LiDAR IN FORESTRY Forests are three-dimensional ecosystems in which trees occupy different vertical layers extending from the forest floor to the upper canopy. Conventional forest inventories primarily measure variables such as diameter at breast height (DBH), girth, tree height, species, basal area and volume through field sampling. Although field inventory remains essential, collecting these measurements over large and inaccessible forest areas can be time-consuming and expensive. Remote sensing has therefore become an important complementary method for forest assessment. Optical satellite imagery such as Landsat and Sentinel-2 provides valuable information on vegetation cover and spectral characteristics, but optical sensors generally provide limited direct information about the vertical structure of forests. LiDAR addresses this limitation by actively measuring the return time of laser pulses. A LiDAR sensor emits laser energy towards the ground and vegetation and records the reflected signal. Because the travel time of the laser pulse can be converted into distance, the sensor can reconstruct the three- dimensional structure of the observed environment. This ability to characterize vegetation in three dimensions makes LiDAR particularly valuable for forestry. NASA's GEDI mission, for example, uses waveform LiDAR to characterize the three-dimensional structure of forests and obtain information on canopy height and the distribution of vegetation at different heights. The Food and Agriculture Organization of the United Nations considers forest inventory to be the systematic collection of information on forest resources and recognizes the use of remote sensing alongside field surveys in forest inventory systems.
1. INTRODUCTION Light Detection and Ranging (LiDAR) is an active remote-sensing technology that uses laser pulses to measure the distance between a sensor and objects on the Earth's surface. Unlike conventional passive optical remote sensing, LiDAR directly provides three-dimensional information about vegetation and terrain. This capability makes LiDAR particularly valuable in forestry, where the vertical structure of forests is an important component of forest inventory, biomass estimation, carbon assessment, habitat characterization and management planning. LiDAR data are commonly represented as three-dimensional point clouds containing the X, Y and Z coordinates of laser returns. By separating ground and vegetation returns, Digital Terrain Models (DTM), Digital Surface Models (DSM) and Canopy Height Models (CHM) can be generated. These products allow estimation of forest height, canopy structure, canopy cover, gap fraction, vertical foliage distribution and other structural attributes. When calibrated with field inventory data, LiDAR metrics can also be used to estimate above-ground biomass, basal area, timber volume and other forest attributes. LiDAR can be acquired from terrestrial, mobile, airborne, unmanned aerial vehicle (UAV) and satellite platforms. Airborne LiDAR is particularly useful for landscape-level forest inventory, while UAV LiDAR provides very high-resolution information over relatively small areas. Spaceborne missions such as NASA's Global Ecosystem Dynamics Investigation (GEDI) have demonstrated the capability of waveform LiDAR to measure forest canopy height, vertical structure and surface elevation over large areas. The technology has applications in forest inventory, working-plan preparation, biomass and carbon estimation, forest health monitoring, disturbance assessment, fire management, habitat modelling, regeneration assessment, terrain analysis and forest-road planning. However, LiDAR is not a substitute for field inventory. Its accuracy depends on point density, sensor characteristics, terrain, canopy structure, data processing and appropriate field calibration. The integration of LiDAR, field inventory and optical or radar satellite data provides a powerful framework for modern forest management.
2. WHAT IS LiDAR? LiDAR stands for: Light Detection and Ranging It is an active remote-sensing technique in which a sensor emits laser pulses and measures the time taken for the reflected energy to return to the sensor. The basic principle is: R= c*ΔT/2 • (c) = speed of light • ( Δt) = round-trip travel time of the laser pulse • 2 = accounts for the outgoing and returning path The distance is then combined with the position and orientation of the sensor to determine the three-dimensional coordinates of the reflecting object. A single laser pulse may produce multiple returns when it interacts with different parts of a forest canopy. The resulting returns provide information about the vertical arrangement of vegetation.
3. WHY LiDAR IS IMPORTANT IN FORESTRY Forests have a complex vertical structure. A conventional two-dimensional image may show that an area is covered by forest, but it does not directly reveal: • How tall the trees are • How many canopy layers are present • Where the forest floor lies beneath the canopy • How dense the canopy is vertically • How large canopy gaps are • How vegetation is distributed with height LiDAR can provide these structural measurements. This is its principal advantage over conventional optical imagery. E.g. GEDI's LiDAR measurements as providing canopy height, canopy vertical structure and surface topography, which can subsequently support above-ground biomass estimation.
4. BASIC COMPONENTS OF A LiDAR SYSTEM A typical LiDAR system consists of: Laser source Produces laser pulses directed towards the target. Scanner Controls the direction in which laser pulses are emitted. Detector Records the returning laser energy. Positioning system Usually includes: • GNSS/GPS • Inertial Measurement Unit (IMU) These determine the position and orientation of the sensor. Data-processing system Converts the measured returns into georeferenced three-dimensional point clouds.
