Showing papers in "Photogrammetric Engineering and Remote Sensing in 2010"

Accuracy Assessment Measures for Object-based Image Segmentation Goodness

Abstract: To select an image segmentation from sets of segmentation results, measures for ranking the segmentations relative to a set of reference objects are needed. We review selected vector-based measures designed to compare the results of object-based image segmentation with sets of training objects extracted from the image of interest. We describe and compare area-based and location-based measures that measure the shape similarity between segments and training objects. By implementing the measures in two object-based image processing software packages, we illustrate their use in terms of automatically identifying parsimonious parameter combinations from arbitrarily large sets of segmentation results. The results show that the measures have divergent performance in terms of the identification of parameter combinations, Clustering of the results in measure space narrows the search. We illustrate combination schemes for the measures for generating rankings of segmentation results. The ranked segmentation results are illustrated and described.

Analysis of Impervious Surface and its Impact on Urban Heat Environment using the Normalized Difference Impervious Surface Index (NDISI)

Abstract: The fast urban expansion has led to replacement of natural vegetation-dominated land surfaces by various impervious materials. This has a significant impact on the environment due to modification of heat energy balance. Timely understanding of spatiotemporal information of impervious surface has become more urgent as conventional methods for estimating impervious surface are very limited. In response to this need, this paper proposes a new index, normalized difference impervious surface index (NDISI), for estimating impervious surface. The application of the index to the Landsat ETM+ image of Fuzhou City and the ASTER image of Xiamen City in China has shown that the new index can efficiently enhance and extract impervious surfaces from satellite imagery, and the normalized NDISI can represent the real percentage of impervious surface. The index was further used as an indicator to investigate the impact of impervious surface on urban heat environment by examination of its quantitative relationship with land surface temperature ( LST ), vegetation, and water using multivariate statistical analysis. The result reveals that impervious surface has a positive exponential relationship with LST rather than a simple linear one. This suggests that the areas with high percent impervious surface will accelerate LST rise and urban heat island development.

Effects of Topographic Variability and Lidar Sampling Density on Several DEM Interpolation Methods

Abstract: This study aims to quantify the effects of topographic variability (measured by coefficient variation of elevation, CV) and lidar (Light Detection and Ranging) sampling density on the DEM (Digital Elevation Model) accuracy derived from several interpolation methods at different spatial resolutions. Interpolation methods include natural neighbor (NN), inverse distance weighted (IDW), triangulated irregular network (TIN), spline, ordinary kriging (OK), and universal kriging (UK). This study is unique in that a comprehensive evaluation of the combined effects of three influencing factors (CV, sampling density, and spatial resolution) on lidar-derived DEM accuracy is carried out using different interpolation methods. Results indicate that simple interpolation methods, such as IDW, NN, and TIN, are more efficient at generating DEMs from lidar data, but kriging-based methods, such as OK and UK, are more reliable if accuracy is the most important consideration. Moreover, spatial resolution also plays an important role when generating DEMs from lidar data. Our results could be used to guide the choice of appropriate lidar interpolation methods for DEM generation given the resolution, sampling density, and topographic variability.

Acquisition, orthorectification, and object-based classification of unmanned aerial vehicle (UAV) imagery for rangeland monitoring.

Abstract: The use of unmanned aerial vehicles (UAVs) for natural resource applications has increased considerably in recent years due to their greater availability, the miniaturization of sensors, and the ability to deploy a UAV relatively quickly and repeatedly at low altitudes. We examine in this paper the potential of using a small UAV for rangeland inventory, assessment and monitoring. Imagery with a ground resolved distance of 8 cm was acquired over a 290 ha site in southwestern Idaho. We developed a semiautomated orthorectification procedure suitable for handling large numbers of small-footprint UAV images. The geometric accuracy of the orthorectified image mosaics ranged from 1.5 m to 2 m. We used object-based hierarchical image analysis to classify imagery of plots measured concurrently on the ground using standard rangeland monitoring procedures. Correlations between imageand ground-based estimates of percent cover resulted in r-squared values ranging from 0.86 to 0.98. Time estimates indicated a greater efficiency for the image-based method compared to ground measurements. The overall classification accuracies for the two image mosaics were 83 percent and 88 percent. Even under the current limitations of operating a UAV in the National Airspace, the results of this study show that UAVs can be used successfully to obtain imagery for rangeland monitoring, and that the remote sensing approach can either complement or replace some ground-based measurements. We discuss details of the UAV mission, image processing and analysis, and accuracy assessment.

