Tích hợp dữ liệu cảm biến từ xa và bioclimatic để dự đoán phân bố của các loài xâm lấn ở các vùng ít dữ liệu: một đánh giá về thách thức và cơ hội

Ahmadi K, Alavi SJ, Amiri GZ, Hosseini SM, Serra-Diaz JM, Svenning J (2020) The potential impact of future climate on the distribution of European yew (Taxus baccata L.) in the Hyrcanian Forest region (Iran). Int J Biometeorol. https://doi.org/10.1007/s00484-020-01922-z Amiri M, Tarkeshi M, Jafari R, Jetschke G (2020) Bioclimatic variables from precipitation and temperature records vs. remote sensing-based bioclimatic variables: which side can perform better in species distribution modeling. Ecol Inform 57(101060):1–16. https://doi.org/10.1016/j.ecoinf.2020.101060 Aranda SC, Lobo JM (2011) How well does presence-only-based species distribution modeling predict assemblage diversity? a case study of the Tenerife flora. Ecography 34:31–38. https://doi.org/10.1111/j.1600-0587.2010.06134.x Arogoundade AM, Odindi J, Mutanga O (2019) Modelling Parthenium hysterophorus invasion in KwaZulu-Natal province using remotely sensed data and environmental variables. Geocarto Int. https://doi.org/10.1080/10106049.2019.1581268 Asner GP, Knapp DE, Kennedy-bowdoin T, Jones MO, Martin RE, Boardman J, Hughes RF (2008) Invasive species detection in Hawaiian rainforests using airborne imaging spectroscopy and LiDAR. Remote Sens Environ 112:1942–1955. https://doi.org/10.1016/j.rse.2007.11.016 Ayanu Y, Jentsch A, Müller-Mahn D, Rettberg S, Romankiewicz C, Koellner T (2014) Ecosystem engineer unleashed: Prosopis juliflora threatening ecosystem services? Reg Environ Change 15(1):155–167. https://doi.org/10.1007/s10113-014-0616-x Bartz R, Kowarik I (2019) Assessing the environmental impacts of invasive alien plants : a review of assessment approaches. NeoBiota 43:69–99. https://doi.org/10.3897/neobiota.43.30122 Beaumont LJ, Hughes L, Poulsen M (2005) Predicting species distributions: use of climatic parameters in BIOCLIM and its impact on predictions of species’ current and future distributions. Ecol Model 186:251–270. https://doi.org/10.1016/j.ecolmodel.2005.01.030 Bekele K, Haji J, Legesse B, Schaffner U (2018) Economic impacts of Prosopis spp. invasions on dryland ecosystem services in Ethiopia and Kenya: evidence from choice experimental data. J Arid Environ. https://doi.org/10.1016/j.jaridenv.2018.07.001 Bellard C, Jeschke JM, Leroy B, Mace GM (2018) Insights from modeling studies on how climate change affects invasive alien species geography. Ecol Evol. https://doi.org/10.1002/ece3.4098 Beyene H, Tessema T (2015) Distribution, abundance, and socio-economic impacts of parthenium (Parthenium hysterophorus). J Poverty Investment Dev 19:22–29 Boyd DS, Foody GM (2011) An overview of recent remote sensing and GIS-based research in ecological informatics. Ecol Inform 6:25–36. https://doi.org/10.1016/j.ecoinf.2010.07.007 Bradley BA (2014) Remote detection of invasive plants: a review of spectral, textural, and phenological approaches. Biol Invasions. https://doi.org/10.1007/s10530-013-0578-9 Bradley BA, Allen JM, O’Neill MW, Wallace RD, Bargeron CT, Richburg JA, Stinson K (2018) Invasive species risk assessments need more consistent spatial abundance data. Ecosphere 9(7):e02302. https://doi.org/10.1002/ecs2.2302 Bradley BA, Fleishman E (2008) Can remote sensing of land cover improve species distribution modeling? J Biogeogr 35:1158–1159. https://doi.org/10.1111/j.1365-2699.2008.01928.x Bradley BA, Mustard JF (2006) Characterizing the landscape dynamics of an invasive plant and risk of invasion using remote sensing. Ecol Appl 16(3):1132–1147 Bucklin DN, Basille M, Romañach SS, Brandt LA, Mazzotti FJ, Watling JI, Mazzotti F (2016) Considerations