Remote Sensing of Land & Vegetation. C S Murthy

Remote Sensing of Land & Vegetation C S Murthy

Remote Sensing current status New era of RS in 21 st Century Increased utilisation of data Quantitative algorithms/improved retrieval methods Better calibrations Variety of bio-physical parameters Monitoring land surface processes and other applications Local and global scales

Remote Sensing of Vegetation - relevance? Terrestrial vegetation key determinant of land surface processes Prime indicator of current ecosystem Land atmosphere interaction Land ocean interaction Hydrological processes Economic relevance Understanding the earth as a system One of the prime interests of the EOS program

Vegetation Agriculture Forest Grasslands Deserts Miscellaneous EO Systems Mapping Monitoring Characterization Phenological changes Bio-physical parameters LAI, LST etc Biomass estimation Parameterization of process

Spectral response of vegetation Leaf pigments Cell structure Water content Dominant factor controlling leaf reflectance 80 R E F L E C T A N C E (%) 70 60 50 40 30 Chlorophyll absorption Water absorption 20 10 0 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 DPC Lecture Series - 04 May 2017 11:15 to 12:15 WAVELENGTH (um)

Current Operational indices Spectral response in V, NIR region Spectral response in the SWIR region Thermal response Mainly using data from polar orbiting satellites Assessing and Monitoring the in situ conditions

Vegetation Index An index - qualifies the intensity of a phenomenon Vegetation indices quantitative measurements of vegetation vigour Better sensitivity than individual spectral bands Vegetation types unique spectral response

REFELECTANCE BASED INDICES Difference Vegetation Index Ratio Vegetation Index Infrared Percent Vegetation Index Perpendicular Vegetation Index Soil Adjusted Vegetation Index Weighted Difference Vegetation Index Greenness Vegetation Index Atmospherically Resistant Vegetation Index Normalized Difference Vegetation Index (NDVI) Normalized Difference Wetness/Water Index (NDWI) Enhanced Vegetation Index

What is NDVI?

Ratio of difference and sum of surface reflectance in NIR and red spectral bands NIR can see roughly 8 layers Red one layer Most successful indicator for describing vegetation Normalisation - reduces the effect of sensor degradation sensitive to changes in vegetation easy to compute and interpret available from most of the sensor systems NDVI Spectral response of vegetation Red more absorption due to chlorophyll Near Infra red more reflection due to leaf structure Normalized Difference Vegetation Index (NDVI) NIR Red / NIR+Red Reflected radiation in Near infrared and red bands. NDVI ranges from -1 to +1 Water negative NDVI Clouds zero NDVI Vegetation positive NDVI represents density, vigor Limitations of NDVI Chlorophyll based index saturates with LAI (=3) Limited capability to detect vegetation water content Over-estimation when the veg. density is less Saturation at peak vegetative phases Conservative index 2017 11:15 to 12:15 Time lag DPC Lecture Series - 04 May

DPC Lecture Series - 04 May 2017 11:15 to 12:15 Atmospheric effects on NDVI

01/May/11 09/May/11 17/May/11 25/May/11 02/Jun/11 10/Jun/11 18/Jun/11 26/Jun/11 04/Jul/11 12/Jul/11 20/Jul/11 28/Jul/11 05/Aug/11 13/Aug/11 21/Aug/11 29/Aug/11 06/Sep/11 14/Sep/11 22/Sep/11 30/Sep/11 08/Oct/11 16/Oct/11 24/Oct/11 01/Nov/11 09/Nov/11 17/Nov/11 25/Nov/11 03/Dec/11 11/Dec/11 19/Dec/11 27/Dec/11 04/Jan/12 12/Jan/12 20/Jan/12 28/Jan/12 05/Feb/12 13/Feb/12 21/Feb/12 29/Feb/12 08/Mar/12 16/Mar/12 24/Mar/12 01/Apr/12 09/Apr/12 17/Apr/12 25/Apr/12 03/May/12 11/May/12 19/May/12 NDVI NDVI profile over agricultural area 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 DPC Lecture Series - 04 May 2017 11:15 to 12:15

Some important VI data sets Satellites Spatial Sensor resolution Temporal resolution Swath Resourcesat 1 & 2 AWiFS 56 m 5 days 750 km LISS III 23 m 26 days 140 km LISS IV 6 m 48 days 70 km LANDSAT 8 OLI 30 m 16 days 185 km Sentinel 2 MSI 10, 20, 60 300 km DPC Lecture Series - 04 May 2017 11:15 to 12:15 Satellite/ Sensor Indices Relevant Parameter NOAA AVHRR (1km) NDVI Crop condition Oceansat 2- OCM (360m) NDVI, ARVI Crop condition Terra MODIS (500 m) SASI, NDWI Surface wetness/ sown area discrimination Terra AMSRE (25 km) Soil moisture Surface wetness/ sown area discrimination INSAT 3A CCD (1 km) NDVI Crop condition

