Linking Essential Biodiversity Variables from space for Wetland conservation and management
November 7-9, 2019
J.K. GargSenior Fellow
TERI School of Advanced Studies, New Delhi 110070
Sustainable Forestry in South Asia-Current Status, Science and Conservation PrioritiesTERI School of Advanced Studies, New Delhi 110070
Essential Biodiversity Variables (EBVs) are defined as the derived measurements required tostudy, report, and manage biodiversity change, focusing on status and trend in elements ofbiodiversity. They provide the first level of abstraction between low-level primaryobservations and high-level indicators of biodiversity (GeoBon).
Criteria for Essential Biodiversity Variables - An ideal EBV should be
• able to capture critical scales and dimensions of biodiversity• biological• a state variable (in general)• sensitive to change• ecosystem agnostic (general - to the degree possible)• technically feasible, economically viable and sustainable in time
Essential Biodiversity Variables (EBVs)
What are wetlands .......Ramsar uses a broad definition of wetlands, including lakes and rivers, swamps and marshes, wet grasslands and peatlands, oases, estuaries, deltas and tidal flats, near-shore marine areas, mangroves and coral reefs, and human-made sites such as fish ponds, rice paddies, reservoirs, and salt pans.
ramsar.org
WetlandEcosystem
Hydrology
Catchment
Chemistry
Physical Habitat
Biology
Wetland – the components
Socioculturalinterrelationships
Wetland ecosystems highly productive and
rich repository of biodiversity than any other ecosystem
Major Threats faced by wetland
ecosystems
Leading to significant losses in biodiversity
Link between Forests and Wetland integrity and Wetland Biodiversity
Water qualityWetland Biodiversity
ResilienceWetland Integrity
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Catchment IntegrityNature of Land use (forested/non forested)Anthropogenic activities (industrialisation,
urbanisation etc.)Geophysical setting (lithology, landforms, slope etc.
• Many of wetlands (especially Ramsar sites, and bird sanctuaries, under the legal jurisdiction of Forest Departments in South Asian countries and elsewhere
• Many wetlands are forested (e.g. Sundarbans in India and Bangladesh)
Wetland Conservation and Management
Geospatial datasets /images In-situ measurements
Wetland Catchment
• Structural components
• Habitat Types
•Land use•Road/rail network•Settlements•Drainage•Slope•Geomorphology•Soils•Etc..
WQ/hydrology Flora Fauna
Macrophytes• %cover in littoral zone• number of species• Shannon diversity• no. of rare species• %area under exotic
speciesPhytoplanktons• total density• Shannon diversity• %blue green algae• %other algae species
•No. of Taxa•Mean No. of Individual Per taxon•%contribution of dominant taxon•Shannon diversity
•BOD•DO•pH•Calcium•Total hardness•Magnesium•Alkanity•Phosphate•Trophic state• Water Balance•.•.
Socio-economic dataand cultural ethos
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Huge requirement of temporal field and geospatial data
Regional
Large Scale Spatial ResolutionHigh
1:1000000
1:250000
1:50000
1:25000
1:10000
1:5000 > 1m
5m
10m
25m
50m
500m
AWiFS
LISS IV
LISS III
Cartosat
Oceansat
LISS IV
National
State
Local
District
IP*
IP* Implementation Plan
Scales and Satellite Data
Small scale Spatial ResolutionLow
India RS satellites provide unique opportunity + international RS satellites
Candidate EBVs amenable to Remote Sensing(More applicable to forests and forested wetlands/catchments)
EBV Class Candidate RS-EBVSpecies populations Species distributionSpecies populations Species abundanceSpecies traits PhenologySpecies traits Plant traits (e.g., specific leaf area, leaf nitrogen content)
Community composition Taxonomic diversity
Community composition Functional diversity
Ecosystem function Productivity (e.g., NPP, LAI, FAPAR)Ecosystem function Disturbance regime (e.g., fire, inundation))Ecosystem structure Habitat structure (e.g., height, crown cover and density)
Ecosystem structure Ecosystem extent and fragmentationEcosystem structure Ecosystem composition by functional type
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Variable Information Need/EBVs RS ComplianceSpatial information Inventory and monitoring
Structural components/ habitat typesAq. veg/weeds extent
Aquatic vegetation type
Extent, type operationalOperationalEmergent, floating weeds possible to delineate on 1:25 000and larger scalesLarge patches can be discerned
Water quality Variable Pollution sources/waste outfalls Large out falls possible to map operationally Thermal pollution Possible to detectPhytoplankton/algal blooms/Chlorophyll Inland wetlands not operationally established; coastal areas
possibleTurbidity/sediment load Possible in conjunction with limited field dataOil pollution Possible in coastal watersPollutant species Ground based studies
Biodiversity Habitat diversityFragmentationHabitat-biotic assemblages etc.