5. TYPES OF LiDAR USED IN FORESTRY Terrestrial Laser Scanning (TLS) TLS systems are positioned on the ground, generally using a tripod. They can capture extremely detailed information about individual trees. Applications include: • stem diameter measurement, • tree height, • stem form, • branch structure, • crown architecture, • individual-tree modelling. Advantages • Very high point density • Detailed tree structure • Excellent for research and calibration Limitations • Limited spatial coverage • Occlusion by vegetation • Time-consuming for large forests Airborne LiDAR Airborne LiDAR systems are mounted on aircraft or helicopters. They can cover large forest areas relatively rapidly. Airborne LiDAR is particularly useful for: • forest inventory, • terrain mapping, • canopy-height mapping, • biomass estimation, • forest-road planning, • watershed analysis, • landscape-scale forest structure.
For large forest areas, airborne LiDAR provides an important link between detailed field measurements and landscape-scale remote sensing. UAV LiDAR LiDAR sensors can also be mounted on unmanned aerial vehicles (UAVs). UAV LiDAR is useful when high spatial resolution is required over relatively small areas. Applications include: • plantation inventory, • research plots, • individual-tree mapping, • regeneration studies, • forest-gap analysis, • small watershed surveys, • difficult terrain, • disaster and disturbance assessment. Its major advantage is the ability to acquire very high-resolution three-dimensional data at relatively flexible flight altitudes. SPACEBORNE LiDAR Spaceborne LiDAR allows forest structure to be studied over very large geographical areas. An important example is NASA's GEDI. Global Ecosystem Dynamics Investigation (GEDI) GEDI was installed on the International Space Station and uses multiple laser systems to measure forest structure. Its measurements include canopy height, vertical vegetation structure and surface elevation. GEDI observations have been used in forestry-related applications including: • canopy-height estimation, • forest structure, • disturbance assessment, • biomass, • carbon storage, • wildlife habitat, • forest fuel characterization. NASA notes that GEDI observations have been integrated with Landsat and other datasets to produce spatially continuous forest canopy-height information.
6. LiDAR DATA TYPES LiDAR data can broadly be divided into: Discrete-return LiDAR Records individual returns from the laser pulse. For example: • first return, • intermediate return, • last return. Full-waveform LiDAR Records the complete pattern of returned laser energy as a function of time. Full-waveform information can provide detailed information about vertical vegetation structure. GEDI uses waveform measurements to characterize forest structure
11. LiDAR POINT CLOUD The basic output of many LiDAR surveys is a point cloud. Each point generally contains: • X coordinate • Y coordinate • Z/elevation • return number • intensity • classification • sometimes additional attributes In a forest, points may represent: • ground, • tree trunks, • branches, • leaves, • shrubs, • understorey, • buildings or other objects. The point cloud is therefore the fundamental dataset from which many forestry products are derived.
12. LiDAR RETURNS IN A FOREST A laser pulse entering a forest may interact with several surfaces. A simplified example is: First return → upper canopy Second return → branches/middle canopy Third return → understorey Last return → ground The distribution of these returns provides information on the vertical structure of the forest. This is particularly important because forest structure cannot be adequately described by canopy cover alone.
13. DIGITAL TERRAIN MODEL (DTM) A Digital Terrain Model (DTM) represents the elevation of the bare-earth surface. In a forest, LiDAR can penetrate canopy gaps and provide ground returns. Ground-classified points are used to generate the DTM. DTM is important for: • slope calculation, • aspect calculation, • drainage analysis, • watershed delineation, • forest-road planning, • terrain correction, • hydrological modelling, • canopy-height calculation.
14. DIGITAL SURFACE MODEL (DSM) A Digital Surface Model (DSM) represents the elevation of the uppermost surface. In a forested landscape, this may include: • tree crowns, • vegetation, • buildings, • other above-ground objects. Thus: DSM ≈ ground elevation + vegetation/objects
15. CANOPY HEIGHT MODEL (CHM) One of the most important LiDAR products in forestry is the Canopy Height Model (CHM). A simplified relationship is- [CHM = DSM - DTM] The CHM represents the height of vegetation above the ground. It can be used to identify: • tall trees, • canopy gaps, • variation in canopy height, • stand structure, • individual tree crowns. NASA identifies canopy height as one of the important forest structural parameters derived from LiDAR observations.
16. IMPORTANT FOREST METRICS DERIVED FROM LiDAR LiDAR can provide numerous structural metrics. Height metrics Examples include: • maximum canopy height, • mean canopy height, • median canopy height, • height percentiles, • standard deviation of height. Density metrics Examples include: • proportion of returns above specified heights, • canopy cover, • vegetation return density, • vertical density profiles. Structural metrics Examples include: • canopy roughness, • gap fraction, • crown dimensions, • vertical foliage distribution. These metrics can be related statistically to field measurements.
