can see, one index might be more appropriate than another based on the purpose of your study and the source of the imagery. The following section lists indices developed to meet the needs of specific research requirements.

Transformed Difference Vegetation Index

The Transformed Difference Vegetation Index (TDVI) was developed to detect vegetation in urban settings where NDVI is often saturated.

TDVI = 1.5 * ( NIR - R ) / √( NIR^2 + R + 0.5)]

Sentinel 2: 1.5 * ( B8 - B4 ) / sqrt( B8^2 + B4 + 0.5)

Landsat 8: 1.5 * ( B5 - B4 ) / sqrt( B5^2 + B4 + 0.5)

Calculating square roots in QGIS Raster Calculator and ArcMap’s Raster Algebra have different syntaxes; QGIS uses ‘sqrt’ while ArcMap uses ‘SquareRoot’.

The Leaf Chlorophyll Index (LCI)  was developed to assess chlorophyll content in areas of complete leaf coverage.

LCI= ( NIR − RedEdge) / (NIR + R)

Sentinel 2: ( B8 - B5 ) / ( B8 + B4 )

Landsat 8: No true equivalent

Vegetation Fraction

The Vegetation Fraction is defined as the percentage of vegetation occupying the ground area; since it’s calculated using values generated from a NDVI, it is subject to the same errors. It’s a comprehensive quantitative index in forest management and an important parameter in ecological models, and can also be used to determine the emissivity parameter when calculating Land Surface Temperature.

Vegetation Fraction: [ NDVI - NDVI(min) ] / [ NDVI(max) - NDVI(min) ]

In this blogpost, we’ve listed down and organised the vegetation indices that we’ve found while improving our ecological monitoring techniques. We make extensive use of both satellite and drone imagery, and will be using this blogpost internally as a quick reference guide to vegetation indices.

Find us on Twitter @techforwildlife if you have any questions or comments, or email us at contact@techforwildlife.com. We’ve also opened up the comments for a few days, so please feel free to point out any errors or leave any other feedback!

P.S.: Hat-tip to Harris Geospatial (@GeoByHarris) for a comprehensive list of vegetation indices, which can be found here.

P.P.S.: We’ll be updating this post with Sentinel-2A imagery in the next few days.

References

·      C. F. Jordan (1969) Derivation of leaf-area index from quality of light on the forest floor. Ecology, vol. 50, no. 4, pp. 663–666, 1969

·      Daughtry, C. S. T., Walthall, C. L., Kim, M. S., De Colstoun, E. B., & McMurtrey Iii, J. E. (2000). Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, 74(2), 229-239.

·      Gitelson, A., Y. Kaufman, and M. Merzylak. (1996) Use of a Green Channel in Remote Sensing of Global Vegetation from EOS-MODIS. Remote Sensing of Environment 58 (1996): 289-298.

·      Huete, A., et al. (2002) Overview of the Radiometric and Biophysical Performance of the MODIS Vegetation Indices." Remote Sensing of Environment 83 (2002):195–213.

·      Hunt, E. Raymond Jr.; Doraiswamy, Paul C.; McMurtrey, James E.; Daughtry, Craig S.T.; Perry, Eileen M.; and Akhmedov, Bakhyt, (2013) A visible band index for remote sensing leaf chlorophyll content at the canopy scale. Publications from USDA-ARS / UNL Faculty. 1156.

·      J. Richardson and C. Weigand, (1977) Distinguishing vegetation from soil background information. Photogrammetric Engineering and Remote Sensing, p. 43, 1977.

·      Jinru Xue and Baofeng Su. (2017) Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications, Journal of Sensors, vol. 2017, Article ID 1353691, 17 pages, 2017.

·      Kim, M. S. (1994). The Use of Narrow Spectral Bands for Improving Remote Sensing Estimations of Fractionally Absorbed Photosynthetically Active Radiation. (Doctoral dissertation, University of Maryland at College Park).

·      Larrinaga, A., & Brotons, L. (2019). Greenness Indices from a Low-Cost UAV Imagery as Tools for Monitoring Post-Fire Forest Recovery. Drones, 3(1), 6.

·      Motohka, T., Nasahara, K. N., Oguma, H., & Tsuchida, S. (2010). Applicability of green-red vegetation index for remote sensing of vegetation phenology. Remote Sensing, 2(10), 2369-2387.

·      Sonnentag, O.; Hufkens, K.; Teshera-Sterne, C.; Young, A.M.; Friedl, M.; Braswell, B.H.; Milliman, T.; O’Keefe, J.; Richardson, A.D. (2012) Digital repeat photography for phenological research in forest ecosystems. Agric. For. Meteorol. 2012, 152, 159–177

·      X. Wang, M. Wang, S. Wang, and Y. Wu. (2015) Extraction of vegetation information from visible unmanned aerial vehicle images. Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering, vol. 31, no. 5, pp. 152–159, 2015. 

·      Y. J. Kaufman and D. Tanré. (1992) Atmospherically Resistant Vegetation Index (ARVI) for EOS-MODIS. IEEE Transactions on Geoscience and Remote Sensing, vol. 30, no. 2, pp. 261–270, 1992.