Each topic below explains the evidence, the business problem it creates, how GLOBEIR solves it, the benefits to your organisation, and how your data stays private and secure.
1. Forest Cover and Density Mapping
India's official forest cover figures come from the Forest Survey of India (FSI), which has published the India State of Forest Report (ISFR) every two years since 1987 [1]. The 2023 edition is based on wall-to-wall interpretation of ISRO's LISS-III data at 23.5 m resolution and 1:50,000 scale, acquired mainly between October and December 2021, when imagery is usually cloud-free and foliage is full [2]. Forest cover means any land over one hectare with tree canopy density of 10 per cent or more, regardless of ownership or legal status, and it is reported in three classes: very dense (70 per cent and above), moderately dense (40 to 70 per cent) and open forest (10 to 40 per cent) [2][4].
The 2023 report puts forest and tree cover at 8,27,357 sq km (25.17 per cent) of the country, and forest cover at 7,15,343 sq km, up from 6,98,712 sq km in 2013 [1]. It also moved closer to the people who manage forests: figures were given for 751 districts instead of 636, forest division-wise cover was reported for the first time where States had supplied digitised division boundaries, and digitised forest boundaries of 25 States and UTs were used to separate cover inside and outside recorded forest areas [2]. FSI also analysed degradation between 2011 and 2021 and identified about 93,000 sq km of potential areas where density could be upgraded [2].
Business problem
A national map at 1:50,000 every two years is a reference, not a management tool for a division. Divisional Forest Officers need to know which compartments are losing density, where open forest is turning to scrub, and how much cover lies outside the recorded boundary, and they need this between national cycles. Many divisions still hold compartment boundaries on paper or as unreferenced scans, so FSI's cover cannot even be summarised by compartment, and divisional reports cannot be checked against national figures.
Our solution
- Digitisation and georeferencing of circle, division, range, beat and compartment boundaries, aligned with the recorded forest boundary.
- Canopy density classification from current satellite imagery in the same classes FSI uses, validated against field plots collected on the My GLOBEIR app.
- Change detection between dates, inside and outside the recorded forest area, with flagged patches sent to field staff for checking.
- Compartment-level cover, change and degradation tables on a WebGIS dashboard, delivered through our remote sensing and GIS mapping services.
Benefits
- Divisions can report cover and change by their own compartments, using the same density classes as the national assessment [2].
- Degraded patches can be prioritised for treatment, in line with FSI's identification of large areas suitable for density upgradation (industry example) [2].
- Digitised boundaries also improve national figures, as the 2023 report's use of division and forest boundaries shows (industry example) [2].
- Change alerts reach field staff while there is still time to act, not two years later.
Privacy & data security
- Forest cover and change layers carry no personal data, but boundary layers may be linked to encroachment cases and land records, which do; these are restricted by role.
- Fine-resolution drone or survey data commissioned for a division is stored and processed in India and never sent to servers of a non-Indian entity, in line with DST rules [19][20].
- Data is encrypted in transit (TLS 1.2 or higher) and at rest (AES-256), with audit logs of access and changes.
2. Forest Fire Alerts and Fire-Risk Zoning
FSI has alerted State Forest Departments to forest fires since 2004 [2]. Its near real-time service uses MODIS (1 km) and SNPP-VIIRS (375 m) detections received at NRSC's Shadnagar earth station, filters out mining and industrial sources, and sends alerts by SMS and as KML and CSV by email, with maps on the Van Agni geo-portal [3]. Large fires are flagged where three contiguous VIIRS pixels burn, and are then tracked on later satellite passes until they die out; weekly pre-fire alerts use a danger rating that combines the Fire Weather Index, forest type and Indian meteorological data [2].
Use has grown sharply. Subscribers rose from 1,31,102 at the end of 2020-21 to 3,07,137 at the end of 2023-24, anyone can register free down to beat level, and more than 112.67 lakh SMS alerts went out in the 2023-24 season [2]. VIIRS detections fell to 2,03,544 in 2023-24 from 2,23,333 in 2021-22 [1][2], and FSI notes that in States such as Madhya Pradesh, Jharkhand and Andhra Pradesh, faster response by a larger subscriber base, together with community action, may explain the decline [2].
FSI also maps fire proneness by analysing seven years of VIIRS detections (2017 to 2023) in a 5 km x 5 km grid: 11.34 per cent of forest cover and scrub is extremely to very highly fire-prone [2]. For the first time, it mapped burnt area nationally using Sentinel-2 and Landsat 8/9 imagery and the differenced Normalised Burn Ratio, finding 34,562 sq km burnt between November 2023 and June 2024; about 93 per cent were surface fires [2].
