Evaluation of machine learning algorithms for forest species mapping based on Sentinel 2 data: a case study of Ait Bouzid forest (Central High Atlas, Morocco)
DOI:
https://doi.org/10.12775/bgeo-2023-0010Keywords
Ait Bouzid forest, forest trees, sentinel 2 image, ML classifiers, mapping, MoroccoAbstract
In arid and semi-arid environments, producing accurate maps of forest tree cover using optical remote sensing data is essential to understand their spatial distributions and dynamics. In this respect, the current study aimed to explore the effectiveness of support vector machine (SVM), K nearest neighbors (KNN), and random forest (RF)machine learning (ML) models to map the forest tree species of Ait Bouzid region (Central High Atlas, Morocco) by using Sentinel-2A data. The results from all models showed that about 19-28%, 21-27%, 16-24%, 15-18%, and 0,3-0,32% of the area was covered by euphorbia, red juniper, cedar, holm oak, bare ground, and water body, respectively. According to the overall accuracy (OA) and kappa coefficient, the SVM classifier showed the highest OA (73%) and kappa (0.66) values, followed by KNN (OA=70%, kappa=0.62) and RF (OA=67%, kappa=0.59). Regarding LC classes, water, bare soil, and holm oak could be identified with the producer's accuracy attaining 100%, while red juniper and cedar were the most challenging classes to determine for all ML classifiers, with the producer's accuracy of 40-50% and 40-67%. This study revealed the potential of ML approaches coupled with multispectral Sentinel-2A data for forest species cartography in arid areas with high accuracy. Furthermore, it provides crucial information about forest tree species distribution for developing forest management plans.
References
Achour Y, Pourghasemi HR (2020) How do machine learning techniques help in increasing accuracy of landslide susceptibility maps? Geoscience Frontiers 11:871-883.
Adugna T, Xu W, Fan J (2022) Comparison of Random Forest and Support Vector Machine Classifiers for Regional Land Cover Mapping Using Coarse Resolution FY-3C Images. Remote Sensing 14:574.
Amiri M, Pourghasemi HR, Ghanbarian GA, Afzali SF (2019) Assessment of the importance of gully erosion effective factors using Boruta algorithm and its spatial modeling and mapping using three machine learning algorithms. Geoderma 340:55-69.
Appeaning Addo K (2010) Urban and Peri-Urban Agriculture in Developing Countries Studied using Remote Sensing and In Situ Methods. Remote Sensing 2:497-513.
Ardabili S, Mosavi A, Dehghani M, Várkonyi-Kóczy AR (2020) Deep learning and machine learning in hydrological processes climate change and earth systems a systematic review. International conference on global research and education. Springer. pp 52-62
Barakat A, Khellouk R, El Jazouli A, Touhami F, Nadem S (2018) Monitoring of forest cover dynamics in eastern area of Béni-Mellal Province using ASTER and Sentinel-2A multispectral data. Geology, Ecology, and Landscapes 2:203-215. doi:10.1080/24749508.2018.1452478
Barakat A, Ouargaf Z, Khellouk R, El Jazouli A, Touhami F (2019) Land use/land cover change and environmental impact assessment in béni-mellal district (morocco) using remote sensing and gis. Earth Systems and Environment:1-13.
Barakat A, Rafai M, Mosaid H, Islam MS, Saeed S (2022) Mapping of Water-Induced Soil Erosion Using Machine Learning Models: A Case Study of Oum Er Rbia Basin (Morocco). Earth Systems and Environment. doi:10.1007/s41748-022-00317-x
Baumgardner MF, Silva LF, Biehl LL, Stoner ER (1986) Reflectance properties of soils. Advances in agronomy 38:1-44.
Belgiu M, Drăguţ L (2016) Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing 114:24-31. doi:https://doi.org/10.1016/j.isprsjprs.2016.01.011
Benabid A (2000) Flore et écosystèmes du Maroc: Evaluation et préservation de la biodiversité.
Bouzekraoui H, El Khalki Y, Mouaddine A, Lhissou R, El Youssi M, Barakat A (2016) Characterization and dynamics of agroforestry landscape using geospatial techniques and field survey: a case study in central High-Atlas (Morocco). Agroforestry Systems 90:965-978. doi:10.1007/s10457-015-9877-8
Breiman L (2001) Random Forests. Machine Learning 45:5-32. doi:10.1023/A:1010933404324
Catani F, Lagomarsino D, Segoni S, Tofani V (2013) Landslide susceptibility estimation by random forests technique: sensitivity and scaling issues. Natural Hazards and Earth System Sciences 13:2815-2831.