Point Clouds: Lidar versus 3D Vision

Abstract: Novel automated photogrammetry is based on four innovations. First is the cost-free increase of overlap between images when sensing digitally. Second is an improved radiometry. Third is multi-view matching. Fourth is the Graphics Processing Unit (GPU), making complex algorithms for image matching very practical. These innovations lead to improved automation of the photogrammetric workflow so that point clouds are created at sub-pixel accuracy, at very dense intervals, and in near real-time, thereby eroding the unique selling proposition of lidar scanners. Two test projects compare point clouds from aerial and street-side lidar systems with those created from images. We show that the photogrammetric accuracy compares well with the lidar-method, yet the density of surface points is much higher from images, and the throughput is commensurate with a fully automated all-digital approach. Beyond this density, we identify 15 additional advantages of the photogrammetric approach.

Land Cover Classification in a Complex Urban-Rural Landscape with Quickbird Imagery.

Abstract: High spatial resolution images have been increasingly used for urban land-use/land-cover classification, but the high spectral variation within the same land-cover, the spectral confusion among different land-covers, and the shadow problem often lead to poor classification performance based on the traditional per-pixel spectral-based classification methods. This paper explores approaches to improve urban land-cover classification with QuickBird imagery. Traditional per-pixel spectral-based supervised classification, incorporation of textural images and multispectral images, spectral-spatial classifier, and segmentation-based classification are examined in a relatively new developing urban landscape, Lucas do Rio Verde in Mato Grosso State, Brazil. This research shows that use of spatial information during the image classification procedure, either through the integrated use of textural and spectral images or through the use of segmentation-based classification method, can significantly improve land-cover classification performance.

Persistent Scatterer Interferometry: Potential, Limits and Initial C- and X-band Comparison

Abstract: This paper is focused on the potential and limits of Persistent Scatterer Interferometry (PSI), a powerful remote sensing technique used to measure deformation phenomena. It only refers to satellite-based PSI techniques, focusing on the most important sources of C-band SAR data: ERS and Envisat. In addition, it compares C- and X-band results, considering data from the high-resolution TerraSAR-X sensor. The paper begins with a description of the main characteristics of PSI. It then discusses the most important PSI products and their performances, analyzing their spatial sampling, the so-called residual topographic error and PSI geocoding, the average displacement rates, and the deformation time series. As C-band products are concerned, the paper reports some relevant PSI validation results, which come from the ESA-funded Terrafirma Validation Project. Regarding the X-band, it describes the results obtained over the City of Barcelona by processing 13 TerraSAR-X images. The last part discusses the main limits of PSI.

Comparison of geo-object based and pixel-based change detection of riparian environments using high spatial resolution multi-spectral imagery.

Abstract: The objectives of this research were to (a) develop a geo-object-based classification system for accurately mapping riparian land-cover classes for two QuickBird images, and (b) compare change maps derived from geo-object-based and per-pixel inputs used in three change detection techniques. The change detection techniques included post-classification comparison, image differencing, and the tasseled cap transformation. Two QuickBird images, atmospherically corrected to at-surface reflectance, were captured in May and August 2007 for a savanna woodlands area along Mimosa Creek in Central Queensland, Australia. Concurrent in-situ land-cover identification and lidar data were used for calibration and validation. The geo-object-based classification results showed that the use of class-related features and membership functions could be standardized for classifying the two QuickBird images. The geo-object-based inputs provided more accurate change detection results than those derived from the pixel-based inputs, as the geo-object-based approach reduced mis-registration and shadowing effects and allowed inclusion of context relationships.

Comparison of Airborne and Terrestrial Lidar Estimates of Seacliff Erosion in Southern California

Abstract: Seacliff changes evaluated using both terrestrial and airborne lidar are compared along a 400 m length of coast in Del Mar, California. The many large slides occurring during the rainy, six-month study period (September 2004 to April 2005) were captured by both systems, and the alongshore variation of cliff face volume changes estimated with the airborne and terrestrial systems are strongly correlated (r 2 = 0.95). How- ever, relatively small changes in the cliff face are reliably detected only with the more accurate terrestrial lidar, and the total eroded volume estimated with the terrestrial system was 30 percent larger than the corresponding airborne estimate. Although relatively small cliff changes are not detected, the airborne system can rapidly survey long cliff lengths and provides coverage on the cliff top and beach at the cliff base.

