Search tips
Search criteria 


Logo of scirepAboutEditorial BoardFor AuthorsScientific Reports
Sci Rep. 2017; 7: 7366.
Published online 2017 August 4. doi:  10.1038/s41598-017-07951-w
PMCID: PMC5544764

The Effects of GLCM parameters on LAI estimation using texture values from Quickbird Satellite Imagery


When the leaf area index (LAI) of a forest reaches 3, the problem of spectrum saturation becomes the main limitation to improving the accuracy of the LAI estimate. A sensitivity analysis of the Grey Level Co-occurrence Matrix (GLCM) parameters which can be applied to satellite image processing and analysis showed that the most important parameters included orientation, displacement and moving window size. We calculated the values of Angular Second Moment (ASM), Entropy (ENT), Correlation (COR), Contrast (CON), Dissimilarity (DIS) and Homogeneity (HOM) from Quickbird panchromatic imagery using a GLCM method. Four orientations, seven displacements and seven window sizes were considered. An orientation of 90° was best for estimating the LAI of black locust forest, regardless of moving window size, displacement and texture parameters. Displacements of 3 pixels appeared to be best. The orientation and window size had only a little influence on these settings. The highest adjusted r2 values were obtained using a 3 × 3 moving window size for ASM and ENT. The tendency of CON, COR, DIS and HOM to vary with window size was significantly affected by orientation. This study can help with parameter selection when texture features from high resolution imagery are used to estimate broad-leaved forest structure information.


Leaf area index (LAI) is an important input variable in forest ecosystem modelling as it is a factor in predicting productivity and assessing forest health over large areas1. Remote sensing technologies have become increasingly important in large-scale ecological studies because of their low cost and ability to provide large amounts of relevant information quickly. Passive optical remote sensing is the most widely used method for obtaining data for LAI estimation2, 3 although there are many studies that focus on LAI estimation using passive airborne laser scanners (Lidar)4, 5. Lidar does not saturate at high values (LAI > 3) and can better separate the understory which includes grasses, herbs and shrubs etc., distributed below the forest canopy. However, due to the high cost of the method, there are limited archives of Lidar images available for analyzing the change in vegetation structure over time6. Currently, the LAI distribution at a landscape scale or a regional scale of forest can be estimated effectively using passive optical remote sensing techniques, especially high resolution satellite remote sensing.

Spectral information has been widely used to analyze large areas of forest. LAI data can be obtained by analysing optical data using regression models based on spectral vegetation indices (SVIs). However, SVIs become saturated when LAI values are larger than 37, 8. This phenomenon is a serious problem when analyzing forest environments exhibiting large heterogeneity with complex vertical and horizontal structures. This is one of the principal limitations to the improvement of LAI estimations of forest canopies. Many studies have demonstrated the potential of high resolution satellite remote sensing sensors (such as IKONOS and QuickBird) for estimating and mapping forest LAI spatially. Texture features, which are frequently used pieces of spatial information and derived from these high-resolution images, have proved to be effective for significantly increasing the accuracy of forest LAI estimation3, 7, 9, 10. Texture analysis involves the measurement of heterogeneity in the tonal values of pixels within a defined area of an image6 and can be used to identify objects or regions of interest11. Song & Dickinson10 demonstrated that image textural information was more useful for estimating LAI than two spectral vegetation indices. Zhou et al.3 confirmed that a combination of texture and SVIs can yield r2 values of 0.84 when they used Quickbird imagery to estimate the LAI of a black locust plantation. Pu & Cheng9 showed that texture-based features from Worldview-2 data are more useful than spectrum-based features and a combination of the two could lead to even higher accuracy of mapping forest LAIs than either one separately. The texture features in high resolution data provide better accuracy than using relatively low resolution data. These studies demonstrated that the accuracy of estimated forest LAI based on remote-sensing data could be significantly increased by considering textural information. Therefore, textural information derived from high resolution satellite imagery has been shown to be unique and can be very useful in estimating and mapping forest LAI.