for building climate-based species distribution models. UF/IFAS extension University of Florida WE375: 1–8. Buermann W, Saatchi S, Smith TB, Zutta BR, Chaves JA, Milá B, Graham CH (2008) Predicting species distributions across the Amazonian and Andean regions using remote sensing data. J Biogeogr. https://doi.org/10.1111/j.1365-2699.2007.01858.x Cayuela L, Golicher DJ, Newton AC, Kolb M, de Alburquerque FS, Arets EJMM, Alkemade JRM, Pérez AM (2009) Species distribution modeling in the tropics: problems, potentialities, and the role of biological data for effective species conservation. Trop Conserv Sci 2(3):319–352 Cord AF, Klein D, Gernandt DS, de la Rosa JAP, Dech S (2014) Remote sensing data can improve predictions of species richness by stacked species distribution models: a case study for Mexican pines. J Biogeogr 41:736–748. https://doi.org/10.1111/jbi.12225 Cord AF, Klein D, Mora F, Dech S (2014) Comparing the suitability of classified land cover data and remote sensing variables for modeling distribution patterns of plants. Ecol Model 272:129–140. https://doi.org/10.1016/j.ecolmodel.2013.09.011 Cord AF, Meentemeyer RK, Leitao PJ, VaclaviK T (2013) Modelling species distributions with remote sensing data: bridging disciplinary perspectives. J Biogeogr 40:2226–2227. https://doi.org/10.1111/jbi.12199 Cord A, Rodder D (2011) Inclusion of habitat availability in species distribution models through multi-temporal remote-sensing data? Ecol Appl 21(8):3285–3298 Deblauwe V, Droissart V, Bose R, Sonké B, Blach-Overgaard A, Svenning J-C, Wieringa JJ, Ramesh BR, Stévart T, Couvreur TLP (2016) Remotely sensed temperature and precipitation data improve species distribution modeling in the tropics. Global Ecol Biogeogr 25:443–454. https://doi.org/10.1111/geb.12426 Diniz Filho JAF, Ferro VG, Santos T, Nabout JC, Dobrovolski R, De Marco JP (2010) The three phases of the ensemble forecasting of niche models: geographic range and shifts in climatically suitable areas of Utetheisa ornatrix. Revista Brasileira de Entomologia 54(3):339–349 Dubula B, Tesfamichael SG, Rampedi IT (2016) Assessing the potential of remote sensing to discriminate invasive Asparagus laricinus from adjacent land cover types. Cogent Geoscience 2:1154650. https://doi.org/10.1080/23312041.2016.1154650 Elkind K, Sankey TT, Munson SM, Aslan CE (2019) Invasive buffelgrass detection using high-resolution satellite and UAV imagery on Google Earth Engine. Remote Sens Ecol Conserv. https://doi.org/10.1002/rse2.116 Engler R, Waser LT, Zimmermann NE, Schaub M, Berdos S, Ginzler C, Psomas A (2013) Combining ensemble modeling and remote sensing for mapping individual tree species at high spatial resolution. For Ecol Manage 310:64–73. https://doi.org/10.1016/j.foreco.2013.07.059 Evangelista PH, Kumar S, Stohlgren TJ, Jarnevich CS, Crall AW, Norman JB, Barnett DT (2008) Modelling invasion for a habitat generalist and a specialist plant species. Diversity Distrib 14:808–817. https://doi.org/10.1111/j.1472-4642.2008.00486.x Feilhauer H, He KS, Rocchini D (2012) Modeling species distribution using niche-based proxies derived from composite bioclimatic variables and MODIS NDVI. Remote Sens 4:2057–2075. https://doi.org/10.3390/rs4072057 Feilhauer H, Thonfeld F, Faude U, He KS, Rocchini D, Schmidtlein S (2013) Assessing floristic composition with multispectral sensors—a comparison based on monotemporal and multiseasonal field spectra. Int J Appl Earth Observ Geoinform 21:218–229. https://doi.org/10.1016/j.jag.2012.09.002 Fernández M, Hamilton H (2015) Ecological niche transferability using invasive species as a case study. PLoS ONE 10(3):e0119891. https://doi.org/10.1371/journal.pone.0119891 Fernandez