17. APPLICATION IN FORESTRY Forest inventory is one of the most important applications of LiDAR. Traditional inventory commonly involves measuring: • species, • DBH/girth, • tree height, • basal area, • number of trees, • volume, • regeneration. LiDAR can provide spatially continuous or highly detailed estimates of several structural variables.
The strongest approach is: Field plots + LiDAR metrics → statistical model → landscape prediction FAO notes that modern forest inventories can combine permanent sample plots with remote-sensing information to assess forest resources. TREE HEIGHT ESTIMATION Tree height is one of the variables for which LiDAR has a major advantage. Traditional tree-height measurement may be affected by: • visibility, • slope, • observer error, • dense vegetation. LiDAR can derive tree height from the difference between the tree top and ground elevation. At the individual-tree level: Tree Height = approx Z{crown} - Z{ground} A CHM can therefore be used to locate local maxima corresponding to potential tree tops. INDIVIDUAL TREE DETECTION High-density LiDAR data can sometimes be used to identify individual trees. Potential outputs include: • tree height, • crown diameter, • crown area, • crown position, • tree spacing. This is particularly useful in plantations and relatively open forests as dense, multi-layered natural forests are more challenging because crowns overlap. A simplified workflow is: Point cloud ↓ Ground classification ↓ DTM ↓ CHM ↓ Tree-top detection ↓ Crown segmentation ↓ Individual-tree attributes
CANOPY COVER ESTIMATION Canopy cover represents the proportion of ground covered by canopy when viewed from a particular direction or under a defined measurement approach. LiDAR can estimate canopy cover using the proportion of vegetation returns above a selected height threshold. Canopy cover information is useful for: • forest-type characterization, • habitat assessment, • regeneration evaluation, • canopy-gap analysis • silvicultural planning. CANOPY GAP ANALYSIS LiDAR-derived CHM can identify gaps in the canopy. A temporal comparison can help determine: Existing gap → gap enlargement → regeneration → canopy closure This provides useful information for forest dynamics and disturbance monitoring. FOREST BIOMASS ESTIMATION Above ground biomass (AGB) is an important forest attribute for: Carbon assessment, climate-change studies, REDD+, ecosystem assessment, forest management. LiDAR is useful because biomass is related to forest structure. Variables such as: • canopy height, canopy density,vertical structure, can be used as predictors of biomass. A simplified empirical relationship may be expressed as: [AGB=f(H,D,C) where: • (H) = canopy height metrics, • (D) = LiDAR density/structural metrics, • (C) = canopy characteristics.
However, LiDAR does not directly measure biomass. Biomass models generally require calibration using field plots. NASA has used GEDI structural measurements to support above-ground biomass estimation, and GEDI-derived canopy height has been combined with other datasets to generate biomass products. CARBON STOCK ESTIMATION Once above-ground biomass has been estimated, carbon stock can be approximated using an appropriate biomass-to-carbon conversion factor. LiDAR can therefore contribute to: • forest carbon inventories, • climate-change assessments, • carbon monitoring, • ecosystem accounting. However, uncertainty from field measurements, allometric equations and LiDAR-derived biomass models must be quantified. TIMBER VOLUME ESTIMATION LiDAR can support timber-volume estimation through variables such as: • tree height, • canopy height, • stand density, • crown dimensions, • field-calibrated structural metrics. This is particularly valuable for reducing the number of field measurements required across large areas. However, species-specific volume equations and field calibration remain important. BASAL AREA ESTIMATION Basal area is an important stand-level variable. LiDAR metrics can be correlated with field-derived basal area because forest structural characteristics are related to stand density and canopy architecture. Such models must be developed and validated using local field inventory data. A typical approach is: LiDAR metrics ↓ Field inventory ↓ Regression/model ↓ Stand volume prediction
FOREST STRUCTURE LiDAR is particularly powerful for analysing vertical forest structure. A forest may contain: • ground layer, • herb layer, • shrub layer, • lower tree layer, • middle canopy, • upper canopy/emergent layer. LiDAR can quantify the distribution of returns at different heights. This helps characterize: • structural complexity, • multi-layered forests, • canopy stratification, • understorey structure. FOREST HEALTH MONITORING LiDAR can contribute to forest-health assessment through changes in canopy structure. Potential indicators include: • reduction in canopy height, • crown loss, • increased canopy gaps, • changes in vertical structure, • tree mortality. LiDAR can therefore complement spectral indicators such as NDVI. A useful integrated approach is: LiDAR → structural change NDVI/EVI → vegetation greenness NDMI → vegetation moisture Together, these provide a more complete assessment of forest condition.
