Forest Canopy Density Classification Using Texture Quantization of Panchromatic Arial Images
Sep 2012 | No Comment
ForestCanopy Density, Image Texture, Classification
Forest canopy density is an important criterion for forestry application. Several methods were introduced to compute these criteria. None manual methods use multispectral images to determine the canopy density. It also could be extracted from Arial images manually. Arial images are valuable data with rather high special resolution, but some of them are panchromatic and are not suitable for spectral processing. Image texture which is valuable contextual information helps the interpreter to distinguish different canopy density areas. In this paper panchromatic Arial images were used to classify forest canopy density cover. Texture features were generated from image and use beside the image in classification. The results shows good improvement in classifying different canopy density covers.
The importance of studying,Forest is a very complex ecosystem. The complexity of forested area in Iran is more than similar area in the world. The Caspian Hyrcanian mixed forest in the north of Iran has a very great diversity. UNESCO classifies the Forested area in Iran as a natural world heritage sites for their great age and diversity. Forest in Iran is habitat of many endemic and semi-endemic and relic species; these exclusive properties make difficult processing and analyzing of satellite images.
Different methods were introduced to estimate forest canopy density; some of them are based on multispectral images which are rather expensive for large areas. Arial panchromatic images have been captured approximately for all the parts of Iran. These images could not be used for spectral processing, but has valuable information which could be used for interpretation and classification of forest canopy density visually. Sotexture quantification could be used to generate new features from panchromatic image, then they are used with the source panchromatic band as input data for classification.
Forest Canopy Density Estimation Models
Since now many models have been used for estimation forest canopy density and biomass inventory from satellite images. Forest canopy density or FCD is a very important factor for forest management and assessment. Some of the general methods for this issue is explained in short term as below:
Multi Linear regression techniques (Iverson et al., 1989 ; Levesque and King, 2003),Multiple linear regression techniques have been used to model the relation between spectral response and closed canopy conifer forest cover (Ripple, 1994). In this study, a multiple linear regression model has been developed, which best described the relation between canopy density and the seven ETM+ spectral bands. The regression equation using n = 186 observations is:
Using these four indices the canopy density for each pixel was calculated in percentage
General Models Used in Operational and Research Project in Iran
In international scale many models have been used in operational projects. Great center and organizations such as USGS, CRC, ITTO& FAO used one of the mentioned models for their activities under operational large projects.
Along with simplification of these models and indices the main reason for this issue is lack of satellite and field data in Iran. Indeed high spatial resolution data of natural and forested area is very low besides related lack of simultaneous field and train data.
Aerial photo interpretation of natural resource is a common activity that is being followed from near 1960 till now. And mid resolution images are accessible from 1973 (1 year after launching landsat#1).although any model that can use the aerial photo as a source of data not face with lack of image data, because as mentioned above they are taken from 1960 by 5-10 year of intervals. According to these capacities and limitations we use Panchromatic Arial Images in our model as aninput image data.
Two panchromatic Arial stereo images from Zagros MountainsGavbarg region inYASUJ province area where used. Images were captured at 1999 in 1:40,000 scale. At first images was oriented using ground control points, then DEM were generated in overlap area. Ortho image were generated using one oriented image and generated DEM.
Different methods were introduced by authors for quantifying image texture. These methods could be used to generate image base features. Generated features could improve image classification accuracy beside spectral features.
First Order Statistical Features
If (I) is the random variable representing the gray levels in the region of interest, the first order histogram P (I) is defined as (Theodoridis, 1999):
Where Ng = number of gray levels.
Geostatistics is the statistical methods developed for and applied to geographical data. These statistical methods are required because geographical data do not usually conform to the requirements of standard statistical procedures, due to spatial autocorrelation and other problems associated with spatial data (http://www.geo.ed.ac.uk).
That is the classical expression of variogram (h) here represents a vectorial lag between pixels. In this study direct variogram, madogramvariogram have been used.
n(h) is the number of pairs that are in mask filter.
Fourier Based Features
Fourier transformation, transforms a signal from space/time domain to frequency domain. The amplitude and phase coefficients are two outputs of a Fourier transformation. So different texture patterns could be identified by their Fourier coefficients but because in this research one value for each pixel is required, raw Fourier coefficients couldn’t be used. Several features can be generated using sum of the Fourier amplitude under different masks (Pratt, 2001). These are comprised ringing, sectorial, horizontal and vertical which are shown in figure 2.