Cheng K, Wang JJF (2019) Forest type classification based on integrated spectral-spatial-temporal features and random forest algorithm—A case study in the qinling mountains. 10:559.
Congalton RG, Green K (2019) Assessing the accuracy of remotely sensed data: principles and practices. CRC press
Decuyper M, Chávez RO, Lohbeck M, Lastra JA, Tsendbazar N, Hackländer J, Herold M, Vågen T-G (2022) Continuous monitoring of forest change dynamics with satellite time series. Remote Sens Environ 269:112829. doi:https://doi.org/10.1016/j.rse.2021.112829
Dinh TV, Nguyen H, Tran X-L, Hoang N-D (2021) Predicting Rainfall-Induced Soil Erosion Based on a Hybridization of Adaptive Differential Evolution and Support Vector Machine Classification. Mathematical Problems in Engineering 2021:6647829. doi:10.1155/2021/6647829
Ettaqy A, Taha A, ElGhiouane A, ElKhou A, Boulli A, Abbas Y (2020) New data on the ecological distribution of Euphorbia resinifera O. Berg in the Beni Mellal-Khenifra region. E3S Web of Conferences. EDP Sciences. p 01001
Evgeniou T, Pontil M (1999) Support vector machines: Theory and applications. Advanced Course on Artificial Intelligence. Springer. pp 249-257
Fassnacht FE, Latifi H, Stereńczak K, Modzelewska A, Lefsky M, Waser LT, Straub C, Ghosh A (2016) Review of studies on tree species classification from remotely sensed data. Remote Sens Environ 186:64-87.
Fix E, Hodges JL (1989) Discriminatory analysis. Nonparametric discrimination: Consistency properties. International Statistical Review/Revue Internationale de Statistique 57:238-247.
Ge W, Cheng Q, Tang Y, Jing L, Gao C (2018) Lithological classification using Sentinel-2A data in the Shibanjing ophiolite complex in Inner Mongolia, China. Remote Sensing 10:638.
George R, Padalia H, Kushwaha SPS (2014) Forest tree species discrimination in western Himalaya using EO-1 Hyperion. International Journal of Applied Earth Observation and Geoinformation 28:140-149. doi:https://doi.org/10.1016/j.jag.2013.11.011
Ghosh A, Fassnacht FE, Joshi PK, Koch B (2014) A framework for mapping tree species combining hyperspectral and LiDAR data: Role of selected classifiers and sensor across three spatial scales. International Journal of Applied Earth Observation and Geoinformation 26:49-63. doi:https://doi.org/10.1016/j.jag.2013.05.017
Ghosh A, Joshi PK (2014) A comparison of selected classification algorithms for mapping bamboo patches in lower Gangetic plains using very high resolution WorldView 2 imagery. International Journal of Applied Earth Observation and Geoinformation 26:298-311.
Godinho S, Guiomar N, Gil A (2018) Estimating tree canopy cover percentage in a mediterranean silvopastoral systems using Sentinel-2A imagery and the stochastic gradient boosting algorithm. International Journal of Remote Sensing 39:4640-4662. doi:10.1080/01431161.2017.1399480
Grabska E, Frantz D, Ostapowicz K (2020) Evaluation of machine learning algorithms for forest stand species mapping using Sentinel-2 imagery and environmental data in the Polish Carpathians. Remote Sens Environ 251:112103. doi:https://doi.org/10.1016/j.rse.2020.112103
Gülci N (2014) Researches on precision forestry in forest planning. Phd thesis, Kahramanmaraş Sütçü İmam University, Faculty of Forestry …
Han H, Shi B, Zhang L (2021) Prediction of landslide sharp increase displacement by SVM with considering hysteresis of groundwater change. Engineering Geology 280:105876. doi:https://doi.org/10.1016/j.enggeo.2020.105876
Huang C, Davis LS, Townshend JRG (2002) An assessment of support vector machines for land cover classification. International Journal of Remote Sensing 23:725-749. doi:10.1080/01431160110040323
Kollert A, Bremer M, Löw M, Rutzinger M (2021) Exploring the potential of land surface phenology and seasonal cloud free composites of one year of Sentinel-2 imagery for tree species mapping in a mountainous region. International Journal of Applied Earth Observation and Geoinformation 94:102208. doi:https://doi.org/10.1016/j.jag.2020.102208
Lary DJ, Alavi AH, Gandomi AH, Walker AL (2016) Machine learning in geosciences and remote sensing. Geoscience Frontiers 7:3-10.