Possible at 1: 50 000 /25 000 and or larger scales
Catchment characteristics Land cover, soils, landforms, hydrologeomorphology, etc.
Possible at 1: 50 000 /25 000 and larger scales
Action Plan for Conservation & Management
Integration and criteria based action plan generation
Possible at 1:50 000/1:25 000 and larger scales
Aquatic Environment and space based EBVs
Inve
ntor
ies o
f Ind
ian
Wet
land
s
Type-wise distribution of wetlands in India
Source: SAC 2011
- swamp/marshType w
ise distribution
Wetland component EBVs
Wetland structural components extracted using LISS IV imagery
WQP’s Algorithm ReferenceChl-a (μg/L) 54.658 + 520.451*B2 –1221.89*B3 +
611.115*B4 – 198.199*B5Lim et al., (2015)
Turbidity (NTU) 1.195+14.45*B4 Carpintero et al., (2013)
Trophic State Index
10 [6-(2.04 -0.68ln(Chl-a)/ln2)] Carlson (1977)
*NoteLandsat 8 OLIBand 2 Blue(B2) (0.45 – 0.51)Band 3 Green(B3) (0.53 – 0.59)Band 4 Red(B4) (0.64 – 0.67)Band 5 NIR(B5) (0.85 – 0.88)
Algorithms used for calculating various EBVs Wetland’s health EBVs
ChattissgarhKotaBilaspurBilhaMatsuri Tahsils
Area: 2853 km2
Area dotted with ponds
Lentic Ecosystems
Spatial variation in TSI based on Chl-a 4/18/2020
Upper Ganga RiverLotic Ecosystems
Lotic Ecosystems𝑴𝑴𝑴𝑴𝑴𝑴𝑴𝑴𝑴𝑴 =
𝑩𝑩𝑩𝑩𝑩𝑩𝑩𝑩 3 − 𝑩𝑩𝑩𝑩𝑩𝑩𝑩𝑩 6𝑩𝑩𝑩𝑩𝑩𝑩𝑩𝑩 3 + 𝑩𝑩𝑩𝑩𝑩𝑩𝑩𝑩 6
)𝑨𝑨𝑴𝑴𝑨𝑨𝑴𝑴𝑩𝑩𝒏𝒏𝒏𝒏 = 4 ∗ 𝑩𝑩𝑩𝑩𝑩𝑩𝑩𝑩 3 − 𝑩𝑩𝑩𝑩𝑩𝑩𝑩𝑩 6 − (0.25 ∗ 𝑩𝑩𝑩𝑩𝑩𝑩𝑩𝑩 5 + 2.75 ∗ 𝑩𝑩𝑩𝑩𝑩𝑩𝑩𝑩 7
SWIR 1 Band Reflectance
Band Thresholding
Variation in Chlorophyll-a Concentration
In situ: 18.2 µg/LCal: 16.7 µg/L
In situ: 34.23 µg/LCal: 65.3 µg/LIn situ: 13.07 µg/L
Cal: 17.66 µg/L
In situ: 15.7 µg/LCal: 9.8 µg/L In situ: 30.2 µg/L
Cal: 73.8 µg/LIn situ: 16.6 µg/L
Cal: 10.5 µg/L
Variation in turbidity values
In situ: 62.4 NTUCal: 33.05 NTU
In situ: 6.75 NTUCal: 19.14 NTU
In situ: 56.7 NTUCal: 93.9 NTU
In situ: 69.4 NTUCal: 78.34 NTU
In situ: 40.9 NTUCal: 29.2 NTU
In situ: 19.2 NTUCal: 24.16 NTU
Wetland Rehabilitation and Restoration
Catchment Health/Integrity EBVsWetland Health EBVs
Land use land cover in buffer zone of Bhindawas wetland
FCC of wetland buffer zone for various years
Grey Heron
Red wattled lapwig
Swamp Hen
White Throated Kingfisher
Chest-nut Bee-eater
Black Winged Stilt
Pheasant Tailed Jacana
Cormorants Bee-eater
Cormorants
Northern Shoveller
Black Headed Ibis
Black Headed Ibis
Waterfowl in the Bhindawas Wetland – Ramsar candidate
Land use land cover maps of Wetland buffer zone
Land use land cover maps for wetland buffer zone
(Blue colour indicates Renuka wetland) (Thakur, 2018).
