This is an outdated version published on 31-12-2023. Read the most recent version.
Forthcoming

Optimization of a soil type prediction method based on the deep learning model and vegetation characteristics

Authors

DOI:

https://doi.org/10.14719/pst.2926

Keywords:

Characteristics, Deep learning, Identification, Spodosols, Vegetation

Abstract

The structure and composition of forest vegetation plays an important role in different ecosystem functions and services. This study aimed to identifying soil types based on vegetation characteristics using a deep learning model in the High Conservation Value (HCV) area of Central Kalimantan, spanning 632.04 hectares. The data on vegetation were collected using a combination method between line transect and quadratic plots were placed. The development of a deep learning model was based on the results of a vegetation survey and the processing of aerial photos using the Feature Classifier method. The results of applying a deep learning model could provide a relatively accurate and consistent prediction in identifying soil types (Entisols 62%, Spodosols 90%, Ultisols 90% accuracy). The composition of vegetation community in Ultisols was dominated of seedling and tree (closed canopy), meanwhile in Entisols and Spodosols was dominated of seedling and sapling (dominantly open canopy). Ultisols exhibited the highest species richness (57 species), followed by Spodosols (31 species) and Entisols (14 species). Ultisols, Entisols, and Spodosols displayed even species distribution(J' close to 1) without dominance of certain species(D < 0.5). The species diversity index was at a low to moderate level(H' < 3), while the species richness index remained at a very low level(D_mg > 3.5).

Downloads

Download data is not yet available.

References

Zhang D, Peng Y, Li F, Yang G, Wang J, Yu J et al. Changes in above-/below-ground biodiversity and plant functional composition mediate soil respiration response to nitrogen input. Funct Ecol. 2021 May 1;35(5):1171-82. https://doi.org/10.1111/1365-2435.13783

Rambey R, Susilowati A, Rangkuti AB, Onrizal O, Desrita, Ardi R et al. Plant diversity, structure and composition of vegetation around barumun watershed, North Sumatra, Indonesia. Biodiversitas. 2021 Aug 1;22(8):3250-56. https://doi.org/10.13057/biodiv/d2208193. Ott RF. How lithology impacts global topography, vegetation and animal biodiversity: A global-scale analysis of mountainous regions. Geophys Res Lett [Internet]. 2020;47(20):1-11. Available from: https://doi.org/10.3929/ethz-b-000449983

Cruz ACR, Corrêa N de M, Murakami MM da S, Amorim T de A, Nunes-Freitas AF, Sylvestre L da S. Importance of the vertical gradient in the variation of epiphyte community structure in the Brazilian Atlantic forest. Flora. 2022 Oct 1;295:152137. https://doi.org/10.1016/j.flora.2022.152137

Fahey RT, Atkins JW, Gough CM, Hardiman BS, Nave LE, Tallant JM et al. Defining a spectrum of integrative trait-based vegetation canopy structural types. Ecology Letters. Blackwell Publishing Ltd. 2019;22:2049-59. https://doi.org/10.1111/ele.13388

Zhao X, Feng Y, Xu K, Cao M, Hu S, Yang Q et al. Canopy structure: An intermediate factor regulating grassland diversity-function relationships under human disturbances. Fundamental Research. 2022 Mar 1;3:179-87. https://doi.org/10.1016/j.fmre.2022.10.007

Walter JA, Stovall AEL, Atkins JW. Vegetation structural complexity and biodiversity in the great smoky mountains. Ecosphere. 2021 Mar 1;12(3). https://doi.org/10.1002/ecs2.3390

Anderle M, Paniccia C, Brambilla M, Hilpold A, Volani S, Tasser E et al. The contribution of landscape features, climate and topography in shaping taxonomical and functional diversity of avian communities and a heterogeneous alpine region. Oecologia. 2022 Jul 1;199:499-512. https://doi.org/10.1007/s00442-022-05134-7

Chen J, Jin S, Du P. Roles of horizontal and vertical tree canopy structure in mitigating daytime and nighttime urban heat island effects. International Journal of Applied Earth Observation and Geoinformation. 2020 Jul 1;89:1-11. https://doi.org/10.1016/j.jag.2020.102060

Verduchi V. How complexity in vegetation structure and distance from the ground-level habitat influence spontaneous plant diversity on green-roofs. 2021 Dec.