Lee S, Hong S-M, Jung H-S (2018) GIS-based groundwater potential mapping using artificial neural network and support vector machine models: the case of Boryeong city in Korea. Geocarto International 33:847-861. doi:10.1080/10106049.2017.1303091
Liaw A, Wiener M (2021) Classification and Regression by randomForest. R news. 2002; 2 (3): 18–22.
Ma L, Li M, Ma X, Cheng L, Du P, Liu Y (2017) A review of supervised object-based land-cover image classification. ISPRS Journal of Photogrammetry and Remote Sensing 130:277-293. doi:https://doi.org/10.1016/j.isprsjprs.2017.06.001
Mahmud S, Redowan M, Ahmed R, Khan AA, Rahman MM (2022) Phenology-based classification of Sentinel-2 data to detect coastal mangroves. Geocarto International 37:14335-14354.
Maldonado S, Weber R (2009) A wrapper method for feature selection using support vector machines. Information Sciences 179:2208-2217.
Maxwell AE, Warner TA, Fang F (2018) Implementation of machine-learning classification in remote sensing: An applied review. International Journal of Remote Sensing 39:2784-2817.
McHugh ML (2012) Interrater reliability: the kappa statistic. Biochemia medica 22:276-282.
Mickelson JG, Civco DL, Silander J (1998) Delineating forest canopy species in the northeastern United States using multi-temporal TM imagery. Photogrammetric Engineering and Remote Sensing 64:891-904.
Mishra AP, Rai ID, Pangtey D, Padalia HJJotISoRS (2021) Vegetation characterization at community level using Sentinel-2 satellite data and random forest classifier in western Himalayan foothills, Uttarakhand. 49:759-771.
Mohammadi J, Shataee S, Næsset E (2020) Modeling tree species diversity by combining ALS data and digital aerial photogrammetry. Science of Remote Sensing 2:100011.
MohanRajan SN, Loganathan A, Manoharan P (2020) Survey on Land Use/Land Cover (LU/LC) change analysis in remote sensing and GIS environment: Techniques and Challenges. Environmental Science and Pollution Research 27:29900-29926.
Mosaffa H, Sadeghi M, Mallakpour I, Jahromi MN, Pourghasemi HR (2022) Application of machine learning algorithms in hydrology. Computers in Earth and Environmental Sciences. Elsevier. pp 585-591
Naghibi SA, Ahmadi K, Daneshi A (2017) Application of support vector machine, random forest, and genetic algorithm optimized random forest models in groundwater potential mapping. Water Resources Management 31:2761-2775.
Nasiri V, Beloiu M, Asghar Darvishsefat A, Griess VC, Maftei C, Waser LT (2023) Mapping tree species composition in a Caspian temperate mixed forest based on spectral-temporal metrics and machine learning. International Journal of Applied Earth Observation and Geoinformation 116:103154. doi:https://doi.org/10.1016/j.jag.2022.103154
Nasiri V, Darvishsefat AA, Arefi H, Pierrot-Deseilligny M, Namiranian M, Le Bris A (2021) Unmanned aerial vehicles (UAV)-based canopy height modeling under leaf-on and leaf-off conditions for determining tree height and crown diameter (case study: Hyrcanian mixed forest). Can J For Res 51:962-971. doi:10.1139/cjfr-2020-0125
Nguyen HTT, Doan TM, Tomppo E, McRoberts RE (2020) Land Use/land cover mapping using multitemporal Sentinel-2 imagery and four classification methods—A case study from Dak Nong, Vietnam. Remote Sensing 12:1367.
Nidamanuri RR (2020) Hyperspectral discrimination of tea plant varieties using machine learning, and spectral matching methods. Remote Sensing Applications: Society and Environment 19:100350.
Potić I, Srdić Z, Vakanjac B, Bakrač S, Đorđević D, Banković R, Jovanović JM (2023) Improving Forest Detection Using Machine Learning and Remote Sensing: A Case Study in Southeastern Serbia. Applied Sciences 13:8289.
Preidl S, Lange M, Doktor D (2020) Introducing APiC for regionalised land cover mapping on the national scale using Sentinel-2A imagery. Remote Sens Environ 240:111673. doi:https://doi.org/10.1016/j.rse.2020.111673
Radhakrishnan S, Lakshminarayanan AS, Chatterjee JM, Hemanth DJJESI (2020) Forest data visualization and land mapping using support vector machines and decision trees. 13:1119-1137.
Ramezan CA, Warner TA, Maxwell AE, Price BS (2021) Effects of training set size on supervised machine-learning land-cover classification of large-area high-resolution remotely sensed data. Remote Sensing 13:368.
