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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