Figure3. Different mask which can be used to generate features from Fourier coefficients
Implementation, Results and Conclusion
To evaluate the effect of using generated features in classification process, firstly the image was classified using dn slicing (parallel pipe classification) because the input is a gray-scale panchromatic image and couldn’t be classified using other methods.
References from Journals
Atkinson, P.M., Tatnall, A.R.L., 1997. Introduction neural networks in remote sensing. Int. J.Remote Sensing 18, 699–709
Boyd, D.S., Foody, G.M., Ripple, W.J., 2002. Evaluation of approaches for forest cover estimation in the Pacific Northwest, USA, using remote sensing. Appl. Geography 22, 375–392
Chudamani Joshi, Jan De Leeuw, Andrew K. Skidmore, Iris C. van Duren, Henk van Oosten, 2005, “Remotely sensed estimation of forest canopy density: A comparison of the performance of four methods”, International Journal of Applied Earth Observation and Geoinformation
Cross, A.M., Settle, J.J., Drake, N.A., Paivinen, R.T.M., 1991. Subpixelmeasurement of tropical forest cover using AVHRR data. Int. J. Remote Sensing 12, 1119–1129.
Haralick, R.M., Shanmugam, K., Dinstein, I., 1973. “Textural features for image classification”, IEEE Transactions on Systems, Man and Cybernetics, vol. 3, no. 6, pp 610-621.
Iverson, L.R., Cook, E.A., Graham, R.L., 1989. A technique forextrapolating and validating forest cover across large regions:calibrating AVHRR data with TM data. Int. J. Remote Sensing 10,1805–1812
Levesque, J., King, D.J., 2003. Spatial analysis of radiometric fractions from high-resolution multispectral imagery for modelling individual tree crown and forest canopy structure and health. Remote Sensing Environ. 84, 589–602.
Skidmore, A.K., Turner, B.J., Brinkhof, W., Knowle, E., 1997. Performance of a neural network: mapping forests using remotely sensed data. Photogrammetric Eng. Remote Sensing 63, 501–514.
Souza Jr., C., Firestone, C.L., Silva, L.M., Roberts, D., 2003. Mapping forest degradation in the Eastern Amazon from SPOT-4 through spectral mixture models. Remote Sensing Environ. 87, 494–506.
References from Books
John A. Richards, 1999, “Remote Sensing Digital Image Analysis an Introduction”, Springer-Verlag
Pratt, 2001,” Digital Image Processing”
SergiosTheodoridis, 1999, “Pattern Recognition”, Academic Press
References from Other Literature
Ashoori, H., Alimohammadi, A., ValadanZoej, M. J., Mojarradi, B., 2006. Generating Imagebased Features for Improving Classification Accuracy of High Resolution Images, May, ISPRS Mid-term Symposium, Netherlands.
Dorren, L.K., Maier, A.B., Seijmonsbergen, A.C., 2003. Improved Landsat-based forest mapping in steep mountainous terrain using object-based classification. Forest Ecol. Manage. 183, 31–46.
Goodenough, David, A.S. Bhogal, R. Fournier, R.J. Hall, J. Iisaka, D. Leckie, J.E. Luther, S.Magnussen, O. Niemann, and W.M. Strome, Earth Observation for Sustainable Development of Forests (EOSD), Victoria, B.C.: Natural Resources Canada, http://www.aft.pfc.forestry.ca,1998
P.S. Roy, S. Miyatake and A. Rikimaru, “Biophysical Spectral Response Modeling Approach for Forest Density Stratification”, ACRS 1997
Rikimaru, A., 1996. Landsat TM data processing guide for forest canopy density mapping and monitoring model. In: International Tropical Timber Organization (ITTO) workshop on utilization of remote sensing in site assessment and planning for rehabilitation of logged-over forest, Bangkok, Thailand, pp. 1–8.
References from websites
Visual classified image which have been used as training source is received from “Forests, Range and Watershed Management Organization (FRWO); Engineering and Evaluation Bureau”