Research Article
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Year 2023, , 34 - 40, 30.06.2023
https://doi.org/10.33904/ejfe.1320121

Abstract

References

  • Bayat, F., Arefi, H., Alidoost, F. 2019. Individual tree detection and determination of tree parameters using uav-based, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42(4/48):179-182. https://doi.org/10.5194/isprs-archives-XLII-4-W18-179-2019
  • Culbert, P.D., Radeloff, V.C., St-Louis, V., Flather, C.H., Rittenhouse, C.D., Albright, T.P., Pidgeon, A.M. 2012. Modeling broad-scale patterns of avian species richness across the Midwestern United States with measures of satellite image texture. Remote Sensing of Environment. 118: 140–150.
  • De Ocampo, A.L., Dadios, E.P. 2021. Integrated Weed Estimation and Pest Damage Detection in Solanum melongena Plantation via Aerial Vision-based Proximal Sensing. Philippine Journal of Science. 150: 1041-1052.
  • Fallatah, A., Jones, S., Mitchell, D. 2020. Object-based random forest classification for informal settlements identification in the Middle East: J eddah a case study. International Journal of Remote Sensing, 41(11):4421–4445.
  • Finn, A., Brinkworth, R., Griffiths, D., Peters, S. 2019. Determining morphometric properties of radiata pine using long wave infrared sensing and biologically-inspired vision, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42(2):277-281, https://doi.org/10.5194/isprs-archives-XLII-2-W13-277-2019 . Franklin, S.E. Wulder, M.A. Lavigne, M.B. 1996. Automated derivation of geographic window sizes for use in remote sensing digital image texture analysis. Computers & Geosciences, 22: 665–673.
  • Haralick, R.M., Shanmugam, K., Dinstein, R. 1973. Textural features for image classification. IEEE Tran. Syst. Man Cybern. 3: 610–621.
  • Horng, M.H. , Huang, X.J., Zhuang, J.H. 2003.Texture Feature Coding Method for Texture Analysis and It’s Application. Journal of Optical Engineering, 42(1):228-238.
  • Jung, K.Y., Park, J.K. 2019, Analysis of vegetation infection information using unmanned aerial vehicle with optical sensor, Sensors and Materials, (10):3319-3326. doi.org/10.18494/SAM.2019.2465
  • Junttila, S., Näsi, R., Koivumäki, N.; Imangholiloo, M., Saarinen, N.; Raisio, J., Holopainen, M., Hyyppä, H., Hyyppä, J., Lyytikäinen-Saarenmaa, P., et al. 2022. Multispectral Imagery Provides Benefits for Mapping Spruce Tree Decline Due to Bark Beetle Infestation When Acquired Late in the Season. Remote Sensing. 14: 909.
  • Kuffer, M, Pfeffer, K., Sliuzas, R., Baud, I., Maarseveen, M. 2017. Capturing the diversity of deprived areaswith image-based features: The case of Mumbai. Remote Sensing. 9(4):384
  • Kupidura, P. 2019. The Comparison of Different Methods of Texture Analysis for Their Efficacy for Land Use Classification in Satellite Imagery, Remote Sensing 11(10): 1233. https://doi.org/10.3390/ rs11101233 Lai, F., Yang, X. 2020. Integrating spectral and non-spectral data to improve urban settlement mapping in alarge Latin-American city. GISci Remote Sensing. 57(6):830–844
  • Lan, Z., Liu Y. 2018. Study on Multi-Scale Window Determination for GLCM Texture Description in High-Resolution Remote Sensing Image Geo-Analysis Supported by GIS and Domain Knowledge, ISPRS International Journal of Geo-Information 7(5): 175. https://doi.org/10.3390/ijgi7050175
  • Lausch, A., Borg, E., Bumberger, J., Dietrich, P., Heurich, M., Huth, A., Jung, A., Klenke, R., Knapp, S., Mollenhauer, H., et al., 2018. Understanding Forest Health with Remote Sensing, Part III: Requirements for a Scalable Multi-Source Forest Health Monitoring Network Based on Data Science Approaches. Remote Sensing, 10: 1120.
  • Lin, L., Hao, Z., Post, C.J. 2023. Mikhailova, E.A. Protection of Coastal Shelter Forests Using UAVs: Individual Tree and Tree-Height Detection in Casuarina equisetifolia L. Forests. Forests, 14:233. https://doi.org/10.3390/f14020233
  • Lin, Q., Huang, H., Wang, J., Huang, K., Liu, Y. 2019. Detection of Pine Shoot Beetle (PSB) Stress on Pine Forests at Individual Tree Level using UAV-Based Hyperspectral Imagery and Lidar. Remote Sensing, 11: 2540. https://doi.org/10.3390/rs11212540