Positional accuracy evaluation of declassified hexagon KH-9 mapping camera imagery.

Abstract: This paper examines the positional accuracy of the declassified KH-9 Hexagon imagery and derived DEM. Aimed at geodesy and mapmaking, the KH-9 program (1973 to 1980) resulted in an image archive with worldwide stereo coverage at 6 to 9 m. We used six KH-9 images acquired in 1980 over two testfields in Central Asia. Using reseau marks on the scanned KH- 9 frames, we found and corrected image distortions. In bundle orientation with Ground Control Points (GCPS) from QuickBird images, we achieved horizontal accuracies below 6 m for a flat terrain testfield and approximately 10 m for a mountainous terrain testfield. With three GCPS the image orientation horizontal accuracy degraded by only 20 percent. We generated a DEM from the KH-9 images and estimated its vertical accuracy using IceSAT laser altimetry data and an additional DEM from 1:25 000 topographic maps. The DEM RMSE was 6.18 m over flat terrain and 20.0 m over mountainous terrain.

Improved land cover mapping using random forests combined with landsat thematic mapper imagery and ancillary geographic data.

Abstract: Large area land-cover mapping involving large volumes of data is becoming more common in remote sensing applications. Thus, there is a pressing need for increased automation in the land-cover mapping process. The main objective of this research was to compare the performance of three machine learning algorithms (MLAS) for mapping wetlands in the Sanjiang Plain combined Landsat TM imagery with ancillary geographical data. Three MLAS included random forest (RF), classification and regression tree (CART), and maximum likelihood classification (MLC). Comparisons were based on several criteria: overall accuracy, sensitivity to data set size, and noise. Our results indicated that first, the random forest and CART approach can achieve substantial improvements in accuracy over the traditional MLC method. Random forest produced the highest overall accuracy (91.3 percent) the kappa coefficient 0.8943, with marsh class accuracies ranging from 77.4 percent to 90.0 percent. Secondly, the random forest method was least sensitive to reduction in training sample size, and it was most resistant to the presence of noise compared to CART and MLC. The comparison between three MLAS revealed that the random forest approach was most resistant to training data deficiencies while improved land-cover map accuracy in marsh area.

Generation of Complex Polyhedral Building Models by Integrating Stereo-Aerial Imagery and Lidar Data

Abstract: The integration of lidar data and aerial imagery is a promising approach for accurate building model generation. In this research, lidar data and stereo-aerial imagery are incorporated in the generation of complex polyhedral building models whose rooftops are bounded by straight lines. The process starts by utilizing lidar data for deriving building hypotheses and the initial boundaries of the planar patches constituting the buildings' rooftops. The boundaries of these patches are then refined through the incorporation of stereo-aerial imagery while utilizing 3D geometric and spectral constraints. Precise building boundaries are derived through resolving feature matching problems and fully utilizing spectral information. Finally, an efficient manual mono-plotting procedure is introduced to remove incorrect and add missing boundaries. The performance of the developed procedures is evaluated through experimental results from real data where the correctness, completeness, and accuracy of the derived building models are evaluated through comparison with a manually generated DBM.

Object-based Class Modeling for Cadastre-constrained Delineation of Geo-objects

Abstract: Remote sensing technology still faces challenges when it comes to monitoring tasks that must be able to stand up to validation from technical, scientific, and practical points of view, in other words, when entering into established, fully operational workflows. In this paper, we present an approach for delineating and monitoring aggregated spatial units relevant to regional planning tasks, which has been fully validated within a 3,654 km 2 area in the Stuttgart Region of southwestern Germany. This has been achieved by developing algorithms for semi-automated (geo-) object-based class modeling of biotope complexes, which are aggregated, functionally homogenous (but not necessarily spectrally homogeneous) units. High levels of complexity in the target classes and the need for integration of auxiliary geodata as a priori knowledge meant that different methods of information extraction were required to be combined in an operational workflow, and that new validation strategies were needed for quality assessment. A total of 31,698 biotope complexes were delineated for the entire Stuttgart Region, with an average size of 11.5 ha for each complex. Approximately 86 percent of the biotope complex boundaries were shown to have been correctly delineated.