M, Hamilton H, Kueppers L (2013) Characterizing uncertainty in species distribution models derived from interpolated weather station data. Ecosphere 4(5):61. https://doi.org/10.1890/ES13-00049 Fischer D, Thomas SM, Suk JE, Sudre B, Hess A, Tjaden NB, Beierkuhnlein C, Semenza JC (2013) Climate change effects on chikungunya transmission in Europe: Geospatial analysis of vector’s climatic suitability and virus’ temperature requirements. Int J Health Geographics 12(51):1–12. https://doi.org/10.1186/1476-072X-12-51 Fithian W, Elith J, Hastie T, Keith DA (2014) Special feature paper: bias correction in species distribution models: pooling survey and collection data for multiple species. Methods Ecol Evol. https://doi.org/10.1111/2041-210X.12242 Fritz A, Li L, Storch I, Koch B (2018) UAV-derived habitat predictors contribute strongly to understanding avian species—habitat relationships on the Eastern Qinghai-Tibetan Plateau. Remote Sens Ecol Conserv. https://doi.org/10.1002/rse2.73 Früh L, Kampen H, Kerkow A, Schaub GA, Walther D, Wieland R (2018) Modelling the potential distribution of an invasive mosquito species: comparative evaluation of four machine learning methods and their combinations. Ecol Model 388:136–144. https://doi.org/10.1016/j.ecolmodel.2018.08.011 Ge S, Everitt J, Carruthers R, Gong P, Anderson G (2006) Hyperspectral characteristics of canopy components and structure for phenological assessment. Environ Monit Assess 120:109–126. https://doi.org/10.1007/s10661-005-9052-1 Girma A, de Bie CAJM, Skidmore AK, Venus V, Bongers F (2015) Hyper-temporal SPOT-NDVI dataset parameterization captures species distributions. Int J Geographical Inform Sci. https://doi.org/10.1080/13658816.2015.1082565 Godoy O, de Lemos-Filho JP, Valladares F (2011) Invasive species can handle higher leaf temperatures under water stress than Mediterranean natives. Environ Exp Bot 71:207–214. https://doi.org/10.1016/j.envexpbot.2010.12.001 Gormley AM, Forsyth DM, Griffioen P, Lindeman M, Ramsey DSL, Scroggie MP, Woodford L (2011) Using presence-only and presence-absence data to estimate the current and potential distributions of established invasive species. J Appl Ecol 48:25–34. https://doi.org/10.1111/j.1365-2664.2010.01911.x Guisan A, Zimmermann NE, Elith J, Graham CH, Phillips S, Peterson AT (2007) What matters for predicting the occurrences of trees: techniques, data, or species’ characteristics? Ecol Monogr 77(4):615–630. https://doi.org/10.1890/06-1060.1 He KS, Bradley BA, Cord AF, Rocchini D, Tuanmu M, Schmidtlein S, Turner W, Wegmann M, Pettorelli N (2015) Will remote sensing shape the next generation of species distribution models ? Remote Sens Ecol Conserv. https://doi.org/10.1002/rse2.7 Hernandez PA, Graham CH, Master LL, Albert DL (2006) The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography 29:773–785 Heshmati I, Khorasani N, Shams-Esfandabad B, Riazi B (2019) Forthcoming risk of Prosopis juliflora global invasion triggered by climate change: implications for environmental monitoring and risk assessment. Environ Monit Assess 191(72):1–12. https://doi.org/10.1007/s10661-018-7154-9 Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978. https://doi.org/10.1002/joc.1276 Huang C-Y, Asner GP (2009) Applications of remote sensing to alien invasive plant studies. Sensors 9:4869–4889. https://doi.org/10.3390/s90604869 Hundessa N, Fufa A (2016) Distribution and socio-economic impacts of Prosopis juliflora in East Shewa and West Arsi Zones, Ethiopia. Int J African Asian Stud 24:31–41 Iluker J, Birner R, Tilahun M, Getu S (2014) A social-economic assessment of the impact of Prosopis juliflora invasion and participative management approach in the Afar