Abstract
The exponential population growth of Karachi city has taken a very heavy toll on the indigenous environment of this South Asian megacity. In this context, the unlikely roadsides plantation of nonnative species by the city decision-makers was a short-term solution to cultivate green belts, ultimately, to improve city environment. However, this has backfired and become a very serious ecological crisis as the species become invasive alien species and destroyed the native ecological environment of the city significantly. This study provided an accurate spatial extent of invasive alien species and evaluated the intensity of the problem using remote sensing technology. Firstly, the importance value index was developed to highlight the intensity of invasive alien species in native plant communities. Afterwards, the hot spots were sampled to develop training areas for Object Based Image Analysis to map the extent of invasive alien species in the city using high-resolution GeoEye satellite imagery. The overall accuracy of mapping was 93.17% with Kappa coefficient value of 0.904. In addition, to explore hyperspectral remote sensing analysis, two techniques—Derivative Vegetation Index and Spectral Angle Mapper—were employed on EO-I hyperspectral imagery to map invasive alien species using spectral signatures. The analysis revealed 99.94% correlation between the two mapping techniques. Finally, to identify the impact of invasive alien species on the microclimate of the city, a spatiotemporal analysis (1992–2016) at selected sites was performed on Landsat thermal images. The study ruled out the concept of invasive alien species influence on the microclimate of the city as both vegetation growth and ambient air temperature change were not interdependent.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Availability of data and material (data transparency)
All data used in this study can easily be downloaded and processed.
References
Adam HE, Csaplovics E, Elhaja ME (2016) A comparison of pixel-based and object-based approaches for land use land cover classification in semi-arid areas, Sudan. IOP Conf Ser Earth Environ Sci 37:012061. https://doi.org/10.1088/1755-1315/37/1/012061
Alvarez-Taboada F, Paredes C, Julián-Pelaz J (2017) Mapping of the invasive species hakea sericea using unmanned aerial vehicle (UAV) and worldview-2 imagery and an object-oriented approach. Remote Sens 9:913. https://doi.org/10.3390/rs9090913
Asner GP, Jones MO, Martin RE, Knapp DE, Hughes RF (2008) Remote sensing of native and invasive species in Hawaiian forests. Remote Sens Environ 112:1912–1926. https://doi.org/10.1016/j.rse.2007.02.043
Bano S (2014) Monitoring the flora through geo-informatic techniques: a case study of Karachi university campus. PhD Thesis, University of Karachi
Barry P (2001) EO-1/ Hyperion Science Data User’s Guide. 65
Beck R, (2003) EO-1 user guide v. 2.3. Satellite systems branch, USGS earth resources observation systems data center (EDC)
Blaschke T (2010) Object based image analysis for remote sensing. ISPRS J Photogramm Remote Sens 65:2–16. https://doi.org/10.1016/j.isprsjprs.2009.06.004
Boardman JW, Kruse FA (1994) Automated spectral analysis: a geological example using AVIRIS data, north Grapevine Mountains, Nevada. In: Proceedings of the thematic conference on geologic remote sensing, vol 1. pp I–407. https://doi.org/10.1016/j.molcel.2014.02.002
Bradley BA (2014) Remote detection of invasive plants: a review of spectral, textural and phenological approaches. Biol Invasions 16:1411–1425. https://doi.org/10.1007/s10530-013-0578-9
Burciaga UM (2020) Sustainability assessment in housing building organizations for the design of strategies against climate change. HighTech Innov J 1:136–147. https://doi.org/10.28991/HIJ-2020-01-04-01
Dahdouh-Guebas F, Koedam N (2006) Empirical estimate of the reliability of the use of the Point-Centred Quarter Method (PCQM): solutions to ambiguous field situations and description of the PCQM+ protocol. For Ecol Manage 228:1–18. https://doi.org/10.1016/j.foreco.2005.10.076