Business problem
Alerts arrive, but the response is often tracked by phone and register. A division cannot easily say how long crews took to reach each fire, which alerts were false or outside forest, how much area burnt in each beat, or whether last year's fire lines and watchers were in the right places. Without this, budgets for fire lines, watchers and equipment are set by habit rather than risk.
Our solution
- A division fire dashboard that ingests FSI near real-time, large fire and pre-fire information and shows it against beat boundaries, crew locations, roads, water points and fire lines.
- Alert assignment and closure in the My GLOBEIR app: the crew records arrival, action taken and photos, so each alert has a geo-tagged response record.
- Beat-level fire-risk zoning that combines historical detections, FSI fire-prone classes and burnt area mapped from Sentinel-2 and Landsat.
- Season reports on response times, burnt area and hotspots through our monitoring systems and heatmap views.
Benefits
- Each alert has a measurable response, from assignment to closure.
- Pre-season fire line clearing and watcher deployment can follow mapped risk, as FSI recommends fire-prone maps be used for optimal use of scarce resources (industry example) [2].
- Burnt area can be tracked beat by beat, season on season, using the same index FSI uses nationally [2].
- Faster response is linked by FSI to falling detections in several States (industry example) [2].
Privacy & data security
- Crew locations are personal data of staff under the DPDP Act [16]; background tracking in the My GLOBEIR app is optional and opt-in with a persistent notification, and is used for fire duty only.
- Supervisors see only their own range or division through role-based access, and all access is logged.
- Fire records about private land or named villages are restricted to authorised users and shared publicly only in aggregate.
3. Working Plans and Compartment GIS
Every forest division is managed under a working plan. The National Working Plan Code 2023 updates how these plans are prepared. According to an FSI presentation on the Code, grids are no longer to be laid by hand on toposheets: divisions may adopt the 5 x 5 km National Forest Inventory grid or lay grids in GIS, generate random plot centres, and use circular plots split into sub-plots [6]. The Code asks States to take up continuous forest inventory, to upload data to a national portal, and for the working plan officer to prepare maps on GIS; the nodal officer submits the draft plan on an online portal, and FSI is mandated to supply geospatial datasets such as forest cover, forest type and trees outside forests on request [6].
The same national inventory underpins ISFR's estimates of growing stock and carbon, which reached 6,429.64 million cubic metres of growing stock in 2023 [2]. The Code links divisional planning to this national system [6].
Business problem
Working plan preparation is a large, periodic effort that often starts from scratch: old stock maps, inventory sheets and registers are collected, reconciled and redrawn. Errors in compartment boundaries or plot locations carry through to yield and prescription calculations. Once the plan is approved, the data is rarely kept up to date, so the next revision starts again from the beginning, now under a Code that expects continuous inventory and GIS maps.
Our solution
- A compartment and felling series database for the division, with forest type, density, slope, drainage, roads and settlements.
- Inventory grid and random plot generation in GIS to the Code's design, with plot navigation and data entry on the My GLOBEIR app.
- Automated checks that each plot record was captured at its planned location.
- Standard working plan maps and statistical tables generated from the database, ready for portal upload, through our GIS mapping and cartography and geospatial data science services.
Benefits
- Grids, plots and maps follow the structured, GIS-based approach the 2023 Code describes [6].
- Inventory data stays in a maintained database, supporting the continuous forest inventory the Code expects [6].
- Fewer transcription errors between field sheets, maps and calculations.
- The next revision starts from current data, not from paper.
Privacy & data security
- Working plan data is mainly about forest resources, but socio-economic surveys and rights records within it can contain personal data and are held with restricted access [16].
- The department chooses the deployment: its own data centre, a MeitY-empanelled government cloud [21], or GLOBEIR-managed India cloud.
- Full export of all layers and tables in open formats at the end of the engagement.
4. Afforestation and Plantation Monitoring
India runs plantation and restoration programmes at large scale. Under the Compensatory Afforestation Fund Act, 2016, the National Authority approved 2,52,000.44 ha of compensatory afforestation between 2019 and 2024, while 95,724.99 ha of forest land was approved for diversion to non-forestry use between April 2019 and March 2024 [4]. The National Mission for a Green India had released Rs 944.48 crore to 17 States and one UT for plantation and eco-restoration, and Nagar Van Yojana had 546 projects approved in 31 States and UTs with Rs 431.77 crore allocated [1].
The national monitoring tool for CAMPA works is e-Green Watch, designed by NIC [5]. State departments upload polygons for compensatory afforestation land, diverted land, plantations and assets; FSI reviews them on Google Earth imagery, classifies each as correct, incorrect or unascertainable, and sends a performance report to the Ministry every month [5]. The system has built a geospatial database of CAMPA plantations and diverted land, and helps ensure multiple plantation works are not approved on the same site. FSI also records its limits: weeding, gap planting, replacement of plants, and assisted or natural regeneration cannot be confirmed from satellite imagery [5].