Mapping Cropland and Major Crop Types across the Great Lakes Basin using MODIS-NDVI Data

Abstract: This research evaluated the potential for using the MODIS Normalized Difference Vegetation Index (NDVI) 16-day composite (MOD13Q) 250 m time-series data to develop an annual crop type mapping capability throughout the 480,000 km 2 Great Lakes Basin (GLB). An ecoregion-stratified approach was developed using a two-step processing approach that included an initial differentiation of cropland versus non-cropland and subsequent identification of individual crop types. Major crop types were mapped for the calendar years of 2002 and 2007. National Agricultural Statistics Service (NASS) census data were used to assess county level accuracies on a unit area basis (2002), and the NASS Crop Data Layer (C DL ) was used to generate 231,616 reference data points to support a pixel-wise assessment of the MODIS crop type classification (2007) accuracy across the US portion of the CLB. County level comparisons for 2002 indicated 2.2, ―6.8, ―6.0, and ―5.8 percent of area bias errors for corn, soybeans, wheat, and hay, respectively. Detailed pixel-wise accuracy assessments resulted in an overall crop type classification accuracy of 84 percent (Kappa = 0.73) for 2007. Kappa coefficients ranged from 0.74 to 0.69 for individual ecoregions. The user's accuracies for corn, soybean, wheat, and hay were 87, 82, 81, and 70 percent, respectively. There were spatial variations of classification performances across ecoregions, especially for soybean and hay. Field sizes had a direct impact on the variable classification performances across the GLB.

Co-registration of surfaces by 3D least squares matching.

Abstract: A method for the automatic co-registration of 3D surfaces is presented. The method utilizes the mathematical model of Least Squares 2D image matching and extends it for solving the 3D surface matching problem. The transformation parameters of the search surfaces are estimated with respect to a template surface. The solution is achieved when the sum of the squares of the 3D spatial (Euclidean) distances between the surfaces are minimized. The parameter estimation is achieved using the Generalized Gauss-Markov model. Execution level implementation details are given. Apart from the co-registration of the point clouds generated from spaceborne, airborne and terrestrial sensors and techniques, the proposed method is also useful for change detection, 3D comparison, and quality assessment tasks. Experiments using terrain data examples show the capabilities of the method.

Automatic Segmentation of Lidar Data into Coplanar Point Clusters Using an Octree-Based Split-and-Merge Algorithm

Abstract: Lidar (light detection and ranging) point cloud data contain abundant three-dimensional (3D) information. Dense distribution of scanned points on object surfaces prominently implies surface features. Particularly, plane features commonly appear in a typical lidar dataset of artificial structures. To explore implicitly contained spatial information, this study developed an automatic scheme to segment a lidar point cloud dataset into coplanar point clusters. The central mechanism of the proposed method is a split-and-merge segmentation based on an octree structure. Plane fitting serves as an engine in the mechanism that evaluates how well a group of points fits to a plane. Segmented coplanar points and derived parameters of their best-fit plane are obtained through the process. This paper also provides algorithms to derive various geometric properties of segmented coplanar points, including inherent properties of a plane, intersections of planes, and properties of point distribution on a plane. Several successful cases of handling airborne and terrestrial lidar data as well as a combination of the two are demonstrated. This method should improve the efficiency of object modelling using lidar data.

Evaluation of the RPC model for spaceborne SAR imagery.

Abstract: The rational polynomial coefficient (RPC) model has raised considerable interest in the photogrammetry and remote sensing community. Much work has been done on frame camera imagery and/or push-broom scanner imagery, and it has been widely accepted that the RPC model can be taken as an alternative to rigorous sensor models for photogrammetric processing. However, there have been few publications discussing the application of the RPC model to SAR image processing. In this paper, we first review the geometric effects of SAR imagery and compare SAR imagery with optical satellite imagery. Then, the unbiased RPC estimators for a series of SAR images are derived. Based on numerous tests with the rigorous sensor model available, the modeling error of the RPC is analyzed. This study found that the RPC model is suitable for SAR imagery and can be used as a replacement for the rigorous sensor model for photogrammetric processing.

