VEGETATION CHANGE DETECTION USING VEGETATION INDICES
ANSHU GANGWAR Ph.D AGRIL. ENGG. (SWCE) PF-15057 DEPARTMENT OFFFARM ENGINEERING INSTITUTE OF AGRICULTURAL SCIENCES, BANARAS HINDU UNIVERSITY, VARANASI
Anshu Gangwar
Digitally signed by Anshu Gangwar DN: C=IN, O=BHU, CN=Anshu Gangwar,
[email protected] m Reason: I am the author of this document Location: Date: 2018-07-24 12:55:48
Why Vegetation Indices? • For classifying and mapping vegetation cover from remote sensed images at a species level. • To determine the density of green on a patch of land, researchers must observe the distinct colours (wavelengths) of visible and near-infrared sunlight reflected by the plants. • To developed vegetation indices (VI) for qualitatively and quantitatively evaluating vegetative covers using spectral measurements.
• Traditional methods are not effective to acquire vegetation covers because they are time consuming, date lagged and often too expensive.
What are they based on? • Healthy green vegetation has a characteristic interaction with energy in the
visible and near-infrared region. • Visible: chlorophyll adsorbs power to perform photosynthesis, especially in the red and blue bands. • Near infrared: the internal structure of leaves is strongly reflective (spongy parenchyma, part of the mesophyll)
Vegetation vs. Soil and Water
Fig. Typical Spectral reflectance curves for vegetation, soil and water (Lillesand and Kiefer, 2000).
VEGETATION INDICES • A vegetation index is a simple mathematical formula.
• Make use of the red vs. NIR reflectance differences for green vegetation. • Vegetation index (VI) is a simple and effective measurement parameter, which is used to indicate the earth surface vegetation covers and crops growth status in remote sensing field.
• Vegetation indices allow us to delineate the distribution of vegetation and soil based on the characteristic reflectance patterns of green vegetation. • A Vegetation Index (VI) is a spectral transformation of two or more bands designed to enhance the contribution of vegetation properties and allow reliable spatial and temporal inter-comparisons of terrestrial photosynthetic activity and canopy structural variations.
BENEFITS
• Species and community distribution • Land cover mapping • Estimating biodiversity
• Phenological (growth) cycles • Vegetation health • Temporal variations (change detection) • Land cover change • Slow vs. Fast changes
• Reduce atmospheric and topographic effects if possible
TYPES • All vegetation indexes derive from measurements on at least two bands. • Jackson and Huete (1991) classification of vegetation indexes: i.
Slope-based
ii. Distance-based
i.
Slope-based Vegetation Index
• Slope based VI’s computations are done using data acquired in visible red and near IR bands. These values indicate both the status and abundance of green vegetation cover and biomass. • This is the first level of classification and aid in delineating areas under vegetation from non-vegetation. • Examples: SI or RVI, NDVI, DVI, TVI, NRVI etc.
Simple Ratio Vegetation Index • The first true vegetation index which was introduced by Jordan in 1969, for ground-based forest mapping, and then applied to satellite images.
SI or RVI =
NIR RED
• It takes advantage of the inverse relationship between the red and infrared bands for vegetated pixels with high index values being produced by combinations of low red and high infrared reflectance. • Ratio value 1.0 = vegetation.
• Reduces the effects of atmosphere and topography. • The major draw back in this method is the division by zero.
Normalized Differential V.I. (NDVI) • It was introduced by Rouse et al.1974 in order to produce a spectral VI that separates green vegetation from its background soil brightness using IRS 1C MSS digital data.
NIR− RED NDVI = NIR + RED • • • • •
Ranges from -1 to 1. producing a linear scale. Never (Rarely?) divide by zero . Does not eliminate atmospheric effects. The use of NDVI for vegetation monitoring, assessing the crop cover, drought monitoring and agricultural drought assessment at national and global level.
• Vegetation is assumed to be present for values >0; the higher, • The more vegetation, but unlikely to reach beyond 0.7-0.8.
Fig. NDVI Calculation (illustration by Simmon R. 2000)
Fig. NDVI as an indicator of drought (NASA earth observatory, 2000)
Transformed Vegetation Index (TVI) • The Transformed Vegetation Index (TVI) proposed by Deering et al. (1975) is aimed at eliminating negative values and transforming NDVI histograms into a normal distribution.
