Assessment of Resources of by FSI

Dr. Subhash Ashutosh Director General Forest Survey of India

SARI Meeting, 7th Nov, 2019 FOREST SURVEY OF INDIA A National organisation with the mandate of monitoring and assessment of forest resources of the country

Nation-wide Forest Cover Mapping National India State of Forest Report • Grid based design (5km x 5km) • Biennial report • Biennial cycle • Observation on over 50 variables • FC, Mangrove, FI, TOF, Cover, • 1:50,000 scale, MMU 1ha. • Circular plots Forest Carbon, Bamboo, Forest Fire • Forest Cover in VDF, MDF & OF • Over 7,000 plots for forest inventory and and State wise information • Change Maps over 10,000 plots for TOF every year • Change in FC along with change • 5 year cycle, interim biennial estimates. matrix. • Widely used primary data Forest and Tree Cover 2017 Assessment Percent of Class Area (sq km) Geographical Area Forest Cover Very Dense Forest (> 70% canopy) 98,158 2.99 Moderately Dense Forest (40-70%) 3,08,318 9.38 Open Forest (10-40%) 3,01,797 9.18 Total Forest Cover* 7,08,273 21.54 Tree Cover 93,815 2.85 Total Forest and Tree Cover 8,02,088 24.39 Scrub (<10%) 45,979 1.40 Non Forest 25,33,217 77.06 Total Geographical Area 32,87,469 100.00

 Forest & Tree Cover of India increased by 8,021 sq km as compared to 2015 assessment

• increase in Forest Cover - 6,778 sq km • increase in Tree Cover - 1,243 sq km Trend in Forest Cover in India - ISFR 2005 to ISFR 2017 Assessment Year Class 2005 2009 2011 2013 2015 2017 83,472 83,428 83,471 83,502 88,633 98,158 VDF (2.54%) (2.54%) (2.54%) (2.54%) (2.70%) (2.99%) 3,19,948 3,20,238 3,20,736 3,18,745 3,12,739 3,08,318 MDF (9.73%) (9.74%) (9.76%) (9.70%) (9.51%) (9.38%) 2,86,751 2,88,728 2,87,820 2,95,651 3,00,123 3,01,797 OF (8.72%) (8.78%) (8.76%) (8.99%) (9.13%) (9.18%) 6,90,171 6,92,394 6,92,027 6,97,898 7,01,495 7,08,273 Total (21.00%) (21.06%) (21.05%) (21.23%) (21.34%) (21.54%) Trend in Forest Cover in India – ISFR 2005 to ISFR 2017 7,08,273 710,000 21.54% 705,000 km 700,000 7,01,495 sq 21.34% 6,97,898 in in 6,92,394 695,000 6,90,171 21.06% 21.23% 21.00% 6,92,027 Area 690,000 21.05% 685,000 2005 2007 2009 2011 2013 2015 2017 Assessment Nation-wide Forest Cover Mapping in India The Forest Cover assessment published in ISFRs is a very important source of primary information on of the country which are widely used across , State Governments, Honourable courts, State Forest Departments, academia and other stakeholders. Objectives • monitor progress towards policy goal of achieving 33% of country’s area under forest cover • monitor forest cover and forest cover changes at various levels (District, State and National) to provide inputs for policy, planning and management • generate data and statistics on forest cover, density classes, forest cover changes for planning and scientific management of forests of the country. • provide base data for forest carbon assessment in the country. • provide inputs for international reporting and tracking progress on forestry related parameters. Definition of Forest Cover

“All Lands more than one hectare in area, with a tree canopy density of more than 10 percent irrespective of ownership and legal status. Such lands may not necessarily be a recorded forest area. It also includes orchards, bamboo and palm”

• started since 1987 • 15 cycles completed so far • methodology has evolved with the changing technology and satellite-sensor development • extensive ground truthing Classification Scheme for Forest Cover Mapping

