A COMPARATIVE ANALYSIS OF PIXEL-BASED AND OBJECT-BASED APPROACHES FOR FOREST ABOVE-GROUND BIOMASS ESTIMATION USING RANDOM FOREST MODEL

Providing an accurate above-ground biomass (AGB) map is of paramount importance for carbon stock and climate change monitoring. The main objective of this study is to compare the performance of pixel-based and object-based approaches for AGB estimation of temperate forests in north-eastern of New York State. Second, the capabilities of optical, SAR, and optical + SAR data were investigated. To achieve the goals, the random forest (RF) regression algorithm was used to model and predict the AGB values. Optical (i.e. Landsat 5TM, Landsat 8 OLI, and Sentinel-2), synthetic aperture radar (SAR) (Sentinel-1 and global phased array type L-band SAR (PALSAR/PALSAR-2)), and their integration have been used to estimate the AGB. It is worth mentioning that the airborne light detection and ranging (LiDAR) AGB raster has been used as a reference data for training/testing purposes. The results demonstrate that the OBIA approach enhanced the RMSE of AGB estimation about 5.32 Mg/ha, 8.9 Mg/ha, and 5.29 Mg/ha for optical, SAR, and optical + SAR data, respectively. Moreover, optical + SAR data with the RMSE of 42.63 Mg/ha and R2 of 0.72 for pixel-based and RMSE of 37.31 Mg/ha and R2 of 0.77 for object-based approach provided the best results

Mineração de dados aplicada a métodos de seleção de variáveis para a modelagem de estoque de carbono acima do solo

The objective of this work was to apply the random forest (RF) algorithm to the modelling of the aboveground carbon (AGC) stock of a tropical forest by testing three feature selection procedures – recursive removal and the uniobjective and multiobjective genetic algorithms (GAs). The used database covered 1,007 plots sampled in the Rio Grande watershed, in the state of Minas Gerais state, Brazil, and 114 environmental variables (climatic, edaphic, geographic, terrain, and spectral). The best feature selection strategy – RF with multiobjective GA – reaches the minor root-square error of 17.75 Mg ha-1 with only four spectral variables – normalized difference moisture index, normalized burn ratio 2 correlation texture, treecover, and latent heat flux –, which represents a reduction of 96.5% in the size of the database. Feature selection strategies assist in obtaining a better RF performance, by improving the accuracy and reducing the volume of the data. Although the recursive removal and multiobjective GA showed a similar performance as feature selection strategies, the latter presents the smallest subset of variables, with the highest accuracy. The findings of this study highlight the importance of using near infrared, short wavelengths, and derived vegetation indices for the remote-sense-based estimation of AGC. The MODIS products show a significant relationship with the AGC stock and should be further explored by the scientific community for the modelling of this stock.O objetivo deste trabalho foi aplicar o algoritmo “random forest” (RF) à modelagem do estoque de carbono acima do solo (CAS) de uma floresta tropical, por meio da testagem de três procedimentos de seleção de variáveis: remoção recursiva e algoritmos genéticos (AGs) uniobjetivo e multiobjetivo. Os dados utilizados abrangeram 1.007 parcelas amostradas na bacia hidrográfica do Rio Grande, no estado de Minas Gerais, Brasil, e 114 variáveis ambientais (climáticas, edáficas, geográficas, de terreno e espectrais). A melhor estratégia de seleção de variáveis – a RF com AG multiobjetivo – chega ao menor erro quadrático de 17,75 Mg ha-1 com apenas quatro variáveis espectrais – índice de umidade por diferença normalizada, textura de correlação do índice de queimada por razão normalizada 2, cobertura arbórea e fluxo de calor latente –, o que representa redução de 96,5% no tamanho do banco de dados. As estratégias de seleção de variáveis ajudam a obter melhor desempenho da RF, ao melhorar a acurácia e reduzir o volume dos dados. Embora a remoção recursiva e o AG multiobjetivo mostrem desempenho semelhante como estratégias de seleção de variáveis, esta último apresenta menor subconjunto de variáveis, com maior precisão. As descobertas deste trabalho destacam a importância do uso de infravermelho próximo, comprimentos de onda curtos e índices de vegetação derivados para a estimativa de CAS baseada em sensoriamento remoto. Os produtos MODIS mostram relação significativa com o estoque de CAS e precisam ser melhor explorados pela comunidade científica para a modelagem deste estoque