Rodriguez-Galiano VF, Ghimire B, Rogan J, Chica-Olmo M, Rigol-Sanchez JP (2012) An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS journal of photogrammetry and remote sensing 67:93-104.
Sabat-Tomala A, Raczko E, Zagajewski B (2020) Comparison of Support Vector Machine and Random Forest Algorithms for Invasive and Expansive Species Classification Using Airborne Hyperspectral Data. Remote Sensing 12:516.
Sahin EK (2022) Implementation of free and open-source semi-automatic feature engineering tool in landslide susceptibility mapping using the machine-learning algorithms RF, SVM, and XGBoost. Stoch Environ Res Risk Assess:1-26.
Sajedi-Hosseini F, Malekian A, Choubin B, Rahmati O, Cipullo S, Coulon F, Pradhan B (2018) A novel machine learning-based approach for the risk assessment of nitrate groundwater contamination. Sci Total Environ 644:954-962. doi:https://doi.org/10.1016/j.scitotenv.2018.07.054
Soleimannejad L, Ullah S, Abedi R, Dees M, Koch B (2019a) Evaluating the potential of sentinel-2, landsat-8, and irs satellite images in tree species classification of hyrcanian forest of iran using random forest. Journal of Sustainable Forestry 38:615-628. doi:10.1080/10549811.2019.1598443
Soleimannejad L, Ullah S, Abedi R, Dees M, Koch BJJoSF (2019b) Evaluating the potential of sentinel-2, landsat-8, and irs satellite images in tree species classification of hyrcanian forest of iran using random forest. 38:615-628.
Sothe C, De Almeida C, Schimalski M, La Rosa L, Castro J, Feitosa R, Dalponte M, Lima C, Liesenberg V, Miyoshi GJG, Sensing R (2020) Comparative performance of convolutional neural network, weighted and conventional support vector machine and random forest for classifying tree species using hyperspectral and photogrammetric data. 57:369-394.
Suleymanov A, Gabbasova I, Komissarov M, Suleymanov R, Garipov T, Tuktarova I, Belan L (2023) Random Forest Modeling of Soil Properties in Saline Semi-Arid Areas. Agriculture 13:976.
Taïbi AN, El Khalki Y, El Hannani M (2015) Atlas régional Région du Tadla Azilal Maroc. Université d'Angers
Thanh Noi P, Kappas M (2018) Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery. Sensors 18:18.
Tuček P, Caha J, Janoška Z, Vondráková A, Samec P, Bojko J, Voženílek V (2014) Forest vulnerability zones in the Czech Republic. Journal of Maps 10:179-182. doi:10.1080/17445647.2013.866911
Tuominen S, Näsi R, Honkavaara E, Balazs A, Hakala T, Viljanen N, Pölönen I, Saari H, Ojanen H (2018) Assessment of classifiers and remote sensing features of hyperspectral imagery and stereo-photogrammetric point clouds for recognition of tree species in a forest area of high species diversity. Remote Sensing 10:714.
Vega Isuhuaylas LA, Hirata Y, Ventura Santos LC, Serrudo Torobeo NJRS (2018) Natural forest mapping in the Andes (Peru): A comparison of the performance of machine-learning algorithms. 10:782.
Verhegghen A, Kuzelova K, Syrris V, Eva H, Achard F (2022) Mapping canopy cover in african dry forests from the combined use of sentinel-1 and sentinel-2 data: Application to tanzania for the year 2018. Remote Sensing 14:1522.
Wessel M, Brandmeier M, Tiede D (2018) Evaluation of different machine learning algorithms for scalable classification of tree types and tree species based on Sentinel-2 data. Remote Sensing 10:1419.
Wilson K, Newton A, Echeverría C, Weston C, Burgman M (2005) A vulnerability analysis of the temperate forests of south central Chile. Biol Conserv 122:9-21. doi:https://doi.org/10.1016/j.biocon.2004.06.015
Zagajewski B, Kluczek M, Raczko E, Njegovec A, Dabija A, Kycko MJRS (2021) Comparison of random forest, support vector machines, and neural networks for post-disaster forest species mapping of the Krkonoše/Karkonosze transboundary biosphere reserve. 13:2581.
Zounemat-Kermani M, Batelaan O, Fadaee M, Hinkelmann R (2021) Ensemble machine learning paradigms in hydrology: A review. Journal of Hydrology 598:126266.
FAO (Food and Agriculture Organization), 2023. Les societies tirent des avantages colossaux des services écosystémiques forestiers, qui représentent plus d’un cinquième des richesses contenues dans les actifs fonciers. https://www.fao.org/3/cb9360fr/online/src/html/forests-ecosystem-services-wealth.html. Available on 25 july 2023.
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