Texture is a complex parameter and texture values measured with the GLCM method are highly sensitive to moving window size, orientation, displacement and physiographic conditions3, 1217. The sensitivity of these texture parameters in relation to LAI estimation using GLCM has not been thoroughly studied16. How to set the value of the moving window size, orientation and displacement when extracting GLCM texture features is still confusing and literature about setting parameters of GLCM is relatively rare. For example, there is contradictory advice as to whether a large or small moving window size should be used. Some studies have shown that image texture measures calculated using a small window size from high resolution imagery were most strongly associated with vegetation structure as observed on the ground3, 6. Others considered that a small moving window size contributes to the sparsity and instability of the GLCM18. Coburn & Roberts13 showed that texture features cannot be described clearly by only using one moving window size. A small window size should be chosen when the type of land under investigation is homogeneous, as opposed to a large window size. Puissant et al.16 determined the best window size for land classification by comparing the variable coefficient of texture parameters extracted using different window sizes.

Displacement is another important factor influencing the value of GLCM parameters. Large pixel displacement leads to low comparability. The probability of occurrence of particular grey levels along the diagonal of the GLCM is small. However, there have been few studies that have investigated the effects of displacement on the texture features, or its potential to estimate LAI. Kayitakire et al.15 considered that the displacement and moving window size were the most sensitive input variables when they estimated forest structure parameters. However, in their research, the influence of displacement on texture features was not studied. The orientation used for the GLCM was also generally ignored, with the average value of texture from four orientations usually applied. The influence of orientation on texture features was not studied in detail. In the study by Kayitakire et al.15, orientation had little effect on the accuracy of forest parameter estimation. However, Clausi19 suggested that each orientation should be used to calculate texture value. Thus, the determination of orientation needs further analysis13.

In conclusion, the sensitivity of these GLCM parameters to the texture features has been studied to help choose the appropriate parameter values for estimating forest variables. Only rarely have these parameters been optimised. Instead, trial and error has been used with the user accepting the best possible option from those tried intuitively. There is a lack of deep understanding of how texture features work, and only locally-based studies have been found in the literature. It is therefore important to carry out a study into how to set the parameters for GLCM features. This work could contribute to the building of a deeper knowledge of the topic. Therefore, the main objective of this study was to test the influence of GLCM parameters on the LAI estimation of black locust plantations in mountain areas of the Loess Plateau in China by calculating texture features from a Quickbird panchromatic image. The GLCM parameters examined included moving window size, orientation and pixel displacement. In this study, an effort was made to understand how the accuracy of LAI estimation changed with varying orientation, displacement and window size of texture features. Four orientations (horizontal 0°, right-diagonal 45°, vertical 90°, left-diagonal 135°) and seven displacement values (3, 5, 7, 9, 11, 13 and 15 pixels) were chosen. Seven moving window sizes (3 × 3, 5 × 5, 7 × 7, 9 × 9, 11 × 11, 13 × 13 and 15 × 15) were tested with a panchromatic Quickbird image. Three texture measures that were computed from each GLCM parameter combination provided 1372 texture variables. This study can help to improve understanding of the relationship between broad-leaved forest LAI and texture features in very high spatial resolution imagery, and also provide suggestions for the selection of GLCM parameters when textural information is used to estimate forest LAI values across a large area.


Effects of the orientation parameter on the accuracy of LAI estimation

Testing was carried out to determine an appropriate orientation for extracting texture features from a panchromatic Quickbird image. The coefficient of determination r2 was clearly sensitive to the orientation. The influence of orientation on the accuracy of the LAI estimation was similar for different texture features when they were calculated using a 3 × 3 moving window size and displacement values of 3 pixels (Figs 1 and and2).2). The lowest adjusted r2 value for all texture features was observed for calculations using the 45° orientation. The best choice of orientation to estimate LAI was 90° for all texture features. Using a fixed 3 × 3 window size, Fig. 1 shows the test results for six texture features and seven displacements: 3, 5, 7, 9, 11, 13 and 15. As can be seen, the 90° orientation still created a slightly better adjusted r2 across the six texture features. With a fixed displacement of 3 pixels, we further tested the effects of four directions (0°, 45°, 90°, and 135°) on the adjusted r2 with the four texture features and seven moving window sizes: 3 × 3, 5 × 5, 7 × 7, 9 × 9, 11 × 11, 13 × 13 and 15 × 15. The test results for the four texture measures with each of the four directions are shown in Fig. 2. Use of 90° orientation gave a slight improvement in the adjusted r2 value compared to the other directions.