TVI =
NDVI + 0.5
• However, it cannot be calculated when NDVI < –0.5. • There is no technical difference between NDVI and TVI in terms of image output or active vegetation detection. • Ratio values < 0.71 is taken as non-vegetation and value > 0.71 gives the vegetation area.
Distance-based V.I.
• Distance-based Vegetation indices are evaluated on the basis of soil line intercept concept. • The main objective of these vegetation indexes is to minimize the interference of soil brightness in case of: → sparse vegetation
→ pixels contain a mixture of green vegetation and soil back ground → Important in arid and semi-arid regions
Soil line • The soil line is a hypothetical line in spectral space that describes the variation in the spectrum of bare soil in the image. • The soil line represents a description of the typical signatures of soils in red/near infrared bi-spectral plot.
• It is obtained through liner regression of the infrared band against the red band for sample of bare soil pixels. • Equation of the soil lines is given below:
Y1 = 0.841333x + 10.781234 (red band independent variable) Y2 = 0.985684x + 9.501355 (infra-red band as independent variable)
Soil Adjusted Vegetation Index (SAVI) •
The improved indices incorporate a soil adjustment factor and/or a blue band for atmospheric normalization. The soil adjusted vegetation index (SAVI) introduces a soil calibration factor, L, to the NDVI equation to minimize soil background influences resulting from first order soil-plant spectral interactions (Huete et al., 1988):
NIR −RED SAVI = (1+L) NIR + RED + L •
An soil adjustment factor (L) introduced to minimizes soil brightness variations on vegetation quantification and eliminates the need for additional calibration for different soils (Huete and Liu, 1994). • • •
High vegetation cover = L is 0.0 (or 0.25), and Low vegetation Cover = L is – 1.0 Intermediate vegetation cover = L is 0.5 (Widely Used)
Difference Vegetation Index (DVI) • DVI weigh up the near-infrared band by the slope of the soil line (Richerdson and
Wiegand, 1997) and is given:
DVI = gNIR − RED where, g = the slope of the soil line
• Distinguishes between soil and vegetation • Does NOT deal with the difference between reflectance and radiance caused by the
atmosphere or shadows. • DVI zero indicates bare soil, values less than zero indicate water, and those greater than zero indicate vegetation
Enhanced Vegetation Index (EVI) • EVI was developed as a standard vegetation index product for the MODIS sensor. • To optimize the vegetation signal with improved sensitivity in high biomass regions and improved vegetation monitoring while correcting for canopy background signal reducing atmospheric influences.
NIR − RED EVI = G * NIR + C1 RED − C2 BLUE + L RED = Reflectance in red channel NIR = Reflectance in NIR channel BLUE = Reflectance in blue channel C1 = Atmospheric resistance red correction coefficient (C1 = 6) C2 = Atmospheric resistance red correction coefficient (C2 = 7.5) L = Canopy background brightness correction factor (L = 1) G = Gain factor (G = 2.5)
Perpendicular Vegetation Index (PVI) • Introduced by Richardson and Wiegand (1977), parent of distance-based indices.
• PVI uses the perpendicular distance from each pixel co-ordinate to the soil line and this was derived to define vegetation and non-vegetation for arid and semi arid region. • The pixels, which are close to soil line, are considered as non-vegetation while pixels, which are away from soil lines, represent vegetation.
• PVI values for data taken at different dates require an atmospheric correction of data, as PVI is quite sensitive to atmospheric variations.
PVI = Sin(a) NIR − Cos(a) RED where, a: Angle between the soil line and the NIR axis.
NIR Reflectance
E
D
A
C
B
RED Reflectance
A & B = Pixels of Bare Soil C & D = = Pixels of Partial green Vegetation E = Pixels of green Vegetation
CASE STUDY G. G. Meera, Parthiban S., Thummalu N., Christy. A. (2015), NDVI: Vegetation change detection using remote sensing and GIS– A case study of Vellore District, Procedia Computer Science, 57, 1199 – 1210.
• To identify areas containing significant vegetation and other different features. • Vellore district (6077 sq. km.) of Tamil Nadu. • It lies between the north latitudes 12° 55' N and east longitudes of 79° 11’ E.