Datt B, Paterson M (2000) Vegetation-soil spectral mixture analysis. Geosci Remote Sens Symp IGARSS 2000:1936–1938. https://doi.org/10.1109/IGARSS.2000.858186
Duro DC, Franklin SE, Dubé MG (2012) A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Remote Sens Environ 118:259–272. https://doi.org/10.1016/j.rse.2011.11.020
Eckert S, Kneubühler M, (2004) Application of hyperion data to agricultural land classification and vegetation properties estimation in Switzerland. In: Proceedings of XXth ISPRS Conference, pp 12–23
Engel K, Tollrian R, Jeschke JM (2011) Integrating biological invasions, climate change, and phenotypic plasticity. Commun Integr Biol 4:247–250. https://doi.org/10.4161/cib.4.3.14885
Everitt JH, Yang C, Fletcher RS, Drawe DL (2006) Evaluation of high-resolution satellite imagery for assessing rangeland resources in South Texas. Rangel Ecol Manag 59:30–37. https://doi.org/10.2111/04-093.1
Everitt JH, Yang C, Sriharan S, Judd FW (2008) Using high resolution satellite imagery to map black mangrove on the Texas Gulf Coast. J Coast Res 246:1582–1586. https://doi.org/10.2112/07-0987.1
Fahad S, Adnan M, Hassan S, Saud S, Hussain S, Wu C, Wang D, Hakeem KR, Alharby HF, Turan V, Khan MA, Huang J (2019) Chapter 10 - rice responses and tolerance to high temperature. In: Hasanuzzaman M, Fujita M, Nahar K, Biswas JK (eds) Advances in rice research for abiotic stress tolerance. Woodhead Publishing, Sawston, pp 201–224. https://doi.org/10.1016/B978-0-12-814332-2.00010-1
Fenu G, Malloci FM (2020) DSS LANDS: a decision support system for agriculture in Sardinia. HighTech. Innov J 1:129–135. https://doi.org/10.28991/HIJ-2020-01-03-05
Filella I, Penuelas J (1994) The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status. Int J Remote Sens 15:1459–1470. https://doi.org/10.1080/01431169408954177
Fuller DO (2005) Remote detection of invasive Melaleuca trees (Melaleuca quinquenervia) in South Florida with multispectral IKONOS imagery. Int J Remote Sens 26:1057–1063. https://doi.org/10.1080/01430060512331314119
He KS, Rocchini D, Neteler M, Nagendra H (2011) Benefits of hyperspectral remote sensing for tracking plant invasions. Divers Distrib 17:381–392. https://doi.org/10.1111/j.1472-4642.2011.00761.x
Heenkenda M, Maier S, Joyce K (2016) Estimating mangrove biophysical variables using worldview-2 satellite data: rapid creek, northern territory. Aust J Imaging 2:24. https://doi.org/10.3390/jimaging2030024
Hestir EL, Khanna S, Andrew ME, Santos MJ, Viers JH, Greenberg JA, Rajapakse SS, Ustin SL (2008) Identification of invasive vegetation using hyperspectral remote sensing in the California Delta ecosystem. Remote Sens Environ 112:4034–4047. https://doi.org/10.1016/j.rse.2008.01.022
Huang C, Asner GP (2009) Applications of remote sensing to alien invasive plant studies. Sensors 9:4869–4889
Husain T, Agnihotri P (2009) Invasive alien species and climate change. In: National conference on invasive alien species. Lucknow, India, pp 36–38
Hussain SS, Ahmed M, Siddiqui MF (2010) Threatened and endangered native plants of karachi. Int J Biol Biotechnol 7:259–266
Hussin YA, Gilani H, Van Leeuwen L, Murthy MSR, Shah R, Baral S, Tsendbazar NE, Shrestha S, Shah SK, Qamer FM (2014) Evaluation of object-based image analysis techniques on very high-resolution satellite image for biomass estimation in a watershed of hilly forest of Nepal. Appl Geomatics 6:59–68. https://doi.org/10.1007/s12518-014-0126-z
Iqbal M, Shafiq M (1996) Plant communities on the sandy areas of Karachi University campus. J Islamic Acad Sci 9:89–98
Jafri SKH (1966) The flora of Karachi. The book corporation Karachi, pp 246–247
Kamal M, Phinn S (2011) Hyperspectral data for mangrove species mapping: a comparison of pixel-based and object-based approach. Remote Sens 3:2222–2242. https://doi.org/10.3390/rs3102222
Kavzoglu T (2009) Increasing the accuracy of neural network classification using refined training data. Environ Model Softw 24:850–858. https://doi.org/10.1016/j.envsoft.2008.11.012
