Deep learning approach to help Chenopodiaceae biodiversity protection to prevent soil erosion (case study: Yazd province, Iran)
محورهای موضوعی : Soil
Ahmad Heidary-Sharifabad
1
(Department of Computer Engineering, Maybod Branch, Islamic Azad University, Maybod, Iran)
Najma Soltani
2
(Department of Computer Engineering, Maybod Branch, Islamic Azad University, Maybod, Iran)
کلید واژه: soil erosion, deep learning, Biodiversity protection, Chenopodiaceae, Super-pixel,
چکیده مقاله :
Background and objective:Chenopodiaceae species are important vegetation around the world, especially in the desert and semi-desert areas. Preserving the biodiversity of Chenopodiaceae species is crucial to preventing soil erosion. In addition, most of them are of ecological and economic importance and also play an important role in biodiversity around the world. Conservation of this biodiversity is vital to the survival and sustainability of the ecosystem. To protect plant biodiversity, it is essential to know the plant species in their natural habitats. Therefore, automatic identification of plant species in their habitat helps to analyze the species and thus take care of their biodiversity. Computer vision approaches can be used to automatically identify and classify plant species. Modern approaches use deep learning in computer vision.Materials and methods:In this study, the ACHENY data set that consists of 27030 images of 30 species of Chenopodiaceae are used. Firstly, using the SuperPixel method, larger size images (448×448) than existing ACHENY dataset images size (224×224) are created. Secondly, based on the newly created dataset we introduce a proper deep learning model to identify Chenopodiaceae species.Results and conclusion:The results of the evaluation confirm the improvement of the classification accuracy of ACHENY species by the proposed model compared to the previously presented models. The results of the experiments indicate a superiority of about 3% accuracy of the proposed method and all evaluation parameters of the research have increased to a reasonable extent.
Abedini, M., & Toulabi, S. (2013). Efficiency comparison of EPM and WEPP in estimation of soil erosion and sediment rate of Solachai watershed. Journal of Researches of Quantification Geomorphology, 2(1), 79–96.
Alkharabsheh, M. M., Alexandridis, T. K., Bilas, G., Misopolinos, N., & Silleos, N. (2013). Impact of Land Cover Change on Soil Erosion Hazard in Northern Jordan Using Remote Sensing and GIS. Procedia Environmental Sciences, 19, 912–921. https://doi.org/10.1016/j.proenv.2013.06.101
Butzer, K. W. (2005). Environmental history in the Mediterranean world: cross-disciplinary investigation of cause-and-effect for degradation and soil erosion. Journal of Archaeological Science, 32(12), 1773–1800. https://doi.org/10.1016/j.jas.2005.06.001
Casati, P., Andreo, C. S., & Edwards, G. E. (1999). Characterization of NADP-malic enzyme from two species of Chenopodiaceae: Haloxylon persicum (C4) and Chenopodium album (C3). Phytochemistry, 52(6), 985–992. https://doi.org/10.1016/S0031-9422(99)00355-6
Cerdà, A., Lucas-Borja, M. E., Franch-Pardo, I., Úbeda, X., Novara, A., López-Vicente, M., Popović, Z., & Pulido, M. (2021). The role of plant species on runoff and soil erosion in a Mediterranean shrubland. Science of The Total Environment, 799, 149218. https://doi.org/10.1016/j.scitotenv.2021.149218
Chen, L., Wei, W., Fu, B., & Lü, Y. (2007). Soil and water conservation on the Loess Plateau in China: review and perspective. Progress in Physical Geography, 31(4), 389–403. https://doi.org/10.1177/0309133307081290
Escudero, A., Iriondo, J. M., Olano, J. M., Rubio, A., & Somolinos, R. C. (2000). Factors affecting establishment of a gypsophyte: the case of Lepidium subulatum (Brassicaceae). American Journal of Botany, 87(6), 861–871. https://doi.org/10.2307/2656894
Fu, B., Wang, S., Liu, Y., Liu, J., Liang, W., & Miao, C. (2017). Hydrogeomorphic ecosystem responses to natural and anthropogenic changes in the Loess Plateau of China. Annual Review of Earth and Planetary Sciences, 45, 223–243. https://doi.org/10.1146/annurev-earth-063016-020552
García‐Ruiz, J. M., Beguería, S., Lana‐Renault, N., Nadal‐Romero, E., & Cerdà, A. (2017). Ongoing and Emerging Questions in Water Erosion Studies. Land Degradation & Development, 28(1), 5–21. https://doi.org/10.1002/ldr.2641
Ghane Ezabadi, N., Azhdar, S., & Jamali, A. A. (2021). Analysis of dust changes using satellite images in Giovanni NASA and Sentinel in Google Earth Engine in western Iran. Journal of Nature and Spatial Sciences (JONASS), 1(1), 17–26. https://dx.doi.org/10.30495/jonass.2021.680327
He, S., Wang, D., Li, Y., Zhao, P., Lan, H., Chen, W., ... & Chen, X. (2021). Social-ecological system resilience of debris flow alluvial fans in the Awang basin, China. Journal of Environmental Management, 286, 112230. https://doi.org/10.1016/j.jenvman.2021.112230
Heidary-Sharifabad, A., Zarchi, M. S., Emadi, S., & Zarei, G. (2021a). An efficient deep learning model for cultivar identification of a pistachio tree. British Food Journal, ahead-of-p(ahead-of-print).