FOREST DISTURBANCE DETECTION LiDAR can detect structural changes resulting from: • logging, • fire, • storms, • disease, • insect outbreaks, • tree mortality, • construction, • mining. For example: [Delta CHM = CHM{t2} – CHM {t1}] A substantial reduction in canopy height can indicate a structural disturbance. However, the cause of disturbance should be established using field observations and complementary imagery. FOREST FIRE APPLICATIONS LiDAR has several applications in fire management. Pre-fire It can characterize: • canopy height, • canopy density, • fuel structure, • vertical fuel distribution. Post-fire It can identify: • canopy loss, • tree mortality, • structural damage, • changes in terrain or debris. LiDAR-derived structural information can therefore contribute to fire-risk and post-fire assessment.
WILDLIFE HABITAT ASSESSMENT Forest structure strongly influences wildlife habitat. LiDAR can characterize: • canopy height, • canopy complexity, • vertical vegetation layers, • canopy gaps, • structural heterogeneity. These variables can be incorporated into habitat models. REGENERATION ASSESSMENT & PLANTATION MONITORING LiDAR can support regeneration studies, plantation monitoring especially where young vegetation produces measurable structural signals. Applications include: • detecting vegetation establishment, • monitoring canopy development, • measuring height growth, • assessing spatial distribution. However, very small seedlings may be below the effective detection capability of many airborne or spaceborne systems. Therefore, field regeneration surveys remain essential for small seedlings and saplings. FOREST ROAD AND TERRAIN ANALYSIS LiDAR's ability to generate highly detailed terrain models makes it valuable for forest engineering. DTMs can be used for: • road alignment, slope analysis, • drainage planning, stream crossing identification, • watershed delineation, • erosion-risk assessment, • access planning. This is especially useful in hilly and mountainous forest areas where terrain strongly influences road construction and harvesting operations.
WATERSHED AND HYDROLOGICAL APPLICATIONS High-resolution LiDAR terrain models can reveal: • drainage channels, • depressions, • ridges, • watershed boundaries, • flow paths. Applications include: • soil and water conservation, • check-dam planning, • drainage management, • erosion assessment, • watershed restoration. APPLICATION IN WORKING PLAN PREPARATION LiDAR has considerable potential for modern working-plan preparation. It can support: Stock mapping Estimation of canopy height, stand structure, biomass, volume. Compartment characterization Different compartments can be compared using structural metrics. Site classification Terrain and vegetation information can be combined. Management prioritization Areas showing low canopy height, high canopy gaps, disturbance, degradationcan be prioritized for field verification. Monitoring management interventions Re.peated LiDAR acquisitions can assess structural changes after: silvicultural treatments, plantations, restoration, harvesting, fire.
18 LiDAR-DERIVED PRODUCTS FOR FORESTRY Product Forestry significance Point cloud Fundamental 3D forest data DTM Ground elevation and terrain DSM Surface/canopy elevation CHM Tree/canopy height Canopy cover Stand density/structure Height percentiles Stand structural metrics Gap map Disturbance and regeneration Crown map Individual-tree assessment Slope map Forest engineering Aspect map Site and ecological analysis Biomass map Carbon and resource assessment
19. LiDAR WORKFLOW IN FORESTRY A generalized workflow is-
20. FACTORS AFFECTING LiDAR ACCURACY Several factors influence LiDAR-derived Forest measurements. Point density Higher point density generally allows more detailed structural characterization. Canopy density Very dense forests may reduce penetration to the ground. Terrain Steep terrain can introduce geometric and classification challenges. Scan angle Off-nadir observations can affect canopy and ground detection. Season Leaf-on and leaf-off conditions can produce different structural observations. Sensor characteristics Different LiDAR systems have different: • pulse rates, • wavelengths, • footprint sizes, • scanning patterns. Geolocation accuracy Errors in GNSS/IMU positioning can affect point-cloud accuracy.
21. LIMITATIONS OF LiDAR IN FORESTRY Despite its advantages, LiDAR has limitations. 1. Acquisition can be expensive for large areas using airborne systems. 2. Data processing requires specialized software and technical expertise. 3. Dense canopy can limit ground-return availability. 4. Individual tree detection is difficult in complex multi-layered forests. 5. Biomass estimation requires field calibration. 6. Species identification from LiDAR alone is generally limited. 7. Very small regeneration may not be detectable. 8. Data volume can be very large. 9. Different acquisition dates can complicate temporal comparisons. 10. Accuracy can vary with terrain, canopy structure and point density. Therefore, LiDAR should be integrated with field observations and other remote-sensing technologies.