Fusing simulated GEDI, ICESat-2 and NISAR data for regional aboveground biomass mapping

Accurate mapping of forest aboveground biomass (AGB) is critical for better understanding the role of forests in the global carbon cycle. NASA's current GEDI and ICESat-2 missions as well as the upcoming NISAR mission will collect synergistic data with different coverage and sensitivity to AGB. In this study, we present a multi-sensor data fusion approach leveraging the strength of each mission to produce wall-to-wall AGB maps that are more accurate and spatially comprehensive than what is achievable with any one sensor alone. Specifically, we calibrate a regional L-band radar AGB model using the sparse, simulated spaceborne lidar AGB estimates. We assess our data fusion framework using simulations of GEDI, ICESat-2 and NISAR data from airborne laser scanning (ALS) and UAVSAR data acquired over the temperate high AGB forest and complex terrain in Sonoma County, California, USA. For ICESat-2 and GEDI missions, we simulate two years of data coverage and AGB at footprint level are estimated using realistic AGB models. We compare the performance of our fusion framework when different combinations of the sparse simulated GEDI and ICEsat-2 AGB estimates are used to calibrate our regional L-band AGB models. In addition, we test our framework at Sonoma using (a) 1-ha square grid cells and (b) similarly sized irregularly shaped objects. We demonstrate that the estimated mean AGB across Sonoma is more accurately estimated using our fusion framework than using GEDI or ICESat-2 mission data alone, either with a regular grid or with irregular segments as mapping units. This research highlights methodological opportunities for fusing new and upcoming active remote sensing data streams toward improved AGB mapping through data fusion.</p

Mapping Natural Forest Remnants with Multi-Source and Multi-Temporal Remote Sensing Data for More Informed Management of Global Biodiversity Hotspots

Global terrestrial biodiversity hotspots (GBH) represent areas featuring exceptional concentrations of endemism and habitat loss in the world. Unfortunately, geospatial data of natural habitats of the GBHs are often outdated, imprecise, and coarse, and need updating for improved management and protection actions. Recent developments in satellite image availability, combined with enhanced machine learning algorithms and computing capacity, enable cost-efficient updating of geospatial information of these already severely fragmented habitats. This study aimed to develop a more accurate method for mapping closed canopy evergreen natural forest (CCEF) of the Eastern Arc Mountains (EAM) ecoregion in Tanzania and Kenya, and to update the knowledge on its spatial extent, level of fragmentation, and conservation status. We tested 1023 model possibilities stemming from a combination of Sentinel-1 (S1) and Sentinel-2 (S2) satellite imagery, spatial texture of S1 and S2, seasonality derived from Landsat-8 time series, and topographic information, using random forest modelling approach. We compared the best CCEF model with existing spatial forest products from the EAM through independent accuracy assessment. Finally, the CCEF model was used to estimate the fragmentation and conservation coverage of the EAM. The CCEF model has moderate accuracy measured in True Skill Statistic (0.57), and it clearly outperforms other similar products from the region. Based on this model, there are about 296,000 ha of Eastern Arc Forests (EAF) left. Furthermore, acknowledging small forest fragments (1-10 ha) implies that the EAFs are more fragmented than previously considered. Currently, the official protection of EAFs is disproportionally targeting well-studied mountain blocks, while less known areas and small fragments are underrepresented in the protected area network. Thus, the generated CCEF model should be used to design updates and more informed and detailed conservation allocation plans to balance this situation. The results highlight that spatial texture of S2, seasonality, and topography are the most important variables describing the EAFs, while spatial texture of S1 increases the model performance slightly. All in all, our work demonstrates that recent developments in Earth observation allows significant enhancements in mapping, which should be utilized in areas with outstanding biodiversity values for better forest and conservation planning.Peer reviewe

Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass

This Special Issue (SI), entitled "Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass”, resulted from 13 peer-reviewed papers dedicated to Forestry and Biomass mapping, characterization and accounting. The papers' authors presented improvements in Remote Sensing processing techniques on satellite images, drone-acquired images and LiDAR images, both aerial and terrestrial. Regarding the images’ classification models, all authors presented supervised methods, such as Random Forest, complemented by GIS routines and biophysical variables measured on the field, which were properly georeferenced. The achieved results enable the statement that remote imagery could be successfully used as a data source for regression analysis and formulation and, in this way, used in forestry actions such as canopy structure analysis and mapping, or to estimate biomass. This collection of papers, presented in the form of a book, brings together 13 articles covering various forest issues and issues in forest biomass calculation, constituting an important work manual for those who use mixed GIS and RS techniques