Comparison of Matching Algorithms for DSM Generation in Urban Areas from Ikonos Imagery

Abstract: The extraction of the third dimension from remote sensing data is a well known technique. Since in a number of countries aerial images and laser scanner data are unavailable, expensive or classified, stereoscopic high-resolution optical satellite images provide a viable alternative for generating digital surface and digital terrain models. Especially the automatic extraction of highly accurate 3D surface models in urban areas is still a very complicated task due to occlusions, large differences in height and the variety of objects and surface material. In this paper an analysis and a visual and quantitative comparison of three different matching algorithms for generating urban DSMs based on very high-resolution satellite images is presented. The three algorithms are least squares matching (LSM) in a region growing fashion, dynamic programming (DP) and semiglobal matching (SGM). The characteristics of the three algorithms as applied to four different Ikonos stereo pairs with a ground sampling distance of 1 m are shown. The following results were obtained: visually, in the LSM results the shape of the buildings is considerably smoothed. While in the DP results the building shape is sharper, only little detail is visible on the building roofs, and streaking along the epipolar lines causes problems. With SGM more details can be extracted and the results visually have the best quality. Based on reference data for the different test sites, the standard deviation of the building heights determined by LSM and DP is in the range of one pixel or slightly better, while it is in the range of half a pixel for SCM.

The Forward Propagation of Integrated System Component Errors within Airborne Lidar Data

Abstract: Error estimates of lidar observations are obtained by applying the General Law of Propagation of Variances ( GLOPOV ) to the direct georeferencing equation. Within the formulation of variance propagation, the most important consideration is the values used to describe the error of the hardware component observations including the global positioning system, inertial measurement unit, laser ranger, and laser scanner (angular encoder noise and beam divergence). Data tested yielded in general, pessimistic predictions as 85 percent of residuals were within the predicted error level. Simulated errors for varying scan angles and altitudes produced horizontal errors largely influenced by IMU subsystem error as well as angular encoder noise and beam divergence. GPS subsystem errors contribute the largest proportion of vertical error only at shallow scan angles and low altitudes. The transformation of the domination of GPS related error sources to total vertical error occurs at scan angles of 23°, 13°, and 8° at flying heights of 1,200 m, 2,000 m, and 3,000 m AGL , respectively.

A Multi-scale Approach for Delineating Individual Tree Crowns with Very High Resolution Imagery

Abstract: This paper presents a multi-scale approach for delineating individual tree crowns (ITC) from high spatial resolution imagery. By analyzing the evolution of image gradients over the scale-space constructed with orthogonal wavelets, tree crown boundaries are effectively strengthened while the textures resulted from tree branches and twigs are largely suppressed. Two scale consistency checks, a scale and a geometric consistency check, were devised to account for tree crown's radiometric and geometric characteristic. After an edge-enhanced image was acquired, a previously developed marker-controlled watershed segmentation method was adopted to delineate ITC. An experiment was carried out in a study site in California. Field measurements of crown size of 58 trees were compared with those derived from aerial imagery. An R square value of 0.68 was achieved. It was found that crown size was underestimated from the photo interpretation compared to that from the ground survey. The result can be attributed to the fact that pixels lying on the tree crown boundaries are poorly represented in the image.

Investigations on surface reflection models for intensity normalization in airborne laser scanning (ALS) data

Abstract: The analysis of laser scanner data is of great interest for gaining geospatial information. Especially for segmentation, classification, or visualization purposes, the intensity measured with a laser scanner device can be helpful. For automatic intensity normalization, various aspects are of concern, like beam divergence and atmospheric attenuation, both depending on the range. Additionally, the intensity is influenced by the incidence angle between beam propagation direction and Surface orientation. To gain the surface orientation, the eigenvectors of the covariance matrix for object points within a nearby environment are determined. After normalization the intensity does no longer depend on the incidence angle and is influenced by the material of the surface only. For surface reflection modeling, (a) the Lambertian, (b) the extended Lambertian, and (c) the Phong reflection model are introduced, to consider diffuse and specular backscattering characteristics of the surface. An airborne measurement campaign was carried out to investigate the influences of the incidence angle on the measured intensity. For investigations, 17 urban areas, such as traffic, building, and vegetation regions were studied and the derived improvements are depicted. The investigation shows that large intensity variation caused by the object surface orientation and the distance between sensor and object can be normalized by utilizing the standard Lambertian reflection model.

A pixel based regeneration index using time series similarity and spatial context

Abstract: Although the regeneration index based on control plots provides a valuable tool to quantify fire impact and subsequent vegetation regrowth, the practical implementation at large scale levels remains limited due to the need for detailed reference maps. The objective of this research therefore was the development of an image-based selection approach for control pixels based on time series similarity (TSS). The TSS approach allows the computation of a perpixel regeneration index on regional to global scale without the need for reference maps. Evaluation of the control plot selection approaches based on un-burnt focal pixels confirmed the validity of the TSS approach and showed optimal results for the TSSRMSD approach with and due to beneficial averaging effects and minimal window size effects. As such, the effects of spatial heterogeneity and noise are minimized and a preliminary quality indicator can be derived

GEOBIA vegetation mapping in Great Smoky Mountains National Park with spectral and non-spectral ancillary information.