22. ADVANTAGES OF LiDAR IN FORESTRY The major advantages are: • Three-dimensional forest measurement • Accurate terrain representation • Direct measurement of canopy height • Detailed canopy structure • Individual-tree analysis at suitable point densities • Large-area Forest inventory • Biomass estimation support • Carbon-stock assessment • Habitat characterization • Fire-fuel characterization • Forest-road planning • Change detection • Integration with GIS and satellite imagery
23. LiDAR COMPARED WITH OPTICAL REMOTE SENSING
24. FUTURE PROSPECTS The future of forestry is likely to involve increasing integration of: • LiDAR, • optical satellites, • Synthetic Aperture Radar (SAR), • UAVs, • GIS, • artificial intelligence, • machine learning, • field inventory. LiDAR will increasingly serve as a source of three-dimensional structural information against which other remote-sensing datasets can be calibrated. Recent research demonstrates the growing use of GEDI LiDAR with Landsat, Sentinel and radar datasets for spatially continuous forest-height and biomass estimation. At the same time, new satellite missions are expanding the capability of large-area forest observation. ESA's Biomass mission, launched in April 2025, uses P-band radar rather than LiDAR, but represents an important complementary development for large-scale forest biomass assessment
25. CONCLUSION LiDAR represents a major advancement in forest remote sensing because it provides direct three- dimensional information about vegetation and terrain. Its ability to characterize canopy height, vertical structure, canopy density and ground elevation makes it particularly valuable for forest inventory and management. Among its most important forestry applications are tree-height estimation, canopy-cover assessment, individual-tree detection, forest-structure analysis, biomass and carbon estimation, timber-volume modelling, fire-fuel characterization, disturbance detection, habitat assessment, plantation monitoring and terrain analysis. The greatest strength of LiDAR is not that it eliminates the need for field measurements, but that it extends the information obtained from field measurements across larger areas. Field plots provide accurate biological and mensurational observations, while LiDAR provides spatially extensive three- dimensional structural information. When combined with optical satellite imagery, radar, GIS and machine learning, this approach can support more efficient and scientifically robust forest monitoring. For forest departments and working-plan preparation, LiDAR therefore has the potential to transform forest assessment from predominantly sample-based structural information towards spatially explicit, three-dimensional and increasingly individual-tree-level forest inventories. The appropriate approach is consequently not "LiDAR instead of field inventory", but: "LiDAR-supported field inventory and integrated remote sensing for sustainable forest management."
26. IMPORTANT TERMS LiDAR: Light Detection and Ranging. Point cloud: Collection of three-dimensional LiDAR returns. DTM: Digital Terrain Model representing bare-earth elevation. DSM: Digital Surface Model representing the upper surface. CHM: Canopy Height Model, commonly derived as DSM − DTM. ALS: Airborne Laser Scanning. TLS: Terrestrial Laser Scanning. UAV LiDAR: LiDAR mounted on an unmanned aerial vehicle. GEDI: Global Ecosystem Dynamics Investigation. AGB: Above-ground biomass. Canopy height: Vertical distance between ground and canopy surface. Return: Refleced laser signal detected by the sensor. Point density: Number of LiDAR points per unit area. Waveform LiDAR: Records the complete returned energy signal rather than only discrete returns. Data fusion: Combining LiDAR with optical, radar or field data to improve forest estimation.
NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI) Forests are dynamic ecosystems whose condition varies spatially and temporally due to natural processes and anthropogenic disturbances. Conventional forest assessment is largely based on field inventories, which provide detailed information but require considerable time, manpower and financial resources. Remote sensing provides an efficient complementary approach by allowing vegetation to be observed repeatedly over large geographical areas. Satellite sensors record reflected electromagnetic radiation from Earth's surface in different wavelength bands. Vegetation has a distinctive spectral response, particularly in the red and near- infrared regions. This characteristic provides the scientific basis for vegetation indices such as NDVI. In forestry, NDVI provides a spatially continuous method for assessing vegetation condition and monitoring temporal changes over large areas. It can be used for forest health assessment, vegetation-density mapping, drought and moisture-stress assessment, fire and disturbance monitoring, regeneration assessment, plantation monitoring, forest degradation studies and analysis of seasonal vegetation dynamics. Landsat and Sentinel-2 satellite imagery provide particularly useful spatial resolutions for forest-level studies, while MODIS and VIIRS are useful for regional and national-scale monitoring. USGS defines Landsat NDVI as (NIR − Red)/(NIR + Red) and describes it as useful for quantifying vegetation greenness, vegetation density and changes in plant health. 1. Introduction The Normalized Difference Vegetation Index (NDVI) is one of the most widely used vegetation indices in remote sensing for assessing vegetation greenness, density and vigour. It is derived from the contrasting spectral response of vegetation in the red and near-infrared (NIR) regions of the electromagnetic spectrum. NDVI is a normalized ratio designed to emphasize the difference between red and NIR reflectance. It is a dimensionless indicator of vegetation greenness and is widely used to characterize vegetation abundance and condition. Green vegetation strongly absorbs red radiation because of chlorophyll pigments, while its internal leaf structure strongly reflects NIR radiation. NDVI exploits this contrast to generate a dimensionless index ranging theoretically from −1 to +1. For forestry, the importance of NDVI lies not simply in obtaining a numerical value for each pixel, but in analysing spatial patterns and changes through time.