Figure 1
The effect of the orientation parameter on the values of adjusted r2 for different texture features calculated using a 3 × 3 moving window size and 3, 5, 7, 9, 11, 13 and 15 pixels (ASM, CON, COR, DIS, ENT, HOM and VAR are abbreviations ...
Figure 2
The effect of the orientation parameter on the values of adjusted r2 for different texture features calculated using 3 pixel displacement and 3 × 3, 5 × 5, 7 × 7, 9 × 9, ...

Effects of the displacement parameter on the accuracy of LAI estimation

The adjusted r2 values for Contrast (CON), Correlation (COR), Dissimilarity (DIS) and Homogeneity (HOM) decreased for the most part with increasing displacement when the orientation was set to 0° and the window size was set to 3 × 3 pixels (Fig. 3a). This trend was not influenced by the window size. Compared to a window size of 15 × 15 pixels, the highest values of adjusted r2 were also observed for a 3 × 3 pixel window size (Fig. 3b). The orientation had a slight influence on this trend (Fig. 3a,c and d). The adjusted r2 values of all the texture measures as a function of the displacement showed similar trends based on the orientation and the window size when the orientation was set to 90° (Fig. 3c), 45° (Fig. 3d) and the window size was set to 3 × 3 pixels. Apart from these observations, ASM and Entropy (ENT) seemed to be less sensitive to the displacement than CON, COR, DIS or HOM (Fig. 4).

Figure 3
The effect of the displacement parameter on the values of adjusted r2 for different texture features when the orientation was set to 0° and the window size was set to 3 × 3 pixels (a); when the orientation was set to 0° ...
Figure 4
The effect of the displacement parameter on the values of adjusted r2 for different texture features when the orientation was set to 45° and the window size was set to 3 × 3, 5 × 5, 7 × 7, ...

Effects of the window size parameter on the accuracy of LAI estimation

The moving window size was also an important factor influencing the adjusted r2 values. The performance of ASM and ENT decreased with increasing moving window size, with the highest adjusted r2 value obtained for a 3 × 3 pixel window when the orientation was set to 45° and the displacement was set to 5 pixels (Fig. 5a). For CON, COR, DIS and HOM parameters, the moving window size had little effect on the adjusted r2 value regardless of the values of displacement and orientation (Fig. 5a,b and c). Therefore, the displacement and orientation did not obviously affect the influence that the moving window size had on the adjusted r2 values. Figures 6 and and77 showed that adjusted r2 values obtained for ASM and ENT clearly decreased as the window size increased, regardless of the values selected for the displacement and orientation parameters. The highest adjusted r2 values of 0.73 and 0.74 were obtained using the 3 × 3 moving window size. On the other hand, the moving window size had nearly no effect on the retrieval results of HOM, CON and DIS when the orientation was set to 90°, regardless of the value selected for the displacement parameter. With respect to COR, adjusted r2 values decreased initially and then increased (Fig. 8).

Figure 5
The effect of the window size on the values of adjusted r2 for different texture features when the orientation was set to 45° and the displacement was set to 5 pixels (a); when the orientation was set to 135° and the displacement was set ...
Figure 6
The effect of the window size on the values of adjusted r2 for ASM.
Figure 7
The effect of the window size on the values of adjusted r2 for ENT.
Figure 8
The effect of the window size on the values of adjusted r2 for different texture features when the orientation was set to 90°.


Our study indicated that orientation, displacement and moving window size had a significant influence on the accuracy of LAI estimation when Quickbird imagery was used to calculate the GLCM parameters. Moreover, our study confirmed the results of previous work showing that the effect of orientation is greater than that of displacement20.