https://doi.org/10.1108/BFJ-12-2020-1100
Heidary-Sharifabad, A., Zarchi, M. S., Emadi, S., & Zarei, G. (2021b). Efficient Deep Learning Models for Categorizing Chenopodiaceae in the Wild. International Journal of Pattern Recognition and Artificial Intelligence, 2152015. https://doi.org/10.1142/S0218001421520157
Heidary-Sharifabad, A., Sardari Zarchi, M., Emadi, S., & Zarei, G. (2021c). ACHENY : A Standard Chenopodiaceae Image dataset for Deep Learning Models. Mendeley Data, v1. https://doi.org/10.17632/fpfty8nn7j.1
Heidary-Sharifabad, A., Zarchi, M. S., & Zarei, G. (2021d). ICPTC: Iranian commercial pistachio tree cultivars standard dataset. Data in Brief, 38, 107348. https://doi.org/10.1016/j.dib.2021.107348
Hong, C., Chenchen, L., Xueyong, Z., Huiru, L., Liqiang, K., Bo, L., & Jifeng, L. (2020). Wind erosion rate for vegetated soil cover: A prediction model based on surface shear strength. CATENA, 187, 104398. https://doi.org/10.1016/j.catena.2019.104398
Huang, G., Zheng, M., & Peng, J. (2021). Effect of Vegetation Roots on the Threshold of Slope Instability Induced by Rainfall and Runoff. Geofluids, 2021, 1–19. https://doi.org/10.1155/2021/6682113
Ishnava, K., Ramarao, V., Mohan, J. S. S., & Kothari, I. L. (2011). Ecologically important and life supporting plants of little Rann of Kachchh, Gujarat. Journal of Ecology and the Natural Environment, 3(2), 33–38.
Jäger, H., Achermann, M., Waroszewski, J., Kabała, C., Malkiewicz, M., Gärtner, H., Dahms, D., Krebs, R., & Egli, M. (2015). Pre-alpine mire sediments as a mirror of erosion, soil formation and landscape evolution during the last 45ka. CATENA, 128, 63–79. https://doi.org/10.1016/j.catena.2015.01.018
Jamali, A. A., Kalkhajeh, R. G., Randhir, T. O., & He, S. (2022). Modeling relationship between land surface temperature anomaly and environmental factors using GEE and Giovanni. Journal of Environmental Management, 302, 113970. https://doi.org/10.1016/j.jenvman.2021.113970
Jamali, A. A., Naeeni, M. A. M., & Zarei, G. (2020). Assessing the expansion of saline lands through vegetation and wetland loss using remote sensing and GIS. Remote Sensing Applications: Society and Environment, 20, 100428. https://doi.org/10.1016/j.rsase.2020.100428
Jueyi, S. U. I., Yun, H. E., & Cheng, L. I. U. (2009). Changes in sediment transport in the Kuye River in the Loess Plateau in China. International Journal of Sediment Research, 24(2), 201–213.
https://doi.org/10.1016/S1001-6279(09)60027-5
Liu, X., Zhang, S., Zhang, X., Ding, G., & Cruse, R. M. (2011). Soil erosion control practices in Northeast China: A mini-review. Soil and Tillage Research, 117, 44–48. https://doi.org/10.1016/j.still.2011.08.005
Mokhtari, M. H., Busu, I., & Parvizi, S. (2021). METRIC based evapotranspiration mapping of pistachio orchard in the semi-arid region. Journal of Nature and Spatial Sciences (JONASS), 1(2). https://dx.doi.org/10.30495/jonass.2021.1927551.1010
Morgan, R. P. C. (2009). Soil erosion and conservation. John Wiley & Sons.