  • Liu, X. (2008). Summary of texture research. Application Reasearch of Computers, 25(8), 2284-2287.
  • Manfreda, S., McCabe, M.F., Miller, P.E., Lucas, R., Madrigal, V.P., Mallinis, G., Ben Dor, E., Helman, D., Estes, L., Ciraolo, G., et al. 2018. On the Use of Unmanned Aerial Systems for Environmental Monitoring. Remote Sensing, 10:641.
  • Milz, S., Wäldchen, J., Abouee, A. et al. 2023. The HAInich: A multidisciplinary vision data-set for a better understanding of the forest ecosystem. Sci Data, 10: 168 https://doi.org/10.1038/s41597-023-02010-8.
  • Mugiraneza, T., Nascetti, A., Ban, Y. 2019. Worldview-2 data for hierarchical object-based urban land coverclassification in Kigali: Integrating rule based approach with urban density and greenness indices. Remote Sensing, 11(18):2128.
  • Ozdemir, I., Mert, A., Ozkan, U.Y., Aksan, S., Unal, Y. 2018. Predicting bird species richness and micro-habitat diversity using satellite data, Forest Ecology and Management, 424:483-493. https://doi.org/10.1016/j.foreco.2018.05.030 .
  • Ozdemir, I., Mert, A., Senturk, O. 2012. Predicting Landscape Structural Metrics Using Aster Satellite Data, Journal of Environmental Engineering and Landscape Management, 20(2):168-176. https://doi.org/10.3846/16486897.2012.688371
  • Qin, Q. 2000. Problems in Automatic Interpretation of Remote Sensing Images and Ways to Solve. Sci. Technol. Surv. Mapp. 25:21–25.
  • Palmer, M.W., Wohlgemuth, T., Earls, P.G., Arévalo, J.R., Thompson, S.D. 2000. Opportunitites for long-term ecological research at the Tallgrass Prairie Preserve. In: Oklahoma, K., Lajtha, K., Vanderbilt (Eds.), Cooperation in Long Term Ecological Research in Central and Eastern Europe: Proceedings of ILTER Regional Workshop, Budapest, Hungary, 22–25 June, 1999, pp. 123–128.
  • Palmer, M.W., Earls, P.G., Hoagland, B.W., White, P.S., Wohlgemuth, T., 2002. Quantitative tools for perfecting species lists. Environmetrics, 13: 121–137.
  • Roumi, M. 2009. Implementing Texture Feature Extraction Algorithms on FPGA. Master thesis, Delft University of Technology, Faculty of Electrical Engineering, Mathematics and Computer Science, Delfth, Netherlands. 15.
  • Seto, K.C., Fleishman, E., Fay, J.P., Betrus, C.J. 2004. Linking spatial patterns of bird and butterfly species richness with Landsat TM derived NDVI. International Journal of Remote Sensing, 25: 4309–4324.
  • Stasolla, M., Gamba, P. 2008. Spatial Indexes for the extraction of formal and informal human settlementsfrom high-resolution SAR images. IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing. 1(2):98–106.
  • St-Louis, V., Pidgeon, A. M., Radeloff, V. C., Hawbaker, T. J., & Clayton, M. K. (2006). High-resolution image texture as a predictor of bird species richness. Remote Sensing of Environment, 105(4), 299-312.
  • St-Louis, V., Pidgeon, A.M., Clayton, M.K., Locke, B.A., Bash, D., Radeloff, V.C. 2009. Satellite image texture and a vegetation index predict avian biodiversity in the Chihuahuan Desert of New Mexico. Ecography, 32: 468–480.
  • Torresan, C., Berton, A., Carotenuto, F., Di Gennaro, S.F., Gioli, B., Matese, A., Miglietta, F., Vagnoli, C., Zaldei, A., Wallace, L. 2018. Forestry applications of UAVs in Europe: A review. International Journal of Remote Sensing, 38: 2427–2447.
  • Tian, J., Chen, D. M. 2007. Optimization in Multi-Scale Segmentation of High-Resolution Satellite Images for Artificial Feature Recognition, International Journal of Remote Sensing, 28(20): 4625–4644.
  • Williams, J., Jackson, T. D., Schönlieb, C. B., Swinfield, T., Irawan, B., Achmad, E., ... & Coomes, D. A. (2022). Monitoring early-successional trees for tropical forest restoration using low-cost UAV-based species classification. Frontiers in Forests and Global Change, 214.
  • Wood, E.M., Pidgeon, A.M., Radeloff, V.C., Keuler, N.S. 2012. Image texture as a remotely sensed measure of vegetation structure. Remote Sensing of Environment. 121: 516–526.