Landscape Fragmentation EBV in forested wetland catchment
Action Plan for Deeper Beel
Biophysical parameters should be regulatedImprove water holding capacityNo action requiredRemoval of vegetation/Dredging
Research carried out so far has proved utility of remote sensing and GIS ininventorying, monitoring and assessment of health of Wetland Ecosystems. For‘wise use’ of wetlands following are the needs:
• Use of Landscape Ecological Approach for Conservation and Restoration ofWetlands.
• Development of metrics/indicators/EBVs for assessment of health of wetlandand its catchment integrity.
• Development of socio-cultural indicators (metrics) for assessing the impact onhealth of wetlands (of people directly or indirectly dependent on wetlands).
The Way Forward
Selected References1. Carlson, R. E. (1977). A trophic state index for lakes. Limnology and oceanography, 22(2), 361-369.2. Carpintero, M., Contreras, E., Millares, A., & Polo, M. J. (2013). Estimation of turbidity along the Guadalquivir
estuary using Landsat TM and ETM+ images. In SPIE Remote Sensing (pp. 88870B-88870B). International Societyfor Optics and Photonics.
3. Garg, JK (2015). Wetland assessment, monitoring and management in India using geospatial techniques. Jour. Environment Mgt. Vol. 148, 112-123.
4. Garg, J.K., Singh, T.S. and Murthy, T.V.R. (1998). Wetlands of India. Project Report: RSAM/SAC/RESA/PR/01/98, 240 p. Space Applications Centre, Ahmedabad.
5. Garg J.K. and Patel J. G. (2007). National Wetland Inventory and Assessment, Technical Guidelines and Procedure Manual, Technical Report, SAC/EOAM/AFEG/NWIA/TR/01/2007, June 2007, Space Applications Centre, Ahmedabad
6. National Wetland Atlas (2011). SAC/EPSA/ABHG/NWIA/ATLAS/34/2011, Space Applications Centre, India, 310p. 7. Gao, H., Wang, L., Jing, L., & Xu, J. (2016). An effective modified water extraction method for Landsat-8 OLI
imagery of mountainous plateau regions. In IOP Conference Series: Earth and Environmental Science (Vol. 34, No.1, p. 012010). IOP Publishing.
8. Gholizadeh, M. H., Melesse, A. M., & Reddi, L. (2016). A Comprehensive Review on Water Quality ParametersEstimation Using Remote Sensing Techniques. Sensors, 16(8), 1298.
9. Lim, J., & Choi, M. (2015). Assessment of water quality based on Landsat 8 operational land imager associatedwith human activities in Korea. Environmental monitoring and assessment, 187(6), 384.
10. Patra, P.P., Dubey, S. K., Trivedi, R. K., Sahu, S. K., & Rout, S. K. (2017). Estimation of chlorophyll-a concentrationand trophic states in Nalban Lake of East Kolkata Wetland, India from Landsat 8 OLI data. Spatial InformationResearch, 1-13.
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