Diversity In The Boreal Forest Of Alaska: Distribution And Impacts On Ecosystem Services

Thesis (Ph.D.) University of Alaska Fairbanks, 2012Within the forest management community, diversity is often considered as simply a list of species present at a location. In this study, diversity refers to species richness and evenness and takes into account vegetation structure (i.e. size, density, and complexity) that characterize a given forest ecosystem and can typically be measured using existing forest inventories. Within interior Alaska the largest forest inventories are the Cooperative Alaska Forest Inventory and the Wainwright Forest Inventory. The limited distribution of these inventories constrains the predictions that can be made. In this thesis, I examine forest diversity in three distinct frameworks; Recruitment, Patterns, and Production. In Chapter 1, I explore forest management decisions that may shape forest diversity and its role and impacts in the boreal forest. In Chapter 2, I evaluate and map the relationships between recruitment and species and tree size diversity using a geospatial approach. My results show a consistent positive relationship between recruitment and species diversity and a general negative relationship between recruitment and tree size diversity, indicating a tradeoff between species diversity and tree size diversity in their effects on recruitment. In Chapter 3, I modeled and mapped current and possible future forest diversity patterns within the boreal forest of Alaska using machine learning. The results indicate that the geographic patterns of the two diversity measures differ greatly for both current conditions and future scenarios and that these are more strongly influenced by human impacts than by ecological factors. In Chapter 4, I developed a method for mapping and predicting forest biomass for the boreal forest of interior Alaska using three different machine-learning techniques. I developed first time high resolution prediction maps at a 1 km2 pixel size for aboveground woody biomass. My results indicate that the geographic patterns of biomass are strongly influenced by the tree size class diversity of a given stand. Finally, in Chapter 5, I argue that the methods and results developed for this dissertation can aid in our understanding of forest ecology and forest management decisions within the boreal region

Kliimamuutuse mõju hindamine rannaniidu taimekooslusele mesokosmi katse ja mehitamata õhusõidukiga kogutud andmete põhjal