Most studies have tended to use the average value or all the values using 0°, 45°, 90° and 135° orientations to estimate forest variables and generate a forest classification17, 2125. They have often neglected to investigate the orientation feature of GLCM parameters. For example, Franklin and Peddle26 indicated that there was a significant increase in classification accuracy when all four orientations of co-occurrence (0°, 45°, 90° and 135°) were used when compared to average textures. However, the influence of orientation on the accuracy of forest variable estimation has rarely been studied. In our study, the orientation was an important factor related to the accuracy of LAI estimation (Figs 1 and and2).2). The highest value of adjusted r2 was obtained when we used an orientation of 90° to compute the GLCM features and 45° was the worst choice when estimating the LAI value. The displacement and moving window size did not affect this trend (Figs 1 and and2).2). These results were the same as those reported by Pu & Cheng9, who demonstrated that an orientation of 90° might be used to calculate the eight second-order texture measures from all eight WV2 bands and thus obtain the highest accuracy of LAI estimation. However, our results were different to those obtained by Kayitakire et al.15, which indicated that the orientation parameter had minimal effect on the R2 values, especially when the displacement parameter was set to 1 pixel, even when it influenced the values of the texture features. In their study, R2 obtained with CON and COR was smaller in the 45° and 135° orientations than in the 0° and 90° orientations. Our results were different to those of Kayitakire et al.15 because a different image type was used and different forest variables were estimated. First, in the study by Kayitakire et al.15, texture features were extracted from digitized orthophotos with a spatial resolution of 0.80 m. Spatial resolution was close to that of IKONOS panchromatic data but different to remote sensing image data. Second, the forest variables estimated by Kayitakire et al.15 included age, top height, circumference, stand density and basal area, rather than LAI.

The accuracy of the estimate of the forest variables is greater in the 45° and 135° orientations than in the 0° and 90° orientations. According to Barber et al.20, the orientation and displacement parameters had a significant effect on the statistical distribution of textural features when they used texture parameters to determine the area of sea ice. Moreover, results obtained with an orientation of 0° were significantly better than with either 45° or 90° because 0° was parallel to the look direction of the sensor. In our study, the best results were obtained at an orientation of 90°, which was parallel to the look direction of the Quickbird imaging sensor. So, when GLCM texture parameters are used to estimate forest LAI, it appears to be better to select such an orientation.

The displacement determined the distribution of factors in the GLCM. So, it is critical to select a suitable pixel interval. Most studies set the displacement to 1 pixel when they used the GLCM method to calculate textural values. How the displacement affected textural values and LAI estimation has not been widely discussed in the recent literature27. In our study, the adjusted r2 values obtained from GLCM texture parameters nearly all decreased with increasing displacement. This might be related to the heterogeneous forest structure of the black locust plantation, where trees have a clumped distribution26. Larger displacements may not reflect completely the non- uniform and non-random spatial distribution of black locust plantations. The adjusted r2 values of CON, COR, DIS, HOM were more obviously affected than those for ASM and ENT (Figs 3 and and5a).5a). The fact that ASM and ENT behave differently may be attributed to variations in texture dimensions. ASM and ENT describe the primitive elements that comprise an image. Other texture parameters describe spatial dependence or interactions between these texture primitives28, 29. Kayitakire et al.15 concluded that COR values decrease with increasing displacement, as was the case in our study. However, the change in CON was different to the result of our study and changes in other parameters as a result of changes in displacement were not observed.

Window size influences the resultant texture, possibly due to the amount of variance included30. Small window sizes were more sensitive to interpixel differences in the proportions of tree crown and shadow, whereas a larger window might not extract texture information efficiently due to over-smoothing of textural variations17, 31. In this study, CON, DIS and HOM achieved the highest accuracy of LAI estimation using 3 × 3 or 5 × 5 moving windows when the orientation was set to 135°; for ASM and ENT, the best accuracy with these window sizes occurred with a displacement of 45°. The displacement did not influence the trend in the changes of adjusted r2 related to moving window size. ASM and ENT were constant, regardless of displacement and orientation. These results contradict the findings of Colombo et al.32, who reported that the best textural indicator for this purpose was the dissimilarity index, computed using a 6 × 6 pixel window. Zhou et al.3 indicated that a 3 × 3 or 5 × 5 moving window size was most suitable for estimating LAI values but did not investigate the effects of displacement and orientation variation. The GLCM parameters that most influenced the estimates of forest LAI were displacement, moving window size and orientation. Our study indicated that their effects were interrelated and complex. The optimum selection of window size is dependent on the spatial resolution of the image, the spatial characteristics of the forest and sun-target-sensor geometry during image acquisition.