Very All lands with tree cover (including Dense mangrove cover) of canopy density of Forest 70% and above. Mod. All lands with tree cover (including Dense Mangrove cover) of canopy density Forest between 40% and 70% above. Open All lands with tree cover (including Forest mangrove cover) of canopy density between 10 - 40%. All forest lands with poor tree growth Scrub mainly of small or stunted having canopy density less than 10 percent. Any area not included in the above Non-forest classes. Methodology : Work-flow Refinement in Methodology • switch over to change centric approach • DIP algorithms aided visual analysis • NDVI and MLE part of the methodology • steps coded in the Manual • uniform application (in Hq and zones) • minimise subjectivity • concurrent QC-QA • use of ortho-rectified satellite data • use of App for ground truthing • GIS coverage of ground truth observations • broad based accuracy assessment • storage on cloud On-Screen Delineation of change polygons

Previous Cycle Current Cycle Forest Cover, Recorded Forest Area, Tree Cover & TOF

Forest Cover within Forest Cover outside Recorder Forest Recorder Forest Area Total Forest Area & Greenwash & Greenwash Area Cover (sq km) Area (sq km) (sq km) 510,916 197,357 708,273 Ground Truthing • the doubt points are generated in a manner that aids the interpretation. • doubt points with significant change, signature mixing, radiometric distortion- haze, poor reflectance are taken are marked for validation • whenever, major interpretational changes are identified on the image, they are thoroughly verified on the ground through Ground Truth (GT) • all the points for ground truth which are to be visited in the field for GT are approved by the supervisors • prior to ground truthing, a tentative area figure has to be generated for the increase/decrease in forest cover. • geo-tagged field photos(usually panoramic view, close and in between) are taken, the photos are properly referenced. • after completing ground truthing exercise all the field forms, field photographs are maintained in a GIS coverage and a photo library. • GT is carried out jointly with the State Forest Departments • Ground truthing also helps in gathering signature of different forest types in different regions Ground Truth Map 2019 Sampling of grids on 5 yr cycle 1st yr – all 1s 2nd yr- all 3s 3rd yr – all 5s National Forest Inventory 4th yr – all 2s 5th yr- all 4s 6th yr- all 1s • quantifying forest resources for meeting vital information needs of forestry sector of the INDIA 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 5x5 km grids country 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 • assess forest carbon stock at the state and 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 national level 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 • Data needs of different organisations e.g NITI 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 Ayog, Finance Commission, SFDs, Universities and Research Organisations etc 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 • International reporting e.g. GFRA, UNFCCC 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 •reducing inventory cycle from 20 years 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 to 5 years for forests and 10 years for approx 7,000 forested grids & 10,000 TOF grids to be inventoried/ year TOF •over 50 variables Outcomes of Inventory of Forest & TOF • estimate growing stock (stems and volume) New parameters of NFI design inside and outside forest areas • availability of water • estimate and carbon stock in the India’s • forest • NTFPs • estimate growth and productivity • incidence of Disease (tree) • inventory of Important NTFPs • incidence of Insect (tree) • mortality • growing stock of bamboo • dead standing tree • estimate important characteristic of forest such as regeneration, grazing, fire incidence etc. Modernization & Value Addition in NFI

. geospatial interface of the sample plot data • for visual analysis, retrieval of data with spatial criteria, generating estimates, sample plot, stratification with other necessary layers, planning of smooth field work etc . automation in field data collection • use of PDAs/tabs and apps, seamless transfer of the data to the servers . software development and customization • for data processing in real time, ease and flexibility in analysis and report generation . modernization of equipments and kit used by the field crews • use of latest equipments and kit used by field crews, with logistic support Forest Growing Stock 4218.380 million cum  Total Growing Stock is estimated 5822.377 million TOF cubic meters 1603.997  Average Growing Stock per ha in Forests - 54.97 million cum cubic meters

5822.377 million cum

FORESTS TOF 72.45% 27.25% Carbon Stock in India’s Forests

Carbon Stock in Carbon stock in Net change in Annual increase % of different Component forest in 2015 forest in 2017 Carbon stock in Carbon stock carbon pools (million tones) (million tones) (million tones) (million tones)