Hamka H, Hapid A, Maiwa A. Analisis vegetasi di kawasan lindung desa betania kabupaten poso. Jurnal Pendidikan MIPA. 2022 Sep 16;12(3):808-13. https://doi.org/10.37630/jpm.v12i3.688

Singh V, Shukla S, Singh A. The principal factors responsible for biodiversity loss. Open J Plant Sci [Internet]. 2021;6(1):11-14. Available from: https://dx.doi.org/10.17352/jps.000026

Gizachew GT. Spatial-temporal and factors influencing the distribution of biodiversity: A review. Scientific Reports in Life Sciences. 2021;2(4):1-19.

Damptey FG, Birkhofer K, Menor IO, de la Riva EG. The functional structure of tropical plant communities and soil properties enhance ecosystem functioning and multifunctionality in different ecosystems in Ghana. Forests. 2022 Feb 1;13(2). https://doi.org/10.3390/f13020297

Javed A, Ali E, Binte Afzal K, Osman A, Riaz DrS. Soil fertility: Factors affecting soil fertility and biodiversity responsible for soil fertility. International Journal of Plant, Animal and Environmental Sciences. 2022;12(01). https://doi.org/10.26502/ijpaes.202129

Montagna M, Brunetti M, Spada A, Cussigh A, Alali S, Cremonesi P et al. Factors affecting soil invertebrate biodiversity in agroecosystems of the po plain area (Italy). ARPHA Conference Abstracts. 2022 Sep 29;5. https://doi.org/10.3897/aca.5.e95808

Kooch Y, Noghre N. The effect of shrubland and grassland vegetation types on soil fauna and flora activities in a mountainous semi-arid landscape of Iran. Science of The Total Environment. 2020 Feb 10;703:135497. https://doi.org/10.1016/j.scitotenv.2019.135497

Lu P, Tan Y, Dai N, Di M, Weng X, Zhan Y et al. Composition and structure of soil fauna communities and their relationships with environmental factors in copper mine waste rock after re-vegetation. Glob Ecol Conserv. 2021 Dec 1;32:e01889. https://doi.org/10.1016/j.gecco.2021.e01889

Chaparro MAE, Moralejo M del P, Böhnel HN, Acebal SG. Iron oxide mineralogy in Mollisols, Aridisols and Entisols from Southwestern Pampean region (Argentina) by environmental magnetism approach. Catena (Amst). 2020 Jul 1;190:104534. https://doi.org/10.1016/j.catena.2020.104534

Suwardi, Sutiarso L, Wirianata H, Nugroho AP, Sukarman, Primananda S. Substantial changes in physical and chemical properties of spodosols soil by hardpan breaking and mounding in oil palm plantation. In: Proceedings of the International Conference on Sustainable Environment, Agriculture and Tourism (ICOSEAT 2022). Atlantis Press. 2023. https://doi.org/10.2991/978-94-6463-086-2_20

Junior AJ de S, Camêlo D de L, Arruda DL de, Souza Junior VS de, Rocha AT da, Corrêa MM. Spodosol formation on sandy ruins in a semi-arid climate in the Catimbau National Park, Northeast Brazil. Catena (Amst). 2023 Aug 1;229:107226. https://doi.org/10.1016/j.catena.2023.107226

Shi Y xiao xiao, Cui J qi, Zhang F, Li K wei, Jiang J, Xu R kou. Effects of soil pH and organic carbon content on in vitro Cr bioaccessibility in Ultisol, Alfisol, and Inceptisol. Chemosphere. 2023 Sep 1;336:139274. https://doi.org/10.1016/j.chemosphere.2023.139274

Xu P, Liu Y, Zhu J, Shi L, Fu Q, Chen J et al. Influence mechanisms of long-term fertilizations on the mineralization of organic matter in Ultisol. Soil Tillage Res. 2020 Jul 1;201:104594. https://doi.org/10.1016/j.still.2020.104594