2. Objectives The major objectives of NDVI-based forest assessment are: 1. To understand the spectral basis of vegetation detection. 2. To calculate and map vegetation greenness using satellite imagery. 3. To assess relative vegetation density and vigour. 4. To monitor spatial and temporal changes in forest condition. 5. To identify areas affected by forest degradation and disturbance. 6. To assess vegetation response after fire, harvesting or other disturbances. 7. To support forest management and planning. 8. To identify areas requiring field verification. 9. To integrate satellite-based information with conventional forest inventories. 10. To develop a repeatable method for monitoring forest condition.
3. Principle of NDVI The fundamental principle of NDVI is based on the contrasting behaviour of green vegetation in two spectral regions: Red region Healthy green leaves contain chlorophyll, which strongly absorbs radiation in the red portion of the electromagnetic spectrum. Near-infrared region The cellular structure of healthy leaves strongly reflects NIR radiation. Therefore: Healthy vegetation → low red reflectance + high NIR reflectance Whereas: Sparse/stressed vegetation → relatively higher red reflectance + lower NIR reflectance This difference is expressed mathematically through NDVI. NDVI Formula The normalization by the sum of the two bands makes NDVI less sensitive to certain variations in illumination and converts the result into a dimensionless index. NDVI= NIR+Red/NIR−Red
4. Spectral Basis of Vegetation A typical healthy green leaf has three important spectral characteristics: 4.1 Visible region Vegetation absorbs much of the incoming visible radiation for photosynthesis. Chlorophyll particularly absorbs radiation in: • Blue region • Red region Green light is relatively more reflected, which is why leaves appear green to our eyes. 4.2 Red-edge region Reflectance increases rapidly between the red and NIR regions. This transition is called the red edge and is particularly useful for studying vegetation condition. 4.3 Near-infrared region Healthy leaves exhibit high NIR reflectance due to internal leaf structure. This creates the fundamental spectral contrast used by NDVI.
5. NDVI Range and Interpretation The theoretical NDVI range is: −1 ≤NDVI≤ +1 In practical land-surface applications, values are commonly interpreted approximately as follows: NDVI value General interpretation −1 to 0 Water, cloud, snow, some non-vegetated surfaces 0–0.1 Bare soil, rock, built-up or very sparse vegetation 0.1–0.2 Very sparse vegetation 0.2–0.3 Sparse vegetation 0.3–0.5 Moderate vegetation 0.5–0.7 Dense/healthy vegetation 0.7–0.9 Very dense, vigorous vegetation These should not be treated as universal forest-class thresholds. Local climate, forest type, season, sensor and atmospheric conditions affect the actual values. A forest with NDVI = 0.65 should not automatically be called "better" than a forest with NDVI = 0.55 without considering: • forest type, • season, • rainfall, • canopy structure, • soil, • topography, • acquisition date, • sensor, • atmospheric conditions. NDVI is therefore best considered a relative indicator.
6. NDVI and Different Forest Conditions 6.1 Dense healthy forest Dense forests generally show high NDVI because of abundant green foliage. Expected characteristics: • high NIR reflectance, • low red reflectance, • relatively high NDVI. However, very dense forests can cause NDVI saturation. 6.2 Open Forest Open forests contain greater proportions of: • soil, grasses, shrubs, • exposed litter. Consequently, their NDVI may be lower than that of closed-canopy forests. 6.3 Degraded Forest Forest degradation can reduce NDVI because canopy cover and green leaf area decline. However, interpretation must consider whether the decrease is caused by: • logging, grazing, • fire, • drought, • disease, • insect attack, • seasonal leaf fall. 6.4 Plantation NDVI can be particularly useful for monitoring plantations because plantations often have relatively uniform vegetation. Time-series NDVI can help monitor: • establishment, • canopy development,
• seasonal growth, • stress, • mortality, • harvesting. 6.5 Deciduous forest This is especially important in India. A deciduous forest can show: Monsoon → high NDVI Post-monsoon → high/moderate NDVI Dry season → lower NDVI A reduction in dry-season NDVI does not necessarily indicate degradation. It may simply represent natural phenology.