Kaur R, Gonzáles WL, Llambi LD, Soriano PJ, Callaway RM, Rout ME, Gallaher TJ, Inderjit (2012) Community Impacts of Prosopis juliflora Invasion: Biogeographic and Congeneric Comparisons. PLoS ONE 7:e44966. https://doi.org/10.1371/journal.pone.0044966
Kazmi JH (1995) Applications of remote sensing techniques for monitoring of desertification: an appraisal of malir valley. In: Proceedings of the second Asia-Pacific conference on multilateral cooperation of space technology (APC-MCSTA), pp 196–203
Kazmi SJH, Shaikh S, Bin Zamir U, Zafar H, Rasool A, Tariq F, Arif T, Hasan JSK (2010) Ecological and socio-economic evaluation of the use of Prosopis juliflora for bio-char production in Pakistan, pp 1–54
Kochubey SM, Kazantsev TA (2012) Derivative vegetation indices as a new approach in remote sensing of vegetation. Front Earth Sci 6:188–195. https://doi.org/10.1007/s11707-012-0325-z
Köppen-Geiger (2012) The Köppen climate classification. Wikipedia
Kumar RM, Acharya RP (2013) Impact assessment of invasive plant species in selected ecosystems of Bhadaure Tamagi VDC, Kaski, p 66
Kundu A, Dwivedi S, Dutta D (2016) Monitoring the vegetation health over India during contrasting monsoon years using satellite remote sensing indices. Arab J Geosci 9:1–15. https://doi.org/10.1007/s12517-015-2185-9
Laba M, Downs R, Smith S, Welsh S, Neider C, White S, Richmond M, Philpot W, Baveye P (2008) Mapping invasive wetland plants in the hudson river national estuarine research reserve using quickbird satellite imagery. Remote Sens Environ 112:286–300. https://doi.org/10.1016/j.rse.2007.05.003
Laliberte AS, Fredrickson EL, Rango A (2007) Combining decision trees with hierarchical object-oriented image analysis for mapping arid rangelands. Photogramm Eng Remote Sens 73:197–207. https://doi.org/10.14358/PERS.73.2.197
Liang W, Abidi M, Carrasco L, McNelis J, Tran L, Li Y, Grant J, Liang W (2020) Mapping vegetation at species level with high-resolution multispectral and lidar data over a large spatial area: a case study with Kudzu. Remote Sens 12:1–18. https://doi.org/10.3390/rs12040609
Masters G, Norgrove L (2010) Climate change and invasive alien species. CABI Working Paper, vol 1. pp 30
Miller JR, Hare EW, Wu J (1990) Quantitative characterization of the vegetation red edge reflectance 1. an inverted-Gaussian reflectance model. Int J Remote Sens 11:1755–1773. https://doi.org/10.1080/01431169008955128
Mittapalli G (2017) Extemporizing spectral signatures using spectroradiometer for different species of family graminaceae
Mitchell K (2010) Quantitative analysis by the point-centered quarter method, pp 1–56
Mitra A (2013) Sensitivity of mangrove ecosystem to changing climate. Sensitivity Mangrove Ecosyst Changing Clim 9788132215:1–323. https://doi.org/10.1007/978-81-322-1509-7
Mooney HA, Hobbs RJ (2000) Global change and invasive species: where do we go from here. Invasive species in a changing world, pp 425–434
Müllerová J, Pergl J, Pyšek P (2013) Remote sensing as a tool for monitoring plant invasions: testing the effects of data resolution and image classification approach on the detection of a model plant species Heracleum mantegazzianum (giant hogweed). Int J Appl Earth Obs Geoinf 25:55–65. https://doi.org/10.1016/j.jag.2013.03.004
Mwangi E, Swallow B (2005) Invasion of Prosopis juliflora and local livelihoods: case study from the Lake Baringo area of Kenya ICRAF Working Paper no. 3 68. https://doi.org/10.5716/WP13657.PDF
Ouko E, Omondi S, Mugo R, Wahome A, Kasera K, Nkurunziza E, Kiema J, Flores A, Adams EC, Kuraru S, Wambua M (2020) Modeling invasive plant species in Kenya’s Northern Rangelands. Front Environ Sci 8:69–69. https://doi.org/10.3389/fenvs.2020.00069
Paciecznik NM, Felker P, Harris PJC, Harsh LN, Cruz G, Tewari JC, Cadoret K, Maldonado LJ, Pasiecznik N, Felker P, Harris PJC, Harsh LN, Cruz G, Tewari JC, Cadoret K, Maldonado LJ (2001) The “Prosopis juliflora” - “Prosopis pallida” complex : a monograph. HDRA, Coventry. https://doi.org/10.1111/jsap.12202
Qureshi H, Arshad M, Bibi Y (2014) Invasive flora of Pakistan: a critical analysis. Int J Biosci (IJB) 4:407–424. https://doi.org/10.12692/ijb/4.1.407-424