Abstract: Vegetation mapping was performed using geographic object-based image analysis (GEOS1A) and very high spatial resolution (VHR) imagery for two study areas in Great Smoky Mountains National Park. This study investigated how accurately GEOB1A with ancillary data emulates manual interpretation in rugged mountain areas for multi-level vegetation classes of the National Vegetation Classification System (NVCS). It was discovered that the incorporation of texture and topographic variables with spectral data from scanned color infrared aerial photographs increased the overall accuracy of GEOBIA vegetation classification by 2.8 percent and 5.0 percent Kappa. In a separate study using multispectral Ikonos imagery, the use of elevation, aspect, slope and proximity to streams produced NVCS macro-group vegetation segmentations that resembled manual interpretation and significantly improved the overall accuracy to 76.6 percent, Kappa 0.57. Ancillary information may thus aid in GEOBIA vegetation mapping for updating vegetation inventories in rugged mountain areas.

Factors Influencing Pulse Width of Small Footprint, Full Waveform Airborne Laser Scanning Data

Abstract: Small footprint, full waveform airborne laser scanning provides the opportunity to derive high-resolution geometric and physical information simultaneously from a single scanner system. This study evaluates the influence of various factors on the shape of the returned waveform and investigates the possibility of improving terrain classification by applying waveform-derived information. The factors discussed are surface roughness, slope angle, scan angle, amplitude, and footprint size. It is statistically demonstrated that roughness is the most significant factor affecting pulse width, and that, over relatively smooth surfaces, there is no significant variation in pulse width behavior resulting from different footprint sizes. Pulse width also exhibits a relatively stable behavior when amplitude, range distance, or scan angle vary substantially. The overall accuracy of classification achieved by applying pulse width information over all the different land-cover types examined in this study (including scrub, hillside, single trees, and forest areas) was greater than 85 percent, with >94 percent achieved for open vegetation areas. Physical surface information provided by small footprint waveform data is considered to be at the microscale, therefore it is recommended to combine such information with geometry (e.g., filtering algorithms) for the optimal identification of terrain points.

A Piecewise Approach to Epipolar Resampling of Pushbroom Satellite Images Based on RPC

Abstract: Epipolar line determination and image resampling are important steps for stereo image processing. Unlike frame cameras that have well-known epipolar geometry, the pushbroom camera does not produce straight epipolar lines and the epipolar pair does not exist for the entire scene. These properties make it difficult to establish epipolar geometry of the pushbroom camera for epipolar image resampling. Therefore, some researchers have adopted approximate models to avoid these problems. In this study, a new method for the conjugate epipolar curve pair determination and epipolar resampling of spaceborne pushbroom images based on RPC is proposed. The proposed method assumes that the conjugate epipolar curve pairs exist approximately for the local scene area and the global epipolar pairs can also exist if the local pairs are sequentially linked. Then, epipolar image resampling is established by reassigning the generated conjugate epipolar curve pair points to satisfy the epipolar resampling image condition. Ikonos stereo images are tested for the evaluation, and the proposed method showed a maximum y-parallax of 1.25 pixels for manually measured tie points, while the resampling method by the parallel projection model showed a maximum of 4.59 pixels.

Linearization of NDVI Based on its Relationship with Vegetation Fraction

Abstract: The Normalized Difference Vegetation Index (NDVI) is widely used for global monitoring of land surface vegetation dynamics from space. However, it is well documented that the NDVI approaches saturation asymptotically over highly vegetated areas. In this study, a linearized NDVI (LNDVI) is derived by introducing a linearity-adjustment factor, b, into the NDVI equation to improve the linearity of the relationship with vegetation fraction and mitigate the saturation problem encountered by NDVI. The linearity of the LNDVI is demonstrated using a ground-observed data set and a modelsimulated data set. A functional relationship and consistence of LNDVI with other NDVI adaptations are found, providing independent justification of the value of the NDVI adaptations. Due to its improved linearity with vegetation fraction, this index would provide more accurate monitoring of vegetation dynamics and estimation of biophysical parameters. The LNDVI can be derived from historical NDVI datasets directly without knowledge of input reflectances.