Region, Ethiopia. Managing Prosopis Juliflora for Better (Agro-) Pastoral Livelihoods in the Horn of Africa 30–38. Ilukor J, Rettberg S, Treydte A, Birner R (2016) To eradicate or not to eradicate? Recommendations on Prosopis juliflora management in Afar, Ethiopia, from an interdisciplinary perspective. Pastoralism Res Policy Pract 6(14):1–8. https://doi.org/10.1186/s13570-016-0061-1 Immitzer M, Neuwirth M, Böck S, Brenner H, Vuolo F, Atzberger C (2019) Optimal Input Features for tree species classification in Central Europe Based on Multi-Temporal Sentinel-2 Data. Remote Sens 11(2599):1–23. https://doi.org/10.3390/rs11222599 Jensen T, Hass FS, Akbar MS, Petersen PH, Arsanjani JJ (2020) Employing machine learning for detection of invasive species using Sentinel-2 and AVIRIS data: the case of Kudzu in the United States. Sustainability 12(3544):1–16. https://doi.org/10.3390/SU12093544 Jones CC (2012) Challenges in predicting the future distributions of invasive plant species. For Ecol Manage 284:69–77. https://doi.org/10.1016/j.foreco.2012.07.024 Kapitza K, Zimmermann H, Martín-lópez B, Von Wehrden H (2019) Research on the social perception of invasive species: a systematic literature review. NeoBiota 43:47–68. https://doi.org/10.3897/neobiota.43.31619 Khare S, Latifi H, Rossi S, Ghosh SK (2019) Fractional cover mapping of invasive plant species by combining very high-resolution stereo and multi-sensor multispectral imageries. Forests 10(540):1–15. https://doi.org/10.3390/f10070540 Kiala Z, Mutanga O, Odindi J, Peerbhay K (2019) Feature selection on sentinel-2 multispectral imagery for mapping a landscape infested by parthenium weed. Remote Sens 11(1892):1–17. https://doi.org/10.3390/rs11161892 Kissling WD, Ahumada JA, Bowser A, Fernandez M, Fern´andez N, García EA, et al (2018) Building essential biodiversity variables (EBVs) of species distribution and abundance at a global scale. Biol Rev 93:600–625. https://doi.org/10.1111/brv.12359 Klerk H, Buchanan G (2017) Remote sensing training in African conservation. Remote Sens Ecol Conserv. https://doi.org/10.1002/rse2.36 Leitão PJ, Santos MJ (2019) Improving models of species ecological niches: a remote sensing overview. Ecol Evol Front. https://doi.org/10.3389/fevo.2019.00009 Li G, Xu G, Guo K, Du S (2014) Mapping the global potential geographical distribution of black locust (Robinia Pseudoacacia L.) using herbarium data and a maximum entropy model. Forests 5:2773–2792. https://doi.org/10.3390/f5112773 Lima TA, Beuchle R, Langner A, Grecchi RC, Griess VC, Achard F (2019) Comparing sentinel-2 MSI and landsat 8 OLI Imagery for monitoring selective logging in the Brazilian Amazon. Remote Sens 11(961):1–21. https://doi.org/10.3390/rs11080961 Loew A, Bell W, Brocca L, Bulgin CE, Burdanowitz J, Calbet X et al (2017) Validation practices for satellite-based Earth observation data across communities. Rev Geophys 55:779–817. https://doi.org/10.1002/2017RG000562 Matongera TN, Mutanga O, Dube T, Lottering RT (2016) Detection and mapping of bracken fern weeds using multispectral remotely sensed data: a review of progress and challenges. Geocarto Int. https://doi.org/10.1080/10106049.2016.1240719 Meroni M, Ng W-T, Rembold F, Leonardi U, Atzberger C, Gadain H, Shaiye M (2017) Mapping Prosopis juliflora in West Somaliland with Landsat 8 Satellite imagery and ground information. Land Degrad Dev 28:494–506. https://doi.org/10.1002/ldr.2611 Müllerová J, Bruna J, Bartalos T, Dvorak P, Vitkova M, Pyšek P (2017) Timing is important : unmanned aircraft vs. satellite imagery in plant invasion monitoring. Front Plant Sci. https://doi.org/10.3389/fpls.2017.00887 Ng W-T, Silva ACO, Rima P, Atzberger C, Immitzer M (2018) Ensemble approach