The reason why texture features were used to estimate LAI was to avoid the problem of SVI saturation in regression models when LAI is larger than 3. In our study, the accuracy of using texture parameter to estimate the LAI of a black locust plantation was tested. However, we did not analyze relationships between different LAI values and the parameters statistically. The main reason for this was that a large quantity of field LAI data, ranging from small to large values, would have been necessary to develop a robust model of LAI estimation using texture features measured with a large number of parameter combinations. We tried our best to choose plots with full representation and ensure a random distribution. The LAI values ranged from 0.95 to 6.80. The trees in 76 plots had differing ages, ranging from 9 years to greater than 50 years old. The plots had different slopes and aspects including shaded and sunny aspects. If we separated the field LAI data into two parts or more (LAI less than 3 and larger than 3), the correlation between field data and texture features became weak.


The experimental results show the best parameter values (orientation, displacement and moving window size) to choose when calculating GLCM features from high spatial resolution imagery to estimate forest LAI values. The best orientation was 90° for estimation of LAI. A displacement of 3 pixels outperformed other displacements significantly. Using a 3 × 3 moving window size could lead to even higher accuracy of a LAI estimation than other window sizes. The high resolution of the Quickbird imagery can offer detailed textural information that is potentially helpful in estimating more accurate LAI. However, the textural information is very complex and the influences of orientation, displacement and moving window size on the accuracy of any LAI estimation are interconnected. Our study tested the sensitivity of GLCM parameters on the estimation of forest LAI. There are few studies from the existing literature that can confirm our findings, and there appear to be no studies that have examined, in detail, the influence of GLCM parameters on the evaluation of LAI. Our study only considered broad-leaved forest i.e. black locust plantations and only used the panchromatic band of imagery from the Quickbird satellite. More testing and validation work is needed, in particular using various forest ecosystems and different satellite sensors.

Materials and Methods

Study area

All LAI measurements were taken within the experimental area known as Huaiping forest farm which is located in Yongshou County of Shaanxi Province on the Loess Plateau of China. Its elevation ranges from 1113 to 1417 metres above sea level. The area studied was 258 km2. The forest vegetation is primarily composed of black locust (Robinia pseudoacacia L.), Platycladus orientalis (L.) Franco and Pinus tabulaeformis Carr. Black locust of different ages could be found in this area. The LAI values ranged from 0.95 to 6.80, with an average of 4.43 and a median of 4.33. The DBH values of black locust trees ranged from 5.10 to 25.5 cm with an average of 11.25 (±3.45) cm. The tree height values varied from 3.8 to 31.8 m with a mean value of 11.02 (±5.28) m. The field data showed a wide range of Above Ground Biomass (AGB), from 5.68 to 169.91 t/ha. The average value was 65.94 t/ha. There is a good correlation between LAI and biomass using the model Y = −53.27 + 81.99X  21.06X2 + 1.86X3 (where r2 = 0.51, F = 150.96, P = 0.00, X is LAI and Y is biomass). The canopy of the black locust plantation on the Loess Plateau was simpler than that of a subtropical or tropical forest. The understorey layer was mainly composed of grasses with no herbs. The mean temperature was 7 °C to 13.3 °C and annual mean precipitation was 600.6 mm. The local growing season usually starts in early April and lasts until late October33.

Ground-based LAI measurements

LAI measurements were made from 16 June to 15 July 2012, under diffuse radiation conditions at sunrise and sunset, using a single sensor. The LAI-2200 instrument (LI-COR Inc., Lincoln, NE, USA; Li-Cor, 2010) was used to indirectly measure LAI in 76 black locust plantation plots. Their location is shown in Fig. 9. At each site, two above-canopy and nine below-canopy readings were taken with an opaque, 180° view-restricting cap placed over the sensor in order to mask out the operator. Setting Ring 5 was excluded from these analyses in order to obtain the most accurate LAI estimates possible.

Figure 9
A subset of ASM features (which was calculated using a 3 × 3 moving window size, 3 pixel displacement and 135° orientation) and the location of the sample plots in the Loess Plateau region of Yongshou County, Shaanxi Province, ...