7. Satellite Data Used for NDVI Several satellite systems can be used. Landsat Landsat is particularly useful for forest studies because of its long historical record and approximately 30-metre multispectral spatial resolution. or Landsat 8 and 9: NDVI=Band 5+Band 4Band 5−Band 4 where: • Band 5 = NIR • Band 4 = Red USGS specifically provides Landsat 8–9 NDVI using these bands. Advantages • long historical record, • 30 m spatial resolution, • suitable for forest compartments and landscape analysis, • freely available data, • Useful for change detection. Sentinel-2 Sentinel-2 provides multispectral imagery with several bands particularly useful for vegetation studies. The standard NDVI can be calculated using: • Band 8 – NIR • Band 4 – Red NDVI=B8+B4B8−B4 Sentinel-2 also provides red-edge bands, which are useful for advanced forest vegetation studies. The Sentinel-2 processing documentation describes NDVI as a contrast between high NIR reflectance and low red reflectance of green vegetation. Advantages • high temporal frequency, • 10 m resolution for relevant bands, • useful for small forest patches,
• excellent for plantation and forest-health studies, • red-edge bands enable additional vegetation indices. MODIS and VIIRS MODIS and VIIRS are useful when the study requires: • egional-scale monitoring, • national-scale monitoring, • frequent observations, • seasonal analysis, • drought monitoring, • fire monitoring. Their major advantage is frequent temporal coverage, although their spatial resolution is generally coarser than Landsat or Sentinel-2. USGS, for example, uses 1-km, 7-day VIIRS NDVI composites for operational vegetation- condition/fire-danger applications. Recommended Satellite for Forest Studies Requirement Suitable satellite Small forest patch Sentinel-2 Forest compartment Sentinel-2 / Landsat Working-plan landscape Landsat / Sentinel-2 Long-term change Landsat Plantation monitoring Sentinel-2 National-scale monitoring MODIS/VIIRS Frequent seasonal monitoring Sentinel-2 / MODIS / VIIRS Detailed forest health Sentinel-2 + field data
8. Methodology for NDVI-Based Forest Assessment A proper NDVI study should follow a systematic workflow. Step 1 – Define study area The study area may be: • forest range, compartment, division, protected area, • plantation, watershed, landscape. Obtain its boundary as a GIS shapefile or geodatabase layer. Step 2 – Acquire satellite imagery Select appropriate satellite imagery according to: • spatial resolution, temporal resolution, • cloud cover, season, study objective. For a forest-change study, imagery from comparable seasons should preferably be selected. Step 3 – Pre-processing Satellite imagery should be prepared before NDVI calculation. Important steps include: 1. atmospheric correction, cloud and cloud-shadow masking, 2. geometric correction, radiometric calibration where applicable, 3. mosaicking if required, clipping to study area. Using surface-reflectance products is generally preferable for quantitative comparison. Step 4 – Calculate NDVI For Landsat 8/9: NDVI=B5+B4/B5−B4 For Sentinel-2: NDVI=B8+B4/B8−B4 Step 5 – Generate NDVI map The calculated raster is classified into suitable NDVI classes. For example:
• very low, low, • moderate, • high, very high. The classification should be based on the study area's distribution rather than blindly applying universal thresholds. Step 6 – Calculate area under each NDVI class For forest management, calculate: Area class= Number of pixels × Pixel area This allows the researcher to report, for example: "Approximately 42% of the study area falls within the high-NDVI category." Step 7 – Temporal analysis NDVI should ideally be calculated for multiple dates. For example: 2015 → 2018 → 2021 → 2024 → 2026 This allows detection of: • improvement, degradation, disturbance, recovery.
9. NDVI Change Detection One of the most important applications in forestry is comparing NDVI between two dates. A simple change layer can be calculated as: ΔNDVI= NDVIt2− NDVIt1 Interpretation ΔNDVI Possible interpretation Large positive Vegetation improvement/recovery Small positive Slight improvement Near zero Relatively stable Small negative Slight decline Large negative Possible degradation/disturbance However, a negative change must be field-verified because it may result from seasonal differences, drought or phenological changes rather than permanent forest loss.
10. NDVI Application in Forestry 1. Forest Health Assessment NDVI can assist in identifying areas showing abnormal vegetation response. Potential indicators include: • drought stress, insect attack, • disease, fire damage, • canopy opening, logging, • storm damage, grazing-related degradation. For example, a sudden decline in NDVI between two comparable-season images may indicate a disturbance. 2. Forest Fire Assessment Fire generally destroys green vegetation and exposes soil/charred material. Consequently: NDVI post−fire< NDVI pre−fire Large positive values can indicate severe vegetation loss. NDVI can subsequently be monitored to assess post-fire recovery. However, specialized burn indices such as NBR (Normalized Burn Ratio) are often more appropriate for fire-severity analysis. 3. Forest Regeneration NDVI can support regeneration studies at landscape scale. Areas undergoing successful regeneration may show: Bare ground → grass/shrub → young forest → increasing canopy Therefore, a long-term NDVI trajectory can provide evidence of vegetation recovery. However, NDVI alone cannot distinguish: • desirable tree regeneration, invasive weeds, • grasses, shrubs. Therefore, field sampling is essential. 4. Encroachment and Forest Degradation NDVI can be integrated with land-use/land-cover classification to identify changes around forest boundaries. For example: Forest → agriculture may produce a distinctive change in vegetation pattern.