Image Processing Techniques based Feature Extraction for Insect Damage Areas

Year 2023, , 34 - 40, 30.06.2023
https://doi.org/10.33904/ejfe.1320121

Abstract

Monitoring of forests is important for the diagnosis of insect damage to vegetation. Detection and monitoring of damaged areas facilitates the control of pests for practitioners. For this purpose, Unmanned Aerial Vehicles (UAVs) have been recently used to detect damaged areas. In order to distinguish damage areas from healthy areas on UAV images, it is necessary to extract the feature parameters of the images. Therefore, feature extraction is an important step in Computer Aided Diagnosis of insect damage monitored with UAV images. By reducing the size of the UAV image data, it is possible to distinguish between damaged and healthy areas from the extracted features. The accuracy of the classification algorithm depends on the segmentation method and the extracted features. The Grey-Level Co-occurrence Matrix (GLCM) characterizes areas texture based on the number of pixel pairs with specific intensity values arranged in specific spatial relationships. In this paper, texture characteristics of insect damage areas were extracted from UAV images using with GLCM. The 3000*4000 resolution UAV images containing damaged and healthy larch trees were analyzed using Definiens Developer (e-Cognition software) for multiresolution segmentation to detect the damaged areas. In this analysis, scale parameters were applied as 500, shape 0.1, color 0.9 and compactness 0.5. As a result of segmentation, GLCM homogeneity, GLCM contrast and GLCM entropy texture parameters were calculated for each segment. When calculating the texturing parameters, neighborhoods in different angular directions (0,45,90,135) are taken into account. As a result of the calculations made by considering all directions, it was found that GLCM homogeneity values ranged between 0.08 - 0.2, GLCM contrast values ranged between 82.86 - 303.58 and GLCM entropy values ranged between 7.81 - 8.51. On the other hand, GLCM homogeneity for healthy areas varies between 0.05 - 0.08, GLCM contrast between 441.70 - 888.80 and GLCM entropy between 8.93 - 9.40. The study demonstrated that GLCM technique can be a reliable method to detection of insect damage areas from UAV imagery.