Remote sensing data and data pre-processing

This study was based on a single Quickbird panchromatic image that was acquired on 22 June 2012 under clear sky conditions. The panchromatic image had a spatial resolution of 0.6 m. The solar azimuth angle was 108.3° and the solar elevation angle was 66.1°. Fifty well-distributed ground control points (GCPs) and a high-resolution (1:10000) digital elevation model were used to orthorectify the data. The overall error was 0.68 pixels.

Texture analysis

The GLCM method suggested by Haralick et al.11 was employed to measure texture features using a Quickbird panchromatic image. A GLCM is a symmetric matrix with each value representing the probability values of nearest neighbour grey tone at a given distance and orientation28. It reveals the spatial arrangement of grey levels in an image object. The seven features HOM, CON, DIS, ENT, VAR, ASM and COR were considered the most relevant for remote sensing analysis14 and were used in this study. Orientation, moving window size and displacement were the most important features influencing the values of GLCM parameters. In this study, four main orientations (0°, 45°, 90°, 135°) and seven moving window sizes (3 × 3, 5 × 5, 7 × 7, 9 × 9, 11 × 11, 13 × 13, 15 × 15) were chosen. The displacement values were set to odd numbers varying from 3 to 15. The three texture features that were computed from each GLCM parameter combination produced 1372 texture variables. Equations 1 to 7 were used for calculating the texture parameters. P(i, j) is the frequency that two pixels occur in the image, one with grey level i and the other with grey level j.











HOM is a measurement of lack of variability or the amount of local similarity in the scene. High HOM values suggest small grey tone differences in pair elements. CON is a measure of the amount of local variation in pixel values between neighbouring pixels. It is high for regions exhibiting large local variations and is the opposite of HOM. DIS is similar to CON and inversely related to HOM. It is high when the local region has a high CON. ENT is a measure of the degree of disorder in an image. ENT is larger when the image is texturally non-uniform or heterogeneous. It is the opposite of ASM. VAR is high when there is a large standard deviation of grey level in the local region. ASM and uniformity are measures of textural uniformity and pixel pair repetition, respectively. They are high when the GLCM is locally homogenous; they are similar to HOM. COR is a measure of grey level linear dependencies in the image. High COR values denote a linear relationship between the grey levels of pixel pairs. Figure 10 shows subsets of seven texture features.

Figure 10
Subsets of ASM, CON, COR, DIS, ENT, HOM and VAR (which were calculated using a 3 × 3 moving window size, 3 pixel displacement and 135° orientation).

Statistical analysis

Empirical relationships between texture parameters and LAI were investigated by carrying out linear analyses using LAI as the independent variable and the texture parameter as the dependent variable. Different texture features were extracted for all field plots using an area of interest mask (AOI) of 20 m × 20 m. The average of AOI texture values was used to establish the simple linear models. Adjusted r2 values were computed based on linear relationships between the two variables, as required34. All statistical analyses were carried out using the SAS software package (version 8.0) for Windows.


This research was mainly financed by two Projects “2662016PY039” and “2662015QC048”, supported by the Fundamental Research Funds for the Central Universities. It is also a part of a Research Special Topic under the auspices of the Forestry Science and Technology Support Plan, Researches and Demonstration of the Key Technology for Plantation Sustainable Management in the Loess Plateau, headed by Professor Zhong Zhao (Grant No. 2012BAD22B0302). We thank the anonymous reviewers for their constructive and valuable comments and the editors for their assistance in refining this paper.

Author Contributions

Author Contributions

J.Z. carried out all the experiments and data analysis. R.G., M.S., T.D. S.W. and J.Z. helped with data analysis. J.Z. and Z.Z. conceived the project, designed the experiments and wrote the manuscript. All authors read and approved the final manuscript.


Competing Interests

The authors declare that they have no competing interests.


Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


1. Pope G, Treitz P. leaf area index (lai) estimation in boreal mixedwood forest of Ontario, Canada using light detection and ranging (LiDAR) and WorldView-2 imagery. Remote Sens. 2013;5:5040–5063. doi: 10.3390/rs5105040. [Cross Ref]
2. Rautiainen M, Heiskanen J, Korhonen L. Seasonal changes in canopy leaf area index and moDis vegetation products for a boreal forest site in central Finland. Boreal Environ. Res. 2012;17:72–84.
3. Zhou JJ, et al. A comparison of three methods for estimating the LAI of black locust Robinia pseudoacacia L. plantations on the Loess Plateau, China. Int. J. Remote. Sens. 2014;35:171–188. doi: 10.1080/01431161.2013.866289. [Cross Ref]
4. Riaño D, Valladares F, Condés S, Chuvieco E. Estimation of leaf area index and covered ground from airborne laser scanner (Lidar) in two contrasting forests. Agr. Forest Meteorol. 2004;124:269–275. doi: 10.1016/j.agrformet.2004.02.005. [Cross Ref]
5. Thomas V, Noland T, Treitz P, McCaughey JH. Leaf area and clumping indices for a boreal mixed-wood forest: lidar, hyperspectral, and Landsat models. Int. J. Remote. Sens. 2011;32:8271–8297. doi: 10.1080/01431161.2010.533211. [Cross Ref]
6. Wood EM, Pidgeon AM, Radeloff VC, Keuler NS. Image texture as a remotely sensed measure of vegetation structure. Remote Sens. Environ. 2012;12:516–526. doi: 10.1016/j.rse.2012.01.003. [Cross Ref]
7. Gray J, Song CH. Mapping leaf area index using spatial, spectral, and temporal information from multiple sensors. Remote Sens. Environ. 2012;119:173–183. doi: 10.1016/j.rse.2011.12.016. [Cross Ref]
8. Kalácska M, et al. Leaf area index measurements in a tropical moist forest: A case study from Costa Rica. Remote Sens. Environ. 2004;91:134–152. doi: 10.1016/j.rse.2004.02.011. [Cross Ref]
9. Pu R, Cheng J. Mapping forest leaf area index using reflectance and textural information derived from WorldView-2 imagery in a mixed natural forest area in Florida, US. Int. J. Appl. Earth Obs. 2015;42:11–23. doi: 10.1016/j.jag.2015.05.004. [Cross Ref]
10. Song C, Dickinson MB. Extracting forest canopy structure from spatial information of high resolution optical imagery: tree crown size versus leaf area index. Int. J. Remote Sens. 2008;29:5605–5622. doi: 10.1080/01431160802060904. [Cross Ref]
11. Haralick RM, Shanmugam K, Dinstein IH. Textural Features for Image Classification. Systems Man & Cybernetics IEEE Transactions on smc. 1973;3:610–621. doi: 10.1109/TSMC.1973.4309314. [Cross Ref]
12. Ling, C., Hao, W. Q. & Gao, D. L. The latest applications of optical image texture in forestry. J. Beijing Forestry University (2015). (in Chinese with English abstract).
13. Coburn CA, Roberts ACB. A multiscale texture analysis procedure for improved forest stand classification. Int. J. Remote Sens. 2004;25:4287–4308. doi: 10.1080/0143116042000192367. [Cross Ref]
14. Franklin SE, Wulder MA, Lavigne MB. Automated derivation of geographic window sizes for use in remote sensing digital image texture analysis. Comput. Geosci. 1996;22:665–673. doi: 10.1016/0098-3004(96)00009-X. [Cross Ref]
15. Kayitakire F, Hamel C, Defourny P. Retrieving forest structure variables based on image texture analysis and IKONOS-2 imagery. Remote Sens. Environ. 2006;102:390–401. doi: 10.1016/j.rse.2006.02.022. [Cross Ref]
16. Puissant A, Hirsch J, Weber C. The utility of texture analysis to improve per‐pixel classification for high to very high spatial resolution imagery. Int. J. Remote Sens. 2005;26:733–745. doi: 10.1080/01431160512331316838. [Cross Ref]
17. Sarker LR, Nichol JE. Improved forest biomass estimates using ALOS AVNIR-2 texture indices. Remote Sens. Environ. 2011;115:968–977. doi: 10.1016/j.rse.2010.11.010. [Cross Ref]