A Thesis for applying for the degree of Doctor of Philosophy in Environmental Protection.Väitekiri filosoofiadoktori kraadi taotlemiseks keskkonnakaitse erialal.Semi-natural grasslands are an essential part of the cultural landscape of Europe. Semi-natural grasslands are commonly characterised by a very high biodiversity, including rare species. Beyond the high biodiversity value, semi-natural grasslands worldwide provide many ecosystem services, including: carbon sequestration and storage, nutrient cycling, regulation of soil quality, habitats for migrating birds, erosion control, and flood regulation. Within the realm of semi-natural grasslands, coastal meadows are particularly important. However, coastal grasslands are threatened by a range of factors such as coastal squeeze, transformation into monoculture ponds, pollution, and climate change. Coastal areas are threatened at a range of spatial scales as a result of sea-level rise, and can include higher flooding frequency in coastal areas, salt water intrusion in aquifers, and potential declines in the extent of coastal wetlands. A warmer climate also implies a modification in precipitation patterns affecting runoff into the sea. In coastal areas, both water levels and salinity have a strong impact on species distribution and therefore on the structure and composition of aquatic and coastal floral and faunal communities. Consequently, plant communities in coastal meadows are expected to undergo changes in their composition and structure. The current thesis explores different methodologies to assess plant community distribution, above-ground biomass, and the effects of management type, duration, and intensity on sward structure using UAV-derived multispectral data and aerial photogrammetry. In addition, the keystone of this thesis is a mesocosm experiment that was used to assess shifts in species richness and abundance in plant community types in Estonian coastal meadows related to future change scenarios of water level and salinity for the Baltic Sea. a. Unmanned Aerial Vehicle (UAV) The use of UAV demonstrated to be able to identify plant community extent and distribution in high biodiversity value coastal meadows in West Estonia. Species diversity and biomass significantly influence the quality of data and this should be accounted for when planning the sample collection to achieve better results. This study has shown that UAVs are useful tools of mapping grasslands at a plant community level. Also, UAV showed to be possible to reveal the structure of the grassland and how it is affected by the management history. For example, the grassland turns more homogeneous under long-term monospecific grazing, b. Mesoscosm Experiment The mesocosm experiment in the present study revealed different temporal changes of wetland communities to altered salinity and water conditions, highlighting the response of plant species to environmental variables. These changes were not significant according to alteration of water level and salinity in the Open Pioneer community, but they were over time. On the other hand, Lower Shore and Upper Shore had significant changes according to time and treatments. These could be explained by dynamic differences in the communities, since Open Pioneer was more variable. c. Conclusions Both methodologies, remote sensing and the mesocosm experiment, are evidently important to evaluate the structure and function of Estonian coastal meadows. The mapping of the extent and structure of coastal plant communities allows an evaluation of the current state of the ecosystem. The mesocosm experiment helps to understand changes in plant community composition under altered conditions of water level and salinity in Estonian coastal meadows and consequently, understand how species richness, abundance, and biomass will respond to those changes. This information is important when considering the protection and potential management of these areas taking into account the species diversity of fauna and flora as well as that of livestock.Uuring viidi läbi kahel tasandil: uuringukohtades Lääne-Eestis ja katsekeskkonnas. Esimesel juhul valiti Silma looduskaitsealal, Matsalu rahvuspargis ja Vormsi saarel ranniku taimekoosluste ja maapealse biomassi kaardistamiseks kokku üheksa rannaniiduala (I, II). Teine osa hõlmab mesokosmi katset (III), mille käigus kasutati katse seadmiseks ja eksperimenteerimiseks Silma looduskaitsealalt kogutud proove. Vaatamata oma suhteliselt väikesele pindalale (45 228 km2) iseloomustab Eestit mitmekesine geoloogia, pinnamood ja kliima. Läänemere rannaniidud on tekkinud ja need säilivad maa isostaatilise tõusu, setete kogunemise ja alade vähese intensiivsusega majandamise – karjatamise