for potential habitat mapping of invasive Prosopis spp. in Turkana Kenya. Ecol Evol 8(23):1–11. https://doi.org/10.1002/ece3.4649 Ng W-T, Rima P, Einzmann K, Immitzer M, Atzberger C, Eckert S (2017) Assessing the potential of sentinel-2 and pléiades data for the detection of prosopis and vachellia spp. In Kenya. Remote Sens 9(74):1–29. https://doi.org/10.3390/rs9010074 Oumar Z (2016) Assessing the utility of the SPOT 6 sensor in detecting and mapping lantana camara for a community clearing project in KwaZulu-Natal, South Africa. South Afr J Geomatics 5(2):214–226. https://doi.org/10.4314/sajg.v5i2.9 Ouyang Z-T, Gao Y, Xie X, Guo H-Q, Zhang T-T, Zhao B (2013) Spectral discrimination of the invasive plant spartina alterniflora at multiple phenological stages in a saltmarsh wetland. PLoS ONE 8(6):e67315. https://doi.org/10.1371/journal.pone.0067315 Paz-Kagan T, Silver M, Panov N, Karnieli A (2019) Multispectral approach for identifying invasive plant species based on flowering phenology characteristics. Remote Sens 11(953):1–18. https://doi.org/10.3390/rs11080953 Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190:231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026 Piiroinen R, Fassnacht FE, Heiskanen J, Maeda E, Mack B, Pellikka P (2018) Invasive tree species detection in the Eastern Arc Mountains biodiversity hotspot using one-class classification. Remote Sens Environ 218:119–131. https://doi.org/10.1016/j.rse.2018.09.018 Prates-Clark C, Saatchi SS, Agosti D (2008) Predicting geographical distribution models of high-value timber trees in the Amazon Basin using remotely sensed data. Ecol Model 211:309–323. https://doi.org/10.1016/j.ecolmodel.2007.09.024 Rajah P, Odindi J, Mutanga O (2018) Evaluating the potential of freely available multispectral remotely sensed imagery in mapping American bramble (Rubus cuneifolius). S Afr Geogr J. https://doi.org/10.1080/03736245.2018.1461683 Rajah P, Odindi J, Mutanga O, Kiala Z (2019) The utility of Sentinel-2 Vegetation Indices (VIs) and Sentinel-1 Synthetic Aperture Radar (SAR) for invasive alien species detection and mapping. Nat Conserv 35:41–61. https://doi.org/10.3897/natureconservation.35.29588 Re D, Tordoni E, De Pascalis F, Negrı´n-Pe´rez Z, Ferna´ndez-Palacios J, Are´valo J, et al (2020) Invasive fountain grass (Pennisetum setaceum (Forssk) increases its potential area of distribution in Tenerife island under future climatic scenarios. Plant Ecol. https://doi.org/10.1007/s11258-020-01046-9 Richter K, Atzberger C, Hank TB, Mauser W (2012) Derivation of biophysical variables from Earth observation data: validation and statistical measures. Appl Remote Sens 6:1–24. https://doi.org/10.1117/1.JRS.6.063557 Rocchini D, Boyd DS, Feret J-B, Foody GM, He KS, Lausch A, Nagendra H, Wegmann M, Pettorelli N (2015) Satellite remote sensing to monitor species diversity: potential and pitfalls. Remote Sens Ecol Conserv. https://doi.org/10.1002/rse2.9 Rocchini D, Andreo V, Forster M, Garzon-lopez CX, Gutierrez AP, Gillespie TW (2015) Potential of remote sensing to predict species invasions: a modeling perspective. Prog Phys Geogr 39(3):283–309. https://doi.org/10.1177/0309133315574659 Royimani L, Mutanga O, Odindi J, Dube T (2018) Advancements in satellite remote sensing for mapping and monitoring of alien invasive plant species (AIPs). Phys Chem Earth. https://doi.org/10.1016/j.pce.2018.12.004 Saatchi S, Buermann W, Steege H, Mori S, Smith TB (2008) Modeling distribution of Amazonian tree species and diversity using remote sensing measurements. Remote Sens Environ 112:2000–2017. https://doi.org/10.1016/j.rse.2008.01.008 Sakate DM, Kashid DN (2016) A new robust model selection method in GLM with application to