Niazi, Y., Mendoza, M. E., Talebi, A., & Bidaki, H. (2021). GIS-based support vector machine model in shallow landslide hazards prediction: A case study on Ilam dam watershed, Iran. Journal of Nature and Spatial Sciences (JONASS), 1(1), 59–84. https://dx.doi.org/10.30495/jonass.2021.680329
Onyando, J. O., Kisoyan, P., & Chemelil, M. C. (2005). Estimation of potential soil erosion for river perkerra catchment in Kenya. Water Resources Management, 19(2), 133–143. https://doi.org/10.1007/s11269-005-2706-5
Panagos, P., Borrelli, P., Meusburger, K., Alewell, C., Lugato, E., & Montanarella, L. (2015). Estimating the soil erosion cover-management factor at the European scale. Land Use Policy, 48, 38–50. https://doi.org/10.1016/j.landusepol.2015.05.021
Qanbari, V., & Jamali, A. A. (2015). The relationship between elevation, soil properties and vegetation cover in the Shorb-Ol-Ain watershed of Yazd. J Biodivers Environ Sci (JBES), 49-56.
Sharma, A., Tiwari, K. N., & Bhadoria, P. B. S. (2011). Effect of land use land cover change on soil erosion potential in an agricultural watershed. Environmental Monitoring and Assessment, 173(1–4), 789–801. https://doi.org/10.1007/s10661-010-1423-6
Shi, W., Caballero, J., Huszár, F., Totz, J., Aitken, A. P., Bishop, R., Rueckert, D., & Wang, Z. (2016). Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network. http://arxiv.org/abs/1609.05158
Stromsoe, N., Marx, S. K., Callow, N., McGowan, H. A., & Heijnis, H. (2016). Estimates of late Holocene soil production and erosion in the Snowy Mountains, Australia. CATENA, 145, 68–82. https://doi.org/10.1016/j.catena.2016.05.013
Suchorukow, A. P. (2008). Flora Iranica–Family Chenopodiaceae. Annalen Des Naturhistorischen Museums in Wien. Serie B Für Botanik Und Zoologie, 153–158. http://www.jstor.org/stable/41767424
Tan, M., & Le, Q. V. (2019). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. http://arxiv.org/abs/1905.11946
Vanmaercke, M., Poesen, J., Verstraeten, G., de Vente, J., & Ocakoglu, F. (2011). Sediment yield in Europe: Spatial patterns and scale dependency. Geomorphology, 130(3–4), 142–161. https://doi.org/10.1016/j.geomorph.2011.03.010
Wei, W., Chen, L., Fu, B., Huang, Z., Wu, D., & Gui, L. (2007). The effect of land uses and rainfall regimes on runoff and soil erosion in the semi-arid loess hilly area, China. Journal of Hydrology, 335(3–4), 247–258. https://doi.org/10.1016/j.jhydrol.2006.11.016
Welsh, S. L., Crompton, C. W., Clemants, S. E., & Committee, F. of N. A. E. (2003). Chenopodiaceae. Flora of North America, 4(part 1), 258–404.
Wen, X., & Zhen, L. (2020). Soil erosion control practices in the Chinese Loess Plateau: A systematic review. Environmental Development, 34, 100493. https://doi.org/10.1016/j.envdev.2019.100493
Wu, G., Liu, Y., Cui, Z., Liu, Y., Shi, Z., Yin, R., & Kardol, P. (2020). Trade‐off between vegetation type, soil erosion control and surface water in global semi‐arid regions: A meta‐analysis. Journal of Applied Ecology, 57(5), 875–885. https://doi.org/10.1111/1365-2664.13597
Xin, Z., Ran, L., & Lu, X. X. (2012). Soil erosion control and sediment load reduction in the Loess Plateau: policy perspectives. International Journal of Water Resources Development, 28(2), 325–341. https://doi.org/10.1080/07900627.2012.668650
Zhao, G., Mu, X., Wen, Z., Wang, F., & Gao, P. (2013). Soil erosion, conservation, and eco‐environment changes in the Loess Plateau of China. Land Degradation & Development, 24(5), 499–510. https://doi.org/10.1002/ldr.2246