või niitmise – tõttu. Eesti rannikumärgaladel on ebatavaline hüdroloogiline režiim. Kuna loodete ulatus on väga väike (~0,02 m), põhjustab rannaniitude üleujutusi valdavalt tsüklonaalne aktiivsus Põhja-Atlandil ja Fennoskandias. Üleujutuste sagedus ja ulatus on ebaregulaarne ning varieerub kogu rannikumaastikul, sõltudes tuule kiirusest ja suunast. Hiljutised hinnangud suhtelise meretaseme tõusu kohta kolmelt mõõnamõõturilt piki Eesti rannikut on järgmised: Tallinnas 1,5–1,7 mm a-1, Narva-Jõesuus 1,7–2,1 mm a-1 ja Pärnus 2,3–2,7 mm a-1 (Ward et al., 2014). Taimekoosluse klassifitseerimiseks ja biomassi prognoosimiseks analüüsiti üheksat rannikumärgala kolmes kohas Silma looduskaitsealal, kahes kohas Matsalu rahvuspargis ja neljas kohas Vormsi saarel. Neis kohtades esinevad kõik väitekirjas käsitletud taimekooslused. Uurimiskohtade taimekooslused liigitati vastavalt Burnside´i jt fütosotsioloogilisele klassifikatsioonile (2007): pilliroostik, võsasoo, madal rannik, kõrgrannik, pioneerliikidega paljakud, kõrgrohustu, võsa ja metsamaa. Võsasoo ning võsa ja metsamaa jäeti nende marginaalse esinemise tõttu uurimusest välja. Uurimistöö käigus tehti kaks erinevat analüüsi, kasutades UAV-ga kogutud multispektraal- ja rgb-fotosid. UAV multispektraalseid pilte kasutati taimekoosluste kaardistamiseks Silma looduskaitsealal Põhja-Tahu, Lõuna-Tahu ja Kudani rannaniidul (I). Järgnevalt kasutati multispektraalseid ja rgb-pilte kõrge ruumilise eraldusvõimega kaartide koostamiseks maapealse biomassi tuvastamiseks kõigis üheksas uuringukohas (II). Taimekoosluste kaardistamiseks (I) ja maapealse biomassi prognoosimiseks (II) kasutati otsustusmetsa klassifikatsiooni. Seejärel analüüsiti maapealse biomassi kaartide abil majandamisviisi ja intensiivsuse mõju rannaniitude heinamaade struktuurile (II). Teavet rannaniitude kasutusviisi kohta saadi maaomanikega isiklikult suheldes. Uurimistöö teises osas valiti mesokosmi katse jaoks kolm taimekooslust: pioneerliikidega paljakud, madal rannik ja kõrgrannik. Need kooslused valiti sealsete võtmeliikide spetsiifilise autökoloogilise kasvukohaeelistuse tõttu (nt soolsus ja mulla veesisaldus). Katsest välja jäetud pilliroostikus ja võsasoos domineerivad üleujutust taluvad liigid; kõrgrohustu kujutab endast maismaa ja märgalade ökosüsteemi vahelist kooslust, ning võsa on täielikult maismaa. Silma looduskaitsealal varuti Põhja-Tahu alalt 2018. aasta juunis kolmest valitud taimekooslusest 15 mätast (suurus 50 x 70 cm, paksus 30 cm). Mesokosmi katse varustus koosnes mahutitest (90L, mõõtmed 56 x 79 x 32 cm), mis olid täidetud 2:1:1 mullaseguga, mis koosneb pestud sõmera struktuuriga liivast, savist ja kompostist, mis on väga sarnane märgala põhjasubstraadiga. Mahutid numereeriti ja varustati vastava tähisega. Mahutid asusid kogu katse jooksul samal kohal. Katse käiku hinnati alalise gradueeritud 50 cm2 kvadraadi abil, mis jaotati 25 kvadraadiks (10 x 10 cm), ja määrati kindlaks muutused esinevate taimeliikide arvukuses pinnakatte pindala järgi (katteprotsent). Katse kestis kolme aastat veetaseme ja soolsuse tingimustes, mis tuletati kliimamuutuste prognoosidest 2100. aastaks. Liikide arvukus ja liigirikkus arvutati 2018., 2019. ja 2020. aastaks iga taimekoosluse kohta eraldi. Liigirikkuse erinevusi aastati ja kasvutingimuste suhtes hinnati Kruskal Wallise testiga, mis põhineb Bonferroni kohandustega Dunni testil, et tuvastada liigirikkuse erinevusi igal aastal. Liigilise arvukuse esitamiseks kasutati arvukuse kõveraid. Taimekoosluse koostise erinevuste uurimiseks kasutati permutatsioonilist mitmemõõtmelist analüüsi Bray-Curtise erinevusega. Aasta ristmõju analüüsis käsitleti töötlemisviisi fikseeritud mõju ja valimeid juhusliku mõjuna. Tulemused ja järeldused Rannaniitudel hinnati taimekoosluste levikut, maismaa biomassi ja taimestiku vertikaalset struktuuri. Fleissi kapa kordaja 0,89 põhjal kaardistati põhjalikult taimekooslused (I). Otsustusmetsa klassijärgsed vead näitavad, et homogeensema struktuuriga piirkondi on kergem klassifitseerida kui keerulise struktuuriga koosluseid. Otsustusmetsa algoritmi jõudlusanalüüs näitas, et biomassi hindamisel saadi parim tulemus, kui multispektraalne info kombineeriti fotogramm-meetriliselt loodud digitaalse maastikumudeliga (DTM, ingl digital terrain model) (II). Tulemused viitavad sellele, et mitme anduri kombinatsiooni saab kasutada ökosüsteemi omaduste mõõtmiseks, mida ainult spektraalinformatsiooni analüüsides ei pruugi tuvastada. Siinse uuringu maapealse biomassi prognooside suur täpsus näitab, et rannaniitude jälgimisel on kaugseire UAV-ga sobiv meetod. Struktuurianalüüsi tulemused näitasid, mil määral mõjutab biomassi jaotust karjatamise kestus ja heterogeensus. Pidevalt majandatavatele