Business problem
Plantation departments face scrutiny from auditors, the Ministry and courts. Polygons are often drawn after the fact, overlap older sites, or do not match the ground, and they are then marked incorrect or unascertainable. Young plantations are invisible on medium-resolution imagery for several years, so survival evidence relies on field registers that are hard to verify. Compensatory afforestation linked to diverted land must also be traceable back to the diversion it offsets.
Our solution
- Polygon cleaning and validation before upload: topology checks, overlap detection against all earlier plantations, and comparison with recent imagery.
- Site boundary capture in the field with the My GLOBEIR app, including plant counts, species and photos at planting and at each survival count.
- Time-series canopy tracking on every site using remote sensing, with sites that fail to develop flagged for inspection.
- Scheme-wise dashboards for CAMPA, Green India Mission and Nagar Van works, linking compensatory afforestation sites to the diversion cases they relate to.
Benefits
- Fewer polygons rejected as incorrect or unascertainable when they are captured and checked on the ground first [5].
- Overlap checks help prevent the same land being counted twice, a purpose e-Green Watch was built for (industry example) [5].
- Field survival data fills the gap FSI identifies for works that imagery cannot see [5].
- Audit-ready records for each site from planting to establishment.
Privacy & data security
- Plantation data is largely non-personal, but photos may show workers or community members; photos are used for site verification only and are not published.
- Records of labour and beneficiaries, where held, are kept separate from public dashboards and accessed by role.
- Department data is never sold or shared and is not used to train models for other clients.
5. Wildlife Habitat, Corridors and Tiger Estimation
The All India Tiger Estimation, led by the National Tiger Conservation Authority with the Wildlife Institute of India every four years, is described by the Government as the world's largest biodiversity survey [9]. In 2022 it estimated an average of 3,682 tigers, with an upper limit of 3,925 and growth of 6.1 per cent a year [8]. The method is spatial from the start: since 2006 India has been divided into a fixed 100 sq km grid; in Phase I, frontline staff in 20 States recorded carnivore signs, prey, vegetation and human disturbance at beat scale on the M-STrIPES mobile app, covering 10,146 grid cells; Phase II at WII builds landscape covariates from remote sensing; and Phase III places paired camera traps in 2 sq km cells, at 32,588 locations across 174 sites, producing 4,70,81,881 photographs of which 97,399 were of tigers [7]. The CaTRAT tool sorts camera images by species and ExtractCompare identifies individual tigers from stripe patterns [9].
The estimation also shows why connectivity matters. Tiger occupancy grew from 1,758 to 1,792 grid cells between 2018 and 2022, but linear infrastructure has left the congested corridor between western and eastern Rajaji functionally unusable for large carnivores and elephants, and local populations have disappeared from several areas [7]. India now has 58 tiger reserves covering about 84,500 sq km [9], and the Ministry has ground-validated 150 elephant corridors in 15 States and asked State Governments to protect them [10].
Business problem
Large carnivores and elephants increasingly use territorial divisions, plantations and farmland outside reserves, where management and data are thinner. Infrastructure agencies, planners and forest departments need defensible maps of habitat quality and corridors when roads, railways, canals or mines are proposed. Camera-trap programmes, meanwhile, generate millions of images and station records that are hard to organise outside national exercises.
Our solution
- Habitat suitability and connectivity models built from forest cover, terrain, water, roads, settlements, night lights and species records, using our GeoAI and machine learning capability.
- Least-cost corridor mapping between reserves and habitat patches, validated against field records and published corridor lists.
- A spatial database for camera-trap stations, deployment history and detections, with image-sorting workflows that hand uncertain images to experts.
- Restricted WebGIS views that let planners see corridor and habitat constraints without exposing exact animal locations.
Benefits
- Habitat and corridor maps that support siting and mitigation decisions, where the tiger estimation shows infrastructure can break connectivity (industry example) [7].
- Faster handling of camera-trap data, as automated species sorting does in the national estimation (industry example) [9].
- A consistent grid-based structure that can be compared over time, the principle behind India's fixed 100 sq km sampling grid [7].
- Work that complements NTCA and WII protocols rather than replacing them.
Privacy & data security
- Sensitive species locations are restricted data. Exact coordinates of tigers, dens, nests, roosts and camera traps are visible only to authorised roles and never shown on public maps.
- When data is shared, locations are generalised to coarser grids following GBIF guidance, which recommends rounding to about 10 km, 1 km or 100 m depending on sensitivity, or withholding coordinates altogether for the most sensitive taxa [15].
- Camera-trap images that capture people are excluded from analysis outputs and kept under restricted access.