Tamang B, Hedman C, Haines F, Stone D, Andreu M. Upland forest community composition and structure by ecoregion in 73 Florida state parks – Insights for ongoing management. For Ecol Manage [Internet]. 2023 Oct 1 [cited 2023 Jul 16];545:121237. https://doi.org/10.1016/j.foreco.2023.121237

Jamison EAK, D’Amato AW, Dodds KJ. Describing a landscape mosaic: Forest structure and composition across community types and management regimes in inland northeastern pitch pine barrens. For Ecol Manage. 2023 May 15;536:120859. https://doi.org/10.1016/j.foreco.2023.120859

Morgan GR, Wang C, Li Z, Schill SR, Morgan DR. Deep learning of high-resolution aerial imagery for coastal marsh change detection: A comparative study. ISPRS Int J Geoinf. 2022 Feb 1;11(2):1-21. https://doi.org/10.3390/ijgi11020100

Jiang G, Zheng Q. Remote sensing recognition and classification of forest vegetation based on image feature depth learning. Mobile Information Systems. 2022;2022:1-11. https://doi.org/10.1155/2022/9548552

Liu M, Fu B, Xie S, He H, Lan F, Li Y et al. Comparison of multi-source satellite images for classifying marsh vegetation using DeepLabV3 Plus deep learning algorithm. Ecol Indic. 2021 Jun 1;125:107562. https://doi.org/10.1016/j.ecolind.2021.107562

Fu B, Zuo P, Liu M, Lan G, He H, Lao Z et al. Classifying vegetation communities karst wetland synergistic use of image fusion and object-based machine learning algorithm with Jilin-1 and UAV multispectral images. Ecol Indic. 2022 Jul 1;140:108989. https://doi.org/10.1016/j.ecolind.2022.108989

Paramananthan S. Managing marginal soils for sustainable growth of oil palms in the tropics. Journal of Oil Palm and the Environment. 2013 Jan;4(1):1-16. https://doi.org/10.5366/jope.2013.1

Hartati W, Arifin J, Sudarmadji T, Ruhiyat D. Spodosols of east kalimantan: Land cover disturbances induced degradation of soil properties. In: Proceedings of the Joint Symposium on Tropical Studies (JSTS-19). Atlantis Press BV. 2021; p. 403-09. https://doi.org/10.2991/absr.k.210408.066

Meng F, Zhang T, Yin D. The effects of soil drought stress on growth characteristics, root system and tissue anatomy of Pinus sylvestris var. mongolica. PeerJ. 2023 Jan 9;11. https://doi.org/10.7717/peerj.14578

Sukarman, Saidy AR, Rusmayadi G, Adriani DE, Primananda S, Suwardi et al. Effect of water deficit of Ultisols, Entisols, Spodosols and Histosols on oil palm productivity in Central Kalimantan. Sains Tanah. 2022 Dec 1;19(2):180-91. https://doi.org/10.20961/stjssa.v19i2.65455

Nottingham AC, Thompson JA, Wood F, Edwards PJ, Strager MP. Mapping pedomemory of spodic morphology using a species distribution model. Geoderma. 2019 Oct 15;352:330-41. https://doi.org/10.1016/j.geoderma.2017.10.044

Schaetzl RJ, Kasmerchak C, Samonil P, Baish C, Hadden M, Rothstein D. Acidification and weathering associated with deep tongues in sandy Spodosols, Michigan, USA. Geoderma Regional. 2020 Dec 1;23:e00332. https://doi.org/10.1016/j.geodrs.2020.e00332

Souza Junior AJ de, Camêlo D de L, Arruda DL de, Souza Junior VS de, Rocha AT da, Corrêa MM. Spodosol formation on sandy ruins in a semi-arid climate in the Catimbau National Park, Northeast Brazil. Catena (Amst). 2023 Aug 1;229:107226. https://doi.org/10.1016/j.catena.2023.107226

Suwardi. Manajemen tanah spodosols melalui sistem pecah hardpan dan mounding untuk meningkatkan produksi tanaman kelapa sawit. Instiper Yogyakarta. Instiper. 2021.