rohumaadele on iseloomulikud suuremad ja homogeensemad alad (II). Üldine lineaarne mudelianalüüs ja Mann-Whitney u-testid näitasid, kuidas taimtoidulised liigid mõjutavad rohumaa struktuuri. Rohumaad, millel karjatatakse erinevaid taimtoidulisi, on mitmekesisema struktuuriga kui veiste karjamaa (II). Mesokosmi katse tulemused näitasid, et kõigis kolmes Läänemere ranniku märgalade koosluses ilmnesid aja jooksul vee- ja soolsusrežiimis märkimisväärsed muutused, mis tõi esile taimeliikide reaktsiooni keskkonnamuutuste suhtes (III). Pioneerliikidega paljakutel suurenes liigirikkus ja taimkate kõigi keskkonnamuutuste korral, sellega võrreldes esines madalal rannikul ja kõrgrannikul nii veetaseme kui ka soolsusega seotud muutusi vähemal määral. Pioneerliikidega paljakuid mõjutab enamasti soolsus, seda isegi peamiselt sõmerast, keskmise fraktsiooniga ja peenest liivast koosnevas pinnases, mis säilitab vähem toitaineid kui peenema fraktsiooniga muld. Spergularia marina ja Glaux maritima aitasid kaasa liigirikkuse suurenemisele mulla suurenenud ja vähenenud soolsuse tingimustes. Üldiselt ei ilmnenud madala ranniku ja kõrgranniku taimekooslustes soolsuse muutumise korral olulisi muutusi võrreldes kontrollkatsega. Nendes kooslustes on liike, mis kasvavad nii soolases kui ka mittesoolases keskkonnas. Veetaseme muutus mõjutas pioneerliikidega paljakute taimekooslust sarnaselt soolsuse muutmisega. Selle koosluse liigirikkus suurenes kõrgema veetaseme korral, võrreldes kontrollkatsega. Kõrgema veetasemega kohanenud liike nagu Eleocharis palustris esines kõrgenenud veetaseme korral kolmandal aastal rohkem; alanenud vees leidus katse lõpus rohkem väiksema veevajadusega liike, nagu Glaux maritima ja Centaurium littorale. Madalal rannikul registreeriti madalama veetaseme korral liigirikkuse muutus, võrreldes kontrollkatsega. Aja jooksul toimuv liikide varieeruvus ilmnes vähese pinnakatvusega liikide puhul, nt ahenesid Carex flacca ja Triglochin palustris´e kasvukohad. Madal rannik asub veetasemelt pioneerliikidega paljaku ja kõrgranniku vahel ning see võib seletada, miks sealsed liigid taluvad mulla mitmesuguseid niiskustingimusi. Kõrgranniku koosluses vähenes kõrgenenud veetaseme korral liikide arv ja sellest tulenevalt ka liigirikkus; sealjuures laienesid vähese pinnakatvuse ja madala veetasemega kohastunud liikide Stellaria graminea ja Viola canina kasvukohad. See uurimus näitas, et ökoloogilistes uuringutes võib erinevate metoodikate kombinatsioon osutuda tõhusaks. Vaid vähestes uuringutes kombineeritakse ökosüsteemiprotsesside mõistmiseks erinevaid lähenemisviise, nt kaugseiret ja katseplatvorme, antud töös mesokosmi katset. Uued tehnoloogilised edusammud kaugseire vallas võivad lahendada küsimusi, millele vastuse leidmine traditsiooniliste ökoloogiliste meetodite abil oleks keeruline või ebapraktiline. Samas on traditsioonilise lähenemisviisiga, nt mesokosmi katsega saadud teadmised uue tehnoloogilise potentsiaali rakendamiseks väga vajalikud. Uurimus näitas, et UAV on sobiv vahend rannikurohumaade struktuuri ja taimekoosluste leviku täpse eraldusvõimega kaartide koostamiseks. Teisest küljest aitab mesokosmi katse mõista taimekoosluse koostise muutusi eri veetaseme ja soolsuse tingimustes.Publication of this thesis is supported by the Estonian University of Life Sciences and by the Doctoral School of Earth Sciences and Ecology created under the auspices of the European Social Fund

Forest Species Mapping using Sentinel 2A images for the Central Alentejo Region (Portugal)

In past years, studies about Land Use and Land Cover (LULC) have been approached extensively in remote sensing for providing information on the environmental and global changes in the landscape. In the forest species mapping, one of the major challenges when using Sentinel-2 (S2A) multispectral data is to delineate and discriminate areas of heterogeneous forest components with spectral similarity at the canopy level. In this context, the main objective of this study was to evaluate the S2A data performance for LULC mapping, using a Random Forest classifier (RF). A set of 26 independent variables derived from the 2019 summer period S2A data, with a spatial resolution of 10 m, was used. A total of eight object-based LULC classes were created, four forest classes (Quercus suber, Quercus rotundifólia, Eucalyptus sp, and Pinus pinea) and four other uses. For this propose supervised classification method was applied using the RF classifier. The cartography accuracy assessment was performed using the statistics confusion matrix and Kappa coefficient (k). This study showed that the RF classifier achieved high overall accuracy (92%) and Kappa (91%) for the four forest classes defined using S2A data