ecological data. Environ Syst Res 5(9):1–8. https://doi.org/10.1186/s40068-016-0060-7 Sankey TT, Mcvay J, Swetnam TL, Mcclaran MP, Heilman P, Nicholas M (2017) UAV hyperspectral and lidar data and their fusion for arid and semi-arid land vegetation monitoring. Remote Sens Ecol Conserv. https://doi.org/10.1002/rse2.44 Shackleton RT, Le Maitre DC, Van Wilgen BW, Richardson DM (2015) The impact of invasive alien Prosopis species (mesquite) on native plants in different environments in South Africa. South Afr J Botany 97:25–31. https://doi.org/10.1016/j.sajb.2014.12.008 Shackleton RT, Le Maitre DC, Pasiecznik NM, Richardson DM (2014) Prosopis: A global assessment of the biogeography, benefits, impacts and management of one of the world’s worst woody invasive plant taxa. AoB PLANTS 6: plu027. https://doi.org/10.1093/aobpla/plu027 Shiferaw W, Demissew S, Bekele T (2018) Invasive alien plant species in Ethiopia: ecological impacts on biodiversity a review paper. Int J Mol Biol 3(4):169–176 Shiferaw H, Schaffner U, Bewket W, Alamirew T, Zeleke G, Teketay D, Eckert S (2019a) Modelling the current fractional cover of an invasive alien plant and drivers of its invasion in a dryland ecosystem. Sci Rep 9(1576):1–12. https://doi.org/10.1038/s41598-018-36587-7 Shiferaw H, Bewket W, Alamirew T, Zeleke G, Teketay D, Bekele K, Schaffner U, Eckert S (2019b) Implications of land use/land cover dynamics and Prosopis invasion on ecosystem service values in Afar Region, Ethiopia. Sci Total Environ 675:354–366. https://doi.org/10.1016/j.scitotenv.2019.04.220 Shiferaw H, Bewket W, Eckert S (2019c) Performances of machine learning algorithms for mapping fractional cover of an invasive plant species in a dryland ecosystem. Ecol Evol. https://doi.org/10.1002/ece3.4919 Shoko C, Mutanga O (2017) Examining the strength of the newly-launched Sentinel 2 MSI sensor in detecting and discriminating subtle differences between C3 and C4 grass species. ISPRS J Photogrammetry Remote Sens 129:32–40. https://doi.org/10.1016/j.isprsjprs.2017.04.016 Singh K, Forbes A, Akombelwa M (2013) The Evaluation of high-resolution aerial imagery for monitoring of bracken fern. South Afr J Geomatics 2(4):296–308 Sintayehu DW, Dalle G, Bobasa AF (2020) Impacts of climate change on current and future invasion of Prosopis juliflora in Ethiopia: environmental and socio-economic implications. Heliyon 6:e04596. https://doi.org/10.1016/j.heliyon.2020.e04596 Somers B, Asner GP (2012) Hyperspectral time series analysis of native and invasive species in hawaiian rainforests. Remote Sens 4:2510–2529. https://doi.org/10.3390/rs4092510 Sun Y, Müller-schärer H, Maron JL, Schaffner U (2015) Origin matters : diversity affects the performance of alien invasive species but not of native species. Am Nat 185(6):725–736. https://doi.org/10.1086/681251 Taddese H (2014) Suitability analysis for Jatropha curcas production in Ethiopia—a spatial modeling approach. Environ Syst Res 3:25. https://doi.org/10.1186/s40068-014-0025-7 Tamiru G (2017) Invasive alien weed species distribution, impacts on agriculture, challenge, and reaction in ethiopia: a review. J Biol Agric Healthcare 7(7):136–146 Thamaga KH, Dube T (2018a) Remote sensing of invasive water hyacinth (Eichhornia crassipes): a review on applications and challenges. Remote Sens Appl Soc Environ. https://doi.org/10.1016/j.rsase.2018.02.005 Thamaga KH, Dube T (2018b) Testing two methods for mapping water hyacinth (Eichhornia crassipes) in the Greater Letaba river system, South Africa: discrimination and mapping potential of the polar-orbiting Sentinel-2 MSI and Landsat 8 OLI sensors. Int J Remote Sens. https://doi.org/10.1080/01431161.2018.1479796 Tola J, Tessema T (2019) Abundance