Seasonal NDVI profiles for drought assessment NDVI 0.6 0.5 0.4 0.3 0.2 0.1 0 June July Aug. Sept. Oct. Nov. Month Normal delayed season Drought DPC Lecture Series - 04 May 2017 11:15 to 12:15 (1) relative deviation from normal, (2) vegetation Condition Index, (3) in season rate of transformation

Date 1 Date 2 Date 3 Time composition of NDVI Maximum value compositing (MVC) procedure DPC Lecture Series - 04 May 2017 11:15 to 12:15

Date 1 Date 2 Date 3 Time composite NDVI DPC Lecture Series - 04 May 2017 11:15 to 12:15

NDWI/LSWI Reflectance in 0.9 2.5 microns dominated by liquid water absorption Sensitive to surface wetness/ vegetation moisture Less affected by atmosphere Reflectance 1.5-2.5 microns does not saturate till LAI reaches 8 SWIR availability AWiFS, LISS-III, MODIS, INSAT 3A MODIS 3 SWIR bands 1240 nm, 1640 nm, 2100 nm LSWI Land Surface Wetness Index uses SWIR at 2100 nm very sensitive to wetness NDWI/LSWI uses Agriculture crop stress detection, crop yield, classification of succulent crops, surface moisture Forest Monitoring, burnt area detection DPC Lecture Series - 04 May 2017 11:15 to 12:15 (NIR-SWIR/NIR+SWIR)

Enhanced Vegetation Index VI corrected for atmosphere and soil background Available from MODIS data EVI = G* ((NIR RED) / (NIR + C 1* Red C 2 * Blue + L)) Where NIR = NIR reflectance Red = Red reflectance NDVI i is current month NDVI NDVI Mean, m Long term NDVI mean for calendar month m Blue = Blue reflectance C 1 = Atmospheric resistance red correction coefficient (6) C 2 = Atmospheric resistance Blue correction coefficient (7.5) L = Canopy background brightness correction factor (1) G = Gain factor (2.5) Higher dynamic range

ARVI (Atmospherically Resistant VI) Self correction for atmospheric effect Atmosphere contribution = Refl. (Blue-Red) Red reflectance = Red (Blue-Red) RB = R cf (B-R) cf to be derived for different atm. Conditions generally assumed as 1 GVI (Green Vegetation Index) NIR Green / NIR+Green Good for leaf chlorophyll assessment

Shortwave Angle Slope Index (SASI) Relationship between bands Coarse spectral fitting analogous to hyper-spectral data β SWIR1 = cos -1 [ (a 2 + b 2 + c 2 ) / (2*a*b) ] Slope = (SWIR2 NIR) SASI = β SWIR1 * Slope (radians) where a, b and c are Euclidian distances between vertices NIR and SWIR1, SWIR1 and SWIR2, and NIR and SWIR2, respectively Palacios et al. 2006, Khanna et al. 2007 Features β SWIR1 Slope SASI Dry soil high high and +ve highly positive Wet soil low small and +ve low positive Dry vegetation low small and -ve low negative Moist vegetation high high and -ve highly negative

-1-0.8-0.6-0.4-0.2 0 0.2 0.4 01/May/11 09/May/11 17/May/11 25/May/11 02/Jun/11 10/Jun/11 18/Jun/11 26/Jun/11 04/Jul/11 12/Jul/11 20/Jul/11 28/Jul/11 05/Aug/11 13/Aug/11 21/Aug/11 29/Aug/11 06/Sep/11 14/Sep/11 22/Sep/11 30/Sep/11 08/Oct/11 16/Oct/11 24/Oct/11 01/Nov/11 09/Nov/11 17/Nov/11 25/Nov/11 03/Dec/11 11/Dec/11 19/Dec/11 27/Dec/11 04/Jan/12 12/Jan/12 20/Jan/12 28/Jan/12 05/Feb/12 13/Feb/12 21/Feb/12 29/Feb/12 08/Mar/12 16/Mar/12 24/Mar/12 01/Apr/12 09/Apr/12 17/Apr/12 25/Apr/12 03/May/12 11/May/12 19/May/12 SASI Seasonal SASI profile