Ali Z, Merrium S, Habib-ur-Rahman M, Hakeem S, Saddique MAB, Sher MA. Wetting mechanism and morphological adaptation; leaf rolling enhancing atmospheric water acquisition in wheat crop—A review. Environmental Science and Pollution Research. 2022 May 1;29(21):30967-85. https://doi.org/10.1007/s11356-022-18846-3

Cai G, Carminati A, Abdalla M, Ahmed MA. Soil textures rather than root hairs dominate water uptake and soil-plant hydraulics under drought. Plant Physiol. 2021 Oct 1;187:858-72. https://doi.org/10.1093/plphys/kiab271

Shoaib M, Banerjee BP, Hayden M, Kant S. Roots’ drought adaptive traits in crop improvement. Plants. MDPI. 2022;11:1-20. https://doi.org/10.3390/plants11172256

Dong X, Zhang Z, Wang S, Shen Z, Cheng X, Lv X et al. Soil properties, root morphology and physiological responses to cotton stalk biochar addition in two continuous cropping cotton field soils from Xinjiang, China. PeerJ. 2022 Feb 16;10. https://doi.org/10.7717/peerj.12928

Phillips AJ, Govedich FR, Moser WE. Leeches in the extreme: Morphological, physiological, and behavioral adaptations to inhospitable habitats. Int J Parasitol Parasites Wildl. 2020 Aug 1;12:318-25. https://doi.org/10.1016/j.ijppaw.2020.09.003

Li WT, Liang N, Zhan J, Wang H, Zhang P. Morphological and anatomical characteristics of eelgrass Zostera marina L. at two distinct environments of Shandong Peninsula, China: An implication of adaptation strategy of seagrasses. Aquat Bot. 2023 May 1;186:103612. https://doi.org/10.1016/j.aquabot.2022.103612

Lynch JP, Strock CF, Schneider HM, Sidhu JS, Ajmera I, Galindo-Castañeda T et al. Root anatomy and soil resource capture. Plant and Soil. Springer Science and Business Media Deutschland GmbH. 2021;466:21-63. https://doi.org/10.1007/s11104-021-05010-y

Li X, Dang X, Gao Y, Meng Z, Chen X, Wang Y. Response mechanisms of adventitious root architectural characteristics of Nitraria tangutorum shrubs to soil nutrients in Nabkha. Plants. 2022 Dec 1;11(23):1-17. https://doi.org/10.3390/plants11233218

Sellan G, Thompson J, Majalap N, Brearley FQ. Soil characteristics influence species composition and forest structure differentially among tree size classes in a Bornean Heath forest. Plant Soil. 2019;1-39. https://doi.org/10.1007/s11104-019-04000-5

Kome GK, Enang RK, Tabi FO, Yerima BPK. Influence of clay minerals on some soil fertility attributes: A review. Open Journal of Soil Science. 2019;09:155-88. https://doi.org/10.4236/ojss.2019.99010

Reichert JM, Morales B, Lima EM, de Bastos F, Morales CAS, de Araújo EF. Soil morphological, physical and chemical properties affecting Eucalyptus spp. productivity on Entisols and Ultisols. Soil Tillage Res. 2023 Feb 1;226:105563. https://doi.org/10.1016/j.still.2022.105563

Xiang Q, Kan A, Yu X, Liu F, Huang H, Li W et al. Assessment of topographic effect on habitat quality in mountainous area using InVEST model. Land (Basel). 2023 Jan 1;12(1):1-17. https://doi.org/10.3390/land12010186

Shang R, Li S, Huang X, Liu W, Lang X, Su J. Effects of soil properties and plant diversity on soil microbial community composition and diversity during secondary succession. Forests. 2021 Jun 1;12(805):1-12. https://doi.org/10.3390/f12060805

Kumar U, Saqib HSA, Islam W, Prashant P, Patel N, Chen W et al. Landscape composition and soil physical–chemical properties drive the assemblages of bacteria and fungi in conventional vegetable fields. Microorganisms. 2022 Jun 1;10(1202):1-18. https://doi.org/10.3390/microorganisms10061202