For an encroachment study, NDVI should ideally be combined with: • satellite imagery, historical imagery, • cadastral boundaries, forest compartment maps, • LULC classification, field GPS observations. NDVI should therefore be regarded as supporting evidence rather than standalone legal evidence of encroachment. 5. Working Plan Preparation NDVI can provide useful supporting information for working-plan exercises. Potential applications include: Forest condition mapping Identify areas with relatively: • high vegetation vigour, • moderate vegetation vigour, • low vegetation vigour. Management prioritization Areas with persistent low NDVI can be investigated for: • degradation, fire, grazing, • invasive species, poor regeneration, • anthropogenic pressure. Temporal comparison NDVI trends can be compared between: • management periods, working-plan periods, • compartments, ranges, forest types. Treatment evaluation NDVI can be used to evaluate vegetation response following: • plantation, assisted natural regeneration, • soil-moisture conservation, fire protection, • restoration measures.
6. NDVI and Biomass NDVI is sometimes described as a proxy for biomass, but this needs careful qualification. Higher NDVI generally indicates greater green vegetation abundance, and therefore NDVI can correlate with biomass under particular conditions. However: NDVI is not a direct measurement of forest biomass. A forest with substantial woody biomass but relatively sparse green foliage may have a lower NDVI than expected. For actual biomass estimation, NDVI should be calibrated against field measurements or supplemented with: EVI, LiDAR, radar, canopy-height data, field inventory.
11. Factors Affecting NDVI NDVI is influenced by several factors. Season Deciduous forests exhibit strong seasonal variation. Rainfall Increased rainfall can stimulate vegetation growth and increase NDVI. Soil background Exposed soil influences the reflectance recorded by the satellite. Atmospheric conditions Aerosols, haze and atmospheric scattering can affect spectral measurements. Cloud cover Clouds can produce erroneous NDVI values. Topography Slope and aspect affect illumination and reflectance. Forest structure Leaf area, canopy density and vertical structure affect NDVI. Sensor characteristics Different satellites use different spectral bandwidths and spatial resolutions.
12. Limitations of NDVI The major limitations are: 1. Saturation in dense vegetation As canopy becomes very dense, NDVI may stop increasing proportionally with additional vegetation. For example: Forest condition NDVI Open forest 0.45 Moderately dense 0.60 Dense forest 0.75 Very dense forest 0.78 2. Sensitivity to soil background 3. Influence of atmospheric conditions 4. Influence of seasonality 5. Difficulty distinguishing vegetation types 6. Cannot directly estimate timber volume 7. Cannot directly determine species composition 8. Cannot distinguish natural seasonal decline from degradation without temporal context 9. Mixed-pixel problem 10.Topographic effects in mountainous terrain
13. NDVI vs Other Vegetation Indices Index Formula/Principle Major forestry application NDVI NIR + Red General vegetation condition EVI NIR + Red + Blue Dense forest/canopy condition SAVI NIR + Red + soil correction Sparse/open forest NDMI NIR + SWIR Vegetation moisture NBR NIR + SWIR Burn severity NDRE Red Edge + NIR Forest chlorophyll/stress GNDVI NIR + Green Chlorophyll/vegetation vigour NDMI, for example, specifically uses NIR and SWIR to estimate vegetation water content.
14. Why NDVI + NDMI + NBR Is Better for Forest Management A very useful forestry approach is to combine indices. NDVI How green/dense is the vegetation? NDMI How much vegetation moisture/stress is present? NBR Has the forest been affected by fire/burning? Together they provide substantially more information than NDVI alone.
15. Field Validation Remote sensing should be validated using field observations. Field measurements may include: • tree density, basal area, canopy cover, DBH/girth, • tree height, regeneration density, species composition, • crown condition, biomass, soil condition, fire damage. The field measurements can then be compared with NDVI value
16. Conclusion NDVI is one of the most important and widely used remote-sensing indicators for vegetation assessment. Its scientific basis lies in the strong absorption of red radiation by chlorophyll and high reflectance of near-infrared radiation by healthy vegetation. The resulting normalized ratio provides a simple, dimensionless measure that can be mapped spatially and analysed temporally. In forestry, NDVI has significant applications in assessing vegetation greenness, monitoring forest condition, detecting disturbance, analysing seasonal dynamics, monitoring plantations and restoration, assessing fire impacts and supporting working-plan and forest-management decisions. Nevertheless, NDVI should be interpreted cautiously. High NDVI does not necessarily mean high timber volume, and low NDVI does not necessarily mean forest degradation. Seasonality, forest type, soil background, atmospheric conditions, canopy structure and sensor characteristics must be considered. In dense forests, NDVI can also saturate. Therefore, the most scientifically sound approach is to use NDVI as a remote-sensing indicator, integrate it with other spectral indices and GIS layers, and validate the results through field observations. With appropriate temporal analysis, NDVI can become a powerful tool for monitoring the condition and dynamics of forest ecosystems over large areas