References

  • Bayat, F., Arefi, H., Alidoost, F. 2019. Individual tree detection and determination of tree parameters using uav-based, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42(4/48):179-182. https://doi.org/10.5194/isprs-archives-XLII-4-W18-179-2019
  • Culbert, P.D., Radeloff, V.C., St-Louis, V., Flather, C.H., Rittenhouse, C.D., Albright, T.P., Pidgeon, A.M. 2012. Modeling broad-scale patterns of avian species richness across the Midwestern United States with measures of satellite image texture. Remote Sensing of Environment. 118: 140–150.
  • De Ocampo, A.L., Dadios, E.P. 2021. Integrated Weed Estimation and Pest Damage Detection in Solanum melongena Plantation via Aerial Vision-based Proximal Sensing. Philippine Journal of Science. 150: 1041-1052.
  • Fallatah, A., Jones, S., Mitchell, D. 2020. Object-based random forest classification for informal settlements identification in the Middle East: J eddah a case study. International Journal of Remote Sensing, 41(11):4421–4445.
  • Finn, A., Brinkworth, R., Griffiths, D., Peters, S. 2019. Determining morphometric properties of radiata pine using long wave infrared sensing and biologically-inspired vision, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42(2):277-281, https://doi.org/10.5194/isprs-archives-XLII-2-W13-277-2019 . Franklin, S.E. Wulder, M.A. Lavigne, M.B. 1996. Automated derivation of geographic window sizes for use in remote sensing digital image texture analysis. Computers & Geosciences, 22: 665–673.
  • Haralick, R.M., Shanmugam, K., Dinstein, R. 1973. Textural features for image classification. IEEE Tran. Syst. Man Cybern. 3: 610–621.
  • Horng, M.H. , Huang, X.J., Zhuang, J.H. 2003.Texture Feature Coding Method for Texture Analysis and It’s Application. Journal of Optical Engineering, 42(1):228-238.
  • Jung, K.Y., Park, J.K. 2019, Analysis of vegetation infection information using unmanned aerial vehicle with optical sensor, Sensors and Materials, (10):3319-3326. doi.org/10.18494/SAM.2019.2465
  • Junttila, S., Näsi, R., Koivumäki, N.; Imangholiloo, M., Saarinen, N.; Raisio, J., Holopainen, M., Hyyppä, H., Hyyppä, J., Lyytikäinen-Saarenmaa, P., et al. 2022. Multispectral Imagery Provides Benefits for Mapping Spruce Tree Decline Due to Bark Beetle Infestation When Acquired Late in the Season. Remote Sensing. 14: 909.
  • Kuffer, M, Pfeffer, K., Sliuzas, R., Baud, I., Maarseveen, M. 2017. Capturing the diversity of deprived areaswith image-based features: The case of Mumbai. Remote Sensing. 9(4):384
  • Kupidura, P. 2019. The Comparison of Different Methods of Texture Analysis for Their Efficacy for Land Use Classification in Satellite Imagery, Remote Sensing 11(10): 1233. https://doi.org/10.3390/ rs11101233 Lai, F., Yang, X. 2020. Integrating spectral and non-spectral data to improve urban settlement mapping in alarge Latin-American city. GISci Remote Sensing. 57(6):830–844
  • Lan, Z., Liu Y. 2018. Study on Multi-Scale Window Determination for GLCM Texture Description in High-Resolution Remote Sensing Image Geo-Analysis Supported by GIS and Domain Knowledge, ISPRS International Journal of Geo-Information 7(5): 175. https://doi.org/10.3390/ijgi7050175
  • Lausch, A., Borg, E., Bumberger, J., Dietrich, P., Heurich, M., Huth, A., Jung, A., Klenke, R., Knapp, S., Mollenhauer, H., et al., 2018. Understanding Forest Health with Remote Sensing, Part III: Requirements for a Scalable Multi-Source Forest Health Monitoring Network Based on Data Science Approaches. Remote Sensing, 10: 1120.
  • Lin, L., Hao, Z., Post, C.J. 2023. Mikhailova, E.A. Protection of Coastal Shelter Forests Using UAVs: Individual Tree and Tree-Height Detection in Casuarina equisetifolia L. Forests. Forests, 14:233. https://doi.org/10.3390/f14020233
  • Lin, Q., Huang, H., Wang, J., Huang, K., Liu, Y. 2019. Detection of Pine Shoot Beetle (PSB) Stress on Pine Forests at Individual Tree Level using UAV-Based Hyperspectral Imagery and Lidar. Remote Sensing, 11: 2540. https://doi.org/10.3390/rs11212540
  • Liu, X. (2008). Summary of texture research. Application Reasearch of Computers, 25(8), 2284-2287.
  • Manfreda, S., McCabe, M.F., Miller, P.E., Lucas, R., Madrigal, V.P., Mallinis, G., Ben Dor, E., Helman, D., Estes, L., Ciraolo, G., et al. 2018. On the Use of Unmanned Aerial Systems for Environmental Monitoring. Remote Sensing, 10:641.