Above Ground Biomass 2220 2238 18 9.00 32

Below Ground Biomass 695 699 4 2.00 10

Dead 29 30 1 0.50 0.42

Litter 131 136 5 2.50 2

Soil 3969 3980 11 5.50 56

Total 7044 7083 39 19.50

Biomass Mapping Using Synthetic Aperture Radar (SAR) Data

Objectives Above Ground Forest Biomass Map of Assam • estimate above ground forest biomass of India (Based on ALOS PALSAR(SAR) and Inventory data) using SAR data • create a forest biomass map of India • build capacity in the organisation for use of data from future missions like NISAR, GEDI etc and use of L-band data. Salient Features • Collaboration with SAC, NRSC and NESAC • two pilot states- Assam and to start Tonnes /Ha <20 with 20 - 40 • use of ALOS-PALSAR and Sentinel-1 data 40 - 60 60 - 80 along with FSI forest inventory data for 80 - 120 biomass values on the sample plots for the 120 - 160 >160 analysis Non Forest • A joint working group has been formed Water Body Methodology Workflow consist of following stages: . Processing of Inventory Data . Processing of SAR Data Mean DN value converted to backscatter 0 value(DB) using formula: σ (dB) = 20log10 (DN p ) − KdB Where, σ 0 ( dB ) = radar backscatter coefficient in dB DN = The average value of pixels(DN) falling under plots of 0.1ha • Statistical analysis(Correlation of SAR data with Reference biomass) • Multi variate regression model • Estimation of biomass

0 0 YBIOMASS (t/Ha) = A + (B * γ HH) + (C * γ HV) Forest Fire Monitoring & Early Warning . Near real time forest fire monitoring . Forest Fire Monitoring: Version 3.0 launched; more than 34000 registered users; over 15 lakhs SMS sent in 6 months . Pre-fire alert system for forest fire on a pilot basis . IMD Data . Forest Type . Advanced model needed . Burnt area assessment since 2015 on experimental basis carried out . An interactive portal for displaying forest fire hotspots. . MODIS and SNPP-VIIRS Data used for fire alerts from 2017 Identifying Forest Fire Prone areas in India • A GIS analysis has been done by overlaying all the detected forest fire points (MODIS) i.e. 2,77,758 from 2004-05 to 2017 on a nation-wide coverage of 5 Km X 5 Km grids. • Based on average annual frequency of fire alert points, the 5 X5 km grids are classified into 5 forest fire prone classes • fire prone forest areas (grids) for all the states of the country in different fire prone intensity class have been identified Criteria for identification of forest fire prone classes

Category Range Extremely fire prone forest area Average frequency of forest fire ≥4 in a grid per year Very Highly fire prone forest area Average frequency of forest fire (≥ 2 and <4) in a grid per year Highly Fire prone forest area Average frequency of forest fire (≥ 1 and <2) in grid per year Moderately fire prone forest area Average frequency of forest fire (≥ 0.5 and <1) in grid per year Less fire prone forest area Average frequency of forest fire (< 0.5) in grid per year Forest areas under different fire prone classes Statistics at a glance Total no of grids (5km x 5km) 1,34,043 Total no of forested grids 69,652 No. of forest fire point/FFP (from 2004 to 2017) 2,77,758 No. of grids where forest fire were detected 29,345 Min - 1 Frequency of forest fire Max - 176

Forest cover in different fire prone classes Forest Sl. Forest Fire Prone No. of % of Total Cover** No. Classes Grids* forest cover (in km2) 1 Extremely fire prone 665 25,617 3.89 2 Very Highly fire prone 2,259 39,500 6.01 3 Highly Fire prone 3,708 75,952 11.50 4 Moderately fire prone 5,496 96,422 14.70 5 Less fire prone 57,489 4,20,625 63.90 Total 69,617 6,58,116 100.00 Decision Support System (DSS) DSS is a web based application that provides information w.r.t. geo- referenced area. DSS has been developed to facilitate, informed, unbiased and expeditious decisions on management of forest in general and implementation of FC Act in particular. CURRENT LAYERS : • Forest Cover Map (FCM) - FSI • Forest Type Map (FTM) - FSI • Biological Richness (BR) - IIRS • Landscape Integrity (LI) – FSI • Hydrological Layer - CWC • Protected Area (PA) - WII • Tiger Reserves (TR) - WII • Wildlife Corridors - WII Way Forward • a new Centre for Methodology Research & Development has been established • emerging technologies of machine learning, artificial intelligence, data mining, cloud computing and data analytics in forest cover mapping and other assessments • take forest cover mapping to next higher level i.e. higher scale, more attributes in mapping • integration of national forest inventory with forest cover and Foret Type spatial layers • crowd sourcing for ground truth information • alerts for forest cover loss and forest degradation