- Air-gapped or on-premise deployment is available where a department requires it.
6. Human-Wildlife Conflict Hotspot Mapping
The Ministry's February 2021 advisory asks States to act across departments, identify conflict hot spots, follow standard operating procedures, set up rapid response teams and pay ex-gratia relief quickly, preferably within 24 hours of a death or injury [12]. Species-specific guidelines issued in March 2023 cover elephant, gaur, leopard, snake, crocodile, rhesus macaque, wild pig, bear, blue bull and blackbuck, and the 2022 guidelines encourage add-on crop cover under PMFBY and unpalatable crops in forest-fringe areas [11][12]. Ex-gratia for a death or permanent incapacitation was raised from Rs 5 lakh to Rs 10 lakh in December 2023 [11].
Peer-reviewed studies show how strongly conflict follows landscape. In Chhattisgarh, records from 19 forest divisions over 2000 to 2023 showed 737 human fatalities and 91 injuries from elephant conflict, concentrated in a few divisions, mostly in the monsoon, and close to forest edges [13]. In Jharkhand, 1,740 incidents from 22 divisions over the same period included 1,340 fatalities; hotspots lay near protected areas and fragmented forest, and forest patch density was significantly associated with conflict intensity [14]. Both studies recommend prioritising high-conflict villages for targeted mitigation [13][14].
Business problem
Conflict is usually recorded as compensation files and rescue calls, not as points on a map. Without hotspot analysis, barriers, early-warning systems and rapid response teams are spread thinly, seasonal patterns are missed, and every new incident is handled as if it were the first. Relief payments slow down when verification depends on paper reports with no location evidence.
Our solution
- Geocoding of incidents, compensation claims and rescue calls to the village or field, with type, species and date.
- Hotspot mapping by species and season, and analysis of drivers such as distance to forest edge, water, roads and cropland, through our geospatial data science service.
- Village prioritisation for barriers, early-warning systems, response team positions and crop advice, shown on heatmap and WebGIS views.
- Geo-tagged incident verification in the My GLOBEIR app so relief cases can move faster.
Benefits
- Mitigation effort can be focused where conflict concentrates, the approach both Indian studies recommend (industry example) [13][14].
- Location-backed verification supports the advisory's aim of relief within 24 hours [12].
- Seasonal patterns, such as monsoon peaks reported in the studies, can guide staffing and early warning [13][14].
- Before-and-after maps show whether mitigation is working.
Privacy & data security
- Incident and compensation records identify affected people and their land, and are personal data under the DPDP Act [16]; they are visible only to authorised roles.
- Hotspot maps for wider use are aggregated to village or grid level, with no names or household locations.
- Analysis is about places, landscapes and seasons; it never profiles people.
7. Patrol Tracking and Field Reporting
India's tiger monitoring already runs on a field data system. M-STrIPES (Monitoring System for Tigers: Intensive Protection and Ecological Status) uses GPS, GPRS and remote sensing for field monitoring, GPS-tagged photographs improve data accuracy, and observations flow into a central database for analysis [9]. In the 2022 estimation, all Phase I data was collected on the M-STrIPES app by frontline staff, supported by field guides in nine regional languages, and the whole exercise took more than 6.41 lakh man-days [7]. The Management Effectiveness Evaluation of tiger reserves, which had covered 51 reserves by 2022, assesses planning, protection and management processes [9].
Business problem
Outside programmes that already use M-STrIPES, much patrolling in territorial divisions, sanctuaries and plantations is still recorded in paper diaries. Senior officers cannot see which beats were covered, how often, or what was found, and cannot match patrol effort to fire, conflict and protection risk. Frontline observations of sightings, damage or fire rarely reach a map in time to act.
Our solution
- Patrol, sighting, sign, fire, damage and conflict forms in the My GLOBEIR app, with GPS tracks, time stamps and photos, working offline and syncing later.
- Live tracking of patrol teams where the department chooses it, for safety and coordination.
- A patrol coverage dashboard by beat and month, overlaid on fire-risk, conflict and habitat layers, through our mobile GIS and monitoring services.
- Exports in formats the department can combine with its existing systems.
Benefits
- Patrol effort becomes visible by beat and season and can be directed to high-risk areas.
- GPS-tagged evidence improves accuracy, as M-STrIPES has shown for tiger monitoring (industry example) [9].
- Field observations reach managers in days rather than at month end.
- A single field app can serve patrols, fire response, plantation checks and conflict verification.
Privacy & data security
- Patrol tracks are personal data of staff [16]; background tracking is optional and opt-in with a persistent notification, and is used for work only.
- Patrol routes and timings are operationally sensitive and are restricted to authorised supervisors.
- Sightings of sensitive species follow the same restricted handling as other species location data [15].