Zaldívar-Cruz B, Pérez-Ceballos R, Zaldívar-Jiménez A, Canales-Delgadillo J, Endañu-Huerta E, Flores AB et al. Structural and diversity changes in coastal dunes from the Mexican Caribbean: The case of the invasive Australian pine (Casuarina equisetifolia). Management of Biological Invasions. 2022 Mar 1;13(1):131-46. https://doi.org/10.3391/mbi.2022.13.1.08

de Francesco MC, Tozzi FP, Buffa G, Fantinato E, Innangi M, Stanisci A. Identifying critical thresholds in the impacts of invasive alien plants and dune paths on native coastal dune vegetation. Land (Basel). 2023 Jan 1;12(135):1-16. https://doi.org/10.3390/land12010135

Debouk H, Emeterio LS, Marí T, Canals RM, Sebastià MT. Plant functional diversity, climate and grazer type regulate soil activity in natural grasslands. Agronomy. 2020 Sep 1;10(1291):1-20. https://doi.org/10.3390/agronomy10091291

Diatta SBD, Tall LN, Ndour YB, Sembene M, Assigbetsé K. Composition and diversity of soil bacterial communities along an environmental gradient in the Sudano-Sahelian region of Senegal. Open Journal of Soil Science. 2020;10(02):58-89. https://doi.org/10.4236/ojss.2020.102004

Yaseen M, Fan G, Zhou X, Long W, Feng G. Plant diversity and soil nutrients in a tropical coastal secondary forest: Association ordination and sampling year differences. Forests. 2022 Mar 1;13(376):1-10. https://doi.org/10.3390/f13030376

Liu Y, Yin X, Yue G, Zheng Z, Jiang J, He Q et al. Blind omnidirectional image quality assessment with representative features and viewport oriented statistical features. J Vis Commun Image Represent. 2023 Mar 1;91:103770. https://doi.org/10.1016/j.jvcir.2023.103770

Stampoulis D, Damavandi HG, Boscovic D, Sabo J. Using satellite remote sensing and machine learning techniques towards precipitation prediction and vegetation classification. Journal of Environmental Informatics. 2021;37(1):1-15.

Saputro IW, Sari BW. Uji performa algoritma naïve bayes untuk prediksi masa studi mahasiswa. Citec Journal. 2019;6(1):1-11. https://doi.org/10.24076/citec.2019v6i1.178

Piaser E, Villa P. Evaluating capabilities of machine learning algorithms for aquatic vegetation classification in temperate wetlands using multi-temporal sentinel-2 data. International Journal of Applied Earth Observation and Geoinformation. 2023 Mar 1;117:1-12. https://doi.org/10.1016/j.jag.2023.103202

Yang P. Exploring the interrelated effects of soil background, canopy structure and sun-observer geometry on canopy photochemical reflectance index. Remote Sens Environ. 2022 Sep 15;279:113133. https://doi.org/10.1016/j.rse.2022.113133

Atemkeng CC, Tapimo R, Tonnang EHZ, Tchinda R. Inverse radiative transfer problem for soil properties retrieval from bidirectional reflectance measurements. Results in Optics. 2023 May 1;11:100409. https://doi.org/10.1016/j.rio.2023.100409

Bouguettaya A, Zarzour H, Kechida A, Taberkit AM. Deep learning techniques to classify agricultural crops through UAV imagery: A review. Neural Computing and Applications. Springer Science and Business Media Deutschland GmbH. 2022;34:9511-36. https://doi.org/10.1007/s00521-022-07104-9

Ramli NE, Yahya ZR, Said NA. Confusion matrix as performance measure for corner detectors. Journal of Advanced Research in Applied Sciences and Engineering Technology. 2022;29(1):256-65. https://doi.org/10.37934/araset.29.1.256265

Asfour M, Menon C, Jiang X. Feature–classifier pairing compatibility for sEMG signals in hand gesture recognition under joint effects of processing procedures. Bioengineering. 2022 Nov 1;9(11):1-18. https://doi.org/10.3390/bioengineering9110634

Published

31-12-2023

Versions

How to Cite

1.
Suwardi, Sutiarso L, Wirianata H, Nugroho AP, Sukarman, Primananda S, Dasrial M, Hariadi B. Optimization of a soil type prediction method based on the deep learning model and vegetation characteristics . Plant Sci. Today [Internet]. 2023 Dec. 31 [cited 2024 Nov. 21];. Available from: https://horizonepublishing.com/journals/index.php/PST/article/view/2926

Issue

Section

Research Articles

Most read articles by the same author(s)