  • Milz, S., Wäldchen, J., Abouee, A. et al. 2023. The HAInich: A multidisciplinary vision data-set for a better understanding of the forest ecosystem. Sci Data, 10: 168 https://doi.org/10.1038/s41597-023-02010-8.
  • Mugiraneza, T., Nascetti, A., Ban, Y. 2019. Worldview-2 data for hierarchical object-based urban land coverclassification in Kigali: Integrating rule based approach with urban density and greenness indices. Remote Sensing, 11(18):2128.
  • Ozdemir, I., Mert, A., Ozkan, U.Y., Aksan, S., Unal, Y. 2018. Predicting bird species richness and micro-habitat diversity using satellite data, Forest Ecology and Management, 424:483-493. https://doi.org/10.1016/j.foreco.2018.05.030 .
  • Ozdemir, I., Mert, A., Senturk, O. 2012. Predicting Landscape Structural Metrics Using Aster Satellite Data, Journal of Environmental Engineering and Landscape Management, 20(2):168-176. https://doi.org/10.3846/16486897.2012.688371
  • Qin, Q. 2000. Problems in Automatic Interpretation of Remote Sensing Images and Ways to Solve. Sci. Technol. Surv. Mapp. 25:21–25.
  • Palmer, M.W., Wohlgemuth, T., Earls, P.G., Arévalo, J.R., Thompson, S.D. 2000. Opportunitites for long-term ecological research at the Tallgrass Prairie Preserve. In: Oklahoma, K., Lajtha, K., Vanderbilt (Eds.), Cooperation in Long Term Ecological Research in Central and Eastern Europe: Proceedings of ILTER Regional Workshop, Budapest, Hungary, 22–25 June, 1999, pp. 123–128.
  • Palmer, M.W., Earls, P.G., Hoagland, B.W., White, P.S., Wohlgemuth, T., 2002. Quantitative tools for perfecting species lists. Environmetrics, 13: 121–137.
  • Roumi, M. 2009. Implementing Texture Feature Extraction Algorithms on FPGA. Master thesis, Delft University of Technology, Faculty of Electrical Engineering, Mathematics and Computer Science, Delfth, Netherlands. 15.
  • Seto, K.C., Fleishman, E., Fay, J.P., Betrus, C.J. 2004. Linking spatial patterns of bird and butterfly species richness with Landsat TM derived NDVI. International Journal of Remote Sensing, 25: 4309–4324.
  • Stasolla, M., Gamba, P. 2008. Spatial Indexes for the extraction of formal and informal human settlementsfrom high-resolution SAR images. IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing. 1(2):98–106.
  • St-Louis, V., Pidgeon, A. M., Radeloff, V. C., Hawbaker, T. J., & Clayton, M. K. (2006). High-resolution image texture as a predictor of bird species richness. Remote Sensing of Environment, 105(4), 299-312.
  • St-Louis, V., Pidgeon, A.M., Clayton, M.K., Locke, B.A., Bash, D., Radeloff, V.C. 2009. Satellite image texture and a vegetation index predict avian biodiversity in the Chihuahuan Desert of New Mexico. Ecography, 32: 468–480.
  • Torresan, C., Berton, A., Carotenuto, F., Di Gennaro, S.F., Gioli, B., Matese, A., Miglietta, F., Vagnoli, C., Zaldei, A., Wallace, L. 2018. Forestry applications of UAVs in Europe: A review. International Journal of Remote Sensing, 38: 2427–2447.
  • Tian, J., Chen, D. M. 2007. Optimization in Multi-Scale Segmentation of High-Resolution Satellite Images for Artificial Feature Recognition, International Journal of Remote Sensing, 28(20): 4625–4644.
  • Williams, J., Jackson, T. D., Schönlieb, C. B., Swinfield, T., Irawan, B., Achmad, E., ... & Coomes, D. A. (2022). Monitoring early-successional trees for tropical forest restoration using low-cost UAV-based species classification. Frontiers in Forests and Global Change, 214.
  • Wood, E.M., Pidgeon, A.M., Radeloff, V.C., Keuler, N.S. 2012. Image texture as a remotely sensed measure of vegetation structure. Remote Sensing of Environment. 121: 516–526.
There are 33 citations in total.

Details

Primary Language English
Subjects Photogrammetry and Remote Sensing
Journal Section Research Articles
Authors

Ece Alkan 0000-0002-1942-313X

Abdurrahim Aydın 0000-0002-6572-3395

Early Pub Date June 30, 2023
Publication Date June 30, 2023
Published in Issue Year 2023

Cite

APA Alkan, E., & Aydın, A. (2023). Image Processing Techniques based Feature Extraction for Insect Damage Areas. European Journal of Forest Engineering, 9(1), 34-40. https://doi.org/10.33904/ejfe.1320121

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The works published in European Journal of Forest Engineering (EJFE) are licensed under a  Creative Commons Attribution-NonCommercial 4.0 International License.