18. Franklin SE, Wulder MA, Gerylo GR. Texture analysis of IKONOS panchromatic data for Douglas-fir forest age class separability in British Columbia. Int. J. Remote Sens. 2001;22:2627–2632. doi: 10.1080/01431160120769. [Cross Ref]
19. Clausi DA. An analysis of co-occurrence texture statistics as a function of grey level quantization. Can. J. Remote Sens. J. Can. De Télédétection. 2002;28:45–62. doi: 10.5589/m02-004. [Cross Ref]
20. Barber DG, Ledrew EF, Barber DG. SAR sea ice discrimination using texture statistics: A multivariate approach. Photogramm. Eng. Rem. S. 1991;57:385–395.
21. Gallardo-Cruz JA, et al. Predicting tropical dry forest successional attributes from space: is the key hidden in image texture? Plos One. 2012;7:e30506. doi: 10.1371/journal.pone.0030506. [PMC free article] [PubMed] [Cross Ref]
22. Ghosh A, Joshi PK. A comparison of selected classification algorithms for mapping bamboo patches in lower Gangetic plains using very high resolution WorldView 2 imagery. Int. J. Appl. Earth Obs. 2014;26:298–311. doi: 10.1016/j.jag.2013.08.011. [Cross Ref]
23. Pasher J, King DJ. Multivariate forest structure modelling and mapping using high resolution airborne imagery and topographic information. Remote Sens. Environ. 2010;114:1718–1732. doi: 10.1016/j.rse.2010.03.005. [Cross Ref]
24. Rodriguez-Galiano VF, Chica-Olmo M, Abarca-Hernandez F, Atkinson PM, Jeganathan C. Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture. Remote Sens. Environ. 2012;121:93–107. doi: 10.1016/j.rse.2011.12.003. [Cross Ref]
25. Sarker MLR, Nichol J, Ahmad B, Busu I, Rahman AA. Potential of texture measurements of two-date dual polarization PALSAR data for the improvement of forest biomass estimation. Isprs J. Photogramm. 2012;69:146–166. doi: 10.1016/j.isprsjprs.2012.03.002. [Cross Ref]
26. Franklin SE, Peddle DR. Spectral texture for improved class discrimination in complex terrain. Int. J. Remote Sen. 1989;10:1437–1443. doi: 10.1080/01431168908903979. [Cross Ref]
27. Rao PVN, et al. Textural analysis of IRS-1D panchromatic data for land cover classification. Int. J. Remote Sens. 2002;23:3327–3345. doi: 10.1080/01431160110104665. [Cross Ref]
28. Haralick RM. Statistical and structural approaches to texture. P. IEEE. 1979;67:786–804. doi: 10.1109/PROC.1979.11328. [Cross Ref]
29. Wulder MA, Franklin SE, Lavigne MB. High spatial resolution optical image texture for improved estimation of forest stand leaf area index. Can. J. Remote Sens. 1996;22:441–449. doi: 10.1080/07038992.1996.10874668. [Cross Ref]
30. Wulder MA, Ledrew EF, Franklin SE, Lavigne MB. Aerial image texture information in the estimation of northern deciduous and mixed wood forest leaf area index (LAI) Remote Sens. Environ. 1998;64:64–76. doi: 10.1016/S0034-4257(97)00169-7. [Cross Ref]
31. Fuchs H, Magdon P, Kleinn C, Flessa H. Estimating aboveground carbon in a catchment of the Siberian forest tundra: Combining satellite imagery and field inventory. Remote Sens. Environ. 2009;113:518–531. doi: 10.1016/j.rse.2008.07.017. [Cross Ref]
32. Colombo R, Bellingeri D, Fasolini D, Marino CM. Retrieval of leaf area index in different vegetation types using high resolution satellite data. Remote Sens. Environ. 2003;86:120–131. doi: 10.1016/S0034-4257(03)00094-4. [Cross Ref]
33. Zhao, Z. Difference of fine root vertical distribution of robinia pseudoacacia under the different climate regions in the Loess Plateau. Scientia Silvae Sinicae42, 1–7 (in Chinese with English abstract) (2006).
34. Pu R. Mapping leaf area index over a mixed natural forest area in the flooding season using ground-based measurements and Landsat TM imagery. Int. J. Remote Sens. 2012;33:6600–6622. doi: 10.1080/01431161.2012.692887. [Cross Ref]

Articles from Scientific Reports are provided here courtesy of Nature Publishing Group