VI Derivatives VCI = (NDVI-NDVI MIN ) / (NDVI MAX -NDVI MIN )*100 TCI = (BT MAX -BT) / (BT MAX -BT MIN )*100 VTI = a*vci + b*tci Where NDVI NDVI Max and NDVI min are smoothed weekly NDVI absolute maximum and its minimum BT, BT Max and BT min are smoothed weekly brightness temperature absolute maximum and its minimum VCI is Vegetation Condition Index, TCI is Thermal Condition Index VTI is Vegetation Health Index Kogan 1995

Crop Maturity Hyper-spectral indices Sensitive to plant structural and biochemical attributes plant disease detection senescence detection stress monitoring Red-edge point of inflection - 680-740 nm abrupt increase in reflectance increase in chlorophyll shift the absorption to longer wavelength decrease in chlorophyll shift to shorter wavelength Plant Water content Normalized Difference Water Index (NDWI) : (R857-R1241)/(R857+R1241) Normalized Difference Infrared Index (NDII) : (R819-R1649)/(R819+R1649) Plant Structural Index Normalized Difference Vegetation Index (NDVI) : (R800-R680)/(R800+R680) Red edge Normalized Difference Vegetation Index (NDVI 705 ) : (R750-R705)/(R750+R705) Plant Senescence Reflectance Index (PSRI) : (R680-R500)/R750 DPC Lecture Series - 04 May 2017 11:15 to 12:15

Bio-physical parameters Leaf Area Index (LAI) Cumulative area under leaves per unit area of land at nadir orientation Indicative crop yield, canopy resistance, heat fluxes Photosynthetically Active Radiation (PAR) Solar radiation available for photosynthesis Absorbed Photosynthetically Active Radiation (APAR) fraction of PAR absorbed by chlorophyll pigments Net Primary Productivity Bio-physical component of eco system quantitative measure of plant growth and carbon uptake NPP = APAR X LUE (light use efficiency)

PROCESS BASED INDICATORS Rn = G + H + E Rn = Net Radiation H = Sensible heat flux E = Latent heat flux E = (Rn-G) c p * air * 1 / r a * (T s -T x ) c p= Sp. Heat of air air= Density r a= Surface roughness T s= Surface temperature T x= Maximum temperature Net radiation Difference between incoming and outgoing short and long wave radiation Latent Heat Flux energy consumed for vaporization of water (AET) Aerodynamic resistance mechanical friction between land and atmosphere controlling vapor removal EF = E / (R n G) EF is Evaporative fraction

Soil moisture Soil moisture important data for hydrology, agriculture, environment, climate system etc. Non-spatial data I. Insitu measurements non-spatial data Manual accurate inadequate coverage Automatic systems calibration related issues large area coverage is expensive Sources of soil moisture data Hydrological models Mass balance approach Profile level moisture Parameterisation of models challenge Soil moisture products from NRSC VIC hydrological models daily soil moisture images AMSR 2 LPRM soil moisture 25 km, 2 day frequency Spatial data Satellite based Large area, daily coverage 25-50 km resolution Increasing popularity Several microwave sensors SMRR 1978-1987 TRMM TMI since 1997 Scatterometer ERS 1 & 2 ASCAT MetopA AMSRE 2002-2011 SMOS 2009 SMAP - 2015 Retrieval algorithms from passive systems NASA LPRM PRI

01 jun 05 Jun 09 Jun 13 Jun 17 Jun 21 Jun 25 Jun 29 Jun 03 Jul 07 Jul 11 Jul 15 Jul 19 Jul 23 Jul 27 Jul 31 Jul 04 Aug 08 Aug 12 Aug Soil moisture (m3/m3) Tracking the drought conditions of 2014 using LPRM Soil Moisture datasets of NRSC Soil moisture deviations from normal in 2014 4_11 JUNE 12_18 JUNE 19_25 JUNE 26_2 JULY 3_9 JULY 10_16 JULY 17_23 JULY 24_30 JULY 31_06 AUGUST 17_23 JULY 0.40 0.35 0.30 0.25 0.20 0.15 0.10 0.05 0.00 Drought frequency in the sowing period LPRM_SM 2014 LPRM_SM 2013

Some specific applications. Forest inventory and monitoring Forest fire mapping Forest burnt area assessment Forest fire alerts Crop inventory Crop modelling Hydrological models Agricultural Drought Monitoring Agricultural drought vulnerability Precision farming Land cover change detection Climate change

Remote Sensing Reviews Publication details, including instructions for authors and subscription information: http://www.informaworld.com/smpp/title~content=t724921233 A review of vegetation indices A. Bannari a; D. Morin a; F. Bonn a; A. R. Huete b a Centre d'applications et de recherches en télédétection (CARTEL), Université de Sherbrooke, Sherbrooke, Québec, Canada b Department of Soil and Water Science, University of Arizona, Tucson, AZ, USA Thank you