and distribution of invasive alien plant species in Illu Ababora Zone abundance and distribution of invasive alien plant species in Illu Ababora Zone of Oromia National Regional State, Ethiopia. J Agric Sci Food Technol 1(7):94–100 Transon J, Andrimont R, Maugnard A, Defourny P (2018) Survey of hyperspectral earth observation applications from space in the sentinel-2 context. Remote Sens 10(157):1–32. https://doi.org/10.3390/rs10020157 Tricarico E (2016) Do alien invasive species and climate change foster conservation behavior ? Aquatic Conserv: Mar. Freshw Ecosyst 26:228–232. https://doi.org/10.1002/aqc.2637 Truong TTA, Hardy G, Andrew ME (2017) Contemporary remotely sensed data products refine invasive plants risk mapping in data-poor regions. Front Plant Sci 8:1–18. https://doi.org/10.3389/fpls.2017.00770 Tuanmu M-N, Jetz W (2014) A global 1-km consensus land-cover product for biodiversity and ecosystem modeling. Global Ecol Biogeogr 23(9):1–15. https://doi.org/10.1111/geb.12182 Urbina-Cardona N, Blair ME, London MC, Loyola R, Vela´squez-Tibata J, Morales-Devia H, (2019) Species distribution modeling in Latin America: a 25-year retrospective review. Trop Conserv Sci 12:1–19. https://doi.org/10.1177/1940082919854058 Václavík T, Meentemeyer K (2012) Equilibrium or not? Modelling the potential distribution of invasive species in different stages of invasion. Diversity Distrib 18:73–83 Vega GC, Pertierra LR, Olalla-tárraga MÁ (2017) Data descriptor: MERRAclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modeling. Sci Data. https://doi.org/10.1038/sdata.2017.78 Vilà M, Espinar JL, Hejda M, Hulme PE, Jarošík V, Maron JL, Pergl J, Schaffner U, Sun Y, Pyšek P (2011) Ecological impacts of invasive alien plants: a meta-analysis of their effects on species, communities, and ecosystems. Ecol Lett 14:702–708. https://doi.org/10.1111/j.1461-0248.2011.01628.x Vuolo F, Neuwirth M, Immitzer M, Atzberger C, Ng W-T (2018) How much does multi-temporal Sentinel-2 data improve crop type classification ? Int J Appl Earth Obs Geoinformation 72:122–130. https://doi.org/10.1016/j.jag.2018.06.007 Wakie TT, Evangelista PH, Jarnevich CS, Laituri M (2014) Mapping current and potential distribution of non-native Prosopis juliflora in the Afar region of Ethiopia. PLoS ONE 9(11):3–11. https://doi.org/10.1371/journal.pone.0112854 Wakjira M (2011) An invasive alien weed giant sensitive plant (Mimosa diplotricha Sauvalle) invading Southwestern Ethiopia. Afr J Agric Res 6(1):127–131. https://doi.org/10.5897/AJAR10.421 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 Sens 10(1419):1–21. https://doi.org/10.3390/rs10091419 West AM, Wakie T, Evangelista PH (2014). Mapping distribution and forecasting invasion of mesquite (Prosopis juliflora) in Ethiopia Ecological Forecasting Issue December):1–18 Wilson JW, Sexton JO, Todd Jobe R, Haddad NM (2013) The relative contribution of terrain, land cover, and vegetation structure indices to species distribution models. Biol Cons 164:170–176. https://doi.org/10.1016/j.biocon.2013.04.021 Xie Y, Sha Z, Yu M (2008) Remote sensing imagery in vegetation mapping : a review. J Plant Ecol 1(1):9–23. https://doi.org/10.1093/jpe/rtm005 Zhao M, Peng C, Xiang W, Deng X, Tian D, Zhou X, Yu G, He H, Zhao Z (2013) Plant phenological modeling and its application in global climate change research: overview and future challenges. Environ Rev 21:1–14. https://doi.org/10.1139/er-2012-0036 Zimmermann NE, Edwards TC Jr, Moisen GG, Frescino TS, Blackard JA (2007) Remote sensing-based predictors improve distribution models of rare, early successional and broadleaf tree species in Utah. J Appl Ecol 44:1057–1067. https://doi.org/10.1111/j.1365-2664.2007.01348.x