Location map of Vellore Disrtict
In this Study, the NDVI technique is used for extracting the various features presented in the 3-band Satellite image of VELLORE district.
NIR − RED RNDVI = NIR +RED
where (01)
NIR − GREEN GNDVI = NIR + GREEN
where (01)
For land cover, land use and species level analyses hierarchical classification approach was adopted and the classification procedures are illustrated through a flow chart: Vegetation Indices LEVEL 1 Land Cover Analysis
0 Area with Vegetation
MLC (MSS Data)
LEVEL 2 Land Use Analysis Built up, Water Body, Waste Land
Classification Based on Spectral Value LEVEL 3 Species Level Mapping
Water Bodies- Shallow, Deep, Turbid etc. Waste Land- Barren Built up- Road , Building etc.
Agriculture, Forest, Plantation
Classification Based on Spectral Value
Eucalyptus, Mixed Forest, Mixed Agriculture, Paddy etc.
T hemat i c bands of NASA ar e as LANDSAT s at el l i t e S.No. Name of Band
Wave Length (µm)
Characteristics and Usage
1.
Visible Blue
0.45-0.52
Maximum Water penetration
2.
Visible Blue
0.52-0.60
Good for measuring plant Vigour
3.
Visible Blue
0.63-0.69
Vegetation Discrimination
4.
Near Infrared
0.76-0.90
Biomass and Shoreline measurement
5.
Middle Infrared
1.55-1.75 2.08-2.35
Moisture content of soil Mineral mapping
6.
Thermal Infrared
10.4-12.5
Soil and Thermal mapping
Satellite image of Vellore district
NDVI classification 2001
Classes discrimination
NDVI 2006
NDVI classification 2006
Changes between 2001 and 2006
Area (ha.)
Land Cover Change detection By using NDVI, it has been clearly shown that the forest or shrub land and open area cover types have decreased by about 6% and 23% from 2001 to 2006 respectively, while agricultural land, built-up and water areas have increased by about 19%, 4 % and 7% respectively.
CONCLUSION • The Change Detection analysis is an efficient way of describing the changes observed in each land use category. • The NDVI method gives superior results for vegetation varying in densities and also for scattered vegetation from a multispectral remote sensing image. • Vegetation Indices should highlight the amount of vegetation, the difference between vegetation and soil, and they should reduce atmospheric effects. • Soil background effects should be minimized if possible. • Indices can be customized for particular applications.
• The study clearly shows the percentage of vegetation is found to be 32.13% at NDVI threshold of 0.3, agricultural area is found to be 23.49%. In the year 2006, the percentage of vegetation in the given study area is found to be 46.43% at NDVI threshold of 0.2, agricultural area found to be 30.84% and the remaining area is found to be 14.0227%.
REFERENCES • Birth, G. S., and G. R. McVey, 1968, Measuring color of growing turf with a reflectance spectrophotometer. Agronomy Journal 60:640-649. • C.F. Jordan. Derivation of leaf-area index from quality of light on the forest floor. Ecology 50 (1969). pp. 663– 666. • Deering, D. W., J. W. Rouse, R. H. Haas, and J. A. Schell, 1975, Measuring forage production of grazing units from Landsat MSS data. Proceedings, 10th International Symposium on Remote Sensing of Environment 2:11691178. • Huete, A. R., 1988, A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment 25:295-309. • Jackson. R.D. and Huete. A.R.. 1991. Interpreting vegetation indexes. Prev. Vet. Med. 11. pp. 185–200. • Rouse, J. W., R. H. Haas, J. A. Schell, and D. W. Deering, 1973, Monitoring vegetation systems in the Great Plains with ERTS. Proceedings, Third ERTS Symposium, NASA SP-351, 1:309-317. • Liu, H. Q., and A. Huete, 1995, A feedback based modifcation of the NDVI to minimize canopy background and atmospheric noise. IEEE Transactions on Geoscience and Remote Sensing 33:457-465. • NASA (2000) NDVI as an Indicator of Drought. • Qi, J., A. Chehbouni, A. R. Huete, and Y. H. Kerr, 1994, A modified soil adjusted vegetation index. Remote Sensing of Environment 48:119-126. • Lillesand M.T. and Keifer R.W (2000) Remote Sensing and Image interpretation, forth edition John Wiley & Sons, Inc., USA.