Ragavan K, Johnny CJ (2015) Quantification of invasive colonies of Prosopis juliflora using remote sensing and GIS techniques. Int J Eng Tech Res 3:110–115
Sánchez-Flores E, Rodríguez-Gallegos H, Yool SR (2008) Plant invasions in dynamic desert landscapes. a field and remote sensing assessment of predictive and change modeling. J Arid Environ 72:189–206. https://doi.org/10.1016/j.jaridenv.2007.05.013
Silver M, Tiwari A, Karnieli A (2019) Identifying vegetation in arid regions using object-based image analysis with RGB-only aerial imagery. Remote Sens. https://doi.org/10.3390/rs11192308
Smith KL, Steven MD, Colls JJ (2004) Use of hyperspectral derivative ratios in the red-edge region to identify plant stress responses to gas leaks. Remote Sens Environ 92:207–217. https://doi.org/10.1016/j.rse.2004.06.002
Solangi GS, Siyal AA, Siyal P (2019) Spatiotemporal dynamics of land surface temperature and its impact on the vegetation. Civ Eng J 5:1753–1763. https://doi.org/10.28991/cej-2019-03091368
Somers B, Asner GP (2013) Multi-temporal hyperspectral mixture analysis and feature selection for invasive species mapping in rainforests. Remote Sens Environ 136:14–27. https://doi.org/10.1016/j.rse.2013.04.006
Thenkabail PS, Smith RB, De Pauw E (2000) Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sens Environ 71:158–182. https://doi.org/10.1016/S0034-4257(99)00067-X
Thenkabail PS, Enclona EA, Ashton MS, Van Der Meer B (2004) Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications. Remote SensEnviron 91:354–376. https://doi.org/10.1016/j.rse.2004.03.013
Trimble Documentation (2013) eCognition Developer 8.9: User Guide. User Guide
Tsai F, Philpot WD (1998) Derivative analysis of hyperspectral data. Remote Sens Environ 66:41–51. https://doi.org/10.1016/S0034-4257(98)00032-7
Underwood E, Ustin S, DiPietro D (2003) Mapping nonnative plants using hyperspectral imagery. Remote Sens Environ 86:150–161. https://doi.org/10.1016/S0034-4257(03)00096-8
Walther GR, Roques A, Hulme PE, Sykes MT, Pyšek P, Kühn I, Zobel M, Bacher S, Botta-Dukát Z, Bugmann H, Czúcz B, Dauber J, Hickler T, Jarošík V, Kenis M, Klotz S, Minchin D, Moora M, Nentwig W, Ott J, Panov VE, Reineking B, Robinet C, Semenchenko V, Solarz W, Thuiller W, Vilà M, Vohland K, Settele J (2009) Alien species in a warmer world: risks and opportunities. Trends Ecol Evol 24:686–693. https://doi.org/10.1016/j.tree.2009.06.008
Acknowledgements
This study was initiated during the German Academic Exchange Service (DAAD) fellowship of the first author in 2018-2019. Authors gratefully acknowledge the financial support of DAAD to conduct this study and the Humboldt University Berlin for hosting and supporting the DAAD fellow during his stay in Berlin.
Funding
German Academic Exchange Service (DAAD) fellowship of the first author in 2019.
Author information
Authors and Affiliations
Contributions
Author codes are given as Syed Jamil Hasan Kazmi (SK), Dagmar Haase (DH), Atif Shahzad.(AS), Saima Shaikh (SS), Syeda Maha Zaidi (SZ) and Salman Qureshi (SQ). SK and DH were involved in conceptualization. SK and AS contributed to methodology. AS, SZ and SS contributed to software. AS and SQ were involved in validation. DH contributed resources. SK and DH were involved in writing original draft. AS and SQ were involved in writing, review and editing. SZ and SS were involved in cartographic visualization. SK, SS and AS contributed photographic evidences. SK and DH were involved in supervision. SK and DH contributed to project administration. SK, DH and SQ contributed to funding acquisition.
Corresponding author
Ethics declarations
Conflict of interest
Not applicable.
Additional information
Editorial responsibility: J Aravind.
Rights and permissions
About this article
Cite this article
Kazmi, J.H., Haase, D., Shahzad, A. et al. Mapping spatial distribution of invasive alien species through satellite remote sensing in Karachi, Pakistan: an urban ecological perspective. Int. J. Environ. Sci. Technol. 19, 3637–3654 (2022). https://doi.org/10.1007/s13762-021-03304-3
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13762-021-03304-3